(PDF) With a Little Help From My Friends: Long-term Self-perceived Health, Neuroendocrine, and Well-being Correlates of Early Adult Social Activity

With a Little Help From My Friends:

Long-term Self-perceived Health, Neuroendocrine,

and Well-being Correlates of Early Adult Social Activity

by

Cheryl L. Carmichael

Submitted in Partial Fulfillment

of the

Requirements for the Degree

Doctor of Philosophy

Supervised by

Professor Harry Reis

Department of Clinical and Social Sciences in Psychology

Arts, Sciences and Engineering

School of Arts and Sciences

University of Rochester

Rochester, New York

2011

ii

Dedication

For my late parents, Robert and Francine Carmichael. Their unconditional

love, support, and belief in me gives me the purpose to pursue my path, and the

courage to stay the course even when the path grows dark, and the way is dim.

Though their time was cut short, the love, kindness, and generosity they showed to all

carries on in the lives they touched. They set an example to live by, and the world is a

better place for them having been in it.

iii

Curriculum Vitae

Cheryl Carmichael was born in Schenectady, New York on March 15, 1978.

She matriculated at Northeastern University in 1996 and graduated in 2001 with a

Bachelor of Science degree in Psychology. She came to the University of Rochester

in the Fall of 2001 and began graduate studies in Social Psychology. She pursued her

research in close relationships under the direction of Professor Harry Reis, and

received a Master of Arts degree from the University of Rochester in 2005. She was

awarded a National Science Foundation Research Experience for Undergraduates

supplement in 2000 to study attentional biases to emotion with Judith Hall. She was

an instructor at St. John Fisher College in Rochester, NY from 2005 to 2007, and was

the 2006 recipient of the Vincent and Helen Nowlis Award for Excellence in

Teaching. In 2007 she and Dr. Reis were awarded a Rochester Center for Mind-Body

Research pilot grant to conduct this research on the long-term correlates of early adult

social activity, which marks the culmination of her academic training. She was a

visiting scholar at the Columbia University Graduate School of Business from 2009

to 2010, and currently resides in Brooklyn, New York where she is an instructor at

Fordham University and City University of New York, Brooklyn College.

List of Publications:

Lane, R.D., Carmichael, C.L., & Reis, H.T. (in press). Differentiation in the

momentary rating of somatic symptoms covaries with trait emotional awareness

in patients at risk for sudden cardiac death. Psychosomatic Medicine.

iv

Tomlinson, J.M., Carmichael, C.L., Reis, H.T., & Aron, A. (2010). Affective

forecasting and individual differences: Accuracy for relational events and anxious

attachment. Emotion, 10, 447-453.

Reis, H.T., Smith, S.M., Carmichael, C.L., Caprariello, P.A., Tsai, F.F., Rodrigues,

A., & Maniaci, M.R. (2010). Are you happy for me? How sharing positive events

with other provides personal and interpersonal benefits. Journal of Personality

and Social Psychology, 99, 311-329.

Carmichael, C.L., Tsai, F.F., Smith, S.M., Caprariello, P.A., & Reis, H.T. (2007).

The self and intimate relationships. In C. Sedikides & S. Spencer (Eds.) The Self.

(pp. 285 – 309). London: Psychology Press.

Carmichael, C.L., & Reis, H.T. (2005). Attachment, sleep quality, and depressed

affect. Health Psychology, 24, 526-531.

v

Acknowledgements

I am so very grateful to everyone who helped to make this dissertation

possible; the list is long, and for that I consider myself lucky! First and foremost, I

wish to extend heartfelt thanks to my advisor, Harry Reis. It has been my privilege

and pleasure to work with someone who is so instinctually attuned to the field, and

has been so unreservedly willing to share his vast experience and soaring intellect

with me. I am indebted to him for his generosity. He has indelibly influenced my

development as researcher, and his devotion to his work continues to inspire and

impress me. Harry has been an amazing mentor and friend, and I am grateful for the

depths of encouragement, support, patience, and care he has shown me.

This project is better off for the contributions of my committee members, Paul

Duberstein and Miron Zuckerman, thanks to both for investing in my intellectual

growth. Additional thanks to Miron for his unassuming approach to mentoring, and

for the warmth and understanding that he showed me.

Several people provided administrative and technical support that was vital to

this project. My appreciation to Jan Moynihan for her advice on cortisol sampling; to

April Engram for processing participant payments, and collecting saliva samples from

my mailbox in my absence; to James Walton for finding the time not only to assay

my saliva samples, but also allowing me to participate in the process and patiently

teaching me about immunoassays; to Nathan Franus for being a kind guide when I

was new to the RCMBR; and to Patrick Davies and Karin Gasaway for sharing

freezer space. I wish to extend thanks to Ilene LaMalfa, Karen Medalle, Audrey

vi

Nolan, and Loretta Pratt who so generously volunteered their time to pilot test the

surveys used in this research. I am especially indebted to Maryann Gilbert for her

tireless assistance and support, and for easing what would have been an otherwise

harrowing process given my circumstances.

This work could not have been completed without the generous support of a

Rochester Center for Mind-Body Research pilot grant, funded by National Institute

on Aging grants R24AG031089 and R21AG023956, or the research participants who

gave of their time and effort with more enthusiasm than I could have hoped for.

My family has been a source of great strength and encouragement. I am

thankful for the support of my brother, John, and for my Aunt Janet and Uncle Gary’s

unwavering faith in my ability to succeed. I am also grateful to Dana Carney, my first

mentor, and now lifelong friend. I became a scientist because of her. Thanks also to

my fellow graduate students Denise Gettman, Shannon Smith, Amy Rodrigues, Feng-

Fang Tsai, Tony Monacelli, Marcia Winter, Ryan O’Loughlin, Peter Caprariello, and

Mike Maniaci. They enriched my life in Rochester and my experience in graduate

school; I am fortunate to count them among my friends.

Finally, I am eternally grateful to my patient, caring, and giving partner,

Marty. His whole-hearted support of every kind was immeasurable, and I could not

have done without it. He cultivated joy, laughter, and fun in our lives, even in the

bleakest of times. He unfailingly believed in my capabilities, and stood by my side

through every obstacle I encountered, absorbing the impact to shelter me from the

storm and shepherd me forward. I get by with a little help from him.

vii

Abstract

This research investigated longitudinal associations between social activity

during college (a phase of early adulthood singled out among developmental theorists

as crucial to relationship development), and social, emotional, and health outcomes in

midlife. Men and women age 48-50 (n = 133) were recruited from a pool of

University of Rochester graduates who completed two-weeks of event-contingent

social interactions records during college in the late 1970s. College interaction diaries

provided measures of structural social integration (i.e., interaction quantity), and

functional perceived support (i.e., interaction quality), which were simultaneously

tested as predictors of midlife health and well-being. Recruited participants reported

on current social relationship quality, emotional adjustment, and physical health, and

provided a series of saliva samples to capture diurnal cortisol rhythm. Midlife social

relationship quality, emotional adjustment, and self-reported health were positively

interrelated. Longitudinally, college social interaction quantity predicted enhanced

social (friendship quality, intimacy with romantic partner, social network size), and

emotional (diminished negative affectivity, enhanced positive affectivity and self-

actualization) outcomes 30 years later, in midlife. Perceived social relationship

quality in college was longitudinally associated with enhanced social (friendship

quality, romantic intimacy), and health (better cardiovascular health) outcomes in

midlife. The discussion focuses on possible mechanisms responsible for longitudinal

associations that span 30 years, the relative impact of structural versus functional

support, and the general invariance of these effects across sex.

viii

Table of Contents

Dedication ii

Curriculum Vitae iii

Acknowledgements v

Abstract vii

Table of Contents viii

List of Tables xii

List of Figures xv

Foreword 1

Chapter 1: Introduction 2

Relationships and Health: The Good, The Bad, and The Absence 5

The absence: The harms of social isolation 6

Social engagement: Structure or function? 7

The bad: The negative consequences of aversive relationships 11

The good: The benefits of constructive relationships. 12

A Life Span Approach 19

Aging and health 19

Early adulthood to midlife 21

Social relationships as the mechanism 23

The Current Research 26

The necessity of longitudinal designs 27

The existing sample 29

ix

Experience sampling methods 30

The current assessment 32

Hypotheses 34

Social well-being hypothesis 35

Emotional adjustment hypothesis 35

Health hypothesis 35

Chapter 2: Method 36

Participants 36

Procedure 37

College Measures 38

Midlife Measures 38

Surveys 38

Emotional adjustment measures 39

Social well-being measures 40

Self perceived health measures 42

Other measures 43

Neuroendocrine function 44

Sample storage and shipping 46

Chapter 3: Results 47

Participant attrition and comparability 47

Data Reduction 48

Outcomes 48

x

Social well-being measures 49

Emotional adjustment measures 50

Physical health measures 50

Neuroendocrine function 51

Sex, Original Sample, and Freshman Versus Senior Status 53

Concurrent Analyses 55

Sex differences in concurrent correlations 57

Longitudinal Prediction of Midlife Outcomes 58

Analysis strategy 58

Social life 58

Emotional adjustment 62

Physical health 64

Neuroendocrine function 66

Summary of longitudinal findings 67

Controlling midlife social relationship quality 67

Controlling personality 68

Chapter 4: Discussion 71

Summary of Concurrent Findings 71

Summary of Longitudinal Findings 74

Unsupported Longitudinal Hypotheses 77

Romantic relationship quality 77

Physical health 80

xi

Measurement concerns 81

Health status 84

His, Hers, or Theirs: Sex Differences 86

Assessing Support: Form Versus Function 89

Limitations 94

Conclusions and Future Directions 99

References 102

Footnotes 121

Tables 123

Figures 150

Appendix A 161

xii

List of Tables

Table Title Page

1 Summary of function and measurement of three

physiological systems 123

2 Summary of previous samples and recruitment for

current study 124

3 Comparison of recruited versus unrecurited participants

on college social activity variables 125

4 Factor loadings for individual social relationship quality

scales on three principle components with oblimin rotation 126

5 Factor loadings for individual emotional adjustment scales

on three principle components with oblimin rotation 127

6 Factor loadings for individual physical health scales on

three principle components with oblimin rotation 128

7 Effects of original sample and year in college (freshman

vs. senior) on midlife social well-being 129

8 Effects of original sample and year in college (freshman

vs. senior) on midlife emotional adjustment 130

9 Effects of original sample and year in college (freshman

vs. senior) on midlife physical health 131

10 Effects of original sample and year in college (freshman

vs. senior) on neuroendocrine function 132

11 Concurrent correlations among midlife social, emotional,

and physical health composite variables 133

12 Concurrent correlations among midlife social well being,

emotional adjustment, physical health and neuroendocrine

composites and individual measures of social well being and

emotional adjustment 134

xiii

Table Title Page

13 Concurrent correlations among midlife social well being,

emotional adjustment, physical health and neuroendocrine

composites and individual measures of physical health

and neuroendocrine function 135

14 Concurrent correlations among midlife social well being,

emotional adjustment, physical health, and neuroendocrine

function composites for men (below the diagonal) and

women (above the diagonal) 136

15 Prediction of midlife romantic relationship quality from

college social activity quantity and quality 137

16 Prediction of midlife friendship quality from college social

activity quantity and quality 138

17 Prediction of midlife social network from college social

activity quantity and quality 139

18 Prediction of midlife negative affectivity from college

social activity quantity and quality 140

19 Prediction of midlife positive affectivity from college

social activity quantity and quality 141

20 Prediction of midlife self-actualization from college social

activity quantity and quality 142

21 Prediction of midlife daily symptomatology from college

social activity quantity and quality 143

22 Prediction of midlife cardiac health I from college social

activity quantity and quality 144

23 Prediction of midlife cardiac health II from college social

activity quantity and quality 145

24 Prediction of midlife neuroendocrine function from

college social activity quantity and quality 146

xiv

Table Title Page

25 Summary of effects of overall early adult social activity

on midlife composite social well being, emotional

adjustment, and physical health variables 147

26 Summary of effects of overall early adult social activity on

midlife composite social well being, emotional adjustment,

physical health, and neuroendocrine function variables

controlling for midlife social well being variables 148

27 Summary of effects of overall early adult social activity

on midlife composite social well being, emotional

adjustment, physical health, and neuroendocrine function

controlling for midlife emotional stability/neuroticism and

extraversion/surgency 149

xv

List of Figures

Figure Title Page

1 Hypotheses 150

2 Original Sample X Participation Status interaction on

number of opposite sex interactions per day 151

3 Original Sample X Sex interaction on midlife daily

symptomatology 152

4 Original Sample X Sex interaction on cardiac health II 153

5 College Year X Sex interaction on positive affectivity 154

6 Same-sex Quantity X Sex interaction on perceived

partner responsiveness 155

7 Overall Quantity X Sex interaction on perceived social

support from friends 156

8 Same-sex Quality X Sex interaction on social network size 157

9 Overall Quantity X Sex interaction on personal growth 158

10 Overall Quality X Sex interaction on cardiac diagnosis history 159

11 Overall Quality X Sex interaction on cardiac health II

composite 160

1

Foreword

The author collected all of the data in the current assessment.

Cortisol assays were performed by James Walton, and assisted by the author, at the

Rochester Center for Mind-Body Research, Strong Memorial Hospital, University of

Rochester, Rochester, NY.

College social activity data were collected by Ladd Wheeler (samples I, II, & III),

Harry Reis (samples II & III), and John Nezlek (samples I & II).

2

Chapter 1: Introduction

There is no hope of joy except in human relations. –Antoine de Sainte-Exupéry

The list of maxims that one could collect enumerating the benefits of good

relationships would undoubtedly be lengthy. These incisive, yet simple observations

conceived across the years by philosophers, writers, musicians, and lay people alike,

are substantiated by the scientific literature; socially integrated individuals tend to

live longer (e.g., Holt-Lundstad, Smith, & Layton, 2010; House, Landis, &

Umberson, 1988) and tend to be better psychologically adjusted (e.g., Argyle, 1999;

Umberson, Chen, House, Hopkins, & Slaten, 1996).

Showing that characteristics of social life are systematically associated with

health and longevity gives rise to several important questions about how and why

such benefits might materialize. First, are there causal links between social life and

health outcomes? Although experimental manipulation is the gold standard for

establishing causality, manipulating certain variables (such as relationship quality, for

instance) is more than an issue of adequate research design; it is also a question of

ethics and feasibility. Our ability to make causal inferences about the relationship

between social life and health hinges on well-designed prospective longitudinal

investigations of social life.

Second, if relationships are causally linked to health, what are the mechanisms

by which such an effect may emerge? It remains to be seen whether health varies as a

function of immediate social circumstance, or reflects something more long standing,

such as an accumulation of benefits acquired over the life course. For example,

3

emergent research has linked positive emotional expression in early adulthood with

later life health and well being (Danner, Snowden, & Friesen, 2001; Harker &

Keltner, 2001), and positive social relationships are the hypothesized mediating

mechanism. However, little evidence exists to bear out this claim. Perhaps an

existence imbued with warm and supportive relationships provides opportunities for

positive emotional expression, promotes psychological well-being and adaptive

coping, and thus enhances health and longevity. These possibilities remain yet

untested because few studies have examined these associations longitudinally, and

even fewer have contemporaneously examined social life during early adulthood, the

period most critical to intimate relationship development (Arnstein, 1984; Erikson,

1959).

Despite some provocative and suggestive findings, our understanding of the

links between social connection and health remains muddied by several limiting

factors. First, this type of research has been largely conducted among a limited birth

cohort. It remains to be seen whether they will generalize to those who were born a a

different time, and lived through a different set of historical events and societal

conditions. In addition, for individual studies, effect sizes are rather small, and the

results of various studies do not always converge on the same conclusions. For

example, the literature diverges with regard to whether men or women benefit equally

from social integration (Keicolt-Glaser & Newton, 2001). Some studies have found

the benefits of social relationships to be relatively greater for men (Diener, Suh,

Lucas, & Smith, 1999; Keicolt-Glaser & Newton, 2001), whereas others have

4

suggested quite the reverse (Glenn, 1975; Wood, Rhodes, & Whelan, 1989).

The purpose of the current investigation is to adopt a lifespan approach, and

longitudinally examine the midlife health and well-being correlates of individuals’

social activity in college (referred to as early adulthood for convenience). Several

theories of lifespan development identify early adulthood as the critical stage for the

flourishing of intimacy and close social relationships (Erickson, 1959; Levinson,

1978; Neugarten, Moore, & Lowe, 1965; White, 1966). Moreover, Hartup and

Stevens’ (1997) argue that throughout life, friends play a vital role in adaptation by

fostering a sense of well being, and facilitating adjustment to life’s transitions.

Stability in social activity from college to age 30 (Reis, Lin, Bennett, & Nezlek,

1993) may connote stability throughout adult life. If key skills that are cultivated

during college can be carried through early adulthood, there is good reason to believe

these skills will continue to be employed throughout the remainder of adult life. Thus,

those with an enriched social network in college may be armed with tools that help

them reap lifelong benefits of good social ties. Those lifelong benefits may include

the provision of social support, an outlet for the experience and expression positive

affect, encouragement of healthy behaviors, and attenuation of the negative effects of

stress, all of which may promote the long-term welfare of recipients (Berkman, Glass,

Brissette, & Seeman, 2000). To investigate whether early adulthood social activity is

related to midlife health and well being, adults (approximately 50 years of age) who

participated in detailed social interaction diary research during college will be

recruited for an assessment of current self-perceived health, neuroendocrine function,

5

and social and emotional well being.

In the following pages I will first provide a detailed overview of the

connection between social life and health and well-being outcomes. I will then

explain why midlife presents an opportune time for examining health and well being.

I will argue for the utility of a lifespan approach in this domain, and provide a

theoretical rationale for linking social activity in early adulthood to outcomes in

midlife. Next, I will describe the characteristics of the target sample and explain the

unique features that are provided by using a longitudinal design with this particular

group of individuals. Finally, I will offer hypotheses about the relationship between

social engagement in early adulthood and health and well being in midlife.

Relationships and Health: The Good, the Bad, and the Absence

Berkman et al. (2000) present an overarching framework for linking social life

to health whereby large-scale factors such as culture and social network size may

facilitate or hinder the availability of intermediate psychosocial mechanisms such as

social engagement, person-to-person contact, and social support. Psychosocial

mechanisms, in turn, are hypothesized to have an effect on health through a number

of specific physiological (e.g., cardiovascular reactivity) and psychological (e.g.,

depression) pathways. Their model suggests that the presence of relationships is

necessary, but not sufficient, for the attainment of benefits. In other words,

relationships provide the means for subsequent events that have more proximal

implications for health. Consistent with this idea, in the following sections I will

selectively review research that supports three broad conclusions with regard to

6

health and well being outcomes: 1) a lack (or perceived lack) of relationships may be

harmful; 2) caustic relationships seem to produce negative consequences; and 3)

positive social relations appear to have beneficial effects.

The broader term health has been employed to refer to a great many specific

and interrelated outcomes, both subjective and objective. The scope of the current

project encompasses self-perceived healthfulness, neuroendocrine functioning, and

psychological well-being. However, when reviewing the empirical evidence in

support of the three claims above, I will report findings beyond the scope of the

current investigation. In addition to reviewing psychological health and well-being

outcomes, I will review physiological outcomes for the cardiovascular system, the

immune system, and the neuroendocrine system, which includes the autonomic

nervous system and the hypothalamic-pituitary-adrenal (HPA) axis (see Table 1 for a

summary of major functions of these three systems, and the most common measures

associated with each). Although the current project will examine neuroendocrine

function as the sole measure of objective health status, I will maintain a broader focus

in my review because these interrelated systems are critical to the physiological

processes of homeostasis (maintaining consistency), allostasis (maintaining

consistency through change), and thus long-term health (McEwen, 1998), a point that

will be elaborated upon in the next section.

The absence: The harms of social isolation. A seminal paper by Berkman

and Syme (1979) showed that the presence of each of four types of social ties (a

spouse, extended family and friends, a church, and other formal and casual groups)

7

was associated with reduced mortality risk up to nine years later among residents of

Almeida County, California. These findings remained significant when controlling

various self-reported mortality-relevant variables such as physical health, smoking,

alcohol consumption, obesity, socioeconomic status, use of preventative health

services, and life satisfaction. Although the use of self-reports arguably undermines

the credibility of these findings, the pattern has been replicated in subsequent

prospective studies with culturally diverse samples across the United States and

Europe. Social integration is positively and consistently associated with reduced

mortality and morbidity rates (Berkman, 1995; House, Landis, & Umberson, 1988).

An examination of the research on social integration and health reveals

variations in the measurement of social integration. Some researchers ask about the

number of relationships one has, others ask about the frequency of social interaction,

and yet others ask about subjective feelings of closeness and connection with others.

Thus, it is important to be clear about what is meant by social engagement, and the

outcomes predicted by each conceptualization.

Social engagement: Structure or function? Among the ways to quantify

social connectedness, two seem to predominate. The structural approach examines

social integration in a relatively objective or quantitative manner; that is, by looking

at the presence versus absence of specific social relationships (e.g., marital partner,

extended family, neighbors, etc.), the number of social ties one has, and the frequency

with which one interacts with those social contacts. As described above, research

stemming from this paradigm demonstrates that structural social integration is

8

associated with physical health gains (e.g., Berkman, 1995; Berkman & Syme, 1979;

House, Landis, & Umberson, 1988). For example, individuals with fewer types of

social connections were found to be more susceptible to the common cold than their

socially integrated counterparts. In fact, those with the fewest kinds of social ties

were 4.2 times as likely (adjusted relative risk ratio) to contract a cold as those with

the greatest diversity of social ties. Moreover, controlling health behaviors (e.g.,

smoking and exercise) did not eliminate the effect of social integration on cold virus

susceptibility (Cohen, Doyle, Skoner, Rabin, & Gwaltney, 1997). Even temporary

bouts of isolation may have negative health consequences. A 4-7 day separation from

one’s romantic partner resulted in heightened salivary cortisol levels among those

high in attachment anxiety (Diamond, Hicks & Otter-Henderson, 2008). High levels

of salivary cortisol reflect HPA axis dysregulation, which may have negative

implications for immune system function and thus the body’s ability to fight disease

(McEwen, 1998).

The functional approach, on the other hand, examines social life from a

relatively subjective standpoint; that is, by looking at the perceived availability of

social support and feelings of loneliness and belonging, apart from the number of

social relationships an individual may have. Research stemming from this paradigm is

consistent with findings from the structural approach; when the functions fulfilled by

social relationship are unsated, health and well being suffer. For example, Cohen,

Mermelstein, Kamarck, and Hoberman (1985) reviewed studies that showed a

perceived lack of available social support predicted poorer mental and physical health

9

outcomes. This was especially true for appraisal support (believing one has someone

to discuss problems with), esteem support (believing one has someone to make a

positive social comparison with), and belonging support (believing one has someone

to spend time with), but less clear for tangible support (believing one has someone

who will provide material aid). More specifically, in a study of metastatic breast

cancer patients, women who felt they had few relatives or friends with whom they

could discuss private matters showed elevations in mean salivary cortisol levels

(Turner-Cobb, Sephton, Koopman, Blake-Mortimer, & Spiegel, 2000).

Loneliness, another functional measure of integration, has garnered a great

deal of recent attention as a predictor of health-relevant outcomes. Feelings of

loneliness may be partly attributed to a discrepancy between an individual’s desired

and actual social circumstances (Peplau & Perlman, 2000). Lonely people (relative to

non-lonely people) exhibit a variety of physiological differences that may contribute

to impoverished health. Lonely people are less likely to enjoy the restorative benefits

of sleep (Cacioppo et al., 2000), and display a lower basal heart rate, which may

reflect emotional withdrawal (Cacioppo et. al., 2000). Moreover, they respond poorly

to stressors, exhibiting increased total peripheral resistance (TPR) and decreased

cardiac output (CO; Cacioppo et al., 2002), a pattern associated with inhibited cardiac

recovery from stress (Dienstbier, 1989; Mendes, Reis, Seery, & Blascovich, 2000).

Lastly, the chronically lonely display increased levels of salivary cortisol (indicating a

higher level of stress), and show maladaptive HPA axis activity whereby cortisol

levels are elevated in the evening (when cortisol is typically on the decline and lowest

10

for most people; Cacioppo et al., 2000). In short, across the body’s systems, the

psychophysiological costs borne by lonely people may contribute to poorer health.

Whereas structural measures of support are relatively objective, functional

measures of support capture subjective interpretations of social life quality, and tend

to vary as a function of more enduring aspects of a person, such as their personality,

self-esteem, or attachment orientation. A body of research shows that secure,

emotionally stable, high self-esteem individuals are more likely to feel supported; to

believe that the close others in their lives are reliabile, trustworthy, and will be there

when needed; and perceive the objectively same support-relevent behaviors as more

comforting and supportive than their less secure counterparts (Murray, Rose,

Bellavia, Holmes, & Kusche, 2002; Shaver & Mikulincer, 2002). Nevertheless,

studies of structural and functional measures of social integration converge on similar

conclusions; social isolation, real or imagined, is associated with impoverished health

and well being. Although structural social isolation may give rise to feelings of

loneliness (Wenger & Burholt, 2004) and a diminished perception of functional

support, the relationship is not perfect. Relationship partners can be responsible for

feelings of loneliness or discontent when conflict arises or rejection occurs, and

spending time alone may have restorative benefits (Kiecolt-Glaser, Glaser, Cacioppo,

& Malarkey, 1998). This point underscores two important considerations. First, it

highlights a key issue that remains to be addressed. Do structural and functional

social networks produce similar effects when measured simultaneously? The current

research will attempt to shed light on this question by examining both structural

11

measure of integration, and functional measures of perceived relationship quality.

College social activity will be assessed in terms of both quantity of social engagement

and quality of social interactions. Second, it calls attention to the potentially harmful

aspects of close relationships.

The bad: The negative consequences of aversive relationships. Although

the absence (or perceived absence) of social connection may be detrimental to health,

simply having relationships is not uniformly beneficial. Hartup and Stevens (1997)

note that “…good outcomes are most likely when one has friends, one’s friends are

well socialized, and when one’s relationships with these individuals are supportive

and intimate.” (p. 365). Negative social relationships can be just as (if not more)

harmful as being or feeling alone.

Negative marital function is associated with poor outcomes for spouses.

Destructive marriages indirectly impact individuals’ health through depression and

poor health habits (Keicolt-Glaser & Newton, 2001). In addition, dissatisfaction and

doubt surrounding the relationship with one’s spouse is associated with sleep

disruptions (Carmichael & Reis, 2005), and poorer subjective health (Levenson,

Carstensen, & Gottman, 1993). Furthermore, marital conflict appears to take a direct

toll on the cardiovascular, neuroendocrine, and immune systems. (Kiecolt-Glaser &

Newton, 2001). During conflict discussion dissatisfied wives evinced higher systolic

blood pressure and heart rate than satisfied wives (Carels, Sherwood, & Blumenthal,

1998). This sort of cardiovascular reactivity to stress may contribute to the

development of cardiovascular disorders (Manuck, 1994), and coronary heart disease

12

(Orth-Gomer, Rosengren, & Wilhelmsen, 1993). Moreover, displays of hostility or

other negative behaviors (e.g., criticism, denying responsibility, making excuses,

negative mind reading) during conflict are associated with poor physiological

responses for both partners. For instance, negative behaviors during conflict were

associated with the production and secretion of catecholamines, which are HPA axis

hormones generated in response to stress (Malarkey, Kiecolt-Glaser, Pearl, & Glaser,

1994). Those same types of negative behaviors have also been linked to down-

regulation of the immune system (Kiecolt-Glaser, Malarkey, Chee, Newton,

Cacioppo, Mao, & Glaser, 1993). Neuroendocrine and immune system dysfunction

may have negative repercussions for health and well being, especially if conflict

occurs frequently.

Notwithstanding the problems that negative relationships may create, poor

quality marital relations may hinder disease recovery. Myocardial infarction (MI)

patients who perceived their marital partners to be relatively unsupportive were more

likely to report post-MI chest pain, and were also more likely to have been

rehospitalized up to one year after the initial heart attack (Helgeson, 1991).

Considerable evidence indicates that negative social relationships may

interfere with adaptive psychophysiological responding, and thus good health.

However, given the health gains associated with structural integration (e.g., Holt-

Lundstad, Smith, & Layton, 2010; House, Landis, & Umberson, 1988), positive

social interactions evidently may be beneficial to health and well being.

The good: The benefits of constructive relationships. Whereas negative

13

social relationships and feelings of social disconnectedness appear to be harmful, a

variety of benefits may be reaped from caring and supportive close relationship

partners who are able provide intimate social contact in the face of life’s ups and

downs. Positive social relationships may serve as sources of self-worth and well

being throughout all stages of life (Hartup & Stevens, 1997), a known factor in

cardiovascular and neuroendocrine health (Ryff et al., 2006).

It is important to note that apparent benefits of good relationships are believed

to be more than just an absence of harm associated with bad relationships. Research

by Uchino and colleagues suggests that a relationship partner can be supportive

(mostly positive), aversive (mostly negative), or ambivalent (a combination of the

two; Uchino, Holt-Lunstad, Uno, & Flinders, 2001). In other words, rather than a

model consisting of a single dimension with “good” and “bad” as opposite ends of the

same continuum, a bivariate model is more appropriate. Bivariate models are

comprised of two independent dimensions, in this case, one dimension representing

relational positivity (running from low to high), the other representing relational

negativity (also running from low to high). Where a partner falls on each dimension is

unrelated to his or her position on the other dimension. Research in a variety of

domains supports bivariate models, despite frequent inverse associations between the

two dimensions. This has been demonstrated for attitudes and affective experiences

(Cacioppo & Berntson, 1994; Larsen, McGraw, & Cacioppo, 2001), in the motivation

literature (Gable, Reis, & Elliot, 2003), and more recently the domain of

psychological well being (Ryff et al., 2006). Although Uchino et al.’s (2001) research

14

has largely focused on outcomes associated with ambivalent network ties (those who

are high in both positivity and negativity), their research demonstrates that positive

and negative aspects of relationships are independent, and benefits of good

relationships may not simply reduce to the absence of harm from bad relationships.

The literature distinguishes two models for explaining the ways in which

social support networks consisting of friends and loved ones may be beneficial to

health: the stress-buffering model, and the main effect model. The stress-buffering

model states that social support networks produce health benefits by reducing the

pathogenic effects of stress (Cassel, 1976; Cobb, 1976). As I will discuss shortly,

stress is a leading contributor to health throughout the life course, and the negative

effects of stress may accumulate over time creating a long-term adverse impact on

biological functioning (McEwen, 1998). According to this model, in the absence of

stress, support is irrelevant. However, when stress is present, relationship partners

may act as a protective buffer, promoting health by alleviating the mental and

physical toll of stress on the individual. This is consistent with Hartup and Stevens’s

(1997) argument that close others help individuals adjust to changing circumstances

and adapt to life’s stressors from childhood, through adolescence, and even into

adulthood.

Supportive social interactions have been shown to reduce cardiovascular

reactivity to stressors in the laboratory (Uchino, Cacioppo, & Kiecolt-Glaser, 1996).

When participants were given a stress induction, those who perceived high levels of

support showed the typical pattern of cardiovascular reactivity, a relative decrease in

15

reactivity among older versus younger participants. However, among those who

perceive low levels of social support the reverse was true; older participants showed

relatively increased cardiovascular reactivity in response to stress (Uchino, Kiecolt-

Glaser, & Cacioppo, 1992). Likewise, Gump, Polk, Kamarck, and Shiffman (2001)

examined ambulatory blood pressure in a naturalistic setting. They found that

interactions with a committed romantic partner, compared to interactions with any

other persons and non-interaction, were associated with reduced ambulatory blood

pressure. In short, supportive relationships can mitigate the harmful cardiovascular

effects of stress and promote healthy cardiovascular functioning.

Partners may also help buffer the damaging effects of stress on

neuroendocrine function. Stress activates the HPA axis, producing elevated levels of

cortisol, which can be harmful to the brain and other organs with prolonged or

persistent exposure (Sapolsky, 1990; 1996). Whereas a lack of social support is

associated with neuroendocrine dysregulation and elevations in cortisol levels

(Cacioppo et al., 2002; Turner-Cobb et. al., 2000), positive social engagement has

been shown to engender adaptive HPA axis responses to stress. During a conflict

discussion task, husbands’ positive behavior was associated with sharper declines in

two stress-related hormones (adrenocorticotropic hormone and cortisol) among wives

(Robles, Shaffer, Malarkey, & Keicolt-Glaser, 2006). This efficient recovery pattern

suggests that constructive social interaction promotes adaptive physiological

responding, which could potentially protect against the deleterious effects of stress.

In contrast to the evidence that negative social relationships appear to impede

16

disease recovery (Helgeson, 1991), positive social contact has been shown to

facilitate disease recovery. For example, coronary artery surgery patients who

received support that demonstrated caring and respect exhibited lower levels of

functional disruption and higher levels of emotional well-being up to one year after

surgery (King, Reis, Porter, & Norsen, 1993). In addition, among persons diagnosed

with congestive heart failure (CHF), better marital functioning (i.e., higher

satisfaction, more marital routines, more useful discussions, and greater positivity

expressed during disagreements) was associated with higher 4-year survival rates,

controlling for CHF severity (Coyne, Rohrbaugh, Shoham, Sonnega, Nicklas, &

Cranford, 2001). Finally, in a study of mild hypertension sufferers, Baker and

colleagues found that marital adjustment was associated with better coronary health

(measured by left ventricular mass; the weight of the chamber primarily responsible

for the pumping of the heart). More importantly, spousal contact had an influence on

3-year diastolic blood pressure (DBP), but the effect was moderated by marital

adjustment. Spousal contact was associated with a decrease in DBP among well-

adjusted couples, whereas the reverse was true for poorly adjusted couples (Baker et.

al., 2000). In other words, interacting with one’s partner promotes cardiovascular

health only if one has a good relationship.

The main effect model, on the other hand, suggests that social support

promotes health notwithstanding stress levels. In other words, social support is

always beneficial, whether or not stress is present. Consistent with this model, close

others may provide individuals with the opportunity to engage in positive social

17

interaction, which can produce positive affect, and allow for the expression of

positive emotion. In an extensive review, Lyubomirsky and colleagues describe

numerous benefits of positive affect, including benefits to social life and health

(Lyubomirsky, King, & Diener, 2005). Frequent positive affect is associated with

longevity, fewer colds and injuries, less pain, less frequent disease relapse, and, for

some diseases, decreased severity (Pressman & Cohen, 2005). Furthermore, positive

emotional expression has been linked to enhanced well-being (Harker & Keltner,

2001) and longevity (Danner, Snowden, & Friesen, 2001), presumably because of the

positive social interactions one experiences across the lifespan. Positive emotions also

appear to have an “undoing effect” on negative emotions (Fredrickson, 2000).

Positive emotional experiences facilitated participants’ return to cardiovascular

baseline following a negative mood induction, thus undoing the cardiovascular

consequences of negative emotion (Fredrickson & Levenson, 1998; Tugade &

Fredrickson, 2004). Perhaps when positive emotions are socially shared, partners aid

in the undoing of negative affect, contributing to better cardiovascular function.

In addition to fostering positive affective experiences, close others may

cultivate health through the provision of instrumental benefits. Providing caregiving,

loaning money, or accompanying an individual to a doctor’s appointment are just a

few examples of the many welfare-promoting forms of tangible support that friends

may offer. Furthermore, relationship partners may instrumentally promote each

other’s welfare by raising each other’s awareness of health-relevant information,

encouraging healthy lifestyle choices, and motivating each other to care for

18

themselves (Kiecolt-Glaser & Newton, 2001.

Empirical evidence exists in support of both models, and methodological

issues surrounding the operationalization of social support have been identified as a

source of divergent results. In a review of studies testing one or both models, Cohen

and Wills (1985) concluded that when social support was measured structurally (e.g.,

number of social contacts), the main effect model typically received support.

However, when social support was measured functionally (e.g., appraisal of

interpersonal resources), the stress-buffering hypothesis was usually supported (see

also Cohen, 1991). It may be noteworthy to point out that few people are stress-free

(Almeida & Horn, 2004), thus making it difficult to distinguish main effects from

stress buffering effects in the absence of a stress-fee sample. The position adopted

here is consistent with Hartup and Stevens (1997) thesis; close relationship partners

are apt to provide benefits in good times and bad (i.e., both main and buffering effects

are likely to occur). The proposed study contains structural and functional measures

of social life, and accounting for both will provide a more comprehensive

understanding of the links between social relationships and health.

Taken together these findings suggest social factors are influential

contributors to psychological adjustment, and to the functioning of the major

physiological systems important to long-term health, morbidity, and mortality risk

(McEwen, 1998). Longitudinally examining the connection between social

engagement and health and well-being outcomes has the potential to reveal important

information about this relationship. A strained social life could act as a chronic

19

stressor, contributing to poor psychological adjustment, and long-term health risk

(McEwen, 1998). Although the body may be particularly resilient in early adulthood,

the negative repercussions associated with lacking, or having poor social relationships

throughout adulthood may begin to manifest themselves more clearly during midlife,

when health begins to deteriorate. Conversely, the benefits associated with an

enriched social life may begin to show their value at the same stage, when resilience

is on the decline. Although testing multiple birth cohorts would be necessary to make

any conclusive statements to that effect, if the current hypotheses are supported it

lends evidence to such a theory.

A Lifespan Approach

Aging and health. According to the Trends in Health and Aging initiative

organized by the Centers for Disease Control, the prevalence of diabetes,

hypertension, and coronary heart disease each triple among individuals age 45 and

over (National Center for Health Statistics, 2002; 2005). This increase in illness and

disease prevalence is likely due to decreased resiliency that accompanies aging.

Across the years, repeated exposure to physical and psychological stressors weakens

the body’s stress response efficiency, and it becomes increasingly difficult to return to

baseline levels of physiological functioning (Sapolsky, 1992; Sterling & Eyer, 1981).

Decreased resilience is a corollary of the glucocorticoid cascade hypothesis of

aging (Sapolsky, 1992). In a complex set of processes, stress activates the HPA axis

to ultimately produce glucocorticoid hormones (e.g., cortisol). Activation of the HPA

axis is generally adaptive because it mobilizes physical resources that facilitate

20

coping with the demands of stressors. However, glucocorticoids have the side effect

of causing hippocampal senescence. When the hippocampus deteriorates it is

prevented from serving the important neuroendocrine function of inhibiting the HPA

axis from further glucocorticoid production (thus making it a cycle or “cascade” of

aging surrounding the regulation of stress hormone secretion; McEwen, 1998;

Sapolsky, 1992). Chronic stress, and/or failure to inhibit cortisol production may

result in a flattening of the natural diurnal cortisol pattern (which typically peaks

about 45 minutes after awakening, and declines over the course of the day), or an

elevation in its mean level. Such dysregulation signals risk for disease because

cortisol suppresses the immune system, and may advance the development chronic

diseases such as diabetes and hypertension (McEwen, 1998; Sapolsky, 1990; 1996).

Negative health consequences become progressively more apparent as individuals age

and once-efficient homeostatic mechanisms are reset to less effective levels of

functioning (Sterling & Eyer, 1981). By mid and later adulthood the glucocorticoid

cascade may be well underway, and resilience may already be diminished (Sapolsky,

1992). Research on loneliness supports this idea; physiological costs to the

cardiovascular and neuroendocrine systems associated with loneliness heighten with

age (Hawkley & Cacioppo, 2007).

HPA axis activity is only one of the body’s responses to stress. In broader

terms, exposure to stress triggers the allostatic mechanism, which brings about

changes in all of the body’s physiological systems (e.g., neuroendocrine,

cardiovascular, and immune) in order to protect the body from harm (McEwen,

21

1998), and return the body to basal levels of functioning. Over time, and under certain

conditions (such as frequent stress, or difficulty adapting to repeated stressors), the

“cumulative biological burden exacted on the body through attempts to adapt to life’s

demands” may have a lasting negative effect, termed allostatic load (McEwen, 1998;

McEwen & Steller, 1993; Seeman, McEwen, Rowe, & Singer, 2001, p. 4770).

HPA axis activation is one of the most common allostatic responses

(McEwen, 1998), and dysregulation is considered an early marker of allostatic load

(Abercrombie, Giese-Davis, Sephton, Epel, Turner-Cobb, & Spiegel, 2004). More

substantively, the HPA axis is particularly responsive to socially situated stressors

(Dickerson & Kemeny, 2004), presumably because group belonging and social

acceptance have strong functional significance to survival (Baumeister & Leary,

1995). Laboratory stressor that contained social-evaluative threats (e.g. public

speaking) produced significantly larger cortisol effects than stressors that did not

contain social-evaluative threats (e.g., noise exposure; Dickerson & Kemeny, 2004).

Thus, cortisol level and rhythm will be focal outcomes of interest in the proposed

study.

Early adulthood to midlife. The glucocorticoid cascade hypothesis seems to

suggest that stress in early and mid adulthood may be particularly important to later

life health. In a large-scale study of daily stress, the frequency of stressors was

highest in early adulthood (age 25-39), declined slightly into mid-adulthood (age 40-

59), and declined more sharply into older adulthood (age 60-74; Almeida, 2005;

Almeida & Horn, 2004; Birditt, Fingerman, & Almeida, 2005). Paradoxically, there is

22

relatively little research on the phenomena transpiring during adulthood that may

contribute to stress response efficiency. Skimming the contents of most human

development textbooks reveals that the lion’s share of attention surrounds changes

that occur through childhood and into adolescence. There is typically at least an entire

chapter devoted to old age; however, the section on middle adulthood usually

amounts to no more than a few pages. Believing that more than a few paragraphs

worth of interesting and important things are happening to people between the ages of

18 and 60 has energized an effort to study adulthood more closely. Large-scale

ventures such as the Midlife in the United States (MIDUS) study of more than 7000

American adults aged 25 through 74, and the Wisconsin Longitudinal Study (WLS)

of over 10,000 graduates from Wisconsin high schools, have begun to make headway

in filling this void. The current research is an attempt to contribute to that effort.

Adopting a lifespan approach is particularly useful for investigating the

relationship between social life and health. Not only is midlife a time when health

problems begin to emerge more rapidly, a growing and provocative literature

implicates experiences in early adulthood as a predictor of important outcomes in

later life. Findings from large epidemiological surveys, such as the MIDUS study,

show links between early life trauma (e.g., physical abuse, parental divorce) and

poorer mental and physical health in midlife (Goodwin, Hoven, Murison, & Hotopf,

2003; Maier & Lachman, 2000). In addition, positive emotional expression in early

adulthood predicts enhanced health and well-being outcomes decades later. In the so-

called “Nun Study,” a study of aging and Alzheimer’s among hundreds of nuns

23

belonging to the School Sisters of Notre Dame, a sub-sample of nuns were asked to

submit an autobiography prior to taking final vows in the early 1930s when the nuns

were approximately 22 years old. These handwritten autobiographies were coded for

emotional content (positive, negative, and neutral), and the nuns were surveyed

through the 1990s for mortality. Controlling for age and education, positive emotional

expression in the handwritten autobiographies was associated with decreased

mortality risk nearly 60 years later (Danner, Snowden & Friesen, 2001).

In a conceptually similar study, women’s college yearbook photos from the

late 1950s were coded for positive emotional expression. Women who displayed

relatively more “Duchenne” (genuine) smiles in their yearbook photos were

psychologically better adjusted, were more likely to become and stay married, and

were more likely to be satisfied in their marital relationship up to 30 years later. Of

importance, those results were not an artifact of physical attractiveness (Harker &

Keltner, 2001). Longitudinal effects such as these are believed to be mediated through

social channels. For example, emotional expressivity is hypothesized to shape social

experiences, which in turn contribute to health (Harker & Keltner, 2001; Keltner,

2003). In fact, in the yearbook study, women who displayed greater positive affect

were judged more favorably by independent observers, and were expected to be more

rewarding interaction partners (Harker & Keltner, 2001). However, little empirical

evidence exists to verify these mechanisms.

Social relationships as the mechanism. Social relationships provide a

theoretically plausible mechanism for linking early adulthood experiences to midlife

24

outcomes. At any stage in life social relationships have a powerful impact on human

development, influencing cognition, emotion, and the formation and maintenance of

subsequent relationships (Reis, Collins, & Berscheid, 2000). Object relations theorists

such as Sullivan (1953) and Bowlby (1969) argued that early social relationships give

rise to affective-cognitive structures related to self and social relations. Those

structures are believed to guide expectations about the manner in which interactions

between self and other should unfold throughout all of life (Bowlby, 1969; 1979;

Bretherton & Munholland, 1999).

Several lifespan development theorists concur that early adulthood marks the

point at which the formation of long-term adult friendships and love relationships

become the primary concern. Erikson (1959) was among the first to speculate about

development in adulthood, identifying the formation of close relationships as the

pivotal task of young adulthood. His psychosocial crisis of intimacy versus isolation

surrounds the acquisition, development, and maintenance of intimate social

relationships with friends and romantic partners during the late teens and early

twenties. Others have since argued for an analogous theme at this stage in life. For

example, Neugarten’s research has similarly identified early adulthood as the

prescriptive time on the “social clock” for emphasizing close intimate relationships,

such as marriage (Neugarten, Moore, & Lowe, 1965); White (1966) argued that a

developmental growth tendency in adulthood involved attending to relationships with

important others; and Levinson (1978) suggested that finding a spouse was one of the

key features of entering the adult world. Successfully establishing intimacy in early

25

adulthood fosters the ability to enter and maintain close relationships throughout the

remainder of adult life. For example, college students who achieved a higher level of

intimacy had greater knowledge and understanding of a same- or opposite-sex friend

(Orlofsky, 1976), and were more in touch with their own emotional experiences

(Orlofsky & Ginsberg, 1981), two qualities that would enhance their ability to form

and sustain close relationships.

Affirming the idea that there is something particularly important about the

relationships formed in late teens and early twenties, there is some evidence for

continuity in social relations throughout early adulthood (from college to age 30; Reis

et al., 1993), and even into old age (Field, as cited in Hartup & Stevens, 1997; Field,

1999; Roberto, 1997). Reis et al. (1993) found that although there were changes in the

nature of social interaction from the early twenties to age 30, intimacy levels

exhibited a substantial amount of stability across this time span. This suggests that the

quality of a person’s social ties is likely to persist over time, increasing the likelihood

of consistency in the benefits that will be afforded to individuals throughout life. If

individuals are surrounded with positive social ties when faced with various stressful

transitions occurring throughout adulthood (e.g., selecting a career, becoming self-

sufficient, buying a home, starting a family), those supportive others may repeatedly

facilitate adaptive psychological and physiological responses to stress.

Notwithstanding the continuity of relationship quality, the benefits gained

from good friends at any one point in time (e.g., psychological well being, coping

abilities, etc.) may be carried forth to future adaptations (Hartup & Stevens, 1997). In

26

other words, having an available and supportive network to aid one through a crisis

may provide an individual with certain tools that can be applied to future problematic

situations. Perhaps individuals embedded in extensive social networks of caring and

supportive friends will have repeated opportunities to develop adaptive coping

strategies that may be drawn upon over the life course (Hartup & Stevens, 1997).

Preliminary evidence indicates that a lifetime of positive social relation is associated

with reduced allostatic load among adults in their late 50s (Seeman, Singer, Ryff,

Love & Levy-Storms, 2002), suggesting that good relationships may indeed have a

cumulative positive effect on health. However, the sample is cross-sectional, and

reports are retrospective, two problems discussed in greater detail below.

In summary, models of relationships may shape the nature of intimacy

development in early adulthood, which sets the stage for the rest of adult social life.

If one has difficulty developing intimate relationships in early adulthood, or develops

an aversive pattern of social relations, this may act as a chronic stressor, wearing

down one’s resilience and increasing risk for disease (McEwen, 1998; Sapolsky,

1992). On the other hand, individuals who successfully navigate the intimacy task of

early adulthood have the potential to reap the psychosocial and physiological rewards

of good relationships for years to come.

The Current Research

Suggesting that social engagement may have a cumulative long-term effect on

health and well-being seems plausible based on the numerous studies reviewed above,

which concurrently link social factors with physiological and psychological

27

functioning. However, to better test the viability of such an argument, longitudinal

investigations are needed. An existing group of individuals who participated in detail-

rich social interaction research while they were undergraduates in college offers an

exceptional opportunity to longitudinally examine the long-term health and well

being correlates of early adult social life.

The necessity of longitudinal designs. Although longitudinal designs have

drawbacks of their own (they are expensive, they require extensive time and effort,

they are subject to attrition, and they possibly confound socioculturally significant

historal events with developmental changes) they offer at least three important

methodological advantages over cross-sectional designs. First, cross sectional studies

that show certain characteristics of social life to have similar means and correlates for

different age groups may not necessarily reflect true stability across time. A variety of

important changes may occur at the individual level, but change may be disguised if

individuals are changing in different directions and group means offer the only

comparison. For example, some individuals’ social life may worsen over time, while

others’ may improve, resulting in no overall mean difference. True change, and the

outcomes that may accompany it, is only revealed when same individuals are

followed across time. This is important for the current research because the

developmental journey does not end at age 18; a great number of transitions occur in

the lives of adults from age 18 to age 50. Transitions may include physical relocation,

finding a new social network, selecting a career, establishing financial independence,

buying a home, making a long-term romantic commitment (and possibly dissolving

28

that commitment), having children, watching grown children leave the nest, and

caring for elderly parents, just to name a few. Each of these relatively normative

events requires substantial adjustment, and is likely to bring about considerable

differences in social activity. Longitudinal research is able to illustrate individual

differences in the way people adjust to normative transitions, and relate those

differences to important later life outcomes.

Longitudinal investigations have the added benefit of reducing certain biases

associated with cross-sectional research designs. Cross-sectional studies must rely on

an individual’s retrospective aggregated impression of social life, the accuracy of

which cannot be determined. When reporting on events in the past, individuals tend to

rely on current circumstances to make heuristic-driven inferences about past behavior

and experience (Schwarz, Groves, & Schuman, 1998), and the retrospection bias

worsens with increased temporal distance from the recalled event (Skowronski, Betz,

Thompson, & Shannon, 1991). Furthermore, retrospective reports may be unduly

influenced by the participant’s mood at the time the measure is completed (Blaney,

1986). For example, in one study, individuals in a positive mood recalled fewer

negative life events, whereas individuals in a negative mood perceived social support

to be less available to them (Cohen, Towes, & Flocco, 1988). Thus, asking people to

retrospect about what social life was like in early adulthood produces a qualitatively

different type of evaluation. Although retrospective accounts are interesting in their

own right, longitudinal studies like this one, which obtain contemporaneous accounts

of social life, allow for a greater degree of accuracy by eliminating retrospection bias.

29

Finally, longitudinal investigations may help to shed light on causal

relationships. Undoubtedly, experimental manipulation is the standard criterion for

making cause and effect inferences. However, testing whether social life causes

disparities in long term health and well being is complicated because manipulating

individuals’ social lives on a large scale does not seem a viable (or reasonable)

option. When experimental manipulation is unsuitable, prospective longitudinal

studies may be the best strategy for exploring causal relationships (Cook & Campbell,

1979; Kenny, 1975; Taris, 2000). The design of this research is such that social life in

early adulthood was studied contemporaneously, and temporally precedes midlife

health and well-being outcomes. In addition, there is a theoretical rationale for the

hypothesized effects. If the data show the predicted effects, and if alternative

explanations are ruled out, the criteria for making a causal inference will be met

(Taris, 2000).

The existing sample. Three college-student samples from the University of

Rochester participated in event-contingent diary studies of social interaction between

1974 and 1980, and are currently between 49 and 52 years old (additional details

about the samples can be found in the Method section). The original studies were

aimed at uncovering sex differences in the patterns of social interaction among

college students (Wheeler & Nezlek, 1977), and the relationship between physical

attractiveness and social competence (Reis, Nezlek, & Wheeler, 1980; Reis, Wheeler,

Spiegel, Kernis, Nezlek, & Perri, 1982). These samples possess particularly desirable

characteristics for a longitudinal investigation of the correlates of early adult social

30

life because experience sampling methods were used to assess social life in early

adulthood. A weakness of the existing longitudinal research in this area is the one-

time assessments of social functioning using broad, global indicators. Such measures

have limited usefulness because they are vulnerable to the heuristics used in making

everyday prediction (Kahneman & Tversky, 1982), and they preclude identifying

which particular features of social life (e.g., frequency of interaction, number of

contacts, perceived interaction quality) may contribute to health. Experience

sampling methods have the ability to refine our assessment of social life.

Experience sampling methods. Experience sampling involves studying

ongoing social experience as it occurs in the natural ebb and flow of everyday life.

During the sampling period, participants complete a diary and report on the subject

matter of interest. Experiences are typically sampled using one of three protocols: at a

specified time interval (e.g., every evening before going to bed), in response to a

fixed or random signal (e.g., participants are alerted by a pager several times over the

course of a day), or following the occurrence of a specific event (e.g., each time a

social interaction occurs, as in the existing research).

The Rochester Interaction Record (RIR; Reis & Wheeler, 1991) is a

prominent experience-sampling instrument, and was used to assess early adulthood

social activity in the current research. The RIR (see Appendix A for a sample, but

note the earliest two samples completed a version that contained fewer questions,

namely intimacy and satisfaction as the only indicators of interaction quality) is an

event-contingent diary, which has participants complete a record describing pre-

31

selected characteristics of each social interaction lasting 10 minutes or more during

the designated diary period (typically 14 days). Investigators have successfully used

the RIR to examine an array of social interaction topics including conflict

(Pietromonaco & Feldman-Barrett, 1997), courtship (Milardo, Johnson, & Huston,

1983), friendship (Lydon, Jamieson, & Holmes, 1997), and intimacy (Reis et al.,

1993), among others.

Despite the fact that diaries are self-reports, an experience sampling approach

provides a more valid and reliable description of social life than that which is

obtained with single measurement surveys. Records are completed immediately

following each social interaction, lessening the delay between the occurrence and

report of events, and further reducing the amount of distortion associated with

aggregation and retrospection (Reis & Gable, 2000). Equally important, the RIR

contains diverse questions about the nature of each interaction, including both

objective (duration, number of people, sex composition) and subjective (intimacy,

satisfaction,) items. The variety of items allow for a more fine-grained analysis of

exactly which features of social life are predictive of later health and well being.

Although it would be desirable to use an experience sampling approach in the current

assessment, the resources and time commitment required for such an endeavor are

prohibitive.

Results from the original studies indicate that reports of college social life

collected with the RIR are related to social and health variables. For example, lonely

men and women interacted with women less frequently, and perceived their

32

interactions with men and women to lack meaning, relative to non-lonely people

(Wheeler, Reis, & Nezlek, 1983). In addition, individuals with poor quality

interactions visited the health center more often (Reis et al., 1982).

A subset of 114 participants were recruited from the three college samples and

followed up in 1985 when participants were approximately 30 years old. Participants

once again completed event-contingent social interaction diaries for 14 days, as well

as global reports of social functioning, emotional well being, and self perceived

health. Results revealed that over time, opposite-sex socializing became more

frequent, whereas same- and mixed-sex socializing became less frequent. In addition,

social interactions became more intimate over time, but not more satisfying. The

onset of these changes occurred slightly earlier for women (during college) than men

(between college and age 30; Reis et al., 1993). Although there were changes in the

nature of interactions across time, Reis et al. (1993) also found marked stability in the

number of interactions, the amount of time spent interacting with others, and

particularly the quality of interactions from college to age 30. These findings lend

support to the idea that socialization in early adulthood influences the remainder of

adult social life. Despite the numerous transitions occurring between college and age

30, and the changes in social activity that they impose, individuals maintained

important characteristics of the social milieu.

The current assessment. The research reviewed thus far demonstrates that

social relationships play a powerful role in determining concurrent health and well

being, and suggests that they may also have similar long-term effects. Given the

33

notable stability in certain features of social life across early adulthood (Reis et al.,

1993), lacking, or have poor social relationships during that period may imply

problematic relationships across the remainder of adulthood, which may act as a

chronic stressor. However, having good social relationships at that time is likely to

facilitate healthy adaptation to the transitions occurring throughout adulthood. Thus,

social circumstances may have a cumulative impact on health and well being, which

would be evident in midlife indices of adjustment and health. The existing data

provide an ideal opportunity for investigating this possibility.

The proposed research employs a longitudinal design to investigate the

midlife social and emotional well being, and biological health correlates of social

activity during college. The use of RIR data to assess social activity across early

adulthood improves upon previous longitudinal research because it allows for the

examination of specific and distinct features of the social environment as predictors

of later life outcomes, and it is less vulnerable to retrospection biases. Moreover,

these detailed accounts of social activity were obtained during the period critical to

adult relationship development (Erikson, 1959; Neugarten et al., 1965), and not

retrospectively.

Participants from the three previously described studies of college social

activity (between 49 and 52 years old at the time of the current assessment) were

recruited to participate in a follow-up study of health and well being. Midlife social

and emotional well-being was assessed with a series of internet-hosted

questionnaires. Health was measured with one objective (neuroendocrine function)

34

and several subjective (self report) indicators. A series of salivary cortisol samples

were collected over the course of an average day to capture HPA axis activity as one

indicator of physiological function, and self-reported health was assessed in the

online questionnaire.

Hypotheses. Support is sought for hypotheses in three domains of midlife

functioning: social well-being, emotional adjustment, and physical health (specific

measures of each are described in the Method section). Because structural (social

integration) and functional (relationship quality) measures of social life have both

been implicated in the outcomes of interest, quantity and quality relevant indicators of

social life will be tested as predictors for each hypothesis. Both types of variables are

provided by the RIR. Social integration will be operationalized as the average amount

of time spent socializing with others per day (time per day), and the average number

of different social interactions one has per day (number per day). Relationship quality

will be operationalized with ratings of intimacy and satisfaction for social

interactions.

In addition to testing overall social activity, hypotheses will also be separately

evaluated for same-sex and opposite-sex social activity. The nature of social

engagement changes over time whereby individuals tend to engage less in same-sex

interactions and more in opposite-sex interactions (Reis et al, 1993), however, it is

unclear whether relative differences in the amount of same- versus opposite-sex

social interaction in college are associated with differential later outcomes. Testing

each separately will allow us to evaluate whether those differences exist. The

35

following hypotheses are offered.

Social well-being hypothesis. Structural and functional social engagement in

college will be positively associated with better social well-being in midlife (Figure

1a).

Emotional adjustment hypothesis. Structural and functional social

engagement in college will predict enhanced emotional adjustment in midlife (Figure

1b).

Health hypotheses. Structural and functional social engagement in college

will predict better health in midlife (Figure 1c). This hypothesis will be tested

separately for self-reported (subjective) health and neuroendocrine function.

36

Chapter 2: Method

Participants

One hundred thirty three (59.9%) participants age 48-52 (Mage = 49.28; 73

female, 59 male, 1 transgender) were recruited for the current research from a pool of

222 eligible adults who had previously participated in one of three event-contingent

diary studies while they were undergraduates at the University of Rochester. The

remaining 89 potential participants could not be located (n = 42, 18.9%), explicitly (n

= 15, 6.8%) or tacitly (i.e., did not respond to multiple contact attempts) refused to

participate (n = 23, 10.4%), or were deceased (n = 9, 4.1%). Seventeen of the 133

participants (12.8%) refused to submit saliva samples, but provided survey data.

College data could not be recovered for four of the 133 current participants,

effectively resulting in a sample of 129 participants who provided both college and

adult data.

Of the 133 current participants, 34 (25.6%) were recruited from college

sample I (original n = 58). These participants had engaged in one to two waves of a

ten-day diary study of sex differences in social interaction during the 1974 – 1975

academic year, when they were freshman (Wheeler & Nezlek, 1977). Forty-three

(32.3%) participants were recruited from college sample II (original n = 77). These

participants were also college freshman at the time of initial participation. They

completed between two and four waves (administered twice each in the fall and

spring semesters) of a ten-day diary study of physical attractiveness and social

competence in the 1976 – 1977 academic year (Reis, Nezlek, & Wheeler, 1980).

37

Lastly, 56 (42.1%) participants were recruited from college sample III (original n =

113).1 This sample consisted of seniors who participated in a fourteen-day diary study

of physical attractiveness and social competence in the 1979 – 1980 academic year

(Reis, Wheeler, Spiegel, Kernis, Nezlek, & Perri, 1982; see Table 2 for a summary).

Procedure

Participants were mailed a recruitment invitation at their most recently

available mailing address. Current contact information was obtained based on

information provided at the last assessment, as well as information made available

through online locator services including the University of Rochester alumni

database, online phone books, Yahoo people search, www.zabasearch.com, and

www.merlindata.com.

Recruitment invitations requesting phone numbers and e-mail addresses were

mailed to potential participants. Return receipt of the contact sheet prompted a

telephone call to give an overview of the project, and discuss details of participation.

Those who agreed to participate were mailed a participation kit including consent

forms, a cover letter with complete instructions for participation, a saliva sampling kit

(described below), and a postage-paid, self-addressed return envelope. In addition,

participants were emailed links to the multi-part web survey of health and well-being

(described below), along with detailed instructions for completing the web surveys,

and an ID code to use in place of other identifying information. Participants were

instructed to return all materials to our laboratory on the day following saliva

collection. Upon receipt of the completed materials, participants received a fifty-

38

dollar honorarium.

College Measures

As discussed above, the RIR (Reis & Wheeler, 1991) includes questions that

assess interaction quantity, or structural social integration, as well as questions that

assess interaction quality, or functional social support. Two questions were selected

to represent interaction quantity (average time per day spent interacting and average

number per day of interactions), and two questions were selected to represent

interaction quality (average interaction intimacy, and average interaction satisfaction).

Time per day and number per day were significantly correlated for overall social

activity (r = .42, p < .001), same-sex social activity (r = .81, p < .001) and opposite-

sex social activity (r = .88, p < .001), thus time per day and number per day were

standardized and combined into three respective interaction quantity variables

(overall, same-sex, opposite-sex). Similarly, intimacy and satisfaction were

significantly correlated for overall social activity (r = .43, p < .001), same-sex social

activity (r = .41, p < .001), and opposite-sex social activity (r = .30, p < .001), and

were combined into three respective interaction quality variables (overall, same-sex,

opposite-sex).

Midlife Measures

Surveys. An Internet survey-hosting website (www.surveymonkey.com)

hosted a three-part assessment of social and emotional well-being, and self-reported

health, described in detail below. Participants logged into the survey with a unique

identifying code and completed the self-paced questionnaire at their convenience. The

39

majority of survey sections were completed in one sitting (93.1%) with an average

completion rate of 38 minutes per section.

Emotional adjustment measures. A set of emotional well-being measures was

carefully selected to reflect both the positive and negative dimensions of well-being,

which have recently been described as independent (Ryff et al., 2006). The measures

were intended to cover key emotional adjustment constructs with minimal

redundancy.

Psychological well being. A 54-item measure of psychological well-being

assessed six domains of wellness (autonomy, environmental mastery, self-acceptance,

positive relations with others, personal growth, and purpose in life) with 9 items per

subscale (Ryff & Keyes, 1995). Responses were rated on a 6-point scale ranging from

disagree strongly to agree strongly. Respective Cronbach’s alphas for the six

subscales are: .86, .81, .90, .91, .80, and .79.

Depression. The Center for Epidemiological Studies 20-item measure

assessed depression during the preceding week (CES-D; Radloff, 1977; ! = .90).

Responses were rated on a four-point scale ranging from rarely or none of the time

(less than 1 day) through most or all of the time (5-7 days). In addition, the 13-item

depression subscale of Symptom Checklist 90 (SCL-90; Derogatis, 1977) measured

depressive symptomatology during the preceding four weeks (! = .88). Responses

were recorded on a 5-point scale from not at all through extremely.

Anxiety. A 10-item subscale of the Symptom Checklist 90 (SCL-90;

Derogatis, 1977) measured anxiety during the preceding four weeks (! = .74).

40

Responses were recorded on a 5-point scale from not at all through extremely.

Stress. The 10-item Perceived Stress Scale (PSS; Cohen, Kamarck, &

Mermelstein, 1983) measured feelings of stress over the last month (! = .87).

Responses were made on a 5-point scale from never through very often.

Life satisfaction. Life satisfaction was measured with the 5-item Satisfaction

with Life Scale (SWLS; Diener, Emmons, Larsen, & Griffin, 1985). Responses were

made on a 7-point scale ranging from strongly disagree through strongly agree (! =

.88).

Loneliness. A 10-item version of the UCLA loneliness scale (UCLA-LS;

Russell, 1996) measured feelings of social isolation with responses given on a 4-point

scale from never to often (! = .89). The 10 items were the same ones that had been

used with one of Russell’s (1996) samples (teachers) during scale validation.

Mood. Positive and negative affect experienced during the preceding week

was assessed with the 18-item version of the Positive and Negative Affect Schedule

(PANAS-X; Watson & Clark, 1994). Eight positive and 10 negative emotions were

rated on a 5-point scale ranging from very slightly or not at all through extremely

(respective Cronbach’s alphas were .90 and .87).

Social well-being measures. A set of questionnaires was selected to assess

social adjustment at broad and specific levels. General questionnaires captured

overall level social integration, whereas detailed questionnaires assessed specific

types of relationships relevant to most individuals in midlife (including romantic

relationships, and closest friends).

41

Social integration. The Social Network Index (Cohen, Doyle, Skoner, Rabin,

& Gwaltney, 1997) assessed levels of social integration. Participants reported on the

number of different kinds of relationships they had, and the frequency with which

they had contact with each. Indices of social network size (i.e., number of different

social contacts), and social network diversity (i.e., number of social roles filled) were

derived from this measure.

Perceived social support. The Kessler Perceived Support Scale (KPSS;

Kessler, Kendler, Heath, Neale, & Eaves, 1992) measured perceptions of the

availability of tangible, emotional and esteem support from six sources: family (! =

.85), friends (! = .87), spouse/partner (! = .97), people from church/synagogue (! =

.97), people from neighborhood (! = .93), and professional colleagues (! = .94).

Ratings are made on a 4-point scale ranging from 1 not at all through a great deal.

Relationship quality with core group of friends. Participants were asked to

think about their core network of closest friends. This was described as “the people

they see and interact with most often, participate in activities with (e.g., dinner,

shopping, golf, etc.), and would include in the planning of a get-together.”

Participants completed four 6-item subscales (social intimacy, ! = .60; emotional

intimacy, ! = .79; intellectual intimacy, ! = .57; recreational intimacy, ! = .77) of the

Personal Assessment of Intimacy in Relationships (PAIR; Schaefer & Olson, 1981) to

measure how intimately connected they felt to this core group of relationship

partners. Ratings were made on a 9-point scale ranging from not at all true through

completely true. In addition, participants reported their closeness to this group using

42

the 1-item Inclusion of Other in the Self (IOS) scale, in which they select one of

seven pairs of increasingly overlapping circles that best represents their feelings of

closeness to the group (Aron, Aron, & Smollan, 1992).

Romantic relationship quality. Participants who reported being involved in a

primary romantic relationship were asked to describe the quality of that relationship.

Relationship quality measures included the IOS (Aron et al., 1992) to measure

closeness; the same four 6-item subscales of the PAIR (social intimacy, ! = .79;

emotional intimacy, ! = .92; intellectual intimacy, ! = .85; and recreational intimacy,

! = .81; Schaefer & Olson, 1981) used to measure intimacy with friends; the Couples

Satisfaction Inventory (CSI; Funk & Rogge, 2007) to measure relationship

satisfaction (! = .98); and the Perceived Partner Responsiveness scale (PPR;

Caprariello & Reis, 2006) to measure the extent to which participants believed that

their romantic partners understand, validate, and care for core aspects of the self (! =

.98).

Self perceived health measures. A selection of self-report measures of health

was chosen to assess participants’ feelings about their health status, health behaviors,

and any existing health conditions they may have. These measures were selected to

succinctly obtain as complete a picture of physical health status as possible. In

addition to the measures described below, participants reported height, weight,

cholesterol level, blood pressure levels, and how recently they had a medical

checkup, cholesterol test, and blood pressure check.

SF-36. Twenty-seven items from the Medical Outcomes Study Short Form 36

43

(SF-36; Ware & Sherbourne, 1992) assessed participants’ health in six domains:

physical functioning, physical limitations, emotional limitations, social functioning,

pain, and general health. Nine items related to depression were excluded to prevent

redundancy. Some items referred to experiences over the preceding four weeks, and

others assessed global perceptions without indicating a time frame.

Symptoms. The Cohen-Hoberman Inventory of Physical Symptoms (CHIPS;

Cohen & Hoberman, 1983) measured the extent to which participants were bothered

by 33 different symptoms of physical illness experienced over the past two weeks.

Ratings were made on a 5-point scale from not at all through extremely.

Prior conditions. Participants reported on whether they had ever been

diagnosed with any of 25 medical conditions (e.g., high cholesterol, high blood

pressure, diabetes, stroke, hayfever, etc.). Items were obtained from the National

Health Interview Survey (NHIS) conduced by the National Center for Health

Statistics of the Centers for Disease Control.

Sleep quality. The Pittsburgh Sleep Quality Index (PSQI; Buysse, Reynolds,

Monk, Berman, & Kupfer, 1989) assessed sleep quality. Participants responded to

questions about their sleep over the last month, spanning 7 categories including sleep

quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances,

use of sleeping medication, and daytime dysfunction.

Other measures. Participants completed the following additional self-report

measures in the online survey.

Demographic information. Participants reported on several demographic

44

variables including age, gender, race, ethnicity, education, and subjective

socioeconomic status (Adler, Epel, Castellazzo, & Ickovics, 2000), and financial

security.

Personality. A 50-item measure of the Big-Five personality traits

(surgency/extraversion, ! = .88; agreeableness, ! = .85; conscientiousness, ! = .86;

emotional stability/neuroticism, ! = .81; intellect/imagination, ! = .84; Goldberg et

al., 2006) assessed participants’ personality with 10 items per subscale. Responses

were rated on a 9-point scale ranging from extremely inaccurate to extremely

accurate.

Neuroendocrine function. To determine diurnal cortisol rhythm, participants

provided five saliva samples during a single sampling day using a protocol developed

by Kirschbaum and colleagues (Kirschbaum, Kudielka, Gaab, Schommer, &

Hellhammer, 1999; Schmidt-Reinwald, Preessner, Hellhammer, Federenko, Rohleder,

Schumeyer, & Kirschbaum, 1999; Wust, Federenko, Hellhammer, & Kirschbaum,

2000) and the John D. and Catherine T. MacArthur Research Network on

Socioeconomic Status and Health (Stewart & Seeman, 2000).

The number and timing of saliva samples to be collected was based on

guidelines determined at a meeting of the MacArthur Research Network on

Socioeconomic Status and Health held at Rockefeller University in December 1999

(Stewart & Seeman, 2000). The meeting participants were internationally recognized

researchers investigating diurnal cortisol measures. They agreed on a strategy of a

one-day, 6-sample protocol for establishing changes in magnitude and rhythm of

45

cortisol in study populations. We collected 5 of the 6 samples, dispensing with the

bedtime sample. Collection times were: (1) awakening (before getting out of bed); (2)

45 min after awakening; (3) 2.5 h after awakening; (4) 8 h after awakening; and (5)

12 h after awakening.

Saliva was collected using the Salivette collection system (Sarstedt,

Germany). Participants were instructed not to brush their teeth for at least 30 min

prior to any of the five saliva samplings to avoid possible contamination of samples

with blood from micro injuries, and were asked to refrain from brushing their teeth

until after the second sample of the day (see below) had been collected. Participants

were also asked to refrain from eating for 10 min prior to saliva sampling; particularly

foods low in pH (i.e., fruit juice). At the designated times, participants gently chewed

on the cotton Salivette swab for approximately 1 min. This generally resulted in a

saliva sample of 0.5-1 ml. Following collection, participants were asked to refrigerate

the samples, until mailing the entire set to our laboratory the next day.

A number of considerations are important for analyzing cortisol data. The

MacArthur Network established 6 categories of factors that have important

implications for variation in cortisol rhythm including (1) individual characteristics

(e.g., age and gender); (2) menstrual cycle, contraceptive use, medication use; (3)

general health, especially infectious disease, which may be associated with flattening

of the diurnal rhythm; (4) food intake, exercise, wake-up time; (5)

psychological/psychosocial factors; and (6) alcohol/smoking. Participants maintained

a log on the sampling day, and provided information on five of the six categories,

46

including food intake, smoking/alcohol usage, medications taken, exercise,

awakening time, and hours of sleep. The daily log did not contain measures of

psychological/psychosocial factors, which were assessed in the larger online

questionnaire.

Sample storage and shipping. Samples were mailed to the laboratory at room

temperature. When samples arrived, they were frozen at -20°C to precipitate mucins.

Samples were assayed in bulk, with all samples from a given subject run in the same

batch. At the time of assay, samples were thawed completely, vortexed, and

centrifuged at 1500g for 5 min. The clear saliva was pipetted into appropriate wells of

the assay plate.

Cortisol Assay. All samples were assayed for salivary cortisol using a HS-

cortisol high sensitivity enzyme immunoassay kit (Salimetrics, PA). The test has a

lower limit of sensitivity of .007 !l/dl, range of sensitivity from .007 to 1.2 !g/dl, and

average intra-and inter-assay coefficients of variation 4.13% and 8.89%, respectively.

The correlation between saliva and serum is highly significant, r (17)=0.96, p<.0001.

The kit contains a built-in pH indicator that warns the user of acidic or basic samples.

As noted below, samples with an unusually high pH were excluded from analyses.

47

Chapter 3: Results

Participant attrition and comparability. Independent samples t-tests

compared the 129 participants in the current wave of data collection to 80 of the 89

eligible individuals from the original samples who did not participate in the current

study (9 non-participants were missing college data). There were no significant

differences in the college interaction quantity or quality variables for the two groups

(all ts < -1.50, ns; see Table 3), suggesting that recruited participants did not differ

from the unrecruited participants in any meaningful way in college social activity.

Two additional ANOVAs tested whether 1) sex, and 2) original sample from

which participants were recruited (I, II, or III, as described above), interacted with

current participation status. A 2 (Sex: male, female)2 x 2 (Participation Status:

recruited, unrecruited) ANOVA on 18 measures of college social activity revealed no

main effects of participation status, and no Sex X Participation Status interaction

reached significance. Similarly, a 3 (Original Sample: I, II, II) x 2 (Participation

Status: recruited, unrecruited) on the same 18 measures of college social activity

revealed that only 1 (5.6%) Original Sample X Participation Status interaction

reached significance. Figure 2 shows that currently recruited participants from sample

I had fewer opposite-sex interactions in college (M = 1.33) than unrecruited

participants from sample I (M = 1.98), whereas the reverse was true for sample II

(recruited M = 1.76, unrecruited M = .107), and there were no differences in the

number of opposite-sex interactions between recruited (M = 2.00) and unrecurited (M

= 1.79) participants from sample III, F(2, 201) = 3.68, p < .05.

48

Although there were sex differences in the social activity variables, the null

findings involving current participation status suggest that recruited participants did

not differ from unrecruited participants in any meaningful way on college social

activity variables.

An additional set of independent samples t-tests compared the college social

activity of unrecriuted participants who expressly declined (n = 15, but college data

were missing for two) versus those who tacitly refused by non-response to repeated

contact attempts (n = 23, but college data were missing for two). These two groups

did not differ in quantity or quality of overall, same-, or opposite-sex college social

activity (all ts < 1.36, all ps > .18). Moreover, neither of these sub-groups of

unrecruited participants differed from recruited participants on any of the college

social activity predictors (all ts < 0.89, all ps > .38). Thus, differences in the

willingness to respond to this outreach attempt did not vary as a function of early

adult social life.

Data Reduction

Outcomes. Multiple measures assessed three broad categories of midlife

outcomes: social well-being, emotional adjustment, and self-reported physical health.

For parsimony and ease of interpretability, principal components analysis was

undertaken to reduce the number of outcomes to a smaller number of summary

variables representing each broader category. Principal components were accepted

based on three criteria: 1) Kaiser's criterion - that each principal component’s

eigenvalue is greater than 1; 2) scree test - a clear leveling-off of eigenvalues after the

49

last interpretable factor with an eigenvalue greater than one; and 3) interpretability.

To account for the differential scaling of individual measures, z-scores were

computed before combining measures to create unit-weighted composite factor

scores.

Social well-being measures. A principal components analysis with oblimin

rotation3 reduced the 10 measures of social relationship quality (social network

diversity; social network size; perceived support, closeness and intimacy with core

friend group; and perceived support, closeness, intimacy, satisfaction, and perceived

partner responsiveness from romantic partner for those participants currently

involved in a romantic relationship) to three principal components (see Table 4 for

factor loadings, eigenvalues, and communalities). The first factor, containing four

measures assessing romantic relationship quality (perceived support, closeness,

satisfaction, and perceived partner responsiveness for romantic partners), accounted

for 44.0% of variance. Factor two consisted of three scales assessing relationship

quality with core friend group (perceived support, closeness, and intimacy), and

accounted for 22.9% of variance. The third component, capturing social network,

contained two items (social network diversity, and social network size), accounting

for 14.5% of variance. Intimacy with romantic partner did not load strongly enough

with the other romantic relationship quality variables, instead loading on the

friendship quality factor. However, because of its lack of theoretical fit with the

friendship variables, this variable was not included on either factor, but was retained

50

for separate analysis. Three social well-being summary variables were created:

romantic relationship quality, friendship quality, and social network.

Emotional adjustment measures. A principal components analysis with

oblimin rotation reduced 14 measures of emotional well-being (satisfaction with life,

loneliness, perceived stress, anxiety, two measures of depression, positive and

negative affect, and the six subscales of Ryff’s psychological well-being measure:

personal growth, purpose in life, self acceptance, autonomy, environmental mastery,

and positive relations with others) to three principal components. Table 5 reports

factor loadings, eigenvalues, and communalities. The first factor, accounting for

52.0% of variance, contained items associated with positive emotional adjustment

(life satisfaction, self-acceptance, positive affect, positive relations with others, and

the absence of loneliness). The second factor, accounting for 11.2% of variance,

contained items associated with impoverished emotional adjustment (negative affect,

both measures of depression, anxiety, and perceived stress). Factor three, accounting

for 7.3% of variance, contained items related to self-actualization, or maximizing

one’s inner potential (personal growth, purpose in life, and autonomy). The

environmental mastery subscale loaded equally on all three factors, and was omitted

from any factor because of its inconsistency. Three emotional adjustment summary

variables were created: positive affectivity, negative affectivity, and self-actualization.

Physical health measures. A third principal components analysis reduced

nine self-reported indicators of physical health (cholesterol level, systolic blood

pressure, diastolic blood pressure, body mass index, history of cardiac diagnoses

51

[proportion of 7 conditions: high cholesterol, high blood pressure, coronary heart

disease, angina, heart attack, other heart condition, and stroke], history of respiratory

diagnoses [proportion of 4 conditions: emphysema, asthma, sinusitis, and chronic

bronchitis], history of endocrine diagnoses [proportion of 7 conditions: diabetes, pre-

diabetes, impaired fasting glucose, borderline diabetes, high blood sugar, and family

history of diabetes], symptomatology, and a combination of the 4 physical health

related subscales of the SF-36 [physical functioning, the extent to which physical

health limits activity, pain, and a global measure of self-perceived healthfulness]) to

three principal components. Factor loadings, eigenvalues, and communalities are

reported in Table 6. Three variables loaded on the first component (symptomatology,

SF-36 physical health, and respiratory diagnoses), accounting for 25.7% of variance.

The second factor also contained 3 variables (cardiac diagnoses, systolic blood

pressure, and diastolic blood pressure), accounting for 16.1% of variance. The third

and final factor contained 3 variables (cholesterol, body mass index, and endocrine

diagnoses), accounting for 12.5% of variance. Cardiac diagnoses loaded on both the

second and third factors, but had a stronger factor loading on component two, and

was placed there with the two blood pressure items. Three physical health summary

variables were created: daily symptomatology, cardiac health I, and cardiac health II.

Neuroendocrine function. One hundred sixteen of 133 current participants

provided between 3 and 5 saliva samples over the course of one sampling day for a

total of 571 saliva samples. The majority of participants provided all five samples (n

= 109, 94.0%), five participants missed one sample, and two participants missed two

52

samples. The samples were assayed for cortisol, as described above. Twenty-one

cortisol values were omitted because the pH of the sample was outside of an

acceptable range, and the cortisol assay thus could not be trusted. An additional 21

values were omitted because they were more than two standard deviations away from

the mean cortisol level for that sampling time, and were also considered unreliable.

The multiple cortisol measurements taken over the course of one day were

combined into two cortisol variables (Pruessner, Kirschbaum, Meinlschmid, and

Hellhammer, 2003). One variable captured total cortisol concentration over the course

of the day (area under the curve with respect to ground; AUCg). The second variable

removes the area between zero and the first cortisol value of the day to capture

changes in cortisol over the course of the day with respect to a person’s starting

cortisol value (area under the curve with respect to increase4; AUCi). Cortisol

concentration (AUCg) and change (AUCi) were negatively correlated (r = -.24, p <

.05); a greater change (i.e., steeper decline in cortisol values) over the course of the

day was associated with a lower overall cortisol concentration.

For all analyses involving these two cortisol variables, an additional set of

analyses were conducted that also controlled for the previously described factors

reputed to influence cortisol levels: sex, age, hours of sleep the previous night,

whether the participant exercised, smoked, or experienced any symptoms of illness on

the sampling day, and oral contraceptive use (for females only). Although only two of

these factors were significantly related to cortisol (participants who got more sleep on

the night prior to sampling had lower cortisol concentration, AUCg r = -.28, p < .01;

53

and participants who smoked on the sampling day had a smaller change in cortisol

values over the course of the day (AUCi r = -.21, p < .05), they were all included as

controls, and negligibly altered the results obtained without their inclusion. The

effects presented thus do not include the aforementioned controls.

Sex, Original Sample, and Freshman Versus Senior Status

A set of MANOVAs examined differences in midlife outcomes as a function

of sex, original college sample, and their interaction. A contrast on the original

sample variable compared individuals who participated as freshman (samples I and

II) to those who participated as seniors (sample III). As previously noted, 26

individuals participated in both samples II and III, and were randomly assigned to one

of the two samples (see Footnote 1 for an explanation). There were no significant

main effects of original sample on any of the midlife social well-being (see Table 7),

emotional adjustment (see Table 8), or physical health (see Table 9) composites, or

cortisol concentration (see Table 10). There was a significant effect of the college

year contrast on the friendship quality composite; participants from the freshman

samples had better quality friendships at midlife (M = .12) than participants from the

senior sample (M = -.09; F[1,122] = 5.64, p < .05). The college year contrast did not

have a significant effect on any other midlife composite variable (see Tables 7-10).

The Original Sample X Sex interaction reached significance for two of the

physical health composite variables: daily symptomatology (F[2,126] = 4.65, p <

.05), and cardiac health II (F[2,68] = 5.85, p < .01). As shown in Figure 3, simple

effects tests revealed that men and women from samples I and III did not differ

54

significantly from each other in daily symptomatology (sample I means: men = -.10,

women = -.16, t[31] < 1, ns; sample III means: men = .00, women = -.06, t[54] < 1 ,

ns). However, women from sample II reported significantly more symptoms (M =

.44) than men from sample II (M = -.36; t[41] = -3.48, p < .01). For cardiac health II,

as shown in Figure 4, simple effects revealed that women from sample I had

significantly more cardiac health problems (M = .88) than men from sample I (M = -

.32; t[16] = -4.01, p < .01), however the sex difference in cardiac health II was not

significant for men and women from samples II (men M = .25, women M = -.08; t[21]

= 1.04, ns), or III (men M = -.04, women M = -.01; t[33] < 1, ns).

The College Year X Sex interaction reached significance for one of the

emotional adjustment composite variables, positive affectivity, F(1,120) = 5.05, p <

.05. Simple effects tests show there was no difference in positive affectivity for men

(M = -.16) and women (M = .07) who participated as freshman (t[72] < 1, ns),

however women who participated as seniors reported significantly more midlife

positive affectivity (M = .35) than men who participated as seniors (M = -.32; t[52] =

-3.19, p < 01) (see Figure 5).

Eleven of 192 (5.7%) tested main and/or interactive effects involving original

college sample and college year emerged as significant. The largely null findings

involving these two variables suggest that original college sample and freshman

versus senior status did not have a reliable or consistent impact on midlife social,

emotional, health, or neuroendocrine outcomes, and these two variables are not

further discussed. Sex main effects emerged on several midlife variables (also

55

included in Tables 7-10), and are explored below when they moderate the key

analyses of interest.

Concurrent Analyses

Pearson correlations were conducted to examine relationships among midlife

outcomes. Table 11 contains a matrix of correlations among social well-being,

emotional adjustment, and physical health composite scores, which are described

below. Correlations between composite scores and the individual measures that were

used to create each composite are contained in Table 12 (social well-being and

emotional adjustment individual measures) and Table 13 (physical health and

neuroendocrine individual measures).

Correlations revealed that, at midlife, those who had better social lives were

also better emotionally adjusted. Adults with better friendships and romantic

relationships had less negative affectivity in midlife (respective rs = -.40, -.36, both

ps < .001), and more positive affectivity (respective rs = .62, .47, both ps < .001) and

self-actualization (respective rs = .28, .27, both ps < .001). A similar pattern emerged

between midlife social network and emotional adjustment, though the effect was

somewhat weaker for negative affectivity outcomes (respective correlations between

social network and negative affectivity, positive affectivity, and self actualization: r =

-.16, p < .08, r = .38, p < .01, r = .22, p < .01)

Social relationship quality was generally unrelated to physical health.

Romantic relationship quality and social network were not significantly correlated

with daily symptomatology or either cardiac health composite (all "r"s < .14, all ps >

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.17). Friendship quality was not significantly related to daily symptomatology (r = -

.05, ns), or the second cardiac health composite (r = -.12, ns), but was negatively

correlated with the first cardiac health composite (consisting of prior cardiac

diagnoses, and systolic and diastolic blood pressure; r = -.27, p < .01). In other words,

individuals who have better social relationships at midlife also have better cardiac

health at midlife.

Midlife emotional adjustment was not consistently related to midlife health

outcomes. Negative affectivity was associated with greater daily symptomatology (r

= .29, p < .01), but unrelated to either cardiac health composite (both"r"s < .10, both

ps > .32). Positive affectivity was unrelated to daily symptomatology or the second

cardiac health composite (both "r"s < .12, both ps > .18), but was negatively

correlated with the first cardiac health composite (r = -.20, p < .05). Participants

experiencing more positive emotional adjustment reported better cardiac health. Self-

actualization was unrelated to any of the health composites (all "r"s < .12, all ps >

.19).

Midlife social relationship quality was not significantly related to cortisol

concentration (AUCg) or change (AUCi) for romantic relationship quality (respective

rs = .08, and -.07, ns), friendship quality (respective rs = -.06, and -.03 ns), or social

network (respective rs = .13, and -.03, ns). Emotional adjustment was also unrelated

to neuroendocrine function; correlations with negative affectivity, positive affectivity,

and self-actualization did not reach significance for cortisol concentration (respective

rs with AUCg= -.02, .03, .13, all ps > .19) or cortisol change (respective rs with

57

AUCi= .04, -.05, .02, all p’s > .19). Daily symptomatology and the first cardiac health

composite were not significantly correlated with cortisol concentration (respective rs

= -.05, .03, both ps > .60), but the second set of cardiac health outcomes was

marginally associated with higher cortisol concentration (r = .19, p = .06).

Participants who reported poorer cardiac health (higher cholesterol, higher BMI, and

a greater proportion of endocrine diagnoses) had higher mean levels of cortisol. None

of the physical health composites were significantly correlated with cortisol change

(daily symptomatology r = .06, cardiac health I r = .13, cardiac health II r = -.11, all

ps > .25).

Sex differences in concurrent correlations. Table 14 contains correlations

among the composite variables separately for men (below the diagonal) and women

(above the diagonal), revealing the general pattern of concurrent relationships

reported above to be highly similar for the two sexes. A few notable sex differences

emerged, and when they did, for the most part concurrent correlations between social

well-being and physical and emotional outcomes were stronger for men than for

women. The correlation between social network and self-actualization (r = .22, p <

.05) was stronger for men (r = .39, p < .01), but was not significant for women (r =

.10). The correlation between friendship quality and cardiac health I (r = -.27, p <

.01) was also predominantly driven by men (r = -.25, p < .10); the relationship

between these two variables was not significant for women (r = -.06). Although the

overall relationship between friendship quality and daily symptomatology was not

58

significant, and the same was true for men (r = -.01) women with better friendships

had marginally lower daily symptomatology (r = -.22, p < .10).

Longitudinal Prediction of Midlife Outcomes

Analysis strategy. Each midlife summary outcome variable was

simultaneously regressed onto the two primary college social activity composite

predictors: interaction quantity (the proxy for structural integration) and interaction

quality (the proxy for functional support). Sex, and the interaction between sex and

each predictor were also included in a second, incremental model.5 Analyses were

conducted for overall college social activity, as well as separately for same-sex and

opposite-sex college social activity. The text reports effects from the first model. As

mentioned above, sex main effects emerged on several midlife outcomes. When sex

moderated the effect of interaction quantity or quality on an outcome variable, the

results are described below.6

Tables 15 through 24 (as indicated below) present regression coefficients

(standardized #s) for both models on the composite outcome variable in the first

column, as well as the effects of college interaction quantity and quality on each of

the individual outcome measures comprising the summary variable in the remaining

columns.

Social life

Romantic relationship quality. Analyses of midlife romantic relationship

quality variables were limited to those participants who indicated they were

“currently involved in an exclusive, committed, romantic relationship” (n = 113).

59

Prior to examining romantic relationship quality, romantic relationship status was

tested as an outcome, and was not significantly predicted by overall college social

activity (quantity # = .02, ns; quality # = -.05, ns), same-sex college social activity

(quantity # = .10, ns; quality # = -.01, ns), or opposite-sex college social activity

(quantity # = -.07, ns; quality # = -.05, ns).

College social activity was not significantly related to midlife romantic

relationship quality (quantity # = .12, ns; quality # = -.04, ns). The same was true for

same-sex social activity (quantity # = .10, ns; quality # = -.04, ns) and opposite-sex

social activity (quantity # = .04, ns; quality # = -.07, ns). See Table 15 for a complete

summary of romantic relationship quality effects.

Although sex did not moderate the effects of college interaction quantity or

quality on the midlife romantic relationship quality composite for overall, same- or

opposite-sex interactions, there was a significant Same-sex Quantity X Sex

interaction on variable in the romantic relationship quality composite: perceived

partner responsiveness (# = .23, p < .05). Calculation of simple slopes revealed that

the quantity of same-sex interaction one had in college was marginally positively

associated with midlife perceived partner responsiveness for women (# = .25, p =

.09), whereas the association was not significant, for men (# = -.19, ns; see Figure 6).

Because intimacy with romantic partner (PAIR-R) did not load with the other

romantic relationship quality measures in the principal components analysis, it was

examined on its own. Both frequency and quality of social activity in college were

positively associated with romantic relationship intimacy in midlife (quantity # = .24,

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p < .05; quality # = .22, p < .05). In other words, participants with more frequent

social activity in college, and participants with higher quality social activity in

college, reported more intimate romantic relationships in midlife.

Friendship quality. Participants who had more frequent social activity in

college had better quality friendships in midlife (quantity # = .21, p < .05). Although

there was not a significant effect of social activity quantity on perceived support from

friends (# = .12, ns), participants with more frequent college social activity felt

marginally closer to their core friend group (# = .16, p < .10), and had significantly

greater intimacy with their friend group (# = .24, p < .01). The effect of college

interaction quantity on midlife friendship quality was not significant for same- or

opposite-sex college interactions (both #s < .13, ns).

College social interaction quality was also associated with enhanced

friendship quality in midlife (# = .28, p < .01). In other words, participants who had

more intimate and satisfying interactions in college also reported better quality

friendships at age 50. This effect was consistent across all three measures making up

the friendship quality composite: perceived support # = .21, p < .05; closeness # =

.24, p < .01; intimacy # = .23, p < .01. College interaction quality was a significant

predictor of midlife friendship quality for both same-sex social activity (# = .31, p <

.01) as well as opposite-sex social activity (# = .19, p < .05), although across the

measures making up the friendship quality composite, the effects were consistently

stronger for same-sex college social activity quality (perceived support # = .24, p <

.01; closeness # = .26, p < .01; intimacy # = .27, p < .01) than for opposite-sex

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college social activity quality (perceived support # = .08, ns; closeness # = .21, p <

.05; intimacy # = .17, p < .10). See Table 16 for complete results.

Although sex did not moderate the effect of interaction quantity or quality on

friendship quality for overall, same-, or opposite-sex social activity, there was a

significant Overall Interaction Quantity X Sex interaction on one of the measures in

the friendship quality composite: perceived support (# = .19, p < .05). Calculation of

simple slopes revealed that more frequent social activity in college was associated

with an increase in midlife perceived support from friends for women (# = .23, p <

.05), thought the relationship was not significant for men (# = -.16, ns; see Figure 7).

Social network. Quantity of social activity in college was positively associated

with social integration in midlife (# = .24, p < .01). People who were well connected

in college had larger (network size # = .24, p < .01) and more diverse (network

diversity # = .20, p < .05) social networks in midlife. The effect of college interaction

quantity on the social network composite was weaker for same-sex quantity (# = .15,

p < .10), and non-significant for opposite-sex quantity (# = .03, ns).

College interaction quality was unrelated to social integration in midlife, for

overall (# = .04, ns), same-sex (# = .04; ns), and opposite-sex social activity (# = .05,

ns). See Table 17 for a complete summary of effects.

Although sex did not significantly moderate the effects of social activity on

the social network composite variable for overall, same- or opposite-sex social

activity, there was a significant Same-sex Interaction Quality X Sex interaction on

one of the variables in the social network composite, network size (# = -.22, p < .05).

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Calculation of simple slopes revealed that better quality same-sex social interaction in

college was associated with having a larger social network in midlife for men (# =

.29, p < .05), but not for women (# = -.08, ns; see Figure 8). The same pattern of sex-

differences in the relationship between college social activity and midlife social

network size emerged for overall and opposite-sex interaction quality, but both

interactions were marginally significant (both #s = -.18, both ps < .10).

Emotional adjustment

Negative affectivity. College social activity quantity was marginally associated

with reduced negative affectivity in midlife (# = -.17, p < .10). In other words,

participants who had more frequent social interaction in college reported less negative

affectivity in midlife. This effect was predominately driven by marginally lower

midlife negative affect (NA # = -.15, p < .10), and significantly lower midlife

depression (CESD # = -.19, p < .05; SCL90D # = -.19, p < .05); frequency of college

social activity was not associated with midlife anxiety (SCL90A # = -.07, ns) or

perceived stress (PSS # = -.11, ns). The effect of social activity quantity on negative

affectivity was not significant for same-sex social activity (# = -.03, ns) or opposite-

sex social activity (# = -.09, ns). See Table 18 for these results.

Interaction quality was not significantly related to midlife negative affectivity

for overall (# = -.06, ns), same-sex (# = -.08, ns), or opposite-sex (# = -.05, ns)

college social activity.

Positive affectivity. College social activity quantity was positively associated

with midlife positive affectivity (# = .29, p < .01). Participants with more frequent

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social activity in college reported greater life satisfaction (# = .24, p < .01), self-

acceptance (# = .26, p < .01), more positive social relationships (# = .30, p < .01),

more positive affect (# = .23, p < .05), and marginally less loneliness (# = -.16, p <

.10) in midlife. The same general pattern was true for same-sex and opposite-sex

college interaction quantity, but the effects were attenuated for the composite social-

activity variable (same-sex # = .14, p < .10; opposite-sex # = .19, p < .05), as well as

the individual positive emotional adjustment measures making up the positive

affectivity composite (see Table 19 for individual regression coefficients).

College social activity quality was marginally positively associated with

midlife positive affectivity (# = .17, p < .10). This effect was predominately driven by

lower midlife loneliness (# = -.24, p <.01), and more positive relationships with

others (# = .23, p < .05); social activity quality was not significantly related to life

satisfaction (# = .05, ns), self-acceptance (# = .10, ns), or positive affect (# = .05, ns).

The pattern was identical for same-sex college social activity quality (positive

affectivity # = .18, p < .05; loneliness # = -.27, p < .01; positive relations with others

# = .29, p < .01), but was not significant for opposite-sex social activity quality. See

Table 19 for regression coefficients.

Self-actualization. Participants with more frequent social activity in college

reported higher levels of self-actualization in midlife (# = .25, p < .01). Time spent

interacting with others in college was positively associated with later personal growth

(# = .24, p < .01), and purpose in life (# = .22, p < .01, and marginally positive

associated with midlife autonomy (# = .16, p < .10). When examining same- and

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opposite-sex social activity quantity, the effects on midlife self-actualization are not

significant for either (same-sex quantity # = .08, ns; opposite-sex quantity # = .11,

ns).

College interaction quality was not significantly related to later life self-

actualization for overall (# = .03, ns), same- (# = .04, ns), or opposite-sex (# = .01,

ns) social activity. Although the Interaction Quantity X Sex interaction did not reach

significance for the self-actualization component, there was a significant Interaction

Quantity X Sex interaction on one of the measures making up the self-actualization

composite, personal growth (# = -.18, p < .05). Simple slope calculations revealed

that more frequent social activity in college is associated with greater personal growth

for men (# = .44, p < .01), but the effect was not significant for women (# = .08, ns;

see Figure 9). Table 20 contains complete regression coefficients for self-

actualization outcomes.

Physical health

Daily symptomatology. College social activity was not significantly related to

midlife daily symptomatology (quantity # = .05, ns; quality # = -.05, ns). The same

was true for same-sex social activity (quantity # < .01, ns; quality # = -.05, ns), and

opposite-sex social interaction quality (# = -.09, ns). However, there was a positive

relationship between opposite-sex social interaction quantity in college and midlife

daily symptomatology (# = .20, p < .05). Participants who had more frequent social

activity in college reported experiencing more symptoms in the 4-weeks prior to

completing the current project (# = .19, p < .05). The effect was not significant for

65

global evaluations of health status (SF-36 # = -.13, ns) or respiratory conditions (# =

.14, ns). See Table 21 for a complete summary of effects

Cardiac health I. Social interaction quantity in college was not significantly

related to the first set of midlife cardiac health outcomes (cardiac health I: cardiac

diagnosis history, systolic and diastolic blood pressure) for overall (quantity # = -.09,

ns), same- (quantity # = -.08, ns) or opposite-sex (quantity # = -.07, ns) social

activity.

College social activity quality was, however, associated with enhanced

midlife cardiac health (# = -.21, p < .05). The effect was significant for same-sex

interaction quality (# = -.23, p < .01) and marginally significant for opposite sex

interaction quality (# = -.17, p < .10). Closer inspection revealed that participants who

had more intimate and satisfying interactions in college reported lower systolic blood

pressure in midlife (overall # = -.25, p < .01; same-sex # = -.28, p < .01; opposite-sex

# = -.19, p < .10). The effects for cardiac diagnosis history and diastolic blood

pressure were weaker. See Table 22 for the relevant statistics.

There was a significant Social Activity Quality X Sex interaction on one of

the individual cardiac health measures, cardiac diagnosis history (# = -.21, p < .05).

Calculation of simple slopes revealed that higher quality interactions in college were

associated with fewer midlife cardiac diagnoses for women (# = -.26, p < .05),

whereas the effect was not significant for men (# = .16, ns; see Figure 10). An

identical pattern of sex differences emerged on cardiac diagnosis history for opposite-

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sex social interaction quality (# = -.20, p < .05), but the interaction was weaker and

not significant for same-sex college social interaction quality (# = -.15, ns).

Cardiac health II. College social activity was not significantly related to

midlife cardiac health for the second set of cardiac outcomes (cardiac health II:

cholesterol, BMI, and endocrine diagnoses; quantity # = .09, ns; quality # = -.07, ns).

The same was true for same-sex (quantity # = .05, ns; quality # = -.07, ns), and

opposite-sex (quantity # = .02, ns; quality # = -.10, ns) college social activity. See

Table 23 for the regression results.

There was a significant Social Activity Quality X Sex interaction on the

cardiac health II composite (# = -.24, p < .05). As with cardiac diagnosis history,

calculation of simple slopes revealed that better quality social activity in college was

associated with a reduction in cardiac problems for women (# = -.23, p < .05),

whereas the effect was not significant for men (# = .25, ns; see Figure 11). The same

pattern of sex differences in this effect was evident for same- (# = -.22, p < .05) and

opposite-sex (# = -.23, p < .05) college social interaction quality.

Neuroendocrine function. College social activity was not significantly related

to cortisol concentration (AUCg; quantity # = .14, quality # = -.08, ns) or change in

cortisol values over the course of the day (AUCi; quantity # = -.08, quality # = -.04).

The same was true for same- and opposite-sex college social activity; neither quantity

nor quality was a significant predictor of cortisol concentration or cortisol slope. See

Table 24 for regression coefficients.

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Summary of longitudinal findings. Table 25 contains a summary of the

overall effects of social activity quantity and quality, sex, and the Quantity X Sex and

Quality X Sex interactions on the nine social well-being, emotional adjustment, and

physical health composite variables, the two cortisol variables, and romantic

intimacy. College social interaction quantity showed the hypothesized effects on three

of four social well-being outcomes, and all three emotional adjustment outcomes, but

no physical health outcomes. College social interaction quality showed the predicted

effects on two of four social well-being outcomes, one of three emotional adjustment

outcomes (though the effect was marginal), and one self-reported physical health

outcome. College interaction quantity was thus a more consistent predictor of midlife

adjustment than college interaction quality.

Controlling midlife social relationship quality. Given that college social

activity was positively and significantly correlated with midlfe social relationship

quality, it might be argued that the effects of college social activity on midlife health

and well-being outcomes are attributable to current social well being. Indeed, there is

some continuity in social activity from college to early adulthood (Reis et al., 1993),

and individuals are likely to reap benefits of immediately available positive social

relationships. However, early adult social activity was expected to exert unique

effects on midlife outcomes. To examine this hypothesis, a set of regression analyses

was conducted to examine the effects of college social activity on midlife emotional

adjustment, physical health, and neuroendocrine function, controlling for midlife

social well-being outcomes.

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These analyses revealed the effects of college social interaction quantity on

midlife emotional adjustment to be relatively unchanged (see Table 26). Although the

marginal effect of social activity quantity on midlife negative affectivity (# = -.17, p

< .10) was attenuated when controlling midlife social well-being (#s ranged from -.06

to -.14, depending on which social well-being variable was controlled, all ns), social

activity quantity remained a significant predictor of midlife positive affectivity and

self-actualization when controlling romantic relationship quality (positive affectivity

# = .21, self-actualization # = .20, both p’s < .05), friendship quality (positive

affectivity # = .17, self-actualization # = .18, both p’s < .05), and social network

(positive affectivity # = .21, self-actualization # = .21, both p’s < .05).

The effects of college social activity quality on the first midlife cardiac health

composite were also relatively robust. Social activity quality continued to predict

cardiac health I when controlling midlife romantic relationship quality (# = -.26, p <

.05), friendship quality (# = -.18, p < .10), and social network (# = -.21, p < .05).

In sum, controlling midlife social relationship quality seemed to have little

impact on the other longitudinal findings, suggesting that the predictive effects cannot

simply be reduced to current social circumstances, or long-term continuity in social

adjustment.

Controlling personality. Participants’ perceptions of their social worlds, and

thus their self-reports of social activity, social relationship quality, and emotional

adjustment may be subject to perceptual biases associated with certain personality

traits. In an attempt to discern whether personality factors were responsible for the

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obtained effects, analyses were conducted in which the two most social interaction

relevant personality characteristics were controlled: neuroticism and extraversion.

Results are presented in Table 27.

The first set of analyses attempted to replicate the longitudinal findings while

controlling neuroticism, a dispositional bias toward perceiving and experiencing the

world negatively (John & Srivastava, 1999). The longitudinal effects of social

interaction quantity and quality on social, emotional, and health outcomes remained

virtually unchanged with neuroticism controlled. A comparison of the neuroticism-

controlled effects presented in the left side of Table 27 to the uncontrolled effects

presented in Table 25 revealed that the only substantive changes were that the two

marginal effects (of college interaction quantity on negative affectivity, and college

interaction quality on positive affectivity) became significant (respective #s = -.15

and .16, both ps <.05) with neuroticism controlled.

The second set of analyses controlled extraversion, a personality trait

associated with a high level of sociability and positive emotionality (John &

Srivastava, 1999). Controlling extraversion weakened some of the effects of college

interaction quantity (though not quality) on midlife social relationship quality (see

right side of Table 27). The effects of college interaction quantity on midlife romantic

intimacy (# = .14), friendship quality (# = .06), negative affectivity (# = -.13), and

self-actualization (# = .10) were reduced to non-significance (all ps > .05. The effect

of interaction quantity on positive affectivity was attenuated as well, but remained

significant (# = .17, p < .01). The effects of college interaction quality, however, were

70

more robust when extraversion was controlled. College social interaction quality

continued to predict romantic intimacy (# = .21, p < .05), friendship quality (# = .24,

p < .01), and the first cardiovascular health variable (# = -.23, p < .01). One college

social interaction quality effect was attenuated, the effect on midlife positive

affectivity (# = .12, ns).

Where personality variables did impact the findings of this research, it is

noteworthy that the less subjective college social activity variable (interaction

quantity), which is expected to be least vulnerable to self-report biases associated

with personality traits, was attenuated when extraversion was controlled.

Nevertheless, these results must be interpreted with caution because personality was

assessed currently, and not in college. Although personality is said to remain stable

across time, it is unclear how socialization in early adulthood may impact later self-

reports of personality. An assessment of neuroticism and extraversion in college

would allow more confidence in these results, a point I will address further in the

discussion.

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Chapter 4: Discussion

This research explored the interconnections among social, emotional, and

physical health in a sample of midlife (age 48-52) adults. Concurrent relationships

between midlife health and well-being were investigated as well as longitudinal

effects of early adult (age 18-22) social engagement on the same midlife outcomes. A

summary and discussion of concurrent findings will be followed by a summary and

discussion of longitudinal results.

Summary of Concurrent Findings

The concurrent results of this research are consistent with some widely

accepted, and empirically supported basic assumptions about the interplay between

social relationship quality and emotional well-being. Midlife adults who were better

socially adjusted (i.e., those who reported better romantic and friend relationships,

and larger and more diverse social networks) also reported being better emotionally

adjusted (e.g., they experienced less negative affect, depression, anxiety, stress, and

loneliness, and more positive affect, life satisfaction, positive relationships, self-

acceptance, purpose, growth, and autonomy). Fifteen of 16 composite variables were

significantly correlated, and one correlation was marginally significant, all in the

expected direction (shown in the top two panels of Table 11). Among the individual

variables making up the composites, 57 of 66 correlations were significant, and three

were marginal (shown in the left two panels of Table 12). This is consistent with prior

theory and research indicating that social and emotional adjustment are reciprocally

related (Arnstein, 1984; Bowlby, 1969, 1979; Harker & Keltner, 2001; Hartup &

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Stevens, 1997; Reis, Collins, & Berscheid, 2000; Rook, 1984). However, whereas

many studies show one facet of social life to be related to one or two emotional

adjustment outcomes, the current research incorporated a variety of measures to

capture multiple dimensions of social and emotional well-being, couching individual

associations between social and emotional life in a broader context of positive

adjustment.

As for self-reported physical health, only one significant correlation with

midlife social well-being emerged: People who felt more closely connected with their

core group of friends also reported better cardiac health -- that they were less likely to

have been previously diagnosed with cardiovascular conditions. Although this single

result might be due to chance, the finding fits well with existing research showing

that good social ties enhance cardiac health. It is consistent with Uchino and

colleagues’ work showing that a greater number of positive and supportive

relationships reduce cardiovascular reactivity to stress (Uchino et al., 2001; Uchino et

al., 1992), and also dovetails with other work showing that supportive social ties

enhance recovery and prognosis following acute cardiovascular problems such as

congestive heart failure (Coyne et al. 2001), heart attack (Helgeson, 1991), and

coronary artery bypass surgery (King et al, 1993). However, cardiac diagnosis history

was the only measure of physical health that was significantly correlated with any

measure of social well-being, and it was correlated with friendship quality, not

romantic relationship quality. Several of the studies mentioned above have been

conducted with married couples, and the effects have been shown for spousal support.

73

Only Uchino’s work looks at cardiac health in the context of other types of

relationships. Why friendship quality, but not romantic relationship quality, predicts

cardiac health in this research remains unclear. It is noteworthy that this particular

outcome was relatively less subjective as far as self-report measures go (in that

participants were asked to indicate how many of several cardiac disease or disorders

had been previously diagnosed by a doctor). Given the outside source of diagnosis,

this measure of health may be less susceptible to the biases associated with memory

or personality traits like neuroticism discussed previously.

Another noteworthy finding that emerged from the concurrent analysis was

the correlation between positive emotional adjustment and cardiac health (again, a

reduction in cardiovascular condition diagnoses). Some evidence has suggested that

the health benefits associated with positive and supportive ties may reflect a social

life enriched with positive emotional experiences. Other research has found short-

term and long-term health benefits of emotional experience and expression (Danner,

Snowden, & Friesen, 2001; Fredrickson, 2000; Fredrickson & Levenson, 1998;

Harker & Keltner, 2001; Mendes et al., 2003), and theorized that social life is the

mechanism. Broadened social resources (Fredrickson, 2000) and more frequent

engagement in high quality social interaction (Keltner, 2003) are two explanations.

This chicken-and-egg problem yet remains unsolved. Although mediation analysis is

statistically possible (and reveals that friendship quality, rather positive emotional

adjustment, remains the significant predictor of cardiac health when both variables

are entered into a regression equation), the lack of temporal difference in assessment

74

of the two variables prevents an unequivocal causal statement. In all likelihood,

others gravitate toward happy people who express positivity because they anticipate

rewarding social interactions with them (Byrne & Clore, 1970). Disclosure of positive

emotion is well received and reinforced by interaction partners, fostering further

positive emotional experience, and creating a social and emotional upward spiral.

Summary of Longitudinal Findings

The results of this research partially supported the longitudinal hypotheses.

The first hypothesis concerned midlife social well-being, and was largely supported;

social activity in college was generally associated with better midlife social outcomes.

High quantity and high quality college social activity was associated with better

friendships -- that is, friendships characterized by greater perceived support, closeness

and intimacy with a core group of confidants -- in midlife. Individuals who reported

more frequent and better quality social encounters in college also reported more

intimate relationships with their romantic partners as adults. Moreover, frequent

social interaction in college was associated with participant reports of larger and more

diverse social network in midlife, though the quality of college social activity was

unrelated to social network size in midlife. Thus, frequent socializers in college were

well connected to sizeable, diverse networks in middle adulthood, a pattern that

presumably remained stable throughout adulthood.

The emotional adjustment hypothesis was also largely supported: Social

activity in college was generally associated with participant reports of enhanced

emotional adjustment in midlife. Frequent social activity in college was associated

75

with higher levels of midlife hedonic and eudaimonic well-being (less negative affect,

depression, and loneliness, and more positive affect, life satisfaction, self-acceptance,

positive relationships, personal growth, purpose, and autonomy). High quality (i.e.,

intimate and satisfying) social activity in college was also associated with certain

positive emotional adjustment outcomes in midlife, particularly those relating to

positive social relationships (i.e., the absence of loneliness and the presence of

positive relationships).

The emotional adjustment findings are consistent with Hartup and Stevens's

(1997) theorizing that positive social engagement throughout life facilitates

adaptation and contributes to well-being. Their review indicated that a major benefit

of having friends in early life is the enhancement of feelings of self-worth. The

current research suggests that this carries through life because those who were more

socially embedded in college seemed to accept, value, and feel more positively about

themselves in midlife.

By and large, the quantity of social activity during college was more strongly

related to midlife psychological well-being than was the quality of social activity

during college (which was also generally true for social well-being outcomes).

College interaction quantity effects were relatively robust across the emotional

adjustment composites and individual measures, with the exception of two midlife

outcomes: anxiety and perceived stress. Perhaps fluctuations in these two variables

are more vulnerable to small environmental variations (e.g., a particularly stressful

day, week, or month may be more likely to alter perceptions, and thus reports of

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anxiety and stress) than are the other variables included in the emotional adjustment

composites (e.g., depression, life satisfaction, self-acceptance, positive and negative

affect, loneliness, and positive relationships with others). Effects were less consistent

for college interaction quality, which seemed to best predict the two midlife measures

that were theoretically most closely associated with interaction quality: loneliness,

and positive relationships with others. I will address this in greater detail below, when

I discuss the relative effects of structural and functional support.

The results for the physical health hypothesis were generally weaker.

However, there was some evidence that midlife cardiac health was enhanced among

individuals who reported having high quality social activity in college – one of two

cardiac health composites was predicted by college social interaction quality, and the

effect was driven by a reduction in self-reported systolic blood pressure. Consistent

with this finding, there was also a negative trend between college social interaction

quality and cardiac diagnosis history, such that people with more intimate and

satisfying social lives in college were less likely to report having been diagnosed with

high cholesterol, high blood pressure, coronary heart disease, angina, heart attack,

stroke, or any other heart condition by the time they had reached midlife. This trend,

however, did not reach significance.

The effect sizes obtained in the current study were modest. The proportion of

variance explained (R2) by college social activity ranged from .03 for the smallest

marginal effect (on negative affectivity) to .12 for the largest significant effect (on

friendship quality). However, considering these effects were obtained over 30 years,

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small effect sizes are not surprising, and the consistency of the results within each

category of outcomes lends further credibility to the authenticity of these findings.

Unsupported Longitudinal Hypotheses

Notwithstanding the effects that were uncovered, several noteworthy non-

significant results emerged as well, particularly for romantic relationship and physical

health outcomes.

Romantic relationship quality. Although college social activity was related

to midlife romantic intimacy, it did not significantly predict romantic relationship

status, or quality (e.g., closeness, support, responsiveness) for those who were

involved in committed, monogamous relationships. It is interesting that romantic

intimacy was predicted by college social life, whereas the other measures of romantic

relationship quality were not. Intimacy was positively and significantly correlated

with each of the other measures of romantic relationship quality (rs ranged from .37

to .46, all ps < .001), but loaded with friendship quality variables in the principal

components analysis. The elements of intimacy that are predicted by early adult

socialization apparently differ from the elements that intimacy shares with feeling

close to, satisfied with, and supported and responded to by a romantic partner. This

may be partly explained by examining the different facets of intimacy assessed by the

PAIR. Some subscales addressed the extent to which romantic partners engage, as a

couple, in activities and relationships external to their primary relationship, such as

spending time with other couples, sharing interests, and mutually enjoying fun

recreational activities (e.g., social and recreational intimacy). Other subscales (e.g.,

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emotional and intellectual intimacy) are more similar to the other romantic

relationship specific measures included in this study (perceived support, closeness,

satisfaction, and responsiveness), which more specifically focused on participants’

feelings about the romantic partner, and what the romantic partner provides to the

participant within the dyad. In an ancillary analysis, a closer examination of the

subscales revealed college social activity was more strongly related to the social and

recreational intimacy subscales of the PAIR (both quantity #s > .17, both ps < .07)

than to the emotional or intellectual intimacy subscales of the PAIR (both quantity #s

< .15, ns). Thus, socialization in college predicted a better social life in the context of

later romantic relationships (what may be characterized as an interpersonal benefit),

but was not related to specific emotional benefits derived solely from the romantic

partner (perhaps characterized as an intrapersonal benefit).

It is somewhat surprising that midlife romantic relationship quality was not

predicted by early adult social life. Childhood friendships are said to be the precursors

to adolescent romantic life (e.g., Hartup & Stevens, 1997), thus it would seem to

follow that early adult social engagement would foster later life romantic ties. Hartup

and Stevens (1997) argued that same-sex relationships in childhood and adolescence

set the stage for fulfilling intimacy needs in early adulthood. Once established, they

may play a key role in later romantic relationship development by preparing young

adults for the opposite-sex socializing that facilitates the pivotal relationship-relevant

developmental transitions common to early adulthood. These transitions include

finding a romantic partner, getting married, and starting a family, as described by

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Erikson (1959), Levinson (1978), White (1966), and others (e.g., Neugarten et al.,

1965). Sex-specific social activity was examined to see if it would account for the

lack of longitudinal effects on romantic relationship quality, but there was little

difference in midlife romantic relationship quality as a function of same-sex versus

opposite-sex social engagement in college. In fact, neither same-sex nor opposite-sex

social activity in college predicted midlife romantic relationship quality. Midlife

romantic intimacy, however, was predicted by same-sex and opposite-sex college

social activity quality (but not quantity). This is similar to the sex-specific effects for

midlife friendship quality outcomes, where both same- and opposite-sex social

activity show similar patterns of prediction – college social activity quality, but not

quantity, was associated with better midlife friendships.

Just as same-sex friends play an important role in bolstering well-being and

feelings of self-worth through adolescence (Hartup & Stevens, 1997), the current

research shows they continue to do so into midlife. However, the results of this study

suggest a somewhat different pattern than Hartup and Stevens discussed. Although

same-sex social engagement was still important for many aspects of adaptation and

adjustment, in the current sample it was not instrumental in later romantic life (the

longitudinal effects of same-sex social activity on midlife romantic relationship

quality were not significant). It also appeared that while same-sex relationships

remain important in early adulthood (e.g., same-sex social activity in college

longitudinally predicted enhanced midlife friendship quality and positive affectivity),

as Hartup and Stevens suggest, opposite-sex relationships gain importance (e.g.,

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longitudinal effects of opposite-sex college social activity on midlife friendship

quality and positive affectivity were also significant). Although midlife romantic

relationship quality was not predicted by same- or opposite-sex social activity, other

important outcomes were. Apparently socializing with opposite-sex peers in early

adulthood becomes increasingly important to later-life adjustment, notwithstanding

the development of a romantic relationship, or the quality of that relationship.

Physical health. Several studies have shown evidence for subjective and

objective physical health gains as a function of social integration (Berkman & Syme,

1979; Cacioppo et al., 2000; Cohen et al., 1997; House et al., 1988) and positive

social interaction (Baker et al., 2000; Berkman, 1995; Carels et al., 1998; Coyne et

al., 2001; Gump et al., 2001; King et al., 1993; Robles et al., 2006; Uchino et al.,

2001). However, the current research generally failed to replicate these effects. For

the most part, subjective physical health outcomes and neuroendocrine function were

unrelated to concurrent measures of social well-being or emotional adjustment, or

college social activity.

Although one of the cardiac health composites was predicted by both college

interaction quality, and midlife friendship quality, daily healthfulness outcomes (e.g.,

physical functioning, pain, participants’ global self-perceptions of healthfulness, daily

experience of specific symptoms, or respiratory problems including asthma and

sinusitis) and other measure of cardiac health (e.g., cholesterol, BMI, and endocrine

system disorders such as diabetes) were unrelated to college social activity variables,

or midlife social and emotional adjustment variables. Concurrently three of eighteen

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correlations (16.7%) with subjective physical health outcomes were significant, and

longitudinally, one of six effects (16.7%) of college social activity on self-reported

physical health was significant. Moreover, despite several studies indicating that

social relationships and social activity are related to HPA axis activity and

neuroendocrine function (Cacioppo et al., 2002; Kiecolt-Glaser et al., 1998; Malarkey

et al., 1994; Robles et al., 2006), neither the midlife measures of social well-being nor

the college measures of social engagement were significant predictors of cortisol

concentration or diurnal rhythm. The lack of findings begs explanation, and (apart

from the straightforward explanation that they are simply unrelated) two possible

alternative explanations come to mind: measurement issues and health status.

Measurement concerns. The measurement of social life in this study,

particularly in college, was quite different from measures typically employed in

research of this nature. In assessing social activity with the RIR this research captured

facets of social life that are typically unrepresented. Social life is typically measured

in terms of social integration (e.g., Berkman & Syme, 1979; Cohen et al., 1997;

House et al., 1988), or global perceptions of relationship quality in a general sense

(e.g., Cacioppo et al., 2000; Uchino et al., 2001), or with a specific target (e.g., Baker

et al., 2000; Coyne et al., 2001; Gump et al., 2001; King et al., 1993). This is among

the first research to use such rich and in-depth measures of social activity to represent

social life, particularly in a longitudinal study. The RIR captures social activity across

diverse relationships, ranging from superficial to exclusive, from frequent interaction

partners to chance encounters, and combines across all types of relationships and

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interactions to provide what is arguably a relatively more objective picture of social

life than that captured by more global measures. Undoubtedly, all kinds of measures

are informative and useful, however, the different results seem to possibly reflect a

pattern in which more global measures appear to better relate to health. Perhaps the

ways in which individuals subjectively weave together the details of their social lives

to build a coherent story is partly responsible for these effects.

Whereas the measurement of social life may have been relatively more

objective than is customary, the current measurement of health was relatively more

subjective than is typical with this type or research. As reviewed above, health effects

have been observed for a large range of outcomes, all of which were objectively

evaluated by a blood test, a biomarker, or medical professional. The assessment of

self-reported health in this research may have been influenced by biases associated

with self-report measures, as discussed previously. Also, as noted above, the least

subjective measure of health (prior diagnoses) was one of the only health outcomes to

show an effect.

Neuroendocrine function was the only truly objective biological measure

included in this study, and may have been affected by the time frame of assessment

and the level of specificity with which it was measured. Many studies showing an

association between social variables and HPA axis activity measure both variables in

the same session, looking, for example, at how cortisol levels fluctuate as a function

of interacting with a specific significant other, or looking at how the presence of a

meaningful other alters HPA axis reactivity to an outside stimulus. Given the

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dispersion of participants across the United States and abroad, it was not possible to

collect this data in the lab, where the simultaneity of the self-report and saliva data

could have been ensured. This design prioritized the flexible accommodation of

participant’s schedules in order to maximize the amount of data obtained by allowing

take-home materials to be completed on a self-paced timetable. Thus, the time frame

between saliva sampling and survey completion varied among participants. To be

sure, I explicitly made participants aware of the importance of adhering to the saliva-

sampling schedule and completing the surveys in a timely manner. An electronic time

stamp on survey submissions made compliance transparent, but adherence to saliva

sampling procedures could not be verified by anything other than the participant’s

report. Some participants completed all tasks on the same day, whereas others spread

them out over several weeks. More than 75% of participants completed all tasks

within a three-week time frame. Nevertheless, concurrent effects involving

neuroendocrine function were not obtained.

Second, perhaps the slice of HPA axis activity that was captured to represent

neuroendocrine function was too narrow. Cortisol was sampled on one day, and it is

unclear whether that day was adequately representative of a participant’s typical HPA

axis activity. Although participants were asked to select a day that they expected to be

typical, the day could have turned out to be unexpectedly stressful or relaxed.

Perceived stress was not assessed on the sampling day, and thus could not be

controlled.

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Health status. Attempting to reconcile the non-significant findings in this

study with prior longitudinal findings (Berkman & Syme, 1979; Holt-Lundstad,

Smith, & Layton, 2010; House, Landis, & Umberson, 1988) brings to mind a second

plausible explanation for the lack of health effects in this research. The current

sample, just approaching their early fifties, is barely on the precipice of a time frame

when health begins to deteriorate. As it stands, there was a relatively low incidence of

illness in the sample. Only 16.5% of participants had been diagnosed with respiratory

illness, less than 40% had been diagnosed with any kind of cardiac issue, and only

21% had been diagnosed with any kind of endocrine system problem. Fewer than

10% of the sample had dealt with cancer and only one participant had kidney or liver

problems. Ninety percent of the sample reported daily symptom (CHIPS) scores of

less than .50 on a 0-4 scale and more than 70% of participants earned a score of 85 or

better (out of 100) on the physical health subcales of the SF-36. Taken together, it

appears that the sample was generally quite healthy. Prior research has examined

samples of a wider age range, which include a larger number of older participants.

For studies in which the population was already suffering a serious health

complication, participants were older, on average, than the current sample, and

contained a greater number of older participants (e.g., average age = 53 for Coyne et

al., 2001, with participants up to 78 years of age; average age = 60 for King et al.,

1993, with participants up to 80 years of age). Where mortality risk was the key

outcome of interest, it was most common among the highest age bracket (e.g. 60-69

years of age; Berkman & Syme, 1979). At the time of assessment nine participants

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from the original pool of 222 had died (college data for 8 was available). Although an

n of eight is insufficient for comparison, an analysis was undertaken comparing the

college social activity of the eight deceased participants with the college data from

the 163 of 171 participants who were confirmed to be alive at the time of current

assessment (college data for 8 living participants were missing).7 No differences in

college social activity were uncovered.

As discussed previously, the age range of the participants at the time of

current assessment seemed ideal, given the general tendency for onset of several

serious health problems at this juncture. However, in this healthy sample, health

problems were relatively uncommon and showed little variance. Socioeconomic

status (SES) may provide one plausible explanation for the lack of health findings.

Poor socioeconomic standing is associated with greater susceptibility to illness and

disease (Adler et al., 2000; Williams & Collins, 1995), yet the current sample was

relatively comfortable, well educated, well employed, and financially secure. Over

98% were college graduates and more than 70% of the sample reported having an

advanced degree. Seventy percent indicated that it was “not at all difficult” to meet

expenses, just over 90% of the sample saw themselves at step seven or higher on

Adler et al.’s (2000) 10-rung ladder of subjective socioeconomic status, and fewer

than 15% indicated that there was not enough money to meet their family's needs.

Thus, given the relative lack of variability in subjective SES, it comes as no surprise

that controlling for SES left the findings of this research virtually unaltered. The

sample's relatively high socioeconomic standing may have served as a health-

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protective factor, delaying the onset of vulnerability to health problems. Seeman

(1996) reported that evidence for mortality and mental health outcomes was stronger

and more consistent than evidence for other physical health outcomes, especially

when looking at reasonably healthy people. Although it may be premature to examine

mortality in this relatively young sample, mental health (i.e., emotional adjustment)

outcomes showed some of the strongest effects in this research. It is particularly

interesting to note that the cardiac diagnosis history variable, one of the only physical

health variables for which effects of early adult social activity were found, was the

most frequently endorsed health issue. It was nearly twice as common as the next

closest health issue (endocrine related problems, such as diabetes or high blood

sugar). It would be informative to track this sample over time, and observe whether

health disparities related to early life social activity emerge as participants continue to

age and health concerns become increasingly prevalent.

In sum, the lack of significant health results might be a function of

measurement or procedural problems, or might reflect a true lack of association

between early adult social activity and midlife health (an explanation deemed less

likely given extant published research indicating otherwise), or perhaps this sample

was too young and healthy at the time of assessment to uncover health effects.

His, Hers, or Theirs: Sex Differences

Sex differences (or lack thereof) also warrant discussion. Several mean

differences between men and women were uncovered on midlife outcomes. Women

reported better friendships than men did, and scored higher on positive affectivity

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outcomes, whereas men reported more cardiac health problems than women did.

These mean differences are not surprising; women tend to have more emotional

closeness, and share greater intimacy with friends than do men (e.g., Caldwell &

Peplau, 1982; Hartup, 1993; Wheeler & Nezlek, 1977), and it is widely known that

men more frequently suffer from heart-related problems than do women (e.g.,

Nikiforov & Mamaev, 1998). However, and perhaps more interesting, men and

women were overwhelmingly similar in the concurrent and longitudinal associations

among social, emotional, and health variables that were uncovered. Despite mean

differences in some of the variables that were measured, the pattern of associations

among the variables was less likely to show sex differences. Of 55 concurrent

correlations among social, emotional, and physical health outcomes, the results of a

comparison of correlation coefficients revealed men and women differed significantly

in the magnitude of only one (1.8%) effect (the correlation between the two cardiac

health composites was significant and strong for women, but much weaker for men;

see Table 14). Longitudinally, of 54 possible overall, same-, and opposite-sex

quantity and quality effects on 18 composite health and well being outcomes, only 3

(5.6%) were significantly moderated by sex (the effect of overall, same- and opposite-

sex social interaction quality on the second cardiac health composite).

Although research has shown that social factors provide protective benefits

for men and women (e.g. Holt-Lundstad, Smith, & Layton, 2010; Kiecolt-Glaser &

Newton, 2001), the literature diverges with regard to whether the benefits of social

support and social integration apply equally to the two sexes. Some authors have

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argued that social relationships are more important to women's well being (e.g.,

Glenn, 1975; Taylor et al. 2000; Wood, Rhodes, & Whelan, 1989). Women, who, for

a variety of reasons including socialization and evolution (Eagly & Wood, 1999),

place greater value on social relationships, are more likely to cope with stress by

affiliating (as opposed to the standard male fight-or-flight response; Taylor et al.,

2000), and have friendships that are characterized by higher levels of self-disclosure

and intimacy (Caldwell & Peplau, 1982; Hartup, 1993). Thus, social ties are

presumed to be more instrumental in shaping women's health and well-being.

However, other authors have suggested that men reap greater benefits from good

social ties, particularly a good marriage (e.g., Diener et al., 1999). Although married

persons generally tend to be physically and mentally better off than their unmarried

counterparts, sex discrepancies still emerge in some studies. For example, the

reduction in mortality risk associated with marriage was, in one review, estimated to

be five times greater for men than for women (Kiecolt-Glaser & Newton, 2001). Two

popular explanations for this divergence are 1) that wives are more likely to exert

positive influence by taking an active role in the management their husbands' health,

and 2) whereas women have a variety of social connections beyond marriage to

provide health and well-being protective benefits, men derive the majority of such

benefits from the marital relationship (Kiecolt-Glaser & Newton, 2001; Umberson,

2002). Despite the controversy over which sex gains more from an enriched social

life, the current data suggest that both men and women derive equivalent short-term

and long-term benefits from social connection (particularly good friendships) and that

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those benefits come in the form of enhanced long-term social and emotional

adjustment. Very few of the longitudinal effects were moderated by sex, indicating

that benefits garnered from early adult social engagement did not vary significantly

by sex. The few effects that were moderated by sex did not show one sex to

experience a clear and consistent advantage over the other.

These findings do not resolve the sex-difference debate, as physical health

gains were not predicted by social life for either sex, by either romantic ties or

friendships. Potential explanations for the lack of physical health results were

discussed above. Mortality, the variable that has shown the biggest disparity for the

two sexes in some research (e.g., Kiecolt-Glaser & Newton, 2001), could not be

tested reliably as an outcome because of the small number of deceased participants.

However, others like Holt-Lundstad, Smith, & Layton (2010) find sex similarities,

and in the current research men were just as likely as women to enjoy emotional and

social benefits of being socially integrated in early adulthood, and having good

quality social connections in midlife, contrary to some prior research and theory

(Diener et al., 1999; Kiecolt-Glaser & Newton, 2001).

Assessing Support: Form Versus Function

The relative merits of structural versus functional support were previously

discussed, and although empirical evidence suggests that there are benefits of both

kinds of support (e.g., Berkman, 1995; Berkman & Syme, 1979; Cacioppo et al.,

2000; Cacioppo et al., 2002; Cohen et al., 1985; House et al., 1988; Uchino et al.,

1996), rarely are the two examined simultaneously. The current research set out to

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examine differences between structural (social integration/quantity) and functional

(perceived support/quality) indicators of social support. Generally speaking, although

both quantity and quality of college social activity were influential in longitudinal

analyses, interaction quantity was a stronger and more consistent predictor of long-

term health and well-being than quality of college social activity. Although simply

having social connections has been shown to provide benefits, the nature of those

relationships has also been deemed important. For example, several findings indicate

that ambivalent or hostile social ties are detrimental to health and well-being (Kiecolt-

Glaser & Newton, 2001; Uchino et al., 2001).

Many prior findings about relationship quality have been obtained in studies

with a shorter time span and with greater specificity. That is, concurrent studies often

examine the correlation between relationship quality with one specific target (e.g. a

spouse, or romantic partner) and a more narrowly focused health outcome (e.g.,

cardiovascular stress reactivity). Taking a broader perspective, as this research did, it

becomes clearer why interaction quantity may have yielded stronger results. The time

frame in which the social interaction data were collected represents a time when

young adults are exploring the social world on their own for the first time, outside the

safety net of their family environment. The time period is developmentally critical; it

is a point at which one has the potential for a great deal of growth. More frequent

interaction at this stage provides more opportunities to learn about oneself, about

others, and about how to navigate the ever-changing social world. Frequent social

contact may teach lasting lessons to be taken forward through life, preparing young

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adults to adapt to the challenges that lie before them. This preparation develops self-

knowledge about one’s strengths and weaknesses, arming individuals with coping

skills, and fostering feelings of efficacy when facing obstacles down the road.

Acquiring this knowledge, skill, and ability through high quality social interactions is

presumably even more beneficial. However at this critical stage of social

development in early adulthood, frequent social interaction, which may also imply

many social outlets for learning about the self and the world, appears to be relatively

more influential.

This explanation is consistent with Baltes and Baltes’s (1990) model of

successful lifespan development, which involves selectively focusing on

developmentally relevant goals that provide the most positive gain, optimizing the

attainment of those goals by devoting resources to them, and compensating for any

loss in the ability to achieve those goals (see also Baltes, 1997). Although the

selective optimization with compensation (SOC) model (Baltes & Baltes, 1990) is

applied to multiple domains of development, whereby certain domains of functioning

may take priority at any given life phase, Carstensen and colleagues’s theory of

socioemotional selectivity (Carsentsen, 1995; Carstensen, Isaacowitz, & Charles,

1999) has zeroed in on the application of the SOC model to the social domain. Their

research has found that as perceived time grows short, social goals shift from

knowledge acquisition to emotion regulation. Time perspective is unavoidably tied to

age (although not completely dependent upon it - research has also shown a shift

toward emotion regulation goals among younger groups who perceived time to be

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limited; Carstensen & Fredrickson, 1998). When participants in this research were in

college, time would likely have been perceived as open ended to the overwhelming

majority, and knowledge acquisition social goals would have reigned supreme.

Frequent social interaction would (and, as evidenced here, did) allow them to

successfully fulfill those goals in the service of long-term adaptive development.

However as time wore on and became conspicuously more limited, it is likely that the

social goals of these participants became ever-more geared toward emotion

regulation, and social interaction quality would have become increasingly critical to

long-term health and well being.

In fact, the quality of the contemporaneous midlife social environment was

beneficial to emotional well-being, and even some indicators of health. However,

analyses looking at the unique effects of early adult social activity on midlife health

and well-being outcomes, while controlling for midlife social well-being, showed that

the effects of early adult social activity on midlife outcomes remained largely

unchanged when controlling midlife social relationship quality. Thus, there is

something unique and important about the early adult social world, particularly the

frequency of socializing, that remains influential in later life.

Longitudinal effects spanning 30 years, such as these, beg the question of

mechanism. In all likelihood, it is probably a combination of factors that contribute to

consistency over time. Temperament, which is partly biologically based (Goldsmith

et al., 1987), likely influenced early social development (long prior to the time period

investigated here), giving rise to scripts that would guide socialization throughout life

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(Bowlby, 1969; Sullivan, 1953). These early-developed scripts would have shaped

social interactions throughout adolescence and into adulthood. Socially secure

individuals (i.e., those with positive models of self and other) would have been prone

to approach the social world with a positive and optimistic outlook that facilitated

rewarding social interactions, reinforcing the individual’s views and aiding in the

development of social skills in a self-fulfilling prophecy. For individuals who

followed such a trajectory, the new social milieu encountered in college was yet

another opportunity to apply their skills in achieving the affiliation goals that became

relevant at this developmental stage (Erikson, 1959). Thus, although personality

variables may have had an impact on the socialization processes that took place, the

social experiences in college were necessary for the accumulation of long-term

benefits, which is why these effects cannot be reduced to temperament (or

personality) alone.

This general framework suggests a probable normative pattern, and those who

follow it are expected to regularly engage in many of the previously reviewed

relationship processes that have been shown to provide short-term benefits in

controlled laboratory settings. For example, the reduced cardiovascular reactivity to

laboratory-induced stress associated with supportive partners (Uchino et al., 1996),

and the efficient HPA-axis recovery pattern associated with positive behaviors during

a laboratory conflict discussion (Robles et al., 2006) capture a snapshot of the fleeting

processes that are expected to recur on a larger scale in everyday life and to have a

cumulative positive impact on a recipient’s health and well-being. In addition,

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regularly engaging with relationship partners in activities that produce positive affect

is also expected to contribute to long-term health and well-being (Lyubomirsky et al.,

2005) in much the same way. Thus, social relationships provide the vehicle by which

various specific health and well-being bolstering mechanisms are able to manifest.

Limitations

Several limitations to the current research warrant note. First, the sample was

relatively homogenous. Participants came from the same university, and the social

life they experienced in that setting may not generalize to social life experienced by

students at other colleges and universities, young adults who lived at home and

commuted to college, or those who did not attend college at all. Persons who did not

have the residential college experience may not have the same kinds of social

experiences that come with living in a self-contained residential environment (e.g,

planned activities, meals in a shared facility) for four years of early adulthood, when

the primary developmental social goal is establishing intimacy and sharing one’s life

with others (Erikson, 1959). It would be important to determine whether similar

findings would be obtained among non-college students to understand if the

transitions that occur at this time are developmental transitions associated with a

phase of life, or rather a function of the unique social circumstances associated with

attending a residential college.

Second, the current sample was composed of relatively happy, well-adjusted,

well-educated, financially secure, mostly white, midlife adults, who lived in

residential halls during college, which maximized opportunities for social contact.

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The findings uncovered in this research may not generalize to more diverse samples.

However, if anything, this sample’s relatively high level of socioeconomic status, low

level of social isolation, and high level of emotional adjustment may have worked

against the longitudinal hypotheses. As discussed above, the socioeconomic

advantage enjoyed by this group of adults may have protected them from some of the

health pitfalls common to this age group. If so, early-adult social activity might be a

stronger predictor of health problems in a relatively more impoverished sample.

Those who are more susceptible to poor health may also have more to gain from

frequent, supportive socializing. Examining these associations with a wider range of

socioeconomic classes would illuminate this question. Moreover, although there was

variability in college social activity, social integration may have been skewed toward

the high end in this sample because the social environment of a college residential

living facility afforded participants constant, built-in opportunities for socializing.

Socializing is central to what college students do on a day-to-day basis, and having

been surrounded by roommates, hall-mates, and classmates around the clock fostered

participants’ ability to be socially integrated if they chose to take advantage of their

environment. Examining less socially embedded people may improve our ability to

detect health disparities as a function of social activity.

Third, this research was limited to a single birth cohort. These participants,

born in the mid 1950s, lived through a unique set of social circumstances. Growing

up in a time when social ideals were shifting from the traditional wife-as-homemaker

and husband-as-breadwinner model, through periods of free love and women’s

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liberation, presented varied models for lifestyle choices about work, marriage, social

relations, and parenting. The first wave of research with this sample, conducted in the

late 1970s, may have caught participants just on the tail end of a time when the

transition from college to the work world was associated with marriage and starting a

family. Marital status data for the U.S. (as reported by the United Nations, 2009)

revealed that in 1980 (when the last of this cohort graduated college, and the youngest

was about 21 years old), 41.4% of women and 27.9% of men in the U.S. aged 20-24

were married. Twenty years later, in 2000, those figures had dropped to 22.5% for

women and 12.9% for men. Gender roles and social scripts had become ever more

loose and varied. The transitions made by this sample may not have the same

meaning for today's college students, who often put off goals of marriage and family

in favor of developing a career. Women in the current study were only just beginning

to make such a choice more commonly. Women born in previous cohorts may have

attended college with the goal of attracting a husband looming larger than educational

aspirations, or may never attended college at all, and missed out on this cohort’s

social experience altogether. It would be informative to replicate this research with a

new cohort of college students, following them into midlife to see what impact, if

any, shifting social scripts and standards may have on the findings uncovered here.

A fourth limitation concerns the nature of the measures. Notwithstanding the

previously discussed benefits of experience sampling over other kinds of self-report

measures, the RIR remains a somewhat subjective self-report measure. Although

retrospection bias may be less of a concern because reports are completed online, the

97

tendency for individuals to pessimistically peer out at the world from under a dark

rain cloud and devalue the quality of their social interactions (or, conversely, to see

the world through rose-colored glasses) may still have resulted in reporting biases. In

an attempt to unravel these kinds of personality effects, a series of analyses were

undertaken in which neuroticism and extraversion were controlled. The effects of

college social activity (quantity and quality) were virtually identical when

neuroticism was controlled. However, when extraversion was controlled, the effects

of college social interaction quantity (but not the effects of quality) were weakened. It

is noteworthy that the quantity variable, which was altered by extraversion, should

have been less susceptible to global biases because of its relatively more objective

nature (the length, and number of interactions). On the other hand, the more

subjective (and thus more vulnerable to global reporting biases) quality variable

(consisting of reports of interaction intimacy and satisfaction), was not altered when

extraversion was controlled, suggesting that global positivity reporting biases are not

likely responsible for the longitudinal effects uncovered here. Extraversion, however,

is associated with increased sociability, which would be reflected in the interaction

quantity variable. This suggests, as discussed above, that personality may foster the

kind of socializing that must take place in early adulthood for long-term benefits to be

obtained.

Regardless, these results must be interpreted with a degree of caution. We

cannot necessarily assume these results suggest personality caused a bias in

participant’s perceptions of college social activity, as personality was assessed 30

98

years later, in the current assessment. College socializing conceivably influenced

participants’ tendency to view the world through rose-colored glasses at age-50.

Although personality is said to remain stable over time, credible causal statements

would require an assessment of personality in college, along with an assessment of

the same midlife outcome measures of social and emotional adjustment. Only then

would it would be possible to further tease apart changes in those outcomes as a

function of both personality and social activity. Alas, the personality and outcome

measures were not collected (and in many cases, did not yet exist!) when these

participants were in college.

At midlife, virtually all measures (except neuroendocrine function) were self-

report. It would have been ideal to collect behavioral measures (e.g., a social

interaction task with a romantic partner or friend, or a medical evaluation by a doctor,

replete with blood work) and re-execute the experiencing sampling procedure using

the RIR. However, not only were participants spread across the world, it seemed

important to make participation as brief and simple as possible to achieve the lowest

possible rate of attrition.

A final limitation concerns the number of measures collected and thus the

number of analyses conducted. The chance of making Type I errors (mistakenly

rejecting the null hypothesis and assuming an effect to be real when in fact it is not)

was inflated because of the number of statistical analyses that were conducted.

Statistical corrections may reduce the likelihood of Type I errors, but were not done

here because these procedures tend to lower the power to detect real effects (possibly

99

resulting in Type II errors). A study of this type is somewhat exploratory by nature,

and correcting for the possibility of Type I errors risks the possibility of failing to find

real effects because the test is too stringent. Instead of jeopardizing the Type II error

rate by controlling for chance, observation of a consistent pattern of effects was used

to gauge the meaningfulness of these results. Inspection of Table 25 shows that six of

12 (50.0%) quantity effects reached significance and four of 12 (33.3%) quality

effects reached significance. In all, 41.7% of longitudinal effects were significant, in a

direction that supported the hypotheses. Moreover, the pattern of findings was

consistent for outcome type as well; for the most part, college social activity effects

emerged on social well-being (five of eight, or 62.5% of the effects were significant)

and emotional adjustment outcomes (four of six, or 66.7% of the effects were

marginally significant or better), and less so on physical health outcomes. The gold

standard, of course, would be to replicate these results with other samples.

Conclusions and Future Directions

This research is among the first to demonstrate that the features of one’s social

environment in early adulthood may have lasting implications for long-term social

and emotional well-being, and possibly health, although the results were substantially

weaker, and less reliable, for health outcomes. While many interesting and

provocative findings were uncovered, the study also raised further questions.

Longitudinally, although both structural social engagement (i.e., interaction

quantity) and functional support (i.e., interaction quality) in early adulthood predicted

later life outcomes, interaction quantity emerged as a relatively more robust and

100

consistent predictor of midlife outcomes over interaction quality. Other research has

identified both kinds of measures of support to be important to health and well-being,

and the findings of the current study raise questions of whether the relative impact of

structural and functional integration may vary a function of the time frame of

assessment. Perhaps during early adulthood, when socializing permeates the day-to-

day activities of the residential college student who is constantly surrounded by a sea

of potential interaction partners, frequent social activity is especially relevant and

important. However, as individuals move on from the residential hall to less socially

saturated living situations (e.g. with one or two housemates or a significant other), the

number of interaction partners likely shrinks and the quality of interactions with a few

selected close others may come to play a more important role.

The relative invariance of concurrent and longitudinal effects across sex raises

questions about the causes of sex differences in the strength and type of benefits

garnered from social life that have been identified in other research (reviewed above).

Sex effects that favor women typically manifest on well-being outcomes, which failed

to emerge for the current research – both men and women experienced social and

emotional adjustment benefits. Sex differences that favor men typically manifest on

health and mortality outcomes, however in the present research these results were

weak for both men and women. Two important questions that deserve further

exploration arise from these findings, one concerning the cause of sex differences in

other research, and the other having to do with the lack of health effects in this

research. A health-protective benefit associated with the relatively high subjective

101

socioeconomic status of this particular sample was offered as one explanation for the

lack of health findings at this stage. It will be particularly interesting to determine

whether health and well-being disparities vary as a function of early adult social life

as these individuals age and their health deteriorates. Moreover, it remains to be seen

whether sex differences will emerge in those effects, if indeed they do emerge.

This research offered a unique approach that contributed to our understanding

of the associations between social life and physical and mental health and wellness. It

examined richly detailed social activity data at a critical period in early adulthood,

and followed individuals into midlife. It illuminated the long-lasting impact of

socialization at an important life stage (college), and spoke to the importance of

considering the history from which a person’s social environment evolved. The

results suggest that people do, indeed, get by with a little help from their friends.

102

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121

Footnotes

1. The total sample size from which we are recruiting (n = 222) differs from the sum

of the number of participants in the three individual studies (n = 248) because 26

individuals (19F, 7M) participated in both studies II and III. Half of these 26

participants were randomly assigned to sample II (10F, 3M), and the other half to

sample III (9F, 4M) in order to determine which college data (sample II or III)

would be used in longitudinal analyses.

2. One transgendered participant (male in college) had to be dropped from all

analyses of sex because a comparison with a group consisting of one member is

meaningless.

3. Oblique solutions were chosen to best capture the factor structure of the variables

while not forcing principal components to be mutually independent. Given the

types of variables included in each principle component analysis, modest

correlations among factors were expected.

4. Although this cortisol variable is termed area under the curve with respect to

increase, because cortisol values tend to decrease over the day, many of the

values for this calculation become negative, and represent the decrease in cortisol

over the course of the day (see Pruessner et al., 2003 for a further explanation).

5. A set of analyses was undertaken to test for effects of the interaction between

college interaction quantity and quality, as well as the three-way interaction with

sex. Neither the Quantity X Quality interaction, nor the Sex X Quantity X Quality

had significant effects on midlife social well-being, emotional adjustment, or

122

physical health outcomes. These variables were thus deleted from the model and

are not discussed further.

6. The transgendered participant was again omitted from analyses including sex

(Model II). Inclusion of this participant did not meaningfully alter the quantity

and quality main effects reported in Model I throughout.

7. College data for the additional 42 of the original 222 participants were excluded

because those participants were never located, and mortality status could not be

definitively determined.

123

Table 1. Summary of function and measurement of three physiological systems

Physiological

System

Major Functions & Organs

Common Measures

Cardiovascular

Function(s):

Transport oxygen to, and remove

carbon dioxide from cells and

organs in the body to maintain

cell and organ function.

Organs:

Heart: generates force for

circulatory processes

Arteries, veins, & capillaries (the

vasculature): conduit for the

pumping of the heart

Heart rate: beats per minute

of heart (increased by

sympathetic nervous system,

decreased by parasympathetic

nervous system)

Systolic blood pressure (SBP):

force of blood against arterial

wall during ventricular

contraction (highest pressure)

Diastolic blood pressure

(DBP): force of blood against

arterial wall during ventricular

relaxation (lowest pressure)

Immune

Function(s):

Defend body against disease

Organs:

Thymus, bone marrow, lymph

nodes, spleen, tonsils, appendix

Immune cell counts: quantity

of immune cells (helper T,

suppressor T, NK cells)

Balance of immune cells: ratio

of types of immune cells such

as helper T cells to suppressor

T cells

Immune cell performance:

proliferation of immune cells

in response to antigens

Neuroendocrine

Function(s):

Regulate cardiovascular system,

metabolize stored glucose and fat,

down regulate immune system

Organs:

Adrenal cortex, adrenal medulla,

hypothalamus, pancreas,

pituitary, thyroid

(Note: the hypothalamus,

pituitary, and adrenal cortex

make up the HPA axis)

Glucocorticoid levels:

catecholamines (epinephrine

& norepinephrine, produced

by adrenal medulla), CRH

(released by hypothalamus),

ACTH (released by pituitary),

cortisol (released by adrenal

cortex)

Diurnal glucocorticoid

rhythm:

Catecholamines and cortisol

follow daily cycle peaking at

awakening and gradually

declining over the day

124

Table 2. Summary of previous samples and recruitment for current study

Sample

size

Year of initial

participation

Age at initial

participation

n recruited for

early adulthood

(1985) follow-up

n recruited

for current

follow-up

I

58

1974

18-19

(freshman year)

22

34

II

77 (64)

1976

18-19

(freshman year)

33

43

III

113 (100)

1979

21-22

(senior year)

56

Note: Sample size reflects the number of individuals who participated in each original study. Parenthetically reported is the

number of unique individuals assigned to that study, with half of the 26 individuals who participated in both the second and

third studies being randomly assigned to each sample.

124

125

Table 3. Comparison of recruited versus unrecurited participants on college social

activity variables.

College interaction quantity

Predictor Unrecruited M Recruited M t df p

Overall Quantity -.01 .06 -1.34 207 ns

TPD 327.44 353.10 -1.50 207 ns

#PD 5.07 5.45 < 1 207 ns

Same Sex .00 .00 < 1 207 ns

TPD 158.92 160.09 < 1 207 ns

#PD 3.61 3.57 < 1 207 ns

Opposite Sex -.12 .04 -1.38 207 ns

TPD 75.71 93.41 -1.66 207 = .10

#PD 1.55 1.75 -1.04 207 ns

College interaction quality

Predictor Unrecruited M Recruited M t df p

Overall Quality -.06 .04 < 1 207 ns

INT 3.52 3.55 < 1 207 ns

SAT 3.62 3.82 -1.13 207 ns

Same Sex -.06 .04 < 1 207 ns

INT 3.49 3.54 < 1 207 ns

SAT 3.68 3.84 -1.01 207 ns

Opposite Sex -.03 .02 < 1 207 ns

INT 4.02 3.99 < 1 207 ns

SAT 3.58 3.74 < 1 207 ns

Note: N = 222. Missing college data for 13 Ss. Recruited n with college data = 129,

unrecruited n with college data = 80.

126

Table 4. Factor loadings for individual social relationship quality scales on three

principle components with oblimin rotation.

Romantic

Relationship Friendship Social

Item Quality Quality Network Communalities

KPSS-R .898 .236 .173 .807

IOS-R .878 .182 .220 .775

PAIR-R .460 .892 .283 .855

CSI .957 .241 .231 .917

PPR .936 .271 .144 .881

KPSS-F .087 .818 .094 .697

IOS-F .237 .760 .334 .598

PAIR-F .210 .941 .289 .888

NTWK-DV .200 .241 .930 .866

NTWK-SZ .183 .266 .922 .850

Eigenvalues 4.397 2.286 1.451

Note: Bolded values indicate the item belongs on that principle component. KPSS-R:

perceived support from romantic partner, IOS-R: inclusion of other in self with

romantic partner, PAIR-R: intimacy with romantic partner, CSI: satisfaction with

romantic relationship, PPR: perceived partner responsiveness, KPSS-F: perceived

support from core friend group, IOS-F: inclusion of other in self with core friend

group, PAIR-F: intimacy with core friend group, NTWK-DV: social network

diversity, NTWK-SZ: social network size

127

Table 5. Factor loadings for individual emotional adjustment scales on three principle

components with oblimin rotation.

Positive Negative Self-

Item affectivity affectivity actualization Communalities

SWLS .737 -.526 .420 .586

LONELY -.867 .437 -.307 .764

SELF-ACC .806 -.605 .680 .809

POS-REL .876 -.353 .430 .774

PA .647 -.444 .544 .508

NA -.394 .880 -.276 .777

CESD -.612 .840 -.446 .770

SCL90D -.628 .816 -.395 .741

SCL90A -.215 .789 -.177 .658

PSS -.531 .847 -.448 .752

PRS-GRTH .463 -.281 .803 .658

PURP .601 -.387 .781 .685

AUT .221 -.267 .844 .749

ENV-MAST .653 -.661 .585 .650

Eigenvalues 7.283 1.574 1.024

Note: Bolded values indicate the item belongs on that principle component. SWLS:

satisfaction with life scale; LONELY: UCLA-Loneliness, SELF-ACC: Ryff self-

acceptance, POS-REL: Ryff positive relations with others, PA: positive affect, NA:

negative affect, CESD: center for epidemiological studies – depression, SCL90D:

SCL90 depression subscale, SCL90A: SCL90 anxiety subscale, PSS: perceived stress

scale, PERS-GRTH: Ryff personal growth, PURP: Ryff purpose in life, AUT: Ryff

autonomy, ENV-MAST: Ryff environmental mastery

128

Table 6: Factor loadings for individual physical health scales on three principle

components with oblimin rotation.

Daily Cardiac Cardiac

Item Symptomatology Health I Health II Communalities

CHIPS .823 -.046 -.090 .688

SF-36 PHYS -.713 -.186 -.085 .550

RESPIR .773 .035 -.013 .598

CARDIAC .099 .467 .419 .404

SYSBP .114 .869 .088 .776

DIABP -.019 .890 -.054 .796

CHOLEST -.225 -.030 .687 .524

BMI .301 .336 .649 .624

ENDOCRN .161 -.021 .663 .466

Eigenvalues 2.567 1.607 1.252

Note: Bolded values indicate the item belongs on that principle component. CHIPS:

daily symptoms, SF-36 PHYS: physical health subscales of the SF-36, RESPIR: prior

respiratory diagnoses, CARDIAC: prior cardiac diagnoses, SYSBP: systolic blood

pressure, DIABP: diastolic blood pressure, CHOLEST: cholesterol level, BMI: body

mass index, ENDOCRN: prior endocrine system diagnoses

129

Table 7. Effects of original sample and year in college (freshman vs. senior) on midlife social well-being

Sample I

Sample II

Sample III

Sample

F

Year

contrast

Sex

F

Sample

X Sex F

Year X

Sex F

M

F

M

F

M

F

Romantic RQ

-.24

-.14

.30

-.04

.15

-.03

<1

KPSS-R

3.84

3.50

3.76

3.56

3.73

3.53

<1

1.50

<1

IOS-R

4.95

4.78

6.00

5.27

5.41

5.45

2.03

<1

CSI

3.30

3.66

3.85

3.47

3.82

3.57

<1

PPR

6.29

6.62

7.09

6.84

6.88

6.65

<1

PAIR-R

6.53

7.12

6.61

7.03

6.98

7.29

1.80

5.61*

7.00**

<1

Friendship RQ

-.36

.25

-.32

.07

-.22

.45

1.06

5.64*

14.19**

<1

2.54

KPSS-F

3.07

3.73

3.28

3.51

3.18

3.71

<1

3.96*

29.29**

2.01

2.78†

IOS-F

4.27

4.46

4.19

4.39

5.00

1.25

4.27*

1.88

<1

2.27

PAIR-F

6.61

7.10

6.45

7.01

6.75

7.29

1.11

3.79*

9.19**

<1

Social Network

-.12

.60

-.17

-.12

.12

-.01

1.47

<1

1.55

2.06

<1

NTWK-SZ

25.05

34.46

23.81

25.48

29.55

26.31

1.34

<1

1.79

<1

NTWK-DV

7.00

8.64

7.00

6.96

7.35

7.29

1.18

<1

1.69

1.84

1.33

Note: Degrees of freedom vary from 1,105 to 2,125

**p ! .01, *p ! .05, †p ! .10

129

130

Table 8. Effects of original sample and year in college (freshman vs. senior) on midlife emotional adjustment

Sample I

Sample II

Sample III

Sample

F

Year

contrast

Sex

F

Sample

X Sex F

Year X

Sex F

M

F

M

F

M

F

Negative Affectivity

.13

.08

.01

.10

.08

-.19

<1

1.39

<1

1.43

NA

1.65

1.61

1.64

1.56

1.58

1.47

<1

1.20

<1

CES-D

1.41

1.31

1.38

1.37

1.26

<1

1.08

<1

1.16

SCL90D

1.50

1.32

1.34

1.58

1.51

1.36

<1

2.55†

2.22

SCL90A

1.31

1.40

1.37

1.34

1.32

1.23

<1

1.82

<1

1.20

PSS

2.16

2.29

2.08

2.13

2.14

2.02

<1

Positive Affectivity

-.28

.22

-.04

-.09

-.32

.35

<1

2.11

5.96*

2.32†

5.05*

SWLS

4.88

5.38

5.35

5.37

4.74

5.46

<1

3.49†

1.02

1.59

LONELY

2.20

2.01

2.04

2.06

2.14

1.86

<1

3.07†

3.63†

1.35

3.23†

SELF-ACC

4.38

4.92

4.76

4.50

4.41

5.04

<1

2.20

3.29†

3.08*

4.62*

POS-REL

4.56

5.31

4.67

4.68

4.49

5.42

1.07

4.58*

10.23**

3.07*

7.68**

PA

3.84

3.98

3.74

3.78

3.72

3.99

<1

1.35

<1

1.37

Self-actualization

-.16

.29

.10

-.12

-.04

.07

<1

1.30

<1

PRS-GRW

5.10

5.43

5.07

5.18

4.99

5.05

1.25

1.80

1.30

<1

PURP

4.80

5.12

5.08

4.80

4.99

5.18

<1

3.16†

2.07

1.56

AUT

4.58

4.83

4.88

4.58

4.75

4.72

<1

1.04

<1

Note: Degrees of freedom vary from 1,119 to 2,122

**p ! .01, *p ! .05, †p ! .10

130

131

Table 9. Effects of original sample and year in college (freshman vs. senior) on midlife physical health

Sample I

Sample II

Sample III

Sample

F

Year

contrast

Sex

F

Sample

X Sex F

Year X

Sex F

M

F

M

F

M

F

Daily Sympt.

-.10

-.16

-.36

.44

.00

-.06

<1

2.67

4.65*

3.10†

SF-36

87.66

88.71

88.31

83.07

83.51

87.66

<1

1.16

<1

1.80

2.74†

CHIPS

.23

.29

.15

.35

.26

<1

4.47*

2.28

1.99

RESP

.05

.00

.12

.02

.04

1.21

<1

2.50

5.48**

<1

Cardiac Health I

.49

.08

.03

-.27

.21

-.26

2.54†

<1

7.56**

<1

CARDIAC

.12

.07

.06

.08

.10

.06

<1

1.10

<1

SYSBP

127.33

122.50

120.29

115.50

122.21

114.26

2.51†

1.73

4.72*

<1

DIABP

79.44

75.90

76.64

71.30

76.91

73.67

1.88

<1

7.64**

<1

Cardiac Health II

-.32

.88

.25

-.08

-.04

-.01

1.00

<1

3.17†

5.85**

<1

CHOLEST

169.50

224.40

189.09

184.92

181.73

183.21

1.16

<1

5.24*

4.93*

<1

BMI

24.53

28.28

25.62

23.79

25.97

25.27

<1

1.37

<1

ENDOCRN

.01

.11

.09

.05

.03

.05

<1

2.32

<1

Note: DF vary from 1,68 to 2,126

**p ! .01, *p ! .05, †p ! .10

131

132

Table 10. Effects of original sample and year in college (freshman vs. senior) on neuroendocrine function

Sample I

Sample II

Sample III

Sample

F

Year

contrast

Sex

F

Sample

X Sex F

Year X

Sex F

M

F

M

F

M

F

AUCg

3368.66

3259.14

2471.11

2949.52

2809.76

3150.50

<1

AUCi

-5414.83

-6398.66

-6618.58

-5139.59

-5761.05

-6890.94

<1

Waking

11.32

13.40

12.01

10.03

11.57

13.59

<1

+45m

14.23

16.33

8.92

10.98

8.91

12.44

2.64†

<1

1.78

<1

+2.5h

3.98

3.72

2.61

3.94

3.90

4.64

<1

1.21

<1

+8h

2.23

1.61

1.47

2.14

2.94

2.22

2.06

1.93

<1

1.47

<1

+12h

1.69

1.44

.80

1.70

.88

1.15

<1

2.08

<1

Note: Degrees of freedom vary from 1,76 to 2,76

**p ! .01, *p ! .05, †p ! .10

132

133

Table 11. Concurrent correlations among midlife social, emotional, and physical health composite variables.

Social Well Being

Emotional Adjustment

Physical Health

Neuroendocrine

Romantic

RQ

Friend

RQ

Social

Network

Negative

Affectivity

Positive

Affectivity

Self-

Actualization

Daily

Sympt.

Cardiac

Health I

Cardiac

Health II

Conc.

(AUCg)

Change

(AUCi)

Social Well Being

Romantic RQ

-

Friendship RQ

.22*

-

Social Network

.20*

.26**

-

Emotional Adjustment

Negative affectivity

-.36**

-.40**

-.16†

-

Positive affectivity

.47**

.62**

.38**

-.66**

-

Self-actualization

.27**

.28**

.22*

-.45**

.63**

-

Physical Health

Daily Sympt.

-.01

-.05

.07

.29**

-.12

-.11

-

Cardiac Health I

-.04

-.27**

-.13

.09

-.20*

-.03

.16†

-

Cardiac Health II

-.02

-.12

-.02

-.01

-.04

.12

.21*

.40**

-

Neuroendocrine Function

AUCg

.08

-.06

.13

-.02

.03

.13

-.05

.03

.19†

-

AUCi

-.07

-.03

.04

-.05

.02

.06

.13

-.11

-.24*

-

Note: **p ! .01, *p ! .05, †p ! .10

133

134

Table 12. Concurrent correlations among midlife social well being, emotional adjustment, physical health and neuroendocrine

composites and individual measures of social well being and emotional adjustment.

Social Well Being

Emotional Adjustment

Physical Health

Neuroendocrine

Romantic

RQ

Friend

RQ

Social

Network

Negative

Affectivity

Positive

Affectivity

Self-

Actualization

Daily

Sympt.

Cardiac

Health I

Cardiac

Health II

Conc.

(AUCg)

Change

(AUCi)

Social Well Being

KPSS-R

.90**

.21*

.16†

-.33**

.40**

.18†

-.02

-.04

-.09

.01

-.02

IOS-R

.88**

.17†

.19*

-.23*

.39**

.23*

.01

-.02

.02

.06

-.09

CSI

.95**

.19*

.23*

-.38**

.48**

.31**

-.04

-.09

.00

.10

-.07

PPR

.94**

.22*

.15

-.40**

.44**

.28**

-.00

-.01

.01

.11

-.06

KPSS-F

.11

.84**

.14

-.32**

.52**

.21*

-.03

-.31**

-.11

-.17†

.07

IOS-F

.24**

.84**

.27**

-.22*

.42**

.19*

-.02

-.18†

-.08

-.04

-.15

PAIR-F

.21*

.89**

.28**

-.49**

.65**

.34**

-.09

-.23*

-.14

.05

.03

NTWK-SZ

.18†

.25**

.92**

-.13

.36**

.21*

.13

-.07

-.04

.06

.04

NTWK-DV

.19*

.22*

.92**

-.16†

.34**

.19*

-.00

-.17†

.00

.18†

-.09

Emotional Adjustment

NA

-.34**

-.28**

-.09

.86**

-.51**

-.31**

.29**

.08

.02

-.13

.09

CES-D

-.40**

-.33**

-.25**

.88**

-.67**

-.48**

.20*

.06

-.04

-.05

.04

SCL90D

-.35**

-.21**

.86**

-.67**

-.43**

.19*

.17†

-.05

-.03

.11

SCL90A

-.12

-.39**

-.00

.77**

-.33**

-.23*

.32**

.04

.07

.04

.00

PSS

-.34**

-.36**

-.09

.85**

-.62**

-.46**

.24**

.05

-.01

.07

-.07

SWLS

.54**

.32**

.30**

-.56**

.80**

.43**

-.10

-.23*

-.07

-.01

-.03

LONELY

-.36**

-.66**

-.37**

.52**

-.82**

-.42**

.07

.15

.06

.01

-.01

SELF-ACC

.45**

.40**

.28**

-.65**

.88**

.68**

-.15†

-.10

.02

.10

-.13

POS-REL

.31**

.73**

.36**

-.45**

.84**

.50**

-.07

-.19*

-.10

.06

-.05

PA

.23*

.36**

.25**

-.50**

.73**

.53**

-.10

-.13

.06

-.01

PRS-GRW

.24**

.19*

-.35**

.54**

.83**

-.02

.01

.07

.12

.07

PURP

.20*

.29**

-.45**

.62**

.83**

-.09

-.10

.15†

.14

.04

AUT

.23*

.16†

.05

-.30**

.37**

.81**

-.16†

.02

.07

.08

-.06

Note: **p ! .01, *p ! .05, †p ! .10

134

135

Table 13. Concurrent correlations among midlife social well being, emotional adjustment, physical health and neuroendocrine

composites and individual measures of physical health and neuroendocrine function.

Social Well Being

Emotional Adjustment

Physical Health

Neuroendocrine

Romantic

RQ

Friend

RQ

Social

Network

Negative

Affectivity

Positive

Affectivity

Self-

Actualization

Daily

Sympt.

Cardiac

Health I

Cardiac

Health II

Conc.

(AUCg)

Change

(AUCi)

Physical Health

SF-36

.00

.05

-.03

-.24**

.24**

.18*

-.74**

-.13

-.19*

.06

-.12

CHIPS

-.11

-.09

.02

.37**

-.12

-.07

.85**

.16†

.20*

-.04

.06

RESP

.08

.00

.12

.04

.09

.01

.69**

.08

.10

-.03

-.04

CARDIAC

.02

-.30*

-.11

.04

-.19*

.03

.13

.63**

.26**

.08

.19†

SYSBP

-.03

-.15

-.10

.07

-.14

.04

.16†

.81**

.32**

.02

.00

DIABP

-.09

-.10

-.04

.04

-.10

-.08

.10

.78**

.33**

-.01

.03

CHOLEST

-.05

-.14

.00

-.13

.04

.03

-.02

.05

.62**

.19

-.35**

BMI

.07

-.04

.05

-.04

-.01

.11

.23**

.43**

.80**

.25*

-.00

ENDOCRN

-.07

-.08

-.07

.06

-.08

.10

.18*

.24*

.72**

.04

.05

Neuroendocrine Function

Waking Cort.

.05

.03

-.09

.05

-.10

-.06

-.09

.11

.27*

.91**

+45m

-.00

-.08

-.03

.07

-.08

.06

-.16

-.06

.01

.74**

.26*

+2.5h

-.04

.05

.04

-.07

.09

.01

-.08

.20*

.73**

.09

+8h

.09

.04

.06

.05

-.02

.00

.14

.22*

.21*

.48**

.14

+12hr

-.04

.05

-.01

-.02

.06

.09

-.02

.02

.09

.46**

.05

Note: **p ! .01, *p ! .05, †p ! .10

135

136

Table 14. Concurrent correlations among midlife social well being, emotional adjustment, physical health, and neuroendocrine

function composites for men (below the diagonal) and women (above the diagonal)

Social Well Being

Emotional Adjustment

Physical Health

Neuroendocrine

Romantic

RQ

Friend

RQ

Social

Network

Negative

Affectivity

Positive

Affectivity

Self-

Actualization

Daily

Sympt.

Cardiac

Health I

Cardiac

Health II

Conc.

(AUCg)

Change

(AUCi)

Social Well Being

Romantic RQ

-

.24†

.13

-.28*

.44**

.31*

-.02

-.06

.01

.17

-.08

Friendship RQ

.22

-

.25*

-.46**

.58**

.36**

-.22†

-.06

-.00

.10

.02

Social Network

.30*

.31*

-

-.14

.29*

.10

.09

-.08

-.01

.16

.00

Emotional Adjustment

Negative affectivity

-.48**

-.43**

-.18

-

-.74**

-.43**

.20†

-.04

-.11

-.23†

.03

Positive affectivity

.55**

.64**

.53**

-.59**

-

.66**

-.13

-.11

-.02

.09

.05

Self-actualization

.24†

.27*

.39**

-.46**

.60**

-

-.01

.10

.20†

.04

-.01

Physical Health

Daily Sympt.

.00

-.01

.02

.45**

-.22†

-.28*

-

.31*

.24*

-.16

.06

Cardiac Health I

-.06

-.25†

-.19

.22

-.18

-.12

.17

-

.60**

.07

-.06

Cardiac Health II

-.02

-.17

-.03

.18

-.07

-.03

.22†

.18

-

.23†

-.10

Neuroendocrine Function

AUCg

.03

-.18

.13

.15

-.01

.25

.08

-.06

.16

-

-.27*

AUCi

-.06

-.07

-.06

.06

-.15

.06

.08

.37*

-.13

-.22

-

**p ! .01, *p ! .05, †p ! .10