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Loneliness and Social Isolation as Risk Factors for Mortality: A Meta-Analytic Review


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Actual and perceived social isolation are both associated with increased risk for early mortality. In this meta-analytic review, our objective is to establish the overall and relative magnitude of social isolation and loneliness and to examine possible moderators. We conducted a literature search of studies (January 1980 to February 2014) using MEDLINE, CINAHL, PsycINFO, Social Work Abstracts, and Google Scholar. The included studies provided quantitative data on mortality as affected by loneliness, social isolation, or living alone. Across studies in which several possible confounds were statistically controlled for, the weighted average effect sizes were as follows: social isolation odds ratio (OR) = 1.29, loneliness OR = 1.26, and living alone OR = 1.32, corresponding to an average of 29%, 26%, and 32% increased likelihood of mortality, respectively. We found no differences between measures of objective and subjective social isolation. Results remain consistent across gender, length of follow-up, and world region, but initial health status has an influence on the findings. Results also differ across participant age, with social deficits being more predictive of death in samples with an average age younger than 65 years. Overall, the influence of both objective and subjective social isolation on risk for mortality is comparable with well-established risk factors for mortality. © The Author(s) 2015.
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Perspectives on Psychological Science
2015, Vol. 10(2) 227 –237
© The Author(s) 2015
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DOI: 10.1177/1745691614568352
Several lifestyle and environmental factors are risk factors
for early mortality, including smoking, sedentary lifestyle,
and air pollution. However, in the scientific literature,
much less attention has been given to social factors dem-
onstrated to have equivalent or greater influence on mor-
tality risk (Holt-Lunstad, Smith, & Layton, 2010). Being
socially connected is not only influential for psychologi-
cal and emotional well-being but it also has a significant
and positive influence on physical well-being (Uchino,
2006) and overall longevity (Holt-Lunstad et al., 2010;
House, Landis, & Umberson, 1988; Shor, Roelfs, & Yogev,
2013). A lack of social connections has also been linked
to detrimental health outcomes in previous research.
Although the broader protective effect of social relation-
ships is known, in this meta-analytic review, we aim to
narrow researchers’ understanding of the evidence in
support of increased risk associated with social deficits.
Specifically, researchers have assumed that the overall
effect of social connections reported previously inversely
equates with risk associated with social deficits, but it is
presently unclear whether the deleterious effects of social
deficits outweigh the salubrious effects of social connec-
tions. Currently, no meta-analyses focused on social iso-
lation and loneliness exist in which mortality is the
outcome. With efforts underway to identify groups at risk
and to intervene to reduce that risk, it is important to
understand the relative influence of social isolation and
Living alone, having few social network ties, and hav-
ing infrequent social contact are all markers of social iso-
lation. The common thread across these is an objective
quantitative approach to establish a dearth of social con-
tact and network size. Whereas social isolation can be an
568352PPSXXX10.1177/1745691614568352Holt-Lunstad et al.Loneliness and Isolation as Mortality Risk Factors
Corresponding Author:
Julianne Holt-Lunstad, Department of Psychology, Brigham Young
University, 1024 Spencer W. Kimball Tower, Provo, UT 84602-5543
Loneliness and Social Isolation as Risk
Factors for Mortality: A Meta-Analytic
Julianne Holt-Lunstad1, Timothy B. Smith2, Mark Baker1,
Tyler Harris1, and David Stephenson1
1Department of Psychology and 2Department of Counseling Psychology, Brigham Young University
Actual and perceived social isolation are both associated with increased risk for early mortality. In this meta-analytic
review, our objective is to establish the overall and relative magnitude of social isolation and loneliness and to examine
possible moderators. We conducted a literature search of studies (January 1980 to February 2014) using MEDLINE,
CINAHL, PsycINFO, Social Work Abstracts, and Google Scholar. The included studies provided quantitative data on
mortality as affected by loneliness, social isolation, or living alone. Across studies in which several possible confounds
were statistically controlled for, the weighted average effect sizes were as follows: social isolation odds ratio (OR) =
1.29, loneliness OR = 1.26, and living alone OR = 1.32, corresponding to an average of 29%, 26%, and 32% increased
likelihood of mortality, respectively. We found no differences between measures of objective and subjective social
isolation. Results remain consistent across gender, length of follow-up, and world region, but initial health status has an
influence on the findings. Results also differ across participant age, with social deficits being more predictive of death
in samples with an average age younger than 65 years. Overall, the influence of both objective and subjective social
isolation on risk for mortality is comparable with well-established risk factors for mortality.
social isolation, loneliness, mortality
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228 Holt-Lunstad et al.
objectively quantifiable variable, loneliness is a subjective
emotional state. Loneliness is the perception of social iso-
lation, or the subjective experience of being lonely, and
thus involves necessarily subjective measurement.
Loneliness has also been described as the dissatisfaction
with the discrepancy between desired and actual social
relationships (Peplau & Perlman, 1982).
Is there a need to distinguish between social isolation
and loneliness in assessing mortality risk? People lacking
human contact often feel lonely (Yildirim & Kocabiyik,
2010); however, social isolation and loneliness are often
not significantly correlated (Coyle & Dugan, 2012;
Perissinotto & Covinsky, 2014), suggesting that these may
be independent constructs and that one may occur with-
out the other. For instance, some may be socially isolated
but content with minimal social contact or actually prefer
to be alone; others may have frequent social contact but
still feel lonely. Because of the conceptual distinction
between social isolation and loneliness, understanding
their relative influence on mortality may provide insights
into possible independent pathways by which each influ-
ences risk and, in turn, guides intervention efforts.
There are several processes by which actual and per-
ceived social isolation may influence mortality risk (also
see other reviews in this special section). Social connec-
tions, or the lack thereof, can influence health and risk of
mortality via direct and indirect pathways (see Uchino,
2006). Both loneliness and social isolation are associated
with poorer health behaviors including smoking, physi-
cal inactivity, and poorer sleep (Cacioppo et al., 2002;
Hawkley, Thisted, & Cacioppo, 2009; Theeke, 2010).
Each is also associated with health-relevant biological
processes, including higher blood pressure, C-reactive
protein, lipid profiles, and poorer immune functioning
(Grant, Hamer, & Steptoe, 2009; Hawkley & Cacioppo,
2010; Pressman et al., 2005). Researchers that have
included both social isolation and loneliness have linked
these factors independently to poorer health behaviors
and biological risk factors (Pressman et al., 2005; Shankar,
McMunn, Banks, & Steptoe, 2011). However, few
researchers have examined these concurrently, and little
is known about their relative or synergistic influence.
In this meta-analytic review, our primary aim was to
focus on the relative effects of objective and subjective
social isolation on mortality (the likelihood of death over a
given time), to determine the magnitude and nature of the
association with risk of mortality, and to identify potential
moderating variables. We reviewed studies of mortality
that included measures of loneliness, social isolation, or
living alone. Because it is important to determine the effect
of social isolation and loneliness independent of corre-
lated lifestyle (e.g., smoking, physical activity) and psycho-
logical factors (e.g., depression, anxiety), we also examined
inclusion of covariates.
Identification of studies
We identified published and unpublished studies of the
association between social relationships and mortality
using two techniques. First, we searched for studies
appearing from January 1980 to February 2014 using sev-
eral electronic databases: MEDLINE, CINAHL, PsycINFO,
Social Work Abstracts, and Google Scholar. To capture
relevant articles, we used multiple search terms, includ-
ing mortality, death, decease(d), died, dead, and
remain(ed) alive, which were crossed with synonyms of
the terms social isolation, loneliness, and living alone. To
minimize inadvertent omissions, we searched each data-
base twice, with searches ending on February 24, 2014.
Second, we manually examined the reference sections of
past reviews and of studies meeting the inclusion criteria
to locate articles not identified in the database searches.
A team of research assistants who were trained and
supervised by the authors conducted the searches.
Inclusion criteria
We included in the meta-analysis studies written in English
that provided quantitative data regarding individuals’ mor-
tality as a function of objective and subjective social isola-
tion (operational definitions of social isolation, loneliness,
and living alone are provided in Table 1). All studies needed
to be prospective in design, meaning that the researchers
measured one’s objective or subjective social isolation at the
study initiation and then followed participants over time
(typically several years) to determine who remained alive
and who was dead at the follow-up. Thus, risk for mortality
is an estimate of the extent to which social isolation, living
alone, and loneliness significantly predict the likelihood of
being dead at follow-up.
We extracted data when authors used measures includ-
ing the terms found in Table 1. In some cases, authors oper-
ationalized social isolation by contrasting the participants
from the bottom quartile or quintile on a social network or
integration measure (e.g., Social Network Index; Cohen,
Doyle, Skoner, Rabin, & Gwaltney, 1997) but otherwise did
not code data from measures of social networks/integration.
Because we were interested in the impact of social deficits
on disease, we excluded studies in which mortality was a
result of suicide or accident. We also excluded studies in
which the outcome could not be isolated to mortality (e.g.,
combined outcomes of morbidity and mortality). Although
we excluded single-case designs and reports with exclu-
sively aggregated data (e.g., census-level statistics), we
included all other types of quantitative research designs that
yielded a statistical estimate of the association between
mortality and loneliness/isolation. Figure 1 shows the flow
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Loneliness and Isolation as Mortality Risk Factors 229
diagram containing the details of study inclusion (included
in the Supplemental Material available online).
Data abstraction
A team of research assistants and the authors performed
the data searches and coding. To reduce the likelihood
of human error in coding, a team of two raters coded
each article twice. Two different raters performed the
second coding of each article. Thus, two distinct coding
teams (four raters) coded each article. Coders extracted
several objectively verifiable characteristics of the stud-
ies: (a) the number of participants and their composi-
tion by age, gender, health status, and preexisting health
Table 1. Descriptive Coding of the Measures Used to Assess Objective and Subjective Isolation
Type of measure Description Example of measure
Social isolation Pervasive lack of social contact or communication,
participation in social activities, or having a
Social Isolation Scale (Greenfield, Rehm, & Rogers,
Social Network Index (bottom quartile; Berkman &
Syme, 1979)
Living alone Living alone versus living with others Binary item: yes, no
Number of people in household
Loneliness Feelings of isolation, disconnectedness, and not
Loneliness Scale (De Jong-Gierveld & Kamphuis, 1985)
UCLA Loneliness Scale (Russell, Peplau, & Cutrona,1980)
Note: UCLA = University of California, Los Angeles.
1,384 Potentially Relevant Reports Identified
154 Full-text Reports Retrieved for Detailed Evaluation
84 Reports Excluded Based on Detailed Review
29 Social Isolation/Loneliness was not an Independent Variable
17 Mortality was not the Outcome Variable
9 Duplicate Report of Data Contained in another Report
8 Insufficient Information to Extract an Effect Size
8 Mortality Data was not Linked to Social Isolation/Loneliness
7 Contained No Quantitative Data
4 Non-human Subjects
2 Cause of Mortality was Suicide/Violence
70 Reports Included in the Meta-Analysis
1,230 Reports Excluded Based on Title/Abstract
339 No Mortality Indicator (Including Mixed Morbidity/Mortality)
296 No Mention of Social Isolation/Loneliness
217 No Quantitative Data (Editorial/Review/Commentary)
207 Irrelevant to Social Support/Mortality Association
81 Cause of Mortality was Suicide/Violence
42 Written in a Language other than English
31 Mortality Data was not Linked to Social Isolation/Loneliness
17 Non-human Subjects
Fig. 1. Reports evaluated for inclusion in the meta-analysis.
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230 Holt-Lunstad et al.
conditions (if any), as well as the cause of mortality; (b)
length of follow-up; (c) research design; (d) type of
social isolation (actual/perceived) evaluated; (e) num-
ber and class of covariates included in the statistical
model; and (f) exclusion of participants who were
severely ill or who died shortly after study initiation.
The latter two variables helped to address possible con-
founds (e.g., depression, health status, physical mobil-
ity, age) and reverse causality, whereby individuals with
impaired health would be more likely to report increased
social isolation or loneliness because of an inability to
engage in social contact.
For each study, we extracted the reported effect size,
making sure that odds ratio (OR) values greater than one
represented an increase in mortality as a function of
social isolation, loneliness, or living alone—and a
decrease in mortality when individuals were not isolated,
lonely, or living alone. Effect sizes less than one indicated
the opposite. To analyze the data, we temporarily trans-
formed the reported effect sizes to the natural log of the
OR and subsequently transformed them back to ORs for
purposes of interpretation.
When researchers reported multiple effect sizes within
a study at the same point in time, we averaged the sev-
eral values (weighted by standard error) to avoid violat-
ing the assumption of independent samples. We therefore
used the shifting units of analysis approach (Cooper,
1998), which minimizes the threat of nonindependence
in the data while allowing for more detailed follow-up
analyses. In a few cases in which researchers reported
multiple effect sizes across different levels of social isola-
tion (high vs. medium, medium vs. low), we extracted
only the value with the greatest contrast (high vs. low).
When a study contained multiple effect sizes across time,
we extracted the data from the longest follow-up period.
We extracted both unadjusted data and the data from the
model involving the greatest number of statistical con-
trols (although we also extracted the data from the model
utilizing the fewest number of statistical controls for a
subsequent comparison after recording the type and
number of statistical controls used within both models).
Overall, the interrater agreement for data abstraction
was adequately high for categorical variables (with
Cohen’s kappa averaging .73) and for continuous vari-
ables (with intraclass correlations for single measures
averaging .95). We resolved discrepancies across coding
teams through further scrutiny of the article until we
obtained consensus.
Description of the retrieved literature
We located 79 articles reporting pertinent data, 9 of which
were excluded because they contained the same data as
another article, resulting in 70 independent studies that
met the full inclusion criteria. The complete list of refer-
ences and a table summarizing the characteristics of
those studies (Table S1) are found in the Supplemental
Material available online. Studies typically involved older
adults, with a mean age of 66.0 years at initial data col-
lection and with a mean length of follow-up being 7.1
years. Most studies (63%) involved normal community
samples, but 37% of studies involved patients with a
medical condition, such as heart disease. See Table 2 for
further descriptive data.
Three studies included data on both loneliness and
one of the objective independent variables: two for lone-
liness and social isolation, and one for loneliness and
living alone. Using a shifting units of analysis approach
(Cooper, 1998), we included data from those distinct
measures in the analyses specific to the type of measure-
ment, but all other studies contributed a single data point
to the analyses.
Effect sizes in the 70 studies had been calculated by
researchers using a variety of methods, with some
researchers reporting unadjusted values and with other
researchers using a variety of covariates. ORs ranged
from 0.64 to 3.85, with exceptionally high heterogeneity
across studies (I2 = 97.8%, 95% CI [97.6%, 98.1%]; Q =
3,328.9, p < .0001), suggesting excessive variability in
findings across all types of data. We therefore divided the
analyses according to the number of covariates used. In
the unadjusted data group, the researchers controlled for
no other variables in the analyses. In the partially
adjusted data group, the researchers typically controlled
for one or two variables, usually age and gender. The
fully adjusted data are the model within studies with the
largest number of covariates. Effect sizes from each cat-
egory were evaluated separately, such that a single study
could contribute effect sizes to more than one category
(see Table 3).
Overall, each of the measures (social isolation, loneli-
ness, and living alone) for each type of data (unadjusted,
partially adjusted, or fully adjusted) had an OR between
1.26 and 1.83. The three measures did not differ in their
ORs for any of the three types of data, meaning that there
was no overall difference among the two objective and
one subjective factors. (Random-effects weighted analy-
ses of variance across the measures yielded all ps > .20.)
However, the type of data did matter in the analysis.
Unadjusted data yielded effect sizes of greater magnitude
than fully adjusted data (see Table 3). The differences
between unadjusted and fully adjusted data also reached
statistical significance (p < .001) when comparing data
within 27 studies in which researchers reported more
than one statistical model (e.g., unadjusted compared
with fully adjusted values) using multivariate meta-ana-
lytic methods after accounting for the .74 correlation of
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Loneliness and Isolation as Mortality Risk Factors 231
Table 2. Characteristics of 70 Studies of the Association of Mortality With Subjective and Objective Measures of Social Isolation
Characteristic MNumber of studies (k) %
Year of initial data collection 1,993 46
Years of participant follow-up 7 70
% deceased by the end of data collection 24.7 66
% female 52.6 67
% smokers 31.2 28
Sample size 48,673
<200 6 9
200–499 7 10
500–999 10 14
1,000–2,999 20 29
3,000–9,999 16 23
>10,000 11 16
Age of participantsa66.0
<50 years 8 11
50–59 years 12 17
60–69 years 11 16
70–79 years 21 30
>80 years 10 14
Location of data collection
Inpatient medical treatment setting 15 21
Outpatient medical treatment setting 11 16
Community setting (normal populations) 44 63
World region of data collection
Europe 38 54
North America 19 27
Asia 7 10
Australia 3 4
Multiple regions 3 4
Note: Not all variables sum to the total number of studies because of missing data.
aAverage age category of participants at study initiation, although not all participants within the study would necessarily be in the category listed.
Table 3. Weighted Mean Effect Sizes (Odds Ratios) by Type of Measurement
Measure kOR+SE 95% CI
Unadjusted data
Social isolation 3 1.83 0.185 [1.27, 2.63]
Living alone 20 1.51 0.072 [1.32, 1.74]
Loneliness 8 1.49 0.105 [1.22, 1.84]
Overall 31 1.53 0.035 [1.38, 1.70]
Partially adjusted dataa
Social isolation 6 1.46 0.162 [1.06, 2.00]
Living alone 8 1.55 0.132 [1.20, 2.00]
Loneliness 7 1.52 0.213 [0.99, 2.30]
Overall 21 1.51 0.117 [1.27, 1.79]
Fully adjusted datab
Social isolation 14 1.29 0.100 [1.06, 1.56]
Living alone 25 1.32 0.075 [1.14, 1.53]
Loneliness 13 1.26 0.099 [1.04, 1.53]
Overall 52 1.30 0.116 [1.16, 1.46]
Note: k = number of studies; OR+ = random-effects weighted odds ratio; CI = confidence interval.
aTypically one or two covariates, most often age and gender. bData from the statistical model in studies that contained the most covariates; these
adjusted data yielded effect sizes that were statistically significantly (p < .05) smaller than unadjusted data.
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232 Holt-Lunstad et al.
effect sizes within studies. Thus, unadjusted and fully
adjusted data not only represented conceptually distinct
classes of data but also yielded findings of different
Moderator analyses
Given the substantial heterogeneity of the overall results
2 > 80%), we analyzed the extent to which the variabil-
ity in effect sizes could be attributable to study or partici-
pant characteristics. These analyses involved only the
fully adjusted data because multiple factors predictive of
mortality had been controlled (thus minimizing possible
confounding explanations). Study and participant charac-
teristics included both categorical and continuous data,
so we report those analyses separately.
Categorical variables. We examined categorical vari-
ables using random-effects weighted analyses of vari-
ance, beginning with the type of covariates used in the
fully adjusted models. Eight studies included multiple
covariates that were directly relevant to social support,
such as marital status, social networks, and loneliness.
These eight studies had lower averaged effect sizes (OR
= 1.17) than those of 33 studies in which no covariates
directly relevant to social support were included in the
statistical model (OR = 1.27). Otherwise, the averaged
effect sizes remained of similar magnitude irrespective of
the particular covariates that were or were not included
in the models (p > .20), including covariates relevant to
depression, socioeconomic status, health status, physical
activity, smoking, gender, and age. Different combina-
tions of covariates across studies yielded similar results.
We found no substantive differences in effect sizes (p
> .15) across the other categorical variables evaluated:
world region, data collection setting, cause of mortality,
research design, health status, and medical condition at
intake. Finding no significant differences across partici-
pant health status when using the fully adjusted data was
particularly notable because of a difference that we
observed with the unadjusted data: Studies in which par-
ticipants had a medical condition and were recruited
from a medical setting had larger unadjusted average
effect sizes (OR = 1.82) than studies with ostensibly
healthy participants recruited from the general commu-
nity (OR = 1.34, p = .003). Furthermore, with the unad-
justed data, studies in which the researchers excluded
participants with terminal conditions or participants who
died shortly after baseline data collection (whose social
isolation or social support may have been affected by
their medical condition) had higher averaged effect sizes
(OR = 1.95) than the studies in which the researchers did
not report such exclusions (OR = 1.38, p < .05). Thus,
accounting for participants’ initial health condition in the
research design resulted in systematically different find-
ings across studies. In most (81%) of the multivariate sta-
tistical models, researchers had controlled for participant
health status variables, such that we found no differences
across those conditions in the fully adjusted data. Studies
in which the researchers controlled for health status vari-
ables yielded substantially different findings from those
studies in which this was not done.
Continuous variables. We examined study and par-
ticipant characteristics involving continuous data in rela-
tion to the observed effect sizes using random-effects
weighted regression coefficients (meta-regression). We
observed no coefficients greater than the absolute value
of .20 between effect sizes and the year of initial data col-
lection, the length of follow-up, or the percentage of
female participants in each study. However, the number
of covariates included in multivariate models was moder-
ately associated with effect size (r = −.27). Visual inspec-
tion of the corresponding scatter plot indicated that when
studies included seven or more covariates, effect sizes
tended to be more homogeneous, without extremely
high values. To clarify, the inclusion of many covariates
did not substantively reduce the magnitude of the gen-
eral findings, which tended to remain in the range of
OR = 1.20–1.40, but it did eliminate all OR values greater
than 1.66.
Analyses also indicated that the association between
the effect size and the average age of participants at
intake was of a moderately strong magnitude (r = −.34
for adjusted data, and r = −.46 for unadjusted data). This
association with participant age remained of the same
magnitude when accounting for length of study follow-
up (and participants’ age at the end of the study) and
when age was or was not used as a statistical covariate.
Examination of the scatter plot and breaking down the
data into three approximately equal categories of initial
participant age helped to interpret the correlation: Studies
involving participants of an average age less than 65
years had an average effect size of OR = 1.57 for adjusted
data, and OR = 1.92 for unadjusted data; studies involv-
ing participants of an average age between 65 and 75
years had an average effect size of OR = 1.25 for adjusted
data, and OR = 1.32 for unadjusted data; and studies
involving participants of an average age greater than 75
years had an average effect size of OR = 1.14 for adjusted
data, and OR = 1.28 for unadjusted data. Adults less than
65 years of age appeared to be at greater risk of mortality
when they lived alone or were lonely compared with
older individuals in those same conditions, even after
controlling for the effect of age and other covariates on
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Loneliness and Isolation as Mortality Risk Factors 233
Likelihood of publication bias
adversely influencing the results
Publication bias occurs when the data obtained in a
meta-analysis fail to represent the entire population of
studies because of the increased probability of nonsig-
nificant results remaining unpublished (and therefore
less accessible for meta-analytic reviews). As can be seen
in Figure 2, the data in this meta-analysis were highly
variable, and the distribution of effect sizes appeared
somewhat imbalanced toward the right side of the graph.
The distribution of the data was relatively sparse toward
the bottom of the white-shaded center of the graph, the
area of nonsignificance. This kind of distribution can sug-
gest that some nonsignificant studies were missing from
the meta-analysis. However, neither Egger’s regression
test nor an alternative to that test recommended for OR
data (Peters, Sutton, Jones, Abrams, & Rushton, 2006)
reached statistical significance (p > .05), which dimin-
ished the likelihood of possible publication bias. We
found the fail-safe N—the number of hypothetically miss-
ing studies needed to reduce the present results to zero—
to be 1,268, a number higher than the plausible number
of studies conducted. Furthermore, using the trim and fill
method (Duval & Tweedie, 2000), we did not estimate
any “missing” studies; the distribution was overall fairly
symmetric relative to the average effect size. It thus
seemed unlikely that publication bias substantively
affected the results of this meta-analysis.
Social isolation results in higher likelihood of mortality,
whether measured objectively or subjectively. Cumulative
data from 70 independent prospective studies, with
3,407,134 participants followed for an average of 7 years,
revealed a significant effect of social isolation, loneliness,
and living alone on odds of mortality. After accounting
for multiple covariates, the increased likelihood of death
was 26% for reported loneliness, 29% for social isolation,
and 32% for living alone. These data indicated essentially
no difference between objective and subjective measures
of social isolation when predicting mortality.
The prospective designs of these studies and the sta-
tistical models that controlled for initial health status (and
several other potential confounds) provide evidence for
the directionality of the effect. Although we cannot con-
firm causality, the data show that individuals who were
socially isolated, lonely, or living alone at study initiation
were more likely to be deceased at the follow-up, regard-
less of participants’ age or socioeconomic status, length
of the follow-up, and type of covariates accounted for in
the adjusted models.
We caution scholars perusing the expanding research
literature on the association of social isolation and loneli-
ness with physical health against reliance on unadjusted
data because those data fail to account for participant
health status, a factor contributing to reverse causality
(when individuals with impaired health report increased
loneliness or social isolation because their health condi-
tion limits their social contacts). Averaged results with
unadjusted data were of greater magnitude than the
results from fully adjusted models (see Table 3), particu-
larly when participants had a preexisting health condition
and when physically ill participants were not excluded
from the unadjusted analyses. In fully adjusted models
accounting for health status and in studies with physically
ill individuals removed from analyses (thus accounting for
reverse causality), social isolation and loneliness remained
predictive of mortality. Future researchers will need to
confirm the hypothesis that when individuals are ill (and
ostensibly needing support) their risk for mortality
increases substantially when lacking social support.
Overall, the findings from this meta-analysis are con-
sistent with prior evidence that has demonstrated higher
survival rates for those who are more socially connected
(Holt-Lunstad et al., 2010) and extend those findings by
focusing specifically on measurement approaches that
assess the relative absence of social connections. Notably,
the present meta-analysis included more than double the
number of studies and 10 times the number of partici-
pants compared with the previous meta-analysis. Thus,
the field now has much stronger evidence that lacking
social connections is detrimental to physical health.
Standard error
–1 012
Effect estimate (lnOR)
p < 1%
1% < p < 5%
5% < p < 10%
p > 10%
Fig. 2. Contour-enhanced funnel plot of effect sizes (natural log of
the odd ratio [lnOR]) by standard error for 70 studies with measures
of the association between mortality and social isolation, loneliness, or
living alone.
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234 Holt-Lunstad et al.
The average effect sizes identified in this meta-analysis
were lower than those reported previously for measures
of social networks (OR = 1.45, 95% CI [1.32, 1.59]) and
social integration (OR = 1.52, 95% CI [1.36, 1.69]) and
were much lower than complex measures of social inte-
gration (OR = 1.91, 95% CI [1.63, 2.23]; see Table 4 of
Holt-Lunstad et al., 2010). This difference may suggest
that the salubrious effects of being socially connected
may be stronger than the adverse effects of lacking con-
nections. However, it is also likely that research methods
that account for the multidimensionality of social rela-
tionships better predict mortality than measurement
focused on any single aspect of sociality, such as social
isolation. Nonetheless, identification of the relative effects
of each component may be useful in targeting those that
may be modifiable.
There is also presently no research evidence to sug-
gest a threshold effect. The aggregate results suggest
more of a continuum than a threshold at which risk
becomes pronounced. Although it is possible that indi-
viduals who are extremely lonely or socially isolated may
account for much of the elevated risk, presently too few
researchers target extremely isolated individuals in stud-
ies. Given the complexity (including objective and sub-
jective aspects) of social relationships, identifying such a
threshold seems unlikely.
Objective versus subjective isolation
Using the meta-analytic data, had we found that either
social isolation or loneliness was more predictive of mor-
tality, interventions to reduce risk could have become
more targeted. However, we presently have no evidence
to suggest that one involves more risk than the other for
mortality. Unfortunately, in the vast majority of studies,
researchers examined only one measurement approach
(social isolation, loneliness, or living alone), precluding
direct comparisons. Among the few studies in which
researchers contrasted social isolation and loneliness, the
evidence was mixed, with researchers finding that loneli-
ness was more influential in one study (Holwerda et al.,
2012), and with other researchers finding that social isola-
tion had stronger effects than loneliness in a later study
(Steptoe, Shankar, Demakakos, & Wardle, 2013). This
inconsistency may be due to differences in methodologi-
cal approaches to handling correlated psychological
states, such as depression (Booth, 2000). Our analyses
indicated that the elevated risk of mortality persisted even
when controlling for correlated components of social net-
works and multiple other factors, including depression,
with the use of covariates negating large effect sizes. In
any case, the multiple, overlapping components of social-
ity make reliance on statistical adjustment less desirable
than direct comparisons between components, such as
loneliness and social isolation.
The equivalent effects of social isolation and loneli-
ness reported here do not indicate interchangeability of
these risk assessments. Rather, the available data suggest
that efforts to mitigate risk should consider both social
isolation and loneliness without the exclusion of the
other. Because social isolation and loneliness are often
weakly correlated (Coyle & Dugan, 2012), simply increas-
ing social contact may not mitigate loneliness. Likewise,
exclusively altering one’s subjective perceptions among
those who remain objectively socially isolated may not
mitigate risk. The evolutionary perspective of loneliness
proposed by Cacioppo and colleagues (Cacioppo et al.,
2006; Cacioppo, Cacioppo, & Boomsma, 2014) presents
loneliness as an adaptive signal, similar to hunger and
thirst, that motivates one to alter behavior in a way that
will increase survival. Accordingly, loneliness is a power-
ful motivator to reconnect socially, which, in turn,
increases survival and opportunity to pass on genes.
Consistent with this perspective, intervention attempts to
alter the signal (e.g., hunger, loneliness) without regard
to the actual behavior (e.g., eating, social connection)
and vice versa would likely be ineffective. Extending this
possibility, some data have shown that those who are
both high in loneliness and social isolation had the poor-
est immune response (Pressman et al., 2005). Therefore,
both objective and subjective measures of social isolation
should be considered in risk assessment.
It is only through direct comparisons of social isola-
tion and loneliness in the same sample that researchers
can establish independent, relative, and synergistic
effects. Consequently, it is possible that different combi-
nations of social isolation and loneliness may represent
different levels of risk. For instance, those low in both
isolation and loneliness would presumably be at lowest
risk, those high in both at highest risk, and those who are
isolated but not lonely or lonely but not isolated to be at
intermediate risk. Nonetheless, there is currently insuffi-
cient empirical evidence to test this hypothesis, highlight-
ing an important weakness of the current literature that
needs to be addressed in future research.
Isolation and aging
The data in this meta-analysis should make researchers
call into question the assumption that social isolation
among older adults places them at greater risk compared
with social isolation among younger adults. Using the
aggregate data, we found the opposite to be the case.
Middle-age adults were at greater risk of mortality when
lonely or living alone than when older adults experi-
enced those same circumstances.
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Loneliness and Isolation as Mortality Risk Factors 235
The moderating effect of age may seem counterintui-
tive in light of data indicating that individuals more than
65 years of age are more likely to report loneliness
(Dykstra, van Tilburg, & de Jong Gierveld, 2005), but
there are at least four plausible explanations for why
middle-age adults may differ from older adults in terms
of the relevance of social networks to physical health.
First, it is possible that individuals who do not die early
may be a particularly resilient group, with different social
or health characteristics than those who die at earlier
ages. Thus, the observed difference across age could be
confounded with preexisting health status, although this
interpretation is qualified by the fact that the researchers
using multivariate statistical models accounted for partici-
pant age and health status. A second explanation involves
changes in social networks as individuals transition from
full-time employment to retirement, with decreases in
socialization in occupational and public forums that are
seen as culturally normative. This possible explanation is
supported by one study in which researchers examined
loneliness after retirement and found an effect for mental
health (anxiety and depression) but not for physical
health (functional status and number of chronic condi-
tions; Bekhet & Zauszniewski, 2012). Third, it is plausible
that individuals who are alone or lonely before retire-
ment age may be more likely to engage in risky health
behaviors or less likely to seek medical treatment early,
whereas after retirement, people may attend more assid-
uously to their physical health. Finally, it is possible that
the different results across participant age are confounded
with marital status: Older adults are much more likely to
be widows/widowers than middle-age adults. Our meta-
analysis cannot shed light on these four possible expla-
nations because the first three explanations involve
variables inadequately evaluated in the present research
literature, and the variable associated with the fourth
explanation, marital status, was not coded in our analy-
ses. Although many studies indicate that loneliness dif-
fers across marital status (Cacioppo & Patrick, 2008;
Hughes et al., 2004; Victor & Bowling, 2012) and that
marital status is significantly associated with mortality
(Roelfs, Shor, Kalish, & Yogev, 2011), we did not include
marital status as an indicator of social isolation because
being unmarried does not necessarily mean that one is
socially isolated, living alone, or lonely. Moreover, there
would be multiple qualitative differences in the social
networks of an older individual who had never been
married compared with one who had been married and
raised children but whose spouse had recently died, even
though both are living alone. Rather than include all pos-
sibly correlated variables (e.g., marital status, depression,
substance abuse), we evaluated only direct measures of
social isolation, living alone, or loneliness. Given the lim-
itations of the present meta-analysis, future researchers
should confirm the apparent differences across partici-
pant age and should evaluate the relative merits of the
several plausible explanations for that finding.
To better evaluate differences across age, future
researchers should involve participants from a broad
range of age groups. Most of the data in this meta-analy-
sis came from older adults. Only 24% of studies involved
people with an average age of 59 years or younger, and
only 9% of studies involved people younger than 50
years of age at intake. If future data collection with
younger adult samples confirms the age differences we
observed in this meta-analysis, then widespread beliefs
about the health risks of social isolation being greatest
among older adults are inaccurate. In any case, the meta-
analytic data, taken together with evidence for detrimen-
tal influences across the life span (Qualter et al., 2015,
this issue), suggest that future research (and possibly
interventions) should expand beyond older adults.
Substantial evidence now indicates that individuals lacking
social connections (both objective and subjective social
isolation) are at risk for premature mortality. The risk asso-
ciated with social isolation and loneliness is comparable
with well-established risk factors for mortality, including
those identified by the U.S. Department of Health and
Human Services (physical activity, obesity, substance
abuse, responsible sexual behavior, mental health, injury
and violence, environmental quality, immunization, and
access to health care; see A
substantial body of research has also elucidated the psy-
chological, behavioral, and biological pathways by which
social isolation and loneliness lead to poorer health and
decreased longevity (for reviews, see Cacioppo, Cacioppo,
Capitanio, & Cole, 2015, this issue; Shankar et al., 2011;
Thoits, 2011; see also Cacioppo et al., 2015; Hawkley &
Cacioppo, 2003, 2010). In light of mounting evidence that
social isolation and loneliness are increasing in society
(McPherson & Smith-Lovin, 2006; Perissinotto, Stijacic
Cenzer, & Covinsky, 2012; Victor & Yang, 2012; Wilson &
Moulton, 2010), it seems prudent to add social isolation
and loneliness to lists of public health concerns. The pro-
fessional literature and public health initiatives can accord
social isolation and loneliness greater recognition.
To draw a parallel, several decades ago scientists who
observed widespread dietary and behavior changes
(increasing consumption of processed and calorie-rich
foods and increasingly sedentary lifestyles) raised warn-
ings about obesity and related health problems (e.g.,
Brewster & Jacobson, 1978; Dietz & Gortmaker, 1985).
The present obesity epidemic (Wang & Beydoun, 2007)
had been predicted. Obesity now receives constant cov-
erage in the media and in public health policy and
by guest on June 21, 2015pps.sagepub.comDownloaded from
236 Holt-Lunstad et al.
initiatives. The current status of research on the risks of
loneliness and social isolation is similar to that of research
on obesity 3 decades ago—although further research on
causal pathways is needed, researchers now know both
the level of risk and the social trends suggestive of even
greater risk in the future. Current evidence indicates that
heightened risk for mortality from a lack of social rela-
tionships is greater than that from obesity (Flegal, Kit,
Orpana, & Graubard, 2013; Holt-Lunstad et al., 2010),
with the risk from social isolation and loneliness (control-
ling for multiple other factors) being equivalent to the
risk associated with Grades 2 and 3 obesity. Affluent
nations have the highest rates of individuals living alone
since census data collection began and also likely have
the highest rates in human history, with those rates pro-
jected to increase (e.g., Euromonitor International, 2014).
In a recent report, researchers have predicted that loneli-
ness will reach epidemic proportions by 2030 unless
action is taken (Linehan et al., 2014). Although living
alone can offer conveniences and advantages for an indi-
vidual (Klinenberg, 2012), this meta-analysis indicates
that physical health is not among them, particularly for
adults younger than 65 years of age. Further research is
needed to address the complexities of social interactions,
interdependence, and isolation (Parigi & Henson, 2014;
Perissinotto & Covinsky, 2014), but current evidence cer-
tainly justifies raising a warning.
We express deep appreciation to Spencer Ford, Devin Peterson,
Lauren Brown, Naomi Worden, Breydan Wright, Eric R. Smith,
Chris Badger, and Jae Cho for their work with database searches
and coding.
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest with
respect to the authorship or the publication of this article.
This research was generously supported by grants from Brigham
Young University awarded to Timothy B. Smith and Julianne
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... This means, on average, an increased risk of 29%, and for those who lived alone 32%. The degree of influence of social isolation showed the greatest degree of influence on premature mortality compared to other risk factors, which are loneliness and living alone (Table 1) (Holt-Lunstad et al., 2015). It is necessary to take into consideration the fact that the pandemic itself impacted the number of deaths on a global scale. ...
... However, the effects on loneliness and subsequent mental health problems could be more long-term and more severe if future restrictive measures are taken repeatedly or at longer intervals. Based on the above-mentioned studies, we emphasize the survey by Holt-Lunstad et al. (2015), which pointed out that the degree of influence of social isolation showed the greatest degree of influence on premature mortality, as a result of which the seriousness of the issue in need of solution and planning is emphasized strategies to protect and restore the quality of life of the elderly population in the future. ...
... Weighted mean effect sizes (odds ratios) by type of measurement(Holt-Lunstad et al., 2015) ...
The COVID-19 pandemic, accompanied by stringent social restrictions, wrought profound changes across various facets of human existence. Unprecedented measures, such as compulsory quarantines, curfews, and restrictions on mobility and social interactions, were implemented to mitigate infection rates. This paper delves into the repercussions of isolation, with a specific focus on its impact on the elderly population—an exceptionally vulnerable demographic. The primary objective of this study is to discern the ramifications of pandemic-induced isolation on the mental and physical well-being of senior citizens. This contribution underscores the comparative analysis of three prior studies that have illuminated the nexus between pandemic-induced isolation and heightened levels of anxiety, depression, and loneliness. A notable strength of this research lies in its comprehensive dataset, derived from comparisons with extant scientific literature and the utilization of diverse scientific methodologies. The preceding investigations centered on the Austrian populace, juxtaposing the effects of loneliness among senior citizens before and during the pandemic. However, these studies were constrained by their inability to explore the enduring consequences of isolation and loneliness post-repeal of anti-pandemic measures, and their incapacity to ascertain its correlation with senior citizens' mortality, particularly those residing in solitary circumstances. This article represents a partial outcome of the VEGA 1/0595/21 project, which investigates public administration interventions during the COVID-19 era and their influence on the quality of life of selected community residents.
... Likewise, lonely individuals may not always be socially isolated based on objective measures but nevertheless may feel lonely in a crowd. If social isolation and loneliness tap different aspects of social relationships, there is a need to distinguish them in research studies that investigate their roles in the etiology and treatment of psychiatric disorders (Holt-Lunstad et al., 2015). To date, however, studies have typically treated social isolation and loneliness interchangeably with little effort to disentangle their effects or have included only one of them in the analysis (Loades et al., 2020). ...
... Incorporating both social isolation and loneliness in analytic models would be necessary to clarify which construct is more predictive of individuals' psychological well-being. One metaanalytic review found that objective social isolation and subjective loneliness did not differ in their influence on the risk of mortality (Holt-Lunstad et al., 2015), suggesting that both social isolation and loneliness should be considered equally important to mitigate the risk of mortality. However, in relation to mental health, which serves as one pathway to early mortality (Walker et al., 2015), loneliness, not social isolation, was found to be associated with an increased risk for mental health problem (Coyle & Dugan, 2012). ...
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This study examined the mechanism by which social isolation and loneliness may become associated with major psychiatric disorders using the data from a nationally representative sample of Korean adults who participated in the National Mental Health Survey of Korea 2021 (N = 5511, Mage = 48.57, SD = 15.27). Of particular interest were the roles of depressive and anxiety disorders as mediators of the association between social isolation or loneliness, and other psychiatric disorders. Social isolation, loneliness, and psychiatric conditions (i.e., depressive disorders, anxiety disorders, tobacco use disorder, alcohol use disorder, and suicidal behavior) of participants were assessed by trained interviewers administering clinical diagnostic interviews and inventories. The results indicated that only loneliness was related to depressive disorders, while both social isolation and loneliness were related to anxiety disorders. Second, only depressive disorders were associated with alcohol use disorder and suicidal behavior, while anxiety disorders were not associated with any other psychiatric disorders. Specifically, the influence of loneliness on alcohol use disorder and suicidal behavior was significantly mediated by depressive disorders. The findings suggest that social relations and affective disorders may be considered potential targets for intervention in major psychiatric disorders.
... The risk of isolation, loneliness, and living alone across different age groups was significantly stronger among younger individuals compared to those over 65 years. 101 Similarly, more recent evidence demonstrates loneliness significantly predicted earlier mortality among young and middle-aged adults (18-59) but not among older adults (60+). 102 Though young and middle-aged adults seem to be at higher risk for isolation and loneliness and earlier mortality than older adults, evidence still suggests the critical role that social con- ...
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Society within the Brain provides insightful accounts of scientific research linking social connection with brain and cognitive aging through state-of-the-art research. This involves comprehensive social network analysis, social neuroscience, neuropsychology, psychoneuroimmunology, and sociogenomics. This book provides a scientific discourse on how a society, community, or friends and family interact with individuals' cognitive aging. Issues concerning social isolation, rapidly increasing in modern societies, and the controversy in origins of individual difference in social brain and behaviour are discussed. An integrative framework is introduced to explicate how social networks and support alleviate the effects of aging in brain health and reduce dementia risks. This book is of interest and useful to a wide readership: from gerontologists, psychologists, clinical neuroscientists and sociologists, to those involved in developing community-based interventions or public health policy for brain health, to people interested in how social life influences brain aging or in the prevention of dementia.
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Background: While existing research establishes a connection between increased self-determination and decreased mortality rates among older adults, there is still limited understanding of how this association varies across demographic variables such as gender and age. Methods: The present study examines the nuances in the relationship between self-determination and mortality risk in a large, nationally-representative cohort of individuals aged over 60 (n=23,522). Self-determination was quantified using a validated measure that evaluates self-direction and agency. Mortality was monitored over a span of 16 years. To minimize sample selection bias, propensity scores were utilized to compute weights, following which a weighted Cox regression model was applied. Results: The findings indicated that self-determination significantly predicted reduced mortality. Notably, the relationship between self-determination and mortality was modulated by both gender and age, with the effects being most pronounced for men and those in the younger age brackets. Conclusions: By shedding light on the intricate interplays between self-determination and health outcomes, this research offers insights for designing tailored interventions aimed at mitigating mortality rates and promoting healthy aging across a heterogeneous elderly population.
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Aim This study aims to establish a nomogram model to predict the relevance of SA in Chinese female patients with mood disorder (MD). Method The study included 396 female participants who were diagnosed with MD Diagnostic Group (F30–F39) according to the 10th Edition of Disease and Related Health Problems (ICD-10). Assessing the differences of demographic information and clinical characteristics between the two groups. LASSO Logistic Regression Analyses was used to identify the risk factors of SA. A nomogram was further used to construct a prediction model. Bootstrap re-sampling was used to internally validate the final model. The Receiver Operating Characteristic (ROC) curve and C-index was also used to evaluate the accuracy of the prediction model. Result LASSO regression analysis showed that five factors led to the occurrence of suicidality, including BMI ( β = −0.02, SE = 0.02), social dysfunction ( β = 1.72, SE = 0.24), time interval between first onset and first dose ( β = 0.03, SE = 0.01), polarity at onset ( β = −1.13, SE = 0.25), and times of hospitalization ( β = −0.11, SE = 0.06). We assessed the ability of the nomogram model to recognize suicidality, with good results (AUC = 0.76, 95% CI: 0.71–0.80). Indicating that the nomogram had a good consistency (C-index: 0.756, 95% CI: 0.750–0.758). The C-index of bootstrap resampling with 100 replicates for internal validation was 0.740, which further demonstrated the excellent calibration of predicted and observed risks. Conclusion Five factors, namely BMI, social dysfunction, time interval between first onset and first dose, polarity at onset, and times of hospitalization, were found to be significantly associated with the development of suicidality in patients with MD. By incorporating these factors into a nomogram model, we can accurately predict the risk of suicide in MD patients. It is crucial to closely monitor clinical factors from the beginning and throughout the course of MD in order to prevent suicide attempts.
This chapter explores the aesthetic tourism market in relation to well-being and quality of life. Following an overview of the aesthetics tourism market and the reasons for engaging in aesthetics tourism activities by tourists, the chapter attempts to explain some of the main motivations why people may engage in aesthetics tourism procedures and surgeries. The main motivations of tourists are explained with reference to the needs hierarchy, intrinsic and extrinsic motivation, self-determination, and prospect theories. The chapter also shows the potential relationships between the self-concept, certain personality characteristics, and the involvement in aesthetic aesthetics tourism procedures and surgeries.
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The development of an adequate assessment instrument is a necessary prerequisite for social psychological research on loneliness. Two studies provide methodological refinement in the measurement of loneliness. Study 1 presents a revised version of the self-report UCLA (University of California, Los Angeles) Loneliness Scale, designed to counter the possible effects of response bias in the original scale, and reports concurrent validity evidence for the revised measure. Study 2 demonstrates that although loneliness is correlated with measures of negative affect, social risk taking, and affiliative tendencies, it is nonetheless a distinct psychological experience.
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Loneliness typically refers to the feelings of distress and dysphoria resulting from a discrepancy between a person's desired and achieved levels of social relations, and there is now considerable evidence that loneliness is a risk factor for poor psychological and physical health. Loneliness has traditionally been conceptualized as a uniquely human phenomenon. However, over millions of years of evolution, efficient and manifold neural, hormonal, and molecular mechanisms have evolved for promoting companionship and mutual protection/assistance and for organizing adaptive responses when there is a significant discrepancy between the preferred and realized levels of social connection. We review evidence suggesting that loneliness is not a uniquely human phenomenon, but, instead, as a scientific construct, it represents a generally adaptive predisposition that can be found across phylogeny. Central to this argument is the premise that the brain is the key organ of social connections and processes. Comparative studies and animal models, particularly when integrated with human studies, have much to contribute to the understanding of loneliness and its underlying principles, mechanisms, consequences, and potential treatments. © The Author(s) 2015.
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Most people have experienced loneliness and have been able to overcome it to reconnect with other people. In the current review, we provide a life-span perspective on one component of the evolutionary theory of loneliness-a component we refer to as the reaffiliation motive (RAM). The RAM represents the motivation to reconnect with others that is triggered by perceived social isolation. Loneliness is often a transient experience because the RAM leads to reconnection, but sometimes this motivation can fail, leading to prolonged loneliness. We review evidence of how aspects of the RAM change across development and how these aspects can fail for different reasons across the life span. We conclude with a discussion of age-appropriate interventions that may help to alleviate prolonged loneliness. © The Author(s) 2015. Access to on-line version of the paper at
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Social isolation has been recognized as a major risk factor for morbidity and mortality in humans for more than a quarter of a century. Although the focus of research has been on objective social roles and health behavior, the brain is the key organ for forming, monitoring, maintaining, repairing, and replacing salutary connections with others. Accordingly, population-based longitudinal research indicates that perceived social isolation (loneliness) is a risk factor for morbidity and mortality independent of objective social isolation and health behavior. Human and animal investigations of neuroendocrine stress mechanisms that may be involved suggest that (a) chronic social isolation increases the activation of the hypothalamic pituitary adrenocortical axis, and (b) these effects are more dependent on the disruption of a social bond between a significant pair than objective isolation per se. The relational factors and neuroendocrine, neurobiological, and genetic mechanisms that may contribute to the association between perceived isolation and mortality are reviewed. Expected final online publication date for the Annual Review of Psychology Volume 66 is November 30, 2014. Please see for revised estimates.
The association of television viewing and obesity in data collected during cycles II and III of the National Health Examination Survey was examined. Cycle II examined 6,965 children aged 6 to 11 years and cycle III examined 6,671 children aged 12 to 17 years. Included in the cycle III sample were 2,153 subjects previously studied during cycle II. These surveys, therefore, provided two cross-sectional samples and one prospective sample. In all three samples, significant associations of the time spent watching television and the prevalence of obesity were observed. In 12- to 17-year-old adolescents, the prevalence of obesity increased by 2% for each additional hour of television viewed. The associations persisted when controlled for prior obesity, region, season, population density, race, socioeconomic class, and a variety of other family variables. The consistency, temporal sequence, strength, and specificity of the associations suggest that television viewing may cause obesity in at least some children and adolescents. The potential effects of obesity on activity and the consumption of calorically dense foods are consistent with this hypothesis.
The relationship between social and community ties and mortality was assessed using the 1965 Human Population Laboratory survey of a random sample of 6928 adults in Alameda County, California and a subsequent nine-year mortality follow-up. The findings show that people who lacked social and community ties were more likely to die in the follow-up period than those with more extensive contacts. The age-adjusted relative risks for those most Isolated when compared to those with the most social contacts were 2.3 for men and 2.8 for women. The association between social ties and mortality was found to be independent of self-reported physical health status at the time of the 1965 survey, year of death, socioeconomic status, and health practices such as smoking, alcoholic beverage consumption, obesity, physical activity, and utilization of preventive health services as well as a cumulative index of health practices.
Many older adults live alone. For example, in the United States, over 45 % of women over the age of 75 years live alone.1 Much attention has been placed on older adults who are living alone, because of the recent studies that have shown that both loneliness and social isolation are associated with poor health outcomes.1–4 These studies have also suggested that living alone is not necessarily indicative of having poor social support or of feeling lonely. While it may be reasonable to believe that living alone is a good proxy for these types of social measures, there is increasing recognition that the measures of social well-being are complex concepts and go beyond simply describing the situational facts of a person’s life. In actuality, social isolation and loneliness are complex self-perceptions that may not be fully captured by whether or not someone lives alone. This demands that as clinicians, we must dig deeper into a patient’s personal perspective. For example, measures of lonelin ...