Psychosocial stress from pre- to post-pandemic times: a latent class growth analysis using data from a German cohort

Psychosocial stress from pre- to post-pandemic times: a latent class growth analysis using data from a German cohort | 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 Research Article Psychosocial stress from pre- to post-pandemic times: a latent class growth analysis using data from a German cohort Daniela Costa, Johannes Horn, Janka Massag, Oliver Purschke, Alexander Kluttig, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8196631/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Social Psychiatry and Psychiatric Epidemiology → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose The COVID-19 pandemic and containment measures disrupted daily life and worsened mental health. Stress, a key driver of mental disorders, likely intensified during this period. However, longitudinal studies tracking stress trajectories in the general population remain limited. This study aims to identify psychosocial stress trajectories from the pre- to post-pandemic period and examine associated characteristics in a population-based sample from a German city. Methods 966 participants from the German National Cohort study-centre in Halle (240,000 inhabitants in Eastern Germany) were included. Those participated in a six-monthly intensified assessment and completed at least four questionnaires between 2019 and 2024 containing PHQ-Stress module. First, latent class growth analysis identified heterogeneous stress trajectories. Second, associations between class membership and covariates were tested with multinomial logistic regressions. Results We identified four psychosocial stress trajectory classes. Most participants followed an intermediate-level-stress trajectory (58%), while others showed low (30%), high (10%), or peak-recovery (3%) trajectories. Across classes, stress rose over time, with small to moderate changes, mostly early in the pandemic. Membership in the intermediate- or high-stress trajectories was associated with greater pre-pandemic stress, depressive and anxiety symptoms, lower life satisfaction, and greater loneliness at the pandemic’s onset. The peak-recovery class was associated with lower agreeableness and divorce before the pandemic, and reductions in sport activities at its onset. Conclusion Stable patterns of stress predominated, while a small subgroup showed stronger reactivity to pandemic-related strain. These findings suggest that, for most individuals, pre-existing vulnerability and stress levels shape long-term trajectories despite substantial contextual change. Covid-19 latent classes mental health psychological stress trajectories Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Mental disorders have a multifactorial origin, emerging from a complex interplay of biological, psychological, and social factors ( 1 – 4 ), such as early life trauma ( 5 ), social isolation ( 6 ), academic and work pressure ( 7 , 8 ), economic instability ( 8 , 9 ), socio-political dynamics ( 10 ), and major global events ( 11 – 13 ). Psychosocial stress is a common pathway linking these factors and is relevant to the development and exacerbation of mental disorders ( 14 , 15 ); chronic stress can also impair physical health ( 16 ). Thus, stress is an important target for prevention. The COVID-19 pandemic and subsequent containment measures profoundly disrupted daily life and increased uncertainty ( 17 – 19 ). Early studies, mainly cross-sectional and based on convenience samples ( 20 , 21 ), suggested widespread elevated distress symptoms ( 22 , 23 ). However, many focused on considered high-risk groups, such as individuals with comorbidities or pre-existing health conditions ( 24 ), and pandemic-related essential workers ( 25 ), rather than using population-based samples. By late 2020, systematic reviews began reporting heterogeneity in mental health responses, questioning the hypothesis of a general deterioration, and highlighting the lack of and need for pre-pandemic and longitudinal assessments ( 23 , 26 ). Consequently, longitudinal studies including pre-pandemic data reported an average deterioration in mental health in early 2020, followed by a general attenuation of symptoms. However, the extent of change varied across populations ( 27 – 29 ), whereby younger adults, women, individuals with lower socioeconomic status, and those with pre-existing mental health conditions experienced greater distress ( 29 – 31 ). Nonetheless, longitudinal studies tracking mental health trajectories starting before the pandemic remain scarce, particularly those focusing on stress in the general population ( 32 ). One of the few studies that investigated trajectories of stress during the pandemic was conducted in Germany (April 2020 – January 2021), using the DASS-21 questionnaire and a convenient sample composed primarily by young women. This research identified four stress trajectories during the early months of the pandemic. In that study, 91% of participants showed stable or slightly rising stress levels, while 9% exhibited fluctuating patterns ( 33 ). In sum, most existing studies focused on early stages of the pandemic, with little research on its later phases. With this background, this study aims ( 1 ) to identify psychosocial stress trajectories among residents from an urban population and its surrounding rural areas in Germany, covering the period from before the COVID-19 pandemic onset until its late stage; and ( 2 ) examine how trajectory membership is associated with participants’ characteristics at three specific data-collection points: baseline (three to five years before pandemic), shortly before the pandemic, and early in the pandemic. Methods Participants This study is part of the German National Cohort (NAKO Gesundheitsstudie), a population-based study conducted at 18 study centres across Germany. Study design details have been published elsewhere ( 34 ). In brief, between 2014 and 2019, approximately 205,000 individuals were recruited and examined. The first follow-up examination (FU1) was conducted about five years after enrollment (2019–2024). Data was collected via medical exams, face-to-face interviews, and touch-screen questionnaires. During the COVID-19 pandemic, two additional questionnaires were administered in May 2020 and September 2022, distributed by e-mail or regular mail. In addition to the general study examinations, supplemental modules were implemented at selected NAKO centres as Level-3 projects. At the Halle (Saale) centre, we initiated an intensified six-month follow-up in 2019 for participants who agreed to receive additional online surveys, on physical activity, stress, and sleep. At baseline, 10,139 individuals were examined in Halle, with 6,875 participating in FU1. For the current study, we included individuals who completed FU1 between April 1, 2019 and March 31, 2020, i.e. before the first COVID-19 containment measures, ensuring a recent pre-pandemic assessment. We aimed to include six-monthly questionnaire data for each participant until their last available assessment, with data collection until early 2024. Participants were included if they had completed at least four questionnaires. Measurements Outcome variable We assessed psychosocial stress using the German version of the Patient Health Questionnaire – stress module (PHQ-st) ( 35 ). PHQ-st inquires about the previous four weeks and contains ten items. Items are rated on a scale, where 0 means ‘not bothered’, 1 ‘bothered a little’ and 2 ‘bothered a lot’, leading to a score ranging from 0 to 20, where a higher score indicates greater stress (Fig. 1 ). This total score was used as a continuous variable in all analyses. PHQ-st has good psychometric properties, such as reliability (ω = 0.776) and validity ( 36 ). Covariates To characterise individuals with different stress trajectories, we examined baseline, FU1, and the first corona questionnaire (CO1) covariates, identified in previous literature as potential predictors of stress and related mental health outcomes. Time-invariant variables were collected at baseline: sex, month and year of birth, personality traits (Big Five) ( 37 ), and early childhood trauma (Childhood Trauma Screener) ( 38 ). Potentially time-varying covariates included sociodemographic characteristics (marital and relationship status, education ( 39 )), international social-economic index of occupation status (ISEI) ( 40 , 41 ), anxiety and depressive symptoms (assessed using GAD-7 and PHQ-9, respectively) ( 42 , 43 ), health-related quality of life (HRQOL) ( 44 ), prior mental disorder diagnoses, body mass index (BMI) ( 45 ), and life satisfaction ( 46 ), sleep quality assessed by one item from the Pittsburgh Sleep Quality Index (PSQI) from baseline and/or FU1 ( 47 ). In addition, we used current employment and housing situation in the beginning of the pandemic, as well as newly introduced items on behavioural changes (physical activity), household financial strain, loneliness ( 48 ), and number of fears from CO1. Figure 2 shows the study timeline, and more detailed item descriptions are provided in Tables’ footnotes and supplementary material (SP). Statistical analysis We performed a latent class growth analysis (LCGA) to identify heterogeneous trajectories of psychosocial stress from April 2019 to the beginning of 2024. Analyses were conducted in R 4.4.0. ( 49 ) using the lcmm R-package ( 50 ). Time was treated as a continuous variable, with a common time-zero for all participants, and individual time points based on questionnaire completion date. To account for individual variability in trajectories, random effects were specified for linear, quadratic, and cubic terms of time. The subject identifier was included as a grouping variable to account for the repeated measures. The model including random effects up to the cubic term showed the best performance based on fit indices (log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC)) and was thus selected for further analyses. A beta link function was used to accommodate the non-Gaussian distribution of the outcome. PHQ-st scores exhibited zero-inflation and left-skewed distribution typical of stress measures. We compared available link functions (linear, beta, splines) in the lcmm R-package, with the beta link function providing best model fit (AIC improvement of 1363 and 172 compared to linear link and splines, respectively). The models with beta and spline links showed a 98.6% concordance. Following the approach described by Schoot et al. ( 51 ), and expecting the best solution to include four or five classes, we progressively fitted models with one to seven classes. For multi-class models, starting values were generated from the maximum-likelihood estimates of the single-class model. Model fit statistics for 1–7 class solutions are presented in SP. The four-class solution demonstrated good classification quality (entropy = 0.63), indicating adequate separation between classes. Although statistical measures (fit indices and entropy values) played a role in our selection process, the ultimate decision prioritised interpretability, conceptual relevance, and practical meaning in elucidating heterogeneous trajectories of psychosocial stress over time. For each class, a spaghetti plot was created using the ggplot2 R-package ( 52 ), to visualize individual trajectories over time, with a LOESS-smoothed trend line capturing the overall trend across individual trajectories. While assessments were continuous, for the interpretation of the visual effects, we used mean differences and Cohen’s d with 95% confidence intervals across the three periods (pre-pandemic, early 2021, and late pandemic) categorized as trivial (|d| < 0.20), small (0.20–0.49), medium (0.50–0.79), and large (≥ 0.80) ( 53 ). Following the approach described by Schoot et al. ( 51 ), no covariates were included in the LCGA model. Instead, the covariates of the class membership were assessed in a subsequent multinomial logistic regression. For the covariates, we performed imputation using the mice R-package ( 54 ). Predictive mean matching was used for numeric and ordinal variables. For nominal variables, we performed multiple imputations with ten datasets and computed logistic regressions. Imputation was performed using a dataset containing data from baseline, FU1, and CO1 (6,823 participants). Initial covariate selection for the multinomial logistic regression was guided by literature and an examination of a correlation matrix. Additionally, we tested models including variables not previously assessed, but considered relevant in the context of the pandemic. To address small subgroup sizes and improve interpretability, categorical variables were dichotomised where appropriate. Three models were run: The first included only baseline covariates; the second added FU1 time-varying measures (the most recent pre-pandemic data) to the baseline time-invariant variables; and the third additionally incorporated the most recent time-varying data up to early in the pandemic (May 2020). In models two and three only the most recent data were used, ensuring that repeated measurements of the same variable were not simultaneously included in a model. To restrict predictors to the most essential, models were rerun applying stepwise selection with the MASS R-package ( 55 ). Variance inflation factor values indicated no problematic multicollinearity. Nagelkerke’s R-squared was used as an indicator of the explanatory power of the models. Results Participants’ characteristics We included 966 participants in the LCGA. Of these, 42% (403) completed ten surveys containing the outcome variable between April 2019 and January 2024, while the rest completed between four and nine surveys (SP). The final analyses included 961 participants, after excluding five participants due to missing Level-3 identification in the baseline dataset (Fig. 3 ). The mean age of participants in January 2019 was 52.4 years, and 53% (512) of the sample were women. Most participants were married at FU1 (570, 59%), and employed at CO1 (689, 72%). In FU1, 144 participants (15%) reported a lifetime diagnosis of depression, and 89 (9%) reported a lifetime diagnosis of anxiety and/or panic attacks (Table 1 ). Table 1 a – Mean and Standard Deviation of Participant Characteristics at Baseline, FU1, and CO1, Overall and by PHQ-Stress Trajectory Class (Numeric Covariates) Collection time Participants Covariates PHQ-st trajectories’ classes All sample, n (%) 1: High, n (%) 2: Peak-recovery, n (%) 3: Intermediate, n (%) 4: Low, n (%) 961 (100) 92 (9.6) 27 (2.8) 559 (58.2) 283 (29.4) Baseline Age in January 2019 a , mean (SD) 52.40 (12.54) 48.13 (11.08) 49.22 (10.17) 51.14 (12.81) 56.58 (11.59) ISEI, mean (SD) 53.08 (15.05) 47.96 (14.22) 50.85 (14.34) 53.44 (14.91) 54.24 (15.36) BIG 5 Extraversion score, mean (SD) 4.71 (1.29) 4.47 (1.47) 4.81 (1.33) 4.71 (1.31) 4.78 (1.19) BIG 5 Neuroticism score, mean (SD) 3.50 (1.34) 4.71 (1.34) 3.55 (1.43) 3.58 (1.24) 2.94 (1.22) BIG 5 Consciousness score, mean (SD) 5.87 (0.91) 5.78 (0.92) 6.09 (0.83) 5.80 (0.93) 6.00 (0.85) BIG 5 Agreeableness score, mean (SD) 5.64 (0.95) 5.25 (1.01) 5.17 (1.00) 5.61 (0.94) 5.86 (0.90) BIG 5 Openness score, mean (SD) 4.69 (1.26) 4.62 (1.19) 5.00 (1.17) 4.66 (1.29) 4.73 (1.25) Childhood trauma (CTS score), mean (SD) 6.76 (2.26) 7.75 (3.08) 7.37 (3.26) 6.69 (2.16) 6.50 (1.92) Psychosocial stress (PHQ-st score), mean (SD) 3.49 (2.97) 7.96 (3.81) 2.44 (2.42) 3.68 (2.40) 1.75 (1.91) Health-related quality of life (SF36–1 item), mean (SD) 2.68 (0.66) 3.09 (0.69) 2.78 (0.75) 2.70 (0.62) 2.49 (0.65) Life satisfaction (L-1), mean (SD) 8.87 (1.73) 7.51 (2.06) 9.11 (1.05) 8.78 (1.70) 9.48 (1.43) Anxiety symptoms (GAD-7 score), mean (SD) 3.04 (3.01) 6.90 (4.55) 2.96 (2.52) 3.14 (2.73) 1.26 (1.95) Depressive symptoms (PHQ-9 score), mean (SD) 3.46 (3.33) 7.59 (5.10) 2.85 (3.08) 3.60 (2.86) 1.91 (2.09) FU1 Health-related quality of life (SF36–1 item), mean (SD) 2.69 (0.66) 3.15 (0.71) 2.44 (0.64) 2.73 (0.62) 2.48 (0.63) Life satisfaction (L-1), mean (SD) 9.03 (1.66) 7.57 (2.03) 8.93 (2.00) 8.91 (1.51) 9.74 (1.39) Anxiety symptoms (GAD-7 score), mean (SD) 2.91 (2.94) 7.43 (4.03) 2.37 (2.20) 3.04 (2.47) 1.24 (1.45) Depressive symptoms (PHQ-9 score), mean (SD) 3.51 (3.14) 7.88 (4.24) 3.19 (2.80) 3.73 (2.72) 1.68 (1.63) Education years (ISCED97), mean (SD) 16.25 (2.07) 16.04 (1.92) 15.56 (2.19) 16.31 (2.10) 16.29 (2.03) CO1 Loneliness (3-item scale), mean (SD) 4.76 (1.48) 5.79 (1.67) 5.04 (1.79) 4.77 (1.47) 4.36 (1.23) Number of fears b , mean (SD) 2.55 (2.35) 3.97 (2.52) 2.41 (2.36) 2.66 (2.27) 1.88 (2.19) Abbreviations: CO1, 1st Corona questionnaire; FU1, 1st follow-up examination; ISEI, international social-economic index of occupation status; Scores ranges: - Ascending order: Big-5 traits: 1 to 7; ISEI: 10 to 89; GAD-7: 0 to 21; number of fears: 0 to 8; L-1: 1 to 11; Loneliness: 3 to 9; PHQ-9: 0 to 27; PHQ-st: 0 to 20; CTS: 5 to 21; - Descending order: SF36-1 item: 1 (excellent) to 5 (poor) a calculated from month and year of birth collected at baseline b Number of fears among: road traffic accident, natural disaster, corona-virus infection, cancer, stroke, heart attack, diabetes mellitus, hereditary disease Table 1 b - Participant Characteristics at Baseline, FU1, and CO1, Overall and by PHQ-Stress Trajectory Class (Frequencies and Percentages for Categorical Variables) Collection time All sample, n (%) PHQ-st trajectories’ classes 1: High 2: Peak-recovery 3: Intermediate 4: Low Expected number of participants 961 (100) 92 (9.6) 27 (2.8) 559 (58.2) 283 (29.4) Baseline Sex Female, n (%) 512 (53.3) 60 (65.2) 14 ( 51.9) 311 (55.6) 127 (44.9) Male, n (%) 449 (46.7) 32 (34.8) 13 ( 48.1) 248 (44.4) 156 (55.1) FU1 Marital Status Married, n (%) 570 (59.3) 48 (52.2) 15 ( 55.6) 317 (56.7) 190 (67.1) Married, living apart from spouse, n (%) 17 (1.8) 1 (1.1) 1 (3.7) 14 (2.5) 1 (0.4) Single, n (%) 221 (23.0) 29 (31.5) 4 (14.8) 147 (26.3) 41 (14.5) Divorced, n (%) 112 (11.7) 12 (13.0) 7 (25.9) 64 (11.4) 29 (10.2) Widowed, n (%) 41 (4.3) 2 ( 2.2) 0 ( 0.0) 17 (3.0) 22 (7.8) Ever diagnosed with Depression no, n (%) 817 (85.0) 56 (60.9) 24 (88.9) 478 (85.5) 259 (91.5) yes, n (%) 144 (15.0) 36 (39.1) 3 (11.1) 81 (14.5) 24 ( 8.5) Ever diagnosed with Anxiety/Panic attack no, n (%) 872 (90.7) 75 (81.5) 26 (96.3) 504 (90.2) 267 (94.3) yes, n (%) 89 (9.3) 17 (18.5) 1 (3.7) 55 ( 9.8) 16 ( 5.7) Body Mass Index under-/normal-weight (< 25kg/m 2 ), n (%) 417 (43.4) 36 (39.1) 12 (44.4) 231 (41.3) 138 (48.8) overweight/obesity (≥ 25kg/m 2 ), n (%) 544 (56.6) 56 (60.9) 15 (55.6) 328 (58.7) 145 (51.2) CO1 Employment status a Employed, n (%) 689 (72.1) 75 (82.4) 25 (92.6) 416 (75.0) 173 (61.1) Unemployed, n (%) 257 (26.9) 14 (15.4) 1 (3.7) 133 (24.0) 109 (38.5) Non-working person, n (%) 10 (1.1) 2 (2.2) 1 (3.7) 6 (1.1) 1 (0.4) Household Financial Situation a Easy, n (%) 562 (58.8) 29 (31.9) 17 ( 63.0) 311 (55.9) 205 (72.4) Reasonably easy, n (%) 299 (31.3) 39 (42.9) 7 ( 25.9) 188 (33.8) 65 (23.0) With some difficulties, n (%) 8 (0.8) 3 (3.3) 1 (3.7) 4 (0.7) 0 (0.0) With great difficulty, n (%) 88 (9.2) 20 (22.0) 2 (7.4) 53 (9.5) 13 (4.6) Living situation a People living alone, n (%) 176 (18.4) 18 (19.8) 5 (18.5) 101 (18.2) 52 (18.4) Couples without children in the household, n (%) 468 (49.0) 28 (30.8) 8 (29.6) 264 (47.5) 168 (59.4) Couples with minor child(ren) in the household, n (%) 201 (21.0) 25 (27.5) 10 (37.0) 124 (22.3) 42 (14.8) Couples with adult child(ren) child(ren) in the household, n (%) 50 (5.2) 8 (8.8) 2 (7.4) 29 (5.2) 11 (3.9) Single parents, n (%) 28 (2.9) 5 (5.5) 2 (7.4) 17 (3.1) 4 (1.4) Other multi-person households, n (%) 34 (3.6) 7 (7.7) 0 (0.0) 21 (3.8) 6 (2.1) Impact of COVID-19 pandemic on sports activity (e.g. jogging, athletic cycling, weight training) a Much less than before, n (%) 221 (23.1) 28 (30.8) 10 ( 37.0) 134 (24.1) 49 (17.3) Slightly less than before, n (%) 214 (22.4) 21 (23.1) 6 (22.2) 126 (22.7) 61 (21.6) Remained the same, n (%) 379 (39.6) 32 (35.2) 7 (25.9) 203 (36.5) 137 (48.4) A little more than before, n (%) 114 (11.9) 9 (9.9) 2 (7.4) 72 (12.9) 31 (11.0) Much more than before, n (%) 29 (3.0) 1 ( 1.1) 2 ( 7.4) 21 ( 3.8) 5 ( 1.8) Abbreviations: CO1, 1st Corona questionnaire; FU1, 1st follow-up examination a Covariates with missing data PHQ-st trajectories Based on the criteria described in the Methods section, we selected a four-class model (SP). Predicted trajectories of PHQ-st scores are shown in SP, and individual PHQ-st trajectories by class are presented in Fig. 4 . Compared to the other three groups, class 1 started with elevated pre-pandemic stress levels (Mean = 8.35) and showed only minimal, unsubstantial fluctuations over time, resulting in a largely flat trajectory with consistently high stress (Δ final−initial = 1.58, d = 0.42, small effect). This high-stress class, comprised 92 participants, had the highest proportion of women (65%, 60) and the lowest mean age (48, SD = 11). Class 2 began with low pre-pandemic stress (Mean = 1.78) and exhibited the steepest increase in the first year (Δ 2021−intial = 5.35, d = 1.49, large effect), followed by a decline that did not return to the pre-pandemic values (Δ final−initial = 1.70, d = 0.53, medium effect). This peak-recovery class included 27 participants. Class 3 showed intermediate pre-pandemic stress (Mean = 3.46) and displayed a sharper increase early in the pandemic than in later stages (Δ 2021−intial = 1.80, d = 0.68, medium effect vs Δ final−2021 = 0.19, d = 0.07, trivial effect), resulting in elevated levels at the end of observation period (Δ final−initial = 2.10, d = 0.73, medium effect). Most participants (559, 58%) belonged to this intermediate-stress class. Class 4 presented the lowest pre-pandemic stress levels (Mean = 1.21) and remained low overall, though increased over time (Δ final−initial = 1.19, d = 0.56, medium effect). The rise occurred mainly early in the pandemic (Δ 2021−intial = 0.95, d = 0.53, medium effect vs Δ final−2021 = 0.12, d = 0.07, trivial effect). This low-stress class, was the second biggest (29%, n = 283), had the highest proportion of men (55%, n = 156) and with the highest mean age (57 years, SD = 12). Stress levels rose across all classes, with higher mean scores at the end of the observation period than at beginning. The increase was most pronounced up to early 2021. Table 2 Within-subject changes in PHQ-st scores for each class at three time-points. N T_initial (M, SD) T_final (M, SD) Δ 1 (M, SD) Effect size [95% CI] Interpretation Class 1 84 8.35 (2.76) 9.93 (2.34) 1.58 (3.75) 0.42 [0.20, 0.64] Small Class 2 27 1.78 (2.22) 3.48 ( 2 , 15 ) 1.70 (3.23) 0.53 [0.12, 0.93] Medium Class 3 527 3.46 (1.93) 5.57 (2.44) 2.10 (2.87) 0.73 [0.64, 0.83] Medium Class 4 271 1.21 (1.12) 2.40 (1.92) 1.19 (2.14) 0.56 [0.43, 0.68] Medium N T_initial (M, SD) T_2021 (M, SD) Δ 2 (M, SD) Effect size [95% CI] Interpretation Class 1 71 8.49 (2.87) 9.45 (2.72) 0.96 (3.52) 0.27 [0.03, 0.51] Small Class 2 23 1.83 (2.33) 7.17 (3.45) 5.35 (3.60) 1.49 [0.88, 2.07] Large Class 3 485 3.52 (1.99) 5.32 (2.33) 1.80 (2.67) 0.68 [0.58, 0.77] Medium Class 4 243 1.19 (1.13) 2.14 (1.53) 0.95 (1.78) 0.53 [0.40, 0.66] Medium N T_2021 (M, SD) T_final (M, SD) Δ 3 (M, SD) Effect size [95% CI] Interpretation Class 1 66 9.42 (2.71) 9.91 (2.36) 0.49 (3.45) 0.14 [-0.10, 0.38] Trivial Class 2 23 7.17 (3.45) 3.57 (2.27) -3.61 (3.01) -1.20 [-1,73, -0,65] Large Class 3 457 5.32 (2.35) 5.51 (2.47) 0.19 (2.73) 0.07 [-0.02, 0.16] Trivial Class 4 233 2.18 (1.54) 2.31 (1.78) 0.12 (1.89) 0.07 [-0.06, 0.19] Trivial N is the number of participants with both measurements in the given comparison. T_initial was each participant’s first available measure up to March 31, 2020 since the beginning of the observation; T_2021 was the single measure closest to January 1, 2021 (between October 1, 2020 and March 31, 2021); and T_final was the most recent measure from July 1, 2022 to the end of observation. For each time-point we report the mean ( M ) and standard deviation (SD), the raw change score ( Δ 1 = Mt_final - Mt_initial, Δ2 = T_ 2021 - Mt_initial, Δ3 = Mt_final - Mt_initial ), and the paired-samples Cohen’s d (with its 95% confidence interval) quantifying the within‐person effect size, with respective interpretation ( 53 ). Multinomial logistic regression Compared to the low-stress class (reference), participants with higher stress at baseline, higher depressive and anxiety symptoms, and lower life satisfaction at FU1 (pre-pandemic), and greater loneliness at CO1 (May 2020), had higher odds of belonging to the intermediate- and high-stress trajectories. These associations were consistent in direction and increased in magnitude from the intermediate- to the high-stress classes, suggesting a gradient of psychological vulnerability. The peak-recovery class showed stronger associations with lower agreeableness, being divorced, and reported reductions in sport activities at CO1. However, these findings should be interpreted with caution due to the class’ small size (Table 3 ). Table 3 – Odds ratios and 95% confidence intervals from multivariable multinomial logistic regression comparing membership in the consistently lower scores latent class with other classes associated with covariates from baseline, at FU1 and at CO1 Collection time PHQ-st trajectories’ classes Covariates 1: High (n = 91, 9.5%) 2: Peak-recovery (n = 27, 2.8%) 3: Intermediate (n = 555, 58.1%) 4: Low (n = 283, 29.6%) OR [95% CI] Baseline Age in January 2019 a 0.97 [0.93, 1.01] 0.99 [0.95, 1.05] 0.96 [0.94, 0.98] Ref BIG 5 Agreeableness score 0.74 [0.51, 1.06] 0.51 [0.34, 0.77] 0.89 [0.73, 1.08] Ref Psychosocial stress (PHQ-st) 1.89 [1.64, 2.19] 1.00 [0.79, 1.26] 1.34 [1.21, 1.48] Ref FU1 Depressive symptoms score (PHQ-9) 1.38 [1.16, 1.65] 1.35 [1.07, 1.69] 1.24 [1.10, 1.39] Ref Anxiety symptoms score (GAD-7) 1.68 [1.40, 2.02] 1.20 [0.93, 1.55] 1.33 [1.17, 1.51] Ref Health-related quality of life (SF36) 2.99 [1.59, 5.62] 0.91 [0.42, 1.96] 1.68 [1.19, 2.36] Ref Life satisfaction (L-1) 0.74 [0.59, 0.93] 0.80 [0.59, 1.06] 0.88 [0.75, 1.02] Ref Divorced Yes (Ref = No) 2.44 [0.82, 7.20] 4.59 [1.59, 13.26] 1.47 [0.83, 2.61] Ref BMI ≥ 25kg/m 2 (Ref = < 25kg/m 2 ) 1.98 [0.90, 4.33] 2.02 [0.83, 4.95] 1.79 [1.22, 2.63] Ref CO1 Number of fears 1.25 [1.08, 1.46] 1.10 [0.91, 1.32] 1.10 [1.02, 1.20] Ref Loneliness score 1.76 [1.39, 2.23] 1.42 [1.07, 1.88] 1.19 [1.04, 1.36] Ref Employment Yes (Ref = No) 8.18 [2.60, 25.72] 6.08 [1.32, 38.03] 1.57 [0.94, 2.62] Ref Living with partner and young child(ren) Yes (Ref = No) 3.88 [1.50, 10.00] 3.37 [1.21, 9.44] 1.26 [0.76, 2.10] Ref Decreased sport activities Yes (Ref = No) 2.35 [1.13, 4.88] 2.62 [1.11, 6.17] 1.47 [1.02, 2.11] Ref a calculated from month and year of birth collected at baseline Abbreviations: BMI, body mass index; CO1, 1st Corona questionnaire; FU1, 1st follow-up examination; GAD-7, the Generalised Anxiety Disorder-7 screener; kg, kilogram; n, number of participants; m, meter; PHQ-9, the Patient Health Questionnaire; PHQ-st, the Patient Health Questionnaire - stress module; ref, reference; Scores ranges: - Ascending order: Big-5 agreeableness: 1 to 7; GAD-7: 0 to 21; number of fears at CO1: 0 to 8; L-1: 1 to 11; Loneliness: 3 to 9; PHQ-9: 0 to 27; PHQ-st: 0 to 20; - Descending order: SF36-1 item: 1 (excellent) to 5 (poor) Nagelkerke’s R 2 = 0.5936; AIC = 1314.652; n (total) = 956 Discussion Main Findings and Integration with Previous Literature We identified four distinct psychosocial stress trajectories from pre- to post-pandemic: low, intermediate, high, and peak-recovery, indicating heterogeneity in stress responses. Most participants followed an intermediate-stress trajectory. Stress increased across all classes, mainly early in the pandemic, and remained elevated thereafter, with moderate changes for most classes and small changes for the high-stress class. Higher-stress trajectories were characterized by pre-existing psychological vulnerability, lower life satisfaction, and poorer HRQOL, whereas peak-recovery class showed stronger association with lower agreeableness and contextual stressors, such as divorced, and reductions in sport activities. These trajectories align with prior evidence of heterogeneous mental health responses to the pandemic: Also in other studies, most participants experienced deterioration in the early phases, while a minority followed more unstable patterns ( 30 , 31 , 33 ). Containment measures disrupted work, household routines, and social interactions, while also limiting access to social and emotional support ( 56 – 60 ). Moreover, while not everyone experienced additional adverse events (e.g., bereavement or illness), when such events occurred, their impact was potentially intensified by pandemic-related constraints, such as limited visits, support, and mourning rituals ( 61 – 64 ). Within this context, Bonanno’s framework ( 65 ) offers a useful lens, describing four typical trajectories after adversity: resilience (stable psychological well-being), recovery (impairment followed by improvement), chronic dysfunction (persistent distress), and delayed dysfunction (later deterioration). In this framework, resilience predominates (55–85%), while recovery (15–25%), chronic (5–30%), and delayed (≤ 15%) trajectories are less frequent ( 65 ). Because resilience is most common, population averages can mask subgroup patterns. In our data, trajectories show patters of resilience (low- or intermediate-stress), chronic dysfunction (high-stress) and recovery (peak-recovery), while a delayed reaction is missing. This partial divergence possibly reflects the fact that Bonanno’s framework was developed based on acute events, while the pandemic was a prolonged and multi-domain experience. It had many layers beyond infection itself, amplifying distress for some individuals and influencing post-traumatic adaptation in ways that differ from past crises, positioning the pandemic as a unique challenge for understanding trauma and recovery. Further potential traumatic events, such as the Russian invasion of Ukraine in February 2022 ( 11 , 66 ) and the subsequent energy crisis and inflation ( 9 , 13 ), may have hampered a recovery in stress levels. Although we did not observed an acute reaction, stress related to these events cannot be excluded, as our questionnaires did not assess war-related stressors. Other instruments, including items more sensitive to war experiences, or different aspects of mental health, such as anxiety or depression symptoms, might indicate changes not captured here. Factors Associated with Class Membership Higher depressive symptoms-scores before the pandemic and their association with higher stress levels might be related to increased susceptibility to stressful events, as described by Liu and Alloy in a systematic review ( 67 ). Another aspect that may exacerbate the stress response is the lack of social interactions and support. Kang et al. found that individuals with high loneliness are more susceptible to everyday stressors and tend to experience prolonged emotional reactions ( 68 ). During the pandemic, physical distancing likely disrupted social interactions and potentially amplified stress responses ( 63 , 69 ). Consistent with this, all classes reported higher loneliness than the reference class early in the pandemic. Studies have suggested that physical activity plays a role in relieving stress ( 70 – 73 ). Thus, the decrease in sports activities may have affected one coping mechanism. Additionally, sports provide important social interactions for some people ( 74 , 75 ). Beyond shared associations, each trajectory class exhibited distinct demographic and psychological profiles, indicating different groups of stress vulnerability and adaptation. Except for class 2, where stress peaked during the pandemic’s first year, the direct impact of the pandemic was less pronounced in other classes. Class 2 represented only 3% of the sample, limiting estimate precision; yet may represent the most stress-reactive subgroup, despite partial return to initial values. Overall, pre-existing stress and health profiles seem to play a central role in shaping trajectory membership. Individuals in the high- and intermediate-stress classes (vs. reference class) reported worse health and higher anxiety before the pandemic, and more fears at its early stage. Similar findings have been reported ( 76 , 77 ), showing that pre-existing worries, anxiety, and depressive symptoms predicted worse mental health during the pandemic, whereas fewer prior issues were associated to greater resilience ( 63 , 78 – 81 ). Pre-pandemic BMI was positively associated with later stress, particularly among intermediate-stress class. As a risk factor for chronic conditions, stigma, and low self-esteem, higher BMI may increase psychological distress ( 82 ) and perceived health risks during the pandemic. Among all participants, the ones in the high-stress class reported the lowest pre-pandemic life satisfaction and HRQOL, consistent with a chronic stress pattern ( 83 ). Additional factors associated with high-stress class included living with a partner and young child(ren), and being employed. Stress among parents of young children during the pandemic is well documented, as many parents had to adjust to new work arrangements ( 57 ), while managing increased childcare responsibilities due to childcare facilities closures. This group was predominantly women, who already carried higher stress-loads, with the pandemic adding further challenges. Previous studies identified female sex and having young children as risk factors for elevated stress ( 84 – 87 ). Pre-pandemic data indicated that this trajectory represents chronically high stress rather than a pandemic-related response. Living with a partner and young child(ren), and being employed were positively associated with the peak-recovery class (3%), though the small class size limits the stability and generalizability of the estimates. Membership in this class was negatively associated with agreeableness, a personality trait linked to trust, cooperation, and empathy. Low agreeableness, characterized by scepticism, individualism, and less adaptability ( 88 ), may exacerbate stress vulnerability when routines, relationships, or coping mechanisms are disrupted, as during the pandemic. Such individuals report more stressors, and greater difficulty managing relationships and stress ( 89 ). This class was also associated with divorce, a potentially traumatic event with long-lasting psychological effects on multiple life dimensions ( 90 ). Additionally, co-parenting with a former partner during the pandemic may have added further stress ( 91 ). Taken together, stress response is shaped by a complex interplay of individual characteristics and contextual factors. Even under the same environmental conditions, responses may differ depending on sociodemographic background, personality traits, life experiences, prior trauma, and coping mechanisms ( 84 , 92 ). Strengths and limitations To our knowledge, no other studies in Germany have investigated heterogeneous stress trajectories both before the pandemic and over a comparable duration. Most previous research defined groups by sociodemographic characteristics before examining mental health outcomes. A major strength of this study that we first identified stress trajectories and subsequently analysed class characteristics, enabling identification of smaller subgroups that may be obscured when looking at the population trajectory. Despite these strengths, several limitations should be acknowledged. Vulnerable groups, such as migrants, who tend to have a higher prevalence of stress and mental health problems ( 93 , 94 ), are underrepresented in the NAKO study. Similarly, other known vulnerability factors in Germany, such as younger age and financial difficulties ( 84 ), were less frequent in our predominantly older and financially comfortable sample, potentially underestimating the prevalence and patterns of more adverse stress trajectories typical of vulnerable groups. Selection bias may have been introduced through the inclusion criteria, requiring participants to complete at least four assessments, potentially excluding individuals with different stress trajectories. For instance, severely affected participants may be underrepresented due to dropout. While not a limitation per se , the number and form of latent classes identified through LCGA may vary depending on sample characteristics and measurement intervals. Thus, different trajectory patterns may have emerged under alternative study conditions. Conclusions The identification of four stress trajectories reveals heterogeneity in population-level responses to the COVID-19 pandemic. Most participants exhibited stress levels that were already elevated before the pandemic, with small to moderate increases occurring mainly early on. None of the classes returned to initial levels, suggesting lasting psychological impact of varying degrees. While high- and intermediate-stress trajectories reflected pre-existing psychological vulnerability and reduced well-being, the peak-recovery class appeared driven by lower agreeableness and contextual stressors, including family and routine disruptions. These findings suggest that public health efforts to reduce chronic stress and support for individuals with psychosocial, health, or socio-demographic disadvantages, should be sustained beyond periods of crisis. Clinicians should identify individual vulnerabilities and stressors when assessing and supporting patients. Declarations Ethics Approval Ethical approval for the NAKO study was obtained from all local ethics committees of the 18 study centres. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975 (in the current, revised version), with the exception that the study was not registered before the recruitment started. Informed Consent All participants provided written informed consent. Competing interests The authors declare none. Financial support This analysis was supported by internal institutional funding. The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C, 01ER1511D, 01ER1801A/B/C/D and 01ER2301A/B/C], federal states of Germany and the Helmholtz Association, the participating universities and the institutes of the Leibniz Association. Author Contribution Conceptualization: D.C., A.F., R.M.; Methodology: D.C. and J.H.; Formal analysis: D.C. and J.H.; Data curation: O.P. and A.K.; Funding acquisition: R.M.; Project administration: O.P. and A.K.; Software: O.P.; Supervision: A.F. and R.M.; Writing - original draft preparation: D.C.; Writing - review and editing: All authors; All authors read and approved the final manuscript. Acknowledgement This project was conducted with data (Application No. NAKO-909) from the German National Cohort (www.nako.de). We thank all participants and staff of this research initiative. Data Availability The NAKO data that support the findings of this study are available from TransferHub (https://transfer.nako.de/transfer/index), but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. The data are, however, available upon request and with the permission of NAKO e.V. 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J Fam Nurs 30(3):199–217 Gencer H, Brunnett R, Staiger T, Tezcan-Güntekin H, Pöge K (2024) Caring is not always sharing: A scoping review exploring how COVID-19 containment measures have impacted unpaid care work and mental health among women and men in Europe. PLoS ONE 19(8):e0308381 John OP, Srivastava S (1999) The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research, 2nd edn. Guilford Press, New York, NY, US, pp 102–138 Leger KA, Charles ST, Turiano NA, Almeida DM (2016) Personality and stressor-related affect. J Personal Soc Psychol 111(6):917–928 Sbarra DA, Law RW, Portley RM (2011) Divorce and Death: A Meta-Analysis and Research Agenda for Clinical, Social, and Health Psychology. Perspect Psychol science: J Association Psychol Sci 6(5):454–474 Goldberg AE, Allen KR, Smith JZ (2021) Divorced and separated parents during the COVID-19 pandemic. Fam Process 60(3):866–887 Kilby CJ, Sherman KA, Wuthrich V (2018) Towards understanding interindividual differences in stressor appraisals: A systematic review. Pers Indiv Differ 135:92–100 Hajak V, Sardana S, Verdeli H, Grimm S (2021) A Systematic Review of Factors Affecting Mental Health and Well-Being of Asylum Seekers and Refugees in Germany. Front Psychiatry. ;12 Cindik-Herbrüggen DED (2023) Investigating Changes in the Psychological Health Before and During the COVID Pandemic: A Comparison Study among Turkish Immigrants living in Germany. Eur Psychiatry 66:S213–S Additional Declarations No competing interests reported. Supplementary Files SPSPPE.docx Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Social Psychiatry and Psychiatric Epidemiology → Version 1 posted Editorial decision: Revision requested 21 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 24 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 23 Dec, 2025 Reviewers invited by journal 22 Dec, 2025 Editor assigned by journal 12 Dec, 2025 Submission checks completed at journal 25 Nov, 2025 First submitted to journal 24 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8196631","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":564992537,"identity":"570ad8a4-89a6-4529-9511-8bad2f1abaa4","order_by":0,"name":"Daniela Costa","email":"","orcid":"","institution":"Martin-Luther-Universität Halle-Wittenberg - IMEBI","correspondingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"","lastName":"Costa","suffix":""},{"id":564992538,"identity":"20cef891-5880-40ed-8c0c-70554cf1e596","order_by":1,"name":"Johannes Horn","email":"","orcid":"","institution":"Martin-Luther-Universität Halle-Wittenberg - IMEBI","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Horn","suffix":""},{"id":564992539,"identity":"817c01a2-b2ea-4b79-943a-ca4deb7bdd53","order_by":2,"name":"Janka Massag","email":"","orcid":"","institution":"Martin-Luther-Universität Halle-Wittenberg - IMEBI","correspondingAuthor":false,"prefix":"","firstName":"Janka","middleName":"","lastName":"Massag","suffix":""},{"id":564992540,"identity":"fede7d02-5822-4631-ab41-2c30e7890067","order_by":3,"name":"Oliver Purschke","email":"","orcid":"","institution":"Martin-Luther-Universität Halle-Wittenberg - IMEBI","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Purschke","suffix":""},{"id":564992541,"identity":"cc870787-9dd6-4be3-8136-b7634b608e8a","order_by":4,"name":"Alexander Kluttig","email":"","orcid":"","institution":"Martin-Luther-Universität Halle-Wittenberg - 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01:18:17","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":220590,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8196631/v1/7c32c5cdc8389febcb056c07.html"},{"id":99193049,"identity":"b5f0dde4-eddb-4f20-8440-348079b56db0","added_by":"auto","created_at":"2025-12-30 01:18:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107572,"visible":true,"origin":"","legend":"\u003cp\u003eEnglish adaptation of the German version of the Patient Health Questionnaire - stress module (PHQ-st)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8196631/v1/bdabfe0e2def451db50357ae.png"},{"id":99317846,"identity":"5367f4ea-ba2c-4a90-839c-932e57723dd7","added_by":"auto","created_at":"2025-12-31 16:30:49","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":120500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNAKO study timeline and collection of the data used in the current study\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8196631/v1/c4890cde223e6c1d7971aca5.jpeg"},{"id":99317316,"identity":"55b04304-5047-4b00-be87-36657ebb0464","added_by":"auto","created_at":"2025-12-31 16:30:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlowchart of participants\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8196631/v1/ed7d0be2b71174d4a18147c0.png"},{"id":99193061,"identity":"7c904964-be6f-47cd-b88e-4f7f250e231b","added_by":"auto","created_at":"2025-12-30 01:18:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":198356,"visible":true,"origin":"","legend":"\u003cp\u003eIndividual trajectories of PHQ-st by class with LOESS smoothed trend line by latent growth class trajectory\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8196631/v1/4cd67cdb9269acac61858182.png"},{"id":107928294,"identity":"4c7e284c-7a75-468c-a573-5feb3b68f313","added_by":"auto","created_at":"2026-04-27 16:09:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1140481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8196631/v1/02de33d7-cc80-4358-a3a5-38200bc06f28.pdf"},{"id":99319405,"identity":"84d50716-557d-48b7-8236-81c50080b32e","added_by":"auto","created_at":"2025-12-31 16:37:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":169372,"visible":true,"origin":"","legend":"","description":"","filename":"SPSPPE.docx","url":"https://assets-eu.researchsquare.com/files/rs-8196631/v1/580ee075d1d0d7313169c836.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePsychosocial stress from pre- to post-pandemic times: a latent class growth analysis using data from a German cohort\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eMental disorders have a multifactorial origin, emerging from a complex interplay of biological, psychological, and social factors (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), such as early life trauma (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), social isolation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), academic and work pressure (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), economic instability (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), socio-political dynamics (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and major global events (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Psychosocial stress is a common pathway linking these factors and is relevant to the development and exacerbation of mental disorders (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e); chronic stress can also impair physical health (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Thus, stress is an important target for prevention.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic and subsequent containment measures profoundly disrupted daily life and increased uncertainty (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Early studies, mainly cross-sectional and based on convenience samples (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), suggested widespread elevated distress symptoms (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, many focused on considered high-risk groups, such as individuals with comorbidities or pre-existing health conditions (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), and pandemic-related essential workers (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), rather than using population-based samples.\u003c/p\u003e \u003cp\u003eBy late 2020, systematic reviews began reporting heterogeneity in mental health responses, questioning the hypothesis of a general deterioration, and highlighting the lack of and need for pre-pandemic and longitudinal assessments (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Consequently, longitudinal studies including pre-pandemic data reported an average deterioration in mental health in early 2020, followed by a general attenuation of symptoms. However, the extent of change varied across populations (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), whereby younger adults, women, individuals with lower socioeconomic status, and those with pre-existing mental health conditions experienced greater distress (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNonetheless, longitudinal studies tracking mental health trajectories starting before the pandemic remain scarce, particularly those focusing on stress in the general population (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). One of the few studies that investigated trajectories of stress during the pandemic was conducted in Germany (April 2020 \u0026ndash; January 2021), using the DASS-21 questionnaire and a convenient sample composed primarily by young women. This research identified four stress trajectories during the early months of the pandemic. In that study, 91% of participants showed stable or slightly rising stress levels, while 9% exhibited fluctuating patterns (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn sum, most existing studies focused on early stages of the pandemic, with little research on its later phases.\u003c/p\u003e \u003cp\u003eWith this background, this study aims (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to identify psychosocial stress trajectories among residents from an urban population and its surrounding rural areas in Germany, covering the period from before the COVID-19 pandemic onset until its late stage; and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) examine how trajectory membership is associated with participants\u0026rsquo; characteristics at three specific data-collection points: baseline (three to five years before pandemic), shortly before the pandemic, and early in the pandemic.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis study is part of the German National Cohort (NAKO Gesundheitsstudie), a population-based study conducted at 18 study centres across Germany. Study design details have been published elsewhere (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In brief, between 2014 and 2019, approximately 205,000 individuals were recruited and examined. The first follow-up examination (FU1) was conducted about five years after enrollment (2019\u0026ndash;2024). Data was collected via medical exams, face-to-face interviews, and touch-screen questionnaires. During the COVID-19 pandemic, two additional questionnaires were administered in May 2020 and September 2022, distributed by e-mail or regular mail.\u003c/p\u003e \u003cp\u003eIn addition to the general study examinations, supplemental modules were implemented at selected NAKO centres as Level-3 projects. At the Halle (Saale) centre, we initiated an intensified six-month follow-up in 2019 for participants who agreed to receive additional online surveys, on physical activity, stress, and sleep.\u003c/p\u003e \u003cp\u003eAt baseline, 10,139 individuals were examined in Halle, with 6,875 participating in FU1. For the current study, we included individuals who completed FU1 between April 1, 2019 and March 31, 2020, i.e. before the first COVID-19 containment measures, ensuring a recent pre-pandemic assessment. We aimed to include six-monthly questionnaire data for each participant until their last available assessment, with data collection until early 2024. Participants were included if they had completed at least four questionnaires.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome variable\u003c/h2\u003e \u003cp\u003eWe assessed psychosocial stress using the German version of the Patient Health Questionnaire \u0026ndash; stress module (PHQ-st) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). PHQ-st inquires about the previous four weeks and contains ten items. Items are rated on a scale, where 0 means \u0026lsquo;not bothered\u0026rsquo;, 1 \u0026lsquo;bothered a little\u0026rsquo; and 2 \u0026lsquo;bothered a lot\u0026rsquo;, leading to a score ranging from 0 to 20, where a higher score indicates greater stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This total score was used as a continuous variable in all analyses. PHQ-st has good psychometric properties, such as reliability (ω\u0026thinsp;=\u0026thinsp;0.776) and validity (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eTo characterise individuals with different stress trajectories, we examined baseline, FU1, and the first corona questionnaire (CO1) covariates, identified in previous literature as potential predictors of stress and related mental health outcomes. Time-invariant variables were collected at baseline: sex, month and year of birth, personality traits (Big Five) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), and early childhood trauma (Childhood Trauma Screener) (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePotentially time-varying covariates included sociodemographic characteristics (marital and relationship status, education (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)), international social-economic index of occupation status (ISEI) (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), anxiety and depressive symptoms (assessed using GAD-7 and PHQ-9, respectively) (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), health-related quality of life (HRQOL) (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), prior mental disorder diagnoses, body mass index (BMI) (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), and life satisfaction (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), sleep quality assessed by one item from the Pittsburgh Sleep Quality Index (PSQI) from baseline and/or FU1 (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In addition, we used current employment and housing situation in the beginning of the pandemic, as well as newly introduced items on behavioural changes (physical activity), household financial strain, loneliness (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), and number of fears from CO1.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the study timeline, and more detailed item descriptions are provided in Tables\u0026rsquo; footnotes and supplementary material (SP).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe performed a latent class growth analysis (LCGA) to identify heterogeneous trajectories of psychosocial stress from April 2019 to the beginning of 2024. Analyses were conducted in R 4.4.0. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) using the lcmm R-package (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Time was treated as a continuous variable, with a common time-zero for all participants, and individual time points based on questionnaire completion date. To account for individual variability in trajectories, random effects were specified for linear, quadratic, and cubic terms of time. The subject identifier was included as a grouping variable to account for the repeated measures. The model including random effects up to the cubic term showed the best performance based on fit indices (log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC)) and was thus selected for further analyses. A beta link function was used to accommodate the non-Gaussian distribution of the outcome. PHQ-st scores exhibited zero-inflation and left-skewed distribution typical of stress measures. We compared available link functions (linear, beta, splines) in the lcmm R-package, with the beta link function providing best model fit (AIC improvement of 1363 and 172 compared to linear link and splines, respectively). The models with beta and spline links showed a 98.6% concordance.\u003c/p\u003e \u003cp\u003eFollowing the approach described by Schoot et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), and expecting the best solution to include four or five classes, we progressively fitted models with one to seven classes. For multi-class models, starting values were generated from the maximum-likelihood estimates of the single-class model. Model fit statistics for 1\u0026ndash;7 class solutions are presented in SP. The four-class solution demonstrated good classification quality (entropy\u0026thinsp;=\u0026thinsp;0.63), indicating adequate separation between classes. Although statistical measures (fit indices and entropy values) played a role in our selection process, the ultimate decision prioritised interpretability, conceptual relevance, and practical meaning in elucidating heterogeneous trajectories of psychosocial stress over time. For each class, a spaghetti plot was created using the ggplot2 R-package (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), to visualize individual trajectories over time, with a LOESS-smoothed trend line capturing the overall trend across individual trajectories. While assessments were continuous, for the interpretation of the visual effects, we used mean differences and Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e with 95% confidence intervals across the three periods (pre-pandemic, early 2021, and late pandemic) categorized as trivial (|d| \u0026lt; 0.20), small (0.20\u0026ndash;0.49), medium (0.50\u0026ndash;0.79), and large (\u0026ge;\u0026thinsp;0.80) (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFollowing the approach described by Schoot et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), no covariates were included in the LCGA model. Instead, the covariates of the class membership were assessed in a subsequent multinomial logistic regression. For the covariates, we performed imputation using the mice R-package (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Predictive mean matching was used for numeric and ordinal variables. For nominal variables, we performed multiple imputations with ten datasets and computed logistic regressions. Imputation was performed using a dataset containing data from baseline, FU1, and CO1 (6,823 participants).\u003c/p\u003e \u003cp\u003eInitial covariate selection for the multinomial logistic regression was guided by literature and an examination of a correlation matrix. Additionally, we tested models including variables not previously assessed, but considered relevant in the context of the pandemic. To address small subgroup sizes and improve interpretability, categorical variables were dichotomised where appropriate. Three models were run: The first included only baseline covariates; the second added FU1 time-varying measures (the most recent pre-pandemic data) to the baseline time-invariant variables; and the third additionally incorporated the most recent time-varying data up to early in the pandemic (May 2020). In models two and three only the most recent data were used, ensuring that repeated measurements of the same variable were not simultaneously included in a model. To restrict predictors to the most essential, models were rerun applying stepwise selection with the MASS R-package (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Variance inflation factor values indicated no problematic multicollinearity. Nagelkerke\u0026rsquo;s R-squared was used as an indicator of the explanatory power of the models.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u0026rsquo; characteristics\u003c/h2\u003e \u003cp\u003eWe included 966 participants in the LCGA. Of these, 42% (403) completed ten surveys containing the outcome variable between April 2019 and January 2024, while the rest completed between four and nine surveys (SP). The final analyses included 961 participants, after excluding five participants due to missing Level-3 identification in the baseline dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mean age of participants in January 2019 was 52.4 years, and 53% (512) of the sample were women. Most participants were married at FU1 (570, 59%), and employed at CO1 (689, 72%). In FU1, 144 participants (15%) reported a lifetime diagnosis of depression, and 89 (9%) reported a lifetime diagnosis of anxiety and/or panic attacks (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea \u0026ndash; Mean and Standard Deviation of Participant Characteristics at Baseline, FU1, and CO1, Overall and by PHQ-Stress Trajectory Class (Numeric Covariates)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCollection time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003ePHQ-st trajectories\u0026rsquo; classes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll sample, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: High, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2: Peak-recovery, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3: Intermediate, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4: Low, n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e961 (100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (9.6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (2.8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e559 (58.2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e283 (29.4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"12\" rowspan=\"13\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge in January 2019\u003csup\u003ea\u003c/sup\u003e, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.40 (12.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.13 (11.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.22 (10.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.14 (12.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.58 (11.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eISEI, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.08 (15.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.96 (14.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.85 (14.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.44 (14.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.24 (15.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIG 5 Extraversion score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.71 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.47 (1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.81 (1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.71 (1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.78 (1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIG 5 Neuroticism score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50 (1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.71 (1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.55 (1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.58 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.94 (1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIG 5 Consciousness score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.87 (0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.78 (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.09 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.80 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.00 (0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIG 5 Agreeableness score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.64 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.25 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.17 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.61 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.86 (0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIG 5 Openness score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.69 (1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.62 (1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.00 (1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.66 (1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.73 (1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChildhood trauma (CTS score), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.76 (2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.75 (3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.37 (3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.69 (2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.50 (1.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychosocial stress (PHQ-st score), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.49 (2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.96 (3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.44 (2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.68 (2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.75 (1.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth-related quality of life (SF36\u0026ndash;1 item), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.09 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.78 (0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.70 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.49 (0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLife satisfaction (L-1), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.87 (1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.51 (2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.11 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.78 (1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.48 (1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety symptoms (GAD-7 score), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.04 (3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.90 (4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.96 (2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.14 (2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.26 (1.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepressive symptoms (PHQ-9 score), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46 (3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.59 (5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.85 (3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.60 (2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.91 (2.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eFU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth-related quality of life (SF36\u0026ndash;1 item), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.69 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.44 (0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.73 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.48 (0.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLife satisfaction (L-1), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.03 (1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.57 (2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.93 (2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.91 (1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.74 (1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety symptoms (GAD-7 score), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.91 (2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.43 (4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.37 (2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.04 (2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24 (1.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepressive symptoms (PHQ-9 score), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.51 (3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.88 (4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.19 (2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.73 (2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.68 (1.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation years (ISCED97), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.25 (2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.04 (1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.56 (2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.31 (2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.29 (2.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoneliness (3-item scale), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.76 (1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.79 (1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.04 (1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.77 (1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.36 (1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of fears \u003csup\u003eb\u003c/sup\u003e, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55 (2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.97 (2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.41 (2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.66 (2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.88 (2.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: CO1, 1st Corona questionnaire; FU1, 1st follow-up examination; ISEI, international social-economic index of occupation status;\u003c/p\u003e \u003cp\u003eScores ranges:\u003c/p\u003e \u003cp\u003e- Ascending order: Big-5 traits: 1 to 7; ISEI: 10 to 89; GAD-7: 0 to 21; number of fears: 0 to 8; L-1: 1 to 11; Loneliness: 3 to 9; PHQ-9: 0 to 27; PHQ-st: 0 to 20; CTS: 5 to 21;\u003c/p\u003e \u003cp\u003e- Descending order: SF36-1 item: 1 (excellent) to 5 (poor)\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e calculated from month and year of birth collected at baseline\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Number of fears among: road traffic accident, natural disaster, corona-virus infection, cancer, stroke, heart attack, diabetes mellitus, hereditary disease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb - Participant Characteristics at Baseline, FU1, and CO1, Overall and by PHQ-Stress Trajectory Class (Frequencies and Percentages for Categorical Variables)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCollection time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll sample, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003ePHQ-st trajectories\u0026rsquo; classes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: High\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2: Peak-recovery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3: Intermediate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4: Low\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpected number of participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e961 (100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (9.6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (2.8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e559 (58.2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e283 (29.4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 ( 51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e311 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e127 (44.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e449 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 ( 48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e248 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e156 (55.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eFU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e570 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (52.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 ( 55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e317 (56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e190 (67.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried, living apart from spouse, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e147 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41 (14.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 ( 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 ( 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22 (7.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eEver diagnosed with Depression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e817 (85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e478 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e259 (91.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 ( 8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eEver diagnosed with Anxiety/Panic attack\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e872 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e504 (90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e267 (94.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 ( 9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16 ( 5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunder-/normal-weight (\u0026lt;\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e417 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e231 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e138 (48.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoverweight/obesity (\u0026ge;\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e544 (56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e328 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e145 (51.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"21\" rowspan=\"22\"\u003e \u003cp\u003eCO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eEmployment status\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e689 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e416 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e173 (61.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e109 (38.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-working person, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eHousehold Financial Situation\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEasy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e562 (58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 ( 63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e311 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e205 (72.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReasonably easy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 ( 25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e188 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65 (23.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith some difficulties, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith great difficulty, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eLiving situation\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeople living alone, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52 (18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCouples without children in the household, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e468 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e264 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e168 (59.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCouples with minor child(ren) in the household, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42 (14.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCouples with adult child(ren) child(ren) in the household, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11 (3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle parents, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther multi-person households, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eImpact of COVID-19 pandemic on sports activity (e.g. jogging, athletic cycling, weight training) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuch less than before, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 ( 37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49 (17.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlightly less than before, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61 (21.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemained the same, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e203 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e137 (48.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA little more than before, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31 (11.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuch more than before, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 ( 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 ( 7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 ( 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 ( 1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: CO1, 1st Corona questionnaire; FU1, 1st follow-up examination\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Covariates with missing data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePHQ-st trajectories\u003c/h3\u003e\n\u003cp\u003eBased on the criteria described in the Methods section, we selected a four-class model (SP). Predicted trajectories of PHQ-st scores are shown in SP, and individual PHQ-st trajectories by class are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCompared to the other three groups, class 1 started with elevated pre-pandemic stress levels (Mean\u0026thinsp;=\u0026thinsp;8.35) and showed only minimal, unsubstantial fluctuations over time, resulting in a largely flat trajectory with consistently high stress (Δ\u003csub\u003efinal\u0026minus;initial\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.58, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42, small effect). This high-stress class, comprised 92 participants, had the highest proportion of women (65%, 60) and the lowest mean age (48, SD\u0026thinsp;=\u0026thinsp;11). Class 2 began with low pre-pandemic stress (Mean\u0026thinsp;=\u0026thinsp;1.78) and exhibited the steepest increase in the first year (Δ\u003csub\u003e2021\u0026minus;intial\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.35, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.49, large effect), followed by a decline that did not return to the pre-pandemic values (Δ\u003csub\u003efinal\u0026minus;initial\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.70, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53, medium effect). This peak-recovery class included 27 participants. Class 3 showed intermediate pre-pandemic stress (Mean\u0026thinsp;=\u0026thinsp;3.46) and displayed a sharper increase early in the pandemic than in later stages (Δ\u003csub\u003e2021\u0026minus;intial\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.80, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.68, medium effect \u003cem\u003evs\u003c/em\u003e Δ\u003csub\u003efinal\u0026minus;2021\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07, trivial effect), resulting in elevated levels at the end of observation period (Δ\u003csub\u003efinal\u0026minus;initial\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.10, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.73, medium effect). Most participants (559, 58%) belonged to this intermediate-stress class. Class 4 presented the lowest pre-pandemic stress levels (Mean\u0026thinsp;=\u0026thinsp;1.21) and remained low overall, though increased over time (Δ\u003csub\u003efinal\u0026minus;initial\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.19, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56, medium effect). The rise occurred mainly early in the pandemic (Δ\u003csub\u003e2021\u0026minus;intial\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.95, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53, medium effect \u003cem\u003evs\u003c/em\u003e Δ\u003csub\u003efinal\u0026minus;2021\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07, trivial effect). This low-stress class, was the second biggest (29%, n\u0026thinsp;=\u0026thinsp;283), had the highest proportion of men (55%, n\u0026thinsp;=\u0026thinsp;156) and with the highest mean age (57 years, SD\u0026thinsp;=\u0026thinsp;12).\u003c/p\u003e \u003cp\u003eStress levels rose across all classes, with higher mean scores at the end of the observation period than at beginning. The increase was most pronounced up to early 2021.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWithin-subject changes in PHQ-st scores for each class at three time-points.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT_initial (M, SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT_final (M, SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔ\u003csub\u003e1\u003c/sub\u003e (M, SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect size [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.35 (2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.93 (2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58 (3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42 [0.20, 0.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78 (2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.48 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.70 (3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53 [0.12, 0.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46 (1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.57 (2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.10 (2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73 [0.64, 0.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40 (1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19 (2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56 [0.43, 0.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eT_initial (M, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eT_2021 (M, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eΔ\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(M, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEffect size [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eInterpretation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.49 (2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.45 (2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 (3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27 [0.03, 0.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83 (2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.17 (3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.35 (3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.49 [0.88, 2.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.52 (1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.32 (2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80 (2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68 [0.58, 0.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.14 (1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53 [0.40, 0.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eT_2021 (M, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eT_final (M, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eΔ\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(M, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eEffect size [95% CI]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eInterpretation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.42 (2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.91 (2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49 (3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14 [-0.10, 0.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrivial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.17 (3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.57 (2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.61 (3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.20 [-1,73, -0,65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.32 (2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.51 (2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19 (2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07 [-0.02, 0.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrivial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18 (1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31 (1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12 (1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07 [-0.06, 0.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrivial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e is the number of participants with both measurements in the given comparison.\u003c/p\u003e \u003cp\u003e\u003cb\u003eT_initial\u003c/b\u003e was each participant\u0026rsquo;s first available measure up to March 31, 2020 since the beginning of the observation;\u003c/p\u003e \u003cp\u003e\u003cb\u003eT_2021\u003c/b\u003e was the single measure closest to January 1, 2021 (between October 1, 2020 and March 31, 2021);\u003c/p\u003e \u003cp\u003eand \u003cb\u003eT_final\u003c/b\u003e was the most recent measure from July 1, 2022 to the end of observation.\u003c/p\u003e \u003cp\u003eFor each time-point we report the mean (\u003cem\u003eM\u003c/em\u003e) and standard deviation (SD), the raw change score (\u003cem\u003eΔ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eMt_final\u003c/em\u003e - \u003cem\u003eMt_initial, Δ2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;T_\u003cem\u003e2021\u003c/em\u003e - \u003cem\u003eMt_initial, Δ3\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eMt_final\u003c/em\u003e - \u003cem\u003eMt_initial\u003c/em\u003e), and the paired-samples Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e (with its 95% confidence interval) quantifying the within‐person effect size, with respective interpretation (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultinomial logistic regression\u003c/h2\u003e \u003cp\u003eCompared to the low-stress class (reference), participants with higher stress at baseline, higher depressive and anxiety symptoms, and lower life satisfaction at FU1 (pre-pandemic), and greater loneliness at CO1 (May 2020), had higher odds of belonging to the intermediate- and high-stress trajectories. These associations were consistent in direction and increased in magnitude from the intermediate- to the high-stress classes, suggesting a gradient of psychological vulnerability. The peak-recovery class showed stronger associations with lower agreeableness, being divorced, and reported reductions in sport activities at CO1. However, these findings should be interpreted with caution due to the class\u0026rsquo; small size (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Odds ratios and 95% confidence intervals from \u003cb\u003emultivariable multinomial logistic regression\u003c/b\u003e comparing membership in the consistently lower scores latent class with other classes associated with covariates from baseline, at FU1 and at CO1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCollection time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePHQ-st trajectories\u0026rsquo; classes\u003c/p\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1: High\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;91, 9.5%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2: Peak-recovery\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;27, 2.8%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3: Intermediate (n\u0026thinsp;=\u0026thinsp;555, 58.1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4: Low (n\u0026thinsp;=\u0026thinsp;283, 29.6%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eOR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge in January 2019\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 [0.93, 1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 [0.95, 1.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 [0.94, 0.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIG 5 Agreeableness score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 [0.51, 1.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51 [0.34, 0.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89 [0.73, 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychosocial stress (PHQ-st)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89 [1.64, 2.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 [0.79, 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34 [1.21, 1.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eFU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepressive symptoms score (PHQ-9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38 [1.16, 1.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35 [1.07, 1.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24 [1.10, 1.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety symptoms score (GAD-7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68 [1.40, 2.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 [0.93, 1.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33 [1.17, 1.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth-related quality of life (SF36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.99 [1.59, 5.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 [0.42, 1.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68 [1.19, 2.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLife satisfaction (L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 [0.59, 0.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80 [0.59, 1.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88 [0.75, 1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (Ref\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44 [0.82, 7.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.59 [1.59, 13.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47 [0.83, 2.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e (Ref\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98 [0.90, 4.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.02 [0.83, 4.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79 [1.22, 2.63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eCO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of fears\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 [1.08, 1.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 [0.91, 1.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10 [1.02, 1.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoneliness score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.76 [1.39, 2.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42 [1.07, 1.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19 [1.04, 1.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (Ref\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.18 [2.60, 25.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.08 [1.32, 38.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57 [0.94, 2.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eLiving with partner and young child(ren)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (Ref\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.88 [1.50, 10.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.37 [1.21, 9.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26 [0.76, 2.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eDecreased sport activities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (Ref\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35 [1.13, 4.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.62 [1.11, 6.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47 [1.02, 2.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e calculated from month and year of birth collected at baseline\u003c/p\u003e \u003cp\u003eAbbreviations: BMI, body mass index; CO1, 1st Corona questionnaire; FU1, 1st follow-up examination; GAD-7, the Generalised Anxiety Disorder-7 screener; kg, kilogram; n, number of participants; m, meter; PHQ-9, the Patient Health Questionnaire; PHQ-st, the Patient Health Questionnaire - stress module; ref, reference;\u003c/p\u003e \u003cp\u003eScores ranges:\u003c/p\u003e \u003cp\u003e- Ascending order: Big-5 agreeableness: 1 to 7; GAD-7: 0 to 21; number of fears at CO1: 0 to 8; L-1: 1 to 11; Loneliness: 3 to 9; PHQ-9: 0 to 27; PHQ-st: 0 to 20;\u003c/p\u003e \u003cp\u003e- Descending order: SF36-1 item: 1 (excellent) to 5 (poor)\u003c/p\u003e \u003cp\u003eNagelkerke\u0026rsquo;s R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.5936; AIC\u0026thinsp;=\u0026thinsp;1314.652; n (total)\u0026thinsp;=\u0026thinsp;956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMain Findings and Integration with Previous Literature\u003c/h2\u003e \u003cp\u003eWe identified four distinct psychosocial stress trajectories from pre- to post-pandemic: low, intermediate, high, and peak-recovery, indicating heterogeneity in stress responses. Most participants followed an intermediate-stress trajectory. Stress increased across all classes, mainly early in the pandemic, and remained elevated thereafter, with moderate changes for most classes and small changes for the high-stress class. Higher-stress trajectories were characterized by pre-existing psychological vulnerability, lower life satisfaction, and poorer HRQOL, whereas peak-recovery class showed stronger association with lower agreeableness and contextual stressors, such as divorced, and reductions in sport activities.\u003c/p\u003e \u003cp\u003eThese trajectories align with prior evidence of heterogeneous mental health responses to the pandemic: Also in other studies, most participants experienced deterioration in the early phases, while a minority followed more unstable patterns (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eContainment measures disrupted work, household routines, and social interactions, while also limiting access to social and emotional support (\u003cspan additionalcitationids=\"CR57 CR58 CR59\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Moreover, while not everyone experienced additional adverse events (e.g., bereavement or illness), when such events occurred, their impact was potentially intensified by pandemic-related constraints, such as limited visits, support, and mourning rituals (\u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this context, Bonanno\u0026rsquo;s framework (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e) offers a useful lens, describing four typical trajectories after adversity: resilience (stable psychological well-being), recovery (impairment followed by improvement), chronic dysfunction (persistent distress), and delayed dysfunction (later deterioration). In this framework, resilience predominates (55\u0026ndash;85%), while recovery (15\u0026ndash;25%), chronic (5\u0026ndash;30%), and delayed (\u0026le;\u0026thinsp;15%) trajectories are less frequent (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Because resilience is most common, population averages can mask subgroup patterns. In our data, trajectories show patters of resilience (low- or intermediate-stress), chronic dysfunction (high-stress) and recovery (peak-recovery), while a delayed reaction is missing. This partial divergence possibly reflects the fact that Bonanno\u0026rsquo;s framework was developed based on acute events, while the pandemic was a prolonged and multi-domain experience. It had many layers beyond infection itself, amplifying distress for some individuals and influencing post-traumatic adaptation in ways that differ from past crises, positioning the pandemic as a unique challenge for understanding trauma and recovery.\u003c/p\u003e \u003cp\u003eFurther potential traumatic events, such as the Russian invasion of Ukraine in February 2022 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) and the subsequent energy crisis and inflation (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), may have hampered a recovery in stress levels. Although we did not observed an acute reaction, stress related to these events cannot be excluded, as our questionnaires did not assess war-related stressors. Other instruments, including items more sensitive to war experiences, or different aspects of mental health, such as anxiety or depression symptoms, might indicate changes not captured here.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFactors Associated with Class Membership\u003c/h2\u003e \u003cp\u003eHigher depressive symptoms-scores before the pandemic and their association with higher stress levels might be related to increased susceptibility to stressful events, as described by Liu and Alloy in a systematic review (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother aspect that may exacerbate the stress response is the lack of social interactions and support. Kang et al. found that individuals with high loneliness are more susceptible to everyday stressors and tend to experience prolonged emotional reactions (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). During the pandemic, physical distancing likely disrupted social interactions and potentially amplified stress responses (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Consistent with this, all classes reported higher loneliness than the reference class early in the pandemic.\u003c/p\u003e \u003cp\u003eStudies have suggested that physical activity plays a role in relieving stress (\u003cspan additionalcitationids=\"CR71 CR72\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). Thus, the decrease in sports activities may have affected one coping mechanism. Additionally, sports provide important social interactions for some people (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond shared associations, each trajectory class exhibited distinct demographic and psychological profiles, indicating different groups of stress vulnerability and adaptation. Except for class 2, where stress peaked during the pandemic\u0026rsquo;s first year, the direct impact of the pandemic was less pronounced in other classes. Class 2 represented only 3% of the sample, limiting estimate precision; yet may represent the most stress-reactive subgroup, despite partial return to initial values.\u003c/p\u003e \u003cp\u003eOverall, pre-existing stress and health profiles seem to play a central role in shaping trajectory membership.\u003c/p\u003e \u003cp\u003eIndividuals in the high- and intermediate-stress classes (vs. reference class) reported worse health and higher anxiety before the pandemic, and more fears at its early stage. Similar findings have been reported (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e), showing that pre-existing worries, anxiety, and depressive symptoms predicted worse mental health during the pandemic, whereas fewer prior issues were associated to greater resilience (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan additionalcitationids=\"CR79 CR80\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePre-pandemic BMI was positively associated with later stress, particularly among intermediate-stress class. As a risk factor for chronic conditions, stigma, and low self-esteem, higher BMI may increase psychological distress (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e) and perceived health risks during the pandemic.\u003c/p\u003e \u003cp\u003eAmong all participants, the ones in the high-stress class reported the lowest pre-pandemic life satisfaction and HRQOL, consistent with a chronic stress pattern (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditional factors associated with high-stress class included living with a partner and young child(ren), and being employed. Stress among parents of young children during the pandemic is well documented, as many parents had to adjust to new work arrangements (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e), while managing increased childcare responsibilities due to childcare facilities closures. This group was predominantly women, who already carried higher stress-loads, with the pandemic adding further challenges. Previous studies identified female sex and having young children as risk factors for elevated stress (\u003cspan additionalcitationids=\"CR85 CR86\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). Pre-pandemic data indicated that this trajectory represents chronically high stress rather than a pandemic-related response.\u003c/p\u003e \u003cp\u003eLiving with a partner and young child(ren), and being employed were positively associated with the peak-recovery class (3%), though the small class size limits the stability and generalizability of the estimates. Membership in this class was negatively associated with agreeableness, a personality trait linked to trust, cooperation, and empathy. Low agreeableness, characterized by scepticism, individualism, and less adaptability (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e), may exacerbate stress vulnerability when routines, relationships, or coping mechanisms are disrupted, as during the pandemic. Such individuals report more stressors, and greater difficulty managing relationships and stress (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e). This class was also associated with divorce, a potentially traumatic event with long-lasting psychological effects on multiple life dimensions (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e). Additionally, co-parenting with a former partner during the pandemic may have added further stress (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTaken together, stress response is shaped by a complex interplay of individual characteristics and contextual factors. Even under the same environmental conditions, responses may differ depending on sociodemographic background, personality traits, life experiences, prior trauma, and coping mechanisms (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eTo our knowledge, no other studies in Germany have investigated heterogeneous stress trajectories both before the pandemic and over a comparable duration. Most previous research defined groups by sociodemographic characteristics before examining mental health outcomes. A major strength of this study that we first identified stress trajectories and subsequently analysed class characteristics, enabling identification of smaller subgroups that may be obscured when looking at the population trajectory.\u003c/p\u003e \u003cp\u003eDespite these strengths, several limitations should be acknowledged. Vulnerable groups, such as migrants, who tend to have a higher prevalence of stress and mental health problems (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e), are underrepresented in the NAKO study. Similarly, other known vulnerability factors in Germany, such as younger age and financial difficulties (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e), were less frequent in our predominantly older and financially comfortable sample, potentially underestimating the prevalence and patterns of more adverse stress trajectories typical of vulnerable groups.\u003c/p\u003e \u003cp\u003eSelection bias may have been introduced through the inclusion criteria, requiring participants to complete at least four assessments, potentially excluding individuals with different stress trajectories. For instance, severely affected participants may be underrepresented due to dropout.\u003c/p\u003e \u003cp\u003eWhile not a limitation \u003cem\u003eper se\u003c/em\u003e, the number and form of latent classes identified through LCGA may vary depending on sample characteristics and measurement intervals. Thus, different trajectory patterns may have emerged under alternative study conditions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe identification of four stress trajectories reveals heterogeneity in population-level responses to the COVID-19 pandemic. Most participants exhibited stress levels that were already elevated before the pandemic, with small to moderate increases occurring mainly early on. None of the classes returned to initial levels, suggesting lasting psychological impact of varying degrees.\u003c/p\u003e \u003cp\u003eWhile high- and intermediate-stress trajectories reflected pre-existing psychological vulnerability and reduced well-being, the peak-recovery class appeared driven by lower agreeableness and contextual stressors, including family and routine disruptions.\u003c/p\u003e \u003cp\u003eThese findings suggest that public health efforts to reduce chronic stress and support for individuals with psychosocial, health, or socio-demographic disadvantages, should be sustained beyond periods of crisis. Clinicians should identify individual vulnerabilities and stressors when assessing and supporting patients.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003e Ethical approval for the NAKO study was obtained from all local ethics committees of the 18 study centres. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975 (in the current, revised version), with the exception that the study was not registered before the recruitment started.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e \u003cp\u003e All participants provided written informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare none.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFinancial support\u003c/h2\u003e \u003cp\u003eThis analysis was supported by internal institutional funding. The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C, 01ER1511D, 01ER1801A/B/C/D and 01ER2301A/B/C], federal states of Germany and the Helmholtz Association, the participating universities and the institutes of the Leibniz Association.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: D.C., A.F., R.M.; Methodology: D.C. and J.H.; Formal analysis: D.C. and J.H.; Data curation: O.P. and A.K.; Funding acquisition: R.M.; Project administration: O.P. and A.K.; Software: O.P.; Supervision: A.F. and R.M.; Writing - original draft preparation: D.C.; Writing - review and editing: All authors; All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis project was conducted with data (Application No. NAKO-909) from the German National Cohort (www.nako.de). We thank all participants and staff of this research initiative.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe NAKO data that support the findings of this study are available from TransferHub (https://transfer.nako.de/transfer/index), but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. The data are, however, available upon request and with the permission of NAKO e.V.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchiele MA, Gottschalk MG, Domschke K (2020) The applied implications of epigenetics in anxiety, affective and stress-related disorders - A review and synthesis on psychosocial stress, psychotherapy and prevention. Clin Psychol Rev 77:101830\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinderman P (2005) A Psychological Model of Mental Disorder. 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Front Psychol. ;Volume 15\u0026ndash;2024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoren Y, Leveille S, You T (2021) Tai Chi Interventions Promoting Social Support and Interaction Among Older Adults: A Systematic Review. Res Gerontol Nurs 14(3):126\u0026ndash;137\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiedermeier M, Dremetsikas V, Herzog S, Kopp-Wilfling P, Burtscher M, Kopp M (2019) Is the Effect of Physical Activity on Quality of Life in Older Adults Mediated by Social Support? Gerontology 65(4):375\u0026ndash;382\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShabani MJ, Mohsenabadi H, Gharraee B, Taghizadeh Firoozjaie I (2022) The relationship between Covid-19 related anxiety and health anxiety: The mediating role of physical concern component. 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Eur Psychiatry 66:S213\u0026ndash;S\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":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Covid-19, latent classes, mental health, psychological stress, trajectories","lastPublishedDoi":"10.21203/rs.3.rs-8196631/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8196631/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic and containment measures disrupted daily life and worsened mental health. Stress, a key driver of mental disorders, likely intensified during this period. However, longitudinal studies tracking stress trajectories in the general population remain limited. This study aims to identify psychosocial stress trajectories from the pre- to post-pandemic period and examine associated characteristics in a population-based sample from a German city.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e966 participants from the German National Cohort study-centre in Halle (240,000 inhabitants in Eastern Germany) were included. Those participated in a six-monthly intensified assessment and completed at least four questionnaires between 2019 and 2024 containing PHQ-Stress module. First, latent class growth analysis identified heterogeneous stress trajectories. Second, associations between class membership and covariates were tested with multinomial logistic regressions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified four psychosocial stress trajectory classes. Most participants followed an intermediate-level-stress trajectory (58%), while others showed low (30%), high (10%), or peak-recovery (3%) trajectories. Across classes, stress rose over time, with small to moderate changes, mostly early in the pandemic. Membership in the intermediate- or high-stress trajectories was associated with greater pre-pandemic stress, depressive and anxiety symptoms, lower life satisfaction, and greater loneliness at the pandemic\u0026rsquo;s onset. The peak-recovery class was associated with lower agreeableness and divorce before the pandemic, and reductions in sport activities at its onset.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eStable patterns of stress predominated, while a small subgroup showed stronger reactivity to pandemic-related strain. These findings suggest that, for most individuals, pre-existing vulnerability and stress levels shape long-term trajectories despite substantial contextual change.\u003c/p\u003e","manuscriptTitle":"Psychosocial stress from pre- to post-pandemic times: a latent class growth analysis using data from a German cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 01:18:11","doi":"10.21203/rs.3.rs-8196631/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-21T20:14:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T00:39:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T20:03:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77819083716499453522929484771878557482","date":"2026-01-12T16:13:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141578917121222956766259669668584002165","date":"2025-12-24T03:59:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-22T12:07:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-12T13:03:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-25T12:56:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Social Psychiatry and Psychiatric Epidemiology","date":"2025-11-24T19:42:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5e5f99b6-e988-403a-b174-e5076bae7de2","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:07:07+00:00","versionOfRecord":{"articleIdentity":"rs-8196631","link":"https://doi.org/10.1007/s00127-026-03092-6","journal":{"identity":"social-psychiatry-and-psychiatric-epidemiology","isVorOnly":false,"title":"Social Psychiatry and Psychiatric Epidemiology"},"publishedOn":"2026-04-20 15:58:22","publishedOnDateReadable":"April 20th, 2026"},"versionCreatedAt":"2025-12-30 01:18:11","video":"","vorDoi":"10.1007/s00127-026-03092-6","vorDoiUrl":"https://doi.org/10.1007/s00127-026-03092-6","workflowStages":[]},"version":"v1","identity":"rs-8196631","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8196631","identity":"rs-8196631","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}