1

1 Research in Nursing & Health, 2009, 32, 634–646

Correlates of Physical Activity in

Low Income College Students

Joyce L. Maglione,1* Laura L. Hayman2**

3 1 Drew University Health Service, Drew University, Madison, NJ 2 College of Nursing and Health Sciences, University of Massachusetts - Boston, Boston, MA

Accepted 5 August 2009

2

Research in Nursing & Health First published year: 1978

ISSN: 01606891 EISSN: 1098240x Publisher: JOHN WILEY & SONS INC Homepage: http://www3.interscience.wiley.com/cgi-bin/jtoc?

ID=33706 Reader accessibility Open Access: No Allows self-archiving of reviewed manuscript: Yes Subscription price per article: $24,62 Subscription price per citation: $22,11 Quality Databases indexing the journal: PsycINFO --- Science Citation

Index --- Social Sciences Citations Index --- Medline (PubMED) --- CINAHL

Article Influence: 0,41 FRIDA level: Leading scholarly ISI impact factor: Available to Journal Citation Report subscribers Provided by Lund University Libraries, Head Office with support

from the National Library of Sweden.

3

تعالی بسمه

بهداشت آموزش گروه کالب ژورنال موضوع

 Correlates of Physical Activity in Low Income College

Students

واحدیان : محمد دهنده ارائه

بهداشت   آموزش تخصصی دکترای دانشجوی

دیماه  : دوم و بیست ارائه 1388تاريخ

: ارائه  12الي 10ساعت

بهداشت  : آموزش گروه جلسات سالن مکان

4

Abstract: The importance of physical activity as a

health promoting behavior has been well documented.

We examined the relationship of social support, self-efficacy, and commitment to a plan of physical activity on physical activity behaviors in a sample of low income college students.

5

Those with higher levels of social support, self-efficacy, and commitment to a plan of physical activity reported more physical activity behaviors.

Commitment to a plan of physical activity mediated the relationships of social support and physical activity behavior, and of self-efficacy and physical activity behavior.

6

The results support the importance of exploring the psychosocial correlates of physical activity in explaining the decision process that underlies physical activity behavior.

Keywords: physical activity ;psychosocial correlates ; social support ;self-efficacy ; commitment to a plan.

7

The importance of physical activity as a health promoting behavior has been well documented (Hayman et al., 2004; Pate et al., 1995).

Physical inactivity is challenging tobacco use as the leading indirect cause of death in the United States (Mokdad, Marks, Stroup, &

Gerberding,2004).

8

Despite evidence of a causal relationship between physical inactivity and increased morbidity and mortality from illnesses such as cardiovascular disease and diabetes (Paffenbarger , Kampert, & Lee, 1997),

70% of Americans fail to meet the national recommended guidelines for physical activity (US Department of Health and Senior Services (USDHHS), 2000).

Physiological, psychosocial, and behavioral factors associated with an individual’s decision to engage in physical activity have been identified in the literature (Allen, 2003; Pender, Murdaugh & Parsons, 2006; Wallace, Buckworth, Kirby, & Sherman, 2000).

9

Exploring the psychosocial correlates of physical activity in particular has shown promise in explaining the decision process that underlies physical activity behavior (Rovniak, Anderson, Winett, & Stephens, 2002; Sallis, Prochaska, Taylor, Hill, & Geraci, 1999).

10

An age related decline in physical activity is well established (Kimm et al., 2002; Sallis

& Saelens, 2000), with the greatest rate of decline occurring between 18 and 24 years of age (USDHHS, 2000).

College students represent a large portion of this young adult population (Gerald

& Hussar, 2000), and the majority of them fail to meet the national guidelines for physical activity (Douglas & Collins, 1997).

11

Existing research demonstrates that socioeconomic factors are also strongly associated with physical activity (Trost, Owen,

Bauman, Sallis, & Brown, 2002) and are considered a major determinant of health (World Health Organization, 2003).

More affluent populations are generally more physically active than less affluent populations (USDHHS).

12

Although there is sufficient research that links psychological factors associated with physical activity behaviors in children, younger adolescents, and middle/older adults; research that includes college students, especially an economically diverse college population, is needed.

The purpose of this study was to explore the relationship of the psychosocial correlates (self-efficacy, social support, commitment to a plan) of physical activity behavior in low income college students.

13

PHYSICAL ACTIVITY

  In the middle of the 20th century, health education specialists and health care workers became increasingly aware of the positive health related outcomes of exercise (Allen, 2003).

As exercise science matured, more precise definitions of physical activity were developed, and expert panels began making recommendations for physical activity to achieve fitness and health benefits.

14

Guidelines from the American College of Sports Medicine have evolved from emphasizing vigorous activity for cardio-respiratory fitness to accumulating 30 minutes or more of moderate intensity physical activity (activity that makes you breathe somewhat harder than normal) 5 days of the week (Haskell et al., 2007).

Several researchers have shown that lack of physical activity increases disease morbidity and mortality.

15

Paffenbarger et al.’s classic study (1997)

followed both Harvard College and University of Pennsylvania alumni from the classes of 1916 and 1928 respectively and found that

alumni who expended <2,000 kcal/week in activities such as walking and sports activities faced a 31% increased risk of disease compared to those who expended more energy.

These findings have been replicated in recent research that linked physical inactivity and the development of chronic disease and premature death (Warburton, Nicol, & Bredin, 2006).

16

LOW INCOME COLLEGE STUDENTS

Researchers have demonstrated that low income is associated with increased mortality risks from illnesses such as heart disease and diabetes (Krieger et al., 2002). Despite its demonstrated health benefits, the likelihood of participation in a program of physical activity decreases with age (National Center for Chronic Disease, Prevention & Health Promotion [NCCDPHP], 2006).

Although physical activity has been adequately studied in low income school children, there is little research on what happens as they develop, and especially as they enter college (Frenn et al., 2003).

17

The college years are a time of growth and development and are a propitious time to educate, motivate, and prepare students to lead healthier lives. College students have opportunities to participate in physical activities that may not have been available to them previously.

Poor physical activity behaviors associated with low income may be alleviated by the promise a college education has to improve socioeconomic status.

18

Correlates of physical activity that may have resulted from childhood socioeconomic status require identification and explanation so that targeted interventions may be created or modified to enable individuals to engage in enjoyable physical activity.

CONCEPTUAL MODEL Numerous theoretical frameworks have

been used to investigate and understand the process by which relationships between psychosocial variables affect health behaviors.

19

The health belief model, social cognitive theory, and the theory of planned behavior are the dominant psychological theories and models used in the health literature (Glanz,Rimer, & Lewis, 2002) to guide health behavior and physical activity research.

Pender’s health promotion model (HPM), a nursing model, is also appropriate for exploring the relationship among these concepts.

20

The HPM is an explanatory model of health behavior. First appearing in the 1980s, Pender’s model ‘‘proposed a framework for integrating nursing and behavioral science perspectives on factors influencing health behaviors’’ (Pender et al., 2006, p. 47; Fig. 1).

The HPM attempts to depict the multidimensional nature of persons interacting with their interpersonal and physical environments as they pursue health.

21

The HPM suggests that health protecting and health promoting behaviors can be viewed as complementary components of a healthy lifestyle (Robbins, Gretebeck, Kazanis, & Pender, 2006).

Embedded within the HPM is an emphasis on the nursing perspective of holistic human functioning.

The model provides a comprehensive explanation of the psychosocial correlates that motivate individuals to engage in positive health behaviors coupled with a holistic philosophy.

22

This holistic philosophy views individuals as interacting with their interpersonal and physical environments and is consistent with the mission and goals of nursing

The HPM posits that individual characteristics and experiences, behavior-specific cognitions and affect, and behavioral contingencies influence individuals to engage in health promoting behavior.

23

FIGURE 1.

The health promotion model (revised)

. (From Pender, N., 2006.Home Page. Retrieved March 7, 2006, from www.nursing.umich.edu/faculty/ pender_nola.html).

24

25

Individual characteristics and experiences incorporate prior related behavior and personal factors such as biological, psychological, and socio-cultural factors.

Behavior-specific cognitions and affect, such as perceived benefits and barriers to action, perceived self-efficacy, activity related affect, interpersonal influences (family and peers), and situational influences, are considered to have major motivational significance within the model.

26

These behavior-specific cognitions

and affect lead to health promoting behavior either directly or modified by a commitment to a plan of action or competing demands and preferences (Pender et al., 2006).

The HPM provides a comprehensive explanation of the psychosocial determinants of physical activity behavior.

It has several strong points.

27

It draws on a number of theories to help

understand health behavior. The concepts are well defined and have been operationalized in the literature or by Pender making it user friendly for health researchers

Unlike other behavior change theories it does not rely on ‘‘fear’’ or ‘‘threat of susceptibility’’ as a construct to motivate behavior change.

This makes the HPM especially useful for children, adolescents, and young adults who tend to take good health for granted and often perceive themselves as invulnerable to disease and illness (Pender et al., 2006).

28

It takes into account prior related behavior and the influence of family and friends, notably missing in other theories of health behavior (Rosen, 2000; Thomson, 2000).

Drawing on the constructs of the HPM, the purpose of this research was to explore the relationship of social support, self-efficacy, and commitment to a plan of physical activity, and physical activity behaviors.

Self-efficacy and social support are powerful determinants of behavior.

29

Although self-efficacy and social support have been previously explained in the published research on physical activity behaviors, researchers have called for further testing of the commitment to a plan of action construct (Wang& Chen, 2003).

Further explanation of self-efficacy, social support, commitment to a plan, and physical activity behaviors guided by the HPM has the potential to expand its use in diverse groups across the lifespan

30

This study was designed to explore the relationships of the psychosocial correlates social support, self-efficacy, and commitment to a plan of physical activity and physical activity behavior in low income college students.

The following hypotheses were tested:

31

(1) Social support, self-efficacy, and commitment to a plan of physical activity together are positively related to physical activity behavior in low income college students.

(2) Commitment to a plan of physical activity mediates the relationship between social support and physical activity behavior in low income college students.

(3) Commitment to a plan of physical activity mediates the relationship between self-efficacy and physical activity behavior in low income college students.

32

METHODS

Sample The study sample frame for this purposive

convenience sample included undergraduate college students enrolled in the Educational Opportunity Fund (EOF) program at a large public university in New Jersey.

The EOF is a New Jersey state supported program developed in 1968 to assist students from economically and educationally disadvantaged backgrounds in their efforts to access higher education (New Jersey Commission on Higher Education, 2006).

33

To be considered for the EOF program, eligible students had to meet New Jersey state income eligibility criteria.

To participate in the study students had to be

at least 18 years old, speak English, be able to use a computer, and have access to the university’s EOF Listserv.

Students also had to be free of any physical

or mental limitations that prevented them from participation in low, moderate, or vigorous physical activity.

34

The sample frame consisted of the 416 students enrolled in the EOF program during the spring 2007 semester. The responding sample of 95 students had a mean age of 19.7 years (SD = 1.57).

Participants represented all 4 years of college with

35.8% (n = 34) in the first year, 23.2% (n = 22) in the second year, 22.1% (n = 21) in the third year and 19.9% (n = 18) in the fourth year of college.

35

Five racial/ethnic groups were included:

42.1% (n = 40) were African American, 8.4% (n = 8) were Asian/Pacific

Islander, 44.2% (n = 42) were Hispanic, 2.1% (n = 2) were Native American,

and 3.2% (n = 3) were White/Non Hispanic.

36

Seventy nine percent of the students were female(n=75)

54.7% (n = 52) of the students indicated that they commuted to school.

Most students were employed part time (64.2%, n = 61),

10.5% (n = 10) were employed full time, and 25.3%(n = 24) were unemployed. Previous high school athletic participation

was almost equally represented with non-participation;

51.6% (n = 49) indicated they had participated in an athletic program while in high school.

37

Institutional Review Board approval was received from the university. An announcement on the Listserv for the EOF students at the university was used to recruit the sample.

Students were instructed they could complete the study questionnaire either online or using a paper copy available in the EOF office.

38

Participants who completed the paper copy of the questionnaire did so in the EOF office after receiving instructions read by an EOF staff member trained by the research investigator.

Participants were instructed to complete the questionnaire only once and to provide their email address that would be used in a raffle to thank them for their participation in the study.

39

This address was stored in a separate dataset and could not be directly linked to the questionnaire dataset.

Online survey completion allowed for the data to be directly entered into the database.

Paper survey responses were entered into the database by the investigator and double checked for accuracy of data entry.

All data were password protected in an electronic file on the computer, accessible only by the principal investigator.

40

Measures

Physical activity. Physical activity was defined for this study as ‘‘any bodily movement produced by skeletal muscles that result in energy expenditure’’ (Caspersen, Powell, & Christenson, 1985, p.126).

Physical activity was measured by using the International Physical Activity Questionnaire (IPAQ, 2005) short version, a seven item instrument that evaluates four types of physical activity behaviors (vigorous, moderate, walking, and sitting) over the last 7 days.

41

Reliability and validity of the IPAQ were evaluated in a sample of 2,450 males and females with a mean age ranging from 25 to 49 years in 14 countries.

Test–retest reliability conducted over a 3–7-day period clustered round )r= .80(.

Criterion validity was estimated by having subjects wear a Computer Science Applications (CSA) accelerometer for 7 consecutive days.

42

Criterion validity had a median rho of about .30 against the CSA accelerometer for minutes of moderate, vigorous, walking and sedentary behaviors.

The IPAQ short version is considered a viable method of monitoring physical activity levels in populations 18–69 years old, has established reliability and validity (IPAQ) and has been used globally across several cultures and languages (Hazzaa & Al-Hazzaa, 2007;IPAQ).

43

The short IPAQ provides separate scores for walking, moderate-intensity, and vigorous-intensity activity.

The summation of the duration in minutes and frequency in days of vigorous-intensity, moderate-intensity, and walking yielded the total physical activity score.

Both categorical and continuous indicators of physical activity are possible from the IPAQ.

44

For this study, data were reported as a continuous measure.

Consistent with the instruction for IPAQ scoring , the volume of activity was computed by weighting each type of activity by its energy requirements defined in Metabolic Equivalency Tasks (METs) to yield a score in MET-minutes.

45

METs are multiples of the resting metabolic rate.

A MET-minute is computed by multiplying the MET score of an activity by the minutes performed.

The following values were used for the analyses of the data:

walking = 3.3 METs, moderate-intensity physical activity = 4.0 METs and vigorous-intensity physical activity = 8.0

METs.

46

The following calculations were used to obtain the physical activity scores:

Walking MET-minutes/week = 3.3 × walking minutes ×walking days;

Moderate MET-minutes/week = 4.0 × moderate-intensity activity minutes × moderate days;

47

Vigorous MET-minutes/week = 8.0 × vigorous-intensity activity minutes × vigorous-intensity days;

Total physical activity MET-minutes/week = sum of walking + moderate + vigorous MET-minutes/week scores (IPAQ, 2005).

48

Social support. Social support was defined as a ‘‘network of interpersonal relationships that provide companionship, assistance, and emotional nourishment.’’ (Pender et al., 2006, p. 226).

The definition was applied to social support for physical activity.

The construct was measured using the Social Support from Family and Friends for Physical Activity Survey, a self-administered 10 item instrument (Sallis, Grossman, Pinski, Patterson, & Nader, 1987).

49

Alphas reported in the literature range from 0.84 to 0.91.

This instrument has been used extensively to measure social support for physical activity in the college age population (Calfas et al., 2000; Wallace & Buckworth, 2003; Wallace et al., 2000).

50

Cronbach’s α for total social support scores in this study was .93.

Self-efficacy. Self-efficacy was defined as judgment of one’s ability to successfully perform the behavior in question (Bandura, 1986).

For this study, exercise was defined as engaging in physical activity to improve health and fitness.

51

Self-efficacy was measured using the Physical Exercise Self-Efficacy Scale (Schwarzer &Renner, 2006), a 5 item scale that inquires about an individual’s self-beliefs about being able to perform physical exercise.

The response range of each item is 1 – 4 with a higher score reflecting higher self-efficacy

52

Published research reports good item-total correlations (r = 0.64 to 0.76)

and excellent measures of reliability (Cronbach’s α

= 0.88) given the small number of items (Brown, 2005).

Construct validity was estimated by correlating the scores to physical exercise intention and self-reported physical exercise 6 months later.

53

Results demonstrated significant relationships (r = 0.33 and 0.39, respectively, p = 0.01).

Cronbach’s α was0 .85 for the current study.

Although initially developed in German, this measure has been used recently in the English speaking populations (Brown; Shen et al., 20 07).

54

Commitment to a plan of physical activity Commitment to a plan of physical

activity was measured using Pender’s 11 item planning for Exercise Scale (Pender, 2006).

The instrument measures commitment to carry out a specific action, as well as definitive strategies to execute the intended behavior, using a 3-point response scale ranging

55

from never (1) to often (3).

Internal consistency found in this study (Cronbach’s α = 0.89) was consistent with the psychometric properties reported by Pender (Cronbach’s α = 0.82).

Pender reported a 2-week test–retest reliability estimate of) r = 0.90 (.

56

Although it has been used in adult populations (Shin, Pender, & Yun, 2003; Shin, Yun, Pender,

& Jang, 2005) no reported use of this instrument in the college age population was found in the published literature.

The construct, commitment to a plan of action, is unique to Pender’s HPM, and the instrument was developed by the theorist,

57

therefore further exploration of this instrument in various populations is warranted to support its use for selection in research with a college age population.

Demographic information. A 7 item self report questionnaire was used to elicit information including:

gender, age, year in college, residential/ commuter status, ethnicity, employment status, and previous athletic participation.

58

Data Analyses Data were analyzed using SPSS

(version 15) for descriptive statistics, inferential statistics, and multiple regression analyses.

The Sobel test for mediation was also calculated.

The Sobel test addresses the details of mediation and is considered to be a strong test of mediation (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002).

59

Descriptive statistics including means, medians, standard deviations (Table 1), and correlations were computed (Table 2).

The assumptions of multiple regression analyses methods were assessed.

Normality of each variable was assessed by visual inspection of normal plots; no transformations were needed in the overall analysis.

60

Linearity and homoscedasticity of the variables were examined by scatter plots (Tabachnick & Fidell, 2006).

The plots of the dependent and independent variables were not found to violate assumptions of linearity or homoscedasticity.

Sample size for the analysis was calculated assuming a medium effect (0.15) with a significance level of a = 0.05 and power = 0.80, using Cohen’s formula for the regression analysis (Cohen, 1992).

61

Seventy-six participants were necessary for regression modeling for this study design with three independent variables (social support, self-efficacy, and commitment to a plan of physical activity).

The first hypothesis was addressed using simultaneous multiple regression.

62

The second and third hypotheses were addressed using multiple regressions and the Sobel test for mediation.

To allow accurate comparisons of total physical activity scores, the data cleaning and processing recommendations of the IPAQ Research Committee (IPAQ, 2005) were followed.

63

Response options such as radial buttons and drop down menus in the online survey minimized the number of missing data and contributed to more precise answers.

For example, respondents had to use a different drop down menu for minutes and hours when entering the amount of time they participated in physical activity.

64

Because both the number of days and daily time were required for the calculation of total physical activity if responses of ‘‘don’t know’’ were entered for time or days then that case was removed from the analyses.

Also removed were cases of unreasonably high outliers as determined by the data processing rules of the IPAQ.

65

Unreasonably high outliers were cases in which the daily sum total of walking, moderate, and vigorous time variables was greater than 960 minutes (16 hours).

One questionnaire considered to be an unreasonably high outlier was excluded from the analyses.

66

Based on these data processing rules, 10 questionnaires that were randomly distributed across the online and paper questionnaires were excluded from the physical activity analyses due to inappropriate responses.

Additionally, to normalize the distribution of activity levels that are typically skewed in large data sets, the IPAQ requires any walking, moderate, or vigorous time that exceeds 3 hours to be re-coded to 180 minutes.

67

This allows a maximum of 21 hours/week of activity for each category (maximum of 63 hours total).

Rules for minimum values of physical activity were also addressed; responses of less than 10 minutes per day of physical activity were re-coded to zero in the calculation of summary scores.

Nineteen questionnaires were re-coded to 180 minutes and one questionnaire was re-coded to zero.

68

RESULTS

The hypothesis that social support, self-efficacy, and commitment to a plan of physical activity would account for a significant proportion of variance in physical activity in low income college students was supported.

69

Table 1. Descriptive Statistics for Major Study Variables

Note: SS, social support; SE, self-efficacy; CPPA, commitment to a plan of physical activity; TMET, total physical activity scores measured in metabolic equivalency task units.

70

Table 2. Pearson Product Moment Correlations of Independent and Dependent Variables

Note: TMET, total physical activity scores measured in metabolic equivalency task units; SS, social support; SE, self-efficacy.

71

Simultaneous multiple regression was used to explore the combined relationship of social support, self-efficacy, and commitment to a plan of physical activity on physical activity.

Results presented in Table 3 demonstrate that the overall regression model with these 3 independent variables was statistically significant (F[3,81] = 7.124, p < 0.0005).

72

Approximately 21% of the variance in physical activity behavior was accounted for by the combination of these three independent variables.

Commitment to a plan of physical activity was a significant predictor of physical activity scores

however neither social support nor self-efficacy’s unique contributions were significant in the overall regression model.

73

The prediction that commitment to a plan of physical activity would mediate the relationship of social support and physical activity behavior was supported.

Results of the hierarchical multiple regression analyses are presented in Table 4.

If mediation was to be demonstrated, four criteria must be met: (Baron & Kenny, 1986):

74

social support must significantly predict physical activity,

commitment to a plan of physical activity must significantly predict physical activity,

social support must significantly predict commitment to a plan of physical activity, and

social support no longer predicts physical activity when both social support and commitment to a plan of physical activity are in the equation.

75

The important principle is that the presence of the mediator statistically explains the relationship between the predictor (social support) and the outcome (physical activity; MacKinnon et al., 2002).

The model indicated evidence of mediation; social support predicted physical activity, commitment to a plan of physical activity predicted physical activity (b = .445, p < .001), and social support predicted commitment to a plan of physical activity (b = .594, p < .001(.

As noted in Table 4, social support was no longer able to significantly predict physical activity when both social support and commitment to a plan of physical activity behavior were in the model.

76

Although social support was a statistically significant independent predictor of physical activity,

it lost significance in the relationship to physical activity when commitment to a plan of physical activity was introduced into the model and commitment to a plan of physical activity remained significant in the model.

The Sobel test also indicated a statistically significant mediation effect (p = 0.003).

77

Table 3. Simultaneous Multiple Regression Explaining Physical Activity Behavior From Social Support ,Self-Efficacy, and Commitment to a Plan of Physical Activity

Note: SS, social support; SE, self-efficacy; CPPA, commitment to a plan of physical activity; PA, physical activity.

78

Table 4. Hierarchical Multiple Regression of Mediator Effect on Social Support and Physical Activity

Note: SS, social support; CPPA, commitment to a plan of physical activity.

79

The third hypothesis, that commitment to a plan of physical activity would mediate the relationship of self-efficacy and physical activity, was supported.

A statistically significant relationship was shown in this sample between self-efficacy and physical activity.

A statistically significant relationship was shown in this sample between commitment to a plan of physical activity and physical activity (b = .445, p < .001).

80

A statistically significant relationship was shown in this sample between self-efficacy and commitment to a plan of physical activity (b = .477, p < .001).

As noted in Table 5 commitment to a plan of physical activity met the criteria of a mediator of the relationship of self-efficacy and physical activity.

Self-efficacy, an independent predictor of physical activity, lost significance in the relationship to physical activity when commitment to a plan of physical activity was introduced into the model.

81

Commitment to a plan of physical activity remained significant in the relationship of self-efficacy and commitment to a plan of physical activity to physical activity.

Additionally, the Sobel test indicated a statistically significant mediation effect (p = .001).

82

Post-Hoc Analyses

Median total physical activity scores (the sum of vigorous, moderate, and walking behaviors) demonstrated that aggregate values for this sample of college students met the recommended levels of total physical activity that an individual should accumulate in a week (median = 3,030 MET-minutes/ week, mean = 3,847MET-minutes/week,

SD = 3,277).

83

In exploring the relationships among the demographic variables, Pearson correlation coefficients indicated that both gender and previous high school athletic participation each revealed statistically significant associations with physical activity behavior (r = 0.33, p = 0.002; and r = 0.29, p = 0.008, respectively),

however, only gender demonstrated a relationship of medium magnitude to be considered clinically significant.

84

Analyses of gender related variables in relation to the main study variables revealed a notable gender gap (Table6).

Approximately 11.1% of the variance in  physical activity could be explained by gender.

This was statistically significant (F [ 1 , 83 ] = 10.36 , p = .002).

85

The proportion of variance in physical activity scores that was explained by gender, social support, self-efficacy, and commitment to a plan of physical activity was approximately 25%.

This amount of variance explained was statistically significant (F change [4,80] = 6.71, p < .001).

86

Controlling for gender, the proportion of variance in physical activity scores that was explained by social support, self-efficacy, and commitment to a plan of physical activity was approximately 14%, (Fchange[3,80] = 4.99, p = .003).

The measure of previous participation in high school athletics was considered as a proxy for ‘‘prior related behavior’’ in the Pender model (Pender et al., 2006).

87

Table 5. Hierarchical Multiple Regression of Mediator Effect on Self-Efficacy and Physical Activity

Note: SE, self-efficacy; CPPA, commitment to a plan of physical activity

88

Table 6. Hierarchical Multiple Regression Explaining the Relationship of Gender and Psychosocial Variables With Physical

Activity

Note: SS, social support; SE, self-efficacy; CPPA, commitment to a plan of physical activity.

89

Approximately 8.2% of the variance in physical activity was explained by high school athletic participation and was significant (F[1,83] = 7.41, p = .008).

Overall, men had higher mean scores than women, however, these results were only statistically significant for commitment to a plan of physical activity (p = .003).

Males’ scores on the commitment to a plan of physical activity measure were .77 standard deviations higher than females, on average, a large effect according to Cohen’s scale (Cohen, 1992).

Additional findings demonstrated that the relationship of family and friend social support was different for commuter students and residential students.

90

Pearson correlation coefficients indicated that for commuters social support from family was slightly stronger (r= .34, p= .022)than social support from friends (r= .30 , p = .044); whereas for residential students social support from family and friends was similar (r = .27, p = .109; and r = .28, p = .119, respectively).

Students had the option of completing the questionnaire either online or in the EOF office.

Controlling for the three independent variables of this study, there was no statistically significant difference in physical activity scores between those that completed the questionnaire online and those that completed the paper version

91

DISCUSSION The aim of this study was to explore the

relationships of social support, self-efficacy, and commitment to a plan of physical activity, and physical activity behavior.

The results supported the hypothesized univariate relationships among the independent and dependent variables.

Although the majority of participants reported high levels of physical activity, these findings conflicted with other researchers’ reports that young adults fail to meet national guidelines for physical activity (Douglas & Collins,

1997; USDHHS, 2000) and that low income populations are especially vulnerable to low levels of physical activity (USDHHS).

92

Differences in definition and measurement of physical activity have led to inconsistent results across studies.

The large proportion of our students who were physically active may be explained by the instrument used to assess physical activity levels.

Because the IPAQ measures total physical activity in all domains over the past 7 days, most adults in a population achieve moderate-intensity activity recommendations (IPAQ, 2005); as compared to other physical activity instruments that only examine leisure time physical activity (Okun et al., 2003).

This narrower focus may not be sufficient to adequately capture many of the behaviors that contribute to the physical activity of students

93

In contrast, participants in this study were given examples of different types of physical activity such as aerobics, heavy lifting, and bicycling.

Additionally, environmental and neighborhood characteristics contribute to levels of physical activity such that individuals living in areas that are safe  have pleasant surroundings and accessible recreational facilities demonstrate higher levels of physical activity (Frank, Kerr, Chapman ,& Sallis , 2007; Kumanyika & Grier, 2006; Loukaitou- Sideris & Eck, 2007).

This suburban college campus with its low crime rate, presence of public safety officers, open spaces and accessible fitness center, may be a protective environment for low income young adults that contributes to increased physical activity behaviors and merits further research.

94

The results with respect to gender are similar to existing research that suggests females are less physically active than males (Clemmens & Hayman, 2004).

This finding has implications for tailoring interventions to improve low income college students’ physical activity behaviors.

Consistent with other research, we found that social support is an important predictor of physical activity behavior (Okun et al., 2003).

The moderate magnitude of effect of social support on total physical activity indicates its importance when considering initiatives aimed at enhancing this healthy behavior.

95

The instrument to measure social support, Social Support from Family and Friends, allowed two types of social support to be measured.

Although both family and friends together significantly predicted physical activity behavior, only family social support independently had a statistically significant correlation with physical activity behavior, suggesting the relationship of family support is more important.

96

Additional findings suggested the relationship to family support was stronger for commuter students than residential students.

This is a reasonable finding considering commuters may interact more with family members than students who live on campus.

Despite findings of a significant association between physical activity and social support, research on these two distinct types of social support and their unique relationships with physical activity behavior is limited in the population of young adults enrolled in college (Sallis & Owen, 1999).

97

Interventions should enlist the support and involvement of friends and family,

especially family for commuter students, as suggested by these findings, when discussing physical activity goals and assessing accomplishments .

Self-efficacy demonstrated a statistically significant association with physical activity behavior.

This supports the findings of other researchers who have suggested self-efficacy is an important predictor of physical activity behavior (Wu & Pender, 2002).

98

Our findings indicate that interventions focused on improving physical activity behaviors that reflect consideration to enhance self-efficacy should concentrate on a discussion about feelings of fatigue, minimizing worrisome situations, and managing a busy schedule as demonstrated by scores on the items of the self-efficacy instrument.

Commitment to a plan of physical activity also fully mediated the relationships of social support and physical activity behavior and of self-efficacy and physical activity behavior in this sample of college students.

In light of the paucity of published research on the construct of commitment to a plan of physical activity, the findings of this study contribute to the expansion of the HPM through its use in a low income college age population .

99

Implications for Practice

The college atmosphere can be an important environment to establish and maintain healthy behaviors.

Given that physical activity behaviors decrease with age (USDHHS, 2000) and that low income populations are especially vulnerable to inactivity (NCCDPHP, 2006), the present findings of acceptable levels of physical activity in this sample suggest that the college setting may positively influence physical activity behaviors for low income young adults.

100

This study rendered valuable information about students’ lifestyle behaviors.

Existing research suggests that school-based multi-level interventions are effective in increasing physical activity behaviors in younger adolescents (Clemmens & Hayman, 2004).

Additional research is needed to determine if similar outcomes are found in older adolescents and young adults .

101

To improve the physical activity behaviors of a campus community, college health nurses might incorporate what is known about effective physical activity practices into their discussions with students and have those discussions concentrate on how to encourage commitment to physical activity planning.

They need to be knowledgeable about the various audiences, such as low income students; since it is unlikely that any one message about healthy physical activity practices will be appropriate for all audiences.

102

Implications for Future Research

Although progress has been made in understanding physical activity behaviors there are gaps in the physical activity literature that identify socioeconomic differences and gender specific analyses (Sallis et al., 1999; Trost et al., 2002).

This study has extended knowledge of physical activity behaviors by explaining these behaviors in a new population and its exploration of gender differences within this population.

103

The present study has also expanded existing knowledge of the construct commitment to a plan, a psychosocial variable that has now been shown to be distinctively influential in predicting physical activity outcomes.

When college health professionals design physical activity interventions, they need to consider incorporating specific tasks that increase or enhance features identified with committing to a plan of physical activity, such as record keeping, posting reminder notes, and specific time allocation for physical activity.

104

One direction for future researchers would be to consider this construct in other low income populations, such as young adults that do not attend college or those that are not subjected to the strengths of group affiliation that these EOF students experienced

105

Limitations Several limitations should be noted

when considering these results.

Self-selection of the study participants might account for the high levels of physical activity in this sample.

Only 23% of the 416 students in the sample frame participated.

It is possible that only those EOF students interested in physical activity chose to participate.

106

The EOF program itself may also contribute to positive behavior outcomes

Each student is assigned a counselor who provides academic, personal, and social counseling.

The interactions of this counselor– student relationship may have targeted social support and self-efficacy skills and positively influenced healthy behaviors in this group of students.

Another limitation of the study is the cross-sectional study design, which limits inferences on the causality of the relation between the dependent and independent variables

107

Finally, this study relied on self-reported physical activity data recalled over a 7-day period.

The instrument selected to study physical activity was chosen because it had sound psychometric properties and provided detailed information about physical activity behaviors that would demonstrate a less biased self-report measure of physical activity and provide high quality data.

108

In summary, the data bring to light the importance of improving physical activity behaviors and developing meaningful programs to increase physical activity behaviors within campus communities.

This requires a multidisciplinary approach calling on the efforts of many.

109

This research addressed just one small part, the psychosocial correlates, within a distinctive population, low income college students that participated in a recognized organization.

Clarifying the relationships of these variables within vulnerable populations may be an important step in developing interventions to improve physical activity behaviors.

110

REFERENCES Allen, N. (2003). voices from the past. Physical culture in Amherst

College. American Journal of Public Health, 93(5), 720–722. Bandura, A. (1986). Social foundations of thought and action: A

social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Baron, R.M., & Kenny, D.A. (1986). The moderatormediator

variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.

Journal of Personality and Social Psychology, 51(6), 1173–1182. Brown, S.A. (2005). Measuring perceived benefits and perceived

barriers for physical activity. American Journal of Health Behavior, 29(2), 107–116. Calfas, K.J., Sallis, J.F., Nichols, J.F., Sarkin, J.A., Jognson, M.F.,

Caparosa, S., et al. (2000). Project GRAD: Two-year outcomes of a randomized controlled physical

activity intervention among young adults. American Journal of Preventive Medicine,18(1), 28–37. Caspersen, C.J., Powell, K.E., & Christenson, G.M. (1985). Physical

activity, exercise, and physical fitness: Definitions and distinctions for health-related research.

Public Health Reports, 100(2), 126–131.

111

Clemmens, D., & Hayman, L.L. (2004). Increasing activity to reduce obesity in adolescent girls: A

research review. Journal of Obstetric, Gynecologic, and Neonatal Nursing, 33(6), 801–808.

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.

Douglas, K.A., & Collins, J.L. (1997). Results from the 1995 National College Health Risk Behavior Survey. Journal of American College Health, 46(2), 55–66.

Frank, L., Kerr, J., Chapman, J., & Sallis, J. (2007). Urban form relationships with walk trip frequency

and distance among youth. American Journal of Health Promotion, 21(4 Suppl.), 305–311.

Frenn, M., Malin, S., Bansal, N., Delgado, M., Greer,Y., Havice, M., et al. (2003). Addressing health disparities in middle school students’ nutrition and exercise. Journal of Community Health Nursing, 20(1), 1–14.

Gerald, D.E., & Hussar, W.J., (2000). Projection of education statistics to 2010. (Report No. NCES

2000071). National Center for Education Statistics Web site. Retrieved July 27, 2009 from http://

nces.ed.gov/pubsearch. Glanz, K., Rimer, B., & Lewis, G.M. (2002). Theory, research, and

practice in health behavior and health education. In K. Glanz, B.K. Rimer, & R.M. Lewis (Eds.), Health behavior

and health education theory, research and practice (3rd ed., pp. 22–40). San Francisco: Jossey-Bass

Publishers.

112

Haskell,W.L., Lee, I.-M., Pate, R.P., Powell, K.E., Blair, S.N., Franklin, B.A., et al. (2007). Physical activity and public health: Updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation, 116(9), 1081–1093.

Hayman, L.L., Williams, C.L., Daniels, S.R., Steinberger, J., Paridon, S., Dennison, B.A., et al. (2004).

Cardiovascular health promotion in the schools: A statement for health and education professionals and

child health advocates from the Committee on Atherosclerosis, Hypertension, and Obesity in Youth

(AHOY) of the Council on Cardiovascular Disease in the Young, American Heart Association. Circulation, 110(15), 2266–2275.

Hazzaa, M., & Al-Hazzaa, H.M. (2007). Healthenhancing physical activity among Saudi adults using the International Physical Activity Questionnaire (IPAQ). Public Health Nutrition, 10(1), 59–64.

International Physical Activity Questionnaire. 2005. IPAQ Home Page (November, 2005). Retrieved July

27, 2009 from http://www.ipaq.ki.se/ipaq.htm. Kimm, S.Y.S., Glynn, N.W., Kriska, A.M., Barton, B.A., Kronsbert,

S.S., Daniels, S.R., et al. (2002). Decline in physical activity in black girls and white girls during

adolescence. The New England Journal of Medicine, 347(10), 709–715.

113

Krieger, N., Chen, J.T.,Waterman, P.D., Soobader, M.J., Subramanian, S.V., & Carson, R. (2002). Geocoding and monitoring of US socioeconomic inequalities in

mortality and cancer incidence: Does the choice of area-based measure and geographic level matter

American Journal of Epidemiology, 156(5), 471– 482. Kumanyika, S., & Grier, S. (2006). Targeting interventions for ethnic

minority and low income populations. Future of Children, 16(1), 187–207. Loukaitou-Sideris, A., & Eck, J.E. (2007). Crime prevention and active

living. American Journal of Health Promotion, 21(4 Suppl.), 380–389. MacKinnon, D.P., Lockwood, C.M., Hoffman, J.M.,West, S.G., &

Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects.

Psychological Methods, 7, 83–104. Mokdad, A.H., Marks, J.S., Stroup, F.F., & Gergerding, J.L. (2004).

Actual causes of death in the United States, 2000. Journal of American Medical Association, 291(10),

1238–1245. National Center for Chronic Disease, Prevention and Health

Promotion. (2006). Physical activity and health: A report of the Surgeon General. Retrieved July 27, 2009 from

http://www.cdc.gov/nccdphp. New Jersey Commission on Higher Education. (2006). Educational

Opportunity Fund Home Page (February8, 2006). Retrieved July 27, 2009 from http://www.nj.gov/highereducation.eof.

114

Okun, M.A., Ruehlman, L., Karoly, P., Lutz, R.,Fairholme, C., & Schaub, R. (2003). Social support

and social norms: Do both contribute to predicting leisure-time exercise? American Journal of Health

Behavior, 27(5), 493–507. Paffenbarger, R.S., Kampert, J.B., & Lee, I. (1997).Physical activity

and health of college men: Longitudinal observations. International Journal of Sports Medicine, 18(Suppl. 3), S200–S203.

Pate, R.R., Pratt, M., Blair, S.N., Haskell,W.L., Macera, C.A., Bouchard, C., et al. (1995). Physical activity

and public health: A recommendation from the Centers for Disease Control and Prevention and

the American College of Sports Medicine. Journal of American Medical Association, 273(5), 402–

407. Pender, N.J., 2006. Home Page. Retrieved July 27, 2009 from

ww.nursing.umich.edu/faculty/pender_nola. html. Pender, N.J., Murdaugh, C.L., & Parsons, M. (2006).Health

promotion in nursing practice (5th ed.) Upper Saddle River, NJ: Pearson Prentice Hall. Robbins, L.B., Gretebeck, K.A., Kazanis, A.S., &Pender, N.J. (2006).

Girls on the move program to increase physical activity participation. Nursing Research, 55(3),

206–216.

115

Rosen, C.S. (2000). Integrating stage and continuum models to explain processing of exercise messages

and exercise initiation among sedentary college students. Health Psychology, 19(2), 172–180.

Rovniak, L.S., Anderson, E.S., Winett, R.A., & Stephens, R.S. (2002). Social cognitive determinants

of physical activity in young adults: A prospective structural equation analysis. Annals of Behavioral

Medicine, 24(2), 149–156. Sallis, J.F., Grossman, R.M., Pinski, R.B., Patterson, T.L., &

Nader, P.R. (1987). The development of scales to measure social support for diet and exercise

behaviors. Preventive Medicine, 16(6), 825–836. Sallis, J.F., & Owen, N. (1999). Physical activity and

behavioral medicine. Thousand Oaks, CA: Sage. Sallis, J.F., Prochaska, J.J., Taylor, W.C., Hill, J.O., & Geraci,

J.C. (1999). Correlates of physical activity in a national sample of girls and boys in grades 4 through 12. Health Psychology, 18(4), 410–415.

Sallis, J.F., & Saelens, B.E. (2000). Assessment of physical activity by self-report: Status, limitations,

and future directions. Research Quarterly for Exercise and Sport, 71(2), 1–14.

116

Schwarzer, R., & Renner, B., (2006). Health-specific self-efficacy scales. Available at: http://userpage.fuberlin. de/_health/ healself.pdf. Retrieved: March 16, 2006. Wang, R., & Chen, C. (2003). Evaluating Pender’s health promotion model from literature review

[Chinese]. Journal of Nursing (China), 50(6), 62–68. Warburton, D.E.R., Nicol, C.W., & Bredin, S.S.D. (2006). Health

benefits of physical activity: The evidence. Canadian Medical Association Journal, 174(6), 801–809. World Health Organization. (2003). The European health report

2002. Retrieved July 27, 2009 from http://www.euro.who.int. Shen, C.L., Feng, D., Esperat, M.C.R., Irons, B.K., Chyu, M., Valdez,

G.M., et al. (2007). Effect of Tai Chi exercise on type 2 diabetes: A feasibility study. Integrative

Medicine Insights, 2, 15–23. Shin, Y., Pender, N.J., & Yun, S. (2003). Using methodological

triangulation for cultural verification of commitment to plan for exercise scale among Korean adults

with chronic disease. Research in Nursing & Health, 26(4), 312–321. Shin,Y.,Yun, S., Pender, N.J.,& Jang, H. (2005). Test of the health

promotion model as a causal model of commitment to a plan for exercise among Korean adults with

chronic disease. Research in Nursing & Health, 28(2), 117–125.

117

Tabachnick, A.G., & Fidell, L.L.S. (2006). Using multivariate statistics (5th ed.) Old Tappan, NJ:

Pearson Higher Education. Thomson, P. (2000). Exercise and health: A review of the

usefulness of behavioural change theories in helping patients to become habitually active. International Journal

of Health Promotion and Education, 38(1), 10–18. Trost, S.G., Owen, N., Bauman, A.E., Sallis, J.F., & Brown,W. (2002).

Correlates of adults’ participation in physical activity: Review and update. Medicine & Science in

Sports & Exercise, 34(12), 1996– 2001. US Department of Health and Human Services. (2000). Healthy

people 2010: Understanding and improving health. Washington, DC: US Government Printing Office.

Wallace, L.S., & Buckworth, J. (2003). Longitudinal shifts in exercise stages of change in college students.

Journal of Sports Medicine and Physical Fitness, 43(2), 209–212. Wallace, L.S., Buckworth, J., Kirby, T.E., & Sherman, W.M. (2000).

Characteristics theory to predicting stage of change. Preventive Medicine, 31(5), 494–505.