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Examining the longitudinal influence of the physical and social environments on social isolation and cognitive health: contextualizing the role of technology

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Examining the longitudinal influence of the physical and social environments on social isolation and cognitive health: contextualizing the role of technology

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EXAMINING THE LONGITUDINAL INFLUENCE OF THE PHYSICAL AND SOCIAL
ENVIRONMENTS ON SOCIAL ISOLATION AND COGNITIVE HEALTH:
CONTEXTUALIZING THE ROLE OF TECHNOLOGY

By

Kexin Yu

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(SOCIAL WORK)

May 2022

Copyright 2022 Kexin Yu
ii
Dedication
The dissertation work was inspired by my grandparents, Fengying Liu, Chunhua Tu,
Zhonglan Chen, and Zhirong Yu. I grew up with their care, and the time we spent together
brought me to aging research. I could not have completed this dissertation work in good mental
and physical health without the support of my partner, Qi Wang. It is a great honor and pleasure
for me to know him a bit more every day after many years of being in our relationship. I owe the
most to my parents, Guorong Tu and Guojiang Yu. They gave me life and the courage to live it
to the fullest.

iii
Acknowledgement
I want to express my deep appreciation to my mentors, Drs. Iris Chi and Shinyi Wu, for
believing in me and guiding me through the doctoral training journey. Many thanks to Dr.
Jennifer Ailshire for challenging me to do better research in the dissertation proposal stage,
supporting me to produce more rigorous results with longitudinal data analysis, and providing
thoughtful comments on the dissertation manuscript. Many other faculty members, here at USC
and externally, have generously offered help and supported my professional development: Drs.
Michael Hurlburt, Yuri Jang, Jeremy Goldbach, Maria Aranda, Chih-Ping Chou, Kate Wilber,
Lawrence Palinkas, Suh-Chen Hsiao, Hiroko Dodge, Lisa Silbert, Jeff Kaye, Helena Chui,
Hussein Yassine, Judy Pa, and John Ringman. Please pardon me that I cannot mention everyone
who has provided great help to me. Deep down, I am grateful to you all for serving as role
models and inspiring me at this early stage of my academic career. My fellow doctoral students,
thank you for encouraging me when I felt down. The time spent with you has been one of the
most valuable experiences over the past few years. The dissertation project was funded by an
NIA F99 grant (F99AG068492), and I am thankful for the investment in me as an emerging
scholar.

iv
Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgement ......................................................................................................................... iii
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Abstract ........................................................................................................................................ viii
Chapter 1. Overview ....................................................................................................................... 1
Chapter 2. Physical Social Environments and Social Isolation among Older Adults: A
Longitudinal Analysis by Racial and Income Groups .................................................................... 8
Abstract ....................................................................................................................................... 8
Introduction ............................................................................................................................... 10
Methods..................................................................................................................................... 15
Results ....................................................................................................................................... 19
Discussion ................................................................................................................................. 22
Chapter 3. Contextualizing the Longitudinal Influence of Information and Communication
Technology Use on Social Isolation within the Living Environments: Do Purposes of Use
Matter? .......................................................................................................................................... 36
Abstract ..................................................................................................................................... 36
Introduction ............................................................................................................................... 38
Methods..................................................................................................................................... 41
v
Results ....................................................................................................................................... 44
Discussion ................................................................................................................................. 46
Chapter 4 A Longitudinal Assessment of the Relationships between Living Environment and
Cognitive Health: The Mediating Effect of Social Isolation ........................................................ 56
Abstract ..................................................................................................................................... 56
Introduction ............................................................................................................................... 58
Methods..................................................................................................................................... 61
Results ....................................................................................................................................... 65
Discussion ................................................................................................................................. 67
Chapter 5. Conclusion ................................................................................................................... 77
References ................................................................................................................................. 82

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List of Tables
Table 2.1. Baseline Sample Characteristics of the Full Sample and by Racial Groups…………28
Table 2.2. Weighted Descriptive Results of Time-Varying Variables across Waves…………...29
Table 2.3. The Results of Multilevel Modeling on the Outcome Social Isolation of the Full
Sample and by Racial Groups……………………………………………………………………31
Supplementary Table 2.1. Baseline (Wave 5) Sample Characteristics by Wave 6 Attrition
Conditions………………………………………………………………………………………..33
Supplementary Table 2.2. Crosstab of Social Isolation Score across Waves……………………35
Table 3.1. Sample Characteristics and Descriptive Results of Time-Varying Variables………..50
Table 3.2. Multilevel Modeling Results for Social, Instrumental and Medical ICT use and Social
Isolation Over Time……………………………………………………………………………...52
Table 4.1. Wave 5 NHATS Weighted Sample Characteristics………………………………….72
Table 4.2. Longitudinal Mediation with Latent Growth Curve Model Results…………………73

vii
List of Figures
Figure 1.1. Conceptual framework of the dissertation project…………………………………….6
Figure 2.1. Interaction Effects Illustrated Using Full Sample and by Racial Groups…………...32
Supplementary Figure 2.1. Social Isolation Scores By Time of Attrition……………………….34
Figure 3.1. Conceptual Framework and Analytical Model………………………………………54
Figure 3.2. Interacted Effect of Medical ICT Use and In-Home Disorder on Social Isolation….55
Figure 4.1. Conceptual Framework……………………………………………………………...75
Figure 4.2. Longitudinal Latent Growth Curve Analytical Model and Results…………………76

viii
Abstract
Social connectedness and cognitive health are two vital components that could determine
the quality of life in later adulthood. While the existing literature studied individual-level factors
that were associated with social isolation, fewer studies have examined the extent to which the
living environment influences the experience of social isolation and cognitive health among
older adults. The Ecological Theory of Aging (ETA) posits that the living environment
profoundly influences the aging process through the interaction between individual competence
and environmental press. A new development of ETA, the COntext Dynamics in Aging (CODA)
framework, posits that the process of personal and environmental resource exchange determines
the aging outcomes through building their identity, evoking or reducing stress, and maintaining
or hindering the autonomy of an older person. The CODA framework categorized the living
environment into four domains, physical environment, social environment, technology
environment, and socioeconomic environment. Three studies within this dissertation project
examined the longitudinal relationships between these four aspects of living environment, social
isolation, and cognitive health of older adults.
Chapter one gives an overview of relevant literature, sets conceptual foundations for
study objectives, and explains the interconnections of the three studies within the dissertation
project. Informed by the ETA and CODA frameworks, chapters two to four report three
independent yet related empirical studies examining the relationships of social isolation,
information and communication technology (ICT) use, and cognitive health from an ecological
perspective. Longitudinal data from waves 5 to 9 of the National Healthy Aging Trend Study
(NHATS) were analyzed with multilevel modeling (Chapters 2 and 3) and Structure Equation
Modeling (Chapter 4) methods. Longitudinal data analysis method was selected to differentiate
ix
changes at between-person and within-person levels and to produce robust study findings with
multiple time points of empirical data.
Chapter two reports a study that examines the longitudinal association of physical and
social environments with social isolation among racially diverse older adults. The physical
environment did not impact social isolation, while a more cohesive social environment was
related to less social isolation among the respondents. Income moderated the association between
social environment and isolation in Hispanic and non-Hispanic White subgroups. The protective
effect of the social environment was more prominent among individuals with higher incomes.
The findings highlight the importance of social environment and socioeconomic status (SES) on
the experience of social isolation in later life.
Chapter three studied the influence of ICT use for three different purposes (i.e., social,
instrumental, and medical) on social isolation over time. The moderating effects of ICT use on
the longitudinal associations between physical social environments and social isolation were
explored. Only social and medical ICT use remained significantly associated with social
isolation. The in-home disorder was positively related to social isolation among medical ICT
users. Social intervention programs can be embedded in medical ICT tools to prevent and
address social isolation, especially for those living in more disadvantaged physical home
environments.
Chapter four examined the mediating effect of social isolation on the hypothesized
longitudinal association between cognitive health of older participants and their physical and
social environments. Social isolation partially mediated the baseline association between in-
home disorder and cognition and fully mediated the relationships between social environment
and baseline cognition. The third study addresses the question - how might the living
x
environment "get under the skin?" The living environment affects cognition by influencing one's
social life.
Chapter five summarizes findings from all three studies and discusses the current
dissertation project's theoretical contributions and practical implications. This dissertation project
aimed to build knowledge and prepare for developing future intervention programs for
improving person-environment fit, addressing social isolation, and preventing cognitive decline.
1
Chapter 1. Overview
Social connectedness and cognitive health are two essential components of successful
aging (Rowe & Kahn, 1997). Three decades of research have consistently shown that social
isolation negatively impacts older adults' health and wellbeing (Courtin & Knapp, 2017; Holt-
Lunstad et al., 2015). Social isolation was associated with the onset of cardiovascular disease
(Cornwell & Waite, 2009a; Holt-Lunstad et al., 2015; House, 2001), declines in cognitive
function (El Haj et al., 2016; Evans et al., 2018), higher risks of depression and anxiety (Saito et
al., 2012), and lower overall life satisfaction (Cacioppo et al., 2010; Cacioppo & Hawkley, 2009;
Kawachi & Berkman, 2001). Perceived social isolation was associated with a higher cortical β-
amyloid burden in older adults, supporting the crucial role of social isolation in preclinical
Alzheimer’s disease and related dementia (Donovan et al., 2016).
The factors that could contribute to social isolation are multi-faceted. Existing literature
studied individual-level factors that were associated with social isolation (J. Wang et al., 2017)
and found that older age (Courtin & Knapp, 2017; C. Victor et al., 2000), specific personality
characteristics such as self-centeredness and withdrawal were related to a higher likelihood of
experiencing social isolation (Cacioppo et al., 2017; Ernst & Cacioppo, 1999; Pikhartova et al.,
2016). Furthermore, health conditions and chronic conditions were also recognized as common
risk factors of social isolation (Hawkley et al., 2008; Jang et al., 2016; Pronk et al., 2013; C. R.
Victor & Bowling, 2012). Nonetheless, when compared to studies focused on individual-level
factors, fewer studies have examined the extent to which the living environment influences the
experience of social isolation and cognitive health among older adults. Older persons tend to live
in a neighborhood for a longer tenure than their young adult counterparts (Keene et al., 2018;
2
Wickes et al., 2019). It might be especially beneficial to examine the environmental effect
longitudinally as the influence of the living environment is likely to be gradual and over time.
The Ecological Theory of Aging (ETA) posits that the living environment profoundly
influences the aging process through the interaction between individual competence and
environmental press (Lawton, 1983; Wahl et al., 2012). The docility hypothesis suggests as
individual age and has a decline in their competence, there are more likely to be subjected to the
environmental influence (Lawton & Nahemow, 1973). Person-environment fit would be
achieved when the environmental press matches the older person’s competence (Lawton &
Nahemow, 1973). Personal and environmental resource exchange determines the aging outcomes
through building their identity, evoking or reducing stress, and maintaining or hindering the
autonomy of an older person (Wahl et al., 2012).
Older adults with lower income levels often live in under-invested communities with
more disorders in the physical environment and have been exposed to more insecurity in the
community. Empirical evidence built with cross-sectional data showed that having
disadvantaged physical and social environments is associated with increased likelihood social
isolation (Jiang et al., 2020; Portacolone et al., 2018). Racial minority older adults have been
disproportionally exposed to disadvantaged physical environments and are at higher risk of being
socially isolated (Cornwell & Cagney, 2014; Hayward et al., 2015; Millar, 2019). However, it is
rarely empirically examined to what extent do the physical and social environments affect the
experience of social isolation longitudinally, especially among racial minority older adults and
those with lower income. Building empirical evidence with longitudinal data will allow the
researcher to distinguish between-individual and within-individual changes or describe the time
sequence of the associations, and thus, yielding findings with causal implications (Hamaker et
3
al., 2015; Krull & MacKinnon, 2001). Empirical evidence suggests that loneliness is as stable as
personality traits across life span (Mund et al., 2020). Nonetheless, less is known regarding
whether social isolation is stable or subject to more frequent change in later life. Employ five
years of longitudinal data to examine social isolation, this paper could help to address the
question.
The physical and social environment might influence the experience of social isolation
differently for racial minority older adults through 1) poverty/low socioeconomic status, 2)
systematic discrimination and racism. The difference in income might partially explain the racial
disparities in social isolation, given poverty is a known risk factor and racial minority older
adults have a lower average household income than their non-Hispanic counterparts (Jiang et al.,
2020; Parsons et al., 2021; Stewart et al., 2007). However, existing research found that racial
minority older adults with higher incomes may still be subject to a frequent experience of social
isolation because they still suffer from discrimination and social exclusion (Bell et al., 2020; Qin
et al., 2020). Chapter 2 of this dissertation project examined the longitudinal interacted effects of
income and living environment on social isolation. Subgroup analyses were conducted to
scrutinize the environmental impact on social isolation by race.
The COntext Dynamics of Aging (CODA) framework hypothesizes technology forms an
emerging living environment in the aging process (Wahl & Gerstorf, 2018). CODA framework
hypothesizes that technology affects the aging outcomes by influencing the older adults' context-
person agency. The concept of agency refers to the older adults' autonomy and ability to act
according to their wills in their lives (Wahl & Gerstorf, 2018). Technology has been recognized
as a means of changing the living environment for older adults, and technology adoption could
change older adults' agency by increasing the knowledge, motivation, and opportunities for daily
4
activities (Horgas & Abowd, 2004; Sallinen et al., 2015). Common types of technology adopted
by the older adult population include information and communication technology (ICT), mobile
health tools, wearable devices, and ambient technology (Nelson Kakulla, 2020). ICT has brought
significant changes to the way people communicate and access resources (Khvorostianov, 2016;
Zickuhr & Madden, 2012). As one of the more common technological tools adopted by older
adults, ICT use could have a more significant impact on the experience of social isolation among
older adults.
With increased popularity among older people (Gell et al., 2015; Xie et al., 2013), ICT
provides opportunities to address isolation (Beneito-Montagut et al., 2018). An increasing
amount of research investigating the association between ICT use and social isolation has been
published. For example, ICT use has been empirically supported with cross-sectional and
longitudinal data to reduce perceived social isolation and increase social contact (Chen & Schulz,
2016; Xie et al., 2013; Yu, Wu, & Chi, 2020). ICT use has primarily been reported to protect
older individuals from the experience of social isolation. Only one study identified has examined
the effect of ICT use for different purposes (Szabo et al., 2019). ICT use for social purposes was
reported to be associated with decreased loneliness and increased social engagement in one year,
while the influence of informational and instrumental use of ICT was insignificant (Szabo et al.,
2019). Nonetheless, previous research has not examined the effect of ICT use within the context
of living environments. Whether ICT use would interact with the physical living environments to
influence the risk of social isolation has not yet been empirically tested (Beneito-Montagut et al.,
2018). Previous research reported that using ICT could increase the person-environment fit as
ICT helps older adults to adapt to changes in the environments, such as the emerging technology
environment (Schlomann et al., 2020). Because ICT increases access to information and
5
resources, using ICT for medical, social, and instrumental purposes could increase older adults’
agency and mitigate the effect of a disadvantaged living environment on social isolation (Horgas
& Abowd, 2004; Sallinen et al., 2015). Chapter 3 of the dissertation examines how using ICT for
informational, medical, and social purposes might interact with physical social environments and
impact older adults' social isolation.
Environments not only affect social isolation among older adults, but they also serve as
one of the most pervasive and complex stimuli to the brain and contributes to shaping cognitive
functionality through the mechanism of neuroplasticity (Cassarino & Setti, 2015). Socially
isolated individuals are at higher risk of cognitive decline (Evans et al., 2018; Poey et al., 2017).
Social isolation has been supported to have a more significant effect size on the onset of
dementia in later life when compared to some common chronic conditions (Evans et al., 2018;
Livingston et al., 2020). For example, a Lancet commission report suggests 2% of dementia can
be prevented if social isolation was eradicated, and such association rate for diabetes was 1%
(Livingston et al., 2020). Previous research with cross-sectional data suggested physical and
social environments of living could influence the experience of social isolation (Jiang et al.,
2020; Portacolone et al., 2018). Additionally, social isolation has been increasingly recognized as
a risk factor for cognitive decline. Social isolation could explain the linkage between
disadvantaged living environments and cognitive decline. More specifically, social isolation
could mediate the hypothesized longitudinal association between disadvantaged living
environments and cognitive decline. Chapter 4 reports the third study of the dissertation project
which empirically examined social isolation as a mediator for the hypothesized longitudinal
association between physical social environments and cognitive health.
6
This dissertation aimed to investigate the longitudinal associations of multiple
dimensions of living environments on older adults' social isolation and cognitive health. The
moderating effect of ICT use on the proposed longitudinal association between living
environment and social isolation will be examined. More specifically, paper 1 examines the
extent to which living environments influence social isolation longitudinally. Racial minority
groups have been disproportionally exposed to less privileged living environments. The impact
of physical, social environments on social isolation could be different for individuals with high
or low-income levels. Subgroup analysis by racial groups was conducted, and income was
hypothesized to moderate the social environment’s influence on social isolation. The second
paper studies the moderating effect of ICT use for social, medical, and instrumental purposes on
the longitudinal association between physical and social environments. The third paper explores
the influence of physical and social environments and the trajectory of cognitive health over
time. Social isolation was hypothesized to mediate the hypothesized longitudinal associations
between physical, social environments and cognition. The conceptual framework of the
dissertation project is illustrated in Figure 1.1.

Figure 1.1. Conceptual framework of the dissertation project
7
Note: Black paths were included in all three papers; Green paths were tested in paper 1 (chapter
2); Blue paths were tested in paper 2 (chapter 3), And red paths were examined in paper 3
(chapter 4).
Longitudinal empirical evidence on the environmental influences on social isolation and
cognitive health is scarce in the existing literature. Building empirical evidence with longitudinal
data will allow the researcher to distinguish between-individual and within-individual changes,
to describe the time sequence of the associations, and thus, yielding findings with causal
implications. Three distinctive papers have been produced from this dissertation work.
Successfully achieving the objectives of this dissertation proposal contributed to building
knowledge on the environmental risk and protective factors of social isolation and cognitive
health among older adults over time. Examining the role of ICT use within context could inform
the development of technology-facilitated health social work services for low-income families in
deprived neighborhoods. By contextualizing the role of technology in older adults' living
environments, knowledge built in the dissertation could assist older adults to age in place with
technology tools and improve person-environment fit. The dissertation work helped to expand
the scope of CODA by studying both the physical and social environments and their
relationships with social isolation and cognitive health.

8
Chapter 2. Physical Social Environments and Social Isolation among Older Adults: A
Longitudinal Analysis by Racial and Income Groups
Abstract
Background and Objectives: The physical environment provides setting and the social
environment set atmosphere for social interaction. How do physical and social environments
affect social isolation among racial minority older adults is under-researched. The current paper
scrutinizes the longitudinal relationships between physical, social environments and social
isolation by race and income levels.
Research Design and Methods: This study used wave 5-9 data from the National Health and
Aging Trend Study (N = 5807). Interviewers visited respondents’ homes evaluated two layers of
physical environments: community and in-home. Social environment was measured by
respondents’ perceived community cohesion. Social isolation score was the count of isolating
events, such as living alone, small network size, and rare social participation. Income was the
sum of self-reported annual total income of self and the spouse. The longitudinal association and
subgroup analysis for non-Hispanic Black, non-Hispanic White, and Hispanic participants were
conducted using multilevel modeling.
Results: More than 30% of participants self-identified as Hispanic, non-Hispanic Black or other
race. Physical environment variables were not significantly related to social isolation over time.
For the non-Hispanic Black and non-Hispanic White participants, better social environment was
longitudinally related to reduced social isolation. A higher annual income was directly related to
a lower level of social isolation in non-Hispanic Black participants. Income moderated the
influence between social environment and social isolation among Hispanic participants over
time.
9
Discussion and Implications: Social environment longitudinally influences social isolation in
later life. Income both directly affect and moderate the longitudinal relationships between social
environment and social isolation. Subgroup analysis results suggest the influence of income
levels was especially salient among Hispanic participants. Intervention programs aim to address
social isolation could adopt community cohesion approaches and targeting minority older adults
with lower incomes.
Keywords: Community cohesion; SES; community disorder; context dynamics in aging
ecological theory of aging; social connectedness

10
Introduction
Social isolation is the absence of contact with other individuals (Pohl et al., 2017).
Socially isolated individuals had a 29% increased likelihood of mortality compared to their not
isolated counterparts (Holt-Lunstad et al., 2015). AARP estimated that about 4 million older
adults enrolled in Medicare are socially isolated, and Medicare spends an additional $6.7 billion
on isolated individuals than otherwise if they are socially connected (Elder & Retrum, 2012).
While the current literature has examined the individual-level risk and protective factors of social
isolation, such as personality and health concerns, the extent to which physical and social
environments affect the experience of social isolation is under-researched.
Lawton's Ecological Theory of Aging (ETA) posits that older adults could be more
sensitive to environmental press compared to younger adults because of increased vulnerability
as a result of functional loss (Lawton, 1983). Building on the seminal ecological theory of aging,
Wahl and Gerstorf (2018) proposed the COntext Dynamics in Aging (CODA) framework. The
CODA framework assumes that the environment makes an impact through changing older adults'
sense of agency and belonging (Wahl et al., 2012; Wahl & Gerstorf, 2018). Agency is the extent
to which older adults can maintain autonomy and keep routines (Wahl & Lang, 2004). Belonging
refers to the older person's familiarity with the living environment, sense of meaning, and
identity attached to the environment (Wahl & Lang, 2004). The physical environment, which are
the objective aspects of the neighborhood and the facilities, for example, access to amenities,
green open spaces, and sidewalks, mainly impacts older persons' agency to accomplish desired
activities (Wahl et al., 2012; Wahl & Gerstorf, 2018). On the other hand, individuals search for a
sense of belonging in the living environment, which constructs the social aspect of a living
environment. The social environment contributes to ones’ place attachment and identity. Both
11
physical and social environments could have fundamental impacts on older persons’ wellbeing
through impacting their agency and sense of belonging.
Physical Environments and Social Isolation
Disadvantages in the community’s physical environments, such as litters on the streets,
deserted buildings, and vandalism, could lead to the perception of insecurity and prevent older
adults from engaging in social activities. Having outdoor space and common areas contribute to
the older residents’ general wellbeing (Moran et al., 2014). Scrutinizing the influence of physical
environment from the lenses of CODA, the disorder in the community limits older adults’
autonomy and thus could lead to social isolation.
Physical environments have layers. Older persons have more autonomy in changing the
physical environment within their own homes than in the community. The role of in-home
environment and community environment could be different in later life. Minimal previous
research identified has explored the potentially distinct effects of community and in-home
environments (Cornwell, 2014; Schafer & Upenieks, 2015). Cornwell (2014) reported a cross-
sectional association between poor in-home environment and weaker social ties and less social
support, suggesting a higher risk of being socially isolated. Schafer and Upenieks (2015) found
that both neighborhood and household disorder were related to functional decline in older adults,
which has been recognized as a risk factor of social isolation (Courtin & Knapp, 2017;
Domènech-Abella et al., 2021; Yu, Wu, Jang, et al., 2020). In the current paper, the impacts of
community physical environment and in-home environment on social isolation will be
differentiated. Although existing literature shed light on the role of the physical environment on
social isolation, the evidence is built mainly on cross-sectional data, their longitudinal
relationships are unknown. Cross-sectional evidence is based on between-person comparisons.
12
On the other hand, longitudinal analysis enables us to answer the question whether
disadvantaged living environments would influence the extent of social isolation of the same
individual over time. The current study explores the longitudinal influence of the physical
environmental disorder in home and in the community on older adults’ social isolation.
Social Environments and Social Isolation
Perceived social environment, such as togetherness and trust, were found to be associated
with a less sedentary lifestyle and more social participation among older adults, and thus,
suggests a lower extent of social isolation (Jolanki & Vilkko, 2015). Residential satisfaction and
a sense of belonging were found negatively associated with perceived social isolation (Prieto-
Flores et al., 2011). Middle to older age adults who perceive the community as trustworthy
experienced less subjective social isolation (i.e., loneliness) over four years (Yang & Moorman,
2019). Older residents of a high-crime neighborhood are more likely to be socially isolated
despite their strong desire for interpersonal connections (Portacolone et al., 2018). Existing
literature, primally with cross-sectional data, cohesively suggest that a positive social
environment is protective of social isolation.
Interaction between Physical and Social Environments
Physical and social environments are not independent of each other (Wahl & Lang,
2004). The perception of the social environment is likely to be influenced by the physical
environment characteristics. Physical environment disorder has a social meaning (Sampson &
Raudenbush, 2004). Negative physical characteristics of a living environment could be related to
less perceived interpersonal interaction opportunities in the neighborhood. For example, visible
vandalism on the street could prevent residents from walking on the streets and increase the
incidence of social isolation (Aiyer et al., 2015). In a conceptual paper discussing the integration
13
of physical and social environments, Wahl and Lang (2004) raised the questions that if a good
social environment would "compensate" the effects of disadvantaged the physical environment,
or if there is an additive effect for living in an adverse physical environment with low social
cohesion. While most previous studies studied those two as separate concepts, the current paper
studies the possible interacted influence of physical and social environments on social isolation
(Moran et al., 2014; Poey et al., 2017; Van Cauwenberg et al., 2014; Wahl & Lang, 2004).
The Influence of Environments Examined by Race and Income
The community environments might affect the social life of racial minority older adults
differently from their non-Hispanic White counterparts (Wilkinson et al., 2017). We hypothesize
two penitential reasons that could illustrate why physical and social environments might affect
social isolation differently for racial minority older adults: 1) poverty/low social economic status,
2) systematic discrimination and racism. Racial minority older adults who struggles with poverty
are disproportionately exposed to disadvantaged physical environments which could serve as risk
factors for social isolation (Cornwell & Cagney, 2014; Hayward et al., 2015; Millar, 2019).
Nonetheless, minority older adults with higher income levels are not at all immune of racism and
discrimination. Previous research report that racial minority older adults with higher incomes are
could still frequently experience social isolation (Bell et al., 2020). They are also at high risk of
experiencing discrimination and social exclusion (Qin et al., 2020). On the other hand, racial
minority older adults has developed mechanism of resilience that could help them to overcome
hardships. For example, Hispanic older adults are more likely to live in multi-generational
households and African American older adults tend to have more extensive non-kin relationships
compared to White older adults (Taylor et al., 2013). Although rarely reported in the existing
14
literature, the longitudinal influences of physical and social environments on social isolation
need to be scrutinized by racial groups
Social economic status, which could be reflected by one’s income levels, may interact
with the social environments and influence the experience of social isolation among racially
diverse groups of older adults. Many existing literature on environmental justice discusses the
environmental hazards that the poverty groups are exposed to, such as community disorder and
pollutions (Hajat et al., 2015). Nonetheless, community cohesions and social inclusion are
arguably other important aspects of environment justice. Racial minority older adults with high
income levels may be able to afford to live in more affluent neighborhoods, yet the sense of
belongingness could be low if they do not feel culturally and socially connected to others in the
community (Angel, 2009; Epps et al., 2018). Racial minority older adults might prefer to live in
communities with stronger sense of belonging over one with better developed physical
environment yet low social cohesion, such as a gentrifying neighborhood (R. J. Smith et al.,
2018). Although having lower income and being racial minority has been separately shown as
risk factors for social isolation, the effects of income have yet to be explored within racial
groups.
Purpose of the Study
The current study conducts subgroup analysis of White, Hispanic and Black participants
to scrutinize the within group variation of physical and social environmental effect on social
isolation over time. The longitudinal data analysis could help to delineate the within-individual
vs. between-individual changes. The moderating effects of income on the longitudinal
relationships between social environments and social isolation will be explored. Because income
15
levels and physical environment tend to be highly correlated, we did not hypothesize income
would moderate the effect of physical environments on social isolation.
Methods
This study used data from the wave 5 (collected in 2015) to 9 (collected in 2019) data
from the National Health and Aging Trend Study (NHATS). NHATS is a longitudinal study that
annually surveys Medicare beneficiaries aged 65 and above in the contiguous United States
(excluding Alaska, Hawaii, and Puerto Rico). Medicare beneficiaries constitute more than 98%
of the US population age 65 and older (Mold et al., 2004). NHATS used a stratified three-stage
sample design: 95 individual counties or groups of counties were selected as primary sampling
units (PSUs), and 655 zip codes were sampled from them as secondary sampling units (SSUs).
Individuals were sampled from the Medicare enrollment data bases (EDBs) of the SSUs with
oversampling of non-Hispanic black individuals and the oldest old. The probabilities of sampling
at all three stages aimed to create equal probability samples and achieve the goals of sample sizes
by age group and race/ethnicity. The NHATS oversampled the oldest old and non-Hispanic
Black older adults. The waves 5 to 9 were selected because measures of physical environment
were included since wave 4, and the NHATS sample was replenished in the 5
th
wave. Starting
the analysis from wave 5 prevents the problem of having a high percentage of missing in the
baseline of longitudinal analysis. The overall response rate at wave 5 was 76%, which consist of
96% of the continuing sample and 63% of the newly enrolled sample (J. DeMatteis et al., 2016).
The working sample of the dissertation project excluded facility-dwelling individuals
because their living environment could be drastically different from community samples. Proxy
responded cases were also excluded from the analysis because the environmental assessments
were not administered with proxy responded cases. In total, there were 5,807 respondents in the
16
wave 5 working sample. On average, a respondent was surveyed for 3.5 waves. In order to
diagnose possible reasons for attrition, dichotomously coded flag variables were generated for
the missingness in each dependent and independent variable. Baseline demographic
characteristics of the participants were regressed on the missing flag variables. Non-Hispanic
Black, racially identified as others, and individuals aged above 75 years old were more likely to
have missing in the social isolation in the follow-up waves. We compared the baseline sample
characteristics between participants who completed wave 6 and those who did not (see
supplementary table 2.1). Compared to the dropped out participants, individuals who stayed in
the study were younger, more likely to be male, had higher education, reported higher social
isolation and lower social environment scores. However, no differences were found in the
physical environment indicators. Supplementary figure 2.1 shows the difference in social
isolation by attrition status. Individuals who dropped out had a higher mean score of social
isolation. These attrition sample diagnoses suggest the study findings might be biased towards
less isolated people and need to be interpreted with caution.
Measurements
Physical environments were reported by the interviewers who visited the respondents at
their homes. Standing in front of the respondent’s home/building and looking around, the
interviewer rated on a four-point scale (1=none, 4=a lot) regarding 1) the amount of litter on the
street, 2) graffiti on buildings and walls, 3) vacant or deserted houses or stores. The disorder
inside the responder’s home/apartment was assessed by observing the existence of 1) peeling
paint, 2) evidence of pests, 3) broken furniture, 4) flooring need of repair, and 5) tripping
hazards. The sum score of the physical conditions of the community and inside home items were
first calculated. Histograms showed the distribution of the community and in-home disorders
17
were highly positively skewed across all waves. About 10% of all respondents had any
community disorder or in-home hazards in their living environments in any given wave.
Therefore, both the community and in-home conditions were dichotomously coded to
differentiate the participants lived in disadvantaged physical environments from those who did
not (1= any disorders/hazards existed, 0= no disorder/hazards observed).
Social environment. This paper operationalizes social environment as community
cohesion perceived by older adults. Respondents reported whether the people in the community
know each other very well, willing to help each other, and can be trusted on a three-point scale
(0= do not agree , 1=agree a little, 2= agree a lot). A sum score of the three items was used as the
scale score for the social environment. Social environment scale scores ranged from 0 to 6, with
a higher score indicates more neighborhood cohesion.
Social isolation was constructed with multiple dimensions of older adults’ social life,
including living arrangements, social network size, and social participation (Cornwell & Waite,
2009b; Cudjoe et al., 2018). The social isolation score is the sum of four dichotomously coded
indicators: living alone, having less than two people in the individuals' social network in the past
year, not attending religious services, and participating in clubs, organization meetings, or
volunteering in the past month (Cudjoe et al., 2018). All items of social isolation were
dichotomously coded as a score of 1 indicates a more isolated situation. The sum score ranged
from 0 to 4. A respondent with a higher score is more socially isolated.
Income. Respondents reported theirs’ and their spouses’ total annual income from
different sources, including social security, supplementary security income, Veteran’s
administration, pension plan, earned income, interests or dividend income, and retirement
account withdrawals. A total income score was calculated. To facilitate understanding of the
18
model results, total income was divided by 1,000 and analyzed in the unit of 1,000 USD. We did
not perform log transformation on the variable income because: 1) income is an independent
variable; 2) it would be difficult to interpret the interaction effect with a log-transformed scale.
Control variables. Age, sex, race, marital status, education, self-rated health, and
depression have been reported in existing literature as predictors for social isolation in older
adults (Cattan et al., 2005a; Courtin & Knapp, 2017; C. R. Victor & Bowling, 2012). The current
study includes the variables listed above as control variables. Sex was binarily measured
(1=female, 0=male). Race was self-reported by the participants. NHATS assessed race and
ethnicity with two questions, one asked about whether the respondent self-identified as Hispanic
or not and the other asked the respondent to choose if they consider their race as White, Black, or
Others. These two variables were recoded into a categorical variable indicating one’s racial
identity, including non-Hispanic White, non-Hispanic Black, Hispanic, and Others. Marital
status was dichotomously coded (1=married/partnered, 0=not married nor partnered). Education
level were coded as 1=have a college or higher degree vs. 0= not having a college degree.
Respondents reported self-rated health on a five-point scale, a higher score indicate better health
conditions. Depression was assessed with the 2-item Patient Health Questionnaire depression
module (PHQ-2) (Kroenke et al., 2003). The PHQ-2 score were dichotomously coded: a score ≥
3 suggests major depression (Kroenke et al., 2003).
Data Analysis Strategies
Data management, variable re-coding, and analysis were conducted using Stata 15 SE.
Descriptive analyses was run before model building. Multilevel modeling was employed to
model the longitudinal relationships between physical social environments and social isolation.
The multilevel models with longitudinal repeated measures have two levels: within-individual
19
and between-individual. A step-wise model building approach was employed for the models with
full sample and racial subgroups. To examine the relationship between time and social isolation,
we first ran models with random intercepts only and with random slopes of time. Likelihood
ratio tests were used to examine which model fitted the data better. The model with random
slope had a better model fit, suggesting the trajectory of change differed between individuals. We
did not choose to model a quadratic term of time because no theoretical reasons suggest that
social isolation would have a curvilinear change over time. Upon establishing the relationships
between time and social isolation, physical and social environments were included. And then, we
added between-individual level sociodemographic control variables, such as age, gender, race,
marital status, education, and income. Lastly, the interaction terms between physical and social
environments, as well as between social environment and income levels were included. In order
to present the results parsimoniously, only the final model results were presented in this
manuscript. Complex sample design of NHATS were accounted for by applying sampling
weight. Subgroup analyses were conducted to scrutinize the influence of living environments by
racial groups.
Results
Table 1 presents the baseline (wave 5) NHATS sample characteristics of the weighted
full sample and weighted descriptive results by racial groups. At baseline, the sample was
represented by 78.19% of non-Hispanic White, 8.21% non-Hispanic Black, 6.67%
Hispanic/Latino, and 6.93% Others. Approximately 58.86% of the participants were married or
partnered. About 28.91% respondents had a bachelor’s degree or above. The average annual total
income of Hispanic and Black older adults (Black participants’ baseline mean = 40,991 USD;
Hispanic participants’ baseline mean = 40,768 USD) were about half of the annual income of
20
their non-Hispanic White counterparts (mean=81,713 USD). The weighted descriptive results by
racial groups show that about 31.59% of non-Hispanic White participants had a bachelor’s
degree. However, the percentages of receiving undergraduate education were about 16.49%
among Black participants and 10.41% among Hispanic participants.
The weighted descriptive results of key variables across four waves are summarized in
Table 2. At baseline wave, about 23.92% of the Black, 30.06% of Hispanic, and 5.84% of non-
Hispanic White participants lived in communities with at least one community disorder
indicator. The longitudinal descriptive results show a decline in the presence of community
disorder among Hispanic and Black participants, but not among non-Hispanic White
participants. At baseline, A higher percentage of Black (19.09%) and Hispanic participants
(16.61%) lived in homes with disorder and hazards than non-Hispanic White participants
(9.57%). However, the percentage of in-home disorder varied across four waves in all racial
subgroups, yet no clear trend was observed. The full sample’s mean score of perceived social
environment was 4.21 at the baseline, indicating a moderate to high level of community
cohesion. Black and Hispanic participants rated their community cohesion lower than White
participants. The means score of social isolation was about 1.66 (SE=0.02) at baseline. Hispanic
and Black participants consistently reported higher levels of social isolation than White
participants over time.
The results of the multilevel models that examines the longitudinal relationships between
physical social environments, income and social isolation with the full sample and by racial
subgroups were summarized in Table 3. The model results with the full sample and with
subgroups by race consistently show that the social isolation score did not change at within
person level over time.
21
The model 1 result for the full study sample shows that disadvantages in the physical
environments, both in-home and in the community, were not longitudinally associated with
social isolation of its older residents. Social environment was negatively related to the extent of
social isolation over time (B=-0.032, SE=0.006, p<.001). Income has a significant main effect on
social isolation (B=0.0002, SE=0.000, p=.025), indicating a positive relationship between
income and social isolation in the full sample. The interaction effect between physical
environment and social environment was not statistically significant. However, the effect of in
social environment on social isolation was moderated by income (B=-0.0001, SE=0.000,
p=0.029). Figure 1 (B) shows the interaction effects between social environment and income.
The beneficial effect of social cohesion was more substantial for individuals with higher
incomes. In the model results with the full sample, being female, married/partnered, having a
bachelor’s degree, better self-rated health, and no major depression were negatively associated
with being socially isolated. The Intra-class Correlation Coefficient (ICC) for model 1 was
0.573, suggesting 57.3% of the total variance in social isolation was at between individual level.
The subgroup analysis results with non-Hispanic Black participants (model 2) showed
that both physical environment indicators were not related to social isolation longitudinally.
Social environments were associated with decreased social isolation (B=-0.033, SE=0.016,
p=0.039). A higher income was protective for Black older participants from being socially
isolated (B=-0.002, SE=0.001, p=0.016). None of the interaction terms were statistically
significant for Black participants. For model 2, about 49.0% of the total variance in the outcome
was at the between individual level.
For Hispanic participants (model 3), the longitudinal main effects of both physical and
social environment were not statistically significant. A higher income was associated with
22
increased social isolation (B=0.004, SE=0.001, p<.001). The interaction between social
environment and income (B=-0.002, SE=0.000, p<.001) were statistically significant. Figure 1
(A) shows the cross-over interaction between social environment and income levels. Among
Hispanic participants with less than median income, an increase in social environment score was
positively related to increased social isolation. However, advantaged social environment was
negatively associated with social isolation among the participants with higher income levels. The
between individual variance account for 49.5% of total variance in model 3.
Physical environment was again not significantly related to social isolation among non-
Hispanic White participants (model 4). Social environment was related to decreased social
isolation over time (B=-0.037, SE=0.007, p<.001). The longitudinal relationship between social
environment and social isolation was moderated by income levels (B=-0.001, SE=0.000,
p=0.015). Figure 1 (B) illustrates the interaction effect. Similar to the relationship observed with
full sample, the negative association between social cohesion and social isolation was stronger
among those with higher income levels. Between level variance account for 59.6% of total
variance in social isolation in model 4.
Discussion
The current study examined the longitudinal relationship between characteristics of
physical environment, social environment and social isolation of older adults. To better
understand the nuance of environmental effect on social isolation, the interacted effect between
physical environment, social environment and income levels were explored using full sample
data and within racial subgroups (i.e., non-Hispanic Black, non-Hispanic White, and Hispanic).
The participants’ levels of social isolation did not vary significant over time. The analytical
results showed physical environment was not directly associated with social isolation at the
23
within individual level. A higher income had a main effect on isolation among all participants
regardless of their race. After adjusting the main and the moderating effects of income levels, a
more cohesive social environment was related to a decreased level of social isolation for non-
Hispanic Black and non-Hispanic White participants, but the association was not significant for
Hispanic participants. An interacted effect between social environment and income was also
found among Hispanic and non-Hispanic White participants.
Because the mean level of social isolation did not vary much over the 5 years of
observation period, it is not feasible for the current study to model the longitudinal change in
social isolation. It is possible that the mean level of social isolation tends to be stable over time.
The mean score of loneliness, which is the subjective evaluation of the social isolation
experience, was found to be essentially the same from adolescence to older adulthood (Mund et
al., 2020). The no change observed could also be attributed to the nature of the measure and the
relatively short follow-up period: with a four-point scale that counts isolating events (living
alone, small social network, not attending religious services and no social participation), it is
possible that five years is still a rather short follow-up duration to capture any significant amount
of changes. Future studies might consider employ other types of social isolation scales that has
the potential to capture more sensitive changes, such as the Lubben Social Network Scale
(Lubben et al., 2006). However, the level of social isolation did change for some individuals at
the within person level. As shown in supplementary table 2.2, from wave 5 to wave 9, about
30.44% of participants' social isolation scores increased, about 46.46% remained the same, and
23.10% had a decreased social isolation score. Because of the within-level changes observed, we
decided to keep the longitudinal analysis as it allows within-individual comparison rather than
between-person comparisons.
24
This study did not find a longitudinal relationship between in-home and in-community
disorders and social isolation of older adults. Previous research reported disadvantages in the
neighborhood was associated with having smaller social network and weaker social ties in later
life (Cornwell, 2014; Cornwell & Behler, 2015). Residents’ of a highly walkable neighborhood
are more likely to engage in social activities (Van Holle et al., 2014). Less previous paper has
looked at the longitudinal relationships between physical environment and social isolation. The
cross-sectional findings could support the between individual disparities, that is, individuals
living in a disadvantaged neighborhood are at higher risk of social isolation than others living in
better physical environments. However, the insignificant findings of the current study imply that
living in a disadvantaged physical environment may not change the extent of social isolation
within individual over time.
The importance of social environment was highlighted by the study results. The current
study found that a more cohesive social environment was longitudinally associated with reduced
level of isolation for older adults. Previous research with cross-sectional data have also reported
similar findings (Bui, 2020; Cornwell & Goldman, 2020; Kearns et al., 2015). Nonetheless, this
assumption did not hold for Hispanic participants. The direct and moderating effect of incomes
could be overpowering the effects of social environment for Hispanic participants. A
considerable income gap was observed between Hispanic and non-Hispanic White participants.
Financial distress could play a more essential role on social isolation among Hispanic origin
older adults when compared to non-Hispanic White counterparts.
The longitudinal interaction effect between physical and social environments on social
isolation was not significant. In other words, social environment did not “compensate” or “add
to” the effects of the physical environments. NHATS Study assessed litter, graffiti, vacant
25
buildings in the community, and peeling paints, pests, broken furniture and tripping hazards in
home. These disadvantages in the physical living environment were hypothesized to serve as
indicators for social disorder and lack of cohesion. However, these characteristics could also be
more distally related to social engagement when compared to other measures, such as the
availability of green space and walkability of the neighborhood. The latter types of physical
environment are conceptually more likely to affect older adults’ autonomy, sense of belonging
and stress. The CODA framework theorized environment influence on aging process through
these three mechanisms (Wahl & Gerstorf, 2018). Pervious research with cross-sectional data
reported certain physical environment characteristics, such as having green space, access to
facilities, and rest areas, were beneficial to older adults’ social wellbeing (Moran et al., 2014;
Tyvimaa, 2011). When such measures are available, future study could consider to explore
whether having access to such social venues in the community, when compounded with different
levels of social cohesion of the neighborhood, would have a different influence on older adults’
social life.
The sub-group analysis results show the moderating effect of income on the longitudinal
association between social environment and social isolation was significant among the Hispanic
and non-Hispanic White participants. The protective effect of a more cohesive social
environment was observed among the group with higher incomes, yet a better social environment
was more positively related to isolation among Hispanic older adults with lower incomes. It is
worth notice that Hispanic participants’ 75th percentile income was $25,000, which was merely
$2,000 higher than non-Hispanic White participants’ 25th percentile income ($23,000). The
disparity in income might be more meaningful for explaining the difference in social isolation.
This moderation again shows the importance of financial resources to the social wellbeing of
26
Hispanic older adults. Because data on the residential area and state were not available, we could
not interpret the results in light with local cost of living, which has the potential to explain the
interaction between social environment and income. Furthermore, Hispanic participants of this
study sample are mostly with lower incomes when compared to non-Hispanic White participants.
To further enhance knowledge on this issue, future research could consider collecting data with
more representative sample of Hispanic older adults with broader spectrum of income and more
detailed measure of living context.
The results ought to be interpreted with cautious as the relatively small sample size for
racial minority older adults might limit the study findings. NHATS did not include Asian older
adults in their sample design. Instead, they were lumped in the Others category. Therefore, it was
not possible for the current study to examine the environmental impacts on social isolation
among Asian older adults in the US. There were 348 Hispanic participants and 1,181 non-
Hispanic Black participants in the 2015 NHATS dataset, compared to 3,979 non-Hispanic White
participants. The proportion for Hispanic participants was especially small given that about 8%
of older adults in the US self-identified as of Hispanic origin in 2015 (Administration for
Community Living, 2016). Individuals who were older, female, and had a higher social isolation
score are more likely to drop out in the follow-up waves. The study results could be biased
towards those who had more observation data points. The 5-year long observation period could
be short to find mode substantial environmental influence on social isolation. Because publicly
available datasets does not include sensitive information such as the zip codes of the participants.
This study also could not take into consideration the more extreme change in the living
environment, such as moving out of the neighborhood. The interviewer reported neighborhood
physical environment. Their perception of the environment could be subject to reporters’ effect.
27
Interviewers’ characteristics, such as race, age, and familiarity with the neighborhood, could
affect their judgement of the environment.
Conclusion
In conclusion, this study found a more cohesive social environment was negatively
associated with social isolation in later life. Income both directly influence social isolation and
moderate the relationships between social environment and social isolation over time. Subgroup
analysis results suggests the influence of income levels were especially salient among Hispanic
participants. Intervention programs aim to address social isolation could consider focusing on
community cohesion and targeting minority older adults with lower income.
28

29

30

31

32

Figure 2.1. Interaction Effects Illustrated Using Full Sample and by Racial Groups

33

Supplementary Table 2.1. Baseline (Wave 5) Sample Characteristics by Wave 6 Attrition
Conditions

Notes: * p<.05, ** p<.01, ***p<.001

Participants
completed wave 6
assessments
Participants
dropped out since
wave 6

Mean/n SD/% Mean/n SD/% t/chi-sq p
Age

172.84*** 0.00
65-74 2347 40.56% 399 28.87%

75-84 2357 40.74% 506 36.61%

85+ 1082 18.70% 477 34.52%

Sex - female 3310 57.21% 861 62.30% 11.90*** 0.00
Education- Bachelor's and above 1500 26.37% 181 18.95% 23.81*** 0.00
Race

45.12*** 0.00
Non-Hispanic White 4020 69.48% 896 64.83%

Non-Hispanic Black 1183 20.45% 294 21.27%

Hispanic 329 5.69% 71 5.14%

Other 254 4.39% 121 8.76%

Social Isolation 1.71 1.05 2.04 1.04% 8.98*** 0.00
Physical Environment - with
disadvantages

Home environment 641 11.94% 111 13.64% 1.92 0.17
Community environment 626 11.02% 117 11.19% 0.03 0.87
Social Environment 4.25 1.63 4.04 1.70 -3.52*** 0.00

34

Supplementary Figure 2.1. Social Isolation Scores By Time of Attrition

0
0.5
1
1.5
2
2.5
3
3.5
4
wave 5 wave 6 wave 7 wave 8 wave 9
Social Isolation
Dropped out after 1 survey interview (n=932) Dropped out after 2 survey interviews (n=652)
Dropped out after 3 survey interviews (n=509) Dropped out after 4 survey interviews (n=413)
Completed all survey interviews (n=4186)

35
Supplementary Table 2.2. Crosstab of Social Isolation Score across Waves

Last wave - n(%)
Baseline 0 1 2 3 4
0 272(6.1%) 247(5.5%) 69(1.6%) 19(0.4%) 1(0.0%)
1 200(4.5%) 677(15.2%) 426(9.6%) 98(2.2%) 11(0.2%)
2 40(0.9%) 323(7.3%) 694(15.6%) 336(7.5%) 59(1.3%)
3 8(0.2%) 68(1.5%) 284(6.4%) 364(8.2%) 89(2.0%)
4 0(0.0%) 3(0.1%) 23(0.5%) 79(1.8%) 61(1.4%)

Notes: The upper triangle sum represents the individuals who had increased social isolation
scores from baseline to the last wave (wave 9), about 30.44% of participants' social isolation
scores increased. The number on the diagonal are those with the same social isolation scores
from baseline to the last wave, about 46.46% remained the same. And 23.10% had a decreased
social isolation score, which is the sum of the lower triangle.

36
Chapter 3. Contextualizing the Longitudinal Influence of Information and Communication
Technology Use on Social Isolation within the Living Environments: Do Purposes of Use
Matter?
Abstract
Introduction: Older adults live in disadvantaged neighborhoods are at higher risk of being
socially isolated. Meanwhile, older adults have been increasingly adopting Information and
Communication Technology (ICT) to gain social contact and increase access to information. ICT
use could attenuate the association between disadvantaged physical and social environments and
social isolation. An increasing amount of research investigating the association between ICT use
and social isolation have been published, however, whether ICT use would interact with the
effect of environmental factors on the risk of social isolation has not yet been empirically tested.
This paper aims to study the longitudinal influence of ICT use for instrumental, social and
medical purposes on social isolation within the physical and social living environments of older
adults.
Methods: Data from the wave 5 (2015) to 9 (2019) National Health and Aging Trend Study
(NHATS) was employed (N=5,807). Physical environments were reported by the interviewers
who visited the respondents’ homes, including community and in-home environments. This
study operationalized social environment as community cohesion perceived by older adults.
Instrumental ICT use was assessed by asking if the respondents shop online and pay bills or
banking with the internet. Social ICT use was indicated by if the respondent visited social
network sites. Use the internet for medical purposes included order or refill prescriptions, contact
medical providers, handle health insurance, and get health information. Social isolation was
constructed with the sum of isolating events in older adults’ life, including living alone, small

37
social network size, and little social participation. Multilevel modeling methods were conducted
to address the nested effect of repeated measure over time of the same individuals
Results: All three types of ICT use were negatively associated with social isolation
longitudinally before adjusting for environmental factors. Upon controlling for physical and
social environments, only medical (B=-0.096, SE=0.019, p=0.001) and social (B=-0.069,
SE=0.021, p<0.001) ICT were still related to social isolation over time. The interaction between
home environment and medical ICT use was significant (B=0.174, SE=0.071, p=0.015). The
existence of home disorder positively associated with higher social isolation among medical ICT
users.
Discussion: ICT use for these three purposes influenced social isolation in different modes when
taken the physical and social environments into consideration. ICT use could serve as tools for
alleviating social isolation among older residents of disadvantaged communities. Home visits
paid to older individuals with more medical issues may help to reduce the risk of social isolation.

Keywords: Information and Communication Technology, Ecological Theory of Aging, NHATS,
Multilevel modeling.

38
Introduction
Social isolation has been recognized as a public health issue for the aging society. The
outburst of the COVID-19 pandemic revealed the significance of social isolation on both mental
and physical health of older adults and also highlighted the importance of using technological
tools for social connection (Stefana et al., 2020). Information and Communication Technology
(ICT) use has been found associated with reduced social isolation (Chen & Schulz, 2016; Cotten
et al., 2013). A systematic review summarized that ICT use alleviates social isolation through
four mechanisms: connecting to the outside world, gaining social support, engaging in activities
of interests, and boosting self-confidence (Chen & Schulz, 2016). ICT has brought significant
changes to the way people communicate and access resources (Khvorostianov, 2016; Zickuhr &
Madden, 2012). With increased popularity among older people (Gell et al., 2015; Xie et al.,
2013), ICT provides excellent opportunities to address social isolation (Beneito-Montagut et al.,
2018). An increasing amount of research investigating the association between ICT use and
social isolation have been published, however, whether ICT use would interact with the effect of
environmental factors on the risk of social isolation has not yet been empirically tested (Beneito-
Montagut et al., 2018).
Both physical and the social environment could influence the risk of experiencing social
isolation for its older residents (O’Sullivan et al., 2021; Poey et al., 2017; Suen et al., 2017). For
instance, older adults living in a high-crime neighborhood reported increased likelihood of being
socially isolated (Portacolone et al., 2018; Saito et al., 2012). Disorders in the home has been
found associated with weaker social ties (Cornwell, 2014). The social cohesion and sense of
togetherness in the community is critical to prevent the experience of social isolation among its
older residents. Living in an environment with a stronger sense of belongingness was found

39
associated with more active lifestyles and increased social participation (Jolanki & Vilkko,
2015). Older adults who report people in the community are trust worthy were less likely to be
socially isolated (Yang & Moorman, 2019).
ICT increases access to information and resources and could bring opportunities of social
interactions, and thus, alleviate social isolation. Adopting ICT possesses the potential to mitigate
the effect of a disadvantaged physical and social environments on social isolation among older
residents. ICT access inequalities exist for older adults with lower social-economic status (SES).
Individuals residing in deprived communities are less likely to have the resources to purchase
technology devices and to use ICT. However, among those older adults who use ICT in
disadvantaged communities, beneficial effects have been reported (Kearns & Whitley, 2019).
ICT users were more physically active, had more frequent contact with neighbors, used social
amenities more frequently, and reported lower level of loneliness (Kearns & Whitley, 2019). ICT
adoption has the potential to reduce the negative influence of disadvantaged physical
environments by providing a new avenue for older adults to interact with family, peers, and
society (Khvorostianov, 2016; Zickuhr & Madden, 2012). The influence of disadvantaged
physical environments could be mitigated by ICT use as its use enables older adults to shop and
receive service online, which could increase the sense of agency.
ICT use has been empirically supported for reducing perceived social isolation and
increase sense of belonging and autonomy (Chen & Schulz, 2016; Xie et al., 2013; Yu, Wu, &
Chi, 2020). ICT users reported that ICT made it easier for them to reach people and increased
their frequency of communicating with others as well as made them felt less isolated (Cotten et
al., 2013). Using ICT to compensate for the absence of interpersonal connection opportunities
could help increase the person-environment fit (Schlomann et al., 2020). ICT use might moderate

40
the longitudinal relationships between community cohesion and social isolation. The interaction
effect of different purposes of ICT use within the living context has not been empirically studied
in previous studies and will be explored for the first time in this dissertation work. No hypothesis
was posited for the interaction effects between ICT use and physical social environments on
social isolation.
The nature and purpose of ICT use might make a difference on social isolation. Most
existing evidence studied the effect of ICT use without discerning the nature of such use due to
restrictions in measurements (Cotten et al., 2013; Yu, Wu, & Chi, 2020). However, it is
reasonable to hypothesize that using ICT for different purposes could have different effects on
social isolation. Only one study identified has examined the effect of ICT use for different
purposes (Szabo et al., 2019). ICT use for social purposes was reported to be associated with
decreased perceived social isolation in one year, while the association between social isolation
with instrumental use of ICT was insignificant (Szabo et al., 2019). More longitudinal empirical
evidence is needed to study the effect of internet use for specific purposes. This paper examines
the longitudinal influence of ICT use for instrumental, medical, and social purposes on social
isolation. Based on existing evidence, the following hypotheses were derived.
H1: ICT use for social purposes alleviate social isolation over time.
H2: ICT use for non-social purposes are not related to social isolation longitudinally.
This paper aims to study the longitudinal influence of ICT use on social isolation within
the physical and social environments of older adults. Findings of the current study could
contribute to the literature with longitudinal evidence on what kind of ICT is effective in
alleviating social isolation in what social and physical environments.

41
Methods
Dataset
Data from the wave 5 (collected in 2015) to 9 (collected in 2019) National Health and
Aging Trend Study (NHATS) was used to examine the hypothesized longitudinal relationships
between living environment, ICT use and social isolation. NHATS collects data annually with
Medicare beneficiaries aged 65 and above. The measures of physical environments were
introduced to NHATS since wave 4, and the NHATS sample was replenished in wave 5. Hence,
the author decided to use the data collected in wave 5 as the baseline.
The working sample excluded facility-dwelling individuals because their living
environment could be drastically different from their community-dwelling counterparts. Proxy
responded cases were also excluded from the analysis because they did not respond to the
environmental assessments. In total, there were 5,807 respondents in the wave 5 working sample.
The sample sizes for wave 6 to wave 9 were 5,108, 4,697 and 4,430, 4022, respectively. The
corresponding attrition rates were 12.04%, 8.05%, 5.68%, 9.21%. To diagnose the possible
reasons for missing in the data, flag variables were generated for the missingness in each
dependent and independent variable. Baseline demographic characteristics of the participants
were regressed on the missing flag variables. Female participants, racial minorities (i.e., African
American, Hispanic and Others), and individuals aged above 75 years old were more likely to
have missing in social isolation scores in the follow-up waves.
Measurements
Physical environments were reported by the interviewers who visited the respondents at
their homes. The interviewer rated the disorder in the community on a four-point scale (1=none,
4=a lot) regarding 1) the amount of litter on the street, 2) graffiti on buildings and walls, 3)

42
vacant or deserted houses or stores. The physical condition inside the responder’s
home/apartment was assessed by observing the existence of 1) peeling paint, 2) evidence of
pests, 3) broken furniture, 4) flooring need of repair, and 5) tripping hazards. The use of
interviewers’ observation could provide more accurate information regarding the physical
environment of each participant than using the environmental condition data grouped by zip
code, which tends to reduce the complexity of the real-life condition. The distribution of the
community and in-home conditions were highly positively skewed across all waves. Only about
10% of the respondents had any community disorder or in-home hazards in their living
environments in any given wave. Therefore, both of the community and in-home conditions were
dichotomously coded to distinguish the participants lived in disadvantaged physical
environments from those who did not.
Social Environment. Social environment was operationalized as community cohesion
perceived by older adults (Cornwell, 2014). Respondents reported whether the people in the
community know each other very well, willing to help each other, and people in the community
can be trusted on a three-point scale (1=agree a lot, 2=agree a little, 3=do not agree). A sum
score of the three items was used as the scale score for the social environment. Social
environment scale scores ranged from 0 to 6. A higher score indicates a more cohesive social
environment.
ICT use. NHATS assessed three types of ICT use: instrumental, social, and medical use.
Instrumental use was measured by asking whether the respondents shop online and pay bills or
banking with the internet. Social use was measured with one item asking in the last month if the
respondent went online to visit social network sites (such as Facebook or Linkedin). Use the
internet for medical purposes had four items: order or refill prescriptions, contact medical

43
providers, handle health insurance matters, and get information about one's health conditions. All
three domains of ICT use were dichotomously coded (1=yes, 0=no). The three aspects of ICT
use will be included in the analytical models one at a time to differentiate the moderating effect
of different purposes of ICT use.
Social isolation was constructed with multiple dimensions of older adults’ social life,
including living arrangements, social network size, and social participation (Cornwell & Waite,
2009b; Cudjoe et al., 2018). The scale score takes the sum of four dichotomously coded
indicators: living with others, having two or more people in the individuals' social network in the
past year, attending religious services and participating in clubs, organization meetings, or
volunteering in the past month. All items of social isolation were dichotomously coded as a score
of 1 indicates a more isolated situation. The scale score ranged from 0 to 4. A respondent with a
higher score is more socially isolated. Individuals with score less than 2 are considered socially
integrated, a score of 3 suggests the participant was socially isolated, and a score of 4 indicates
severe social isolation (Cudjoe et al., 2018). The construct validity for Living arrangement,
social network size and social participation were supported to form an indicator of social
isolation (Cornwell & Waite, 2009b).
Covariates. All models controlled for baseline age, sex, race, income, levels of
education, marital status, self-rated health and depression. Race was self-reported by the
participants, including non-Hispanic White, non-Hispanic Black, Hispanic, and Others.
Respondents reported theirs’ and their spouses’ total annual income from various sources and a
sum score was calculated for the total annual income. Education was dichotomously coded, a
score of one indicates having college or above education. Marital status was also dichotomously
coded to married/partnered vs. not married/partnered. Participants reported self-rated health on a

44
five-point scale, a higher score indicate better health conditions. Depression was assessed with
the 2-item Patient Health Questionnaire depression module (PHQ-2), a score equal to or greater
than 3 suggests major depression (Kroenke et al., 2003).
Data Analysis Strategies
Data management, variable re-coding, and analysis were conducted using Stata 15 SE.
Prior to model building, descriptive univariate analyses were run. Correlation of key variables
were run to estimate the effect size and detect any potential multicollinearity issues. Multilevel
modeling methods were employed to address the nested effect of repeated measure over time of
the same individuals (Krull & MacKinnon, 2001). Because ICT use for different purposes are not
mutually exclusive, the MLM included one type of ICT use at a time. A stepwise model building
approach was employed. First, each ICT variable and socio-demographic control variables were
included in the models; then physical and social environment indicators were added; lastly, the
physical social environment variables were interacted with ICT use for three purposes. Sampling
weight was applied to the models to match the representativeness of the racial/ethnic groups as
designed in the sampling strategies. The analytical framework for the paper is shown in Figure 1.
Results
Table 1 present the statistics on the study weighted sample’s demographics at baseline,
and the weighted longitudinal descriptive results for key variables of interests. The weighted
results show that, at baseline, about 55.01% of the participants were female, 78.19% were non-
Hispanic White, 8.21% were non-Hispanic Black, 6.67% were Hispanic/Latinx, and 6.93% self-
identified as other race. About 58.86% of the study sample were either married or partnered, and
28.91% of the participants had a bachelor’s degree. The weighted mean household annual
income was 74,000 USD.

45
At the baseline wave of the study (2015), about 9.26% participants had any disorder in
the community. The percentage of community disorder declined over the 5-year long observation
period. About 1 in 10 participants had any disorder in their homes, and the percentage slightly
declined over time. On a scale range from 0 to 6, the average of the participants’ reported social
environment was 4.21 in 2015 and the mean score increased in the following waves. Among all
three types of ICT use assessed, instrumental use was mostly commonly reported (39.93%) and
the social use was least prevalent (30.06%). An increased in ICT use were found in all three
types of ICT use over time. The mean score of social isolation was 1.66 at baseline and the
sample mean remained stable over time. The averaged self-rated health score was 3.47 at
baseline and had a subsequent decline over time. During the five years follow-up period, about
10% of the sample was categorized as having major depression based on their PhQ-2 scores.
The multilevel modeling results for three types of ICT use interacted with the community
environments are summarized in Table 2. Step one model (M1) included time, three types of ICT
use and sociodemographic covariates. In M1, all three types of ICT use were negatively
associated with social isolation longitudinally. After adding physical and social environmental
variables in step 2 model (M2), instrumental ICT use no longer significantly related to social
isolation, while social (B=-0.069, SE=0.021, p=0.001) and medical ICT (B=-0.096, SE=0.019,
p<0.001) use remained significantly related to social isolation over time. Upon controlling for
the interaction between three types of ICT use and environmental factors (M3), only social ICT
(B=-0.121, SE=0.058, p=0.037) use remained directly related to social isolation over time. H1
was supported. The association between social isolation and the other two types of ICT use
became no longer significant. H2 was conditionally supported. Social environment consistently
related to reduced extent of social isolation in all steps of model building. The main effects of in-

46
home and community physical environment on social isolation were not significant in all three
models.
Among all the interaction terms tested in M3, only the interaction between home
environment and medical ICT use was statistically significant (B=0.225, SE=0.087, p=0.013).
ICT use for medical purposes moderated the association between home environment and social
isolation. Figure 2 shows that the existence of home disorder only had a positive association with
social isolation among medical ICT users, but not with individuals who did not use ICT for
medical purposes.
Discussion
The objective of the current study is to examine the longitudinal influence of ICT use for
three purposes on social isolation of older adults within the living environment. The multilevel
analysis with 5 years of data shows that all three types of ICT use were negatively related to
social isolation over time. The longitudinal negative associations for social ICT and social
isolation remained robust after adjusting for the effects of physical and social environments.
Previous research suggested that internet use, a generic type of ICT adoption without
specificizing purposes, could help older adults maintain more social contacts over time than if
they would not use internet (Yu, Wu, & Chi, 2020). Although previous research has looked at the
association between ICT use and social isolation, few has securitized the effect by purposes of
use (Baker et al., 2018; Chen & Schulz, 2016). Szabo et al., (2019) found instrumental and
information internet use enriched the range of activities that older adults could engage with, and
social internet use increased their social contacts and reduced sense of loneliness. With a
nationally representative longitudinal dataset, the findings of the current paper shows that all
three types of ICT use could help to address social isolation. However, when physical and social

47
environments were controlled, the association between instrumental ICT use became no longer
significant. After adding the interaction terms, only social ICT use was still directly related to
social isolation. The findings suggest that, among the three types of ICT use examined, social
ICT use has the strongest association with social isolation while instrumental ICT use was less
related to the outcome. Medical ICT use interacts with the environmental factors on its effect on
social isolation.
Although ICT use has become increasingly popular among older adults and has been
shown to be associated with better wellbeing, technology may not be a one-size-fit-all solution
for addressing social isolation in later life (Sims et al., 2017). Unfortunately, lower income older
adults living in disadvantaged communities are less likely to have access to ICT devices and
training programs. The longitudinal influence of technology uses has not been studied within the
living context of older adults. The current paper contributes to the literature by investigating to
what extent would ICT use for three purposes moderate the effects of disadvantaged physical
social environments on social isolation. A more cohesive social environment was found to be
related to less social isolation among the communities’ older residents. Nonetheless, none of the
three types of ICT use moderated the association between social environment and social
isolation. This finding can be explained by the “person-environment fit” concept (Lawton, 1983).
When a fit between person and the environment has been achieved, for example, if they lived in
an environment with high social cohesion, the effect of ICT use on social isolation is attenuated.
Medical ICT use moderated the effect of in-home environment. The interactions between
the other two types of ICT use and physical environments were not significant. In-home disorder
was found to be related to higher extent of social isolation only among medical ICT users. The
authors hypothesized that medical ICT users might suffer from more health concerns than

48
nonusers. The existence of in-home disorder could be a signal of lack of necessary social support
from family and friends or lack of financial resources. Both health concerns and lower incomes
have been supported as risk factors of social isolation in existing literature (Cattan et al., 2005b;
Parsons et al., 2021; Stewart et al., 2007; Yu, Wu, Jang, et al., 2020). Findings of the current
study suggest the accumulative effect of disadvantages in the physical environment and medical
ICT use. This finding might suggest the need of social isolation interventions for older adults
with health concerns who live in disadvantaged physical environments. The interaction term
between community environment and medical ICT use was not statistically significant, which
could indicate home environment has a larger influence on social isolation than community
environments. The shelter-in-place order during the COVID-19 pandemic further revealed the
importance of the in-home environments to the wellbeing of older adults. Regular home visits to
older adults with health concerns might be valuable for social isolation prevention.
Findings of the current study might be limited by the measures of community physical
environments. The measures of community conditions focused on the existence of physical
disorders, such as vacant buildings, graffiti, and litter on the street. However, community
physical environments that are more directly related to the mobility of older adults, for example,
frequency of the public transportation and continuity of the pedestrian sidewalk, could have
greater predictive values for older adults’ social activities and extent of isolation (Latham-Mintus
et al., 2022; Levasseur et al., 2015; Richard et al., 2009). For the protection of the participants’
privacy, it was not included in the dataset whether the older adults moved during the study
period. Hence we could not account for the change in physical social environments as a result of
moving.

49
Given the research on the effects of ICT use on social isolation within the living
environment is rather scarce, future research might consider to further explore the topic with
more sophisticated measures of ICT use, such as companion robotics, ambient technology, smart
home monitoring and wearable devices. Technology environment has been proposed as a new
domain of living environment in the context dynamics of aging theoretical frame work (Wahl &
Gerstorf, 2018). The new developments in technology, such as companion robotics, ambient
technology, smart home monitoring and wearable devices, possess great potential for increasing
the life space of older adults and reducing the risk of social isolation. Future research could also
consider study the new development of technology in the context of living environments.
In sum, this study found ICT use for instrumental, social and medical purpose were
related with reduced level of social isolation longitudinally. After controlling for environmental
factors, the association between instrumental ICT use and social isolation became no longer
significant. Nonetheless, social and medical ICT was still related to social isolation over time.
ICT use also was found to moderate relationship between environment and social isolation.
Specifically, in-home disorder was associated with increased social isolation among older adults
who used ICT for medical purposes. ICT use for these three purposes influenced social isolation
in different manners when taken the physical and social environments into consideration. ICT
use could serve as tools for alleviating social isolation among older residents of disadvantaged
communities.

50
Table 3.1. Sample Characteristics and Descriptive Results of Time-Varying Variables.
Baseline demographics weighted

Variables %/mean
(Linearized
SE)

Age

65-74 58.37%

75-84 31.09%

85+ 10.53%

Gender - Female 55.01%

Race

Non-Hispanic White 78.19%

Non-Hispanic Black 8.21%

Hispanic/Latinx 6.67%

Others 6.93%

Marital Status –
Married/Partnered
58.86%

Education – Bachelor’s
degree and above
28.91%

Income
1
74.00(7.08)

Weighted descriptive
time-varying variables
2015
(n=5807)
2016
(n=5108)
2017
(n=4697)
2018
(n=4430)
2019
(n=4022)
%/mean %/mean %/mean %/mean %/mean
Physical Environment

Disorder in the
community
2

9.26% 9.36% 8.92% 8.26% 7.33%
Disorder in the home
3
10.10% 10.36% 10.77% 10.18% 9.20%
Social Environment
4
4.21(0.03) 4.30(0.03) 4.30(0.03) 4.31(0.03) 4.33 (0.03)
ICT use
5

Instrumental use 39.93% 41.56% 41.69% 40.49% 42.84%
Social use 30.06% 31.32% 30.90% 30.60% 31.38%
Medical use 31.35% 31.93% 32.23% 30.50% 33.96%
Self-rated health 3.47(0.02) 3.46(0.02) 3.40(0.02) 3.37(0.02) 3.29(0.02)
Major depression (PHQ-2 ≥
3)
10.40% 10.34% 10.53% 8.94% 10.89%
Social Isolation
6
1.66(0.02) 1.65(0.02) 1.66(0.02) 1.65(0.02) 1.66(0.02)
1
Income was presented in units of 1,000 USD.
2
Disorder in the community score was dichotomously coded. 1= disorders existed in the
community, 0= no community disorder observed
3
In-home disorder was dichotomously coded. 1= hazards existed in home, 0= no in-home
hazards observed
4
Social Environment scores ranged from 0 to 6. A higher score indicates more neighborhood
cohesion.
5
ICT use was dichotomously coded, 1 = using ICT, 0 = not using ICT.

51
6
Social isolation scores ranged from 0 to 4. A higher score indicates more severe social
isolation. A score of 0 suggests no social isolation.

52

53

54

Figure 3.1. Conceptual Framework and Analytical Model

55

Figure 3.2. Interacted Effect of Medical ICT Use and In-Home Disorder on Social Isolation.

1.65 1.7 1.75 1.8 1.85
Linear Prediction, Fixed Portion
0 1
Home Environment (1="had in-home disadvantages")
Medical ICT use "No" Medical ICT use "Yes"
Interaction between medical ICT use and home environment

56
Chapter 4 A Longitudinal Assessment of the Relationships between Living Environment
and Cognitive Health: The Mediating Effect of Social Isolation
Abstract
Introduction: Previous conceptual work pointed out the significance of living environment on
older adults’ cognitive health, yet the longitudinal environmental influence on cognitive health
has not been extensively researched and underlying mechanism of the living environment’s
impact on cognition is largely unknown. This paper examines the longitudinal relationships
between physical and social environments and cognitive health among older adults. The
mediating effect of social isolation on the proposed longitudinal association was examined.
Methods: This paper uses data from the waves 5 to 9 of the National Healthy Aging Trend
Study (NHATS). The working sample included 5,807 community-dwelling older adults. Physical
environments were reported by the interviewers who visited the respondents at their homes.
Disorders in participants’ homes and in the communities were dichotomously coded. Social
environment was measured by the sum score of self-reported familiarity with, trustworthiness of,
and willingness to help others in the neighborhood. Social isolation was operationalized using
sum score of isolation situations, such as living alone, small social network size, and little social
participation. Three domains of cognition were assessed, that are, memory, orientation, and
executive function. The sum score of these three domains was the indicator of global cognition.
Longitudinal growth curve modeling methods were employed to examine the longitudinal
association between wave 5 physical and social environments, social isolation, and cognitive
health change trajectory from wave 5 to 9.
Results: The findings suggest that physical environments in one’s home and in the community
were cross-sectionally associated lower global cognition scores. A higher extent of social

57
isolation was related to worse global cognition. Social isolation partially mediated the
association between in-home disorder and baseline cognition, and it fully mediated the
relationships between social environment and baseline cognition. All the environmental variables
and the mediator were not significantly related with the change trajectory of global cognition in
the next five years.
Discussion: The study findings indicate that environmental influence on cognitive health and the
mediating effect of social isolation are at between-person but not within-person level. Future
studies with more comprehensive assessments of living environments and a longer observation
period or more targeted population are needed if one is to explore the environmental effect on
social isolation and cognitive health change at within individual level.

Keywords: Built environment, social cohesion, global cognition, NHATS, community disorder

58
Introduction
Living environments serve as one of the most pervasive and complex stimuli to the brain
and contribute to shaping cognitive functionality of older adults through the mechanism of
neuroplasticity (Cassarino & Setti, 2015). Environment-associated challenges, as simple as daily
tasks like shopping, could simulate the brain because it requires one to remember the route and
shopping list despite the distractors in the environments. Environmental stimuli, just like
education and cognitively-demanding jobs, have been theorized to be associated with an
increased cognitive reserve and prevent cognitive decline (Cassarino & Setti, 2015). Previous
conceptual work pointed out the vital influence of the living environment on older adults’
cognitive health.
Living environments include both physical and social aspects (Wahl & Lang, 2004). The
construct of physical environment describes the objective characteristics of the neighborhood,
street, and the older adults’ home, and the social environment reflects the community cohesion
and perceived quality of relationships with neighbors (Wahl & Lang, 2004). Physical disorders
in the community, such as cluttered environment, could increase the cognitive burden and the
environmental stressors. Older adults living in communities with a higher number of adverse
physical environmental characteristics are more likely to be deprived of health care resources
and cognitively stimulating activities (Sheffield & Peek, 2009). Limited mobility, which is often
associated with high amount of disadvantages in the community’s physical environment, has
been found to predict cognitive decline in four years among community-dwelling older adults
(Crowe et al., 2008). Existing literature has less reported empirical evidence regarding the in-
home environment and cognitive health. This paper aims to contribute to the literature by
investigating the longitudinal relationships between the layered physical environment (including

59
both in-home and community disorders) and the cognitive health of older adults. The following
hypothesis was derived for the longitudinal association between physical environments and
cognitive health.
H1: Disadvantaged physical environments, i.e., the disorder in the community and at homes,
predict deterioration in cognitive health over time.
Positive social environments provide the resources and opportunities that are necessary
for cognitive simulating engagements (Lee & Waite, 2018; Van Cauwenberg et al., 2014). Social
cohesion and support were found to be significantly associated with cognitive functioning with
cross-sectional data (Lee & Waite, 2018). A systematic review reported that having more social
activities and a larger supportive social networks were related to better global cognition among
cognitively healthy older adults (Kelly et al., 2017). More frequent social contact in mid-life
predicts a lower risk of dementia and a higher score in global cognition (Sommerlad et al., 2019).
Social contact with friends, which is more likely to occur in a cohesive social environment, were
found to be more beneficial for cognitive health than social contact with relatives (Sommerlad et
al., 2019). Although existing literature has not yet examined the longitudinal relationships
between social environment and cognition, an physically and socially active lifestyle has been
shown to have long term benefit on cognitive health among older adults (Fratiglioni et al., 2004;
Oremus et al., 2019). A more cohesive social environment could promote social interaction and
activities outside one’s home (Van Cauwenberg et al., 2014; Wahl & Lang, 2004). Based on
existing relevant evidence, the following hypothesis was derived for the longitudinal relationship
between social environment and social isolation of older residents.
H2: Positive social environment has a direct longitudinal effect on cognitive health among older
adults.

60
Besides lack of longitudinal evidence on environment and cognition, the current literature
rarely address how does the living environment “get under the skin”. The proposed research
project aspires to contribute to the literature by empirically examine one possible mechanism
through which living environments make an impact on cognitive health. Social isolation could
mediate the association between physical and social environments and cognitive functioning.
The literature review in the previous section established the reasoning for the associations
between physical and social environments and social isolation. Social isolation was found to
predict cognitive decline longitudinally (Evans et al., 2018; Sommerlad et al., 2019). Perceived
social isolation was associated with a higher cortical Amyloid burden in older adults, supporting
the critical role of social isolation in preclinical Alzheimer’s disease and related dementia
(Donovan et al., 2016). However, social isolation is unlikely to fully explain the effects of living
environment on cognitive health. Other factors that related to living context, such as chronic
health conditions, could also serve as explanatory mechanisms of the link between living
environments and cognitive health (Cockerham et al., 2017; Crowe et al., 2008; Lee & Waite,
2018). This study hypothesized the following longitudinal relationships between social isolation
and cognitive health within the environment.
H3: Social isolation partially mediates the association between physical and social environments
and cognitive health over time.
This paper examines the longitudinal relationships between physical and social
environments, social isolation, and cognitive health among older adults. Knowledge built in this
paper could contribute to the literature by building longitudinal evidence of associations between
physical and social environments and cognitive health of older adults. The possible mediating
pathway through social isolation was explored.

61
Methods
Dataset
This paper uses waves 5 to 9 data from the National Healthy Aging Trend Study
(NHATS). NHATS annually interviews older adults sampled from the Medicare beneficiaries.
Because the characteristics of the community environment and long-term care facilities is likely
to be different, the working dataset only included the community-dwelling participants. The
physical environment measurements were first introduced since wave 4, and the NHATS sample
was replenished in the 5
th
wave, therefore, the analysis included longitudinal data from wave 5.
Starting the analysis from wave 5 addresses the issue of missing in the baseline of longitudinal
analysis. The physical and social environments of long-term care facilities could be very
different from independent living in the community. Therefore, the study sample only included
community dwelling participants. In total, there were 5,807 respondents in the wave 5 working
sample. The corresponding attrition rates were 12.04% and 8.05% for wave 6 and 7,
respectively. In order to diagnose possible reasons for attrition, flag variables were generated for
the missingness in each dependent and independent variable. Baseline demographic
characteristics of the participants were regressed on the missing flag variables. Female
participants, racial minorities (i.e., African American, Hispanic, and Others), and individuals
aged above 85 years old were more likely to have missing in the social isolation and global
cognition in the follow-up waves.
We chose to study the association between wave 5 physical, social environmental factors,
social isolation and the trajectory of change in global cognition since wave 5. Physical and social
environments were built in the past, prior to when the wave 5 data was collected, and it is
relatively more difficult to change compared to individual level variables. Previous research has

62
shown that the level of social isolation could remine stable even after significant change in health
conditions, such as hip fracture (T. O. Smith et al., 2018). Conceptually, it is meaningful to use
the physical, social environments and social isolation at baseline to predict the longitudinal
trajectory of global cognition.
Measurements
Physical environments were reported by the interviewers who visited the respondents at
their homes. Standing in the community where the participants lived in, the interviewer rated on
a four-point scale (1=none, 4=a lot) regarding 1) the amount of litter on the street, 2) graffiti on
buildings and walls, 3) vacant or deserted houses or stores. The physical condition inside the
responder’s home/apartment was assessed by observing the existence of 1) peeling paint, 2)
evidence of pests, 3) broken furniture, 4) flooring need of repair, and 5) tripping hazards. The
sum score of the physical conditions of the community and inside home items were first
calculated. The use of interviewers’ observation could provide more accurate information
regarding the physical environment of each participant than using the environmental condition
data grouped by zip code, which tends to reduce the complexity of the real-life condition. The
distribution of the community and in-home conditions were highly positively skewed across all
waves. Only about 10% of the respondents had any community disorder or in-home hazards in
their living environments in any given wave. Therefore, both of the community and in-home
conditions were dichotomously coded to differentiate the participants lived in disadvantaged
physical environments and those who did not.
Social Environment. Social environment was operationalized as community cohesion
perceived by older adults. Respondents reported whether the people in the community know each
other very well, willing to help each other, and people in the community can be trusted on a

63
three-point scale (1=agree a lot, 2=agree a little, 3=do not agree). A sum score of the three items
was used as the scale score for the social environment. Social environment scale scores ranged
from 3 to 9, with a higher score indicates more neighborhood cohesion.
Social isolation was constructed with multiple dimensions of older adults’ social life,
including living arrangements, social network size, and social participation (Cornwell & Waite,
2009b; Cudjoe et al., 2018). The scale score takes the sum of four dichotomously coded
indicators: living with others, having two or more people in the individuals' social network in the
past year, attending religious services and participating in clubs, organization meetings, or
volunteering in the past month. All items of social isolation were dichotomously coded as a score
of 1 indicates a more isolated situation. The scale score ranged from 0 to 4. A respondent with a
higher score is more socially isolated. Individuals with scores equal to or less than 2 are
considered socially integrated, a score of 3 suggests the participant was socially isolated, and a
score of 4 indicates severe social isolation (Cudjoe et al., 2018). The Cronbach’s alpha for the
social isolation scale was 0.67 across all four waves. Although the alpha score is slightly below
the cut-off point of good internal reliability, i.e., Cronbach’s alpha=0.70, similar internal
consistency reliability has been reported for social isolation measurement with national panel
studies for individuals aged 65 and above (Cornwell & Waite, 2009b). Living arrangements,
social network size, and social participation are supported to have construct validity to form an
indicator of social isolation (Cornwell & Waite, 2009b).
Global cognitive health scale score was assessed through three domains: 1) memory:
using immediate and delayed words recall; 2) orientation: date, month, year and day of the
week, naming the President and Vice President of the United States; and 3) executive function
measured by clock drawing test (Kasper et al., 2013). The three domains of cognitive functioning

64
scores were summed up to formulate a global cognitive score, ranged from 0 to 32. A higher
global cognition score indicates better global cognitive health (Farooqui et al., 2017; Kasper et
al., 2013; Seitz et al., 2018). The Cronbach’s alpha for the global cognitive health scale was 0.78
for NAHTS wave 5-6, 0.79 for wave 7 and 0.80 for wave 8, suggesting good internal consistency
reliability.
Control variables. The analytical models controlled for age, gender, race, education
level, income, marital status, depression, and self-rated health. The participants self-reported
whether they identify as non-Hispanic White, non-Hispanic Black, Hispanic, or Others.
Educational background is dichotomously coded into having vs. not having a bachelor’s degree.
Income is measured by annual total income (personal combined with spousal) in 1000 US
Dollars. Marital status is dichotomously coded as 1=married/partnered, 0 = not married nor
partnered. Depression was assessed with the 2-item Patient Health Questionnaire depression
module (PHQ-2) (Kroenke et al., 2003). The PHQ-2 score were dichotomously coded: a score ≥
3 suggests major depression (Kroenke et al., 2003). Self-rated health was reported on a 5-item
scale. A higher score indicates better health.
Analytical strategies
Data managing and descriptive analysis were run with Stata 15 SE. The model building
uses structural equation modeling methods, including latent growth curve modeling (LGCM) and
mediation analysis. Because the current paper was focused on investigating changes in cognition
from baseline to following observational time points, baseline (that is, wave 5) analytical weight
was applied to all models (J. M. DeMatteis et al., 2019). To model the trajectory in cognitive
health, a LGCM model for global cognition was fitted. The global cognition score from wave 5
to 9 were used to fit intercept and slope of linear change. Longitudinal mediation models were

65
employed to examine the longitudinal association between wave 5 physical and social
environments and cognitive health trajectory from wave 5 to 9 (H1-H2). Social isolation score in
wave 5 is hypothesized to mediate the relationship between environmental variables and
cognitive health (H3). The longitudinal mediation model with the latent growth curve modeling
was run with Mplus 8. Full Information Maximum Likelihood (FIML) estimation was used to
handle missing data in the models. FIML is a single step maximum likelihood approach for
missing data that has been widely used in structural equation modeling (Allison, 2003). Root
mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis
index (TLI) were used to evaluate the extent to which each model fits the data. RMSWEA<0.05,
CFI, and TFI > 0.90 indicates an acceptable model fit; CFI and TFI greater than 0.95 indicate a
good model fit (Hu & Bentler, 1999).
Results
Table 1 presents the weighted baseline (wave 5) NHATS sample composition by gender,
race/ethnicity, and age groups. NHATS data provided age information in categories. The sample
represents about 58.37% of the participants who were between 65 and 74, about 31.09% aged
between 75 to 84, and about 10.53% of the participants were 85 and above years old. The
weighted sample was made up of about 78.19% of the sample self-reported as non-Hispanic
White, 8.21% non-Hispanic Black, 6.67% Hispanic/Latino, and about 6.93% others. A little over
half (55.01%) of the sample were female. 58.86% participants were married or partnered. About
one in four participants had a bachelor’s degree or above education level. The mean of annual
income of the respondent and his/her spouse, if there was one, was 74,000 USD at baseline.
About 9.26% of the sampled persons lived in communities with at least one characteristic that
indicated community disorder. A similar percentage (10.10%) of participants had at least one

66
hazardous condition in their homes. On a scale ranging from 0 to 6, the weighted mean score of
social environments perceived by respondents was around 4.21(SE=0.03) in the baseline,
indicating a moderate to high level of community cohesion. The weighted means score of social
isolation was about 1.66 (SE=0.02) at baseline. The weighted mean score of global cognition
was 20.55 (SE=0.08) at baseline.
The linear LGCM for global cognition from wave 5 to wave 9 fit the data well
(CFI=0.996; TLI=0.997; RMSEA=0.022, 90% confidence interval (CI) 0.016-0.027), suggesting
the longitudinal trajectory of global cognition follows a linear trend. The mean of the intercept
for global cognition was 19.07. The mean of the slope was -0.231 (SE=0.016, p<.001), indicating
the global cognition score of the respondents decreased over time.
The longitudinal mediation analysis with LGCM results was visualized in Figure 2. The
detailed results including the regression coefficients of the control variables can be found in
Table 2. The model fit indices for the longitudinal mediation with LGCM suggest a good model
fit (CFI=0.996; TLI=0.994; RMSEA=0.014, 90% CI 0.009-0.018). A higher social isolation
score was related to a lower global cognitive score at baseline (B=-0.732, SE=0.078, p<0.001).
Disorder in the community (B=-0.727, SE=0.256, p=0.005) and disorder in the home (B=-0.588,
SE=0.239, p=0.014) both were negatively related to the intercept of global cognition. However,
none of the environmental variables nor social isolation were statistically significantly associated
with the slope, i.e., the magnitude of change of global cognition. The association between social
environment and the slope of global cognition was marginally significant (B=0.021, SE=0.012,
p=0.086).
The existence of any disorder in home was significantly related to higher level of social
isolation (B=0.170, SE=0.061, p=.006). However, disorder in community was not significantly

67
related to social isolation. A more cohesive social environment was negatively related to social
isolation (B=-0.064, SE=0.013, p<0.001). Social Isolation fully mediated the baseline association
between social environments and global cognition, as well as partially mediated the cross-
sectional relationships between in-home environments and cognition. The environmental factors
and social isolation were not related to the longitudinal change in global cognition.
The intercept of global cognition was significantly associated with the slope of global
cognition (B=0.335, SE=0.097, p=0.001). The slope of global cognition is negative. The positive
correlation between the intercept and slope suggests individuals with a higher baseline cognition
score (intercept) had a less steep slope, i.e., their cognitive functioning declined slower.
Discussion
This study aimed to examining the influence of the physical and social environments on
the trajectory of global cognition among community-dwelling older adults longitudinally. The
LGCM model showed a declining trend in global cognition overtime. A higher baseline
cognition score was associated with a less steep decreasing trend longitudinally. Physical
environment disorders in home and in the community both were negatively associated with the
baseline global cognition, but not with the longitudinal change in cognition. H1 was not
supported. Social environments were not find to be associated with the intercept and slope of
global cognition after accounting for social isolation as the mediator. The model results did not
support H2. Social isolation partially mediates the linkage between disorder in home and the
intercept of global cognition. Social Isolation fully mediated the cross-sectional association
between social environment and global cognition. H3 was not supported.
The findings on the cross-sectional relationships between physical, social environments
and social isolation are in accordance with but did not extend what has been reported in the

68
literature. Disadvantaged built environment were found related to higher extent of social
isolation. A systematic review found that a more walkable built environment were related with
increased social capital and lower rate of depression among its residents (Renalds et al., 2010).
CODA hypothesized that the physical environment affects individuals’ wellbeing through
changing one’s sense of agency and levels of stress (Wahl & Gerstorf, 2018). Results of the
current study suggested that disorders the respondents’ homes were related with increased level
of social isolation cross-sectionally. Nonetheless, we did not find disorder in the community,
such as disserted building, litter, and graffiti, would influence social isolation. In-home
environment was found to be a better indicator for the older person’s cognition when it is
compared to the community environment, which the older adult has less control over (Lee &
Waite, 2018). Nonetheless, a reported based on the American community survey found that the
risk of social isolation in older adults varies by the county they live in (American’s Health
Ranking, 2021). Future study could consider examining which aspects of the community
physical environment would impact the experience of social isolation. In the current study, social
environment was related to baseline global cognition, yet only marginally significantly
associated with the slope of global cognition. Previous longitudinal studies also found that
perceived social support was not significantly associated with cognition change (Eisele et al.,
2012; Seeman et al., 2001).
Extensive evidence has been built on the importance of social relationships on cognitive
health of older adults (Cacioppo & Hawkley, 2009; Cattan et al., 2005a; Evans et al., 2018;
Sommerlad et al., 2019). We hypothesized that social isolation would mediate the association
between physical social environments and cognitive health. The findings suggest social isolation
mediates the relationships cross-sectionally, but not longitudinally. Higher risk of social isolation

69
could be one underlying mechanism for the association between in-home disorder, social
environment, and overall cognitive health. In fact, social isolation fully mediated the association
between baseline social environment and global cognition in this study. However, the
hypothesized model only hold cross-sectionally could suggest that the difference we observed
are at between individual level, i.e., older adults living in disadvantaged neighborhoods had
lower cognition score than those did not have in-home disorder or in communities with a
stronger sense of cohesion. The mediated relationship might not influence the trajectory of
change in cognition for the same person. Nonetheless, the intercept of global cognition was
positively related to its slope, suggesting individuals with higher baseline cognition score had a
slower trend of decrease of cognition.
Despite the supportive evidence from previous cross-sectional research and theoretical
reasonings, the study findings indicate that baseline physical environmental factors, social
environment, as well as social isolation were not associated with the respondents’ subsequent
change in global cognition. A number of methodological reasons might need to be considered for
interpreting the insignificance results. First, the study sample included only community-dwelling
participants because the living environment of long-term care facilities could be meaningfully
different from independent living in the community. Community dwelling population tend to be
healthier in terms of cognitive health when compared to their facility dwelling counterparts
(Rashedi et al., 2014). The study findings could be different if it were conducted with older
adults in the long-term care system. Future studies might consider exploring conceptually and
empirically on whether the living environments of these two groups are comparable, and if so,
how to compare these two types of environments.

70
Second, the five-year period of observation could still be too short to find major changes
in cognition. Over a 7.5 year observation period, social relationships were still found to only
associated with baseline cognition, yet the longitudinal relationship was not significant (Seeman
et al., 2001). The suitable length of observation could be dependent on participants’
characteristics. For example, Machulda et al. (2017) divided clinically normal participants into
four groups, amyloid passivity (A+ or A-), and the presence of neurodegeneration (N+ or N-).
They found group differences in cognitive performance at 15- and 30- month observation
(Machulda et al., 2017). Older old participants showed more variation in the cognitive ability
over 2.5 years of observation (Salthouse, 2010). Future study might need to determine the length
of observation for cognitive change based on the characteristics of the participants. It would be
ideal to follow the participants for a longer period of time or recruiting the older old participants
to investigate the change in cognition.
Third, the physical environment assessments were reported by the interviewers and may
not be sufficient to reflect the aspect of the living environment that are more closely related to
simulating the cognition of older people, such as transportation and walkability, etc (Latham-
Mintus et al., 2022; Levasseur et al., 2020). Living active lifestyles, which could be facilitated by
residing in a safer community with more green space, has been found associated with delayed
cognitive decline (Küster et al., 2016; Moniruzzaman et al., 2020; Van Cauwenberg et al., 2014).
A call for collecting systematic physical environment data and link it to the health and wellbeing
of older people (Suen et al., 2017). The NHATS dataset did not include information on changing
address for the participants. Hence, it currently is not feasible to explore the impact of moving,
which could be the most dramatic type of environment change, on cognitive health. Future study

71
might need to consider studying the longitudinal impact of moving in later life on social isolation
and cognitive functioning.
In conclusion, this study investigates the cognitive health of older adults within their
living contexts. Social isolation was hypothesized as one potential pathway through which
physical and social environments influences older residents’ cognitive functioning. The findings
suggest that disorders in home and in the community was cross-sectionally but not longitudinally
associated with lower global cognition scores. A higher extent of social isolation was related to
worse global cognition. Social isolation partially mediates the association between in-home
disorder and cognition, and it fully mediated the relationships between social environment and
baseline global cognition. All the environmental variables and the mediator were not
significantly related with the change trajectory of global cognition in the next five years. Our
findings conclude the environmental influence and the mediating effect of social isolation on
global cognition among older adults at between-person level. Future studies could explore the
environmental effect on social isolation and cognitive health change at within individual level
with more comprehensive assessments of living environments and a longer observation period.

72

Table 4.1. Wave 5 NHATS Weighted Sample Characteristics
Variables %/mean (Linearized
SE)
Age

65-74 58.37%
75-84 31.09%
85+ 10.53%
Gender - Female 55.01%
Race

Non-Hispanic White 78.19%
Non-Hispanic Black 8.21%
Hispanic/Latino 6.67%
Others 6.93%
Married/Partnered 58.86%
Education – Bachelor’s degree and above 28.91%
Income (KUSD) 74.00 (7.08)
Physical Environment
Disorder in community
1
9.26%
Disorder in home
2
10.10%
Social Environment
3
4.21(0.03)
Social Isolation
4
1.66(0.02)
Major Depression (PHQ-2≥3)
8.28%
Self-rated health 3.60 (0.02)
Global Cognition
5
20.55(0.08)
1
Disorder in the community score was dichotomously coded. 1= disorders existed in the
community, 0= no community disorder observed
2
In-home disorder was dichotomously coded. 1= hazards existed in home, 0= no in-home
hazards observed
3
Community cohesion scores ranged from 0 to 6. A higher score indicates more neighborhood
cohesion.
4
Social isolation scores ranged from 0 to 4. A higher score indicates more severe social
isolation. A score of 0 suggests no social isolation.
5
Global cognition scores ranged from 0 to 32. A higher score indicates better global cognitive
health.

73
Table 4.2. Longitudinal Mediation with Latent Growth Curve Model for the Direct Relations
between Environment and Global Cognition and Mediated association through Social Isolation

B SE p
Intercept of global cognition ON

Home environment
-0.588 0.239 0.014
Community environment
-0.727 0.256 0.005
Social Environment
0.028 0.048 0.557
Social isolation
-0.732 0.078 0.000
Education - bachelor’s or above degree
1.994 0.162 0.000
Marital status - married/partnered
-0.395 0.163 0.015
Age - reference group 65-74

75-84
-2.020 0.157 0.000
85+
-4.503 0.235 0.000
Income
0.000 0.000 0.740
Race - reference group non-Hispanic White

Black
-2.208 0.210 0.000
Hispanic
-3.402 0.314 0.000
Others
-2.670 0.508 0.000
Self-rated health
0.431 0.077 0.000
Major depression
-1.084 0.269 0.000
Slope of global cognition ON

Home environment
-0.062 0.064 0.338
Community environment
0.116 0.074 0.115
Social Environment
0.021 0.012 0.086
Social isolation
0.009 0.019 0.641
Education - bachelor’s or above degree
-0.022 0.042 0.602
Marital status - married/partnered
0.086 0.043 0.043
Age - reference group 65-74

75-84
-0.261 0.040 0.000
85+
-0.514 0.066 0.000
Income
0.000 0.000 0.032
Race - reference group non-Hispanic White

Black
0.103 0.053 0.052
Hispanic
0.090 0.088 0.309
Others
0.324 0.100 0.001
Self-rated health
0.065 0.021 0.002
Major depression
-0.004 0.074 0.962

74
Social isolation ON

Home environment
0.170 0.061 0.006
Community environment
0.052 0.062 0.396
Social Environment
-0.064 0.013 0.000
Education - bachelor’s or above degree
-0.170 0.046 0.000
Marital status - married/partnered
-0627 0.042 0.000
Age - reference group 65-74

75-84
-0.020 0.040 0.615
85+
0.038 0.052 0.468
Income
0.000 0.000 0.012
Race - reference group non-Hispanic White

Black
-0.244 0.050 0.000
Hispanic
0.071 0.071 0.312
Others
0.074 0.127 0.558
Self-rated health
-0.096 0.020 0.000
Major depression
0.243 0.063 0.000
Correlation between the intercept and slope of global cognition
0.335 0.097 0.001
Intercepts

Social isolation
2.716 0.097 0.000
Intercept of global cognition
20.916 0.424 0.000
Slope of global cognition
-0.463 0.112 0.000

75

Figure 4.1. Conceptual Framework

76

77
Chapter 5. Conclusion
Social isolation is an emerging social determinants of health (Cudjoe et al., 2018; Evans
et al., 2018; J. Wang et al., 2017). It is of public health importance to examine the environmental
impact on social isolation and develop an ecological theory for social isolation. Informed by the
Ecological Theory of Aging (ETA) and the COntext Dynamics in Aging (CODA) framework,
this dissertation project examined the longitudinal associations between physical and social
characteristics of the living environment and older adults’ social isolation and cognitive health.
To contribute to the literature on how might the physical and social environments influence
social isolation among diverse older adults, we conducted subgroup analysis by race and income
levels. The moderating effects of ICT use for social, medical, and instrumental purposes on the
longitudinal association between physical and social environments and social isolation were
explored. Social isolation was hypothesized to mediate the longitudinal environmental effects on
older adults' cognitive health.
Paper 1 focused on the longitudinal association between physical and social
environments and the extent of social isolation among older people. Multilevel modeling method
was selected because it is uniquely suited for differentiating between- and within-person changes
over time. The total sample modeling results with five waves of data showed physical
environment did not impact social isolation, while a more cohesive social environment was
related to less social isolation among the respondents. Findings of paper 1 suggest the
environmental effects on social isolation observed were primarily at the between-person level.
The within-person level social isolation scores did not vary significantly over time. Subgroup
analysis produced more nuanced outcomes. A more cohesive social environment was negatively
associated with social isolation only among non-Hispanic White and non-Hispanic Black

78
participants. Income moderated the association between social environment and isolation in
Hispanic and non-Hispanic White subgroups. The protective effect of the social environment
was more prominent among individuals with higher incomes. These findings highlight the
importance of social environment and socioeconomic status (SES) on the experience of social
isolation in later life, yet the reason might need to be further researched. The first paper applied
parts of the CODA framework, i.e., the concepts of physical, social, and SES environments, to
examine social isolation among diverse older people. The CODA posits that environments affect
older persons' well-being through three pathways: context-person agency, context-person stress,
and context-person belonging (Wahl & Gerstorf, 2018). This dissertation project contributes to
the CODA framework by empirically tested the interaction of physical and social environments
and SES on social isolation. The findings of this study support the posits of parts of CODA and
expand its scope to study social isolation among diverse older adults. Future research might
consider investigating mediating effects of the three context-person pathways. Intervention
programs aimed at addressing social isolation could adapt community cohesion approaches for
minority older adults, especially for those with lower incomes. For example, interventions that
adopt a community cohesion building approach in common areas, organizing social programs in
places like parks, grocery stores, and pharmacies in the community could help improve person-
environment fit and address social isolation.
Paper 2 explored the direct and moderating effect of ICT use for three different purposes
(i.e., social, instrumental, and medical) on the longitudinal association between social isolation
and physical and social environments. Again, multilevel modeling methods were employed to
model the hypothesized relationships between physical environments, social environments,
different types of ICT use, and social isolation over time. The effect of ICT use was not

79
scrutinized by racial groups because there are not enough empirical or theoretical reasons to
support that ICT use would have a different impact by race. In the analysis with the NHATS
community-dwelling sample, the direct effects of all three types of ICT use on social isolation
were significant. Using ICT for any purpose was negatively related to social isolation over time.
After adjusting for environmental factors, only social and medical ICT use remained
significantly associated with social isolation. The in-home disorder was positively related to
social isolation among medical ICT users. This finding supports and extends the docility
hypothesis of ETA, suggesting medical ICT use could be an emerging indicator for docility to
environmental press. Implication of the finding for social work practice suggests social
intervention programs to be embedded in medical ICT tools to prevent and address social
isolation, especially for those living in more disadvantaged physical home environments.
However, previously the interaction between the physical and social environments and ICT use
was not empirically studied. The second paper of this dissertation contributes to the literature by
building knowledge on how ICT use might affect social isolation in various environmental
settings. ICT-based tools have been gaining popularity among older adults, and the COVID-19
pandemic accelerated the process. It is of considerable practical and policy implications to study
the role of technology in later life through innovative approaches. Contextualizing the role of
ICT within physical, social environments could be one way to achieve this goal.
Paper 3 examined the mediating effect of social isolation on the hypothesized
longitudinal association between physical, social environments, and cognitive health of older
participants. The author used baseline physical environments, social environments, and social
isolation to be associated with the longitudinal change in global cognition as modeled with the
longitudinal growth curve modeling (LGCM) methods. LGCM was chosen over multilevel

80
modeling because this type of model is better suited for testing the mediation effect. The results
suggest that physical and social environments were only associated with cognition at baseline but
were not related to the magnitude of change in global cognition over time. Social isolation
partially mediated the baseline association between in-home disorder and cognition and fully
mediated the relationships between social environment and baseline cognition. These findings
suggest that the environmental effects on cognition in later life might be mainly at the between-
individual level, yet it did not affect the change trajectory of global cognition within the same
person. Nonetheless, the findings might be limited by the current measurements of physical and
social environments; the study sample included only community-dwelling participants who tend
to be healthier; the 5-year follow-up period could be too short to observe the change in cognition.
Future studies might consider addressing these limitations of the current study to extend
knowledge on the within-individual relationships between living environment, social isolation,
and cognitive health among older people. Paper 3 contributes to the knowledge by presenting
evidence on the mediating effect of social isolation at the between-person level. It addresses the
question - How does the living environment "get under the skin?" The living environment affects
cognition by influencing one's social life.
This dissertation project generated knowledge on longitudinal associations between
environmental factors, social isolation, and cognitive health of older adults. It contributes to the
existing literature on the mechanisms of social isolation on the association between living
environments and cognitive health. To the author's knowledge, the influence of ICT use has been
examined for the first time in the context of living environments in this dissertation work.
Synthesizing findings from all three papers using longitudinal analysis methods, we found that
the social environment plays an essential role in the experience of social isolation among older

81
adults. The effects of physical and social environments on social isolation differed dependent on
one’s racial background and income level. More specifically, non-Hispanic Black and Hispanic
minority older adults and individuals with lower income were at higher risk of experiencing
social isolation. ICT use for medical and social purposes directly impacted social isolation over
time, and medical ICT use moderated the impact of physical and in-home environments on social
isolation. Future studies could further explore how environmental factors influence cognitive
health changes at the within-person level with more comprehensive environmental measures, a
more extended observation period, and/or more targeted population. Social isolation mediated
the cross-sectional association between in-home environment, social environment, and cognition.
These findings suggest that environmental factors need to be considered for aging service
design and policymaking. For example, aging-friendly community initiatives could consider
addressing social isolation and cognitive decline by building a more walkable physical
environment and implementing social service programs that could enhance community cohesion,
especially for underserved communities where individuals with lower income reside. ICT could
serve as a promising intervention platform for alleviating social isolation, especially for
individuals living in disadvantaged home environments and with lower income levels. The
dissertation is an initial step towards more future investigations for understanding the
mechanisms through which living environments might affect two pillars of healthy aging, that is,
social connectedness and cognitive health. It serves as preliminary work to develop technology-
facilitated behavioral intervention programs for alleviating social isolation and preventing
cognitive decline.

82
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Asset Metadata

Creator Yu, Kexin (author)

Core Title Examining the longitudinal influence of the physical and social environments on social isolation and cognitive health: contextualizing the role of technology

School Suzanne Dworak-Peck School of Social Work

Degree Doctor of Philosophy

Degree Program Social Work

Degree Conferral Date 2022-05

Publication Date 04/08/2022

Defense Date 02/15/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag community cohesion,ecological theory of aging,OAI-PMH Harvest,older adults,social connectedness,socioeconomic status

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Chi, Iris (committee chair), Wu, Shinyi (committee chair), Ailshire, Jennifer (committee member)

Creator Email kexin@usc.edu,yukexin7@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC110883180

Unique identifier UC110883180

Document Type Dissertation

Format application/pdf (imt)

Rights Yu, Kexin

Type texts

Source 20220408-usctheses-batch-920 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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Repository Name University of Southern California Digital Library

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