I Ran, Fell, but Managed to be Third: Energy Allocation in Primary School Children in Rural

Uganda

A thesis presented by

Brennan Ann Vail

To the Department of Human Evolutionary Biology

in partial fulfillment of the requirements

for the degree with honors

of Bachelor of Arts

Harvard University

Cambridge, Massachusetts

March 9th, 2012

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Acknowledgements

I would like to thank my thesis advisor, Professor Peter Ellison, for his tremendous

wisdom and guidance. Neither designing this fieldwork nor writing this thesis would

have been possible without him.

An enormous thank you to Patrick Tusiime, my translator in Uganda. His help in the field

made this research possible and his friendship is something that I always treasure.

To Michelle Sirois, thank you for being an amazing friend and the perfect travel partner.

Thank you to Caroline Riss and Zarin Machanda for their assistance with travel

preparations and in the field. Caroline made me feel at home the moment I arrived in

Kibale, and Zarin was immeasurably helpful in preparing my presentation for the schools.

Thank you to Meredith Reiches for her patient assistance with the accelerometers.

I would like to thank Elizabeth Ross and Kate Wrangham-Briggs for permitting me to

conduct this research under the Kasiisi Project and the Kasiisi Porridge Project. Their

support was tremendous and this research would not have been possible without their

assistance.

I am extremely grateful to all of the children who participated in this research. They filled

my days with laughter and hugs. Thank you to the parents of these children for being so

open to and supportive of my research. Thank you to the headmasters and teachers of

Kyanyawara, Kasiisi, Kigarama, and Kiko primary schools for welcoming me into their

communities.

Thank you to the Harvard Global Health Institute and the Goelet Fund for their generous

grant support that enabled me to travel to Uganda. Also, thank you to the Uganda

National Council for Science and Technology for their permission to conduct this

research in the Kabarole district.

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Abstract

BACKGROUND- Under-nutrition remains an enormous global health problem that has

a tremendous impact on child development. Children with inadequate energy intake

cannot satisfy their body’s somatic and reproductive energy demands. Using life history

theory, this thesis explores how children in resource scarce environments in rural Uganda

allocate limited energetic resources. It also explores the effect of food aid, specifically the

Kasiisi Porridge Project, has on this pattern of allocation.

SUBJECTS- All participants were healthy children ages 8 to 18 from Kyanyawara,

Kasiisi, Kigarama, and Kiko primary schools in the Kabarole district of Uganda. Of the

129 participants, 65 were female and 64 were male.

METHODS- Nutritional status was assessed with anthropometric measurements. Energy

expenditure was measured during the school day using ActiTrainer accelerometers.

Finally, each study participant completed a verbal 24-hour diet recall.

RESULTS- Stunting was the most common physical manifestation of malnutrition

followed by underweight and low BMI for age, respectively. The porridge provided by

the Kasiisi Porridge Project reduced the number of children eating nothing for lunch, but

did not have a significant effect on mean nutritional status. Weight for height and weight

for age were both positively correlated with energy expenditure in activity for females.

The relationship accounted for the most variation in females who had started puberty.

CONCLUSIONS- In resource scarce environments, children allocate energy

conservatively by allocating more energy to gaining weight than to growing taller.

Females also allocate energy to activity with lower priority than energy is allocated to

growth, particularly when resource demands increase at the onset of puberty. This

conservative pattern of allocation contributes to the ability of humans to sustain a long

period of juvenile development. Food aid, as it is currently distributed in this study

population, is not an effective way to improve the nutritional status or increase the mean

energy expenditure in activity of primary school children. Future collaboration between

the fields of human evolutionary biology and global health is imperative for designing

better interventions and improving child health worldwide.

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Table of Contents

Introduction....................................................................................................................... 1

A Student's Story ............................................................................................................ 2 Objectives ....................................................................................................................... 2 Nutrition Patterns Globally ............................................................................................. 3 Nutrition and Child Development................................................................................... 5 Food Aid ....................................................................................................................... 10 Nutrition Patterns in Rural Uganda .............................................................................. 11 Kasiisi Project ............................................................................................................... 12 Hypotheses.................................................................................................................... 14 Summary....................................................................................................................... 23

Methods............................................................................................................................ 24 Location ........................................................................................................................ 25 Participant Selection ..................................................................................................... 25 Permissions ................................................................................................................... 26 Anthropometric Data .................................................................................................... 26 Energy Expenditure Data.............................................................................................. 27 Dietary Recall and Additional Information .................................................................. 28 Use of Data ................................................................................................................... 29 Statistics ........................................................................................................................ 29 Research Ethics............................................................................................................. 31

Results .............................................................................................................................. 32 Hypothesis 1 ................................................................................................................. 33 Hypothesis 2 ................................................................................................................. 39 Hypothesis 3 ................................................................................................................. 45 Summary....................................................................................................................... 51

Discussion ........................................................................................................................ 53 General Findings........................................................................................................... 54 Support for Findings by Hypothesis ............................................................................. 56 Summary....................................................................................................................... 60 Literature Comparison .................................................................................................. 61 Significance in Human Evolutionary Biology.............................................................. 65 Significance in Public and Global Health..................................................................... 68 Limitations .................................................................................................................... 69 Conclusion .................................................................................................................... 71 APPENDIX 1............................................................................................................... 73 REFERENCES............................................................................................................ 74

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Figures and Tables Figure 1: Prevalence of low BMI for age ........................................................................ 33 Figure 2: Prevalence of underweight ............................................................................... 34 Figure 3: Prevalence of stunting ...................................................................................... 34 Figure 4: Lunch patterns at schools with porridge........................................................... 36 Figure 5: Lunch patterns at schools without porridge ..................................................... 36 Figure 6: Effect of porridge on mean weight for height .................................................. 37 Figure 7: Effect of porridge on mean weight for age....................................................... 37 Figure 8: Effect of porridge on mean height for age ....................................................... 37 Figure 9: Regression of weight for height and activity.................................................... 39 Figure 10: Regression of weight for age and activity ...................................................... 39 Figure 11: Regression of height for age and activity ....................................................... 40 Figure 12: Effect of porridge on energy expenditure in activity ..................................... 41 Figure 13: Regression of height for age and activity (females)....................................... 42 Figure 14: Regression of weight for age and activity (females) ...................................... 42 Figure 15: Regression of weight for height and activity (females) ................................. 43 Figure 16: Regression of height for age and activity (males) .......................................... 43 Figure 17: Regression of weight for age and activity (males) ......................................... 43 Figure 18: Regression of weight for height and activity (males)..................................... 44 Figure 19: Regression of weight for height and activity (pre pubertal females) ............. 45 Figure 20: Regression of weight for age and activity (pre pubertal females).................. 45 Figure 21: Regression of height for age and activity (pre pubertal females)................... 46 Figure 22: Regression of weight for height and activity (post pubertal females)............ 46 Figure 23: Regression of weight for age and activity (post pubertal females) ................ 46 Figure 24: Regression of height for age and activity (post pubertal females) ................. 47 Figure 25: Regression of weight for height and activity (pre pubertal males) ................ 47 Figure 26: Regression of weight for age and activity (pre pubertal males)..................... 47 Figure 27: Regression of height for age and activity (pre pubertal males)...................... 48 Figure 28: Regression of weight for height and activity (post pubertal males)............... 48 Figure 29: Regression of weight for age and activity (post pubertal males) ................... 48 Figure 30: Regression of height for age and activity (post pubertal males) .................... 49

Table 1: Difference in mean nutritional status of children consuming porridge and children not consuming porridge ...................................................................................... 37 Table 2: Results of t-tests when children are separated by sex, pubertal status, orphan status, breakfast size, and household size ......................................................................... 38 Table 3: Results of t-tests when children are separated by height for age ....................... 38 Table 4: Predictive value of nutritional status for energy expenditure in activity ........... 40 Table 5: Predictive value of nutritional status for energy expenditure in activity when children are separated according to sex, pubertal status, orphan status, breakfast size and household size................................................................................................................... 40 Table 6: Predictive value of nutritional status for energy spent in activity when children are separated according to sex and pubertal status ........................................................... 49 Table 7: Amount of variance explained by the regressions for females pre and post puberty .............................................................................................................................. 49

1

~ Chapter One ~

Introduction

2

A STUDENT’S STORY

In the morning I got out of my bed, put on my sandals, and washed my body with

soap and clean water. I went back into the house, dried my body with a towel, and

smeared myself with the help of my mom. I removed my casual clothes, I put on my

school clothes, and I ate and drank tea. I packed food, water, and books into my bag, and

then I said bye to my brothers and my mom. I walked and ran to school.

At school I first played a game called dodging. Then I went to the morning

assembly and marched to my classroom with my classmates. I sat down and then stood to

greet my teacher. In class I sang, sat, studied, wrote and drew. At break time I jumped

rope, shared bread with my two friends, and ran back to class after the bell. In class I sat,

stood to sing and dance, sat again, studied, and wrote. At lunchtime I took porridge and

played. In class in the afternoon I was sent by my teacher to find chalk from the staff

room. After school I went for athletics training. I ran, fell, but managed to be the third in

the group of little ones.

I walked and ran home, greeted my mom, fetched water, ate and drank. At night I

helped cook by adding wood to the fire. I carried a neighbor's baby, bathed and dried my

body with the help of my mom, and I tried to fight my sister. I was punished for that. I just

ate dinner and as am telling this story I am heading to my bed.

~ Primary School Student

OBJECTIVES

This thesis will examine the energy allocation of primary school children, like the

child whose story is above, living in a resource poor environment in the rural Kabarole

district of Uganda. The effect of a school feeding program administered by the Kasiisi

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Project on energy expenditure in activity and on nutritional status is a sub topic that will

be addressed. In this thesis nutritional status refers to caloric intake and is measured by

weight for height, weight for age, and height for age. Using these anthropometric

measurements in addition to energy expenditure data and dietary recalls this thesis will

explore three main hypotheses: (I) In environments of chronic resource scarcity children

and adolescents allocate more energy to weight than to height; (II) In energy limited

environments, children and adolescents allocate energy to activity at a lower priority than

they allocate energy to gaining and maintaining weight; (III) In resource-limited

environments, females who have started puberty allocate energy to activity at an even

lower priority than do pre pubertal females. The significance of this research for

improving human health, for understanding the long juvenile period in humans, and for

delivering caloric supplements both at schools and also more generally will be explored.

NUTRITION PATTERNS GLOBALLY Malnutrition is, most basically, a problem of energy input not matching energy

output. Recently, the scientific and global health communities have been keenly

interested in the consequences of energy input exceeding energy output. Over-nutrition

has indeed become a global problem. Overweight and obesity rates are rapidly increasing

in the developed world (Caballero, 2007). Moreover, the developing world is now facing

the double burden of over- and under-nutrition that has come with what public health

professionals are calling the ‘nutrition transition’ (Popkin, 2001) The human diet has

changed from being primarily composed of low fat and high fiber foods to being largely

made up of foods high in fat and refined carbohydrates and low in fiber. Urbanization and

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industrialization are two proximate causes of this transition (Popkin, 1997; Popkin,

2001).

Despite this present interest in the rising rates of overweight and obesity

worldwide, under-nutrition remains a very serious issue particularly in rural areas of the

developing world. The fundamental problem is the same: energy input does not equal

energy output. In the case of under-nutrition, caloric energy input is not adequate given

energy expenditure. There are three standard measures of under-nutrition: stunting (low

height for age), underweight (low weight for age), and wasting (low weight for height). A

child is classified as stunted, underweight or wasted if he or she falls two standard

deviations below the mean height for age, weight for age, or weight for height,

respectively. The means and standard deviation cutoffs used in this report were published

by the Center for Disease Control (CDC) (Center for Disease Control and Prevention,

2009a; 2009b; 2009c). CDC references are based on a sample of children in the United

States. They are, however, still appropriate to use in this research because studies have

show that children around the world have the same growth potential given favorable

environmental conditions (Graitcher & Gentry, 1981; Habicht et al., 1974).

The World Health Organization (WHO) Department of Nutrition reported the

prevalence of stunting, underweight, wasting, and overweight in preschool children in the

developing world in 1990 and again in 2010. The highest rates of malnutrition are in

Africa and Asia. The prevalence of stunting is highest on both continents, followed by

underweight, and finally by wasting. The table below presents the respective percentages

in both Africa and Asia (WHO Department of Nutrition, 2009a; WHO Department of

Nutrition, 2009b; De Onis et al., 2010; De Onis et al., 2011).

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Africa Asia 1990 2010 1990 2010

Stunting 40.3% 38.2% 48.6% 27.6% Underweight 21.5% 19.3% 33.8% 19.5% Wasting 8.3% 10.0% 11.6% 10.6% Overweight and Obesity

4.9% 8.5% 3.2% 4.9%

These percentages demonstrate that although the prevalence of under-nutrition in

children has, for the most part, declined since 1990, it is still a much larger problem in the

developing world than over-nutrition. Therefore, it is imperative that under-nutrition not

be overlooked or forgotten by medical professionals, public health professionals,

scientists, and policy makers.

NUTRITION AND CHILD DEVELOPMENT Nutrition has a substantial impact on child development because it dictates how

much energy is available to allocate to the various energetic demands associated with

development. Life history theory provides a useful lens for understanding energy

allocation. The theory is based on the fundamental idea that energy is limited. Thus

during energy allocation, tradeoffs must be made (Gadgil & Bossert, 1970). Energy

demands can be placed into two broad categories: demands for somatic efforts and

demands for reproductive efforts. Somatic efforts include growth, maintenance, and

energy used more generally for survival. Reproductive efforts include all behaviors

related to gestation in females, to parenting, and to mating, including courting and

copulation (Bogin et al., 2007; Chisholm, 1993; Stearns, 1992).

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The balance between somatic and reproductive efforts throughout the life span

differs between species. Compared to primates, humans have a uniquely long juvenile

period, particularly with respect to the length of the reproductive period which is

approximately equal to, if not shorter than that of chimpanzees (Shultz, 1969). Humans

have a longer life span and a lower early adult mortality and thus can afford to delay

reproduction for this uniquely long juvenile period. Hill and Kaplan (1999) demonstrated

that, on average, Ache hunter gatherers in Paraguay live for 56 years, with an early adult

mortality rate of 1.5%, as compared to wild chimpanzees who live an average of only 28

years with an early adult mortality rate of 4%. Consistent with this pattern, chimpanzees

typically begin to reproduce at age 13-15 whereas hunter-gatherers do not reproduce until

age 18-20. During the juvenile period humans and chimpanzees grow at approximately

the same rate, but humans continue to growth several years after chimpanzees begin to

reproduce. This long juvenile period makes energy allocation in human children an

interesting topic of research. There are many factors that influence optimal energy

allocation in human children including- pubertal status, sex, health status, and household

environment.

Pubertal Status

Before puberty all of a child’s energy is allocated to somatic efforts such as

growth, activity, and maintenance. However, upon the onset of puberty, adolescents must

allocate some energy to preparing for reproduction, an additional demand on a limited

supply of energy. In both sexes reproductive organs grow and body composition and

stature change (Tanner, 1990). In females, energy demands for weight gain increase to

facilitate adipose deposition because having a positive energy status increases the

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probability of conception. Energy status is the amount of stored energy that can be

mobilized for reproductive effort. In addition to increased adipose deposition, females

may limit energy expenditure in activity because having a positive energy balance also

increases fecundity. Energy balance is the amount of energy entering the body minus the

amount of energy spent and thus is also an indicator of the amount of energy available to

be mobilized for reproduction (Ellison, 2003; Lipson & Ellison, 1996). For males, gamete

production is not influenced by energy status or balance. However, males may divert

more energy to male-male competition and to developing secondary sex characteristics,

such as muscle mass, to increase their access to mates (Ellison, 2003).

Sex

As highlighted above, another important factor in adolescent energy allocation is

sex. While both sexes have additional energetic demands once puberty begins, these

demands manifest themselves in different ways. Females are likely to allocate more

energy to weight gain and males are likely to allocate more energy to activity as

described above. Additionally, the cost of preparing for reproduction for females is

obligate. Conception is highly unlikely for a female with a very low energy status or

negative energy balance. Males, on the other hand, can reproduce without the

development of secondary sex characteristics or male-male competition if they have

access to fecund females (Ellison, 2003; Nelson, 2005). Thus females, compared to

males, might have to devote a greater proportion of resources in energy limited

environments to reproductive efforts.

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Health Status

Another host factor that influences energy allocation is health status or disease

burden. McDade et al. (2008) conducted a study of Tsimane children in Bolivia that

found a negative correlation between levels of C-reactive protein and gains in height for

both children ages 2-4 and also those with low body fat. C-reactive protein is a protein

released in the acute phase of the innate immune response that can be used to measure

immune system activation. Immune function is a maintenance mechanism that is

energetically very costly. To compensate for this increased maintenance demand,

children will allocate less energy to growth. This reduction will have physical for

consequences for children who are growing quickly (ages 2-4) and for children who do

not have a lot of energy reserves (low body fat).

Household Environment

Finally, household environment plays a large role in how children allocate energy

because human children are dependent on parental care for an extended period of time

after weaning (Hill and Kaplan, 1999). Provisioning is a critically important aspect of

parental care. Studies typically focus on fathers because a mother’s parental investment is

obligate whereas father’s investment is facultative (Nelson, 2005). If parental care is

unnecessary, it would be in a father’s biological interest to mate with other females rather

than care for his offspring. Thus, high levels of paternal care are a good indicator of the

necessity of parental care. Marlowe (2003) studied the Hadza population in Tanzania and

found that typically male and female energetic contributions, as measured in kilocalories,

to the family through foraging or hunting were equal. However, when the family had a

child under the age of three, the man contributed 58% of kcals, and with a child under the

9

age of one, the man contributed 69% of kcals. Moreover, a UN survey demonstrated that

the child mortality rate was higher in 11 of 14 developing countries if the father was

absent. If the father had died the rates were even higher than if the father had simply

divorced the mother (Geary, 2000). Both of these studies underscore the importance of

paternal care and thus parental care in humans. Human children cannot adequately

provide for themselves. Thus, the amount of energy they have to allocate to somatic and

reproductive efforts is highly dependent on parental care, an environmental factor.

Orphan status and household size can affect the quality parental care. There is a

consistent negative relationship between household size and nutritional status. This

finding suggests that energetic resources may be spread too thin in large households, and

underscores the significance of parental investment in child growth and development

(Alderman & Garcia, 1994; Garrett & Ruel, 1999). Additionally, Smyke et al. (2007)

demonstrated that orphaned children in Romanian institutions living without parents had

decreased physical growth compared to non-orphans living with their families in the

community. Moreover, the longer a child was institutionalized the more retarded his or

her growth. This research highlights the effect that parents can have on both energetic

and psychosocial conditions that influence a child’s growth. Other studies on the

nutritional status and physical growth of orphans show mixed results (Panpanich et al.,

1999; Rivers et al., 2008; Zidron et al., 2009), likely because the level of care in different

orphanages varies tremendously.

Summary

Humans have a very long juvenile period. Optimal energy allocation among

growth, maintenance, and preparation for reproduction once puberty begins is very

10

important so that reproductive fitness is not compromised during this long juvenile

period. The amount of energy allocated to each of these demands in any individual

depends on pubertal status, sex, disease burden, and household environment. This report

focuses on how children in a resource-limited setting in rural Uganda prioritize energy

allocation within and among energetic demands for growth, activity, and preparation for

reproduction.

FOOD AID Food aid is one way to increase the amount of energy an individual has to allocate

among the energy demands of child development. Sufficient energy intake is so

important that two of the eight Millennium Development Goals (MDGs) are directly

related to child nutrition. The MDGs are specific targets aimed at improving the lives of

the world’s poorest people by 2015. These targets have been agreed upon by all of the

countries of the world. The first MDG related to child nutrition is goal one: to eradicate

extreme poverty and hunger. Goal number four is to reduce child mortality. Malnutrition

is a contributing factor to all of the leading causes of child mortality (“Millennium

Development Goals”, 2010).

In 2005, 8.25 million tons of food aid was delivered throughout the world. Africa

received 56% of this food aid, the largest portion. Asia followed receiving 29%. Uganda,

specifically, was given 309,100 tons of food aid in 2005. This was the third highest

amount of aid, just behind Ethiopia and Sudan (UN World Food Programme, 2006).

School Feeding Programs are one specific type of food aid, the type studied in

this research. The World Food Program and the World Bank outlined three goals for

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school feeding programs: to provide a safety net for families, to improve scholastic

performance, and to improve child nutrition (Bundy et al., 2009). Studies carried out

between 1990 and 2009 provide conclusive evidence that school feeding programs

decrease incidence of illness as well as improve energy intake, vitamin and mineral

intake, and school enrollment and attendance. On the other hand, there is inconclusive

evidence that school feeding programs increase weight, height, cognition, classroom

behavior, and overall educational achievement (Jomaa et al., 2010). This research

attempts to add to the body of evidence about the effect of school feeding programs on

the nutritional status of primary school children.

NUTRITION PATTERNS IN RURAL UGANDA

The primary malnutrition challenges in rural Uganda are ones of under-nutrition.

The most recent country profile of Uganda published in 2010 reveals that in 2006,

Uganda’s population was 27.357 million and 87% of this population lived in rural areas

where poverty is a major concern. More specifically, 52% of individuals lived on less

than $1 per day and could only afford 2 meals per day. Consequently, dietary diversity in

Uganda is low. The typical diet in rural Uganda consists of starch (cassava and sweet

potatoes), plantains (matooke- cooked banana), cereals (maize, millet, sorghum), and

sauces made of beans, ground nuts, peas, or green leafy vegetables. Life expectancy at

birth in Uganda is 48 years, and children make up an extraordinarily large portion of the

population. In 2005, 50% of the population was under 15 years of age (Kikafunda et al.,

2010). Thus, child health and nutrition are of significant concern to the country’s future.

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The infant mortality rate in Uganda was 76 deaths per 1000 birth from 2001 to

2005. This is down from 89 per 1000 in the 1990s but is still quite high compared to the

developed world. In 2006 the United States had the second highest infant mortality rate in

the developed world, but it was still only 5 deaths per 1000 births (Green, 2006). The

under five mortality in Uganda has also declined since the 1990s but remains high at 137

per 1000. The most common childhood illnesses are malaria, diarrhea, and acute

respiratory infections (Kikafunda et al., 2010). These illnesses both exacerbate and are

exacerbated by under-nutrition.

The Uganda Demographic Health Survey, conducted in 2006, indicated that

38.1% of preschool children were stunted, 15.9% were underweight, and 6.1% were

wasted. The prevalence of stunting and underweight was higher in rural areas with 39.5%

of pre-school children stunted in rural areas compared to 25.5% in urban areas, and

16.5% of children underweight in rural areas compared to 10.6% in urban areas

(Kikafunda et al., 2010). Similar anthropometric indicators of school-aged children were

not part of the Demographic Health Survey. However, a survey conducted 6 kilometers

outside of Kampala, the capital city, indicated that 20.1% of school-aged children were

stunted, 13.9% were underweight, and 2.3% were wasted (Kikafunda et al., 2006). This

survey might not be representative of the country because of the close proximity of the

study sight to the capital city.

KASIISI PROJECT The Kasiisi Porridge Project is a non-profit organization working to address the

problem of under-nutrition in western Uganda with a school feeding program. The

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Kasiisi Porridge Project is part of a larger non-profit called the Kasiisi Project. The

Kasiisi Project was founded in 1997 and is based in Cambridge, MA. The project is

directed by Elizabeth Ross in the United States, and has a partner organization in Uganda

called the Kibale Forest Schools and Student Support Project (KFSSSP) directed by John

Kasenene. The project works in villages in the Kabarole district of Uganda, a district that

borders the Kibale forest in the south west of the country. The mission of the Kasiisi

project is “to create an educated community in and around Kibale National Park with

access to alternative sources of livelihoods which have a low dependency on park

resources and thus increases its conservation.” The project serves 7000 children in 14

schools but is most involved with the five schools closest to Kibale National Park-

Kasiisi, Kyanyawara, Kigarama, Kiko, and Rweterra (The Kasiisi Project, 2011; Kasiisi

Project, “Fact Sheet”; Kasiisi Project, “KFSSSP Road Map”; Kasiisi Project, “The

Kasiisi Project Annual Report”). This research was conducted in the first four schools

listed.

The Kasiisi Porridge Project is one of many programs, including scholarships,

conservation education, health education, and literacy initiatives, that the Kasiisi Project

has implemented to achieve its mission. The Kasiisi Porridge Project is a British non-

profit that was founded in May 2009 by Kate Wrangham-Briggs. It provides one cup of

porridge each day at lunch time to 1200 children in Kasiisi and Kyanyawara primary

schools. The porridge is made of maize flour, water, and a bit of sugar. Not only does this

school lunch program provide porridge, but it has also built two school kitchens and

water tanks necessary to cook the porridge. Recently, the program has also helped Kasiisi

purchase 20 acres of land to build a community farm in hopes of making the porridge

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program self-sufficient (The Kasiisi Project, 2011; Kasiisi Project, “Fact Sheet”; Kasiisi

Project, “KFSSSP Road Map”; Kasiisi Project, “The Kasiisi Project Annual Report”).

This research has been conducted in collaboration with the Kasiisi Project, and

specifically with the Kasiisi Porridge Project. In gratitude for their support, I hope that

this report will prove useful for their continued service to the children and families living

around Kibale National Park.

HYPOTHESES

This thesis will focus on three main hypotheses that explore how children and

adolescents in a resource-limited setting in rural Uganda allocate energy to growth,

activity, and preparation for reproduction.

Hypothesis 1- Growth: In environments of chronic resource scarcity children and

adolescents allocate more energy to weight than to height.

In environments of limited resources, optimal growth is often not possible

because children must allocate a fixed amount of energy devoted to growth between

gaining height and gaining weight. This hypothesis suggests that, if resources are scarce,

children allocate more energy to gaining weight than to growing taller. This strategy

maintains flexibility because energy allocated to weight, can be reclaimed for future use

if conditions become more adverse. Energy allocated to height, on the other hand, is more

permanent.

Patterns of stunting (low height for age), underweight (low weight for age), and

wasting (low weight for height) in resource poor settings around the world support the

15

hypothesis that more energy is allocated to weight than to height. Stunting is usually the

most common physical manifestation of under-nutrition. A higher prevalence of stunting

compared to underweight and wasting, suggests that more energy is allocated to weight,

causing height to falter first. A study in rural Kenya, the country on Uganda’s eastern

border, found that 49% of children were stunted compared to only 19.6% underweight.

This same study found that in rural areas, where resource scarcity is more common,

children are more likely to be stunted than children in urban areas (Abubakar et al.,

2008). A study of a foraging society in lowland Bolivia also found a greater prevalence of

stunting compared to underweight and to wasting (Foster et al., 2005). Finally, nutrition

patterns in Uganda reviewed previously reveal that a higher percentage of both preschool

and primary school children are stunted compared to underweight and to wasted

(Kikafunda et al., 2006; Kikafunda et al., 2010).

Observations of populations in times when food is plentiful and times when food

is scarce also support the hypothesis that more energy is allocated to weight than to

height in resource-limited settings. Branca et al. (1993) studied the growth of children in

rural Ethiopia before harvest and after harvest. They found that weight for height did not

change but height velocity was lower before harvest. This suggests that energy allocation

to weight was consistent in times of plenty and times of scarcity but that energy

allocation to height was not. This supports the hypothesis that more energy is allocated to

weight than to height in resource-limited environments.

Finally, evidence from school feeding programs also supports the hypothesis that

more energy is allocated to weight gain than to growing taller. Kristjansson et al. (2006)

did a review of school feeding programs and found that weight gains were consistent

16

across studies but height gains were not. Assuming that the school feeding programs were

targeted at undernourished children and increased caloric intake, the Kristjansson et al.

findings support the idea that more energy is allocated to weight in environments of

resource scarcity. A single study done on primary school children in Jamaica provided

breakfast to undernourished and adequately nourished primary school children. The

height of adequately nourished children increased more than that of undernourished

children but there was no difference in weight gain between the two groups (Powell et al.,

1998). This finding suggests that the extra calories from the breakfast were allocated to

weight in the undernourished group in support of hypothesis one. Nonetheless, a review

by Jomaa et al. (2010) found inconsistent effects of school feeding programs on weight,

height, and BMI suggesting that the impact of these programs remains unresolved and

warrants further study.

Prediction 1- Prevalence of stunting is greater than prevalence of underweight or

wasting.

Stunting reflects low height for age whereas underweight and wasting reflect low weight

for age and low weight for height, respectively. If more energy is allocated to weight than

to height when resources are scarce, then height will falter before weight. Thus, stunting

will be more common than underweight and wasting in populations facing resource

scarcity.

Prediction 2- School feeding programs, assuming they increase caloric intake, should

reduce the prevalence of underweight and wasting.

17

If more energy is allocated to weight when resources are scarce, then the extra calories

provided by school feeding programs to children in energy limited environments should

be allocated to weight. Increases in weight will decrease the prevalence of low weight for

age (underweight) and low weight for height (wasting) among students in schools with

feeding programs.

Hypothesis 2- Activity: In energy limited environments, children and adolescents

allocate energy to activity at a lower priority than they allocate energy to gaining

and maintaining weight.

According to life history theory, energy must be allocated between somatic efforts

and reproductive efforts (Chisholm, 1993). Somatic efforts include growth and activity.

This hypothesis states that energy is allocated to activity at a lower priority than energy is

allocated to growth in terms of weight. Energy allocated to activity cannot be regained.

However, energy allocated to weight gain can be reclaimed. Thus, allocating energy to

activity at a lower priority than growth ensures future flexibility in unpredictable

environments.

There are very few studies on energy expenditure in malnourished children.

Rutishauser and Whitehead (1972) conducted one such study that offers support for this

hypothesis. Ugandan children ages 2 and 3 who had an energy intake 30% below what

was expected based on the energy intake of English children but followed a pattern of

weight gain similar to English children, spent less time running and walking. This

decrease in activity but not in weight gain is presumably because allocation to activity

was a lower priority than allocation to growth in terms of weight.

18

Another study in Senegal found that activity in Senegalese girls was positively

correlated with BMI (Benefice et al., 2001). This finding supports the hypothesis that

energy is allocated to activity at a lower priority than growth because it suggests that

energy is allocated to activity only when children have higher energy intakes and can thus

allocate more energy to both growth and activity.

However, results are inconsistent. Spurr et al. (1986) predicted that reducing

energy expenditure in activity would be the “first line of defense” against retarded

growth. They found, however, that undernourished, school-aged Colombian boys show

no decrease in activity when compared to well-nourished boys. They propose that peer

pressure could be at work in school-aged boys causing them to maintain high levels of

activity despite poor nutritional status (Spurr et al., 1986). Overall, more information is

needed on patterns of energy expenditure in activity in populations at risk of under-

nutrition.

Prediction 1- Energy expenditure in activity is positively correlated with nutritional

status.

If energy is allocated to activity at a lower priority than energy is allocated to weight,

then energy expenditure in activity will increase as caloric intake increases and more

energy is available to allocate to lesser priorities. Nutritional status as it is defined in this

thesis is a measure of caloric intake. Better-nourished children have a higher total energy

intake. Thus nutritional status should be positively correlated with energy expenditure in

activity. This relationship is a positive phenotypic correlation because total energy input

is higher so more energy can be allocated to both growth and activity. A negative

19

relationship would instead exist if energy input were fixed. In this case, growth and

activity would be in competition for a set amount of limited resources.

Prediction 2- Energy expenditure in activity is more strongly correlated with weight than

with height.

If energy is allocated to activity at a lower priority than energy is allocated to weight,

then children with greater weights should be able to allocate more energy to activity.

According to hypothesis one, more energy is allocated to weight than to height in

resource-limited settings. Thus, weight is a more sensitive indicator of caloric intake than

height. Because energy expenditure in activity is related to total caloric intake, as

described in prediction one, weight will be a better predictor of energy expenditure in

activity than height.

Prediction 3- Predictions 1 and 2 should be true for both sexes if energy allocation is

only between growth and activity.

Both boys and girls must allocate energy between growth and activity. Assuming the

tradeoff is simply between growth and activity, there should be no significant sex

difference because the largest sex difference in energy allocation is with respect to

reproductive effort. Hypothesis three will examine the sex difference that appears when

preparation for reproduction is considered as a third allocation domain.

20

Hypothesis 3- Reproduction: In resource-limited environments, females who have

started puberty allocate energy to activity at an even lower priority than do pre

pubertal females.

Once children begin puberty energy must be allocated to an additional domain:

preparation for reproduction. Since energy allocated to activity cannot be regained, the

addition of new energy demands in post pubertal individuals living in resource scarce

environments will make energy allocation to activity an even lower priority compared to

pre pubertal individuals.

Moreover, as previously discussed, preparation for reproduction differs between

sexes. Females allocate energy towards increasing adipose deposition whereas males

allocate energy towards male-male competition and building secondary sex

characteristics such as muscle mass (Ellison, 2003; Tanner, 1990). Both male-male

competition and building muscle mass require activity but adipose deposition does not.

Therefore when energetic demands on a fixed amount of resources increase at the onset

of puberty, energy allocation to activity should become an even lesser priority in females

but not in males.

Consistent with this hypothesis, Goran et al. (1998) found a 50% reduction in

activity from age 6.5 to 9.5 in a sample of girls living in Vermont. The authors suggest

that this pattern represents an “energy conserving mechanism”. Additionally, Drenowatz

et al. (2009) investigated activity levels in early maturing girls versus late maturing girls.

They found that early maturing girls had lower levels of activity compared to late

maturing girls of the same age. This result, however, was not independent of body mass.

Thus the difference in activity could have been linked to the burdens of extra weight

21

rather the energetic demand of preparing for reproduction. Nonetheless, adipose

deposition increases in preparation for reproduction (Tanner, 1990). Therefore an

increase in body mass could be evidence of energy allocation to preparation for

reproduction. If this is the case, it makes sense that activity is related to body mass. By

showing a decrease in energy expenditure in activity with age and upon the onset of

puberty, these two studies by Goran et al. and Drenowatz et al. support the hypothesis

that puberty increases energy demands making energy allocation to activity an even

lower priority.

Research also demonstrates that levels of physical activity, particularly vigorous

physical activity, are higher in boys compared to girls throughout adolescence (Sallis et

al., 2000; Trost et al., 2002). Moreover boys show an increase in total energy expenditure

with age whereas girls show a decrease because of reduced physical activity (Goran et al.,

1998). These studies are consistent with the increased demand for physical activity in

preparation for reproduction in males (Ellison, 2003).

Prediction 1- Energy expenditure in activity is more strongly positively correlated with

nutritional status in children who have started puberty.

If energy is allocated to activity at an even lower priority in post pubertal females

compared to pre pubertal females because of the additional energetic demands associated

with preparation for reproduction, then post pubertal females must be even better

nourished than pre pubertal females to be able to allocate energy to activity. Thus,

nutritional status will be a better predictor of energy expenditure in activity for post

pubertal females compared to pre pubertal females.

22

Prediction 2- This positive correlation between energy expenditure in activity and

nutritional status should be more pronounced for nutritional status measured by weight

than by height.

Females preparing for reproduction face increased demands for weight gain. If energy is

allocated to activity at an even lower priority in post pubertal females compared to pre

pubertal females because of the additional energetic demands associated with preparation

for reproduction, then females with higher weights should be able to allocate more

resources to activity. Since height is not related to fecundity, height will not be as good of

a predictor as weight for energy expenditure in activity.

Prediction 3- This positive correlation between energy expenditure in activity and

nutritional status should be more pronounced for females than for males after the onset

of puberty.

When individuals must allocate energy to preparation for reproduction in addition to

growth and physical activity, a sex difference will appear. If energy is allocated to

activity at an even lower priority in post pubertal females compared to pre pubertal

females because of the additional obligate demands to increase adipose deposition, then

caloric intake/nutritional status will be a predictor of energy expenditure in activity.

However, if the additional demands associated with reproduction for males manifest in

increased demand for physical activity, then energy expenditure in activity might be

independent of nutritional status in males.

23

SUMMARY

Although trends of obesity and overweight have recently caught the attention of

the scientific community, under-nutrition remains a significant problem, especially in the

developing world. Uganda is one such country struggling with high levels of under-

nutrition. This problem deserves the attention of the scientific community because

nutrition, or energy input, affects child development. Children with inadequate caloric

intake cannot satisfy their body’s energetic demands, which vary with pubertal status,

sex, health status, and environment. This thesis will explore three hypotheses for how

children in resource poor settings allocate limited energy among the demands imposed by

growth, activity, and preparation for reproduction: (I) In environments of chronic

resource scarcity children and adolescents allocate more energy to weight than to height;

(II) In energy limited environments, children and adolescents allocate energy to activity

at a lower priority than they allocate energy to gaining and maintaining weight; (III) In

resource-limited environments, females who have started puberty allocate energy to

activity at an even lower priority than do pre pubertal females. Food aid offers one way to

combat under-nutrition. As part of the analysis of hypothesis one, this thesis will look at

the effect of the Kasiisi Porridge Project on the nutritional status of primary school

children.

24

~ Chapter Two ~

Methods

25

LOCATION

Research was conducted at four primary schools, Kyanyawara, Kasiisi, Kigarama,

and Kiko, in the Kabarole district of Uganda just outside of Kibale National Park. The

Kabarole district is located about 50 kilometers from the border of Uganda and the

Democratic Republic of the Congo at 0.6°N and 30.3°E. The nearest major city to the

research site is Fort Portal, located approximately 20 kilometers to the north.

PARTICIPANT SELECTION

Participants were selected randomly from printed class registers at Kyanyawara,

Kasiisi, Kigarama, and Kiko primary schools. Seven students were selected from each

grade level beginning with Primary 3 (equivalent to 3rd grade) and ending with Primary 7

(equivalent to 7th grade). Each group of seven consisted of 3 girls and 4 boys or 4 girls

and 3 boys. This gender breakdown was alternated by grade. The ages of participants

ranged from 8 to 18 years old and none of the participants were visibly ill. After random

participant selection, the research was presented to an entire grade of students. The

selected students were called outside the classroom after the presentation and given the

option to participate or to opt-out without penalty. If children opted out or if a guardian

could not come to sign a permission form, another child of the same gender was

randomly selected in the classroom.

All interactions were translated into the local language, Ruturoo, with the help of

a secondary school graduate and current medical school student, Patrick Tusiime. In total

138 children were selected and 129 participated due to absences or failed parent

permission. Of the 129 participants, 65 were female and 64 were male.

26

PERMISSIONS

Written permission from the headmaster of each primary school was obtained

prior to participant selection. The parents or guardians of selected children were invited

to their child’s respective school to hear a presentation about the research and to ask any

questions. After the presentation, written or verbal permission was obtained from each

willing parent or guardian. Immediately prior to beginning data collection, children were

read an assent script and gave a verbal agreement to participate. All interactions with

parents and children were translated into Ruturoo.

ANTHROPOMETRIC DATA

Each child’s height was measured with a standard tape measure that was fixed

against the outside of a classroom wall. Children were instructed in Ruturoo to remove

their shoes, to stand with their heels touching the wall, and to look straight ahead. Height

was originally recorded in centimeters. Weight was measured on a Health-o-Meter digital

scale placed on a hard, flat surface. Children were asked to remove shoes and jackets but

remained dressed in school uniforms, which were similar at each school. Weight was

originally recorded in pounds. Mid-upper arm circumference (MUAC) was measured half

way between the shoulder and the elbow with a standard tape measure. MUAC

measurements, however, were not used in analysis.

All measurements were collected in the morning between 8:30 and 10:30am, prior

to any breaks in the school day that would have provided opportunities for food

consumption. The means and standard deviation cutoffs for weight for age, height for

age, and BMI for age for both males and females used in this report were published by

27

the Center for Disease Control (CDC) (Center for Disease Control and Prevention,

2009a; 2009b; 2009c). Comparison to the CDC references is appropriate for studies have

shown that children around the world have the same growth potential given favorable

environmental conditions (Graitcher & Gentry, 1981; Habicht et al., 1974). BMI for age

was used in place of weight for height only when comparing children to the CDC

references because tables for weight for height end at a height of 121 centimeters,

approximately 3 feet and 11.5 inches. BMI was calculated according to the following

formula.

BMI = weight (kg) height2 (m2)

See Appendix 1 for an example of a data collection sheet.

ENERGY EXPENDITURE DATA

Energy expenditure was measured using ActiTrainer accelerometers developed by

ActiGraph (Pensacola, FL). Each accelerometer is 8.6 cm by 3.3 cm by 1.5 cm and

weighs 51g. The accelerometers were charged and data was uploaded through a standard

USB connection. The software, ActiLife v5.5.5, operates on a PC. Each accelerometer

was sealed in a small plastic bag and placed in a neoprene pouch attached to a nylon belt.

Children wore the accelerometers on their left hip. Boys wore them around the beltline of

uniform pants and girls wore the accelerometers over the waist sash of their uniform

dresses. Activity was measured for each child on a single day from the beginning of

school (approximately 8:30am) until the end of classes (approximately 3:30pm). Children

were instructed to leave the belt on all day including during a 10:30-11am break and

28

lunch from 1-2pm that provided time for free play. Children were also instructed to go

about their activities as usual. Because energy expenditure in activity was only recorded

during the school day, the measurements do not take into account activity outside of

school such as chores at home or walking to and from school. The accelerometer

measured activity counts, which were then converted into kilocalories using the work-

energy theorem built into the ActiLive v5.5.5 software. Kcal counts were standardized by

the exact amount of time the accelerometer belt was worn by each child.

DIETARY RECALL AND ADDITIONAL INFORMATION

Each child was asked if he or she received porridge at school and was also led

through a more extensive verbal 24-hour diet recall on the day that he or she wore the

accelerometer belt. If the previous day was a school day the child was asked about the

foods he or she consumed before school, on the way to school, in the morning at school,

at lunch, in the afternoon at school, on the way home from school, for dinner (usually just

after reaching home), and in the evening before bed. If the previous day was not a school

day the child was asked about foods eaten for breakfast, in the morning, at lunch, in the

afternoon, for dinner, and in the evening before bed. For each food item the child

described where and when the food was eaten. Using a typical plastic plate and cup the

child was also asked to estimate portion size.

Following the diet recall the child reported the total number of children in his or

her household, including him or herself, and which adults he or she lived with. If the

child did not live with both of his or her parents, follow up questions were asked to

determine if the parents were deceased. See Appendix 1 for data collection sheet.

29

USE OF DATA

Following data collection, a preliminary analysis of the data was presented to the

children and teachers at each school. A final version of this thesis was given to the

Kasiisi Project for their own programmatic evaluation and improvement. A list of all

children whose height for age or weight for age fell below the 5th percentile on the CDC

growth charts (with a special designation for children who fell below the 5th percentile in

both categories) was given to the community nurse for appropriate follow up and care.

STATISTICS

Data was analyzed using JMP Pro 9.0.0 licensed to Harvard University and

Microsoft Excel 2008 for Mac version 12.2.9. All regressions and t-tests were produced

with the JMP statistics package and other figures were produced with Microsoft Excel. A

chi-squared test for homogeneity was used to evaluate the difference in the probability of

falling a certain number of standard deviations from the mean BMI for age, weight for

age, and height for age. A t-test was used to compare the mean weight for height, weight

for age, and height for age of children consuming and not consuming porridge. A t-test

was also used to compare the mean energy expenditure in activity of children consuming

and not consuming porridge. Finally, linear regressions were performed to evaluate the

relationship between nutritional status and energy expenditure in activity. For the

majority of regressions significance was used to test the strength of the correlation

between a particular indicator nutritional status and energy expenditure in activity. This

was valid because the sample size is constant.

30

The significance level used was 0.05 but special designation was made when p-

values and F-values fell below 0.01. Bonferroni corrections were made where appropriate

and are indicated. This correction shifts the cut-off point used to determine significance

but does not shift the significance level of values relative to one another.

When children were separated into groups they were separated in the following

fashion. Puberty status was assumed by age. Girls were assumed to have started puberty

if they were 13 and older (46.2% of girls). This cutoff is based on a survey conducted by

the Kasiisi project showing that 60% of girls in the study community had reached

menarche by age 13 (Ross et al., 2010). This cutoff seemed appropriate because girls

reach menarche later in puberty after peak height velocity is attained and breast

development begins (Tanner, 1990). Therefore, if 60% of girls had reached menarche, a

larger percentage would have likely started puberty. Boys were assumed to have started

puberty if they were 14 and older (40.6% of boys). This was based on the knowledge that

males begin puberty later than females (Tanner, 1990). The term pre pubertal is used to

mean before the onset of puberty. Post pubertal refers to after the onset of puberty, not

after the completion of puberty. Orphan status was assessed by household composition.

Children were considered orphans if at least one parent was not living in the household

either because that parent had moved away or had died. Children were placed in breakfast

categories based on the 24-hour diet recall. A child was considered to have eaten no

breakfast if they consumed nothing. A small breakfast consisted of only tea or a piece of

bread for example. A normal breakfast was more substantial such as bananas and beans,

sweet potatoes and beans, or Irish potatoes and ground nuts. Household size was based on

the total number of individuals living and eating in a home. A household was considered

31

large if it consisted of five or more individuals and small if it consisted of less than five

individuals.

RESEARCH ETHICS

All research methods were reviewed and approved by both the Harvard

Committee on the Use of Human Subjects in Research (application number F20476-101)

and also the Uganda National Council for Science and Technology (reference number SS

2556).

32

~ Chapter Three ~

Results

33

The following results are presented according to their relevance to the three

hypotheses as outlined in chapter one. Please refer to that chapter for a full explanation of

the hypotheses. For all tests, except where indicated otherwise, data were collected from

a sample of 129 children.

HYPOTHESIS 1- Growth: In environments of chronic resource scarcity children

and adolescents allocate more energy to weight than to height.

Prediction 1- Prevalence of stunting is greater than prevalence of underweight or

wasting.

Figure 1: Prevalence of low BMI for age

0"5"10"15"20"25"30"

'2" '1.5" '1" '0.5" 0" 0.5" 1" 1.5"

Percentage)of)Children)

Standard)Deviations)from)Mean)

BMI)

34

Figure 2: Prevalence of underweight

Figure 3: Prevalence of stunting

Figures 1-3 above demonstrate the percentage of children falling a designated

number of standard deviations above and below the CDC mean BMI for age, weight for

age, and height for age. Children are classified according to the CDC Z-scores for

0"5"10"15"20"25"30"

'2" '1.5" '1" '0.5" 0" 0.5" 1" 1.5"

Percentage)of)Children)

Standard)Deviations)from)Mean)

Weight)for)Age)

0"5"10"15"20"25"

'2" '1.5" '1" '0.5" 0" 0.5" 1" 1.5"

Percentage)of)Children)

Standard)Deviations)from)Mean)

Height)for)Age)

35

children ages 2 to 20. In agreement with the CDC data, weight is reported in kilograms

and height is reported in centimeters

Typically, a child is considered stunted, wasted, or underweight if he or she falls 2

standard deviations or more below the mean. According to this cutoff, 2.3% of children

attending Kyanyawara, Kasiisi, Kigarama, and Kiko primary schools have a low BMI for

their age, 8.5% are underweight, and 14.0% are stunted. Thus, there are slightly more

children who are underweight than would be expected in a normal population and even

more children who are stunted. When considering all children that fall below the mean,

the data reveals that 44.2% of children have at least a mildly low BMI for age, 65.1% of

children are at least mildly underweight, and 72.1% of children are at least mildly

stunted. Again there are slightly more children who are underweight than would be

expected in a normal population and even more children who are stunted.

This pattern is evident in the degree to which each histogram is right skewed. The

histogram for height for age is the most right skewed. A chi-squared test for homogeneity

(p = 9.02 x 10-5) revealed that the probability of falling a certain number of standard

deviations away from the mean is not identical for BMI for age, weight for age, and

height for age. Therefore a child does not have an equal probability of becoming stunted,

falling underweight, or having a low BMI for age. Looking at the distributions of the

histograms, it appears that the likelihood of becoming stunted is higher than the

likelihood of falling underweight or developing a low BMI for age. However, the chi-

squared test does not distinguish which probability is different from the others.

36

Prediction 2- School feeding programs, assuming they increase caloric intake, should

reduce the prevalence of underweight and wasting.

Figure 4: Lunch patterns at schools Figure 5: Lunch patterns at schools

with porridge without porridge

Figures 4 and 5 above demonstrate the eating habits of children during lunch time

at schools that do offer porridge (Kyanyawara and Kasiisi) and schools that do not offer

porridge (Kigarama and Kiko) as inferred from the 24-hour diet recalls. A snack is

defined as an item that would not be considered a meal on its own. For example, a piece

of bread, sugar cane, and maize are all considered snacks. Comparatively, a meal might

consist of bananas and beans or sweet potatoes and beans. Dietary recalls were evaluated

from a total of 40 children at schools with porridge and 49 children at schools without

porridge. These totals do not sum to 129 because children were excluded if the previous

day was not a school day. At schools without porridge, 20% of children eat nothing at

lunch time compared to only 2% of children at schools that do offer porridge.

Nothing"2%"

Porridge"Only"35%"

Porridge+Snack"12%"

Porridge+Meal"38%"

Meal"Only"13%" Nothing"

20%"

Snack"Only"27%"

Meal"Only"53%"

37

Figure 6: Effect of porridge on mean Figure 7: Effect of porridge on mean weight for height weight for age

Figure 8: Effect of porridge on mean height for age

Table 1: Difference in mean nutritional status of children consuming porridge and children not consuming porridge T-Test Results p-value Difference in Means Weight for height 0.373 -0.764 Weight for age 0.517 -0.055 Height for age 0.707 -0.001 * value < 0.05

Figures 6-8 above are the results of t-tests for the difference in mean weight for

height, weight for age, and height for age between children who eat porridge and children

who do not. For this test weight was reported in kilograms and height in meters. None of

20

25

30

35

40

45W

eig

ht fo

r H

eig

ht

no yesPorridge

2

2.5

3

3.5

4

4.5

Weig

ht fo

r A

ge

no yesPorridge

0.08

0.09

0.1

0.11

0.12

0.13

0.14

0.15

0.16

0.17

Heig

ht fo

r A

ge

no yesPorridge

38

the tests were significant at the 0.05 level as seen by the p-values in table 1 above (p-

value weight for height = 0.373, p-value weight for age = 0.517, p-value height for age =

0.707). Although not significant, all of the differences were negative suggesting the

possibility of a slight trend towards a higher mean weight for height, weight for age, and

height for age in children that eat porridge. As demonstrated in table 2 below, the

difference in means remained insignificant even when children were separated according

to sex, assumed pubertal status, orphan status, breakfast size, and household size as

explained in the methods.

Table 2: Results of t-tests when children are separated by sex, pubertal status, orphan status, breakfast size, and household size

p-values Weight for Height Weight for Age Height for Age Male 0.592 0.487 0.890 Sex Female 0.437 0.667 0.582 Male Pre 0.441 0.065 0.238 Male Post 0.440 0.420 0.751 Female Pre 0.543 0.342 0.594

Puberty

Female Post 0.772 0.813 0.333 Yes 0.982 0.529 0.226 Orphan Status No 0.255 0.802 0.224 No 0.951 0.922 0.722 Small 0.264 0.691 0.138

Breakfast

Normal 0.064 0.477 0.151 Small (<5) 0.902 0.741 0.814 Household Size Large (≥5) 0.119 0.186 0.666

* value < 0.05

Table 3: Results of t-tests when children are separated by height for age

p-values Height for Age < -1 SD from Mean

Height for Age > -1 SD from Mean

Weight for Height 0.773 0.089 Weight for Age 0.682 0.094 * value < 0.05

39

Additionally, as seen in table 3 above, when children were separated according to

whether or not their height for age was more than 1.5 standard deviations below the CDC

mean, there was no significant difference in mean weight for height (p-value stunted =

0.773, p-value not stunted = 0.089) and weight for age (p-value stunted = 0.682, p-value

not stunted = 0.094) between children who ate porridge and those who did not.

HYPOTHESIS 2- Activity: In energy limited environments, children and

adolescents allocate energy to activity at a lower priority than they allocate energy

to gaining and maintaining weight.

Prediction 1- Energy expenditure in activity is positively correlated with nutritional

status.

~ AND ~

Prediction 2- Energy expenditure in activity is more strongly correlated with weight

than with height.

Figure 9: Regression of weight for height Figure 10: Regression of weight for age and activity and activity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Activity K

cal/m

in

20 25 30 35 40 45Weight for Height

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Activity K

cal/m

in

2 2.5 3 3.5 4 4.5Weight for Age

40

Figure 11: Regression of height for age and activity

Table 4: Predictive value of nutritional status for energy expenditure in activity

Equation RSquare Prob > F Weight for Height Activity = -0.023 + 0.010(Weight for Height) 0.110 0.0001** Weight for Age Activity = -0.010 + 0.096(Weight for Age) 0.098 0.0003** Height for Age Activity = 0.404 – 1.049(Height for Age) 0.011 0.236 * significant p-value < 0.05 ** significant p-value < 0.01 Table 5: Predictive value of nutritional status for energy expenditure in activity when children are separated according to sex, pubertal status, orphan status, breakfast size and household size

F-values Weight for Height Weight for Age Height for Age Male 0.127 0.070 0.987 Sex Female <0.0001**^ <0.0001*^ 0.220 Male Pre 0.487 0.393 0.832 Male Post 0.317 0.192 0.251 Female Pre 0.032* 0.018* 0.907

Puberty

Female Post 0.023* 0.003**^ 0.144 Yes 0.016* 0.060 0.082 Orphan Status No 0.005**^ 0.002**^ 0.940 No 0.104 0.243 0.968 Small 0.009** 0.005**^ 0.948

Breakfast

Normal 0.828 0.657 0.945 Small (<5) 0.001**^ 0.004**^ 0.147 Household Size Large (≥5) 0.041* 0.129 0.339

* value < 0.05 ** value < 0.01 ^ significant after Bonferroni correction (0.05/n)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Activity K

cal/m

in

0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17Height for Age

41

Figure 12: Effect of porridge on energy expenditure in activity

p-value = 0.390

The linear regression plots (figures 9-11) above demonstrate the relationship

between weight for height, weight for age, and height for age and energy expenditure in

activity. For these analyses, weight was reported in kilograms, height in meters, and

energy expenditure in activity in kcal/minute. As demonstrated in table 4, both weight for

height and weight for age have a significant predictive value at the 0.05 level for energy

expenditure in activity (F-value = 0.0001 and F-value = 0.0003 respectively). Moreover

this significant relationship is positive as indicated by the sign of the slopes (m = 0.010

and m = 0.096 respectively). Height for age displays a negative relationship (m = -1.049)

and is not a significant predictor (F-value = 0.236) of energy expenditure in activity at the

0.05 level.

Table 5 demonstrates that the relationship between both weight for age and

energy expenditure in activity and also weight for height and energy expenditure in

activity remained less likely to occur by chance than the relationship between height for

age and activity when children were separated according to sex, assumed pubertal status,

orphan status, breakfast size, and household size. After Bonferroni corrections it is

0

0.1

0.2

0.3

0.4

0.5

0.60.7

0.8

0.9

Activ

ity K

cal/m

inno yes

Porridge

42

evident that the relationship is significant for females, particularly post pubertal females,

non-orphans, and children living in small households. Moreover, figure 12 demonstrates

that there was no mean difference in energy expenditure in activity between children

eating porridge and children not eating porridge (p-value = 0.390). This is not surprising

given porridge was shown to have no effect on mean nutritional status.

Prediction 3- Predictions 1 and 2 should be true for both sexes if energy allocation is

only between growth and activity.

FEMALES

Figure 13: Regression of height for age Figure 14: Regression of weight for age and activity and activity

Activity = 0.432 – 1.442(Height for Age) Activity = -0.256 + 0.161(Weight for Age)

0.1

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cal/m

in

2.5 3 3.5 4 4.5Weight for Age

43

Figure 15: Regression of weight for height and activity

Activity = -0.119 + 0.014(Weight for Height)

MALES Figure 16: Regression of height for age Figure 17: Regression of weight for age and activity and activity

Activity = 0.305 – 0.022(Height for Age) Activity = 0.065 + 0.083(Weight for Age)

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20 25 30 35 40 45Weight for Height

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44

Figure 18: Regression of weight for height and activity

Activity = 0.137 + 0.007(Weight for Height)

Figures 13-18 illustrate the relationship between height for age, weight for age,

and weight for height and energy expenditure in activity for males and females

separately. There is a positive relationship between weight for age and energy

expenditure in activity and weight for height and energy expenditure in activity for males

(m = 0.083 and m = 0.007 respectively) and females (m = 0.161 and m= 0.014

respectively). However, as indicated in table 5, the regression is significantly predictive

for females after Bonferroni corrections (F-value weight for height < 0.0001 and F-value

weight for age < 0.0001), but not for males (F-value weight for height = 0.127 and F-

value weight for age = 0.070). Height for age has a negative relationship with energy

expenditure in activity for males (m = -0.022) and females (m = -1.442) but the

relationship is not significant for either males (F-value = 0.987) or for females (F-value =

0.220).

0

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20 25 30 35Weight for Height

45

HYPOTHESIS 3- Reproduction: In resource-limited environments, females who

have started puberty allocate energy to activity at an even lower priority than do

pre pubertal females.

Prediction 1- Energy expenditure in activity is more strongly positively correlated with

nutritional status in children who have started puberty.

~ AND ~

Prediction 2- This positive correlation between energy expenditure in activity and

nutritional status should be more pronounced for nutritional status measured by

weight than by height.

~ AND ~

Prediction 3- This positive correlation between energy expenditure in activity and

nutritional status should be more pronounced for females than for males after the

onset of puberty.

FEMALES PRE PUBERTY

Figure 19: Regression of weight for height Figure 20: Regression of weight for age and activity and activity

Activity = -0.023 + 0.010(Weight for Height) Activity = -0.052 + 0.086 (Weight for Age)

0.05

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Activity K

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16 18 20 22 24 26 28 30Weight for Height

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Activity K

cal/m

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2.25 2.5 2.75 3 3.25 3.5 3.75Weight for Age

46

Figure 21: Regression of height for age and activity

Activity = 0.191 + 0.120(Height for Age)

FEMALES POST PUBERTY

Figure 22: Regression of weight for height Figure 23: Regression of weight for age and activity and activity

Activity = -0.142 + 0.015(Weight for Height) Activity = -0.268 + 0.172(Weight for Age)

0.05

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47

Figure 24: Regression of height for age and activity

Activity = -0.301 + 5.505(Height for Age)

MALES PRE PUBERTY

Figure 25: Regression of weight for height Figure 26: Regression of weight for age and activity and activity

Activity = 0.116 + 0.008(Weight for Height) Activity = 0.085 + 0.074(Weight for Age)

0

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48

Figure 27: Regression of height for age and activity

Activity = 0.236 + 0.415(Height for Age)

MALES POST PUBERTY

Figure 28: Regression of weight for height Figure 29: Regression of weight for age and activity and activity

Activity = 0.102 + 0.008(Weight for Height) Activity = 0.079 + 0.080(Weight for Age)

0

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49

Figure 30: Regression of height for age and activity

Activity = -0.164 + 4.602(Height for age)

Table 6: Predictive value of nutritional status for energy spent in activity when children are separated according to sex and pubertal status

F-values Weight for Height Weight for Age Height for Age Male Pre 0.487 0.393 0.832 Male Post 0.317 0.192 0.251 Female Pre 0.032* 0.018* 0.907

Puberty

Female Post 0.023* 0.003**^ 0.144 * value < 0.05 ** value < 0.01 ^ significant after Bonferroni correction (0.05/n) Table 7: Amount of variance explained by the regressions for females pre and post puberty R2 Values Weight for Height Weight for Age Female Pre 0.132 0.159 Female Post 0.172 0.277

Figures 19-21 display linear regressions for the relationship between weight for

height and energy expenditure in activity, weight for age and energy expenditure in

activity, and height for age and energy expenditure in activity for pre pubertal girls.

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in0.085 0.09 0.095 0.1 0.105 0.11 0.115 0.12

Height for Age

50

Figures 22-24 display the same regressions for post pubertal girls. Figures 25-27 display

linear regressions for pre pubertal males. Finally, figures 28-30 display regressions for

post pubertal males.

Table 6 indicates which regressions show statistically significant predictive power

both before and after Bonferroni corrections. The relationships between weight for height

and energy expenditure in activity and between weight for age and energy expenditure in

activity are less likely to occur by chance for females pre puberty (F-value = 0.032 and F-

value = 0.018 respectively) and for females post puberty (F-value = 0.023 and F-value =

0.003 respectively) compared to males both pre puberty (F-value = 0.487 and F-value =

0.393 respectively) and post puberty (F-value = 0.317 and F-value = 0.192 respectively).

After Bonferroni corrections only weight for age in post pubertal females is a significant

predictor of energy expenditure in activity. Moreover, the amount of variation explained

by the regressions is higher for post pubertal females as compared to pre pubertal

females. Table 7 displays these R2 values. Weight for height explains 13.2% of the

variation seen in pre pubertal female energy expenditure in activity and 17.2% of the

variation seen in post pubertal female energy expenditure in activity. Likewise, weight

for age explains 15.9% of the variation seen in pre pubertal female energy expenditure in

activity and 27.7% of the variation seen in post pubertal female energy expenditure in

activity.

Height for age is not a significant predictor of energy expenditure in activity for

pre pubertal females (F-value = 0.907), post pubertal females (F-value = 0.144), pre

pubertal males (F-value = 0.832), or post pubertal males (F-value = 0.251).

51

SUMMARY

At Kyanyawara, Kasiisi, Kigarama, and Kiko primary schools 2.3% of children

have a low BMI for their age, 8.5% are underweight, and 14.0% are stunted as defined by

falling 2 standard deviations below the mean BMI for age, weight for age, and height for

age published by the CDC for children ages 2-20. 44.2% of children simply fall below

the mean BMI for age, 65.1% fall below the mean weight for age, and 72.1% fall below

the mean height for age. The probability of having a low BMI for age, being

underweight, and being stunted is not equal (p = 9.02 x 10-5).

At schools with porridge, only 2% of children eat nothing for lunch whereas at

schools without porridge, 20% of children eat nothing for lunch. T-tests for the difference

in mean weight for height, mean weight for age, and mean height for age between

children that eat porridge and children that do not eat porridge did not produce significant

results even when a variety of factors were controlled.

Weight for height and weight for age are significantly predictive of and positively

correlated with energy expenditure in activity (F-value = 0.0001 and F-value = 0.0003

respectively and m = 0.010 and m = 0.096 respectively). Height for age is not

significantly predictive of energy expenditure in activity. When children were separated

by sex this significant predictive value was only seen for females (F-value weight for

height < 0.0001 and F-value weight for age < 0.0001). Porridge did not have a significant

effect (p-value = 0.390) on mean energy expenditure in activity, but this is not surprising

given that the porridge did not have a significant effect on mean nutritional status.

When children were separated by pubertal status, a significant, positive

relationship was observed between weight for age and energy expenditure in activity for

52

post pubertal females (F-value = 0.003). This was not true for males. Moreover the

relationship between weight for age and energy expenditure in activity and the

relationship between weight for height and energy expenditure in activity in both pre

pubertal females (F-value = 0.018 and F-value = 0.032) and post pubertal females (F-

value = 0.003 and F-value = 0.023) are less likely to occur by chance than in males.

Height for age was not a significant predictor of energy expenditure in activity for either

sex pre or post puberty. Finally, more of the variation in energy expenditure in activity

can be explained by the regression model for post pubertal females as compared to pre

pubertal females (R2 values for weight for height are 0.172 and 0.132 respectively and R2

values for weight for age are 0.277 and 0.159 respectively).

53

~ Chapter Four ~

Discussion

54

GENERAL FINDINGS

Primary school children living in the Kabarole district of Uganda face resource

scarcity that places restrictions on their energy intake. These restrictions are manifest in

children with low height for age, low weight for age, and low BMI for age. The

respective percentages of children falling two or more standard deviations below the

CDC reference mean in each of these categories are 14.0%, 8.5%, and 2.3%. The

respective percentages of children simply falling below the CDC mean in each of these

categories are 72.1%, 65.1%, and 44.2%.

In such an environment of scarcity, tradeoffs must be made in the allocation of

limited energetic resources (Gadgil & Bossert, 1970). Life history theory provides a

framework to understand these tradeoffs and their physical manifestations. Life history

theory assumes that humans must allocate limited energetic resources between competing

categories of somatic and reproductive effort. Optimal allocation maximizes an

individual’s fitness in a given environment (Chisholm, 1993; Bogin et al., 2007; Stearns,

1992).

The results from this research suggest that in an environment characterized by

limited or unpredictable resource availability children can maximize their fitness by

allocating energy conservatively. This means children will allocate energy such that it

can be regained if resource scarcity increases, or that they will allocate energy so that it

does not create a long-term commitment to higher energy expenditure. The data points to

three main ways in which children allocate energetic resources conservatively as it is

defined above.

55

First, of the energy allocated to growth, more is allocated to gaining and

maintaining weight than to growing taller. This biologic choice is a conservative one

because it creates flexibility. Weight can be gained and lost. Therefore, resources

allocated to weight can be reclaimed and reused for other purposes, such as immune

function, if resources become scarcer. On the other hand, allocating resources to growth

in terms of height is more permanent and has longer-term metabolic maintenance

requirements. Thus allocating energy to height does not create the same sort of flexibility

in an unpredictable environment.

Second, energy is allocated to activity at a lower priority than energy is allocated

to gaining and maintaining weight. This is conservative because by allocating more

energy to weight gain than to activity, children retain future flexibility. Resources

allocated to weight can be regained, while those allocated to activity are permanently

lost.

Third and finally, energy is allocated to activity at an even lower priority upon the

onset of puberty in females. Beginning at puberty, females face increased energetic

demands due to the introduction of reproductive effort as a category of allocation. With

higher energy demands it is even more important to be conservative with energy

allocation in resource-limited environments. Because energy allocated to activity cannot

be regained it will become an even lesser priority if an individual is forced to be more

conservative in his or her energy allocation.

This third pattern of conservative energy allocation does not appear in males.

Although the absence of the pattern is not proof of a sex difference in the prioritization of

energy allocation, it is in line with the different energy demands males and females face

56

in preparing for reproduction. Unlike females who face increased demands for adipose

deposition, males prepare for reproduction by increasing activity levels to build muscle

mass and to show dominance through male-male competition. These two behaviors help

males gain access to mates (Ellison, 2003). If energy expenditure in activity is important

in preparation for reproduction for males, then it might not become a lesser priority.

In conclusion, the results from this research suggest that children in resource-

limited environments allocate energy conservatively to increase future flexibility. They

accomplish this by allocating more energy to gaining and maintaining weight than to

growing taller. Additionally, children allocate energy to activity at a lower priority than

to weight. This is particularly true for females after the onset of puberty. Males do not

appear to allocate energy to activity at a lesser priority after the onset of puberty, perhaps

because activity is important for males in preparation for reproduction.

The support for these general findings is presented below in relation to each of the

three hypotheses explored in this thesis. In instances where data does not support a

hypothesis, an explanation is offered for why the lack of support does not disprove the

hypothesis and thus the general findings presented above.

SUPPORT FOR FINDINGS BY HYPOTHESIS

Hypothesis 1- Growth: In environments of chronic resource scarcity children and

adolescents allocate more energy to weight than to height.

Hypothesis one predicts the first way in which children allocate energy

conservatively – by allocating more energy to weight gain than to height gain. This

57

hypothesis is supported by the higher percentage of children presenting with low height

for age compared to low weight for age and BMI for age (Figures 1-3). A higher

prevalence of low height for age means that height is likely to falter before weight when

energy intake is limited. This suggests that, when resources are scarce, more energy is

allocated to gaining and maintaining weight than to gaining height.

The lack of a significant difference in mean weight for age and weight for height

between children who eat porridge and children who do not eat porridge (Figures 6-8 and

Table 1) does not offer support for hypothesis one, however. This remained true even

when children were separated according to sex, assumed pubertal status, orphan status,

breakfast size, and household size (Table 2). If more energy were allocated to weight as

opposed to height to maintain flexibility in environments of resource scarcity, then it

would be expected that a difference in weight for height and weight for age should be

observed between children who do and do not eat porridge. Even when children were

separated according to height for age (Table 3), under the assumption that stunted

children definitely experience an environment of resource scarcity, there was no

significant difference.

However, while the porridge’s lack of effect on mean nutritional status does not

support hypothesis one, it does not disprove the hypothesis either. It is possible that

energetic demands for weight gain were being met in the study population prior to the

introduction of the school porridge. If this is the case, it makes sense that mean weight for

age and mean weight for height do not differ between children who do and do not

consume porridge. In this scenario, the extra energy input could have been allocated to an

energy demand other than growth. It is also possible that although the porridge decreased

58

the percentage of children eating nothing for lunch at school from 20% to 2% (Figures 4

and 5), it did not have enough nutritional value or was not consumed for a sufficient

period of time to make a significant difference in nutritional status given that the

Kabarole district of Uganda was only facing a moderate level of resource scarcity at the

time of data collection.

Hypothesis 2- Activity: In energy limited environments, children and adolescents

allocate energy to activity at a lower priority than they allocate energy to gaining

and maintaining weight.

Hypothesis two predicts the second way in which children allocate energy

conservatively – by allocating energy to gaining and maintaining weight with higher

priority than to activity. The hypothesis is supported by the positive correlation between

weight for height and energy expenditure in activity and weight for age and energy

expenditure in activity (Figures 9-11 and Tables 4, 5). This correlation is a phenotypic

correlation (Stearns, 1992) rather than a trade-off. Individuals with greater weights for

age and weights for height can be assumed to take in a higher total amount of energy.

They thus have more energy to allocate to lower priority demands, such as activity. Non-

orphans and children living in small households typically have higher levels of energy

intake than orphans and children living in large households (Smyke et al., 2007;

Alderman & Garcia, 1994; Garrett & Ruel, 1999). Therefore the finding, when children

are divided by orphan status and household size, that the significant relationship only

holds for non-orphans and children who live in small households (Table 5) supports the

59

hypothesis. Moreover, porridge does not affect nutritional status, and consequently does

not have a significant effect on mean energy expenditure in activity (Figure 12).

However, contrary to prediction, the hypothesis only holds for females (Figures

13-15). When children are divided by sex, males do not demonstrate a positive

correlation between weight for height and energy expenditure in activity or between

weight for age and energy expenditure in activity (Figures 16-18). Again, this does not

necessarily disprove the hypothesis and could be explained by the greater amount of peer

pressure faced by boys, in comparison to girls, to engage in active play at school (Spurr et

al., 1986). Additionally, since analyses for this hypothesis do not take pubertal status into

account, the results could be influenced by the increased demands for physical activity

faced by post pubertal boys in order to improve access to mates. This will be explored

further in hypothesis three.

Hypothesis 3- Reproduction: In resource-limited environments, females who have

started puberty allocate energy to activity at an even lower priority than do pre

pubertal females.

Hypothesis three predicts the final way in which children, specifically females,

allocate energy conservatively – by allocating energy to activity at even less of a priority

upon the onset of puberty. This hypothesis is supported by the significant, positive

relationship between weight for age and energy expenditure in activity for adolescent

females and the lack of a significant relationship between these two variables for pre

pubertal females (Figures 19-24, Table 6). Additionally, before the Bonferroni correction,

the relationship between weight for height and energy expenditure in activity and the

60

relationship between weight for age and energy expenditure in activity are significant in

both pre pubertal and post pubertal females. However, the amount of variance explained

by the regression is greater for post pubertal females compared to pre pubertal females

(Table 7). The fact that more of the variation in energy expenditure in activity in post

pubertal females is explained by nutritional status suggests that energy allocation to

activity is a lower priority in post pubertal females because it depends more on high

caloric intake.

Males do not appear to allocate energy to activity at a lesser priority. This is

evident in the lack of a significant positive relationship for post pubertal boys between

weight for height and energy expenditure in activity and between weight for age and

energy expenditure in activity (Figures 28-30, Table 6). The F-values for post pubertal

males and post pubertal females differ by an order of magnitude even before Bonferroni

corrections. Although the lack of a significant relationship is not proof that males do not

allocate energy to activity at a lesser priority upon the onset of puberty, it aligns with the

sex specific demands associated with preparation for reproduction. Whereas females face

increased demands for adipose deposition, males face increased demands for activity,

which perhaps makes energy allocation to activity independent of nutritional status.

SUMMARY

When resources are limited, one way to increase fitness is to allocate energy in a

way that creates energetic flexibility to increase the chances of surviving to reproductive

maturity. The data presented in this thesis suggest that primary school children facing

resource scarcity in the Kabarole district of Uganda allocate energy conservatively by

61

allocating more resources to weight than to height. This is supported by prevalence data

for the various measures of nutritional status. Furthermore, children allocate energy to

activity with lower priority than they allocate energy to weight gain. This is supported by

the positive phenotypic correlation between weight for age and energy expenditure in

activity and between weight for height and energy expenditure in activity. Finally, post

pubertal females allocate energy to activity at an even lower priority because preparation

for reproduction increases demands on limited energetic resources. This is supported by

the fact that when females are separated by pubertal status the positive phenotypic

correlation between weight for age and energy expenditure in activity only holds for post

pubertal females. Additionally, nutritional status explains more of the variation in energy

expenditure in activity for post pubertal females compared to pre pubertal females. Boys

do not appear to lower the priority of energy allocation to activity at the onset of puberty.

This is perhaps because energy allocation to activity is an essential part of preparation for

reproduction for males. This is a situation where spending energy in a resource-limited

environment increases fitness more than allocating energy conservatively.

LITERATURE COMPARISON The number of studies on how children allocate resources in environments

characterized by scarcity is very limited and over-shadowed by the overwhelming

attention currently focused on obesity. Moreover, the demographics of the study

populations included in the literature are vastly different, as are environmental

conditions. This variability makes it difficult to generalize the relationship between

nutritional status and energy expenditure since these two variables are highly dependent

62

on both an individual’s internal and external environment. Where comparison was

possible, the data presented in this thesis are generally consistent with the published

biomedical literature and evolutionary theory. This research is unique, however, in that it

combines these two disciplines. It does not simply present health outcomes but provides

an evolutionary, adaptive explanation for why child growth, nutrition, and activity

patterns are the way they are.

The prevalence of low height for age, weight for age, and weight for height or

BMI for age in study participants is consistent with the worldwide pattern. The

prevalence of stunting is highest, followed by the prevalence of underweight, and then by

the prevalence of wasting (WHO Department of Nutrition, 2009a; WHO Department of

Nutrition, 2009b; De Onis et al., 2010; De Onis et al., 2011). The prevalence data in

Uganda (Kikafunda et al., 2010; Kikafunda et al., 2006) and Kenya (Abubakar et al.,

2008) show similar patterns. The conventional methods for assessing nutritional status in

the field were used in this research, but this thesis goes one step further to suggest that we

observe these trends because more energy is allocated to weight than to height.

In addition to the large variation in population characteristics and levels of

resource scarcity between published studies, the quality and quantity of food provided by

school feeding programs and the outcomes measured when evaluating these programs

vary widely. Therefore, comparing meta-analyses is the best approach for assessing the

impact of nutritional interventions. One meta-analysis by Kristjansson et al. (2006)

including randomized control trials and controlled before and after trials found that

school feeding programs had a significant positive effect on weight but not height. On the

other hand, a more recent meta analysis done by Jomaa et al. (2010), looking at

63

randomized control trials, intervention/control studies (a method similar to the methods

used in this thesis), and crossover studies (a method in which each study participant

receives all treatments so as to be his or her own control), found inconsistent effects of

school feeding programs on weight, height and BMI. The methods and results presented

in this thesis are more consistent with the analysis by Jomaa et al. (2010) and do not

indicate any effect of the porridge on mean nutritional status.

The Kristjansson et al. research, the Jomaa et al. findings, and this research all

looked at primary school aged children in developing countries. The difference in the

results of the Kristjansson et al. study could be due to the level of resource scarcity in the

specific populations studied, the disease burden shouldered by the populations, household

characteristics, or the quality and quantity of the food provided in the interventions. More

research that explicitly controls for these variables is needed to truly understand the

impact of school feeding programs.

The number of studies looking at activity patterns of children living in energy

limited environments is even more limited than the number of nutritional status surveys

and studies on food aid. The conclusion offered by this thesis, that energy is devoted to

activity with less of a priority than to growth in terms of weight, is consistent with the

findings of Rutishauser and Whitehead’s study (1972) on Ugandan children ages 1-3.

However, Rutishauser and Whitehead’s study does not show the sex difference found in

the results presented in chapter three, likely because their study focused on much younger

children. However, this gender difference is supported by the combination of findings

from Benefice et al. (2001), who observed that activity was positively correlated with

BMI and negatively correlated with height in adolescent Senegalese girls, and from Spurr

64

et al. (1986), who did not observe a decrease in activity in undernourished Colombian

boys.

Of these three studies, the one conducted by Benefice et al. is the only research

that used accelerometers to measure energy expenditure. Rutishauser and Whitehead used

observation, and Spurr et al. made estimations based on basal metabolic rate, resting

metabolic rate, VO2, and heart rate. This research is among the first to use accelerometers

in an energy-limited environment with both sexes and with a variety of ages. Moreover,

unlike these other studies, this research provides an evolutionary explanation for why

energy is allocated to activity at a lower priority than energy is allocated to growth.

Finally, the conclusion that energy is allocated to activity with even lower priority

after the onset of puberty in females is consistent with studies showing a reduction in

activity with age up to the onset of puberty in girls (Goran et al., 1998; Benefice et al.,

2001) and lower levels of activity in early maturing girls as compared to late maturing

girls (Drenowatz et al., 2009). The observation that boys do not allocate energy to

activity with lower priority in preparation for reproduction is supported by findings from

Sallis et al. (2000) and Trost et al. (2002) that demonstrate that energy expenditure in

activity is higher in boys compared to girls and from Goran et al. (1998) that found total

energy expenditure in boys actually increased with age. Again this thesis research is

unique in that it does not just demonstrate a sex difference in energy allocation after

puberty, but explains why the sex difference might exists based on sex specific

reproductive strategies.

65

SIGNIFICANCE IN HUMAN EVOLUTIONARY BIOLOGY This thesis contributes both to understanding why humans can afford to have such

a long juvenile period (Hill and Kaplan, 1999; Schultz, 1969) and also what role human

evolutionary biology can play in improving human health.

In order to have a long juvenile period, an organism must delay reproduction.

Delaying reproduction without compromising reproductive fitness is only possible,

however, if an organism is fairly certain to make it to reproductive age in good health.

This is difficult in an unpredictable environment characterized by resource scarcity. For

example, research demonstrates that when energy availability is low, urinary excretion of

C-peptide decreases in orangutans, bonobos, and chimpanzees. C-peptide is released

proportionally to insulin, which is responsible for glucose uptake and energy storage.

Therefore, lower levels of C-peptide are correlated with less energy storage and a

negative energy balance (Deschner et al., 2008; Emery Thompson et al., 2008, Emery

Thompson and Knott, 2008). A prolonged period of negative energy balance as the result

of resource scarcity will be detrimental to an organism’s health and will likely lower

reproductive fitness.

Human children, remarkably, are able to devote resources to development and

future reproductive capacity rather than current reproductive capacity for an extended

period of time without compromising fitness in environments characterized by resource

scarcity. The human ability to maintain such a long juvenile period before reproduction

can be partially explained by the ability to establish an energetic buffer in unpredictable

environments. Humans create this buffer both behaviorally and physiologically.

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The physiological mechanisms are explored in this thesis. Specifically, human

children facing resource scarcity allocate more energy to weight than to height, and girls

allocate energy to activity at a lower priority than to weight gain, especially after the

onset of puberty when energetic demands increase. This pattern of energy allocation is

conservative because energy is allocated such that it can be regained or so that it does not

create a long-term commitment to higher energy expenditure. Being conservative in

energy allocation creates future flexibility or a sort of energy buffer in an unpredictable

environment. Studies involving anthropometric measurements of juvenile, non-human

primates in varying environmental conditions are necessary to determine how the human

ability to create this energy buffer physiologically is unique or enhanced.

Behaviorally, humans establish this energy buffer in many unique ways, including

by pooling resources and by eating cooked food. Cooking increases the energetic value of

a given food (Carmody and Wrangham, 2009). In an unpredictable environment, the

ability to extract the maximum amount of energy from any available resource creates a

buffer from scarcity. A pooled energy budget is the idea that in a group, individuals can

maximize indirect reproductive fitness by sharing the benefit of energetic resources and

the burden of energetic demands with the larger group (Reiches et al., 2009). This allows

children to be provisioned by a broader range of individuals, including grandmothers

(Hawkes et al., 1998). This system creates a social buffer against environmental scarcity.

Neither cooking nor pooling energy resources changes the environment. Instead these

two unique behaviors simply create a buffer against resource scarcity.

Using the behavioral and physiological methods described above, human children

are able to maintain adequate energy supplies and allocate those supplies effectively in

67

resource-limited environments. This ability reduces the fitness cost of delaying

reproduction and consequently permits a long juvenile period of development (Walker et

al., 2006). This longer period of development will result in parents who are larger and

mentally more mature. Larger mothers have larger infants because they are able to

provide more calories to the fetus during pregnancy (Robson et al., 2006). Infants with

more adipose tissue are born with an energetic buffer that increases their chance of

survival. Moreover more mature parents might be better able to care for their infants and

children. Given the amount of investment humans make in each offspring, individuals

can maximize their fitness by ensuring their expensive offspring survive and thrive.

In addition to shedding light on the uniquely long juvenile period in humans, this

study underscores the opportunity the field of human evolutionary biology currently has

to contribute to improving human health. Predicting the outcome of increasing energy

intake has been a downfall of many public health interventions. For example, a study in

Ethiopia revealed that technology aimed at improving nutritional status by reducing the

amount of physical labor required of women actually decreased their inter-birth intervals

and consequently increased rates of child malnutrition (Gibson and Mace, 2006). In

Gambia a similar decrease in inter-birth intervals was seen when malnourished, lactating

women were given nutrition supplements that resulted in an unexpected decrease in the

duration of lactational amenorrhea (Lunn et al., 1981). The research for this thesis

demonstrates, however, that life history theory can help predict patterns of energy

allocation. Thus, human evolutionary biology has a critical role to play in the design and

evaluation of public and global health interventions. With further research on gender

specific, age specific, and environment specific patterns of energy allocation,

68

collaboration between the two disciplines will become even more crucial for improving

human health.

SIGNIFICANCE IN PUBLIC AND GLOBAL HEALTH In the field of public and global health, this thesis has implications for the

implementation of school feeding programs specifically and for the design and

implementation of nutritional interventions more generally.

For school feeding programs specifically, the results suggest that, while it would

be ethically unacceptable to offer food to only a subset of children, particular attention

should be paid to making sure children who are underweight or wasted and girls with

high activity demands who have also started puberty benefit from food aid. Moreover,

this study highlights the importance of evaluating all possible effects of school feeding

programs. Although the results of this research did not find any effects of the school

porridge on the mean height for age, mean weight for age, mean weight for height, and

mean energy expenditure in activity of primary school children in the Kabarole district of

Uganda, the porridge could have a substantial effect on attendance rates at school,

academic performance, and the mental health of children.

Concerning nutritional interventions more broadly, this thesis demonstrates the

value of designing and evaluating interventions with an evolutionary lens. Life history

theory can help predict the alternative outcomes of any supplement. For example, a

caloric supplement given to stunted children might increase their activity but not affect

height. Being able to predict such outcomes will both improve intervention design and

also ensure that evaluation monitors all potential positive effects of an intervention.

69

Additionally, this research highlights the importance of the timing of nutritional

interventions. The amount of energy allocated to long term, relatively inflexible energetic

investments such as height could be largely based on an individual’s initial environment.

Some scientists argue that the important initial environment is early childhood (Alderman

et al., 2006) and some argue that the important initial environment is even earlier in utero

(Bateson et al., 2004). Nevertheless, if the nutritional intervention starts too late it will

have missed this early critical period and might be less effective, if effective at all. This is

perhaps a previously unexplored explanation for the school porridge’s lack of effect on

the mean nutritional status of primary school children in the Kabarole district.

LIMITATIONS There are limitations of this study that should be discussed. One general limitation

is the relatively small sample size of 129 children. Sample size was primarily constrained

by the number of accelerometers available. A small sample size could have decreased the

power of the statistical tests to find significance. Analysis of the data did not include a

power test, however the spread of the data was quite large in the t-tests so an increased

sample size would likely not have significantly improved the power. With respect to the

regressions comparing males, females, and pubertal status, the gender divide was equal

(65 females and 64 males) and the number of males and females in the sample who were

assumed to have started puberty was approximately equal (46.2% of girls and 40.6%

boys). Thus, the relative significance is apparent even with a small sample size. However

it is possible that, with such a small sample size, the study population is not

representative of the entire population despite random participant selection.

70

There are also limitations associated with data collection. First, regarding

anthropometric data, height measurements were not taken electronically or in duplicates,

so there could have been small sources of error. This could have affected where

individuals fell compared to the CDC reference means. Second, energy expenditure data

was only collected during the school day in order to ensure the safety of the

accelerometers. This precluded measuring energy spent at home or walking to and from

school. These two sources of energy expenditure are likely not negligible because

children in this study community are often responsible for chores at home and walk up to

8 kilometers to get to school in the morning. Only capturing a limited amount of daily

energy in activity could have influenced the relationship between nutritional status and

energy expenditure in activity. Perhaps the relationship would have been stronger for

females and apparent for males if all activity were accounted for. It is also possible that

activity at school is more related to the amount of activity outside of school than

nutritional status. Third, there are limitations associated with the 24-hour diet recall.

Primary school children have trouble estimating portion size and relaying the preparation

methods of the foods they eat. Additionally, the fact that a foreigner was asking them

about their diet, despite the translation of all interactions, could have biased responses in

the direction of reporting more than was actually eaten. Due to these limitations it was

not possible to count or estimate total daily caloric intake. This made it impossible to

know if the school porridge increased total caloric intake. If the porridge did not in fact

increase total caloric intake, then it would not be surprising that mean nutritional status

did not change between children eating porridge and children not eating porridge.

71

Finally, there are limitations associated with data analysis. One such limitation is

that pubertal status is assumed by age. For girls, a previous study on the age of menarche

in the sample population (Ross et al., 2010) provided a reference for establishing the age

at which puberty was assumed to begin. This type of reference did not exist for boys,

however. These assumptions could have artificially weakened the relationships between

indicators of nutritional status and energy expenditure if some of the individuals who

were assumed to have started puberty had not actually reached puberty. It is possible then

that the lack of relationship observed in males between nutritional status and energy

expenditure in activity was not due to a sex difference, but was instead due to the

possibility that very few of the males in this study had actually started puberty.

Additionally, the type of analysis performed in this thesis did not permit any conclusions

to be drawn about specific thresholds that must be attained before energy is allocated to

activity or height, for example. This would be an interesting topic for future research.

CONCLUSION

Malnutrition is a growing problem worldwide. While rates of overweight and

obesity are rising, the problem of under-nutrition has not disappeared and thus deserves

the attention of the scientific community. Resource scarcity contributes greatly to under-

nutrition. In environments of resource scarcity, like the Kabarole district of Uganda,

children must allocate limited energetic resources among growth, activity, and

preparation for reproduction upon the onset of puberty. The evidence presented in this

thesis suggests that children allocate energy within and between these three domains

conservatively. The exact pattern of allocation depends on pubertal status, sex, and

72

energy intake. This conservative pattern of allocation contributes to the creation and

maintenance of an energy buffer that partially explains why humans are able to afford

long juvenile periods in the face of resource scarcity.

Food aid and school feeding programs in particular are one way to combat

resource scarcity and increase energy intake in children. However, this thesis suggests

that just increasing caloric intake is not necessarily a solution to physical manifestations

of under-nutrition. That is, increasing input does not guarantee that the extra energy will

be allocated to the target energetic demand. Moreover, the timing of interventions is

likely crucial.

Finally, this thesis has demonstrated the power of combining the fields of human

evolutionary biology and global health to improve the design of health interventions and

evaluate those interventions more effectively. To combat the problem of under-nutrition

and to improve child health worldwide, collaboration between the two disciplines will be

imperative.

Ogume Kurungi

73

APPENDIX 1

Participant ID:____________________

ANTHROPOMETRIC DATA Sex- Age- Weight- Height- MUAC- CDC CLASSIFICATIONS Height for age- Weight for age- BMI for age- OTHER Porridge- YES NO Number of siblings in household including self- Adult caregivers- DIETARY ASSESSMENT Food Item/Preparation Quantity Time Where Consumed

74

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