Journal of Child and Family Studies

https://doi.org/10.1007/s10826-020-01804-3

ORIGINAL PAPER

Trajectories of Early Adolescent Loneliness: Implications for Physical

Health and Sleep

Alice M. Eccles

●

Pamela Qualter

●

Margarita Panayiotou

●

Ruth Hurley

●

Michel Boivin

●

Richard E. Tremblay

Abstract

The current study examines the relationship between prolonged loneliness, physical health, and sleep among young

adolescents (10–13 years; N = 1214; 53% girls). Loneliness was measured at 10, 12 and 13 years of age along with parent-

reported health and sleep outcomes. Using growth mixture modelling, 6 distinct trajector ies were identiﬁed: ‘low increasing

to high loneliness’ (n = 23, 2%), ‘high reducing loneliness’ (n = 28, 3%), ‘medium stable loneliness’ (n = 60, 5%), ‘medium

reducing loneliness’ (n = 185, 15%), ‘low increasing to medium loneliness’ (n = 165, 14%), and ‘low stable loneliness’

(n = 743, 61%). Further analyses found non-signiﬁcant differences between the loneliness trajectories and parent-report

health and sleep outcomes including visits to health professionals, perceived general health, and sleep quality. The current

study offers an imp ortant contribution to the literature on loneliness and health. Results show that the relationship may not be

evident in early adolescence when parent reports of children’s health are used. The current study highlights the importance of

informant choice when reporting health. The implications of the ﬁndings for future empirical work are discussed.

Keywords Loneli ness

●

Health

●

Adolescent

●

Longitudinal

●

Sleep

Highlights

●

Six distinct trajectories of loneliness were identiﬁed in a large, representative sample.

●

Novel examination of loneliness and parent-reported sleep and health outcomes.

●

Highlights the importance of measurement and informant choice for future work.

Loneliness is a negative emotional state caused by a dis-

crepancy between a person’s desired and actual social

relationships (Peplau and Perlman 1982). Loneliness is

related, but distinct from social isolation (Smith and Victor

2019); it is characterised by dissatisfaction with current

relationships (Cacioppo and Cacioppo 2014) and a feeling

of emotional or physical disconnection from others (Qualter

et al. 2015). It involves uncomfortable feelings, including

sadness, which people try to alleviate quickly by recon-

necting with others. Among adults, those feelings contribute

to reductions in well-being, which for those who experience

loneliness frequently contributes to lower reports of quality

of life, and poorer overall physi cal and mental health

(Leigh-Hunt et al. 2017). Much of the research linking

loneliness and poor health is focused on adults and there is

limited examination of the imp act of prolonged loneliness

on physical health and sleep among adolescents. The cur-

rent study addresses that gap in the literature, extending our

understanding of distinct developmental trajectories of

loneliness in adolescence and their impact on health.

Loneliness is a common experience throu ghout life,

peaking when there are substantial changes to our social

environments (Qualter et al. 2015). Loneliness is considered

a normative reaction to those changing social circumstances

* Alice M. Eccles

[email protected]

School of Psychology, University of Central Lancashire, Preston,

Lancashire, UK

Manchester Institute of Education, University of Manchester,

Oxford Road, Manchester, UK

Université Laval, ,Québec QC G1V 0A6, Canada

The University of Montreal, Québec, Canada

Supplementary Information The online version of this article

(https://doi.org/10.1007/s10826-020-01804-3) contains supplementary

material, which is available to authorised users.

1234567890();,:

and psychological challenges; people are motivated to

connect with others, ﬁnding groups to which they can

belong and make friends with people they can trust. There

are two life periods — adolescence and old age—char-

acterised by major changes to social environments, where

increased social needs and expectations increase sensitivity

to feelings of loneliness (Victor and Yang 2012). Loneliness

has been examined extensively among those in old age, but

there is limited work exploring loneliness among youth

despite empirical literature showing the experience is

common among adolescents (Qualter et al. 2015 ). Recent

data from population surveys (Australian Loneliness Report

2018; CIGNA 2018;Ofﬁce of National Statistics 2018),

charity reports (Coop Foundation 2018; Grifﬁn 2010), and

academic research (BBC Loneliness Experiment 2018)

shows that feelings of loneliness are common among ado-

lescents and young adults. Given the evidence that lone-

liness is a particular issue for youth, there is a need to

explore how it impacts their lives: is it the case that the

same health effects of loneliness found among the elderly

are also eviden t among youth.

Peer relationships become increasingly important during

early adolescence (Rubin et al. 2006): concerns relating to

social standing begin to emerge (Haller et al. 2014), inter-

actions with peers’ increase, and independence from parents

occurs (Smetana et al. 2015); such increased focus on peers

ampliﬁes feelings of difference, increasing loneliness. Most

adolescents navigate those normative feelings of loneliness,

but empirical evidence suggests there are small groups of

individuals who are at risk of prolonged feeling of lone-

liness during those crucial years (Harris et al. 2013;

Jobe-Shields et al. 2011 ; Ladd and Ettekal 2013; Qualter

et al. 2013; Schinka et al. 2013; Vanhalst et al. 2013).

Overall, the ﬁndings suggest that the vast majority of youth

experience consistently low or moderate levels of loneliness

and only a small minority suffers from chronically high

levels of loneliness. While there is a growing body of evi-

dence to support the presence of distinct loneliness trajec-

tories, there is a need for further research that examines

loneliness over time and whether following certain trajec-

tories of loneliness predicts important developmental out-

comes, including physical healt h and sleep.

The relationship between loneliness and physical health

has been well researched among adults (for reviews see Holt-

Lunstad et al. 2015; Valtorta et al. 2016)withprolonged

loneliness leading to increasing blood pressure (Hawkley

et al. 2006), higher incidence of coronary heart disease

(Thurston and Kubzansky 2009), and poorer perceived gen-

eral health (Nummela et al. 2011; Segrin and Domschke

2011) and health complaints, such as migraines (Christiansen

et al. 2016). Continued activation of physiological systems,

including the hypothalamic–pituitary–adrenocortical (HPA)

axis, contribute to inﬂammatory processes and are thought to

be the leading mechanism by which loneliness is linked to ill-

health (Hawkley and Cacioppo 2010).

Within young populations, most research has used con-

current data and shown that higher levels loneliness are

associated with higher odds of reporting headaches and

stomach aches (Stickley et al. 2016), and poorer self-

reported health (Eccles et al. 2020; Lohre 2012). The cross-

sectional nature of those studies is a problem because such

an approach fails to consider individual variation in the

experience of loneliness over time. Given the growing body

of research that highlights that it is prolonged loneliness that

links to poor health, it is crucial to distinguish between

those who are chronically lonely and others for whom

loneliness is a transitory experience. Among older adoles-

cent samples, prolonged loneliness predicted later self-

reported health (Qualter et al. 2013; Harris et al. 2013), was

a metabo lic risk factors associated with cardiovascular

disease (Goosby et al. 2013), and increased the number of

GP visits (Qualter et al. 2013). Work is needed to explore

how young adolescents following different trajectories of

loneliness differentially experience health.

As well as explore different trajectories of loneliness

and how those link to health, it is i mportant to control for

earlier health reports in any exploration of how loneliness

predicts poor health: self-reported health is stable from

early adolescence th rough to young adulthood (Foss e and

Haas 2009), and without controlling for earlier poor health,

any analysis that explores the prospe ctive link betwe en

loneliness and health is not rob ust. Such robustness is

missing from the extant empricial work. In addition, the

growing body of evidence that expo sure to poverty during

early childhood is a ssociated with a w ide variety of adult

health conditions irrespective of concurrent deprivation

status (Braveman and Barclay 20 09), means there is a need

to control socioeconomic status (SES) and family afﬂuen ce

when examining the effects of l oneliness on later health.

Seguin et al. (2012) examined the trajectory of so cio-

economic status, including family income, using the

QLSCD data from baselines through to 10 years o f age.

The trend da ta suggests the pre valence of poverty during

childhood declines over time with fewer children living

with families with low income or receiving state support.

Despite that increase in family afﬂuenc e, Se quin hig hlights

how early poverty, despite the increase in afﬂ uen ce later in

childhood, has detrimental impacts upon subseque nt health

as illustrated by previous research (Braveman et al. 2011;

Luo and White 2005; Poulton et al. 2002). Given ﬁndings

that ea rly childhood SES is associated with an elevated risk

of mortality in adulthood, regardless of the individual’s

current SES status (Galobardes et al. 2008), it is important

to account for early exposure to social ine qua lity that might

shapefuturebarriersinlifethatimpacthealth(Ferraro

et al. 2016).

Journal of Child and Family Studies

As well as physical h ealth, social stress is linked to

disturbed sleep (Dahl and Lewin 2002), suggesti ng that

loneliness may be counterproductive to a good night’s

sleep. Loneliness signals to an individual to change

something about their social environment. As such, it

leads to an increased vigilance to social cues and

increased activation of the HPA axis in response to that

social stre ss (Haw kl ey and Cac ioppo 2010). Previous

research supports that idea: loneliness is not a ssociated

with the overall sleep quantity, but it affects overall

quality of the sleep among adults (Cacioppo et al. 2002;

Kurina et al. 2011). Extant literature that has used pro-

spective designs shows loneliness over time strongly

predicts poor sleep quality among adults (Hawkley et al.

2010). Despite empirical work examining the relationship

between loneliness and sleep among adults, there is a

paucity of research examining that association among

youth. In older adolescents, loneliness has been associated

with subjective sleep outcomes such as quality and day-

time dysfunction (Matthews et al. 2017), but not related to

objective measures such as sleep duration and latency

(Doane and Thurston 2014). Majeno et al. (2018)high-

light the importance of loneliness and social stress, sug-

gesting loneliness partially mediates the relationship

between discrimination and sleep in adolescents.

In early adolescents, loneliness has been found to be

associated with increased difﬁculties and experiences of

disturbed sleep, but not will overall sleep quantity (Eccles

et al. 2020). Whilst those previous studies support an

association between loneliness and sleep, they all examine

concurrent data. There is little research examining the

relationship over time. In 8–11 year olds, Harris et al.

(2013) showed that children experiencing a relatively high

reducing loneliness reported greater sleep disturbance

compared to children who followed a low, stable trajectory

of loneliness. Such work with children suggests that lone-

liness may impact sleep, even when social reconnection

occurs. There is a need to further investigate whether dif-

ferent trajectories of loneliness among youth relate to sleep

outcomes. Such work will establish whether loneliness over

timeis associated with poorer sleep quality consistently

across different samples of youth.

Another important aspect to consider is the use of parent

reports of their child’s health, which is commonplace in the

literature (Garbarski 2014). The use of proxy reports and the

agreement with self-reported outcomes has been extensively

researched and ﬁndings have been mixed (Smith and

Goldman 2011; Todd and Goldman 2013). Nolan (2016)

examined both parent and child report health utilising data

collected in the Young Lives survey: moderate correlations

between the two informants support convergent validity;

parent reports also displayed a stronger association with

physical health indicators such as height. There is evidence

to suppor t parental-proxy reports of health and their use

warrants further attention.

The current study examined the presence of distinct

developmental trajectories of loneliness in a sample of

Canadian adolescents aged 10–13 years. First, we examined

how loneliness changed over the course of late childhood

and early adolescence for the whole sample. Second, we

explored whether youth followed particular trajectories of

loneliness, and whether those following particular trajec-

tories of loneliness experienced poorer health and sleep

problems. Our overall aim was to provide data on the

prospective associations between loneliness, physical

health, and sleep among young adolescents. We controlled

for earlier health problems and early family income in our

analyses to determine the uniqueness of loneliness in pre-

dicting health and sleep outcomes.

Method

The data used in the current study were collected as part of

the on-going Québ ec Longitudinal Study of Child Devel-

opment (QLSCD). The QLSCD is a large birth cohort study

organised by the Direction Santé Québec of the Institut de

la Statistique du Québec. The prospective study includes

singleton infants born in the Canadian provinces of Québec

between 1997 and 1998. The QLSCD study took baseline

measurements from 2223 infants and mothers when infants

were between the ages of 15 and 36 weeks (mean age of

5 months). The study continued to collect data, on an annual

or biannual basis, and is still an active research project. The

current study focused on data collected when the partici-

pants were ages 10, 12, and 13 years (in the current study,

referred to as Times 1, 2, and 3 respectively). The QLSCD

received ethical approval from the ethics committee of the

Faulty of Medicine, the University of Montreal and the

Direction Sa nte Quebec, Institu t de la Stati stique du

Quebec.

Participants

In the current study, only participants who completed the

loneliness questions at Time 1 of the current study (age 10

years) and at least one other time point (age 12 or 13 years)

were included in the study. A total of 1214 children (53%

female) met those criteria , with 13.45% missing data. Lit-

tle’s(1988) Missing Completely at Random (MCAR) test

suggested data were not MCAR (χ

(2116) = 2762.12, p <

0.001), but following recommendations (Enders 2010;

Muthen and Muthen 1998–2020), we computed missing

data using FIML on the latent loneliness variables in the

growth curve analysis. Missing data on the distal outcomes

were imputed using multiple imputation (following

Journal of Child and Family Studies

guidelines provided by Asparouhov and Muthen 2010).

Data imputation is typically less biased than other techni-

ques such as list wise deletion (Schafer and Graham 2002).

Procedure

Data used in this study were collected from both the ado-

lescents and their mothers. Youth completed the loneliness

questions when they were ages 10, 12, and 13 years

respectively. At the same time points, mothers completed

measures that asked about their child’s sleep and physical

health. Parental socio-economic status data were collected

from the mother when the children were 17 months olds.

Measures

Loneliness

Children were asked to rate, using a 3-point likert scale

(1 = never,2= sometimes, and 3 = always) how often they

had felt the following in the past 2 weeks: (1) I have no one

to talk to, (2) I feel alone, and (3) I feel left out. Those three

items are similar to those in the 3-item short form of the

UCLA (Hughes et al. 2004). The three item scores were

summed, creating an overall loneliness score with total

scores ranging from 3 to 9. Higher scores represented higher

loneliness. The loneliness measure demonstrated reasonably

good internal reliability at all three time points (T1—α =

0.67; T2— α = 0.68; T3—α = 0.73).

Physical health

The current study includes variables relating to mothers’

perceptions of their child’s physical health. Perceived

general health. Mothers’ were asked to rate their child’s

current general healt h (1 [ Excellent]—5[Poor]), such that a

higher score represented poorer general health. Health

professional visits. Mothers indicated which of the follow-

ing health professionals they had consulted in the past

12 months about their child’s health: general practitioner,

paediatrician, medical doctor and/or public health nurse.

The number of health professionals the mother had con-

sulted was summed. A higher score indicated that the child

had visited a higher number of different health profes-

sionals. Antibiotic Use. Mothers indicated how many times

their child had taken a course of antibiotics in the past

6 months. They used the following 6-point scale: 1 = none,

2 = once,3= twice,4= 3 times ;5= 4 or more,6= one or

more long term antibiotics, and 7 = continuous treatment .

Infection rates . Mothers were asked which of the following

infections their child had suffered from in the past three

months: gastro-intestinal, ear, bronchitis/pneumonia, cold/

ﬂu/laryngitis, and other infections. A total number of

infections variable was created, with a higher score indi-

cating a higher frequency of infections.

Sleep

For sleep behaviour, mothers were asked about the quality

and quantity of their child’s sleep. For sleep quality,

mothers were asked whether their child was drowsy or

sleepy during the day with a higher score representing

poorer quality sleep (1 = Never to 4 = Always). For sleep

quantity, mothers were asked to indicate how long in total

their child slept during the night (on average); they provi ded

answers in hours and minutes. Sleep quantity was calculated

in half hour intervals.

Family Income

When children were 17 months old, mothers provided

information on household income, household size, and type

of family residence. That information was used to calculate

income sufﬁciency using the Statistics Canada’sdeﬁnition

of low income, which considers the number of people in the

household and the family zone of residence. Family income

was coded as sufﬁcient (1) or insufﬁcient (2).

Data Analyses

First, we examined the prevalence of loneliness among the

current sample and explored bivariate correlations between

all variables. Next, we explored the changes in loneliness at

the group level for chil dren in the QLSCD when they were

ages 10–13 years using latent growth curve modelling

(LGCM). LGCM enabled us to estimate the initial level of

loneliness at baseline (intercept) and the degree of change in

loneliness from baseline across the time of the study (slope).

Factor loadings were ﬁxed at 0, 2, 3 to represent the time

difference between measurements. The model ﬁ t was

assessed using common ﬁt indices: chi square index, Root

Mean Square Error of Approximation (RMSEA: should be

less than 0.07; Steiger 2007), and Comparative Fit Index

(CFI: a value of equal or greater than 0.95 is indicative of

good ﬁt; Hu and Bentler 1999). Next, using latent class

growth analysis (LCGA; Jung and Wickrama 2008), we

explored whether there were sub groups of children that

followed distinct trajectories of loneliness from ages 10–13

years, and whether that differential development predicted

health and sleep outcomes controlling for earlier health and

family income. In the ﬁrst stage of LCGA, the basic model

was tested, exploring only the trajectories of loneliness and

without any control variables. The optimal number of

groups following distinct trajectories of loneliness was

determined by comparing K-Class and K-1 class model

based on Bayesian Information Criterion (BIC), sample-size

Journal of Child and Family Studies

adjusted Bayesian Information Criterion (aBIC), and

Akaike’s Information Criteria (AIC). A lower BIC, aBIC,

and AIC are indi cative of a better ﬁt (McLachlan and Peel,

2000). Class number was also evaluated using the Lo-

Mendell-Rubin likelihood ratio test (LMRT) and entropy.

For LMRT, a signiﬁcant p value suggests the model was

signiﬁcantly improved by the additional class when com-

pared to previous solution; for entropy, a value close to 1

indicates less classiﬁcation error and therefore representing

a better ﬁt (Nagin and Odgers 2010). The optimal number

of groups was also determined based on theoretical under-

pinnings and previous ﬁndings (Nagin and Odgers 2010)to

ensure substantive meaning to the ﬁndings. In the second

stage of the LCGA, the relations between the groups fol-

lowing distinct trajectories of lonel iness and later health and

sleep outcomes were examined using the BCH approach

(Bolck et al. 2004; Bakk and Vermunt 2016) in Mplus V8.

BCH estimated the mean differences in health and sleep

between the different trajector y classes using Wald’s chi

square test (Asparouhov and Muthén 2014). In the second

stage of the LCGA, we controlled for earlier reports of

family incom e and baseline measures of health in the

model. All models were run in Mplus V8 (Muthen and

Muthen 1998–2020). We followed van de Schoot et al.

(2017)’s recommendations for writing up the analysis.

Results

Demographics

Table 1 includes variable information for the 1214 partici-

pants whose data wer e analysed. Of those participants, 54%

identiﬁed as Canadian , 15% were French, 21% identiﬁed as

both; the remaining 10% were from other ethnicities. The

majority of the sample came from homes that had been

classiﬁed as having sufﬁcient income to meet basic needs

when the child was 17 months of age. Descriptive statistics,

including correlations within and between variables, as well

as additional analysis relating to SES stabilities, are outlined

in Supplementary Tables S1, S2.

Changes in Loneliness Over Time

Results from LGCA showed a signiﬁcant intercept (β0 =

3.82, p < 0.001) and a non-signi ﬁcant slope (β1 = 0.008,

p = 0.568), such that participants on average scored 3.82 on

the loneliness scale a t baseline, and stayed relatively stable

over time (χ

(3) = 242.65, p < 0.001, CFI = 0.98, RMSEA

= 0.07, CI = 0.024–0.118). The results demonstrated a

signiﬁcant variance in the intercept (0.36, p = 0.014) and

some variation for slope (0.30, p = 0.018), justifying the

examination of different trajectories of loneliness.

Using LCGA, two through to seven-class solutions for

loneliness were estimated, with Table 2 outlining the ﬁt

indices. Considering the model ﬁt indices, and the theore-

tical underpinnings and minimal class size recommenda-

tions (Class k >1% of N; Jung and Wickrama 2008), a 6-

class solution ﬁt the data best.

The six distinct loneliness trajectories are as follows: low

increasing to high lonelines s ( n = 23, 2%), high reducing

loneliness (n = 28, 3%), medium stable loneliness (n = 60,

5%), medium reducing loneliness (n = 185, 15%), low

increasing to medium loneliness (n = 165, 14%), and low

stable loneliness (n = 743, 61%; see Table 3 and Fig. 1).

Children following a low increasing to high loneliness

trajectory reported low levels of loneliness at 10 years old,

but reported increasingly levels of loneliness across the

three years resulting in a high level of loneliness at age 13.

Table 1 Early Family Income and Loneliness reports for all

participants (N = 1214)

% of sample N

Early Family Income

Sufﬁcient 80% 971

Insufﬁcient 20% 243

Time point 1 Time point 2 Time point 3

Mean age in years

10.14 12.13 13.13

Mean loneliness

(range is 3–9)

3.84 (1.42) 3.79 (1.41) 3.89 (1.65)

Mean loneliness scores based on sum score of T1, T2, and T3

loneliness scores. Data on early family income was collected when the

children were aged 17 months. Income sufﬁciency was deﬁned using

the Statistics Canada’sdeﬁnition of low income, whilst also

considering the number of people in the household and the family

zone of residence. The low-income cut-off used by Statistics Canada is

an income level from which, on average, a person (or family) spends

20% more of their total income on food, shelter, and clothing than is

spent by similar persons or families in similar locations (https://www

150.statcan.gc.ca/n1/pub/75f0002m/2012002/lico-sfr-eng.htm)

Mean age calculated for those who provided data at the corresponding

time points (T1: 1180; T2 = 1186; T3 = 1099)

Table 2 Model Fit Indices for 2 through 7 class solutions

Class AIC BIC Adj BIC Entropy LRT

p value

2 10,531.49 10,577.40 10,548.82 0.871 <0.001 204

3 10,318.77 10,379.99 10,341.875 0.856 0.037 130

4 10,101.22 10,177.74 10,130.09 0.907 0.050 58

5 9908.07 9999.90 9942.73 0.924 0.002 40

6 9770.46 9877.59 9810.89 0.931 0.001 23

7 9681.43 9803.87 9727.63 0.939 0.061 17

AIC Akaike’s Information Criteria, BIC Bayesian Information Criter-

ion, Adj BIC adjusted Bayesian Information Criterion, Entropy, LRT

Lo-Mendell-Rubin likelihood ratio test Value, N number of individuals

in the smallest class

Journal of Child and Family Studies

Children following the high reducing lonel iness trajectory

reported relatively high levels of loneliness when they were

10 years of age, but that reduced over the next 3 years to

average levels of loneliness. Children following the medium

stable loneliness had relatively stable, but moderate, levels

of loneliness over the three years. Those following the

medium reducing trajectory reported average levels of

loneliness at ten years of age, which reduced to lower than

average over the next three years. Those children following

the low increasing to medium loneliness trajectory reported

low levels of loneliness at age 10 years and increased on

loneliness steadily, to an average level of loneliness, over

the next three years. The majority of children followed the

low stable loneliness trajectory reporting low levels of

loneliness from ages 10–13 years.

Using the BCH approach, the mean differences on ado-

lescent health and sleep outcomes across the six different

trajectories of loneliness were estimated. To control for

Type 1 errors, a conservative adjusted alpha of <0.001 was

used for all comparisons. In this step, we also controlled for

the effects of earlier family income and health on later

health and loneliness As shown in Table 4, we found no

signiﬁcant differences in physical health and sleep com-

plaints between children who followed different trajectories

of loneliness from 10–13 years.

Discussion

The current study examined the presence of groups of

adolescents who followed distinct trajectories of loneliness

from ages 10–13 years, with the aim of exploring the pro-

spective link between prolonged loneliness and physical

health and sleep outcomes among youth. We controlled for

the impact of early family income, as a known predictor of

poor health outcomes among adolescence and for earlier

reports of poor health, exploring loneliness as a unique

predictor of poor health and inadequate sleep. We found six

distinct trajectories of loneliness—‘Low Increasing to

Medium’, ‘Medium Reducing’, ‘High Reducing’, ‘Low’,

‘Low Increasing to High’ and ‘Medium Stable’—but there

was no support for the hypothesis that increasing or main-

tained loneliness was related to poorer health outcomes as

reported by the mother. Given ﬁndings that loneliness is

linked to poor health and sleep quality among adults, we

discuss why we have diff erent ﬁndings for youth in the

current sample. We discuss why it might be the case that

young adolescents who report higher loneliness, and parti-

cularly those in the current sample, might have mothers who

do not report them as experiencing poor health and sleep

outcomes.

Adolescents in the current sample followed one of six

distinct trajectories of loneliness from 10 to 1 3 years of

age: (1) ‘Low Increasing to Medium’,(2)‘Medium

Reducing’,(3)‘High Reducing’,(4)‘Low Stable’,(5)

‘Low Increasing to High’ and (6) ‘Medium Stable’.The

low stable group explained the loneliness experience for

the majority of the participants (61%), supporting previous

research examining distinct trajectories of loneliness

across adolescence (Harris et al. 2013;Jobe-Shieldsetal.

2011; Ladd and Ettekal 2013; Qualter et al. 2013; Schinka

et al. 2013;Vanhalstetal.2013). The current sample did

not include a group of adolescents who followed a high

stable trajectory of loneliness, a group found in some of

the previous research (Ladd and Ettekal 2013;Qualter

et al. 2013; Schinka et al. 2013

), but there was evidence

that some children followed a trajectory characterised by

high reducing loneliness, and others followed a trajectory

of low increasing to high loneliness. The most li kely

explanation for not ﬁnding a group of adolescents

experiencing prolonged loneliness is the relatively low

levels of loneliness reported by the current sample. Given

other recent data suggesting that loneliness is a signiﬁcant

Table 3 Loneliness mean scores at ages 10, 12 and 13 years for the 6-

class solution

n%of

10 years 12 years 13 years

Low Increasing

to Medium

165 14 3.32 4.32 5.44

Medium Reducing 185 15 5.31 3.71 3.61

High Reducing 38 3 7.46 4.68 4.39

Low Stable 743 61 3.25 3.41 3.20

Low Increasing to High 23 2 3.77 5.76 8.05

Medium Stable 60 5 5.77 5.57 6.35

The range of scores on the loneliness measure is 3 to 9, with 9

representing high reports of loneliness

10 year

s 12 years 13 years

Low Increasing Medium

Medium Reducing

High Reducing

Low Stable

Low increasing high

Medium Stable

Fig. 1 Estimated mean trends for the six loneliness trajectory classes

Journal of Child and Family Studies

issue for adolescents (for example, Ofﬁce of

National Stat isti cs 2018), it becomes i mportant to deter-

mine what is different about the current sample that makes

these adolescents report lower levels of loneliness; given

the current study includes a representative sample of youth

we need further study to understand what might be dif-

ferent about the experiences of youth in Quebec or Canada

that sets them apart from adolescents from other countries

where reports of loneliness among youth are much higher.

It is also note-worthy that the loneliness measure utilised

in the current study is different from those in previous

research, and may explain why higher levels of loneliness

were not captured within the sample.

We did not ﬁnd support for prospective relationships

between loneliness and physical health and loneliness and

sleep outcomes: adolescents in the current study following

the different trajectories of loneliness did not signiﬁcantly

differ on the health and sleep outcomes. Previous research

that examined different pathways of loneliness reported

signiﬁcant differences between distinct loneliness trajec-

tories on health and sleep outcomes (Harris et al. 2013;

Qualter et al. 2013), but our ﬁndings do not support that

earlier work. The lack of relationship between self-reported

loneliness and parent-reported health outcomes could be for

a number of reasons. First, it may be that mothers do not

accurately report the health and sleep of their children.

Indeed, previous research suggests mother may under-

estimate sleep problems within children and adolescents

(Fatima et al. 2016) with up to one third of potential sleep

problems remaining unnoticed (Paavonen et al. 2000).

Second, the negative cognitive bias and affect associated

with loneliness could extenuate discrepancies between

informants. Although adolescents are typically accurate at

reporting their own physical health (Riley 2004), there is

evidence to suggest adolescents are much less optimistic

about their health than their parents as a result of a sensi-

tivity or mental health problem the parents is not aware of

(Waters et al. 2003). It may be the case that self-reported

health provides a ‘window into the internal lives of the

youth’ (Johnson and Wang 2008); adolescents reporting

loneliness are pessimistic and negative in their reﬂections,

and that includes their health, supporting previous asso-

ciations between youth loneliness, health and sleep (Qualter

et al. 2013; Stickley et al. 2016; Matthews et al. 2017;

Harris et al. 2013; Eccles et al. 2020). Further research

should investigate the impact of different informants,

including parents and health professionals, to help deepen

our understanding about the true relationship between youth

loneliness and health.

The current study offers an important contribution to the

literature on loneliness and health. We showed that the

relationship may not be evident in early adolescence when

parent reports of children’s health are used. However, the

study is not without limitations. It is important to note the

health measures were not from a validated scale, and the

current research could be extended through the use of more

objective measures of sleep, (e.g. sleep caps) and health

biomarkers and cortisol). Extending research examining the

relationship between youth loneliness and health and/or

sleep through more objective measures, to mirror those used

in adult literature, will help provide a deeper understanding

behind the relationship as well as insight into the underlying

mechanisms.

It is also important to recognise the overall levels of

loneliness reported in the current sample are low. Given the

reliability of the loneliness measure used it the current study,

the current study highlights the importance of measurement

choice. There are arguments for using short measures of

loneliness with restricted response scales, but there are

counterarguments that such measures do not truly capture the

experience of youth loneliness (Eccles et al. 2020

). However,

Eccles et al. showed that associations between loneliness and

health/sleep complaints were robust across the longer and

shortened version of the UCLA when used with adolescents,

Table 4 Trajectory means and comparisons on physical health and sleep outcomes, controlling for early family income and health outcomes

Low Increasing to Medium Medium Reducing High Reducing Low Stable Low Increasing to High Medium Stable

Visits to Professionals 1.57 1.06 1.60 1.30 2.28 2.65

Perceived Health 0.72 0.95 0.76 0.68 0.90 0.26

Infection Rates 5.51 4.98 3.28

5.10 5.04 4.75

Antibiotic Use 1.06 1.06 0.78 0.89 1.32 1.14

Daytime Drowsiness 0.97 0.73 0.61 0.66 1.34 0.79

Overall Sleep Quantity 36.26 33.58 35.98 33.44 34.48 30.73

No comparison demonstrated a signiﬁcant factor as indicated by p > 0.05

Mothers reported on all health complaints. All values reported for the outcomes are standardised residuals. Differences across proﬁles on T3/age 13

outcomes were obtained after controlling for earlier reports of respective physical health/sleep complaints (measured at T1/age 10) and early family

income effects (data collected when the child was 17 months old)

Analysis were also completed without Earlier Family Income as a control and the results did not differ

High reducing trajectory demonstrated lower rates of infection than the low stable (p = 0.010), medium reducing (p = 0.051) and low increasing

(p = 0.004). However, to control for Type 1 errors, these comparisons failed to reach the Bonferroni adjusted p < 0.001

Journal of Child and Family Studies

suggesting that short measures such as that used in the cur-

rent study should not represent a problem because they

measure loneliness accurately. However, more research using

both standard and shortened versions of loneliness measures

are needed so the potential impact of how we measure

loneliness can be examined. To help overcome that potential

limitation, it is important that future work gathers prevalence

and cross-national survey data relating to the presence,

experience, and feeling of loneliness amongst young people,

and the associated health outcomes.

With the limitations considered, the current study still

offers a valuable contribution to the literature and has many

merits. The study retains a large sample size throughout the

analyses, and utilises a robust statistical method that con-

sidered the health differences between groups following

different trajectories of loneliness, allowing for full analysis

in one statistical programme (Bakk and Vermunt 2016). The

longitudinal nature of the study is certainly a strength,

enabling the examination of the relationship between lone-

liness and health outcomes over a prolonged period of time;

the control of early family income and health also ensured

robust control of important related constructs. The current

study examined the prospective association between lone-

liness, physical health, and sleep, and we provided a robust

design by controlling for early reports of family income and

child health that have been shown to impact later health

outcomes. Contrary to research with adults, we did not ﬁnd a

relationship between increasing loneliness and poor health

and sleep quality. That was, to some extent, a reﬂection of

the fact that young adolescents in the current representative

sample of Canadian youth did not report high levels of

loneliness. Future research will want to (1) explore feelings

of loneliness in different groups to establish whether high,

stable loneliness over time impacts health negatively, and (2)

gather prospective health data from multiple sources,

including via objective measurement, to explore whether the

relationship between loneliness and poor health is primarily

driven by self-perceptions.

Acknowledgements This study uses data from the Quebec Long-

itudinal Study of Child Development (QLSCD). Data collection for the

QLSCD was made possible through funding from the ministère de la

Santé et des Services Sociaux (MSSS) (Ministry of Health and Social

Services), the Lucie and André Chagnon Foundation, the ministère de

la Famille (Ministry of Family), and the Institut de la statistique du

Québec (the Institut).

Author Contributions A.E. conceived the study, performed statistical

analyses, interpreted the ﬁndings, drafted the manuscript and made

revisions to the manuscript based on reviewer feedback; P.Q. con-

ceived the study, helped to perform statistical analyses, interpret the

ﬁndings, and contributed to the draft manuscript; M.P. and R.H.

helped to perform statistical analyses and interpret the ﬁndings, and

contributed to the draft manuscript; M.B. and R.E.T. participated in the

study design, and edited the manuscript; M.B. and R.E.T. coordinated

the original data collection. All authors read and approved the ﬁnal

manuscript.

Data Sharing and Declaration The dataset analysed during the current

study is not publicly available; It is available from Institut de la sta-

tistique du Québec (Information and Documentation Centre, Institut de

la statistique du Québec, 200, chemin Sainte-Foy, 3e étage Québec

(Québec, G1R 5T4).

Compliance with Ethical Standards

Conﬂict of Interest The authors declare that they have no conﬂict of

interest.

Ethical Approval All procedures performed in studies involving

human participants were in accordance with the ethical standards of

the institutional and/or national research committee (include name of

committee + reference number) and with the ‘1964 Helsinki

declaration and its later amendments or comparable ethical standards.’

Informed Consent Informed consent was obtained from all individual

participants included in the study.

Publisher’s note Springer Nature remains neutral with regard to

jurisdictional claims in published maps and institutional afﬁliations.

Open Access This article is licensed under a Creative Commons

Attribution 4.0 International License, which permits use, sharing,

adaptation, distribution and reproduction in any medium or format, as

long as you give appropriate credit to the original author(s) and the

source, provide a link to the Creative Commons license, and indicate if

changes were made. The images or other third party material in this

article are included in the article’s Creative Commons license, unless

indicated otherwise in a credit line to the material. If material is not

included in the article’s Creative Commons license and your intended

use is not permitted by statutory regulation or exceeds the permitted

use, you will need to obtain permission directly from the copyright

holder. To view a copy of this license, visit http://creativecommons.

org/licenses/by/4.0/.

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