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Loneliness can be seen as a social failure subject to causal search: Why am I lonely? Why do I lack friends? According to attribution theory, answers to these questions can influence emotions, motivation, and behaviours. This study examined the relationships between various affiliative causal beliefs (i.e., beliefs about loneliness and friendship development), social participation, and loneliness among older adults (72+ years). Cross-sectional and longitudinal (over five years) results showed that more strongly endorsing internal/controllable causal beliefs (i.e., believing that making friends depends on effort) related to greater social participation. Moreover, greater social participation related to less loneliness. External/uncontrollable causal beliefs predicted greater loneliness. In fully addressing loneliness, it may be important to focus on people's causal beliefs.
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Journal of Social and Personal
DOI: 10.1177/0265407509106718
2009; 26; 273 Journal of Social and Personal Relationships
U. Swift and Joelle C. Ruthig
Nancy E. Newall, Judith G. Chipperfield, Rodney A. Clifton, Raymond P. Perry, Audrey
longitudinal study
Causal beliefs, social participation, and loneliness among older adults: A
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Causal beliefs, social participation,
and loneliness among older
adults: A longitudinal study
Nancy E. Newall, Judith G. Chipperfield, Rodney A. Clifton,
Raymond P. Perry, & Audrey U. Swift
University of Manitoba
Joelle C. Ruthig
University of North Dakota
Loneliness can be seen as a social failure subject to causal
search: Why am I lonely? Why do I lack friends? According to
attribution theory, answers to these questions can influence
emotions, motivation, and behaviours. This study examined
the relationships between various affiliative causal beliefs (i.e.,
beliefs about loneliness and friendship development), social
participation, and loneliness among older adults (72+ years).
Cross-sectional and longitudinal (over five years) results
showed that more strongly endorsing internal/controllable
causal beliefs (i.e., believing that making friends depends on
effort) related to greater social participation. Moreover, greater
social participation related to less loneliness. External/un-
controllable causal beliefs predicted greater loneliness. In fully
addressing loneliness, it may be important to focus on people’s
causal beliefs.
KEY WORDS: attribution theory • causal attributions • health •
loneliness • social participation
Journal of Social and Personal Relationships Copyright © 2009 SAGE Publications
(, Vol. 26(2–3): 273–290. DOI: 10.1177/0265407509106718
This research was supported by a Canadian Institutes of Health Research (CIHR) Canada
Graduate Scholarships Doctoral Award to the first author, and a CIHR operating grant
(MOP-64335) and Mid Career Award in Aging to the second author. Preliminary findings of
this research were presented in a poster at the Annual Scientific and Educational Meeting
of the Canadian Association on Gerontology, Victoria, B.C., Canada, October 2004. Address
correspondence to Nancy E. Newall, Department of Psychology, University of Manitoba,
Winnipeg, Manitoba, Canada, R3T 2N2 [e-mail:]. Duncan Cramer
was the Action Editor on this article.
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Loneliness is commonly conceived of as a feeling resulting from perceived
deficits in social relationships (Dykstra & Fokkema, 2007).These deficits can
appear to take on an almost physiological form as lonely individuals often
describe themselves as feeling empty inside (e.g., Schultz & Moore, 1984;
Weiss, 1973). Loneliness is a negative experience in its own right. Further-
more, research focusing on older adults has shown that loneliness is related
to other negative outcomes such as poor health (e.g., Russell, 1996) and
depression (e.g., Cacioppo, Hughes, Waite, Hawkley, & Thisted, 2006).These
findings emphasize the importance of identifying and understanding the
causes of loneliness in older adults in order to develop intervention strat-
egies aimed at reducing loneliness.
The percentage of older adults reporting moderate loneliness appears to
range from 20%–40% (Pinquart & Sorensen, 2001; Weeks, 1994; Wenger &
Burholt, 2004). In considering research which has focused on age differences
in the prevalence of loneliness, it appears that older adults may be no more
lonely than younger adults (e.g., Green, Richardson, Lago, & Schatten-Jones,
2001; Schulz & Moore, 1988). In their meta-analysis of correlates of loneli-
ness in older adults, Pinquart and Sorensen (2001) found that the relation
between age and loneliness appeared to be U-shaped, such that loneliness
decreased with age for the youngest subgroup (age < 60 years), was un-
related to age for the next oldest subgroup (between 60.1–80 years), and
increased with age for the oldest subgroup (age > 80.1). Therefore, the
oldest may be especially vulnerable to loneliness and this possibility under-
scores the importance of studying the development of loneliness.
Loneliness has been studied from various perspectives. The present study
examined a representative sample of older adults to consider whether lone-
liness develops in part from the way people think about their affiliations
with others. In particular, we examined whether older adults’ explanations
about their affiliations with others (e.g., friendship development) predicted
social participation in activities, as well as their current level of loneliness and
their subsequent loneliness five years later. This focus on the role of cogni-
tive factors in understanding loneliness follows past research on the relation-
ship between loneliness and causal beliefs or attributions (e.g., Anderson,
Horowitz, & French, 1983; Peplau & Caldwell, 1978) as well as perceptions
of control or locus of control (e.g., Moore & Schulz, 1987; Solano, 1987).
Causal beliefs and loneliness
Although Weiner’s attribution theory (e.g., Weiner, 1985) has been devel-
oped and applied most extensively in the academic achievement domain
(e.g., Menec et al., 1994; Perry, Hechter, Menec, & Weinberg, 1993; Ruthig,
Perry, Hall, & Hladkyj, 2004), it also offers a particularly useful framework
for studying causal beliefs in the affiliation domain.This theoretical perspec-
tive is based on the premise that people generally want to understand why
events happen in their lives. For example, people who are lonely may seek
to find an explanation for their loneliness or lack of satisfying relationships.
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According to attribution theory (e.g., Weiner, 1985), the types of explana-
tions that individuals generate can have profound effects on their subsequent
emotions, expectations, behaviours, and motivation. Proposing a cognition–
emotion–action sequence, Weiner (1985) argues that all perceived causes
can be characterized in terms of three dimensions: locus of causality (internal
vs. external), controllability (uncontrollable vs. controllable), and stability
(unstable vs. stable). Each dimension predicts unique emotions, which, in
turn, guide actions.Thus, attribution theory focuses on the underlying dimen-
sions of attributions, rather than specific attributions per se.
Peplau, Perlman, and colleagues proposed that loneliness reflects a dis-
crepancy between individuals’ desired and achieved social networks (Peplau
& Caldwell, 1978; Perlman, 2004). Thus, loneliness can be precipitated by
changes in a person’s achieved social contacts (e.g., widowhood, moving)
or by changes in a person’s desired social contacts (e.g., through social
norms) (Peplau, Russell, & Heim, 1979). Peplau et al. (1979) also argued
that understanding the causes for loneliness can help people regain control
over their social relationships so as to maintain a balance between achieved
and actual social relations. Using Weiner’s theoretical framework, they put
forth that the types of explanations given for loneliness might greatly affect
emotional reactions to loneliness as well as the enactment of coping beha-
viours (Peplau et al., 1979). In terms of emotional reactions, they noted that
embarrassment would follow from people believing that they are lonely
because they are unattractive (an internal and uncontrollable cause). On the
other hand, blaming others for loneliness (external and controllable cause)
might result in feelings of anger towards others. Furthermore, in terms of
coping behaviours, Peplau et al. argued that people may be motivated to
overcome their loneliness only if they perceive that it is due to personally
controllable reasons such as lack of effort. Thus, framed within the discrep-
ancy model of loneliness, high perceived controllability may imply a confi-
dence in the ability to improve actual social networks so that they match
desired social networks (we would like to thank an anonymous reviewer
for this suggestion).
The importance of the controllability dimension in causal attributions is
highlighted by Anderson and his colleagues’ work on loneliness and attri-
butional style among college students (e.g., Anderson & Arnoult, 1985;
Anderson et al., 1983; Anderson & Riger, 1991). Their research shows that
the controllability dimension is most commonly associated with what they
refer to as “problems in living”, namely loneliness, depression, and shyness.
That is, people who are lonely are likely to see their interpersonal failures
as being due to uncontrollable causes. Based on this work, Anderson and
his colleagues developed the Controllability Attributional Model (CAM)
which predicts that attributing failures to uncontrollable causes leads to low
success expectancies, negative affect, and low motivation (Anderson &
Riger, 1991).
However, it should be noted that some researchers have speculated that,
in people who have tried and failed to establish relationships, blaming the
self (i.e., self-blame, making an internal and controllable attribution) may
Newall et al.: Loneliness among older adults 275
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lead to greater feelings of shame and social withdrawal as compared to
blaming others or blaming circumstances beyond control (making an
external and uncontrollable attribution; see Peplau et al., 1979). Related to
this, Perlman (2004) noted that socially-isolated individuals may avoid
feeling lonely by attributing their isolation to uncontrollable external forces.
Notably, these studies focus on the emotional effects (i.e., negative affect,
intensity of loneliness) of attributions in people who are chronically lonely
or socially isolated. In contrast, our study focuses more on the motivational
effects of general causal beliefs about friendship development and lone-
liness. Thus, this may explain different expected relationships between the
beliefs and loneliness.
The present study
The primary objective of this study was to examine older individuals’ causal
beliefs about affiliation as immediate and long-term predictors of loneliness.
In particular, we examined whether older adults’ explanations about their
affiliations with others (e.g., friendship development) predicted social parti-
cipation and subsequent loneliness. Because attributing interpersonal failures
to uncontrollable causes is associated with loneliness and low motivation in
the affiliative domain (Anderson & Riger, 1991), it was hypothesized that
external/uncontrollable causal beliefs would predict lower social partici-
pation and greater loneliness, whereas internal/controllable affiliative causal
beliefs would predict greater social participation and lower loneliness. This
conforms to the logic that an external/uncontrollable belief (e.g., believing
that friends are made through luck) is de-motivating, resulting in a passive
approach to friendship development or social contacts and, in turn, foster-
ing loneliness. In contrast, a more proactive approach to making friends
may be taken by someone who believes that making friends is caused by
one’s own efforts (a controllable/internal attribution), which could then
lead to less loneliness.
Our approach is an improvement over past studies that have typically
examined the role of causal beliefs and loneliness without considering the
contribution of other predictor variables. In order to determine whether or
not causal beliefs predicted social participation and loneliness above and
beyond various demographic and health variables, we specified structural
equation models in which these background variables, together with causal
beliefs, predicted social participation, and, in turn, social participation pre-
dicted loneliness (see Figure 1). We examined this model using both cross-
sectional and longitudinal data.
Age was included in the models to capture possible age-related factors,
although it may be widowhood or physical incapacity rather than age per se
that influences loneliness (e.g., Perlman, 2004; Pinquart & Sorensen, 2001).
Gender was also included because it has been generally been found that
older women are more likely to be lonely than older men (e.g., Jylha, 2004;
Pinquart & Sorensen, 2001); however, this may be due to an artifact of how
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loneliness is measured, resulting in women’s greater willingness to admit to
feeling lonely (Perlman, 2004) or to factors that covary with gender (e.g.,
women being older in age, living alone, or in poorer health). Education level
was included to account for differences in socioeconomic status that relate
to social isolation (Wenger, Davies, Shahtahmasebi, & Scott, 1996) and
may provide more available resources and social opportunities that could
prevent loneliness (Pinquart & Sorensen, 2001). Living alone, versus living
with others, was also considered because it is an important risk factor for
loneliness in older adults (e.g., De Jong Gierveld, 1987; Havens, Hall,
Sylvestre, & Jivan, 2004). Finally, health was included in the models because
it consistently correlates with loneliness (e.g., Wenger et al., 1996), although
the association may become weaker at older ages (Dykstra, Van Tilburg,
& De Jong Gierveld, 2005; but see Hawkley & Cacioppo, 2007). Recent
research shows that loneliness may directly influence health through its
negative effect on immune response (Pressman et al., 2005; Hawkley &
Cacioppo, 2003), although the nature of the relationship between health
and loneliness is likely reciprocal (e.g., Fees, Martin, & Poon, 1999).
Aging in Manitoba (Canada) study. Participants were community-dwelling
individuals who took part in the Aging in Manitoba (AIM) longitudinal
studies that have been ongoing for over 35 years. The initial 1971 survey was
conducted by the Manitoba Provincial Department of Health and Social
Development in order to identify the needs of older persons (Mossey,
Havens, Roos, & Shapiro, 1981). A random sample of Manitobans (65+
years) was drawn from the Manitoba Health population registry, and strat-
ified by place of residence (i.e., community or personal care home) and
region. A total of 4,803 individuals participated in the initial 1971 AIM
study. Using similar techniques, two additional cross-sectional samples of
older individuals (60+ years) were selected in 1976 (N = 1,302) and again in
1983 (N = 2,877). Since then, there have been several follow-up data collec-
tion waves (in 1990, 1996, 2001, 2005, and 2006) in which sociodemographic,
health, and psychosocial information has been obtained via face-to-face
Newall et al.: Loneliness among older adults 277
Social Participation Loneliness
Causal Beliefs
Theoretical model
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interviews in the participants’ homes. A loneliness scale was included in the
interview schedule for the first time in 1996. Our study complements other
Aging in Manitoba research studies on the social and health factors related
to loneliness and social isolation in older Manitobans (e.g., Havens et al.,
Obtaining representative samples of older Manitobans was important to
the objectives of the AIM study. The 1971 sample was representative in
terms of age and gender of the Manitoba population aged 65 and over
(Mossey et al., 1981). Furthermore, the AIM 1990 sample was comparable
to the older Manitoba population in 1990 in terms of gender and marital
status (Chipperfield, Havens, & Doig, 1997) and generally representative in
terms of age, although the oldest age category (90+ years) was over repre-
sented and the youngest age category (70–74 years) was underrepresented
(Chipperfield et al., 1997).
Sample selection. The present study’s cross-sectional sample (N = 1243)
was taken from the AIM 1996 sample of 1,868 individuals to include only
those respondents who: (1) were community-dwelling (i.e., not living in a
personal care home); and (2) had complete data for loneliness and causal
beliefs (the main variables of interest). Participants living in a personal care
home (n = 255) and/or who had incomplete data (n = 370) were excluded,
leaving a final sample of 1,243 participants. Data were incomplete due to
proxy respondents completing the interview who did not answer the subjec-
tive questions (n = 132), participants being interviewed over the phone and
given a shorter interview (n = 6) and due to participants refusing to answer
the questions or providing invalid (e.g., don’t know) responses (n = 232).
Some of these latter participants with missing data had required consider-
able help from a proxy respondent to complete the interview (n = 77).
The longitudinal (i.e., AIM 1996–2001) sample included individuals (N =
688) from the 1996 cross-sectional sample who: (1) also participated in the
2001 study; and (2) had completed the AIM 2001 loneliness scale. Out of the
1,243 individuals from the 1996 cross-sectional data, 798 also participated in
AIM 2001. The reasons for non-participation in AIM 2001 included death,
re-location to another province, hospitalization or being too ill, and refusal.
Of the individuals who had completed both surveys, 110 people had incom-
plete 2001 data, leaving a total of 688 individuals in the longitudinal sample.
Data were incomplete due to proxy respondents completing the interview
(n = 106), and participants being interviewed over the phone and given a
shorter interview (n = 4). Not surprisingly, given the reasons for attrition,
those individuals in the longitudinal sample (N = 688), compared to those
who participated in 1996 only (N = 555), were younger (M ages = 78.9 vs.
82.3 yrs, t(1241) = 11.6, p < .01) and were healthier in 1996, as indicated by
a smaller number of health conditions (M = 3.6 vs. 4.3, t(1241) = 4.9, p < .01).
Table 1 describes the measures used for the cross-sectional and longitudi-
nal samples. Note that, although all the variables were measured at Time 1
in 1996, loneliness was also measured at Time 2 in 2001.
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Sociodemographics. Sociodemographic variables included age (in years),
and gender. Education level, or years they had completed in school, was
included to account for differences in socioeconomic status.A living arrange-
ments variable differentiated between people who lived alone and those who
lived with one or more persons. Living arrangements closely corresponded
with marital status. In the cross-sectional sample, 96% (575/598) of married
individuals lived with one or more individuals. Of the not married (widowed,
n = 534; separated/divorced, n = 28; or single, n = 83), only 16% (101/645)
lived with others.
Newall et al.: Loneliness among older adults 279
Description of study measures for the cross-sectional and longitudinal samples
# of
Measures Anchors items MSDRange
Age (yrs) 1 80.45 5.43 72–98
(78.93) (4.68) (72–95)
Gender 1 = men 1 1.57 .50
2 = women (1.59) (.49)
Education (yrs) 1 9.23 3.15 0–27
(9.42) (2.95) (0–21)
Independence (IADL) 0 = needs help 12 9.70 1.97 2–12
1 = yes, can do (10.20) (1.55) (2–12)
General perceived health 1 = poor 1 2.42 .73 1–4
4 = excellent (2.34) (.69) (1–4)
Health conditions 0 = no 21 3.94 2.55 0–17
1 = yes (3.62) (2.43) (0–13)
Social participation 0 = no 14 5.05 2.11 0–13
1 = yes (5.35) (2.09) (0–13)
Living arrangements 1 = lives alone
2 = lives with 1 or 1 1.54 .50
2 = more persons (1.56) (.50)
Causal belief – effort 1 = total disagreement 1 4.90 1.90 1–7
7 = total agreement (5.04) (1.82) (1–7)
Causal belief – context 1 = total disagreement 1 3.13 2.10 1–7
7 = total agreement (3.08) (2.10) (1–7)
Causal belief – luck 1 = total disagreement 1 3.74 2.05 1–7
7 = total agreement (3.72) (2.07) (1–7)
Loneliness (1996) 0 = no 11 2.76 2.58 0–11
1 = yes
Loneliness (2001) 0 = no 11
1 = yes (2.68) (2.79) (0–11)
Notes.Numbers shown in brackets represent the longitudinal sample values.All measures taken
in 1996, except loneliness assessed in 1996 and 2001.
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Functional status. Participants’ functional status (independence) was meas-
ured by asking whether or not they were independently capable of per-
forming 12 specific instrumental activities of daily living (IADL; e.g.,
light housework, laundry, and food preparation). Based on similar IADL
measures (e.g., Lawton & Brody, 1969), a composite score was created by
summing the items so that higher scores reflected greater independence
(cross-sectional sample: alpha = .75, skewness = 1.13, kurtosis = 1.43).
Health status. Two measures were used to create a health status latent
construct. First, individuals’ self-rated health was assessed by asking them
to rate their health compared to other people who were their own age. This
measure has been shown to predict objective health status (Bailis, Segall,
& Chipperfield, 2003), mortality (Menec, Chipperfield, & Perry, 1999), and
health care use (Mossey et al., 1981). Responses ranged on a 5-point scale
(1 = bad; 2 = poor; 3 =fair; 4 =good; 5 = excellent). This measure was subse-
quently reverse coded and the small number of “bad” responses were re-
categorized to “poor”, resulting in a 4-point scale ranging from 1 = excellent
to 4 = poor.
As a second measure of health, individuals were asked whether they
currently had, or were still feeling, the after-effects of 21 specific health
problems (e.g., heart and circulation problems, arthritis). These items were
summed to create a composite measure, with higher scores indicating
poorer health (cross-sectional sample: alpha = .61, skewness = .84, kurtosis
= .95). Because the two health measures both theoretically tap into people’s
health, and because of their strong empirical association (r = .44), we spec-
ified the two observed health measures as a latent measure in the structural
equation models.
Social participation. In the present study, respondents’ social activity parti-
cipation was measured by tallying the number of social activities they had
participated in during the past week. In particular, respondents were asked
whether they had performed any of 14 activities (e.g., visiting family, visiting
friends, playing sports or games, doing church-related activities, doing com-
munity volunteer work). Affirmative responses were summed to create a
measure of social activity participation (cross-sectional sample: alpha = .56,
skewness = .39, kurtosis = -.03).
Causal beliefs for affiliation. Participants’ causal beliefs for affiliation were
assessed using three items from the Multidimensional Multiattributional
Causality (MMC) scales (Lefcourt, Von Baeyer, Ware, & Cox, 1979). The
original MMC scales were intended to measure causal beliefs (attributions
for outcomes) in the affiliation and achievement domains and items were
classified as: internal/unstable items (Effort subscale); external/stable items
(Context subscale); external/unstable items (Luck subscale); internal/stable
items (Ability subscale). Note that the original MMC scales included items
that varied in only two dimensions (internal/external and stable/unstable) as
identified in Weiner’s early work (Weiner et al., 1971). Thus, the scales did
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not originally include items to tap into the third causal dimension of con-
trollability (i.e., whether events are perceived to be controllable or uncon-
trollable) that was introduced much later by Weiner (1985). Nonetheless, it
was possible to classify the items based on the controllability dimension.
Due to restricted space in the interview schedule, only one item from each
of the four affiliation subscales was included in the 1996 interview schedule,
and only three of those four items were selected for the present study. Note
that we excluded this fourth item that corresponded to the ability subscale
because it was ambiguous in terms of whether people perceive social skills
to be innate (internal/stable/uncontrollable) or modifiable (internal/unstable/
controllable). Weiner (1983) discussed this potential pitfall of researchers
erroneously concluding ability attributions to be innate. Specifically, for the
purposes of the present study, respondents were asked whether they agreed
with statements assessing: (1) Effort: “Do you believe that loneliness comes
from not trying to be friendly?” (internal/unstable/controllable); (2) Context:
“Over your life have you found that no matter what you do, some people
just don’t like you?” (external/stable/uncontrollable); and (3) Luck: “Over
your life, have you found that making friends has largely been a matter of
having the right breaks?” (external/unstable/uncontrollable).
Loneliness. Loneliness was measured in 1996 and again in 2001 using De
Jong Gierveld and Kamphuis’s (1985) 11-item loneliness scale. Five of the
scale’s items measure feelings of belonging (e.g., “I can call on my friends
whenever I need them”), and six items measure emotional loneliness or
missing relationships (e.g., “I miss having a really close friend). Based on
participant agreement with the statements (no; more or less; and yes), a
composite measure was created by summing item scores such that higher
scores reflected greater loneliness. The items were also dichotomized (0 =
no, 1 = more or less; yes), as is typically done with this scale, so that the
potential range was 0–11 (e.g., Dykstra et al., 2005). For both the cross-
sectional and the longitudinal samples, respectively, the scale had good reli-
ability (AIM 1996 (N = 1,243): alpha = .77, skewness = .99, kurtosis = .43;
AIM 2001 (N = 688): alpha = .83, skewness = .99, kurtosis = .20). If scores
of 3 and above reflect loneliness (Lauder, Mummery, Jones, & Caperchione,
2006), then 45% of the 1996 sample could be considered as being lonely.
In addition to the De Jong Gierveld and Kamphuis scale, participants
were categorized based on their self-reported loneliness in 1996 (1 = not
lonely; 2 = moderately lonely; 3 = severely lonely; 4 = extremely lonely;
M = 1.35; SD = .53). Past studies suggest that such 1-item measures may
underreport loneliness (Perlman, 2004; Ernst & Cacioppo, 1999); neverthe-
less, it is of value to obtain a sense of how many participants categorize
themselves as being lonely. In 1996, 1% of participants described them-
selves as “severely” or “extremely” lonely, 32% as “moderately” lonely, and
67% as “not lonely”.
Analytical approach. Structural equation modelling using AMOS (Arbuckle,
1995) tested the hypothesized cross-sectional and longitudinal models.
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Coefficients were estimated using the maximum likelihood method and
the goodness of fit was assessed by Chi-square, the comparative fit index
(CFI), the adjusted goodness of fit index (AGFI), and the root mean square
error of approximation (RMSEA). Both the CFI and AGFI indices range
from 0 to 1, with values close to 1 indicating good fit (Byrne, 2001), and,
more specifically, values at or above .95 recommended for the CFI (Hu
& Bentler, 1999). RMSEA values at or below .06 indicate good fit (Hu &
Bentler, 1999).
Bivariate correlations for the variables are presented in Table 2. Focusing
on the 1996 cross-sectional sample, as expected, being older was associated
with being female, living alone, having less education, less functional capa-
bility, poorer health, and participating in fewer social activities. As past
research suggests, a number of age-related variables, rather than simply age,
relates to loneliness (e.g., Jylha, 2004). For this reason we were interested
in examining age in the context of other social and health variables in the
structural equation models. Also, as expected, greater social activity parti-
cipation was correlated with participants’ functional capability and health
(in particular their perceived health) and with loneliness.
Structural equation modelling results
Cross-sectional results. Indices suggested good fit for the cross-sectional
model, χ
= 48.47, p < .001, CFI = .97, AGFI = .95, and RMSEA = .06. Table
3 shows all the estimated coefficients between the variables. Although not
provided in Table 3, the estimated coefficients between all of the demo-
graphic, health, and causal belief variables were specified in the models
(Figure 1). In general, the results corresponded to theoretical expectations.
Focusing first on social participation, significant predictors included
younger age, higher education level, greater functional independence, and
greater endorsement of the effort causal belief. Turning to loneliness, signi-
ficant predictors included lower education level, poorer health, living alone,
as well as less endorsement of the effort belief, and greater endorsement of
the context and luck beliefs. Although the magnitude of the relationships
between the causal beliefs and loneliness could be considered small, these
magnitudes exceed most of the effects of the other established demo-
graphic and health predictors in the model.
In addition, as expected, greater social participation was associated with
less loneliness, even after controlling for the demographic, health, and
causal belief variables. This is important because we can also examine how
variables may affect loneliness indirectly through social participation. That
is, age and functional status, while not having a direct effect on loneliness,
do appear to indirectly relate to loneliness through greater social participa-
tion. Moreover, education level and endorsement of the effort causal belief
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Newall et al.: Loneliness among older adults 283
Correlations between study variables for the cross-sectional (N = 1,243) and longitudinal (N = 688) samples
Variables 123456789101112
1. Age –
2. Gender .07*
3. Education –.17** .07*
(–.16**) (.09*)
4. Independence (IADL) –.37** –.18** .05
(–.26**) (–.26**) (–.02)
5. General perceived health .01 .07* –.09** –.33**
(.02) (.05) (–.09*) (–.32**)
6. Health conditions .11** .07* –.03 –.34** .44**
(.08*) (.07) (–.03) (–.34**) (–.46**)
7. Social participation –.14** –.04 .11** .24** –.19** –.07*
(–.06) (–.06) (.12**) (.20**) (–.14**) (–.06)
8. Living arrangements –.27** –.35** .01 .09** –.01 –.06* .01
(–.27**) (–.40**) (.03) (.11**) (–.04) (–.09*) (.04)
9. Causal belief – effort –.04 –.03 –.05 .09** –.07* –.07* .13** .08**
(.03) (–.02) (–.06) (.03) (–.08*) (–.07) (.12**) (.06)
10. Causal belief – context –.05 –.12** –.04 –.02 .03 .07* –.03 –.03 –.03
(–.03) (–.08*) (–.09*) (–.03) (.03) (.07) (–.07) (–.06) (–.04)
11. Causal belief – luck .08* –.05 –.08* –.05 .04 .10** –.03 .03 .13** .13**
(.06) (–.02) (–.08*) (–.08**) (.06) (.10*) (–.02) (–.01) (.11**) (.22**)
12. Loneliness 1996/ .06* .00 –.11** –.17** .19** .25** –.17** –.12** –.22** .18** .14**
12. (Loneliness 2001) (.07*) (.07) (–.04) (–.11**) (.09*) (.16**) (–.18**) (–.12**) (–.13**) (.05) (.11**)
Notes. Correlations without parentheses are from the cross-sectional sample and correlations in parentheses are from the longitudinal sample. All variables are from 1996 except
Loneliness measured in 1996 and 2001. *p .05; **p .01.
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are both associated indirectly, through social participation, and directly to
In summary, for the cross-sectional results, causal beliefs predicted lone-
liness in the expected directions even after controlling for the sociodemo-
graphic, social, and health variables. Moreover, the internal/controllable
belief was directly associated with loneliness, as well as indirectly associated
with loneliness through its association with greater social participation.
Longitudinal results. The longitudinal version of the model in Figure 1 was
tested only with those participants who completed both the 1996 and 2001
data collections. Moreover, all of the predictor variables, including social
participation, were from the 1996 data collection whereas the loneliness
variable was from the 2001 data collection.
Indices suggested good fit for the longitudinal model, χ
= 15.58, p = .08,
CFI = .99, AGFI = .97, and RMSEA = .03. Note that although they are not
shown in Table 4, for theoretical reasons the estimated coefficients between
all of the demographic, health, and causal belief variables were specified in
the models (Figure 1). Similar to the cross-sectional sample results, greater
education level, greater functional independence, and greater endorsement
of the effort causal belief in 1996 Time 1 were associated with greater social
participation in 1996 (Table 4). Unlike the cross-sectional results, age was
not associated with social participation. Note that because greater social
participation is, in turn, associated with less loneliness five years later in 2001,
this means that education level, functional status, and effort beliefs can be
seen as having an indirect effect on loneliness through social participation.
Similar to the cross-sectional results, better health, greater endorsement
of the effort causal belief, and less endorsement of the luck causal belief in
1996 Time 1 were directly related to less loneliness in 2001 (Table 4). Unlike
284 Journal of Social and Personal Relationships 26(2–3)
SEM coefficients for the cross-sectional model (N = 1,243)
Endogenous variables
Exogenous variables 1996 Social participation 1996 Loneliness 1996
Age –.06+ –.02
Gender (1 = men; 2 = women) –.01 –.05+
Education level .09** –.08**
Independence (IADL) .17** .02
Poorer health –.08+ .28**
Living arrangements –.04 –.10**
Causal belief-effort .11** –.19**
Causal belief-context –.02 .13**
Causal belief-luck –.01 .11**
Social participation –.09**
.09 .20
+p = .06–.08; *p .05; **p .01.
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the cross-sectional results, endorsement of the causal belief of context and
education level in 1996 were not directly associated with loneliness in 2001,
and living with others in 1996 was associated with loneliness in 2001.
In summary, the longitudinal results replicated to a great extent the cross-
sectional results. This is not surprising given the strong association between
the 1996 and 2001 loneliness ratings (r = .46). As expected, results showed
that strongly endorsing the statement that loneliness stems from not being
friendly (effort belief) was related to less loneliness at both measurement
points separated by five years (Table 3 and 4). Similarly, strongly endorsing
the uncontrollable/external causal belief of luck was related to greater lone-
liness at both measurement points. However, no significant association was
found between the external/uncontrollable causal belief of context and lone-
liness in 2001. Thus, this belief was only related to loneliness in the imme-
diate context, and not over the 5-year time span of the longitudinal study.
This study examines the experience of loneliness among a representative
sample of older adults. Consistent with past research, suggesting that 20–40%
percent of older adults report occasional or moderate loneliness (Pinquart
& Sorensen, 2001; Wenger & Burholt, 2004), our study found that, based on
the single-item self-reported loneliness measure, about 33% of participants
described themselves as “severely” or “moderately” lonely.This implies that
for many people loneliness is a common experience in later life, underscoring
the need to understand how it evolves and how to mitigate its effects.
Our study provides some insight into the development and maintenance
of loneliness. In particular, we showed that beliefs that new relationships
Newall et al.: Loneliness among older adults 285
SEM path coefficients for the longitudinal model (N = 688)
Endogenous variables
Exogenous variables 1996 Social participation 1996 Loneliness 2001
Age .00 .03
Gender (1 = men; 2 = women) –.02 .02
Education level .11** .00
Independence (IADL) .18** .02
Poorer health –.03 .14*
Living arrangements .00 –.08*
Causal belief-effort .11** –.10**
Causal belief- context –.05 .00
Causal belief-luck .01 .10**
Social participation –.15**
.07 .09
+p = .06–.08; *p .05; **p .01.
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are forged through internal/controllable causes, like effort, are associated
with greater social participation and less loneliness, whereas beliefs that
making friends is due to the context or to luck are associated with greater
loneliness. Moreover, the predictive value of endorsing these causal beliefs
about affiliation was demonstrated both immediately and over five years
even after accounting for the effects of other sociodemographic and health
Causal beliefs, social participation, and loneliness
The association found between endorsing internal/controllable affiliative
causal beliefs (i.e., effort) and being less lonely suggests that these types
of beliefs are, in fact, protecting people from loneliness. Moreover, effort
beliefs related to less loneliness both directly and indirectly through greater
social participation. This evidence is consistent with attribution theory that
posits that the explanations individuals give for events can profoundly
influence their expectations, emotions, and behaviours. Perceiving that a
social failure, such as loneliness, stems from a controllable reason, for
example, might at least provide individuals with a sense of control. Such a
sense could lead to subsequent efforts to ameliorate their social situation
and to bring into line their actual versus desired social networks.
In contrast, endorsing external/uncontrollable beliefs was related to
greater loneliness which may imply that uncontrollable attributions for
friendship erode feelings of control for individuals about creating or main-
taining relationships. This may lead people to have low motivation and
effort in the affiliative domain which, in turn, leads to greater loneliness.
In this way, an uncontrollable attribution, such as luck, could foster a
“passive”, rather than an “active”, approach to creating and maintaining
friends. Of note, our results suggested that strongly endorsing the context
or luck beliefs was not related to social participation. Thus, the results
suggest that social participation is not part of the mechanism through which
external/uncontrollable causal beliefs are associated with greater loneliness.
In addition, our findings are not consistent with the speculation that
internal/controllable causal beliefs result in self-blame thereby fostering
greater loneliness. Nor do they support the thinking that loneliness may be
avoided by attributing loneliness to external/uncontrollable forces. These
issues could be addressed more directly, however, in research focusing on
individuals’ feelings of self-blame surrounding their loneliness and their
perceptions of control. For example, given theory which emphasizes the
adaptiveness of a correspondence between control perceptions or control
strategies and the objective potential of control in a given situation (e.g.,
Heckhausen & Schulz, 1998), it may be that our results would have been
different had we examined beliefs among people living in institutions
offering less objective potential for control over social relationships. That
is, it is possible that if a person has little objective potential to control their
social relations, then making external/uncontrollable attributions for lone-
liness may be more adaptive than making internal/controllable attributions.
Based on the potential to change individuals’ causal beliefs through inter-
ventions, these findings have important implications. For example, enabling
286 Journal of Social and Personal Relationships 26(2–3)
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people to become aware of how their perceptions affect their behaviours
and emotions might help them to deal more effectively with their loneliness.
Moreover, increasing practitioners’ awareness of these connections may
help them to provide guidance for their patients. At the same time, it is
important to note that we do not mean to suggest that individuals are solely
responsible for their loneliness.As acknowledged earlier, to understand and
to address the complex phenomenon that is loneliness requires multiple
perspectives. Indeed, our results point to the importance of adults being
given resources and opportunities for social activities and interaction as
they grow older. What we are suggesting is that designing interventions to
address loneliness among older adults may be facilitated by considering the
role that general causal beliefs play in loneliness.
Demographic, health, and loneliness
The present study replicated past research in finding a health–loneliness
relationship (e.g., Fees et al., 1999; Russell, 1996). Our results showed that,
even after accounting for other variables such as gender and education,
poorer health was associated with greater loneliness. This study also repli-
cated past research (e.g., Jylha, 2004) in showing that no direct relationship
between age and loneliness exists, after controlling for other age-related
variables. In the cross-sectional analyses, that increasing age predicted less
social participation suggests one possible intervening variable between age
and loneliness. Participants’ level of functionality or independence was also
indirectly related to loneliness through social participation. That is, parti-
cipants who were more independent participated in more social activities,
which, in turn, related to less loneliness.
Strengths, limitations, and conclusions
There were several strengths of the study, including the opportunity to
longitudinally examine loneliness among a large, representative sample of
older adults that included those who were aged 85 years and older. Access
to longitudinal data enabled us to consider the replicability of results estab-
lished in cross-sectional analyses. We have greater confidence in the gener-
alizability of these results because, unlike past studies using convenience
samples, our sample was initially selected to reflect the population of older
adults in the province of Manitoba, Canada.
Some limitations of the study should also be noted. In particular, we had
available only single items to assess different types of affiliative causal
beliefs. A preferable method would have been to have participants offer
reasons for their lack of friends and greater loneliness and subsequently to
have them rate these causes on the dimensions of internality, controllabil-
ity, and stability. Another limitation is that research questions relating to
causality cannot be definitively answered in this study; it is only possible to
determine how concepts vary in relation to others and not strictly whether
one variable caused changes in another variable. For example, although it
was argued that causal beliefs predicted social participation, it is also
Newall et al.: Loneliness among older adults 287
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possible that the reverse holds true. Although the present study does not
preclude this possible interpretation, the study findings are consistent with
attribution theory and with the Controllability Attributional Model, which
propose that cognitions influence motivation and behaviours (Anderson &
Riger, 1991; Weiner, 1985).
To conclude, our findings provide insights into how the causal dimensions
of internality/externality and controllability relate to loneliness for older
adults. In addition to replicating past research, which shows a relation be-
tween greater loneliness and uncontrollable causal attributions (Anderson
& Riger, 1991), our research provides a more nuanced picture of the
relative importance of causal beliefs for social participation and loneliness.
In particular, the findings point to the value of focusing not only on how
older adults’ health and social activities relates to loneliness, but also on
the potentially modifiable beliefs people hold, how these can be changed,
and the effects these changes will likely have on people’s loneliness in the
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... See also Böger & Huxhold, 2018a. 128 Cacioppo, Hawkley, & Correll, 2013Newall et al., 2009129 Elmer & Campbell, 2016Weiss, 1973, White, 2010 The transition from temporary 130 to chronic loneliness may be due to factors that disrupt the normal reaffiliation process initiated by loneliness. These include intractable social isolation (e.g., being homebound due to illness or disability); personality traits (e.g., dispositional distrustfulness or rejection sensitivity); low self-worth (e.g., feeling invaluable or like you do not deserve to be with people); external locus of control; and even genetic influences (e.g., genes for prosocial behaviour, attention processing, and sensitivity to negative social information). ...
... Of course, as discussed earlier, cognitive impairment may additionally be an outcome of isolation and loneliness (Holwerda et al., 2014;Kuiper et al., 2015;Wilson et al., 2007;Zhong et al., 2017). 247 Liebke et al., 2017248 Martens, 2010249 Beach & Bamford, 2016DiTommasso et al., 2015;Fry & Debats, 2002;Newall et al., 2009Newall et al., , 2014Pikhartova et al., 2016;Qualter et al., 2015;Vanhalst et al., 2015;Watson & Nesdale, 2012. Of note, a recent survey of British seniors found that nearly one-third believe feelings of loneliness are out of their control (Independent Age UK, 2016). ...
... It can allow them to gradually acclimate to social contact and develop greater confidence in their social abilities. 616 SPARC BC, 2017 617 Masi et al., 2011618 Hauge & Kirkevold, 2012Wong, 2015619 Fry & Debats, 2002Newall et al., 2009Newall et al., , 2014Watson & Nesdale, 2012620 Kantar Public, 2016 Avoid advertising programs as being specifically for isolation/loneliness, given the stigma surrounding these issues. Instead, use more generic language (e.g., "Relationship Enrichment Program" or "Friendship Development Program"). ...
... (Cacioppo et al., 2009;Weijs-Perrée et al., 2015). Not just geographical proximity, but also who live alone are more likely to be lonely (Brittain et al., 2017;Dahlberg et al., 2022;Newall et al., 2009;Taube et al., 2013). ...
... Further, men and women were more likely to be socially lonely when they lived alone. Living alone is a well-known factor in loneliness and other adverse health outcomes (Brittain et al., 2017;Bu et al., 2020;Newall et al., 2009;Taube et al., 2013). Also important in social loneliness were feeling less connected to friends, fewer emotional supporters (for men only), fewer informational supporters (for women only), and not had a club membership. ...
Background. Loneliness is a serious public health problem and became even more visible during the COVID-19 pandemic. Yet it is unknown which aspects of social networks are most important. Here, we evaluated social network structure and function and associations with moderate and severe social and emotional loneliness in older adults. Methods. This cross-sectional study includes online questionnaire data (SaNAE cohort, August-November 2020), in independently living Dutch adults aged 40 years and older. For the separate outcomes social and emotional loneliness, associations with structural network aspects (e.g., network diversity - having various types of relationships, and density - having network members who know each other), and functional network aspects (informational, emotional, and practical social support) were assessed and risk estimates were adjusted for the number of contacts, age, educational level, level of urbanization and chronic conditions. Multivariable logistic regression analyses were stratified by sex. Results. Of 3,396 participants (55% men; mean age 65 years), 18% were socially lonely which was associated with a less diverse and less dense network, living alone, feeling less connected to friends, not having a club membership, and fewer emotional supporters (men only) or informational supporters (women only). 28% were emotionally lonely, which was associated with being socially lonely, and more exclusively online (versus in-person) contacts (men only), and fewer emotional supporters (women only). Conclusion. Network structure and function beyond the mere number of contacts is key in loneliness, and in particular, the types of relationships are important. Public health strategies should be sex-tailored and promote network diversity and density, club membership, informational and emotional support, and in-person contact.
... For example, individual traits, such as aversion to social risk-taking, low self-esteem, shyness, and physical attractiveness may predispose individuals to loneliness (Peplau & Perlman, 1979). Demographic and contextual factors also influence loneliness, as older individuals (Newall et al., 2009), diverse individuals (those who feel like an outcast within a social group), and socially isolated individuals are more likely to experience loneliness (Cacioppo et al., 2014;Peplau & Perlman, 1979). Further, those who experience stressful or demanding work may be especially at risk for loneliness (Cacioppo et al., 2014), such as those in extreme occupations, including entrepreneurs. ...
Full-text available
Loneliness, involving a complex set of feelings that occurs when social needs are not adequately met, has been described as a worldwide modern epidemic. Despite its infiltration into all occupations, loneliness may be especially problematic for those in extreme occupations, such as entrepreneurs, who deal with acute levels of uncertainty, resource constraints, responsibility, and time pressure. Disparate prior findings suggest that entrepreneurs may be especially prone to loneliness, less prone to loneliness, or that they may have unique coping mechanisms that allow them to effectively manage loneliness. This conflicting evidence suggests that we have an incomplete understanding of loneliness within entrepreneurship, specifically, and extreme occupational contexts more generally. Integrating literature on loneliness, well‐being, and entrepreneurship, we conduct a qualitative, inductive study analyzing over 9000 Reddit posts drawn from online entrepreneurship communities where individuals seek and offer advice on how to address entrepreneurial loneliness. In applying appraisal theory to interpret our findings, we discover that whereas some entrepreneurs experience loneliness as threatening and harmful, others experience loneliness as positive or irrelevant, contrary to existing literature that points to loneliness as wholly negative. As such, we uncover several different processes through which entrepreneurs appraise and cope with their loneliness, as well as occupationally unique outcomes for entrepreneurs if loneliness is not coped with effectively. Our findings and emergent theoretical model of the loneliness process in this extreme occupation have important implications for research and practice regarding loneliness, well‐being, and the psychological and mental health of entrepreneurs.
... A common misconception is that friendship is an "organic" process, even though forming friendships requires effort and initiation. In a five-year longitudinal study, those who believed friendship relies on chance were lonelier five years later, whereas those who thought friendship requires effort were less lonely (Newall et al., 2009). While many factors may be out of an individual's control, making an effort to initiate social interactions can lead to new friendships. ...
Friendships have been declining for the past 30 years, resulting in severe mental and physical health consequences. However, multiple barriers prevent individuals from initiating and maintaining connections. This paper highlights the individual and societal-level challenges that limit social connection including fear of rejection, insecure attachment style, structural racism, and increased use of technology. To help clients make friends, we recommend clinicians assess loneliness, social competency, and attachment style; administer cognitive behavioral or behavioral activation therapies; and guide clients to assume others like them and be self-compassionate.
... Feeling socially isolated impairs the capacity to self-regulate and these effects are so automatic as to seem outside of awareness for self-regulation of lifestyle behaviours. Regulation of emotion can enhance the ability to regulate self-control behaviours, [13] as is evident from research showing that positive affect predicts increased physical activity. In middleaged and older adults, greater loneliness was associated with less effort applied to the maintenance and optimisation of positive emotions. ...
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World is greying as the proportion of the ageing population increases and the demography is changing both in the developing and developed world. Contact between people is the central part of everyone’s life and the glue that holds communities and society together. Lack of social relations is considered to cause loneliness and isolation for the individual and, simultaneously, on a societal level, leads to marginalisation, social disintegration and diminishing trust between people. This has come to sharp focus during the corona pandemic. Meaningful social connections are central to the physical and mental health of human beings. Off late, the deleterious health implication of social isolation and loneliness has increasingly been noticed, with a higher risk of premature death and accelerated risks of coronary heart disease, stroke, depression, and dementia. Worldwide, there is an increasing awareness regarding the alarming consequences of loneliness, especially among older adults. In response, 2018 saw the launch of a UK loneliness strategy and the first minister for loneliness in the world appointed.
... The study findings also emphasized the value of personalized services based on an assessment of patient social needs and risk factors. For example, environmental or situational factors, such as living alone; limited access to social support or social networks [33,170,281-311]; cognitive, mental or physical deficits (eg, chronic illness, visual or hearing impairments, and depression); and other demographic and socioeconomic attributes such as age, gender, marital status, immigration status, education, and income [33, 170,286,289,292,294,298,299,302,[304][305][306][308][309][310] were more likely to influence or be associated with loneliness [45]. Recognizing the diversity of needs and preferences of older populations and the importance of user participation in the development of interventions to address social isolation and loneliness, it is likely that tailored approaches will be more successful than "one-size-fits-all" solutions. ...
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Background: Social prescription programs represent a viable solution to linking primary care patients to nonmedical community resources for improving patient well-being. However, their success depends on the integration of patient needs with local resources. This integration could be accelerated by digital tools that use expressive ontology to organize knowledge resources, thus enabling the seamless navigation of diverse community interventions and services tailored to the needs of individual users. This infrastructure bears particular relevance for older adults, who experience a range of social needs that impact their health, including social isolation and loneliness. An essential first step in enabling knowledge mobilization and the successful implementation of social prescription initiatives to meet the social needs of older adults is to incorporate the evidence-based academic literature on what works, with on-the-ground solutions in the community. Objective: This study aims to integrate scientific evidence with on-the-ground knowledge to build a comprehensive list of intervention terms and keywords related to reducing social isolation and loneliness in older adults. Methods: A meta-review was conducted using a search strategy combining terms related to older adult population, social isolation and loneliness, and study types relevant to reviews using 5 databases. Review extraction included intervention characteristics, outcomes (social [eg, loneliness, social isolation, and social support] or mental health [eg, psychological well-being, depression, and anxiety]), and effectiveness (reported as consistent, mixed, or not supported). Terms related to identified intervention types were extracted from the reviewed literature as well as descriptions of corresponding community services in Montréal, Canada, available from web-based regional, municipal, and community data sources. Results: The meta-review identified 11 intervention types addressing social isolation and loneliness in older adults by either increasing social interactions, providing instrumental support, promoting mental and physical well-being, or providing home and community care. Group-based social activities, support groups with educational elements, recreational activities, and training or use of information and communication technologies were the most effective in improving outcomes. Examples of most intervention types were found in community data sources. Terms derived from the literature that were the most commonly congruent with those describing existing community services were related to telehealth, recreational activities, and psychological therapy. However, several discrepancies were observed between review-based terms and those addressing the available services. Conclusions: A range of interventions found to be effective at addressing social isolation and loneliness or their impact on mental health were identified from the literature, and many of these interventions were represented in services available to older residents in Montréal, Canada. However, different terms were occasionally used to describe or categorize similar services across data sources. Establishing an efficient means of identifying and structuring such sources is important to facilitate referrals and help-seeking behaviors of older adults and for strategic planning of resources.
Background: Motoric cognitive risk syndrome (MCR) reduces the quality of life, independence, and social interaction in older adults. Social participation is a potentially modifiable factor that benefits cognitive and mental health. This study explored the mediating roles of social participation between MCR and depression and between MCR and loneliness. Methods: We performed a secondary analysis of data from the 2015-2016 National Social Life, Health, and Aging Project. Slow gait speed and cognitive decline were used to assess MCR. Mediation analysis was applied to two models, both of which used MCR as an exposure and social participation as a mediator. The outcomes were depression and loneliness for each model, respectively. Results: Among 1,697 older adults, 196 (11.6%) had MCR. The mediating role of social participation was statistically significant in both models. The indirect effect (β=0.267, p=0.001) of MCR on depression through social participation comprised 11.97% of the total effect (β=2.231, p<0.001). The indirect effect (β=0.098, p=0.001) of MCR on loneliness through social participation was 19.48% of the total effect (β=0.503, p<0.001). Conclusion: Interventions to increase social participation may reduce depression and loneliness of older adults with MCR.
Objectives: Older adults account for 18.5% of the Canadian population and are at risk of experiencing social isolation, compared to other age groups. Researchers define social isolation as a lack of social contact and relationships, but many social isolation measures do not reflect this definition. The aim of our study is to review the existing measures of social isolation with older adults to recommend evidence-based measures to researchers and practitioners. Methods: We conducted a rapid review on PsycInfo and PsycTests. We included articles that were written in English or French, were peer-reviewed, used an older adult sample, included a self-report social isolation measure, and reported psychometric information. Results: Following exclusion of ineligible articles, 12 measures were available for analysis. We further categorized the measures into: five most recommended measures, five measures that require further research, and two measures not recommended for use with older adults. Conclusions: We observed a range of measures with varying suitability to be used with older adults; some were empirically driven but did not have strong psychometric properties, or vice-versa. Clinical implications: It is imperative that interventions aimed to address social isolation in older adults use evidence-based measures to assess progress and report treatment effectiveness.
We propose that individuals low (vs. high) in socioeconomic status (SES) are vulnerable to impaired relationship functioning through two different mutually reinforcing paths that both directly implicate perceptions of control and relational devaluation. The first of these involves chronic exposure to relational devaluation as a function of factors such as stigmatization in broader society that serves to undermine low SES individuals’ perceptions of control. The second involves enhanced reactivity to relationally devaluing experiences such as discrimination and ostracism as a function of this limited reserve of perceived control. We present a perceived control‐relationally devaluing experiences model of low SES vulnerability to impaired relationship functioning that incorporates these predictions and further specifies how low SES individuals’ reduced perceptions of control may help account for documented associations between low SES and negative interpersonal outcomes such as hostility, aggression, and reduced relationship quality. We conclude by considering implications for intervention as well as potential alternative and complementary mechanisms.
This study established a research model to fully understand the relationship of social engagement, loneliness and cognitive function among Chinese older adults. In the latest wave (2018) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a total of 12,852 participants aged 65 years and older were included in the study. Path analysis was performed to examine our hypotheses. The social engagement had a positive relationship with normal cognitive function while loneliness had a negative one, and the standardized regression weights were 0.618 (p < 0.01) and -0.046 (p < 0.01), respectively. Watching TV or listening to the radio had the greatest association with cognitive function, followed by housework, outdoor activities, reading newspapers or books, playing cards or mahjong, and raising domestic animals or pets. Loneliness played a mediating role between social engagement and cognitive function. Social engagement could be considered as one of the interventions to improve older adults' health.
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In this chapter a theory of motivation and emotion developed from an attributional perspective is presented. Before undertaking this central task, it might be beneficial to review the progression of the book. In Chapter 1 it was suggested that causal attributions have been prevalent throughout history and in disparate cultures. Studies reviewed in Chapter 2 revealed a large number of causal ascriptions within motivational domains, and different ascriptions in disparate domains. Yet some attributions, particularly ability and effort in the achievement area, dominate causal thinking. To compare and contrast causes such as ability and effort, their common denominators or shared properties were identified. Three causal dimensions, examined in Chapter 3, are locus, stability, and controllability, with intentionality and globality as other possible causal properties. As documented in Chapter 4, the perceived stability of a cause influences the subjective probability of success following a previous success or failure; causes perceived as enduring increase the certainty that the prior outcome will be repeated in the future. And all the causal dimensions, as well as the outcome of an activity and specific causes, influence the emotions experienced after attainment or nonattainment of a goal. The affects linked to causal dimensions include pride (with locus), hopelessness and resignation (with stability), and anger, gratitude, guilt, pity, and shame (with controllability).
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A large-scale questionnaire study was conducted to test several aspects of different attributional models of everyday problems in living. College students completed scales assessing depression, loneliness, and shyness. In addition, they completed a questionnaire that measured attributional style on five causal dimensions (locus, stability, controllability, globality, and intentionality) for four types of situations (interpersonal success and failure, noninterpersonal success and failure. The results of a series of regression and correlation analyses led to the following major conclusions: (1) Globality, intentionality, and stability may be dropped from attributional models of depression, loneliness, and shyness with little loss of predictive power; (2) controllability is the single most important dimension in predicting a person's level of depression, loneliness, or shyness; (3) locus adds to the prediction of these symptoms only w hen assessed by failure items; and (4) attributional style predicts these ...
Human behavior is very flexible and ontogenetic potential adds to the scope of variability of developmental paths. Therefore, development in the life course needs to be regulated. Developmental regulation by the individual is scaffolded by external constraints. External constraints to development based on biological aging, institutional age-grading, and internalized age norms provide an age-graded agenda for striving for developmental growth and avoiding developmental decline. The life-span theory of control proposes that control of one's environment is the key to adaptive functioning throughout the life span. The theory identifies the evolutionary roots and the life-span developmental course of man striving to control the environment (primary control) and the self (secondary control). Primary control is directed at producing effects in the external world, while secondary control influences the internal world so as to optimize the motivational resources for primary control. In this 1999 book, a series of studies illustrate the rich repertoire of the human control system to master developmental challenges in various age periods and developmental ecologies.
In the last two decades, an approach to the study of motivation has emerged that focuses on specific cognitive and affective mediators of behaviour, in contrast to more general traits or motives. This 'social-cognitive' approach grants goal-oriented motivation its own role in shaping cognition, emotion and behaviour, rather than reducing goal-directed behaviour to cold-blooded information processing or to an enactment of a personality type. This book adds to this process-oriented approach a developmental perspective. Critical elements of motivational systems can be specified and their inter-relations understood by charting the origins and the developmental course of motivational processes. Moreover, a process-oriented approach helps to identify critical transitions and effective developmental interventions. The chapters in this book cover various age groups throughout the life span and stem from four big traditions in motivational psychology: achievement motivation, action theory, the psychology of causal attribution and perceived control, and the psychology of personal causation and intrinsic motivation.