Latent Classes of Adolescent Health Behaviour, Social Covariates and Mental Wellbeing: A Longitudinal Birth Cohort Study

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The expansion of services to support the mental health and wellbeing of young people is a public health priority and a core component of the National Health Service’s Long-Term Plan. In this paper, we contribute to knowledge regarding the epidemiology of adolescent mental wellbeing by leveraging secondary analysis of a very large longitudinal dataset (#BeeWell) to generate insights regarding different patterns of health behaviour, their covariates, and consequences for mental wellbeing one year later. Methods: A Latent Class Analysis was conducted using data on physical activity, sleep, and eating habits collected in 2021 from 18,478 Year 8 pupils from Greater Manchester (United Kingdom) to (1) identify distinct latent classes of adolescent health behaviour; (2) establish factors likely to be associated with latent class membership; and (3) determine whether latent class membership contributes to variance in self-reported mental wellbeing one year later. Results: A three-class solution was identified as an excellent fit to the data, discriminating between: the Wellness Weary ( n = 2,717; 15%); the Balanced Bunch ( n = 7,377; 40%); and the Green and Dream Team ( n = 8,384; 45%). Several factors significantly influenced class membership. Most notably, socio-economic disadvantage and social media use were linked with less favourable health behaviour patterns, whilst cisgender heterosexual girls were likely to endorse healthier patterns. After adjusting for covariates, the Green and Dream Team reported significantly greater mental wellbeing than the Balanced Bunch one year later, signalling that health behaviours endorsed in adolescence may have a long-term impact on mental health. Conclusions: Beyond advancements in fundamental understanding, findings yield significant translation opportunities through their use and application in health, education, and allied professional settings designed to support young people. Adolescents Diet Health Behaviour Physical Activity Sleep Wellbeing Figures Figure 1 Figure 2 Figure 3 Background Health behaviours are a set of practices that promote or impair the health of an individual [ 1 ]. Adolescence is a critical developmental period when several of these behaviours start to emerge, many of which can persist into early adulthood [2]. Physical activity [ 3 ], sleep [ 4 ], and eating habits [ 5 ], previously coined The Big Three modifiable health behaviours, are proposed to have both independent and synergistic associations with adolescent mental health [6,7]. Theoretical models of these associations span neurobiological, psychosocial, and behavioural processes [ 8 ]. For example, physical activity impacts on the functioning of the hypothalamus-pituitary-adrenal axis, which in turn reduces cortisol levels, thereby supporting wellbeing [ 9 ]. Similarly, inadequate sleep can prompt more frequent use of maladaptive emotion regulation strategies, which in turn negatively impacts mental health [10]. A great number of factors may influence the likelihood an individual endorses different health behaviours in adolescence [ 11 ]. Better understanding of these factors can help identify those at-risk and contribute to effective, targeted behaviour change interventions. Covariates of particular relevance during adolescence include gender identity and sexual orientation [12,13]; ethnicity [14–16]; socio-economic disadvantage [17–19]; social media use [20,21]; physical health [ 14 ]; bullying victimisation [22]; and prior levels of wellbeing (given the likely reciprocal nature of the relationship with health behaviours) [ 23 – 25 ]. There is now widespread evidence that being sufficiently active, getting enough sleep, and following a healthy diet can support mental health in adolescence [6]. However, most existing research has focused on mental illness , with relatively few studies elucidating links with wellbeing [ 4 ]. From a population health perspective, a focus on wellbeing arguably has greater utility than a focus on mental illness [ 26 ]. Most young people do not meet diagnostic criteria for a mental health disorder (leading to floor effects), but there is substantial variability in mental wellbeing [27], which has been demonstrated to predict a range of salient outcomes later in life including but not limited to: adult mental and physical health; health behaviours; relationships; and labour market outcomes [ 28 ]. A Person-Centred Approach Previous studies in this area have tended to adopt variable-centred approaches to analysis (e.g., regression) which assume a homogeneous population differing only in the extent to which they engage in health behaviours. In comparison, relatively few adopt person-centred perspectives (e.g., cluster or latent class/profile analysis) which aim to capture the heterogeneity that exists within populations in terms of the extent and pattern of health behaviours they exhibit [ 29 ]. Where variable-centred approaches examine associations between variables, person-centred approaches examine relationships between people, offering evidence of how certain health behaviours might cluster together in distinct patterns that characterise unobserved subgroups (i.e., latent classes) of the population [ 29 , 30 ]. Each latent class comprises individuals who elicit similarities on specific indicators, but who are quantitatively and qualitatively distinct from those in alternative classes, thus capturing homogeneity within groups and heterogeneity between groups [ 30 ]. Adolescence is a transformative life stage characterised by rapid neurological, psychosocial, and emotional development [ 31 ]. As such, the extent to which young people endorse different health behaviours, the effect of social and demographic antecedents of health behaviour, and the collective impact health behaviours may have on wellbeing, is likely to differ significantly from one individual to the next [ 31 ] signalling that person-centred research is both warranted and necessary in this age-group. A useful illustrative example of the utility of person-centred approaches is seen in a cross-sectional study [32], which used cluster analysis to identify three distinct patterns of health behaviour (utilising data on sleep, alcohol use, cannabis use, social media use, and sport and hobby participation) among Irish adolescents, denoted as low, moderate and high health-promoting, respectively. The authors found that membership of these clusters was predicted by socio-demographic characteristics (e.g., high health-promoting adolescents were more likely to be younger and female); they also reported that cluster membership was associated with mental health and wellbeing outcomes (e.g., low health-promoting adolescents reported the highest levels of anxiety and depression, and lowest levels of life satisfaction). Whilst extremely illuminative, existing person-centred studies on adolescent health behaviour and mental health have predominantly been cross-sectional [32] or focused on links with mental illness [ 33 ]. There is therefore an ongoing need to establish temporal precedence between health behaviour participation and adolescent wellbeing both before and after adjusting for a wide range of social and demographic covariates. Aims and Hypotheses Using data from the first two annual waves (T1, T2) of the #BeeWell study in Greater Manchester, United Kingdom, the aims of this study were to establish: (1) latent classes of adolescent health behaviour at T1 when participants were aged 12–13; (2) whether bullying victimisation, social media use, gender identity and sexual orientation, ethnicity, socio-economic disadvantage, self-reported physical health, and/or mental wellbeing at T1 were associated with latent class membership; and, (3) whether latent class membership at T1 contributed to variance in mental wellbeing at T2, when participants were aged 13–14 years. As person-centred analyses of adolescent health behaviours are sparse, the class identification phase of analysis was largely exploratory in nature. Nevertheless, concurrent health behaviours are often positively correlated [34]. Accordingly, we hypothesised there may be a predominantly healthy class (e.g., physically active, sufficient sleep, regular fruit and vegetables, irregular junk food) and a predominantly unhealthy class (e.g., physically inactive, insufficient sleep, irregular fruit and vegetables, regular junk food), among others (H1). We further hypothesised, based on existing evidence noted above, that bullying victimisation, LGBTQ + adolescents, minority ethnicity, socio-economic disadvantage, poor physical health, and poor T1 wellbeing would be significant risk factors for membership of less healthy classes (H2). Finally, we hypothesised that members of more healthy classes would report better wellbeing at T2 after controlling for covariates (H3). An analysis plan detailing the hypotheses and analytical methods used was pre-registered on the Open Science Framework [ 35 ]. Methods Participants #BeeWell is a hybrid population cohort study in Greater Manchester, United Kingdom, comprising: (i) a truncated longitudinal study in which participants are tracked with annual data points from age 12–15 (i.e., from Year 8 to Year 9 to Year 10 of secondary school; Sample 1); and, (ii) a cross-sectional study comprising annual data points for participants aged 14–15 (i.e., those in Year 10 of secondary school at a given data point; Sample 2) [ 36 ]. Our secondary analysis drew on the first (T1) and second (T2) annual data points for Sample 1 conducted in 2021 and 2022, respectively (overall N = 20,241). All young people from Sample 1 who responded to at least one of the health behaviour items at T1 were eligible for inclusion. Data were clustered by school, so consistent with guidance for working with multilevel data, only cases were there were ≥ 5 pupils per school were retained [ 37 ]. This resulted in a final analytical sample of n = 18,478 pupils from 138 schools. Measures #BeeWell is a rich source of individual-level data on a wide range of wellbeing domains/indicators (e.g., life satisfaction, self-esteem, mental wellbeing) and drivers (e.g., social media use, bullying, sleep), as well as multiple individual characteristics (e.g., age, ethnicity, sexual orientation). Data pertaining to latent class indicators (physical activity, sleep, fruit and vegetable consumption, confectionary consumption) and covariates (ethnicity, socio-economic disadvantage, gender identity, sexual orientation, self-reported physical health, social media use, bullying victimisation and mental wellbeing) were drawn from the T1 survey and linked administrative data. Outcome data on mental wellbeing were drawn from the T2 survey. A detailed explanation of the measures used and how scores were interpreted is provided in Table 1 . The #BeeWell survey can also be accessed online [ 38 ]. Table 1 Sample characteristics and measures used to quantify adolescent health behaviour, covariates and wellbeing Variable Measure Description Score Latent Class Indicators Fruit and Vegetable Consumption (missing = 0.49%) Single item adapted from the Health Behaviours in Schools Checklist [ 71 ](Inchley et al., 2020) and the Millennium Cohort Study [ 72 ](Centre for Longitudinal Studies, 2020) targeting weekly consumption of fruit and vegetables. Responses treated as quasi-continuous, rated on a seven-point scale ranging from 0 ( Never ) to 6 ( Everyday more than once ). Mean (S.D.) 5.06 (1.60) Confectionary Consumption (missing = 0.71%) Single item adapted from the Health Behaviours in Schools Checklist [ 71 ](Inchley et al., 2020) and the Millennium Cohort Study [ 72 ](Centre for Longitudinal Studies, 2020) targeting weekly consumption of sweets, chocolate, crisps, and fizzy drinks. Responses treated as quasi-continuous, rated on a seven-point scale ranging from 0 ( Never ) to 6 ( Everyday more than once ). Reverse coded such that higher scores represented healthier behaviour. Mean (S.D.) 3.24 (1.44) Physical Activity (missing = 3.44%) Two items adapted from the Health Behaviours in Schools Checklist [ 71 ](Inchley et al., 2020) measuring weekly minutes of Moderate-to-Vigorous Physical Activity (MVPA). A binary variable was derived discriminating between participants adhering/not adhering to current CMO MVPA guidelines (≥ 420-minutes per week). Responses coded: No = 0; Yes = 1. Yes No n (%) 6,590 (35.66%) 11,253 (60.90%) Sleep (missing = 0.87%) A single item from the Health Behaviours in Schools Checklist asking whether the amount of sleep they normally get is enough to feel awake and concentrate on schoolwork during the day [ 71 ](Inchley et al., 2020). Responses coded: No = 0; Yes = 1. Yes No n (%) 11,732 (63.49%) 6,586 (35.64%) Covariates of Latent Class Membership Wellbeing (missing = 11.69%) The (seven-item) Short Warwick-Edinburgh Mental Wellbeing [ 73 ](Tennant et al., 2007). A five-point Likert-type scale. Total scores range from 7 to 35 with higher scores indicating greater wellbeing. Consistent with recommendations from the scale developer, transformed SWEMWBS scores were used [ 74 ](Stewart-Brown et al., 2009). Mean (S.D.) 21.75 (4.95) Bullying Victimisation (missing = 5.31%) Three items adapted from the Understanding Society Youth Questionnaire [ 74 ](Institute for Social and Economic Research, 2021) and the Health Behaviours in Schools Checklist [ 71 ](Inchley et al., 2020). A binary measure of bullying was derived. Participants who responded quite a lot or a lot to at least one item were classed as bullied. Coded as: Not Bullied = 0; Bullied = 1. Bullied Not Bullied n (%) 3,092 (16.73%) 14,405 (77.96%) Ethnicity (missing = 3.88%) Classed as Asian, Black, Mixed, White, or Any Other Ethnic Group (including Chinese) using linked administrative data provided by Greater Manchester Local Authorities. Dummy variables derived for each ethnic group. Coded as: No = 0; Yes = 1. Asian Black Mixed White AOEG n (%) 3,190 (17.26%) 903 (4.89%) 1,077 (5.83%) 11,998 (64.93%) 593 (3.21%) Self-Reported Physical Health (missing = 0.23%) A single item adapted from Understanding Society [ 75 ](Institute for Social and Economic Research, 2021). A five-point Likert-type scale ranging from 1 ( Poor ) to 5 ( Excellent ). Mean (S.D.) 2.42 (1.022) Gender Identity and Sexual Orientation (missing = 7.33%) A three-category variable was derived (Thornton, Petersen, Marquez & Humphrey, 2024) using sex assigned at birth (linked administrative data), gender identity and sexual orientation (gathered through T1 surveys) [ 76 ]. Cisgender Heterosexual Boys (reference group); Cisgender Heterosexual Girls; and LGBTQ+. Dummy coded as: No = 0; Yes = 1. Cishet Boy Cishet Girl LGBTQ+ n (%) 6,215 (33.63%) 5,366 (29.04%) 5,542 (29.99%) Social Media Use (missing = 5.84%) A single item adapted from the Millennium Cohort Study assessing daily hours spent on social media [ 72 ](Centre for Longitudinal Studies, 2023). A continuous variable whereby higher scores represent more frequent daily use (hours). Mean (S.D.) 4.29 (2.51) Socio-Economic Disadvantage (missing = 3.36%) Index of Multiple Deprivation rank based on the Lower Layer Super Output Area (LSOA) for the young person’s home postcode (provided by Greater Manchester Local Authorities) ranging from 1 ( Most Deprived ) to 32,844 ( Least Deprived ). The reciprocal of IMD rank expressed as a percentage was used such that scores ranged from 0 to 1 and higher scores indicated greater disadvantage. Mean (S.D.) .65 (.30) Outcomes of Latent Class Membership Wellbeing (missing = 46.23%) The (seven-item) Short Warwick-Edinburgh Mental Wellbeing [ 73 ](Tennant et al., 2007). A five-point Likert-type scale. Total scores range from 7 to 35 with higher scores indicating greater wellbeing. Consistent with recommendations from the scale developer, transformed SWEMWBS scores were used [ 74 ](Stewart-Brown et al., 2009). Mean (S.D.) 21.78 (5.04) ***[INSERT Table 1 HERE]*** Statistical Methods All analyses were conducted using Mplus version 8.9 [ 39 ]. A Maximum Likelihood Three-Step Approach was used [40]. This method adopts a stepwise approach wherein the optimal latent class model is identified without the inclusion of covariates, using a range of model fit statistics (step one). Participants are then assigned to their most likely latent class, while accounting for classification error using average posterior probabilities (step two). Finally, covariates and distal outcomes of latent class membership are included in the final latent class regression model as auxiliary variables (step three). A conceptual framework for the current study is illustrated in Fig. 1 . Full Information Maximum Likelihood (FIML) was used to handle missing data [ 41 ], details of which can be found in column one of Table 1 . To investigate whether missing data posed a risk of bias, a full-case sensitivity analysis was conducted whereby missing data were removed listwise. Results of the sensitivity analysis are provided as supplementary material. Latent Class Enumeration Latent class analysis was conducted using four health behaviours as class indicators (physical activity; sleep sufficiency; fruit and vegetable consumption; confectionary consumption). As data were clustered by school, a sandwich estimator was used (in Mplus ‘type = complex’). To identify the most optimal number of classes, starting with a one-class solution, models with a consecutive number of latent classes were run until convergence problems were encountered. The following model fit statistics were consulted to determine which solution offered the best fit to the data: Akaike Information Criteria (AIC); Bayesian Information Criteria (BIC); and Sample Size Adjusted Bayesian Information Criteria (ssaBIC), for which lower values indicate better model fit [ 42 ]. Lo-Mendell-Rubin Adjusted Likelihood Ratio Tests (LMRa) significant at p < .05 suggested a model was a significantly better fit to the data than that containing one less latent class (i.e., k vs. k − 1) [ 43 ]. Classification entropy is not a fit index thus, was not used in the class enumeration process [44]. Nevertheless, entropy values are reported with values ≥ .80 considered to represent acceptable levels of classification accuracy [ 45 ]. To avoid overfitting the model to the data and diluting generalisability of findings, quantitative fit statistics were considered alongside substantive criteria such as the interpretability of the classes, model parsimony (with the simplest solution preferred), and the proportional distribution of the sample (i.e., models with very small classes were considered unstable) [46]. Lastly, to strengthen reliability of the class enumeration process, a split halves analysis was conducted whereby the analytical sample was randomly split in half and the class enumeration process repeated in each to determine whether the best fitting model was consistent throughout [47]. Latent Class Regression Once the most optimal latent class model was identified, a latent class regression analysis was carried out to explore associations between potential covariates, latent class membership, and subsequent mental wellbeing (Fig. 1 ). Associations between latent class membership and later wellbeing were established in both unadjusted and fully adjusted models. Results Class Enumeration Convergence issues arose from the six-class solution onward. Accordingly, fit statistics for the one- to five-class solutions are presented in Table 2 . Information criteria-based fit statistics are also illustrated as an elbow plot to aid interpretation (Fig. 2 ). A clear elbow is visible at the three-class solution indicating that beyond this point, increasing model complexity yielded diminishing returns in model fit. LMRa results inferred the k -solution was a better fit in every instance however, the four- and five-class solutions contained very small latent classes (6% and 3% of the sample, respectively). Conversely, the smallest class for the three-class solution contained 15% of the sample; well above the guideline threshold of 10% recommended to avoid over-extraction [46]. Split halves analysis reinforced findings of the main class enumeration; identifying a three-class model as the best fit to the data (see supplementary material). In summation, the three-class solution was considered the most quantitatively and qualitatively parsimonious model and was advanced for further analysis. Entropy for the three-class solution was .916, indicating excellent classification accuracy. Table 2 Model fit statistics for latent classes of adolescent health behaviours Classes LL AIC BIC ssaBIC LMRa Entropy Model Estimated Class Proportions 1 -90210.016 180432.031 180478.977 180459.910 - - 1 2 -88634.063 177290.125 177376.193 177341.236 .000 .729 .58, .42 3 -87014.105 174060.209 174185.399 174134.552 .000 .916 .45, .40, .15 4 -86865.065 173772.131 173936.442 173869.705 .000 .895 .45, .40, .08, .06 5 -86631.204 173314.408 173517.841 173435.214 .000 .833 .45, .27, .16, .07, .03 Class Structure The structure of the three latent classes is illustrated in Fig. 3 . The red class, henceforth referred to as the Wellness Weary ( n = 2,717, 14.7%), were least likely to be active or get sufficient sleep and ate fruit and vegetables relatively infrequently. Compared to the rest of the sample, the amber class, the Balanced Bunch ( n = 7,377, 39.9%), exhibited a moderate likelihood of being active, getting sufficient sleep and eating fruit and vegetables. The green class, the Green and Dream Team ( n = 8,384, 45.4%) were most likely to be active, get sufficient sleep, and ate substantially more fruit and vegetables than the other classes. Confectionary consumption was largely homogeneous across all latent classes, although the Wellness Weary did consume sweets, chocolate, crisps and fizzy drinks slightly more often than the other two classes. Covariates of Health Behaviour Class Membership For all class comparisons, Wellness Weary were used as a reference hence, covariates with Odds Ratios > 1.00 should be considered factors that increased the likelihood adolescents endorsed healthier patterns of behaviours. Results are presented in Table 3 . In brief, members of healthier classes were significantly more likely to be cisgender heterosexual girls, have better wellbeing at T1, and more favourable self-perceived physical health. Healthier classes were also significantly less likely to be socioeconomically disadvantaged, of Asian or Black ethnicity, and spent less time using social media. The Balanced Bunch were also significantly more likely to be of Mixed ethnicity and less likely to identify as LGBTQ+. Table 3 Covariates of health behaviour class membership 95% CI 95% CI Variable Class OR (S.E.) Lower Upper Variable Class OR (S.E.) Lower Upper Physical WW 1 1 1 Mixed WW 1 1 1 Health BB 1.225 (.037) *** 1.155 1.300 BB 1.277 (.138) * 1.033 1.580 GDT 1.587 (.051) *** 1.491 1.690 GDT 1.226 (.132) .993 1.513 IMD WW 1 1 1 AOEG WW 1 1 1 BB .553 (.061) *** .445 .686 BB .971 (.195) .655 1.440 GDT .276 (.038) *** .210 .361 GDT 1.529 (.359) .965 2.422 Social WW 1 1 1 Cisgender WW 1 1 1 Media BB .929 (.011) *** .908 .950 Heterosexual BB 1.191 (.088) * 1.030 1.377 GDT .841 (.010) *** .821 .861 Girl GDT 1.466 (.118) *** 1.252 1.716 Bullied WW 1 1 1 LGBTQ+ WW 1 1 1 BB .912 (.061) .800 1.040 BB .814 (.057) ** .710 .934 GDT 1.055 (.072) .923 1.206 GDT .954 (.070) .826 1.101 Asian WW 1 1 1 SWEMWBS WW 1 1 1 BB .812 (.058) ** .705 .934 BB 1.041 (.007) *** 1.028 1.054 GDT .756 (.061) ** .645 .886 GDT 1.063 (.007) *** 1.050 1.076 Black WW 1 1 1 BB .586 (.081) *** .446 .769 GDT .487 (.064) *** .376 .630 BB, Balanced Bunch; GDT, Green and Dream Team; WW Wellness Weary * Significant at the .05 level ** Significant at the .01 level *** Significant at the .001 level Later Wellbeing as an Outcome of Health Behaviour Class Membership Results are presented in Table 4 . Before adjusting for covariates, wellbeing scores one year later differed significantly across all latent classes, increasing from the Wellness Weary to the Balanced Bunch (Mean Difference = .813(.196), p = .000, d = .162), and the Balanced Bunch to the Green and Dream Team (Mean Difference = .900(.117), p = .000, d = .180). After adjusting for baseline wellbeing, the difference between Wellness Weary and the Balanced Bunch was non-significant (Mean Difference = .074(.179), p = .682, d = .016). In the fully adjusted model, an effect remained whereby the Green and Dream Team reported significantly greater T2 wellbeing than the Balanced Bunch (Mean Difference = .219(.098), p = .026, d = .050). No other significant differences were observed. In the sensitivity analysis whereby only full cases were analysed, this difference became non-significant however, we attribute this inconsistency to the reduction in sample size (N = 13,683) and the full-case models’ diminished power to detect significant effects (see supplementary materials). Table 4 Mean differences in wellbeing at T2 Unadjusted Model Partially Adjusted Model a Fully Adjusted Model b Comparison SWEMWBS MD (S.E.) d SWEMWBS MD (S.E.) d SWEMWBS MD (S.E.) d WW 20.637 (.176) ref - 21.478 (.171) ref - 21.642 (.168) ref - BB 21.450 (.113) .813 (.196) *** .162 21.551 (.105) .074 (.179) .016 21.560 (.101) − .082 (.178) − .019 GDT 22.350 (.125) 1.713 (.212) *** .342 21.904 (.118) .426 (.190) * .095 21.779 (.117) .138 (.190) .032 BB 21.450 (.113) ref - 21.551 (.105) ref - 21.560 (.101) ref - GDT 22.350 (.125) .900 (.117) *** .180 21.904 (.118) .352 (.103) ** .079 21.779 (.117) .219 (.098) * .050 BB, Balanced Bunch; GDT, Green and Dream Team; MD, Mean Difference ; WW, Wellness Weary * Significant at the .05 level ** Significant at the .01 level *** Significant at the .001 level a Adjusted for baseline wellbeing b Adjusted for baseline wellbeing, physical health, IMD, social media use, bullying victimisation, ethnicity, gender identity and sexual orientation Discussion The purpose of this study was to elucidate patterns of adolescent health behaviour, explore associations with a range of covariate factors prominent in adolescence, and establish whether subscribing to different health behaviour patterns contributes to variance in prospective wellbeing one year later. A three-class solution provided an excellent fit to the data, discriminating between the Wellness Weary (a relatively unhealthy class), the Balanced Bunch (a moderately healthy class), and the Green and Dream Team (a relatively healthy class), in alignment with H1. A large number of covariate factors were significantly associated with latent class membership. Most notably, ethnic minorities and those with higher levels of socio-economic disadvantage were less likely, and cisgender heterosexual girls were more likely, to be members of healthier classes. There were no observed effects of bullying victimisation on health behaviour class membership meaning findings offer partial support for H2. The Green and Dream Team reported significantly better T2 wellbeing than the Balanced Bunch but not the Wellness Weary . Accordingly, we are only able to offer partial support for H3. Previous studies exploring patterns of health behaviour such as physical activity or eating habits have typically identified three to seven clusters depicting healthy, unhealthy and mixed patterns [ 48 ]. Adolescent risk -behaviours have also been found to cluster in a similar way [49]. In one such study, three distinct patterns of risk-behaviours (binge drinking, low fruit and vegetable intake, physical inactivity, insufficient sleep, and smoking) were uncovered, and high-risk classes had significantly poorer mental health [49]. Findings from the present study concur, showing that adolescents vary substantially in the extent to which they engage in health behaviours, and that the collective effect of health behaviour patterns have potential to enhance or diminish their health and wellbeing. The emergence of distinct classes of health behaviour can be explained through the lens of Health Lifestyle Theory [34]. This theory proposes that healthy lifestyles are not the product of uncoordinated behaviours of disconnected individuals, but instead are a consequence of the complex interplay between societal structures, individual agency, group-based identities, and cultural norms [34,50]. Whilst a young person’s free will to make healthy choices undoubtably contributes to variance in health behaviour endorsement, social and demographic factors can greatly limit or enhance the actual and perceived choices available to a specific class or subset of the population [34,50]. For this reason, groups of individuals with similar social and demographic characteristics may encounter similar barriers or drivers that lead to the emergence of common health behavioural patterns. In the present study, factors found to significantly influence adolescent health behaviour class membership were physical health, socio-economic disadvantage, social media usage, minority ethnicity, LGBTQ + status, gender identity and sexual orientation, and prior mental wellbeing. Two of the strongest and arguably most novel of these associations are discussed below. Socio-Economic Disadvantage Evidence concerning the impact of socio-economic variables on adolescents’ physical activity paints a mixed picture. Variability is due in part to the broad range of metrics used to quantify socio-economic status [51] and the dense range of psychosocial factors that precede or accompany activity participation across socio-economic groups [ 52 – 54 ]. For instance, adolescents from disadvantaged households might rely more on active transportation if they lack access to a car but could face barriers such as unaffordable membership fees for after-school sports clubs. Whilst differences in domain-specific activities are common [53,54], it is not always clear how this translates to overall activity performed throughout the day. That said, a recent report published by Sport England suggested young people from low affluence families are less likely to be physically active [ 14 ]. The effects of disadvantage extend to other health behaviours with links to lower fruit and vegetable consumption [18], more sugary drinks [17], less sleep, fragmented sleep, and difficulty in achieving sleep onset [ 19 ]. Those most likely to endorse one health behaviour (e.g., physical activity) were also those most likely to endorse concomitant health behaviours. Consistency in the rank order of health behaviour patterns across all latent class indicators supports Health Lifestyle Theory [34,50] and infers that underlying social disparity may have ubiquitous impact, shaping engagement in multiple health behaviours simultaneously. Indeed, of all the covariates measured, socioeconomic disadvantage was the most strongly associated with membership of both the Wellness Weary and Balanced Bunch classes. Alas, the effects of socio-economic disparity persist and the need create a more fair and equitable society must continue to be of paramount importance to public health advocates [ 55 ]. Social Media Use The association between social media use and adolescent health behaviour is a relatively new topic for research. Young people are extensive social media users, yet there is a lack of research on the effect of social media on health behaviour [56]. Emerging evidence has linked social media use to disrupted sleep patterns, including delayed sleep onset and increased night-time awakenings [20]. Excessive use of social media to share or promote physical activities may also contribute to exercise compulsion and pressure to conform to certain body-image standards which in turn can have negative consequences for wellbeing [21]. The current study contributes to knowledge by establishing that lower daily usage increases the probability of having favourable health behaviour patterns. Efforts to improve health behaviours among young people might usefully focus on finding the balance (i.e., between time spent on social media and time spent engaging in health behaviours. Positive associations have previously been observed between social media use and confectionary consumption; attributed to food and drink marketing involving celebrities and influencers designed to target adolescents; a highly impressionable group in society [57,58]. Our findings diverge from the literature in this respect given there was little variation in confectionary consumption across classes. This infers that even those with extremely high social media use were likely to consume a similar level of confectionary to their peers. Instead, we propose the negative relationship observed between social media use and health behaviour patterns may be a product of time displacement and reductions specifically in physical activity or sleep. Where social media can be consumed alongside eating or drinking, time spent on social media necessarily displaces time available to engage in sleep and many forms of physical activity [59]. Adopting a time displacement perspective helps explain the structure of health behaviour patterns observed herein and may support more consistent replication of findings compared to approaches dependent on assumptions surrounding the content of social media itself. With this in mind, perhaps a primary focus for future research (particularly that where qualitative interpretation of the content being consumed is unavailable) should be to illuminate links between social media and physical activity or sleep as opposed to eating habits. Health Behaviours and Later Wellbeing In the unadjusted analyses, comparisons of the T2 outcomes of the three latent health behaviour classes were directly in line with our predictions, with healthier classes reporting significantly better mental wellbeing (i.e., Green and Dream Team > Balanced Bunch; Green and Dream Team > Wellness Weary ; Balanced Bunch > Wellness Weary ). The magnitude of effect sizes in these contrasts was also in line with expectations, in that the largest difference in T2 wellbeing was between the most and least healthy classes. This pattern of findings is in line with prior research that has utilised person-centred approaches to understand the links between health behaviours and mental health and wellbeing in adolescence [32]. However, after adjusting for prior (T1) wellbeing, differences between classes reduced substantially. This was most noticeable in the difference between the Green and Dream Team and Wellness Weary classes which reduced from d = .342 to d = .095 inferring that prior wellbeing accounts for a substantial proportion of the variation in scores one year later. When adjusting for additional social covariates, differences reduced further still such that the only statistically significant difference remaining in T2 mental wellbeing was between the Green and Dream Team and Balanced Bunch classes. The departure of our findings from the associations identified in prior studies may reflect methodological differences. For example: our analysis was longitudinal as opposed to cross-sectional [32,60,61]; our focus was on wellbeing as opposed to mental illness [60,62]; and our adjusted models also factored in a wide range of sociodemographic covariates which may have attenuated the association between health behaviours and wellbeing making effects seem weaker compared to studies making alternative adjustments [32,60,62]. Reports show a widening gap in the physical activity levels of Black and Asian adolescents compared to those from other ethnic backgrounds [ 14 ]. The current study expands knowledge in this regard, establishing that not only are Black and Asian adolescents least likely to be active, but they are also least likely to get sufficient sleep or have healthy eating habits, placing them among the least healthy generally. The health behaviour patterns of these ethnic minorities conflict with reports from both this cohort and others that Black and Asian adolescents have at least equal (if not higher) levels of wellbeing than many other ethnic groups [ 63 – 65 ]. Similarly, a plethora of sources report girls have lower physical activity levels, lower wellbeing and greater mental health difficulties than boys [ 14 , 63 , 66 ] yet were substantially more likely to be Green and Dream Team members. In essence, there appears to be a partial disconnect between health lifestyles and adolescent wellbeing. Whilst there is evidence for a longitudinal association between the two, the strength of this association is highly subject to demographic and socio-economic influences that should be factored into public health messaging and intervention/prevention strategies going forward. Moreover, disparity on the grounds of ethnicity, socio-economic position, gender and sexual orientation is concerning regardless of any links with wellbeing. Evidence that over one in ten young people are associated with a Wellness Weary (less physical activity, insufficient sleep, and infrequent fruit and vegetable intake) pattern of behaviour is worrying in and of itself. Strengths and Limitations The current study benefited from a very large sample, longitudinal dataset, and use of robust, person-centred statistical techniques. However, there are a number of limitations that should be borne in mind. First, although the previously coined, ‘Big Three’ (physical activity, sleep, diet) were utilised, these are by no means the only health behaviours prevalent in adolescence. Notably, our dataset did not contain information about substance use (e.g., alcohol, cannabis), which becomes increasingly prevalent through the course of adolescence [ 67 ]. Such data are now being collected as part of the recent extension of the #BeeWell study in its second location [ 68 ] and so a future analysis can incorporate these health behaviours to address this limitation. Importantly, whilst the majority of the sample were part of the Green and Dream Team (i.e., the healthiest class) caution is urged when describing this group as ‘healthy’. The Green and Dream Team’s likelihood of endorsing healthy behaviours is relative to the rest of the sample so members of this class and indeed all other classes, may still be insufficiently healthy according to national and/or international public health recommendations [ 69 , 70 ]. Some measures used contain an element of subjectivity (e.g., the single-item used to quantify sleep) and do not readily facilitate comparison against national averages or age-matched cohorts, making quantification of overall health by way of health behaviour endorsement difficult. It is the philosophical stance of #BeeWell that young peoples’ voices should be central to research. As such measures were chosen in co-creation alongside a Youth Steering Group. It is therefore maintained that the data analysed, and findings presented herein provide a meaningful insight into the lives of young people from Greater Manchester. Supplementary material has been provided to facilitate comparison of the demographic characteristics of the analytical sample against those for Greater Manchester and England. Conclusions This study identified three distinct patterns of health behaviour among adolescents, broadly categorised as more, moderate, and less healthy. The observed consistency in engaging in multiple health behaviours corroborates the tenets of Health Lifestyle Theory. Notably, disparities rooted in ethnicity and socio-economic status were evident, with minority and socio-economically disadvantaged adolescents more frequently adopting less healthy behavioural patterns. Public health initiatives must continue to focus on reducing these disparities. Additionally, curbing social media use could present a less impactful, yet more timely strategy for encouraging adherence to healthier behaviour patterns. The healthiest class of adolescents demonstrated significantly greater wellbeing, whilst a considerable segment of the cohort fell into the least healthy category. These findings collectively underscore the imperative to enhance health behaviour during adolescence to foster better long-term health outcomes. Abbreviations AIC Akaike Information Criteria; BIC Bayesian Information Criteria; CMO Chief Medical Officers; FIML Full Information Maximum Likelihood; H1 Hypothesis one; H2 Hypothesis two; H3 Hypothesis three; LGBTQ+ Lesbian Gay Bisexual Transgender Queer LL Loglikelihood; LMRa Lo-Mendell-Rubin Adjusted Likelihood Ratio Test; ssaBIC Sample Size Adjusted Bayesian Information Criteria; T1 Timepoint one; T2 Timepoint two. Declarations Declaration of Interest The authors have no competing interests to declare. Ethics Approval and Consent to Participate Informed consent was obtained from the parents/legal guardians of all study participants. Consistent with the conditions of this approval and related documentation (e.g., parent information and informed consent forms), data were in pseudonymised form during analysis. All methods used in the study were carried out in accordance with the Declaration of Helsinki. Consent for Publication Not applicable. Availability of Data and Materials An anonymised version of the #BeeWell survey responses will be made publicly available in 2026. Due to ethical governance constraints this cannot be brought forward since participants have been given the right to withdraw their data until this point, necessitating the need to maintain a securely stored pseudonymised version until this point. In addition, the linked administrative data (e.g., sex, free school meal eligibility) will never be shared publicly due to the prohibition of onward sharing in the data sharing agreement in place with the Local Authorities who provided it. To request access to the #BeeWell data, please contact [name removed for blind review]. Anonymous Mplus syntax used to analyse the data will be made publicly available via the Open Science Framework upon acceptance of this manuscript for publication. Competing Interests The authors declare that they have no competing interests. Funding The #BeeWell study is kindly funded by the University of Manchester, the Greater Manchester Combined Authority, The National Lottery Community Fund, BBC Children in Need, Big Change, the Gregson Family Foundation, the Paul Hamlyn Foundation, the Holroyd Foundation, the Oglesby Charitable Trust, and the Peter Cundill Foundation. No sources of funding had any input on the design of the study, collection, analysis or interpretation of data, or in writing this manuscript. Authors Contributions All authors had input into study conceptualisation. C.K. E.T. and K.P. analysed the data. All authors interpreted the results and contributed to drafting the manuscript. All authors and read and approved the final manuscript. Acknowledgements We gratefully acknowledge the engagement of the many participating schools and young people across Greater Manchester, without whose efforts this research would not have been possible. References Short SE, Mollborn S. Social determinants and health behaviors: conceptual frames and empirical advances. Curr Opin Psychol., Viner RM, Ross D, Hardy R, Kuh D, Power C, Johnson A, Wellings K, McCambridge J, Cole TJ, Kelly Y, Batty GD. Life course epidemiology: recognising the importance of adolescence. J Epidemiol Community Health. 2015;69(8):719 – 20. doi:10.1136/jech-2014-205300. 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Supplementary Files Additionalfile1.docx Supplementary Material File name: Additional file 1.docx Word document containing the following supplementary items: Table S1: demographic characteristics of the analytical sample, Greater Manchester and national population of adolescents Table S2: model fit statistics for latent classes of health behaviour (half one) Figure S1: elbow plot illustrating model fit (half one) Figure S2: three latent classes of adolescent health behaviour (half one) Table S3: model fit statistics for latent classes of health behaviour (half two) Figure S3: elbow plot illustrating model fit (half two) Figure S4: three latent classes of adolescent health behaviour (half two) Table S4: item response probabilities and model estimated mean scores for class indicators (split halves analysis) Table S5: associations between latent classes of health behaviour and covariates (complete case sensitivity analysis) Cite Share Download PDF Status: Published Journal Publication published 18 Sep, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 09 May, 2024 Submission checks completed at journal 08 May, 2024 Editor assigned by journal 08 May, 2024 First submitted to journal 07 May, 2024 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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3","display":"","copyAsset":false,"role":"figure","size":163253,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThree latent classes of adolescent health behaviour\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4382077/v1/55659ded734334e5e786e5ff.png"},{"id":65103931,"identity":"ecc9a13d-82f6-4e9a-811b-04077dbfd7bf","added_by":"auto","created_at":"2024-09-23 16:09:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1545442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4382077/v1/1e3eaaa9-514f-493e-a3b2-a3c48dadf51d.pdf"},{"id":56514771,"identity":"e3204df7-5f72-4391-8d26-6e44d141909f","added_by":"auto","created_at":"2024-05-15 07:29:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":502873,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material\u003c/p\u003e\n\u003cp\u003eFile name: Additional file 1.docx\u003c/p\u003e\n\u003cp\u003eWord document containing the following supplementary items:\u003c/p\u003e\n\u003cp\u003eTable S1: demographic characteristics of the analytical sample, Greater Manchester and national population of adolescents\u003c/p\u003e\n\u003cp\u003eTable S2: model fit statistics for latent classes of health behaviour (half one)\u003c/p\u003e\n\u003cp\u003eFigure S1: elbow plot illustrating model fit (half one)\u003c/p\u003e\n\u003cp\u003eFigure S2: three latent classes of adolescent health behaviour (half one)\u003c/p\u003e\n\u003cp\u003eTable S3: model fit statistics for latent classes of health behaviour (half two)\u003c/p\u003e\n\u003cp\u003eFigure S3: elbow plot illustrating model fit (half two)\u003c/p\u003e\n\u003cp\u003eFigure S4: three latent classes of adolescent health behaviour (half two)\u003c/p\u003e\n\u003cp\u003eTable S4: item response probabilities and model estimated mean scores for class indicators (split halves analysis)\u003c/p\u003e\n\u003cp\u003eTable S5: associations between latent classes of health behaviour and covariates (complete case sensitivity analysis)\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4382077/v1/ea0af939aa3c178b4be4dd47.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Latent Classes of Adolescent Health Behaviour, Social Covariates and Mental Wellbeing: A Longitudinal Birth Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eHealth behaviours are a set of practices that promote or impair the health of an individual [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Adolescence is a critical developmental period when several of these behaviours start to emerge, many of which can persist into early adulthood [2]. \u003cem\u003ePhysical activity\u003c/em\u003e [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e3\u003c/span\u003e], \u003cem\u003esleep\u003c/em\u003e [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and \u003cem\u003eeating habits\u003c/em\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e5\u003c/span\u003e], previously coined \u003cem\u003eThe Big Three\u003c/em\u003e modifiable health behaviours, are proposed to have both independent and synergistic associations with adolescent mental health [6,7]. Theoretical models of these associations span neurobiological, psychosocial, and behavioural processes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For example, physical activity impacts on the functioning of the hypothalamus-pituitary-adrenal axis, which in turn reduces cortisol levels, thereby supporting wellbeing [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, inadequate sleep can prompt more frequent use of maladaptive emotion regulation strategies, which in turn negatively impacts mental health [10].\u003c/p\u003e \u003cp\u003eA great number of factors may influence the likelihood an individual endorses different health behaviours in adolescence [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Better understanding of these factors can help identify those at-risk and contribute to effective, targeted behaviour change interventions. Covariates of particular relevance during adolescence include gender identity and sexual orientation [12,13]; ethnicity [14\u0026ndash;16]; socio-economic disadvantage [17\u0026ndash;19]; social media use [20,21]; physical health [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; bullying victimisation [22]; and prior levels of wellbeing (given the likely reciprocal nature of the relationship with health behaviours) [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR13\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is now widespread evidence that being sufficiently active, getting enough sleep, and following a healthy diet can support mental health in adolescence [6]. However, most existing research has focused on mental \u003cem\u003eillness\u003c/em\u003e, with relatively few studies elucidating links with wellbeing [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. From a population health perspective, a focus on wellbeing arguably has greater utility than a focus on mental illness [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Most young people do not meet diagnostic criteria for a mental health disorder (leading to floor effects), but there is substantial variability in mental wellbeing [27], which has been demonstrated to predict a range of salient outcomes later in life including but not limited to: adult mental and physical health; health behaviours; relationships; and labour market outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eA Person-Centred Approach\u003c/h3\u003e\n\u003cp\u003ePrevious studies in this area have tended to adopt variable-centred approaches to analysis (e.g., regression) which assume a homogeneous population differing only in the extent to which they engage in health behaviours. In comparison, relatively few adopt person-centred perspectives (e.g., cluster or latent class/profile analysis) which aim to capture the heterogeneity that exists within populations in terms of the extent \u003cem\u003eand\u003c/em\u003e pattern of health behaviours they exhibit [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Where variable-centred approaches examine associations between variables, person-centred approaches examine relationships between people, offering evidence of how certain health behaviours might cluster together in distinct patterns that characterise unobserved subgroups (i.e., latent classes) of the population [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Each latent class comprises individuals who elicit similarities on specific indicators, but who are quantitatively and qualitatively distinct from those in alternative classes, thus capturing homogeneity within groups and heterogeneity between groups [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Adolescence is a transformative life stage characterised by rapid neurological, psychosocial, and emotional development [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As such, the extent to which young people endorse different health behaviours, the effect of social and demographic antecedents of health behaviour, and the collective impact health behaviours may have on wellbeing, is likely to differ significantly from one individual to the next [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e31\u003c/span\u003e] signalling that person-centred research is both warranted and necessary in this age-group.\u003c/p\u003e \u003cp\u003eA useful illustrative example of the utility of person-centred approaches is seen in a cross-sectional study [32], which used cluster analysis to identify three distinct patterns of health behaviour (utilising data on sleep, alcohol use, cannabis use, social media use, and sport and hobby participation) among Irish adolescents, denoted as low, moderate and high health-promoting, respectively. The authors found that membership of these clusters was predicted by socio-demographic characteristics (e.g., high health-promoting adolescents were more likely to be younger and female); they also reported that cluster membership was associated with mental health and wellbeing outcomes (e.g., low health-promoting adolescents reported the highest levels of anxiety and depression, and lowest levels of life satisfaction). Whilst extremely illuminative, existing person-centred studies on adolescent health behaviour and mental health have predominantly been cross-sectional [32] or focused on links with mental illness [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. There is therefore an ongoing need to establish temporal precedence between health behaviour participation and adolescent wellbeing both before and after adjusting for a wide range of social and demographic covariates.\u003c/p\u003e\n\u003ch3\u003eAims and Hypotheses\u003c/h3\u003e\n\u003cp\u003eUsing data from the first two annual waves (T1, T2) of the #BeeWell study in Greater Manchester, United Kingdom, the aims of this study were to establish: (1) latent classes of adolescent health behaviour at T1 when participants were aged 12\u0026ndash;13; (2) whether bullying victimisation, social media use, gender identity and sexual orientation, ethnicity, socio-economic disadvantage, self-reported physical health, and/or mental wellbeing at T1 were associated with latent class membership; and, (3) whether latent class membership at T1 contributed to variance in mental wellbeing at T2, when participants were aged 13\u0026ndash;14 years.\u003c/p\u003e \u003cp\u003eAs person-centred analyses of adolescent health behaviours are sparse, the class identification phase of analysis was largely exploratory in nature. Nevertheless, concurrent health behaviours are often positively correlated [34]. Accordingly, we hypothesised there may be a predominantly healthy class (e.g., physically active, sufficient sleep, regular fruit and vegetables, irregular junk food) and a predominantly unhealthy class (e.g., physically inactive, insufficient sleep, irregular fruit and vegetables, regular junk food), among others (H1). We further hypothesised, based on existing evidence noted above, that bullying victimisation, LGBTQ\u0026thinsp;+\u0026thinsp;adolescents, minority ethnicity, socio-economic disadvantage, poor physical health, and poor T1 wellbeing would be significant risk factors for membership of less healthy classes (H2). Finally, we hypothesised that members of more healthy classes would report better wellbeing at T2 after controlling for covariates (H3). An analysis plan detailing the hypotheses and analytical methods used was pre-registered on the Open Science Framework [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e#BeeWell is a hybrid population cohort study in Greater Manchester, United Kingdom, comprising: (i) a truncated longitudinal study in which participants are tracked with annual data points from age 12\u0026ndash;15 (i.e., from Year 8 to Year 9 to Year 10 of secondary school; Sample 1); and, (ii) a cross-sectional study comprising annual data points for participants aged 14\u0026ndash;15 (i.e., those in Year 10 of secondary school at a given data point; Sample 2) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our secondary analysis drew on the first (T1) and second (T2) annual data points for Sample 1 conducted in 2021 and 2022, respectively (overall N\u0026thinsp;=\u0026thinsp;20,241).\u003c/p\u003e \u003cp\u003eAll young people from Sample 1 who responded to at least one of the health behaviour items at T1 were eligible for inclusion. Data were clustered by school, so consistent with guidance for working with multilevel data, only cases were there were \u0026ge;\u0026thinsp;5 pupils per school were retained [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This resulted in a final analytical sample of \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18,478 pupils from 138 schools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003e#BeeWell is a rich source of individual-level data on a wide range of wellbeing domains/indicators (e.g., life satisfaction, self-esteem, mental wellbeing) and drivers (e.g., social media use, bullying, sleep), as well as multiple individual characteristics (e.g., age, ethnicity, sexual orientation). Data pertaining to latent class indicators (physical activity, sleep, fruit and vegetable consumption, confectionary consumption) and covariates (ethnicity, socio-economic disadvantage, gender identity, sexual orientation, self-reported physical health, social media use, bullying victimisation and mental wellbeing) were drawn from the T1 survey and linked administrative data. Outcome data on mental wellbeing were drawn from the T2 survey. A detailed explanation of the measures used and how scores were interpreted is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The #BeeWell survey can also be accessed online [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\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 measures used to quantify adolescent health behaviour, covariates and wellbeing\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLatent Class Indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruit and Vegetable Consumption\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;0.49%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle item adapted from the Health Behaviours in Schools Checklist [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e71\u003c/span\u003e](Inchley et al., 2020) and the Millennium Cohort Study [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e72\u003c/span\u003e](Centre for Longitudinal Studies, 2020) targeting weekly consumption of fruit and vegetables.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponses treated as quasi-continuous, rated on a seven-point scale ranging from 0 (\u003cem\u003eNever\u003c/em\u003e) to 6 (\u003cem\u003eEveryday more than once\u003c/em\u003e).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (S.D.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.06 (1.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfectionary Consumption\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;0.71%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle item adapted from the Health Behaviours in Schools Checklist [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e71\u003c/span\u003e](Inchley et al., 2020) and the Millennium Cohort Study [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e72\u003c/span\u003e](Centre for Longitudinal Studies, 2020) targeting weekly consumption of sweets, chocolate, crisps, and fizzy drinks.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponses treated as quasi-continuous, rated on a seven-point scale ranging from 0 (\u003cem\u003eNever\u003c/em\u003e) to 6 (\u003cem\u003eEveryday more than once\u003c/em\u003e). Reverse coded such that higher scores represented healthier behaviour.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (S.D.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.24 (1.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;3.44%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo items adapted from the Health Behaviours in Schools Checklist [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e71\u003c/span\u003e](Inchley et al., 2020) measuring weekly minutes of Moderate-to-Vigorous Physical Activity (MVPA).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA binary variable was derived discriminating between participants adhering/not adhering to current CMO MVPA guidelines (\u0026ge;\u0026thinsp;420-minutes per week).\u003c/p\u003e \u003cp\u003eResponses coded: \u003cem\u003eNo\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0; \u003cem\u003eYes\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003cp\u003e6,590 (35.66%)\u003c/p\u003e \u003cp\u003e11,253 (60.90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;0.87%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA single item from the Health Behaviours in Schools Checklist asking whether the amount of sleep they normally get is enough to feel awake and concentrate on schoolwork during the day [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e71\u003c/span\u003e](Inchley et al., 2020).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponses coded: \u003cem\u003eNo\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0; \u003cem\u003eYes\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003cp\u003e11,732 (63.49%)\u003c/p\u003e \u003cp\u003e6,586 (35.64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates of Latent Class Membership\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWellbeing\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;11.69%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe (seven-item) Short Warwick-Edinburgh Mental Wellbeing [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e73\u003c/span\u003e](Tennant et al., 2007).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA five-point Likert-type scale. Total scores range from 7 to 35 with higher scores indicating greater wellbeing. Consistent with recommendations from the scale developer, \u003cem\u003etransformed\u003c/em\u003e SWEMWBS scores were used [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e74\u003c/span\u003e](Stewart-Brown et al., 2009).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (S.D.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.75 (4.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBullying Victimisation\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;5.31%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThree items adapted from the Understanding Society Youth Questionnaire [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e74\u003c/span\u003e](Institute for Social and Economic Research, 2021) and the Health Behaviours in Schools Checklist [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e71\u003c/span\u003e](Inchley et al., 2020).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA binary measure of bullying was derived. Participants who responded \u003cem\u003equite a lot\u003c/em\u003e or \u003cem\u003ea lot\u003c/em\u003e to at least one item were classed as bullied.\u003c/p\u003e \u003cp\u003eCoded as: \u003cem\u003eNot Bullied\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0; \u003cem\u003eBullied\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBullied\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNot Bullied\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003cp\u003e3,092 (16.73%)\u003c/p\u003e \u003cp\u003e14,405 (77.96%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;3.88%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassed as Asian, Black, Mixed, White, or Any Other Ethnic Group (including Chinese) using linked administrative data provided by Greater Manchester Local Authorities.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy variables derived for each ethnic group.\u003c/p\u003e \u003cp\u003eCoded as: \u003cem\u003eNo\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0; \u003cem\u003eYes\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAsian\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBlack\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eMixed\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eWhite\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eAOEG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003cp\u003e3,190 (17.26%)\u003c/p\u003e \u003cp\u003e903 (4.89%)\u003c/p\u003e \u003cp\u003e1,077 (5.83%)\u003c/p\u003e \u003cp\u003e11,998 (64.93%)\u003c/p\u003e \u003cp\u003e593 (3.21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Reported Physical Health\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;0.23%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA single item adapted from Understanding Society [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e75\u003c/span\u003e](Institute for Social and Economic Research, 2021).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA five-point Likert-type scale ranging from 1 (\u003cem\u003ePoor\u003c/em\u003e) to 5 (\u003cem\u003eExcellent\u003c/em\u003e).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMean (S.D.)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.42 (1.022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender Identity and Sexual Orientation\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;7.33%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA three-category variable was derived (Thornton, Petersen, Marquez \u0026amp; Humphrey, 2024) using sex assigned at birth (linked administrative data), gender identity and sexual orientation (gathered through T1 surveys) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCisgender Heterosexual Boys (reference group); Cisgender Heterosexual Girls; and LGBTQ+.\u003c/p\u003e \u003cp\u003eDummy coded as: \u003cem\u003eNo\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0; \u003cem\u003eYes\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCishet Boy\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eCishet Girl\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLGBTQ+\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003cp\u003e6,215 (33.63%)\u003c/p\u003e \u003cp\u003e5,366 (29.04%)\u003c/p\u003e \u003cp\u003e5,542 (29.99%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Media Use\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;5.84%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA single item adapted from the Millennium Cohort Study assessing daily hours spent on social media [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e72\u003c/span\u003e](Centre for Longitudinal Studies, 2023).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA continuous variable whereby higher scores represent more frequent daily use (hours).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (S.D.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.29 (2.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-Economic Disadvantage\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;3.36%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex of Multiple Deprivation rank based on the Lower Layer Super Output Area (LSOA) for the young person\u0026rsquo;s home postcode (provided by Greater Manchester Local Authorities) ranging from 1 (\u003cem\u003eMost Deprived\u003c/em\u003e) to 32,844 (\u003cem\u003eLeast Deprived\u003c/em\u003e).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe reciprocal of IMD rank expressed as a percentage was used such that scores ranged from 0 to 1 and higher scores indicated greater disadvantage.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (S.D.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.65 (.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcomes of Latent Class Membership\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWellbeing\u003c/p\u003e \u003cp\u003e\u003cem\u003e(missing\u0026thinsp;=\u0026thinsp;46.23%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe (seven-item) Short Warwick-Edinburgh Mental Wellbeing [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e73\u003c/span\u003e](Tennant et al., 2007).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA five-point Likert-type scale. Total scores range from 7 to 35 with higher scores indicating greater wellbeing. Consistent with recommendations from the scale developer, \u003cem\u003etransformed\u003c/em\u003e SWEMWBS scores were used [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e74\u003c/span\u003e](Stewart-Brown et al., 2009).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (S.D.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.78 (5.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***[INSERT Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e HERE]***\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003eAll analyses were conducted using Mplus version 8.9 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A Maximum Likelihood Three-Step Approach was used [40]. This method adopts a stepwise approach wherein the optimal latent class model is identified without the inclusion of covariates, using a range of model fit statistics (step one). Participants are then assigned to their most likely latent class, while accounting for classification error using average posterior probabilities (step two). Finally, covariates and distal outcomes of latent class membership are included in the final latent class regression model as auxiliary variables (step three). A conceptual framework for the current study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Full Information Maximum Likelihood (FIML) was used to handle missing data [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e41\u003c/span\u003e], details of which can be found in column one of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To investigate whether missing data posed a risk of bias, a full-case sensitivity analysis was conducted whereby missing data were removed listwise. Results of the sensitivity analysis are provided as supplementary material.\u003c/p\u003e \u003cp\u003eLatent Class Enumeration\u003c/p\u003e \u003cp\u003eLatent class analysis was conducted using four health behaviours as class indicators (physical activity; sleep sufficiency; fruit and vegetable consumption; confectionary consumption). As data were clustered by school, a sandwich estimator was used (in Mplus \u0026lsquo;type\u0026thinsp;=\u0026thinsp;complex\u0026rsquo;). To identify the most optimal number of classes, starting with a one-class solution, models with a consecutive number of latent classes were run until convergence problems were encountered. The following model fit statistics were consulted to determine which solution offered the best fit to the data: \u003cem\u003eAkaike Information Criteria\u003c/em\u003e (AIC); \u003cem\u003eBayesian Information Criteria\u003c/em\u003e (BIC); and \u003cem\u003eSample Size Adjusted Bayesian Information Criteria\u003c/em\u003e (ssaBIC), for which lower values indicate better model fit [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. \u003cem\u003eLo-Mendell-Rubin Adjusted Likelihood Ratio Tests\u003c/em\u003e (LMRa) significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 suggested a model was a significantly better fit to the data than that containing one less latent class (i.e., \u003cem\u003ek\u003c/em\u003e vs. \u003cem\u003ek\u003c/em\u003e \u0026minus;\u0026thinsp;1) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Classification entropy is not a fit index thus, was not used in the class enumeration process [44]. Nevertheless, entropy values are reported with values \u0026ge; .80 considered to represent acceptable levels of classification accuracy [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo avoid overfitting the model to the data and diluting generalisability of findings, quantitative fit statistics were considered alongside substantive criteria such as the interpretability of the classes, model parsimony (with the simplest solution preferred), and the proportional distribution of the sample (i.e., models with very small classes were considered unstable) [46]. Lastly, to strengthen reliability of the class enumeration process, a split halves analysis was conducted whereby the analytical sample was randomly split in half and the class enumeration process repeated in each to determine whether the best fitting model was consistent throughout [47].\u003c/p\u003e \u003cp\u003eLatent Class Regression\u003c/p\u003e \u003cp\u003eOnce the most optimal latent class model was identified, a latent class regression analysis was carried out to explore associations between potential covariates, latent class membership, and subsequent mental wellbeing (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Associations between latent class membership and later wellbeing were established in both unadjusted and fully adjusted models.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClass Enumeration\u003c/h2\u003e \u003cp\u003eConvergence issues arose from the six-class solution onward. Accordingly, fit statistics for the one- to five-class solutions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Information criteria-based fit statistics are also illustrated as an elbow plot to aid interpretation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A clear elbow is visible at the three-class solution indicating that beyond this point, increasing model complexity yielded diminishing returns in model fit. LMRa results inferred the \u003cem\u003ek\u003c/em\u003e-solution was a better fit in every instance however, the four- and five-class solutions contained very small latent classes (6% and 3% of the sample, respectively). Conversely, the smallest class for the three-class solution contained 15% of the sample; well above the guideline threshold of 10% recommended to avoid over-extraction [46]. Split halves analysis reinforced findings of the main class enumeration; identifying a three-class model as the best fit to the data (see supplementary material). In summation, the three-class solution was considered the most quantitatively and qualitatively parsimonious model and was advanced for further analysis. Entropy for the three-class solution was .916, indicating excellent classification accuracy.\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\u003eModel fit statistics for latent classes of adolescent health behaviours\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\"\u003e \u003cp\u003eClasses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003essaBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMRa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel Estimated\u003c/p\u003e \u003cp\u003eClass Proportions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-90210.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180432.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180478.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e180459.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-88634.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177290.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e177376.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e177341.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.58, .42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-87014.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174060.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e174185.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e174134.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.45, .40, .15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-86865.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173772.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173936.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e173869.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.45, .40, .08, .06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-86631.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173314.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173517.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e173435.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.45, .27, .16, .07, .03\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClass Structure\u003c/h2\u003e \u003cp\u003eThe structure of the three latent classes is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The red class, henceforth referred to as the \u003cem\u003eWellness Weary\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,717, 14.7%), were least likely to be active or get sufficient sleep and ate fruit and vegetables relatively infrequently. Compared to the rest of the sample, the amber class, the \u003cem\u003eBalanced Bunch\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7,377, 39.9%), exhibited a moderate likelihood of being active, getting sufficient sleep and eating fruit and vegetables. The green class, the \u003cem\u003eGreen and Dream Team\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8,384, 45.4%) were most likely to be active, get sufficient sleep, and ate substantially more fruit and vegetables than the other classes. Confectionary consumption was largely homogeneous across all latent classes, although the \u003cem\u003eWellness Weary\u003c/em\u003e did consume sweets, chocolate, crisps and fizzy drinks slightly more often than the other two classes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCovariates of Health Behaviour Class Membership\u003c/h2\u003e \u003cp\u003eFor all class comparisons, \u003cem\u003eWellness Weary\u003c/em\u003e were used as a reference hence, covariates with Odds Ratios\u0026thinsp;\u0026gt;\u0026thinsp;1.00 should be considered factors that increased the likelihood adolescents endorsed healthier patterns of behaviours. Results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In brief, members of healthier classes were significantly more likely to be cisgender heterosexual girls, have better wellbeing at T1, and more favourable self-perceived physical health. Healthier classes were also significantly less likely to be socioeconomically disadvantaged, of Asian or Black ethnicity, and spent less time using social media. The \u003cem\u003eBalanced Bunch\u003c/em\u003e were also significantly more likely to be of Mixed ethnicity and less likely to identify as LGBTQ+.\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\u003eCovariates of health behaviour class membership\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e95% CI\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\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (S.E.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (S.E.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.225 (.037)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.277 (.138)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.587 (.051)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.226 (.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAOEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e 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colname=\"c9\"\u003e \u003cp\u003e.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.276 (.038)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.529 (.359)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBullied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLGBTQ+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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\u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.586 (.081)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.769\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.487 (.064)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.630\u003c/p\u003e \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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBB, \u003cem\u003eBalanced Bunch;\u003c/em\u003e GDT, \u003cem\u003eGreen and Dream Team;\u003c/em\u003e WW \u003cem\u003eWellness Weary\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e*\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eSignificant at the .05 level\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e**\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eSignificant at the .01 level\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e***\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eSignificant at the .001 level\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLater Wellbeing as an Outcome of Health Behaviour Class Membership\u003c/h2\u003e \u003cp\u003eResults are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Before adjusting for covariates, wellbeing scores one year later differed significantly across all latent classes, increasing from the \u003cem\u003eWellness Weary\u003c/em\u003e to the \u003cem\u003eBalanced Bunch\u003c/em\u003e (Mean Difference\u0026thinsp;=\u0026thinsp;.813(.196), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.000, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.162), and the \u003cem\u003eBalanced Bunch\u003c/em\u003e to the \u003cem\u003eGreen and Dream Team\u003c/em\u003e (Mean Difference\u0026thinsp;=\u0026thinsp;.900(.117), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.000, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.180). After adjusting for baseline wellbeing, the difference between \u003cem\u003eWellness Weary\u003c/em\u003e and the \u003cem\u003eBalanced Bunch\u003c/em\u003e was non-significant (Mean Difference\u0026thinsp;=\u0026thinsp;.074(.179), \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.682, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.016). In the fully adjusted model, an effect remained whereby the \u003cem\u003eGreen and Dream Team\u003c/em\u003e reported significantly greater T2 wellbeing than the \u003cem\u003eBalanced Bunch\u003c/em\u003e (Mean Difference\u0026thinsp;=\u0026thinsp;.219(.098), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.026, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.050). No other significant differences were observed. In the sensitivity analysis whereby only full cases were analysed, this difference became non-significant however, we attribute this inconsistency to the reduction in sample size (N\u0026thinsp;=\u0026thinsp;13,683) and the full-case models\u0026rsquo; diminished power to detect significant effects (see supplementary materials).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMean differences in wellbeing at T2\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\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\u003eUnadjusted Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003ePartially Adjusted Model\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c16\" namest=\"c13\"\u003e \u003cp\u003eFully Adjusted Model\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWEMWBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMD (S.E.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSWEMWBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMD (S.E.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eSWEMWBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eMD (S.E.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.637 (.176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.478 (.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e21.642 (.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.450 (.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.813 (.196)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.551 (.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.074 (.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e21.560 (.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.082 (.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.350 (.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.713 (.212)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.904 (.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.426 (.190)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e21.779 (.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.138 (.190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.450 (.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.551 (.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e21.560 (.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.350 (.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.900 (.117)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.904 (.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.352 (.103)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e21.779 (.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.219 (.098)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c16\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBB, \u003cem\u003eBalanced Bunch;\u003c/em\u003e GDT, \u003cem\u003eGreen and Dream Team;\u003c/em\u003e MD, \u003cem\u003eMean Difference\u003c/em\u003e; WW, \u003cem\u003eWellness Weary\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e*\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eSignificant at the .05 level\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e**\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eSignificant at the .01 level\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003e***\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eSignificant at the .001 level\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e \u003cem\u003eAdjusted for baseline wellbeing\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e \u003cem\u003eAdjusted for baseline wellbeing, physical health, IMD, social media use, bullying victimisation, ethnicity, gender identity and sexual orientation\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe purpose of this study was to elucidate patterns of adolescent health behaviour, explore associations with a range of covariate factors prominent in adolescence, and establish whether subscribing to different health behaviour patterns contributes to variance in prospective wellbeing one year later. A three-class solution provided an excellent fit to the data, discriminating between the \u003cem\u003eWellness Weary\u003c/em\u003e (a relatively unhealthy class), the \u003cem\u003eBalanced Bunch\u003c/em\u003e (a moderately healthy class), and the \u003cem\u003eGreen and Dream Team\u003c/em\u003e (a relatively healthy class), in alignment with H1. A large number of covariate factors were significantly associated with latent class membership. Most notably, ethnic minorities and those with higher levels of socio-economic disadvantage were \u003cem\u003eless\u003c/em\u003e likely, and cisgender heterosexual girls were \u003cem\u003emore\u003c/em\u003e likely, to be members of healthier classes. There were no observed effects of bullying victimisation on health behaviour class membership meaning findings offer partial support for H2. The \u003cem\u003eGreen and Dream Team\u003c/em\u003e reported significantly better T2 wellbeing than the \u003cem\u003eBalanced Bunch\u003c/em\u003e but not the \u003cem\u003eWellness Weary\u003c/em\u003e. Accordingly, we are only able to offer partial support for H3.\u003c/p\u003e \u003cp\u003ePrevious studies exploring patterns of health behaviour such as physical activity or eating habits have typically identified three to seven clusters depicting healthy, unhealthy and mixed patterns [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Adolescent \u003cem\u003erisk\u003c/em\u003e-behaviours have also been found to cluster in a similar way [49]. In one such study, three distinct patterns of risk-behaviours (binge drinking, low fruit and vegetable intake, physical inactivity, insufficient sleep, and smoking) were uncovered, and high-risk classes had significantly poorer mental health [49]. Findings from the present study concur, showing that adolescents vary substantially in the extent to which they engage in health behaviours, and that the collective effect of health behaviour patterns have potential to enhance or diminish their health and wellbeing.\u003c/p\u003e \u003cp\u003eThe emergence of distinct classes of health behaviour can be explained through the lens of Health Lifestyle Theory [34]. This theory proposes that healthy lifestyles are not the product of uncoordinated behaviours of disconnected individuals, but instead are a consequence of the complex interplay between societal structures, individual agency, group-based identities, and cultural norms [34,50]. Whilst a young person\u0026rsquo;s free will to make healthy choices undoubtably contributes to variance in health behaviour endorsement, social and demographic factors can greatly limit or enhance the actual and perceived choices available to a specific class or subset of the population [34,50]. For this reason, groups of individuals with similar social and demographic characteristics may encounter similar barriers or drivers that lead to the emergence of common health behavioural patterns. In the present study, factors found to significantly influence adolescent health behaviour class membership were physical health, socio-economic disadvantage, social media usage, minority ethnicity, LGBTQ\u0026thinsp;+\u0026thinsp;status, gender identity and sexual orientation, and prior mental wellbeing. Two of the strongest and arguably most novel of these associations are discussed below.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSocio-Economic Disadvantage\u003c/h2\u003e \u003cp\u003eEvidence concerning the impact of socio-economic variables on adolescents\u0026rsquo; physical activity paints a mixed picture. Variability is due in part to the broad range of metrics used to quantify socio-economic status [51] and the dense range of psychosocial factors that precede or accompany activity participation across socio-economic groups [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR31\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. For instance, adolescents from disadvantaged households might rely more on active transportation if they lack access to a car but could face barriers such as unaffordable membership fees for after-school sports clubs. Whilst differences in domain-specific activities are common [53,54], it is not always clear how this translates to overall activity performed throughout the day. That said, a recent report published by Sport England suggested young people from low affluence families are less likely to be physically active [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The effects of disadvantage extend to other health behaviours with links to lower fruit and vegetable consumption [18], more sugary drinks [17], less sleep, fragmented sleep, and difficulty in achieving sleep onset [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThose most likely to endorse one health behaviour (e.g., physical activity) were also those most likely to endorse concomitant health behaviours. Consistency in the rank order of health behaviour patterns across all latent class indicators supports Health Lifestyle Theory [34,50] and infers that underlying social disparity may have ubiquitous impact, shaping engagement in multiple health behaviours simultaneously. Indeed, of all the covariates measured, socioeconomic disadvantage was the most strongly associated with membership of both the \u003cem\u003eWellness Weary\u003c/em\u003e and \u003cem\u003eBalanced Bunch\u003c/em\u003e classes. Alas, the effects of socio-economic disparity persist and the need create a more fair and equitable society must continue to be of paramount importance to public health advocates [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocial Media Use\u003c/p\u003e \u003cp\u003eThe association between social media use and adolescent health behaviour is a relatively new topic for research. Young people are extensive social media users, yet there is a lack of research on the effect of social media on health behaviour [56]. Emerging evidence has linked social media use to disrupted sleep patterns, including delayed sleep onset and increased night-time awakenings [20]. Excessive use of social media to share or promote physical activities may also contribute to exercise compulsion and pressure to conform to certain body-image standards which in turn can have negative consequences for wellbeing [21]. The current study contributes to knowledge by establishing that lower daily usage increases the probability of having favourable health behaviour patterns. Efforts to improve health behaviours among young people might usefully focus on finding the balance (i.e., between time spent on social media and time spent engaging in health behaviours.\u003c/p\u003e \u003cp\u003ePositive associations have previously been observed between social media use and confectionary consumption; attributed to food and drink marketing involving celebrities and influencers designed to target adolescents; a highly impressionable group in society [57,58]. Our findings diverge from the literature in this respect given there was little variation in confectionary consumption across classes. This infers that even those with extremely high social media use were likely to consume a similar level of confectionary to their peers. Instead, we propose the negative relationship observed between social media use and health behaviour patterns may be a product of time displacement and reductions specifically in physical activity or sleep. Where social media can be consumed alongside eating or drinking, time spent on social media necessarily displaces time available to engage in sleep and many forms of physical activity [59]. Adopting a time displacement perspective helps explain the structure of health behaviour patterns observed herein and may support more consistent replication of findings compared to approaches dependent on assumptions surrounding the content of social media itself. With this in mind, perhaps a primary focus for future research (particularly that where qualitative interpretation of the content being consumed is unavailable) should be to illuminate links between social media and physical activity or sleep as opposed to eating habits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHealth Behaviours and Later Wellbeing\u003c/h2\u003e \u003cp\u003eIn the unadjusted analyses, comparisons of the T2 outcomes of the three latent health behaviour classes were directly in line with our predictions, with healthier classes reporting significantly better mental wellbeing (i.e., \u003cem\u003eGreen and Dream Team\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eBalanced Bunch; Green and Dream Team\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eWellness Weary\u003c/em\u003e; \u003cem\u003eBalanced Bunch\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eWellness Weary\u003c/em\u003e). The magnitude of effect sizes in these contrasts was also in line with expectations, in that the largest difference in T2 wellbeing was between the most and least healthy classes. This pattern of findings is in line with prior research that has utilised person-centred approaches to understand the links between health behaviours and mental health and wellbeing in adolescence [32]. However, after adjusting for prior (T1) wellbeing, differences between classes reduced substantially. This was most noticeable in the difference between the \u003cem\u003eGreen and Dream Team\u003c/em\u003e and \u003cem\u003eWellness Weary\u003c/em\u003e classes which reduced from \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.342 to \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.095 inferring that prior wellbeing accounts for a substantial proportion of the variation in scores one year later. When adjusting for additional social covariates, differences reduced further still such that the only statistically significant difference remaining in T2 mental wellbeing was between the \u003cem\u003eGreen and Dream Team\u003c/em\u003e and \u003cem\u003eBalanced Bunch\u003c/em\u003e classes. The departure of our findings from the associations identified in prior studies may reflect methodological differences. For example: our analysis was longitudinal as opposed to cross-sectional [32,60,61]; our focus was on wellbeing as opposed to mental illness [60,62]; and our adjusted models also factored in a wide range of sociodemographic covariates which may have attenuated the association between health behaviours and wellbeing making effects seem weaker compared to studies making alternative adjustments [32,60,62].\u003c/p\u003e \u003cp\u003eReports show a widening gap in the physical activity levels of Black and Asian adolescents compared to those from other ethnic backgrounds [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The current study expands knowledge in this regard, establishing that not only are Black and Asian adolescents least likely to be active, but they are also least likely to get sufficient sleep or have healthy eating habits, placing them among the least healthy generally. The health behaviour patterns of these ethnic minorities conflict with reports from both this cohort and others that Black and Asian adolescents have at least equal (if not higher) levels of wellbeing than many other ethnic groups [\u003cspan additionalcitationids=\"CR64\" citationid=\"CR37\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Similarly, a plethora of sources report girls have lower physical activity levels, lower wellbeing and greater mental health difficulties than boys [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e66\u003c/span\u003e] yet were substantially more likely to be \u003cem\u003eGreen and Dream Team\u003c/em\u003e members. In essence, there appears to be a partial disconnect between health lifestyles and adolescent wellbeing. Whilst there is evidence for a longitudinal association between the two, the strength of this association is highly subject to demographic and socio-economic influences that should be factored into public health messaging and intervention/prevention strategies going forward. Moreover, disparity on the grounds of ethnicity, socio-economic position, gender and sexual orientation is concerning regardless of any links with wellbeing. Evidence that over one in ten young people are associated with a \u003cem\u003eWellness Weary\u003c/em\u003e (less physical activity, insufficient sleep, and infrequent fruit and vegetable intake) pattern of behaviour is worrying in and of itself.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThe current study benefited from a very large sample, longitudinal dataset, and use of robust, person-centred statistical techniques. However, there are a number of limitations that should be borne in mind. First, although the previously coined, \u0026lsquo;Big Three\u0026rsquo; (physical activity, sleep, diet) were utilised, these are by no means the only health behaviours prevalent in adolescence. Notably, our dataset did not contain information about substance use (e.g., alcohol, cannabis), which becomes increasingly prevalent through the course of adolescence [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Such data are now being collected as part of the recent extension of the #BeeWell study in its second location [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and so a future analysis can incorporate these health behaviours to address this limitation.\u003c/p\u003e \u003cp\u003eImportantly, whilst the majority of the sample were part of the \u003cem\u003eGreen and Dream Team\u003c/em\u003e (i.e., the healthiest class) caution is urged when describing this group as \u0026lsquo;healthy\u0026rsquo;. The \u003cem\u003eGreen and Dream Team\u0026rsquo;s\u003c/em\u003e likelihood of endorsing healthy behaviours is relative to the rest of the sample so members of this class and indeed all other classes, may still be insufficiently healthy according to national and/or international public health recommendations [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Some measures used contain an element of subjectivity (e.g., the single-item used to quantify sleep) and do not readily facilitate comparison against national averages or age-matched cohorts, making quantification of overall health by way of health behaviour endorsement difficult. It is the philosophical stance of #BeeWell that young peoples\u0026rsquo; voices should be central to research. As such measures were chosen in co-creation alongside a Youth Steering Group. It is therefore maintained that the data analysed, and findings presented herein provide a meaningful insight into the lives of young people from Greater Manchester. Supplementary material has been provided to facilitate comparison of the demographic characteristics of the analytical sample against those for Greater Manchester and England.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study identified three distinct patterns of health behaviour among adolescents, broadly categorised as more, moderate, and less healthy. The observed consistency in engaging in multiple health behaviours corroborates the tenets of Health Lifestyle Theory. Notably, disparities rooted in ethnicity and socio-economic status were evident, with minority and socio-economically disadvantaged adolescents more frequently adopting less healthy behavioural patterns. Public health initiatives must continue to focus on reducing these disparities. Additionally, curbing social media use could present a less impactful, yet more timely strategy for encouraging adherence to healthier behaviour patterns. The healthiest class of adolescents demonstrated significantly greater wellbeing, whilst a considerable segment of the cohort fell into the least healthy category. These findings collectively underscore the imperative to enhance health behaviour during adolescence to foster better long-term health outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eAIC\u0026nbsp;\u003c/em\u003eAkaike Information Criteria; \u003cem\u003eBIC\u003c/em\u003e Bayesian Information Criteria; \u003cem\u003eCMO\u003c/em\u003e Chief Medical Officers; \u003cem\u003eFIML\u003c/em\u003e Full Information Maximum Likelihood;\u0026nbsp;\u003cem\u003eH1\u003c/em\u003e Hypothesis one; \u003cem\u003eH2\u0026nbsp;\u003c/em\u003eHypothesis two; \u003cem\u003eH3\u003c/em\u003e Hypothesis three;\u0026nbsp;\u003cem\u003eLGBTQ+\u003c/em\u003e Lesbian Gay Bisexual Transgender Queer\u003cem\u003e\u0026nbsp;LL\u003c/em\u003e Loglikelihood;\u0026nbsp;\u003cem\u003eLMRa\u0026nbsp;\u003c/em\u003eLo-Mendell-Rubin Adjusted Likelihood Ratio Test; \u003cem\u003essaBIC\u0026nbsp;\u003c/em\u003eSample Size Adjusted Bayesian Information Criteria;\u0026nbsp;\u003cem\u003eT1\u003c/em\u003e Timepoint one; \u003cem\u003eT2\u003c/em\u003e Timepoint two.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from the parents/legal guardians of all study participants. Consistent with the conditions of this approval and related documentation (e.g., parent information and informed consent forms), data were in pseudonymised form during analysis. All methods used in the study were carried out in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e\n\u003cp\u003eAn anonymised version of the #BeeWell survey responses will be made publicly available in 2026. Due to ethical governance constraints this cannot be brought forward since participants have been given the right to withdraw their data until this point, necessitating the need to maintain a securely stored pseudonymised version until this point. In addition, the linked administrative data (e.g., sex, free school meal eligibility) will never be shared publicly due to the prohibition of onward sharing in the data sharing agreement in place with the Local Authorities who provided it. To request access to the #BeeWell data, please contact [name removed for blind review].\u003c/p\u003e\n\u003cp\u003eAnonymous Mplus syntax used to analyse the data will be made publicly available via the Open Science Framework upon acceptance of this manuscript for publication.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe #BeeWell study is kindly funded by the University of Manchester, the Greater Manchester Combined Authority, The National Lottery Community Fund, BBC Children in Need, Big Change, the Gregson Family Foundation, the Paul Hamlyn Foundation, the Holroyd Foundation, the Oglesby Charitable Trust, and the Peter Cundill Foundation. No sources of funding had any input on the design of the study, collection, analysis or interpretation of data, or in writing this manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthors Contributions\u003c/h2\u003e\n\u003cp\u003eAll authors had input into study conceptualisation. C.K. E.T. and K.P. analysed the data. All authors interpreted the results and contributed to drafting the manuscript. All authors and read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe gratefully acknowledge the engagement of the many participating schools and young people across Greater Manchester, without whose efforts this research would not have been possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShort SE, Mollborn S. Social determinants and health behaviors: conceptual frames and empirical advances. 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A Quick Guide to the Governments Healthy Eating Recommendations 2018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://assets.publishing.service.gov.uk/media/5ba8a087e5274a55c3407c38/A_quick_guide_to_govt_healthy_eating_update.pdf\u003c/span\u003e\u003cspan address=\"https://assets.publishing.service.gov.uk/media/5ba8a087e5274a55c3407c38/A_quick_guide_to_govt_healthy_eating_update.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 24 April 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInchley J, Currie D, Budisavljevic S, Torsheim T, Jaastad A, Cosma A, Samdal O. Findings from the 2017/2018 Health Behaviour in School-Aged Children (HBSC) Survey in Europe and Canada; International Report. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/iris/handle/10665/332104\u003c/span\u003e\u003cspan address=\"https://apps.who.int/iris/handle/10665/332104\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 24 April 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCentre for Longitudinal Studies, UCL Institute of Education, University College London. Millennium Cohort Study. [data series]. 15th Release. UK Data Service. SN: 2000031. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5255/UKDA-Series-2000031\u003c/span\u003e\u003cspan address=\"10.5255/UKDA-Series-2000031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTennant R, Hiller L, Fishwick R, Platt S, Joseph S, Weich S, Stewart-Brown S. The Warwick-Edinburgh mental well-being scale (WEMWBS): development and UK validation. Health Qual Life Outcomes. 2007;5:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1477-7525-5-63\u003c/span\u003e\u003cspan address=\"10.1186/1477-7525-5-63\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStewart-Brown S, Tennant A, Tennant R, Platt S, Parkinson J, Weich S. Internal construct validity of the Warwick-Edinburgh mental well-being scale (WEMWBS): a Rasch analysis using data from the Scottish health education population survey. Health Qual Life Outcomes. 2009;7:1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1477-7525-7-15\u003c/span\u003e\u003cspan address=\"10.1186/1477-7525-7-15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstitute for Social and Economic Research. Understanding society: Waves 1\u0026ndash;11, 2009\u0026ndash;2020 and Harmonised BHPS: Waves 1\u0026ndash;18, 1991\u0026ndash;2009, User Guide. University of Essex; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThornton E, Petersen K, Marquez J, Humphrey N. Do patterns of adolescent participation in arts, culture and entertainment activities predict later wellbeing? A latent class analysis. J Youth Adolesc. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10964-024-01950-7\u003c/span\u003e\u003cspan address=\"10.1007/s10964-024-01950-7\" 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":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Adolescents, Diet, Health Behaviour, Physical Activity, Sleep, Wellbeing","lastPublishedDoi":"10.21203/rs.3.rs-4382077/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4382077/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Adolescent wellbeing has been declining in the United Kingdom for over a decade. The expansion of services to support the mental health and wellbeing of young people is a public health priority and a core component of the National Health Service’s Long-Term Plan. In this paper, we contribute to knowledge regarding the epidemiology of adolescent mental wellbeing by leveraging secondary analysis of a very large longitudinal dataset (#BeeWell) to generate insights regarding different patterns of health behaviour, their covariates, and consequences for mental wellbeing one year later.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A Latent Class Analysis was conducted using data on physical activity, sleep, and eating habits collected in 2021 from 18,478 Year 8 pupils from Greater Manchester (United Kingdom) to (1) identify distinct latent classes of adolescent health behaviour; (2) establish factors likely to be associated with latent class membership; and (3) determine whether latent class membership contributes to variance in self-reported mental wellbeing one year later.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA three-class solution was identified as an excellent fit to the data, discriminating between: the \u003cem\u003eWellness Weary \u003c/em\u003e(\u003cem\u003en\u003c/em\u003e = 2,717; 15%); the \u003cem\u003eBalanced Bunch \u003c/em\u003e(\u003cem\u003en \u003c/em\u003e= 7,377; 40%); and the \u003cem\u003eGreen and Dream Team \u003c/em\u003e(\u003cem\u003en \u003c/em\u003e= 8,384; 45%). Several factors significantly influenced class membership. Most notably, socio-economic disadvantage and social media use were linked with less favourable health behaviour patterns, whilst cisgender heterosexual girls were likely to endorse healthier patterns. After adjusting for covariates, the \u003cem\u003eGreen and Dream Team\u003c/em\u003e reported significantly greater mental wellbeing than the \u003cem\u003eBalanced Bunch\u003c/em\u003e one year later, signalling that health behaviours endorsed in adolescence may have a long-term impact on mental health.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Beyond advancements in fundamental understanding, findings yield significant translation opportunities through their use and application in health, education, and allied professional settings designed to support young people.\u003c/p\u003e","manuscriptTitle":"Latent Classes of Adolescent Health Behaviour, Social Covariates and Mental Wellbeing: A Longitudinal Birth Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 07:29:13","doi":"10.21203/rs.3.rs-4382077/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-09T07:01:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-08T11:00:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-08T11:00:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-05-07T09:47:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"276358e3-a2bc-426b-b27e-43eb95d85714","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-23T15:59:50+00:00","versionOfRecord":{"articleIdentity":"rs-4382077","link":"https://doi.org/10.1186/s12889-024-20004-y","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-09-18 15:56:53","publishedOnDateReadable":"September 18th, 2024"},"versionCreatedAt":"2024-05-15 07:29:13","video":"","vorDoi":"10.1186/s12889-024-20004-y","vorDoiUrl":"https://doi.org/10.1186/s12889-024-20004-y","workflowStages":[]},"version":"v1","identity":"rs-4382077","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4382077","identity":"rs-4382077","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}