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Trajectories of Loneliness During Childhood and Adolescence: Predictors and Health Outcomes

Trajectories of Loneliness During Childhood and Adolescence: Predictors and Health

Outcomes

Article

Other: Qualter, P., Brown, S.L., Rotenberg, K.J., Vanhalst, J,* Harris, R.A.*, Goossens, L,

Bangee, M.* & Munn, P. (in press). Trajectories of Loneliness during Childhood and

Adolescence: Predictors and health outcomes. Journal of Adolescence: Special Issue on

Loneliness. iFirst, doi:10.1016/j.adolescence.2013.01.005,

Available at: http://link.springer.com/article/10.1007/s10802-012-9676-x

It is advisable to refer to the publisher’s version if you intend to cite from the work. To link to

this article http://www.sciencedirect.com/science/article/pii/S0140197113000122

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http://link.springer.com/article/10.1007/s10802-012-9676-x


Title: Trajectories of Loneliness During Childhood and Adolescence: Predictors and Health

Outcomes

Authors: Qualter, P.1, Brown, S. L.2, Rotenberg, K. J.3, Vanhalst, J.4, Harris, R. A.1,

Goossens, L.4, Bangee, M.1 & Munn, P.5

Affiliations: 1University of Central Lancashire, Lancashire, England, UK; 2 University of

Liverpool, Liverpool, England, UK; 3 Keele University, Staffordshire, England, UK; 4

Catholic University of Leuven, Leuven, Belgium; 5 University of Strathclyde, Glasgow,

Scotland.

Key words: Loneliness, Latent growth mixture modeling, trajectories, health, temperament,

social acceptance, sociability, self-worth, attribution, trust beliefs.

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Abstract

The present study employed latent growth mixture modeling to discern distinct

trajectories of loneliness using data collected at 2-year intervals from age 7-17 years (N =

586) and examine whether measures taken at age 5 years were good predictors of group

membership. Four loneliness trajectory classes were identified: (1) low stable (37% of the

sample), (2) moderate decliners (23%), (3) moderate increasers (18%), and (4) relatively high

stable (22%). Predictors at age 5 years for the high stable trajectory were low trust beliefs,

low trusting, low peer acceptance, parent reported negative reactivity, an internalizing

attribution style, low self-worth, and passivity during observed play. The model also included

outcome variables. We found that both the high stable and moderate increasing trajectories

were associated with depressive symptoms, a higher frequency of visits to the doctor, and

lower perceived general health at age 17. We discuss implications of findings for future

empirical work.

ABSTRACT WORD COUNT = 150 words

MAIN BODY = 5, 635 (larger word count agreed with editor).

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Introduction

Loneliness is an unpleasant state that arises due to a discrepancy between the

interpersonal relationships people want to have, and those they perceive they currently have

(Peplau & Perlman, 1982). Loneliness is important, not just because it affects the person’s

current emotional state, but it is associated with poor social, behavioural, and health outcomes

in adult, adolescent, and child samples (see Heinrich & Gullone, 2006, for review).

The Course of Loneliness Through Childhood and Adolescence

Little is known about the course of loneliness over the lifespan. Early reviews

concluded that loneliness peaks during early adolescence, drops between young adulthood

and middle age, and then rises slightly in old age (see Heinrich & Gullone, 2006). Prospective

studies support these claims (e.g., Bartels, Cacioppo, Hudziak, & Boomsma, 2008;

Demakakos, Nunn, & Nazroo, 2006; Dykstra, van Tilburg, & de Jong Gierveld, 2005; van

Roekel, Scholte, Verhagen, Goossens, & Engels, 2010).

Such studies yield important information about trends in loneliness from middle

childhood to old age, but they examine only mean loneliness scores, which assumes

homogeneity within the populations under study. This assumption is not realistic because we

know that loneliness is transitory for some people (Hymel, Tarulli, Hayden-Thomson, &

Terrell-Deutsch, 1999; Young, 1982) and prolonged for others (Koenig & Adams, 1999;

Qualter, Brown, Munn, & Rotenberg 2010). Studies that assume homogeneity of loneliness

claim to offer information about the normative developmental patterns of loneliness, but the

mean may be an artifact of averaging across subgroups of individuals who follow different

courses of development. By not considering heterogeneity, we may have inadequate

information regarding processes that occur within distinct subgroups (see Bauer & Curran,

2003). For example, stable loneliness may have a different set of predictors compared to

transient loneliness.

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Only two studies to date have assessed specific patterns of change over a number of

observations in loneliness during middle childhood to early adolescence (Jobe-Shields,

Cohen, & Parra, 2011; Jones, Schinka, van Dulman, Bossarte, & Swahn, 2011). Jobe-Shields

et al. showed that social withdrawal was a risk factor for increases in loneliness across

childhood. Further, both studies showed that outcomes related to differing loneliness

trajectories, with an increasing loneliness trajectory predicting social withdrawal, self-

harming behaviors and suicidal thoughts.

Both these studies identified a distinct group of children who increased in loneliness

from middle childhood into early adolescence; this increase in loneliness was characterized by

poor adjustment. In the current study, we examined loneliness from middle childhood through

to late adolescence and used the growth mixture-modeling framework (Muthén, 2004) to

identify latent classes of loneliness. Thus, we examine loneliness across the full length of the

school journey. We further examined the extent to which membership of these different

profiles of loneliness were predicted by early childhood risk factors, and predicted later health

outcomes.

Correlates of Loneliness: Potential Early Risk Factors

A significant body of research has documented correlates of loneliness, including

temperament, peer rejection, an unhealthy attribution style, low self-worth, and low trust

beliefs in peers. In the current study, we examined these as possible risk factors for the

development of long-term or increasing loneliness across the school years.

Temperament is likely to be important for understanding the development of

loneliness as it influences interpersonal interactions via the individual’s initial response to

new social encounters, their arousal and reactivity during any social situation, and recovery in

response to a social threat (McClowry, 2003). Research shows that lonely people have a

different pattern of social response to non-lonely people: they feel more threatened in social

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encounters (Jones et al., 1981, 1983), are more vigilant to social threats (Cacioppo &

Hawkley’s, 2009; Qualter et al., 2012), and are typically shy, passive and less sociable than

non-lonely people (for review, see Heinrich & Gullone, 2006). Thus, two temperament

characteristics may act as risk factors in predicting chronic, stable and/or increasing

loneliness: (1) negative reactivity, which involves the tendency to experience fear, anger,

sadness, and discomfort, and (2) withdrawal, portrayed by the child’s initial response to new

people and situations. High levels of negative reactivity and withdrawn behaviour pose risks

to children’s social relationships because they are linked directly to behavioral deficiencies

(de Pauw & Mervielde, 2010), which undermine the opportunities to develop and maintain

positive social relationships; thus, they are implicated in the development and/or maintenance

of loneliness. In the current study, we examined whether aspects of temperament, specifically

negative reactivity and approach/withdrawal are important for the development of loneliness.

Loneliness during childhood is associated with peer rejection and lonely children are

generally not liked by peers and are the target of peer victimization (Asher, Parkhurst, Hymel,

& Williams, 1990; Boivin, Hymel, & Bukowski, 1995; Ladd, Kochenderfer, & Coleman,

1997). Peer rejection serves as a probable cause of loneliness in children (Boivin, Hymel and

Bukowski, 1995). However, there has been no concerted focus on the question of whether low

social acceptance is associated with long-term loneliness.

Loneliness is also correlated with an unhealthy attribution style and low self-worth in

adults (Perlman & Peplau, 1981; Young, 1982), and children (Qualter & Munn, 2002). In the

current study, we examined whether an unhealthy attribution style in early childhood

predisposes a person to stable feelings of loneliness, as is proposed by Peplau, Russell, and

Heim (1979). Also, we examine whether low self worth in early childhood predicts increases

in loneliness over time and/or helps maintain a high level of loneliness. In line with Snyder,

Stephan & Rosenfield, 1978, we expect that children with low self-worth come to make more

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internal attributions for social failings over time; this continued internal negative talk alters a

person’s self-esteem, especially if social relationships are held to be important, and can lead

to long-term loneliness.

Trust beliefs in peers were also examined in the current study. Three short-term

longitudinal studies (Rotenberg et al., 2010) have shown that low trust beliefs in peers predict

loneliness across time. One limitation with that research is that it did not yield evidence for a

link between trust beliefs and loneliness across childhood to adolescence. The current

research redresses that limitation.

Several researchers have shown that that children’s social behavior in school (i.e.,

social engagement) contributes to loneliness (Qualter & Munn, 2002; Rotenberg et al., 2010)

and we examined this in the current study in relation to long-term loneliness by observing

actual social interactions. We examined the role played by low levels of social interaction

when the child was 5 years and whether this predicted long-term loneliness. Another behavior

that likely contributes to loneliness is low behavior-dependent trust, which according to the

Basis, Target, and Basis interpersonal trust framework (BDT: Rotenberg, 2010), comprises

the extent to which children depend on others to fulfill their promises (reliability trust), keep

secrets (emotional trust), and tell the truth (honesty trust). Low behavior-dependent trust

would result in social disengagement, thus contributing to loneliness. In support of that

hypothesis, Rotenberg, MacDonald and King (2004) found that children’s loneliness was

negatively associated with their behavior-dependent trust as assessed by fulfilling a promise

to classmates who had promised to co-operate. Unfortunately, these findings are limited

because the behavior-dependent trust comprised children’s responses in a mixed motive

game, which was described by an experimenter. Consequently, the relation between

children’s loneliness and behavior-dependent trust in naturalistic interactions remains to be

examined. Children’s naturally occurring behavior-dependent trust was assessed in the

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current investigation by others’ (parents, teachers, and peers) observations of the extent to

which individuals are trusting. It is important to note that parents, teachers and peers have

different experiences of the children’s trusting because, according to the BDT framework,

children vary their trusting behavior as a function of the target of trust.

The studies dealing with the correlates of loneliness have generally been cross-

sectional or predicted loneliness at a specific time point only; predictors have not been

evaluated against trajectories of loneliness established over a number of observations.

Consequently, our capacity to develop appropriate intervention strategies for loneliness is

limited. Thus, in the current study, we used latent class analyses to model the course of

loneliness over a 10-year period, while factoring baseline indicators (at age 5 years) into the

analyses as predictors of loneliness class membership via multinomial logit regression.

Correlates of Loneliness: Adolescent Health Behavior and Well-being

Prospective studies show that peer-related loneliness during childhood (the early and

middle school years) predicts poorer emotional health outcomes in adolescence (Qualter, et

al., 2010; Schinka et al., 2011; Vanhalst et al., 2012). The most commonly advanced reasons

for this are that experiencing loneliness consistently causes psychological and physiological

strains that dispose people toward emotional disorders. Cacioppo and Hawkley’s (2009)

model suggests that lonely individuals experience more stress in everyday life, which can

cause extended periods of hypothalamo-pituitary-adrenal activations leading to both

depression and a range of other somatic and psychological symptoms. Non-physiological

mediators of strain include negative self- and other-related cognitions and attributional states,

associated with loneliness (Heinrich & Gullone, 2006) and implicated in the development of

depression and somatic symptoms (Morley & Moran, 2011). Further, lonely people do not

have access to the supportive social relationships that can moderate the link between stress

and poorer mental health (Cohen, 2004). Depression, frequent use of doctors’ services, and

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poorer self-reported health are consequences of stressful experiences (Axelsson & Ejlertsson,

2002), and we would expect them to be affected by consistent loneliness. Whether trajectories

of loneliness from childhood to late adolescence predict depression has yet to be investigated.

Prospective and cross-sectional research also links loneliness to health compromising

behaviors such as alcohol misuse, smoking and drug use (Hawkley & Cacioppo, 2010). This

can be linked to emotional dysfunction described above, as lonely adolescents attempt to self-

medicate feelings of distress (Carrigan & Randall, 2003). Further, dysfunctional social

relationships appear to have a more direct role in undermining self-regulation of health

behaviors. For example, Baumeister et al. (2005) suggest that dysfunctions in social

relationships create needs for individuals to engage personal resources that might otherwise

be used for executive tasks necessary to successfully self-regulate behavior. Another

possibility is that lonely adolescents’ greater integration needs may lead them to engage with

peers in and out of school whom they otherwise may not, and who influence them to practice

undesirable health behaviors (DeWall & Pond, 2011).

However, the evidence linking loneliness to these health behaviors is inconsistent

(Lauder et al., 2006), possibly because such studies cannot discriminate between groups

characterized by transient versus chronic loneliness, which differentially predict outcomes

(Jones et al., 2011). The longitudinal design of the current study allowed us to assess how

developmental trajectories of peer-related loneliness are associated with health outcomes.

The Current Study

This study aims to (1) describe the number and nature of latent classes of peer-related

loneliness during childhood and adolescence using growth mixture modeling (Muthén, 2004),

(2) examine the hypothesis that temperament, social withdrawal, social preference, self-worth,

attribution style, and trust variables at age 5-years will predict trajectory membership at ages

7-17 years, and (3) examine the hypothesis that trajectory memberships, particularly high

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stable or increasing trajectories, will report greater emotional and physical health problems

and engagement in risky health behaviors. The study covers all years of formal schooling in

the UK offering an examination of loneliness trajectories across the school years.

Method

Participants and Procedure

The study is a prospective study of children from the North West of England, UK. All

participants were enrolled in the state education system, were primarily Caucasian, and

parental permission for participation in the study at each time period was obtained. The

sampling frame was developed to ensure that children were chosen from schools in

Lancashire that were reasonably representative of schools in different areas of the UK as

determined by the UK Government Index of Multiple Deprivation. This index combines

indicators over a range of economic, social and housing issues, into a single deprivation score

for local areas in England, and is commonly used in British educational and health research.

Of those primary schools approached, a representative group of 30 schools agreed to

take part in the study. All children between 58 and 62 months who attended the targeted

schools were possible participants. A total of 842 children were selected, of which 640 (76%)

participated in the study at Time 1 when they were aged 5 years. 50.16% of this sample was

female, 4.84% were from ethnic minority groups that all originated from areas of Southern

Asia, including Bangladesh, India and Pakistan; 97.5% of the sample were born in the UK.

Drop out was relatively high with 586 (92%) children participating at T2 (age 7 years), 400

(62%) at T3 (age 9 years), 389 (61%) at T4 (age 11 years) 296 (46%) at T5 (age 13 years),

364 (57%) at T6 (age 15 years), and 361 (56%) at T7 (age 17 years). Analyses revealed that

children retained at each time point were no different to those that had dropped out at that

particular time point on peer-related loneliness (t < .23, p > .05), or predictor variables (t

< .69, p > .05). There was one exception: children who did not take part at T3 (age 9 years)

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were more likely to have been observed ‘alone’ at Time 1 than those children who provided

data at Time 3 (t = 2.66, p = .009).

All questionnaires for the child at Time 1 were delivered in an interview with the first

author; several trained coders collected observational data of playground behaviour; parent

and teacher measures were given to the parent/teacher respectively to complete at home.

Loneliness measures up to and including age 15 years were completed in school during an

interview with one of several trained researchers, while the loneliness and health

questionnaires at Time 7 were sent to the adolescent’s home and returned via post.

Measures

Loneliness. The peer-related loneliness subscale from the Loneliness and Aloneness

Scale for Children and Adolescents (LACA: Marcoen, Goossens, & Caes, 1987) was used.

Participants rate each statement as it applies to them using a rating of 1 (never) to 4 (often).

Higher scores indicate higher loneliness. An example item is ‘I feel cut-off from other people’.

The questionnaire was developed for school children and early-adolescents, so we adapted the

measure for our participants when they were aged 13 years and above; items were changed so

that they were more appropriate for that age group. For example, ‘ I feel sad because nobody

wants to play with me’ was changed to ‘I feel sad because nobody wants to join in with me’.

This scale demonstrated acceptable internal consistency at each time point (α

= .82, .75, .83, .89, .82, and .78 at ages 7, 9, 11, 13, 15, and 17 years respectively).

Predictors of trajectory class membership. Parent reports, teacher reports,

behavioural observations, and self-reports were used as measures of child risk factors at age 5

years.

Parent reported temperament. Negative Reactivity and Approach/Withdrawal

subscales of the School-aged Temperament Inventory (SATI: McClowry, 1995; McClowry,

Halverson, & Sanson, 2003) were used. Higher scores indicate that the child is high in

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negative reactivity and has a tendency to withdraw in new situations, respectively. Both

subscales have adequate internal consistency (McClowry, 1995, 2002; McClowry et al.,

2003). In the current sample, α = .89 and .84 for negative reactivity and approach/withdrawal

respectively.

Social preference. Using the standard protocol for administering sociometric measures

to young children (Cassidy & Asher, 1992), children were asked to name three children with

whom they most like to play and three children they do not like to play with very much. A

proportion score was computed for each child (i.e., the number of nominations received was

divided by the number of classmates contributing sociometric data) to permit comparison of

scores across classrooms varying in size; these proportion scores were standardized within

each classroom. These standardized most-liked (ML) and disliked (DL) scores were used to

calculate a social preference score following the methods outlined in Coie and Dodge (1983).

A social preference (SP) score was computed as the ML score minus the DL score and is an

indication of likability and acceptance by peers.

Attribution style. The interview instrument designed by Qualter and Munn (2002) was

used to assess children’s habitual style of explaining social events. There are 10 social

outcomes (i.e., 5 for positive-relational outcomes, 5 for negative relational outcomes) and

children are asked to provide reasons for that event. Responses were transcribed and coded as

attributing outcome either to external or internal mechanisms. Total scores used in the current

study are (1) number of positive events that were attributed to external causes, and (2) number

of negative social events that were attributed to internal causes. The inter-rater agreement for

the coding of the attributions was good (kappa co-efficient = .92).

Global self-worth. The global self-worth subscale of the Self-Perception Profile for

Children (Harter, 1985) constitutes a global judgment of one’s worth as a person. The

subscale includes six items. The young child is presented with two pictures and is asked to

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decide which of these children is most like them. They are then asked whether this is sort of

true or really true of them. Each item was scored according to Harter (1985), with a total

mean score of 4 indicating the highest level of perceived self-worth. Reliability was good (α =

.85).

Observed sociability. Children were observed for a 5-minute period on four separate

occasions whilst they were at recess. The protocol is described in detail elsewhere (Qualter

and Munn, 2002). Behaviors of interest for this study are (1) amount of time playing alone,

and (2) aloneness (child is alone, non-involvement in play or interaction with peers or

watches other children). Greater frequencies of these behavior denoted higher social

withdrawal.

Trust beliefs in peers. Trust beliefs in peers were measured using the peer subscale

from Imber's Children's Trust Scale. This scale comprises 10 items with a forced-choice, two-

alternative-answer format (Imber, 1971, 1973). The possible scores ranged from 10 to 20. The

items pertain to the belief in the sincerity of words or behavior of a person or a group of

people; the items in the scale depict issues regarding keeping promises by generic peers and

peer friends. This scale demonstrated adequate internal consistency (α = .75). Scores on the

T-CARS were reversed so that higher scores were indicative of higher trust on all measures.

Trusting (Behavior-Dependent Trust). Teacher and parent reports of children’s

trusting were measured using the single item ‘doesn’t trust other people’ from The Teacher-

Classroom Adjustment Rating Scale (T-CARS: Connors, 1969). Peers reported on trust using

the item ‘trusts other kids’ during sociometric testing. Children’s peer ratings as trusting were

standardized within classes.

Loneliness at 5 years. In order to determine whether early reports of loneliness could

also predict loneliness trajectories, we collected loneliness data from all children at age 5

years. Here we were interested to determine whether all of our other risk factors were able to

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predict membership of loneliness trajectory once a self-report measure of loneliness taken at

age 5 years was included in the model. The LACA has been adapted for use with 5-year-old

children (Qualter & Munn, 2002) and the peer-subscale was used in the current sample.

Internal consistency for the revised measure was good (α = .72).

Health Outcome Measures. Participants completed a series of self-report measures

of health at age 17.

Health Status Questionnaire (HSQ). Three items from the Roy (1994) HSQ were used

in the current study: (1) how would you describe your health generally? (rated ‘excellent’ to

‘very bad’), (2) When did you last consult your GP about your own health, other than for a

check-up required for work or insurance, or for a vaccination? (rated ‘in the last week’ to ‘so

long ago I can’t remember’), and (3) If you had a consultation with your doctor or with a

specialist within the last year, how many consultations did you have? (rated ‘more than 4 per

month’ to ‘less that 4 per year’). General health was reverse scored so that a higher score

indicated better health. The possible ranges of scores were general health (1-5), last visit to

the doctors (1-7), and frequency of visits to doctors (1-4).

Behavioral Risk Factor Surveillance System (BRFSS). Questions from this measure

(Center for Disease Control and Prevention [CDC], 2006) were used to assess health-

impacting behaviors carried out by participants. Responses to each question are treated as

separate measures. Smoking information was assessed by the question ‘do you smoke

cigarettes everyday, some days, or not at all?’. Alcohol intake was assessed with two

questions: ‘on the days you drank in the last 30 days about how many units did you drink on

average per session?’ (units are explained and examples given) and ‘How many times during

the past 30 days did you have 5 units or more on one occasion?’. Drug taking was assessed

using the question ‘During the past 30 days on approximately how many days have you taken

non prescribed drugs or used substances?’.

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Depressive symptoms. Depressive feelings and behavior were assessed using the 19

items from the CES-D (Radloff, 1977). The item 'I felt lonely’ was removed from the scale.

This scale includes items such as “I felt depressed”. Participants were asked to rate how often

they felt the way described during the past week on a scale ranging from 0 (rarely or none of

the time) to 3 (most or all of the time). Items were summed, with higher scores indicating

higher levels of depressive symptoms. Cronbach’s alpha was .85.

Overview of Data Analyses

First, Latent Growth Curve Modeling (LGCM) was used to examine mean level

changes in loneliness. Second, we employed Latent Growth Mixture Modeling (LGMM) to

identify discrete growth classes of loneliness, to test predictors of class membership, and

establish health outcomes (Muthén, 2004). All analyses were conducted in Mplus version 5.1

(Muthén & Muthén, 1998-2008), and missing values were estimated with full information

maximum likelihood estimation (FIML), which is widely endorsed (Schafer & Graham,

2002).

The LGMM proceeded in three stages. First, a series of models, with varying numbers

of classes, were fit to the loneliness data to determine the optimal number of latent classes that

underlie the data and the form of changes over time in loneliness within each class. We

compared 2-, 3- 4- and 5-group trajectory models. The Bayesian information criteria (BIC),

Akaike Information Criteria (AIC), Adjusted BIC, entropy, and the Lo-Mendell-Rubin

likelihood ratio test (LMR) were used to inform the decision of number of classes

(McLachlan & Peel, 2000). Lower AIC, BIC, and adjusted BIC values indicate a more

parsimonious model. Entropy is a measure of classification accuracy, with values closer to 1

indexing greater precision (range: 0-1); the LMR test provides a k−1 likelihood ratio-based

method for determining the ideal number of trajectories, with low p value indicating a better

fit to the data.

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In the second stage of the LGMM, covariates taken when the child was 5 years were

explored as predictors of membership of the loneliness classes using logistic regression.

Third, once the latent loneliness classes were identified and described, within the final model,

we examined the relation between loneliness classes and health outcomes at 17 years of age

using Wald tests of mean equality (Morin et al., 2011).

Results

Identifying Different Classes of Loneliness

Examination of the mean scores for the full sample showed that loneliness was

relatively stable up until age 13 at which point it peaked, but then dropped quickly (see Table

1). LGCM revealed that all estimates of variance related to the intercept and slope were

significant (intercept = 28.05, slope = -1.26, p < .001), which justified an examination of

interindividual differences in loneliness over time using LGMM. Table 2 summarizes the fit

statistics for the 2-5 models from the LGMM. Based on the fit statistics and interpretation of

the later predictor variable analyses, four latent classes of loneliness were identified1. Figure 1

presents the four–class solution. Descriptive statistics on loneliness by age and latent class are

presented in Table 1. We found that most of the children (37%) followed a low stable

trajectory of loneliness, whilst another 23% followed a moderate declining trajectory of

loneliness. Furthermore, 22% followed a high stable trajectory, whilst another 18% followed

an increasing loneliness trajectory that started at a moderate start point on the loneliness scale.

The predictor variables were incorporated into our LGMM model (Table 3). Six

loneliness contrasts were examined: high stable lonely vs. moderate decreasers, high stable

lonely vs. low stable loneliness, high stable lonely vs. moderate increasers, moderate

increasers vs. moderate decreasers, increasers vs. low stable lonely, and low stable vs.

1 The means and percentages reported here are from the model with all covariates included. The class membership changed when the covariates were added to the model, but, for reasons of parsimony, the changes in class membership are not noted; they are available from the corresponding author upon request.

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moderate decreasers. Table 3 shows that seven risk factors differentiated the high stable

lonely adolescents from the other three latent classes in the LGMM model: high loneliness

scores at age 5, low social preference, high negative reactivity, greater frequency of playing

alone, externalization of positive events, low self worth, and trust beliefs. Further, the

moderate increasers could be differentiated from the two non-lonely classes by two risk

factors: (1) low social acceptance by peers, and (2) low trusting scores as reported by teachers

and trust beliefs in peers. These increasers also reported higher self-worth than all other

groups at age 5 years. Approach/Withdrawal was the only variable that did not predict

membership of classes.

Age 17 Health Outcomes as a Function of Loneliness Class

In the model, we included the predictor variables at age 5 years, but also health

outcome variables at age 17 years. Using Wald’s tests of mean equality, we examined

whether membership of a given loneliness class predicted health behavior and outcome. We

found that the high stable lonely and moderate increasers could be differentiated from the

non-lonely groups by their higher reports of depression (W >1.03, p > .05, ORs > 1.29 [1.14-

1.89]), frequency of visits to the doctor (W >5.71, p > .02, ORs > 2.30 [1.18-4.72]), and

general health (W >2.13, p > .03, ORs < .63 [.35-.93]). The high stable loneliness class was

also different to the non-lonely classes in the amount of alcohol units they consumed (W

>4.69, p > .03, ORs > 1.19 [1.02-1.39]) and in the number of days they consumed over 5 units

of alcohol in the last month (W >1.27, p > .05, ORs > 1.27 [1.19-1.46]). For completeness, we

tested for differences between low stable lonely and moderate, decreasing groups: they

showed no differences on any of the outcome variables. Descriptive statistics on health

outcome measures can be found in Table 4.

Discussion

This study is one of the first to prospectively describe the developmental trajectories

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of loneliness from middle childhood to late adolescence. We examined loneliness from 7-17

years, covering the compulsory years of schooling in the UK, and explored whether early risk

factors were associated with different profiles of loneliness, and investigated health outcomes

measured in late adolescence. First, four trajectory profiles were identified. Results showed

that most children followed a consistently low level of loneliness throughout the 12 years.

Another group showed a falling trajectory of modest to low loneliness. A third group included

children who started followed a rising trajectory of loneliness from an average starting point;

the fourth group of children had relatively high levels of loneliness throughout the 12 years.

These patterns of results are in line with previous studies (Jobe-Shields et al., 2011; Jones et

al., 2011; others in this issue) that show heterogeneity in the course of loneliness through

childhood and early adolescence.

In the current study, there were considerably more individuals in the high loneliness

group than in other trajectory studies and more than we would expect based on recent survey

statistics that show 11% of British adults report feeling lonely often (The Mental Health

Foundation: Griffin, 2010). This difference probably represents the fact participants in the

high lonely class in the current study were only relatively high on loneliness; they did not

score very high levels on the loneliness measure, even though they always scored higher than

other groups. However, the fact that this pattern of relatively high loneliness was associated

with health outcomes provides validation that this subgroup is ‘at risk’.

Predictive Links Between Early Risk Factors and Loneliness Trajectories

We examined the extent to which different longitudinal profiles of loneliness were

predicted by a number of risk factors. Negative reactivity, low levels of social engagement

and low social preference increased the risk of following the relatively high stable loneliness

trajectory. How these risk factors work together has not been established here, and should be

examined in future research. For example, this pre-existing temperamental vulnerability may

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amplify the effects of environmental stressors on lonely feelings. So, children following the

relatively high stable loneliness trajectory may be emotionally reactive children who are

simply less able to cope than other children with negative peer experiences.

Cognitive style also acted as a significant risk factor. Specifically, those individuals

who externally attributed positive events, had low self worth, reported low generalized trust

beliefs, and were noted by peers and teachers to have low trust in peers were at increased odds

of following the relatively high stable lonely trajectory. This suggests that as young as 5 years

of age, lonely children have already developed a sense of self that is linked to not trusting

peers, not liking themselves, and seeing positive things that happen to them as luck or out of

their control. We might expect to see low self-worth linked to this profile because (1) feelings

of pessimism and hopelessness about the future often lead to an unhealthy attribution style

(Ickes & Layden, 1978), but (2) causal attributions may alter self-worth, especially when

social relationships are important for that individual (Snyder et al., 1978). The current study

shows that it is the combination of these characteristics that predisposes an individual to

relatively high stable loneliness across childhood/adolescence.

High self-worth, low social acceptance, low trust beliefs in others and low trusting in

early childhood increased the odds of following the increasing loneliness trajectory. Others

(Murrey-Close et al., 2010) have noted that inflated self-perceptions often place children at

risk for rejection by peers, so it may not be surprising that overly high self-worth is associated

with low social acceptance in this sample. Further, decreases in positive biases regarding

social and behavioral competence have been shown to be associated with increases in

depressive symptoms over time (Hoza et al., 2010). In line with these findings, we found that

overly positive self-perceptions are associated with the development of loneliness, but we

have not shown that loneliness increases alongside decreases in positive self-perception bias.

Thus, future research should establish (1) whether this positive self-bias is evident for specific

19


domains of self-perception for children following this increasing trajectory of loneliness, and

(2) whether an increase in loneliness over time is associated with a decrease in positive bias.

The findings further demonstrate that children’s low trust beliefs in others, as well as

low trusting, predict sustained loneliness across childhood to adolescence. The findings

extend those of Rotenberg et al. (2010) that showed low trust beliefs in others predicted

loneliness at early childhood, middle and young adulthood. Our findings also complement

those obtained by Rotenberg, MacDonald and King (2004) in which children’s loneliness was

negatively associated with behavior-dependent trust. The finding that trusting reported by

teachers and peers predicted loneliness, rather than that reported by parents, may be the result

of two principles. First, teachers and peers are reporting the extent to which children showed

low trusting behavior in peer interactions. Second, the low trusting quality of those peer

interactions contributed to the children’s loneliness.

Loneliness Trajectories and Health Outcomes

Loneliness trajectories between ages 7-17 years predicted depression, more frequent

use of services offered by doctor’s surgeries, poorer self-reported general health and more

units of alcohol consumed per drinking occasion at age 17. Interestingly, patterns of

association differed between the outcomes. High stable loneliness and moderately increasing

loneliness trajectories were associated with greater depression than moderate declining and

low-stable loneliness trajectories. Visits to the doctors and self-reported health status showed

a similar profile of association with the trajectories.

This notion that loneliness in early childhood precedes depression in adolescents is

one we tested previously with data from this same sample (Qualter et al., 2010). The current

analysis shows that this predictive relationship extends into later adolescence. Here, we also

found that loneliness is associated with the frequency of doctors’ visits and reported concerns

20


about health. It is not clear whether these indicators reflect objectively compromised physical

health or somatic complaints that are related to emotional distress.

Greater alcohol use was associated with the relatively high stable loneliness group, but

not the moderately increasing loneliness trajectory. This suggests that loneliness may affect

drinking differently to the way in which it influences distress. This is more consistent with the

social processes suggested by Baumeister et al (2005) or de Wall and Pond (2011), than it is

with an emotional dysregulation explanation.

Strengths and Limitations

The present study is one of the first to explore different developmental trajectories of

loneliness during childhood and adolescence, and our findings offer important new insights

regarding the development of loneliness, its predictors, and how it impacts on health in late

adolescence. The prospective design, with early measurement of predictive variables assessed

when the participants were aged 5 years provides a broad picture of the development of

loneliness during childhood and adolescence. In addition, self-reports were combined with

parent-reports, peer-reports and observational data, limiting shared method variance.

Despite these strengths, the present study has some limitations. First, the participants

were primarily Caucasian. A sample with greater ethnic diversity would be more appropriate

to ensure findings are generalizable. Also, there was significant drop-out over the course of

the study, which may have impacted on the overall results. Third, growth mixture modeling is

a sample-specific technique and replication of the present findings in other samples is

recommended.

These limitations not withstanding, we believe these findings provide important new

insights into the development of loneliness during the school years, from young childhood to

late adolescence. Moreover, when considered alongside the findings from the other papers in

this special issue, our findings have implications for prevention and intervention programs

21


with children and adolescents at risk of long-term loneliness. Specifically, providing

interventions for young people who have low self-worth, are not socially accepted, and do not

trust others may prevent them from embarking on a stable high or increasing trajectory of

loneliness through middle childhood and adolescence. Emotion regulation and social skills

training may be particularly helpful in this regard too.

22


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Table 1. Adjusted Loneliness Scores Over Time by Peer Loneliness Class

_____________________________________________________________________

Loneliness class

_______________________________________

Participant’s Relatively Moderate Moderate Low/Stable Whole

age in years High Increasing Decreasing Sample

7 32.87 (4.82) 26.72 (5.67) 28.55 (5.29) 22.13 (4.05) 27.00 (6.34)

9 33.68 (5.23) 27.46 (4.66) 25.45 (3.07) 24.83 (3.39) 27.55 (5.36)

11 32.84 (7.98) 30.34 (8.66) 24.57 (7.23) 18.86 (5.75) 26.05 (9.44)

13 37.87 (4.82) 31.72 (5.67) 23.08 (5.30) 21.19 (5.23) 31.20 (5.83)

15 36.03 (6.96) 32.80 (6.81) 21.31 (7.26) 18.36 (5.46) 26.18 (9.93)

17 30.41 (6.63) 32.41 (5.72) 22.49 (4.24) 21.81 (3.85) 24.46 (6.11)

___________________________________________________________________________

Note. Scores are adjusted for missing data using full information maximum likelihood

estimation (FIML) within Mplus version 5.1. Scores for trajectories are also adjusted for the

following covariates shown to distinguish class membership: social preference, negative

reactivity, self-worth, playing alone, externalization of positive events, and trust beliefs.

Scores on peer loneliness range from 12-48.

29


Table 2. Conditional Growth Mixture Modeling for Peer Loneliness: Global Fit Statistics

________________________________________________________________________

Class AIC BIC Adjusted BIC Entropy LRT p value

________________________________________________________________________

1 22526.93 22575.04 22540.12 - -

2 22449.43 22510.66 22466.22 .85 .0807

3 22371.37 22445.72 22391.75 .87 .0001

4 22347.53 22434.99 22371.51 .95 .0001

5 22353.53 22464.12 22391.10 .91 .50

_________________________________________________________________________

Notes: AIC = Akaike Information Criteria, BIC = Bayesian information criteria, LRT = Lo-

Mendell-Rubin test. AIC, BIC, Adjusted BIC = lower values indicate a more parsimonious

model; Entropy = values closer to 1 index greater precision (range: 0-1). The LRT = a low p

value indicates a better fit to the data. The final model selection of a four-class solution was

based on the model fit information detailed above, in combination with the predictor and

outcome effects noted in the remainder of this paper.

30


Table 3. Latent growth mixture modeling (LGMM) of loneliness predicted by risk factors

____________________________________________________________________________________________________________________

Loneliness Class Comparisons

_________________________

Relatively Relatively Relatively

High Stable Lonely vs. High Stable Lonely vs. High Stable Lonely vs.

Moderate Increasers Moderate Decreasers Low Stable Lonely

______________________________________________________________________________________________________________

Variable OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value

______________________________________________________________________________________________________________

Risk factor

Loneliness age 5 1.15 (1.10-1.20) .001*** 1.05 (1.01-1.09) .008** 1.06 (.02-1.09) .001***

socpref .78 (.56-1.09) .15 .55 (.34-1.05) .05* .53 (.37-.76) .001**

Approach/withdrawal 2.06 (.84-5.06) .12 1.05 (.47-2.30) .90 1.40 (.59-3.34) .47

NREACT 3.29 (1.40-7.55) .005*** 3.19 (1.35-7.56) .008** 3.04 (1.23-7.54) .01**

playing alone 1.18 (.95-1.09) .16 1.28 (1.03-1.60) .03* 2.67 (.93-7.65) .05*

31


alone 1.02 (.95-1.09) .63 1.10 (.98-1.11) .19 .99 ( .88-1.16) .83

intneg .87 (.72-1.04) . 11 .85 (.66-1.09) .21 1.01 (.83-1.21) .94

expos 1.72 (1.23-2.41) .001*** 2.33 (1.65-3.39) .001*** 2.08 (1.10-3.94) .02*

Lself-worth .24 (.11-.49) .05* .56 (.30-1.04) .05* .47 (.26-.85) .01**

Trusting Teacher .95 (.17-5.31) .95 .44 (.24-.81) .01** .49 (.04-1.92) .01**

Trusting Peers 1.23 (.61-2.46) .57 .47 (.01-1.47) .05* .91 (.51-1.66) .75

Trust Beliefs .95 (.67-1.31) .76 .31 (.10-.92) .03* .53 (.37-.76) .001**

_______________________________________________________________________________________________________________

Loneliness Class Comparisons

___________________________________

Moderate Increasers vs. Moderate Increasers vs. Low, Stable Lonely vs.

Moderate Decreasers Low Stable Lonely Moderate Decreasers

______________________________________________________________________________________________________________

Variable OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value

______________________________________________________________________________________________________________

Risk factor

32


Loneliness age 5 .92 (.88-.96) .001*** .92 (.89-.96) .001*** 1.007 (.97-1.04) .70

socpref .57 (.45-.71) .001*** .73 (.50-.99) .04* 1.16 (.98-1.38) .11

Approach/withdrawal .51 (.22-1.21) .13 .68 (.28-1.67) .34 .76 (.32-1.79) .52

NREACT .97 (.44-2.15) .94 .51 (.24-1.08) .11 .98 (.97-1.07) .86

playing alone .92 (.70-1.20) .53 1.19 (.95-1.49) .13 1.00 (.99-1.02) .93

alone 1.06 (.99-1.13) .09 1.01 (.95-1.07) .86 1.05 (.98-1.07) .89

negative int. .97 (.84-1.10) .61 1.01 (.92-1.12) .70 .98 (.89-1.13) .67

intneg .58 (.30-1.13) .11 .88 (.74-1.03) .10 .96 (.89-1.06) .53

expos 1.28 (.51-1.52) .69 1.07 (.61-1.84) .82 1.30 (.98-1.74) .07

Lself-worth 2.33 (1.14-4.76) .02* 1.99 (.99-3.88) .05 1.02 (1.01-1.04) .93

Trusting Teacher .12 (.01-1.02) .05* .42 (.08-1.21) .07 .93 (.29-2.99) .91

Trusting Peers .59 (.27-1.27) .17 .84 (.65-1.91) .69 .98 (.72-1.59) .57

Trust Beliefs .24 (.04-.98) .03* .36 (.06-.75) .01** 1.06 (.82-1.89) .68

___________________________________________________________________________________________________________________

Notes. CI = confidence interval; OR = odds ratio. socpref = social preference score, NREACT = negative reactivity (SATI), Approach =

Approach/withdrawal (SATI), , expos = externalizing positive events (attribution style), intneg = internalizing negative events (attribution style),

33


Lself-worth = low global self-worth, TrustingT = Teacher’s reports of child’s trusting, TrustingP – Parent’s reports of child’s trusting, TrustingPe

= Peers reports of child’s trusting, Trust Beliefs = the child’s trust beliefs in peers as measured using the peer subscale of the Imber's Children's

Trust Scale.

34


Table 4. Health Outcomes at 17 years by Loneliness Class

___________________________________________________________________________

Loneliness class

_____________________________________________________________________

Relatively

High/Stable Moderate Increasers Moderate Decreasers Low/Stable

Outcome M SE M SE M SE M SE

___________________________________________________________________________

GenH*** 3.09 .15 3.27 .14 3.98 .15 3.66 .13

GPLast 1.93 .15 2.01 .16 1.93 .17 2.19 .14

GP Freq*** 1.69 .09 1.92 .10 1.47 .08 1.48 .06

Smoker 1.60 .09 1.55 .09 1.43 .08 1.52 .07

Alc.units*** 4.36 .39 3.23 .39 2.25 .23 2.30 .21

Alc.5unit*** 5.53 .26 3.11 .22 1.08 .27 1.70 .15

DrugDays 1.29 .26 .90 .06 .76 .03 .88 .03

CES-D*** 8.23 .94 7.78 .99 5.18 .79 5.28 .86

__________________________________________________________________________

Notes. GenH = General Health, Reversed scored so higher score indicates better health,

GPLast = ‘When did you last consult your GP?’ (1-in the last week, to 7 -so long ago I can’t

remember), GP Freq = Frequency of visits to GP for the same problem (1-more than 4 per

month, to 4 -less that 4 per year). Smoker – ‘how often you do smoke?’ (1= everyday, 2=

some days, 3= not at all), Alc.units = ‘when drinking, how many units did you drink?’,

Alc.5units = ‘How many times during the past 30 days did you have 5 units or more on one

35


occasion?’, DrugDays = ‘During the past 30 days on approximately how many days have you

taken non prescribed drugs or used substances?’ ***p < .001.

36


Figure 1. Four-Class Solution for Peer Loneliness

37


38

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