Moving Through Time: Stability and Change in Adolescent Movement Behaviour and links with Future Depressive Symptoms

Moving Through Time: Stability and Change in Adolescent Movement Behaviour and links with Future Depressive Symptoms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Moving Through Time: Stability and Change in Adolescent Movement Behaviour and links with Future Depressive Symptoms Christopher Knowles, Gavin Breslin, Angela Carlin, Kyle Paradis, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5610144/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background Movement behaviours are consistently recognised as having long-term implications for health and wellbeing. Adolescence is often accompanied by diminishing bodily movement and an increased onset of depressive symptoms. Time Displacement dictates that when awake, altering time spent in one movement state (i.e., Sedentary, Light- Moderate-, or Vigorous-Physical Activity) must alter time spent in others. However, few studies evaluate these interdependent movement behaviours as indicators within a composite profile of overall daily movement. The current study included a Random Intercepts Latent Transition Analysis (RI-LTA) to establish latent profiles of movement behaviour across three timepoints in adolescence; the likelihood and predictors of profile transitions over time; and whether differences in transition patterns contributed to variance in future depressive symptoms. Methods Data were represented from 4,964 participants of the Avon Longitudinal Study of Parents and their Children. Movement behaviours were measured using Actigraph AM7164 2.2 accelerometers age 12, 14, and 16. Depressive symptoms were measured using the Short Mood and Feelings Questionnaire at ages 18 and 22. Results A 3x3 non-invariant RI-LTA was an excellent fit to the data (BIC = 410417; Entropy = .902) distinguishing between Maximal-, Moderate- , and Minimal-Movers . Once accounting for non-invariance (i.e., that all profiles moved less over time), transition probabilities presented the Moderate-Mover profile as extremely stable across adolescence. Females, and those with higher BMI and more educated parents were more likely to transition to profiles characterised by lesser movement. Transition patterns containing a period of minimal movement were associated with worse depressive symptoms at ages 18 and 22. Similarly, maximal movement age 12 conferred protection against depressive symptoms age 22 even after shifting to an enduring period of moderate movement thereafter. Conclusions Maximising PA and minimising time spent sedentary when age 12 can protect against depressive symptoms in early adulthood, even if daily movement later decreases. Early intervention has potential to promote health-supportive behaviour and mitigate depressive symptoms across the lifespan. Implications extend to the promotion of PA, and public-health strategies centred on young peoples’ movement behaviour and the reduction of depressive symptoms. Biological sciences/Psychology/Human behaviour Health sciences/Risk factors Health sciences/Diseases/Psychiatric disorders/Depression Health sciences/Health care/Public health Health sciences/Health care/Public health/Epidemiology ALSPAC Adolescence Depressive Symptoms Latent Transition Analysis Light Physical Activity Mixture Modelling MVPA Sedentary Behaviour Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Adolescence is closely linked to the emergence of depressive symptoms, a major contributor to disability across the lifespan.[ 1 , 2 ] Evidence supports that early intervention may be critical for the prevention of clinically relevant symptoms.[3,4] This early life stage is characterized by heightened neurological and developmental plasticity making young people receptive to behaviour change and habit formation.[ 5 , 6 ] Patterns of health and risk behaviour established in these formative years can shape long-term health trajectories and impact future wellbeing.[7,8,9,10] Physical Activity (PA) and sedentary behaviour are particularly relevant health/risk behaviours due to high prevalence of inactivity and sedentary behaviour in this age group.[ 11 ] While regular Moderate-to-Vigorous Physical Activity (MVPA) has long been recognised as a low-risk, low-cost, method of managing depressive symptoms (and indeed, all-round health and wellbeing),[12] associations with sedentary behaviour are more nuanced. For example, social media use – an increasingly prevalent sedentary activity[ 13 ] – has been linked to unhealthy behavioural patterns in youth including a “ Wellness Weary ” pattern comprising little PA, insufficient sleep, and low-quality diet.[ 10 ] Contrastingly, a report from the Health Behaviours in School-Aged Children study indicates that heavy but not problematic social media use can foster feelings of peer support and social connectedness.[ 13 ] While international guidelines recommend minimising sedentary time for optimal physical health;[ 14 ] evidently, the same universal recommendation cannot be directly applied to mental health outcomes. Complexity in the association between physical movement and depressive symptoms in adolescence is compounded by a critical lack of insight into the effects of Light Physical Activity (LPA).[ 15 ] A growing body of evidence demonstrates that reductions in depressive symptoms can be achieved via LPA in adulthood[ 16 ] however, care should be taken not to generalise these findings to younger populations. Young people experience many psychological, social, and developmental challenges distinct from those faced in adulthood including academic pressure and ongoing identity formation, influencing their motivation and ability to be active. Similarly, a fit and healthy teenager may find LPA insufficiently taxing to induce a sense of task-mastery; and therefore less likely to support wellbeing.[ 17 ] Nevertheless, there is some evidence that LPA has mental health benefits for youth[ 18 ] and given that less than half of young people in England currently meet MVPA guidelines,[ 11 ] efforts to get the nation moving will likely continue to incorporate LPA as part of the solution. There is, therefore, an ongoing need to better understand associations with all types, intensities, and volumes of movement behaviour in adolescence. PA and sedentary behaviour are not simply two sides of the same coin and are rightly treated as independent variables in respective fields of research. However, the necessity that when awake, one must be either sedentary, lightly-, or moderate-to-vigorously active means PA and sedentary behaviour are inextricably linked.[ 19 ] The finitude of time dictates that when awake, one must decrease time spent in at least one state in order to boost time spent in another. Accordingly, recommendations for epidemiologists now emphasise that analytical approaches integrate all forms of movement into composite patterns of daily behaviour.[20,21] Such approaches align with Health Lifestyle Theory and the Overflow Hypothesis both of which speak to the effect of co-dependence among concomitant health-influencing behaviours.[ 22 , 23 ] Failure to holistically evaluate compositions of adolescent movement and the impact of relative transitions between movement profiles risks mismeasurement of prospective movement-based public health strategies, and advocacy for preventative action that may end up less effective than is to be expected. One study performing compositional data analysis reported evidence that displacing sedentary time with MVPA yields more pronounced physical health benefits than if displacing LPA, which itself offers some advantages for disease markers (e.g., cardiovascular capacity, obesity).[ 19 ] Although associations with mental health are less clear cut due to the potential for sedentary activities to support wellbeing,[ 13 ] emerging person-centred studies suggest the benefits of adolescent movement for depressive symptoms are dependent on a “synergistic interaction” and the ideal combination of co-dependent movement behaviours performed throughout the day.[ 24 ] Examining MVPA, LPA, and sedentary behaviour as component parts of an integrated movement profile would therefore provide deeper insight into how different combinations of these behaviours impact both physical and mental health, and thereby inform more nuanced, targeted, and effective public health strategies. A person-centred approach Person-centred approaches assume population heterogeneity and profiles individuals appropriately according to shared characteristics, yielding advanced theoretical knowledge applicable to public health advocates where variable-centred studies cannot.[ 25 , 26 ] In particular, Latent Transition Analysis (LTA) probabilistically assigns individuals to the profile with which they most closely correspond repeatedly across multiple timepoints, and calls on relevant predictor variables to estimate the likelihood of transitioning profiles for subgroups of the population. Evidence suggests “high-decreasers” (those most physically active in childhood who trend down to the mean by late adolescence) experience fewer depressive symptoms in early adulthood than those who only started moving more later in adolescence.[9] This infers a portion of the variance in depressive symptoms among individuals exhibiting similar volumes of movement may be explained by a lagged and pervasive effect of historic movement-based behaviours. A powerful feature of LTA in this regard, is it enables examination of how transition patterns can account for variance in mental health outcomes by considering historic volumes of movement and the path one took to arrive at a particular latent profile.[27] LTA thus provides a framework to better understand why comparable activity levels at a single timepoint do not consistently predict mental health in adolescence. Recent methodological advancements now suggest including random intercepts within LTA models can greatly enhance model accuracy by parsing stable, individual traits from other factors influencing profile changes.[28] The present study is among the first to utilise Random Intercepts Latent Transition Analysis (RI-LTA) generally, and the very first to use accelerometer data to objectively assess holistic movement profiles within a RI-LTA framework. By tracking changes in movement profiles across three stages of adolescence (age 12, 14, 16) this study offers unique insight into how transitions in movement behaviour influence depressive symptoms in late adolescence (age 18) and early adulthood (age 22). Aims and hypotheses The purpose of the current study is to establish: (i) distinct movement behaviour profiles at multiple timepoints across adolescence; (ii) predictors of profile transitions; and (iii) the extent to which transition patterns predict future depressive symptoms. Depressive symptoms at age 18 and 22 were evaluated to determine the extent to which movement behaviours may influence depressive symptoms over multiple timeframes. Health Lifestyle Theory posits that barriers to engagement in one health behaviour likely to extend to other health behaviours.[ 23 ] We therefore did not expect to find profiles showing high levels of MVPA paired with low levels of LPA (or vice versa). Instead, we anticipated profiles where both MVPA and LPA would increase in tandem, and antagonistically to sedentary behaviour [H 1 ]. In line with extant literature, we hypothesised the sample would become more sedentary and less physically active over time [H 2 ]. We hypothesised that female sex [H 3a ], higher Body Mass Index (BMI) [H 3b ], lower parental education [H 3c ] and higher baseline depressive symptoms [H 3d ] would significantly increase the likelihood of transitioning to profiles characterised by lesser movement. We hypothesised that compared to those exhibiting moderate volumes of movement consistently over time, those with a history of greater movement would report fewer depressive symptoms at age 18 [H 4a ] and age 22 [H 4b ], and that those with a history of lesser movement would report greater depressive symptoms at age 18 [H 5a ] and age 22 [H 5b ]. See Figs. 1 and 2 for illustrative examples of hypothesised profile characteristics and differences in depressive symptoms. While we did not hypothesise identifying a specific number of profiles at any timepoint, in both figures, three profiles are depicted to support interpretation. Methods Participants Participants were part of the Avon Longitudinal Study of Parents and Children (ALSPAC).[ 29 , 30 , 31 ] All pregnant women resident in Avon, UK with expected delivery dates between 1st April 1991 and 31st December 1992 were invited to take part. After subsequent attempts to bolster the initially enrolled sample of 15,454 pregnant women, the total number of foetuses alive at one year of age was 14,901. Study data were collected and managed using Research Electronic Data Capture (REDCap) tools hosted at the University of Bristol.[ 32 ] REDCap is a secure, web-based software platform designed to support data capture for research studies. Further information regarding research ethics can be found via the study website.[ 33 ] The ALSPAC study website contains details of all available data through a fully searchable data dictionary and variable search tool.[ 34 ] The analytical sample comprised all participants with at least 4 valid days (≥ 600minutes) of accelerometer data containing ≥ 3 weekdays and ≥ 1 weekend day at ≥ 1 timepoint were included in analysis ( n = 4,964). Physical activity PA was measured using Actigraph AM7164 2.2 accelerometers [Actigraph LLC, Fort Walton Beach, FL, USA] in 2005–2006 when participants were 11.7 years of age (henceforth referred to as age 12; T1) and again when age 13.9 (henceforth 14 years; T2), and age 15.5 (henceforth 16 years; T3). Devices were worn on the right hip for 7 consecutive days during waking hours except when doing aquatic activities. Time spent sedentary, in LPA, and MVPA was quantified using count per minute (cpm) thresholds determined in a previous calibration study (sedentary behaviour < 200cpm; LPA ≥ 200cpm, 10 minutes with consecutive zero counts.[ 35 , 36 ] Depressive symptoms At age 12, 18, and 22, participants completed the Short Mood and Feelings Questionnaire (SMFQ).[37] The SMFQ is a 13-item self-report measure of depressive symptoms over the past two weeks with responses scored on a 3-point scale: 0 = Not True; 1 = Sometimes True; and 2 = True. Total scores range from 0–26 with higher scores indicating greater symptom severity. The SMFQ has been validated for use as a screening tool for depression in adolescence[38] and early adulthood[ 39 ] with high construct, content, and criterion validity. The SMFQ had high internal reliability at each timepoint in the present sample (α = .84 to .90). Covariates Data on sex (male/female), BMI, parental education, and baseline depressive symptoms were collected via questionnaires age 12. Data handling Missing data were handled using Multiple Imputation by Chained Equations (MICE) in the Statistical Package for the Social Sciences version 28 [IBM Corp., Amonk, NY, USA]. Twenty imputed datasets were generated with aggregated scores derived following the Bar Procedure.[ 40 ] SMFQ scores were positively skewed hence, missing values were imputed using Predictive Mean Matching.[ 41 ] Statistical analysis Analyses were performed using Mplus version 8.9 in four key stages.[ 42 ] Stage one: profile enumeration at each timepoint A sequence of cross-sectional Latent Profile Analyses were performed age 12 (T1), 14 (T2), and 16 (T3). Models were fit using only latent profile indicators without adjusting for covariates.[ 43 ] Solutions with k + 1 profiles were enumerated up to a 10-profile solution. Relative fit indices were used to identify the best fitting model: Akaike Information Criterion (AIC); Bayesian Information Criterion (BIC); Sample Size Adjusted BIC (ssaBIC); and the Lo–Mendell–Rubin Adjusted Likelihood Ratio Test (LMRa). For AIC, BIC, and ssaBIC, lower values represent better fit. An elbow plot was generated to identify the point at which increased model complexity yielded diminished returns in model fit.[ 43 ] Significant LMRa tests indicate the k solution fits better than that containing k –1 profiles.[ 44 ] The proportional distribution of the sample was also evaluated to qualify stability, interpretability and generalisability of each model. Solutions with the smallest profiles were regarded as least stable. Although not a fit index, classification entropy is reported with values > .80 considered clear separation between profiles.[ 43 ] The best fitting LPAs were advanced and combined into a single LTA model with profiles at each timepoint estimated simultaneously. Stage two: model modifications, measurement invariance and inclusion of random intercepts Fit statistics were compared between the regular LTA and RI-LTA models (both invariant and non-invariant) to quantitatively validate that the more complex RI-LTA models were a better fit.[ 45 ] Only BIC is used to assess fit of RI-LTA models in line with current recommendations.[28] Satorra-Bentler Scaled Mean-adjusted Chi Square tests of model fit formally tested the assumption of measurement invariance for regular LTA and RI-LTA models.[ 46 ] Satorra-Bentler tests determine whether a more complex non-invariant (comparison) model, within which measurement parameters (i.e., profile indicators) are freely estimated, is a significantly better fit than a simpler invariant (nested) model with parameters fixed to their T1 values at all timepoints. Non-significant tests suggest the simpler invariant model is preferable. All stage two models were adjusted for accelerometer wear time at each timepoint to reduce risk of bias in profile estimation. As a sensitivity analysis, this stage was also conducted without adjusting for wear time to illustrate the extent to which failure to make such adjustments may bias parameter estimates for future studies (supplementary material). Stage three: profile transition probabilities and their predictors Transition probabilities were obtained by regressing profile membership at T2 on membership at T1, then T3 membership on T2. All parameters were then fixed to their final starting values such that the structure of profiles remained unchanged when introducing auxiliary predictors to the model.[ 42 , 43 ] Predictors of profile transitions were fit simultaneously to account for potential between-predictor interactions. As risk of Type II Error increases when adding multiple predictors simultaneously, sensitivity analyses were performed with predictors tested individually in separate models (supplementary material). Stage four: comparing depressive symptoms across transition probabilities Where possible, differences in depressive symptoms at ages 18 and 22 were assessed via Wald tests to compare estimated SMFQ scores for each transition pattern (e.g., 2→2→2 versus 3→3→2) for minimally and fully adjusted models (refer to Fig. 2 for support interpreting transition patterns). The largest stable transition pattern was used as reference against which all viable unstable transition patterns (i.e., increasing/decreasing/fluctuating) were compared. Some transition patterns contained very few participants (e.g., 3→1→2) and were considered unreliable so have not been analysed. Results Stage one: profile enumeration Fit statistics for each timepoint are provided in Table 1. A clear elbow was visible at T1 for three profiles inferring beyond this point, increased model complexity yielded diminished returns in fit (Figure 3). The three-profile solution was well distributed with three large groups identified, unlike the four-profile solution which contained a group comprising only 5% of the sample. At T2, an elbow was observed at three profiles (Figure 3) and again, the sample was well distributed. Unlike T1 and T2, no clear elbow was observed at T3 (Figure 3). However, a series of LMRa likelihood ratio tests indicated three profiles were better than two, but four profiles were not better than three. Likewise, the four-profile solution contained an unstable group comprising just 1% of the sample. For these reasons, three-profile solutions were selected as those which fit the data best at T1, T2 and T3 independently. Table 1 Model fit indices for cross-sectional latent profiles of adolescent movement behaviour Classes LL AIC BIC ssaBIC LMRa Entropy Model Estimated Class Proportions Age 12 1 -74439 148890 148929 148910 - - 1 2 -73306 146632 146697 146665 .000 .745 .77, .23 3 -72446 144920 145011 144967 .000 .804 .64, .21, .15 4 -72172 144381 144498 144441 .000 .826 .62, .21, .12, .05 5 -72010 144065 144208 144138 .001 .835 .60, .20, .14, .04, .02 6 -71905 143863 144033 143950 .000 .847 .59, .20, .13, .05, .01, .01 7 -71805 143671 143866 143771 .000 .828 .55, .22, .12, .05, .04, .01, .01 8 -71732 143532 143753 143645 .001 .830 .54, .21, .11, .06, .04, .02, .02, .01 9 -71695 143466 143714 143593 .058 .810 .52, .21, .09, .06, .05, .04, .02, .01, .01 10 -71660 143404 143677 143544 .045 .815 .51, .21, .09, .06, .04, .02, .01, .01, .01, .01 Age 14 1 -71687 143386 143425 143406 - - 1 2 -70555 141131 141196 141164 .000 .939 .91, .09 3 -69543 139114 139205 139160 .000 .922 .81, .10, .09 4 -69228 138493 138610 138553 .047 .930 .79, .10, .07, .03 5 -69020 138085 138228 138158 .135 .933 .78, .11, .07, .02, .02 6 -68847 137746 137915 137832 .050 .940 .76, .11, .07, .02, .03, .01 7 -68742 137545 137740 137645 .041 .925 .75, .07, .07, .05, .03, .02, .01 8 -68627 137323 137545 137437 .214 .921 .73, .07, .07, .05, .03, .03, .02, .01 9 -68518 137112 137359 137238 .104 .922 .72, .07, .07, .05, .03, .03, .02, .01, .01 10 -68420 136924 137197 137064 .331 .924 .71, .07, .06, .05, .03, .03, .02, .01, .01, .01 Age 16 1 -66872 133756 133795 133776 - - 1 2 -65418 130856 130921 130890 .000 .986 .97, .03 3 -64455 128938 129029 128984 .004 .979 .91, .05, .04 4 -63913 127862 127979 127922 .176 .980 .89, .05, .05, .01 5 -63630 127305 127448 127378 .691 .978 .86, .07, .04, .02, .01 6 -63232 126517 126687 126604 .284 .975 .85, .04, .04, .04, .03, .01 7 -62876 125812 126008 125912 .163 .979 .84, .04, .04, .03, .03, .02, .01 8 -62581 125231 125452 125344 .696 .978 .83, .04, .04, .03, .02, .02, .02, .01 9 -62324 124725 124972 124851 .202 .980 .82, .04, .03, .03, .03, .02, .02, .01, .01 10 -62083 124251 124524 124391 .177 .980 .82, .04, .03, .03, .03 .02, .01, .01, .01, .01 Stage two: model modifications Regular (invariant and non-invariant) LTA models were estimated by incorporating all latent profile analyses in a single 3x3 design (i.e., three profiles across three timepoints). This process was repeated for models including random intercepts such that invariant and non-invariant RI-LTA models, also with a 3x3 design were fit to the data (Table 2). Table 2 Model fit of invariant and non-invariant regular LTA and RI-LTA models Model BIC Satorra-Bentler ( p ) Entropy Model Estimated Class Proportions 3x3 416762 - Overall = .853 Regular LTA T1 = .739 T1 = .60, .39, .01 (invariant) T2 = .848 T2 = .72, .18, .10 T3 = .963 T3 = .92, .06, .02 3x3 415442 - Overall = .850 RI-LTA T1 = .758 T1 = .63, .37, <.01 (invariant) T2 = .822 T2 = .73, .17, .10 T3 = .956 T3 = .93, .05, .02 3x3 410900 <.050 Overall = .901 Regular LTA T1 = .806 T1 = .61, .23, .16 (non-invariant) T2 = .918 T2 = .77, .10, .04 T3 = .975 T3 = .89, .07, .04 3x3 410417 <.050 Overall = .902 RI-LTA T1 = .808 T1 = .61, .24, .15 (non-invariant) T2 = .919 T2 = .77, .13, .10 T3 = .975 T3 = .89, .07, .04 Invariant models contained very small classes and sample distributions for non-invariant models were more closely aligned with those established during initial profile enumeration (stage one). Highly significant Satorra-Bentler tests demonstrated measurement non-invariance for both regular LTA and RI-LTA models while BIC was lowest for the non-invariant RI-LTA model overall. Therefore, in line with recent evidence that RI-LTAs represent an advancement of the state of the art, the 3x3 non-invariant RI-LTA was considered the most quantitatively and qualitatively robust measurement model and was selected for advancement. Entropy for the 3x3 non-invariant RI-LTA overall was .902 (T1=.808; T2=.919; T3=.975) signalling excellent classification accuracy. The final model is illustrated in Figure 4. Estimated mean values for profile indicators and wear time are provided as supplementary material. Stage three: profile transitions and their predictors Transition probabilities are expressed as Odds Ratios (ORs) with 95% Confidence Intervals (CIs) in Table 3. For each transition, the pattern denoting stability (i.e., the diagonal of the probability table) serves as reference. Predictors of profile transitions are reported in Table 4. Table 3 Odds of profile transition from T1 to T2 and T2 to T3 for each latent movement profile Wave 2 Maximal Movers (10.1%) Moderate Movers (76.5%) Minimal Movers (13.4%) Wave 1 n OR [95%CI] n OR [95%CI] n OR [95%CI] Maximal Movers (15.5%) 164 ref. 568 4.65 [3.76 to 5.76] * 10 .05 [.022 to .114] * Moderate Movers (60.9%) 317 .09 [.08 to .11] * 2,436 ref. 316 .07 [.06 to .09] * Minimal Movers (23.6%) 7 .03 [.01 to .08] * 823 4.25 [3.56 to 5.07] * 323 ref. Wave 3 Maximal Movers (4.2%) Moderate Movers (89.2%) Minimal Movers (6.6%) Wave 2 n OR [95%CI] n OR [95%CI] n OR [95%CI] Maximal Movers (10.1%) 68 ref. 416 13.53 [9.69 to 18.90] * 4 .03 [.01 to .10] * Moderate Movers (76.5%) 138 .02 [.02, 0.03] * 3,505 ref. 184 .01 [.01 to .02] * Minimal Movers (13.4%) 6 0.11 [.04 to .29] * 509 15.74 [11.60 to 21.37] * 134 ref. Odds adjusted for accelerometer wear time, sex, BMI, parental education and baseline depressive symptoms Table 4 Predictors of profile transitions with 95% confidence intervals Wave 2 Sex Wave 1 Maximal Movers Moderate Movers Minimal Movers Maximal Movers ref. 1.96 [1.55 to 2.48] * 3.68 [2.74 to 4.94] * Moderate Movers .51 [.40 to .65] * ref. 1.88 [1.49 to 2.35] * Minimal Movers .27 [.20 to .37] * 0.53 [.43 to .67] * ref. Wave 3 Wave 2 Maximal Movers Moderate Movers Minimal Movers Maximal Movers ref. 1.34 [.97 to 1.85] 2.20 [1.44 to 3.36] * Moderate Movers .75 [.54 to 1.03] ref. 1.64 [1.17 to 2.31] * Minimal Movers .46 [.30 to .70] * .61 [.43 to .86] * ref. Wave 2 BMI Wave 1 Maximal Movers Moderate Movers Minimal Movers Maximal Movers ref. 1.04 [1.00 to 1.08] 1.08 [1.03 to 1.13] * Moderate Movers .97 [.93 to 1.00] ref. 1.04 [1.01 to 1.08] * Minimal Movers .93 [.88 to .97] * .96 [.93 to .99] * ref. Wave 3 Wave 2 Maximal Movers Moderate Movers Minimal Movers Maximal Movers ref. 1.00 [.95 to 1.05] .96 [.90 to 1.02] Moderate Movers 1.00 [.96 to 1.05] ref. .96 [.91 to 1.01] Minimal Movers 1.04 [.98 to 1.11] 1.04 [.99 to 1.09] ref. Parental Wave 2 Education Wave 1 Maximal Movers Moderate Movers Minimal Movers Maximal Movers ref. 1.20 [1.09 to 1.33] * 1.31 [1.16 to 1.49] * Moderate Movers .83 [.76 to .92] * ref. 1.09 [.99 to 1.21] Minimal Movers .76 [.67 to .86] * .92 [.83 to 1.01] ref. Wave 3 Wave 2 Maximal Movers Moderate Movers Minimal Movers Maximal Movers ref. 1.09 [.94 to 1.24] 1.09 [.91 to 1.29] Moderate Movers .93 [.81 to 1.07] ref. 1.01 [.88 to 1.16] Minimal Movers .92 [.77 to 1.10] .99 [.87 to 1.14] ref. Baseline Wave 2 SMFQ Wave 1 Maximal Movers Moderate Movers Minimal Movers Maximal Movers ref. 1.00 [.97 to 1.03] 1.01 [.98 to 1.05] Moderate Movers 1.00 [.97 to 1.03] ref. 1.01 [.98 to 1.04] Minimal Movers .99 [.95 to 1.03] 0.99 [.96 to 1.02] ref. Wave 3 Wave 2 Maximal Movers Moderate Movers Minimal Movers Maximal Movers ref. 1.00 [.96 to 1.05] 1.03 [.97 to 1.09] Moderate Movers 1.00 [.95 to 1.05] ref. 1.03 [.98 to 1.07] Minimal Movers .97 [.92 to 1.03] .98 [.94 to 1.02] ref. * Predictor significantly contributed to the probability to profile transition All predictors were added to the model simultaneously Sex coded as 0=male 1=female so odds >1 infer greater likelihood for females From T1 to T2, the odds of transitioning from moderate to maximal ( n =317, OR=.09 [.08 to .11]) or moderate to minimal ( n =316, OR=.07 [.06 to .09]) were very low compared to remaining in the same category ( n= 2,436) indicating a high-level of stability for the moderate mover profile. Stability of the moderate mover profile was also observed across T2 to T3 ( n =3,505) with very low odds of transitioning from moderate to maximal (OR=.02 [.02 to .03]), or moderate to minimal ( n =184, OR=.01 [.01 to .02]). Females were half as likely as males to transition from moderate to maximal movers from T1 to T2 (OR=.51 [.40 to .65]) and much more likely to transition from moderate to minimal from T1 to T2 (OR=1.88 [1.49 to 2.35]) and T2 to T3 (OR=1.64 [1.17 to 2.31]). Higher BMI increased odds of transitioning from moderate to minimal from T1 to T2 (OR= 1.04 [1.01 to 1.08]) while higher level of parental education decreased odds of transitioning from moderate to maximal movers from T1 to T2 (OR=.83 [.76 to .92]). Baseline depressive symptoms did not influence the odds of transitioning profiles across any timepoints. Sensitivity analyses wherein predictors were estimated sequentially in separate models produced effect sizes of equal magnitude and significance, strengthening the robustness of findings (supplementary material). Stage four: comparing depressive symptoms across profiles and transition probabilities Wald difference tests comparing depressive symptoms at ages 18 and 22 for those with different transition patterns are reported in Table 5. As the focus of the current study was on profile transitions , cross-sectional between-profile differences in depressive symptoms are not reported herein. For completeness, these analyses are provided as supplementary material. Table 5 Depressive symptoms associated with transition patterns with consistently moderate movers serving as reference Transition SMFQ Wald Test Statistics a SMFQ Wald Test Statistics a Pattern n (%) at 18 ( SE ) ( SE ) p d at 22 ( SE ) ( SE ) p d Minimally 1→1→2 139 (2.8) 4.46 (.37) -.64 (.38) .10 .15 2.82 (.32) -.64 (.33) .05 .15 Adjusted b 1→2→2 525 (10.6) 4.56 (.19) -.54 (.22) * .01 .13 2.61 (.16) -.85 (.19) * <.001 .20 2→1→2 272 (5.5) 4.86 (.27) -.24 (.29) .40 .06 3.19 (.22) -.27 (.25) .28 .07 2→2→2 2,213 (44.6) 5.10 (.10) ref. 3.46 (.10) ref. 2→3→2 252 (5.1) 6.27 (.36) 1.17 (.38) * <.01 .27 4.59 (.42) 1.13 (.44) * .01 .27 3→2→2 763 (15.4) 5.55 (.18) .45 (.22) * .04 .10 3.90 (.18) .44 (.22) * .05 .11 3→3→2 252 (5.1) 5.86 (.32) .75 (.34) * .03 .17 4.87 (.40) 1.41 (.41) * <.01 .34 Fully 1→1→2 135 (2.7) 1.56 (.51) -.20 (.33) .55 .05 -.11 (.50) -.25 (.31) .43 .06 Adjusted c 1→2→2 522 (10.5) 1.56 (.46) -.19 (.21) .38 .05 -.42 (.44) -.56 (.18) * <.01 .14 2→1→2 278 (5.6) 1.77 (.50) .02 (.27) .95 .00 .12 (.47) -.02 (.24) .92 .01 2→2→2 2,202 (44.4) 1.75 (.45) ref. .14 (.44) ref. 2→3→2 255 (5.1) 2.62 (.57) .87 (.36) * .02 .21 .98 (.60) .84 (.42) * .05 .21 3→2→2 769 (15.5) 1.92 (.49) .17 (.21) .43 .04 .28 (.48) .14 (.21) .51 .04 3→3→2 249 (5.0) 2.65 (.57) .62 (.33) .06 .15 .98 (.60) 1.29 (.41) * <.01 .32 1 = maximal movers; 2 = moderate movers; 3 = minimal movers a Wald tests subtracted reference group mean/intercept from comparison group mean/intercept hence, a negative value indicates comparison group had fewer symptoms, a positive value indicates comparison group had greater symptoms b adjusted for wear time during model modifications process in stage two c additionally adjusted for sex, BMI, parental education, baseline depressive symptoms * significant difference In all comparisons, those with a consistent moderate mover pattern (i.e., 2→2→2) were used as the reference against which all other transition patterns were compared. After adjusting for covariates, those whose prior movement levels fluctuated but contained a period of moving less often (i.e., 2→3→2 vs. 2→2→2) consistently reported significantly greater depressive symptoms at age 18 ( p =.02, d =.21), and age 22 ( p =.05, d =.21). The largest effect was observed for those who consistently used to move less (i.e., 3→3→2 vs. 2→2→2) with these individuals reporting significantly greater depressive symptoms at age 22 ( p <.01, d =.32). Finally, those who moved more during early adolescence (i.e., 1→2→2 vs. 2→2→2) also reported significantly fewer depressive symptoms at age 22 ( p <.01, d =.14). Discussion The aim of this study was to establish latent profiles of movement behaviour throughout adolescence, evaluate the stability of these profiles over time, and the extent to which transitioning between profiles contributes to variance in depressive symptoms in both late adolescence and early adulthood. Characteristics of movement behaviour profiles Three latent profiles were identified across all three timepoints differentiating between Maximal Movers , Moderate Movers , and Minimal Movers . MVPA and LPA decreased while sedentary behaviour increased antagonistically between and within profiles at every timepoint, in line with H 1 . Although maximal, moderate, and minimal movers were consistently identified throughout adolescence, quantitative assessment of measurement non-invariance presented statistical evidence that all profiles became more sedentary and less physically active over time, providing support for H 2 . Findings align with extant literature suggesting that adolescent movement becomes increasingly homogeneous over time;[9] contradicts evidence of the “Active Couch Potato Hypothesis” (i.e., the notion that volumes of MVPA can persist despite increases in sedentary behaviour);[ 47 , 48 ] and contributes new knowledge with evidence that convergence of movement behaviour persists even when sedentary behaviour is included as a key profile indicator, underscoring the need for a holistic approach in public health initiatives. Interventions should concurrently target improvements in PA and reductions sedentary behaviour to promote well-balanced movement profiles indicative of a healthy lifestyle. Furthermore, for initiatives to be maximally efficacious, they should target adolescents when health behaviours are most malleable.[49] Our evidence posits, the earlier, the better. Adolescents’ daily lives are predominantly sedentary and become increasingly so as they age. To foster optimal compositions of 24-hour movement, public health guidelines should specify that increases in LPA or MVPA should specifically displace time spent sedentary, as opposed to each other. This is substantiated by evidence that displacing sedentary behaviour with MVPA yields a greater collective health benefit than if displacing LPA.[ 19 ] The centrality of school-based intervention efforts makes this challenging due to the practical constraints of integrating PA into environments that are inherently sedentary in nature. Potential scalable solutions include: “Classroom Movement Breaks” – structured 5-10minute lesson breaks for stretching and dynamic movements; and “Physically Active Learning” – practices that require pupils to move around the classroom to complete tasks, both of which are promising for enhancing movement volume, academic performance and wellbeing.[50]. There is also evidence among younger children (aged < 10) that integrating a ‘Daily Mile’ into everyday school routines improves visual spatial working memory and physical fitness.[ 51 ] However, fostering an autonomously motivating PA experience that ensures reliable sustainability of such significant changes in children’s behaviour remains an elusive goal for public health, requiring increased application of behaviour change theory science.[ 52 ] Conclusions Distinct profiles of movement behaviour exist throughout adolescence. Most individuals exhibit moderate levels of movement at age 12, or shift to moderate levels later as they progress through adolescence, although what constitutes ‘moderate movement’ incrementally declines as all adolescents moved less over time. Females, those with higher BMI, and with more educated parents were at greater risk of early transition to profiles marked by reduced PA and increased sedentary behaviour. Moving often when age 12 led to significantly fewer depressive symptoms in early adulthood, even after transitioning to a prolonged period of moderate movement. Collectively, findings highlight early adolescence as a critical period for the delivery of intervention/prevention strategies designed to either initiate or maintain healthy patterns of movement behaviour and mitigate future depressive symptoms. Such approaches may benefit from adopting evidence-based contemporary models of behaviour change that focus on nurturing individuals’ psychological needs, along with strategies that are attentive to adolescents’ social circumstances and cultural context, to ultimately facilitate autonomously motivating and lasting behaviour change. Declarations Ethics approval and consent to participate Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained in writing from all participants (or their parent/legal guardian when participants were minors) following the recommendations of the ALSPAC Ethics and Law Committee at the time. All methods were performed in accordance with the most up-to-date guidelines and regulations. Clinical trial number Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and [author names removed for blind review] will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ); This research was specifically funded by: NIH (grant ref: PD301198SC101645); Wellcome Trust and MRC: (grant ref: 092731). Author Contribution CK conceptualised the study, analysed the data and drafted the manuscript. GB, AC, KP and SS contributed to reviewing the manuscript and have read and approved the final version. Acknowledgement We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. Data Availability The data that support the findings of this study are available from the ALSPAC Executive but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the ALSPAC Executive upon submission of a research proposal through the ALSPAC website (https://www.bristol.ac.uk/alspac/researchers/access/) where researchers can also access a data dictionary, variable catalogue and variable search tool. References Solmi, M. et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry. ;27(1):281 – 95. https://doi.org/10.1038/s41380-021 -01161-7 Institute for Health Metrics and Evaluation (IHME). GBD Results. Seattle, WA: IHME, University of Washington, 2024. (2022). https://www.healthdata.org/data-tools-practices/interactive-visuals/gbd-results (accessed 7/11/2024). Davey CG, McGorry PD. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5610144","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":392586259,"identity":"a5ac2302-45c6-499b-b890-48651d8b432f","order_by":0,"name":"Christopher Knowles","email":"data:image/png;base64,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","orcid":"","institution":"University of Manchester","correspondingAuthor":true,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Knowles","suffix":""},{"id":392586260,"identity":"34ed05f6-597f-4716-9460-4a425c137276","order_by":1,"name":"Gavin Breslin","email":"","orcid":"","institution":"Queen’s University Belfast","correspondingAuthor":false,"prefix":"","firstName":"Gavin","middleName":"","lastName":"Breslin","suffix":""},{"id":392586261,"identity":"50e3c458-c956-4a25-a132-5f5666120135","order_by":2,"name":"Angela Carlin","email":"","orcid":"","institution":"Ulster University","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"","lastName":"Carlin","suffix":""},{"id":392586262,"identity":"b492540b-1b1d-412d-a29b-7de4e37990c2","order_by":3,"name":"Kyle Paradis","email":"","orcid":"","institution":"Ulster University","correspondingAuthor":false,"prefix":"","firstName":"Kyle","middleName":"","lastName":"Paradis","suffix":""},{"id":392586263,"identity":"036e5eff-244b-40c3-8ddf-f58ee6dca160","order_by":4,"name":"Stephen Shannon","email":"","orcid":"","institution":"Ulster University","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Shannon","suffix":""}],"badges":[],"createdAt":"2024-12-09 15:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5610144/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5610144/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-04466-7","type":"published","date":"2025-07-01T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72290584,"identity":"861439fe-ab7c-4dfe-9f70-ab2cce25df0f","added_by":"auto","created_at":"2024-12-24 17:23:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePredicted profile characteristics relating to H\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e and H\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e. Each bar represents an hypothesised movement profile and are clustered by age at measurement. Each profile is associated with less movement than its equivalent at the timepoint prior\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage111.png","url":"https://assets-eu.researchsquare.com/files/rs-5610144/v1/c52e0b447cfe3b091bbc909e.png"},{"id":72290581,"identity":"e65f76d4-82c9-4898-97f9-f9353558cac6","added_by":"auto","created_at":"2024-12-24 17:23:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":190384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHypothesised differences in depressive symptoms among those exhibiting different transition patterns proposed for H\u003c/em\u003e\u003csup\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u0026amp; H\u003c/em\u003e\u003csup\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"floatimage28.png","url":"https://assets-eu.researchsquare.com/files/rs-5610144/v1/71c34281ad421b6cf2997f91.png"},{"id":72290551,"identity":"42a72a0e-e0f6-43e7-ac3f-4be2a6a64b94","added_by":"auto","created_at":"2024-12-24 17:23:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":306758,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eElbow plots illustrating model fit cross-sectional latent profile models with k+1 solutions\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage39.png","url":"https://assets-eu.researchsquare.com/files/rs-5610144/v1/c3782250a3d0639c73e057bb.png"},{"id":72290578,"identity":"b035e05c-3fa2-46cb-949d-5df0dae638ac","added_by":"auto","created_at":"2024-12-24 17:23:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":163852,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e3x3 non-invariant RI-LTA model with movement behaviours expressed as the proportion of daily accelerometer wear time (%)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage49.png","url":"https://assets-eu.researchsquare.com/files/rs-5610144/v1/e8bbb11e74568d0fc48d432b.png"},{"id":86179154,"identity":"d5269b04-c92c-40e4-bfd2-c3a9a6959fa6","added_by":"auto","created_at":"2025-07-07 16:16:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2449651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5610144/v1/c208f941-bf7c-4d6d-a980-396d4b37546b.pdf"},{"id":72290576,"identity":"337fd897-617e-4a35-87dc-4b04112aff65","added_by":"auto","created_at":"2024-12-24 17:23:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":90564,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5610144/v1/3cbe8d3715ff4cef97aa001b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Moving Through Time: Stability and Change in Adolescent Movement Behaviour and links with Future Depressive Symptoms","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdolescence is closely linked to the emergence of depressive symptoms, a major contributor to disability across the lifespan.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Evidence supports that early intervention may be critical for the prevention of clinically relevant symptoms.[3,4] This early life stage is characterized by heightened neurological and developmental plasticity making young people receptive to behaviour change and habit formation.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Patterns of health and risk behaviour established in these formative years can shape long-term health trajectories and impact future wellbeing.[7,8,9,10] Physical Activity (PA) and sedentary behaviour are particularly relevant health/risk behaviours due to high prevalence of inactivity and sedentary behaviour in this age group.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e] While regular Moderate-to-Vigorous Physical Activity (MVPA) has long been recognised as a low-risk, low-cost, method of managing depressive symptoms (and indeed, all-round health and wellbeing),[12] associations with sedentary behaviour are more nuanced. For example, social media use \u0026ndash; an increasingly prevalent sedentary activity[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e13\u003c/span\u003e] \u0026ndash; has been linked to unhealthy behavioural patterns in youth including a \u0026ldquo;\u003cem\u003eWellness Weary\u003c/em\u003e\u0026rdquo; pattern comprising little PA, insufficient sleep, and low-quality diet.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Contrastingly, a report from the Health Behaviours in School-Aged Children study indicates that heavy but not problematic social media use can foster feelings of peer support and social connectedness.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e13\u003c/span\u003e] While international guidelines recommend minimising sedentary time for optimal physical health;[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e14\u003c/span\u003e] evidently, the same universal recommendation cannot be directly applied to mental health outcomes.\u003c/p\u003e \u003cp\u003eComplexity in the association between physical movement and depressive symptoms in adolescence is compounded by a critical lack of insight into the effects of Light Physical Activity (LPA).[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] A growing body of evidence demonstrates that reductions in depressive symptoms can be achieved via LPA in adulthood[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e16\u003c/span\u003e] however, care should be taken not to generalise these findings to younger populations. Young people experience many psychological, social, and developmental challenges distinct from those faced in adulthood including academic pressure and ongoing identity formation, influencing their motivation and ability to be active. Similarly, a fit and healthy teenager may find LPA insufficiently taxing to induce a sense of task-mastery; and therefore less likely to support wellbeing.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Nevertheless, there is some evidence that LPA has mental health benefits for youth[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and given that less than half of young people in England currently meet MVPA guidelines,[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e] efforts to get the nation moving will likely continue to incorporate LPA as part of the solution. There is, therefore, an ongoing need to better understand associations with all types, intensities, and volumes of movement behaviour in adolescence.\u003c/p\u003e \u003cp\u003ePA and sedentary behaviour are not simply two sides of the same coin and are rightly treated as independent variables in respective fields of research. However, the necessity that when awake, one \u003cem\u003emust\u003c/em\u003e be either sedentary, lightly-, or moderate-to-vigorously active means PA and sedentary behaviour are inextricably linked.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] The finitude of time dictates that when awake, one \u003cem\u003emust\u003c/em\u003e decrease time spent in at least one state in order to boost time spent in another. Accordingly, recommendations for epidemiologists now emphasise that analytical approaches integrate all forms of movement into composite patterns of daily behaviour.[20,21] Such approaches align with Health Lifestyle Theory and the Overflow Hypothesis both of which speak to the effect of co-dependence among concomitant health-influencing behaviours.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFailure to holistically evaluate compositions of adolescent movement and the impact of relative transitions between movement profiles risks mismeasurement of prospective movement-based public health strategies, and advocacy for preventative action that may end up less effective than is to be expected. One study performing compositional data analysis reported evidence that displacing sedentary time with MVPA yields more pronounced physical health benefits than if displacing LPA, which itself offers some advantages for disease markers (e.g., cardiovascular capacity, obesity).[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Although associations with mental health are less clear cut due to the potential for sedentary activities to support wellbeing,[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e13\u003c/span\u003e] emerging person-centred studies suggest the benefits of adolescent movement for depressive symptoms are dependent on a \u0026ldquo;synergistic interaction\u0026rdquo; and the ideal combination of co-dependent movement behaviours performed throughout the day.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Examining MVPA, LPA, and sedentary behaviour as component parts of an integrated movement profile would therefore provide deeper insight into how different combinations of these behaviours impact both physical and mental health, and thereby inform more nuanced, targeted, and effective public health strategies.\u003c/p\u003e\n\u003ch3\u003eA person-centred approach\u003c/h3\u003e\n\u003cp\u003ePerson-centred approaches assume population heterogeneity and profiles individuals appropriately according to shared characteristics, yielding advanced theoretical knowledge applicable to public health advocates where variable-centred studies cannot.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] In particular, Latent Transition Analysis (LTA) probabilistically assigns individuals to the profile with which they most closely correspond repeatedly across multiple timepoints, and calls on relevant predictor variables to estimate the likelihood of transitioning profiles for subgroups of the population.\u003c/p\u003e \u003cp\u003eEvidence suggests \u0026ldquo;high-decreasers\u0026rdquo; (those most physically active in childhood who trend down to the mean by late adolescence) experience fewer depressive symptoms in early adulthood than those who only started moving more later in adolescence.[9] This infers a portion of the variance in depressive symptoms among individuals exhibiting similar volumes of movement may be explained by a lagged and pervasive effect of historic movement-based behaviours. A powerful feature of LTA in this regard, is it enables examination of how \u003cem\u003etransition patterns\u003c/em\u003e can account for variance in mental health outcomes by considering historic volumes of movement and the path one took to arrive at a particular latent profile.[27] LTA thus provides a framework to better understand why comparable activity levels at a single timepoint do not consistently predict mental health in adolescence.\u003c/p\u003e \u003cp\u003eRecent methodological advancements now suggest including random intercepts within LTA models can greatly enhance model accuracy by parsing stable, individual traits from other factors influencing profile changes.[28] The present study is among the first to utilise Random Intercepts Latent Transition Analysis (RI-LTA) generally, and the very first to use accelerometer data to objectively assess holistic movement profiles within a RI-LTA framework. By tracking changes in movement profiles across three stages of adolescence (age 12, 14, 16) this study offers unique insight into how transitions in movement behaviour influence depressive symptoms in late adolescence (age 18) and early adulthood (age 22).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAims and hypotheses\u003c/h2\u003e \u003cp\u003eThe purpose of the current study is to establish: (i) distinct movement behaviour profiles at multiple timepoints across adolescence; (ii) predictors of profile transitions; and (iii) the extent to which transition patterns predict future depressive symptoms. Depressive symptoms at age 18 and 22 were evaluated to determine the extent to which movement behaviours may influence depressive symptoms over multiple timeframes.\u003c/p\u003e \u003cp\u003eHealth Lifestyle Theory posits that barriers to engagement in one health behaviour likely to extend to other health behaviours.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e23\u003c/span\u003e] We therefore did not expect to find profiles showing high levels of MVPA paired with low levels of LPA (or vice versa). Instead, we anticipated profiles where both MVPA and LPA would increase in tandem, and antagonistically to sedentary behaviour [H\u003csup\u003e1\u003c/sup\u003e]. In line with extant literature, we hypothesised the sample would become more sedentary and less physically active over time [H\u003csup\u003e2\u003c/sup\u003e]. We hypothesised that female sex [H\u003csup\u003e3a\u003c/sup\u003e], higher Body Mass Index (BMI) [H\u003csup\u003e3b\u003c/sup\u003e], lower parental education [H\u003csup\u003e3c\u003c/sup\u003e] and higher baseline depressive symptoms [H\u003csup\u003e3d\u003c/sup\u003e] would significantly increase the likelihood of transitioning to profiles characterised by lesser movement. We hypothesised that compared to those exhibiting moderate volumes of movement consistently over time, those with a history of greater movement would report fewer depressive symptoms at age 18 [H\u003csup\u003e4a\u003c/sup\u003e] and age 22 [H\u003csup\u003e4b\u003c/sup\u003e], and that those with a history of lesser movement would report greater depressive symptoms at age 18 [H\u003csup\u003e5a\u003c/sup\u003e] and age 22 [H\u003csup\u003e5b\u003c/sup\u003e]. See Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for illustrative examples of hypothesised profile characteristics and differences in depressive symptoms. While we did not hypothesise identifying a specific number of profiles at any timepoint, in both figures, three profiles are depicted to support interpretation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eParticipants were part of the Avon Longitudinal Study of Parents and Children (ALSPAC).[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e31\u003c/span\u003e] All pregnant women resident in Avon, UK with expected delivery dates between 1st April 1991 and 31st December 1992 were invited to take part. After subsequent attempts to bolster the initially enrolled sample of 15,454 pregnant women, the total number of foetuses alive at one year of age was 14,901.\u003c/p\u003e \u003cp\u003eStudy data were collected and managed using Research Electronic Data Capture (REDCap) tools hosted at the University of Bristol.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e32\u003c/span\u003e] REDCap is a secure, web-based software platform designed to support data capture for research studies. Further information regarding research ethics can be found via the study website.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e33\u003c/span\u003e] The ALSPAC study website contains details of all available data through a fully searchable data dictionary and variable search tool.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe analytical sample comprised all participants with at least 4 valid days (\u0026ge;\u0026thinsp;600minutes) of accelerometer data containing\u0026thinsp;\u0026ge;\u0026thinsp;3 weekdays and \u0026ge;\u0026thinsp;1 weekend day at \u0026ge;\u0026thinsp;1 timepoint were included in analysis (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4,964).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhysical activity\u003c/h3\u003e\n\u003cp\u003ePA was measured using Actigraph AM7164 2.2 accelerometers [Actigraph LLC, Fort Walton Beach, FL, USA] in 2005\u0026ndash;2006 when participants were 11.7 years of age (henceforth referred to as age 12; T1) and again when age 13.9 (henceforth 14 years; T2), and age 15.5 (henceforth 16 years; T3). Devices were worn on the right hip for 7 consecutive days during waking hours except when doing aquatic activities. Time spent sedentary, in LPA, and MVPA was quantified using count per minute (cpm) thresholds determined in a previous calibration study (sedentary behaviour\u0026thinsp;\u0026lt;\u0026thinsp;200cpm; LPA\u0026thinsp;\u0026ge;\u0026thinsp;200cpm, \u0026lt;\u0026thinsp;3600cpm; MVPA\u0026thinsp;\u0026ge;\u0026thinsp;3600cpm).[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Raw accelerometer counts were recorded in 60-second epochs. ALSPAC accelerometer data have a pre-imposed non-wear cut-point of any period\u0026thinsp;\u0026gt;\u0026thinsp;10 minutes with consecutive zero counts.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\n\u003ch3\u003eDepressive symptoms\u003c/h3\u003e\n\u003cp\u003eAt age 12, 18, and 22, participants completed the Short Mood and Feelings Questionnaire (SMFQ).[37] The SMFQ is a 13-item self-report measure of depressive symptoms over the past two weeks with responses scored on a 3-point scale: 0\u0026thinsp;=\u0026thinsp;Not True; 1\u0026thinsp;=\u0026thinsp;Sometimes True; and 2\u0026thinsp;=\u0026thinsp;True. Total scores range from 0\u0026ndash;26 with higher scores indicating greater symptom severity. The SMFQ has been validated for use as a screening tool for depression in adolescence[38] and early adulthood[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] with high construct, content, and criterion validity. The SMFQ had high internal reliability at each timepoint in the present sample (α\u0026thinsp;=\u0026thinsp;.84 to .90).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eData on sex (male/female), BMI, parental education, and baseline depressive symptoms were collected via questionnaires age 12.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData handling\u003c/h3\u003e\n\u003cp\u003eMissing data were handled using Multiple Imputation by Chained Equations (MICE) in the Statistical Package for the Social Sciences version 28 [IBM Corp., Amonk, NY, USA]. Twenty imputed datasets were generated with aggregated scores derived following the Bar Procedure.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e40\u003c/span\u003e] SMFQ scores were positively skewed hence, missing values were imputed using Predictive Mean Matching.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAnalyses were performed using Mplus version 8.9 in four key stages.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStage one: profile enumeration at each timepoint\u003c/h2\u003e \u003cp\u003eA sequence of cross-sectional Latent Profile Analyses were performed age 12 (T1), 14 (T2), and 16 (T3). Models were fit using only latent profile indicators without adjusting for covariates.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] Solutions with \u003cem\u003ek\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e profiles were enumerated up to a 10-profile solution. Relative fit indices were used to identify the best fitting model: \u003cem\u003eAkaike Information Criterion\u003c/em\u003e (AIC); \u003cem\u003eBayesian Information Criterion\u003c/em\u003e (BIC); \u003cem\u003eSample Size Adjusted BIC\u003c/em\u003e (ssaBIC); and the \u003cem\u003eLo\u0026ndash;Mendell\u0026ndash;Rubin Adjusted Likelihood Ratio Test\u003c/em\u003e (LMRa). For AIC, BIC, and ssaBIC, lower values represent better fit. An elbow plot was generated to identify the point at which increased model complexity yielded diminished returns in model fit.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] Significant LMRa tests indicate the \u003cem\u003ek\u003c/em\u003e solution fits \u003cem\u003ebetter\u003c/em\u003e than that containing \u003cem\u003ek\u003c/em\u003e\u0026ndash;1 profiles.[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] The proportional distribution of the sample was also evaluated to qualify stability, interpretability and generalisability of each model. Solutions with the smallest profiles were regarded as least stable. Although not a fit index, classification entropy is reported with values\u0026thinsp;\u0026gt;\u0026thinsp;.80 considered clear separation between profiles.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] The best fitting LPAs were advanced and combined into a single LTA model with profiles at each timepoint estimated simultaneously.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStage two: model modifications, measurement invariance and inclusion of random intercepts\u003c/h2\u003e \u003cp\u003eFit statistics were compared between the regular LTA and RI-LTA models (both invariant and non-invariant) to quantitatively validate that the more complex RI-LTA models were a better fit.[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] Only BIC is used to assess fit of RI-LTA models in line with current recommendations.[28] Satorra-Bentler Scaled Mean-adjusted Chi Square tests of model fit formally tested the assumption of measurement invariance for regular LTA and RI-LTA models.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] Satorra-Bentler tests determine whether a more complex non-invariant (comparison) model, within which measurement parameters (i.e., profile indicators) are freely estimated, is a significantly better fit than a simpler invariant (nested) model with parameters fixed to their T1 values at all timepoints. Non-significant tests suggest the simpler invariant model is preferable. All stage two models were adjusted for accelerometer wear time at each timepoint to reduce risk of bias in profile estimation. As a sensitivity analysis, this stage was also conducted without adjusting for wear time to illustrate the extent to which failure to make such adjustments may bias parameter estimates for future studies (supplementary material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStage three: profile transition probabilities and their predictors\u003c/h2\u003e \u003cp\u003eTransition probabilities were obtained by regressing profile membership at T2 on membership at T1, then T3 membership on T2. All parameters were then fixed to their final starting values such that the structure of profiles remained unchanged when introducing auxiliary predictors to the model.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] Predictors of profile transitions were fit simultaneously to account for potential between-predictor interactions. As risk of Type II Error increases when adding multiple predictors simultaneously, sensitivity analyses were performed with predictors tested individually in separate models (supplementary material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStage four: comparing depressive symptoms across transition probabilities\u003c/h2\u003e \u003cp\u003eWhere possible, differences in depressive symptoms at ages 18 and 22 were assessed via Wald tests to compare estimated SMFQ scores for each transition pattern (e.g., 2\u0026rarr;2\u0026rarr;2 versus 3\u0026rarr;3\u0026rarr;2) for minimally and fully adjusted models (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for support interpreting transition patterns). The largest stable transition pattern was used as reference against which all viable unstable transition patterns (i.e., increasing/decreasing/fluctuating) were compared. Some transition patterns contained very few participants (e.g., 3\u0026rarr;1\u0026rarr;2) and were considered unreliable so have not been analysed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eStage one: profile enumeration\u003c/h2\u003e\n\u003cp\u003eFit statistics for each timepoint are provided in Table 1. A clear elbow was visible at T1 for three profiles inferring beyond this point, increased model complexity yielded diminished returns in fit (Figure 3). The three-profile solution was well distributed with three large groups identified, unlike the four-profile solution which contained a group comprising only 5% of the sample. At T2, an elbow was observed at three profiles (Figure 3) and again, the sample was well distributed. Unlike T1 and T2, no clear elbow was observed at T3 (Figure 3). However, a series of LMRa likelihood ratio tests indicated three profiles were better than two, but four profiles were not better than three. Likewise, the four-profile solution contained an unstable group comprising just 1% of the sample. For these reasons, three-profile solutions were selected as those which fit the data best at T1, T2 and T3 independently.\u003c/p\u003e\n\u003ch3\u003eTable 1 \u003cem\u003eModel fit indices for cross-sectional latent profiles of adolescent movement behaviour\u003c/em\u003e\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"759\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003eClasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003essaBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLMRa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 293px;\"\u003e\n \u003cp\u003eModel Estimated Class Proportions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eAge 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-74439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e148890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e148929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e148910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-73306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e146632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e146697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e146665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.77, .23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-72446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e144920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e145011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e144967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.64, .21, .15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-72172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e144381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e144498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e144441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.62, .21, .12, .05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-72010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e144065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e144208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e144138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.60, .20, .14, .04, .02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-71905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e143863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e144033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e143950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.59, .20, .13, .05, .01, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-71805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e143671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e143866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e143771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.55, .22, .12, .05, .04, .01, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-71732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e143532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e143753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e143645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.54, .21, .11, .06, .04, .02, .02, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-71695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e143466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e143714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e143593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.52, .21, .09, .06, .05, .04, .02, .01, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-71660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e143404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e143677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e143544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.51, .21, .09, .06, .04, .02, .01, .01, .01, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eAge 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-71687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e143386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e143425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e143406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-70555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e141131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e141196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e141164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.91, .09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-69543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e139114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e139205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e139160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.81, .10, .09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-69228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e138493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e138610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e138553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.79, .10, .07, .03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-69020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e138085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e138228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e138158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.78, .11, .07, .02, .02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-68847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e137746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e137915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e137832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.76, .11, .07, .02, .03, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-68742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e137545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e137740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e137645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.75, .07, .07, .05, .03, .02, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-68627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e137323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e137545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e137437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.73, .07, .07, .05, .03, .03, .02, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-68518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e137112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e137359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e137238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.72, .07, .07, .05, .03, .03, .02, .01, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-68420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e136924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e137197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e137064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.71, .07, .06, .05, .03, .03, .02, .01, .01, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eAge 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-66872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e133756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e133795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e133776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-65418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e130856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e130921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e130890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.97, .03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-64455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e128938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e129029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e128984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.91, .05, .04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-63913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e127862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e127979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e127922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.89, .05, .05, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-63630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e127305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e127448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e127378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.86, .07, .04, .02, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-63232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e126517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e126687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e126604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.85, .04, .04, .04, .03, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-62876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e125812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e126008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e125912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.84, .04, .04, .03, .03, .02, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-62581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e125231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e125452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e125344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.83, .04, .04, .03, .02, .02, .02, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-62324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e124725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e124972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e124851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.82, .04, .03, .03, .03, .02, .02, .01, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-62083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e124251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e124524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e124391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 293px;\"\u003e\n \u003cp\u003e.82, .04, .03, .03, .03 .02, .01, .01, .01, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003eStage two: model modifications\u003c/h2\u003e\n\u003cp\u003eRegular (invariant and non-invariant) LTA models were estimated by incorporating all latent profile analyses in a single 3x3 design (i.e., three profiles across three timepoints). This process was repeated for models including random intercepts such that invariant and non-invariant RI-LTA models, also with a 3x3 design were fit to the data (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 2 \u003cem\u003eModel fit of invariant and non-invariant regular LTA and RI-LTA models\u003c/em\u003e\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"543\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSatorra-Bentler\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 112px;\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 139px;\"\u003e\n \u003cp\u003eModel Estimated Class Proportions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e3x3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e416762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eOverall = .853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eRegular LTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT1 = .739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT1 = .60, .39, .01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e(invariant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT2 = .848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT2 = .72, .18, .10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT3 = .963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT3 = .92, .06, .02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e3x3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e415442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eOverall = .850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eRI-LTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT1 = .758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT1 = .63, .37, \u0026lt;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e(invariant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT2 = .822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT2 = .73, .17, .10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT3 = .956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT3 = .93, .05, .02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e3x3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e410900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eOverall = .901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eRegular LTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT1 = .806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT1 = .61, .23, .16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e(non-invariant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT2 = .918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT2 = .77, .10, .04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT3 = .975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT3 = .89, .07, .04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e3x3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e410417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026lt;.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eOverall = .902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eRI-LTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT1 = .808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT1 = .61, .24, .15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e(non-invariant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT2 = .919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT2 = .77, .13, .10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eT3 = .975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eT3 = .89, .07, .04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eInvariant models contained very small classes and sample distributions for non-invariant models were more closely aligned with those established during initial profile enumeration (stage one). Highly significant Satorra-Bentler tests demonstrated measurement non-invariance for both regular LTA and RI-LTA models while BIC was lowest for the non-invariant RI-LTA model overall. Therefore, in line with recent evidence that RI-LTAs represent an advancement of the state of the art, the 3x3 non-invariant RI-LTA was considered the most quantitatively and qualitatively robust measurement model and was selected for advancement. Entropy for the 3x3 non-invariant RI-LTA overall was .902 (T1=.808; T2=.919; T3=.975) signalling excellent classification accuracy. The final model is illustrated in Figure 4. Estimated mean values for profile indicators and wear time are provided as supplementary material.\u003c/p\u003e\n\u003ch2\u003eStage three: profile transitions and their predictors\u003c/h2\u003e\n\u003cp\u003eTransition probabilities are expressed as Odds Ratios (ORs) with 95% Confidence Intervals (CIs) in Table 3. For each transition, the pattern denoting stability (i.e., the diagonal of the probability table) serves as reference. Predictors of profile transitions are reported in Table 4.\u003c/p\u003e\n\u003ch3\u003eTable 3 \u003cem\u003eOdds of profile transition from T1 to T2 and T2 to T3 for each latent movement profile\u003c/em\u003e\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"737\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eMaximal Movers (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eModerate Movers (76.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMinimal Movers (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eWave 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003eOR [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eOR [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eOR [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMaximal Movers (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e4.65 [3.76 to 5.76]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e.05 [.022 to .114]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eModerate Movers (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e.09 [.08 to .11]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e2,436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e.07 [.06 to .09]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003cp\u003e(23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e.03 [.01 to .08]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e4.25 [3.56 to 5.07]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003eWave 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eMaximal Movers (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eModerate Movers (89.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMinimal Movers (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003eOR [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eOR [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eOR [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMaximal Movers (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e13.53 [9.69 to 18.90]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e.03 [.01 to .10]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eModerate Movers (76.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e.02 [.02, 0.03]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3,505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e.01 [.01 to .02]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003cp\u003e(13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.11 [.04 to .29]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e15.74 [11.60 to 21.37]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eOdds adjusted for accelerometer wear time, sex, BMI, parental education and baseline depressive symptoms\u003c/em\u003e\u003c/p\u003e\n\u003ch3\u003eTable 4 \u003cem\u003ePredictors of profile transitions with 95% confidence intervals\u003c/em\u003e\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"700\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 483px;\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eWave 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 189px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.96 [1.55 to 2.48]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.68 [2.74 to 4.94]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.51 [.40 to .65]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.88 [1.49 to 2.35]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.27 [.20 to .37]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.53 [.43 to .67]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 483px;\"\u003e\n \u003cp\u003eWave 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.34 [.97 to 1.85]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.20 [1.44 to 3.36]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.75 [.54 to 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.64 [1.17 to 2.31]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.46 [.30 to .70]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.61 [.43 to .86]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 483px;\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eWave 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.04 [1.00 to 1.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.08 [1.03 to 1.13]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.97 [.93 to 1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.04 [1.01 to 1.08]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.93 [.88 to .97]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.96 [.93 to .99]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 483px;\"\u003e\n \u003cp\u003eWave 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.00 [.95 to 1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e.96 [.90 to 1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e1.00 [.96 to 1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e.96 [.91 to 1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e1.04 [.98 to 1.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.04 [.99 to 1.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eParental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 483px;\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eWave 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.20 [1.09 to 1.33]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.31 [1.16 to 1.49]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.83 [.76 to .92]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.09 [.99 to 1.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.76 [.67 to .86]\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.92 [.83 to 1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 483px;\"\u003e\n \u003cp\u003eWave 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.09 [.94 to 1.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.09 [.91 to 1.29]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.93 [.81 to 1.07]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.01 [.88 to 1.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.92 [.77 to 1.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.99 [.87 to 1.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 483px;\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSMFQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eWave 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.00 [.97 to 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.01 [.98 to 1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e1.00 [.97 to 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.01 [.98 to 1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.99 [.95 to 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.99 [.96 to 1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 483px;\"\u003e\n \u003cp\u003eWave 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 152px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMaximal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e1.00 [.96 to 1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.03 [.97 to 1.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e1.00 [.95 to 1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.03 [.98 to 1.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMinimal Movers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e.97 [.92 to 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e.98 [.94 to 1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003e*\u003c/sup\u003ePredictor significantly contributed to the probability to profile transition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAll predictors were added to the model simultaneously\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSex coded as 0=male 1=female so odds \u0026gt;1 infer greater likelihood for females\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom T1 to T2, the odds of transitioning from moderate to maximal (\u003cem\u003en\u003c/em\u003e=317, OR=.09 [.08 to .11]) or moderate to minimal (\u003cem\u003en\u003c/em\u003e=316, OR=.07 [.06 to .09]) were very low compared to remaining in the same category (\u003cem\u003en=\u003c/em\u003e2,436) indicating a high-level of stability for the moderate mover profile. Stability of the moderate mover profile was also observed across T2 to T3 (\u003cem\u003en\u003c/em\u003e=3,505) with very low odds of transitioning from moderate to maximal (OR=.02 [.02 to .03]), or moderate to minimal (\u003cem\u003en\u003c/em\u003e=184, OR=.01 [.01 to .02]).\u003c/p\u003e\n\u003cp\u003eFemales were half as likely as males to transition from moderate to maximal movers from T1 to T2 (OR=.51 [.40 to .65]) and much more likely to transition from moderate to minimal from T1 to T2 (OR=1.88 [1.49 to 2.35]) and T2 to T3 (OR=1.64 [1.17 to 2.31]). Higher BMI increased odds of transitioning from moderate to minimal from T1 to T2 (OR= 1.04 [1.01 to 1.08]) while higher level of parental education decreased odds of transitioning from moderate to maximal movers from T1 to T2 (OR=.83 [.76 to .92]). Baseline depressive symptoms did not influence the odds of transitioning profiles across any timepoints. Sensitivity analyses wherein predictors were estimated sequentially in separate models produced effect sizes of equal magnitude and significance, strengthening the robustness of findings (supplementary material).\u003c/p\u003e\n\u003ch2\u003eStage four: comparing depressive symptoms across profiles and transition probabilities\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWald difference tests comparing depressive symptoms at ages 18 and 22 for those with different transition patterns are reported in Table 5. As the focus of the current study was on \u003cem\u003eprofile transitions\u003c/em\u003e, cross-sectional between-profile differences in depressive symptoms are not reported herein. For completeness, these analyses are provided as supplementary material.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 5 \u003cem\u003eDepressive symptoms associated with transition patterns with consistently moderate movers serving as reference\u003c/em\u003e\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"765\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eTransition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSMFQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 166px;\"\u003e\n \u003cp\u003eWald Test Statistics \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSMFQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 178px;\"\u003e\n \u003cp\u003eWald Test Statistics \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003ePattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eat 18 (\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003e(\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003eat 22 (\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e(\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eMinimally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u0026rarr;1\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e139 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4.46 (.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-.64 (.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e2.82 (.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-.64 (.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eAdjusted \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u0026rarr;2\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e525 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4.56 (.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-.54 (.22)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e2.61 (.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-.85 (.19)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2\u0026rarr;1\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e272 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4.86 (.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-.24 (.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e3.19 (.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-.27 (.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u0026rarr;2\u0026rarr;2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,213 (44.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.10 (.10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eref.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.46 (.10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eref.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2\u0026rarr;3\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e252 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e6.27 (.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.17 (.38)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026lt;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4.59 (.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.13 (.44)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3\u0026rarr;2\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e763 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e5.55 (.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.45 (.22)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e3.90 (.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e.44 (.22)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3\u0026rarr;3\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e252 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e5.86 (.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.75 (.34)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4.87 (.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.41 (.41)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026lt;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eFully\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u0026rarr;1\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e135 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.56 (.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-.20 (.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-.11 (.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-.25 (.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eAdjusted \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u0026rarr;2\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e522 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.56 (.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e-.19 (.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-.42 (.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-.56 (.18)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026lt;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2\u0026rarr;1\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e278 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.77 (.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.02 (.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.12 (.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-.02 (.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u0026rarr;2\u0026rarr;2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2,202 (44.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.75 (.45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eref.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.14 (.44)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eref.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2\u0026rarr;3\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e255 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.62 (.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.87 (.36)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.98 (.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e.84 (.42)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3\u0026rarr;2\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e769 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.92 (.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.17 (.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.28 (.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e.14 (.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3\u0026rarr;3\u0026rarr;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e249 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.65 (.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.62 (.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e.98 (.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.29 (.41)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026lt;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e1 = maximal movers; 2 = moderate movers; 3 = minimal movers\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eWald tests subtracted reference group mean/intercept from comparison group mean/intercept hence, a negative value indicates comparison group had fewer symptoms, a positive value indicates comparison group had greater symptoms\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003e\u0026nbsp;b\u0026nbsp;\u003c/sup\u003eadjusted for wear time during model modifications process in stage two\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eadditionally adjusted for sex, BMI, parental education, baseline depressive symptoms\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003e*\u003c/sup\u003esignificant difference\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn all comparisons, those with a consistent moderate mover pattern (i.e., 2\u0026rarr;2\u0026rarr;2) were used as the reference against which all other transition patterns were compared. After adjusting for covariates, those whose prior movement levels fluctuated but contained a period of moving less often (i.e., 2\u0026rarr;3\u0026rarr;2 \u0026nbsp;vs. \u0026nbsp; 2\u0026rarr;2\u0026rarr;2) consistently reported significantly greater depressive symptoms at age 18 (\u003cem\u003ep\u003c/em\u003e=.02, \u003cem\u003ed\u003c/em\u003e=.21), and age 22 (\u003cem\u003ep\u003c/em\u003e=.05, \u003cem\u003ed\u003c/em\u003e=.21). The largest effect was observed for those who consistently used to move less (i.e., 3\u0026rarr;3\u0026rarr;2 \u0026nbsp;vs. \u0026nbsp;2\u0026rarr;2\u0026rarr;2) with these individuals reporting significantly greater depressive symptoms at age 22 (\u003cem\u003ep\u003c/em\u003e\u0026lt;.01, \u003cem\u003ed\u003c/em\u003e=.32). Finally, those who moved more during early adolescence (i.e., 1\u0026rarr;2\u0026rarr;2 \u0026nbsp;vs. \u0026nbsp; 2\u0026rarr;2\u0026rarr;2) also reported significantly fewer depressive symptoms at age 22 (\u003cem\u003ep\u003c/em\u003e\u0026lt;.01, \u003cem\u003ed\u003c/em\u003e=.14).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to establish latent profiles of movement behaviour throughout adolescence, evaluate the stability of these profiles over time, and the extent to which transitioning between profiles contributes to variance in depressive symptoms in both late adolescence and early adulthood.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of movement behaviour profiles\u003c/h2\u003e \u003cp\u003eThree latent profiles were identified across all three timepoints differentiating between \u003cem\u003eMaximal Movers\u003c/em\u003e, \u003cem\u003eModerate Movers\u003c/em\u003e, and \u003cem\u003eMinimal Movers\u003c/em\u003e. MVPA and LPA decreased while sedentary behaviour increased antagonistically between and within profiles at every timepoint, in line with H\u003csup\u003e1\u003c/sup\u003e. Although maximal, moderate, and minimal movers were consistently identified throughout adolescence, quantitative assessment of measurement non-invariance presented statistical evidence that all profiles became more sedentary and less physically active over time, providing support for H\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFindings align with extant literature suggesting that adolescent movement becomes increasingly homogeneous over time;[9] contradicts evidence of the \u0026ldquo;Active Couch Potato Hypothesis\u0026rdquo; (i.e., the notion that volumes of MVPA can persist despite increases in sedentary behaviour);[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and contributes new knowledge with evidence that convergence of movement behaviour persists even when sedentary behaviour is included as a key profile indicator, underscoring the need for a holistic approach in public health initiatives. Interventions should concurrently target improvements in PA and reductions sedentary behaviour to promote well-balanced movement profiles indicative of a healthy lifestyle. Furthermore, for initiatives to be maximally efficacious, they should target adolescents when health behaviours are most malleable.[49] Our evidence posits, the earlier, the better.\u003c/p\u003e \u003cp\u003eAdolescents\u0026rsquo; daily lives are predominantly sedentary and become increasingly so as they age. To foster optimal compositions of 24-hour movement, public health guidelines should specify that increases in LPA or MVPA should specifically displace time spent sedentary, as opposed to each other. This is substantiated by evidence that displacing sedentary behaviour with MVPA yields a greater collective health benefit than if displacing LPA.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] The centrality of school-based intervention efforts makes this challenging due to the practical constraints of integrating PA into environments that are inherently sedentary in nature. Potential scalable solutions include: \u0026ldquo;Classroom Movement Breaks\u0026rdquo; \u0026ndash; structured 5-10minute lesson breaks for stretching and dynamic movements; and \u0026ldquo;Physically Active Learning\u0026rdquo; \u0026ndash; practices that require pupils to move around the classroom to complete tasks, both of which are promising for enhancing movement volume, academic performance and wellbeing.[50]. There is also evidence among younger children (aged\u0026thinsp;\u0026lt;\u0026thinsp;10) that integrating a \u0026lsquo;Daily Mile\u0026rsquo; into everyday school routines improves visual spatial working memory and physical fitness.[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] However, fostering an autonomously motivating PA experience that ensures reliable sustainability of such significant changes in children\u0026rsquo;s behaviour remains an elusive goal for public health, requiring increased application of behaviour change theory science.[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eDistinct profiles of movement behaviour exist throughout adolescence. Most individuals exhibit moderate levels of movement at age 12, or shift to moderate levels later as they progress through adolescence, although what constitutes \u0026lsquo;moderate movement\u0026rsquo; incrementally declines as all adolescents moved less over time. Females, those with higher BMI, and with more educated parents were at greater risk of early transition to profiles marked by reduced PA and increased sedentary behaviour. Moving often when age 12 led to significantly fewer depressive symptoms in early adulthood, even after transitioning to a prolonged period of moderate movement. Collectively, findings highlight early adolescence as a critical period for the delivery of intervention/prevention strategies designed to either initiate or maintain healthy patterns of movement behaviour and mitigate future depressive symptoms. Such approaches may benefit from adopting evidence-based contemporary models of behaviour change that focus on nurturing individuals\u0026rsquo; psychological needs, along with strategies that are attentive to adolescents\u0026rsquo; social circumstances and cultural context, to ultimately facilitate autonomously motivating and lasting behaviour change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained in writing from all participants (or their parent/legal guardian when participants were minors) following the recommendations of the ALSPAC Ethics and Law Committee at the time. All methods were performed in accordance with the most up-to-date guidelines and regulations.\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\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 UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and [author names removed for blind review] will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf\u003c/span\u003e\u003c/span\u003e); This research was specifically funded by: NIH (grant ref: PD301198SC101645); Wellcome Trust and MRC: (grant ref: 092731).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eCK conceptualised the study, analysed the data and drafted the manuscript. GB, AC, KP and SS contributed to reviewing the manuscript and have read and approved the final version.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the ALSPAC Executive but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. 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Disord\u003c/em\u003e. \u003cb\u003e174\u003c/b\u003e, 447\u0026ndash;463. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2014.11.061\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2014.11.061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ALSPAC, Adolescence, Depressive Symptoms, Latent Transition Analysis, Light Physical Activity, Mixture Modelling, MVPA, Sedentary Behaviour","lastPublishedDoi":"10.21203/rs.3.rs-5610144/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5610144/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMovement behaviours are consistently recognised as having long-term implications for health and wellbeing. Adolescence is often accompanied by diminishing bodily movement and an increased onset of depressive symptoms. Time Displacement dictates that when awake, altering time spent in one movement state (i.e., Sedentary, Light- Moderate-, or Vigorous-Physical Activity) must alter time spent in others. However, few studies evaluate these interdependent movement behaviours as indicators within a composite profile of overall daily movement. The current study included a Random Intercepts Latent Transition Analysis (RI-LTA) to establish latent profiles of movement behaviour across three timepoints in adolescence; the likelihood and predictors of profile transitions over time; and whether differences in transition patterns contributed to variance in future depressive symptoms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were represented from 4,964 participants of the Avon Longitudinal Study of Parents and their Children. Movement behaviours were measured using Actigraph AM7164 2.2 accelerometers age 12, 14, and 16. Depressive symptoms were measured using the Short Mood and Feelings Questionnaire at ages 18 and 22.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA 3x3 non-invariant RI-LTA was an excellent fit to the data (BIC\u0026thinsp;=\u0026thinsp;410417; Entropy\u0026thinsp;=\u0026thinsp;.902) distinguishing between \u003cem\u003eMaximal-, Moderate-\u003c/em\u003e, and \u003cem\u003eMinimal-Movers\u003c/em\u003e. Once accounting for non-invariance (i.e., that all profiles moved less over time), transition probabilities presented the \u003cem\u003eModerate-Mover\u003c/em\u003e profile as extremely stable across adolescence. Females, and those with higher BMI and more educated parents were more likely to transition to profiles characterised by lesser movement. Transition patterns containing a period of minimal movement were associated with worse depressive symptoms at ages 18 and 22. Similarly, maximal movement age 12 conferred protection against depressive symptoms age 22 even after shifting to an enduring period of moderate movement thereafter.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMaximising PA and minimising time spent sedentary when age 12 can protect against depressive symptoms in early adulthood, even if daily movement later decreases. Early intervention has potential to promote health-supportive behaviour and mitigate depressive symptoms across the lifespan. Implications extend to the promotion of PA, and public-health strategies centred on young peoples\u0026rsquo; movement behaviour and the reduction of depressive symptoms.\u003c/p\u003e","manuscriptTitle":"Moving Through Time: Stability and Change in Adolescent Movement Behaviour and links with Future Depressive Symptoms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 17:23:32","doi":"10.21203/rs.3.rs-5610144/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-31T09:39:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-22T20:10:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-18T21:06:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325894196051536177706841585433637959405","date":"2025-01-10T12:14:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320595355454404556185701350060627817830","date":"2025-01-10T02:51:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-10T02:01:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-10T02:00:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-12-20T05:41:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-18T12:22:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-12-09T15:00:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"276358e3-a2bc-426b-b27e-43eb95d85714","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41852286,"name":"Biological sciences/Psychology/Human behaviour"},{"id":41852287,"name":"Health sciences/Risk factors"},{"id":41852288,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"},{"id":41852289,"name":"Health sciences/Health care/Public health"},{"id":41852290,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2025-07-07T16:05:49+00:00","versionOfRecord":{"articleIdentity":"rs-5610144","link":"https://doi.org/10.1038/s41598-025-04466-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:58:11","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2024-12-24 17:23:32","video":"","vorDoi":"10.1038/s41598-025-04466-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-04466-7","workflowStages":[]},"version":"v1","identity":"rs-5610144","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5610144","identity":"rs-5610144","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}