Demographic and coping predictors of severe forms of loneliness in young people aged 12-25 years

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F. Lau This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4406667/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Loneliness is common in young people and predicts a range of concurrent psychiatric conditions. Yet, young people feel there are few resources to support them. Who develops severe forms of youth loneliness and which modifiable psychological correlates are associated with loneliness severity could help in developing resources to support groups of young people who are most vulnerable. Here, we explored which demographic characteristics (age, gender, minority ethnic status, and indices of socioeconomic status) predicted more severe forms of loneliness. Based on strategies that young people said they would recommend to a friend to manage loneliness, we also explored whether specific coping strategies and coping flexibility predicted severe loneliness. We explored these questions using loneliness data gathered during the COVID-19 pandemic, a time when social restriction policies heightened loneliness experiences. Latent class growth analysis identified five loneliness trajectory classes. Among these was a “high stable” group (11% of the sample) who reported frequent loneliness that also endured across time-points. Other groups included a moderate decreasing (15%), a low increasing (16%), a moderate stable (23%), and a low stable (35%) group. The high stable loneliness class also reported significantly lower wellbeing scores compared to the many of the other groups. Entry into the high stable loneliness group was predicted by being female. Recommendation of approach coping strategies predicted lower likelihood of being in the high stable loneliness group. Future research and clinical work should explore the utility of coping strategies to manage loneliness to reduce the impact on well-being and psychiatric outcomes. adolescents youth chronic loneliness social isolation coping Figures Figure 1 Introduction Loneliness is the distressing emotional state resulting from a discrepancy between a person’s desired and perceived quantity and quality of social relationships [1]. Loneliness is associated with poorer health [2] including a broad range of psychiatric difficulties [3]. Young people are vulnerable to loneliness and its’ negative impacts [4, 5]. Elevations in loneliness in youth may be transient, reflecting age-normative transitions in the social environment (e.g., starting/leaving education/work, leaving home) and on developmental changes in non-social aspects e.g., emerging independence, self-identity [6, 7]. But some young people experience more severe loneliness, when these feelings are frequent, persistent and harmful to mental and physical health and other areas of functioning [8]. Indeed, longitudinal studies have identified a group of young people (between 1-22%) who experience prolonged and/or increasing loneliness across time [9-14]. But young people have said that there are few (tailored) resources to help them manage loneliness. They call for a research agenda that includes identifying who has more “severe” forms of loneliness and more accessible resources for manging loneliness [15]. Here, we investigate which demographic sub-groups of young people report more severe loneliness and whether loneliness severity is associated with differences in modifiable coping strategies. Identifying demographic characteristics of those with more severe loneliness can facilitate support to those most vulnerable. In adults, being female or being of a lower socioeconomic status (SES) are associated with more frequent and persistent loneliness [16-19]. In young people, some studies have reported gender differences while others have not [20, 21]. A few studies have found that ethnic minority status is associated with greater loneliness [22, 23] but another only found that (lower) family income, but not sex and ethnicity, predicted membership of the “chronic” loneliness group [13]. Finally, another study found that among young people aged 12 to 18 years, being female and being older predicted membership of a high, stable group [24]. Most of these studies of youth, however, have captured loneliness at a single time-point only, limiting the dimension of loneliness severity to frequency only, rather than capturing the persistence of loneliness. Identifying modifiable psychological characteristics of those with more severe loneliness can help innovate programmes and interventions to support those experiencing loneliness. Investigating how coping variables relate to youth loneliness may shed some light on these modifiable characteristics. Coping refers to an individual’s cognitive and behavioural efforts to manage internal and external demands [25]. Many different categories of coping strategies have been suggested but there is a lack of consensus on coping typologies and categories [26]. In adults, across studies [27], problem-focused coping styles associated with lower loneliness levels and emotion-focused coping styles with higher loneliness levels. There are far fewer studies of coping in relation to loneliness in youth. One study found that avoidant coping strategies, such as rumination, were associated with higher levels of loneliness in adolescents and young adults [28]. However, this research does not inform more specific strategies within these broad coping categories that are more or less helpful to those experiencing loneliness. Recently, we coded the qualitative responses of young people when asked to recommend coping strategies when faces with social isolation to a friend [29]. We then mapped these onto more specific coping strategies in the wider literature. Coping strategies identified included social (contact seeking), behavioural (approach, distraction and self-care) and psychological-cognitive (self-talk, self-compassion, and gratitude) strategies but we did not explore associations between coping strategies and loneliness. Here, we sought to investigate the demographic and coping predictors of loneliness in a large study of young people in the UK. Loneliness data was gathered at multiple time-points during the COVID-19 pandemic, a period of unprecedented restrictions to social interactions. We focussed on young people aged 12-25 years to capture a broad period of developmental sensitivity and to identify within this period, age-specific differences (within youth) in the propensity to experience more severe forms of loneliness. We first explored the proportion of young people with more severe forms of loneliness, defined as those with more frequent and persistent loneliness experiences across time and also with the lowest well-being scores. Given prior data, we expected this to be between 1-22%. Next, we explored whether age, sex, ethnicity, and socioeconomic status predicted different loneliness trajectories, especially those with more severe forms. Finally, we compared the endorsement of different self-reported coping strategies identified in our previous study [29] across different loneliness trajectories. However, as some have argued that it is flexibility in using different coping strategies when faced with various stressful situations, rather than an over-reliance on a single strategy, that may be linked to better psychological wellbeing [30], we compared overall number of coping strategies across the different loneliness sub-groups. The dataset used here overlaps with data reported in our other study of young people aged 12-18 years [24]. While the earlier study included parallel loneliness data from young people from India and Israel, allowing a focus on cross-cultural differences alongside, age and gender, the current study uses data from young people across a broader age range (12-25 years) alongside data on ethnicity, indices of socioeconomic status and data on coping strategies recommended by young people. Methods Participants and Procedure We analysed data from a multi-wave study investigating the impact of the COVID-19 pandemic on young people’s emotional wellbeing. The study received ethical approval from the Psychiatry, Nursing and Midwifery Research Ethics Committee at Kings College London (Ref: HR-19/20-18250). The eligibility criteria included being aged 12–25 years old, being able to read the questionnaire in English, and residing in the UK at the time of the data collection for the first timepoint. Participants were invited, through schools, colleges and universities in the UK, websites, social media, and charity mailing lists, to complete an online survey using Qualtrics about their demographics (age, sex, ethnicity, and highest parental education qualifications), their loneliness and wellbeing and what advice they would give to others over managing social distancing and isolation situations. Young people either provided consent (if aged 16 years and above) or were instructed on how to obtain parental consent (if under 16 years). Participants were surveyed fortnightly across 8 time-points and received vouchers for their time/efforts. Data collection occurred between May 2020 and April 2021. Measures Demographics Age, sex assigned at birth, country currently living in, and ethnicity of participants were collected. Ethnicity data was collected as a multiple-choice option from a list according to the Office of National Statistics’ recommendations [ 31 ]. Highest academic qualification obtained by either of their parents were collected as a proxy SES. Young people’s reports of parental education levels may be a less biased indicator of SES compared to proxies based on young people’s reports of other indicators, such as parental occupation [ 32 ]. Loneliness Participants completed the short three-item University of California Los Angeles (UCLA) Loneliness Scale [ 33 ]. The three-item UCLA scale has been advised for use in young people aged 10–15 years [ 34 ]. Total scores range from 3 to 9, with higher scores indicating more loneliness. The original 20-item UCLA scale has shown high internal consistency (alpha between 0.71 and 0.96) with children and adolescents but the psychometrics of the shorter UCLA scale in younger populations is more mixed [ 35 ]. In our sample, the internal consistency of the UCLA at each assessment point was good ( α = 0.80–0.84). Mental Well-being The short 7-item version of the Warwick-Edinburgh Mental Well-being scale (SWEMWBS; [ 36 ]) indexed well-being. Total scores ranged from 7 to 35 with higher scores indicating better mental well-being. The reliability of the SWEMWBS at the initial and final assessment was good ( α = 0.78–0.86). Coping Strategies Participants were asked, “Based on your experiences, what advice would you give to other young people on managing the isolating experiences of social distancing?” and provided with a free text response. The development of a coding scheme of coping strategy categories is described in [ 29 ]. In brief, strategies were informed by the general emotion regulation and coping strategy literature (without reliance on one sole framework) and the range of therapeutic techniques used in psychological treatments for affective conditions, along with the themes that emerged from the participants’ responses using thematic analysis guidelines [ 37 ]. The final coding scheme consisted of 7 categories: contact-seeking, distraction, approach, self-care, self-talk, self-compassion, and gratitude. Responses that were vague, unclear, or expressed not knowing what to recommend were coded “None / Vague”. The total number of different coping strategy categories recommended indexed coping flexibility [ 30 ]. Statistical analyses Data cleaning 4,872 responses were collected initially at baseline. After data cleaning [see 24], and the inclusion of participants with loneliness data at three or more time points, the final sample for the analysis consisted of 1,624 participants. Participants who were included were significantly older than those who were excluded (had data at less than three timepoints) ( MD = 0.91, t (2600)=-6.285, p < .001, d = 3.57). Females were more likely to be included than males ( χ 2 (1) = 43.56, p < .001). To investigate the overall change in loneliness over time, we used latent growth curve modelling (LGCM) using the Lavaan package [ 38 ] for R Version 4.2.0. LGCM estimates the initial level of loneliness (intercept) and the change in loneliness over time (slope). To examine whether there were individual differences in the loneliness trajectory, the variance in the intercept and slope were estimated. Fixed growth factor loadings of 0, 1, 2, 3, 4, 5, 6, 7 using maximum likelihood estimation with robust Huber-White standard errors and a scaled test statistic were used to fit a linear model. A comparative fit index (CFI) > 0.97 and a Standardised Root Mean Square Residual (SRMR) < 0.05 is considered a good model fit while a CFI = 0.95–0.97 and SRMR = 0.05–0.10 is considered acceptable. We used latent class growth analysis (LCGA, implemented in Version 8.8 Mplus) to investigate the individual differences in loneliness trajectories, identifying different classes of loneliness trajectories. A series of models with different number of classes were fit to the loneliness data to determine the best number of classes. The 2-, 3-, 4-, 5- and 6-class trajectory models were compared. To inform our decision of the number of optimal classes, we used similar criteria as described in our previous paper [ 24 ]: Model convergence Comparing K-class and K-1 class model fit statistics, using the Akaike information criterion (AIC), Bayesian information criterion (BIC) and sample size adjusted BIC (aBIC). Bootstrapped Lo, Mendell, Rubin likelihood ratio test (LRT), comparing K-class and K-1 class models. Entropy. A measure of subgroup classification quality, higher is preferrable. Minimum subgroup n. E.g., more than 5% of total sample. Qualitatively different subgroup trajectories. Model parsimony and the theoretical meaning and relevance of classes. After selecting the optimal model, and identifying the latent loneliness classes, the relationship between loneliness class membership and wellbeing outcome at the final timepoint was examined while controlling for sex, age, SES, ethnicity, and initial wellbeing scores. The manual Bolck, Croons, and Hagenaars (BCH) method [ 39 ] was carried out. This estimates a distal outcome model (final wellbeing) with an arbitrary secondary model (controlling for covariates in addition to the latent classes). The BCH model produces final wellbeing mean intercepts specific to each loneliness class, which indicate the loneliness classes’ influence on the final wellbeing scores, and an omnibus test was the carried out to detect differences between the loneliness trajectory groups. If the omnibus test was significant, pairwise comparisons were carried out with the mean intercepts across the loneliness classes using the “MODEL CONSTRAINT” function in Mplus. Next, covariates: sex, age, ethnicity, highest level of parental education and coping strategy variables were added to the model using MPlus’ R3STEP. This method regresses the loneliness classes on the covariates while preventing the inclusion of the covariates from changing the latent class structure and measurement error. Another separate model using R3STEP was carried out using demographic factors and coping flexibility (number of coping strategies endorsed). Missing data in the LGCM and LCGA models were modelled using full information maximum likelihood estimation. As participants’ survey start date and length of participation varied due the study’s rolling start date and number of surveys completed, we regressed out both variables from the loneliness scores at each assessment timepoint and used the residuals in the LCGA models. Exact p-values were reported for all tests. The Benjamini-Hochberg adjustment with a false discovery rate of 0.05 controlled for multiple comparisons. Results Descriptive statistics Table 1 shows participants’ demographic characteristics, loneliness and well-being scores at each time-point. The mean age of the participants was 18.2 years ( SD = 3.55). 75% of participants were female and 63% were White. More than half of the participants reported that their parents were university educated (66%), with a quarter of parents obtaining a post-graduate degree. The seven coping categories (contact seeking, distraction, approach, self-care, self-talk, self-compassion, gratitude) derived from the free-text responses to the question “Based on your experiences, what advice would you give to other young people who are managing the isolating experiences of social distancing?” are published in an earlier paper [ 29 ] along with other analysis linking these with demographic characteristics. In brief, the three most frequently recommended were contact seeking (51.7%), self-care (35.8%) and distraction (23.2%). The least recommended strategy was gratitude (9.0%). 5.8% of participants did not recommend a coping strategy. As for coping flexibility, young people recommended between one and six coping strategies ( M = 1.72, SD = 0.99) with one being the most frequently recommended number of coping strategies (41.6%) and the percentage of participants decreasing as number of coping strategies increased. Table 1 Sample characteristics and mean responses at the initial timepoint Variables Participants at Timepoint 1 ( N = 1624) Mean ( SD ) or % n Age 18.2 (3.55) 1,624 Sex Male 25 414 Female 75 1210 Ethnicity White / White British 63 1019 Asian / Asian British 20 326 Black / Black British 4 60 Mixed or Other 11 170 Prefer Not to Say 3 49 Highest Parent Education Primary 2 40 GCSE 13 205 A-level 19 300 Undergraduate 41 664 Master 19 314 PhD 6 102 Loneliness (UCLA) 5.31 (1.84) 1,624 Mental Wellbeing (SWEMWBS) 21.9 (4.42) 1,624 Latent Growth Curve Modelling analysis The LGCM fit the data well, χ 2 (31) = 168.15, p < .001, CFI = 0.98, SRMR = 0.04. The intercept ( standardised estimate = 3.48, unstandardised = 5.36, p < .001) and slope ( standardised estimate =-0.18, unstandardised =-0.04, p < .001) were significant, indicating that the mean loneliness scores decreased over time. There were significant variances in both the intercept ( standardised = 1.00, unstandardised = 2.38, p < .001) and slope ( standardised = 1.00, unstandardised = 0.04, p < .001), which supported further analyses of distinct trajectories. Figure S1 in Supplementary Material shows the overall loneliness trajectory across the eight assessments grouped by age, sex, parental education level and ethnicity. Latent Class Growth Analysis The LCGA was carried out to identify different loneliness trajectory classes. For the fit statistics of the 2 through to 6-class models see Table S2 in Supplementary Material. All classes’ bootstrapped LRT were significant. Considering the fit indices, entropy, minimum number of participants in each class, and the substantive meaning of the classes, the 5-class model was chosen. In the 5-class model, the largest class consisted of 35% of the sample and shows a low, stable loneliness trajectory ( M intercept =-1.60, p < .001; M slope =0.02, p = .064). There were classes that showed: a moderate stable loneliness trajectory (23%; M intercept =1.26, p < .001; M slope =-0.002, p = .946), low increasing loneliness trajectory (16%; M intercep t =-0.76, p < .001; M slope =0.24, p < .001) and a moderate decreasing loneliness trajectory (15%; M intercept =0.69, p < .001; M slope =-0.25, p < .001). Lastly, the smallest class (11%) shows a high, stable loneliness trajectory ( M intercept =2.59, p < .001; M slope =0.03, p = .354). The loneliness trajectories of the 5 classes are shown in Fig. 2, which depicts the trajectories using the raw loneliness scores. Final wellbeing as a function of loneliness class membership Table 2 presents results from the BCH method investigating loneliness class differences in predicting final wellbeing outcome. The omnibus Wald χ 2 test indicated that overall, there were significant differences in the wellbeing mean intercept between the loneliness classes at the final assessment timepoint ( p < .001). We ran further pairwise comparisons between the high stable loneliness class and the other four classes. After Benjamini-Hochberg adjustment (false discovery rate of 0.05), participants in the high stable loneliness class reported significantly lower final wellbeing scores compared to the low stable class ( z =-4.43, p < .001), moderate stable class ( z =-1.74, p = .001) and the moderate decreasing class ( z =-4.87, p < .001). There were no significant differences in the final wellbeing scores between high stable loneliness class and low increasing loneliness class ( z =-0.59, p = .290). For the initial and final wellbeing of participants according to the five trajectory classes, see Table S3 in Supplementary Material. Table 2 Latent class growth analysis of loneliness predicted by demographic factors and coping strategy variables High stable vs. Low stable loneliness High stable vs. Moderate stable loneliness High stable vs. Low increasing loneliness High stable vs. Moderate decreasing loneliness Variable B (SE) OR [95% CI] p B (SE) OR [95% CI] p B (SE) OR [95% CI] p B (SE) OR [95% CI] p Age -0.03 (0.03) 0.97 [0.92, 1.02] .240 -0.04 (0.03) 0.96 [0.91, 1.03] .250 -0.03 (0.03) 0.97 [0.91, 1.04] .419 0.01 (0.04) 1.01 [0.94, 1.07] .764 Sex -1.23 (0.30) 0.28 [0.16, 0.52] < .001*** -0.68 (0.36) 0.51 [0.25, 1.02] .058 -0.67 (0.36) 0.51 [0.26, 1.03] .059 -1.09 (0.35) 0.33 [0.17, 0.66] .002** Ethnicity 0.03 (0.21) 1.03 [0.68, 1.56] .880 0.05 (0.26) 1.04 [0.64, 1.72] .860 -0.21 (0.26) 0.81 [0.48, 1.35] .414 0.28 (0.26) 1.32 [0.77, 2.09] .283 Parental Education -0.24 (0.21) 0.79 [0.52, 1.19] .253 0.07 (0.25) 1.07 [0.65, 1.76] .792 -0.05 (0.26) 0.95 [0.57, 1.58] .847 -0.08 (0.27) 0.92 [0.54, 1.51] .766 Coping Strategy Contact Seeking 0.05 (0.21) 1.05 [0.69, 1.59] .819 0.22 (0.26) 1.24 [0.75, 2.06] .400 0.06 (0.26) 1.06 [0.64, 1.77] .813 0.02 (0.27) 1.02 [0.60, 1.74] .934 Distraction 0.01 (0.23) 1.01 [0.64, 1.59] .964 -0.05 (0.28) 0.95 [0.55, 1.63] .845 0.32 (0.30) 1.38 [0.77, 2.46] .280 0.34 (0.31) 1.40 [0.77, 2.57] .273 Approach -0.39 (0.29) 0.68 [0.38, 1.20] .178 -0.57 (0.33) 0.57 [0.30, 1.08] .084 0.06 (0.39) 1.06 [0.50, 2.27] .879 -0.99 (0.33) 0.37 [0.20, 0.71] .003** Self-care -0.41 (0.24) 0.66 [0.41, 1.05] .079 -0.53 (0.28) 0.59 [0.34, 1.01] .054 -0.31 (0.28) 0.74 [0.42, 1.28] .275 -0.78 (0.29) 0.46 [0.26, 0.81] .007** Self-talk 0.28 (0.23) 1.31 [0.83, 2.08] .235 0.41 (0.30) 1.51 [0.84, 2.69] .166 0.52 (0.31) 1.68 [0.91, 3.09] .096 0.25 (0.32) 1.28 [0.68, 2.41] .445 Self-compassion 0.16 (0.29) 1.17 [0.66, 2.06] .586 -0.35 (0.33) 0.71 [0.37, 1.33] .282 0.33 (0.39) 1.40 [0.65, 3.01] .395 0.61 (0.47) 1.84 [0.73, 4.65] .199 Gratitude 0.04 (0.32) 1.04 [0.56, 1.95] .901 0.84 (0.45) 2.32 [0.96, 5.59] .061 0.06 (0.43) 1.06 [0.46, 2.44] .897 -0.04 (0.43) 0.96 [0.42, 2.23] .931 SE = Standard Error; OR = Odds Ratio; CI = Confidence Intervals. Sex: female = 0, male = 1; ethnicity: marginalised = 0; majority = 1; parental education level: below undergraduate = 0, undergraduate and above = 1. * p < .05, ** p < .01, *** p < .001, bold values represent statistical significance based on Benjamini-Hochberg adjustment (false discovery rate of 0.05) Predictors of loneliness class membership Table 3 presents the results from the LCGA with predictors, showing the associations between both demographic factors and coping strategies and the likelihood of membership in the loneliness trajectory classes. Compared to the low stable loneliness class, participants in the high stable loneliness class were more likely to be females compared to males, OR = 0.28, 95% CI [0.16, 0.52], p < .001. Participants in the high stable loneliness class were also more likely to be female, compared to male, than those in the moderate decreasing class, OR = 0.33, 95% CI [0.17, 0.66], p = .002. There were no significant differences between males and females in the likelihood of class membership between those in the high stable loneliness class compared to those in the moderate stable class nor the low increasing class. Age, ethnicity, and parental education were not significant predictors of loneliness class membership. For coping strategy, participants who recommended approach coping strategies were less likely to be in the high stable loneliness class compared to the moderate decreasing loneliness class, OR = 0.37, 95% CI [0.20, 0.71], p = .003. While not significant after Benjamini-Hochberg corrections, self-care as a coping strategy approached significance: participants who recommended self-care as a coping strategy were also less likely to be in the high stable loneliness class compared to the moderate decreasing loneliness class, OR = 0.46, 95% CI [0.26, 0.81], p = .007. Endorsement of the other coping strategies did not significantly predict loneliness class membership. Table 3 Final wellbeing estimated means by loneliness class Outcome Loneliness class High stable ( n = 172) Low stable ( n = 574) Moderate stable ( n = 371) Moderate decreasing ( n = 243) Low Increasing ( n = 264) Latent subgroup comparison omnibus test M 1 (SE) M 1 (SE) M 1 (SE) M 1 (SE) M 1 (SE) Wald χ 2 p-value Final Wellbeing 10.12 (0.96) 14.56 (1.16) 11.94 (1.02) 14.99 (1.02) 10.73 (1.13) 178.32 < .001 Note . SE = Standard Error. Loneliness scores are adjusted for baseline survey date and study participation duration. 1 Means estimated from class-specific intercepts (BCH-generated) for the final wellbeing in a latent class auxiliary regression model, which represents the influence of the latent loneliness class on final wellbeing. Another regression model with the latent classes and demographics as covariates in addition to coping flexibility (number of coping strategies endorsed) was ran. Coping flexibility did not significantly predict loneliness class membership above and beyond the other predictors. Discussion We investigated the proportion of young people with the most severe forms of loneliness across time, and which demographic characteristics and coping strategies predicted these more severe subgroups. We addressed these questions during the COVID-19 pandemic, a time when many young people’s lives were affected by social restrictions, which impacted their education, professional life, finances, relationships and daily routines. Overall loneliness levels experienced by young people was moderate and decreased slightly over time in line with some [ 40 ] but not all previous data [ 41 ], gathered during the pandemic. Five distinct loneliness trajectories were identified with the high-stable group comprising 11% of the sample, consistent with non-pandemic [ 10 – 14 ] and pandemic data [ 24 ]. The high stable loneliness was associated with significantly lower wellbeing (compared to the low stable, moderate stable, and moderate decreasing loneliness classes) at the final timepoint, after controlling for initial wellbeing and demographic factors. The link between loneliness and adverse psychological impacts has been well documented in both cross-sectional and longitudinal studies prior to COVID-19 with both lower overall wellbeing and increased mental health difficulties [ 42 ]. Large proportions of the sample also had loneliness trajectories that were at least moderate in level or increasing (23% were stable moderate and 16% were low increasing) too, again consistent with adult data [ 16 ]. Sex predicted loneliness class membership above and beyond other demographic predictors, such as age, ethnicity, and SES. Females were more likely than males to experience chronic high loneliness compared to low stable or moderate decreasing loneliness. This is similar to a longitudinal adult study [ 16 ] and cross-sectional young people studies during COVID [ 19 , 43 , 44 ], although some have reported no gender differences in changes in perceived social isolation [ 45 ]. These differences may have emerged because more social support mitigates loneliness in females, and social distancing would create barrier in sustaining social support [ 46 ]. Alternatively, females may be more willing to express emotions including loneliness. Our findings highlight the need to consider modifiable strategies to manage youth loneliness. Contact seeking, the most frequently recommended coping strategy in our study and perhaps the most direct form of increasing social support, was not associated with loneliness, consistent with reports that increased social contact, such as over the phone or texting, during the pandemic did not reduce loneliness in adolescents [ 43 ]. Instead, young people who recommended approach strategies (e.g., picking up a new hobby, learning something new) were more likely to have a moderate decreasing loneliness trajectory compared to a high stable trajectory. Indeed, approach strategies which relates to providing a range of rewarding activities is similar to behavioural activation principles often applied in depression treatments. Although no longer significant after controlling for multiple comparisons, our data tentatively suggested that recommending self-care (e.g., scheduling, exercise, productive work) predicted decreasing loneliness. Research into coping strategies and loneliness is important because coping can be modified and learnt through interventions such as coping skills training and are likely to be acceptable and easily adoptable by young people. There are some study limitations. First, the trajectory classes found in this study were statistically derived using LGCM and LCGA rather than through direct measurement/observation, so caution over interpreting group differences is warranted. Secondly, our sample had a larger representation of females, individuals of higher SES and individuals of minoritized ethnic groups than the general UK population limiting the generalisability of our findings to other young people in the UK (and beyond). The higher representation of minoritized ethnic groups in this study can be seen as a strength for enabling a better understanding of groups that are less represented. Parents’ educational attainment was used as a proxy for SES, which is not uncommon for this age group but nonetheless has limitations. Third, we measured loneliness as a single dimension based on 3 items, and future studies may wish to consider different dimensions of loneliness such as social and emotional loneliness in future studies. Finally, our question on coping strategies was worded as recommendations rather than what participants used to cope themselves. As associations between people’s recommendations and their behaviours [are not always aligned [49], future studies may benefit from exploring what coping strategies young people employed, how effective were they in implementing the strategies, and how effective the strategies were for managing loneliness. In summary, this study identified a minority of young people who report prolonged high levels of loneliness, along with the negative impact of chronic loneliness on wellbeing. These findings speak to the importance of encouraging young people to use their psychological resources (functional coping strategies) to manage loneliness especially given the few effective targeted interventions for young people who are lonely. Approach and self-care coping strategies may have potential in alleviating young people’s feelings of loneliness and improving their wellbeing but it is also important to recognise that those who struggle with loneliness are not a homogenous group, and strategies most useful to young people may vary [ 48 ]. Declarations Funding and competing interests: This work was supported by funding from the Rosetrees Trust (ref M949) and the UK Economic and Social Research Council (ES/T00004X/1). In addition, JYFL and DF have funding from UKRI which currently supports LR (MR/X002381/1). The authors have no relevant financial or non-financial interests to disclose. Ethics approvals and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. The study received ethical approval from the Psychiatry, Nursing and Midwifery Research Ethics Committee at Kings College London (Ref: HR-19/20-18250). Informed consent was obtained from all individual participants included in the study or if under 18 years, by their legal guardians. Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. The first draft of the manuscript was written by Angelina Jong and Jennifer Lau, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank Alan Meehan and Ted Barker for their advice on the statistical analyses for the current study. Data Availability Data are currently being prepared for sharing in a data repository. During this process, data will be made available on request. References Peplau L, Perlman D (1982) Perspectives on loneliness. In: Peplau, Perlman (eds) Loneliness: A sourcebook of current theory, research and therapy. Wiley, New York, pp 1–20 Henriksen J, Larsen ER, Mattisson C, Andersson NW (2019) Loneliness, health and mortality. Epidemiol Psychiatr Sci 28(2):234–239. 10.1017/S2045796017000580 Mann F et al (2022) Loneliness and the onset of new mental health problems in the general population. Soc Psychiatry Psychiatr Epidemiol 57(11):2161–2178. 10.1007/s00127-022-02261-7 Victor CR, Yang K (2012) The Prevalence of Loneliness Among Adults: A Case Study of the United Kingdom. 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Cross-cohort analyses of predictors of loneliness before and during the COVID-19 pandemic. Public Health 186:31–34. https://doi.org/10.1016/j.puhe.2020.06.036 Lee CM, Cadigan JM, Rhew IC (2020) Increases in Loneliness Among Young Adults During the COVID-19 Pandemic and Association With Increases in Mental Health Problems. J Adol Health 67(5):714–717. https://doi.org/10.1016/j.jadohealth.2020.08.009 Li LZ, Wang S (2020) Prevalence and predictors of general psychiatric disorders and loneliness during COVID-19 in the United Kingdom. Psychiatry Res 291:113267. https://doi.org/10.1016/j.psychres.2020.113267 Heinrich LM, Gullone E (2006) The clinical significance of loneliness: a literature review. Clin Psychol Rev 26(6):695–718. 10.1016/j.cpr.2006.04.002 Weeks MS, Asher SR (2012) Loneliness in Childhood. In: Elsevier (ed) Advances in Child Development and Behavior, Vol. 42, pp. 1–39 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 Psychiatric Epid 51(10):1373–1384. https://doi.org/10.1007/s00127-016-1279-3 van Bergen DD, Smit JH, van Balkom AJLM, van Ameijden E, Saharso S (2008) Suicidal Ideation in Ethnic Minority and Majority Adolescents in Utrecht, The Netherlands. Crisis 29(4):202–208. https://doi.org/10.1027/0227-5910.29.4.202 Riddleston L et al (2022) Identifying characteristics of adolescents with persistent loneliness during COVID-19: A multi-country eight-wave longitudinal study. JCPP Adv 4(1):e12206. 10.1002/jcv2.12206 Ray C, Lindop J, Gibson S (1982) The concept of coping. Psychol Med 12(2):385–395. https://doi.org/10.1017/S0033291700046729 Besevegis E, Galanaki EP (2010) Coping with loneliness in childhood. Eur J Dev Psychol 7(6):653–673. https://doi.org/10.1080/17405620903113306 Deckx L, van den Akker M, Buntinx F, van Driel M (2018) A systematic literature review on the association between loneliness and coping strategies. Psychol Health Med 23(8):899–916. https://doi.org/10.1080/13548506.2018.1446096 Seepersad S (2004) Coping with Loneliness: Adolescent Online and Offline Behavior. CyberPsychol Beh 7(1):35–39. https://doi.org/10.1089/109493104322820093 Jong A et al (2023) Young people’s self-reported coping strategies to manage social isolation: Lessons learnt from the COVID-19 pandemic lockdowns. Curr Res Beh Sci 5:100133. https://doi.org/10.1016/j.crbeha.2023.100133 Cheng C, Cheung MWL (2005) Cognitive Processes Underlying Coping Flexibility: Differentiation and Integration. J Pers 73(4):859–886. https://doi.org/10.1111/j.1467-6494.2005.00331.x Office of National Statistics (2017) Measuring Equality: A Guide for the Collection and Classification of Ethnic Group, National Identity and Religion Data in the UK. https://www.ons.gov.uk/methodology/classificationsandstandards/measuringequality/ethnicgroupnationalidentityandreligion Diemer MA, Mistry RS, Wadsworth ME, López I, Reimers F (2013) Best Practices in Conceptualizing and Measuring Social Class in Psychological Research: Social Class Measurement. ASAP (1):77–113. https://doi.org/10.1111/asap.12001 Russell DW (1996) UCLA Loneliness Scale (Version 3): Reliability, Validity, and Factor Structure. J Pers Assess 66(1):20–40. https://doi.org/10.1207/s15327752jpa6601_2 Office for National Statistics (2018b) Testing of Loneliness Questions in Surveys: Overview of Our Loneliness Question Testing, Methodology and Findings. https://https://www.ons.gov.uk/peoplepopulationandcommunity /wellbeing/compendium/nationalmeasurementofloneliness/2018/testingoflonelinessquestionsinsurveys Cole A, Bond C, Qualter P, Maes M (2021) A Systematic Review of the Development and Psychometric Properties of Loneliness Measures for Children and Adolescents. Int J Env Res Pub Health 18(6):3285. https://doi.org/10.3390/ijerph18063285 Tennant R et al (2007) The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): Development and UK validation. Health Qual Life Outcomes 5(1):63. https://doi.org/10.1186/1477-7525-5-63 Braun V, Clarke V (2006) Using thematic analysis in psychology. Qual Res Psychol 3(2):77–101. https://doi.org/10.1191/1478088706qp063oa Rosseel Y (2012) lavaan: An R Package for Structural Equation Modeling. J Stat Softw 48(2):1–36. https://doi.org/10.18637/jss.v048.i02 Asparouhov T, Muthén B (2014b) Auxiliary Variables in Mixture Modeling: Using the BCH Method in Mplus to Estimate a Distal Outcome Model and an Arbitrary Secondary Model. Mplus Web Notes 21(2):1–22 Ray C (2021) The trajectory and determinants of loneliness during the early months of the COVID-19 pandemic in the United States. J Social Pers Rel 38:026540752110165. https://doi.org/10.1177/02654075211016542 Hu Y, Gutman LM (2021) The trajectory of loneliness in UK young adults during the summer to winter months of COVID-19. Psychiatry Res 303:114064. https://doi.org/10.1016/j.psychres.2021.114064 Farrell AH, Vitoroulis I, Eriksson M, Vaillancourt T (2023) Loneliness and Well-Being in Children and Adolescents during the COVID-19 Pandemic: A Systematic Review. Children 10(2). https://doi.org/10.3390/children10020279 . Article 2 Cooper K et al (2021) Loneliness, social relationships, and mental health in adolescents during the COVID-19 pandemic. J Aff Dis 289:98–104. https://doi.org/10.1016/j.jad.2021.04.016 Geulayov G, Mansfield K, Jindra C, Hawton K, Fazel M (2022) Loneliness and self-harm in adolescents during the first national COVID-19 lockdown: Results from a survey of 10,000 secondary school pupils in England. Cur Psychol. https://doi.org/10.1007/s12144-022-03651-5 Houghton S et al (2022) Adolescents’ longitudinal trajectories of mental health and loneliness: The impact of COVID-19 school closures. J Adol 94(2):191–205. https://doi.org/10.1002/jad.12017 Rose AJ, Rudolph KD (2006) A review of sex differences in peer relationship processes: Potential trade-offs for the emotional and behavioral development of girls and boys. Psychol Bull 132:98–131. https://doi.org/10.1037/0033-2909.132.1.98 Allom V, Panetta G, Mullan B, Hagger MS (2016) Self-report and behavioural approaches to the measurement of self-control: Are we assessing the same construct? Pers Ind Diffs 90:137–142. https://doi.org/10.1016/j.paid.2015.10.051 Pearce E et al (2021) Loneliness as an active ingredient in preventing or alleviating youth anxiety and depression: A critical interpretative synthesis incorporating principles from rapid realist reviews. Trans Psychiatry 11(1):628. https://doi.org/10.1038/s41398-021-01740-w Additional Declarations No competing interests reported. Supplementary Files SupplementaryMateriallonelinesstrajectory.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4406667","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304965706,"identity":"35cc6c72-511e-4c9a-8054-92903cfd08f5","order_by":0,"name":"Angelina Jong","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Angelina","middleName":"","lastName":"Jong","suffix":""},{"id":304965707,"identity":"9a1c2619-38fe-4976-b816-cdf5e67096bd","order_by":1,"name":"Laura Riddleston","email":"","orcid":"","institution":"Wolfson Institute of Population Health, Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Riddleston","suffix":""},{"id":304965708,"identity":"547ab851-03d0-421b-bad7-db4b61d0e838","order_by":2,"name":"Delia Fuhrmann","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Delia","middleName":"","lastName":"Fuhrmann","suffix":""},{"id":304965710,"identity":"a0be9ac3-3f68-4eac-ab8f-c029ccbb7583","order_by":3,"name":"Jennifer Y. F. Lau","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACxgbmBgaGCiCLHSJgQIQWRqCWM0AWM7FaQJoYGNtI0cLcwNj4mXeedbTBYQbGDz8YDhsT47Bmad5t6bkbDjMwS/YwHDYjRksDUMthkBYGaQaGwzZE2fKbdw5YC/NvYrW0SfM2gLWwgWwhwmHNjG2Wc46l5848DGT0GKQT9r5he/PhG29qrHP7jgMZPyqsDRsIamkGU8xgNxIXkfIMcC2jYBSMglEwCnAAAFIcN0TgQ4PdAAAAAElFTkSuQmCC","orcid":"","institution":"Wolfson Institute of Population Health, Queen Mary University of London","correspondingAuthor":true,"prefix":"","firstName":"Jennifer","middleName":"Y. F.","lastName":"Lau","suffix":""}],"badges":[],"createdAt":"2024-05-11 21:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4406667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4406667/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56911813,"identity":"d4a3fb0d-767a-4193-8f69-7e17d42156f5","added_by":"auto","created_at":"2024-05-22 05:22:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMean trajectory of loneliness scores in different classes from Assessment 1 to 8\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003eThe sample proportion of each class is shown in brackets.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4406667/v1/ea3ee25d535d84da40b4d43d.png"},{"id":58500384,"identity":"b779565e-e7bc-45f6-bcd5-ffc358b15cbc","added_by":"auto","created_at":"2024-06-17 13:16:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":789898,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4406667/v1/a293737e-673d-4165-8f68-2c5f322697b5.pdf"},{"id":56911812,"identity":"86f20d73-1be9-4320-b417-f3872ea3e517","added_by":"auto","created_at":"2024-05-22 05:22:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":260642,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMateriallonelinesstrajectory.docx","url":"https://assets-eu.researchsquare.com/files/rs-4406667/v1/e36867fc6941041a10eb8bbc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Demographic and coping predictors of severe forms of loneliness in young people aged 12-25 years","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLoneliness is the distressing emotional state resulting from a discrepancy between a person\u0026rsquo;s desired and perceived quantity and quality of social relationships [1]. Loneliness is associated with poorer health [2] including a broad range of psychiatric difficulties [3]. Young people are vulnerable to loneliness and its\u0026rsquo; negative impacts [4, 5]. Elevations in loneliness in youth may be transient, reflecting age-normative transitions in the social environment (e.g., starting/leaving education/work, leaving home) and on developmental changes in non-social aspects e.g., emerging independence, self-identity\u0026nbsp;[6, 7]. But some young people experience more severe loneliness, when these feelings are frequent, persistent and harmful to mental and physical health and other areas of functioning\u0026nbsp;[8]. Indeed, longitudinal studies have identified a group of young people\u0026nbsp;(between 1-22%) who experience prolonged and/or increasing loneliness across time [9-14]. But young people have said that there are few (tailored) resources to help them manage loneliness. They call for a research agenda that includes identifying who has more \u0026ldquo;severe\u0026rdquo; forms of loneliness and more accessible resources for manging loneliness [15]. Here, we investigate which demographic sub-groups of young people report more severe loneliness and whether loneliness severity is associated with differences in modifiable coping strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIdentifying demographic characteristics of those with more severe loneliness can facilitate support to those most vulnerable. In adults, being female or being of a lower socioeconomic status (SES) are associated with more frequent and persistent loneliness [16-19]. In young people, some studies have reported gender differences while others have not\u0026nbsp;[20, 21]. A few studies have found that ethnic minority status is associated with greater loneliness [22, 23] but another only found that (lower) family income, but not sex and ethnicity, predicted membership of the \u0026ldquo;chronic\u0026rdquo; loneliness group [13]. Finally, another study found that among young people aged 12 to 18 years, being female and being older predicted membership of a high, stable group [24]. Most of these studies of youth, however, have captured loneliness at a single time-point only, limiting the dimension of loneliness severity to frequency only, rather than capturing the persistence of loneliness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIdentifying modifiable psychological characteristics of those with more severe loneliness can help innovate programmes and interventions to support those experiencing loneliness. Investigating how coping variables relate to youth loneliness may shed some light on these modifiable characteristics. Coping refers to an individual\u0026rsquo;s cognitive and behavioural efforts to manage internal and external demands [25]. Many different categories of coping strategies have been suggested but there is a\u0026nbsp;lack of consensus on coping typologies and categories [26]. In adults, across studies [27], problem-focused coping styles associated with lower loneliness levels and emotion-focused coping styles with higher loneliness levels.\u0026nbsp;There are far fewer studies of coping in relation to loneliness in youth. One study found that avoidant coping strategies, such as rumination, were associated with higher levels of loneliness in adolescents and young adults [28]. However, this research does not inform more specific strategies within these broad coping categories that are more or less helpful to those experiencing loneliness.\u0026nbsp;Recently, we coded the qualitative responses of young people when asked to recommend coping strategies when faces with social isolation to a friend [29]. We then mapped these onto more specific coping strategies in the wider literature. Coping strategies identified included social (contact seeking), behavioural (approach, distraction and self-care) and psychological-cognitive (self-talk, self-compassion, and gratitude) strategies but we did not explore associations between coping strategies and loneliness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we sought to investigate the demographic and coping predictors of loneliness in a large study of young people in the UK. Loneliness data was gathered at multiple time-points during the COVID-19 pandemic, a period of unprecedented restrictions to social interactions. We focussed on young people aged 12-25 years to capture a broad period of developmental sensitivity and to identify within this period, age-specific differences (within youth) in the propensity to experience more severe forms of loneliness. We first explored the proportion of young people with more severe forms of loneliness, defined as those with more frequent and persistent loneliness experiences across time and also with the lowest well-being scores. Given prior data, we expected this to be between 1-22%. Next, we explored whether age, sex, ethnicity, and socioeconomic status predicted different loneliness trajectories, especially those with more severe forms. Finally, we compared the endorsement of different self-reported coping strategies identified in our previous study [29] across different loneliness trajectories. However, as some have argued that it is flexibility in using different coping strategies when faced with various stressful situations, rather than an over-reliance on a single strategy, that may be linked to better psychological wellbeing [30], we compared overall number of coping strategies across the different loneliness sub-groups. The dataset used here overlaps with data reported in our other study of young people aged 12-18 years [24]. While the earlier study included parallel loneliness data from young people from India and Israel, allowing a focus on cross-cultural differences alongside, age and gender, the current study uses data from young people across a broader age range (12-25 years) alongside data on ethnicity, indices of socioeconomic status and data on coping strategies recommended by young people.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Procedure\u003c/h2\u003e \u003cp\u003eWe analysed data from a multi-wave study investigating the impact of the COVID-19 pandemic on young people\u0026rsquo;s emotional wellbeing. The study received ethical approval from the Psychiatry, Nursing and Midwifery Research Ethics Committee at Kings College London (Ref: HR-19/20-18250). The eligibility criteria included being aged 12\u0026ndash;25 years old, being able to read the questionnaire in English, and residing in the UK at the time of the data collection for the first timepoint. Participants were invited, through schools, colleges and universities in the UK, websites, social media, and charity mailing lists, to complete an online survey using Qualtrics about their demographics (age, sex, ethnicity, and highest parental education qualifications), their loneliness and wellbeing and what advice they would give to others over managing social distancing and isolation situations. Young people either provided consent (if aged 16 years and above) or were instructed on how to obtain parental consent (if under 16 years). Participants were surveyed fortnightly across 8 time-points and received vouchers for their time/efforts. Data collection occurred between May 2020 and April 2021.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDemographics\u003c/h2\u003e \u003cp\u003eAge, sex assigned at birth, country currently living in, and ethnicity of participants were collected. Ethnicity data was collected as a multiple-choice option from a list according to the Office of National Statistics\u0026rsquo; recommendations [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Highest academic qualification obtained by either of their parents were collected as a proxy SES. Young people\u0026rsquo;s reports of parental education levels may be a less biased indicator of SES compared to proxies based on young people\u0026rsquo;s reports of other indicators, such as parental occupation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eLoneliness\u003c/h2\u003e \u003cp\u003eParticipants completed the short three-item University of California Los Angeles (UCLA) Loneliness Scale [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The three-item UCLA scale has been advised for use in young people aged 10\u0026ndash;15 years [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Total scores range from 3 to 9, with higher scores indicating more loneliness. The original 20-item UCLA scale has shown high internal consistency (alpha between 0.71 and 0.96) with children and adolescents but the psychometrics of the shorter UCLA scale in younger populations is more mixed [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In our sample, the internal consistency of the UCLA at each assessment point was good (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80\u0026ndash;0.84).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eMental Well-being\u003c/h2\u003e \u003cp\u003eThe short 7-item version of the Warwick-Edinburgh Mental Well-being scale (SWEMWBS; [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]) indexed well-being. Total scores ranged from 7 to 35 with higher scores indicating better mental well-being. The reliability of the SWEMWBS at the initial and final assessment was good (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.78\u0026ndash;0.86).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCoping Strategies\u003c/h2\u003e \u003cp\u003eParticipants were asked, \u0026ldquo;Based on your experiences, what advice would you give to other young people on managing the isolating experiences of social distancing?\u0026rdquo; and provided with a free text response. The development of a coding scheme of coping strategy categories is described in [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In brief, strategies were informed by the general emotion regulation and coping strategy literature (without reliance on one sole framework) and the range of therapeutic techniques used in psychological treatments for affective conditions, along with the themes that emerged from the participants\u0026rsquo; responses using thematic analysis guidelines [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The final coding scheme consisted of 7 categories: contact-seeking, distraction, approach, self-care, self-talk, self-compassion, and gratitude. Responses that were vague, unclear, or expressed not knowing what to recommend were coded \u0026ldquo;None / Vague\u0026rdquo;. The total number of different coping strategy categories recommended indexed coping flexibility [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eData cleaning\u003c/h2\u003e \u003cp\u003e4,872 responses were collected initially at baseline. After data cleaning [see 24], and the inclusion of participants with loneliness data at three or more time points, the final sample for the analysis consisted of 1,624 participants. Participants who were included were significantly older than those who were excluded (had data at less than three timepoints) (\u003cem\u003eMD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003et\u003c/em\u003e(2600)=-6.285, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.57). Females were more likely to be included than males (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;43.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003eTo investigate the overall change in loneliness over time, we used latent growth curve modelling (LGCM) using the Lavaan package [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] for R Version 4.2.0. LGCM estimates the initial level of loneliness (intercept) and the change in loneliness over time (slope). To examine whether there were individual differences in the loneliness trajectory, the variance in the intercept and slope were estimated. Fixed growth factor loadings of 0, 1, 2, 3, 4, 5, 6, 7 using maximum likelihood estimation with robust Huber-White standard errors and a scaled test statistic were used to fit a linear model. A comparative fit index (CFI)\u0026thinsp;\u0026gt;\u0026thinsp;0.97 and a Standardised Root Mean Square Residual (SRMR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered a good model fit while a CFI\u0026thinsp;=\u0026thinsp;0.95\u0026ndash;0.97 and SRMR\u0026thinsp;=\u0026thinsp;0.05\u0026ndash;0.10 is considered acceptable.\u003c/p\u003e \u003cp\u003eWe used latent class growth analysis (LCGA, implemented in Version 8.8 Mplus) to investigate the individual differences in loneliness trajectories, identifying different classes of loneliness trajectories. A series of models with different number of classes were fit to the loneliness data to determine the best number of classes. The 2-, 3-, 4-, 5- and 6-class trajectory models were compared. To inform our decision of the number of optimal classes, we used similar criteria as described in our previous paper [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eModel convergence\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eComparing K-class and K-1 class model fit statistics, using the Akaike information criterion (AIC), Bayesian information criterion (BIC) and sample size adjusted BIC (aBIC).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBootstrapped Lo, Mendell, Rubin likelihood ratio test (LRT), comparing K-class and K-1 class models.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEntropy. A measure of subgroup classification quality, higher is preferrable.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMinimum subgroup n. E.g., more than 5% of total sample.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eQualitatively different subgroup trajectories.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModel parsimony and the theoretical meaning and relevance of classes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAfter selecting the optimal model, and identifying the latent loneliness classes, the relationship between loneliness class membership and wellbeing outcome at the final timepoint was examined while controlling for sex, age, SES, ethnicity, and initial wellbeing scores. The manual Bolck, Croons, and Hagenaars (BCH) method [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] was carried out. This estimates a distal outcome model (final wellbeing) with an arbitrary secondary model (controlling for covariates in addition to the latent classes). The BCH model produces final wellbeing mean intercepts specific to each loneliness class, which indicate the loneliness classes\u0026rsquo; influence on the final wellbeing scores, and an omnibus test was the carried out to detect differences between the loneliness trajectory groups. If the omnibus test was significant, pairwise comparisons were carried out with the mean intercepts across the loneliness classes using the \u0026ldquo;MODEL CONSTRAINT\u0026rdquo; function in Mplus.\u003c/p\u003e \u003cp\u003eNext, covariates: sex, age, ethnicity, highest level of parental education and coping strategy variables were added to the model using MPlus\u0026rsquo; R3STEP. This method regresses the loneliness classes on the covariates while preventing the inclusion of the covariates from changing the latent class structure and measurement error. Another separate model using R3STEP was carried out using demographic factors and coping flexibility (number of coping strategies endorsed).\u003c/p\u003e \u003cp\u003eMissing data in the LGCM and LCGA models were modelled using full information maximum likelihood estimation. As participants\u0026rsquo; survey start date and length of participation varied due the study\u0026rsquo;s rolling start date and number of surveys completed, we regressed out both variables from the loneliness scores at each assessment timepoint and used the residuals in the LCGA models. Exact p-values were reported for all tests. The Benjamini-Hochberg adjustment with a false discovery rate of 0.05 controlled for multiple comparisons.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows participants\u0026rsquo; demographic characteristics, loneliness and well-being scores at each time-point. The mean age of the participants was 18.2 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.55). 75% of participants were female and 63% were White. More than half of the participants reported that their parents were university educated (66%), with a quarter of parents obtaining a post-graduate degree. The seven coping categories (contact seeking, distraction, approach, self-care, self-talk, self-compassion, gratitude) derived from the free-text responses to the question \u0026ldquo;Based on your experiences, what advice would you give to other young people who are managing the isolating experiences of social distancing?\u0026rdquo; are published in an earlier paper [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] along with other analysis linking these with demographic characteristics. In brief, the three most frequently recommended were contact seeking (51.7%), self-care (35.8%) and distraction (23.2%). The least recommended strategy was gratitude (9.0%). 5.8% of participants did not recommend a coping strategy. As for coping flexibility, young people recommended between one and six coping strategies (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.72, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.99) with one being the most frequently recommended number of coping strategies (41.6%) and the percentage of participants decreasing as number of coping strategies increased.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSample characteristics and mean responses at the initial timepoint\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eParticipants at Timepoint 1\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1624)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e (\u003cem\u003eSD\u003c/em\u003e) or %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.2 (3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite / White British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian / Asian British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack / Black British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed or Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrefer Not to Say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest Parent Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness (UCLA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.31 (1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental Wellbeing (SWEMWBS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.9 (4.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLatent Growth Curve Modelling analysis\u003c/h2\u003e \u003cp\u003eThe LGCM fit the data well, \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(31)\u0026thinsp;=\u0026thinsp;168.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, CFI\u0026thinsp;=\u0026thinsp;0.98, SRMR\u0026thinsp;=\u0026thinsp;0.04. The intercept (\u003cem\u003estandardised estimate\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.48, \u003cem\u003eunstandardised\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and slope (\u003cem\u003estandardised estimate\u003c/em\u003e=-0.18, \u003cem\u003eunstandardised\u003c/em\u003e=-0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) were significant, indicating that the mean loneliness scores decreased over time. There were significant variances in both the intercept (\u003cem\u003estandardised\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00, \u003cem\u003eunstandardised\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and slope (\u003cem\u003estandardised\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00, \u003cem\u003eunstandardised\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), which supported further analyses of distinct trajectories. Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in Supplementary Material shows the overall loneliness trajectory across the eight assessments grouped by age, sex, parental education level and ethnicity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLatent Class Growth Analysis\u003c/h2\u003e \u003cp\u003eThe LCGA was carried out to identify different loneliness trajectory classes. For the fit statistics of the 2 through to 6-class models see Table S2 in Supplementary Material. All classes\u0026rsquo; bootstrapped LRT were significant. Considering the fit indices, entropy, minimum number of participants in each class, and the substantive meaning of the classes, the 5-class model was chosen. In the 5-class model, the largest class consisted of 35% of the sample and shows a low, stable loneliness trajectory (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eintercept\u003c/em\u003e\u003c/sub\u003e=-1.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eslope\u003c/em\u003e\u003c/sub\u003e=0.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.064). There were classes that showed: a moderate stable loneliness trajectory (23%; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eintercept\u003c/em\u003e\u003c/sub\u003e=1.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eslope\u003c/em\u003e\u003c/sub\u003e=-0.002, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.946), low increasing loneliness trajectory (16%; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eintercep\u003c/em\u003et\u003c/sub\u003e=-0.76, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eslope\u003c/em\u003e\u003c/sub\u003e=0.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a moderate decreasing loneliness trajectory (15%; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eintercept\u003c/em\u003e\u003c/sub\u003e=0.69, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eslope\u003c/em\u003e\u003c/sub\u003e=-0.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Lastly, the smallest class (11%) shows a high, stable loneliness trajectory (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eintercept\u003c/em\u003e\u003c/sub\u003e=2.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eslope\u003c/em\u003e\u003c/sub\u003e=0.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.354). The loneliness trajectories of the 5 classes are shown in Fig.\u0026nbsp;2, which depicts the trajectories using the raw loneliness scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFinal wellbeing as a function of loneliness class membership\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents results from the BCH method investigating loneliness class differences in predicting final wellbeing outcome. The omnibus Wald χ\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e test indicated that overall, there were significant differences in the wellbeing mean intercept between the loneliness classes at the final assessment timepoint (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). We ran further pairwise comparisons between the high stable loneliness class and the other four classes. After Benjamini-Hochberg adjustment (false discovery rate of 0.05), participants in the high stable loneliness class reported significantly lower final wellbeing scores compared to the low stable class (\u003cem\u003ez\u003c/em\u003e=-4.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), moderate stable class (\u003cem\u003ez\u003c/em\u003e=-1.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001) and the moderate decreasing class (\u003cem\u003ez\u003c/em\u003e=-4.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). There were no significant differences in the final wellbeing scores between high stable loneliness class and low increasing loneliness class (\u003cem\u003ez\u003c/em\u003e=-0.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.290). For the initial and final wellbeing of participants according to the five trajectory classes, see Table S3 in Supplementary Material.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLatent class growth analysis of loneliness predicted by demographic factors and coping strategy variables\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eHigh stable vs. Low stable loneliness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHigh stable vs. Moderate stable loneliness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eHigh stable vs. Low increasing loneliness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eHigh stable vs. Moderate decreasing loneliness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eB (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eB (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eB (SE)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e[0.92, 1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e[0.91, 1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003cp\u003e[0.91, 1.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.01 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e[0.94, 1.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.23 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003cp\u003e[0.16, 0.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.68 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003cp\u003e[0.25, 1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.67 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003cp\u003e[0.26, 1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.09 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003cp\u003e[0.17, 0.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e.002**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e[0.68, 1.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003cp\u003e[0.64, 1.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.21 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003cp\u003e[0.48, 1.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.28 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003cp\u003e[0.77, 2.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.24 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003cp\u003e[0.52, 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003cp\u003e[0.65, 1.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.05 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e[0.57, 1.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.08 (0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003cp\u003e[0.54, 1.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoping Strategy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContact Seeking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003cp\u003e[0.69, 1.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003cp\u003e(0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003cp\u003e[0.75, 2.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003cp\u003e[0.64, 1.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003cp\u003e(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003cp\u003e[0.60, 1.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003cp\u003e[0.64, 1.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e[0.55, 1.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.32 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003cp\u003e[0.77, 2.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003cp\u003e(0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003cp\u003e[0.77, 2.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003cp\u003e(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003cp\u003e[0.38, 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003cp\u003e(0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003cp\u003e[0.30, 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003cp\u003e[0.50, 2.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003cp\u003e(0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003cp\u003e[0.20, 0.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e.003**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.41 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003cp\u003e[0.41, 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003cp\u003e(0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003cp\u003e[0.34, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003cp\u003e(0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003cp\u003e[0.42, 1.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.78\u003c/p\u003e \u003cp\u003e(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003cp\u003e[0.26, 0.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.007**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-talk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003cp\u003e[0.83, 2.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003cp\u003e(0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003cp\u003e[0.84, 2.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.52 (0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003cp\u003e[0.91, 3.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.25 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003cp\u003e[0.68, 2.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-compassion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003cp\u003e[0.66, 2.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003cp\u003e(0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003cp\u003e[0.37, 1.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003cp\u003e[0.65, 3.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.61 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003cp\u003e[0.73, 4.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGratitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003cp\u003e[0.56, 1.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003cp\u003e(0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003cp\u003e[0.96, 5.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003cp\u003e[0.46, 2.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003cp\u003e(0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e[0.42, 2.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standard Error; \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Odds Ratio; CI\u0026thinsp;=\u0026thinsp;Confidence Intervals. Sex: female\u0026thinsp;=\u0026thinsp;0, male\u0026thinsp;=\u0026thinsp;1; ethnicity: marginalised\u0026thinsp;=\u0026thinsp;0; majority\u0026thinsp;=\u0026thinsp;1; parental education level: below undergraduate\u0026thinsp;=\u0026thinsp;0, undergraduate and above =\u0026thinsp;1.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, bold values represent statistical significance based on Benjamini-Hochberg adjustment (false discovery rate of 0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of loneliness class membership\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results from the LCGA with predictors, showing the associations between both demographic factors and coping strategies and the likelihood of membership in the loneliness trajectory classes. Compared to the low stable loneliness class, participants in the high stable loneliness class were more likely to be females compared to males, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28, 95% CI [0.16, 0.52], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. Participants in the high stable loneliness class were also more likely to be female, compared to male, than those in the moderate decreasing class, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33, 95% CI [0.17, 0.66], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002. There were no significant differences between males and females in the likelihood of class membership between those in the high stable loneliness class compared to those in the moderate stable class nor the low increasing class. Age, ethnicity, and parental education were not significant predictors of loneliness class membership. For coping strategy, participants who recommended approach coping strategies were less likely to be in the high stable loneliness class compared to the moderate decreasing loneliness class, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37, 95% CI [0.20, 0.71], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003. While not significant after Benjamini-Hochberg corrections, self-care as a coping strategy approached significance: participants who recommended self-care as a coping strategy were also less likely to be in the high stable loneliness class compared to the moderate decreasing loneliness class, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46, 95% CI [0.26, 0.81], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007. Endorsement of the other coping strategies did not significantly predict loneliness class membership.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eFinal wellbeing estimated means by loneliness class\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eLoneliness class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh stable\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;172)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow stable\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;574)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate stable\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;371)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate decreasing\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;243)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow Increasing\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;264)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLatent subgroup comparison\u003c/p\u003e \u003cp\u003eomnibus test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(SE)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(SE)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(SE)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(SE)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(SE)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWald χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal Wellbeing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.12 (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.56 (1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.94 (1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.99 (1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.73 (1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e178.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standard Error. Loneliness scores are adjusted for baseline survey date and study participation duration.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e1\u003c/sup\u003eMeans estimated from class-specific intercepts (BCH-generated) for the final wellbeing in a latent class auxiliary regression model, which represents the influence of the latent loneliness class on final wellbeing.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnother regression model with the latent classes and demographics as covariates in addition to coping flexibility (number of coping strategies endorsed) was ran. Coping flexibility did not significantly predict loneliness class membership above and beyond the other predictors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated the proportion of young people with the most severe forms of loneliness across time, and which demographic characteristics and coping strategies predicted these more severe subgroups. We addressed these questions during the COVID-19 pandemic, a time when many young people\u0026rsquo;s lives were affected by social restrictions, which impacted their education, professional life, finances, relationships and daily routines.\u003c/p\u003e \u003cp\u003eOverall loneliness levels experienced by young people was moderate and decreased slightly over time in line with some [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] but not all previous data [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], gathered during the pandemic. Five distinct loneliness trajectories were identified with the high-stable group comprising 11% of the sample, consistent with non-pandemic [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and pandemic data [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The high stable loneliness was associated with significantly lower wellbeing (compared to the low stable, moderate stable, and moderate decreasing loneliness classes) at the final timepoint, after controlling for initial wellbeing and demographic factors. The link between loneliness and adverse psychological impacts has been well documented in both cross-sectional and longitudinal studies prior to COVID-19 with both lower overall wellbeing and increased mental health difficulties [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Large proportions of the sample also had loneliness trajectories that were at least moderate in level or increasing (23% were stable moderate and 16% were low increasing) too, again consistent with adult data [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSex predicted loneliness class membership above and beyond other demographic predictors, such as age, ethnicity, and SES. Females were more likely than males to experience chronic high loneliness compared to low stable or moderate decreasing loneliness. This is similar to a longitudinal adult study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and cross-sectional young people studies during COVID [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], although some have reported no gender differences in changes in perceived social isolation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These differences may have emerged because more social support mitigates loneliness in females, and social distancing would create barrier in sustaining social support [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Alternatively, females may be more willing to express emotions including loneliness.\u003c/p\u003e \u003cp\u003eOur findings highlight the need to consider modifiable strategies to manage youth loneliness. Contact seeking, the most frequently recommended coping strategy in our study and perhaps the most direct form of increasing social support, was not associated with loneliness, consistent with reports that increased social contact, such as over the phone or texting, during the pandemic did not reduce loneliness in adolescents [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Instead, young people who recommended approach strategies (e.g., picking up a new hobby, learning something new) were more likely to have a moderate decreasing loneliness trajectory compared to a high stable trajectory. Indeed, approach strategies which relates to providing a range of rewarding activities is similar to behavioural activation principles often applied in depression treatments. Although no longer significant after controlling for multiple comparisons, our data tentatively suggested that recommending self-care (e.g., scheduling, exercise, productive work) predicted decreasing loneliness. Research into coping strategies and loneliness is important because coping can be modified and learnt through interventions such as coping skills training and are likely to be acceptable and easily adoptable by young people.\u003c/p\u003e \u003cp\u003eThere are some study limitations. First, the trajectory classes found in this study were statistically derived using LGCM and LCGA rather than through direct measurement/observation, so caution over interpreting group differences is warranted. Secondly, our sample had a larger representation of females, individuals of higher SES and individuals of minoritized ethnic groups than the general UK population limiting the generalisability of our findings to other young people in the UK (and beyond). The higher representation of minoritized ethnic groups in this study can be seen as a strength for enabling a better understanding of groups that are less represented. Parents\u0026rsquo; educational attainment was used as a proxy for SES, which is not uncommon for this age group but nonetheless has limitations. Third, we measured loneliness as a single dimension based on 3 items, and future studies may wish to consider different dimensions of loneliness such as social and emotional loneliness in future studies. Finally, our question on coping strategies was worded as recommendations rather than what participants used to cope themselves. As associations between people\u0026rsquo;s recommendations and their behaviours [are not always aligned [49], future studies may benefit from exploring what coping strategies young people employed, how effective were they in implementing the strategies, and how effective the strategies were for managing loneliness.\u003c/p\u003e \u003cp\u003eIn summary, this study identified a minority of young people who report prolonged high levels of loneliness, along with the negative impact of chronic loneliness on wellbeing. These findings speak to the importance of encouraging young people to use their psychological resources (functional coping strategies) to manage loneliness especially given the few effective targeted interventions for young people who are lonely. Approach and self-care coping strategies may have potential in alleviating young people\u0026rsquo;s feelings of loneliness and improving their wellbeing but it is also important to recognise that those who struggle with loneliness are not a homogenous group, and strategies most useful to young people may vary [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding and competing interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funding from the Rosetrees Trust (ref M949) and the UK Economic and Social Research Council (ES/T00004X/1). In addition, JYFL and DF have funding from UKRI which currently supports LR (MR/X002381/1). The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approvals and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. The study received ethical approval from the Psychiatry, Nursing and Midwifery Research Ethics Committee at Kings College London (Ref: HR-19/20-18250). Informed consent was obtained from all individual participants included in the study or if under 18 years, by their legal guardians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. The first draft of the manuscript was written by Angelina Jong and Jennifer Lau, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to thank Alan Meehan and Ted Barker for their advice on the statistical analyses for the current study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are currently being prepared for sharing in a data repository. During this process, data will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePeplau L, Perlman D (1982) Perspectives on loneliness. In: Peplau, Perlman (eds) Loneliness: A sourcebook of current theory, research and therapy. Wiley, New York, pp 1\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenriksen J, Larsen ER, Mattisson C, Andersson NW (2019) Loneliness, health and mortality. 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Trans Psychiatry 11(1):628. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41398-021-01740-w\u003c/span\u003e\u003cspan address=\"10.1038/s41398-021-01740-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"adolescents, youth, chronic loneliness, social isolation, coping","lastPublishedDoi":"10.21203/rs.3.rs-4406667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4406667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLoneliness is common in young people and predicts a range of concurrent psychiatric conditions. Yet, young people feel there are few resources to support them. \u003cem\u003eWho\u003c/em\u003e develops severe forms of youth loneliness and \u003cem\u003ewhich\u003c/em\u003e modifiable psychological correlates are associated with loneliness severity could help in developing resources to support groups of young people who are most vulnerable. Here, we explored which demographic characteristics (age, gender, minority ethnic status, and indices of socioeconomic status) predicted more severe forms of loneliness. Based on strategies that young people said they would recommend to a friend to manage loneliness, we also explored whether specific coping strategies and coping flexibility predicted severe loneliness. We explored these questions using loneliness data gathered during the COVID-19 pandemic, a time when social restriction policies heightened loneliness experiences. Latent class growth analysis identified five loneliness trajectory classes. Among these was a \u0026ldquo;high stable\u0026rdquo; group (11% of the sample) who reported frequent loneliness that also endured across time-points. Other groups included a moderate decreasing (15%), a low increasing (16%), a moderate stable (23%), and a low stable (35%) group. The high stable loneliness class also reported significantly lower wellbeing scores compared to the many of the other groups. Entry into the high stable loneliness group was predicted by being female. Recommendation of approach coping strategies predicted lower likelihood of being in the high stable loneliness group. Future research and clinical work should explore the utility of coping strategies to manage loneliness to reduce the impact on well-being and psychiatric outcomes.\u003c/p\u003e","manuscriptTitle":"Demographic and coping predictors of severe forms of loneliness in young people aged 12-25 years","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-22 05:22:14","doi":"10.21203/rs.3.rs-4406667/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"afaa0fa8-1c94-4c4e-9c7f-9451fb4b3759","owner":[],"postedDate":"May 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-17T13:08:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-22 05:22:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4406667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4406667","identity":"rs-4406667","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}