ArticlePDF Available

Abstract and Figures

Purpose Loneliness is a recognised public-health concern that is traditionally regarded as a unidimensional construct. Theories of loneliness predict the existence of subtypes of loneliness. In this study, latent class analysis (LCA) was used to test for the presence of loneliness subtypes and to examine their association with multiple mental health variables. Methods A nationally representative sample of US adults (N = 1839) completed the De Jong Gierveld Loneliness Scale, along with self-report measures of childhood and adulthood trauma, psychological wellbeing, major depression, and generalized anxiety. Results When treated as a unidimensional construct, 17.1% of US adults aged 18–70 were classified as lonely. However, the LCA results identified four loneliness classes which varied quantitatively and qualitatively: ‘low’ (52.8%), ‘social’ (8.2%), ‘emotional’ (26.6%), and ‘social and emotional’ (12.4%) loneliness. The ‘social and emotional’ class were characterised by the highest levels of psychological distress, followed by the ‘emotional’ class. The ‘social’ loneliness class had similar mental health scores as the ‘low’ loneliness class. Childhood and adulthood trauma were independently related to the most distressed loneliness classes. Conclusions Current findings provide support for the presence of subtypes of loneliness and show that they have unique associations with mental health status. Recognition of these subtypes of loneliness revealed that the number of US adults aged 18–70 experiencing loneliness was twice as high as what was estimated when loneliness was conceptualized as a unidimensional construct. The perceived quality, not the quantity, of interpersonal connections was associated with poor mental health.
This content is subject to copyright. Terms and conditions apply.
Vol.:(0123456789)
1 3
Social Psychiatry and Psychiatric Epidemiology
https://doi.org/10.1007/s00127-018-1597-8
ORIGINAL PAPER
Quality notquantity: loneliness subtypes, psychological trauma,
andmental health intheUS adult population
PhilipHyland2,8 · MarkShevlin3· MaryleneCloitre4,5· ThanosKaratzias6,7· FrédériqueVallières2·
GráinneMcGinty1· RobertFox8· JoannaMcHughPower1
Received: 6 July 2018 / Accepted: 18 September 2018
© Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract
Purpose Loneliness is a recognised public-health concern that is traditionally regarded as a unidimensional construct.
Theories of loneliness predict the existence of subtypes of loneliness. In this study, latent class analysis (LCA) was used to
test for the presence of loneliness subtypes and to examine their association with multiple mental health variables.
Methods A nationally representative sample of US adults (N = 1839) completed the De Jong Gierveld Loneliness Scale,
along with self-report measures of childhood and adulthood trauma, psychological wellbeing, major depression, and gen-
eralized anxiety.
Results When treated as a unidimensional construct, 17.1% of US adults aged 18–70 were classified as lonely. However, the
LCA results identified four loneliness classes which varied quantitatively and qualitatively: ‘low’ (52.8%), ‘social’ (8.2%),
‘emotional’ (26.6%), and ‘social and emotional’ (12.4%) loneliness. The ‘social and emotional’ class were characterised
by the highest levels of psychological distress, followed by the ‘emotional’ class. The ‘social’ loneliness class had similar
mental health scores as the ‘low’ loneliness class. Childhood and adulthood trauma were independently related to the most
distressed loneliness classes.
Conclusions Current findings provide support for the presence of subtypes of loneliness and show that they have unique
associations with mental health status. Recognition of these subtypes of loneliness revealed that the number of US adults aged
18–70 experiencing loneliness was twice as high as what was estimated when loneliness was conceptualized as a unidimen-
sional construct. The perceived quality, not the quantity, of interpersonal connections was associated with poor mental health.
Keywords Loneliness· Latent class analysis· Mental health
* Philip Hyland
philip.hyland@mu.ie
Mark Shevlin
m.shevlin@ulster.ac.uk
Marylene Cloitre
Marylene.Cloitre@va.gov
Thanos Karatzias
t.karatzias@napier.ac.uk
Frédérique Vallières
fvallier@tcd.ie
Gráinne McGinty
Grainne.mcginty@gmail.com
Robert Fox
rfox@live.ie
Joanna McHugh Power
Joanna.power@ncirl.ie
1 School ofBusiness, National College ofIreland, Dublin,
Ireland
2 Centre forGlobal Health, School ofPsychology, Trinity
College Dublin, Dublin, Ireland
3 School ofPsychology, Ulster University, Derry,
NorthernIreland, UK
4 National Center forPTSD, Veterans Affairs Palo Alto Health
Care System, PaloAlto, CA, USA
5 Department ofPsychiatry andBehavioral Science, Stanford
University, PaloAlto, CA, USA
6 School ofHealth andSocial Care, Edinburgh Napier
University, Edinburgh, UK
7 Rivers Centre forTraumatic Stress, NHS Lothian, Edinburgh,
UK
8 School ofPsychology, Maynooth University, Kildare, Ireland
Social Psychiatry and Psychiatric Epidemiology
1 3
Introduction
Loneliness is increasingly recognised as a global health
concern [1], and is known to be correlated with, and pre-
dictive of, psychological and physical disorders [2, 3]. The
number of people experiencing loneliness varies across
nations. Prevalence rates of loneliness in nine former
Soviet Union countries ranged from 4.4% (Azerbaijan) to
17.9% (Moldova) [4]. In a nationally representative sam-
ple of Danish adults, 21% of people reported being either
moderately (16.4%) or severely (4.6%) lonely [5]. In Que-
bec, 14.5% of individuals aged 15years and older reported
loneliness [6]. No study has yet examined the prevalence
rates of loneliness amongst the adult population of the
United States (US); however, a nationally representative
survey of US adults aged 45years and older found that
35% reported loneliness [7]. The relatively high rate of
loneliness in this US study was likely due to the use of
an older adult sample given that loneliness rises substan-
tially in older age [5]. Determining the prevalence rate
of loneliness is exceptionally challenging as there is no
established diagnostic algorithm for classifying loneliness.
Moreover, variation in the methods used to measure loneli-
ness (single-item vs. multiple-item scales) and to classify
individuals as “being lonely” (a certain response option
for a single-item measure or use of a given cut-off score
for multi-item scales) is likely to lead to considerable vari-
ation in estimates of the prevalence rates of loneliness.
Loneliness is typically treated as a unidimensional con-
struct, and consequently, prevalence rates of loneliness
tends to be determined based on whether or not an indi-
vidual exceeds a total score [e.g., 57]. However, many
have challenged the assumption that loneliness is a unidi-
mensional construct and have instead argued that multiple
types of loneliness exist [8]. Weiss’ [9] multidimensional
theory of loneliness, for example, distinguishes between
‘social’ (deficiencies of social integration) and ‘emotional’
(deficiencies of close attachments) loneliness. Factor ana-
lytic studies indicate that measurement models which dis-
tinguish between these dimensions of loneliness are supe-
rior to unidimensional models [10, 11], and that social
and emotional loneliness are only moderately correlated
[12]. Failure to recognise naturally occurring subtypes of
loneliness may, therefore, lead to unreliable estimates of
the prevalence rate of loneliness.
Further support for the existence of subtypes of loneli-
ness comes from studies indicating distinct antecedents
of social and emotional loneliness. Social loneliness has
been shown to be related to reductions in social network
size, whereas emotional loneliness has been shown to be
related to deficits in intimate partner relationships [13].
Additionally, males tend to display higher social and lower
emotional loneliness, while females show the opposite pat-
tern. Social and emotional loneliness also share similar
risk-correlates such as partnership status, increasing age,
low subjective wellbeing, widowhood, and lower levels
of self-esteem [10, 13]. Childhood and adulthood trauma-
tization have both been linked to an increased likelihood
of experiencing loneliness [1418], and loneliness has
been shown to mediate the relationship between traumatic
exposure and psychiatric morbidity [19]. No study has
yet investigated the relationship between loneliness and
childhood and adulthood trauma simultaneously, and more
importantly, no study has yet examined if the developmen-
tal timing of traumatic exposure is differentially associ-
ated with proposed subtypes of loneliness. The existing
literature is also inconclusive regarding the relationship
between loneliness subtypes and mental health status. For
example, some studies have found depression and anxi-
ety to be associated with social loneliness [20, 21]; oth-
ers have found depression to be more strongly associated
with emotional loneliness [2123]; and yet others show
that depression is similarly related to social and emotional
loneliness [24].
The inconsistent findings are likely due to multiple factors
including variation in the measurement of loneliness, the
use of non-representative samples, and imprecise methods
of classifying loneliness subtypes. Traditionally, purported
subtypes of loneliness are represented by summed subscale
scores from measures of loneliness, and these subscales are
known to be moderately correlated [12]. This method does
not discriminate between different types of loneliness and
leaves results vulnerable to the effects of multicollinear-
ity. The application of latent class analysis (LCA) offers a
methodologically rigorous approach to (1) determining if
unique subtypes of loneliness exist, and (2) if so, isolating
these subtypes through the construction of non-overlapping,
homogeneous classes of individuals (e.g., ‘emotionally
lonely’ individuals and ‘socially lonely’ individuals). To
date, however, only one study has used LCA methods to
determine if distinct subtypes (or latent classes) of loneliness
exist [25]. In this study of Northern Irish adolescents who
completed the UCLA-Loneliness Scale [26], four distinct
loneliness classes were identified. The classes differed quan-
titatively (‘low’, ‘moderate’, and ‘high’ loneliness classes)
and qualitatively (one class was characterised by high levels
of ‘social loneliness’). Moreover, the classes were also found
to significantly differ in relation to their risk of psychiatric
morbidity.
Given the possible therapeutic and prevention implica-
tions of identifying naturally occurring loneliness subtypes
in the population, as well as the extant methodological limi-
tations in this field of research, the current study, based on
a nationally representative sample of US adults aged 18–70
years, was performed to investigate five objectives:
Social Psychiatry and Psychiatric Epidemiology
1 3
1. To determine the prevalence rate of loneliness in the
US adult population aged 18–70 years using a standard
method employed in the literature when loneliness is
conceptualised as a unidimensional construct.
2. Using LCA techniques, we examined if qualitatively
distinct subtypes of loneliness existed as predicted by
Weiss’ [9] multidimensional theory of loneliness (i.e.,
‘social’ and ‘emotional’ loneliness). We predicted that
multiple latent classes of loneliness would be identified.
Loneliness classes that differed on purely quantitative
grounds (e.g., ‘high’, ‘medium’, and ‘low’ loneliness
classes) would falsify the hypothesis that subtypes
of loneliness exist. Evidence of qualitatively distinct
classes (e.g., classes that have similar levels of loneli-
ness but are markedly distinct in their profile of lone-
liness) would support the hypothesis that subtypes of
loneliness exist.
3. We examined if loneliness subtypes were differentially
related to psychological wellbeing, major depressive dis-
order (MDD), and generalized anxiety disorder (GAD).
4. We examined if specific relationships existed between
loneliness subtypes and antecedent risk-factors includ-
ing childhood and adulthood traumatization.
5. We investigated if the relationships between childhood
and adulthood traumatization and psychological wellbe-
ing, MDD, and GAD, respectively, were influenced by
the specific subtype of loneliness that one was charac-
terised by.
Methods
Participants andprocedures
This study used a nationally representative household
sample of non-institutionalised adults currently residing
in the United States. Data were collected in March 2017
using an online research panel randomly recruited through
probability-based sampling. To be included in the current
study, respondents had to be aged between 18 and 70years
at the time of the survey, and have experienced at least
one traumatic event in their lifetime. A total of 3953 par-
ticipants were screened to meet the inclusion criteria and
a total of 1839 people qualified as valid cases (eligibil-
ity rate = 46.3%). The survey design oversampled among
females and minority populations (African American and
Hispanic), each at a 2:1 ratio. To adjust for this oversam-
pling, and to ensure the nationally representative nature of
the sample, the data were weighted to be representative of
the entire US adult population aged 18–70 years. All self-
report surveys were completed on-line and the median time
of completion was 18min. Individuals received no pay-
ment for participation, but were incentivised to participate
through entry into a raffle for prizes. The study received
ethical approval from the Research Ethics committee of the
institution to which the first author is affiliated.
The mean age of the weighted sample was 44.55years
(SD = 14.89) and included a similar number of males (48%,
n = 883) and females (52%, n = 956). The majority of the
sample was married (55.3%, n = 1016) and 8.1% (n = 149)
indicated that they were co-habiting with a partner. These
individuals were subsequently combined to reflect a group
that were ‘in a relationship’. The remainder of the sample
indicated that they were single (23.3%, n = 428), divorced
(10.9%, n = 202), or widowed (2.4%, n = 44). These indi-
viduals were combined to reflect a group that were ‘not in
a relationship’. The majority of the sample were ‘White,
Non-Hispanic’ (63.8%, n = 1173), followed by ‘Hispanic’
(16.9%, n = 310), ‘Black, Non-Hispanic’ (11.8%, n = 217),
‘Other, Non-Hispanic’ (6.3%, n = 115), and ‘2 + Races,
Non-Hispanic’ (1.3%, n = 24). Approximately one-third of
the sample reported that their highest level of educational
achievement was a ‘Bachelor’s degree or higher’ (31.8%,
n = 585), while similar amounts indicated ‘some college’
(30.3%, n = 558), or ‘finishing high school’ (28.7%, n = 528),
and 9.1% (n = 168) indicated that they ‘did not finish high
school’. Nearly half of the sample earned US$75,000 or more
per year (48.5%, n = 891), 29.8% (n = 547) earned between
US$35,000 and US$74,999 per year, 11.0% (n = 202) earned
between US$20,000 and US$34,999 per year, and 10.8%
(n = 199) earned between US$0–US$19,999 per year.
Measures
Loneliness
The six-item De Jong Gierveld Loneliness Scale [27] was
used to measure feelings of social and emotional loneli-
ness, each measured by three items. The emotional lone-
liness items are phrased in a negative manner and the
social loneliness items are phrased in a positive manner.
All items were answered using a three-point Likert scale
of ‘Very much agree’ (1), ‘Somewhat agree’ (2), and ‘Do
not agree’ (3). Following the scoring guidelines provided
by the scale authors [27], all items were dichotomised to
reflect the ‘presence’ (1) or ‘absence’ (0) of an indicator of
loneliness. For the emotional loneliness items, agreement
responses were taken to indicate item endorsement, while
for the social loneliness items, disagreement responses were
taken to indicate item endorsement. This measure has been
shown to be reliable and valid in large-scale general popula-
tion surveys [28]. The internal reliability (Cronbach’s alpha)
of the full scale (α = 0.81) and the ‘social’ (α = 0.88) and
‘emotional’ (α = 0.74) subscales were satisfactory within the
current sample. There is no agreed upon cut-off score for
the six-item De Jong Gierveld Loneliness Scale to identify
Social Psychiatry and Psychiatric Epidemiology
1 3
loneliness cases. In the current study, we followed the rec-
ommendations of Shevlin etal. [29] that caseness for loneli-
ness should be determined by selecting only those individu-
als with a score 1 standard deviation above the sample mean.
Childhood andadulthood traumatic exposure
A modified version of the Life Events Checklist for DSM-5
[30] was used to measure traumatic exposure during child-
hood and adulthood. Individuals answered on a ‘Yes’ (1) or
‘No’ (0) basis if they had experienced any of 14 common
traumatic events ‘before the age of 18’ (childhood) or ‘at
or after the age of 18’ (adulthood). Three items from the
Adverse Childhood Experiences questionnaire [31] assess-
ing physical abuse, sexual abuse, and neglect were also
used to supplement the measurement of childhood trauma.
Summed total scores of childhood (0–17) and adulthood
(0–14) trauma were calculated.
Psychological wellbeing
Psychological wellbeing was assessed using the five-item
World Health Organization Well-Being Index (WHO-5)
[32]. The WHO-5 is an internationally validated measure of
positive psychological health. A recent review of 213 inter-
national studies supported the reliability and validity of the
scale [33]. Respondents are asked to indicate how they have
been feeling over the past 2weeks to each positively phrased
statement along a six-point Likert scale ranging from ‘At
no time’ (0) to ‘All of the time’ (5). Scores range from 0
to 25, with higher scores reflecting greater psychological
wellbeing. Scores 13 are indicative of poor mental health
and the possible presence of a psychiatric disorder [34]. The
reliability of the WHO-5 among the current sample was high
(α = 0.93).
Major depressive disorder (MDD) andgeneralized anxiety
disorder (GAD)
Symptoms of MDD and GAD were measured using the
eight-item Patient Health Questionnaire Depression Scale
(PHQ-8) [35] and the Generalized Anxiety Disorder 7-item
Scale (GAD-7). These scales assess the symptoms of MDD
and GAD in-line with DSM-5 criteria (the PHQ-8 excludes
one item reflecting the suicidality/self-harm symptom for
MDD). For both measures respondents indicate how often
they have been bothered by each symptom over the last
2weeks using a four-point Likert scale ranging from ‘Not
at all’ (0) to ‘Nearly every day’ (3). Scores on the PHQ-8
range from 0 to 24 and scores on the GAD-7 range from 0 to
21. In both cases, higher scores reflect greater symptomatol-
ogy, and scores 10 are considered indicative of diagnos-
tic status [35, 36]. The PHQ-8 [37] and the GAD-7 [38]
have demonstrated excellent psychometric properties. The
internal reliability of the PHQ-8 (α = 0.93) and the GAD-7
(α = 0.94) were excellent within the current sample.
Data analysis
The analytic process for the current study included three
linked phases and all analyses were conducted using
Mplus 7.4 [39]. First, LCA was performed based on binary
responses to the six De Jong Gierveld Loneliness Scale
items so as to determine the optimal number of latent
classes of loneliness. The fit of six models (1–6 classes)
were assessed and all models were estimated using robust
maximum likelihood [40]. Missing data were low (1.5%)
and the models were estimated using all available informa-
tion. To avoid solutions based on local maxima, 500 ran-
dom sets of starting values were used followed by 100 final
stage optimizations. The relative fit of the latent class mod-
els were compared using three information theory based fit
statistics: the Akaike information criterion (AIC) [41], the
Bayesian information criterion (BIC) [42] and the sample-
size-adjusted BIC (ssaBIC) [43]. The model that produces
the lowest value on each criterion can be judged to be best.
Additionally, the Lo–Mendell–Rubin adjusted likelihood
ratio test (LMR-A) [44] was used to compare models with
increasing numbers of latent classes, whereby a non-signifi-
cant value suggests that the model with one less class should
be accepted. Evidence from simulation studies indicates that
the BIC is the best index to identify the correct number of
latent classes [45].
Second, mean differences on the mental health variables
(psychological wellbeing, MDD, and GAD) were compared
across the identified latent classes. To avoid shifts in the
latent classes due to the inclusion of auxiliary variables, an
automatic Bolck–Croon–Hagenaars (BCH) method [46] was
implemented. The BCH method has been shown in simula-
tion studies to outperform alternative approaches such as
the ‘3-step method’ or the ‘Lanza method’ [47, 48]. The
BCH method overcomes the primary limitation of the 3-step
method (shifting latent classes as a result of the inclusion of
auxiliary variables) due to the fact that it “uses a weighted
multiple group analysis, where the groups correspond to the
latent classes, and thus the class shift is not possible because
the classes are known” [49, p.2]. Additionally, unlike the
Lanza method, the BCH method does not require homogene-
ity of variance for the auxiliary variables.
Third, a manual BCH method [49] was conducted to
evaluate: (1) the unique associations between five covariates
(age, sex, relationship status, childhood trauma, and adult-
hood trauma) and class membership; and (2) class-specific
associations between these covariates and psychological
wellbeing, MDD, and GAD. This manual BCH process
is completed in two steps. In the first step, the latent class
Social Psychiatry and Psychiatric Epidemiology
1 3
measurement model is estimated and the BCH class weights
are saved. In the second step, the general auxiliary model
is evaluated. In this case, the latent classes were (1) simul-
taneously regressed on all covariates, and (2) the mental
health variables were simultaneously regressed on all covari-
ates conditional on the latent class variable. This analyti-
cal process allows for the effect of each covariate on class
membership to be determined without any shift in the latent
classes, and for the class-specific relationships between the
covariates and the mental health variables to be determined
simultaneously.
Results
Objective 1—prevalence rate ofloneliness intheUS
adult population whentreated asaunidimensional
construct
The mean score for the six-item De Jong Gierveld Loneli-
ness Scale was 1.76 (SD = 1.77). A total of 17.1% (n = 307)
of the sample had a mean score of loneliness greater than 1
SD above the sample mean and were, therefore, classified
as lonely.
Objective 2—LCA results
The BIC and ssaBIC results were lowest for the four-class
solution, suggesting its statistical superiority, however, the
LMR-A became non-significant at four-classes suggesting
the superiority of a three-class solution. Based on the simu-
lation work of Nylund etal. [44] which indicated that the
BIC is the best method for determining the optimal class
solution, along with the interpretability of the different
class solutions, it was determined that the four-class model
was the best representation of the latent class structure of
loneliness. The profile plot of the four-class solution is pre-
sented in Fig.1 and all fit indices for the LCA are presented
Table1.
Class 1 was the largest (52.8%, n = 984) and was char-
acterised by low probabilities of endorsing each loneliness
item. This class was labelled the ‘low loneliness’ class. Class
2 was the smallest (8.2%, n = 138) and was characterised
by low probabilities of endorsing the emotional loneliness
items and high probabilities of endorsing the social loneli-
ness items. This class was labelled the ‘social loneliness’
class. Class 3 (26.6%, n = 472) was characterised by high
probabilities of endorsing the emotional loneliness items and
low probabilities of endorsing the social loneliness items.
EL1 EL2 EL3 SL1 SL2 SL3
Class 1: Low Loneliness (52.8%) 0.0420.268 0.0160.026 0.064 0.002
Class 2: Social Loneliness (8.2%) 0.2150.216 0.0290.845 0.959 0.712
Class 3: Emoonal Loneliness (26.6%) 0.746 0.7890.660.094 0.1510.089
Class 4: Social and Emoonal Loneliness (12.4%) 0.893 0.7010.938 0.8280.944 0.732
0
0.2
0.4
0.6
0.8
1
1.2
PROBABILITY OF ENDORSING LONELINESS ITEM
Class 1: Low Loneliness (52.8%) Class 2: Social Loneliness (8.2%)
Class 3: Emoonal Loneliness (26.6%) Class 4: Social and Emoonal Loneliness (12.4%)
Fig. 1 Latent class profile of loneliness
Table 1 LCA fit statistics based
on responses to the De Jong
Gierveld Loneliness Scale
(N = 1815)
Best-fitting model in bold
Classes Log likelihood AIC BIC ssaBIC LMR-A (p) Entropy
1− 6350 12,712 12,745 12,726
2− 5464 10,955 11,027 10,986 1737 (< 0.001) 0.84
3− 5156 10,352 10,462 10,399 605 (< 0.001) 0.82
45057 10,169 10,317 10,231 194 (0.203) 0.83
5− 5042 10,153 10,340 10,232 29 (0.415) 0.87
6− 5031 10,144 10,370 10,240 22 (0.395) 0.87
Social Psychiatry and Psychiatric Epidemiology
1 3
This class was labelled the ‘emotional loneliness’ class.
Finally, class 4 (12.4%, n = 222) was characterised by high
probabilities of endorsing all loneliness items. This class
was labelled the ‘social and emotional loneliness’ class.
Objective 3—class differences onmental health
variables
There were statistically significant overall differences
between the classes on psychological wellbeing, MDD,
and GAD, and all pairwise comparisons between the latent
classes were statistically significant (see Table2). The pat-
tern of results was similar across all mental health variables.
There was a clear gradient of psychological distress across
classes with the ‘low loneliness’ class the least distressed,
followed by the ‘social loneliness’ class, then the ‘emotional
loneliness’ class, and then the ‘social and emotional loneli-
ness’ class being the most distressed. These results indicate
that while the experience of social loneliness is associated
with slight diminutions in overall mental health, relative to
the low loneliness class, the experience of emotional loneli-
ness has a substantially greater, and more negative impact
on overall mental health status. Furthermore, the combina-
tion of social and emotional loneliness is associated with the
poorest mental health status.
Objective 4—correlates ofclass membership
Table3 reports the results of a multinomial logistic regres-
sion analysis assessing the unique associations between class
membership and each covariate. Compared to the ‘low lone-
liness’ class, membership of the ‘social loneliness’ class was
significantly associated with younger age. Membership of
the ‘emotional loneliness’ class was significantly associ-
ated with younger age, being female, not being in a rela-
tionship, and an increased number of childhood traumas.
Table 2 Tests of differences of
means (standard errors) across
loneliness classes (N = 1815)
Statistical significance = **p < 0.001, *p < 0.01
a All tests have 3 degrees of freedom
b All tests have 1 degree of freedom
Psychological wellbeing Depression Generalized anxiety
Class 1: Low loneliness 18.20 (0.18) 1.17 (0.10) 1.23 (0.10)
Class 2: Social loneliness 15.93 (0.89) 2.78 (0.62) 2.48 (0.45)
Class 3: Emotional loneliness 11.96 (0.39) 7.06 (0.38) 6.06 (0.34)
Class 4: Social and emotional loneliness 7.10 (0.48) 10.64 (0.63) 8.96 (0.58)
Overall testa (Wald χ2) 618.19*** 463.14*** 357.05***
Pairwise testsb (Wald χ2)
Class 1 vs. 2 6.24* 6.61* 7.34*
Class 1 vs. 3 192.40** 211.94** 169.53**
Class 1 vs. 4 480.21** 225.55** 172.38**
Class 2 vs. 3 16.52** 34.51** 40.06**
Class 2 vs. 4 71.31** 74.18** 72.35**
Class 3 vs. 4 57.29** 21.89** 17.00**
Table 3 Correlates of class
membership based on results of
a multinomial logistic regress
analysis (N = 1772)
Reference group for all analyses if Class 1 (the ‘Low Loneliness’ class)
Sex is scored (0 = male, 1 = female); relationship status is scored (0 = married or in a relationship, 1 = wid-
owed, divorced, or single)
B unstandardized beta value, SE standard error, OR odds ratio
Statistical significance = *p < 0.01, **p < 0.001
Class 2: Social loneliness
B (SE) [OR]
Class 3: Emotional loneliness
B (SE) [OR]
Class 4: Social and
emotional loneliness
B (SE) [OR]
Age 0.03 (0.01)** [0.97] 0.02 (0.01)** [0.98] 0.03 (0.01)** [0.97]
Sex 0.21 (0.25) [0.81] 0.59 (0.18)** [1.80] 0.62 (0.22)* [1.86]
Relationship 0.17 (0.29) [0.84] 0.64 (0.18)** [1.90] 0.42 (0.22) [1.52]
Adult trauma 0.09 (0.07) [1.09] 0.04 (0.06) [1.04] 0.16 (0.06)* [1.17]
Child trauma 0.08 (0.07) [1.08] 0.25 (0.05)** [1.28] 0.23 (0.06)** [1.26]
Social Psychiatry and Psychiatric Epidemiology
1 3
Membership of the ‘social and emotional loneliness’ class
was significantly associated with younger age, being female,
an increased number of childhood traumas, and an increased
number of adulthood traumas.
Objective 5—class‑specific associations
betweencovariates andmental health variables
The results of the class-specific associations between each
covariate and each mental health variable are presented in
Table4. In the ‘low loneliness’ class, the model explained
almost no variance in each of the mental health variables.
Adulthood trauma was significantly associated with poorer
psychological wellbeing, and higher levels of MDD and
GAD. Additionally, being female was significantly associ-
ated with increased levels of MDD and GAD. In the ‘social
loneliness’ class, the model explained > 10% of variance
in each mental health variable, and increased frequency of
adulthood trauma was significantly and positively associated
with MDD and GAD scores. In the ‘emotional loneliness’
class, the model explained > 20% of variance in MDD and
GAD scores, and < 10% of variance in psychological well-
being scores. Increased frequency of childhood trauma was
significantly associated with lower levels of psychological
wellbeing, and higher levels of MDD and GAD. Finally,
in the ‘social and emotional loneliness’ class, the model
explained a robust percentage of variance in MDD (27%)
and GAD (35%) scores, but substantially less variance in
psychological wellbeing (6%) scores. Increased frequency
of adulthood trauma was significantly associated with psy-
chological wellbeing and MDD scores; being female was
significantly associated with increased levels of MDD and
Table 4 Class-specific
association between each
covariate and all mental health
variables (N = 1772)
Sex is scored (0 = male, 1 = female); Relationship status is scored (0 = married or in a relationship, 1 = wid-
owed, divorced, or single)
β standardized beta value, SE standard error, OR odds ratio
Statistical significance = *p < 0.05, **p < 0.01, ***p < 0.001
Psychological wellbeing
β (SE)
Depression
β (SE)
Generalized anxiety
β (SE)
Class 1: Low loneliness (52.8%)
Age 0.03 (0.04) − 0.00 (0.03) − 0.04 (0.03)
Sex − 0.05 (0.04) 0.07 (0.02)** 0.10 (0.03)***
Relationship status 0.01 (0.04) − 0.03 (0.03) 0.00 (0.03)
Adult trauma − 0.15 (0.05)*** 0.09 (0.04)* 0.08 (0.04)*
Childhood trauma 0.08 (0.05) 0.01 (0.04) − 0.00 (0.04)
R20.02 0.01 0.02
Class 2: Social loneliness (8.2%)
Age − 0.21 (0.12) 0.07 (0.08) 0.05 (0.08)
Sex 0.05 (0.16) − 0.08 (0.14) − 0.05 (0.11)
Relationship status 0.13 (0.15) − 0.14 (0.12) − 0.14 (0.10)
Adult trauma − 0.27 (0.19) 0.29 (0.12)** 0.30 (0.11)**
Childhood trauma 0.04 (0.18) − 0.02 (0.15) − 0.05 (0.14)
R20.17 0.11 0.11
Class 3: Emotional loneliness (26.6%)
Age − 0.07 (0.08) 0.08 (0.09) − 0.11 (0.08)
Sex − 0.15 (0.08) 0.13 (0.09) 0.22 (0.08)
Relationship status 0.11 (0.07) 0.03 (0.08) − 0.06 (0.08)
Adult trauma − 0.01 (0.11) 0.12 (0.13) 0.12 (0.14)
Childhood trauma − 0.17 (0.05)* 0.33 (0.10)*** 0.35 (0.10)***
R20.08 0.21 0.25
Class 4: Social and emotional loneliness (12.4%)
Age 0.00 (0.08) − 0.28 (0.11) − 0.34 (0.10)***
Sex − 0.15 (0.11) 0.33 (0.15)* 0.38 (0.14)**
Relationship status 0.01 (0.10) − 0.05 (0.14) − 0.03 (0.13)
Adult trauma − 0.23 (0.09)** 0.38 (0.15)** 0.28 (0.17)
Childhood trauma 0.03 (0.10) 0.08 (0.20) 0.21 (0.19)
R20.06 0.27 0.35
Social Psychiatry and Psychiatric Epidemiology
1 3
GAD; and younger age was significantly associated with
higher levels of GAD.
Discussion
Loneliness is typically treated as a unidimensional construct
and prevalence rates have been derived from this conceptu-
alization [47]. However, theoretical models and empirical
data suggest that loneliness may in fact be multidimensional
in nature [812], and if so, prevalence estimates are likely
to be in error. Moreover, empirical findings regarding the
risk-factors for loneliness are also likely to be in error if the
construct is not conceptualised in an accurate manner. The
objective of this study was to investigate whether subtypes of
loneliness were identifiable within a nationally representa-
tive sample of US adults aged 18–70; and if so, to determine
how recognition of loneliness subtypes would influence the
prevalence rate of loneliness, as well as the associations with
risk-factors and mental health variables.
Using a typical method employed in the literature for
determining prevalence rates when loneliness is treated as a
unidimensional construct [29], we found that 17.1% of US
adults aged 18–70 would have been classified as experienc-
ing loneliness. This finding is generally consistent with pop-
ulation prevalence rates from similarly aged representative
samples from Quebec (14.0%), Denmark (21.0%), Arme-
nia (10.7%), Belarus (8.9%), Georgia (12.3%), Moldova
(17.9%), and Ukraine (10.8%) [46]. However, the LCA
results indicated that loneliness was not unidimensional in
nature. Two of the four classes, the ‘social’ and ‘emotional’
loneliness classes, differed qualitatively. These findings not
only provided novel empirical support for the longstanding
theoretical predictions of Weiss [9] and Russell etal. [21],
but they also indicated that classifying individuals as lonely
based on a particular cut-off score is possibly misguided
as such an approach fails to recognise naturally occurring
subtypes of loneliness.
Based on the LCA results, approximately one-in-eight US
adults aged 18–70 (12.4%) were characterised by the simul-
taneously presence of social and emotional loneliness. This
class had mean levels of psychological wellbeing, MDD,
and GAD that were reflective of psychiatric morbidity. Addi-
tionally, approximately one-in-four US adults aged 18–70
(26.6%) were characterised exclusively by the experience
of emotional loneliness. This group of people, while less
psychologically distressed than the ‘social and emotional
loneliness’ class, were nonetheless characterised by mean
levels of psychological wellbeing, MDD, and GAD that
were also reflective of psychiatric morbidity. The combined
proportion of individuals in these latent classes of loneli-
ness who were characterised by clinically relevant levels of
psychological distress was 39.0%. This finding indicates that
by recognising naturally occurring subtypes of loneliness,
the number of people experiencing a form of loneliness that
is likely to be of clinical relevance is more than double the
number identified when loneliness is conceptualised as a
unidimensional construct (39.0% vs. 17.1%).
Although another 8.2% of the population were charac-
terised exclusively by the experience of social loneliness,
individuals in this latent class were characterised by mental
health scores reflective of healthy psychological function-
ing. Individuals characterised by ‘social loneliness’ had
mental health scores that were not meaningfully different
from individuals in the ‘low loneliness’ class. Our results
show that when subtypes of loneliness are identified in a
methodological rigorous manner, it is ‘emotional’ but not
‘social’ loneliness that is associated with poorer psycho-
logical health. These findings suggest that not all types of
loneliness are necessarily detrimental to one’s mental health.
More importantly, these results indicate that the perception
of inadequate close attachments to others is considerably
more detrimental to one’s mental health than the perception
of inadequate social integration. To put it another way, it is
the quality, not the quantity, of interpersonal connections
that makes the difference when it comes to one’s psycho-
logical health.
Support for the discriminant validity of the loneliness
subtypes was found in relation to the specific correlates of
class membership. For example, being single, divorced, or
widowed increased the likelihood of belonging to the ‘emo-
tional loneliness’ class by nearly two-times, but had no
association with membership of the ‘social loneliness’ class.
Similarly, females were approximately two-times more likely
than males to belong to the ‘emotional loneliness’ class, but
no sex differences were evident in relation to membership
of the ‘social loneliness’ class; findings that are generally
consistent with prior observations [10, 13]. Childhood trau-
matization was associated with ‘emotional’ but not ‘social’
loneliness, with every childhood traumatic experience
increasing the odds of belonging to the ‘emotional loneli-
ness’ class by 28%. It appears therefore that traumatization
during childhood is associated with feelings of insufficient
interpersonal attachments in later life. Childhood trauma has
been demonstrated to disrupt healthy attachment relation-
ships throughout life [50] and to lead to social withdrawal
and social isolation [51]. It was interesting to note that child-
hood and adulthood trauma were independently associated
with an increased likelihood of belonging to the ‘social and
emotional loneliness’ class. The current study was the first
to simultaneously assess the relationship between loneliness
and both childhood and adulthood trauma, and our results
indicated that traumatic exposure in these different develop-
mental periods were positively associated with feelings of
deficiencies in both social network size and intimate con-
nections. Current results add to a growing literature attesting
Social Psychiatry and Psychiatric Epidemiology
1 3
to the importance of trauma history in understanding the
characteristic nature of the experience of loneliness [1419].
Although distinguished by multiple factors, member-
ship of the ‘social’, ‘emotional’, and ‘social and emotional’
loneliness classes was associated with younger age. These
findings are consistent with the existing literature that loneli-
ness follows a ‘U-shaped distribution’ of increasing levels of
loneliness in early adulthood before declining through adult-
hood and then peaking again in older adulthood [5]. Given
that this sample did not include individuals over the age of
70, it is unsurprising that age was negatively correlated with
all types of loneliness.
The importance of trauma history in the context of
loneliness was further demonstrated by the results of the
class-specific analyses. Amongst the ‘low-loneliness’ class,
adulthood traumatization was significantly associated with
poorer psychological wellbeing, MDD, and GAD. Of note,
adulthood trauma was significantly associated with MDD
and GAD for those characterised by ‘social loneliness,
whereas, childhood trauma was significantly associated with
MDD, GAD, and psychological wellbeing for those char-
acterised by ‘emotional loneliness’. Our results show that
not only are the loneliness subtypes differentially associated
with childhood and adulthood trauma, but the relationship
between mental health status and developmental timing of
traumatic exposure is dependent upon the specific subtype of
loneliness that one experiences. These findings support the
value of considering different types of social/interpersonal
clinical interventions depending on trauma history. Social
interventions are likely to be of benefit to those with adult
trauma; interpersonal/attachment interventions are likely to
be of benefit to those with childhood trauma; and social and
interpersonal interventions are likely to be of benefit to those
with a history of both childhood and adulthood trauma.
A particularly curious finding was that the explanatory
power of the regression models was highly dependent upon
the type of loneliness being experienced, and, whether one
considered positive or negative mental health indicators.
Trauma history and demographic factors explained almost
no variation in psychological wellbeing, MDD, and GAD
scores for those in the ‘low-loneliness’ class (1–2% of
variance explained) and explained a higher percentage of
variation in each mental health variable (11–17% of vari-
ance explained) for those in the ‘social loneliness’ class.
Furthermore, these variables explained a substantial level
of variation in MDD and GAD scores for those individuals
in both the ‘emotional’ (21% and 25%, respectively) and
‘social and emotional’ (27% and 35%, respectively) loneli-
ness classes. However, the same variables accounted for very
little variance in psychological wellbeing scores amongst the
‘emotional’ (8%) and ‘social and emotional’ (6%) loneli-
ness classes. One might have expected that factors such as
sex, age, relationship status, and traumatic history would
contribute to an understanding of mental health variables
irrespective of the type of loneliness one was character-
ised by; however, our results demonstrate that the explana-
tory power of these variables was highly dependent on (1)
whether one was lonely or not, (2) the type of loneliness
that one was experiencing, and (3) whether indicators of
positive or negative mental health were being considered.
These results have important implications for how clinical
researchers should think about how loneliness might mod-
erate the relationship between well recognised risk-factors
and mental health.
The nationally representative nature of the sample, along
with the application of sophisticated latent variable model-
ling techniques to identify subtypes of loneliness and their
relationship to a variety of risk-factors and mental health
variables, overcomes many of the limitations of the exist-
ing literature in this area. However, the current study is not
without its limitations. For example, old age is a period of
life where loneliness increases however the current sample
did not include any members of the population over the age
of 70. It will be important to replicate this study amongst
cohorts of the population that include persons over the age of
70. Additionally, the study findings are reflective of the US
adult population, and therefore, the cross-cultural validity of
these findings is unknown. It will be particularly important
to determine if current findings replicate in culturally dis-
tinct populations. Finally, the cross-sectional nature of the
study precludes any inferences regarding the predictive rela-
tionships between traumatic exposure and loneliness class
membership, or, the predictive relationships between trauma
history and mental health status dependent upon one’s lone-
liness subtype.
In sum, the current study provides empirical support for
the existence of distinct subtypes of loneliness. Our study
findings highlight the importance of recognising subtypes
of loneliness given the considerable variation in mental
health status, the unique associations with demographic and
traumagenic variables, and the influence that these subtypes
of loneliness have on the associations between established
risk-factors (e.g., childhood and adulthood traumatization)
and mental health status. The current findings also revealed
that as a result of recognizing the naturally occurring sub-
types of loneliness, the number of US adults aged 18–70
who experienced loneliness of a type that is associated with
serious mental health difficulties is more than twice as high
as the figure obtained when loneliness is treated as a unidi-
mensional construct. Finally, our findings revealed that the
perception of reduced quality, not quantity, of interpersonal
relationships was associated with poor psychological health.
From a societal perspective, and in the interests of reducing
the burden of psychological distress, efforts should be made
to enhance the quality of social connections as opposed to
promoting the virtues of larger social networks.
Social Psychiatry and Psychiatric Epidemiology
1 3
Author contributions PH, MS, MC, and JMP developed the study
concept. PH, MS, GM, and RF conducted the statistical analyses. JMP
wrote the introduction. TK, FV, and MC contributed to the writing
of the discussion. All authors reviewed, revised, and contributed to
the writing of the final version of the manuscript. All authors have
approved the final version of the paper for submission.
Funding This work was supported by the National Institutes of Mental
Health (Grant number R01 MH08661).
Compliance with ethical standards
Conflict of interest On behalf of all authors, the corresponding author
states that there is no conflict of interest.
References
1. Hunter D (2012) Loneliness: a public health issue. Perspect Public
Health 132:153–153. https ://doi.org/10.1177/17579 13912 44956 4
2. Cacioppo JT, Hawkley L, Thisted RA (2010) Perceived social
isolation makes me sad: 5-year cross-lagged analyses of loneli-
ness and depressive symptomatology in the Chicago health, aging,
and social relations study. Psychol Aging 25:453–463. https ://doi.
org/10.1037/a0017 216
3. Caspi A, Harrington H, Moffitt TE, Milne BJ, Poulton R (2006)
Socially isolated children 20 years later: risk of cardiovascular
disease. Arch Pediatr Adolesc Med 160:805–811. https ://doi.
org/10.1001/archp edi.160.8.805
4. Stickley A, Koyanagi A, Roberts B, Richardson E, Abbott P,
Tumanov S, McKee M (2013) Loneliness: its correlates and
association with health behaviours and outcomes in nine coun-
tries of the former Soviet Union. PLoS One 8:e67978. https ://doi.
org/10.1371/journ al.pone.00679 78
5. Lasgaard M, Friis K, Shevlin M (2016) “Where are all the lonely
people?” A population-based study of high-risk groups across
the life span. Soc Psychiatry Psychiatr Epidemiol 51:1373–1384.
https ://doi.org/10.1007/s0012 7-016-1279-3
6. Stravynski A, Boyer R (2001) Loneliness in relation to suicide
ideation and parasuicide: a population-wide study. Suicide Life
Threat Behav 31:32–40
7. Anderson OG (2010) Loneliness among older adults: a national
survey of adults 45+. AARP Research, Washington, DC. https ://
doi.org/10.26419 /res.00064 .001
8. Sønderby LC, Wagoner B (2013) Loneliness: an integrative
approach. J Integr Soc Sci 3:1–29
9. Weiss RS (1974) The provisions of social relationships. In: Rubin
Z (ed) Doing unto others: Joining, molding, conforming, helping,
loving. Prentice-Hall, Englewood Cliffs, pp17–26
10. Gierveld JDJ, Van Tilburg T (2010) The De Jong Gierveld short
scales for emotional and social loneliness: tested on data from 7
countries in the UN generations and gender surveys. Eur J Ageing
7:121–130. https ://doi.org/10.1007/s1043 3-010-0144-6
11. Liu BS, Rook KS (2013) Emotional and social loneliness in later
life: associations with positive versus negative social exchanges.
J Soc Pers Relatsh 30:813–832. https ://doi.org/10.1177/02654
07512 47180 9
12. Dahlberg L, McKee KJ (2014) Correlates of social and emo-
tional loneliness in older people: evidence from an English
community study. Aging Ment Health 18:504–514. https ://doi.
org/10.1080/13607 863.2013.85686 3
13. Dykstra PA, Fokkema T (2007) Social and emotional loneliness
among divorced and married men and women: comparing the
deficit and cognitive perspectives. Basic Appl Soc Psych 29:1–12.
https ://doi.org/10.1080/01973 53070 13308 43
14. van der Velden PG, Pijnappel B, van der Meulen E (2017) Poten-
tially traumatic events have negative and positive effects on
loneliness, depending on PTSD-symptom levels: evidence from
a population-based prospective comparative study. Soc Psychia-
try Psychiatr Epidemiol 53:1–12. https ://doi.org/10.1007/s0012
7-017-1476-8
15. Cohen-Mansfield J, Shmotkin D, Goldberg S (2009) Loneliness in
old age: longitudinal changes and their determinants in an Israeli
sample. Int Psychogeriatr 21:1160–1170. https ://doi.org/10.1017/
S1041 61020 99909 74
16. Gibson RL, Hartshorne TS (1996) Childhood sexual abuse and
adult loneliness and network orientation. Child Abuse Negl
20:1087–1093
17. Merz EM, Jak S (2013) The long reach of childhood. Childhood
experiences influence close relationships and loneliness across
life. Adv Life Course Res 18:212–222. https ://doi.org/10.1016/j.
alcr.2013.05.002
18. Stein JY, Itzhaky L, Levi-Belz Y, Solomon Z (2017) Traumatiza-
tion, loneliness, and suicidal ideation among Ex-POWs: a longi-
tudinally assessed sequential mediation model. Front Psychiatry
8:281. https ://doi.org/10.3389/fpsyt .2017.00281
19. Shevlin M, McElroy E, Murphy J (2015) Loneliness mediates
the relationship between childhood trauma and adult psychopa-
thology: evidence from the adult psychiatric morbidity survey.
Soc Psychiatry Psychiatr Epidemiol 50:591–601. https ://doi.
org/10.1007/s0012 7-014-0951-8
20. DiTommaso E, Spinner B (1997) Social and emotional loneliness:
a re-examination of Weiss’ typology of loneliness. Pers Individ
Differ 22:417–427. https ://doi.org/10.1016/S0191 -8869(96)00204
-8
21. Russell D, Cutrona CE, Rose J, Yurko K (1984) Social and emo-
tional loneliness: an examination of Weiss’s typology of loneli-
ness. J Pers Soc Psychol 46:1313–1321
22. Peerenboom L, Collard RM, Naarding P, Comijs HC (2015) The
association between depression and emotional and social loneli-
ness in older persons and the influence of social support, cogni-
tive functioning and personality: a cross-sectional study. J Affect
Disord 182:26–31. https ://doi.org/10.1016/j.jad.2015.04.033
23. Schnittger RI, Wherton J, Prendergast D, Lawlor BA (2012) Risk
factors and mediating pathways of loneliness and social support
in community-dwelling older adults. Aging Ment Health 16:335–
346. https ://doi.org/10.1080/13607 863.2011.62909 2
24. Drageset J, Espehaug B, Kirkevold M (2012) The impact of
depression and sense of coherence on emotional and social lone-
liness among nursing home residents without cognitive impair-
ment—a questionnaire survey. J Clin Nurs 21:965–974. https ://
doi.org/10.1111/j.1365-2702.2011.03932 .x
25. Shevlin M, Murphy S, Murphy J (2014) Adolescent loneliness
and psychiatric morbidity in the general population: identifying
“at risk” groups using latent class analysis. Nord J Psychiatry
68:633–639. https ://doi.org/10.3109/08039 488.2014.90734 2
26. Russell DW (1996) UCLA loneliness scale (version 3): reliability,
validity, and factor structure. J Pers Assess 66:20–40. https ://doi.
org/10.1207/s1532 7752j pa660 1_2
27. de Jong Gierveld J, van Tilburg TG (2006) A 6-item scale for over-
all, emotional, and social loneliness: confirmatory tests on survey
data. Res Aging 28:582–598. https ://doi.org/10.1177/01640 27506
28972 3
28. De Jong Gierveld J, Van Tilburg T (2010) The De Jong Gierveld
short scales for emotional and social loneliness: tested on data
from 7 countries in the UN generations and gender surveys. Eur J
Ageing 7:121–130. https ://doi.org/10.1007/s1043 3-010-0144-6
29. Shevlin M, Murphy S, Mallet J, Stringer M, Murphy J (2013) Ado-
lescent loneliness and psychiatric morbidity in Northern Ireland.
Social Psychiatry and Psychiatric Epidemiology
1 3
Br J Clin Psychology 52:230–234. https ://doi.org/10.1111/
bjc.12018
30. Weathers FW, Blake DD, Schnurr PP, Kaloupek DG, Marx BP,
Keane TM (2013) The life events checklist for DSM-5 (LEC-5).
Instrument available from the National Center for PTSD at http://
www.ptsd.va.gov. Accessed 25 June 2017
31. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM,
Edwards V, etal. (1998) Relationship of childhood abuse and
household dysfunction to many of the leading causes of death in
adults. The Adverse childhood experiences (ACE) study. Am J
Prev Med 14:245–258
32. World Health Organization: Regional Office for Europe (1998)
Wellbeing measures in primary health care: the DepCare project.
In: Consensus meeting, Stockholm
33. Topp CW, Østergaard SD, Søndergaard S, Bech P (2015)
The WHO-5 well-being index: a systematic review of the
literature. Psychother Psychosom 84:167–176. https ://doi.
org/10.1159/00037 6585
34. Awata S, Bech P, Koizumi Y, Seki T, Kuriyama S, Hozawa A,
etal. (2007) Validity and utility of the Japanese version of the
WHO-five well-being index in the context of detecting suicidal
ideation in elderly community residents. Int Psychogeriatr 19:77–
88. https ://doi.org/10.1017/S1041 61020 60042 12
35. Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mok-
dad AH (2009) The PHQ-8 as a measure of current depression in
the general population. J Affect Disord 114:163–173. https ://doi.
org/10.1016/j.jad.2008.06.026
36. Spitzer RL, Kroenke K, Williams JB, Lowe B (2006) A brief
measure for assessing generalized anxiety disorder: the GAD-7.
Arch Intern Med 166:1092–1097. https ://doi.org/10.1001/archi
nte.166.10.1092
37. Manea L, Gilbody S, McMillan D (2015) A diagnostic meta-
analysis of the Patient Health Questionnaire-9 (PHQ-9) algorithm
scoring method as a screen for depression. Gen Hosp Psychiatry
37:67–75. https ://doi.org/10.1016/j.genho sppsy ch.2014.09.009
38. Kertz S, Bigda-Peyton J, Bjorgvinsson T (2013) Validity of the
generalized anxiety disorder-7 scale in an acute psychiatric sam-
ple. Clin Psychol Psychother 20:456–464. https ://doi.org/10.1002/
cpp.1802
39. Muthén LK, Muthén BO (2013) Mplus user’s guide, 7thedn.
Muthén & Muthén, Los Angeles
40. Yuan KH, Bentler PM (2000) Three likelihood-based meth-
ods for mean and covariance structure analysis with nonnor-
mal missing data. Sociol Methodol 30:165–200. https ://doi.
org/10.1111/0081-1750.00078
41. Akaike H (1987) Factor analysis and the AIC. Psychometrika
52:317–332
42. Schwartz G (1978) Estimating the dimension of a model. Ann Stat
6:461–464
43. Sclove SL (1987) Application of model-selection criteria to some
problems in multivariate analysis. Psychometrika 52:333–343
44. Lo Y, Mendell N, Rubin DB (2001) Testing the number of com-
ponents in a normal mixture. Biometrika 88:767–778
45. Nylund KL, Asparouhov T, Muthén B (2007) Deciding on the
number of classes in latent class analysis and growth mixture
modeling. A Monte Carlo simulation study. Struct Equ Model
14:535–569. https ://doi.org/10.1080/10705 51070 15753 96
46. Bakk Z, Vermunt JK (2016) Robustness of stepwise latent class
modeling with continuous distal outcomes. Struct Equ Model
23:20–31. https ://doi.org/10.1080/10705 511.2014.95510 4
47. Bakk Z, Tekle FB, Vermunt JK (2013) Estimating the associa-
tion between latent class membership and external variables using
bias adjusted three-step approaches. In: Liao TF (ed) Sociological
methodology. SAGE Publications, Thousand Oaks
48. Vermunt J (2010) Latent class modeling with covariates: two
improved three-step approaches. Political Anal 18:450–469
49. Asparouhov T, Muthén B (2014) Auxiliary variables in mixture
modelling: using the BCH method in Mplus to estimate a distal
outcome model and an arbitrary secondary model. Stat Model.
https ://www.statm odel.com/do wnl oad/aspar ouhov _muthe n_2014.
pdf. Accessed 25 June 2017
50. Pearce J, Simpson J, Berry K, Bucci S, Moskowitz A, Varese
F (2017) Attachment and dissociation as mediators of the link
between childhood trauma and psychotic experiences. Clin Psy-
chol Psychother 24:1304–1312. https ://doi.org/10.1002/cpp.2100
51. Walsh K, Fortier MA, DiLillo D (2010) Adult coping with child-
hood sexual abuse: a theoretical and empirical review. Aggress
Violent Behav 15:1–13. https ://doi.org/10.1016/j.avb.2009.06.009
... A large body of evidence supports the long-term impact of early life experience on psychological outcomes (Hyland et al., 2018;Lin and Chiao, 2020;Musetti et al., 2021b). The adolescent years involve formulating a social identity, which has long been recognized as an important developmental stage and largely determined by a range of influences that includes parents, peers, and school (Musetti et al., 2021a). ...
... Research has identified various factors associated with a range of domains of loneliness (Hyland et al., 2018;Chiao et al., 2019;Lin and Chiao, 2020). These factors include individual socio-economic characteristics, such as age, gender, and work characteristics, partnership status, individual self-esteem (Mund et al., 2020), and family cohesion (Fujimori et al., 2017;Chiao et al., 2019). ...
... Prior research has demonstrated that specific indicators of loneliness tend to cluster within certain loneliness domains (Hyland et al., 2018;Chiao et al., 2019;Lin and Chiao, 2020). We have extended this line of inquiry and created a representation of the latent structure of the loneliness during adulthood. ...
Article
Full-text available
Background A common life-course hypothesis is that negative early-life experiences contribute to poor health in later-life. However, little is known about perceived peer relationships during adolescence and the feeling of loneliness in emerging adulthood. This study explores the perception of adolescent peer relationships in a school context and its association with loneliness in adulthood and in workplace contexts. Methods This study used data from a cohort sample of 2,520 adolescents from the Taiwan Youth Project ( N = 2,520), consisting of eleven waves of data collected from 2000 to 2017. Major measures included the Loneliness Scale (6-item de Jong Gierveld short scale) and perceived peer relationships (classroom cohesion and perceived popularity among classmates) in middle school. Multivariate multinomial logistic regressions were used to estimate the associations of perceived peer relationships during adolescence and workplace characteristics with loneliness in adulthood. Results Positive perceived peer relationships in adolescence were significantly related to decreased risk of serious social loneliness [Relative risk ratios (RRR) 0.70, 95% CI: 0.58–0.85] and severe social/emotional loneliness (RRR = 0.76, 95% CI: 0.63–0.91) in adulthood. Workplace satisfaction was a protective factor of severe social/emotional loneliness in employed adults. Conclusion Adolescents who perceived peer relationships in middle school as positive were less likely to report social and emotional loneliness during adulthood. Satisfaction in the workplace characteristics was also associated with lower risk of loneliness in adulthood. Theoretical and policy implications are discussed.
... Although existing research has shown a connection between emotional abuse/neglect and elevated levels of loneliness, most studies typically used a composite emotional abuse and neglect score (e.g., Hyland et al., 2018;Luo et al., 2020;Ma et al., 2020), which fails to examine the unique impacts of emotional abuse and neglect on adolescents' loneliness. Given that the literature indicates that emotional abuse and neglect may have different impacts on individuals' internalizing problems (Berzenski, 2019;McNeil et al., 2020), it is important to compare such influences by further breaking down the concept. ...
Article
Full-text available
This study investigated the unique impacts of emotional abuse and neglect on loneliness. Further, it explored the moderating effect of peer acceptance in these associations among Chinese rural left-behind adolescents with non-left-behind adolescents from the same rural areas as a comparison group. A total of 914 rural adolescents (Mage = 13.71 years, SD = 0.90) participated in the study. Participants completed self-report measures of emotional abuse, emotional neglect, and loneliness. Classroom-based peer nominations were used to assess participants’ peer acceptance. Hierarchical regression analyses were used to test the independent and interactive associations linking emotional abuse/neglect and peer acceptance with loneliness. The results showed that left-behind adolescents experienced higher levels of emotional neglect than non-left-behind adolescents. Emotional neglect was more strongly associated with adolescents’ loneliness than emotional abuse. The associations between emotional neglect and loneliness were stronger for left-behind adolescents than non-left-behind adolescents. In addition, peer acceptance moderated the association between emotional abuse and loneliness among left-behind adolescents. Gender differences were found in the link between peer acceptance to loneliness among non-left-behind adolescents. Implications of these findings for future research and the development of interventions are discussed.
... Kamiya et al. found that parental substance abuse and financial distress during childhood are associated with an increased level of loneliness in later life [37]. Childhood trauma (and adulthood trauma) were independently related to the most distressed loneliness classes [58]. ...
Article
Full-text available
The aim of this paper is to study the association between childhood circumstances and loneliness in older adults in Europe. Based on rich information collected by the Survey on Health, Ageing, and Retirement in Europe (SHARE) on childhood characteristics and individual characteristics at age 50+, the study is able to control for personality traits, socioeconomic and demographic factors, social support and health in later life, and country-specific characteristics. The analyses show strong correlations between life circumstances in childhood and feeling lonely in older age; these correlations remain significant after adjusting for covariates. While ill health is the main factor correlated with loneliness at 50+, as expected, the analysis of the relative importance of the determinants reveals that personality traits account for more than 10% of the explained variance and that life circumstances during childhood account for 7%. Social support at older ages is the second highest category of factors, accounting for 27%—with, interestingly, support at home and social network characteristics contributing about 10% each, engaging in activities and computer skills accounting for 7% of the explained variance. Demographic and socioeconomic factors account for 6% and country-level characteristics contribute 5%. This paper points out the relevance of early life interventions to tackling loneliness in older age, and it shows that early interventions and interventions aiming at increasing social support in later life need to be adapted to all personality types. Thus, the role of childhood circumstances and the mechanisms explaining the association between loneliness in childhood and loneliness in later life deserve more attention in future research.
... Loneliness has been divided into emotional and social subtypes based on Weiss's early work (Weiss, 1973) and subdivided even further in some reports. Such subtyping could be helpful as shown by a recent epidemiology study in the US showing that those with both social and emotional loneliness are characterised by the highest level of psychological distress (Hyland et al., 2018). Relatedly, the definition of loneliness used is unidimensional; other, multidimensional conceptualisations and definitions of loneliness have been proposed that emphasise the emotional and social subtypes (e.g., De Jong Gierveld, 1998) that could also be of potential utility. ...
Article
It is now widely accepted that loneliness is influenced by a combination of psychological factors, including attitudes to participating in social interactions and mental health problems, as well as environmental factors such as living far from family and friends and life events and transitions such as bereavement and moving away from home. Despite increased recognition of the importance of individual-level processes and meanings that influence the experience of loneliness, there is a gap in our knowledge of how best to address the psychological factors that contribute to chronic loneliness. In this report, we aim to synthesise information from a range of sources in order to identify the psychological pathways to loneliness and relevant psychological barriers to accessing strategies which target social isolation. The report highlights promising interventions that have potential to target the psychological aspects of loneliness. It makes a series of recommendations to improve understanding and delivery of effective psychological interventions to address loneliness and how the interaction between such strategies and community-based interventions. We conducted an extensive scoping review of the academic literature, including online database searches and broader searches reviewing conference abstracts and reports from the Third Sector. We obtained expert opinions by speaking to relevant stakeholders including people with lived experiences of loneliness, charitable organisations working with people who are experiencing chronic loneliness, and those involved in developing and evaluating interventions to tackle loneliness. Much of the work focused on older adults but we also looked at interventions delivered across the age range. We report the findings from this work, including an overview of the wide range of psychological factors which might explain why some people who are chronically lonely struggle to engage with community strategies and other sources of support that are available. These factors include having mental health problems, personality characteristics and having unhelpful beliefs and behaviours related to social interactions. We recommend that interventions that target either the psychological or social aspects of loneliness should not be provided in isolation, and that multi-modal interventions are likely to be most successful. Further research evidence is needed to evaluate the feasibility, acceptability, effectiveness and cost-effectiveness of delivering psychological interventions in conjunction with community-based strategies. Social prescribing is a potential opportunity for the successful delivery of psycho-social interventions. For example, integration of psychological and community-based support could be promoted by including directories of psychological support in guides to community based resources, and by connecting social prescribing link workers with their local improving access to psychological therapies services. The social psychological approaches such as the Groups 4 Health model (Haslam et al., 2019; Haslam, Cruwys, Haslam, Dingle & Chang, 2016) show promise and potentially could bridge psychological and social understandings of loneliness. There is preliminary research evidence that interventions that address the psychological factors involved in loneliness can be successful, and there are various approaches to addressing these factors across the UK, although many initiatives have not yet been fully evaluated. The strongest research evidence was found for cognitive behavioural interventions, and there are some promising developments, including digital initiatives which are designed to change individuals’ thoughts and feelings about loneliness, that are worthy of further evaluation. We would also recommend that acceptance and commitment therapy is formally evaluated as an intervention for loneliness. We noted that the research base in this area is still underdeveloped and more work is needed to demonstrate which interventions are most accessible to people who are chronically lonely and can feasibly be delivered within NHS and community settings. Research into the potential adverse effects of psychological interventions, individual differences in responsiveness and the longer term impact on loneliness is also needed. It is likely that including measures of loneliness in evaluations of interventions for social anxiety and grief and in routine work with older adults in improving access to psychological therapies services would yield data that will contribute to the growing evidence base in this area. We hope that bringing together the research evidence and expert opinion in this report will increase awareness of the wide range of psychological factors implicated in loneliness and lead to further provision of psychological interventions for loneliness, in combination with community based support for social isolation.
... In contrast to this, we found no association between how frequently participants contacted friends and family and PWB, suggesting that quality of social support may be more important for wellbeing than the frequency of social contact. These findings align with previous research that found that having poor quality social relationships was associated with poor psychological health, while a low quantity of social connections was not (Hyland et al., 2019). ...
Article
Introduction: Loneliness is prevalent among young people. But, there is little work exploring the association between loneliness with well-being among this age group. Framed by social-ecological theory, we examined demographic, interpersonal, and community factors associated with personal wellbeing and, critically, identified malleable moderators of the relationship between loneliness and well-being that could be targeted in intervention efforts. Methods: We used cross-sectional, secondary data from 965 young people (aged 16-24) from the Community Life Survey in England. Loneliness was measured using a single-item direct measure; personal wellbeing was measured through a composite measure containing items assessing happiness, life satisfaction, and a sense that life is worthwhile (α = 0.88). Regression techniques were used to assess associations between individual, interpersonal, and community factors and well-being, and to identify moderators of the relationship between loneliness and well-being. Results: Loneliness was negatively associated with well-being. Chatting with neighbors and having people to provide help moderated the relationship between loneliness and well-being. Full-time students and those with good physical health had higher well-being while being a carer was predictive of lower well-being. All community variables were strongly associated with increased well-being. Of all interpersonal variables investigated, only having people to count on was associated with increased well-being. Conclusions: Our results demonstrate that supportive relationships and close community ties are important for reducing the negative impact of loneliness on youth well-being. Interventions to improve well-being could benefit from targeting these aspects of young people's social and community lives, while acknowledging individual vulnerabilities, such as poor physical health.
The present article analyzes the connection between, on the one hand, gender equality and, on the other hand, loneliness and social isolation. It hypothesizes that modern relational institutions that support gender equality, such as no-fault divorce laws, reduce loneliness in close relationships. This hypothesis is put to the test through a multilevel analysis of the International Social Survey Program (ISSP) 2017. The analysis reveals that the data agree, to a large extent, with the theoretical arguments. The prevalence of loneliness is higher in countries with higher levels of gender inequality (as measured by the Gender Inequality Index (GII)). This can be attributed to a moderation effect; at lower levels of gender inequality, partnerships provide better protection from loneliness. These results are robust to controls for demographic composition, level of health, educational attainment, income poverty, and interview mode. Last, the analyses show that the threat of emotional isolation is more widespread in countries with low gender inequality. These findings, however, are only significant before controlling for demographic composition, level of health, educational attainment, income poverty, and interview mode, and they require further analysis. The concluding section relates these findings to the popular tendency to argue that modern society has created a “loneliness epidemic” and discusses policy implications.
Article
This study explores the effect of unprecedented mass isolation during COVID-19 lockdowns through the lens of self-disclosure of loneliness on Twitter. Using a dataset of 30 million public tweets, we use machine learning to identify tweets that contain self-disclosure of loneliness. We find that thousands more people turned to Twitter to express their loneliness during the lockdowns; however, this effect normalized within a month, demonstrating the “ordinization” effect on a collective level. Furthermore, lockdown brought a marked shift in the weekly timings of posting and a change in the accompanying emotions, which were more positive and other-focused. Finally, based on a qualitative analysis, we propose an updated typology of loneliness that captures the possibilities offered by the affordances of social media. Our findings illustrate the profound effect lockdowns had on the societal psychological state and emphasize the importance of mental health resources during extreme and isolating events.
Article
Loneliness is common in adults of all ages. Prior research among older adults has shown that social loneliness (feelings of missing a wider social network) and emotional loneliness (missing an intimate relationship) differ in risk factors. Therefore, this study examined risk factors of social and emotional loneliness among adults aged 19-65 years. This study was conducted within the framework of a community-based health study in the northwest of the Netherlands in 2016. Cross-sectional data of 7,885 participants were analysed using structural equation modelling. Social and emotional loneliness were measured using the validated scale of de Jong-Gierveld. Socio-demographic and health-related risk factors were self-reported. Multiple socio-demographic, health outcomes and health behaviours were associated with higher scores on both types of loneliness, although the predictive power of multiple risk factors differed by type. Additionally, female gender, younger age, medium or high educational level and smoking were associated with lower social loneliness scores specifically, while having a paid job and lower BMI were associated with lower emotional loneliness scores. To conclude, associations with risk factors were partly consistent across social and emotional loneliness, however, some important differences have been shown. These differences are important to consider when developing targeted prevention and intervention strategies.
Article
Emotion recognition deficit is related to impaired community functioning. Loneliness is also associated with impaired social performance. However, the way in which emotion recognition and loneliness may contribute to social functioning remains unclear in euthymic patients with bipolar disorder. We aimed to examine emotion recognition ability in Han Chinese euBD patients relative to healthy controls (HCs) and to investigate the associations between emotion recognition, loneliness, and social functioning. Thirty‐nine HCs and 46 euthymic BD patients completed an emotion recognition task and nonsocial cognitive measures related to executive function and attention. The UCLA loneliness scale and Social Performance Scale were administered to evaluate psychological loneliness and social functioning, respectively. We observed lower emotion recognition accuracy, higher loneliness, and poorer social functioning in the BD patients after adjustment for demographic data. Loneliness was negatively associated with global social functioning in both the BD and HC groups. Higher loneliness and lower emotion recognition accuracy were associated with poorer social functioning in euthymic BD in different subdomains. Our study confirmed a subtle impairment of emotion recognition ability in euthymic BD. Loneliness impacts globally on social functioning, while emotion recognition ability may affect specific subdomains of social functioning in euthymic BD. Alleviation of loneliness and enhancement of social cognition might improve social functioning in BD patients.
Article
Full-text available
PurposeExamine to what extent adults affected by recent potentially traumatic events (PTE) with different PTSD-symptom levels are more at risk for post-event loneliness than non-affected adults are in the same study period. Methods We extracted data from the Dutch longitudinal LISS panel to measure pre-event loneliness (2011) and post-event loneliness (2013 and 2014), pre-event mental health problems (2011), PTE and PTSD symptoms (2012). This panel is based on a traditional random sample drawn from the population register by Statistics Netherlands. ResultsResults of the multinomial logistic regression analyses showed that affected adults with high levels of PTSD symptoms were more at risk for high levels of post-event loneliness than affected adults with very low PTSD-symptom levels and non-affected adults, while controlling for pre-event loneliness, pre-event mental health problems and demographics. However, affected adults with very low levels of PTSD symptoms compared to non-affected adults were less at risk for medium and high levels of post-event loneliness while controlling for the same variables. Yet, pre-event loneliness appeared to be the strongest independent predictor of loneliness at later stages: more than 80% with high pre-event levels had high post-event levels at both follow-ups. Conclusions Remarkably, potentially traumatic events have depending on PTSD-symptom levels both negative and positive effects on post-event loneliness in favor of affected adults with very low PTSD symptoms levels. However, post-event levels at later stages are predominantly determined by pre-event loneliness levels.
Article
Full-text available
Although highly researched among veterans, the underlying mechanisms of suicidal ideation (SI) among former prisoners of war (ex-POWs), especially in the long-term, have rarely been investigated. Furthermore, while posttraumatic stress symptoms (PTSS) and loneliness have been individually associated with veteran SI, and both may be differentially implicated by captivity versus war traumas, the interplay between them has yet to be examined. Filling this gap, the current longitudinal study examined a hypothetical sequential model wherein war captivity, compared with combat-induced trauma, is implicated in worse PTSS, which is then implicated in worse loneliness and PTSS, which together may explain subsequent SI. Two groups of Israeli veterans of the 1973 Yom Kippur War, 163 ex-POWs and 185 matched non-captive veterans were assessed 18 (T1) and 30 (T2) years after the war. Analyses indicated that compared with war, captivity was implicated in worse PTSS, which was implicated in worse loneliness, and these worked in tandem to implicate SI. Loneliness, however, was not directly affected by the type of trauma, nor was its relation to SI linked to its implication in subsequent PTSS. These results may inform future research and clinical practice as the study underscores the importance of both PTSS and loneliness in ex-POWs’ long-term SI.
Article
Full-text available
Background Loneliness is a prevalent and urgent public health issue. Optimal planning of community approaches to loneliness requires a differentiated understanding of loneliness across the life span. We identified groups at high risk of loneliness by exploring the relationship between loneliness and socio-demographic and health-related factors across multiple age groups. Methods This was a combined population-based questionnaire survey and register data study based on a representative sample, including 33,285 Danish individuals aged 16–102 years. Loneliness was measured using the Three-Item Loneliness Scale. ResultsThe relation between loneliness and age took a shallow U-shaped distribution. Ethnic minority status, receiving disability pensions or being unemployed, living alone, prolonged mental disorder, and psychiatric treatment were strongly associated with severe loneliness. Socio-demographic and health-related factors were associated with an increased risk of severe loneliness in specific age groups. Being female, having a low educational level and living in a deprived area were only associated with loneliness in adolescence/emerging adulthood. Receiving disability pensions and living alone (i.e., divorced), on the other hand, were strongly associated with loneliness in early and middle adulthood and young-old age. Conclusion Ethnic minority status, living alone, and prolonged mental disorder may well be key factors in determining the generic level of loneliness in a given population. Other conditions are associated with an increased risk of severe loneliness in specific age groups and may moderate the age–loneliness relation. These findings may help to identify populations within communities at risk of loneliness and thereby support the implementation of policies and public health interventions across the life span.
Article
Full-text available
Researchers using latent class (LC) analysis often proceed using the following three steps: (1) an LC model is built for a set of response variables, (2) subjects are assigned to LCs based on their posterior class membership probabilities, and (3) the association between the assigned class membership and external variables is investigated using simple cross-tabulations or multinomial logistic regression analysis. Bolck, Croon, and Hagenaars (2004) demonstrated that such a three-step approach underestimates the associations between covariates and class membership. They proposed resolving this problem by means of a specific correction method that involves modifying the third step. In this article, I extend the correction method of Bolck, Croon, and Hagenaars by showing that it involves maximizing a weighted log-likelihood function for clustered data. This conceptualization makes it possible to apply the method not only with categorical but also with continuous explanatory variables, to obtain correct tests using complex sampling variance estimation methods, and to implement it in standard software for logistic regression analysis. In addition, a new maximum likelihood (ML)-based correction method is proposed, which is more direct in the sense that it does not require analyzing weighted data. This new three-step ML method can be easily implemented in software for LC analysis. The reported simulation study shows that both correction methods perform very well in the sense that their parameter estimates and their SEs can be trusted, except for situations with very poorly separated classes. The main advantage of the ML method compared with the Bolck, Croon, and Hagenaars approach is that it is much more efficient and almost as efficient as one-step ML estimation. © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved.
Research
Full-text available
Recently, several bias-adjusted stepwise approaches to latent class modeling with continuous distal outcomes have been proposed in the literature and implemented in generally available software for latent class analysis. In this article, we investigate the robustness of these methods to violations of underlying model assumptions by means of a simulation study. Although each of the 4 investigated methods yields unbiased estimates of the class-specific means of distal outcomes when the underlying assumptions hold, 3 of the methods could fail to different degrees when assumptions are violated. Based on our study, we provide recommendations on which method to use under what circumstances. The differences between the various stepwise latent class approaches are illustrated by means of a real data application on outcomes related to recidivism for clusters of juvenile offenders.
Article
Full-text available
The 5-item World Health Organization Well-Being Index (WHO-5) is among the most widely used questionnaires assessing subjective psychological well-being. Since its first publication in 1998, the WHO-5 has been translated into more than 30 languages and has been used in research studies all over the world. We now provide a systematic review of the literature on the WHO-5. We conducted a systematic search for literature on the WHO-5 in PubMed and PsycINFO in accordance with the PRISMA guidelines. In our review of the identified articles, we focused particularly on the following aspects: (1) the clinimetric validity of the WHO-5; (2) the responsiveness/sensitivity of the WHO-5 in controlled clinical trials; (3) the potential of the WHO-5 as a screening tool for depression, and (4) the applicability of the WHO-5 across study fields. A total of 213 articles met the predefined criteria for inclusion in the review. The review demonstrated that the WHO-5 has high clinimetric validity, can be used as an outcome measure balancing the wanted and unwanted effects of treatments, is a sensitive and specific screening tool for depression and its applicability across study fields is very high. The WHO-5 is a short questionnaire consisting of 5 simple and non-invasive questions, which tap into the subjective well-being of the respondents. The scale has adequate validity both as a screening tool for depression and as an outcome measure in clinical trials and has been applied successfully across a wide range of study fields. © 2015 S. Karger AG, Basel.
Chapter
Full-text available
Bumper stickers reading "Friends can be good medicine!" were distributed by the California Department of Mental Health in 1981 as part of a statewide health promotion initiative (California Department of Mental Health, 1981). The objectives of the initiative were to increase awareness of the health-promoting influence of supportive relationships and to encourage personal involvement providing support to others. Although the ultimate success of this project is unknown, its implementation reflects the degree to which a link between social support and health has become part of our belief system. Correlations between social support and health outcomes have been found in a range of contexts and using a variety of methods (for recent reviews, see Broadhead et al. Although links between social support and health are consistently found, our understanding of the nature of this relation remains limited. A problem in past research was that social support was conceptualized unidimensionally, although it was operationalized in many different ways (e.g., marital status, community involvement, availability of confidants). More recent efforts have analyzed social support into component functions. Theorists differ somewhat with respect to the specific functions served by social support, but most conceptualizations include emotional sustenance, self-esteem building, provision of information and feedback, and tangible assistance (e.g.. Once support is defined in terms of its functions, it is possible to generate hypotheses concerning the psychological processes through which social support has its effects. Although clear theoretical formulations of the helping functions served by relationships arc crucial in the generation of hypotheses, these predictions cannot be empirically tested without appropriate assessment instruments. As described in House and Kahn's (1985) recent review, a number of social support measures have been developed. The measures differ widely in their implicit models of social support, some assessing number of supporters, others tapping frequency of supportive acts, and still others measuring degree of satisfaction with support. A number of problems have plagued these measurement efforts. At the theoretical level, the authors of social support measures have rarely articulated the assumptions underlying their instruments. For example, if a measure assesses the number of supportive individuals, the assumption is that better outcomes are associated with the quantity of support sources. If a measure taps satisfaction with support, the assumption is that better outcomes are associated with the perception that support is adequate for one's needs, regardless of tile number of supporters. Although these differences are rarely articulated, different research questions are posed and answered as a function of the manner in which social support is assessed. Inconsistencies in the literature nay be related to differences in the aspects of social support that are assessed in different studies (see Cohen & Wills, 1985).
Article
Exposure to childhood trauma has been implicated in the development of paranoia and hearing voices, but the mechanisms responsible for these associations remain unclear. Understanding these mechanisms is essential for ensuring that targeted interventions can be developed to better support people experiencing distress associated with paranoia and voices. Recent models have proposed that dissociation may be a mechanism specifically involved in the development of voices and insecure attachment in the development of paranoia. Recent theoretical proposals have added to this and argued that fearful attachment could also lead to increased vulnerability for voices. This study was the first to examine whether dissociation and insecure attachment styles mediated the relationship between childhood trauma and these psychotic experiences. One hundred and twelve participants experiencing clinical levels of psychosis completed measures of dissociation, childhood trauma, attachment, voices, and paranoia. Results revealed positive associations between fearful (but not dismissive and anxious) attachment, dissociation, trauma, and psychotic experiences. Mediation analyses indicated that dissociation, but not fearful attachment, significantly mediated the relationship between trauma and voices. Conversely, both dissociation and fearful attachment significantly mediated the relationship between trauma and paranoia. The findings suggest that insecure attachment might be more strongly related to paranoia than hallucinations and suggest that fearful attachment may be a more promising mechanism to explain this relationship. Furthermore, the findings suggest that the impact of dissociation on psychotic experiences may extend to paranoia. Future research is required to replicate these findings using interview-based attachment measures.
Article
We investigated the association between old age depression and emotional and social loneliness. A cross-sectional study was performed using data from the Netherlands Study of Depression in Older Persons (NESDO). A total of 341 participants diagnosed with a depressive disorder, and 125 non-depressed participants were included. Depression diagnosis was confirmed with the Composite International Diagnostic Interview. Emotional and social loneliness were assessed using the De Jong Gierveld Loneliness Scale. Socio-demographic variables, social support variables, depression characteristics (Inventory of Depressive Symptoms), cognitive functioning (Mini Mental State Examination) and personality factors (the NEO- Five Factor Inventory and the Pearlin Mastery Scale) were considered as possible explanatory factors or confounders. (Multiple) logistic regression analyses were performed. Depression was strongly associated with emotional loneliness, but not with social loneliness. A higher sense of neuroticism and lower sense of mastery were the most important explanatory factors. Also, we found several other explanatory and confounding factors in the association of depression and emotional loneliness; a lower sense of extraversion and higher severity of depression. We performed a cross-sectional observational study. Therefore we cannot add evidence in regard to causation; whether depression leads to loneliness or vice versa. Depression in older persons is strongly associated with emotional loneliness but not with social loneliness. Several personality traits and the severity of depression are important in regard to the association of depression and emotional loneliness. It is important to develop interventions in which both can be treated. Copyright © 2015 Elsevier B.V. All rights reserved.