thesis final manuscript 2014

ASSOCIATIONS WITH STRESS: A CROSS-SECTIONAL COMPARISON OF

WELLNESS IN OLDER ADULTS

SHELBY BENCI, B.S. (California Polytechnic State University, San Luis Obispo) 2012

CHAD EARL, B.S. (Bradley University) 2000

APRIL IRVINE, B.S. (Johnson & Wales University) 2012

JULIE LONG, B.S. (California Polytechnic State University, San Luis Obispo) 2012

NIKKI NIES, B.S. (Montclair State University) 2013

JESSICA SCHIAPPA, B.S. (Benedictine University) 2013

RESEARCH MANUSCRIPT

Submitted in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE in NUTRITION AND WELLNESS

In the College of Education and Health Service,

Benedictine University, Lisle, Illinois

Research Advisor:

Dr. Bonnie Beezhold, MHS, CHES

December 2014

SHELBY BENCI, B.S.

CHAD EARL, B.S.

APRIL IRVINE, B.S.

JULIE LONG, B.S.

NIKKI NIES, B.S.

JESSICA SCHIAPPA, B.S.

The Research Manuscript submitted has been read and approved by the Research

Advisor. It is hereby recommended that this Research Manuscript be accepted as

fulfilling part of the Master of Science in Nutrition and Wellness graduate degree in the

College of Education and Health Services at Benedictine University, Lisle, Illinois.

________________________________ _________________________________

Signature of Bonnie Beezhold, PhD, Signature of Karen Plawecki, M.S., Ph.D.

MHS, CHES, Research Advisor Director, M.S. in Nutrition and Wellness

APPROVED FOR BINDING

_________________________________

Signature of Catherine Arnold, M.S., Ed.D.

Chairperson, Nutrition Department

APPROVED COMPLETION OF

RESEARCH REQUIREMENT

__________________________________

Signature of Alan Gorr, Ph.D., M.P.H.

Dean, College of Education and Health

Services

December 11, 2014________________ December, 2014___________________

Date of Oral Defense Intended Graduation Date (December 2014)

Shelby Benci, Chad Earl, April Irvine, Julie Long, Nikki Nies, Jessica Schiappa

TABLE OF CONTENTS

LIST OF TABLES vii

ACKNOWLEDGEMENTS x

STRUCTURED RESEARCH ABSTRACT xi

CHAPTER 1: INTRODUCTION 1

Introduction 1

Study Purpose 3

Hypotheses 4

Variables to be Examined 6

CHAPTER 2: LITERATURE REVIEW 8

Dimensions of Wellness 8

Mental Wellness by Chad Earl 9

Physical Wellness by Chad Earl 9

Social Wellness by Chad Earl 10

Spiritual Wellness by Chad Earl 11

Health and Wellness of Older Adults 12

Depression by Jessica Schiappa 13

Associations of Stress and Depression by Jessica Schiappa 14

Weight by April Irvine 15

Physical Activity by April Irvine 16

Dietary Patterns in Older Adults by April Irvine 17

Health and Wellness of the Vowed Religious Community 19

Dietary Patterns in Vowed Religious Communities by Nikki Nies 19

Blood Pressure by Nikki Nies 19

Comparison of Groups by Nikki Nies 20

Mechanisms of Stress 22

Impact of Chronic Stress on Physical Wellness by Shelby Benci 24

Impact of Chronic Stress on Mental Wellness by Shelby Benci 26

Impact of Chronic Stress Eating Behaviors and Weight by Shelby Benci 28

Stress and Aging by Shelby Benci 30

Mitigating Factors of Stress 30

Multivitamin/Mineral Supplementation by Julie Long 31

Omega 3 Fatty Acids by Julie Long 32

Fruits and Vegetables by Julie Long 34

Physical Activity by Chad Earl 35

Social Support by Chad Earl 36

Spiritual Practices by Chad Earl 37

CHAPTER 3: METHODOLOGY 39

Research Study Design 39

Research Study Recruitment 39

Data Collection Methods and Process 41

Validity and Reliability of Methods 44

Measurement Tools 48

Statistical Procedures 54

CHAPTER 4: FINDINGS 56

Stress & Health & Lifestyle Factors Hypotheses 1-4

by Shelby Benci 59

Alcohol & Health & Lifestyle Factors Hypotheses 5-8

by Jessica Schiappa 62

Sweets Intake & Health & Lifestyle Factors Hypotheses 9-13

by Julie Long 65

Physical Health Measures & Health & Lifestyle Factors Hypotheses 14-18

by Chad Earl 68

Geriatric Depression Scale & Health & Lifestyle Factors Hypotheses 19-23

by Nikki Nies 72

Amount of Sleep & Health & Lifestyle Factors Hypotheses 24-27

by April Irvine 75

CHAPTER 5: DISCUSSION 80

Overall Findings 80

Stress 80

Depression 84

Sweets Intake 85

Alcohol Intake 88

Sleep 90

Physical Health Measures 91

Strengths and Limitations 93

Conclusions 94

REFERENCES 95

APPENDIX A: Cross-Sectional Wellness Study IRB Document 115

APPENDIX B: Wellness Survey 136

APPENDIX C: Recruitment Tools 143

APPENDIX D: Signed Informed Consent Form 146

APPENDIX E: Registration and Testing Procedures 147

APPENDIX F: Health Assessment Data Collection Tools 150

LIST OF TABLES

Table Page

1. Demographic and Lifestyle Characteristics by Group…………………………...57

2. Health and Wellness Characteristics by Group………………………………….58

3. Comparison of Means between Living Groups………………………………….59

4. Associations between PSS and Health and Lifestyle Factors……………………61

5. PSS Multiple Linear Regression Analysis……………………………………….62

6. Comparison of Means of Weekly Alcohol Intake between Living Groups……...63

7. Comparison of Means between Binned Weekly Alcohol Intake Groups………..64

8. Associations between Alcohol Intake and Stress………………………………...65

9. Comparison of Means of Sweet Intake…………………………………………..66

10. Significant Correlations of Variables with Sweets Intake……………………….67

11. Multiple Linear Regression Analysis of Sweets Intake………………………….68

12. Comparison of Means between Groups of Heart Rate & Body Fat……………..69

13. Correlations with Physical Parameters and Perceived Stress……………………69

14. Associations with Muscle Mass………………………………………………….70

15. Body Fat and Heart Rate Associations…………………………………………..71

16. Comparison of Stress Means between Muscle Mass Groupings………………...72

17. Comparison of Means with Geriatric Depression Scale Scores…………………73

18. Significant Correlations of Variables with Geriatric Depression Scale………….73

19. Multivariate Analyses of Predictors of Depression……………………………...74

20. Significant Correlations of Variables with Geriatric Depression Scale………….75

21. Comparison of Means between Genders and Hours of Sleep……………………75

22. Comparison of Means between Living Groups with Hours of Sleep……………76

23. Comparison of Means between Sleep Hour Binned Groups…………………….77

24. Significant Correlations of Variables with Sleep Hours…………………………79

ACKNOWLEDGEMENTS

We would like to first thank our advisor, Dr. Bonnie Beezhold, for her support

and guidance through this entire process. We would also like to thank our family and

friends for their endless love and support. We express our sincere gratitude and thanks to

one another, as none of this could have been completed without each other’s support,

effort, and time. Thank you to all who have helped us on this journey.

ABSTRACT OF RESEARCH MANUSCRIPT

SHELBY BENCI, B.S.

CHAD EARL, B.S.

APRIL IRVINE, B.S.

JULIE LONG, B.S.

NIKKI NIES, B.S.

JESSICA SCHIAPPA, B.S.

Benedictine University, Lisle, Illinois

December 2014

Research Advisor: Bonnie Beezhold, PhD, MHS, CHES

Background: Chronic stress negatively impacts wellness and is associated with physical

and mental chronic disease. Certain lifestyle factors can mitigate stress and improve

health outcomes.

Objective: To examine the relationships of stress with physical, emotional, social,

spiritual health measures, and diet and lifestyle factors in older adults living in two

different communal environments.

Methods: Cross-sectional study of 67 participants were recruited from vowed religious

communities and an independent retirement community. Study assessments included a

survey containing demographic and lifestyle questions, brief validated questionnaires

measuring perceived stress and other wellness dimensions, a 24-hour recall questionnaire

and anthropometric measurements.

Results: Of the 67 participants, 35 resided in vowed religious communities and 32

resided in an independent retirement community. A significant difference in reported

depression, as measured by the Geriatric Depression Scale-15, was found with the vowed

religious community reporting a higher mean score than the independent retirement

community (2.12 vs. 1.16, p = .020). Percent body fat (38.55 vs. 33.26, p = .025) and

heart rate (75.86 vs. 68.41, p = .029) were also significantly different by living group,

with higher values in the vowed religious community compared to the independent

retirement community. Spirituality, vitamin D intake, and daily sweets intake explained

50% of the variance in perceived stress scores in multivariate analyses.

Conclusion: Our findings suggest that older adults living in vowed religious

communities do not experience greater well-being than those living in independent

retirement community. Perceived stress in older adults may be reduced by certain

lifestyle practice.

CHAPTER 1

INTRODUCTION

Problem Description and Rational

Worldwide, stress is the second most common health problem that can negatively

impact an individual’s wellness 1. Unhealthy levels of stress can negatively impact both

mental and physical health in every age group. The U.S. Census Bureau projects that by

2050, 20% of the U.S. population will be over the age of 65 2. Increased exposure to

stressful life events and oxidative damage from chronic stress may specifically impact

older adults over younger generations 3. Stress and negative emotions activate the

hypothalamic-pituitary-adrenal (HPA) axis to release cortisol into circulation 4.

Prolonged activation of this axis has been associated with inflammation, physical and

mental health problems, and mortality 5. Specifically, stress can also induce

inflammatory brain-altering processes and are now thought to exacerbate brain aging 6,7.

Chronic exposure to acute stress and cortisol is related to DNA and RNA damage in older

adults 7. In a recent study that compared stress levels of caregivers and non-caregiving

controls, it was shown that the cumulative effect of daily stressors promoted elevations in

blood inflammatory markers 8.

Moreover, chronic stress is associated with negative physical and mental health

outcomes such as cardiovascular disease, metabolic syndrome, weight gain and late-life

depressive symptoms 2,9. Stress can affect mental health through dysfunction of the HPA

axis and increased serum cortisol levels, which may cause depression, decreased quality

of life and negative emotions. In older adults, increased perceived stress and stressful life

events can lead to an increase in depressive symptoms 2. Research also suggests that there

is increasing variability in self-esteem at progressively older ages, which increases stress

levels 10,11. Age-related declines in older adults’ self-esteem could derive from a loss of

social roles, social isolation, or an increase in physical health problems 12. In fact,

optimism has been found to buffer the association between perceived stress and elevated

levels of diurnal cortisol 13.

Dietary factors can influence mental health. A healthy diet and physical activity

has been shown to decrease perceived stress and improve health outcomes and health-

related quality of life 14. A recent prospective study published in the Journal of the

Academy of Nutrition and Dietetics assessed the associations between self-reported stress

and dietary intakes and dietary behaviors of adults in the United States 15. The study

found that higher perceived stress scores were associated with higher fat intake of the

calories consumed, greater intake of high-fat snacks, and fast food 15. This suggests that

people who perceive themselves to be more stressed are more likely to eat an unhealthy

diet, which over time can lead to health problems including excess weight gain and

obesity 15.

Lifestyle and environmental factors can also be influential with respect to mental

health. The stress or support in one’s everyday living environment may affect perceived

stress or depression. A vowed religious life lived in a close community may positively

influence these factors and even provide physical health benefits. Far removed from 21st

century social and cultural norms and pace, an ascetic lifestyle is one of self-discipline,

an absence of self-indulgence and regular acts of fasting, all of which may benefit ones

mental and physical health 16,17. For example, a prospective study in Italy that

investigated blood pressure, an indicator of stress, followed 144 nuns and 138 similar

laywomen controls for 20 years, and found that blood pressure did not increase with age

in the nuns compared to laywomen, an unexpected result only found in comparisons with

hunter-gatherer groups 18. While monks and nuns live a structured, cohesive, minimalist

lifestyle, adults living in an independent community typically are not limited by such

constraints, which may lead to mental health differences between the two populations. In

American older adults, 80% have at least one chronic disease and 50% have two or more.

In a study of 1085 independently living adults over the age of 60, those with more

chronic disease diagnoses had an increase in depressive symptoms and a decrease in

health-related quality of life 19.

Study Purpose

The aim of our study was to explore various dimensions of wellness: physical,

emotional, social, and spiritual, with a focus on stress, and associations with diet, lifestyle

factors, and physical health parameters in older adults. We also compared the wellness of

adults in different communal environments, by exploring these factors in both a vowed

religious community and an independent living community. Due to the older age of

adults in vowed religious environments, we limited our community group to 65 years and

older. Participants completed a survey with demographic questions as well as four

wellness scales; we also obtained physical measurements and took a 24-hour dietary

recall. We hypothesize that those living in vowed religious communities have less stress

and healthier dimensions of wellness than those living in an independent living

community.

Hypotheses

Relationship between Stress and Health and Lifestyle Factors

o H10: There is no difference in stress reported by gender in older adults.

o H20: There is no difference in stress reported by the vowed religious

community and the independent retirement community in older adults.

o H30: Perceived stress is not related to health and lifestyle factors in older

adults.

o H41: Certain health and lifestyle factors contribute to or predict perceived

stress in older adults.

Relationship between Alcohol and Health and Lifestyle Factors

o H50: There is no difference in weekly alcohol intake between genders in

older adults.

o H60: There is no difference in weekly alcohol intake between the vowed

religious community and independent retirement community in older

adults.

o H70: Perceived stress scores will not be different in levels of alcohol

intake in older adults.

o H80: Weekly alcohol intake is not related to stress or other lifestyle and

health factors in older adults.

Relationship between Sweets Intake and Health and Lifestyle Factors

o H90: There is no difference in sweets intake by gender in older adults.

o H100: Sweets intake per day does not differ in the vowed religious

community compared to the independent retirement community in older

adults.

o H111: Sweets intake is related to perceived stress in older adults and other

health and lifestyle factors

o H121: Perceived stress scores are different by sweets intake level in older

adults.

Perceived Stress and Body Composition:

o H131: Physical health parameters are associated with stress in older adults.

o H141: Muscle mass was associated with lifestyle factors in older adults.

H150: There are no differences in physical health parameters between

older adults in the two living environments.

o H161: Body fat and heart rate are associated with lifestyle factors in older

adults.

o H170: Perceived stress scores do not differ in older individuals with lower

and higher muscle mass.

Relationship between Geriatric Depression Scale and Health and Lifestyle Factors

o H181: Older adults living in the vowed religious group will report less

depression than those living in the independent retirement group.

o H191: Health and lifestyle factors significantly related to depression

explain the difference in depression we observed between living groups in

older adults.

o H201: Depressive symptoms reported by participants will be associated

with health and lifestyle factors in older adults.

Relationship between Amount of Sleep and Health and Lifestyle Factors

o H210: There is no difference in reported hours of sleep per night by gender

in older adults.

o H220: There is no difference in reported hours of sleep per night between

the vowed religious community and independent retirement community in

older adults.

o H230: There is no difference in health and lifestyle factors related to sleep

in three categories in older adults.

o H240: Reported sleep hours per night is not related to health and lifestyle

factors in older adults.

Variables To Be Examined:

o Demographics

o Perceived Stress

o Social Support

o Spirituality

o Depression

o Body Composition and Anthropometrics

o Blood pressure and heart rate

o Diet Composition

CHAPTER 2

LITERATURE REVIEW

Dimensions of Wellness

Wellness can be defined as the quantifiable daily practice, state or condition of

being in adequate physical, emotional, and mental health 20. In 1959, wellness research

originated with the work of Dr. Halbert Dunn. He coined the term “High Level Wellness

for Man and Society”, and his research focused on the synergistic relationship and impact

of health status relating to the mind, body and spirit 21. The research efforts and models of

practitioners since then have attempted to create, clarify and quantify variables that

impact “high level wellness” 20. Currently, in allied healthcare there are several

classification systems used to measure wellness including the Six Dimensional Model,

the Twelve Dimensional Model of Wellness, and the Sixteen Dimensional Model of

Wellness. All the models are multidimensional in nature and attempt to quantify the

physical, mental, social, and spiritual behaviors that contribute to health 20,21. The rest of

this section will focus on the four most common domains of wellness: mental, physical,

social, and spiritual.

Mental Wellness

Positive mental wellness has a positive impact on a person’s overall health.

Mental wellness or health as defined by the World Health Organization is a state of well-

being in which the individual realizes his or her own abilities, can cope with the normal

stresses of life, can work productively and fruitfully, and is able to make a contribution to

his or her community. An optimistic mental outlook has been shown to have a positive

impact on physical health measures, recovery from disease or trauma, and maintenance of

routine social engagement 22-24. A cohort study of 41,275 men diagnosed with clinically

localized prostate cancer from 2004 to 2007 were recruited to examine the relationship

between mood disorders and treatment outcomes 25. The study found that men with

depressive disorder overall had worse mortality than those who were not depressed 25.

In addition, a prospective study of 46 college students looked at the impact of a

positive emotion and risk of depression following a traumatic experience, September 11,

2001 26. The study found that students who exhibited the highest levels of optimism,

maintained a positive outlook on life, experienced frequent positive emotions and tended

to resist depressive symptoms following the events of September 11, 2001 26. These

studies illustrate that individuals who displayed an optimistic perspective tended to better

mental wellness, than those who with a negative outlook.

Physical Wellness

People with better physical wellness including measurements such as BMI,

normal lipid and glucose levels are more likely to have a better quality of life and longer

life expectancy. The second dimension of wellness, physical wellness, is classified by

having healthy ranges of anthropometric values, laboratory blood parameters,

cardiovascular measures, and physical capacity scores 27. A prospective cohort study of

1023 community-dwelling older adults tracked changes in allostatic load, which is a

measure of physiological wear and tear on the body including measurements such as

BMI, lipid panels, and blood glucose levels, over a 10-year period and compared these

factors to the sample’s mortality rate 27. Findings revealed that higher allostatic load or

rapidly increased allostatic load scores significantly increased mortality risk in older

adults 27.

In addition, a prospective study of 489 African American youth in the rural south

were assessed for changes in allostatic load at 11 years old and then at 19 years old 28.

The study illustrated that those who received consistent, supportive parenting and

positive friend influence had more ideal physical health measures, greater emotional

stability, and less behavior problems in school, at home and in the community 28. These

two studies demonstrate that objective physical health change is a valid and reliable

measurement of individual global wellness. Both studies demonstrate that changes to

physical health markers correlate to physiological function that strongly contribute to

overall life span and health.

Social Wellness

The third dimension of wellness that has a large impact on one’s overall health is

social wellness. The social dimension recognizes the need for contribution and positive

interaction with one’s family, friends and community members 22,29,30. In a cross-

sectional study of 316 Korean older adults, the effect of social support, religious

practices, and daily stressors on overall well-being was examined 23. The results showed

that higher perceived stress levels were associated with higher incidence of depression

and decreased life satisfaction. The individuals that received the most social support,

performed regular spiritual practices, engaged in frequent family interaction, and

participated in scheduled group leisure activities had significantly lower stress values and

higher quality of life 23.

In another cross sectional study of 755 pregnant Chinese women in their second

trimester were recruited to examine the direct and moderating effects of social support in

mitigating perceived stress associated with depressive or anxiety symptoms 31. The

findings showed that perceived stress, anxiety, and depression were lower in individuals

who had family members that were actively engaged in their lives. The study also

showed that individuals benefit from positive environments including: occupation, home,

local community, and medical providers that were supportive and showed concern for

their needs 31. These studies demonstrate that those who have more social support and

are actively involved in community activities have better social wellness. This increase in

social wellness positively effects overall wellness.

Spiritual Wellness

The final dimension of interest is spiritual well-being; research shows that those

with increased spirituality are healthier. Spiritual wellness addresses the search for a

meaning and purpose to human existence, and includes a deep appreciation for the

expanse of life and natural forces that exist in the universe. It is important to note that the

terms spiritual and religious are not synonymous. Religiousness as defined by Merriam-

Webster dictionary refers to being dutiful and conscientious when performing a specific

practice. Spiritual defined by Merriam-Webster dictionary means relating to, consisting

of, or affecting the spirit and or relating to sacred matters.

A study of 502 African Americans aged 50-105 years old were surveyed to

observe the impact of church attendance and level of involvement in their congregation

on perceived stress and mental health parameters 29. Results indicated that individuals

who were highly involved in their church community felt that they had a church family

that would help them in times of illness or tragedy, and had the ability to pray to God for

help with their personal burdens, concerns, or crises 29. People who were spiritually

active had lowered perceived stress values when compared to those who did not engage

in similar behaviors 29. In another study of 316 adults, 65 years old or greater living in a

retirement community examined the impact of spiritual coping practices and social

support on depression and life satisfaction. The study found individuals who exhibited

the greatest utilization of spiritual coping practices combined with social support

demonstrated the lowest depression scores and highest life satisfaction 23. These studies

demonstrate that those who are spiritually active had overall better quality of life and

health. This shows that belief in something greater is an important aspect to living a

healthy lifestyle.

Health and Wellness of Older Americans

Mental disorders such as stress, depression, and anxiety are common in the older

American population and can have detrimental effects on a person’s livelihood. Research

shows that mental disorders, such as perceived stress, can be destructive to an

individual’s life. In a cross-sectional study of 689 women aged 45-60, qualitative data,

such as stressful life events, health-related quality of life, mental health, chronic disease,

and depression were collected using the Life Stressor Checklist-Revised (LSC-R), Short

Form Health-Related Quality of Life (SF-12), and the Center for Epidemiologic Studies

Depression Scale (CES-D). Researchers found was that those who reported more life

stressors also reported more chronic disease 32. Similarly, in a population-based study of

6,207 adults measured socio-demographics, health behaviors, psychosocial measures,

cognitive function and health history. The findings of the study included that increasing

levels of stress was associated with cognitive decline in older adults aged 65 years and

older 33. Thus these studies show that perceived stress may lead to more chronic disease

and cognitive decline in older adults.

Depression

Depression in older adults may increase inflammation and risk of chronic illness.

In an experimental study of 138 adults, depressive symptoms, anxiety, and stress were

measured using the Center for Epidemiological Studies Depressive Scale, the Beck

Anxiety Inventory, Trier Social Stress Scale and the Childhood Trauma Questionnaire.

Whole blood was also drawn and analyzed to measure interleukin-6 concentrations. The

researchers found that participants who expressed more depressive symptoms also

demonstrated more inflammation and increased inflammation increased the risk for

chronic diseases, such as cancer, heart disease, and diabetes 34.

In a similar study, data was analyzed from the Health and Retirement Study. This 12-

year prospective study examined 3,645 individuals between the ages of 62-74 years old.

The study used the Center for Epidemiological Studies Depressive Scale to observe self-

reported depression, and a self-report of chronic illness. They found that in older

working adults, participants with depression at baseline had a significantly higher risk of

developing chronic diseases, specifically diabetes mellitus, heart disease and arthritis 35.

This indicates is that depression in older adults may have negative effects on health;

specifically it may increase inflammation and risk of chronic disease.

Associations of Stress and Depression

Research shows that depression and perceived stress in older adults are associated

with one another. In a longitudinal study involving 70 elderly depressed subjects,

hippocampal volume, perceived stress levels and life stressors were evaluated using the

Montgomery-Asberg Depression Rating Scale, MRI data, and a self-report questionnaire

concerning life stress. The researchers found that among the depressed participants there

was a higher prevalence of negative life events and higher perceived stress scores 36.

Similarly, in a cross-sectional study of 54 community-dwelling older women, memory

function, perceived stress, life events, activities, and depression were measured with a

questionnaire that the participants completed, that included the General Frequency of

Forgetting Scale, Perceived Stress Scale, Geriatric Scale of Recent Life Events, Activities

Checklist, and Geriatric Depression Scale 37. Researchers found that perceived stress,

along with anxiety and depression was affiliated with memory complaints, as stress can

impact the brain’s memory center, the hippocampus 37. This suggests that adverse mental

health issues in older adults, in particular stress and depression, may influence one

another and lead to further mental illness and memory problems.

Weight

Weight status plays an important role in overall health and wellness of older adults. In a

cross-section study published in the Journal of American Medical Association, Flegal et

al. examined the prevalence of overweight and obesity in the aging population 38. The

researchers used Body Mass Index (BMI) to measure body fat a person carries based on

height. The results showed that the obesity prevalence in 2011-2012 overall was 35.4%

for the whole sample, 32% for men, and 38.1% for women 38. Over one-third of the older

adult population is obese. Since obesity has been linked with increase risk of heart

disease, diabetes, and hypertension this is a very serious concern.

Similarly, in a cross-sectional analysis of US adults aged 65 years and older,

Fakhouri et al. examined the prevalence of overweight and obesity based on BMI 39. The

researchers found that for both men and women the prevalence of obesity was higher

among those aged 65‒74 years compared with those aged 75 years and older, and over

the past 10 years the prevalence has overweight and obesity has increased in this

population 39. Also, non-Hispanic black women were more obese than non-Hispanic

white women, and those with a college degree were less obese than those with some

college experience 39. These findings suggest that over time there has been a increase in

prevalence of overweight/ obesity in the older adult population and that weight status

varies by ethnicity and education level. However, it appears that those who are over 75

years have a decrease in weigh, which may be due to changes in dietary intake and

physical activity as person ages.

Physical Activity

Physical inactivity of the American older adult may contribute to a decline in

overall well-being. The Older American Report 2012, measured older adults’ physical

activity patterns using self-reported surveys and comparing the results to the 2008 US

Physical Activity Guidelines 40. The results showed there has been a 5% increase in the

number of individuals meeting the federal physical activity guidelines from 1998 to 2010

40. However, even with this improvement in the number of older adults meeting physical

activity recommendations the 11% of individuals meeting US physical activity guidelines

remains substantially low. A cross-sectional study of 975 adults aged 65 years and older

published in the American Journal of Epidemiology, examined associations between

physical activity level (e.g. sedentary to vigorous activity) and well-being variables (e.g.

chronic health complications, BMI, life satisfaction, depression, and perceived stress) 41.

Researchers found that participation in physical activity was positively associated with

physical health and well-being 41.

In addition, greater sedentary time was negatively associated with physical health

and perceived well-being; whereas, light, moderate, and high physical activities were all

positively associated with physical health and perceived well-being 41. Additionally, a

study by Bankoski et al. investigated the association between sedentary activity and

metabolic syndrome among 1,367 older adults, aged 60 and older 42. Sedentary times

during waking hours were measured by an accelerometer and metabolic syndrome was

defined using the Adult Treatment Panel III criteria. Over all, the sample spent 9.5 hours

sedentary, and individuals with metabolic syndrome spent even more time sedentary than

compared with people without metabolic syndrome 42. Independent of physical activity,

the amount of sedentary time was significantly related to metabolic risk 42. Overall,

participation in physical activity was positively associated with physical health and well-

being for the older adult population, but despite this finding, the majority of older adults

spend their time sedentary and physical inactivity, which can increase the risk metabolic

syndrome and other chronic diseases.

Dietary Patterns in Older Adults

Diet plays an important role in overall health and well-being. The current trends

in health and wellness of the older adult population aged 65 years and older is

characterized by poor diet quality, as defined by not meeting United States (US) dietary

guidelines. The Older American Report 2012, a cross-sectional analysis, examined health

and wellness factors of 40 million US adults, aged 65 years and older. Diet was measured

using the Healthy Eating Index-2005, and diet quality of participants was compared to

recommendations of the 2005 Dietary Guidelines for Americans 40. According to the

report, 79% of adults between the ages 65-74 years believed they were in good health,

but the data actually indicated that this population was not meeting US dietary guideline

recommendations through their current dietary patterns 40. This is a concern because

older adults with overall poor quality diets have an increased risk of chronic disease

compared to those with high quality diets.

A 10 year cohort study of 2,200 participants aged 55 years and older, published in

the Journal of Academy of Nutrition and Dietetics, investigated the association between

diet quality, quality of life, and activities of daily living 43. The researchers found that the

majority of participants had poor diet quality; however, those who had adequate nutrient

intake reported better quality of life 43. In fact, participants who consumed more than 5

servings of vegetables per day, ate low-fat dairy products and whole grains, and followed

a low sodium diet had a 50% reduction of disability of activities of daily living in 5 years

43. In addition, improved dietary intake was associated with more education, increased

duration of exercise, and lower body mass index 43. All of these findings support the idea

that regularly eating a high quality diet improves overall health and wellness of older

adults. Additionally, a study from the Journal of Cancer, investigated health behaviors

and associations with quality of life outcomes in 753 participants aged 65-87 years old 44.

The researchers found that individuals who participated in regular moderate-to-vigorous

exercise and consumed a plant based, low-fat diet had better quality of life health

outcomes 44. Also, researchers found that physical inactivity may predict poor diet

quality, decreased social function, and an increase in chronic health complications 44.

The findings from the Older Adult American report and the studies by Gopinath

et al. and Mosher et al., suggest that overall, older adults’ dietary patterns are

characterized by poor quality including consuming below the recommended 5 servings of

vegetables per day, choosing white grains instead of whole wheat grains, and eating

foods high in concentrated sweets and sodium. Furthermore, the data suggests that older

age, less education, and higher BMI were associated with increased risk of activities of

daily living disability. These results suggest that diet may be a predictor in the health and

wellness of the aging population, and diet plays an important role in the overall health

and wellness of the aging population.

Health and Wellness of the Vowed Religious Community

The vowed-religious community adheres to a life of self-discipline and active spiritual

practices, and there is a theory that this kind of lifestyle positively impacts the health and

wellness of this community. People in religious orders often follow a stricter diet and do

not participate in many activities that can increase daily stress 45,46.

Dietary Patterns in Vowed Religious Communities

Monks and nuns often adhere to strict dietary practices that align with their

contemplative lifestyle, which can affect overall health. Many adopt a lacto-ovo

vegetarian diet and as a result of these dietary restrictions, some vowed religious persons

diets’ are low in B vitamins, calcium, iron, magnesium and zinc 45,46. For example, zinc

deficiencies may be explained by high intake of phytate rich foods and decreased calcium

intake due to fasting. Monks who fast regularly have favorable nutrient and food intake

profiles. Overall, they had decreased intake of total fat, saturated fat, and trans fatty acids,

with higher intake of iron, folate, legumes, fish, seafood, and fiber in comparison to

laypersons. While fasting is an integral part of the monastic life, benefits go beyond

spiritually, including a greater nutrient composition 25,46.

Blood Pressure

It appears that those living in religious orders have lower blood pressure, which

may be result psychosocial influences. A 32-year prospective study by Timio et al.,

looked at differences of anthropometric and blood pressure measurements, blood panels

and overall health practices of 144 white nuns and 138 healthy laypersons 18.

Researchers found that over the 32 years laywomen’s blood pressure significantly

increased, whereas nuns’ blood pressure remained nearly stable 18. Researchers noted that

other variables that often affect blood pressure including age, race, lifestyle habits, did

not vary between the two groups. Therefore, researchers speculated that psychosocial

influences including conflict, anxiety and aggression might have been the determining

factor for an increase in blood pressure in laywomen. This study suggests that women in

religious orders may have better mental health factors lead to a healthier lifestyle.

Moreover, an additional study found nuns’ cardioprotective health remains stable

compared to laywomen who have increased blood pressure with age 47,48. The study

found that laywomen had more non-fatal cardiovascular events than nuns, 31 versus 69,

with psychological stress being the underlying cause of such events 47,48. In these studies

it appears that psychosocial experiences of those in religious orders may be a factor in the

prevalence of lower blood pressure.

Comparison of Groups

While older adults’ daily habits are not as uniform as those living in the vowed

religious community, many older adults appear to be living a happy, healthy life. A

cross sectional study conducted by Cha et al., 2012, sought to uncover the successful

aging factors in Korean adults 49. Using the self-liking/self-competence scale, self-

efficacy scale, interpersonal relationship scale, self-achievement instrument, and

successful aging scale, it was found the largest contributor to successful aging was self-

esteem. Additional factors included level of involvement in religious activities, which

provides a positive view on life, including group meditation, social gatherings, prayer,

and increased positive thinking. While religion cannot be held entirely responsible for

older adults’ mindset, it does indicate it is an activity that promotes a positive, active

lifestyle 50.

Moreover, a study led by Schlehofer et al., 2008, aimed to gain a better

understanding of how the average older adult sees religion and spirituality and if there

was a difference in views found between the sample 50. Participants had a hard time

providing concrete definitions of spirituality, even though they considered themselves

highly religious, and subjects saw religion as an opportunity to be part of a community,

ability to make connections with others, and to be part of a larger identity 50. While older

adults’ perspective on religion is not as structured as those living in the vowed religious

community, those living in the independent retirement community recognize the

importance of constant spiritual practices.

It’s evident the vowed-religious community and independent retirement

community older adults have distinct qualities. The vowed-religious live a very

structured life, with activities and roles clearly defined and are given a balance of solitude

and communal scheduled time, recognizing the importance of both for overall personal

growth. Material possessions and food consumption are secondary to serving, with

obedience, fasting, and discipline being key aspects of the culture. Comparatively,

independent older adults living a modern life are characterized more by “free will”. Diets

are more liberal with age, yet the amount of physical activity is often left unadjusted with

increased food intake. Additionally, older adults’ make their own schedules, which are

dictated by personal interests, not by rank or position in the community. While the

vowed-religious live a minimalist life, independent older adults live at the other end of

the spectrum, with less structure and more flexible decisions.

Mechanisms of Stress

Stress is a state of altered homeostasis in response to mental or physical stressors.

Many things can cause stress in an individual’s life such as work, life events and financial

problems. Stress can cause symptoms such as depression, anxiety and sleep issues as well

as negative health outcomes such as cardiovascular disease, weight gain, and insulin

resistance 51.

The normal stress response includes both physiological and behavior responses

that strive to restore homeostasis. Two main physiological systems are involved in the

normal stress response, the sympathetic nervous system and the hypothalamic-pituitary-

adrenal axis (HPA axis) 52. The sympathetic nervous system is very fast acting and works

to quickly adapt to stressful situations through the release of epinephrine and

norepinephrine 52. The HPA axis is slower acting, which allows for long-term adaption to

a stressful condition 52. The HPA axis activates the corticotropin-releasing factor in the

hypothalamus, which then stimulates the release of adrenocorticotropin hormone from

the pituitary gland. This causes the release of glucocorticoids, stress hormones, from the

cortex of the adrenal glands. Glucocorticoids regulate the stress response through a

negative feedback loop with the hypothalamus and pituitary gland. The normal stress

response occurs in response to acute stressors. Prolonged exposure to acute stressors is

known as chronic stress. Chronic stress can alter the body’s normal stress response,

metabolism and homeostasis, and may produce psychological and physiological damage

4. Cortisol is a main glucocorticoid that is associated with pro-inflammatory molecules

and cytokines such as interleukin-6 (IL-6) and C-reactive protein (CRP) 8. High amounts

of daily stressors can lead to chronic low-grade elevation of those inflammatory markers

8. Elevation of IL-6 and CRP is associated with increased risk of weight gain, depression,

cardiovascular disease, insulin resistance, diabetes, cancer, autoimmune disease, frailty,

and mortality 8. A cross sectional study of 53 caregivers and 77 non-caregivers were

observed to determine if daily stressors impact circulating levels of IL-6 and CRP. The

caregivers had a greater occurrence of daily stressors as well as an increase in the

inflammatory markers IL-6 and CRP 8. This demonstrates the inflammatory response that

is associated with chronic stress.

Cortisol levels have been found to increase rapidly after awakening. This

measure, if monitored frequently, can be used as a baseline for adrenocortical and HPA

axis activity 53. After awakening, serum cortisol increases by 50-60% regardless of sleep

duration, quality and routines 53. Since the cortisol awakening response is consistent, if

monitored closely, it can reveal subtle changes in cortisol levels and HPA axis activity 53.

In a twin study of 104 pairs aged 8-64, cortisol awakening was measured with saliva

samples 0, 30, 45 and 60 minutes after awakening, and participants filled out surveys

regarding psychosocial factors such as stress, self-esteem and self-efficacy. Those with

higher perceived chronic stress had increased cortisol awakening responses, showing the

relationship between chronic stress and altered hormonal cycles 53. A study of 22 healthy

individuals had their blood and saliva tested 0, 15, 30, 45 and 60 minutes after awakening

54. The participants who were chronically stressed had an enhanced cortisol awakening

response and those whose chronic stress lasted for years or had reached a burn out stage,

where they were no longer able to cope with their stress, had a blunted cortisol response

and increased feedback sensitivity 54. This demonstrates the relationship between chronic

stress and the disruption of normal hormone responses. The normal stress response in the

body involves multiple physiological and behavioral responses. When there is over

exposure to stress, there can be a dysfunction of the normal stress response, which can

lead to negative health outcomes.

Impact of Chronic Stress on Physical Wellness

Chronic stress has been linked to negative health outcomes such as cardiovascular

disease, metabolic syndrome and weight gain. Job stress, marital stress and financial

stress all impact these negative health outcomes. Repeated social or environmental stress

can cause a dysregulation of the normal stress response and alter the activation HPA axis

and glucocorticoids, leading to cardiovascular, immune and metabolic symptoms 2.

Cardiovascular disease is a common negative health outcome related to chronic

stress. The mechanisms relating stress and cardiovascular disease include both behavioral

and physiological factors such as smoking, lack of exercise, insulin resistance, and

increased blood pressure 55. The INTERHEART study, a case-control study that matched

11,119 participants with a first myocardial infarction and 13,648 healthy controls across

52 countries, measured psychosocial stress, including work, home, financial and life

stress in participants. The results of INTERHEART study demonstrated the association

between increased psychosocial stress and risk for myocardial infarction 51. Behavioral

factors are also involved in cardiovascular risk and stress. A group of 10,308 government

employees, aged 35-55 years old, were studied to determine the risk for coronary heart

disease related to chronic work stress. Work stress was associated with lower physical

activity, poor diet, metabolic abnormalities and a higher rise of morning cortisol 56.

The mechanism by which stress increases cardiovascular risk likely involves HPA

axis dysfunction and cortisol regulation 57. In a prospective cohort study of 479 initially

healthy men and women, blood pressure and cortisol reactivity were measured at baseline

and at a three-year follow up. There was an association between hypertension and cortisol

reactivity 57. Cortisol can directly influence the physiological systems that are

responsible for regulating blood pressure; the results of this study demonstrate that HPA

axis hyperactivity is involved in the mechanism that related stress to cardiovascular risk.

Chronic stress is related to both increased cardiovascular disease and death associated

with cardiovascular disease. A 2012 study focused on perceived stress following an acute

myocardial infarction (AMI) in hospitalized patients in the United States. Patients with

higher perceived stress following their AMI hospitalization had an increase in mortality

within two years 55.

Cortisol is an insulin antagonist; during chronic stress there are high levels of

serum cortisol and this may alter normal insulin production and functionality in the body

58. Metabolic syndrome is a group of risk factors related to cardiovascular disease, insulin

resistance and obesity. Chronic stress can contribute to the risk for metabolic syndrome,

through both physiological mechanisms and behavioral mechanisms associated with

stress such as poor diet and smoking. A prospective cohort study of 10,308 participants

had work stress and biological markers of metabolic syndrome measured four different

times over a course of fourteen years. Work stress increased the risk for metabolic

syndrome in a dose-response manner and those with chronic work stress had double the

chance of developing metabolic syndrome in their lifetime 58. Additionally, in a

prospective cohort study of 120 women followed for fifteen years, psychosocial factors

such as perceived stress and depression were measured, as well as serum metabolic

syndrome markers. Women who had higher amounts of stress in their life had an

increased risk of developing metabolic syndrome 59. Psychosocial factors, such as stress,

can increase risk of developing metabolic syndrome through physiological or behavioral

mechanisms.

The allostatic load model demonstrates how psychological demands such as

excess stress can negatively impact ones physiological health. An Australian cross-

sectional study looked at psychosocial factors and their affect on arthritis in women ages

51-61 years old. Those with moderate to high perceived stress had a 2.5 fold increase in

report of arthritis demonstrating that increased perceived stress can manifest through

negative physiological health outcomes 60. Critical illness is an example of an acute

stressor and therefore often results in elevated cortisol levels and pro-inflammatory

cytokines. A case control study matched 158 patients in the intensive care unit (ICU)

with 64 controls and measured different markers of the hormonal stress response. The

ICU patients had elevated cortisol levels caused from both over production of cortisol as

well as altered cortisol clearance during the time of acute stress 61.

Impact of Chronic Stress on Mental Wellness

The normal stress responses involving glucocorticoids and the HPA axis is also

related to mental health. Chronic stress may be related to cognitive impairment 4.

Increased activity in the HPA axis and elevated amounts of serum cortisol is associated

with depression 62. Cortisol is able to cross the blood brain barrier, where it can activate

receptors and alter central nervous system activity 52. A high concentration of stress

hormones can also inhibit neurogenesis, the creation of new neurons, which may alter

mental health 63. Five hundred and sixty-five participants who met the Diagnostic and

Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for major

depressive disorder were evaluated using surveys and blood samples. Those with

dysfunction in the HPA axis had agitation symptoms and cognitive disorders within their

major depressive disorder diagnosis 62. In 2013, 125 adults ranging from 67-94 years old

were studied to determine an association between allostatic load, the dysfunction of the

HPA axis and glucocorticoid response due to increased environmental and social stress,

and depressive symptoms. Participants were interviewed to determine allostatic load

score and overall depression risk. Higher allostatic load scores were associated with

increased depressive symptoms 2. These studies demonstrate the association between the

HPA axis and mental health. Dysfunction of the normal stress response and increased

exposure to acute stressors can negatively impact mental health.

Mental health may also be affected by the stress response through behavioral side effects

of stress, such as altered sleep patterns and quality of life. A cross-sectional study of 181

older adults focused on the relationship between perceived stress and mental health.

Those with higher perceived stress had reduced quality of life, increased depressive

symptoms, and increased sleep disturbances 32. This demonstrates a different relationship

between stress and mental health.

Stress may also affect mental health through mood and emotions. Stress is often

related to negative affect and may be due to high cortisol levels through the dysfunction

of the HPA axis. A randomized controlled trial of 232 participants underwent either a

Trier Social Stress Test or a placebo stress test and had their saliva tested for cortisol

levels, emotional responses were rated using the Positive and Negative Affect Schedule

52. Those who experienced the Trier Social Stress Test had higher cortisol levels and

higher negative affect than those who underwent the placebo stress test, which

demonstrated that stress can alter mood and emotions through dysfunction of the HPA

axis and hormonal mechanisms 52. Stress can affect mental health through dysfunction of

the HPA axis and increased serum cortisol levels, which may cause depression, decreased

quality of life and negative emotions.

Impact of Stress on Eating Behaviors and Weight

Weight gain is often caused from a positive energy balance, but stress and its

effects on behavior and metabolism can contribute to obesity risk 64. As discussed earlier,

chronic stress can be a predictor of metabolic syndrome and cardiovascular disease, both

of which are also related to obesity 64. In a 2011 longitudinal study of 72 participants,

BMI was measured as well as social stressors, including work and social life. Social

stressors were found to be significant predictors of BMI 65. A prospective, 19-year study

in London aimed to evaluate the relationship between chronic work stress and obesity in

over 10,000 participants aged 35-55 years old. The study revealed that chronic work

stress predicted both general and central obesity 64. Chronic stress alters the normal stress

response, leading to altered adrenocortical activity, insulin resistance, abdominal obesity

and metabolic syndrome 64. A 2013 study examined the relationship between stress and

physical health in older Australian adults ages 60-70 years old. The results showed that

those who reported higher life stressors had higher BMI and increased occurrence of

chronic disease 32.

Chronic stress may impact obesity both directly and indirectly through behaviors

such as poor diet, alcohol consumption and low physical activity 64. An observational

study on government in employees sought to demonstrate a relationship between work

stress and blood pressure. Results showed that increased work stress led to increased use

of coping mechanisms such as alcohol consumption, unhealthy eating patterns and

physical inactivity 66. These coping mechanisms are associated with obesity. Stress may

change diet and exercise behaviors, which may impact and influence body weight and

weight gain during times of chronic stress 67. In a 9-year longitudinal cohort study, 1,355

US adults were followed with psychosocial stress and BMI being measured regularly.

Results showed that psychosocial stress contributed to weight gain in those who had a

higher baseline BMI, and stress can caused some participants to eat more or less than

usual and alter eating habits 67.

A healthy diet and physical activity have shown to decrease perceived stress and

improve health outcomes and health-related quality of life 14. A randomized controlled

trial of overweight and obese women aimed to determine if diet and exercise could

increase psychosocial factors and health related quality of life. The women were assigned

to one of four interventions: dietary weight loss, aerobic exercise, combined diet and

exercise, or control. The combined diet and exercise group saw the largest positive

outcomes on psychosocial factors, including stress, and health related quality of life 14.

Stress can negatively impact weight and eating behaviors through both direct and indirect

mechanisms such as alteration of the normal stress response and coping devices like junk

food and alcohol.

A healthy diet and exercise may improve health outcomes and lead to better quality of

life. Control of stress may improve eating habits, weight and quality of life.

Stress and Aging

The U.S. Census Bureau projects that by 2050, 20% of the U.S. population will be

over the age of 65 2. Within this age group, increased perceived stress and stressful life

events can lead to an increase in depressive symptoms 2. Increased exposure to stress can

accelerate the biological aging mechanisms such as inflammation and telomere length 7.

Psychological stress is associated with increased oxidative damage, which contributes to

aging and age-related chronic diseases such as neurodegenerative, metabolic,

cardiovascular diseases and cancer 7. The stress hormone cortisol is released after

stimulation by an acute stressor and chronic exposure to acute stress is related to DNA

and RNA damage in older adults 7.

Mitigating Factors of Stress

While perceived stress is a mental health factor that affects many Americans,

even those in the older Adult population, there is a growing body of evidence that has

found that there are many healthy ways to mitigate the symptoms of stress 68-70. These

stress relieving tactics range from dietary habits to physical activity to social and spiritual

support. Many researchers focus on dietary patterns and foods that are associated with

increased and decreased stress in a variety of populations. Research has found diets high

in fruits and vegetables, high in omega-3 fatty acids, using multi-vitamin supplements are

predictors for decreased stress in people.

Multivitamin/Mineral Supplements

Currently, there is a belief that regular multivitamin/mineral supplementation may

lower stress. Reasons for taking these supplements include improving mental function

and for improvements in stress and tiredness. Researchers have looked at one supplement

in particular, Berocca, a high dose B-complex vitamin and mineral and its effects on

mood. A double blind randomized control trial of 80 healthy men, between the ages of

18-42 years, were given either the Berocca supplement or placebo for 28 days.

Participants’ health was assessed before the 28-day intervention and after, using the

General Health Questionnaire, Hospital Anxiety and Depression Scale, and the Perceived

Stress Scale 71. Post-hoc test revealed that the treatment group had significantly lower

perceived stress scores than the placebo group after the 28-day intervention 71. Similarly,

a double blind, randomized, placebo controlled trial study of 215 males between the ages

30 and 55 years was given either the Berocca supplement or placebo for 33 days, and

then health and mood was assessed using varies health surveys, including the Perceived

Stress Scale 72. Once again after the 33-day treatment period, participants in the treatment

group had significantly lower perceived stress scores 72. Both of these studies

demonstrate that multi-vitamin/mineral supplementation in males can reduce perceived

stress of healthy individuals.

Finally, a double blind randomized trial of 173 men without a history of

aggression or impulsive behavior assessed how a multivitamin/mineral, DHA, or both

affected aggression, impulsivity, and stress. The men were divided into one of four

groups: placebo group, multivitamin/mineral group, DHA group, or

multivitamin/mineral/DHA group 73. The researchers found that the only group that had a

significant decrease in stress after the intervention was the vitamin/mineral group 73.

Once again, this reiterates that the use of a multivitamin and mineral can be helpful in

reducing stress of healthy males. Little research has looked at how a vitamin/mineral

supplement would affect females or older adults, but based on the current research, one

may hypothesize that these groups would have similar outcomes as younger, healthy

males.

Omega-3 Fatty Acids

In recent years, omega-three fatty acid supplementation and its affect on a

person’s perceived stress level has become increasingly popular area of research. The

thought behind consuming these fatty acids to reduce stress includes idea that the

polyunsaturated fatty acids act on the hypothalamic-pituitary-adrenocortical (HPA) axis,

by reducing pro-inflammatory cytokine production and stop the IL-1 signally pathway,

which in the end reduces corticotropin-releasing factor 1 (CRF) and HPA activation, and

ultimately prevents stress from rising 74. Because there is scientific research to

demonstrate that omega-3 fatty acid supplementation may physiologically affect stress

levels, researchers have tried to demonstrate such findings in human clinical trials.

One study investigated whether omega-3 phosphatidylserine (PS)

supplementation affected the psychological and physiological measures to the acute

stressor, the Trier Social Stress Test (TSST). This was a randomized, double blind,

placebo-controlled trial, and men between the ages of 30-60 years were assigned to either

the placebo group (n =30) or the treatment group (n =30) 75. Stress was measured before

and after the 13-week supplement intervention and perceived stress was measured using

visual analog scales (VAS). The results showed that after 13 weeks participants with high

chronic stress who were given omega-3 phosphatidylserine supplement had significantly

lower stress scores than those who were given the placebo, but this change was not noted

in participants who were characterized as having low chronic stress levels 75. This is an

interesting finding because it suggests that omega-3 fatty acid supplementation may only

lower a person’s perceived stress if the person has relatively high levels of chronic stress.

For people who have short episodes of chronic stress fatty acid supplementation may not

be effective in lower stress levels.

In addition as study published in 2003, assessed the effect of 7.2 gm/day of

omega-3 fatty acid supplementation on the sympathetic nervous system and stress

hormones associated with mental stress. Participants underwent mental stress tests before

beginning supplementation and 3 weeks after, and blood samples were collected to assess

stress hormones including cortisol and insulin, as well as blood pressure and heart rate 68.

After the 3-week supplementation period blood markers of stress in participants who

underwent mental stress tests significantly decreased 68. This shows once again that

omega-3 fatty supplementation may be beneficial in reducing mental stress, which is

important since high levels of chronic stress are associated with increased risk of many

diseases.

While these studies demonstrate that omega-3 fatty acid supplementation has

beneficial effects of perceived stress levels, there is some research that states that omega-

3 fatty acids are not beneficial for improving mood. In a randomized, double-blind,

placebo-controlled trial of 302 independent-living older adults (over 65 years) the effect

of supplementing EPA + DHA on mental wellbeing was assessed 76. Mental well-being

was measured using the Center for Epidemiologic Studies Depression Scale,

Montgomery Asberg Rating Scale, Geriatric Depression Scale, and Hospital Anxiety and

Depression Scale, and participants were either given 1800 mg/d EPA + DHA, 400 mg/d

EPA + DHA, or placebo for 26 weeks. At the end of the study no differences in mental

wellness were found between the three groups, indicating that omega-3 fatty acids do not

improve older adult mental well-being 76. Authors argue that changes in mood may not

have been seen because it is unclear what level of supplementation is needed 76, and that

most of the surveys assessed depression, not stress, therefore for those who were not

depressed, changes in mood may not have been observed.

Fruits and Vegetables

Finally, reducing stress may be that as simple as a well-rounded, primarily plant

based diet. There is a body of evidence that shows that people who eat mostly fruits and

vegetables have lower perceived stress than those who are eating more traditional

Western diet. This diet includes greater consumption of refined grains, added sugars, and

fats and oils. A recent cross-sectional study of 3706 university students in the United

Kingdom looked at how diet affects overall mental health. Participants’ intake was

assessed with a food frequency questionnaire and mood was measured with the Perceived

Stress Scale and Beck Depression Inventory 77. Consumption of healthy foods including

fruits and vegetables was significantly negatively associated with perceived stress and

depression. Similarly, consuming unhealthy foods like sweets, cookies, snacks, and fast

food was positively associated with perceived stress in females only 77.

Similar studies have been produced in the older adult population. A cross-

sectional study of 1336 Puerto Rican older adults, 45-75 years old, looked at associations

between psychological stress and nutrition. Perceived stress was measured with a Spanish

version of the Perceived Stress Scale, and general health status and behaviors were

measured with a survey based on the NHANES III 69. Perceived stress was negatively

associated with lower intake of protein, fruit, vegetables, fiber, and omega-3 fatty acids;

and positively associated with foods characterized by salty snacks and sweets 69. While

these studies demonstrate the negative association between fruits and vegetables and

perceived stress, it does not indicate a casual effect between the two variables. Further

research that can show changes in perceived stress overtime in necessary for one to be

able to make the statement that diet high in fruits and vegetables can reduce stress.

Physical Activity

Research demonstrates that physical activity and formal exercise are associated

with lower perceived stress. While the terms physical activity and exercise are often used

interchangeably, they actually have different meanings. Physical activity is used to

describe low to moderate intensity aerobic chores including household, occupational or

recreational movements and physical hobbies. In comparison, exercise is a subset of

physical activity that can include aerobic movement patterns, but most appropriately used

to describe intense and deliberate physical stress including anaerobic activities. Formal

exercise is planned, structured, progressive, and performed to improve at least one aspect

of physical fitness such as: muscular strength, muscular endurance, flexibility, balance or

cardiovascular conditioning 47.

In a 2013 cross sectional study of 14,804 college students, the association

between vigorous activity and perceived stress was examined to better understand the

relationship between the two 70. Perceived stress and activity level were self-reported, and

the results indicated that individuals who performed at least twenty minutes of vigorous

exercise three days a week had lower perceived stress scores than those who had lower

frequencies of activity 70. A similar study examined the impact of moderate physical

activity and perceived stress in a senior living population. The researchers assessed 164

individuals with who had mean age of 72 over a 4-year timeframe 78. The results showed

that individuals who participated in moderate activities for 2-5 hours a week had lower

perceived stress scores and reduced co-morbidities when compared to those individuals

who did not 78. These studies demonstrate that even if people are physical active only a

few hours a week, perceived stress decreases. This emphasizes the importance of

choosing an active lifestyle.

Social Support

Increased social support and community involvement are associated with better

mental health including lower perceived stress. Research shows that social engagement is

a stand-alone core behavior that can be utilized to strongly improve overall health status

64,79. In several studies social support has demonstrated to be as significant as diet or

physical activity and can work synergistically with them to lower perceived stress and

improve physical health measures. In a 2013 cross sectional study of 14,804 college

students, researchers aimed to investigate the relationship of social activity and perceived

stress. Findings demonstrated that individuals who had five or more close friends or spent

two or more hours a day in some form of social communication or shared group activities

had lower perceived stress scores 70. However, those who also exercised showed further

modulation of stress.

This study highlights that positive, consistent social support from friends and

family members are every bit as significant as diet and physical activity when improving

or sustaining health and lifespan in the long run. Individuals who want to effectively

manage chronic stress levels will need to include some degree of constructive routine

engagement with their family, friends and local community as part of a comprehensive

program 29,70,80.

Spiritual Practices

Research shows that people who are spiritually active experience less perceived

stress than those who are not. A study of 111 undergraduate college students, between the

ages of 18 to 40 years old, looked at whether praying before a stressful situation lowered

physiological and psychological markers of stress 81. Heart rate, blood pressure, an

Anxiety Thermometer, and the State-Trait Anxiety Inventory, Importance of Religion

scale, and Prayer Experience survey were used to measure prayer and stress scores.

Results showed that prayer lowered systolic and diastolic blood pressure values when

exposed to an acutely stressful situation, but self-talk also positively reduced levels of

stress 81. While prayer, an aspect of spiritual practice, reduced stress, which was not the

only factor that reduced stress, in comparison no self-talk prayer did reduce stress.

Similarly, a cross-section study of 316 older adults 65 years and older living in

assisted living facilities assessed how perceived stress, spiritual coping and support,

active, and avoidance coping impacted depression 23. The study found that perceived

stress and spiritual coping are significantly related to psychological well-being in older

adults including stress and depression 23. These studies show that increased spiritual

practices positively impact a person’s stress levels. People who regularly pray, attend

church, and/or meditate may have better levels of stress and overall a healthier lifestyle.

CHAPTER 3

METHODOLOGY

Research Study Design

The research design was a cross-sectional study, the participants were evaluated

with a wellness survey, physical stress measures, (systolic blood pressure, diastolic blood

pressure, heart rate), anthropometric values and 24-hour dietary recall. Participants

signed consent forms that identified study parameters and personal acceptance of risk

during the data collection process.

The three requirements for participation in the study were: participants had to be

65 years of age or older. Second, individuals had to be able to engage in daily activities

without assistance and be without significant cognitive impairment. Last, the study

participant had to live in either a monastic community or in an independent living senior

community exclusively, opposed to a residential home or apartment unit.

Individuals could not participate in the study if they required assistance with

activities of daily living or had significant cognitive difficulties.

Research Study Recruitment

The total study population was collected through a convenient sample of thirty-six

individuals from four monastic communities and thirty-two individuals from an

independent retirement community.

Data of one participant from a monastic community had to be removed from the

sample, due to their inability to complete the survey information. The validity of our

sample was improved by having similar sex, age, ethnicity, physical health, and

socioeconomic status findings between the two communities.

The recruitment protocol began by contacting local suburban monastic and

independent living facility Directors. They were initially contacted through an

introductory standardized email, “I'm a graduate student in the Nutrition Department at

Benedictine University. My study mentor, Dr. Bonnie Beezhold, and a few other

graduate students are conducting a study to investigate diet, lifestyle, and health

measurements associated with perceived wellness….” Several days after the email was

sent out, a follow-up phone call was placed to gauge interest and further clarify

participation questions. The communities that were interested scheduled an onsite

interview with a student representative accompanied by the study mentor for a detailed

overview of the research process. The communities that decided to proceed forward with

the study were given a formal flyer advertising the study to be placed at key

thoroughfares inside their facilities. The flyer was accompanied by a sign-up form several

weeks before the data collection date. The forms and scripts are listed in Appendix C.

Study participants were asked to complete a wellness survey at station one that took

approximately 25 minutes and then move through three additional stations ranging in

time from five to twenty minutes each. Station two collected blood pressure and pulse.

Station three measured anthropometrics and station four recorded previous day’s dietary

intake.

Data Collection Methods

We used objective health measures that could validly assess our study sample and

were deemed reliable to measure our desired wellness dimensions of study. We started

by researching variables that could be used to quantify participants’ health and lifestyle

factors. Our efforts concluded with a set of anthropometric, physical stress measures, diet

and daily behaviors that when combined create a comprehensive summation of physical,

emotional, spiritual and mental health status. We next examined previous research studies

for tools and survey instruments that were appropriate for our study design and age

group. A complete discussion of the all the equipment used during our data collection is

listed in the measurement tools section.

Data Collection Process

The data collection process ran over a three-month period, March through May,

with data collection days occurring on several Fridays and Saturdays.

The on-site data collection followed a sequential process. The survey was

provided in a quiet area, including a consent form notifying the participants of any

potential risks during the assessment process as well as written acknowledgement of the

terms of participation. Second, blood pressure and pulse was gathered in a seated

position. The third station administered height and waist measurements, as well as the

body fat, lean muscle and weight totals. The last station, collected 24-hour dietary recall

performed in a one-on-one interview format. All data was collected on site at each

facility.

The study took place at five locations. The following is a listing of the

participating sites in the study with a brief description of their populations:

● St. Procopius Abbey (5601 College Rd, Lisle, IL 60532)

● Marmion Abbey (850 Butterfield Rd, Aurora, IL 60502)

● Sacred Heart Monastery (1910 Maple Ave, Lisle, IL 60532)

● School Sisters of St. Francis of Christ the King (13900 Main Street, Lemont, IL

60439)

● Monarch Landing (2255 Monarch Dr, Naperville, IL 60563)

The first three vowed religious communities listed practiced Benedictine

monasticism. Their teachings originated in medieval Italy by its principal founder St.

Benedict and can be practiced by both women and men 82. Their lives are arranged by a

charism, or guide book that can be summarized into five large themes of the order:

Hospitality -welcoming all who enter their community, indiscriminate of their

religion or background 83. Prayer- daily mindful focus on God individually and

collectively 83,84. Obedience-Taking an active position of openness and availability to

God’s voice and direction in life 83,84. Stewardship and Stability- respect for wise and

moderate use of natural resources for the good of all. Some even call Benedictines the

forerunners of the green movement and ecological consciousness. Stability refers to

remaining and working diligently in one abbey and community for one’s lifetime. Thus,

fostering the development of deep lasting relationships and concern for fellow brothers,

community organizations and members 82,84. Love of Learning – centers around teaching

the integration of thought and action as complementary aspects of life. The actions

include preserving the intellectual and material works created from previous generations

and creating scholarly, artistic and scientific works which enrich and enlarge human life.

The majority of these monastic communities are in congregations for purposes of mutual

assistance and common discipline. However, Benedictine communities are diverse, with

some individuals pursuing an enclosed life with little involvement in the local church and

society. While, others insist on various degrees of involvement, ranging from

educational instruction at all levels, parochial ministry, evangelization, publication,

health care, etc. 82-84.

School Sisters of St. Francis of Christ the King was the only Franciscan

community who participated in our study. The family of Franciscan orders was founded

in the 13th century by its principal founder St. Francis of Assisi. Franciscans take vows

of poverty, chastity and obedience and all share in the mission of living the Gospel and

serving the poor 85. Similar to the Benedictine orders, men and women can become

followers. Some of the roles they fill in the community along with being constant

witnesses for Christ are educators, administrators, catechists in parishes, religious

teachers in parish, public schools, while simultaneously keeping a focus on promoting

and strengthening Christian values 85,86.

In the independent living community population Monarch Landing offers a robust

independent living experience that promotes a vibrant lifestyle for active seniors. The

independent retirement community is located on a scenic campus, which is thoughtfully

constructed to be in harmony with nature. The various units are designed with welcoming

living areas, dining rooms, country kitchens, artful lighting and specialty accents

throughout its several floor plans. Residents are encouraged to make decisions about their

schedules, dining preferences, social activities, care choices, faith services, cooking,

fitness classes and more. Monarch Landing offers a newly constructed assisted living

memory support and soon to open rehabilitation and skilled nursing services; thus

providing complete continuing care for seniors throughout the later stages of life 87.

The Institutional Review Board at Benedictine University approved this study.

The IRB approval level was exempt based on the anonymous survey data and low risk

nature of the physical health measures collected.

Validity and Reliability of Methods

During our study we utilized wellness surveys that are validated for older adults

in their original form and calculated outcomes based on their specific scoring instruction

88-91. The four instruments that were used to measure the mental wellness of our

population as part of our survey were the 15-question Geriatric Depression Scale (GDS),

the Multidimensional Scale of Perceived Social Support (MSPSS), the 12-item

Spirituality Index of Well-Being and the 10-item Perceived Stress Scale (PSS). The first

instrument was the 15-question GDS, which has been used in many research studies

illustrating high validity and reliability scores (Cronbach’s alpha of 0.80) 88,92,93. One

such study involving sixty-four outpatients aged 60 or older who met criteria for

depressive disorder comparing ICD-10 Checklist of Symptoms, Montgomery-Asberg

Depression Rating Scale (MADRS), and DSM-IV diagnostic criteria to the GDS-15

scoring values. The results were that the GDS-15 produced sensitivity and specificity

rates of 92.7% and 65.2% respectively, and positive and negative predictive values of

82.6% and 83.3% respectively 88. These findings illustrate that the GDS -15 is a good

screening instrument for major depression as defined by both the ICD-10 and DSM-IV.

The second survey used in our assessment was the MSPSS, which is found to

have excellent internal consistency and test retest reliability with a Cronbach’s alpha of

0.81-0.98 in nonclinical samples and 0.92-0.94 in clinical samples 94. The MSPSS

produced item and scale scores with adequate reproducibility; over a 2-3 month period of

time, its reliability is r=.72-.85 94. In regards to validity, MSPSS positively correlates

with a self-concept measure and negatively with measure of depression and anxiety,

which confirms the validity of survey 95.

The third scale added to our survey was the 12-item Spirituality Index of Well-

Being (SIWB). The SIWB is a scale that has been validated to determine subjective well-

being of an individual. Internal reliability analysis performed on the SIWB scale

indicated good reliability with a Cronbach’s alpha of .91. The 6-item subscales also

showed strong reliability values: α = .86 for self-efficacy and α =.89 for life scheme.

The last scale utilized in our survey was the 10-item PSS-10. It has been shown to

have relatively high reliability and validity within all age groups with a Cronbach’s alpha

of .82 90,96,97. In addition to the survey data, we also collected diet information and

physical health measures. These measures have demonstrated in past research to both

independently and collectively measure risk of disease and mortality in older adults and

are also used as standards of care in the fields of traditional medicine, nutrition, public

health, exercise and complementary health. They are as follows: waist circumference,

systolic blood pressure, diastolic blood pressure, pulse, Body Mass Index, body fat and

lean mass 27,98-103. In addition, a 24-hour dietary recall was used to collect dietary data.

The 24-hour recall approach is used in research, but has proven be reliable but invalid 104.

Individuals may not report their food consumption accurately, most commonly

underreporting, due to knowledge deficits, memory lapse, demeanor of the examiner or

the environment interview situation 105.

Threats to Internal and External Validity

Concerns with validity included wellness survey instruments, physical health variables

used to quantify wellness status and the equipment used to capture the physical health

data. To minimize these concerns, the research group members researched the literature

for the appropriate scales that were shown to be valid for the wellness parameters of

interest as well as the age group of our sample. Our team members were careful once the

scales were determined not to alter the instruments in any way and to calculate the survey

totals per the particular scale instruction. The four scales are listed as follows: Geriatric

Depression Scale (GDS), the Multidimensional Scale of Perceived Social Support

Scoring (MSPSS), the Perceived Stress Scale (PSS) and the Spirituality Index of Well-

Being (SIWB). The validity and reliability for each scale was discussed in the previous

paragraph by stating each scales average Cronbach’s value. However, several scales and

measurement techniques used in this study had individual limitations.

The PSS-10 scale is most accurate for capturing acute stress of a specific life

event or stressor that occurs within a 4-8 week period of measurement 90. A limitation in

the SIWB in the validation of this scale is the absence of work that tests the conceptual

framework. A myriad of pathways, sequences, and relationships are suggested in the

framework, which was developed from qualitative data, but the scale lacks robust

empirical testing 89,106.

The 24-hour recall is most prone to study participants “underreporting” their food

consumption 105. To overcome these issues the interview process is conducted by trained

team members who used food models and standardized serving sizes as well as

prompting and probing of the participants to reduce incidence of misreporting or

inaccurate stating of food intake. 24-hour diet recalls are most appropriate for cross-

sectional research investigations when the study purpose requires quantitative estimates

of intake 107.

The group also utilized physical health measures to classify wellness status. The

physical measures chosen in our study were all taken with validated equipment and by

study team members trained in its specific and proper use. To ensure consistency and

reliability each team member performed data collection at only the station they were

trained on and did not interchange between stations at any time during the data intake

process. The dietary intake was captured from the previous day by utilizing the 24-hour

dietary intake process. The 24-hour intake was taken by a member of the research team in

a one-on-one interview format utilizing standardized food models, serving size sizes cups

and questions designed to spur memory and promote accurate caloric intake reporting.

Reliability Concerns

Concerns with potential reliability existed within the data collection process.

Possible concerns were within three areas: inconsistency with the data collection

methods, inconsistency with the data processing, and pre-assessment participant factors.

To minimize reliability concerns the research group members were placed in a training

process which consisted of performing several trials on team members and being given

detailed instruction on their device or tool operation prior to the onsite collection of data.

The training instruction included the proper set-up, use, calibration and preparation of

that device or tool utilized to gather intake data. Team members were careful to perform

pre-checks on the equipment onsite to ensure proper working order before any data from

participants were collected. The participant factors in the study included individuals who

exercised the day of collection, ate or drank directly before the assessment, wore clothing

that was thicker or thinner than an average t-shirt, or had orthopedic injuries that could

compromise their posture and stability during measurement. To minimize these

occurrences data collection was taken in the morning at the majority of the collection

sites used in our sample. Study participants that were dressed is a way that posed a barrier

to proper assessment were asked to change or modify their clothing so that the proper

measurements could be recorded. Any individuals that had compromised posture or

balance were lightly assisted and stabilized into the best positions to gather the most

accurate data readings.

Measurement Tools

Geriatric Depression Scale (GDS-15)

A survey instrument included in our wellness survey was the 15-item Geriatric

Depression Scale (GDS-15). The 15 question Geriatric Depression Scale (GDS-15) is a

tool used to diagnose depression in the older adult population and is frequently used in

the research setting 108,109. The survey has not only proven to successfully diagnosis

depression in the general older adult population, but also in the very old 110,111. Our study

assesses health parameters of adults over 65 years old, therefore the GDS-15 is

appropriate for use in our research population. The questionnaire takes about 10 minutes

or less to complete. Answering ‘yes’ to the first 10 questions indicates depression; and or

answering ‘no’ to the remaining 5 questions also signifies depression. The answers then

that indicate depression are given a positive score of 1. The scores of all 15 questions are

added together, with a sum of 5 or greater being indicative of depression 112.

Multidimensional Scale of Perceived Social Support Scoring (MSPSS)

A second instrument utilized in our study was the 12-question Multidimensional

Scale of Perceived Social Support Scoring (MSPSS). This tool is a subjective assessment

scale that can be used as a predictor of well-being, helps examine the influence of

stressful life events, general depression, health status and treatment effects 91. MSPSS

measures perceived social support and adequacy of emotional support presently available

in an individual’s life. Perceived social support appears to be the most important

measurement in an individual’s perception of received support 94. The MSPSS addresses

the availability of social support from 3 major relationships: significant others (#1, 2, 5

and 10), friends (#6, 7, 9 and 12) and family members (#3, 4, 8 and 11). Each potential

source of support is used to assess a subject’s satisfaction with support on a 7 point Likert

scale of 0 (very strongly disagree) to 7 (very strongly agree). The scoring range for the

12 questions, is between: 7 to 84; with the highest possible social support score being 84.

The categories breakdown into the following rankings: 69-84 High Acuity, 49-68

Moderate Acuity, and 12-48 Low Acuity.

Perceived Stress Scale (PSS-10)

A third tool incorporated in the survey was the 10-item Perceived Stress Scale

(PSS-10). It is a tool used to globally measure how seemingly stressful situations impact

a participant’s life 90. This tool has widely been utilized by clinicians and researchers to

quantify perceived stress in a variety of populations including the older adult 7,14,78,113.

When the PSS-10, is utilized to collect information the participants are asked how often

they feel or have felt in a specific manner over the past month; their options are: never,

almost never, sometimes, fairly often, and very often. The PSS-10 is scored by assigning

point values to how often specific feelings are experienced: never (0), almost never (1),

sometimes (2), fairly often (3), very often (4). Points are then reversed for the four

positively stated items, questions 4, 5, 7 and 8 and summed together 90. The highest total

score possible is 24 and scores of 20 or greater indicate a period of high distress 90.

Spirituality Index of Well-Being (SIWB)

The final tool included in the survey was the Spirituality Index of Well-Being

(SIWB). This is a subjective 12 item scale intended to determine an individual’s

perception of their spiritual quality of life 89. The SIWB is validated and consistently

used to determine general well-being 89. The first six items on the SIWB scale address the

concept of self-efficacy. Items seven through twelve address the concept of life scheme

106. The SIWB employs a five point Likert scale used to determine how the participant

feels about each statement given in the SIWB scale. A “one” signifies they “strongly

agree;” two signifies they “agree;” three signifies they “neither agree nor disagree;” four

signifies they “disagree;” and five signifies the “strongly disagree.” The scale utilizes a

scoring, system that indicates higher SIWB scores translates into greater degrees of

spirituality and/or well-being 89. To score, the mean of the items on each of the two

subscales is calculated, as well as the mean score of the combined scales. Higher scores

indicate increased spirituality and/or self-efficacy, with the highest total score being 60,

and the lowest total score being 0. The Highest score possible for each of the two

concepts, self-efficacy and life scheme, is 30 and the lowest score is 0 for both sections

Instrumentation and Procedures of Physical Health Measures

Physical assessments and stress measures were collected using several tools. The

first measurement collected of these parameters was blood pressure. Systolic, diastolic

and pulse were collected on the same arm three consecutive times in approximately 1

minute recurring intervals. We did not utilize the first value and formed a score by

averaging the next two blood pressure readings. The BP Tru BPM -200 automatic unit

inflates the cuff up to above the systolic pressure 35 mm hg then slowly deflates at a

constant rate until a reading can be established. The unit will automatically calculate the

systolic, diastolic, and pulse values on a screen that must be recopied to a data sheet. The

collection process was initiated with the examiner instructing the participant to sit quietly

for a few minutes prior to measurement. The examiner then asked the participant to place

their arm on the table palm up and maintain constant breathing without speaking. Then

the examiner placed the cuff around the participants arm just above the elbow and

activated the machine.

Standing Height

Height was measured using a portable stadiometer, Seca 213 portable unit. This

model can record height values ranging from 20 to 205 cm with increments as small as a

millimeter. To collect the height information, participants were asked to remove their

shoes, stand with their back towards the measurement post and maintain a light three

point contact position with the post (buttocks, shoulder blades, back of head). They were

instructed to look straight ahead and maintain best possible posture (chin up, head neural,

shoulders pulled back, arms relaxed with hands at the side of the thighs). The headboard

is then lowered to the top of the cranium just to the point of skull contact or significant

hair depression. Height was then recorded in centimeters and the headboard was raised

and the person was asked to step away.

Waist Circumference

Waist circumference was recorded with a pliable, but stretch resistant body tape

measure. The measurement process consisted of the examiner asking permission to locate

the umbilicus and the participant standing with feet together while maintaining optimal

posture (chin up, head neural, shoulders pulled back, arms relaxed with hands at the side

of the thighs). The participant pointed to their umbilicus and the examiner, measured

around a t-shirt, tight enough to prevent any lag in the tape. The examiner recorded the

value to the nearest 0.1 centimeter.

Weight and Body Composition

Weight and body composition were collected by employing the use of the Inbody

230 BIA Scale. This scale allows for the collection of lean body mass, fat mass, dry lean

mass, intracellular, and extracellular water, total body water, body mass index, percent

body fat, and basal metabolic rate. The Inbody can measure these aspects through the use

of eight polar tactile electrodes that sends a 50 khz electrical currents through the various

tissues of the body. That frequency is one of the highest reactance currently available on

the professional market for identifying various components of body composition 114.

Research participants were asked to remove shoes, socks, electrical devices, and jewelry.

The participants were then asked to stand on the scale with bare feet and recite intake

information (height, age, sex) for the scale to tabulate findings. The scale then created a

printed profile. The examiner printed two copies one for data configuration and the other

for personal use for the client. The examiner then used hygienic wipes to clean and

sanitize the hand and feet contact points on the scale after each use.

24-Hour Diet Recall

Diet data was collected from study participants by utilizing the 24-hour diet recall

method. This method consists of listing all foods consumed by the individual during the

previous 24-hour period. The dietary interview was performed face-to-face by trained

research staff to collect general menu information and then probe for critical details. The

questions are standardized and include cooking methods, brands, time of consumption,

food types, recipes and portion sizes. Other data collected from the 24-hour recall

included length of meals and general eating framework (eat alone, eat while watching

TV, etc.). The examiners also asked study participants the time and location of meal

consumption to stimulate memory and help facilitate greater recall accuracy. The

research team members also employed the use of visual aids such as food models and

measuring cups to assist with improved intake precision and correct portion

identification. The data was reviewed again with the participant a final time before being

placed into Elizabeth Stewart Hands and Associates (ESHA) software program. The

energy, macro and micronutrient intakes were determined using a nutrient analysis

software called Food Processor, by ESHA Research, Inc. The Food Processor server

derives food nutrient composition from the United States Department of Agriculture’s

(USDA) national nutrient database, which is used as its primary standard reference.

ESHA Diet software

The Food Processor nutrition software has been used by dietitians, nutrition

professionals, academic institutions and other healthcare professionals for 30 years. The

data reported in the ESHA software comes from over 1800 sources, including the USDA

database, international databases, and nutrient data from food manufacturers, restaurants,

national food councils and associations.

Statistical Procedures

Our data was coded into the Statistical Package for the Social Sciences (SPSS)

numerically. SPSS by IBM is a group of an integrated products that addresses the entire

research analytical process, beginning with organizing the collected data, followed by

analysis of variables and last reporting of results. Any new variables needed based on

combined metrics or varying interpretation were created in the program and added into a

continuously evolving data set.

The following tests of statistical analysis were performed in our research study to

qualify outcomes and generate findings. A Pearson’s Correlation Coefficient was used

measure the nature of the association between two variables and the strength of their

relationship. The associations were reported if they have a p-value of less than .05.

Multiple linear regression was used to determine if a linear relationship between a

dependent variable and one or more independent variables existed, as well as the strength

of those variables to the outcome measure. Results were reported if values were

significant defined by p-values less than .05.

A Mann-Whitney U Test examines the differences between the two living groups

and genders. Results were reported if the p-values were less than 0.05. A Kruskal-

Wallis Test was used to verify that three or more samples were statistically the same

between groups, or if the groups were significantly different between one another.

Results were reported if the p-values were less than 0.05. In our study a Pearson’s

Correlation Coefficient examined the relationship between stress and or depression scores

and lifestyle factors, physical health measures and dietary intake. Mann-Whitney U Tests

were used to compare the values of stress, depression, lifestyle factors, physical health

measures, and dietary intake between groups. Kruskal-Wallis Tests were used to stratify

significant variables with other study data to observe if there was a statistical difference

between low, moderate or high values. Lastly multiple linear regression was utilized on

specific occasion to best understand which lifestyle factors, physical health measures and

dietary intake choices made the greatest contribution to predicting outcome measures.

CHAPTER 4

FINDINGS

Our sample consisted of 67 independent older adults aged 65 years and older of

whom 52.2% (n = 35) were living in vowed religious communities (VRC) and 47.8% (n

= 32) were living in an independent retirement community (IRC). List-wise deletion was

selected as the method for treating missing data. No outliers were deleted for the analysis

of these results. One participant was excluded before analysis because of functional

disability, so 66 participants remained.

Approximately 60% of our sample was female (41/67). The majority of the

sample was Caucasian White (approximately 75%) and very educated, with about half of

the participants holding a graduate degree. Table 1 shows the population characteristics

by the two living groups. Activity hours were significantly different by group (U =

194.50, p = .001), with the vowed religious community reporting 27 more hours a week

of activity than the independent retirement community (r = .46). See Table 1 for

demographic and lifestyle characteristics.

Table 1 - Demographic and Lifestyle Characteristics by Group

Variables

Vowed religious

community

Independent

retirement

community

statistic

Mean ± SE Mean ± SE

Age (years) 67 78.91 ± 1.50 79.28 ± 1.34 542.501 .826

Gender

(males/females)

67 15/20 11/21 0.512 .477

Non-White/White 67 10/25 6/26 0.892 .346

Bachelor’s degree or

higher

67 4/31 15/17 10.332 .001*

Total exercise

(minutes/week)

32 46.79 ± 17.85 29.50 ± 3.18 89.501 .165

Hours of sleep/night 67 7.10 ± 0.16 6.98 ± 0.20 535.001 .749

Work-related activity

(hours/day)

59 29.79 ± 5.11 4.00 ± 0.78 194.501 .001*

Alcohol serving/week 65 1.11 ± 0.28 2.39 ± 0.39 332.501 .008*

Number of

servings/day of

vegetables

64 1.84 ± 0.13 1.72 ± 0.13 459.501 .432

Number of

servings/day of fruits

64 1.78 ± 0.17 2.06 ± 0.14 409.001 .141

Number of

servings/day of sweets

63 1.29 ± 0.15 0.84 ± 0.14 345.001 .026*

*p<0.05 is significant 1Mann-Whitney U test; 2Chi Square test

Mean body mass index for the whole sample was 27.96 ± 4.88. Groups did not vary with

regard to reported stress or blood pressure. See Table 2 for health and wellness

characteristics, all of which were our outcome measures.

Table 2- Health and Wellness Characteristics by Group

Variables

religious

community

Independent

retirement

community

Whitney U

Perceived Stress

61 11.81 ± 0.97 9.76 ± 0.97 362.00 .140

Geriatric Depression

66 2.12 ± 0.37 1.16 ± 0.27 369.00 .020*

Social Support Scale 67 63.77 ± 2.94 68.06 ± 2.69 461.50 .216

Spirituality Index of

Well-Being Scale

67 53.53 ± 1.07 52.06 ± 1.16 471.00 .262

Body mass index

65 28.88 ± 0.93 26.94 ± 0.73 400.00 .095

Waist circumference

66 39.74 ± 1.14 40.13 ± 3.70 465.50 .314

Body fat percentage 64 38.55 ± 1.68 33.26 ± 1.74 345.50 .025*

Muscle mass 62 55.86 ± 2.54 54.43 ± 2.51 463.50 .811

Systolic blood

pressure (mmHg)

67 128.37 ± 4.15 131.89 ± 3.34 447.00 .156

Diastolic blood

pressure (mmHg)

67 71.49 ± 2.62 70.63 ± 1.82 495.50 .418

Heart rate

(beats/minute)

67 75.86 ± 2.08 68.41 ± 1.99 386.50 .029*

*p<0.05 is significant

We also measured the nutrient composition of a 24 hour dietary recall survey, and

found no significant differences by group except for Vitamin C and carbohydrate intake.

The vowed religious community had a mean vitamin C intake of 65.02 mg/day ± 10.52

mg/day vs independent retirement community mean intake of 106.09 mg/day ± 20.23 (U

= 382.0, p = .038). The vowed religious community had a mean carbohydrate intake of

221.38 grams/day ± 26.26 vs independent retirement community mean intake of 280.29

grams/day ± 23.15, (U = 360.0, p = .012).

Relationship between Stress and Health and Lifestyle Factors

H10: There is no difference in stress reported by gender in older adults.

Of the total sample, 61 participants completed the Perceived Stress Scale (PSS)

with the mean score of the sample of 10.84 + 0.69. A Mann-Whitney U test was used to

examine the differences in PSS score by gender. No significant difference of PSS score

was found between males and females (U = 346.00, p > .05). The mean PSS score for

males was 9.50 ± 0.97 and the mean for females was 11.83 ± 0.95. The results failed to

reject the null hypothesis.

H20: There is no difference in stress reported by the vowed religious community and the

independent retirement community in older adults.

A Mann-Whitney U test was used to examine the difference in PSS score between

the vowed religious community and independent retirement community. No significant

difference was found (U = 362.00, p > .05). The vowed religious community had a mean

PSS score of 11.81 and the independent retirement community had a mean PSS score of

9.76. The results failed to reject the null hypothesis. Table 3 shows the comparison of

means between living groups.

Table 3-Comparison of Means between Living Groups

Variable

Vowed religious

community

Independent retirement

community

Statistic1

PSS 11.81 ± 0.97 9.76 ± 0.96 362.00 .140 1Mann-Whitney U test; actual PSS scores shown vs mean ranks.

H30: Perceived stress is not related to health and lifestyle factors in older adults.

A Pearson correlation coefficient was calculated for the relationship between PSS

score and all other scales, anthropometric measures and dietary intake. Table 4 shows

significant associations between PSS scores and health and lifestyle factors. A negative

correlation was found between PSS score and the spirituality index of wellbeing, (r(59) =

-.444, p = .000) indicating a significant linear relationship. Those with higher reported

spirituality also reported lower perceived stress. A positive correlation was found

between PSS score and GDS score, (r(59) = .374, p = .003) indicating a significant linear

relationship. Those with higher reported depression scores also reported higher perceived

stress. A positive correlation was found between PSS and intake of sweets/day, (r(55) =

.328, p = .013) indicating a significant linear relationship. Those with higher reported

sweet intake/day also reported higher perceived stress. A negative correlation was found

between PSS score and muscle mass, (r(54) = -.327, p = .014) indicating a significant

linear relationship. Those with higher muscle mass also reported lower perceived stress.

A negative correlation was found between PSS score and intake of alcohol/week, (r(49) =

-.331, p = .009) indicating a significant linear relationship. Those with higher reported

alcohol/week also reported lower perceived stress. A negative correlation was found

between PSS score and Vitamin D intake, (r(58) = -.305, p = .018) indicating a

significant linear relationship. Those with higher reported Vitamin D intake also reported

lower perceived stress. See Table 4. The null hypothesis was rejected.

Table 4-Associations between PSS and Health and Lifestyle Factors

Variable N Correlation (r) P value

Spirituality Index of Wellbeing 61 -.444 .000

Geriatric Depression Scale 61 .374 .003

Number of servings of sweets/day 57 .328 .013

Muscle mass 56 -.327 .014

Number of servings of alcohol/week 61 -.331 .009

Fiber (g/day) 61 -.271 .035

B6 (mg/day) 60 -.286 .027

B12 (mcg/day) 60 -.269 .038

Vitamin D (IU) 60 -.305 .018

Mg (mg/day) 60 -.256 .048

K (mcg/day) 60 -.287 .026

H41: Certain health and lifestyle factors contribute to or predict perceived stress in older

adults.

Multiple linear regression was used to assess the ability of six health and lifestyle

factors (Spirituality Index of Wellbeing, GDS-15, sweets/day, muscle mass, alcohol/week

and Vitamin D) to predict levels of stress. Preliminary analyses were conducted to ensure

no violation of the assumptions of normality, linearity, multicollinearity and

homoscedasticity. The total variance explained by the model as a whole was 50.5% with

adjusted R squared = .444. In the final model, only the Spirituality Index of Wellbeing

(p=.002), vitamin D (p = .014) and sweets/day (p=.046) were statistically significant. The

Spirituality Index of Wellbeing made the strongest contribution to explaining stress when

other variables were controlled for, uniquely explaining 11% (standardized β = -.347) of

the total variance in stress in the model. See Table 5. The alternative hypothesis was

accepted.

Table 5-PSS Multiple Linear Regression Analysis

Variables Standardized β P value R2 Adjusted R2

Spirituality Scores -.347 .002

Vitamin D (IU) -.271 .010

Sweets per day .216 .046

Muscle Mass -.199 .080

GDS-15 .189 .080

Alcohol

beverages/week

-.186 .083

Model total .505 .444

Relationship between Alcohol and Health and Lifestyle Factors

H50: There is no difference in weekly alcohol intake between genders in older adults.

Of our total sample, 59% reported drinking more than half a drink of alcohol per

week. Descriptive statistics to assess normality were conducted with the alcohol intake

per week variable and we found that the distribution of the variable was not normal. A

Mann-Whitney U test was conducted to examine the difference in reported alcohol intake

per week by gender. No significant difference in alcohol intake per week was found (U =

379.50, p > .05). We stratified the results by gender, and found a significant difference

between the independent retirement community and vowed religious community (U =

77.50, p = .001, r = .54) in females. Males averaged 2.02 drinks per week and females

averaged 1.51 drinks per week. The null hypothesis was rejected. See Table 6 for the

comparison between living groups and gender.

Table 6 - Comparison of Means between Living Groups

Variables

N Weekly alcohol

intake

statistic1

Vowed religious community 34 1.11 0.28

332.50

community

31 2.39 0.39

Males 26 2.02 0.37

379.50

.078 Females 39 1.51 0.33

Females—VRC 19 0.45 0.30

.001 Females—IRC 20 2.53 0.29

Males—VRC 15 1.94 0.35

.853 Males—IRC 11 2.14 0.30 1Mann-Whitney U test; actual alcoholic drinks shown vs mean ranks.

H60: There is no difference in weekly alcohol intake between the vowed religious

community and independent retirement community in older adults.

A Mann-Whitney U test was used to examine the difference in reported alcohol

intake per week between these groups. A significance difference was found in alcohol

intake between the two groups (U = 332.50, p = .008, r = .33). Alcohol intake per week

in the independent retirement community averaged 2.39 drinks and the vowed religious

community averaged 1.11 drinks per week. The null hypothesis was rejected.

H70: Perceived stress scores will not be different in levels of alcohol intake in older

adults.

In order to investigate whether health and lifestyle factors we measured were

different by alcohol intake, we created the following three-level categorical variable: less

than or equal to half an alcohol drink per week, greater than one half and less than or

equal to two alcohol drinks per week, and greater than two alcohol drinks per week. A

Kruskal-Wallis test was conducted comparing perceived stress in the three alcohol intake

levels. A significant result was found in PSS scores (H(2) = 12.08, p = .002), indicating

that the alcohol levels differed from each other. The difference in PSS scores was found

between those who reported less than or equal to one half alcohol drinks per week and

greater than 2 alcohol drinks per week (r = .28). Participants reporting more than two

alcohol drinks per week had lower PSS scores than participants in other weekly alcohol

intake levels. The null hypothesis was rejected. See Table 7.

Table 7 - Comparison of Means

Variables

≤0.50

drinks/week

>0.50 and ≤2.00

drinks/week

statistic1

Mean ± SE Mean ± SE Mean ± SE

PSS2 61 13.36 ± 1.00 10.58 ± 1.24 7.41 ± 1.02 12.00 .002 1Kruskal-Wallis test; 2Differences were between lowest weekly alcohol intake and

highest weekly alcohol intake.

H80: Weekly alcohol intake is not related to stress or other lifestyle and health factors in

older adults.

A Pearson correlation coefficient was calculated for the relationship between

participants’ weekly alcohol intake and PSS scores. A negative correlation was found

(r(59) = -.331, p = .009), indicating a linear relationship between the variables.

Participants who consumed more alcohol weekly reported less stress. When stratified by

gender, however, a positive correlation was found in females only (r(33) = -.422, p =

.001) , indicating a significant linear relationship between the two variables. Females

who drank more alcohol had lower PSS scores. The null hypothesis was rejected. See

Table 8.

Table 8 - Associations between Alcohol Intake and Stress

Variables N Correlations (r) P value

Total sample

PSS Scores 61 -.331 .009

Females Only

PSS Scores 35 -.422 .001

Relationship between Sweets Intake and Health and Lifestyle Factors

H90: There is no difference in sweets intake per day by gender in older adults.

Almost half of the total sample (44.8%) reported eating one sweet per day.

Descriptive statistics were run to assess the normality of the sweets per day variable, and

we found that the variable was not normally distributed. A Mann-Whitney U test was run

to examine the difference in reported sweets per day by gender. No significant

differences in sweets intake was found (U = 456.50, p > .05). Males averaged 1.08 sweets

per day and females averaged 1.05 sweets. The null hypothesis was accepted.

H100: Sweets intake per day does not differ in the vowed religious community compared

to the independent retirement community in older adults.

A Mann-Whitney U test was conducted to examine the difference in reported

sweets per day between these groups. A significant difference was found (U = 345.50, p

= .026), with a medium effect size (r = .28). Sweets per day in the independent retirement

community averaged 0.84, and the vowed religious community averaged 1.29 sweets.

The null hypothesis was rejected.

H111: Perceived stress scores differ by frequency of sweets intake level in older adults.

To compare whether sweets intake and perceived stress varied at different levels,

we created a three-level categorical variable: zero sweets per day, one sweet per day, and

two or more sweets per day. A Kruskal-Wallis test was conducted comparing the variable

perceived stress with the three levels of sweets intake. A significant result was found in

perceived stress levels (H(2) = 7.798, p = .020), indicating that the three levels of sweets

intake differed from each other. The differences in perceived stress were found between

those who reported zero sweets per day and two or more sweets per day with large effect

size (r = .49), and between those who reported one sweet per day and two or more sweets

per day with a medium effect size (r = .33). Participants who reported eating two or more

sweets per day had higher perceived stress levels. See Table 9. The alternative hypothesis

was accepted.

Variable

0 sweets

1 sweets

2 sweets

Test statistic1

P value

Mean ± SE Mean ± SE Mean ± SE

PSS2 57 9.00 ± 1.04 10.32 ± 1.00 14.20 ± 1.41 7.79 .020 1Kruskal-Wallis test; 2Differences were between lowest sweets intake level and greatest

sweets intake level, and between middle sweets intake level and greatest sweets intake

level.

H121: Sweets intake is related to perceived stress in older adults and other health and

lifestyle factors.

In order to compare what health and lifestyle factors measured were correlated

with sweets intake, a Pearson correlation coefficient test was conducted. A positive

correlation was found between sweets intake and perceived stress (r(55) = .328, p =

.013), sweets intake and iron (r(60) = .448, p = .000) and sweets intake and thiamine

(r(60) = .371, p = .003), indicating a significant linear relationship between the variables.

A negative correlation was found between sweets intake and soluble fiber (r(60) = -.261,

p = .039), indicating a significant linear relationship between the two variables. The data

was split by gender and correlations comparing sweets intake and perceived stress were

run. A positive correlation was found between sweets intake and perceived stress in

males only (r(22) = .496, p = .014). The alternative hypothesis was accepted. See Table

Table 10 - Significant Correlations of Variables with Sweets Intake

Variables N Correlation (r) P value

Total sample

Perceived Stress Score 57 .328 .013

Iron (mg/day) 62 .448 .000

Thiamine (mg/day) 62 .371 .003

Riboflavin (mg/day) 62 .269 .035

Soluble Fiber (g/day) 63 -.261 .039

Zinc (mg/day) 62 .260 .041

Perceived Stress Score 24 .496 .014

H131: Certain health and lifestyle factors contribute to or predict sweets intake in older

adults.

A multiple linear regression was used to assess the ability of three health and

lifestyle factors (perceived stress score, iron intake, and thiamine intake) to predict levels

of sweets intake. A significant regression equation was found (F(3,53) = 10.499, p =

.000), with an R2 of .373. Participants’ predicted sweets intake is equal to -.265 + .023

(iron intake) + .313 (thiamine intake) + .057 (perceived stress), where iron and thiamine

is measured in mg and perceived stress is measured using the PSS. Perceived stress

explained 13% of the total variance in sweets intake in our population. Perceived stress

makes the strongest unique contribution to sweets intake. See Table 11. The alternative

hypothesis was accepted.

Table 11 – Multiple Linear Regression Analysis of Sweets Intake

Perceived Stress Score .373 .001

Iron (mg/day) .315 .012

Thiamine (mg/day) .296 .020

Model Total .373 .337

Relationship between Physical Health Measures and Health and Lifestyle Factors

H140: There are no differences in physical health measures between the vowed religious

community and the independent retirement community in older adults.

A Mann-Whitney U test was conducted to examine the difference in physical

health measures between the vowed religious community and independent retirement

community. A significant difference was found between the two groups in regard to body

fat percent (U = 345.5, p = .025, r = .28) and heart rate (U = 386.5, p = .029, r = .27).

Body fat percent was higher for the vowed religious community with a mean of 38.5

percent compared to the independent retirement community of 33.3 percent. Heart rate

for the vowed religious community averaged 76 beats per minute compared to the

independent retirement community which averaged 68.5 beats per minute. See Table 12.

The null hypothesis was rejected.

Table 12 – Comparison of Means between Groups

Variables

Vowed religious

community

Independent

retirement

community

statistic1

Heart Rate

(beats/minute)

67 75.86 ± 12.31 68.41 ± 11.24 386.5 .029

Body Fat

(percentage)

62 38.55 ± 9.51 33.26 ± 9.86 345.5 .025

1Mann-Whitney U test; actual heart rate beats per minute and body fat percent shown vs

mean ranks.

H151: Physical health measures are associated with stress in older adults.

A Pearson correlation test was conducted to examine the relationships between

stress and physical health measures. A negative correlation was found between muscle

mass and stress (r(60) = .-327, p = .014), indicating a significant negative linear

relationship between muscle mass and stress. These findings indicate that as the

participant’s stress increased, their muscle mass decreased. See Table 13. The alternative

hypothesis was accepted.

Table 13 – Correlations with Physical Parameters and Perceived Stress

Muscle mass (kg) 62 -.327 .014

H161: Muscle mass is associated with health and lifestyle factors in older adults.

muscle mass and lifestyle factors. A positive association was found between muscle

mass and activity hours (r(60) = .291, p = .034), chromium intake (r(60) = .304, p =

.017) . These findings show that as participant activity hours per week and chromium

intake per day increased muscle mass increased. Also, a negative correlation was found

between muscle mass and percent body fat (r(60) = -.280, p = .028) and muscle mass and

age (r(60) = -.295, p = .020) indicating an inverse linear relationship. These findings

illustrate that as participant’s body fat and age increased, muscle mass decreased. See

Table 14. The alternate hypothesis was accepted.

Table 14-Associations with Muscle Mass

Activity hours (per week) 53 .291 .034

Chromium (mcg/day) 61 .304 .017

Age (years) 62 -.295 .020

Body Fat (percent) 62 -.280 .028

H171: Body fat and heart rate are associated with lifestyle factors in older adults.

body fat, heart rate and lifestyle factors. Again, as mentioned earlier, a negative

correlation was found between percent body fat and muscle mass (r(60) = -.280, p =

.028), calories from saturated fat (r(62) = -.279, p = .026), calcium (r(61) = -.282, p =

.025), and the b –vitamin group (r(61) = -.275, p = .029). These findings indicate that as

percent body fat decreased, calories from saturated fat, b- vitamin index (Thiamine,

Riboflavin, Niacin, Pantothenic Acid, Pyridoxine, Biotin, Folate, Choline and

Cobalamin), and calcium intake increased. Additionally, as participant’s muscle mass

increased, body fat percentage decreased.

Heart rate had a negative correlation with vitamin C (r(64) = -.291 p = .018),

indicating a linear relationship between the two variables. As vitamin C intake increased

heart rate activity decreased. See Table 15. The alternate hypothesis was accepted.

Table 15- Body Fat and Heart Rate Associations

Body fat percent 62

Muscle mass (kg) 62 -.280 .028

Saturated fat calories 64 -.279 .026

Calcium (mg/day) 63 -.282 .025

B Vitamins Index (B-1,B-2, B-3,B-5,B-

6,B-7,B-9,B-12)

63 -.275 .029

Heart Rate (beats/minute) 67

Vitamin C (mg/day) 66 -.291 .018

H180: Perceived stress scores do not differ in older individuals with lower and higher

muscle mass.

A Mann-Whitney U test was conducted to examine the difference in stress scores

between the lower half of the sample’s muscle mass scores (<54.3 kilograms) and the

upper half of the sample’s muscle mass scores (>54.4 kilograms). No significant

difference was found between the two (U = 307.5, p > .05). Stress scores in the low

muscle mass group averaged 9.96 compared to the high muscle mass group which

averaged 11.65. See Table 16 The null hypothesis was accepted.

Table 16- Comparison of Stress Means between Muscle Mass Groupings

Variable

Muscle mass (lower

Muscle mass (upper

statistic1

61 11.65 ± 1.00 9.96 ± 1.30 307.5 .186

1Mann-Whitney U test; actual muscle mass in kilograms shown vs mean ranks

Relationship between Geriatric Depression Scale and Health and Lifestyle Factors

H191: Older adults living in the vowed religious group will report less depression than

those living in the independent retirement group.

In our sample population, 7.6% of our participants reported to be depressed.

Descriptive statistics to assess normality were conducted with the Geriatric Depression

Scale-15 (GDS-15). Distribution of the variable was not normal. A Mann-Whitney U

test was used to examine the difference in reported depression between the vowed

religious community and independent retirement community. We found a significant

difference between the two groups (U = 369.0, p = .020). The vowed religious

community reported a higher depression score, with a mean score of 2.12 in comparison

to 1.16 for the independent retirement community. The alternative hypothesis was

accepted. See Table 17.

H201: There is no difference in depressive symptoms by gender in older adults.

A Mann-Whitney U test was conducted to examine the difference in reported

depression between males and females. No significant difference was found (U = 443.0,

p > .050). The null hypothesis was rejected. See Table 17.

Variable

N GDS-15

SE M-W Test

Statistic1

Vowed religious community 34 2.12 0.36

369.00

.020 Independent retirement

community

32 1.16 0.27

Males 26 1.65 0.38

.297 Females 40 1.61 0.30

1Mann-Whitney U test actual depression scores shown vs mean ranks.

H211: Depressive symptoms reported by participants will be associated with health and

lifestyle factors in older adults.

To better understand what lifestyle, anthropometric and/or health factors were

related to participants’ depression levels, a Pearson correlation coefficient was calculated.

The three significant variables related to depression was perceived stress (r(59) = .374, p

= .003), social support (r(64) = -.288, p = .019) and living environment (r(64) = -.254, p

= .040). The hypothesis was accepted. See Table 18.

Table 18 - Significant Correlations of Variables with Geriatric Depression Scale

Variables N Correlation with GDS-15 (r) P value

Total Sample Population

Perceived stress 61 .374 .003

Social support 66 -.288 .019

Living environment1 66 -.254 .040 1 Religious vs. nonreligious group

H221: Health and lifestyle factors will be predictors of depression in older adults.

A multivariate analysis was run to better understand of the three significant

correlations (p < .025) found with depression, perceived stress, social support and living

environment, which variables was the most significant contributor of depression. It was

found 21% of the variance in depression scores between the two living groups was

explained. Perceived stress makes the strongest unique contribution, and is the only

statistically significant contribution to depression scores when gender and social support

are controlled for. Perceived stress uniquely explained 8% of the total variance in

depression scores in our population. The alternative hypothesis was accepted. See Table

Table 19 - Multivariate analyses

Perceived Stress .295 .014

Social support -.196 .033

Living environment -.171 .044

Model total .210 .169

H231: Health and lifestyle factors will be predictors of depression in older adults living

in the vowed religious group only.

The population was stratified by living group. A Mann-Whitney U test was

conducted to identify correlations in the vowed religious group only. Perceived stress

contributed to GDS (r(30) = .378, p = .033) and trans fat intake (g) was positively

correlated with GDS (r(33) = .365, p = .034). See table 20. The null hypothesis was

accepted.

Table 20 - Significant Correlations of Variables with Geriatric Depression Scale

Variables N Correlation with GDS-15 (r) P value

Vowed Religious Group 35

Perceived stress 32 .378 .033

Trans fat intake (g) 35 .365 .034

Relationship between Amount of Sleep and Health and Lifestyle Factors

H240: There is no difference in reported hours of sleep per night by gender in older

adults.

More than a quarter of the total sample (28.4%) reported less than seven hours of

sleep. Descriptive statistics to assess normality were conducted with the sleep hours per

night variable and we found that the distribution of the variable hours of sleep was not

normal. A Mann-Whitney U test was used to examine the difference in reported sleep

hours by gender. No significant difference of sleep hours was found (U = 458.50, p >

.05). Males averaged 7.17 hours of sleep and females averaged 6.98 hours of sleep. The

null hypothesis was accepted. See Table 21.

Table 21 - Comparison of Means between Genders

Variables Males Females Test Statistic1 P value

Hours of sleep/night 7.17 ± 0.23 6.98 ± 0.16 458.50 .327 1Mann-Whitney U test; actual hours of sleep shown vs mean rank.

H250: There is no difference in reported hours of sleep per night between the vowed

religious community and independent retirement community in older adults.

A Mann-Whitney U test was used to examine the difference in reported sleep

hours per night between these groups. No significant difference was found (U = 535.00, p

> .05). Sleep hours per night in the independent retirement community averaged 6.98

hours and the vowed religious community averaged 7.12 hours. The null hypothesis was

accepted. See Table 22.

Table 22 - Comparison of Means between Living Groups

Variable

Vowed religious

community

Statistic1

Hours of

sleep/night

7.12 ± 0.16 6.98 ± 0.20 535.00 .749

1Mann-Whitney U test; actual hours of sleep shown vs mean ranks.

H260: There is no difference in health and lifestyle factors related to sleep in three

categories in older adults.

In order to investigate whether health and lifestyle factors we measured was

different by sleep levels; we created the following three-level categorical variable: less

than seven hours of sleep, the recommended seven to eight hours of sleep, and more than

eight hours of sleep. Kruskal-Wallis tests were conducted comparing variables of interest

in the three sleep levels. A significant result was found in iron intakes (H(2) = 10.65, p <

.01, indicating that the sleep levels differed from each other. The difference in iron

intakes was found between those who reported less than seven hours of sleep and greater

than eight hours of sleep (r = .61), and between seven to eight hours of reported sleep and

greater than eight hours of reported sleep (r = .46). Participants reporting more than eight

hours of sleep per night consumed more iron than participants in other sleep levels. See

Table 23. A Pearson correlation coefficient was calculated for the relationship between

participants’ sleep hours and iron intake. A positive correlation was found (r(64) = .360,

p = .003), indicating a significant linear relationship between the two variables.

Participants who consumed more iron slept longer per night. When stratified by gender,

however, a positive correlation was found in males only (r(24) = .417, p = .034),

indicating a significant linear relationship between the two variables. Males who

consumed more iron slept longer per night. The null hypothesis was rejected. See Table

Table 23 – Comparison of Means

Variables N <7 hrs 7-8 hrs >8 hrs Test

statistic1

Mean ±

Sweets/day2 63 0.88 ±

1.02 ± 0.12 2.25 ± 0.25 8.40 .015

(mg/day)2

66 11.8 ±

12.92 ±

44.08 ±

10.60 .005

1Kruskal-Wallis test; 2Differences were between lowest sleep level and greatest sleep

level, and between middle sleep level and greatest sleep level.

A Kruskal-Wallis test also found a significant result in daily sweet intakes (H(2) =

8.46, p < .05), indicating that the sleep levels differed from each other. The difference of

daily sweet intakes was found between those who reported less than seven hours of sleep

and greater than eight hours of sleep (r = .60), and between seven to eight hours of

reported sleep and greater than eight hours of reported sleep (r = .40). Participants

reporting more than eight hours of sleep per night consumed more daily sweets than those

in other sleep levels. See Table 23. A Pearson correlation coefficient was calculated for

the relationship between participants’ sleep hours and daily sweet intakes, and a positive

correlation was found in females only (r(37) = .374, p = .019), indicating a significant

linear relationship between the two variables. Females who consumed more daily sweets

slept longer per night. See Table 24.

H270: Reported sleep hours per night is not related to health and lifestyle factors in older

adults.

A Pearson correlation coefficient was calculated for the relationship between

participants’ sleep hours and exercise variables and found negative correlations between

sleep hours and both mild and moderate exercise in males (r(11) = -.589, p = .034) and

(r(19) = -.464, p = .034, respectively), indicating a significant linear relationship between

the hours of sleep and the two variables. Males who participated in less exercise slept

longer per night. The null hypothesis was rejected. See Table 24.

Table 24 - Significant Correlations of Variables with Sleep Hours

Total sample

Iron (mg/day) 66 .360 .003

Males only

Moderate exercise

(hours/week)

21 -.464 .034

Mild exercise (hours/week) 13 -.589 .034

Iron (mg/day) 26 .417 .034

Females only

Sweets/day 39 .374 .019

CHAPTER 5

DISCUSSION

Overall Findings

Our study was the first to investigate health and lifestyle factors that impact stress

in two different cohesive older adult communities. We found that spirituality was the

largest predictor of perceived stress in these older adults. Each of the major topics in our

results will be discussed.

Stress

The role of stress in older adults was the major focus of this study. We found that

the vowed religious community did not report more stress than the comparison

community, the independent retirement community. As mentioned earlier, mechanisms

of a chronic stress response can negatively impact health 52. Our results showed that

there was no difference in perceived stress between the two communities. However,

based on the results of a regression analysis, spirituality was the biggest predictor of

perceived stress. While there was no difference in spirituality or perceived stress between

communities, in the total sample, as reported spirituality increased, perceived stress

decreased. Therefore, our results confirmed previous literature in this field, which

indicated that a religious lifestyle can positively impact wellness. A 32-year follow up

study of 144 nuns and 138 laypersons in Italy found those living in a religious

community had more stable blood pressures, a common measure of stress, throughout the

study compared to the control group 18.

Another study, in the Netherlands focused on the relationship between a monastic

lifestyle and mortality. In the 1,523 Benedictine and Trappist monks, the religious

lifestyle was associated with longer life expectancy 115. The vowed religious community

in our study actually reported higher stress, albeit not significant and there was no

difference in blood pressure between groups. However, we collected data during the

Lenten season, which is characterized by increased religious services and responsibilities

from the church and may have impacted the stress level of the vowed religious

community.

According to the American Psychological Association, women report more stress

than men, possibly due to disparities in sex hormones, gender-roles and/or different types

of stressors experienced 116. A cross-sectional study of 501 females and 679 male, white-

collar employees that were matched for educational and managerial level looked at the

difference in perceived stress by gender. Females in the study reported higher levels of

stress and work overload due to household responsibilities in addition to job related

duties 116. Additionally, a cross-sectional study of 2816 men and women in Spain focused

on perceived stress and coping mechanism disparities between genders. Results showed

that women have higher perceived stress than men, yet they managed stressful

experiences by using emotion-focused coping mechanisms 117. These studies established

that gender-roles and the type of stressor(s) might contribute to females historically

reporting higher stress than males. In our study, females did report a higher mean

perceived stress, however it was not significant and overall did not confirm past literature

in the field.

In addition to differences in stress between groups, the current study explored

associations between perceived stress and differences in health and lifestyle factors.

Perceived stress was positively correlated with depression and an increased consumption

of sweets per day. Perceived stress was negatively correlated with spirituality, muscle

mass and an increased consumption of alcohol per week. Stress and spirituality was

discussed earlier as it relates to current literature.

Stress was also negatively correlated with the consumption of certain nutrients:

fiber, B6, B12, vitamin D, magnesium and potassium. In our sample, as perceived stress

increased, intake of healthful foods containing these key nutrients decreased. An

observational study on government in employees sought to demonstrate a relationship

between work stress and blood pressure. Increased work stress led to increased use of

coping mechanisms such as alcohol consumption, unhealthy eating patterns and physical

inactivity 66. In a study of 457 women, Groesz et al.,2012 aimed to determine the

association between stress, the drive to eat and food choices made. Increased perceived

stress was associated with a stronger drive to eat, especially comfort foods that were

higher in fat and sugar 118. These studies demonstrate that an increase in perceived stress

is associated with an increase of unhealthy food choices, which may contribute to the

decrease of healthy nutrients that was observed in our population. The findings of the

current study confirmed past literature regarding stress and food choices.

Recent studies have found that the mechanism regarding stress and food choices

may lay in the hormone leptin, which regulates appetite 119,120. Leptin is released from

adipose cells and is involved in the reward center of the brain 119,120. In Brazil, a study of

57 women aimed to determine an association between basal leptin levels, stress and

dietary choices. They found that increased basal leptin levels were related to cravings of

sweets in stressed women 120. Another study of 40 women had their blood leptin levels

and food intake measured in response to acute stressors. Researchers concluded that

leptin might act as a modulator of stress eating comfort foods 119. The results of these

studies exhibit a possible biochemical mechanism that may relate increased stress to poor

dietary choices.

Vitamin D intake was also a major predictor of stress in our sample based on a

multiple regression model. Vitamin D deficiency is a common problem in older adults

and recent literature is showing possible links between vitamin D status and mental

health in older adults. A cross-sectional analysis of the Iowa Women’s Health study by

Motsinger et al.,2012 aimed to determine the association between vitamin D, mood

disorders, such as depression and anxiety, and health related quality of life in older

women. They found that those who consumed less than 400 IU of vitamin D daily had

lower mental health related quality of life 121. A study of 10,086 adults in Norway aimed

to determine the association between blood levels of vitamin D and depression and

mental distress. They found that those with low vitamin D levels had an increase in

mental distress and depressive symptoms 122. A recent review of vitamin D and acute

stress found that while vitamin D can modulate the effects of proinflammatory cytokines,

there is, so far, an inconsistent relationship between vitamin D and inflammation during

times of acute stress 123. Vitamin D receptors are widely distributed in the human brain,

including the hippocampus and myocardium of the heart, which may explain the

mechanism that links vitamin D intake to stress 124.

Depression

We found the vowed religious communities reported higher depressive symptoms

than the independent retirement community, with reported stress the largest contributor to

depression. Since research shows that depression differs by gender, we compared

depressive symptoms by gender, but there was no difference in reported depression

scores when we compared males and females in the whole sample.

While our study is the first to look at monastics’ level of depression, depression is

a ubiquitous part of all life stages. Cortisol is associated with higher levels of depression

and anxiety due to prolonged activation of hypothalamic-pituitary-adrenocortical axis,

which interferes with physiological systems known to cause inflammation 69,125. For

instance, in a study led by Fagundes et al.,2013 they evaluated the relationships between

depressive symptoms and stress-induced inflammation in 138 participants. Findings

included that the more depressive symptoms produced more interleukin (IL)-6 in

response to the stressor, as well as higher levels of IL-6 both at 45 minutes and after the

two hour mark of the stressor 34. Interleukins are a group of cytokines that mediate

communication between cells.

For our study, in men, with lower feelings of social support, reported depressive

symptoms rose. Friend support and family functioning seems to play the largest role in

the prediction of social support levels and reported depression 126. In a longitudinal

survey study led by Jensen et al., 2014, 1416 individuals completed surveys to help

explore how support varied with age and gender, highlighting that maintaining social

support is important in psychological health and in the reduction of depression 127

When the living groups were stratified, the vowed religious community was found

to have increased reported depressive symptoms with increased perceived stress and trans

fat intake. Trans fats are artificial fats that have a longer shelf life than unsaturated fats

and are used in processed foods 128. Those that consume a large amount of trans fats have

been found to have a 48% increase risk of depression due to the biological changes linked

to heart disease and depression and the association with IL-6 and high sensitive-C

reactive protein in women with higher body mass indices 129. Furthermore, those that

consume a large amount of trans fats have been found to have an increased risk of

depression due to the low-grade inflammatory status and endothelial dysfunction 129.

These results show a linear relationship between these variables and we cannot draw

causal conclusions.

Sweets Intake

We looked at sweets intake as it is relates to perceived stress and other health and

lifestyle factors, finding sweets intake is associated with perceived stress levels in our

sample. While researchers know that perceived stress is associated with increased added

sugar intake, the causal pathway between these two factors has yet to be established.

There are a few theories about what drives the association; some researchers believe

chronic stress stimulates the glucocorticoid-augmented central neural network 130. The

glucocorticoids may act on the brain in a forward-feed to increase a person’s desire to eat

calorically dense foods like sweets 130. Alternately, consumption of highly caloric foods

like sugar and fat may down regulate dopamine receptors. Since dopamine reduces stress

sensitivity and depression, the decreased dopamine receptors may lead to increased stress

In our study, we did not find any differences in the amount of sweets consumed by

gender. This is in direct contrast with a longitudinal, observational study of 416 older

men and women by Hsiao et al.,2013 found women were more likely to consume “more

healthy” diets than men 132. These healthy diets consisted of primarily plant-based foods,

and foods lower in saturated fat and added sugar. With a larger sample size, we may

have observed similar divisions in diet quality between genders.

Furthermore, in our study as older adults’ perceived stress levels increased so did

the consumption of sweets per day. This link between higher perceived stress scores and

added sugar intake has been demonstrated in other studies. A cross-sectional study, led

by Mikolajczyk et al., 1839 college students in Europe found that college women who

had higher perceived stress scores ate more carbohydrate dense foods including sweets,

cookies, snacks, and fast food when compared to women who had lower perceived stress

scores 133. Similarly a cross-sectional study of 853 men and women, by Yannakoulia et

al.,2008 reported that greater sweets and meat consumption was associated with anxiety

levels in women 134. While these studies found associations between sweets consumption

and stress and anxiety only in women, we did not find this pattern in our results. In fact,

when we stratified our results by gender, sweets intake and perceived stress was only

significantly associated in males. Our study has a much older aged population compared

to populations sampled in Mikolajczyk and Yannakoulia’s studies. The variance in age

may contribute the difference in our findings.

A cross-sectional study by Laugero et al., 2011 looked at many health measures

associated with perceived stress in adults aged 45-70 years old, and they found that those

who were characterized as being the most stressed had greatest intake of sweets 69. This is

similar to our findings, which found that older adults who ate 2 or more sweets per a day

had the greatest perceived stress scores. It is important to note that our study and the

Laugero et al.,2011 study looked at adults who were older, so these correlations can most

specifically be applied to older adults. While our study and previous studies demonstrate

that higher perceived stress levels are associated with greater consumption of foods with

added sugars, a causal relationship cannot be established. Future longitudinal and

experimental studies should explore the causal relationship between these two variables.

Our study also looked at what health and lifestyle factors, besides perceived

stress, were correlated with sweets intake. We found that sweets intake was associated

with intakes of iron, thiamine, and riboflavin. We believe this association is seen because

in the United States white flour is enriched with iron, thiamine, riboflavin, niacin, and

folate, and most sweets are made with white flour in the United States. Therefore, people

who are consuming more sweets are consuming more enriched flour products. We also

found an association between soluble fiber and sweets intake. We hypothesized that when

people eat more sweets they are displacing the consumption of fruits and vegetables,

foods known to contain large amounts of soluble fiber. In fact, Mikolajczyk et al.,2009

found that people who had higher perceived stress levels consumed more sweets and less

fruits and vegetables, in comparison to people with lower stress levels 133. This study

emphasizes our theory that sweets intake displacing foods with soluble fiber in a diet.

Further research should explore the idea that sweets intake displaces foods with soluble

fiber in people with high levels of perceived stress.

Alcohol Intake

Another domain of stress that was examined was weekly alcohol intake. We

found a strong negative correlation of weekly alcohol intake with the Perceived Stress

Scale (PSS)-10 scale. Older adults consuming moderate amounts of alcohol reported less

stress than those consuming no alcohol to half a drink per week. Moderate alcohol intake

is defined as the consumption of up to one drink per day for women and up to two drinks

per day for men. Twelve fluid ounces of regular beer, 5 fluid ounces of wine, or 1.5 fluid

ounces of 80 proof distilled spirits count as one drink 135. Older adults ages 55 and older

drink about four drinks per week, which would be considered low alcohol intake, as

defined by the US Department of Health and Human Services and the US Department of

Agriculture 135,136.

This is in concurrence with the thought that moderate alcohol intake reduces

stress, since alcohol has a calming effect, with the ability to reduce tension 90,137.

Moreover, alcohol may transmit protective changes in cerebral vasculature, which is the

circulation of blood, as alcohol increases hippocampal acetylcholine release. Those that

drink moderate amounts have a lower prevalence of white matter lesions and subclinical

infarcts, which may be attributed to increased acetylcholine release, higher high density

lipoprotein cholesterol levels and fibrinogens that helps stop bleeding, although they may

only have a modest protective effect 138,139.

In our study, those living in the vowed religious community drank less alcoholic

beverages than those living in the independent living community. Specifically, those in

the vowed religious community drank about one drink per week, while those in the

independent retirement community drank about two and a half drinks per week. This is

in accordance with past studies that measured alcohol consumption within vowed

religious communities, finding monks and nuns consume less alcohol than the general

population 140,141. Vowed religious communities, such as those who participated in our

study, may drink less alcohol as they follow the Rule of St. Benedict “ora et labora,”

which translates from Latin as “pray and work.” The Rule of St. Benedict symbolizes

sobriety, and embodies their way of life 142,143.

Additionally, we found an association with weekly alcohol intake and stress in

our population, when stratified by gender. There was a negative correlation between

female weekly alcohol intake and perceived stress scale. This was in accordance with

recent studies that reported females who drank the least displayed greater perceived stress

15,144. Variances in gender may be the result of differences in both stress and alcohol use,

as research has shown that women undergo more family related stress than men. For

example, a recent epidemiological survey that explored over 4,000 current male and

female drinkers age sixty and over and their perceived stress levels in comparison to

alcohol consumption. It was found that women who drank the least displayed greater

perceived stress, although the mechanism behind this was not identified 144.

Upon further investigation, our study found associations between weekly alcohol

intake and stress among different levels of alcohol intake levels. Those who consumed

two or more drinks per week reported less stress than those who consumed less than one

half alcohol drink, which is in agreement with the theory that alcohol may contribute to a

reduction in tension 90,137 This is in agreement with a recent studies that found those who

drank less, reported higher levels of perceived stress 15,144. In a prospective study that

analyzed over 65,000 men and women between the ages of 50 and 76, participants were

evaluated on self-reported levels of stress and dietary behaviors. It was reported that

those who drank one alcohol serving per week reported higher levels of perceived stress

than those who drank four alcohol servings per week, although the mechanism behind

which alcohol consumption in older adults is related to cognitive health continues to be

unclear 15,145

We looked at the amount of hours of sleep as a related domain of mental and

physical health. In our study, while the hours spent sleeping did not vary between the

independent retirement community and the vowed religious community nor by gender,

the amount of sleep influenced specific variables related to gender. We found that

increasing physical activity was associated with a decrease in sleep duration in men. This

result is inexplicable as current literature supports that physical activity is associated with

better sleep quality, duration, and improved sleep outcomes 146,147. This inexplicable

result may be explained by the discrepancy between self-reported physical activity and

objective measures of physical activity. Aadahl et al., 2003 compared self-reported

physical activity versus an accelerometer measuring physical activity in a randomized

control trial of 2500 participants 148. The researchers found decreased reliability of self-

reported exercise because it depends on the respondents’ ability to accurately quantify

their exercise while the accelerometer measures the actual movement. Furthermore, in a

randomized controlled trial of 248 participants, Hagstromer et al., 2008 investigated the

reliability of self-reported physical activity versus using accelerometer and found self-

reported physical activity does not correspond with objective measures of physical

activity. The participants reported up to three times greater physical activity compared to

the amount actually recorded by the accelerometer 149.

With respect to sleep and dietary intakes, we found that iron intakes were

positively associated with sleep duration. Our results confirmed a previous study that

found as iron intake increased sleep duration increased as well as in a cross-sectional of

3304 participants 150. Additionally, Kuhn et al., 1988 found sleep deprivation can produce

as much as a 50% reduction in serum iron levels due to a disruption in circadian rhythm

151. We found that those who had more than 8 hours of sleep had the greatest iron intakes

followed by those who met the 7-8 hours of sleep recommendation.

We found older adults who reported more than eight hours of sleep also reported a

higher intake of sweets. This result confirms previous studies that found women who

reported a greater intake of sweets, reported longer sleep duration. Because longer sleep

duration disrupts conventional eating times, consequently the dominance of snacks and

sweets over meals may ensue 150,152.

Physical Health Measures

We investigated the relationship of perceived stress on physical health measures

and anthropometric values. Our results found that as perceived stress scores increased,

muscle mass decreased. Our results confirmed a previous cross-sectional study led by

Poornima et al., 2014, who examined the impact of perceived stress on 64 participants’

muscle strength and endurance 153. The results showed perceived stress values were

negatively associated with the ability to reach and hold a maximal peak muscular

contraction 153.

We found that muscle mass was positively associated with weekly activity hours,

which is line with past studies. For example, a cohort study of 4232 participants,

examined activity minutes per day excluding exercise on risk factor and chronic disease

incidence 154. The findings showed at baseline and follow-up evaluations, non-exercise

activity minutes per day was associated with better lower waist circumferences, blood

lipid values, and healthy glucose levels when compared to exercise frequency per week

and sedentary hours per day 154. Furthermore, a prospective cohort study of 357

participants looked at muscle strength’s role in the reduction of muscle mass and muscle

fatigue, hypothesizing grip strength, grip fatigue time, biceps thickness, and fat free mass

would be associated with impaired mobility-related activities of daily living (mrADLs)

155. The study found those that had a decline of mrADLs at 30 days had a significant

difference in weakness, grip fatigue or fat free mass 155.

In contrast, we found inverse correlations between muscle mass and body fat and

between muscle mass and age. Body fat percent is the ratio of skeletal muscle and organ

mass to subcutaneous adipose and visceral fat mass in the body 100. In addition, a

previous study confirmed that as an individual ages, muscle tissue mass and metabolism

decreases 154. This supports our findings that as participants age, muscle mass decreased.

Additionally, we looked at how physical health measures varied by living groups,

finding percentage of body fat and heart rate significantly differed between the two

communities. Body fat percent was positively associated with muscle mass, dietary

calcium and dietary B-vitamin intake. A cross-sectional study of 531 men age 29 to 71

years old were grouped in low, moderate and intense weekly exercise groups to examine

the impact on anthropometric measures. The results showed that individuals who

exercised with the most intensity had the lowest waist circumference, body fat

percentages, overall weight, and highest muscle mass when compared to the moderate

and low categories. 100.

Our results found that as vitamin C increases, heart rate decreases. In a cross-

sectional study that confirms our findings a sample of 541 participants were examined to

determine the relationship between dietary intake and serum blood concentrations of

vitamin C on blood pressure and heart rate values 156. The finding showed that plasma

ascorbate concentration was inversely correlated to systolic and diastolic blood pressures

and pulse rate. In a second study led by Bruno et al., 2012, the study looked at 32

untreated patients with essential hypertension and 20 normotensive subjects who received

3 grams of vitamin C, with heart rate, noninvasive beat-to-beat blood pressure, and

muscle sympathetic nerve activity monitored 157. The results found that the hypertensive

patients’ systolic blood pressure was lowered than normotensive subjects, confirming the

positive affect of vitamin C157.

Strengths and Limitations

Using an affluent independent retirement community as our control group

provided us a better ability to compare the two groups, as both were financially secure.

Additionally, having an equal number of participants in each living group allowed for an

equal comparison of groups. Socio-demographic characteristics were also similar in both

groups, including education, race and age. By conducting the 24-hour recall, the study

was able to obtain detailed dietary data through the process of elaboration and

clarification. Furthermore, we utilized a properly trained research team, validated tools

and instruments to obtain anthropometric measurements. Finally, this was the first study

of its kind to compare a vowed religious community to an independent retirement

community’s perceived stress’ impact on health and lifestyle factors.

Our study was not without its limitations. Since this was a cross-sectional study,

we cannot make causal inferences about the data collected. Not only was our sample size

small, but we had more female participants than male, therefore our results have less

information about the men in our communities. All data recorded was self-reported,

which may have been biased. For example, participants may have had selective memory

in terms of what they ate. Partial data was collected from participants during the Lenten

season, this may have influenced dietary intake.

Conclusion

Those living in the vowed religious community tended to eat more sweets, drink

less alcohol, report higher levels of depression, and have higher amounts of body fat and

higher heart rates than those living in the independent retirement community. Perceived

stress scores were reportedly higher in participants who were less spiritual, ate more

sweets, consumed less vitamin D, had less muscle mass, and drank less weekly alcohol.

As the first study to investigate health and lifestyle factors that impact stress in two

different cohesive older adult communities, further research is needed to examine causal

relationships between variables and validate our findings.

REFERENCES

1. Shah LBI, Torres S, Kannusamy P, Chng CML, He H, Klainin-Yobas P. Efficacy of

the virtual reality-based stress management program on stress-related variables in people

with mood disorders: The feasibility study. Arch Psychiatr Nurs. (0).

2. Kobrosly RW, van Wijngaarden E, Seplaki CL, Cory-Slechta DA, Moynihan J.

Depressive symptoms are associated with allostatic load among community-dwelling

older adults. Physiol Behav. 2014;123:223-230.

3. de Frias CM, Whyne E. Stress on health-related quality of life in older adults: The

protective nature of mindfulness. Aging & Mental Health. 2014:1-6.

4. Sandstrom A, Peterson J, Sandstrom E, et al. Cognitive deficits in relation to

personality type and hypothalamic-pituitary-adrenal (HPA) axis dysfunction in women

with stress-related exhaustion. Scand J Psychol. 2011;52(1):71-82.

5. Rueggeberg R, Wrosch C, Miller GE. The different roles of perceived stress in the

association between older adults' physical activity and physical health. Health Psychol.

2012;31(2):164-171.

6. Kyrou I, Tsigos C. Stress hormones: Physiological stress and regulation of metabolism.

Current Opinion in Pharmacology. 2009;9:787-793.

7. Aschbacher K, O'Donovan A, Wolkowitz OM, Dhabhar FS, Su Y, Epel E. Good

stress, bad stress and oxidative stress: Insights from anticipatory cortisol reactivity.

Psychoneuroendocrinology. 2013;38(9):1698-1708.

8. Gouin JP, Glaser R, Malarkey WB, Beversdorf D, Kiecolt-Glaser J. Chronic stress,

daily stressors, and circulating inflammatory markers. Health Psychol. 2012;31(2):264-

9. Juster R, Bizik G, Picard M, et al. A transdisciplinary perspective of chronic stress in

relation to psychopathology throughout life span development. Dev Psychopathol.

2011;23(Special Issue 03):725-776.

10. Trzesniewski K, Donnellan M, Robins R. Stability of self-esteem across the life span.

J of Personality and Social Psychol. 2003;84(1):205-220.

11. Pruessner JC, Hellhammer DH, Kirschbaum C. Burnout, perceived stress, and

cortisol responses to awakening. Psychosom Med. 1999;61(2):197-204.

12. Orth U, Robins R, Soto C. Tracking the trajectory of shame, guilt, and pride across

the life span. J of Personality and Social Psychol. 2010;99(6):1061-1071.

13. Jobin J, Wrosch C, Scheier M. Associations between dispositional optimism and

diurnal cortisol in a community sample: When stress is perceived as higher than normal.

Health Psychology. 2014;33(4):382-391.

14. Imayama I, Alfano C, Angela K, et al. Dietary weight loss and exercise interventions

effects on quality of life in overweight/obese postmenopausal women: A randomized

controlled trial. Int J of behavioral nutr and physical activity. 2011;8(118).

15. Barrington WE, Beresford SA, McGregor BA, White E. Perceived stress and eating

behaviors by sex, obesity status, and stress vulnerability: Findings from the vitamins and

lifestyle (VITAL) study. J Acad Nutr Diet. 2014.

16. Papadaki A, Vardavas C, Hatzis C, Kafatos A. Calcium, nutrient and food intake of

greek orthodox christian monks during a fasting and non-fasting week. Public Health

Nutr. 2008;11(10):1022-1029.

17. Naidenova LP. Life within monastery walls. Russian Studies in History.

2013;52(1):66-88.

18. Timio M, Saronio P, Verdura C, Schiaroli M, Timio F, Monarca C. A link between

psychosocial factors and blood pressure trend in women. Physiol Behav. 2001;73(3):359-

19. Gallegos-Carrillo K, García-Peña C, Mudgal J, Romero X, Durán-Arenas L,

Salmerón J. Role of depressive symptoms and comorbid chronic disease on health-related

quality of life among community-dwelling older adults. J Psychosom Res.

2009;66(2):127-135.

20. Roscoe LJ. Wellness: A review of theory and measurement for counselors. Journal of

Counseling & Development. 2009;87(2):216-226.

21. DUNN HL. High-level wellness for man and society. Am J Public Health Nations

Health. 1959;49(6):786-792.

22. Bowman SA. Socioeconomic characteristics, dietary and lifestyle patterns, and health

and weight status of older adults in NHANES, 1999-2002: A comparison of caucasians

and african americans. J Nutr Elder. 2009;28(1):30-46.

23. Lee KH, Besthorn FH, Bolin BL, Jun JS. Stress, spiritual, and support coping, and

psychological well-being among older adults in assisted living. Journal of Religion &

Spirituality in Social Work: Social Thought. 2012;31(4):328-347.

24. Seligman ME. Positive health. Appl Psychol. 2008;57(s1):3-18.

25. Prasad SM, Eggener SE, Lipsitz SR, Irwin MR, Ganz PA, Hu JC. Effect of

depression on diagnosis, treatment, and mortality of men with clinically localized

prostate cancer. J Clin Oncol. 2014;32(23):2471-2478.

26. Fredrickson BL, Tugade MM, Waugh CE, Larkin GR. What good are positive

emotions in crisis? A prospective study of resilience and emotions following the terrorist

attacks on the united states on september 11th, 2001. J Pers Soc Psychol. 2003;84(2):365.

27. Hwang A, Peng L, Wen Y, et al. Predicting all-cause and cause-specific mortality by

static and dynamic measurements of allostatic load: A 10-year population-based cohort

study in taiwan. J AM MED DIR ASSOC. 2014;15(7):490-496.

28. Brody GH, Yu T, Chen Y, et al. Cumulative socioeconomic status risk, allostatic

load, and adjustment: A prospective latent profile analysis with contextual and genetic

protective factors. Dev Psychol. 2013;49(5):913.

29. Williams GL, Keigher S, Williams AV. Spiritual well-being among older african

americans in a midwestern city. Journal of religion and health. 2012;51(2):355-370.

30. Everard KM, Lach HW, Fisher EB, Baum MC. Relationship of activity and social

support to the functional health of older adults. J Gerontol B Psychol Sci Soc Sci.

2000;55(4):S208-12.

31. Lau Y, Wong DFK, Wang Y, Kwong DHK, Wang Y. The roles of social support in

helping chinese women with antenatal depressive and anxiety symptoms cope with

perceived stress. Arch Psychiatr Nurs. 2014.

32. Seib C, Whiteside E, Lee K, et al. Stress, lifestyle, and quality of life in midlife and

older australian women: Results from the stress and the health of women study. Womens

Health Issues. 2014;24(1):e43-52.

33. Aggarwal NT, Wilson RS, Beck TL, et al. Perceived stress and change in cognitive

function among adults 65 years and older. Psychosom Med. 2014;76(1):80-85.

34. Fagundes CP, Glaser R, Hwang BS, Malarkey WB, Kiecolt-Glaser JK. Depressive

symptoms enhance stress-induced inflammatory responses. Brain Behav Immun.

2013;31:172-176.

35. Karakus MC, Patton LC. Depression and the onset of chronic illness in older adults:

A 12-year prospective study. J Behav Health Serv Res. 2011;38(3):373-382.

36. Zannas AS, McQuoid DR, Payne ME, et al. Negative life stress and longitudinal

hippocampal volume changes in older adults with and without depression. J Psychiatr

Res. 2013;47(6):829-834.

37. Potter GG, Hartman M, Ward T. Perceived stress and everyday memory complaints

among older adult women. Anxiety Stress Coping. 2009;22(4):475-481.

38. Flegal KM. Prevalence of obesity and trends in the distribution of body mass index

among US adults, 1999-2010. JAMA. 2012;307(5):491-497.

39. Fakhouri TH, National Center for Health Statistics (US). Prevalence of obesity

among older adults in the United States, 2007-2010. US Department of Health and

Human Services, Centers for Disease Control and Prevention, National Center for Health

Statistics; 2012.

40. National Center for Health Statistics (US). Older americans 2012: Key indicators of

well-being. . 2012.

41. Buman MP, Hekler EB, Haskell WL, et al. Objective light-intensity physical activity

associations with rated health in older adults. Am J Epidemiol. 2010;172(10):1155-1165.

42. Bankoski A, Harris TB, McClain JJ, et al. Sedentary activity associated with

metabolic syndrome independent of physical activity. Diabetes Care. 2011;34(2):497-

43. Gopinath B, Russell J, Flood VM, Burlutsky G, Mitchell P. Adherence to dietary

guidelines positively affects quality of life and functional status of older adults. J Acad

Nutr Diet. 2014;114(2):220-229.

44. Mosher CE, Sloane R, Morey MC, et al. Associations between lifestyle factors and

quality of life among older long-term breast, prostate, and colorectal cancer survivors.

Cancer. 2009;115(17):4001-4009.

45. Harland BF, Peterson M. Nutritional status of lacto-ovo vegetarian trappist monks. J

Am Diet Assoc. 1978;72(3):259-264.

46. Pleva HM. Monastic architecture: The household of god. Cistercian Studies

Quarterly. 2012;47(3):335-342.

47. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical

fitness: Definitions and distinctions for health-related research. Public Health Rep.

1985;100(2):126-131.

48. Timio M, Saronio P, Venanzi S, Gentili S, Verdura C, Timio F. Blood pressure in

nuns in a secluded order: A 30-year follow-up. Miner Electrolyte Metab. 1999;25(1-

2):73-79.

49. Cha NH, Seo EJ, Sok SR. Factors influencing the successful aging of older korean

adults. Contemp Nurse. 2012;41(1):78-87.

50. Choi NG, Dinitto DM. Heavy/binge drinking and depressive symptoms in older

adults: Gender differences. Int J Geriatr Psychiatry. 2011;26(8):860-868.

51. Rosengren A, Hawken S, Ounpuu S, et al. Association of psychosocial risk factors

with risk of acute myocardial infarction in 11119 cases and 13648 controls from 52

countries (the INTERHEART study): Case-control study. Lancet. 2004;364(9438):953-

52. Het S, Schoofs D, Rohleder N, Wolf O. Stress-induced cortisol level elevations are

associated with reduced negative affect after stress: Indications for a mood-buffering

cortisol effect. Psychosom Med. 2012;74:23-32.

53. Wust S, Federenko I, Hellhammer DH, Kirschbaum C. Genetic factors, perceived

chronic stress, and the free cortisol response to awakening. Psychoneuroendocrinology.

2000;25(7):707-720.

54. Schmidt-Reinwald A, Pruessner JC, Hellhammer DH, et al. The cortisol response to

awakening in relation to different challenge tests and a 12-hour cortisol rhythm. Life Sci.

1999;64(18):1653-1660.

55. Arnold SV, Smolderen KG, Buchanan DM, Li Y, Spertus JA. Perceived stress in

myocardial infarction: Long-term mortality and health status outcomes. J Am Coll

Cardiol. 2012;60(18):1756-1763.

56. Chandola T, Britton A, Brunner E, et al. Work stress and coronary heart disease:

What are the mechanisms? Eur Heart J. 2008;29(5):640-648.

57. Hamer M, Steptoe A. Cortisol responses to mental stress and incident hypertension in

healthy men and women. J Clin Endocrinol Metab. 2012;97(1):E29-34.

58. Chandola T, Brunner E, Marmot M. Chronic stress at work and the metabolic

syndrome: Prospective study. BMJ. 2006:1-5.

59. Raikkonen K, Matthews K, Kuller LH. Depressive symptoms and stressful life events

predeict metabolic syndrome among middle-aged women. Diabetes Care.

2007;30(4):872-877.

60. Harris ML, Loxton D, Sibbritt DW, Byles JE. The relative importance of

psychosocial factors in arthritis: Findings from 10,509 australian women. J Psychosom

Res. 2012;73(4):251-256.

61. Boonen E, Vervenne H, Meersseman P, et al. Reduced cortisol metabolism during

critical illness. N Engl J Med. 2013;368(16):1477-1488.

62. Min W, Liu C, Yang Y, et al. Alterations in hypothalamic-pituitary-adrenal/thyroid

(HPA/HPT) axes correlated with the clinical manifestations of depression. Prog

Neuropsychopharmacol Biol Psychiatry. 2012;39(1):206-211.

63. Anacker C, Cattaneo A, Musaelyan K, et al. Role for the kinase SGK1 in stress,

depression, and glucocorticoid effects on hippocampal neurogenesis. Proc Natl Acad Sci

U S A. 2013;110(21):8708-8713.

64. Brunner EJ, Chandola T, Marmot MG. Prospective effect of job strain on general and

central obesity in the whitehall II study. Am J Epidemiol. 2007;165(7):828-837.

65. Berset M, Semmer N, Elfering A, Jacobshagen N, Meier L. Does stress at work make

you gain weight? A two-year longitudinal study. Scand J Work Environ Health.

2011;37(1):45-53.

66. Lindquist T, Beilin L, Knuiman M. Influence of lifestyle, coping and job stress on

blood pressure in men and women. Hypertension. 1997;29:1-7.

67. Block J, He Y, Zaslavsky A, Ding L, Ayanian J. Psychosocial stress and change in

weight among US adults. Am J Epidemiol. 2009;170(2):181-192.

68. Delarue J, Matzinger O, Binnert C, Schneiter P, Chiolero R, Tappy L. Fish oil

prevents the adrenal activation elicited by mental stress in healthy men. Diabetes Metab.

2003;29(3):289-295.

69. Laugero KD, Falcon LM, Tucker KL. Relationship between perceived stress and

dietary and activity patterns in older adults participating in the boston puerto rican health

study. Appetite. 2011;56(1):194-204.

70. VanKim NA, Nelson TF. Vigorous physical activity, mental health, perceived stress,

and socializing among college students. American Journal of Health Promotion.

2013;28(1):7-15.

71. Carroll D, Ring C, Suter M, Willemsen G. The effects of an oral multivitamin

combination with calcium, magnesium, and zinc on psychological well-being in healthy

young male volunteers: A double-blind placebo-controlled trial. Psychopharmacology

(Berl). 2000;150(2):220-225.

72. Kennedy DO, Veasey R, Watson A, et al. Effects of high-dose B vitamin complex

with vitamin C and minerals on subjective mood and performance in healthy males.

Psychopharmacology (Berl). 2010;211(1):55-68.

73. Long SJ, Benton D. A double-blind trial of the effect of docosahexaenoic acid and

vitamin and mineral supplementation on aggression, impulsivity, and stress. Hum

Psychopharmacol. 2013;28(3):238-247.

74. Barbadoro P, Annino I, Ponzio E, et al. Fish oil supplementation reduces cortisol

basal levels and perceived stress: A randomized, placebo-controlled trial in abstinent

alcoholics. Mol Nutr Food Res. 2013;57(6):1110-1114.

75. Hellhammer J, Hero T, Franz N, Contreras C, Schubert M. Omega-3 fatty acids

administered in phosphatidylserine improved certain aspects of high chronic stress in

men. Nutr Res. 2012;32(4):241-250.

76. van de Rest O, Geleijnse JM, Kok FJ, et al. Effect of fish-oil supplementation on

mental well-being in older subjects: A randomized, double-blind, placebo-controlled trial.

Am J Clin Nutr. 2008;88(3):706-713.

77. El Ansari W, Adetunji H, Oskrochi R. Food and mental health: Relationship between

food and perceived stress and depressive symptoms among university students in the

united kingdom. Cent Eur J Public Health. 2014;22(2):90-97.

78. Rueggeberg R, Wrosch C, Miller G. The different roles of perceived stress in the

association between older adults’ physical activity and physical health Health

Psychology. 2012;31(2):164-171.

79. Luo Y, Hawkley LC, Waite LJ, Cacioppo JT. Loneliness, health, and mortality in old

age: A national longitudinal study. Soc Sci Med. 2012;74(6):907-914.

80. Berset M, Semmer NK, Elfering A, Jacobshagen N, Meier LL. Does stress at work

make you gain weight? A two-year longitudinal study. Scand J Work Environ Health.

2011;37(1):45-53.

81. Belding JN, Howard MG, McGuire AM, Schwartz AC, Wilson JH. Social buffering

by god: Prayer and measures of stress. Journal of religion and health. 2010;49(2):179-

82. Abbey SJ. The Benedictines: An introduction. Order of Saint Benedict Home Page

Web site. (www.osb.org/gen/benedictines.html).

83. Abbey M. Benedictine charism. Marmion Abbey Home Page Web site.

http://marmion.org/s/1356/abbey/abbey2.aspx?sid=1356&gid=1&pgid= 475). Updated

84. Monastery SP. Charism home page. Saint Procopius Monastery Home Page Web site.

http://procopius.webs.com/our-charism.

85. Sisters SF. Mission School Sisters of Saint Francis Home Page Web site.

http://www.ssfcr.org.

86. Lemont FS. Congregational history. Franciscan Sisters of Lemont Home Page Web

site. www.leomontfranciscans.org.

87. Landing M. Services and amenities. Monarch Landing Home Page Web site.

(http://www.welcometomonarchlanding.com).

88. Almeida OP, Almeida SA. Short versions of the geriatric depression scale: A study of

their validity for the diagnosis of a major depressive episode according to ICD-10 and

DSM-IV. Int J Geriatr Psychiatry. 1999;14(10):858-865.

89. Daaleman T, Frey B. The spirituality index of well-being: A new instrument for

health related quality of life research. . Annals of Family Medicine. 2004;2(5):499-503.

90. Cohen S, Williamson G. Perceived stress in a probability sample of the united states.

In: The social psychology of health. Newbury Park, CA: Sage; 1988:31-67.

91. Stanley MA, Beck JG, Zebb BJ. Psychometric properties of the MSPSS in older

adults. Aging & Mental Health. 1998;2(3):186-193.

92. Burke WJ, Roccaforte WH, Wengel SP. The short form of the geriatric depression

scale: A comparison with the 30-item form. J Geriatr Psychiatry Neurol. 1991;4(3):173-

93. Lesher EL, Berryhill JS. Validation of the geriatric depression scale--short form

among inpatients. J Clin Psychol. 1994;50(2):256-260.

94. Ekbäck M, Benzein E, Lindberg M, Årestedt K. The swedish version of the

multidimensional scale of perceived social support (MSPSS)-a psychometric evaluation

study in women with hirsutism and nursing students. Health and quality of life outcomes.

2013;11(1):1-9.

95. Wongpakaran T, Wongpakaran N, Ruktraku R. Reliability and validity of the

multidimensional scale of perceived social support (MSPSS): Thai version. Clin Pract

Epidemiol Ment Health. 2011;7(11):161-166.

96. Andreou E, Alexopoulos EC, Lionis C, et al. Perceived stress scale: Reliability and

validity study in greece. International journal of environmental research and public

health. 2011;8(8):3287-3298.

97. Ezzati A, Jiang J, Katz M, Sliwinski M, Zimmerman M, Lipton R. Validation of the

perceived stress scale in a community sample of older adults. Int J Geriatr Psych. 2013.

98. Bigaard J, Frederiksen K, Tjønneland A, et al. Body fat and fat‐free mass and all‐

cause mortality. Obes Res. 2004;12(7):1042-1049.

99. De Lorenzo A, Bianchi A, Maroni P, et al. Adiposity rather than BMI determines

metabolic risk. Int J Cardiol. 2013;166(1):111-117.

100. Goh VH, Tong TY. Association of age and physical exercise with bodyweight and

body composition in asian chinese men. The Aging Male. 2010;13(4):265-274.

101. Srikanthan P, Karlamangla AS. Muscle mass index as a predictor of longevity in

older adults. Am J Med. 2014;127(6):547-553.

102. Fox K, Borer JS, Camm AJ, et al. Resting heart rate in cardiovascular disease. J Am

Coll Cardiol. 2007;50(9):823-830.

103. Marz RB. Medical nutrition from marz:(a textbook in clinical nutrition). Omni-

Press; 1999.

104. Keyzer W, Huybrechts I, Vriendt V, et al. Repeated 24-hour recalls versus dietary

records for estimating nutrient intakes in a national food consumption survey. Food Nutr

Res. 2011;55.

105. van Staveren WA, de Groot LC, Blauw YH, van der Wielen RP. Assessing diets of

elderly people: Problems and approaches. The American Journal of Clinical Nutrition.

1994;59(1):221S-223S.

106. Frey B, Daaleman T, Peyton V. Measuring a dimension of spirituality for health

research: Validity of the spirituality index of well-being. . Research on Aging.

2005;27(5):556-577.

107. Johansson G, Wikman Å, Åhrén A, Hallmans G, Johansson I. Underreporting of

energy intake in repeated 24-hour recalls related to gender, age, weight status, day of

interview, educational level, reported food intake, smoking habits and area of living.

Public Health Nutr. 2001;4(04):919-927.

108. Pritchard E, Barker A, Day L, Clemson L, Brown T, Haines T. Factors impacting

the household and recreation participation of older adults living in the community.

Disabil Rehabil. 2014.

109. Klug G, Lacruz ME, Emeny RT, Hafner S, Ladwig KH, Huber D. Aging without

depression: A cross-sectional study. Psychodyn Psychiatry. 2014;42(1):5-22.

110. Conradsson M, Rosendahl E, Littbrand H, Gustafson Y, Olofsson B, Lovheim H.

Usefulness of the geriatric depression scale 15-item version among very old people with

and without cognitive impairment. Aging Ment Health. 2013;17(5):638-645.

111. de Craen AJ, Heeren TJ, Gussekloo J. Accuracy of the 15-item geriatric depression

scale (GDS-15) in a community sample of the oldest old. Int J Geriatr Psychiatry.

2003;18(1):63-66.

112. D'Ath P, Katona P, Mullan E, Evans S, Katona C. Screening, detection and

management of depression in elderly primary care attenders. I: The acceptability and

performance of the 15 item geriatric depression scale (GDS15) and the development of

short versions. Fam Pract. 1994;11(3):260-266.

113. Scott SB, Sliwinski MJ, Blanchard-Fields F. Age differences in emotional responses

to daily stress: The role of timing, severity, and global perceived stress. Psychol Aging.

2013;28(4):1076-1087.

114. Corporation I. Inbody 230 product page. Inbody Home Page Web site.

(http://www.e-inbody.com/global/product/InBody230.aspx).

115. de Gouw HW, Westendorp RG, Kunst AE, Mackenbach JP, Vandenbroucke JP.

Decreased mortality among contemplative monks in the netherlands. Am J Epidemiol.

1995;141(8):771-775.

116. Lundberg U, Mårdberg B, Frankenhaeuser M. The total workload of male and

female white collar workers as related to age, occupational level, and number of children.

Scand J Psychol. 1994;35(4):315-327.

117. Matud MP. Gender differences in stress and coping styles. Personality and

Individual Differences. 2004;37(7):1401-1415.

118. Groesz L, McCoy S, Carl J, et al. What is eating you? stress and the drive to eat.

Appetite. 2012;58(2):717-721.

119. Tomiyama AJ, Schamarek I, Lustig RH, et al. Leptin concentrations in response to

acute stress predict subsequent intake of comfort foods. Physiol Behav. 2012;107(1):34-

120. Macedo DM, Diez-Garcia RW. Sweet craving and ghrelin and leptin levels in

women during stress. Appetite. 2014;80:264-270.

121. Motsinger S, Lazovich D, MacLehose RF, Torkelson CJ, Robien K. Vitamin D

intake and mental health-related quality of life in older women: The iowa women's health

study. Maturitas. 2012;71(3):267-273.

122. Kjærgaard M, Joakimsen R, Jorde R. Low serum 25-hydroxyvitamin D levels are

associated with depression in an adult norwegian population. Psychiatry Res.

2011;190(2–3):221-225.

123. Quraishi SA, Camargo CAJ. Vitamin D in acute stress and critical illness. Curr Opin

Clin Nutr Metab Care. 2012;15(6):625-634.

124. Jiang P, Zhang W, Li H, Cai H, Liu Y, Chen L. Stress and vitamin D: Altered

vitamin D metabolism in both the hippocampus and myocardium of chronic

unpredictable mild stress exposed rats. Psychoneuroendocrinology. 2013;38(10):2091-

125. Liu S, Wrosch J, Miller G, Pruessner J. Self-esteem change and diurnal cortisol

secretion in older adulthood. Psychoneuroendocrinology. 2014;41(0):111-120.

126. Wang J, Zhao X. Family functioning and social support for older patients with

depression in an urban area of shanghai, china. Arch Gerontol Geriatr. 2012;55(3):574-

127. Jensen M, Smith A, Bombardier C, Yorkston K, Miró J, Molton I. Social support,

depression, and physical disability: Age and diagnostic group effects. . Disability and

Health Journal. 2014;7(2):164-172.

128. Ladea P. Nutrition and depression at the forefront of progress. . Journal of Medicine

and Life. 2012;5(4):414-419.

129. Villegas A, Verberne L, De Irala J. Dietary fat intake and the risk of depression: The

SUN project. PLoS ONE. 2011;6(1).

130. Dallman MF, Warne JP, Foster MT, Pecoraro NC. Glucocorticoids and insulin both

modulate caloric intake through actions on the brain. J Physiol. 2007;583(Pt 2):431-436.

131. Gibson EL. Emotional influences on food choice: Sensory, physiological and

psychological pathways. Physiol Behav. 2006;89(1):53-61.

132. Hsiao PY, Mitchell DC, Coffman DL, et al. Dietary patterns and diet quality among

diverse older adults: The University of Alabama at Birmingham study of aging. J Nutr

Health Aging. 2013;17(1):19-25.

133. Mikolajczyk RT, El Ansari W, Maxwell AE. Food consumption frequency and

perceived stress and depressive symptoms among students in three European countries.

Nutr J. 2009;8:31-2891-8-31.

134. Yannakoulia M, Panagiotakos DB, Pitsavos C, et al. Eating habits in relations to

anxiety symptoms among apparently healthy adults. A pattern analysis from the ATTICA

study. Appetite. 2008;51(3):519-525.

135. U.S. Department of Health, Human Services and U.S. Department of Agriculture.

Dietary guidelines for Americans. . 2005;6.

136. Saad L. Majority in U.S. drink alcohol, averaging four drinks a week. Gallup Web

site. http://www.gallup.com/poll/156770/majority-drink-alcohol-averaging-four-drinks-

week.aspx. Updated Last Updated, 2012. Accessed, August/14, Accessed, 2014.

137. Conger J. Reinforcement theory and the dynamics of alcoholism. Quarterly

Journal of Studies on. 1956;17:296-305.

138. Coker L, Rapp S, Stefanick M, et al. Association between reported alcohol intake

and changes in cognition: The women's health intitiative memory study. . The

Gerontologist. 2004;44:82-84.

139. Mukamal KJ, Kuller LH, Fitzpatrick AL, Longstreth WT,Jr, Mittleman MA,

Siscovick DS. Prospective study of alcohol consumption and risk of dementia in older

adults. JAMA. 2003;289(11):1405-1413.

140. van Meel, D., de Vrij, J., Kunst, A., Mackenbach, J. Differences in risk factors for

disease and health problems between monks and the general population in the

netherlands. Ned Tijdschr Geneeskd. 1992;136(32):1551-1555.

141. Meurer J, McDermott R, Malloy M. An exploratory study of health practices of

american catholic nuns. Health Values: The Journal Of Health Behavior, Education &

Promotion. 1990;14(2):9-17.

142. DeGouw H, Westendorp R, Kunst A, Mackenbacj J, Vandenbroucke J. Decreased

mortality among comteplative monks in the netherlands. Am J of Epidemiolgy.

1995;141(8):771-775.

143. Sabbatini S. Attempts to fight palutism and malaria in the middle ages. Role of

benedictine and cistercian monks in the rise of monastic medicine and land reclamation

during the middle ages. Infez Med. 2005;13(3):196-207.

144. Sacco P, Bucholz K, Harrington D. Gender differences in stressful life events, social

support, perceived stress, and alcohol use among older adults: Results from a national

survey. Subst Use Misuse. 2014;49(4):456-465.

145. Lang I, Wallace RB, Huppert FA, Melzer D. Moderate alcohol consumption in older

adults is associated with better cognition and well-being than abstinence. Age Ageing.

2007;36(3):256-261.

146. Reid K, Glazer K, Lu B, Naylor E, Wolfe L, Zee P. Aerobic exercise improves self-

reported sleep and quality of life in older adults with insomnia. Sleep Med.

2010;11(9):934-940.

147. Edinger J, Morey M, Sullivan R, et al. Aerobic fitness, acute exercise

and sleep in older men. Sleep. 1993;16(4):351-359.

148. Aadahl M. Validation of a new self-report instrument for measuring physical

activity. Medicine & Science in Sports & Exercise. 2003;35(7):1196-1202.

149. Hagströmer M, Bergman P, De Bourdeaudhuij I, et al. Concurrent validity of a

modified version of the international physical activity questionnaire (IPAQ-A) in

european adolescents: The HELENA study. Int J Obes. 2008;32:S42-S48.

150. Sato-Mito N, Sasaki S, Murakami K, Okubo H, Takahashi Y, Shibata S. The

midpoint of sleep is associated with dietary intake and dietary behavior among young

japanese women. Sleep Med. 2011;12(3):289-294.

151. Kuhn E, Brodan V. Changes in the circadian rhythm of serum iron induced by a 5-

day sleep deprivation. Europ J Appl Physiol. 1982;49(2):215-222.

152. Sangmi K, DeRoo L, Sandler D. Eating patterns and nutritional characteristics

associated with sleep duration. Public Health Nutr. 2011;14(5):889-895.

153. Poornima K, Karthick N, Sitalakshmi R. Study of the effect of stress on skeletal

muscle function in geriatrics. Journal of clinical and diagnostic research: JCDR.

2014;8(1):8.

154. Ekblom-Bak E, Ekblom B, Vikstrom M, de Faire U, Hellenius ML. The importance

of non-exercise physical activity for cardiovascular health and longevity. Br J Sports

Med. 2014;48(3):233-238.

155. Wilber ST, Frey JA, Poland SA, Pattisapu AK, Kannan JL. 78 muscle strength,

muscle mass, and muscle fatigue and their relationship with mobility-related activities of

daily living in older emergency department patients. Ann Emerg Med. 2012;60(4,

Supplement):S29-S30.

156. Bates CJ, Walmsley CM, Prentice A, Finch S. Does vitamin C reduce blood

pressure? results of a large study of people aged 65 or older. J Hypertens.

1998;16(7):925-932.

157. Bruno RM, Daghini E, Ghiadoni L, et al. Effect of acute administration of vitamin C

on muscle sympathetic activity, cardiac sympathovagal balance, and baroreflex

sensitivity in hypertensive patients. Am J Clin Nutr. 2012;96(2):302-308.

APPENDIX A

CROSS-SECTIONAL WELLNESS STUDY IRB DOCUMENT

Benedictine University--Institutional Review Board

Statement of Exemption Form

Version 10-1-2011

Principal Investigator (Faculty or lead advisor responsible for students): Dr. Bonnie

Beezhold

Title of Investigator: Bonnie Beezhold, PhD, CHES

Department or Program: Nutrition

Address: 5700 College Road Lisle, IL 60532

Phone: 630-829-6528 E-Mail address: bbeezhold@ben.edu

Student Investigator (Grad): Shelby Benci

Address: 5750 Abbey Drive #4D, Lisle, IL 60532

Phone: 760-415-0939 E-Mail address: Shelby_Benci@ben.edu

Student Investigator (Grad): Chad Earl

Address: 1404 Knoll Drive, Naperville, IL 60565

Phone: 630-8546469 E-Mail address: Chad_Earl@ben.edu

Student Investigator (Grad): April Irvine

Phone: 303-887-2356 E-Mail address: April_Irvine@ben.edu

Student Investigator (Grad): Julie Long

Address: 8520 Hillcrest Drive, Orland park, IL 60462

Phone: 708-828-3545 E-Mail address: Julie_Long@ben.edu

Student Investigator (Grad): Nikki Nies

Phone: 201-790-1370 E-Mail address: Nikki_Nies@ben.edu

Student Investigator (Grad): Jessica Schiappa

Address: 1111 W. Hillgrove Avenue, LaGrange, IL 60525

Phone: 720-480-2557 E-Mail: Jessica_Schiappa@ben.edu

Check all that apply: Student project X Faculty project X Joint

faculty/student project X

Other Specify:

Grant research Specify:

Title of Project

Wellness investigation in older adults in community: a cross-sectional comparison

HIPAA: Health Insurance Portability and Accountability Act

Yes x No Will health information be obtained from the covered entity (a

health care provider who bills health insurers)?

Yes x No Will the study involve the provision of healthcare in a covered entity, such

as Benedictine’s student health center?

Yes x No If the study involves the provision of healthcare, will a health insurer or

billing agency be contacted for billing or eligibility?

If you answered “NO” to all three questions, you are not subject to HIPAA and do not

need to address Page 4 of this form. If you answered “YES” to any of the questions

above, you are subject to HIPAA and must attach the HIPAA Worksheet.

Citation of Exempt Category (definitions below): 1 x 2 3 4

EXCEPTIONS: Research involving vulnerable populations such as the mentally or

cognitively impaired, prisoners, parolees, pregnant women, and fetuses, cannot be

exempt from review even though it meets the criteria of one of the categories below.

Research using survey procedures or interview procedures upon children cannot be

exempt. Research involving observation of children’s behavior cannot be exempt if the

investigator is a participant in the behaviors observed.

EXEMPTION CATEGORIES (45 CFR 46.101(b)): Research activities in which the

only involvement of human subjects will be in one or more of the following categories:

1. Research conducted in established or commonly accepted educational settings,

involving normal educational practices, such as (i) research on regular and special

education instructional strategies, or (ii) research on the effectiveness of or the

comparison among instructional techniques, curricular or classroom management

methods. (Cannot implement ANY identifiers or minor children)

2. Research involving the use of educational tests (cognitive, diagnostic, aptitude,

achievement), survey procedures, interview procedures or observation of public

behavior, unless: (i) information obtained is recorded in such a manner that

human subjects can be identified, directly or through identifiers linked to the

subjects; and (ii) any disclosure of the human subjects’ responses outside the

research could reasonably place the subjects at risk of criminal or civil liability or be

damaging to the subjects’ financial standing, employment or reputation. Research

which deals with sensitive aspects of the subject’s own behavior such as illegal

conduct, drug use, sexual behavior, or use of alcohol, cannot be exempt from review.

3. Research involving the use of educational tests (cognitive, diagnostic, aptitude,

achievement), survey procedures, interview procedures, or observation of public

behavior that is not exempt under paragraph (b) (2) of this section if (i) the human

subjects are elected or appointed public officials or candidates for public office; or (ii)

federal statue(s) require(s) without exception that the confidentiality of the personally

identifiable information will be maintained throughout the research and thereafter.

Research which deals with sensitive aspects of the subject’s own behavior such as

illegal conduct, drug use, sexual behavior, or use of alcohol, cannot be exempt from

review.

4. Research involving the collection or study of existing data, documents, records,

pathological specimens or diagnostic specimens, if these sources are publicly

available or if the information is recorded by the investigator in such a manner that

subjects cannot be identified, directly, or through identifiers linked to the subjects.

5. Research and demonstration projects which are conducted by or subject to the

approval of federal department or agency heads and which are designed to study,

evaluate, or otherwise examine: (i) public benefit or service programs; (ii)

procedures for obtaining benefits or services under those programs; (iii) possible

changes in or alternatives to those programs or procedures; or (iv) possible changes in

methods or levels of payment for benefits or services under those programs.

6. Taste and food quality evaluation and consumer acceptance studies, (i) if wholesome

foods without additives are consumed or (ii) if a food is consumed that contains a

food ingredient at or below the level and for a use found to be safe, or agricultural

chemical or environmental contaminant at or below the level found to be safe, by the

U.S. Food and Drug Administration or approved by the Environmental Protection

Agency or the Food and Safety and Inspection Service of the U.S. Department of

Agriculture.

CONTINUING STUDIES:

Is this a continuation of an existing IRB approved study? Yes X No

If yes, please indicate when the IRB approved of the study:

______________________________________

and attach a copy of last year’s approved exempt study.

Description of the Proposed Project: Please give a description of the proposed project

on a separate page. In addition, indicate how subjects will be recruited, from where, and

when, and how they will remain confidential. Follow the checklist to be sure you’ve

covered all necessary questions. Include attachments that are needed to conduct this

study (i.e., Informed/ Parent Consent Form, copies of any instruments/surveys to be used

as well as copyright for use when necessary, permission to use existing data and for what

purpose, research or publication).

Also address the critical elements of your exemption category as indicated below:

Category 1: Specify whether 1. i or 1.ii applies and briefly explain.

Category 2: Assure that condition 2.i will be met and briefly explain how; and assure

that condition 2. ii applies; and confirm that copies of test/survey/interview questions or

items are attached.

Category 3: Explain why conditions 2.i and 2.ii cannot be met; and attach copy of

test/survey/interview questions or items; and either assures and briefly explains that

condition 3. ii applies, or explain subject’s public office and how it precludes anonymity

(i.e., 3.i).

Category 4: Briefly explain the nature of the existing data/documents; and briefly

explain either their public availability or the procedures to ensure anonymity and

confidentiality.

Category 5: Briefly explain method by which the project is reviewed and approved by a

federal department/agency head; and

identify and describe which of the 5.i - 5.iv categories apply.

Category 6: Assure that condition 6.i will be met; and assure via documentation

regarding approved safety levels that condition 6.ii will be met.

SIGNATURES

Principal Investigator (Faculty or lead advisor responsible for student):

______ ________________ Date____3-04-14_______

I certify that I have the appropriate credentials and privileges to conduct this study and

that the facilities are adequate.

Student Investigators (Graduate)

______________________________________________ _ Date____3-04-14_______

______________________________________________ _Date___ _3-04-14_______

Required for student investigators

Department Chair or Program Head:

_________________________________________________ Date___3-04-14_______

I certify that the investigator has the appropriate credentials and privileges to

conduct this study and that the facilities are adequate.

Dean of College:

_________________________________________________Date_________________

(Only required if research involves clinical studies with medical procedures or medical

tests.)

Research Personnel

Please list ALL research personnel (students/ faculty) involved in the conduct of this

study. All faculty and advisors must complete the IRB approved educational program on

the protection of human subjects and provide to the IRB the certification forms verifying

completion of the courses. The IRB will not review a study without such forms on file

for all research personnel.

Name Title

Department__________________

Dr. Bonnie Beezhold Assistant Professor Nutrition

Shelby Benci Graduate student Nutrition

Chad Earl Graduate student Nutrition

April Irvine Graduate student Nutrition

Julie Long Graduate student Nutrition

Nikki Nies Graduate student Nutrition

Jessica Schiappa Graduate student Nutrition

IRB Use Only: IRB Review and Approval

IRB Chair_________________________________________________Approval

Date________________________

Exempt Review Category # ______________Expiration Date______________________

Minimal Risk: yes no

HIPAA: Waiver of Authorization Recruitment Authorization

Description of Study

Study Rationale

Stress and negative emotions activate the hypothalamic-pituitary-adrenal (HPA) axis to

release cortisol into circulation (Dickerson & Kemeny, 2004). Prolonged activation of

this axis has been associated with inflammation, physical health problems, and mortality

(Rueggeberg et al, 2012). Specifically, stress can also induce inflammatory brain-altering

processes and are now thought to exacerbate brain ageing (Kyrou & Tsigos, 2009; Denis

et al, 2013). In a recent study that compared stress levels of caregivers and non-

caregiving controls, it was shown that the cumulative effect of daily stressors promoted

elevations in blood inflammatory markers (Gouin et al, 2011). Moreover, chronic stress is

associated with late-life depressive symptoms (Juster et al, 2011; Kobrosly et al, 2014).

Research also suggests that there is increasing variability in self-esteem at progressively

older ages (Trzesniewski et al., 2003) which increases stress levels (Pruessner et al,

1999). Age-related declines in older adults’ self-esteem could derive from a loss of social

roles, social isolation, or an increase in physical health problems (Orth et al, 2010). In

fact, optimism has been found to buffer the association between perceived stress and

elevated levels of diurnal cortisol (Jobin, 2013).

According to the American Psychological Association, a majority of Americans

have tried to reduce their stress but fewer than 10% report success in doing so (APA,

2012). Dietary factors can be stress-protective. For example, a growing body of evidence

now suggests that omega-3 fats are involved in HPA axis regulation and thus in

individual reactivity and sensitivity to stress (McNamara and Carlson, 2006). A recent

placebo-controlled, double-blind randomized trial compared the effect of fish oil

supplementation with placebo in stressed medical students, and found that those students

who received fish oil had decreases in blood inflammatory markers and reduction in

anxiety symptoms compared to controls. Lifestyle and environmental factors can also be

protective. For example, a prospective study in Italy that investigated blood pressure, an

indicator of stress, followed 144 nuns and 138 similar laywomen controls for 20 years,

and found that blood pressure did not increase with age in the nuns compared to

laywomen, an unexpected result only found in comparisons with hunter-gatherer groups

(Timio et al, 2001).

Aims and Hypotheses

The main aim of our study will be to explore various dimensions of wellness - physical,

emotional, social, and spiritual, with a particular focus on perceived stress - and

associations with diet, lifestyle factors, and physical measurements, such as blood

pressure, BMI and percent body fat, in older adults living in different communities,

focusing on a comparison of ascetic communities, such as St. Procopius, and independent

living senior communities in the western suburbs. This study will provide separate

hypotheses for six master’s students who will be collecting and analyzing the data,

writing a thesis manuscript, and presenting the results to the community.

The project’s general hypotheses are:

1) Older adults living in monastic communities report less stress (and/or depressive

symptoms) than individuals living in lay retirement communities.

2) Older adults who consume a healthy diet (and/or more regular activity) will report

less stress (and/or depressive symptoms) than individuals who do not consume a

healthy diet.

3) Older adults who report a higher level of spiritual wellness or a higher level of social

support will also report less stress (and/or depressive symptoms).

4) Older adults who report higher levels of wellness have lower blood pressures and

heart rates than adults who report lower levels of wellness.

5) Older adults who are obese will report more stress (and/or depression).

Survey design

We plan to conduct a cross-sectional wellness study of older healthy adults residing in

monastic and independent living communities. Our research group has constructed a

survey composed of questions about demographics, health and lifestyle factors, and

wellness dimensions specifically involving stress, depression, spiritual wellness and

social wellness. We will also be recording a 24-hr diet recall, and taking blood

pressure/pulse and weight (BMI, waist circumference, and percent body fat)

measurements. We plan to recruit participants at the Benedictine and possibly other

nearby monastic communities, and at independent living facilities in and around Lisle

and Naperville in order to compare group outcome measures in these two different

environments. We hope to recruit 50 participants in both groups for a total of 100

participants.

Study measures

All study assessments will take place on site. Our survey (attached) is composed of

questions from validated scales used in older adults when possible. The first section

contains demographic, health and lifestyle questions. The second section has four

validated embedded scales that have been used in older adults: the SF-36v2 Health

Survey to measure perceived physical and mental health, the Perceived Stress Scale to

measure stress, The Spirituality Index of Well-being to measure spiritual wellness, and

the Multidimensional Perceived Social Support Scale to measure social wellness. We will

also utilize a 24-hr diet record form to record dietary intakes, and will subsequently enter

the data into the National Cancer Institute’s ASA24 diet software program (with their

permission) to analyze the intakes. Physical measurements will include blood pressure

and pulse, waist circumference, weight and height, and percent body fat. Height will be

measured with a portable stadiometer and weight and percent body fat will be measured

with a segmental bioelectrical impedance analyzer. All equipment will be brought to each

Participant recruitment

Participants will be recruited with the permission and assistance of directors and/or health

care managers at the sites, including St. Procopius Abbey, Sacred Heart Monastery,

Marmion Abbey, and independent living facilities selected in the Naperville/Lisle area. A

flyer (attached) will attract volunteers for the study day which will be handed out to

potential volunteers or posted at various locations at the sites at least a week before we

arrive for data collection. A signup sheet will be given to our contact at each site.

Eligibility criteria

All volunteers who are 65 years of age and older and who do not require assistance to

engage in their daily activities will be eligible to participate.

Methods

The study protocol will include four stations: the survey, the diet recall, the blood

pressure/pulse measurements, and the weight measurements. The survey will begin with

a consent form (attached). Volunteers will be informed of the study eligibility and how

long the study assessment will take to complete. Participants will be instructed that they

can stop participation at any time and that their survey responses and measurements will

be destroyed. Participants should be able to complete the study in one sitting and we

estimate completion time at 30-45 minutes.

Starting and Ending Dates

Data collection will convene once we have IRB approval, estimated to begin at the end of

March, excluding Holy Week, and proceeding through May 2014, however, possibly

requiring additional time in June 2014.

Potential Benefits

Aggregate results of this study will be conveyed in a thesis manuscript and in a

community research presentation in fall 2014, and potentially at research conferences and

in a journal publication. Participants will have the satisfaction of participating in research

that can ultimately improve public health. In addition, participants may also become more

aware of their own diet and lifestyle choices and the link to quality of life and wellness.

Potential Risks

This research does not involve greater than minimal risk for the participants, such as

potentially feeling mild discomfort when completing the wellness scales embedded in the

survey, when disclosing personal information, or when having physical measurements

taken. Anonymity of participants and confidentiality of responses and measurements will

be protected. Surveys and physical measurement records will not contain names but be

numbered only. All paper documents will be stored in the principal investigator’s files

within a locked department office. All electronically stored data will use the numerical

identifiers (no personal identifying or HIPAA information) and be password-protected in

a secure database. Although the paper surveys will have consent forms, only signatures

will be required; after the participant completes their participation, the consent forms will

be separated from the numbered surveys, thus further protecting anonymity. Paper

surveys will be scanned and electronic versions of the surveys and related data files will

be maintained for seven years in the Nutrition Department of Benedictine University;

paper surveys will be destroyed through shredding, once all data has been entered and

analyzed in the SPSS statistical software program.

Study Significance

This research will add to the body of scientific knowledge regarding the role of certain

modifiable lifestyle factors in physical and mental wellness, particularly in older adults,

and potentially contribute to public health recommendations for healthy aging.

[Please see the informed consent and recruitment email on the next page. The

survey and a recruitment flyer are attached.]

Informed Consent

WELLNESS INVESTIGATION OF OLDER ADULTS IN COMMUNITY

Spring, 2014

Dear Study Participant:

My graduate students and I are researchers in the Nutrition Department at Benedictine

University. The main aim of our study will be to explore various dimensions of wellness

- physical, emotional, social, and spiritual – and their relationships with diet, lifestyle

factors, and physical measurements in older adults living in different types of

communities. This research will add to the body of scientific knowledge about factors

that can influence physical and mental wellness, particularly in older adults, and

potentially contribute to public health recommendations for healthy aging.

Thank you for your willingness to participate in important research. You will be asked to

complete a survey, recall your diet over the last 24 hours, and have blood pressure and

weight measures taken. Your participation is completely voluntary. If at any time you do

not want to continue with the study, you may stop. Your time and involvement is

profoundly appreciated. The entire session should take no more than 30 minutes.

All personal data will be anonymous – surveys and measurement data forms will only

have numerical identifiers to protect your identity and confidentiality. Data will be

entered into a statistical program on a password-protected computer. Individual responses

will be compiled and results presented only in the aggregate. The study will be the

subject of a thesis manuscript and a campus research presentation in the fall of 2014, and

will potentially also be the subject of a journal publication. Under no circumstances will

your personal information ever be a focus of attention or your name or identifying

characteristics appear in writing. I personally will secure and ultimately dispose of the

information. You can reach me at 480-620-6773 or at bbeezhold@ben.edu if you have

any further questions about the study.

Please sign below on the line provided to indicate that you have read this form and

consent to participation. The study has been approved by the Institutional Review Board

of Benedictine University. The chair of the board is Dr. Alandra Weller-Clarke, who can

be reached at (630) 829 – 6295 or at aclarke@ben.edu for concerns about the study.

Sincerely,

Dr. Bonnie Beezhold

Assistant Professor

_______________________________________________

____________________________

Participant signature Date

Recruitment email + attached study description

Dear _____:

I'm a graduate student in the Nutrition Department at Benedictine University. My mentor,

Dr. Bonnie Beezhold, and a few other graduate students are conducting a study to

investigate diet, lifestyle, and health measurements associated with perceived wellness.

Building on the results of previous studies, we hope to compare older adults in nearby

monastic communities (St. Procopius Abbey, Sacred Heart Monastery, etc.) with those

residing in independent senior living communities. We are looking for participants who

are at least 65 years old and able to engage in their daily activities without assistance.

Volunteers will be asked to complete a survey of demographic/health questions and a diet

recall, and we will also take blood pressure and weight measurements (BMI, percent

body fat, waist circumference).

Would it be possible for us to meet with you regarding including adults in your

community? All assessments would be done at your site, so we would work together with

you to find a day that would be convenient for us to come out to a common area in your

facility. All assessments will be anonymous with only participant number identifiers, and

results would only be presented in the aggregate in a future research presentation and

publication.

I've attached a brief one-page study description for your review. Thank you so much for

your consideration in participating in this research, and I look forward to hearing from

[student name]

[Attached Study Description below.]

Brief study rationale: Stress and negative emotions activate the hypothalamic-pituitary-

adrenal (HPA) axis to release cortisol into circulation (Dickerson & Kemeny, 2004).

Prolonged activation of this axis has been associated with inflammation, physical health

problems, and mortality (Rueggeberg et al, 2012). Specifically, stress can also induce

inflammatory brain-altering processes and are now thought to exacerbate brain ageing

(Kyrou & Tsigos, 2009; Denis et al, 2013). In a recent study that compared stress levels

of caregivers and non-caregiving controls, it was shown that the cumulative effect of

daily stressors promoted elevations in blood inflammatory markers (Gouin et al, 2011).

Moreover, chronic stress is associated with late-life depressive symptoms (Juster et al,

2011; Kobrosly et al, 2014). Research also suggests that there is increasing variability in

self-esteem at progressively older ages (Trzesniewski et al., 2003) which increases stress

levels (Pruessner et al, 1999). Age-related declines in older adults’ self-esteem could

derive from a loss of social roles, social isolation, or an increase in physical health

problems (Orth et al, 2010). In fact, optimism has been found to buffer the association

between perceived stress and elevated levels of diurnal cortisol (Jobin, 2013).