nutritional and fall risk among older women living in long- term care
TRANSCRIPT
NUTRITIONAL AND FALL RISK AMONG OLDER WOMEN LIVING IN LONG-
TERM CARE FACILITIES OF INDIA
A Thesis
Submitted to the Faculty of Graduate Studies and Research
In partial fulfillment of the requirements
For the Degree of
Doctor of Philosophy
in
Kinesiology and Health Studies
University of Regina
By
Swati Madan
Regina, Saskatchewan
June, 2017
Copyright 2017: S. Madan
UNIVERSITY OF REGINA
FACULTY OF GRADUATE STUDIES AND RESEARCH
SUPERVISORY AND EXAMINING COMMITTEE
Swati Madan, candidate for the degree of Doctor of Philosophy in Kinesiology and Health Studies, has presented a thesis titled, Nutritional and Fall Risk Among Older Women Living in Long-Term Care Facilities of India, in an oral examination held on June 5, 2017. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material. External Examiner: *Dr. Jamuna Duvvuru, Sri Venkateswara University
Supervisor: Dr. Shanthi Johnson, Faculty of Kinesiology and Health Studies
Committee Member: Dr. Rebecca Genoe, Faculty of Kinesiology and Health Studies
Committee Member: Dr. Patrick Neary, Faculty of Kinesiology and Health Studies
Committee Member: Dr. Richard MacLennan, Department of Psychology
Chair of Defense: Dr. Andrei Volodin, Department of Mathematics and Statistics *Via Skype
I
ABSTRACT
The objectives of the study were to assess the nature of nutritional and fall risk in
older adults living in LTC facilities in New Delhi, evaluate the inter-relationship of
nutritional and fall risk, and assess whether depression, fear of falling, and physical
function predict fall risk, and to test the reliability of nutrition and fall risk measures.
Eighty five women aged 60 years and over living in six LTC facilities in New Delhi were
recruited. Measures included the Mini Nutritional Assessment (MNA), Falls Efficacy
Scale International (FES-I), Downton Index, SF-36 Health Survey, Geriatric Depression
Scale (GDS), Mini Mental State Exam (MMSE), and a background profile and physical
activity questionnaire. Mobility was assessed using Timed up and go test (TUG), and
handgrip strength was assessed using Jamar hydraulic hand dynamometer. Using SPSS
software (version 22.0), descriptive statistics, correlation between different variables,
predictors of fall risk, and reliability measures were analysed.
The mean age of participants was 74.21(5.52) years. A majority were widowed
with poor educational and income level. Findings revealed that 54% of the older women
were at a high level of nutritional risk. The factors that accounted for a large proportion
of variance in the nutritional risk level were mobility status, intake of psychotropic
medications, low dietary intake, and poor self-perception of health status. MNA scores
had significant negative correlation with Downton Index scores (R= -.419, p<.001) which
implies that higher MNA scores (lower nutritional risk) were associated with lower
scores on Downton Index (lower fall risk). Multiple regression analysis revealed that fear
II
of falling, fall history, body pain, functional mobility, gait condition, and depression were
predictors of fall risk. Analysis of the psychometric properties of the main constructs
showed that the reliability of MNA was low (Cronbach’s alpha= 0.3) while the reliability
of Downton Index was fair but acceptable (Cronbach’s alpha=0.7). The study highlights
the burden of nutritional and fall risk among older adults living in LTC in India, the need
to establish the psychometric properties of tools for various cultural contexts, and plan
intervention studies to address this significant co-existing health issue.
III
ACKNOWLEDGMENT
First and foremost, I would like to thank my advisor and mentor, Dr. Shanthi
Johnson, for her continuous and sincere support throughout my doctoral research
program. Her kindness, patience, and positive attitude towards every situation have been
a remarkable source of inspiration for me, and have given me motivation, strength and
ambition, and have sharpened my skills as a researcher. I would like to express my heart-
felt gratitude towards Dr. Richard MacLennan who helped me immensely with the
statistical analysis of my data, and for his brilliant advice throughout my research
program. I would like to sincerely thank him for his expert guidance and unconditional
support throughout my PHD program. I am very grateful to my committee members Dr.
Patrick Neary and Dr. Rebecca Genoe for their valuable feedback, and suggestions, and
for their support and cooperation throughout my research study. I would like to thank
them for their time, advice, and research inputs.
My sincere thanks to the Research Ethics Board (REB) at the University of
Regina, and the Directors and Managers of long-term care facilities in New Delhi for
giving me ethics approval to carry out research, and for giving me permission to meet
older women living in the facilities. I am grateful to all the women who participated in
my research study, the nursing staff, and the caregivers for their help and cooperation.
IV
DEDICATION
I would like to dedicate this dissertation to my parents since they have played a
very important role in providing me with motivation, guidance, encouragement,
emotional, and financial support throughout my research program. My parents have been
my pillars of strength while I faced many challenges at different phases of my research
project. I am thankful to them for having faith in me, loving me unconditionally, and
always being there whenever I needed their support and guidance. I also dedicate my
dissertation to Tejas, my loving sister, for her encouragement and for being my source of
inspiration and guidance. I would like to thank my grandparents for their love and
blessings. Without the support provided by my family, this dissertation would not have
been possible.
V
TABLE OF CONTENTS
ABSTRACT.................................................................................................................I
ACKNOWLEDGMENT............................................................................................III
DEDICATION...........................................................................................................IV
TABLE OF CONTENTS............................................................................................V
LIST OF TABLES.....................................................................................................IX
LIST OF APPENDICES.........................................................................................XIV
LIST OF ABBREVIATIONS..................................................................................XV
CHAPTER 1 INTRODUCTION..............................................................................1
CHAPTER 2 LITERATURE REVIEW...................................................................7
2.1 Long-term care..................................................................................................8
2.2 Prevalence of malnutrition in older adults.........................................................9
2.3 Consequences of malnutrition in older adults..................................................12
2.4 Factors contributing to malnutrition in LTC....................................................14
2.4.1 Medical Risk Factors............................................................................15
2.4.2 Social Risk Factors..............................................................................23
2.4.3 Psychological Risk Factors...................................................................26
2.5 Measurement of malnutrition in older adults....................................................29
2.6 Frailty and falls in LTC Facilities.....................................................................34
2.7 Public Health Importance of Falls....................................................................35
2.8 Costs associated with falls and fall-related injuries..........................................39
VI
2.9 Risk factors for falls..........................................................................................41
2.9.1 Lower extremity weakness due to sarcopenia.....................................43
2.9.2 Low functional capacity......................................................................45
2.9.3 Psychotropic medications....................................................................46
2.9.4 Vitamin D deficiency..........................................................................47
2.10 Measurement of Fall Risk................................................................................49
2.10.1 Downton Index.....................................................................................49
2.10.2 Falls Efficacy Scale International (FES-I)...........................................51
2.10.3 Timed-up-and go-test...........................................................................52
2.11 Association between Nutritional and Fall Risk...............................................54
CHAPTER 3 METHODS.........................................................................................57
3.1 Selection of facilities and recruitment of participants.....................................57
3.2 Sample size......................................................................................................58
3.3 Measures of Nutritional and Fall Risk.............................................................59
3.3.1 Background Profile Questionnaire.......................................................59
3.3.2 Mini-Nutritional Assessment (MNA)..................................................59
3.3.3 Fall Efficacy Scale International..........................................................59
3.3.4 Downton Index.....................................................................................61
3.3.5 Geriatric Depression Scale...................................................................61
3.3.6 SF-36 Health Survey............................................................................62
3.4 Measure of Cognitive Status............................................................................63
3.5 Measure of Balance and Mobility....................................................................64
3.6 Measure of Functional Status...........................................................................65
3.7 Data Analysis...................................................................................................66
3.7.1 Multiple Regression Analysis..............................................................66
VII
3.7.2 Factor Analysis....................................................................................67
3.7.3 Reliability Analysis.............................................................................68
CHAPTER 4 RESULTS........................................................................................69
4.1 Background Profile of Participants................................................................69
4.2 Health and Activity Profile of Participants.....................................................71
4.3 Nutritional Characteristics of Participants.....................................................78
4.4 Fall Risk Profile of Participants.....................................................................81
4.5 Correlation Analysis......................................................................................89
4.6 Multiple Regression Analysis........................................................................91
4.7 Factor Analysis.............................................................................................107
4.8 Reliability Analysis.......................................................................................120
CHAPTER 5 DISCUSSION.................................................................................122
5.1 Study Purpose..............................................................................................122
5.2 Background characteristics...........................................................................122
5.3 Health Profile................................................................................................123
5.4 Psychological issues……………….............................................................124
5.5 Nutritional Risk Level..................................................................................126
5.6 Fall Risk Level.............................................................................................128
5.7 Fear of falling...............................................................................................129
5.8 Cognitive Status ..........................................................................................130
5.9 Association between nutritional status and falls..........................................131
5.10 Multiple Regression.....................................................................................131
5.11 Factor Analysis............................................................................................133
VIII
5.12 Reliability Analysis....................................................................................133
5.13 Future Directions and Research.................................................................134
5.14 Strengths and Limitations of the Study......................................................136
REFERENCES......................................................................................................138
IX
LIST OF TABLES
Table 1 Risk factors for falls..........................................................................42
Table 2 Background characteristics of participants………...........................69
Table 3 Health profile of participants............................................................73
Table 4 General health status of participants...........................................76-77
Table 5 Nutritional profile of participants...............................................79-80
Table 6 Fall risk profile of participants...................................................83-84
Table 7 Level of depression assessed using Geriatric Depression Scale in
participants.................................................................................86-87
Table 8 Severity of memory problems and cognitive decline assessment using
MMSE............................................................................................89
Table 9 Correlation analysis of age, TUG test and HGS with MNA, FES-I,
GDS, Downton Index, and MMSE scores......................................90
Table 10 Correlation analysis of MNA with FES-I, Downton Index, GDS,
and MMSE scores............................................................................91
Table 11 Regression analysis with FES-I score as predictor of fall
risk..................................................................................................93
Table 12 Model summary for regression analysis with Downton Index
score as DV and MNA and FES-I score as IVs..............................93
Table 13 Multiple regression analysis with fall hsitory as predictor
X
of fall risk.................................................................................94
Table 14 Model summary for regression involving Downton index score as
DV and MNA score and fall history as IVs..............................94
Table 15 Multiple regression analysis showing a decreased ability to
perform moderate activities acting as a predictor of future
fall risk.....................................................................................95
Table 16 Model summary for multiple regression analysis involving
Downton Index score as outcome variable or DV and MNA score
and difficulty performing moderate level of activity as predictor
variables or IVs........................................................................96
Table 17 Multiple regression analysis showing visual impairment as a
predictor of fall risk................................................................97
Table 18 Model summary for regression analysis involving Downton
Index score as DV and MNA Score and visual impairment
as predictor variables or IVs..................................................98
Table 19 Multiple regression analysis showing limb impairment as a
predictor of future fall risk......................................................98
Table 20 Model summary for regression analysis involving Downton Index
score as outcome variable or DV and MNA score and limb
impairment as predictors or IVs.............................................99
Table 21 Multiple regression analysis showing recently experienced body
pain as a predictor of future fall risk....................................100
Table 22 Model summary for regression analysis involving Downton
XI
score as outcome variable or DV and MNA score and
pain as predictors or IVs......................................................101
Table 23 Multiple regression analysis with Downton Index score
as outcome variable or DV and MNA score and time taken to
perform TUG test as predictors or IVs...............................102
Table 24 Model summary for multiple regression analysis carried out
between Downton Index as DV or outcome variable and MNA
score and time taken tocomplete TUG test as predictors or
IVs.....................................................................................102
Table 25 Multiple regression analysis showing GDS scores as a predictor of
fall risk...............................................................................103
Table 26 Model summary for regression analysis involving Downton Index
score as DV or outcome variable and MNA score and GDS score
as IVs or predictor variables..............................................103
Table 27 Multiple regression analysis showing gait condition acting as a
predictor of fall risk...........................................................104
Table 28 Model summary for regression analysis involving Downton Index
score as DV and MNA score and gait condition as IVs
or predictors of fall risk.....................................................105
Table 29 Multiple regression analysis showing lower social interaction with
friends and family acting as a predictor of fall risk...........106
XII
Table 30 Model summary showing regression analysis between Downton
Index score (DV) and MNA score and lower social interaction with
friends and family as predictors (IVs)......................................107
Table 31 Factor Analysis of MNA items................................................108
Table 32 Total variance explained using factor analysis for determination of
nutritional risk by including MNA items..................................108
Table 33 Rotated Component Matrix of items MNA...............................109
Table 34 KMO and Barlett's test for FES-I .............................................110
Table 35 Total variance explained using factor analysis for
items of FES-I...........................................................................111
Table 36 Rotated Component Matrix for FES-I using Varimax Rotation
technique: factor analysis method............................................111
Table 37 KMO and Barlett's test for Geriatric Depression Scale...........113
Table 38 Total variance explained for depression using factor analysis of
GDS items................................................................................113
Table 39 Rotated component Matrix for items of Geriatric Depression
Scale (GDS)......................................................................114-115
Table 40 KMO and Barlett's test for SF-36 HealthSurvey.....................116
Table 41 Total Variance explained by various items of SF-36 Health Survey
using Factor Analysis...............................................................116
Table 42 Rotated Component Matrix of SF-36 Health Survey Items using
Varimax Rotation technique....................................................117
Table 43 KMO and Barlett's test for items of MMSE............................118
XIII
Table 44 Variance explained by different components extracted by factor
analysis for MMSE.................................................................119
Table 45 Rotated Component Matrix of items of MMSE using Varimax
Rotation procedure.................................................................119
Table 46 Reliability analysis for MNA, FES-I, Downton Index, GDS,
MMSE and SF-36 using Cronbach's alpha...........................120
XIV
LIST OF APPENDICES
APPENDIX A: Letter of Invitation.........................................................195-196
APPENDIX B: Letter of Informed Consent............................................197-198
APPENDIX C: Background Profile and PAL Questionnaire..................199-202
APPENDIX D: Mini Nutritional Assessment Questionnaire...................203-206
APPENDIX E: Downton Index................................................................207-208
APPENDIX F: Falls Efficacy Scale International...................................209-210
APPENDIX G: Geriatric Depression Scale..............................................211-213
APPENDIX H: Mini Mental State Exam.................................................214-217
APPENDIX I: RAND Version of SF-36 Health Survey.........................218-222
APPENDIX J: Ethics Approval Certificate provided by
Research Ethics Board (REB)..........................................223-225
XV
LIST OF ABBREVIATIONS
AD: Alzheimer's disease
ADL: Activities of daily living
BMI: Body mass index
CC: Calf circumference
CDC: Center for Disease Control and Prevention
ESPEN: European Society for Clinical Nutrition and Metabolism
FCI: Functional Co-morbidity Index
FES-I: Falls Efficacy Scale- International
FFMI: Fat free mass index
GDS: Geriatric Depression Scale
GNRI: Geriatric Nutritional Risk Index
HGS: Hand grip strength
HIC: High income countries
HRQoL: Health related quality of life
IADL: Instrumental activities of daily living
INR: Indian Rupee
KMO: Kaiser Meyer Olkin statistic
LMIC: Low and middle income countries
LTC: Long term care
MAC: Mid arm circumference
MCS: Mental Component Summary
MDS: Minimum data set
MMSE: Mini Mental State Exam
XVI
MNA: Mini Nutritional Assessment
MST: Malnutrition Screening Tool
MUST: Malnutrition Universal Screening Tool
NRS: Nutritional Risk Screening
OAHs: Old Age homes
PAL: Physical Activity Level
PCS: Physical Component Summary
PD: Parkinson's disease
ProFANE: Prevention of Falls Network Europe
SNAQ: Short Nutritional Appetite Questionnaire
TUG: Timed-up-and-go test
UN: United Nations
UNPF: United Nations Population Fund
WHO: World Health Organization
1
CHAPTER 1
Introduction
Population aging is taking place at a rapid pace all across the world. The term
“population aging” refers to an increased proportion of older adults in a given population,
and a smaller proportion of younger adults and children (United Nations, 2013).
According to the United Nations Report on World Population Prospects (2015), the total
global population reached 7.3 billion by mid-2015, and the number of individuals aged
60 years and over is 901 million, comprising 12 % of the total population. Of the global
population of 7.3 billion people, 4.4 billion live in Asia (60%). It is projected that by the
year 2030, the number of older adults will reach 1.4 billion and their number will
continually grow as a result of population aging. Many Asian countries have seen rapid
population aging. For example, India is undergoing a rapid and unprecedented population
aging, characterized by demographic changes which have occurred as a result of
declining fertility, and improved longevity, and have led to an increased proportion of
individuals aged 60 years and over.
The fertility rate in India was reported to be 2.3 in 2013 and is projected to drop to
1.88 by 2050 which is considered to be below the replacement level. Life expectancy at
age 60 has increased significantly and is projected to reach 81 years by 2050 (United
Nations World Population Report, 2015). Interestingly, life expectancy at age 80 has also
increased due to advances in the medical field and is expected to rise to 88.5 years by
2050 (United Nations, 2015). In spite of the growing number of older adults in Asia and
countries such as India, most of the aging research continues to emerge from high income
2
countries such as the United States of America, Canada, several European countries,
Japan, and Australia.
Along with population aging, alterations in demographic and family
characteristics have major implications for older adults, who will have fewer individuals
to care for their needs and well being (WHO Report on Global Health & Aging, 2011).
With decreased social support from families, older adults will need to transfer to long-
term care (LTC) facilities, to ensure that their care needs will be met adequately. LTC
facilities are growing in number in low and middle income countries (LMICs) such as
India due to the breakdown of the traditional joint family system and rise of nuclear
family units. Care for older adults was never a major issue when joint families were
functioning successfully in the Indian society (Kumar & Bhargava, 2014). Older adults
who were once considered to be "head of the family" in a traditional Indian joint family
are not given the same status anymore, and are often neglected by younger members of
the family who do not have the time to provide them with the care support they need to
lead healthy lives (Sebastian & Sekher, 2011).
According to the data published in the Directory of Old Age Homes by HelpAge
India, there are 1260 registered care homes in India which provide housing, food, medical
assistance and social support to older adults (HelpAge India, 2009). Care homes are
commonly referred to as “old age homes” or “homes for the aged” in India and have been
in existence since early eighteenth century (Nair, 1995). These institutions traditionally
served destitute persons who had no family members to look after their care needs and no
income. Most of these care homes were free for older adults and were operated by
Christian organizations primarily in the southern part of India (Datta, 2017). Most of the
3
care homes are situated in the southern Indian states of Karnataka, Andhra Pradesh,
Tamil Nadu, and Kerela (Rajan, Mishra, & Sarma, 1999). Some developments have taken
place in the past three decades and have led to the rise of “pay and stay homes” which are
similar to retirement homes in the United States.
These pay and stay homes have been growing in number due to increasing
demands of lower and upper middle class families whose younger members are unable to
provide care support to older adults (Brink, 1998; Datta, 2017). Due to economic
prosperity, higher rates of migration, and growing job mobility within and outside India,
multigenerational co-residence is being replaced by nuclear households and resulting in a
consequential decline in provision of informal care support to older adults (Brink, 1998;
Datta, 2017). In LTC facilities, individualized and coordinated services are provided to
the residents, with the aim of maximizing the quality of life by following a holistic
approach (Singh, 2010). Studies from the high income countries (HIC) show that older
adults especially those residing in LTC facilities have complex clinical profile with high
rates of falls and malnutrition (Daniels, 2002).
Nutritional risk is high in older adults living in LTC facilities (Thompson et al.,
2006), as they most commonly experience age-related problems such as decreased
appetite, and a decline in taste and smell acuity. Aging also contributes to a progressive
deterioration in physiological functioning, changes in gastrointestinal motility, appetite
changes, and modification in secretion of hormones that regulate appetite (Ortolani,
Landi, Martone, Onder, & Bernabei, 2013). In addition, greater intake of medications,
which is quite common in older adults, is strongly associated with a decline in nutritional
status (Jyrkka et al., 2011). While there has been sufficient research related to nutrition
4
risk from the context of HICs, there is limited research related to low and middle income
countries (LMICs) including India. In addition to being at a heightened nutritional risk,
older adults are also at an increased risk of encountering falls.
Falls are one of the leading problems affecting older adults and it is reported that
50% of individuals especially over the age of 80 fall at least once annually (Galantino et
al., 2012). Falls and fall-related injuries are a major clinical issue in geriatric populations,
since they are responsible for hospitalization, chronic pain, reduction in quality of life,
increased risk of fractures, particularly hip fractures, and increased risk of death in cases
where fall-related injuries are serious, and cannot be treated adequately (Statistics
Canada, 2014). Falls are the primary cause of both fatal and non-fatal injuries in older
adults (Centers for Disease Control and Prevention, 2013). They are reported to be the
most common contributory factors towards traumatic brain injuries (Sterling, O’Connor,
& Bonadies, 2001).
It has been estimated that one third of older adults experience at least one fall in a
year (Stevens, 2005). To reduce risk of subsequent falls, many individuals restrict their
level of physical activity as they have an intense fear of falling (Allison et al., 2013).
Leading sedentary lives may result in a greater risk of experiencing falls (Denkinger,
Lukas, Nikolaus, & Hauer, 2015). Injuries caused by fall incidents, such as, hip fractures
and head injury, contribute towards significant costs related to fall-related
hospitalizations and treatment (CDC, 2014). Falls are detrimental to the quality of life of
older adults as they increase risk of disability, and lead to loss of independence. Head
injuries, including intracranial injuries, skull fracture, and hematoma are associated with
considerable care costs including admission to emergency departments of hospitals,
5
surgery, multiple visits paid to the general physicians, need for physiotherapists, nurses
and caregivers (CDC, 2014). Therefore, fall-prevention becomes inevitable, and
programs that are designed to lower fall risk and reduce fear of falling in older adults
need to be implemented (Hoang, Jullamate, Piphatvanitcha, & Rosenberg, 2016).
It is noteworthy that falls and injuries related to them are preventable. Several fall
prevention strategies are available including multi-factorial interventions, exercise-based
programs, and environmental modification (Cameron et al., 2010; Gillespie et al., 2012).
However, the implementation and adherence rates of these fall-prevention strategies are
quite low (Simek, McPhate, & Haines, 2012; Yardley et al., 2008).
Studies show that nutritional risk and fall risk co-exist in older adults given the
commonality of the contributing factors. Neyens et al. (2013) carried out a study to
examine the association between falls and a lower nutritional status in older adults living
in LTC facilities in the Netherlands. They found that undernourished older adults
encountered a greater number of falls as compared to individuals with an optimum
nutritional status. It was also noted that with nutritional intervention the percentage of
fallers in the malnourished group was much lower than the group of older adults who did
not receive any nutritional intervention. The researchers of this study established that
there is a solid relationship between malnutrition and falls in older adults. A recent study
conducted by Chien and Guo (2014) found that malnutrition in older adults is an
independent predictor for falls based on a study carried out in Taiwan. The strong
relationship between malnutrition and falls in frail older adults is supported by previous
studies (Gill, Taylor, & Pengelly, 2005; Johnson, 2003). To date, no study has examined
the association between nutritional and fall risk among Indian older adults residing in
6
LTC facilities of New Delhi, along with the determination of fall risk from predictors
such as depression, cognitive status, functional status (including the measurement of
handgrip strength and timed-up-and go (TUG) test performance), and no study has
carried out factor analysis and reliability analysis of nutritional and fall risk measures
such as MNA, Downton Index, and FES-I.
The World Health Organization has predicted that health care systems will face
numerous challenges with aging of populations across the world (WHO, 2008). The
presence of multiple co-morbidities is very common in LMICs with lower health care
resources, and it is essential that health policies and programs address the health needs of
older adults in LMICs such as India (Joshi, Kumar, & Awasthi, 2003). Nutrition plays a
critical role in the determination of health status of older adults, and therefore it is
imperative to do greater research on understanding the nutritional status of older adults in
India (Vedantam, Subramanium, Rao, & John, 2010). Besides nutritional status,
understanding and addressing fall risk is also important in low and middle income
countries (LMICs), as falls are one of the primary causes of fatal and non-fatal injuries in
older adults. The prevalence of falls in Indian older adults has been reported to be
between 14% and 53% (D'Souza et al., 2014). Falls are an emerging public health
problem in India and are considered to be a major barrier to active aging (D'Souza et al.,
2014). There is an urgent need for collaboration of health care professionals, researchers,
health care delivery systems, and policy makers to develop programs and policies to
prevent falls and fall-related injuries in Indian older adults and provide a boost to active
aging in India (D'souza et al., 2014).
7
CHAPTER 2
LITERATURE REVIEW
The proportion of older adults in India (aged 60 years and over) is expected to
increase from 8% in 2010 to 19 % by 2050 i.e. 323 million people (Population Reference
Bureau, 2012). As discussed in the Introduction, population aging started occurring over
several decades ago in the high income countries (HICs) around the world, and has
occurred over a much shorter timeframe in many low and middle income countries
(LMICs) including India (United Nations, 2009). Currently, two thirds of the world’s
older adults reside in LMICs (United Nations, 2013). Of the projected growth in the
geriatric population, there will be 417 million older adults in HICs of the world and the
LMICs will be home to 1.6 billion older adults in 2050 (United Nations, 2013). This
rapid growth has been attributed to the reduction in the rates of infectious diseases, better
sanitation practices, improved food handling procedures, and advancements in the
medical domain. This profound increase in the proportion of older adults, along with
several changes in the traditional joint family systems, and limited old-age income
support, has many social, economic, and health care policy challenges associated with it
(Population Reference Bureau, 2012). Therefore, the implications of population aging are
magnified in the context of LMICs, since these countries will have less time to respond to
the increasing care needs of the geriatric populations due to limited resources available
(UNFPA, 2012).
8
2.1 Long-term care
With rapid changes taking place in the demographic structure of populations,
institutionalization of older adults in long-term care (LTC) facilities (also called nursing
homes) is becoming common.There are 1,260 LTC facilities in India, as compared to
only 90 facilities in 1950 (HelpAge India, 2009). LTC represents a spectrum of health,
personal, and supportive services that cater to the requirements of individuals who are
unable to look after themselves adequately, due to the presence of chronic disease,
physical, or cognitive disabilities, and other health-related conditions (US Department of
Health and Human Services, 2013). Specifically, LTC involves the provision of
assistance with activities of daily living (ADLs), such as, bathing, dressing, using the
toilet, transferring, and eating; and instrumental activities of daily living (IADLs), such
as, grocery shopping, managing money and medications, preparing meals, using the
telephone, and caring for pets. LTC facilities also provide extensive nursing care services
and supervision over a 24-hour period (US Department of Health and Human Services,
2013). While older adults are the primary recipients of LTC, children and young adults
requiring chronic care are also potential recipients of these care services (Evashwick,
2005). The goal of LTC is to improve the ability of the older individuals to function
independently, and provide optimum care support to enable them to lead a higher quality
of life (Evashwick, 2005).
In LMICs, such as India, the provision of LTC is a relatively new and emerging
trend. This is primarily attributed to population aging, but also to the disintegration of
9
traditional family structure, increasing employment among women who are the
traditional caregivers, and other societal factors (Mahajan and Ray, 2013). LTC residents
are confronted with numerous challenges including presence of multiple chronic health
problems, disability, dementia, depression, and malnutrition. Malnutrition is a very
common challenge faced by older adults living in LTC all over the world. Although
malnutrition present in older adults living in care homes in HICs has been well studied and
recognized as a major research priority, is not given due importance in LMICs (Bell,
2015). It is important to identify potential risk factors, and work towards interventional
strategies to reduce malnutrition, and enhance nutritional health of the LTC residents.
2.2 Prevalence of malnutrition in older adults
Malnutrition has been defined as “a sub-acute or chronic nutritional state in which
the extent of over-nutrition or under-nutrition varies considerably, and leads to an
alteration in body composition and decreased function, with the simultaneous presence of
inflammatory processes” (Soeters et al., 2008, p. 708). Broadly, it is used to characterize
any imbalance in the nutritional state of a person either due to deficiency, or excess of
nutrients. Although there is no gold standard procedure for diagnosing malnutrition in
older adults, it is generally accepted by most researchers that this condition is
characterized by involuntary weight loss with a reduction of dietary intake resulting in
loss of muscle and subcutaneous fat or a low BMI <18.5 kg/m2 (National Center for
Classification in Health, 2010). Malnutrition is frequently observed in the frailest
population groups, particularly older adults having a low income and individuals who are
institutionalized i.e. living in LTC facilities (Donini et al., 2013).There are numerous
10
challenges faced by clinicians, nurses, and dietitians in recognizing individuals at
nutritional risk due to a lack of uniformity in defining malnutrition.
The European Society for Clinical Nutrition and Metabolism (ESPEN) has
recently proposed a consensus definition of malnutrition with the objective of reaching
conformity between different countries as well as previously carried out studies. This
definition considers an individual to be malnourished if she/he has lost weight combined
with a low BMI i.e. <18.5 kg/m2 for individuals <65 years,<20kg/m2 for individuals aged
65-70 years, and <22 kg/m2 for individuals aged >70 years, or low fat-free mass index
(FFMI) i.e. <17 kg/m2 for men and <15 kg/m2 for women (Rondel, Langius, Schueren, &
Kruizenga, 2016).Some definitions of malnutrition contain information on appetite,
fatigue, or quality of life (Kruizenga et al., 2005; Lim et al., 2012). As mentioned earlier,
most definitions of malnutrition are based on a low BMI or unintentional weight loss i.e.
>5% in the past 3 months or >10% in the past year (Dutch Malnutrition Steering Group
(DMG) and Dietitians of Malnutrition Netherlands, 2011; Kruizenga et al., 2005
;Todorovic & Micklewright, 2011).
The prevalence rates of malnutrition among older adults vary widely depending
on the setting, i.e., whether older adults live in the community independently, or live in a
LTC facility. They also vary depending on the definitions of malnutrition used, how it is
measured, and the population. Donini et al. (2013) used the Mini Nutritional Assessment
(MNA) in nursing home residents and community dwelling older adults and found that
malnutrition was present in both men (16.3%) and women (26%). The prevalence of
nutritional risk was reported to be 35.6% in men and 39% in women. Malnutrition
prevalence was significantly higher in nursing home residents with 31% men and 42.5%
11
women being malnourished. Women living in LTC were found to be at a greater
nutritional risk level (43.4%) than men (34.6%) (Donini et al., 2013).In the community
setting, the prevalence of malnutrition is estimated to range between 4 and 44% (Cuervo
et al., 2009; Kaiser et al., 2010).Among those living in LTC facilities, some international
studies have reported that 30 to 50% of older adults are malnourished (Kaiser et al., 2010;
Pauly et al., 2007; Soini et al., 2006; Stratton et al., 2003). Some studies have found that
the prevalence of malnutrition in LTC facilities of Canada and United States ranges
between 40 and 80% (Furman, 2006; Keller, 2000; Shatenstein, Kergoat, & Nadon, 2001).
Bell, Tamura, Masaki, and Amella (2013) have reported that malnutrition
prevalence in older adults living in long-term care (LTC) ranges from 4 to 71%, and
nutritional risk ranges from 29 to 97%. The wide variation in prevalence rate of
malnutrition and nutrition risk can be attributed to selection criteria used to define
malnutrition, and research methodology used which varies considerably, depending on
the setting. In most research carried out in LTC, the prevalence of malnutrition in older
adults has been reported to be between 20 and 39%, and nutritional risk typically ranges
from 47 to 62% (Bell, Tamura, Masaki, &Amella, 2013).
It has been reported that the prevalence of malnutrition in Indian older adults is
often under-represented and under-estimated. A study carried out in the city of Ambala in
the northern state of Haryana assessed the nutritional status of 300 community-dwelling
rural people aged >60 years by using the Mini-Nutritional Assessment Questionnaire
(MNA) and found that 26% individuals were undernourished and 64% were at a high
nutritional risk (Bishnoi, Kumar, Mittal, Goel, Nazir, Preet, Jain, 2016). Another study
was conducted on older adults living in the community setting of Dehradun in the state of
12
Himachal Pradesh and found that greater than 50% of older adults are underweight and
malnourished (Semwal, Vyas, Juyal, & Sati, 2014). Kalaiselvi et al (2016) assessed the
prevalence of under-nutrition in the rural town of Puducherry and revealed that 25% of
older adults were undernourished. Some studies have found that under-nutrition in Indian
older adults ranges from 14% to 52% (Jamir et al., 2015; Lahiri, Biswas, Santra, &
Lahiri, 2015; Vedantam, Subramanium, Rao, & John, 2010).
Malnutrition affects a considerable percentage of older adults living in Indian
LTC facilities (Jamir et al., 2015; Pai, 2011; Saha, Basu, Ghosh, Saha, & Banerjee,
2014). Individuals living in LTC facilities are undernourished due to a decline in food
intake and also have a lower BMI as compared to those living in the community. Several
studies carried out across the globe have discussed the nutritional challenges faced by
LTC residents (Bell, Tamura, Masaki, & Amella, 2013; Kaiser et al., 2010; Kayser-Jones,
2002; Pauly, Stehle, & Volkert, 2007; Serrano-Urrea, & Garcia-Meseguer, 2013). For
many people residing in LTC, aging brings about a continuous and progressive decline in
physiological functioning that increases the susceptibility to nutritional risk and leads to
feeding dependency which compromises autonomy (Porter Starr, McDonald, & Bales,
2015).
2.3 Consequences of Malnutrition in Older Adults
Malnutrition is regarded as a true geriatric syndrome which has a multi-factorial
origin, and leads to frailty and disability in older adults (Volkert, 2013). It is related to
adverse health outcomes in these individuals (Baldwin & Parson, 2004; Locher et al.,
13
2007). Specifically, malnutrition is correlated with a greater mortality rate and decline in
functional capacity (Chan et al., 2010; Ferdous et al., 2009; Ferreira et al., 2011).
Studies have shown that malnutrition contributes to cellular, physical, and
psychological degeneration by interfering with cellular defense mechanisms. Kubrak and
Jensen (2007) studied malnutrition in acute care patients and observed that it leads to
serious wound infections and pneumonia which lengthen hospital stay and increase health
care costs. Fatigue and lethargy are frequently observed in affected individuals, leading to
delayed recovery from illness, and worsening of anorexia in which an individual
experiences lack of appetite (Kubrak & Jensen, 2007). Reduction in taste sensitivity and
olfaction occurs frequently with advancing age and lowers the enjoyment experienced by
older adults during mealtimes. Appetite is severely altered in individuals taking multiple
medications (Morley, 2012). Anorexia which has been shown to predict mortality
independent of other factors (Rolland et al., 2006), leads to a 30% decline in dietary
intake in older men and 20% decline in dietary intake in older women (Morley, 2010).
This decrease in food intake as a result of loss of appetite has been termed as the
"physiological anorexia of aging" (Morley, 2012).
Malnutrition also leads to the formation of pressure ulcers in older adults who are
institutionalized. Studies show that the vulnerability of a malnourished individual to
develop pressure ulcers increases considerably (Neloska et al., 2016), and causes adverse
thermoregulatory effects. A pressure ulcer is a skin and/or tissue injury which typically
takes place over a bony prominence and leads to the progressive destruction of
underlying tissues (European Pressure Ulcer Advisory Panel, 2009). The prevalence of
pressure ulcers in LTC residents ranges between 11 and 29% (Donini et al., 2005;
14
Lahmann, Halfens, & Dassen, 2006). It has been reported in literature that there is a
direct relationship between the severity of malnutrition and occurrence of pressure sores
in LTC residents (Park, 2014). Older adults having pressure ulcers go through a
continuous cycle of losing protein in the form of excess exudate and experience delayed
wound healing (Neloska et al., 2016).
Naber et al. (1997) found that renal functioning is impaired and there is a lowered
absorption of nutrients from the intestine in malnourished individuals. It has also been
reported that malnutrition contributes towards sarcopenia (loss of muscle mass),
retrogression of visceral organs, and an impairment of the respiratory and cardiac muscle
functioning (Holmes, 2007; Kubrack & Jensen, 2007). Malnutrition has a considerable
impact on the functional status, mood, and quality of life of older adults (Rasheed &
Woods, 2013). It has been shown in many studies that the consequences of malnutrition
vary widely. Also, the onset of malnutrition and manifestation of the consequences occur
over an extended period of time influenced by a number of factors such as pre-existing
co-morbidities, physical and psychological state, age, and so on. Given the debilitating
consequences of malnutrition, it is important to identify and address the risk factors that
contribute to its development.
2.4 Factors contributing to malnutrition in LTC
Several research studies have been conducted in developed countries of the
western part of the world which have examined the contributory factors to malnutrition in
older adults. A recent systematic review carried out by Favaro-Moreira et al. (2016)
evaluated the risk factors that make older adults more vulnerable to malnutrition. They
15
included six longitudinal studies in their analysis and identified age, frailty especially in
institutionalized individuals, excessive intake of medications, reduction of physical
function, Parkinson's disease, constipation, poor or moderate self-reported health status,
cognitive deficits, dementia, mealtime dependency, poor appetite, swallowing difficulties
(dysphagia), loss of interest in life and institutionalization as important factors that lead to
malnutrition in older adults. A few studies in India have also examined risk factors
associated with malnutrition in older adults. Kalaiselvi et al. (2016) carried out a study to
assess the determinants of malnutrition in Indian older adults and found that the factors
making older adults more vulnerable to under-nutrition were reported to be age, a lower
socio-economic status, and male gender.
As indicated earlier, there are many factors that make older adults more
susceptible to malnutrition. Hickson (2006) has identified three major categories that lead
to an increased risk of malnutrition- medical, psychological, and social. According to
Hickson (2006), the most important medical factors that contribute to malnutrition are
oral health problems, loss of appetite, changes in taste and smell acuity, gastrointestinal
disorders, and neurological health conditions. Anxiety, depression, and bereavement are
the psychological factors that may increase an individual’s nutrition risk. In addition,
there are certain social factors such as lack of knowledge, loneliness, socio-economic
status, and poverty that lead to malnutrition.
2.4.1 Medical Risk Factors
2.4.1.1 Poor Appetite
16
A decline in appetite in older adults is one of the cardinal causes of malnutrition.
Food intake decreases with the increasing age of an individual (Amarya, Singh, &
Sabharwal, 2016). Various changes take place in the digestive system with increasing
age, including a decrease in gastric acid secretion, lower saliva production, consequently
leading to slow peristalsis and frequent constipation. This contributes towards appetite
loss and alteration of eating patterns which lead to nutritional deficits in older adults
(Amarya, Singh, & Sabharwal, 2016).
Roberts et al. (1994) have also reported that aging leads to impairment in the
mechanisms governing food intake. They carried out a study which found that younger
men are able to regain the weight lost in the ad libitum period, as compared to older men
who failed to gain weight during the same period. Other studies have shown that
cholecystokinin (CCK) levels increase with age and lead to sluggish gastric emptying and
early satiety (Clarkston et al., 1997). In addition, cytokines are involved in the regulation
of food intake in the older individuals and may adversely affect appetite in older adults
(Morley, 2001).
2.4.1.2 Oral health problems, Poor dentition and Dysphagia
The World Health Organization defines “oral health” as the absence of pain in the
mouth and facial tissues, the lack of birth defects such as cleft lip and palate, sores, and
periodontal disease, and no evidence of oral and throat cancer, tooth decay, or disorder
affecting the oral cavity (Petersen, 2003).Unfortunately, older adults in the LTC setting
are at a heightened risk of having oral health problems (Dion, Cotart, & Rabilloud, 2007;
Savikko et al., 2013; Soini et al., 2011). Rauen et al. (2006) have shown that there is a
significant association between poor oral health and compromised nutritional status in
17
older adults residing in institutions. Food intake and choices are restricted due to oral
health problems in older adults residing in LTC facilities. Specifically, tooth decay or
loss, periodontal disease, incompatible dentures, and a reduction in chewing capacity are
associated with an insufficient intake of energy, protein, carbohydrates, fat, fiber,
calcium, and antioxidants. Additionally, the enjoyment that accompanies mealtimes is
decreased (Sahyoun, 2004). Improvement in oral health can take place by using
preventive dentistry, replacement of lost or broken teeth, artificial saliva preparations,
well-fitted dentures, and texture modifications of food (Sahyoun, 2004).
Oral health issues also extend to swallowing difficulty often described as
dysphagia. This condition refers to the discomfort experienced by an individual while
bolus passes from the mouth to the stomach, and may be caused by oropharyngeal or
esophageal dysfunction (Clave, Verdaguer, & Arreola, 2005; Clave, Terre, de Kraa, &
Serra, 2004). Dysphagia is common among older adults and is often caused by dementia,
weakness, stroke, low salivary secretions, inflammatory disorders, infection, Parkinson’s
disease, and obstruction. Most cases of dysphagia are chiefly related to the oropharangeal
and/or the esophageal phases of swallowing.
The first subgroup involves oropharyngeal issues, including chewing problems,
poor neuromuscular coordination needed for normal food passage into the esophagus, and
could be preceded by difficulty in initiating the swallowing process. This may result in
choking, coughing, food getting stuck in the mouth, persistent pneumonia, alterations in
speech or voice, and regurgitation of food or fluids. Studies have reported the prevalence
of oropharyngeal dysphagia to be over 51% in the older individuals who live in LTC
facilities (Lin et al., 2002; Turley & Cohen, 2009).
18
The second subgroup involves esophageal issues where the patients may suffer
from problems related to passage of food from the back of the throat to the esophagus.
There could be a feeling of food being stuck in the throat or chest, pain in the chest
region due to difficulty in swallowing, or regurgitation of food after swallowing.
Involuntary loss of weight, changes in dietary patterns, or loss of appetite could be due to
any of the two above mentioned phases of dysphagia, or due to other problems (Kayser-
Jones & Pengilly,1999; Palmer, Drennan, & Baba, 2000). Individuals having
oropharyngeal dysphagia are particularly at high risk of developing lower respiratory
tract infections (Serra-Prat et al., 2012). Oropharyngeal dysphagia is associated with
tracheobronchial aspiration (inhalation of gastric contents into the respiratory tract) which
leads to pneumonia in about 50% of cases and also leads to increased mortality in
affected individuals (Clave et al., 2005). Therefore, dysphagia needs to be managed
effectively in these individuals to prevent the onset of respiratory problems and also to
prevent the onset of malnutrition.
2.4.1.3 Loss of taste and smell acuity
Loss of taste in older adults is commonly observed and results primarily from a
physiological decline in the number of taste buds and papillae. Loss of taste is also
governed by dryness of mouth and throat, diseases commonly associated with the process
of aging, prescription drugs, environmental factors, and surgical treatments (Donini et al.,
2003; Lang et al., 2006). Impairment of taste and smell in older adults is associated with
a reduced appetite, and thus a lower intake of food (Pilgrim, Robinson, Sayer, & Roberts,
2015).
19
Taste is mainly attributed to non-volatile substances present in food including
sugars, salts, acid, and limonin which primarily determine basic flavours- sweet, salty,
acidic, and bitter (Douglas, 2011). Individuals who have a diminished perception of these
flavours are unable to experience the enjoyment associated with meal consumption and
consequently have a reduced dietary intake (Boyce & Shone, 2006). Taste comprises a
prominent part of the cephalic phase response needed for the process of digestion. It
helps in the determination of food choices and meal consumption by raising satiety and
increasing pleasure during mealtimes (Schiffman, 1997). Loss of taste and smell
sensitivity is not uncommon in older adults and may be worsened due to the presence of
disease, or drug intake (Schiffman, 1997). Evidence indicates that persons having one or
more health problems, and consuming a minimum of three medications need
approximately 11 times more salt and three times sugar to recognize the presence of these
substances in food as compared to young adults (Schiffman & Gatlin, 1993). Studies
carried out in community dwelling older women in the age group 65 to 93 years suggest
that decreased flavour perception leads to a loss of interest in activities related to meal
preparation, and result in a higher consumption of sweets and fats (Duffy et al., 1995).
Some studies have demonstrated that making food flavourful can increase
palatability, and promote food intake in not only hospitalized older adults and LTC
residents, but also in healthy older adults (Mathey, Siebelink, de Graaf, Van Staveren,
2001; Schiffman & Graham, 2000). A decline in gustatory function has to be managed
effectively in older adults by using flavour enhancers in food to encourage eating in
persons who are experiencing multiple co-morbidities, and consume many medications
daily which adversely affect their sense of taste and reduce dietary intake (Toffanello et
20
al., 2013). A recent study carried out by Neumann, Schauren, and Adami (2016)
examined the taste sensitivity of older adults living in LTC institutions and found that
institutionalized older adults have a lower perception of sweet and sour flavour than
younger adults. They suggested that medications, smoking, alcohol consumption, and
gender were not significantly associated with taste perception and sensitivity.
2.4.1.4 Gastrointestinal problems
Decreased peristaltic contractions (Huang et al., 1995), delayed liquid emptying
(Kao et al., 1994) and reductions in solid emptying (Brogna et al., 1999) are the changes
normally associated with the aging process. In addition, a slower transit of fecal contents
through the colon is commonly observed in older adults over the age of 80. This delay in
passage is due to a decline in the number of neurons in the myenteric plexus (major nerve
supply to GI tract which contributes to GI tract motility) (Madsen & Graff, 2004). Some
studies have found a greater prevalence of atrophic gastritis in older adults that ranges
from 50 to 70% (Pilotto & Salles, 2002). Atrophic gastritis associated with the infection
of Helicobacter Pylori, lowers the production of ghrelin, and increases leptin activity.
Both these conditions increase the risk of developing anorexia and undernutrition in older
adults (Azuma et al., 2001; Nwokolo et al., 2003). Frequently occurring problems in
older adults include gastroesophageal reflux disease (GERD), dyspepsia, irritable bowel
syndrome (IBS), and chronic primary constipation (Grassi et al., 2011).
2.4.1.5 Neurological disorders
Neurological disorders refer to the disorders involving malfunctioning of the
nervous system. They involve a range of physical and psychological impairments which
require a multifaceted management (Murray & Lopez, 2002). Neurological disorders are
21
frequently observed in individuals aged 55 years and over and their prevalence ranges
from 5 to 55% in these individuals (Hofman et al., 2013). The most commonly observed
neurological conditions are dementia, Parkinson’s disease, poly-neuropathy, stroke and
essential tremor (Callixte et al., 2015). Dementia and Parkinson’s disease will be
discussed in the following section.
2.4.1.6 Dementia and Parkinson's disease
Dementia is not a normal part of the aging process and reflects multiple
impairments in cognitive functioning. It is an irreversible and progressive syndrome that
involves cognitive deterioration and can considerably lead to a decreased social and
occupational functioning (Health Quality Ontario, 2008). Alzheimer’s disease (AD) is the
most common cause of dementia and its prevalence ranges from 60% to 80% in older
adults (Alzheimer’s Association, 2015). Initially, there is a decline in episodic memory.
In advanced stages, there are marked changes in other aspects of memory and cognition
(Beydoun et al., 2014). Neuropathological studies have demonstrated that dementia in
older adults is representative of more than one pathological state (Kovacs et al., 2008).
Hypertension and hyperlipidemia are also associated with an increased risk of acquiring
dementia (Lautenschlager, Almeida, &Flicker, 2003). Research studies have found
essential links between nutritional status and cognitive functioning (Morris, 2012;
Solfrizzi et al., 2011).
Dietary factors including vitamins, antioxidants such as polyphenols, and fish
have been found to have a protective role against AD, whereas high intake of calories,
saturated fatty acids, and excessive alcohol intake have been shown to promote AD in
older adults (Ramassamy & Belkacemi, 2011). Individuals with a compromised cognitive
22
function are more vulnerable to malnutrition. In addition, depression and poly-pharmacy
are important contributing factors in the development of dementia (LoGiudice & Watson,
2014). Since residents of LTC facilities frequently consume multiple medications on a
daily basis, and often experience episodes of depression, they are at an escalated risk of
developing dementia which can reduce their food intake and place them at a higher risk
of malnutrition.
Alzheimer’s disease (AD) is characterized by decline in memory and cognitive
skills, and gradually leads to the inability to perform simple tasks of daily living. It is a
progressive and irreversible brain disorder, in which symptoms usually start appearing
after the age of 65, and is the most common cause of dementia in older adults (National
Institute of Aging, 2015). Increased pro-inflammatory cytokine levels are linked with a
higher susceptibility to develop Alzheimer’s disease in old age (Kang et al., 2014).
In Parkinson’s disease (PD), there is slow movement, tremor, and postural
instability. People with PD have stiff and aching limbs, along with the presence of gait
impairment (Nutt & Wooten, 2005) which prevent them from engaging in grocery
shopping, meal preparation, or independent feeding (Lorefalt et al., 2006; Miller
&Daniels, 2000). Additionally, individuals with PD have dysphagia, constipation, and
poor appetite leading to early satiety (Verbaan et al., 2007). Therefore, individuals having
these problems are at an elevated risk of malnutrition.
2.4.1.7 Cancer
Cancer is commonly associated with under-nutrition, especially, in advanced
stages. Individuals having cancer often undergo cachexia which is characterized by a
23
gradual weight loss, abnormal metabolic activity, and compromised immunity (Esper &
Harb, 2005; Van Cutsem & Arends, 2005). Cancer treatment involving chemotherapy
leads to modifications in dietary intake, nausea, swallowing difficulties, and depression.
Cancer has the potential to induce anorexia (decreased appetite), asthenia (lack of
strength and energy), and severe reduction in body mass index of affected individuals
(Fearon, Voss, & Hustead, 2006; van Halteren, Bongaerts, & Wagener, 2003).
Diagnosis of cancer is directly associated with anorexia (lack of appetite) and the
severity of anorexia depends on the location of the tumour, mood alterations, and toxicity
related to treatment being provided to patients. It is noteworthy that cancer is the second
most important risk factor for weight loss in older adults and cancer contributes manifold
in the severity of anorexia. Anorexia is a leading cause of weight loss in older adults with
and without cancer (Broughman et al., 2015; Nishino et al., 2015). Besides the medical
risk factors, there are some social risk factors that contribute towards nutritional risk in
older adults. These factors have been discussed in the following section.
2.4.2. Social Risk Factors
2.4.2.1 Inadequate nutrition knowledge and poverty
Individuals who have a low education level are often unaware of the nutritional
benefits of different foods and lack knowledge of health maintenance and well being.
Lower education status has been shown to strongly affect the nutritional risk profile of
community dwelling older adults aged 65 and over (Chor, Leung, Griffiths, & Leung,
2013). It has been reported in the literature that social factors, including poverty and low
level of education, play a role in influencing the nutritional status of older individuals.
Poverty is a strong determinant of health status of an individual (Benzeval & Ken, 2001)
24
and malnutrition is frequently observed in individuals having a low income (Chen,
Schilling, & Lyder, 2001). A dearth of economic resources restricts the availability of
food (Donini, Savina, & Cannella, 2003). The socio-economic status of an individual
determines the food choices that he or she would make and also has an impact on food
availability (Joseph & Carriquiry, 2010; Torheim, Ferguson, Penrose, & Arimond, 2010).
2.4.2.2 Loneliness and social isolation
Loneliness and social isolation are commonly observed in geriatric populations
and their impact on nutritional status of older adults is complex but well established. Loss
of a spouse through divorce or death may lead to grief, loneliness, lack of social support,
and lower social participation, which may have an impact on the nutritional status of
older adults (Quandt et al., 2000). A Finnish study was carried out to determine the
relationship between loneliness and malnutrition in older adults aged 75 years and over in
the city of Kuopio. The researchers assessed nutritional risk using the Mini Nutritional
Assessment (MNA) questionnaire, cognitive status using Mini Mental State Exam
(MMSE) and subjective loneliness was assessed by asking the participants how often
they felt lonely (3-step scale: 1= “often”, 2= “sometimes”, 3= “never”). Findings of this
study revealed that there was a strong association between nutritional risk and feelings of
subjective loneliness. Individuals who were found to be at a higher nutritional risk were
more prone to loneliness and were mostly women, had a lower body mass index (BMI),
lower MMSE scores and higher co-morbidity scores assessed using the Functional Co-
morbidity Index (FCI) (Eskelinen, Hartikainen, & Nykanen, 2016).
In another study involving 1200 older adults(>65 years) in rural areas of Lebanon,
social isolation and loneliness were found to be independently associated with
25
undernutrition. Data was collected using face-to-face interviews along with
administration of Mini Nutritional Assessment (MNA), the Lubben Social Network
Scale, and Jong-Gierveld Loneliness Scale. The Lubben Social Network Scale 6 (LSNS
6) includes three questions regarding the family networking of older adults.
The LSNS 6 determines the closeness of older adults with their relatives and
evaluates whether they have some close relatives whom they can approach for help or for
discussing private matters. The Jong-Gierveld Loneliness Scale is a 5 item scale that
includes questions on feeling empty, missing having people around, feeling a lack of
friends, feeling abandoned, and feeling the absence or lack of a really good friend. Social
isolation was found to be more frequent in the older individuals having physical and
mental status impairment. Individuals who were divorced, from a lower socio-economic
status, and had a lower level of education were at the highest risk of being socially
isolated (Boulos, Salameh, & Barberger-Gateau, 2016).
Studies have shown that the dietary intake of older adults who experience
loneliness and social isolation is severely reduced. For example, in a longitudinal study
carried out in the United States involving 1000 older individuals, social isolation and a
lower income were found to be important factors contributing towards a higher
nutritional risk (Locher et al., 2005). The nutritional status of older adults affected by
loneliness leads to poor food availability (Joseph & Carriquiry, 2010). This could be due
to geographical isolation and distant location of grocery stores. Absence of help with
grocery shopping and food preparation may lead to a compromised nutritional status.
Inadequate social support networks and social isolation have been found to adversely
affect the nutritional health of older adults (Locher et al., 2005; Pirlich et al., 2005).
26
2.4.3 Psychological Risk Factors
2.4.3.1 Depression
Depression is a very common problem faced by individuals staying in LTC
facilities(Diegelmann et al., 2017) and it is considered to be a major psychiatric disorder
in this population group (Nazemi et al., 2013). Studies have shown that prevalence rates
of depression could reach as high as 45% in institutionalized older adults (Jongenelis et
al., 2004; Webber et al., 2005). Untreated depression is a very serious public health
problem (Health Council of Canada, 2012; Cole & Dendukuri, 2003) and has been
associated with decreased health-related quality of life, higher morbidity and loss of
independence, decline in functional capacity, nursing home admissions (Harris &
Cooper, 2006), increased use of healthcare services (Gallegos-Carrillo et al., 2009), and
elevated risk of premature death from suicide and other medical health conditions
(Schoevers et al., 2009). There is a strong link between depression and malnutrition in
older adults (Evers & Marin, 2002; Feldblum et al., 2007; Kvamme et al., 2011;
Visvanathan, 2003). Vafaei et al. (2013) examined the relationship between malnutrition
and depression in rural older adults (> 60 years) and found that malnourished individuals
were 15.5 times more prone to suffer from severe depression. They concluded that both
under-nutrition and depression have deleterious effects on the health and well-being of
older adults.
27
Many studies have reported depression to be an important contributory factor to
weight loss observed in older adults (Morley & Kraenzle, 1994; Blaum, Fries, &
Fiatarone, 1995). Bales, Fischer, and Orenduff (2004) showed that 36% of residents in
nursing homes having involuntary weight loss suffered from depression. A study has
shown that low levels of vitamin B12 and disturbance in the ratio of omega 6 fatty acids to
omega 3 fatty acids is associated with late-life depression, even after controlling for other
risk factors (Tiemeier, 2003).
The presence of depression predisposes older adults to develop anorexia and this
could further increase their nutritional problems (Morley & Silver, 1995). A close
interrelationship exists between depression and malnutrition even after adjusting for the
level of education, smoking, and socioeconomic status (Morley & Kraenzle, 1994;
Cabrera, Mesas, Garcia, & de Andrade, 2007). Some evidence indicates that although
depression remains an under-recognized and under-treated problem in older adults, its
prevalence is high both in HIC and LMICs. Goyal and Kajal (2014) have reported that
that the prevalence of depression in older adults living in India is high. They recruited
100 individuals aged 60 years and above from different socio-economic backgrounds. It
was found that 17% of the participants were suffering from severe depression and 60%
were suffering from moderate depression.
2.4.3.2 Bereavement
Losing a loved one is very stressful and has a serious impact on an individual's
life (Tseng, Petrie, & Leon-Gonzalez, 2017). Many studies have been carried out to
assess the effect of bereavement on health status of older adults. Most studies have
focused their attention on the relationship of bereavement with a lower health care
28
utilization, depression, and mortality (van den Berg et al., 2011; Guldin et al., 2012;
Simeonova, 2013; Koslow, Ruiz, & Nemeroff, 2014). Bereavement also has a
considerable impact on an individual’s nutritional behaviour (Stahl & Schulz, 2014).
Stahl and Schulz (2014) have reported alterations in dietary intake of individuals who are
bereaved as compared to those who are not, and have found that meal skipping is very
common in bereaved individuals. In addition, they observed that bereaved persons
consume fewer servings of fruits and vegetables and have higher fat intake.
During the period of acute grief, men are considered to be at a greater risk of
encountering health problems as compared to females (Stroebe et al., 2001) and
widowers have a greater likelihood of being at high nutritional risk due to problems with
cooking which lead to an insufficient calorie intake (Koehn, 2001; van den Berg et al.,
2011). Women who lose their spouse may suffer from poverty due to financial
dependency on their partner and may be at a greater risk of morbidity and mortality
(McGarry and Schoeni, 2005). This also puts them at an elevated level of nutritional risk.
Depression resulting from loss of spouse leads to sleep disorders, lower immune function,
and increased susceptibility to multiple co-morbidities (Buckley et al., 2010; Hirschfeld,
2011).
Johnson (2002) studied nutritional risk level among bereaved individuals and
compared the results with individuals living in relationships i.e couples. NSI
DETERMINE Checklist was used to assess the nutritional risk profile of participants.
The findings revealed that bereaved individuals were at moderate nutritional risk
regardless of whether they had received grief counselling or not. The level of nutritional
risk in individuals coping with bereavement and those in coupled relationships was
29
significantly different. The study emphasized on the need to address food issues in
interventions targeting grief resolution.
Given the knowledge and understanding of the diverse set of factors contributing
to malnutrition, it is essential to screen older adults for identifying those at risk for
malnutrition or those with varying levels of malnutrition. Nutrition risk screening is
considered to be valuable as it helps in early identification of malnutrition, frailty, falls,
hospitalization, and delays institutionalization and death in older adults (Cederholm et al.,
2011; Bauer & Sieber, 2008).
2.5 Measurement of Malnutrition in Older Adults
Rather than relying on laboratory parameters such as serum albumin, researchers
consider nutrition screening tools to be more suitable for identifying undernourished
older adults and to screen for other nutritional problems including weight loss and lack of
appetite (Agarwal, Marshall, Miller, & Isenring, 2016). Nutritional screening is
considered to be the first step in the prevention and management of under-nutrition in
older adults and therefore several international nutrition organizations recommend
routine nutritional screening of high risk frail individuals in order to prevent
complications of under-nutrition (Gustafsson et al., 2013; Malone & Hamilton, 2013;
Mueller et al., 2011; White et al., 2012). Ideally, the responsibility of nutritional
assessment of older adults belongs to the dietitians, but they are always not available to
carry out assessment in care homes, particularly in LMICs. Clinical nurses are present in
most care facilities and have the ability to carry out routine nutritional assessment and
screening of older adults (Juntao et al., 2017).
30
There are several nutrition screening tools that have been used in identifying older
adults at a greater level of nutritional risk. Van Bokhorst et al. (2014) have reported that
20 nutrition screening tools have been used for assessment of nutritional risk in older
adults living in different settings i.e. community, acute-care, and long-term care. These
tools include the Chinese Nutrition Screen (CNS), Minimum Data Set (MDS), Simplified
Nutrition Appetite Questionnaire (SNAQ), Short Nutritional Assessment Questionnaire
for Residential Care (SNAQ-RC), DETERMINE, Geriatric Nutrition Risk Index (GNRI),
Mini Nutritional Assessment- Long Form(MNA), Mini Nutritional Assessment-Short
Form (MNA-SF), Revised MNA-SF, Malnutrition Universal Screening Tool (MUST),
Nutrition Risk Index (NRI), Nutrition Form For The Elderly (NUFFE), Rapid Screen,
Simple Screening Tool#1, Simple Screening Tool#2, Malnutrition Screening Tool
(MST), Nutrition Risk Screening (NRS), NRS-2002, and Subjective Global Assessment
(SGA). It was noted by the researchers who carried out a systematic review of these 20
screening tools that none of the tools performed consistently well in nutritional status
assessment of older adults. The Mini Nutritional Assessment (MNA) which is the most
commonly used nutrition assessment tool yielded modest psychometric properties. It was
concluded by the researchers that existing screening tools for the LTC setting are fairly
able to predict nutrition-related outcomes. The ideal screening tool for the LTC
environment should contain more items specific to the risk factors that contribute to
under-nutrition in older adults living in this setting (Van Bokhorst et al., 2014). Since
MNA is the most commonly used nutritional assessment and screening tool in older
adults, most emphasis has been placed on a detailed study of this measurement scale.
Mini-Nutritional Assessment (MNA)
31
The Mini-Nutritional Assessment (MNA) is the most widely used tool in care
home settings and has been studied extensively in the older adults (Salva, Corman,
Andrieu, et al., 2004). The complete MNA consists of 18 items (score ranging from 0 to
30) which covers anthropometric assessment (weight, height, mid-arm circumference,
calf circumference, and weight loss in the last 3 months), general assessment (mobility,
level of independence, prescription medications, pressure sores or skin ulcers), dietary
intake assessment (number of meals consumed, food and fluid intake, and self-feeding
capacity), and subjective assessment (an individual’s self-perception of health and well
being, comparison of one’s own nutritional status with that of others ). This questionnaire
is divided into two parts for classifying individuals on the basis of their nutrition risk
score obtained by completing the questionnaire (Guigoz, Lauque, & Vellas, 2002).
The first part consists of items A to F, and if an individual scores 11 and above,
the person is considered to be well nourished. The second part of the MNA has items G
to R and is administered to participants with a score of lower than 11. Individuals getting
a total score >24 are considered as “well nourished,” those getting a score lying between
17 and 23.5 are “at risk of malnutrition,” and those scoring <17 are potentially
“malnourished” (Guigoz, Vellas, & Garry, 1996). The MNA has been found to be useful
and effective in assessing the nutritional status of the geriatric populations in various
settings (Griep et al., 2000; Kaiser et al., 2010; Kaiser et al., 2011).
The development and validation of the MNA was carried out for timely
identification of protein-energy malnutrition in older adults (Guigoz, Vellas, & Garry,
1994; Guigoz, Vellas, & Garry, 1996). With the diagnostic criteria of MNA, this tool has
a sensitivity of 96% and specificity of 98% and predictive value of 97% (Vellas et al.,
32
1999). In addition, it possesses the predictive validity for negative health outcomes,
mortality, number of visits to general practitioner, length of hospitalization, and transition
to a nursing home (Berner, 2003; Kondrup et al., 2003). This makes it the most
appropriate tool for nutrition risk screening in older adults (Volkert et al., 2010).
As the commonly used nutrition screening tool, MNA has been used with more
than 35,000 older adults living in a variety of settings such as home care, hospitals,
institutional care, outpatient care and the community and the rates of malnutrition and
risk for malnutrition have ranged widely depending on the population. When separated
on the basis of their accommodation facilities, 21% older adults staying in service flats,
33% living in homes for elderly, 39% cognitively impaired individuals, and 71% older
adults residing in LTC institutions were in a malnourished state (Guigoz, Lauque, &
Vellas, 2002). Individuals obtaining low MNA scores have a higher likelihood of
suffering from disease (Beck & Ovesen, 1998) and being admitted to nursing homes (Van
Nes et al., 2001).
The reliability of the MNA has been evaluated in institutionalized older adults.
When tested in Spanish older individuals, the internal consistency estimated by
Cronbach’s alpha was found to be 0.83 and 0.74 for the first and second assessment
respectively. The inter-rater reliability was reported to be 0.89 for total MNA score and
higher than 0.89 for the continuous items. The value of Kappa index (inter-rater
agreement) was 0.78 and the researchers observed that the test-retest reliability for the
total MNA was good. In addition, the test-retest reliability of 12 items out of 18 was
found to be “almost perfect” using the Kappa index (Bleda, Bolibar, Pares, & Salva,
2002). This screening instrument is easily administered by professionals working in the
33
healthcare sector in geriatric clinics and also used in hospitals and nursing homes for
identification of patients requiring timely nutrition intervention. It was originally
designed and validated using data from Western countries involving primarily Caucasian
populations (Guigoz, Vellas, & Garry, 1994). It has been used without modification in
some non-white populations such as the Japanese (Kuzuya et al., 2005; Izawa et al.,
2006). However, it is recommended that for obtaining better results in certain
populations, some modifications are desirable in the original version of the MNA. The
MNA must be modified taking into consideration the cultural and anthropometric
characteristics of the population of interest (Chumlea, 1999; Chumlea, 2006).
Diekmann et al (2013) carried out a comparative analysis of three screening tools
among nursing home residents. They used the Mini Nutritional Assessment (MNA), the
Nutritional Risk Screening (NRS 2002), and the Malnutrition Universal Screening Tool
(MUST). They evaluated the applicability, classification of nutritional status, and
predictive value in the nursing home environment. The study was conducted on 200
residents living in two nursing homes of Nuremberg in Germany. The prevalence of
malnutrition using the MNA was 15.4% and it was observed that 8.6% the older
individuals were at nutrition risk using NRS 2002 and MUST. The one-year mortality
rate was highest in individuals categorised as “malnourished” using the MNA. MNA
showed the best predictive value for survival as compared to NRS 2002 and MUST in
LTC residents. The researchers of the study could not establish the superiority of any tool
over the others, but concluded that MNA items reflect specific conditions relevant in
older adults and are based on anthropometric thresholds that are suitable for geriatric
34
populations. It was suggested by the researchers of this study that MNA appears to be
suitable for use in the LTC setting for malnutrition assessment of older adults.
While several nutritional risk screening tools exist, no tool is considered to be
superior as compared to the others. As reported earlier, some of these are more
commonly used in LTC settings with better psychometric properties. Specifically, MNA,
and MNA-SF are more commonly used in LTC settings. After being developed by Vellas
and Guigoz in 1989, the MNA has become the most widely preferred nutritional
assessment tool (Guigoz, Lauque, & Vellas, 2002). It is the most appropriate tool for
assessing the nutritional status of individuals living in LTC facilities, since it offers a
multidimensional approach by including components that correspond well with
characteristic problems faced by LTC residents, such as dementia, pressure sores, feeding
problems, and mobility issues (Kaiser et al., 2010).
Research also suggests that any individual identified to be at nutrition risk should
have a complete nutrition assessment, and if indicated by the health care professional,
implementation of a nutrition intervention must be carried out (Joint Commission
International, 2011). As indicated earlier, nutritional risk predisposes individuals to not
only malnutrition, but also other adverse health consequences including an increased
morbidity and mortality. Nutritional risk and malnutrition have been associated with
compromised immunity (Bohl, Shen, Kayupov, & Della Valle, 2016), reduced functional
capacity (Kruizenga et al., 2016; Ahmed & Haboubi, 2010), and frailty which leads to an
increased propensity for falls (Kamo et al., 2017). The following section will summarize
the literature on falls in LTC context and creating a rationale for examining the
relationship between nutritional risk and fall risk in older adults.
35
2.6 Frailty and Falls in LTC Facilities
Older adults who live in LTC facilities are considered to be at high risk of frailty
(Kojima, 2015; Matusik et al., 2012). Frailty is a syndrome involving a decline in
physiological functioning and a heightened risk of adverse health outcomes resulting
from substantial deficits of multiple systems of an individual (Fried et al., 2001). The
likelihood of developing frailty increases as a result of dementia, sensory deficits (e.g.
visual impairment), poly-pharmacy and presence of multiple health problems (Kamo et
al., 2017). Frailty is an important contributor to falls, disability, institutionalization, and
even death (Lally & Crome, 2007). Prevalence of frailty in LTC residents ranges between
19% and 75.6% (Khater & Mousa, 2012).Since falls are the most common negative
health outcome associated with frail LTC population, the following section focuses on
falls in older adults living in LTC facilities.
2.7 Public Health Importance of Falls
Falls are considered to be a geriatric syndrome since they involve a deterioration
of multiple functions which is commonly observed in individuals who develop age-
related frailty (Montero-Odasso, 2016). Falls and their consequences have far-reaching
outcomes not only for the older adults who have experienced fall events, but also for
caregivers and health care providers. Screening for fall risk and timely identification of
the older individuals at risk of falling is critical for appropriate referral to fall-prevention
interventions. Research has demonstrated that falls have a multi-factorial etiology and
there is no definitive single diagnostic tool available for fall risk assessment (Frith, 2015).
It has not been established by past research studies that a particular performance-based
measure, self-reported measure, fall history information, or a combination of several
36
measures is best at prediction of future fall incidents (Lucardi et al., 2017).
Falls are caused by an interaction of several factors, including a reduction in
efficacy of postural responses, sensory deficits, musculo-skeletal impairment,
deconditioning related to physical inactivity, excessive polypharmacy, depression,
reduction in balance self-efficacy, and neuromuscular dysfunction (Lucardi et al., 2017).
Falls encountered by older adults pose a serious public health problem with an increased
economic burden not only for the affected person, but also for the society. In individuals
aged 65 years and over, falls result in frequent emergency department admissions and
long hospital stays lasting at least three weeks. There is sufficient data available on falls
and fall-related injuries in HICs such as Canada. In 2010/2011, the fall-related
hospitalizations reported in Canadian seniors were 78,330 and hospital stays were nine
days higher in individuals who had experienced falls as compared to individuals who had
been admitted for other reasons. Fall-related hospitalizations in Canadianolder adults
were reported to have increased by 19% between 2006 and 2010 and estimated to be
12,884 in number (Public Health Agency of Canada, 2014). Fall-related hospitalizations
in individuals living in LTC were associated with higher rates of hip fracture (59%) and
mortality as compared to fall-related hospitalization rates in community dwelling older
adults. Individuals living in LTC are a vulnerable population considered to be at a high
fall risk due to the complex health challenges faced by them including advanced
dementia, multiple chronic diseases, and limited mobility (Public Health Agency of
Canada, 2014).
Globally, an estimated 424,000 deaths occur per year as a result of falls and
greater than 50% of these fall-related deaths take place in older adults (aged 60 and over).
37
More than 70% of these deaths occur in developing countries (WHO, 2008). Compared
to older adults in the community setting, those in LTC have higher rates of falls.
Specifically, studies done in LTC institutions have computed that the annual average fall
rate per person is 1.7 which ranges between 0.6 and 3.6, and is higher than that observed
in community dwelling older adults, who have a mean fall rate of 0.65 ranging between
0.3 and 1.6 (Rubenstein, 2006). It has been estimated that a fall occurs every second day
in a LTC institution consisting of about 100 beds. Studies have shown that the rate of
hospitalization varies from 1.6 to 3.0 per 10,000 population in older adults living in
Canada, UK, and Australia (WHO, 2007).
Falls have numerous implications for older adults. Multiple health outcomes are
associated with the occurrence of falls, including, fear of loss of independence, social
isolation, poor mobility, depression, and episodes of confusion. Fallers aged 75 years and
over, are approximately four to five times more likely to be admitted to a LTC facility for
a year or more, as compared to fallers aged 65 to 74 years (Scott, 1990).Research has
shown that falls contribute to 95% of all hip fractures suffered by older adults and have
caused death in 20% of the cases (Wolinsky et al., 2009; Ioannidis et al., 2009; Jiang et
al., 2005). Falls can also contribute to premature transition to LTC facilities. Studies have
shown that one-third of older adults who are hospitalized following a fall are
institutionalized soon after being discharged from the hospital (Scott, Wagar, & Eliott,
2010). Besides having a tremendous impact on the society, falls also affect the quality of
life of older adults (Hartholt et al., 2011b).
Adequate research focusing on fall risk factors, burden, and prevention strategies
has been carried out in developed countries (Lord et al., 2007). However, there has been
38
insufficient research in the field of fall prevention and estimation of true burden, imposed
by the high incidence of falls in middle and low income countries. The high mortality
rates associated with falls in developing countries reflect the severity of morbidity,
disability, and treatment costs that follow in fall-related cases (Jagnoor et al., 2011;
Dandona et al., 2010). The prevalence of falls in Indian older adults has been reported to
be between 14% and 53% (D'souza, Shringapure, & Karol, 2008; Johnson, 2006; Joshi,
Kumar, & Avasthi, 2003).
Few studies have attempted to evaluate risk factors for falls that increase older
adults’ susceptibility to hospital admissions. Ravindram and Kutty (2016) have examined
the risk factors for fall-related injuries in community-dwelling older adults that result in
hospitalization in the city of Thiruvananthapuram in the state of Kerela in India. They
included 251 older adults who were admitted in hospitals following injuries suffered as a
result of falls. Patients admitted in the General Surgery, Orthopaedics, and Neurosurgery
departments were included in the research study. The mean age of the participants was
71.6 (9.1) years and out of a total cases of 251, most of the cases were females (n=165). It
was noted that hip fractures were the most common injury following a fall incident.
Visual impairment was present in 42% cases and 10% of total cases (n=251) had suffered
previous falls. The monthly income of 80% of cases was <INR 5,000 (=CAD$100). A
majority of cases (84%) did not use walking aids for going out of their room. The causes
of falls were mainly intrinsic including syncope (27%). Extrinsic causes included slipping
and tripping (66%) and some falls were a combination of both intrinsic and extrinsic
factors (7%).
39
Joshi, Kumar, and Awasthi (2003) have estimated that 1 in every 5 falls results in
a hip fracture in older adults. Johnson (2006) examined the frequency and nature of falls
in community dwelling and institutionalized older Indian women living in Kerela. A field
survey was used to collect demographic data and falls profile. The study reported a lower
prevalence of falls (45%) in community dwelling women, as compared to that observed
in LTC facilities (64%). Findings revealed that 70% of LTC residents, who encountered
falls, required medical treatment for injuries related to falls. The researcher recommended
the development and implementation of falls prevention strategies within the Indian
context. D'Souza et al. (2008) studied falls history, injuries, and hospitalization rates in
Indian older adults living in cities of Manipal and Udupi. This study found that 59% of
fall cases led to injuries, 16% resulted in fractures, 47% involved physician consultation,
and 19% required hospitalization. Kaushik and D'Souza (2008) reported a lower balance
and confidence level observed in 40% of individuals who had a history of previous falls.
Cardona et al. (2008) studied the burden of injuries in rural areas of Andhra Pradesh and
found that falls were more common in females as compared to males. The study revealed
that 86% of fatal falls occurred in individuals aged 60 and over.
2.8 Costs associated with falls and fall-related injuries
Falls in older adults put an immense burden on the health care system. The high
occurrence of falls has led to a high demand for healthcare services which include
emergency department visits, hospitalization (Hartholt et al., 2011a, 2011b; Stevens et
al., 2006), and LTC admissions (Hartholt et al., 2011b). Costs of treating injuries related
to falls are substantial, and have numerous implications for the family, society, and
40
community. Fall-related costs are increasing at a rapid pace all over the world (WHO,
2007).
In the United States, the direct medical costs of falls in older adults living in the
community were approximately $34 billion (Stevens, Corso, Finkelstein, & Miller,
2006). Few studies have investigated the costs of experiencing falls in nursing home
residents. It has been estimated that a fall encountered in a nursing home by an older
adult costs $1,456 (Carroll et al., 2008). Two surveys were carried out which revealed
that the average cost of a fall was $1,892 and it ranged between $700 and $13,000. This
cost was reported without envisioning the period beyond one year of follow-up, and it
also did not consider the amount spent in case of a second or third fall. In addition, it did
not include the amount needed for nursing care after a typical fall. Some research has
been done to estimate the cost of falls in Canada. For example, the direct expenses
associated with falls in Canadian older adults were estimated to be greater than $2 billion
in the year 2004. The total cost of falls for Canadian older adults (aged 65 and over) was
reported to be $26.8 billion which included direct care costs of approximately $15.9
billion (Parachute, 2015). The economic cost of injury in Canada has risen by 35% since
2004 and it is projected that by the year 2035 the cost of injuries will increase by 180%
and would be $75 billion annually (Parachute, 2015). This suggests that the costs of
treating injuries associated with falls are very high in individuals aged 65 years and over
and hence may exert an enormous burden on health care resources. It is therefore,
essential to screen older adults for falls to implement timely fall-prevention interventions
and save hospitalization and follow-up costs.
41
As mentioned earlier, falls are common with serious health related consequences,
and are a public health concern all around the world. As such, studies have focussed on
understanding the factors contributing to falls, and ways to address them through
appropriate interventions. In the following section, the risk factors associated with falls
will be discussed.
2.9 Risk Factors for Falls
Researchers have come to understand and agree that falls are caused by a complex
array of factors. Falls in older adults are the result of an interaction of long-term or short-
term risk factors; and short term triggers, including a trip, acute disease condition, or an
adverse reaction to medications (Tinetti, 2003). Factors contributing to falls can be
categorized into four major domains: biological, behavioural, environmental, and socio-
economic (WHO, 2007). Biological risk factors include age, gender, and ethnicity and
may play a role in influencing the “postural control system” of an individual. Behavioural
risk factors include alcohol intake, poly-pharmacy, wearing inappropriate footwear, and
lack of physical activity. These risk factors are potentially modifiable and are in a
person’s control. Environmental factors include uneven surfaces, architectural obstacles,
and transportation problems and socio-economic risk factors include poverty.
Falling risk becomes four times higher in older adults discharged from the
hospital and these individuals are at the greatest risk during the first two weeks following
discharge (Mahoney, Sager, Dunham, & Johnson, 1994). Hospitalization puts individuals
at a heightened risk of encountering falls, and approximately 29% of individuals who
have fallen during their hospital stay, would fall at home, 35% are likely to be
42
hospitalized again due to a fall, and 5% have a high likelihood of dying within 30 days
(Davenport et al., 2009). This is mainly due to the compromised nutritional status of
these individuals which makes them more prone to fall. Fall risk factors can also be
categorized as modifiable (e.g., gait, balance) and non-modifiable, as in the case of age
and gender. According to the American Geriatrics Society/British Geriatrics Society
Guidelines (2010), fall risk factors are mainly classified as extrinsic and intrinsic.
Important extrinsic risk factors are intake of multiple medications (polypharmacy),
particularly psychotropic medications, environmental hazards, such as poor lighting, lack
of safety equipment e.g. grab bars in bathrooms, and ill-fitted carpets. Major intrinsic risk
factors for falls include lower extremity weakness, history of falls, female gender, gait
and balance impairment, functional and cognitive decline, dizziness, lower body mass
index (BMI), urinary incontinence, age over 80, visual deficits, and depression. There is a
complex interaction and synergism between both intrinsic and extrinsic risk factors and
falls result from a combination of multiple factors reported above (AGS/BGS Clinical
Practice Guidelines, 2010). Scott, Dukeshire, Gallagher, and Scanlan (2001) have
proposed a model for risk factors that make older adults more vulnerable to encounter
falls. It is shown below.
Table 1. Risk factors for falls (Scott, Dukeshire, Gallagher, & Scanlan, 2001)
Biological/Intrin
sic
Behavioural Social and
Economic
Environmental
Poor mobility Fear of falling Low income Poor building
design
43
Gait/balance
deficits
History of falls Low education
level
Stairs
Muscle
weakness
Polypharmacy Language
barriers
Lack of hand
rails or grab bars
Chronic illness Antipsychotics
Antidepressants
Improper living
conditions
Poor lighting
Cognitive
decline
Alcohol
consumption
Living alone Slippery or
uneven surfaces
Stroke Lack of regular
exercise
Lack of social
support/networks
Obstacles
Parkinson’s
disease
Inappropriate
footwear
Cultural factors Tripping hazards
Arthritis Improper
assistive devices
Diabetes Poor nutrition
A meta-analysis showed that the strongest risk factors for encountering a fall are
having a history of falls, gait impairment, inappropriate use of walking aids, Parkinson’s
disease, vertigo, and consumption of anti-epileptic drugs (Deandrea et al., 2010). A study
carried out recently found that there is an increase in risk of encountering falls with a
sharp rise in musculoskeletal pain, number of joints affected, and the degree of hindrance
in an individual’s daily activities (Leveille et al., 2009). Also, studies have shown that
there is an increase in the risk of falling with the rise in number of risk factors. The one-
44
year falling risk almost doubles with every additional risk factor. When no additional risk
factor is present, the one-year risk of falling is approximately 8% but it rises to 78% in
the presence of four risk factors (Tinetti & Kumar, 2010). Some of the common risk
factors that lead to falls have been discussed in the following section.
2.9.1 Lower extremity weakness due to sarcopenia
Sarcopenia can be defined as the age-related progressive loss of skeletal muscle
mass and function (Fielding et al., 2011). Risk factors for sarcopenia have been identified
as age, female gender, a low body mass index (BMI), a decline in physical activity level,
and poor scores obtained on the six-minute walking test (Tramontano et al., 2017). The
process of sarcopenia involves either a gradual loss of muscle mass alone, or a
combination of an increased fat mass and a lower muscle mass. There are multiple factors
that contribute towards the development of sarcopenia including physical inactivity,
endocrine changes, chronic diseases, inflammation, and nutritional deficits (Tramontano
et al., 2017). The International Sarcopenia Initiative (ISI) has reported the prevalence of
sarcopenia among older adults to range from 1 to 33% (Cruz-Jentoft et al., 2014), but all
the studies on the estimation of sarcopenia prevalence have been conducted on older
adults residing in developed countries (Tramontano et al., 2017).
Sarcopenia is associated with a lower handgrip strength (Woo, Leung, Sham, &
Kwok, 2009), mobility impairment (Reid et al., 2008), poor balance and a higher risk of
falls in older adults (Szulc, Beck, Marchand, & Delmas, 2005). In addition, it increases
the risk of disability (Kim et al., 2015) and can prolong length of hospital stay in these
individuals (Gariballa & Alessa, 2013). Interestingly, sarcopenia is reversible to some
extent, and some studies have been carried out which have demonstrated that exercise can
45
play an essential role in prevention of sarcopenia and change the rate and pattern of
muscle mass decline in older adults (Hassan et al., 2015; Iolascon et al., 2014). Although
sarcopenia is considered to be an important risk factor for falls in older adults (Landi et
al., 2012), it can be managed and prevented with exercise interventions involving
physiotherapists and exercise trainers.
2.9.2 Lower Functional capacity
Individuals with a decreased functional capacity are more susceptible to
experience falls. Functional capacity is the ability to carry out activities of daily living
(ADL) in a normal or accepted way and it has been reported in literature that there is a
decline in functional capacity of older adults as they continue to experience the aging
process (Millan-Calenti et al., 2010). The inability to carry out ADL efficiently is a
growing concern among individuals aged 60 years and over since it may adversely affect
their quality of life and increase their level of dependency on others (Aijanseppa et al.,
2005). The prevalence of functional disability among older adults in rural India has been
reported to be 34.7% (Sharma, Parashar, & Mazta, 2014). In general, data on functional
disability among older adults populations of India is scanty (Agrawal, 2016). A recent
study carried out to predict factors contributing towards functional decline in Indian older
adults revealed that squatting, climbing stairs, and walking was limited in community
dwelling persons. A majority of the participants (67.1%) reported problems with
performing ADL. Factors contributing to a lower functional status were found to be
46
female gender, previous hospitalization, having two or more chronic diseases, memory
loss, and loneliness (Nagarkar & Kashikar, 2017).
With advancing age of an individual, the ability to maintain balance diminishes
(Lajoie & Gallagher, 2004) and studies have shown that balance and gait deficits are
crucial predictors of falls (Bergland, Pettersen, & Laake, 2000). Cognitive decline and
lower affective functioning may adversely affect the functional status of an individual
(Kaminska, Brodowski, & Karakiewicz, 2015).
2.9.3 Psychotropic medications
Psychotropic medications are commonly consumed by older adults living in the
community as well in LTC facilities (Cox et al., 2016).The intake of some psychotropic
medications is strongly associated with falls in older adults living in care homes (Cox et
al., 2016). Anxiolytic medications are used in the management of anxiety disorders, and
are consumed on a daily basis by 50 to 80% of residents of long-term care (Conn &
Madan, 2006). Meta-analyses on this topic have shown that the use of anxiolytics such as
benzodiazepines is associated with a heightened risk of falls in older adults (Ensrud et al.,
2002; Leipzig, Cumming, & Tinetti, 1999; Woolcott et al., 2009). Ray et al. (2000) have
reported that consumption of benzodiazepines in care home residents is significantly
associated with falls. Anti-depressants such as selective serotonin reuptake inhibitors
(SSRIs) have been also recognized as a risk factor for falls (Kerse et al., 2008). SSRIs
have been linked with a lower bone mineral density (Diem et al., 2007) and a higher risk
of fractures in older adults (Richards et al., 2007) and hence are believed to have a high
potential of increasing fall risk in older adults consuming these medications. Sterke et al.
47
(2012) have reported a significant increase in fall risk with low dosage use of anxiolytics,
hypnotics, sedatives, anti-depressants and anti-psychotics in older adults living in nursing
homes particularly those having cognitive impairment (e.g. dementia).
2.9.4 Vitamin D deficiency
Vitamin D is essential for maintenance of bone health and prevention of fractures.
It also plays a role in the prevention of certain chronic diseases such as diabetes and
cancer. In addition, it helps in developing a strong immunity against infections and
disease (Schwalfenberg, 2007). Vitamin D is obtained mainly through sunlight and food
sources of this vitamin are few, most important of which are egg yolk and fatty fish.
Fortified non-dairy drinks and fortified cow’s milk are major sources of vitamin D in the
diet (Vatanparast, Calvo, Green, & Whiting, 2010). Research indicates that fortification
is not adequate to prevent vitamin D deficiency (Vieth, 2012). Vitamin D is not
adequately synthesized during winter months and older adults are at an elevated risk of
deficiency (Whiting, Langlois, Vatanparast, & Green-Finestone, 2011) and therefore it is
important to take vitamin D supplements to meet daily requirements.
The prevalence of vitamin D deficiency in institutionalized older adults is high.
Low concentrations of serum 25(OH)D in older adults are associated with risk of
institutionalization and a high risk of mortality (Visser, Deeg, Puts, Seidell, & Lips,
2006). Older adults have a greater likelihood of having vitamin D deficiency due to lower
48
exposure to sunlight and the decreased formation of vitamin D3 under the influence of
UV light (Holick, Matsuoka, & Wortsman, 1989). Studies have shown that low
concentrations of 25-hydroxyvitamin D [25(OH)D] and 1,25-hydroxyvitamin D
[1,25(OH)D] lead to an elevated bone loss and increase the risk of falling (Bischoff-
Ferrari et al., 2004). Multiple cross-sectional studies have reported the association
between low 25(OH) D concentrations and decreased muscle strength in older adults
(Gerdhem, Ringsberg, Obrant, & Akesson, 2005). Randomized clinical trials have shown
that vitamin D supplementation has the potential to reduce 23 to 53% of falls in older
adults living in nursing homes (Bischoff et al., 2003; Flicker et al., 2005). In addition, a
meta-analysis of vitamin D supplementation and falls found a 22% decline in the number
of falls suffered by residents of nursing homes (Bischoff-Ferrari et al., 2004). Another
meta-analysis conducted by Murad et al. (2011) concluded that vitamin D plays a
cardinal role in improvement of functional capacity and prevention of falls which lead to
significant morbidity in older adults. Most of the evidence was derived from trials
comprising of older women (Murad et al., 2011). A meta-analysis carried out by Kalyani
et al. (2010) has shown the efficacy of vitamin D in falls prevention in older adults living
in the community and in nursing homes. The effect of vitamin D on decreasing the
number of falls was significant in certain subgroups of individuals, such as community
dwelling older adults aged 80 years and below, those having adequate calcium intake, no
previous history of fall or fracture, taking a dose of 800 IU and above.
The combined effects of vitamin D and calcium in fall risk reduction and
decreasing the severity of injurious falls have been studied. A study found that vitamin D
supplementation in combination with calcium reduced the severity of fractures,
49
particularly hip fractures in older adults and reduces the risk of injurious falls (Nowson,
2010). A meta-analysis was carried out by Weaver et al. (2016) to assess the combined
role of calcium and vitamin D in fracture reduction and found that there was a 15%
reduction in fractures in older adults taking these supplements. Vitamin D and calcium
when used in combination have the potential to reduce fall-related injuries such as
fractures and can prevent treatment costs of hospitalizations and surgeries.
Within the LTC environment, the presence of risk factors is further magnified
given the complex health and medical conditions often present in LTC residents. Older
adults living in long-term care institutions often suffer from depression and social
isolation. Their social networks get restricted, and they are unable to maintain close ties
with old friends and family members. A recent study done in Caucasian women aged 70
years and over has shown that stronger family networks lead to lower rates of falls
(Faulkner, Cauley, Zmuda, Griffin, & Nevitt, 2003). Changes associated with increasing
age such as, decreased balance, poor gait, and loss of vision (Tinetti, 1987; van Doorn et
al., 2003) contribute towards the increased proneness of nursing home residents to falls
(Spector et al., 2007).
2.10 Measurement of Fall Risk
Given the severe and debilitating consequences of falls and related injuries, and
the awareness of the risk factors that predispose older adults to falls, various assessment
tools have been developed. The tools vary widely in the extent to which various risk
factors are taken into account. For example, some consider multiple risk factors whereas
others focus only on risk factors related to physical function of individuals, or
50
psychosocial aspects such as the fear of falling. The tool used for assessing risk of falls in
older adults should be simple, reliable, and must have sufficient discriminative ability
(Kelly & Dowling, 2004).
2.10.1 Downton Index
The Downton index has been validated for use in the nursing home setting
(Rosendahl et al., 2003). It is a simple falls risk assessment tool and can be easily
administered by nurses. Studies have reported its predictive value to be similar to that of
other tools (Perell et al., 2001; Scott, Votova, Scanlan, & Close, 2007). It includes
information related to history of falls within the past 12 months, use of tranquilizers and
sedatives, diuretics, antidepressants, and anti-parkinsonian medications. In addition, it
assesses sensory deficits possessed by an individual. Visual and hearing impairment are
assessed by nurses, which is subsequently followed by evaluation of limb dysfunction.
Nurses are also required to examine the patients’ cognitive functioning status. The gait of
individuals being tested is also studied by examining their ambulatory potential and need
for assistive devices (Meyer, Kopke, Haastert, & Muhlhauser, 2009).
Rosendahl and colleagues (2003) observed that the sensitivity of the Downton
index ranged from 81 to 95% and the specificity ranged from 35 to 40%. These
researchers confirmed that relatively few individuals are falsely predicted by the
Downton index as being at low risk of falling. They concluded that low specificity of this
instrument is more acceptable than low sensitivity, as long as the fall intervention does
not cause any injury to the individual through the use of physical restraints, or obstacles.
This tool has been studied in nursing homes in Hamburg (Germany) but has not been
51
tested in developing countries. A recent study examined the characteristics of Spanish
older adults admitted in acute care who are prone to suffer falls. The Downton index was
used to assess the risk of falls in these individuals. Using this index, 56.5% of the older
patients were classified to be “at risk” of suffering a fall and females were found to be at
lower risk of falling as compared to males (Aranda-Gallardo et al., 2014).
2.10.2 Falls Efficacy Scale International (FES-I)
The Falls Efficacy Scale International (FES-I) was developed and validated by the
Prevention of Falls Network Europe (ProFaNE) (Yardley et al., 2005; Delbaere et al.,
2010). It is used extensively for evaluating fear of falling in older adults. There is a
shorter version of FES-I which has been developed and tested as well (Kempen et al.,
2008). Initially, the FES-I was validated in English which was followed by its validation
in several other languages (Kwan, Tsang, Close, & Lord, 2013; Ulus et al., 2012; Billis et
al., 2011; Camargos, Dias, Dias, & Freire, 2010). The FES-I questionnaire can be
completed within three to four minutes. The participant can complete the questionnaire or
the information can be collected using an interview process (Visschedijk et al., 2015).
The FES-I measures response of participants while performing 16 ADLs and assesses
their fear of falling. The response to each question of the questionnaire consists of four
levels ranging from “not at all concerned” to “very concerned.” The score obtained on the
FES-I ranges from 16 to 64 (Kempen et al., 2007).
52
The FES-I has shown good measurement properties in older adults living in the
community. In a group of 94 German older adults, the Cronbach’s alpha was 0.90 and the
intra-class correlation was reported to be 0.79. Another study carried out in a sample of
193 older adults living in the Netherlands, aged 70 years and above, revealed that the
Cronbach’s alpha was 0.96 and intra-class correlation was 0.82 (Kempen et al., 2007).
Visschedijk and colleagues (2015) assessed the reliability and validity of FES-I in older
adults (aged >65 years) living in Netherlands. They reported that the reliability and
construct validity of the FES-I are good. The Cronbach’s alpha was 0.94 which suggests
that the internal consistency of FES-I is desirable and highly acceptable. The ICC for all
raters was found to be 0.72. The researchers of this study concluded that the FES-I has
the desired internal consistency and reliability to measure fear of falling in older adults.
Mane, Sanjana, Patil, and Sriniwas (2014) assessed the prevalence and correlates
of fear of falling (FOF) in older adults living in the southern state of Karnataka in India
using the short version of FES-I and found that 33.2% older adults were struggling with
FOF. The researchers found that gender and socio-economic status were not significantly
associated with FOF among the subjects but a fall history, depression, restriction of
physical activity, education level, family type and presence of multiple health problems
was significantly correlated with FOF in this population group. The FES-I has been
translated into Hindi which is the national language of India and is spoken by most
Indians. Arora (2014) used the Hindi version of FES-I to assess the confidence of Indian
older adults (>60 years) in performing activities of daily living (ADL) and found that the
internal consistency (Cronbach’s alpha=0.831) and test-retest reliability (ICC=0.894)
were significant (p<0.001). Occupational status was found to cause variation between
53
mean FES-I scores of the study participants.The researcher concluded that FES-I is a
valid measure of fear of falling among Hindi speaking Indian older adults.
2.10.3 Timed-up-and-go (TUG) test
One of the commonly used tests is timed up and go (TUG) which was developed
by Mathias et al. (1986) for screening older adults having difficulties in the maintenance
of balance. TUG was taken to another level of sophistication by Podsialdo and
Richardson (1991) and Shumway-Cook, Brauer, and Woollacott (2000) by the addition
of elements such as time, and cognitive loading. TUG has been recommended by the
American Geriatrics Society, the British Geriatric Society, and Nordic Geriatricians as a
screening tool for identifying individuals at risk of falling (American Geriatrics Society,
British Geriatrics Society, American Academy of Orthopedic Surgeons Panel On Falls
Prevention, 2001). Specifically, it is used to detect individuals requiring an elaborate gait
and balance assessment (American Geriatrics Society/British Geriatrics Society, 2011).
This test involves computation of time taken by an individual to rise from a chair having
armrests, walk for a distance of 3 metres using some devices, turn at a predetermined
point, return to the chair, and get seated (Podsialdo & Richardson, 1991).
Cut-off values for TUG have been set between 10 and 25 seconds and are
reported to differentiate between fallers and non-fallers (Chiu, Au-Yeung, & Lo, 2003;
Dite & Temple, 2002). The commonly used version of the TUG involves completion of
the task at a convenient walking speed (Podsialdo & Richardson, 1991). However, some
modifications have been made to the original version such as, walking at the fastest
possible pace (Rikli & Jones, 1999), addition of tasks related to cognitive and motor
54
functioning (Shumway-Cook, Brauer, & Woollacott, 2000), time measurement of
different constituent tasks (Wall et al., 2000), use of a chair that is without arms (Smith,
Segal, & Wolf, 1996), and arranging another chair at the 3 metre path (O’Brien, Culham,
& Pickles, 1997). Timed-up and go (TUG) test has been used in a few Indian studies
(Anuradha et al., 2012; Krishnamurthy & Telles, 2007; Rege & Joshi, 2005) but is not
very commonly used in older adults in India.
2.11 Association between nutritional risk and fall risk
The literature review suggests that both nutritional risk and fall risk are common
among older adults. Malnutrition and falls are debilitating with negative consequences for
individual and health systems perspectives. With aging, muscle weakness and
gait/balance deficits increase the risk of falling by approximately three to fourfold (AGS,
BGS, & American Academy of Orthopedic Surgeons Panel on Falls Prevention, 2001).
Decline in muscle mass and functioning with age is commonly referred to as sarcopenia.
Multiple factors contribute to sarcopenia including imbalance in protein metabolism, lack
of adequate nutritional intake, (Kinney, 2004), presence of chronic diseases and intake of
several medications (Tinetti, 2003). Although literature indicates that there is an
association between malnutrition and an elevated fall risk (Vellas et al., 1990; Vellas,
Baumgartner, & Wayne, 1992), very few studies have been carried out to study this
association in geriatric populations.
Johnson (2003) examined the association between nutritional and falls risk in frail
older adults and found that fallers were at increased nutritional risk and had lower
55
physical and psychological well being as compared to non-fallers. The study found a
significant association between nutritional risk and balance and leg strength which are
important predictors of falls in the older adults. A study conducted by Daniels (2002)
revealed that older adults at high risk of falling in long-term care facilities are also at risk
of being undernourished. A recent study carried out to determine the association between
nutritional status and risk of falling in Taiwanese community dwelling older adults,
revealed that nutritional status is an independent predictor of falls in this population
irrespective of gender, age, falls history, hospital stay within past 12 months, and
difficulties experienced while performing ADLs and IADLs (Chien & Guo, 2014). Given
the prevalence of falls and nutrition risk as well as the adverse consequences, further
research is needed in this area especially from the context of LMICs.
Objectives
The primary objective of the study was to assess the nutritional and fall risk in
older women living in LTC facilities in India. The researcher also wanted to examine the
correlation between nutritional risk and fall risk of female LTC residents living in New
Delhi, India. In addition, the role of variables such as the depression, cognitive status,
general health status, and physical function in predicting fall risk was also examined.
Only women were included in the study. The other criteria were as follows:
Inclusion criteria
Individuals >60 years of age
Living in LTC institutions for at least 6 months
Residents of LTC who can walk independently or are using walking aids
Exclusion criteria
56
Individuals aged <60 years
Community dwelling older adults
Older adults that have been newly admitted in LTC facilities
Having serious health problems such as end-stage renal disease, cirrhosis, cancer,
or severe dementia
Hypotheses
It was expected that Indian older adults living in LTC facilities would have a high
nutritional risk and fall risk level. Also, it was expected that there will be a strong
association between nutritional and fall risk. Based on existing research within the HIC
context, it was further hypothesized that the high rates of depression, poor physical
function and fear of falling will be significant predictors of fall risk.
57
CHAPTER 3
METHODS
3.1 Selection of Facilities and Recruitment of Participants
Six “homes for the aged” (also referred to as old age homes) in New Delhi, were
selected to recruit participants for the study. To recruit the facilities and participants, the
researcher carried out a survey of old age homes in New Delhi registered with HelpAge
India foundation. Old age homes with a sizeable proportion i.e more than 50% of women
irrespective of whether they were Government affiliated care homes or private homes
were contacted. Invitation letters were distributed amongst the residents of each care
facility, and the researcher went to the care homes after a week to meet the residents who
had received the invitation letters. Residents who were willing to participate in the
research study were provided with letters of informed consent and after completing these
letters, interested residents completed the questionnaires administered to them by the
researcher. The data collection took place over a four month period (January to April
58
2016) after ethics approval had been granted by the Research Ethics Board (REB) at the
University of Regina and by the directors of the six care homes visited in New Delhi. A
sample of the letter of invitation and the letter of informed consent can be found in
Appendices A and B. The following section includes details of sample size estimation,
the inclusion and exclusion criteria, information on nutritional and fall risk screening as
well as other questionnaires, and statistical procedures used in the study.
3.2 Sample Size
A priori sample size N was calculated using recommended level of power (1-β or
80%), significance level α (0.01), and the expected effect size (Aberson, 2010; Cohen,
1988). Cohen classified effect size as small (d=0.2), medium (d=0.5), and large (d >0.8)
and stated that a medium effect size of 0.5 is visible to a careful observer and a small
effect size of 0.2 is although noticeably small but not trivial that it should be neglected. In
addition, a large effect size of 0.8 is considered to be the same distance above the
medium effect size as small effect size is below it (Coe, 2002). It is noteworthy that these
designations of effect size do not take into consideration other variables such as the
accuracy of the assessment tool or measure used and also do not account for the diversity
of the study population. However, these effect size designations provide a general guide
for researchers and must be informed by context (Sullivan & Feinn, 2012). G*Power 3
software was used to compute the sample size N (Faul, Erdfedler, Lang, & Buchner,
2007).
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The G*Power was developed as a stand-alone power analysis program for
conducting statistical analysis in social and behavioural research. It involves the
provision of options for carrying out power analysis for a range of commonly used t, F, z,
χ2, and exact tests. G*Power 3 is a very flexible method of power computation of any
statistical test that involves t, F, z, χ2 (Faul, Erdfelder, Lang, & Buchner, 2007). Using
G*Power 3 software, it was determined a sample size of 85 was needed for the study. The
sample size was calculated by using values of α= .01, power=.80 and effect size d=0.5
(medium effect size according to Cohen). Based on sample size estimation, the researcher
recruited a total of 85 older adults from six nursing homes (homes for the aged) in New
Delhi.
3.3 Measures of Nutritional Risk and Fall Risk
3.3.1 Background Profile questionnaire
A background profile questionnaire was used to obtain information about the
participants’ family background, education status, annual income, marital status, and
length of stay in LTC. In addition, information on medication intake and chronic illness
was obtained from the participants of the study. Participants were asked to mention their
self-perceived level of physical activity, and social support availability.
3.3.2 Mini-Nutritional Assessment (MNA)
The Mini-Nutritional Assessment (MNA), which is a non-invasive simple tool
with good psychometric properties, was used to screen individuals for nutritional risk
(Guigos, Vellas, & Garry, 1994). It includes 18 items covering anthropometric indicators,
dietary intake, global and self-viewed nutritional aspects. The score of MNA ranges from
0 to 30. A score of<17 is indicative of malnutrition, a score lying between 17.5 and 23.5
60
indicates moderate nutritional risk, and a score of >24 implies that an individual is at no
nutritional risk (Vellas et al., 1999).
3.3.3 Falls Efficacy Scale- International (FES-I)
The FES-I (Yardley et al. 2005) was used for evaluating the fear of falling which
is a strong risk factor for predicting future falls. The FES-I can be used to assess fear of
falling in older adults with or without a history of falls and fear of falling. The FES-I was
developed to include more challenging activities which older adults might fear
performing in order to prevent falls. The FES-I has been used to measure level of concern
about falling while performing physical and social activities inside and outside the house.
The FES-I was developed jointly by the members of Prevention of Falls Network Europe
(ProFANE), European Committee focused on fall prevention, and the psychology of
falling (Greenberg, 2011). This group assessed fear of falling in older adults living in
different countries and also translated FES-I into several languages such as Chinese,
Spanish, German, Dutch, English, French, Greek, Hindi, Swedish, Norwegian, and
Danish.
In FES-I, the participants were asked for their perceived level of concerns
regarding the likelihood of falling in relation to performance of 16 activities. These
activities included cleaning the house, getting dressed/undressed, preparing meals, taking
a shower, shopping, getting in and out of a chair, going upstairs and downstairs, walking
around inside the house, reaching up or bending down, using the telephone, walking on a
slippery surface, visiting a friend, going to a crowded place, walking on an uneven
surface, going to an event, and walking up and down a slope. The scoring of perceived
concern is done using a four point scale (“not at all,” “somewhat,” “fairly,” and “very
61
concerned”). The score of FES-I varies from 16 to 64 and a higher score(i.e. >23)
(Delbaere et al., 2010) means that the respondent has a strong fear of falling and a greater
fall risk.When Falls Efficacy Scale International (FES-I) was first developed and
validated it was found to have excellent internal consistency (Cronbach's alpha=0.96) and
also had impressive test-retest reliability (ICC=0.96) (Yardley et al., 2005). The FES-I
has shown very good internal consistency in most studies with a Cronbach's alpha value
of at least 0.79 (Delbaere et al., 2010). This questionnaire was developed using factor
analysis and has better psychometric characteristics as compared to the original version
of FES (Greenberg, 2011).
3.3.4 Downton Index
The Downton index (Rosendahl, Lundin-Olsson, Kallin, Jensen, Gustafson, &
Nyberg, 2003) was used for assessing risk of falls. This tool includes well established risk
factors that lead to falls, such as sensory deficits (visual impairment and hearing
impairment), medications (sedatives, diuretics, anti-parkinsonian drugs, antidepressants),
history of falls, gait (with/without use of walking aids), and cognitive status
(oriented/confused). The score ranges from 0 to 11 and obtaining a score of >3 is
indicative of a high risk of falling. This index has a very high sensitivity which suggests
that only few individuals are falsely predicted to be at risk of falling.
3.3.5 Geriatric Depression Scale
The Geriatric Depression Scale (GDS) was developed to detect depressive
disorders in older adults. In the present study, the long form of GDS was used. The GDS
consists of 30 items or questions which concern an individual’s feelings and behaviours
during the past week. It has a simple yes/no format and with no gradation or frequency
62
choices. Participants scoring 0 to 9 are normal or have mild depression; scores between
10 and 19 are indicative of moderate depression; and individuals who obtain scores
between 20 and 30 are suffering from severe depression. Responses to items 1, 5, 7,9, 15,
19, 21, 27, 29, and 30 must be "No" in order to get a score of 1 for each of the responses
to these questions (Yesavage et al., 1983). It was developed to be a self-administered
questionnaire, but can be administered with the help of another person who may not be a
professional (Roman & Callen, 2008). The GDS has been shown to have a sensitivity of
84% and specificity of 95% with a cut-off score greater than 11 in a sample that consisted
of normal controls and depressed individuals (Yesavage et al., 1983). The reliability and
validity of the GDS have been supported through clinical practice and research. The Long
form of GDS has been widely used by researchers and clinicians in the field of geriatric
psychiatry and has been recommended by the Institute of Medicine. The original Long
form is a powerful tool in identification of depression in nursing home residents,
including patients who are suffering from mild dementia (Jongenelis et al., 2005). The
short form GDS is not as robust as the long form GDS in the identification of minor
depressive symptoms. It has a slightly lower specificity and sensitivity when compared to
the long form GDS (Cwikel & Ritchie, 1988), but a large-scale study has found that it has
acceptable internal reliability and construct validity in identification of depression in
cognitively intact older adults (Friedman, Heisel, & Delavan, 2005).
3.3.6 SF-36 Health Survey
The SF-36 (Ware, Gandek, and IQOLA Project, 1994), which is a multi-purpose
health survey consisting of 36 questions or items related to functional status, physical and
mental health, and self-evaluation of general health status, was used in this study. Items
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are grouped into various categories or scales. These scales represent physical functioning,
physical role, bodily pain, general health, vitality, social functioning, emotional role, and
mental health. The first four scales represent physical health status and the last four scales
represent mental health status. It only takes 5 to 10 minutes to complete this health
questionnaire. Lowest score obtained is 0 and highest is 100. Higher scores are
suggestive of better health status. The RAND version of the SF-36 form (Nicholas,
Laucis, Hays, & Bhattacharyya, 2015) was used in the study. The RAND scoring
instructions were followed for grading responses to items of SF-36 questionnaire and
items are scored in a way such that a higher score defines a more desirable health state
(Nicholas, Laucis, Hays, & Bhattacharyya, 2015).
The reliability of the eight scales and summary measures has been estimated using
internal consistency and test-retest methods. Reliability statistics have exceeded 0.7
which is the minimum accepted standard in group comparisons in more than 25 studies
(Tsai, Bayliss, & Ware, 1997). Reliability estimates for physical and mental summary
scores have usually been greater than 0.90 in most studies (Ware et al., 1994). There is
evidence of adequate content, criterion, construct, and predictive validity. The SF-36
questionnaire is suitable for self-administration, computerized administration, or
administration by a trained interviewer in person or by telephone to individuals aged 14
years and older.
Pro-rating of item scores of SF-36 was carried out by the researcher to
compensate for items which were not included in the questionnaire due to their emotional
risk level. Items 16, 17, 18, and 19 were associated with adversely affecting the
64
emotional health of older adults and were thus not a part of the SF-36 form administered
to the participants in care homes.
Pro-rated score= Raw Score/Current number of items * original number of items
3.4 Measure of Cognitive Status
Mini-Mental State Examination (MMSE)
The Mini-Mental State Examination (MMSE) (Folstein,1975) is a questionnaire
designed to assess the cognitive status of individuals and this was used to test for the
presence or absence of cognitive deficits in this study. This questionnaire is quick and
easy to administer and its use requires minimal training. It is useful in identification of
seniors having dementia but it must be noted that its specificity decreases when
cognitively healthy individuals and patients with mild cognitive impairment have to be
discriminated.
The score of MMSE ranges from 0 to 30 and includes an assessment of
orientation, concentration, attention, verbal learning, naming, and visual-construction.
Scores lower than 9 are indicative of severe dementia, scores ranging from 10 to 19
indicate moderate cognitive deficits, scores ranging from 20 to 25 are suggestive of mild
cognitive impairment, and scores higher than 26 indicate a normal cognitive status. The
scores on MMSE are dependent on demographic variables such as age, and education,
where younger and more educated individuals are likely to score higher. Cultural
differences, head trauma, and severe depression may adversely affect MMSE scores.
False-positives may be reported especially among seniors having a lower education status
and lower socio-economic status. Despite some of its weaknesses, it has been used
extensively by clinicians to monitor cognitive functioning in older adults.
65
3.5 Measure of Balance and Mobility
Timed-up-and-go (TUG) test
The Timed-up-and-go (TUG) test (Podsialdo& Richardson, 1991) which measures
functional mobility is a commonly used screening tool for assessing falls risk in older
adults. In this test, the person is required to rise from a seated position in an arm chair,
walk a distance of 3 meters, turn and walk back to the chair, and sit down. The time taken
to complete this test and any difficulties encountered during the test are noted. A cut-off
score of >13.5 seconds is used for determination of falling risk (Rose, Jones, & Lucchese,
2002).
3.6 Measure of Functional Status
Handgrip strength
Handgrip strength (HGS) is considered to be a good measure of physical fitness
and frailty among older adults (Lam et al., 2016). HGS assessment of older adults is
essential to evaluate the degree of frailty and risk of future disability, and enables
researchers to identify functional limitations in early stages. HGS is also used to predict
negative health outcomes such as mortality and hospital discharge outcome (Sasaki,
Kasagi, Yamada, & Fujita, 2007). Measurement of HGS is simple, non-invasive and
inexpensive but most data available on HGS is from Western populations and scanty data
is available on Asian older adults who have a smaller stature, different genetics, lifestyle,
and occupations (Lam et al., 2016).
The standard procedure for measuring HGS using Jamar hydraulic hand
dynamometer (Patterson Medical, Warrenville, IL, USA) was followed which includes
asking participants to be seated on a standard height chair without armrests and
66
positioned as per the American Society of Hand Therapists' recommendation (Fess &
Moran, 1981). The grip bar or handle of the dynamometer was adjusted based on the
participant's hand size in order to get an optimal grip position and the test was performed
by participants after the researcher provided them with clear verbal instructions. HGS
was measured by asking participants to hold the device in their left hand and then right
hand and press as hard as they can. Participants pressed the dynamometer three times for
each hand and the highest value was recorded to get a measure of left hand strength and
right hand strength. Both the readings were added to get the total handgrip strength of
each participant.
3.7 Data Analysis
The data were entered and analysed using SPSS version 22.0. Data analysis
included descriptive and correlational analysis, multiple regression, factor analysis, and
reliability analysis for the key measures. Descriptive analysis was carried out to
determine means, frequencies, percentages, and standard deviations for the demographic
profile, physical activity profile, dietary intake, fall risk factors, depression risk factors,
general health status, and cognitive condition of the participants of the study. Correlation
analyses were carried out to examine the relationship between nutritional risk (MNA
scores) and fear of falling (FES scores), nutritional risk (MNA scores) and fall risk
(Downton Index scores) and to evaluate the relationship of nutritional status and
depression.
3.7. 1 Multiple Regression Analysis
Multiple regression analysis involves fitting a model to a data which is then used
to predict values of the dependent variable (DV) from two or more independent variables
67
(IVs). In other words regression analysis is a method of predicting an outcome variable
from two or more predictor variables. The multiple regression equation involving two
predictor variables can be expressed as follows:
Y= b0 +b1X1 + b2X2
where X1 and X2 represent the predictors or independent variables, b1, and b2 represent
regression coefficients and b0 is the intercept of the regression line. A straight line can be
defined by a slope (or gradient) which is denoted by b1 and the point at which the line
crosses the vertical axis of the graph i.e. b0 which is the intercept of the line. It is
noteworthy that a particular line has a specific intercept and gradient. We used the Enter
method which is also known as forced entry method where the predictor variables are
entered into the model simultaneously and the researcher can make no decision about the
order in which variables are entered into the analysis (Field, 2009). This ensures that
there is no bias involved and it gives replicable results if the model is tested repeatedly. It
is considered to be the only appropriate procedure for theory testing and is preferred over
hierarchical (block-wise method) and stepwise method where investigators can control
which predictor variables to enter first in the analysis (Studenmund & Cassidy, 1987).
3.7.2 Factor Analysis
Factor analysis was carried out to summarize data and interpret relationships
between variables. It is commonly regarded as a data reduction technique and is used to
regroup variables into a smaller set based on shared variance. Factor analysis is based on
the premise that measurable variables can be reduced to a few latent variables that have a
common variance and are unobservable. These factors that are unobservable are not
directly measured but are hypothetical constructs that are used to represent variables. In
68
simplest terms, exploratory factor analysis is used to discover a number of factors that
influence variables and to examine which variables "fit together." This procedure is very
valuable in understanding concepts and identifying constructs. It helps in the
simplification of inter-related measures and helps the researcher in determining patterns
by examining a set of variables.
3.7.3 Reliability Analysis
Reliability of a scale or test is a measure of the extent to which it is a consistent
measure of a concept or construct. Cronbach's alpha is the measure used to evaluate
reliability, or internal consistency, of a set of test items. Cronbach's alpha is usually
computed by correlating the individual score for each scale item with the total score for
each observation and then comparing it to the variance for all item scores. The value of
Cronbach's alpha ranges from 0 to 1 and a value closer to 1 is considered to be desirable.
Cronbach's alpha lower than 0.5 is unacceptable and values ranging from 0.6 to 0.8 are
generally considered to be acceptable. Reliability analysis was carried out for MNA,
FES-I, Downton Index, MMSE, GDS, and SF-36 Health Survey.
69
CHAPTER 4
RESULTS
4.1 Background profile of participants
The background characteristics of participants of the study have been summarized
in Table 2. All participants were of Indian origin and lived in either government -
affiliated or profit- oriented private care homes in New Delhi. Women (n=85) aged 60
years and over, were recruited for the study from a total of six LTC facilities. The mean
age of the LTC residents was 74.21(5.52) years and age range was 62 to 89 years. Most
were widowed (81.8%) and some were never married (5.9%).
Table 2
Background characteristics of Participants
Characteristic n (%)
70
Marital Status
Never married
Separated/Divorced
Widowed
Educational background
10th grade or below
12th grade
Bachelor’s degree
Number of children
None
One
Two
>Three
Annual income
INR <50,000
INR 50,000 to 100,000
INR 100,000 to 500,000
INR >500,000
No income
Frequency of visits by children
Once in 2 weeks
Once a month
Once in six months
5 (5.9%)
11 (12.9%)
69 (81.2%)
43 (50.6%)
17 (20.0%)
25 (29.4%)
10 (11.7%)
17 (20.0%)
40 (47.1%)
18 (21.2%)
26 (30.5%)
23 (27.3%)
8 (9.4%)
2 (2.3%)
26 (30.5%)
11 (12.9%)
30 (35.4%)
19 (22.3%)
71
Once every year
Never
19 (22.3%)
6 (7.1%)
A majority of participants (90.6%) had stayed in the residential care facility for a
period greater than two years. Most of them (68%) had two or more children and some
participants (11.8%) had no children. It was reported by 35.3% of participants that their
children visited them only once every month and provided them with some fruits, letters
and greeting cards sent by their grandchildren. Many residents did not have any source of
income (30.6%) and were supported by their care homes for their daily necessities, such
as food, medications, clothing, and others. Some residents living in these care homes
were getting monthly financial assistance in the form of pension from the government
and/or the former employer (11.8%). These individuals either had annual incomes
ranging between INR 100,000 to 500,000 or above INR 500,000.
4.2 Health and activity profile of participants
The health and physical activity profile information of the participants is shown in
Table 3. The mean body weight of the participants was found to be 59.14 (7.15) kg
72
(range 43 to 78.5 kg) and mean height was 159.62 (3.75) cm. The mean Body Mass Index
(BMI) of the residents of care homes was 23.29 (2.65). The WHO guidelines for BMI
cut-offs classify persons having a BMI of 18.5 to 24.9 kg/m2 as having a normal weight
but optimum BMI cut-offs for Indians proposed by Misra et al. (2009) classify
individuals as normal weight if their BMI is between 18.0 and 22.9 kg/m2, underweight if
their BMI is <18.0 kg/m2, overweight if their BMI is 23 to 24.9 kg/m2 and obese if BMI
is >25 kg/m2. Using Misra et al. (2009) BMI classification for Indians, 42.4% of
participants had normal weight, 28.2% were overweight, 28.2% were obese, and 1.2%
were underweight. Misra et al. (2003) has proposed that WHO guidelines for BMI
classification are based on morbidity data of Caucasian populations and are not
applicable to Indian populations which have a greater percentage of body fat and
abdominal adiposity at lower level of BMI compared to Caucasians. Therefore, BMI cut-
offs for Indians have been lowered and the optimal cut-off in identification of overweight
individuals has been proposed as 23 kg/m2. This has been confirmed in another study
carried out by Mohan et al. (2007) which recommends the ideal BMI cut-off for Indian
men and women to be 23kg/m2.
The study participants spent most of their time in sedentary activities such as
reading books and newspapers, listening to music, talking to friends, and watching
television. The mean sitting time was computed as 8 hours and mean time spent doing
exercise was 16 minutes. Participants were aware of the benefits of doing regular exercise
(99%) and knew that exercise keeps an individual in good health and is useful in
prevention of chronic disease (45%). Only one participant was not aware of the benefits
of engaging in exercise regularly. A majority of participants (84.5%) were willing to
73
participate in an exercise program in their care home, if the director or manager of the
facility was prepared to take some initiatives in organizing such programs on a daily or
weekly basis. Many individuals (48%) preferred individually designed exercise programs
in the presence of an exercise trainer or instructor. The researcher collected information
on the physical activity level (PAL) of the participants throughout their lives as younger
adults, and found that most of them (71.7%) led fairly active lives in the past and
performed exercise regularly. Some participants did not perform exercise at all (23.5%)
when they were younger or middle-aged adults while others (20%) performed
"pranayama," a form of yoga which originated in India and includes deep breathing
exercises comprising inhalation, retention, and exhalation practised at different rates of
respiration. A few participants preferred walking (26.6%) in the facility compound. Only
2.2% women reported doing 30 minutes of moderate-intensity exercise daily. The
researcher gathered information on knowledge of benefits of balance and strength
training in fall prevention. It was observed that only 18.8% of women were aware of the
importance of balance and strength training exercises in prevention of falls and fall-
related injuries.
Table 3
Health profile of participants
Characteristic n (%) or M (SD)
Weight (kg)
Height (cm)
BMI (Kg/m2 )
59.14 (7.9)
159.62 (3.7)
23.29 (2.6)
Mid arm circumference (MAC) 25.77 (1.9)
74
Calf circumference (CC)
Physical Activity profile
Time spent sitting (hours)
Time spent doing exercise (mins)
Preference for doing exercise
Individual exercise
Group exercise
Both individual and group exercise
Neither individual nor group exercise
Physical activity level (PAL) throughout life
Very active (% yes)
Active (% yes)
Not active at all (% yes)
33.34 (2.2)
7.94 (2.4)
15.56 (4.9)
40 (48.2%)
29 (34.9%)
4 (4.8%)
10 (12.1%)
n (%)
4 (4.7%)
61 (71.8%)
20 (23.5%)
The daily mean medication intake was 4.79 (2.34) and 60.6% of participants
consumed 3 to 6 medications in a day. A few women reported that they were healthy and
did not take any medications (8.2%). Some residents (23%) consumed 7 to 10
medications daily. Study participants had several chronic health conditions including
diabetes, hypertension, heart disease, asthma, osteoarthritis, gastrointestinal disorders
(including acidity, gastroesophageal reflux, and constipation), back problems, and
rheumatoid arthritis. It was reported that 94.4% of care home residents had chronic health
problems. The most common chronic health conditions found in the participants were
75
osteoarthritis (44.7%), back problems (39.7%), hypertension (38.8%), and
gastrointestinal disorders (37.6%).
The SF-36 scale was used to evaluate health-related quality of life (HRQoL) and
is comprised of 36 questions or items which assess eight domains including, physical
functioning, limitations due to physical health problems (role-physical, RP), body pain
(BP), general health (GH), vitality (VT), social functioning (SF), limitations resulting
from emotional health problems (role-emotional, RE), and mental health (MH). Four
items on the survey were found irrelevant to the context of LTC and were removed
leaving only 32 items. These items included: difficulty in performing work or other
activities (e.g. requiring extra effort (item 16)), during past 4 weeks emotional problems
(e.g. anxiety, depression) resulted in cutting down time spent on work or other activities
(item 17), accomplishing less due to these problems (item 18), and did not do work as
carefully as usual due to emotional reasons (item 19). The multiple items on the eight
subscales represent physical component summary (PCS) and mental component summary
(MCS). Higher scores represent a greater level of health-related quality of life (HRQoL),
and are suggestive of a better physical and mental health status. The general health
characteristics as assessed using SF-36 Health Survey have been described in Table 4.
Some study participants described their health status as being good (34%) and a
few individuals felt that their health status was rather poor (14%). Several participants
(45%) reported that their health was almost the same when compared to their health one
year ago. Almost half the study participants (48%) had limited performing moderate
activities to some extent, and vigorous activities were not at all performed by 93% of
participants. Doing grocery shopping was limited a lot by most individuals (62%), as they
76
were mostly satisfied with the meals they were being provided at the care home facilities,
and also did not have adequate financial resources to spend on purchasing groceries.
Climbing several stairs was also considered to be a difficult task and was limited a lot by
82% participants. A major proportion of participants (85%) described their inability to
accomplish tasks as per their preference due to physical health problems, and 51% of
women revealed that they were experiencing moderate body pain which restricted their
work productivity and social interaction. About 52% participants reported that they
mostly expected their health to become worse in the following years and a good bit of the
residents often felt blue, down hearted (31%) and were suffering from low energy levels
and fatigue most of the time (31%).
77
Table 4
General health status of study participants
Item No. Characteristic M (SD)
1 Self-perception of general health 35.8 (18.7)
2 Present health condition compared to health
one year ago
41.1 (23.3)
3 Difficulty performing vigorous activity 3.9 (12.9)
4 Performing moderate level of activity 28.5 (26.3)
5 Problems with lifting or carrying groceries 21.8 (25.7)
6 Difficulty climbing several stairs 9.9 (19.2)
7 Difficulty climbing one flight of stairs 47.6 (34.9)
8 Bending to find something on the floor 54.9 (40.1)
9 Walking more than a mile 58.8 (40.0)
10 Walking several blocks 12.6 (23.6)
11 Walking one block 50.8 (35.9)
12 Bathing or dressing up regularly 68.2 (39.4)
13 Cutting down time spent on work / activities
as a result of physical health problems
17.2 (36.2)
14 Accomplishing less than one’s preference due
to physical problems
17.2 (36.2)
15 Limited in the kind of work or activity due to
physical problems
37.0 (47.3)
78
20 Interference of physical problems in social
activities e.g. meeting friends/relatives
53.2 (22.8)
21 Presence of body pain 39.5 (17.7)
22 Interference of body pain with work 42.6 (25.2)
23 Feeling full of pep in the past 4 weeks 37.8 (19.8)
24 Been a nervous person in the last 4 weeks 45.2 (22.7)
25 Feeling low such that nothing could cheer you 56.9 (26.5)
26 Felt calm and peaceful 44.4 (20.9)
27 Had lots of energy 39.5 (19.7)
28 Feeling downhearted and blue 57.7 (22.3)
29 Felt worn out 45 (19.27)
30 Been a happy person 44.4 (22.8)
31 Feeling tired 45 (19.27)
32
How frequently in the past four weeks have
your physical problems prevented you from
social interaction e.g. meeting friends
46.9 (18.6)
33 Getting sick easily as compared to other
people
50.9 (24.5)
34 Are you as healthy as others 52.9 (27.6)
35 Expecting health to get worse 33.4 (17.5)
36 Feeling your health is excellent 22.5 (20.7)
79
Items 16, 17, 18, and 19 were omitted due to their emotional risk level with
participants living in care homes. These items concern difficulty performing work or
activities as a result of emotional problems e.g anxiety, depression. Pro-rating was carried
out to compensate for missing items on emotional health of older adults.
4.3 Nutritional characteristics of participants
The Mini-Nutritional Assessment (MNA) Long-form was used to assess the
nutritional risk profile of older women who participated in the study. The MNA includes
questions on weight loss, number of medications taken daily, dietary intake,
neuropsychological problems and self-perception of health status. The nutritional status
characteristics of the study participants are shown in Table 5. Weight loss between 1 and
3 kg was reported by 24.7% women and weight loss greater than 3 kg was reported by
32.9% women. Some women reported that they did not know if they had lost weight
(20%) in the past few months and 22.3% women had maintained the same weight status
as they did not lose any weight in the past several months.
80
Table 5
Nutritional profile of study participants
Characteristic n (%)
Meal intake
Three meals
Two meals
One meal
56 (65.9%)
28 (32.9%)
1 (1.2%)
Decline in food intake
No decrease in food intake
Moderate decrease in food intake
Severe decrease in food intake
27 (31.7%)
45 (52.9%)
13 (15.4%)
Weight loss during past 3 months
Between 1 and 3 kg
Greater than 3 kg
No weight change
Does not know
21 (24.7%)
28 (32.9%)
19 (22.4%)
17 (20.0%)
At least one serving of dairy products/day
Two or more servings of legumes per week
84 (98.8%)
79 (92.9%)
Two or more servings of fruits and/or
vegetables daily
62 (72.8%)
Fluid consumed per day
3-5 cups
>5 cups
6 (7.1%)
79 (92.9%)
81
Mode of feeding
Unable to eat without assistance
Self-feed with some difficulty
Self-feed without any difficulty
4 (4.8%)
8 (9.4%)
73 (85.8%)
Self-perception of nutritional state
Considers self as malnourished
Views self as having no problem
Uncertain of nutritional status
15 (17.6%)
37 (43.5%)
33 (38.9%)
Self-perception of health condition
Not as good
As good
Not sure
Better
21 (24.7%)
31 (36.4%)
27 (31.8%)
6 (7.1%)
MNA Score
Nutritional risk classification
<17 Malnourished
17.5 to 23.5 At nutritional risk
>23.5 Normal nutritional status
M (SD)
18.44 (4.71)
27 (31.7%)
46 (54.1%)
12 (14.2%)
Three complete meals were consumed by 65.8% of participants and 32.9%
preferred only two meals a day (breakfast and dinner). Milk, cheese, and/or yoghurt were
consumed daily by a majority of women (98.8%). At least two servings of fruits and
vegetables were included in the daily dietary intake of 72.9% of women. Most
participants were able to eat independently (85.8%) without assistance provided by
caregivers. A few persons were totally dependent on caregivers (4.7%) during mealtimes.
Many of them considered themselves to be healthy and without any nutritional problem
82
(43.5%). Some women viewed themselves as malnourished and at a high nutritional risk
(17.6%). The mean MNA Score of the participants was 18.44 (4.71) and they were
classified as “malnourished”, “at nutritional risk” and having a “normal nutritional status”
depending on the score obtained by adding their responses to MNA questions. It was
found that 54.1% of study participants were at nutritional risk based on the computed
MNA scores. Individuals who obtained MNA Score <17 were classified as malnourished
or undernourished and those who scored between 17 and 23.5 were considered to be “at
nutritional risk”. Moreover, individuals who scored greater than 24 on the MNA were
classified as having a normal nutritional status.
4.4 Fall risk profile of participants
The fall risk of participants was assessed using Downton Index, which is used to
determine presence of factors that lead to a heightened risk of falling, and the Fall
Efficacy Scale International (FES-I), which evaluates fear of falling. The Downton Index
measures fall risk and is based on information related to falls history, intake of sedatives,
diuretics, anti-depressants, gait status, and cognitive status. Individuals getting high
scores on Downton Index (>3) questionnaire are at a greater fall risk. Fall risk profile of
the participants has been listed in Table 6. The mean Downton index score was computed
as 3.38 (2.18). Using the Downton index classification, more than half of the total
participants (58%) were found to be at a high risk of falling. Many women faced balance
and mobility problems and an unsteady gait (45.9%). Only 32% were safe while using
assistive devices and had an adequate gait status. Some women were not comfortable
with using walking devices (14%) and were classified as “unsafe while using assistive
devices” such as cane sticks and walkers. These individuals had gait instability and
83
balance deficits and had lower confidence in using walking aids. More than half of the
participants had experienced previous falls (54%) and a majority of them had visual
impairment (71%) while some had hearing impairment (21%) and limb impairment
(33%).
The FES-I questionnaire examines the confidence of older adults in performing
16 activities of daily living and determines their concern about falling. The activities
include cleaning, meal preparation, dressing, bathing, going to answer the phone, grocery
shopping, moving out of chair/bed, climbing stairs, walking in the neighbourhood,
walking up or getting down a slope, walking on an uneven surface, walking on a slippery
surface, visiting friends, going to a crowded area and going to a social event. A higher
score obtained on FES-I is indicative of higher risk of falling. Scores obtained on the FES
typically range between 16 and 64 with higher scores indicating higher levels of fear of
falling. The mean FES-I score was 34.04 (13.8) and 60% of older adults had an intense
fear of falling as evident from their FES-I scores ranging from 28 to 64. Some residents
were very concerned about walking on a slippery surface (20%), walking on an uneven
surface (33%), walking in a crowded area (18%), and going to a social/religious event
(21%). Study participants had a greater confidence level (lower fear of falling) while
cleaning, dressing up, going to attend a phone call, and moving in or out of their chair
and bed, but had a low confidence level (higher fear of falling) while climbing or
descending stairs, walking on a slippery or uneven surface, bathing, visiting friends and
neighbourhood, going to a social or religious event and walking in a crowded place or on
a slope.
84
Table 6
Fall risk profile of participants
Characteristic n (%) or M (SD)
History of falls 46 (54.1%)
Use of tranquillizers 36 (42.3%)
Use of anti-depressants 20 (23.5%)
Use of diuretics 33 (38.8%)
Use of anti-hypertensives 33 (38.8%)
Visual impairment 60 (70.6%)
Hearing impairment 18 (21.2%)
Limb impairment 28 (32.9%)
Gait status
Need for assistive devices
Normal (safe without walking
aids)
Safe with walking aids
Unsafe without walking aids
Unable
45 (52.9%)
27 (31.8%)
12 (14.1%)
1 (1.2%)
Falls Efficacy Scale (FES-I)
Fear of falling while performing the
following activity
M (SD)
Cleaning 1.69 (0.8)
85
Preparing meals 1.68 (0.8)
Dressing or undressing oneself 1.67 (0.8)
Daily
Bathing
2.02 (0.9)
Grocery shopping 1.92 (1.0)
Moving in/or out of a chair 1.71 (0.9)
Climbing stairs 2.32 (0.9)
Walking in neighbourhood 1.99 (1.0)
Reaching for things above (in cabinets) 1.87 (0.9)
Going to answer the phone 1.76 (0.9)
Walking on a slippery surface 2.75 (0.8)
Going to visit friends 2.02 (1.1)
Walking in a crowded place 2.24 (1.1)
Walking on an uneven surface 2.89 (0.9)
Going up/down a slope 3.13 (0.9)
Attending a social event 2.35 (1.1)
FES Score (0 to 64)
Fear of Falling Classification
Low risk (score 16-19)
Moderate risk (score 20-27)
Severe risk (score 28-64)
M (SD)
34.04 (13.8)
n (%)
9 (10.6)
25 (29.4)
51 (60.0)
Most individuals who participated in the study (70%) had some form of visual
impairment. Some women recently had cataract surgeries (20%), while some others had
myopia or short-sightedness (35%); a few had retinopathy (10%), while the remaining
women had some unknown visual impairment (5%). Hearing impairment was also
present in some persons (21%) and only 7% of these women had been using hearing aids.
Others who did not have access to hearing aids felt helpless and experienced
86
communication problems. Limb impairment was present in 33% of women and resulted
in balance and mobility problems in these individuals and increased their susceptibility to
encounter falls.
The depression level was assessed using Geriatric Depression Scale-Long Form
which consists of 30 items with Yes or No response. The depression items included in
GDS are shown in Table 7. Individuals scoring higher than 20 on this questionnaire were
considered to be having severe depression while individuals scoring between 0 and 9
were considered to be having mild depression. Participants who scored between 10 and
19 were moderately depressed. A majority of them faced a drop in interest in daily
activities (80%) and some were dissatisfied with life (24.7%). Boredom was experienced
by 71.7% women and emptiness was present in the lives of 45.8% women. Several
participants felt that unpleasant things would occur in future (60%) and would constantly
worry about an uncertain future (67%).
More than half (53%) participants believed that other individuals are better off
than them 51.7% residents preferred avoiding social events and activities and most
individuals wanted to stay in the care facility (73%) as they found it to be safe and
comfortable as compared to going out. Using the GDS Classification scale, it was found
that severe depression was experienced by 47% women, and it was also observed that
42.4% women were moderately depressed.
87
Table 7
Level of depression as assessed using Geriatric Depression Scale (GDS)
Characteristic n (% Yes or No)
Satisfaction with life (% No) 21 (24.7%)
Drop of interest in activities (% Yes) 68 (80.0%)
Feeling of emptiness (% Yes) 39 (45.8%)
Getting bored often (% Yes) 61 (71.7%)
Hopeful about future (% No) 49 (57.6%)
Bothered by repetitive thoughts (% Yes) 56 (65.8%)
In good spirits most of the time (% No) 43 (50.6%)
Afraid of something bad occurring in
future (% Yes)
Feeling happy most of the time (% No)
Feeling helpless (% Yes)
Getting restless and fidgety often (% Yes)
51 (60.0%)
54 (63.5%)
52 (61.2%)
57 (67.1%)
Prefer staying at home rather than going
out (% Yes)
62 (72.9%)
Often worrying about future
(% Yes)
57 (67.1%)
Having memory problems greater than
others (% Yes)
Enjoy being alive (% No)
33 (38.8%)
31 (36.4%)
88
Feel blue or downhearted (% Yes) 53 (62.3%)
Feeling worthless often (% Yes)
25 (29.4%)
Thinking of past frequently (% Yes) 58 (68.2%)
Find life exciting (% No) 63 (74.1%)
Trouble concentrating (% Yes)
Enjoy getting up in the morning (% No)
Prefer avoiding social gatherings (% Yes)
Ease of making decisions (% No)
Clarity of mind (% No)
Mean GDS Score (0 to 30 points)
Level of Depression using GDS
Normal to mild (score 0-9)
Moderate (score 10-19)
Severe (score 20-30)
56 (65.8%)
39 (45.8%)
44 (51.7%)
21 (24.7%)
15 (17.6%)
M (SD)
16.6 (9.7)
9 (10.6%)
36 (42.4%)
40 (47.1%)
Cognitive deficits present in participants were assessed using the Mini-Mental
State Exam (MMSE) questionnaire which evaluates an individual's ability to remember,
recall, read, write, perform an act after reading the instructions, copy a diagram shown on
a piece of paper, and identify familiar objects. The degree or extent of memory loss can
be determined by this questionnaire. Higher scores obtained on MMSE are suggestive of
a better cognitive status and is indicative of acceptable level of memory, orientation,
89
registration, judgment, identification, attention, and recall. MMSE is a screening test that
is used commonly in geriatric populations to identify presence of cognitive deficits and
determine if dementia is present.
A considerable proportion of participants (41.2%) were aware of the date and
only 2% of them were unable to recall the date (Table 8). Almost half participants
(47.1%) were able to obtain full 5 marks and 34.1% participants obtained 4 marks on
recalling the exact location of self, including details of the care home address, floor
number where their apartment was located, name of care home, and name of the city
where it was located. Most individuals had sufficient object -identification skills (92.9%)
and were able to name three unrelated objects shown by the researcher. A majority of
individuals faced difficulty in spelling "world" backwards and only 7.1% of the
participants were able to spell "world" backwards without making any mistake. A
majority of women had the ability to follow instructions after reading (96.5%), and could
easily perform an act such as, repeating a given phrase (58.8%), and copy a diagram
shown on a piece of paper (85.9%). The mean MMSE score was found to be 21.7 (5.3).
It was observed that on the basis of MMSE cognitive status classification, 4.7% seniors
had severe cognitive deficits (score ranging between 0 and 9), 16.5% seniors had
moderate cognitive impairment (score ranging between 10 and 19), 49.4% seniors had
mild cognitive impairment (scores ranging between 20 and 24), and 29.4% seniors had no
cognitive deficits (scores ranging between 25 and 30) (Table 8).
90
Table 8
Severity of memory problems and cognitive decline assessed by Mini Mental State Exam
(MMSE)
Characteristic Item Score (scored out of 5) M (SD)
Awareness of date, day, month, year,
season
4.0 (1.1)
Awareness of location of self 4.2 (1.0)
Ability to name three unrelated objects 2.8 (0.4)
Recalling names of three objects
mentioned before
2.4 (0.8)
Spelling the word "world" backwards 1.5 (1.4)
Identifying two simple objects 1.8 (0.4)
Repeating a given phrase 0.6 (0.5)
Folding a sheet of paper as instructed 2.3 (1.2)
Reading and performing 0.9 (0.2)
Writing a sentence with both noun and
verb
0.2 (0.4)
Copying a given picture/diagram 0.8 (0.3)
4.5 Correlation Analysis
Correlation analysis was carried out to determine the relationship of variables
such as age, TUG score and total handgrip strength with each of the following test scores-
MNA, FES-I,GDS, Downton Index and MMSE. Results of this analysis have been
91
presented in Table 9 shown below. A higher age was found to be associated with lower
MNA scores (r=-.497) and indicates that advancing age was associated with a greater
nutritional risk. It was also observed that advancing age was associated with increased
fear of falling among study participants (r=.563). Older individuals were more concerned
about performing activities of daily living and feared that they might fall and injure
themselves. Individuals who had a higher fear of falling took longer to complete the
timed-up-and-go (TUG) test (r=.611). Women who scored well on MMSE questionnaire
had a higher functional status as their handgrip strength was greater. Depression severity
was shown to be associated with advanced age (r=.398) and scores obtained on Mini
Mental State Exam dropped with higher age (r=-.387).
Table 9
Correlation analysis of age, TUG time and total handgrip strength with MNA, FES-I,
GDS, Downton Index and MMSE scores
Pearson co-relation coefficient (r)
Scale Age TUG time HGS
MNA -.497
(p<0.001)
-.353
(p=.001)
.472
(p<0.001)
FES-I .563
(p<0.001)
.611
(p<0.001)
-.483
(p<0.001)
GDS .398
(p<0.001)
.353
(p=0.001)
-.504
(p<0.001)
Downton
Index
.329
(p<.002)
.480
(p<0.001)
-.421
(p<0.001)
MMSE -.387 -.318 .524
92
(p<0.001) (p<.005) (p<0.001)
Correlation analysis of Mini Nutritional Assessment (MNA) scores was
conducted with FES-I, Downton Index, Geriatric Depression Scale-Long form and Mini-
Mental State Exam scores and it shown in Table 10 below. It was found that individuals
having higher fear of falling had a lower nutritional status and the older adults who were
severely depressed according to GDS classification criteria had a lower nutritional status.
Individuals who scored high on Mini-Mental State Exam had a higher nutritional status
and consequently a lower nutritional risk.
Table 10
Correlation analysis of MNA with FES-I, Downton Index, GDS and MMSE scores
Pearson's correlation coefficient (r)
FES-I Downton Index GDS MMSE
MNA
-.644
(p<0.001)
-.419
(p<0.001)
-.616
(p<0.001)
.528
(p<0.001)
4.6 Multiple Regression Analysis
Multiple regression analysis was carried out with Downton Index as the outcome
variable or dependent variable (DV) and the MNA score as predictor variable or
independent variable (IV) along with other variables of interest, such as, fear of falling
(measured by FES-I score), fall history, depression (measured by GDS score), cognitive
status (measured by MMSE scores), body pain, gait condition, time taken to perform
93
TUG test and ability to carry out moderate intensity activities. Significant b values
(p<.001 or p<.05) indicated that the predictor variables (IVs) are strong determinants of
fall risk. The b values give us information about the relation between fall risk and
predictor variables. The sign of b-values indicates the direction of relationship between
the predictor and outcome variable. The summary tables contain valuable information
about unstandardized and standardized regression coefficients. Standardized regression
co-efficients are considered to be useful as researchers find them easier to interpret since
they are measured in standard deviation units, and thus are directly comparable and hence
provide a greater insight into the importance of a predictor variable.
Multiple regression analysis was carried out to test whether FES-I (measure of
fear of falling) score acts as a predictor of Downton Index Score i.e. fall risk (Table 11).
It was found that fear of falling represented by FES-I score is a strong predictor variable
and can determine future fall risk. FES-I is indicative of the confidence possessed by
seniors in performing 16 activities of daily living. The unstandardized regression
coefficients associated with MNA score and FES-I score were -.007 and .099
respectively. The standardized regression coefficients associated with MNA score and
FES-I score were -.015 and .627 respectively. As shown in Table 12, the F value was
28.05 and degrees of freedom for regression and residual were 2 and 82. Also, both MNA
score and FES-I score together account for 40.6% variability in fall risk.
94
Table 11
Multiple regression analysis involving FES-I Score as a predictor of fall risk (Downton
Index Score)
Model Coefficients*
Unstandardized
coefficients
Standardized
Coefficients
t-test Sig level
B Standard
error
Beta
Constant .132 1.424 .093 .926
MNA Score -.007 .052 -.015 -.139 .890
FES-I Score .099 .018 .627 5.641 .000
Table 12
Model summary for regression analysis involving Downton Index score (DV) and MNA
score and FES-I score as IVs
DV IVs R R2 F df Sig.
Downton
index score
MNA
score, FES-I
.637 .406 28.05 2
82
.000
The researcher also attempted to determine if previous falls encountered by study
participants act as a predictor of fall risk. Both MNA score and fall history were entered
into the regression analysis as independent variables and Downton Index score was
95
entered as the dependent variable or outcome variable. From Table 13, it can be noted
that the unstandardized and standardized regression coefficients for MNA score were -1.1
and -.227 respecively. For fall history, the value of unstandardized and standardized
regression coefficients was 2.2 and .509. As included in the model summary in Table 14,
F value is 27.16 and degrees of freedom are 2 and 82. R2 is .399 which implies that both
MNA score and fall history together account for 39.9% variability in fall risk.
Table 13
Multiple regression analysis showing fall history as a predictor of fall risk
Model Coefficients** t-test Sig. level
Unstandardized
coefficients
Standardized
coefficients
B Standard
error
Beta
Constant 4.1 .914 4.5 .000
MNA Score -1.1 .043 -.227 -2.4 .016
Falls history 2.2 .403 .509 5.5 .000
Table 14
Model summary for regression involving Downton Index as DV and MNA and fall
history as IVs
DV IVs R R2 F df Sig.
Downton
Index
MNA
Score, Fall
history
.631 .399 27.16 2
82
.000
96
The researcher found that difficulty in carrying out moderate intensity activities
was a predictor of fall risk. The unstandardized and standardized regression coefficients
for MNA score and moderate intensity activity have been shown in Table 15. The
negative sign of b-values suggests that there is an inverse relationship between difficulty
in performing moderate intensity activity and Downton Index score. From Table 16, it
can be noted that the F value was 22.28 and degrees of freedom for the regression and
residual were 2 and 82. Both MNA score and difficulty in doing activities of moderate
intensity accounted for 35.2 % variability in Downton Index scores or fall risk. Difficulty
encountered by participants in doing activities of moderate intensity indicates that they
had low functional status and were vulnerable to develop dependence on caregivers for
performing activities of daily living (ADL).
Table 15
Multiple regression showing a decreased ability to perform moderate activities acting as a
predictor of fall risk
Model Coefficients** t-test Sig.
level
Unstandardized coefficients Standardized
coefficients
B Standard
error
Beta
Constant 5.930 .813 7.29 .000
MNA Score -.083 .047 -.180 -1.76 .082
Difficulty with
moderate
intensity
activity
-.040 .008 -.483 -4.72 .000
Dependent variable: Downton index score
97
Predictor variable: MNA Score, difficulty with moderate level of activity
Table 16
Model summary for regression analysis involving Downton Index score as outcome
variable or DV and MNA score and difficulty performing moderate level of activity as
predictor variables or IVs
DV IVs R R2 F df Sig.
Downton
Index
MNA Score,
Difficulty
performing
moderate
level of
activity
.593 .352 22.28 2
82
.000
Multiple regression analysis was conducted to determine whether visual and limb
impairment act as predictors of fall risk. Both visual and limb impairments were found to
be strong predictor variables as their effect on Downton Index score was found to be
significant. From Table 17, it is clear that the unstandardized and standardized regression
coefficients for visual impairment are 1.995 and .419, respectively. It implies that older
adults living in care homes who suffer from visual impairment are at a greater fall risk.
Visual impairment makes it difficult for older adults to notice obstacles in their path
while walking or descending stairs.
98
Table 17
Multiple regression analysis showing visual impairment as a predictor of future fall risk
Model Coefficients** t-test Sig. level
Unstandardized
coefficients
Standardized
coefficients
B Standard
error
Beta
Constant 4.525 .962 4.705 .000
MNA Score -.139 .043 -.229 -3.189 .002
Visual
impairment
1.995 .447 .419 4.465 .000
It can be noted from Table 18 that both MNA score and visual impairment
account for 33.7% variability in fall risk. The F value was 20.85 and degrees of freedom
for regression and residual were 2 and 82. Obtaining lower scores on MNA questionnaire
is associated with a higher level of nutritional risk and as discussed earlier, loss of visual
acuity makes it difficult to notice impediments, uneven surfaces, and increases the
susceptibility to encounter falls in care home residents. From Table 19, it can be observed
that limb impairment is also a significant predictor of fall risk.
99
Table 18
Model summary for regression analysis involving Downton Index score as DV and
MNA Score and visual impairment as predictor variables or IVs
DV IVs R R2 F df Sig.
Downton
Index
MNA Score,
Visual
impairment
.581 .337 20.85 2
82
.000
Table 19
Multiple regression analysis showing limb impairment as a predictor of future fall risk
Model Coefficients** t-test Sig. level
Unstandardized
coefficients
Standardized
coefficients
B Standard
error
Beta
Constant 3.874 .834 4.646 .000
MNA Score -.077 .041 -.166 -1.889 .062
Limb
impairment
2.793 .406 .605 6.885 .000
Limb impairment was found to have a role in predicting fall risk in care home
residents. Foot problems adversely affect balance and mobility and this has the potential
to result in a fall event. From Table 19, it can be seen that the unstandardized and
100
standardized coefficients for limb impairment are 2.793 and .605, respectively. It can be
noted from Table 20 that the F value was 37.51 and degrees of freedom were 2 and 82.
MNA score and limb impairment together account for 47.8% variability in fall risk. This
implies that both nutritional risk and lower extremity problems can contribute towards an
increased fall risk in older adults living in LTC facilities.
Table 20
Model summary for regression analysis involving Downton Index score as outcome
variable or DV and MNA score and limb impairment as predictors or IVs
DV IVs R R2 F df Sig.
Downton
Index score
MNA score,
limb
impairment
.691 .478 37.51 2
82
.000
As shown in Table 21, the unstandardized and standardized regression
coefficients for body pain were found to be -.053 and -.428, respectively. Body pain
experienced by participants in the past four weeks significantly predicted fall risk.
101
Table 21
Multiple regression analysis showing recently experienced body pain as a predictor of
future fall risk
Model Coefficients** t-test Sig. level
Unstandardized
coefficients
Standardized
coefficients
B Standard
error
Beta
Constant 6.911 .809 8.548 .000
MNA score -.091 .050 -.197 -1.836 .070
Body pain
experienced
in the past 4
weeks
-.053 .013 -.428 -3.992 .000
It can be noted from Table 22 that the F value was 18.42 and the degrees of
freedom were 2 and 82. Both MNA scores and body pain accounted for 31% variability
in fall risk (Table 22). Body pain was responsible for the low physical activity level in
some seniors. The presence of chronic pain in older women restricted their mobility and
affected their functional status. Decreased mobility is a major challenge faced by many
older adults living in LTC facilities and it decreases their ability to move independently
within the LTC environment.
102
Table 22
Model summary for regression analysis involving Downton Index score as outcome
variable or DV and MNA score and body pain as predictors or IVs
DV IVs R R2 F df Sig.
Downton
Index score
MNA
score, body
pain in past
4 weeks
.557 .310 18.42 2
82
.000
The researcher was interested in determining whether time taken to perform
timed-up-and-go test acts as a predictor of future fall risk. Table 23 highlights the
summary of the regression analysis with Downton score as outcome variable and MNA
and TUG test performance as the predictor variables. The unstandardized and
standardized regression coefficients for the TUG test performance are .421 and .400
respectively. As presented in Table 24, the F value is 14.38 and degrees of freedom are 2
and 76, respectively. Both MNA score and TUG test performance together account for
27.5% variability in fall risk. This implies that a low nutritional status and mobility status
can both increase risk of encountering falls. TUG test is a simple test which normally
takes less than 13.5 seconds to complete and older adults who take longer to complete
this test have problems with balance, gait, and mobility. These individuals are at a higher
risk of encountering falls. It was observed that the time taken to perform TUG test can
effectively predict Downton Index scores in care home residents.
103
Table 23
Multiple regression analysis with Downton index score (fall risk) as outcome variable or
DV and MNA score and time taken to complete timed-up-and-go (TUG) test as
predictors or IVs
Model Coefficients** t-test Sig. level
Unstandardized
coefficients
Standardized
coefficients
B Standard
error
Beta
Constant .090 1.974 .046 .964
MNA score -.113 .052 -.226 -2.164 .034
Time taken
to perform
TUG test
.421 .110 .400 3.827 .000
Table 24
Model summary for regression carried out between Downton Index as DV or outcome
variable and MNA score and time taken to complete TUG test as predictors or IVs
DV IVs R R2 F df Sig.
Downton
Index score
MNA score,
TUG test
performance
.524 .275 14.38 2
76
.000
Table 25 contains the information on regression analysis carried out between
Downton Index score as outcome variable and MNA score and GDS score as
independent variables. GDS scores, which are indicative of the level of depression found
104
in older adults, were found to act as a predictor of fall risk. As shown in Table 25, the
unstandardized and standardized regression coefficients for GDS score are .091 and .406,
respectively. From Table 26, it is clear that both MNA score and GDS scores together
account for 27.8% variability in fall risk. In other words, both nutritional status and
depression can cause variance in the risk of encountering falls.
Table 25
Multiple regression analysis involving GDS scores as a predictor of fall risk
Model Coefficients** t-test Sig. level
Unstandardized
coefficients
Standardized
coefficients
B Standard
error
Beta
Constant 3.313 1.351 2.453 .016
MNA score -.078 .055 -.169 -1.423 .159
GDS scores .091 .027 .406 3.413 .001
Table 26
Model summary for regression analysis involving Downton Index score as DV or
outcome variable and MNA score and GDS score as IVs or predictor variables
DV IVs R R2 F df Sig.
Downton
Index score
MNA
score, GDS
score
.528 .278 15.81 2
82
.000
105
Table 27
Multiple regression analysis showing gait condition acting as a predictor of future fall
risk
Model Coefficients t-test Sig. level
Unstandardized
coefficients
Standardized
coefficients
B Standard
error
Beta
Constant 4.462 1.279 3.488 .001
MNA score -.092 .059 -.198 -1.540 .127
Gait
condition
.950 .364 .335 2.608 .011***
***p<.05
From Table 27, it can be noted that the unstandardized and standardized
regression coefficients for gait condition are .950 and .335, respectively. Gait condition
significantly predicts Downton Index scores. It can be noted from Table 28 that the F
value obtained was 12.87 and degrees of freedom were 2 and 82, respectively. Both
MNA score and gait condition together account for 23.9% variability in fall risk. Older
adults living in LTC often experience difficulties with maintaining an optimum gait status
and frequently need assistive devices such as cane sticks and walkers to prevent fall
incidents. Gait disturbances are commonly observed in older adults and are mostly multi-
106
factorial in origin. Sensory impairment, including visual deficits, hearing problems, and
vestibular deficits, along with neurodegenerative processes and medication intake
adversely affects gait status of older adults. Individuals having gait impairment often
complain of joint stiffness, pain, numbness, lower extremity weakness, and an altered gait
pattern. Gait disorders make older adults highly vulnerable to encounter falls since
individuals with gait problems engage in sedentary behaviours and avoid being
physically active which further increases their fall risk. MNA scores and gait condition of
older adults accounted for 23.9% variability in fall risk.
Table 28
Model summary for regression analysis involving Downton Index score as DV and
MNA score and gait condition as IVs or predictors
DV IVs R R2 F df Sig.
Downton
Index score
MNA score,
gait
condition
.489 .239 12.87 2
82
.000
Table 29 contains information on regression analysis between Downton Index
score as outcome variable and MNA score and social interaction as predictor variables.
The unstandardized and standardized regression coefficients for social interaction with
friends are -.059 and -.509, respectively. It can be noted from Table 30 that the F value
was 21.87 and the degrees of freedom were 2 and 82. Both MNA scores and lower social
107
interaction with friends and family together accounted for 35% variability in fall risk
(Table 30). Study participants reported that their social interaction with friends and
family was restricted after being transferred to LTC facilities, and thus their social
support networks were limited to the individuals living in the care homes including their
neighbours and caregivers looking after their care needs. A reduction in social activity
was found to act as a predictor of fall risk. Social support networks play an important role
in determining the satisfaction with life and lack of social interaction adversely affects
the health status of institutionalized older adults. It contributes towards loneliness and
social isolation and diminishes quality of life. Negative b-values represent an inverse
association between social interaction and fall risk. The lower the social networks of
older adults, the higher would be the fall risk and vice versa.
Table 29
Multiple regression analysis showing lower social interaction with friends and family
acting as a predictor of fall risk
Model Coefficients t-test Sig. level
Unstandardized
coefficients
Standardized
coefficients
B Standard
error
Beta
Constant 6.930 .786 8.817 .000
MNA score -.058 .051 -.125 -1.145 .256
Lower social
interaction
-.059 .013 -.509 -4.651 .000
108
Table 30
Model summary showing regression analysis between Downton index score (DV) and
MNA score and lower social interaction with friends and family as predictors (IVs)
DV IVs R R2 F df Sig.
Downton
Index score
MNA score,
reduced
social
interaction
.590 .348 21.87 2
82
.000
4.7 Factor Analysis
Factor analysis was conducted by including items of the Mini-Nutritional
Assessment (MNA) questionnaire as seen in Table 31. It was found that the value of
KMO test of sampling adequacy was .615 and the chi-square value of Barlett's test of
sphericity was 360 (p<.001). The computed KMO value was significant for the MNA.
From Table 32, it is clear that Factor 1 (mobility status) accounted for a large proportion
of variance (23.7%), while other factors accounted for a much smaller proportion of
variance including factor 2 (intake of psychotropic medications) (10.6%), factor 3 (lower
dietary intake) (9.7%), and factor 4 (self-perception of health status) (8.4%). Table 33
contains the factor loadings for the various items of MNA and the interpretations are
based on these factor loadings. The factor structure of MNA is not desirable within the
context of use among the LTC residents in this study as seen in Table 33. There are only
two loadings under each factor that have been taken into consideration. Factor loadings
with value below 0.300 have been excluded from the Table.
109
Table 31
Factor analysis of MNA items
Characteristic Value
Kaiser- Meyer- Olkin (KMO)* test of
sampling adequacy
0.615
Barlett's test of sphericity (chi-square) 360
* KMO test is significant (p<.001)
Table 32
Total variance explained using factor analysis for determination of nutritional risk by
including MNA items
Factor/Component Eigen value % variance
Factor 1 3.8 23.7
Factor 2 1.7 10.6
Factor 3 1.6 9.7
Factor 4 1.3 8.4
Factor 5 1.3 8.0
Factor 6 1.1 6.7
110
Table 33
Rotated Component Matrix of items of Mini Nutritional Assessment (MNA)
Item Component 1 Component 2 Component 3 Component 4
Moving out of
bed/chair
.795
Feeding
assistance
required
.796
Fluid
consumption
.681
Psychological
stress present in
past 4 weeks
.716
Intake of >3
medications
daily
.786
Decreased food
intake
.831
Weight loss in .810
Two or more
servings of
legumes per
week
.793
Comparison of
self health
status with that
of others
.740
111
Factor analysis was done using items of Falls Efficacy Scale-International (FES-I)
as seen in Table 34. Fear of falling concerning 16 activities is presented in the FES-I
questionnaire in the form of 16 items, including cleaning, meal preparation, bathing,
reaching above to get something, moving in or out of a chair or bed, dressing, grocery
shopping, climbing stairs, going up or descending a slope, going to answer the telephone,
walking in a crowded area, walking in the neighbourhood, walking on a slippery surface,
walking on an uneven surface, visiting a friend or relative, and attending a social or
religious event. The KMO test of sampling adequacy and Barlett's test of sphericity were
computed and found to be .955 and 2090 respectively which were significant (p<.001).
As shown in Tables 35 and 36, most of the variance was explained by factor 1 which
indicated concern about moving out of bed or chair to perform activities inside the care
facility (79.6%). The remaining variance was explained by factor 2 which represented
concern about going out of the care facility (6.4%). From Table 36, it can be seen that the
loadings on factor 1 are higher than the loadings on factor 2. The interpretations of factor
structure of FES-I are based on the factor loadings shown in Table 36.
Table 34
KMO and Barlett's test for Falls Efficacy Scale International (FES-I)
Characteristic Value
Kaiser-Meyer-Olkin measure of sampling
adequacy
.955
Barlett's test of sphericity (chi-square) 2090
112
Table 35
Total variance explained using factor analysis for items of FES-I
Component Eigen value % Variance
Factor 1 12.7 79.63
Factor 2 1.2 6.4
Factor 3 0.5 3.1
Factor 4 0.3 1.8
Table 36
Rotated Component Matrix for FES-I using Varimax Rotation technique
Item Factor 1 loadings Factor 2 loadings
Fear of falling while
cleaning
.874
Fear of falling while
preparing meals
.880
Fear of falling while
dressing
.899
Fear of falling while
bathing
.789
Fear of falling while
moving in or out of a chair
.860
Fear of falling while
climbing stairs
.679
Fear of falling while
walking in a neighbourhood
.781
113
Fear of falling while
reaching above for things
.801
Fear of falling while going
to answer the phone
.821
Fear of falling while
walking on a slippery
surface
.664
Fear of falling while
visiting friends at their
place
.558
Fear of falling while
walking on an uneven
surface
.890
Fear of falling while
ascending or descending a
slope
.904
Fear of falling while
attending a social or
religious event
.642
Factor analysis was also carried out using items of Geriatric Depression Scale
(GDS) as seen in Table 37. The KMO and Barlett's chi-square test values were observed
to be .878 and 1898 respectively and were significant (p<.001). From Tables 38 and 39, it
can be noted that worrying about future (Factor 1) accounted for a major proportion of
variance (48.4%) in depression. Remaining variance in depression was accounted by
being unhappy with present condition (factor 2) (6.7%), lacking the energy levels and not
being in good spirits most of the time (factor 3) (5.4%), and drop of interest in activities
and experiencing boredom often (factor 4) (4.4%). Factor loadings shown in Table 39
114
were used to make interpretations about constructs that were underlying the factors. GDS
has a very good factor structure and it adequately represents the underlying construct i.e.
depression.
Table 37
KMO and Barlett's test for Geriatric Depression Scale
Characteristic Value
Kaiser-Meyer-Olkin test of sampling
adequacy
.878
Barlett's test of sphericity (chi-square) 1898
Table 38
Total variance explained for depression using factor analysis of GDS items
Component Eigen value % Variance accounted by
the item
Factor 1
Factor 2
Factor 3
Factor 4
Factor 5
14.0
1.9
1.5
1.2
1.1
48.4
6.7
5.4
4.4
3.9
115
Table 39
Rotated component Matrix for items of Geriatric Depression Scale (GDS)
Variable/Item Component
1
Component
2
Component
3
Component
4
Component
5
Worrying about
future
.673
Feeling
blue/downhearted
.769
Thinking about
past events
.765
Afraid something
bad will happen
.693
Helplessness .659
Do not feel like
getting up in the
morning
.728
Avoid social
gatherings
.778
Frequently feel
like crying
Not finding life
exciting
Not feeling
energetic
.595
.738
.764
Not hopeful
about future
.689
116
Not being in
good spirits most
of the time
.636
Drop of interest
in activities
.765
Avoid going out .561
Worrying about
future
.510
Getting bored
often
.610
Difficult to take
decisions
.659
Dissatisfied with
life
.786
Feeling empty .425
Feeling worthless .622
Results of factor analysis using items of SF-36 Health Survey questionnaire
revealed that KMO was .904 and Barlett's chi-square test value was 1753 (p<.001) (Table
40). Most variance was accounted by a lower functional status (factor 1) (50.9%), and
the remaining proportion of variance was explained by psychological and emotional
health (factor 2) (10.4%), body pain (factor 3) (4.7%), and self-view of health status
(factor 4) (4.3%) (Table 41). Using varimax rotation procedure, it was observed that
loadings on factor 1 are higher than loadings on factor 2, 3, and 4. Table 42 shows the
factor loadings that were used to draw interpretations of the underlying constructs of the
factor structure of SF-36 questionnaire.
117
Table 40
KMO and Barlett's test for SF-36 Health Survey
Characteristic Value
Kaiser-Meyer-Olkin measure of sampling
adequacy
.904
Barlett's test of sphericity (chi-square) 1753
Table 41
Total Variance explained by various items of SF-36 Health Survey using Factor Analysis
Item Eigen value % Variance accounted by
the factor
Factor 1 12.7 50.9
Factor 2 2.6 10.4
Factor 3 1.2 4.7
Factor 4 1.1 4.3
118
Table 42
Rotated Component Matrix of SF-36 Health Survey Items using Varimax Rotation
technique
Item Loadings on
Component 1
Loadings on
Component 2
Loadings on
component 3
Loadings on
Component 4
Difficulty doing moderate
activity
.719
Problems going for
grocery shopping
.709
Difficulty climbing one
flight of stairs
.802
Problemswith walking one
mile
.784
Difficulty walking one
block
.759
General health status
perception
.521
Staying happy often in the
past 4 weeks
.800
Feeling energetic in last 4
weeks
Staying calm in the past 4
weeks
.776
.798
Feeling full of pep
Feeling nervous
.614
.690
Interference of body pain .737
with work or activity
119
Body pain experienced in
last 4 weeks
.789
Interference of physical
problems with work or
activity
.527
Health condition a year
ago
.621
Thinking health will get
worse in coming years
.583
Felt sick in last 4 weeks .746
The KMO statistic for MMSE was computed as .794 and Barlett's test of
sphericity was reported to be 324 (p<.001) (Table 43). Factor 1 which represented the
construct recalling and memory condition accounted for most of the variance in test
scores (39.1%) and the remaining variance was accounted by factor 2 which denoted the
contruct of the ability to follow instructions (11.2%) and factor 3 which denoted
registration and orientation (9.4%) (Table 44). Table 45 contains factor loadings for
various items of MMSE that were used to interpret the constructs underlying the factors.
Table 43
KMO and Barlett's test for items of Mini Mental State Exam (MMSE)
Characteristic Test value
Kaiser-Meyer-Olkin*** measure of
sampling adequacy
.794
120
Barlett's test of sphericity 324
Note: *** p<.001; KMO test is significant
Table 44
Variance explained by different components extracted by factor analysis for MMSE
Component Eigenvalue % Variance explained
1 4.3 39.1
2 1.2 11.2
3 1.0 9.4
Table 45
Rotated Component Matrix of items of MMSE using Varimax Rotation procedure
Item Component 1 Component 2 Component 3
Remembering date .728
Remembering
location
.858
Ability to name 3
unrelated objects
.812
Recalling names of
the 3 objects
mentioned before
.803
Identifying 2 simple
objects
.617
121
Folding a sheet of
paper in accordance
with given
instructions
.569
Reading and
performing the act
.776
Copying a picture
shown
.691
Spelling WORLD
backwards
.850
Repeating a phrase
shown on paper
.543
4.8 Reliability Analysis
The researcher carried out reliability analysis (Table 46) to test whether the items
of each of the questionnaires used in the study (i.e. MNA, FES-I, Downton Index, GDS,
MMSE, and SF-36 Health Survey) measure the construct they denote.
Table 46
Reliability analysis for MNA, FES-I, Downton Index, GDS, MMSE and SF-36 using
Cronbach's alpha
Cronbach's
alpha
coefficient
MNA FES-I Downton
Index
GDS MMSE SF-36
Α .300 .982 .663 .961 .787 .954
122
The internal consistency of items of Falls Efficacy Scale International (FES-I)
(α=.982) was found to be the highest amongst all questionnaires tested using reliability
analysis in SPSS. Geriatric depression scale (GDS) items also had a very high value of
Cronbach's alpha (α=.961) which indicates that the various items on this questionnaire
have a good ability to measure a common construct (i.e. depression). Mini Mental State
Exam and Downton Index items also had an acceptable level of internal consistency. The
items of Mini Nutritional Assessment (MNA) showed a poor measure of internal
consistency (α=.300) in this study (Table 46). It must be noted here that items having
zero variance were eliminated from the reliability analysis to prevent any occurrence of
errors.
123
CHAPTER 5
DISCUSSION
5.1 Study Purpose
The present study was carried out to assess the level of and the association
between nutritional and fall risk among older women living in long-term care (LTC)
facilities in New Delhi (India). The role of variables such as depression, cognitive
impairment, general health status, and physical function level in the prediction of fall risk
was also explored. This is the first study that has attempted to determine the factors that
contribute towards an elevated nutritional and fall risk in older adults living in LTC
facilities of New Delhi, India.
5.2 Background characteristics of participants
The participants involved in the study were older women (average age of 74) and
a majority were widowed (82%) with lower level of education and socioeconomic status.
This is not surprising given that traditional long term care in India emerged as a system to
support poor and destitute women as a charity provided by religions organizations (Datta,
2017; Rajan, Mishra, & Sarma, 1999). Also, studies in India have shown high rates of
illiteracy (70-73%) among the older population (Ingle & Nath, 2008; HelpAge India,
2014). Although a majority of the participants (68%) had two or more children, only 35%
reported having children visit them at least on a monthly basis. The residents also
indicated that most of the visits were for financial reasons only and they did not have
strong emotional ties with their mothers living in care homes. While these kinds of elder
abuse are common, they are largely under-estimated and under-reported by older adults.
124
Although some initiatives have been taken by the government of India in the past three
decades to provide assistance to its senior populations, most of the programs and welfare
schemes have lacunae and are not implemented on a regular basis. A Health Insurance
Plan is available to help older adults with treatment of health problems and surgery and is
known as Rashtriya Swasthya Bima Yojana (RSBJ). This plan was launched in 2008 and
by getting registered with the health authority, beneficiaries are entitled to hospitalization
coverage of INR 30,000 (CAD $ 600) (Maroof, Ahmad, Khalique, & Ansari, 2016).
Awareness of availability of welfare programs and benefits, including the existence of
social security plans and health plans, supported by the government of India is low
among Indian older adults, and this has a tremendous impact on utilization of health care
services (Maroof, Ahmad, Khalique, & Ansari, 2016).
5.3 Health profile of participants
The participants in this study also had poor health with multiple chronic diseases
(94%) with concomitant intake of multiple medications (average of 5 medications daily
and 61% consumed 3 to 6 medications in a day) and experienced psychosocial issues
such as high rates of depression and social isolation. India is undergoing a rapid
demographic transition phase (Visaria, 2011) and is confronted with the twofold burden
of diseases: under-nutrition and infectious diseases on one hand, and lifestyle-related
diseases such as obesity, diabetes, hypertension, heart disease, and cancer on the other
hand. In the present study, the most common chronic health conditions found in seniors
were osteoarthritis, back problems, hypertension, and gastrointestinal disorders. These
figures are very similar to the findings reported by Thakur, Banerjee, and Nikumb (2013)
who have examined the chronic health problems present in rural older adults in India and
125
found that hypertension was present in 30.7% of persons with prevalence higher in
women as compared to men. These researchers suggested that lower literacy levels
among Indian older adults contribute towards a greater number of health problems since
they found that literacy has a positive impact on overall health of older adults (Thakur et
al., 2013).
5.4 Psychological issues
Besides chronic health problems and associated poly-pharmacy, many
participants reported the presence of stress in their daily lives (82%). Inability to afford
treatment for some health problems, lack of regular visits of friends and family members,
and constant thoughts of unpleasant past events added to their stress levels. The long
form of Geriatric Depression Scale was used to assess depression as it is a useful
screening tool for identifying individuals with depressive disorders. The mean GDS score
obtained in the present study was 16.6 (9.7). This score represents moderate level of
depression if we consider the mean scores obtained in the GDS development study by
Yesavage et al. (1982) which found mean GDS scores of 5.75 (4.3) in individuals who
were not depressed at all, 15.1 (6.5) in individuals with mild to moderate level of
depression and score of 22.85 (5.1) in severely depressed individuals.
The study participants were suffering from moderate (42.4%) to high (47%)levels
of depression. Only 10.6% of participants had mild symptoms of depression. The burden
of depression is 50% higher for women compared to men in India and the prevalence of
depression in Indian adults has been estimated to be 9% with average age of onset being
32 years (Bohra, Srivastava, & Bhatia, 2015). In Indian older women, depression is
126
mainly caused by widowhood and loneliness after death of spouse (Sinha, Shrivastava, &
Ramasamy, 2013). Poor nutritional status and a lower socio-economic status further add
to the burden of depression in older adults (Mohandas, 2009). Most of the older women
who participated in the study were widows (81.2%) and at high nutritional risk (54.1%).
This could explain the moderate to severe level of depression observed in these women
living in LTC facilities of India.
A study conducted in OAHs of West Bengal state in India, involving 200 older
women by Saha et al. (2014) has shown that severe depression was present in 59%
residents and 32% residents had mild depression. Interestingly, these researchers found
that these women were also at high nutritional risk. Tiwari, Pandey, and Singh (2012)
reported similar findings in their study carried out in Lucknow city in India and found
depression to be very common (38%) in older adults living in LTC. Other studies have
reported higher rates of anxiety and depression among the geriatric population in India
(Ghosh, 2006). For many individuals, it is difficult to cope with the stress of being
isolated from family members, and not easy to accept that the younger members of their
families who once consulted them for taking important decisions, and treated them with
utmost respect, no longer need them. Rao, Trivedi, and Yadav (2015) examined the
reasons for relocation to LTC, quality of life and coping strategies of 50 residents
(male=33, female=17) of five care homes in the city of Ahmedabad in the state of
Gujarat. They found that most of the residents were very dissatisfied with the behaviour
of their family members, particularly children and were unhappy to be deprived of love,
respect, and affection they deserved from the younger members of their families. Studies
have shown that social determinants of health (lower education, income) along with poor
127
physical and psychosocial health,such as, depression and social isolation,are well known
contributors to poor nutrition (Lahiri, Biswas, Santra, & Lahiri, 2014).
5.5 Nutritional Risk Level
Not surprisingly, the level of nutritional risk assessed using the Mini Nutritional
Assessment (MNA) tool was high in this population with 54.1% of residents at high
nutritional risk. Similar findings were reported by Saha et al. (2014) who reported the
prevalence of nutritional risk to be 57% in older adults living in OAHs in the state of
West Bengal. Pai (2011) who conducted a comparative study of nutritional status of
older adults (>60 years) living in care homes (n=108) and community setting (n=102) in
the city of Mangalore in India using MNA questionnaire also found similar level of
nutritional risk prevalence in India. Results of the study indicated that 57.4% of care
home residents were at a high nutritional risk and only 15% of community dwelling older
adults were at nutritional risk. Most studies on nutritional status assessment of older
adults have been conducted on those who are community dwelling and very scanty
research has been carried out to evaluate the nutritional status of older adults living in
LTC facilities. Kalaiselvi et al. (2016) have reported nutritional problems in community
dwelling older adults. They found that the prevalence of under-nutrition was 25% with
59% of the study participants viewing themselves as undernourished. Agarwalla, Saikia,
and Baruah (2015) assessed the nutritional status of 360 older adults living in the
community setting in the state of Assam using MNA and 24 hour dietary recall method
and found that 55% older adults were at high nutritional risk. They found a significant
association of nutritional status with age, female gender, low functional status, financial
dependency on others, and poor calorie intake.
128
The mean MNA score was found to be 18.44 (4.71) in the present study
participants. The findings of this study are similar to a recent study carried out in older
adults living in the community setting in West Bengal, where mean MNA scores were
found to be 18.41 (3.78) in women and 19.25 (3.51) in men (Lahiri et al., 2014). Another
study was conducted a few years ago which assessed the nutritional risk in rural older
adults living in the state of Tamil Nadu in South India. The mean MNA scores in rural
older women were found to be 22.17 (3.57) (Vedantam, Subramanium, Rao, & John,
2009). These scores are higher than the MNA scores obtained in the current study as
community dwelling older adults have better nutritional status as compared to individuals
living in LTC facilities.
Under-nutrition is a major public health problem in India (Jamir et al., 2013) but
most emphasis has been laid on examining the prevalence and consequences of this
problem in children. Moreover, in India, nationwide surveys such as National Family
Health Survey (NFHS) and United Nations Population Fund (UNPF) Survey conduct
assessment of health status of older adults, but do not pay attention to nutritional risk
assessment (Kalaiselvi, Arjumand, Jayalakshmy, Gomathi, Pruthu, & Palanivel, 2016).
Although more than 50% of older adults in India are malnourished and greater than 90%
have dietary intakes below the recommended level (Mathew, 2016), conducting research
on assessment of nutritional risk is seldom considered to be a priority, and is often
neglected.
Older adults living in long-term care (LTC) facilities, such as nursing homes or
care homes, are a high risk population for developing nutritional problems (Kaiser et al.,
2010; Kojima, 2015; Matusik et al., 2012; Sloane et al., 2008). The level of nutritional
129
risk in individuals residing in LTC is high due to a decline in food intake, and an
increased likelihood of weight loss, resulting from appetite changes, swallowing
problems, reduced gastrointestinal tract motility, and hormonal changes that lead to early
satiety or feeling full too soon (Hickson, 2006). Reduction in metabolic rate, presence of
several co-morbidities, oral health problems including tooth decay, and social neglect,
contribute enormously towards under-nutrition in older adults (Kikafunda & Lukwago,
2005). Economic insecurity coupled with the rise of nuclear families, and breakdown of
traditional joint families leads to inadequate care support available to older adults, and
also puts them at a higher nutritional risk (Ramage-Morin & Garriguet, 2013).
Undernourished older adults are at an elevated risk of being unable to perform activities
of daily living, have feeding difficulty, mobility impairment, and may develop
incontinence (Kikafunda & Lukwago, 2005). It has been reported in the literature that
undernourished older adults often need to be hospitalized, and have a high risk of
mortality (Bose, Bisai, Das, Dikshit, & Pradhan, 2007; Pednekar, Hakama, Hebert, &
Gupta, 2008).
5.6 Fall risk level
Besides under-nutrition, falls are also a prominent public health problem in older
adults leading to disability, loss of independence, and premature death (Sibley, Voth,
Munce, Straus, & Jaglal, 2014). The findings of the present study revealed that fall risk
was high in women living in care homes of New Delhi. Using the Downton index
classification (Rosendahl et al., 2003), more than half of the total residents (58%) were
found to be at a high risk of falling. Many seniors faced balance and mobility problems
and an unsteady gait (46%). More than half of the participants had experienced previous
130
falls (54%) in the previous year and a majority of them had visual (71%) and mobility
impairment (33%), which are well known risk factors of falls. The findings of the study
are consistent with the literature that suggests that balance and gait impairment (Al-
Momani et al., 2016; Salzman, 2010), fall history (Lee, Lee, & Khang, 2013), and
sensory deficits in older adults living in care homes such as visual impairment are
strongly related to falls (Rubenstein, 2006).
5.7 Fear of falling in participants
Fear of falling in the present study was found to be high and the mean FES-I score
of the participants was 34.04 (13.8). This is higher than the mean FES-I score reported in
other studies carried out in older adults living in the community.Fear of falling was
assessed in a study carried out on community dwelling older adults of Canada, Columbia,
Albania, and Brazil using FES-I and the mean FES-I score was found to be 23.3 (8.8) in
individuals having a mean age of 69.0 (2.8). The researchers of this study concluded that
fear of falling has a strong association with the spatial area where a person moves
through in daily life and fear of falling is site specific i.e. it varies depending on the
location of the person (Auais et al., 2017).
Although falls are considered to be a major public health problem in HICs as they
are the leading cause of injury and death in geriatric populations, they are not given the
status of an important public health issue in LMICs and resource-poor countries (United
Nations, 2013; Takanishi, Yu, & Morita, 2008). Even though a substantial proportion of
older adults are affected by falls and fall-related injuries in LMICs, there is a paucity of
research carried out in India regarding the risk factors for falls and fall-related injuries in
131
India along with effective strategies to reduce them. Some researchers have examined the
falls prevalence in India including Joshi et al. (2003), Johnson (2006), and D'souza
(2008). More research needs to be directed towards evaluation of fall risk factors and fall
prevention in LMICs such as India.
5.8 Cognitive status of participants
The cognitive status was assessed using the Mini-Mental State Exam in the
participants of the study. The MMSE is a widely used screening tool for identifying older
adults at risk of developing severe cognitive deficits and dementia. The mean MMSE
score was found to be 21.7 (5.3) in the participants and it was observed that 4.7% women
obtained very low scores (0 to 9) on MMSE and were likely to have severe cognitive
impairment. Normal cognitive functioning was observed in 29.4% of participants as they
obtained desirable scores on MMSE (25-30) while moderate cognitive deficits were
observed in 49.4% participants who scored between 20 and 24 on MMSE. Similar
findings were reported by Gambhir et al (2014) who studied cognitive functioning of
older adults with mean age 65.8 (5.8) and obtained mean MMSE score of 22.9 (4.9) in
individuals with a low level of education i.e. mean primary school training of 5 (3) years
and MMSE mean score of 26.1 (3.9) in individuals who had at least completed higher
secondary school education (12th grade). This study found some interesting results and
reported that regardless of poor cognitive function scores obtained on MMSE, the level of
dementia found in older adults was low. Only 1.5 % males and 1.5% females had
Alzheimer's disease and only 1.2% males and 0.6% females had vascular dementia. The
researchers concluded that a low level of education negatively affects performance on
132
MMSE but does not imply that older adults have cognitive deficits or dementia (Gambhir
et al., 2014).
5.9 Association between nutritional and fall risk
The current study also showed a strong association between nutritional and fall
risk. Nutritional risk and fall risk were found to be highly correlated. It is to be noted that
as the nutritional risk level decreases, nutritional status improves leading to higher MNA
scores and fall risk decreases with a consequent reduction in Downton Index scores.
Scores obtained on MNA questionnaire are inversely correlated with Downton Index
scores. This is the reason we found a negative correlation coefficient while carrying out
correlation analysis using Downton Index scores as the outcome variable (dependent
variable) and MNA scores as the predictor variable (independent variable).
5.10 Multiple Regression Analysis
In this study, several predictors of fall risk level were identified. These variables
include fear of falling, fall history, depression, visual impairment, limb impairment,
presence of body pain, difficulty performing moderate intensity exercise, gait
impairment, and greater time taken to complete the timed-up-and-go (TUG) test. Falls
have a multi-factorial etiology and several factors contribute towards falls. It must be
noted that as the number of risk factors increases, fall risk increases correspondingly.
Older adults who have a high fear of falling are more likely to stay indoors and avoid
being physically active. They do not want to encounter any fall incident and are fearful of
fall-related injuries such as fractures which require hospitalization and nursing care
support. Having previous falls further escalates fall risk and older adults limit their
mobility and prefer having their meals inside their room.
133
Depression is an important factor that predicts falls and is common in widows,
older adults with a lower socio-economic status and lack of social networks. Depression
is commonly found in residents of LTC facilities as they are away from family and
friends and their children do not come to meet them frequently in the care facilities.
Literature has shown a higher prevalence of depression ranging from 8 to 40% in care
home residents. Depression is widely prevalent in Indian women across all age groups.
Depression is under-reported in Indian older adults due to the social stigma associated
with expressing the symptoms present in depressed individuals. In India, the prevalence
of depression increases with age (Barua et al., 2007). It is particularly common in widows
(Poongothai et al., 2009) and older adults with a lower socio-economic status and poor
nutritional intake (Mohandas, 2009).
Visual deficits are frequently observed in LTC residents and vision problems limit
their ability to see any obstacle or hindrance in their path while walking around the LTC
facility. Limb impairment is also common in LTC residents and negatively affects
balance, gait, and mobility. Foot problems are a strong predictor of fall risk and are often
observed in older adults who have neuropathy which is a complication of diabetes.
Presence of severe body pain in back and legs also restricts mobility and intensifies fall
risk. It also adversely affects the ability to perform moderate intensity activities and leads
to poor performance on balance and mobility tests such as timed-up-and-go test. Multiple
regression analysis was therefore very useful in identifying predictors of fall risk in older
women who participated in this study.
134
5.11 Factor Analysis
Factor analysis was helpful in identifying the main constructs represented by the
items on various assessment tools. The researcher studied the factor structure of tools
such as, the Mini Nutritional Assessment, Downton Index, Falls Efficacy Scale
International, Geriatric Depression Scale, Mini Mental State Exam and SF-36 Health
Survey. The factor structure of Downton Index was remarkably poor and since KMO was
not very high, the factor analysis for Downton Index has been excluded from the results.
Mini Nutritional Assessment also did not have a desirable factor structure. The factor
structure of Falls Efficacy Scale International was found to be the best amongst all tools
and the factor structure of SF-36 was also very good. Mini Mental State Exam had an
acceptable factor structure and Geriatric Depression Scale had a highly desirable factor
structure with the value of KMO >0.8 which is very high. Since the factor structures of
FES-I and GDS are clearly of high quality, it can be said that Falls Efficacy Scale-
International and Geriatric Depression Scale both measure fear of falling and depression
very well and are very useful tools for assessment of confidence in performing ADL and
for assessing the presence of depressive symptoms in older adults living in LTC facilities.
5.12 Reliability Analysis
The present study also made contribution to understanding the psychometric
properties of tools commonly used in HIC context. Specifically, modest reliability was
observed for the Downton Index (α=0.7) whereas the level of reliability for MNA was
very poor (α=0.3).Some items had to be excluded from the reliability analysis of the
MNA screening tool since the participants' responses to some items had zero variance.
135
For example most participants consumed milk products on a daily basis and none of the
participants consumed meat, fish or poultry within the care home. Most participants had
some level of psychological stress present in their daily lives in the care facility. Since
these items were removed from the analysis, the researcher was able to use a limited
number of items for carrying out the reliability test. This might explain why the
researcher found a modest value of Cronbach's alpha (α= 0.3) for MNA in the study. No
previous study has been carried out in India which has attempted to assess the reliability
of nutrition and fall risk screening tools.
5.13 Future directions and research
An important finding of the present study is that researchers should use caution in
using MNA and Downton Index as stand alone measures of nutritional and fall risk as
they had low reliability and poor factor structures in the Indian LTC context. It would be
recommended that a comprehensive nutritional and fall risk assessment must be carried
out in LTC residents in India and other LMICs as there is no gold standard tool available
to identify the presence of nutritional or fall risk in older adults. For example, a
comprehensive nutritional assessment can include a detailed diet history, 3 day 24 hour-
dietary recalls, use of MNA, serum albumin level test, determination of quanities of food
consumed by older adults by carrying out plate waste measurements, and monitoring
weight to check for involuntary weight loss on a monthly basis. A comprehensive fall
risk assessment can include assessing fall history, number of risk factors for falls,
evaluation of presence of limb impairment and foot problems, assessing fear of falling by
using FES-I and carrying out TUG test and functional reach test to assess physical
function.
136
The Government of India (GoI) has made some efforts recently to work towards
the improvement of health care services provided to older adults, particularly, individuals
living in rural areas (Verma & Khanna, 2013). For example, the GoI launched the
National Program for the Health Care of the Elderly (NPHCE) which has the following
objectives: to provide accessible, affordable, and high quality long-term and
comprehensive health care services to older adults in India; to build a framework to
promote the concept of healthy and active aging; to provide promotional, preventive,
curative, and rehabilitative services through primary health-care approach which is
community based; to build the capacity of medical and paramedical health care
professionals as well as caregivers within the family system to provide care support to
seniors; and identification of health problems of older adults and provision of health
interventions in the community along with referral services through district hospital
regional medical institutions (Verma & Khanna, 2013). Although some health care
support is provided to community dwelling older adults in India, the needs of older adults
requiring LTC services are largely neglected. The GoI urgently needs to develop
strategies to provide comprehensive and affordable long-term care services to Indian
older adults who are growing in number as a result of population aging and other
demographic changes taking place in India. Given the burden associated with treatment
of under-nutrition and fall-related injuries, efforts should be directed towards prevention
and treatment of under-nutrition in older adults and in formulation and sincere
implementation of fall prevention strategies and intervention programs to help older
adults lead healthy, productive, and fulfilling lives.
137
5.14 Strengths and Limitations of the Present study
The present study had the primary objective of examining the level nutritional and
fall risk and their association among women residing in LTC facilities of New Delhi,
India. Since it is the first research study of its kind, it is of great value and significance.
Older adults living in "homes for the aged" are frequently neglected, and not much
importance is placed on examining their health status, particularly nutritional condition
and susceptibility to encounter falls in the LTC environment. Due to a wide variety of
nutritional and fall risk factors studied and analyzed, the study contributed to a more in-
depth understanding of the factors contributing towards poor nutritional health and
increased risk of falls in older adults. This study will be a valuable addition to the
existing data on nutritional and fall risk profile of older adults living in India and will
inform future programs and policies directed towards health status improvement of older
adults living in OAHs in India.
The study had a few limitations as it included only women staying in OAHs. Men
were not included in the study as they were not willing to participate. The results
obtained by including women are not generalizable to the total population of older adults
living in OAHs in India. Only six LTC facilities were included in the study after
obtaining ethics approval from their directors and managers. Most LTC facilities have
very complex procedures for gaining ethical permission as they take many months to
process the ethics applications and even after waiting for several months, the directors of
OAHs are often unwilling to allow research projects to be implemented due to fears of
older adults being harmed, injured, or revelation of LTC lacunae and gaps in health care
138
systems. An important limitation is that there is no gold standard tool which can
singularly assess nutrition risk and fall risk accurately in older adults living in LTC
facilities in India. Both MNA and Downton Index have poor factor structures and low
levels of reliability as is exhibited by low values of Cronbach's alpha (i.e. <.80).Further
research must be carried out to develop tools for nutritional and fall risk assessment
which have high levels of reliability to identify older adults at an elevated risk of being
undernourished and having a high risk of encountering falls in the LTC environment.
139
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196
APPENDIX A
LETTER OF INVITATION
Dear resident,
We invite you to participate in the research study, “Nutritional and fall risk
assessment inolderwomen livingin long-termcarefacilities of New Delhi.” We are
working with 85 care home residents in NewDelhi(India) to learn what causes these
older adults to have high nutritional and fall risk. We will ask you some questions to
learn about your background, physical activity and general health, but you are only
required to answer those questions you are comfortable with. We will also measure
your balance and mobility, along with hand grip strength. This will take 45 minutes to
complete and give us valuable information about the health of elders in old age homes.
There is no risk in doing these simple tests, but you also can choose not to do these.
Your participation is voluntary and that non-participation will not impact the services
received from the care home. Permission for this study has been obtained from the
director of this care home and the Research Ethics Board at the University of Regina.
By participating, you will help us and other health professionals better
Faculty of Kinesiology and Health Studies
Centre for Kinesiology, Health & Sport
University of Regina, Regina, SK S4S 0A2
197
understand nutritional andfall risk in seniors like you.This will then help to plan better
strategies, programs, and services to help prevent falls and nutrition problems among
seniors.Ifyou areinterested in participating in this researchstudy, kindly contact the
researcherat thefollowingaddress, by phone or mail or when she is back to the care
facility in a week's time.
Swati Madan
1024, Kisik Towers
3737 Wascana Parkway, University of Regina,
Regina, Saskatchewan, S4S0A2
Phone : 306-790-7221
E-mail : [email protected]
Dr. Shanthi Johnson
CK 115, 3737 Wascana Parkway
University of Regina, Regina, SK. S4S0A2
Phone: 306-337-2436
Email: [email protected]
198
APPENDIX B
LETTER OF INFORMED CONSENT
Dear Participant,
Thank you for your interest inour study,“Nutritional and fall risk assessment in
older adults living in long-term carefacilities: A cross cultural comparison.” Your
participation is valuable and appreciated. We want to make sure you understand that:
Your participation in this study is voluntary (you don’t have to participate) and non-
participation will not impact the services received.
You are not obligated to answer anything you are uncomfortable with, and may stop
answering questions anytime for any reason.It is your choice to perform theTUG test
(balance and mobility impairment test), and handgrip strength test, and if you do not wish
to take part, you can inform the researcher. You can withdraw from the study at anytime,
until three months from the time of survey. After that, the data would be analyzed and
it will not be possible to remove your information.
There are minimal risks associated with your participation to your health or the service
you get in the care home.
Your name, address, and personal information will be kept confidential. We will keep all
the files in a secure location in a locked cabinet. Your answers to questionnaires will be
used for data analysis using a statistical analysis software program. The results of the
study will help us understand what causes heightened nutritional and fall risk in older
adults living in care homes.
This project has been approved on ethical grounds by the University of Regina Research
Ethics Board. Any questions regarding your rights as a participant may be addressed to
the committee at 1-306-585-4775 or [email protected]. Out of town participants
may call collect.”
Are you willing to participate in this study? Yes_____ No _____
I have received a copy of this letter of consent. Yes _____ No _____
199
Ifyou have anyotherquestions or concerns, please feelfree to contact:
Swati Madan
1024, Kisik Towers
3737 Wascana Parkway,
University of Regina, Regina,
Saskatchewan, S4S0A2
Phone : 306-790-7221
E-mail : [email protected]
Dr. Shanthi Johnson
CK 115, 3737 Wascana Parkway
University of Regina, Regina,
SK. S4S0A2
Phone: 306-337-2436
Email: [email protected]
200
APPENDIX C
Background Profile and Physical Activity Questionnaire
Please answer the following questions as they describe you. Choose one of the following
options.
1. How long have you lived in this facility?
a. Six months
b. Six months to a year
c. One to two years
d. More than two years
2. Marital status
a. Never married
b. Married
c. Divorced
d. Widowed
3. Indicate the number of children you have
a. None
b. One
c. Two
d. Three
e. >three
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4. Educational status
a. 10th Grade or below
b. 12th Grade
c. Bachelors degree
d. Masters degree and above
5. Annual income
a. INR 50,000
b. INR 50,000 -100,000
c. INR 100,000- 500,000
d. INR > 500,000
6. Level of social support
Do your children or friends frequently visit this nursing home to meet you
(Yes/No).If yes, how often?
a. Once a week
b. Once every two weeks
c. Once a month
d. Once every six months
e. Once every year
7. Physical activity level in the past
a. Very active
b. Active
c. Fairly active
d. Rarely active
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8. Do you take medications? If yes, how many medications per day? Also mention
which ones.
9. Presence of chronic conditions
Do you have any chronic health condition? If yes, mention it below.
10. In a typical week, how many days do you perform moderate intensity exercises
that cause small increases in breathing and heart rate?
------------------------------------------------------------------------------------------
11. Do you perform activities involving stair climbing and walking on a daily basis? ------------------------------------------------------------------------------------------- 12. How much time do you spend sitting or reclining on a typical day? -------------------------------------------------------------------------------------------- 13. Are you aware of the benefits of regular physical exercise in the maintenance
of good health (e.g. strong bones and muscles)?
------------------------------------------------------------------------------------------------ 14. Are you willing to participate in an exercise program designed to improve
your health and well being?
-------------------------------------------------------------------------------------------------
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15. Will you perform exercises that are recommended by a physiotherapist or
exercise trainer?
-------------------------------------------------------------------------------------------- 16. Does your LTC facility have any ongoing exercise programs that you would like to
join? ------------------------------------------------------------------------------------------------ 17. If your LTC facility does not have any exercise training or group activity sessions,
would you like the facility to incorporate training sessions involving group exercises?
----------------------------------------------------------------------------------------------
18. Would you prefer doing group exercise or individually tailored exercise ?
------------------------------------------------------------------------------------------------
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APPENDIX D
Mini-Nutritional Assessment (MNA)
Complete the screen by filling in the boxes with the appropriate numbers. Add the
numbers for the screen. If the score is 11 or less, continue with the assessment to gain a
Malnutrition Indicator Score.
Screening:
A. Has food intake declined over the past three months due to loss of appetite,
digestive problems, chewing or swallowing difficulties?
0=severe decrease in food intake
1=moderate decrease in food intake
2=no decrease in food intake
B. Weight loss during the last three months
0= weight loss greater than 3 kg
1= does not know
2= weight loss between 1 and 3 kg
3= no weight loss
C. Mobility
0= bed or chair bound
1=able to get out of bed/ chair but does not go out
2=goes out
D. Has suffered psychological stress or acute disease in the past three months
0= yes
2=no
E. Neuropschological problems
0= severe dementia or depression
1= mild dementia
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2= no psychological problems
F. Body Mass Index (BMI) (weight in kg)/(height in m2)
0= BMI less than 19
1= BMI 19 to less than 21
2= BMI 21 to less than 23
3= BMI 23 or greater
Screening score (subtotal max. 14 points)
12-14 points: Normal nutritional status
8-11 points: at risk of malnutrition
0-7 points: malnourished
For a more in-depth assessment, continue with questions G-R
Assessment
G. Lives independently (not in nursing home or hospital)
1= yes
0= no
H. Takes more than three prescription medications daily
0= yes
1= no
I. Pressure sores or skin ulcers
0= yes
1= no
J. How many full meals does the patient eat daily
0= 1 meal
1= 2 meals
2= 3 meals
K. Selected consumption markers for protein intake
- At least one serving of dairy products (milk, cheese, yoghurt) per day yes
no
- Two or more servings of legumes or eggs per week yes
no
- Meat, fish, or poultry every day yes
no
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- 0.0= if 0 or 1 yes
- 0.5= if 2 yes
- 1.0= if 3 yes
L. Consumes two or more servings of fruits or vegetables per day?
0= no
1= yes
M. How much fluid (water, juice, coffee, tea, milk) is consumed per day?
0.0 = less than 3 cups
0.5 = 3 to 5 cups
1.0= more than 5 cups
N. Mode of feeding
0= unable to eat without assistance
1= self-fed with some difficulty
2= self-fed without any problem
O. Self-view of nutritional status
0= views self as being malnourished
1= uncertain of nutritional state
2= views self as having no nutritional problem
P. In comparison with other people of the same age, how does the patient consider
his/her health status?
0.0= not as good
0.5= does not know
1.0= as good
2.0= better
Q. Mid-arm circumference (MAC) in cm
0.0= MAC less than 21
0.5= MAC 21 to 22
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1.0= MAC greater than 22
R. Calf-circumference (CC) in cm
0= CC less than 31
1= CC 31 or greater
Assessment (max. 16 points) score
Screening score
Total assessment score (max. 30 points)
Malnutrition Indicator Score
24 to 30 points Normal nutritional status
17 to 23.5 points At risk of malnutrition
Less than 17 points Malnourished
208
APPENDIX E
Downton Index
Item Score
Known previous falls
No
Yes
0
1
Medications
None
Tranquillizers/sedatives
Diuretics
Antihypertensives
Antiparkinsonian drugs
Antidepressants
Other medications
0
1
1
1
1
1
0
Sensory deficits
None
Visual impairment
Hearing impairment
Limb impairment
0
1
1
1
209
Mental state
Oriented
Confused (cognitively impaired)
0
1
Gait
Normal (safe without walking aids)
Safe with walking aids
Unsafe (with/without walking aids)
Unable
0
0
1
0
Item scores are added together to obtain an index total which ranges from 0 to 11, where
a score of 3 or greater is indicative of a high risk of falls
(Adapted from Downton, 1993).
210
APPENDIX F
Falls Efficacy Scale International (FES-I)
For each of the following activities, please tick the box which is closest to your own
opinion to show how concerned you are that you might fall if you did this activity.
Activity Not at all
concerned
Somewhat
concerned
Fairly
concerned
Very
concerned
1. Cleaning the house
(sweep, vacuum,
dust)
1 2 3 4
2. Getting
dressed/undressed
1 2 3 4
3. Preparing meals 1 2 3 4
4. Taking a bath or
shower
1 2 3 4
5. Going to the shop 1 2 3 4
6. Getting in and out of
a chair
1 2 3 4
7. Going up or down the
stairs
1 2 3 4
8. Walking around in
the neighbourhood
1 2 3 4
9. Reaching for
something above
your head or on the
ground
1 2 3 4
10. Going to answer the
telephone before it
1 2 3 4
211
stops ringing
11. Walking on a
slippery surface (wet
or icy)
1 2 3 4
12. Visiting a friend or
relative
1 2 3 4
13. Walking in a place
with crowds
1 2 3 4
14. Walking on an
uneven surface
(rocky ground,
poorly maintained
pavement)
1 2 3 4
15. Walking up or down
the slope
1 2 3 4
16. Going to a social
event (religious
service, family
gathering or a club
meeting)
1 2 3 4
212
APPENDIX G
Geriatric Depression Scale Long Form
1. Are you basically satisfied with your life ? yes no
2. Have you dropped many of your activities and interests ? yes no
3. Do you feel that your life is empty ? yes no
4. Do you often get bored ? yes no
5. Are you hopeful about the future ? yes no
6. Are you bothered by thoughts you can’t get out of your head ? yes no
7. Are you in good spirits most of the time ? yes no
8. Are you afraid that something bad is going to happen to you ? yes no
9. Do you feel happy most of the time ? yes no
10. Do you often feel helpless ? yes no
11. Do you often get restless and fidgety ? yes no
12. Do you prefer to stay at home, rather than going out and doing
new things ? yes no
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13. Do you frequently worry about the future ? yes no
14. Do you feel you have more problems with memory than most ? yes no
15. Do you think it is wonderful to be alive now ? yes no
16. Do you often feel downhearted and blue ? yes no
17. Do you feel pretty worthless the way you are now? yes no
18. Do you worry a lot about the past ? yes no
19. Do you find life very exciting ? yes no
20. Is it hard for you to get started on new projects ? yes no
21. Do you feel full of energy ? yes no
22. Do you feel that your situation is hopeless ? yes no
23. Do you think that most people are better off than you are ? yes no
24. Do you frequently get upset over little things ? yes no
25. Do you frequently feel like crying ? yes no
26. Do you have trouble concentrating ? yes no
27. Do you enjoy getting up in the morning ? yes no
28. Do you prefer to avoid social gatherings ? yes no
214
29. Is it easy for you to make decisions ? yes no
30. Is your mind as clear as it used to be ? yes no
This is the original scoring for the scale: One point for each of these answers. Cutoff:
normal-0 to 9; mild depression-10 to 19; severe depression-20 to30
215
APPENDIX H
Mini Mental State Exam
Date: _____________
Instructions:Score one point for each correct response within each question or
activity.
Total
score
Score Questions
5 “What is the year? Season? Date? Day? Month?”
5 “Where are we now? State? County? Town/city? Hospital? Floor?”
3
The examiner names three unrelated objects clearly and
slowly, then the instructor asks the patient to name all three of
them. The patient’s response is used for scoring.
5
“I would like you to count backward from 100 by
sevens.”(93,86,79, 72, 65,…)
Alternative:“Spell WORLD backwards.”(D-L-R-O-W)
3 “Earlier I told you the names of three things.Can you tell
me what those were?”
2 Show the patient two simple objects,such as a wrist-watch and
a pencil, and ask the patient to name them.
1 “Repeat the phrase:‘No ifs,ands,or buts.’”
3 “Take the paper in your right hand, fold it in half, and put it on
the floor.”
1 “Please read this and do what it says.”(Written instruction is
“Close your eyes.”)
1 “Make up and write a sentence about anything.”(This
sentence must contain a noun and a verb.)
216
1
“Please copy this picture.”(The examiner gives the patient a
blank piece of paper and asks him/her to draw the symbol
below. All 10 angles must be present and two must
intersect.)
30 TOTAL
217
Interpretation of the MMSE:
Method Score Interpretation
Cutoff <24 Abnormal
Range <21
>25
Increased
risk of
dementia
Decreased
risk of
dementia
dementia
21 Abnormal for 8thgrade education
Education <23 Abnormal for high school education
<24 Abnormal for college education
24-30 No cognitive impairment
Severity 18-23 Mild cognitive impairment
0-17 Severe cognitive impairment
Interpretation of MMSEScores:
Score Degree
of
Impair-
ment
Formal
Psychometric
Assessment
Day-to-Day Functioning
25-30
Question
ably
signific
ant
If clinical signs of cognitive
impairment are present,
formal assessment of
cognition may bevaluable.
May have clinically
significant but mild
deficits. Likely to
affect only most
demanding activities
of daily living.
20-24
Mild Formal assessment may be
helpful to better determine
pattern and extent of
deficits.
Significant effect.
May require some
supervision,support
and assistance.
218
10-19 Moderate Formal assessment may be
helpful if there are specific
clinical indications.
Clear impairment. May
require 24-hour
supervision.
0-9
Severe
Patient not likely to be
testable.
Marked impairment.
Likely to require 24-
hour supervision and
assistance withADL.
Source:
Folstein, M.F., Folstein, S.E., McHugh, P.R. (1975). Mini-mental
state: A practical method for grading the cognitive state of patients
for the clinician. Journal of Psychiatric Research, 12, 189-198.
219
APPENDIX I
RAND VERSION OF SF- 36 HEALTH SURVEY
Choose one option for each questionnaire item.
1. In general, would you say your health is:
1 - Excellent
2 - Very good
3 - Good
4 - Fair
5 - Poor
2. Compared to one year ago, how would you rate your health in general now?
1 - Much better now than one year ago
2 - Somewhat better now than one year ago
3 - About the same
4 - Somewhat worse now than one year ago
5 - Much worse now than one year ago
220
The following items are about activities you might do during a typical day. Does your
health now limit you in these activities? If so, how much?
Yes,
limited a
lot
Yes, limited
a little
No, not
limited at all
3. Vigorous activities, such as running, lifting
heavy objects, participating in strenuous sports 1 2 3
4. Moderate activities, such as moving a table,
pushing a vacuum cleaner, bowling, or playing
golf
1 2 3
5. Lifting or carrying groceries 1 2 3
6. Climbing several flights of stairs 1 2 3
7. Climbing one flight of stairs 1 2 3
8. Bending, kneeling, or stooping 1 2 3
9. Walking more than a mile 1 2 3
10. Walking several blocks 1 2 3
11. Walking one block 1 2 3
12. Bathing or dressing yourself 1 2 3
During the past 4 weeks, have you had any of the following problems with your work or
other regular daily activities as a result of your physical health?
Yes No
13. Cut down the amount of time you spent on work or other activities
1 2
14. Accomplished less than you would like
1 2
15. Were limited in the kind of work or other activities
1 2
16. Had difficulty performing the work or other activities (for example, it took
extra effort) 1 2
221
During the past 4 weeks, have you had any of the following problems with your work or
other regular daily activities as a result of any emotional problems (such as feeling
depressed or anxious)?
Yes No
17. Cut down the amount of time you spent on work or other activities 1 2
18. Accomplished less than you would like 1 2
19. Didn't do work or other activities as carefully as usual 1 2
20. During the past 4 weeks, to what extent has your physical health or emotional
problems interfered with your normal social activities with family, friends, neighbors, or
groups?
1 - Not at all
2 - Slightly
3 - Moderately
4 - Quite a bit
5 - Extremely
21. How much bodily pain have you had during the past 4 weeks?
1 - None
2 - Very mild
3 - Mild
4 - Moderate
5 - Severe
6 - Very severe
222
22. During the past 4 weeks, how much did pain interfere with your normal work
(including both work outside the home and housework)?
1 - Not at all
2 - A little bit
3 - Moderately
4 - Quite a bit
5 - Extremely
These questions are about how you feel and how things have been with you during the
past 4 weeks. For each question, please give the one answer that comes closest to the way
you have been feeling.
How much of the time during the past 4 weeks...
All of
the
time
Most of
the time
A good
bit of the
time
Some of
the time
A little
of the
time
None of
the time
23. Did you feel full of pep? 1 2 3 4 5 6
24. Have you been a very
nervous person? 1 2 3 4 5 6
25. Have you felt so down in the
dumps that nothing could cheer
you up?
1 2 3 4 5 6
26. Have you felt calm and
peaceful? 1 2 3 4 5 6
27. Did you have a lot of
energy? 1 2 3 4 5 6
28. Have you felt downhearted
and blue? 1 2 3 4 5 6
29. Did you feel worn out? 1 2 3 4 5 6
30. Have you been a happy
person? 1 2 3 4 5 6
223
All of
the
time
Most of
the time
A good
bit of the
time
Some of
the time
A little
of the
time
None of
the time
31. Did you feel tired? 1 2 3 4 5 6
32. During the past 4 weeks, how much of the time has your physical health or emotional
problems interfered with your social activities (like visiting with friends, relatives, etc.)?
1 - All of the time
2 - Most of the time
3 - Some of the time
4 - A little of the time
5 - None of the time
How TRUE or FALSE is each of the following statements for you.
Definitely
true
Mostly
true
Don't
know
Mostly
false
Definitely
false
33. I seem to get sick a little
easier than other people 1 2 3 4 5
34. I am as healthy as anybody I
know 1 2 3 4 5
35. I expect my health to get
worse 1 2 3 4 5
36. My health is excellent 1 2 3 4 5
224
APPENDIX J
University of Regina
Research Ethics Board REB #
Certificate of Approval 2015-214
Investigator(s) Swati Madan
Department Kinesiology & Health Studies
Funder: Unfunded
Supervisor: Dr. Shanthi Johnson
Title: Nutritional and fall risk assessment in older adults living
in Long-term care facilities APPROVED ON: January 1,
2016
APPROVAL OF:
Application For
Behavioural Research Ethics
Review
Background Profile and Physical
Activity Questionnaire
Falls Efficacy ScaleInternational
Downton Index
Geriatric
Depression
Scale Long Form
SF-36Health Survey
Letter of Invitation
Letter of Informed Consent
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FULL BOARD MEETING DELEGATED REVIEW
The University of Regina Research Ethics Board has reviewed the above-
named research project.The proposal was found to be acceptable on ethical
grounds.The principal investigator has the responsibility for any other administrative
or regulatory approvals that may pertain to this research project, and for ensuring that
the authorized research is carried out according to the conditions outlined in the
original protocol submitted for ethics review. This Certificate of Approval is valid for
the above time period provided there is no change in experimental protocol, consent
process or documents.
Any significant changes to your proposed method, or your consent and recruitment
procedures should be reported to the Chair for Research Ethics Board consideration in
advance of its implementation.
ONGOING REVIEW REQUIREMENTS
In order to receive annual renewal, a status report must be submitted to the REB
Chair for Board consideration within one month of the current expiry date each year
the study remains open, and upon study completion.Please refer to the following
website for further instructions:http://www.uregina.ca/research/for-faculty-staff/ethics-
compliance/human/forms1/ethics-forms.html.
Dr. Larena Hoeber, Chair
University of Regina Research Ethics Board
Please send all correspondence to:
226
Research Office
University of Regina
Research and Innovation Centre 109
Regina, SK S4S 0A2
Telephone: (306)-585-4775