Joint Longitudinal Trajectories of Perceived Stress and Health-Related Quality of Life after First-Time ST-Segment Elevation Myocardial Infarction: A Multicenter Prospective Cohort Study

Joint Longitudinal Trajectories of Perceived Stress and Health-Related Quality of Life after First-Time ST-Segment Elevation Myocardial Infarction: A Multicenter Prospective Cohort Study | 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 Joint Longitudinal Trajectories of Perceived Stress and Health-Related Quality of Life after First-Time ST-Segment Elevation Myocardial Infarction: A Multicenter Prospective Cohort Study Nasrin Salimian, Marjan Mansourian, Masoumeh Sadeghi, Hamidreza Roohafza, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8508163/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Psychosocial recovery after ST-segment elevation myocardial infarction (STEMI) is not well described, especially regarding how perceived stress and health-related quality of life (HRQoL) change together over time. To jointly characterize the trajectories of stress and HRQoL after first-time STEMI and identify the demographic and behavioral determinants of these trajectories. Methods In a multicenter cohort of 1,730 STEMI survivors, perceived stress and HRQoL were assessed 12, 24, and 36 months after discharge. A bivariate Student-t mixed-effects model estimated joint trajectories and within-person associations, adjusting for age, sex, marital status, education, physical activity, coping, and sleep quality. Results Among 1,730 patients (mean age 56.2 ± 9.9 years; 81.9% men), mean stress increased from 1.28 ± 0.85 at year 1 to 4.72 ± 4.43 at year 2 and then decreased to 3.88 ± 4.13 at year 3, whereas HRQoL changed modestly from 32.67 ± 4.39 to 32.74 ± 4.72 and 33.29 ± 4.82. Stress and HRQoL were moderately negatively correlated (r = − 0.39). Poor sleep quality was associated with higher stress (β = 0.85; 95% CI 0.56–1.14) and lower HRQoL (β = −2.68; 95% CI − 3.33–−2.03), while more adaptive coping was associated with lower stress (β = −0.86; 95% CI − 1.02–−0.70) and higher HRQoL (β = 3.45; 95% CI 3.05–3.84). Conclusions Despite contemporary acute care, psychosocial recovery remains incomplete, with stress and HRQoL tied to sociodemographic and behavioral factors. These joint trajectories identified vulnerable subgroups and highlight the need for psychosocial assessment, coping support, and exercise-based rehabilitation into routine follow-up. Post-Traumatic Stress Longitudinal Studies ST Elevation Myocardial Infarction Quality of Life Figures Figure 1 Introduction Cardiovascular diseases remain a leading global health problem, affecting populations across regions and age groups ( 1 ). Despite marked progress in prevention, diagnosis, and treatment, they still account for most morbidity and mortality cases worldwide and are responsible for millions of deaths each year ( 2 ). Myocardial infarction (MI) is a major component of this burden and despite advances in prevention and clinical management, it continues to pose substantial public health and clinical challenges ( 3 ). Among its subtypes, ST segment elevation myocardial infarction (STEMI) is the most severe presentation, resulting from complete coronary occlusion that causes extensive myocardial damage and a high risk of disability and death ( 4 , 5 ). Acute reperfusion strategies and modern drug therapies have improved short-term survival in the early post-event period; however, recovery after discharge often remains incomplete. Many patients experience ongoing psychosocial problems, including stress, anxiety, depressive symptoms, and reduced health-related quality of life (HRQoL), and these factors strongly influence long-term prognosis ( 6 , 7 ). HRQoL is a multidimensional construct that reflects patients’ physical functioning, psychological well-being and social participation ( 8 ). Following an infarction, HRQoL frequently declines and impairments may persist for years with strong associations with poorer prognosis ( 7 , 9 ). Chronic stress turns out to be one of the key determinants of this trajectory ( 10 ). Beyond its subjective impact, stress activates biological pathways involving dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, systemic inflammation, and autonomic imbalance, which together may contribute to recurrent cardiac events and delayed recovery ( 11 , 12 ). Recent evidence further indicates that psychological distress after MI including depression, anxiety, and post-traumatic stress disorder is highly prevalent and consistently linked with increased morbidity and mortality ( 2 , 13 ). The relationship between stress and HRQoL is bidirectional. Higher stress can reduce quality of life across physical, emotional, and social domains, while poor HRQoL can in turn weaken patients’ capacity to cope, creating a cycle of distress and delayed recovery ( 14 , 15 ). This cycle is strongly influenced by demographic and behavioral factors. Age, sex, marital status, and socioeconomic status shape how individuals adjust after MI ( 16 , 17 ). Lifestyle factors such as poor sleep quality, smoking, low physical activity, and unhealthy diet increase stress levels and impede recovery, whereas social support and adaptive coping strategies can be protective ( 16 , 18 – 20 ). Overall, recovery after MI is not solely a matter of myocardial healing but also involves addressing psychological and behavioral determinants that critically influence long-term outcomes ( 14 ). Despite growing interest in psychosocial outcomes after myocardial infarction, important gaps remain in current evidence. Many studies have examined stress and HRQoL separately, often through cross-sectional analyses or single-outcome longitudinal designs, which limits understanding of how these domains affect each other over time ( 15 , 16 , 21 ). Longitudinal research indicates that psychological distress, coping capacity, and quality of life evolve concurrently during recovery; yet traditional models rarely capture these parallel and mutually dependent patterns ( 22 , 23 ). In addition, clinical datasets often show skewed distributions, heavy tails, and outliers which reduce the validity of conventional analyses relying on normality assumptions ( 24 , 25 ). These considerations highlight the need for analytical strategies capable of examining correlated psychosocial outcomes simultaneously while accommodating the complex distributional characteristics of real clinical data. In this study we applied flexible joint longitudinal modeling to evaluate simultaneous changes in stress and HRQoL among patients with first-time STEMI. By integrating demographic and behavioral determinants including sleep quality, smoking, physical activity and nutrition, this approach aims to provide a more comprehensive understanding of post-infarction recovery and support individualized psychosocial and rehabilitation strategies designed to improve long-term quality of life. Methods Study Design and Participants This study is a secondary analysis of data from a large multicenter prospective cohort investigating clinical, behavioral, and psychosocial outcomes among patients with first-time STEMI. The design, recruitment procedures, and data collection protocol of the parent cohort have been previously published in detail ( 26 ). For this secondary analysis, eligible participants were those aged 18–75 years with confirmed first STEMI, as defined by the American Heart Association (AHA) diagnostic criteria ( 3 ). Additional criteria included the ability to provide informed consent and clinical stability after acute management. The exclusion criteria included previous cardiovascular disease, severe comorbidities limiting three-year survival, unwillingness or inability to attend scheduled follow-up visits and participation in concurrent interventional studies. All baseline and follow-up assessments were performed by trained multidisciplinary teams (cardiologists, nurses, and psychologists) using standardized protocols. Data Collection Baseline assessments included socio-demographic characteristics, socioeconomic indicators, anthropometric measurements, and routine clinical information related to the STEMI index, such as infarction characteristics, reperfusion strategy, left ventricular function, in-hospital complications, and laboratory indices. All variables were collected using standardized procedures across the participating centers. Psychosocial and Lifestyle Measures Psychosocial and behavioral characteristics were assessed using validated instruments, each supported by both original and Persian versions. Stress was measured using the Kessler Psychological Distress Scale (K6), a six-item screening tool scored from 0 to 4 per item, yielding a total score of 0–24, with higher values indicating greater psychological distress. K6 was treated as a continuous variable in the analyses, given its established use in longitudinal studies. The original K6 demonstrates strong validity ( 27 ), and the Persian version has shown good reliability in Iranian populations ( 28 ). HRQoL was assessed using the 12-Item Short Form Health Survey (SF-12), which provides a physical and mental component summary score standardized to 0–100 (higher scores indicate better HRQoL) ( 29 ). The Iranian version has been validated and is widely used in cardiovascular research ( 30 ). Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), which consists of 19 items across seven components, producing a global score from 0–21, with higher values indicating poorer sleep quality ( 31 ). The Persian version has demonstrated good psychometric properties ( 32 ). Coping strategies were assessed using the Coping Inventory for Stressful Situations (CISS), a 48-item questionnaire covering problem-focused, emotion-focused, and avoidance coping styles, scored on a 1–5 Likert scale ( 33 ). For the present analyses, a composite coping score was computed and rescaled to a range of 0–2 to facilitate interpretation in the joint model, with higher values indicating more adaptive coping. The Persian version of the instrument has demonstrated acceptable reliability and construct validity in Iranian populations ( 34 ). The nutrition score was a composite derived from self-reported dietary habits, rescaled to 0–1, with higher values indicating healthier nutrition. Socioeconomic status (SES) was assessed using a cohort-specific composite score derived from education, occupation, and household asset indicators, using principal component analysis as described in the parent cohort ( 35 ). Physical activity was measured using a single-item question asking whether participants engaged in at least 30 minutes of physical activity per day (walking, exercise, cycling, etc.). Single-item PA questions have been shown to provide acceptable validity in epidemiological studies ( 36 ). Statistical analysis All analyses followed a prespecified plan to describe longitudinal changes in perceived stress and HRQoL after first-time STEMI and to examine how these two outcomes moved together over time. Baseline demographic, clinical and psychosocial variables were summarized as means (SD) for continuous variables and counts with percentages for categorical variables. The course of stress and HRQoL was described by reporting the mean values and SDs at each annual follow-up visit. These descriptive summaries were used to characterize the cohort and frame the longitudinal modeling; no formal hypothesis testing was performed for baseline comparisons. To estimate the joint trajectories of stress and HRQoL and their within-person association, we fitted a bivariate linear mixed-effects model with shared patient-level random effects. Time, expressed as years since the index STEMI, was entered as a continuous fixed effect in both outcome equations together with a quadratic term (time²) to capture non-linear patterns. To reduce collinearity, time was centered at year 2 when constructing the quadratic term. At the individual level, random intercepts and random slopes for time were specified for each outcome, with an unstructured random-effects covariance to allow for correlation between stress and HRQoL random effects. A prespecified set of baseline covariates was included as fixed effects: age, sex, marital status, education, physical activity (≥ 30 min/day vs < 30 min/day), coping strategy score, and sleep quality. Smoking status, nutrition score, socioeconomic status, BMI, LVEF, and treatment type were evaluated in the preliminary models but were not retained in the final joint model as they were not statistically significant in the preliminary models and were omitted to maintain parsimony. Covariates were evaluated in a block-wise (hierarchical) manner (demographic, lifestyle, and clinical blocks). The within-person association between the two longitudinal processes was obtained from the covariance structure of the random intercepts and slopes and summarized as a random-effects correlation coefficient. All models were estimated in R version 4.3.1 using the brms package ( 37 ), which provides a high-level interface to Stan for fitting multilevel and multivariate mixed-effects models ( 38 , 39 ). Weakly informative priors were used according to the default brms settings. The initial joint model was specified under a Gaussian likelihood and then re-estimated using a Student-t likelihood for the level-1 residuals of each outcome to improve robustness to heavy-tailed and outlying psychosocial scores, while patient-level random effects were kept normally distributed. Models were fitted using 4 Markov chains with 4000 iterations per chain, including warm-up. Convergence and sampling quality were assessed using R̂ (< 1.01), effective sample sizes, and visual inspection of trace plots. Posterior predictive checks were used to compare the Gaussian and Student-t specifications and showed better agreement with the empirical distributions under the Student-t model. Fixed-effect results are reported as regression coefficients with 95% confidence intervals. Missing baseline covariates (including sleep quality, coping strategy score, physical activity, marital status, education, and any others with < 10% missing) were handled using multiple imputation via chained equations (10 imputations) with the mice package in R (version 4.3.1). The imputation model included all fixed-effect covariates from the joint model (age, sex, marital status, education, physical activity, coping, and sleep quality) as predictors, plus auxiliary variables (e.g., baseline BMI and LVEF) to improve imputation accuracy. Missing longitudinal outcomes for stress and HRQoL were accommodated within the joint mixed-effects framework under a missing-at-random assumption, using all available repeated measures. Analyses were performed separately for each imputed dataset, and estimates were pooled across imputations using Rubin's rules to account for imputation uncertainty. Ethics approval The original multicenter STEMI cohort was approved by the Ethics Committee of the National Institute for Medical Research Development (NIMAD), Tehran, Iran (approval code IR.NIMAD.REC.1397.295) and by the ethics committees of the participating universities of medical sciences. Written informed consent was obtained from all the patients at enrollment. The present secondary analysis was additionally approved by the Research Ethics Committee of the Schools of Dentistry, Health Sciences and Advanced Medical Technologies, Isfahan University of Medical Sciences (approval code IR.MUI.DHMT.REC.1404.157). All procedures complied with the Declaration of Helsinki and relevant national regulations and the dataset made available to the investigators for this analysis was de-identified. Results At baseline, 1,730 patients with first-time STEMI were included in the analysis (mean age 56.2 ± 9.9 years; 81.9% men). Baseline assessments were conducted shortly after discharge, with psychosocial measures first collected at 12 months post-discharge and repeated at 24 and 36 months. Follow-up assessments were completed for 1,595 patients at year 1 (92.2% of baseline), 1,450 patients at year 2 (83.8%), and 1,320 patients at year 3 (76.3%). Attrition over the three-year follow-up period was due to loss to follow-up (n = 280; 16.2%) and death (n = 130; 7.5%). The baseline characteristics are summarized in Table 1 , which is provided at the end of the manuscript. Over the 3-year follow-up, observed mean perceived stress showed a non-linear pattern with a peak at year 2 (Table 2 , Fig. 1 ), increasing from 1.28 ± 0.85 at year 1 to 4.72 ± 4.43 at year 2 and decreasing to 3.88 ± 4.13 at year 3. Mean HRQoL changed modestly over time, from 32.67 ± 4.39 at year 1 to 32.74 ± 4.72 at year 2 and 33.29 ± 4.82 at year 3. Table 1 Baseline demographic, socioeconomic, lifestyle, and clinical characteristics Variable Category Value* Demographic and socioeconomic characteristics Age, years – 56.2 (SD 9.9) Sex Male 1,417 (81.9) Female 313 (18.1) Marital status Married 1,450 (88.0) Single 38 (2.3) Divorced 49 (3.0) Widowed 111 (6.7) Education level Less than high school 561 (34.0) High school diploma 788 (47.8) University degree 300 (18.2) Employment status Employed 1,351 (78.1) Unemployed 379 (21.9) Socioeconomic status Low 402 (23.2) Middle 827 (47.8) High 501 (29.0) Lifestyle and clinical characteristics Smoking status Never smoker 817 (47.2) Former smoker 187 (10.8) Current smoker 726 (42.0) Physical activity ≥ 30 min/day Yes 651 (37.6) No 1,079 (62.4) Subjective sleep quality Very good 420 (24.3) Pretty good 884 (51.1) Pretty bad 335 (19.4) Poor 91 (5.3) Coping strategy score – 1.21 (SD 0.34) Nutrition score – 0.89 (SD 0.30) BMI, kg/m² – 27.1 (SD 4.1) LVEF, % – 41.3 (SD 8.7) Type of treatment No reperfusion 38 (2.2) Primary PCI 1,651 (96.6) Thrombolysis 20 (1.2) *Values are n (%) for categorical variables and mean (SD) for continuous variables. BMI: body mass index; LVEF: left ventricular ejection fraction; PCI: percutaneous coronary intervention. Table 2 Trajectories of perceived stress and HRQoL over 3-year follow-up Measure Year 1 Year 2 Year 3 Stress 1.28 (0.85) 4.72 (4.43) 3.88 (4.13) HRQoL 32.67 (4.39) 32.74 (4.72) 33.29 (4.82) Values are mean (SD) HRQoL: health-related quality of life. Because the observed stress means suggested curvature (increase from year 1 to year 2 followed by a decrease in year 3), time was modeled using both linear and quadratic terms. To improve interpretability and reduce collinearity between linear and quadratic components, time was centered at year 2 when constructing the quadratic term. In the joint Student-t mixed-effects model, both linear and quadratic time components were statistically significant for stress (linear β = 1.30, 95% CI 1.21–1.40, p = 0.01; quadratic β = −0.45, 95% CI − 0.52 to − 0.38, p = 0.004) and for HRQoL (linear β = 0.43, 95% CI 0.29–0.56, p < 0.001; quadratic β = 0.12, 95% CI 0.05–0.19, p = 0.008). β coefficients represent the adjusted mean differences (effect sizes) per one-unit change in the predictor. In the adjusted joint model, the overall time effect (joint test of Time and Time²) was significant for both the outcomes (p < 0.01). To address confounding transparently, covariates were evaluated in a hierarchical manner, and results from the final fully adjusted joint Student-t model are presented (Table 3 ). In the final model, female sex was associated with higher stress (β = 0.22, 95% CI 0.06–0.39, p = 0.01) and lower HRQoL (β = −1.13, 95% CI − 1.52 to − 0.75, p < 0.001). Divorced marital status was associated with higher stress (β = 0.46, 95% CI 0.14–0.78, p = 0.005). Poor sleep quality was associated with higher stress (β = 0.85, 95% CI 0.56–1.14, p = 0.002) and lower HRQoL (β = −2.68, 95% CI − 3.33 to − 2.03, p < 0.001). Conversely, higher coping strategy scores were associated with lower stress (β = −0.86, 95% CI − 1.02 to − 0.70, p < 0.001) and higher HRQoL (β = 3.45, 95% CI 3.05–3.84, p < 0.001). Regular physical activity was associated with higher HRQoL (β = 0.47, 95% CI 0.21–0.73, p < 0.001), and having a high school diploma (vs. less than high school) was associated with slightly higher HRQoL (β = 0.35, 95% CI 0.03–0.66, p = 0.029). Table 3 Adjusted fixed-effect estimates (effect sizes) from the final joint Student-t mixed-effects model. Predictor Outcome Coefficient (95% CI) p-value Time (linear), years Stress 1.30 (95% CI: 1.21–1.40) 0.01 HRQoL 0.43 (95% CI: 0.29–0.56) < 0.001 Time 2 (quadratic), years² Stress −0.45 (95% CI: −0.52 – −0.38) 0.004 HRQoL 0.12 (95% CI: 0.05–0.19) 0.008 Sex (female) Stress 0.22 (95% CI: 0.06–0.39) 0.01 HRQoL −1.13 (95% CI: −1.52 – −0.75) < 0.001 Marital status (divorced) Stress 0.46 (95% CI: 0.14–0.78) 0.005 Education (diploma) HRQoL 0.35 (95% CI: 0.03–0.66) 0.029 Physical activity (yes) HRQoL 0.47 (95% CI: 0.21–0.73) < 0.001 Coping strategy score Stress −0.86 (95% CI: −1.02 – −0.70) < 0.001 HRQoL 3.45 (95% CI: 3.05–3.84) < 0.001 Sleep quality (poor) Stress 0.85 (95% CI: 0.56–1.14) 0.002 HRQoL −2.68 (95% CI: −3.33 – −2.03) < 0.001 β coefficients represent adjusted mean differences (effect sizes). The joint Student-t mixed-effects model included shared random intercepts and random slopes for time. Time was modeled using centered linear and quadratic terms. CI: confidence interval; HRQoL: health-related quality of life. The covariance structure of the random effects indicated a moderate negative within-subject correlation between stress and HRQoL over time (r = − 0.39; 95% CI − 0.45 to − 0.33), derived from the random-effects covariance matrix. Discussion The purpose of this study was to clarify how perceived stress and HRQoL change together after first-time STEMI and how key demographic and behavioral factors relate to these trajectories. Using a flexible joint longitudinal Student-t model, we found that stress followed an unfavorable course over time, whereas HRQoL showed only small gains, and that the two processes were moderately and negatively correlated within individuals. Female sex, divorce, and poor sleep quality were consistently associated with higher stress and lower HRQoL, while higher physical activity, more adaptive coping, and higher educational attainment were linked with more favorable psychosocial profiles. Taken together, these results indicate that recovery after STEMI remains incomplete and is strongly shaped by psychosocial and lifestyle factors, even among patients who survive acute events and receive contemporary evidence-based care ( 10 , 14 ). This pattern of results is broadly consistent with and extends previous longitudinal studies on HRQoL after myocardial infarction. Munyombwe et al. (2020) reported heterogeneous HRQoL trajectories in survivors of acute MI and showed that a substantial subgroup remained in persistently low HRQoL classes ( 21 ), supporting our observation that recovery is incomplete for many patients despite small average improvements. Burnos and Wrzosek (2022) also found that maladaptive stress-coping styles and post-traumatic stress symptoms are strongly associated with poorer HRQoL after MI ( 22 ), which aligns with our finding that better coping strategies are linked to both lower stress and higher HRQoL. In contrast, a population-based study of first-time MI by Gąsecka et al. (2021) describes more pronounced HRQoL gains over time ( 7 ), suggesting that our STEMI cohort may represent a clinically more severe group with greater residual burden and psychosocial vulnerability. Moreover, Kolarczyk et al. (2024) identified demographic and clinical correlates of HRQoL in a cross-sectional MI sample but did not observe the same strength of association with sleep or coping ( 15 ), which is plausible given that single-time assessments cannot capture dynamic within-person coupling between stress and HRQoL. Methodological differences in study design (joint longitudinal modeling versus cross-sectional regression), case mix (STEMI vs. broader MI populations) and the inclusion of behavioral determinants such as sleep quality and coping offer reasonable explanations for both convergent and divergent findings across studies. One interpretation of these findings is that stress and HRQoL may represent interdependent dimensions of post-MI recovery consistent with a common mind–heart–body pathway ( 11 , 14 ). The observed negative within-subject correlation between stress and HRQoL is consistent with models in which chronic psychological distress may contribute to autonomic imbalance, hypothalamic–pituitary–adrenal axis dysregulation, and systemic inflammation, potentially sustaining symptom burden and functional limitations long after discharge ( 10 ). The strong impact of poor sleep quality on both outcomes is in line with studies showing that sleep disturbance after MI is associated with adverse clinical outcomes and impaired recovery ( 18 ). The marked vulnerability of women, who showed higher stress and lower HRQoL, is consistent with reports that psychosocial stressors disproportionately affect women with cardiovascular disease and may interact with social roles, caregiving responsibilities, and structural disadvantages ( 12 ). At the same time, the robust associations of higher physical activity, adaptive coping and higher education with better HRQoL profiles indicate that psychosocial and behavioral resources can partially buffer the long-term impact of STEMI and complement biological recovery. These findings are consistent with contemporary multidimensional perspectives on HRQoL ( 8 ). Although the present findings support a clinically meaningful coupling between stress and HRQoL, several limitations should be considered. First, stress and HRQoL were measured annually, which may have missed short-term fluctuations and adaptation processes occurring in the early months after STEMI. Second, despite the advantages of the Student-t joint model in accommodating skewed distributions and outliers ( 25 ), unmeasured confounding by factors such as illness acceptance, social support networks, or participation in structured cardiac rehabilitation cannot be ruled out. Third, informative dropout (e.g., sicker or more distressed patients dying or dropping out) cannot be ruled out and may bias the trajectories. Finally, the cohort consisted of patients with first-time STEMI who survived to discharge and complete follow-up, so the findings may not generalize to patients with recurrent events, non-ST elevation MI, or those with severe frailty or cognitive impairment who are underrepresented in research. Additionally, the observational design and annual measurements limit the ability to establish a causal relationship between stress and HRQoL. Despite these limitations, this study has several implications for both clinical practice and research. The clear and persistent association between higher stress and lower HRQoL indicates that psychosocial recovery should be treated as a core target of STEMI care rather than an optional addition to biological treatment ( 14 ). The finding that poor sleep quality, divorce, and female sex mark patients at a particular risk suggests that brief screening for sleep problems, social isolation, and gender-related stressors could help clinicians prioritize early psychosocial and rehabilitative support. In parallel, the protective roles of physical activity, adaptive coping and higher education point toward the potential value of tailored behavioral counseling and structured cardiac rehabilitation programs that explicitly integrate stress management, coping-skills training, and graded exercise into routine post-MI care. At a health-systems level, these data argue for policies that address social determinants of health, including educational and socioeconomic disparities, which appear to shape both psychological and functional trajectories after STEMI. In terms of future research, these findings suggest several next steps. Longitudinal studies with more frequent assessments of stress, HRQoL, sleep and coping are needed to clarify the sequence of changes and to test whether increases in stress tend to precede declines in HRQoL or the reverse. Randomized trials of multicomponent care that combine standard cardiac rehabilitation with focused psychosocial modules, such as cognitive–behavioral therapy, sleep-focused programs or family-based education, could examine whether modifying these factors improves both trajectories and lowers clinical risk. Adding intermediate biomarkers, including inflammatory markers and measures of autonomic function, would help clarify the biological pathways linking chronic stress and impaired HRQoL to recurrent cardiovascular events ( 11 , 22 ). Extending joint modeling approaches to include hard outcomes, such as rehospitalization and mortality, could then support dynamic risk prediction that integrates psychological and clinical information across the recovery course. To our knowledge, this is one of the first large multicenter STEMI cohorts to apply a robust Student-t bivariate mixed-effects model to jointly analyze longitudinal stress and HRQoL, while incorporating sleep quality and coping as key behavioral determinants. This study adds to the evidence that post-STEMI recovery is driven by closely linked psychological and quality-of-life processes that unfold over time rather than by isolated endpoints. By jointly modeling longitudinal stress and HRQoL and incorporating key demographic and behavioral determinants, we provide a more detailed picture of how vulnerable subgroups, particularly women, patients with poor sleep quality, and those with limited psychosocial resources, experience recovery in daily life. Although confirmation in other settings and populations is needed, the findings support the integration of systematic psychosocial assessments and multifaceted rehabilitation into routine STEMI care. Optimizing survival alone is not sufficient; sustainable recovery after STEMI also depends on actively maintaining psychological well-being and health-related quality of life. Conclusion In patients with first-time STEMI, perceived stress stayed high over follow-up, HRQoL showed only modest gains and the two trajectories were inversely related, with sociodemographic and behavioral factors marking clear psychosocial risk and resilience profiles. This study responds to the previous lack of longitudinal joint analyses of stress and health-related quality of life after STEMI and the limited attention to behavioral determinants in recovery research, narrowing an important gap between psychosocial theory and clinical data. By modeling these two outcomes together instead of treating them as separate endpoints, our findings frame post-STEMI recovery as a patient-centered process that unfolds over time rather than as a short-term response to an acute event. At the level of routine care, the results support the systematic assessment of stress, sleep, coping, and social vulnerability in STEMI follow-up and give a clear rationale for embedding targeted psychosocial and exercise-based elements within standard cardiac rehabilitation programs to modify adverse psychosocial trajectories. These data support integrating brief psychosocial screening tools, including the K6, SF-12, and PSQI, into routine STEMI follow-up to identify patients at elevated risk of adverse stress–HRQoL trajectories. Future studies should link joint psychosocial trajectories with hard cardiovascular endpoints and rigorously test tailored, multicomponent interventions in randomized designs. High-quality STEMI care should focus not only on restoring coronary perfusion but also on actively maintaining psychological well-being and health-related quality of life as core outcomes of treatment. Abbreviations AHA American Heart Association BMI Body Mass Index CI Confidence Interval CISS Coping Inventory for Stressful Situations HRQoL Health-Related Quality of Life HPA Hypothalamic–Pituitary–Adrenal K6 Kessler Psychological Distress Scale LVEF Left Ventricular Ejection Fraction MI Myocardial Infarction NIMAD National Institute for Medical Research Development PA Physical Activity PSQI Pittsburgh Sleep Quality Index R̂ Potential Scale Reduction Factor (R-hat) SD Standard Deviation SES Socioeconomic Status SF-12 12-Item Short Form Health Survey STEMI ST-Segment Elevation Myocardial Infarction Declarations Ethics approval and consent to participate The original multicenter STEMI cohort was approved by the Ethics Committee of the National Institute for Medical Research Development (NIMAD), Tehran, Iran (approval code IR.NIMAD.REC.1397.295) and by the ethics committees of the participating universities of medical sciences. The present secondary analysis was additionally approved by the Research Ethics Committee of the Schools of Dentistry, Health Sciences and Advanced Medical Technologies, Isfahan University of Medical Sciences (approval code IR.MUI.DHMT.REC.1404.157). Written informed consent was obtained from all participants at enrollment. All procedures complied with the Declaration of Helsinki and relevant national regulations. Clinical trial number: not applicable. Consent for publication Not applicable Availability of data and materials The dataset used for the present analysis was provided to the investigators in de-identified form. (The dataset is not publicly available due to participant confidentiality and institutional policies; de-identified data may be made available from the corresponding author upon reasonable request and subject to institutional approvals and data-sharing agreements.) Data presentation statement The data in this manuscript have not been previously presented in abstract form at scientific meetings. Competing interest The authors declare that they have no competing interests. Funding The parent multicenter STEMI cohort was supported by the National Institute for Medical Research Development (NIMAD), Tehran, Iran [grant number 964708]. No additional funding was received for the present secondary analysis. Authors’ contributions Conceptualizing the study, designing the statistical analysis plan, writing the manuscript, and approving the manuscript: NS, MM, MS, HR, and HM; Data acquisition and clinical oversight (multicenter cohort): MS and HR; Data management, data analysis/interpretation, and drafting the first version of the manuscript: NS; Statistical modeling and statistical analysis: NS, with methodological supervision: MM and HM; Clinical interpretation of findings and critical revision of the manuscript for important intellectual content: MS and HR; Supervision or mentorship: MM, MS, HR, and HM. Each author contributed important intellectual content during the manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. All the authors read and approved the final manuscript. Acknowledgment The authors wish to thank the participants of the parent multicenter STEMI cohort and the clinical and research staff at the participating centres for their contributions to data collection. The underlying cohort on which this analysis is based was supported by the National Institute for Medical Research Development (NIMAD), Tehran, Iran (grant no. 964708). Use of artificial intelligence: ChatGPT (OpenAI) was used solely for grammatical editing and language refinement of the manuscript. No AI tools were used in data generation, analysis, interpretation, or creation of figures. The authors take full responsibility for the content of the References Collaboration GBoCD. Global, regional, and national burden of cardiovascular diseases and risk factors in 204 countries and territories, 1990–2023. J Am Coll Cardiol. 2025. 10.1016/j.jacc.2025.08.015 . Chong B, Jayabaskaran J, Jauhari SM, Chan SP, Goh R, Kueh MTW, et al. 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Health-related quality of life and its related factors in coronary heart disease patients: results from the Henan Rural Cohort study. Sci Rep. 2021;11(1):5011. Darvish S, Mahoney SA, Venkatasubramanian R, Rossman MJ, Clayton ZS, Murray KO. Socioeconomic status as a potential mediator of arterial aging in marginalized ethnic and racial groups: current understandings and future directions. J Appl Physiol. 2024;137(1):194–222. Zhu C-Y, Hu H-L, Tang G-M, Sun J-C, Zheng H-X, Zhai C-L, et al. Sleep quality, sleep duration, and the risk of adverse clinical outcomes in patients with myocardial infarction with non-obstructive coronary arteries. Front Cardiovasc Med. 2022;9:834169. Dibben GO, Faulkner J, Oldridge N, Rees K, Thompson DR, Zwisler A-D, et al. Exercise-based cardiac rehabilitation for coronary heart disease: a meta-analysis. Eur Heart J. 2023;44(6):452–69. Arman A, Attar A, Izadpanah P, Bahja H, Jeihooni AK. Enhancing self-care in post-MI patients: a family-supported educational intervention based on the theory of planned behavior. BMC Cardiovasc Disord. 2025;25(1):511. Munyombwe T, Hall M, Dondo TB, Alabas OA, Gerard O, West RM, et al. Quality of life trajectories in survivors of acute myocardial infarction: a national longitudinal study. Heart. 2020;106(1):33–9. Burnos A, Wrzosek M. Quality of life after myocardial infarction as a function of temperamental traits, stress coping styles, and posttraumatic stress disorder symptoms. Front Psychiatry. 2022;12:696544. Tokarewicz J, Jankowiak B, Klimaszewska K, Święczkowski M, Matlak K, Dobrzycki S. Acceptance of Illness and Health-Related Quality of Life in Patients After Myocardial Infarction—Narrative Review. J Clin Med. 2025;14(3):729. Signorelli M, Spitali P, Tsonaka R. Poisson–Tweedie mixed-effects model: A flexible approach for the analysis of longitudinal RNA-seq data. Stat Modelling. 2021;21(6):520–45. Tang Y. A monotone data augmentation algorithm for longitudinal data analysis via multivariate skew-t, skew-normal or t distributions. Stat Methods Med Res. 2020;29(6):1542–62. Roohafza H, Noohi F, Hosseini SG, Alemzadeh-Ansari M, Bagherieh S, Marateb H, et al. A cardiovascular risk assessment model according to Behavioral, Psychosocial and traditional factors in patients with ST-segment elevation myocardial infarction (CRAS-MI): review of literature and methodology of a Multi-center Cohort study. Curr Probl Cardiol. 2023;48(7):101158. Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek DK, Normand S-L, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(6):959–76. Hajebi A, Motevalian A, Amin-Esmaeili M, Rahimi‐Movaghar A, Sharifi V, Hoseini L, et al. Adaptation and validation of short scales for assessment of psychological distress in Iran: the Persian K10 and K6. Int J Methods Psychiatr Res. 2018;27(3):e1726. Ware JE, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220–33. Montazeri A, Vahdaninia M, Mousavi SJ, Omidvari S. The Iranian version of 12-item Short Form Health Survey (SF-12): factor structure, internal consistency and construct validity. BMC Public Health. 2009;9(1):341. Buysse DJ, Reynolds CF III, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213. Farrahi Moghaddam J, Nakhaee N, Sheibani V, Garrusi B, Amirkafi A. Reliability and validity of the Persian version of the Pittsburgh Sleep Quality Index (PSQI-P). Sleep Breath. 2012;16(1):79–82. Endler N, Parker JD. Coping inventory for stressful situations. APA PsycTests; 1990. Shokri O, Taghilou S, Geravand F, Paeizi M, Moulaei M, Abd Elahpour M, et al. Factor structure and psychometric properties of the farsi version of the coping inventory for stressful situations (CISS). Adv Cogn Sci. 2008;10(3):22–33. Evans-Lacko S, Aguilar-Gaxiola S, Al-Hamzawi A, Alonso J, Benjet C, Bruffaerts R, et al. Socio-economic variations in the mental health treatment gap for people with anxiety, mood, and substance use disorders: results from the WHO World Mental Health (WMH) surveys. Psychol Med. 2018;48(9):1560–71. Milton K, Bull F, Bauman A. Reliability and validity testing of a single-item physical activity measure. Br J Sports Med. 2011;45(3):203–8. Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, et al. Stan: A probabilistic programming language. J Stat Softw. 2017;76:1–32. Fieuws S, Verbeke G. Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles. Biometrics. 2006;62(2):424–31. Verbeke G. Linear mixed models for longitudinal data. Linear mixed models in practice: A SAS-oriented approach. Springer; 2000. pp. 63–153. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 17 Feb, 2026 Editor invited by journal 28 Jan, 2026 Editor assigned by journal 26 Jan, 2026 Submission checks completed at journal 26 Jan, 2026 First submitted to journal 03 Jan, 2026 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8508163","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592896153,"identity":"7497f705-f781-404b-ab56-01024866a934","order_by":0,"name":"Nasrin Salimian","email":"","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nasrin","middleName":"","lastName":"Salimian","suffix":""},{"id":592896154,"identity":"446a68a6-3b0d-43d9-b1de-b82839b5ed98","order_by":1,"name":"Marjan Mansourian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACxgYogw3IfgCkefhI0cJsANLCRoqNbBJQvfgB87TDzx7+qDicz8d+9ljl1xw7GTYG5oePbuBz2Ow0c2OeM4ct23jy0m7LbksGOozN2DgHr5YEM2nGtsMGbAw5ZrcltzEDtfCwSePXkv5N8idIC/8bs2LJbfXEaMkxk+AFaZHIMWP8uO0wUVrKpHnOpAO1vDGWZtx2nIeNmYBfDGenb5P8UWFtIN+fY/jx57Zqe3725oeP8WppQOIw84BJPMpBQB7FlT8IqB4Fo2AUjIKRCQBUmT7qsN0Q/QAAAABJRU5ErkJggg==","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Marjan","middleName":"","lastName":"Mansourian","suffix":""},{"id":592896155,"identity":"3224d6fb-7de4-4f6d-b602-a024ff06c1aa","order_by":2,"name":"Masoumeh Sadeghi","email":"","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Masoumeh","middleName":"","lastName":"Sadeghi","suffix":""},{"id":592896156,"identity":"12db2d12-65a2-430c-8bad-27e51b2cd9e2","order_by":3,"name":"Hamidreza Roohafza","email":"","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hamidreza","middleName":"","lastName":"Roohafza","suffix":""},{"id":592896157,"identity":"4ccc14ec-6f6d-4105-bb8c-11a326712053","order_by":4,"name":"Hamid Reza Marateb","email":"","orcid":"","institution":"University of Isfahan","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"Reza","lastName":"Marateb","suffix":""}],"badges":[],"createdAt":"2026-01-03 16:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8508163/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8508163/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103098812,"identity":"17ce50ab-f101-4d8e-879f-a93835d6c3f9","added_by":"auto","created_at":"2026-02-20 19:19:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127433,"visible":true,"origin":"","legend":"\u003cp\u003eObserved mean (95% CI) trajectories of HRQoL and perceived stress over 3 years since discharge.\u003c/p\u003e","description":"","filename":"Figure1longitudinaloutcomes.png","url":"https://assets-eu.researchsquare.com/files/rs-8508163/v1/ce36a32935b89828aa8e0a4e.png"},{"id":105751666,"identity":"cc3758af-4e94-46de-a697-c0c9de5c6db4","added_by":"auto","created_at":"2026-03-30 15:35:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":869214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8508163/v1/93ad0deb-4f47-4b67-b145-70b83099029c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint Longitudinal Trajectories of Perceived Stress and Health-Related Quality of Life after First-Time ST-Segment Elevation Myocardial Infarction: A Multicenter Prospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases remain a leading global health problem, affecting populations across regions and age groups (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite marked progress in prevention, diagnosis, and treatment, they still account for most morbidity and mortality cases worldwide and are responsible for millions of deaths each year (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Myocardial infarction (MI) is a major component of this burden and despite advances in prevention and clinical management, it continues to pose substantial public health and clinical challenges (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong its subtypes, ST segment elevation myocardial infarction (STEMI) is the most severe presentation, resulting from complete coronary occlusion that causes extensive myocardial damage and a high risk of disability and death (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Acute reperfusion strategies and modern drug therapies have improved short-term survival in the early post-event period; however, recovery after discharge often remains incomplete. Many patients experience ongoing psychosocial problems, including stress, anxiety, depressive symptoms, and reduced health-related quality of life (HRQoL), and these factors strongly influence long-term prognosis (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHRQoL is a multidimensional construct that reflects patients\u0026rsquo; physical functioning, psychological well-being and social participation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Following an infarction, HRQoL frequently declines and impairments may persist for years with strong associations with poorer prognosis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Chronic stress turns out to be one of the key determinants of this trajectory (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Beyond its subjective impact, stress activates biological pathways involving dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, systemic inflammation, and autonomic imbalance, which together may contribute to recurrent cardiac events and delayed recovery (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Recent evidence further indicates that psychological distress after MI including depression, anxiety, and post-traumatic stress disorder is highly prevalent and consistently linked with increased morbidity and mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationship between stress and HRQoL is bidirectional. Higher stress can reduce quality of life across physical, emotional, and social domains, while poor HRQoL can in turn weaken patients\u0026rsquo; capacity to cope, creating a cycle of distress and delayed recovery (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This cycle is strongly influenced by demographic and behavioral factors. Age, sex, marital status, and socioeconomic status shape how individuals adjust after MI (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Lifestyle factors such as poor sleep quality, smoking, low physical activity, and unhealthy diet increase stress levels and impede recovery, whereas social support and adaptive coping strategies can be protective (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Overall, recovery after MI is not solely a matter of myocardial healing but also involves addressing psychological and behavioral determinants that critically influence long-term outcomes (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite growing interest in psychosocial outcomes after myocardial infarction, important gaps remain in current evidence. Many studies have examined stress and HRQoL separately, often through cross-sectional analyses or single-outcome longitudinal designs, which limits understanding of how these domains affect each other over time (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Longitudinal research indicates that psychological distress, coping capacity, and quality of life evolve concurrently during recovery; yet traditional models rarely capture these parallel and mutually dependent patterns (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In addition, clinical datasets often show skewed distributions, heavy tails, and outliers which reduce the validity of conventional analyses relying on normality assumptions (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These considerations highlight the need for analytical strategies capable of examining correlated psychosocial outcomes simultaneously while accommodating the complex distributional characteristics of real clinical data. In this study we applied flexible joint longitudinal modeling to evaluate simultaneous changes in stress and HRQoL among patients with first-time STEMI. By integrating demographic and behavioral determinants including sleep quality, smoking, physical activity and nutrition, this approach aims to provide a more comprehensive understanding of post-infarction recovery and support individualized psychosocial and rehabilitation strategies designed to improve long-term quality of life.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eThis study is a secondary analysis of data from a large multicenter prospective cohort investigating clinical, behavioral, and psychosocial outcomes among patients with first-time STEMI. The design, recruitment procedures, and data collection protocol of the parent cohort have been previously published in detail (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor this secondary analysis, eligible participants were those aged 18\u0026ndash;75 years with confirmed first STEMI, as defined by the American Heart Association (AHA) diagnostic criteria (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Additional criteria included the ability to provide informed consent and clinical stability after acute management. The exclusion criteria included previous cardiovascular disease, severe comorbidities limiting three-year survival, unwillingness or inability to attend scheduled follow-up visits and participation in concurrent interventional studies. All baseline and follow-up assessments were performed by trained multidisciplinary teams (cardiologists, nurses, and psychologists) using standardized protocols.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eBaseline assessments included socio-demographic characteristics, socioeconomic indicators, anthropometric measurements, and routine clinical information related to the STEMI index, such as infarction characteristics, reperfusion strategy, left ventricular function, in-hospital complications, and laboratory indices. All variables were collected using standardized procedures across the participating centers.\u003c/p\u003e\n\u003ch3\u003ePsychosocial and Lifestyle Measures\u003c/h3\u003e\n\u003cp\u003ePsychosocial and behavioral characteristics were assessed using validated instruments, each supported by both original and Persian versions.\u003c/p\u003e \u003cp\u003eStress was measured using the Kessler Psychological Distress Scale (K6), a six-item screening tool scored from 0 to 4 per item, yielding a total score of 0\u0026ndash;24, with higher values indicating greater psychological distress. K6 was treated as a continuous variable in the analyses, given its established use in longitudinal studies. The original K6 demonstrates strong validity (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), and the Persian version has shown good reliability in Iranian populations (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). HRQoL was assessed using the 12-Item Short Form Health Survey (SF-12), which provides a physical and mental component summary score standardized to 0\u0026ndash;100 (higher scores indicate better HRQoL) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The Iranian version has been validated and is widely used in cardiovascular research (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), which consists of 19 items across seven components, producing a global score from 0\u0026ndash;21, with higher values indicating poorer sleep quality (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The Persian version has demonstrated good psychometric properties (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoping strategies were assessed using the Coping Inventory for Stressful Situations (CISS), a 48-item questionnaire covering problem-focused, emotion-focused, and avoidance coping styles, scored on a 1\u0026ndash;5 Likert scale (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). For the present analyses, a composite coping score was computed and rescaled to a range of 0\u0026ndash;2 to facilitate interpretation in the joint model, with higher values indicating more adaptive coping. The Persian version of the instrument has demonstrated acceptable reliability and construct validity in Iranian populations (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The nutrition score was a composite derived from self-reported dietary habits, rescaled to 0\u0026ndash;1, with higher values indicating healthier nutrition. Socioeconomic status (SES) was assessed using a cohort-specific composite score derived from education, occupation, and household asset indicators, using principal component analysis as described in the parent cohort (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Physical activity was measured using a single-item question asking whether participants engaged in at least 30 minutes of physical activity per day (walking, exercise, cycling, etc.). Single-item PA questions have been shown to provide acceptable validity in epidemiological studies (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses followed a prespecified plan to describe longitudinal changes in perceived stress and HRQoL after first-time STEMI and to examine how these two outcomes moved together over time. Baseline demographic, clinical and psychosocial variables were summarized as means (SD) for continuous variables and counts with percentages for categorical variables. The course of stress and HRQoL was described by reporting the mean values and SDs at each annual follow-up visit. These descriptive summaries were used to characterize the cohort and frame the longitudinal modeling; no formal hypothesis testing was performed for baseline comparisons.\u003c/p\u003e \u003cp\u003eTo estimate the joint trajectories of stress and HRQoL and their within-person association, we fitted a bivariate linear mixed-effects model with shared patient-level random effects. Time, expressed as years since the index STEMI, was entered as a continuous fixed effect in both outcome equations together with a quadratic term (time\u0026sup2;) to capture non-linear patterns. To reduce collinearity, time was centered at year 2 when constructing the quadratic term. At the individual level, random intercepts and random slopes for time were specified for each outcome, with an unstructured random-effects covariance to allow for correlation between stress and HRQoL random effects. A prespecified set of baseline covariates was included as fixed effects: age, sex, marital status, education, physical activity (\u0026ge;\u0026thinsp;30 min/day vs\u0026thinsp;\u0026lt;\u0026thinsp;30 min/day), coping strategy score, and sleep quality. Smoking status, nutrition score, socioeconomic status, BMI, LVEF, and treatment type were evaluated in the preliminary models but were not retained in the final joint model as they were not statistically significant in the preliminary models and were omitted to maintain parsimony. Covariates were evaluated in a block-wise (hierarchical) manner (demographic, lifestyle, and clinical blocks). The within-person association between the two longitudinal processes was obtained from the covariance structure of the random intercepts and slopes and summarized as a random-effects correlation coefficient.\u003c/p\u003e \u003cp\u003eAll models were estimated in R version 4.3.1 using the brms package (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), which provides a high-level interface to Stan for fitting multilevel and multivariate mixed-effects models (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Weakly informative priors were used according to the default brms settings. The initial joint model was specified under a Gaussian likelihood and then re-estimated using a Student-t likelihood for the level-1 residuals of each outcome to improve robustness to heavy-tailed and outlying psychosocial scores, while patient-level random effects were kept normally distributed. Models were fitted using 4 Markov chains with 4000 iterations per chain, including warm-up. Convergence and sampling quality were assessed using R̂ (\u0026lt;\u0026thinsp;1.01), effective sample sizes, and visual inspection of trace plots. Posterior predictive checks were used to compare the Gaussian and Student-t specifications and showed better agreement with the empirical distributions under the Student-t model. Fixed-effect results are reported as regression coefficients with 95% confidence intervals. Missing baseline covariates (including sleep quality, coping strategy score, physical activity, marital status, education, and any others with \u0026lt;\u0026thinsp;10% missing) were handled using multiple imputation via chained equations (10 imputations) with the mice package in R (version 4.3.1). The imputation model included all fixed-effect covariates from the joint model (age, sex, marital status, education, physical activity, coping, and sleep quality) as predictors, plus auxiliary variables (e.g., baseline BMI and LVEF) to improve imputation accuracy. Missing longitudinal outcomes for stress and HRQoL were accommodated within the joint mixed-effects framework under a missing-at-random assumption, using all available repeated measures. Analyses were performed separately for each imputed dataset, and estimates were pooled across imputations using Rubin's rules to account for imputation uncertainty.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthics approval\u003c/h3\u003e\n\u003cp\u003e The original multicenter STEMI cohort was approved by the Ethics Committee of the National Institute for Medical Research Development (NIMAD), Tehran, Iran (approval code IR.NIMAD.REC.1397.295) and by the ethics committees of the participating universities of medical sciences. Written informed consent was obtained from all the patients at enrollment.\u003c/p\u003e \u003cp\u003e The present secondary analysis was additionally approved by the Research Ethics Committee of the Schools of Dentistry, Health Sciences and Advanced Medical Technologies, Isfahan University of Medical Sciences (approval code IR.MUI.DHMT.REC.1404.157). All procedures complied with the Declaration of Helsinki and relevant national regulations and the dataset made available to the investigators for this analysis was de-identified.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAt baseline, 1,730 patients with first-time STEMI were included in the analysis (mean age 56.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 years; 81.9% men). Baseline assessments were conducted shortly after discharge, with psychosocial measures first collected at 12 months post-discharge and repeated at 24 and 36 months. Follow-up assessments were completed for 1,595 patients at year 1 (92.2% of baseline), 1,450 patients at year 2 (83.8%), and 1,320 patients at year 3 (76.3%). Attrition over the three-year follow-up period was due to loss to follow-up (n\u0026thinsp;=\u0026thinsp;280; 16.2%) and death (n\u0026thinsp;=\u0026thinsp;130; 7.5%). The baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which is provided at the end of the manuscript. Over the 3-year follow-up, observed mean perceived stress showed a non-linear pattern with a peak at year 2 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), increasing from 1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85 at year 1 to 4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.43 at year 2 and decreasing to 3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13 at year 3. Mean HRQoL changed modestly over time, from 32.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39 at year 1 to 32.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.72 at year 2 and 33.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82 at year 3.\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\u003eBaseline demographic, socioeconomic, lifestyle, and clinical characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDemographic and socioeconomic characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.2 (SD 9.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,417 (81.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313 (18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,450 (88.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e561 (34.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e788 (47.8)\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\u003eUniversity degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300 (18.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,351 (78.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379 (21.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e402 (23.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e827 (47.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e501 (29.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLifestyle and clinical characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e817 (47.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187 (10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e726 (42.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhysical activity\u0026thinsp;\u0026ge;\u0026thinsp;30 min/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e651 (37.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,079 (62.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSubjective sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e420 (24.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretty good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e884 (51.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretty bad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e335 (19.4)\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\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoping strategy score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21 (SD 0.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89 (SD 0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.1 (SD 4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.3 (SD 8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo reperfusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (2.2)\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\u003ePrimary PCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,651 (96.6)\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\u003eThrombolysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*Values are n (%) for categorical variables and mean (SD) for continuous variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eBMI: body mass index; LVEF: left ventricular ejection fraction; PCI: percutaneous coronary intervention.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrajectories of perceived stress and HRQoL over 3-year follow-up\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYear 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.28 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.72 (4.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.88 (4.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.67 (4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.74 (4.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.29 (4.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are mean (SD)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHRQoL: health-related quality of life.\u003c/p\u003e \u003cp\u003eBecause the observed stress means suggested curvature (increase from year 1 to year 2 followed by a decrease in year 3), time was modeled using both linear and quadratic terms. To improve interpretability and reduce collinearity between linear and quadratic components, time was centered at year 2 when constructing the quadratic term. In the joint Student-t mixed-effects model, both linear and quadratic time components were statistically significant for stress (linear β\u0026thinsp;=\u0026thinsp;1.30, 95% CI 1.21\u0026ndash;1.40, p\u0026thinsp;=\u0026thinsp;0.01; quadratic β = \u0026minus;0.45, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.52 to \u0026minus;\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;0.004) and for HRQoL (linear β\u0026thinsp;=\u0026thinsp;0.43, 95% CI 0.29\u0026ndash;0.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; quadratic β\u0026thinsp;=\u0026thinsp;0.12, 95% CI 0.05\u0026ndash;0.19, p\u0026thinsp;=\u0026thinsp;0.008). β coefficients represent the adjusted mean differences (effect sizes) per one-unit change in the predictor. In the adjusted joint model, the overall time effect (joint test of Time and Time\u0026sup2;) was significant for both the outcomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eTo address confounding transparently, covariates were evaluated in a hierarchical manner, and results from the final fully adjusted joint Student-t model are presented (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the final model, female sex was associated with higher stress (β\u0026thinsp;=\u0026thinsp;0.22, 95% CI 0.06\u0026ndash;0.39, p\u0026thinsp;=\u0026thinsp;0.01) and lower HRQoL (β = \u0026minus;1.13, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1.52 to \u0026minus;\u0026thinsp;0.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Divorced marital status was associated with higher stress (β\u0026thinsp;=\u0026thinsp;0.46, 95% CI 0.14\u0026ndash;0.78, p\u0026thinsp;=\u0026thinsp;0.005). Poor sleep quality was associated with higher stress (β\u0026thinsp;=\u0026thinsp;0.85, 95% CI 0.56\u0026ndash;1.14, p\u0026thinsp;=\u0026thinsp;0.002) and lower HRQoL (β = \u0026minus;2.68, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;3.33 to \u0026minus;\u0026thinsp;2.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, higher coping strategy scores were associated with lower stress (β = \u0026minus;0.86, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1.02 to \u0026minus;\u0026thinsp;0.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher HRQoL (β\u0026thinsp;=\u0026thinsp;3.45, 95% CI 3.05\u0026ndash;3.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regular physical activity was associated with higher HRQoL (β\u0026thinsp;=\u0026thinsp;0.47, 95% CI 0.21\u0026ndash;0.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and having a high school diploma (vs. less than high school) was associated with slightly higher HRQoL (β\u0026thinsp;=\u0026thinsp;0.35, 95% CI 0.03\u0026ndash;0.66, p\u0026thinsp;=\u0026thinsp;0.029).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted fixed-effect estimates (effect sizes) from the final joint Student-t mixed-effects model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTime (linear), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30 (95% CI: 1.21\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43 (95% CI: 0.29\u0026ndash;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTime\u003csup\u003e2\u003c/sup\u003e (quadratic), years\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.45 (95% CI: \u0026minus;0.52 \u0026ndash; \u0026minus;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12 (95% CI: 0.05\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22 (95% CI: 0.06\u0026ndash;0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.13 (95% CI: \u0026minus;1.52 \u0026ndash; \u0026minus;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (divorced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46 (95% CI: 0.14\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (diploma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35 (95% CI: 0.03\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47 (95% CI: 0.21\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCoping strategy score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.86 (95% CI: \u0026minus;1.02 \u0026ndash; \u0026minus;0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.45 (95% CI: 3.05\u0026ndash;3.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSleep quality (poor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 (95% CI: 0.56\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRQoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.68 (95% CI: \u0026minus;3.33 \u0026ndash; \u0026minus;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eβ coefficients represent adjusted mean differences (effect sizes). The joint Student-t mixed-effects model included shared random intercepts and random slopes for time. Time was modeled using centered linear and quadratic terms.\u003c/p\u003e \u003cp\u003eCI: confidence interval; HRQoL: health-related quality of life.\u003c/p\u003e \u003cp\u003eThe covariance structure of the random effects indicated a moderate negative within-subject correlation between stress and HRQoL over time (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.39; 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.45 to \u0026minus;\u0026thinsp;0.33), derived from the random-effects covariance matrix.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe purpose of this study was to clarify how perceived stress and HRQoL change together after first-time STEMI and how key demographic and behavioral factors relate to these trajectories. Using a flexible joint longitudinal Student-t model, we found that stress followed an unfavorable course over time, whereas HRQoL showed only small gains, and that the two processes were moderately and negatively correlated within individuals. Female sex, divorce, and poor sleep quality were consistently associated with higher stress and lower HRQoL, while higher physical activity, more adaptive coping, and higher educational attainment were linked with more favorable psychosocial profiles. Taken together, these results indicate that recovery after STEMI remains incomplete and is strongly shaped by psychosocial and lifestyle factors, even among patients who survive acute events and receive contemporary evidence-based care (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis pattern of results is broadly consistent with and extends previous longitudinal studies on HRQoL after myocardial infarction. Munyombwe et al. (2020) reported heterogeneous HRQoL trajectories in survivors of acute MI and showed that a substantial subgroup remained in persistently low HRQoL classes (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), supporting our observation that recovery is incomplete for many patients despite small average improvements. Burnos and Wrzosek (2022) also found that maladaptive stress-coping styles and post-traumatic stress symptoms are strongly associated with poorer HRQoL after MI (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), which aligns with our finding that better coping strategies are linked to both lower stress and higher HRQoL. In contrast, a population-based study of first-time MI by Gąsecka et al. (2021) describes more pronounced HRQoL gains over time (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), suggesting that our STEMI cohort may represent a clinically more severe group with greater residual burden and psychosocial vulnerability. Moreover, Kolarczyk et al. (2024) identified demographic and clinical correlates of HRQoL in a cross-sectional MI sample but did not observe the same strength of association with sleep or coping (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), which is plausible given that single-time assessments cannot capture dynamic within-person coupling between stress and HRQoL. Methodological differences in study design (joint longitudinal modeling versus cross-sectional regression), case mix (STEMI vs. broader MI populations) and the inclusion of behavioral determinants such as sleep quality and coping offer reasonable explanations for both convergent and divergent findings across studies.\u003c/p\u003e \u003cp\u003eOne interpretation of these findings is that stress and HRQoL may represent interdependent dimensions of post-MI recovery consistent with a common mind\u0026ndash;heart\u0026ndash;body pathway (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The observed negative within-subject correlation between stress and HRQoL is consistent with models in which chronic psychological distress may contribute to autonomic imbalance, hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis dysregulation, and systemic inflammation, potentially sustaining symptom burden and functional limitations long after discharge (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The strong impact of poor sleep quality on both outcomes is in line with studies showing that sleep disturbance after MI is associated with adverse clinical outcomes and impaired recovery (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The marked vulnerability of women, who showed higher stress and lower HRQoL, is consistent with reports that psychosocial stressors disproportionately affect women with cardiovascular disease and may interact with social roles, caregiving responsibilities, and structural disadvantages (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). At the same time, the robust associations of higher physical activity, adaptive coping and higher education with better HRQoL profiles indicate that psychosocial and behavioral resources can partially buffer the long-term impact of STEMI and complement biological recovery. These findings are consistent with contemporary multidimensional perspectives on HRQoL (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the present findings support a clinically meaningful coupling between stress and HRQoL, several limitations should be considered. First, stress and HRQoL were measured annually, which may have missed short-term fluctuations and adaptation processes occurring in the early months after STEMI. Second, despite the advantages of the Student-t joint model in accommodating skewed distributions and outliers (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), unmeasured confounding by factors such as illness acceptance, social support networks, or participation in structured cardiac rehabilitation cannot be ruled out. Third, informative dropout (e.g., sicker or more distressed patients dying or dropping out) cannot be ruled out and may bias the trajectories. Finally, the cohort consisted of patients with first-time STEMI who survived to discharge and complete follow-up, so the findings may not generalize to patients with recurrent events, non-ST elevation MI, or those with severe frailty or cognitive impairment who are underrepresented in research. Additionally, the observational design and annual measurements limit the ability to establish a causal relationship between stress and HRQoL.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study has several implications for both clinical practice and research. The clear and persistent association between higher stress and lower HRQoL indicates that psychosocial recovery should be treated as a core target of STEMI care rather than an optional addition to biological treatment (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The finding that poor sleep quality, divorce, and female sex mark patients at a particular risk suggests that brief screening for sleep problems, social isolation, and gender-related stressors could help clinicians prioritize early psychosocial and rehabilitative support. In parallel, the protective roles of physical activity, adaptive coping and higher education point toward the potential value of tailored behavioral counseling and structured cardiac rehabilitation programs that explicitly integrate stress management, coping-skills training, and graded exercise into routine post-MI care. At a health-systems level, these data argue for policies that address social determinants of health, including educational and socioeconomic disparities, which appear to shape both psychological and functional trajectories after STEMI.\u003c/p\u003e \u003cp\u003eIn terms of future research, these findings suggest several next steps. Longitudinal studies with more frequent assessments of stress, HRQoL, sleep and coping are needed to clarify the sequence of changes and to test whether increases in stress tend to precede declines in HRQoL or the reverse. Randomized trials of multicomponent care that combine standard cardiac rehabilitation with focused psychosocial modules, such as cognitive\u0026ndash;behavioral therapy, sleep-focused programs or family-based education, could examine whether modifying these factors improves both trajectories and lowers clinical risk. Adding intermediate biomarkers, including inflammatory markers and measures of autonomic function, would help clarify the biological pathways linking chronic stress and impaired HRQoL to recurrent cardiovascular events (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Extending joint modeling approaches to include hard outcomes, such as rehospitalization and mortality, could then support dynamic risk prediction that integrates psychological and clinical information across the recovery course.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is one of the first large multicenter STEMI cohorts to apply a robust Student-t bivariate mixed-effects model to jointly analyze longitudinal stress and HRQoL, while incorporating sleep quality and coping as key behavioral determinants. This study adds to the evidence that post-STEMI recovery is driven by closely linked psychological and quality-of-life processes that unfold over time rather than by isolated endpoints. By jointly modeling longitudinal stress and HRQoL and incorporating key demographic and behavioral determinants, we provide a more detailed picture of how vulnerable subgroups, particularly women, patients with poor sleep quality, and those with limited psychosocial resources, experience recovery in daily life. Although confirmation in other settings and populations is needed, the findings support the integration of systematic psychosocial assessments and multifaceted rehabilitation into routine STEMI care. Optimizing survival alone is not sufficient; sustainable recovery after STEMI also depends on actively maintaining psychological well-being and health-related quality of life.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn patients with first-time STEMI, perceived stress stayed high over follow-up, HRQoL showed only modest gains and the two trajectories were inversely related, with sociodemographic and behavioral factors marking clear psychosocial risk and resilience profiles. This study responds to the previous lack of longitudinal joint analyses of stress and health-related quality of life after STEMI and the limited attention to behavioral determinants in recovery research, narrowing an important gap between psychosocial theory and clinical data. By modeling these two outcomes together instead of treating them as separate endpoints, our findings frame post-STEMI recovery as a patient-centered process that unfolds over time rather than as a short-term response to an acute event. At the level of routine care, the results support the systematic assessment of stress, sleep, coping, and social vulnerability in STEMI follow-up and give a clear rationale for embedding targeted psychosocial and exercise-based elements within standard cardiac rehabilitation programs to modify adverse psychosocial trajectories. These data support integrating brief psychosocial screening tools, including the K6, SF-12, and PSQI, into routine STEMI follow-up to identify patients at elevated risk of adverse stress\u0026ndash;HRQoL trajectories. Future studies should link joint psychosocial trajectories with hard cardiovascular endpoints and rigorously test tailored, multicomponent interventions in randomized designs. High-quality STEMI care should focus not only on restoring coronary perfusion but also on actively maintaining psychological well-being and health-related quality of life as core outcomes of treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAHA\u0026emsp;American Heart Association\u003c/p\u003e\n\u003cp\u003eBMI\u0026emsp;Body Mass Index\u003c/p\u003e\n\u003cp\u003eCI\u0026emsp;Confidence Interval\u003c/p\u003e\n\u003cp\u003eCISS\u0026emsp;Coping Inventory for Stressful Situations\u003c/p\u003e\n\u003cp\u003eHRQoL\u0026emsp;Health-Related Quality of Life\u003c/p\u003e\n\u003cp\u003eHPA\u0026emsp;Hypothalamic\u0026ndash;Pituitary\u0026ndash;Adrenal\u003cbr\u003e\u0026nbsp;K6\u0026emsp;Kessler Psychological Distress Scale\u003c/p\u003e\n\u003cp\u003eLVEF\u0026emsp;Left Ventricular Ejection Fraction\u003c/p\u003e\n\u003cp\u003eMI\u0026emsp;Myocardial Infarction\u003c/p\u003e\n\u003cp\u003eNIMAD\u0026emsp;National Institute for Medical Research Development\u003c/p\u003e\n\u003cp\u003ePA\u0026emsp;Physical Activity\u003c/p\u003e\n\u003cp\u003ePSQI\u0026emsp;Pittsburgh Sleep Quality Index\u003c/p\u003e\n\u003cp\u003eR̂\u0026emsp;Potential Scale Reduction Factor (R-hat)\u003c/p\u003e\n\u003cp\u003eSD\u0026emsp;Standard Deviation\u003c/p\u003e\n\u003cp\u003eSES\u0026emsp;Socioeconomic Status\u003c/p\u003e\n\u003cp\u003eSF-12\u0026emsp;12-Item Short Form Health Survey\u003c/p\u003e\n\u003cp\u003eSTEMI\u0026emsp;ST-Segment Elevation Myocardial Infarction\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original multicenter STEMI cohort was approved by the Ethics Committee of the National Institute for Medical Research Development (NIMAD), Tehran, Iran (approval code IR.NIMAD.REC.1397.295) and by the ethics committees of the participating universities of medical sciences. The present secondary analysis was additionally approved by the Research Ethics Committee of the Schools of Dentistry, Health Sciences and Advanced Medical Technologies, Isfahan University of Medical Sciences (approval code IR.MUI.DHMT.REC.1404.157). Written informed consent was obtained from all participants at enrollment. All procedures complied with the Declaration of Helsinki and relevant national regulations. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used for the present analysis was provided to the investigators in de-identified form. (The dataset is not publicly available due to participant confidentiality and institutional policies; de-identified data may be made available from the corresponding author upon reasonable request and subject to institutional approvals and data-sharing agreements.)\u003cbr\u003e\u003cstrong\u003eData presentation statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data in this manuscript have not been previously presented in abstract form at scientific meetings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe parent multicenter STEMI cohort was supported by the National Institute for Medical Research Development (NIMAD), Tehran, Iran [grant number 964708]. No additional funding was received for the present secondary analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualizing the study, designing the statistical analysis plan, writing the manuscript, and approving the manuscript: NS, MM, MS, HR, and HM; Data acquisition and clinical oversight (multicenter cohort): MS and HR; Data management, data analysis/interpretation, and drafting the first version of the manuscript: NS; Statistical modeling and statistical analysis: NS, with methodological supervision: MM and HM; Clinical interpretation of findings and critical revision of the manuscript for important intellectual content: MS and HR; Supervision or mentorship: MM, MS, HR, and HM. Each author contributed important intellectual content during the manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the participants of the parent multicenter STEMI cohort and the clinical and research staff at the participating centres for their contributions to data collection.\u003cbr\u003e\u0026nbsp;The underlying cohort on which this analysis is based was supported by the National Institute for Medical Research Development (NIMAD), Tehran, Iran (grant no. 964708).\u003c/p\u003e\n\u003cp\u003eUse of artificial intelligence: ChatGPT (OpenAI) was used solely for grammatical editing and language refinement of the manuscript. No AI tools were used in data generation, analysis, interpretation, or creation of figures. The authors take full responsibility for the content of the\u0026nbsp;\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCollaboration GBoCD. Global, regional, and national burden of cardiovascular diseases and risk factors in 204 countries and territories, 1990\u0026ndash;2023. J Am Coll Cardiol. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2025.08.015\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2025.08.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChong B, Jayabaskaran J, Jauhari SM, Chan SP, Goh R, Kueh MTW, et al. Global burden of cardiovascular diseases: projections from 2025 to 2050. 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Socio-economic variations in the mental health treatment gap for people with anxiety, mood, and substance use disorders: results from the WHO World Mental Health (WMH) surveys. Psychol Med. 2018;48(9):1560\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilton K, Bull F, Bauman A. Reliability and validity testing of a single-item physical activity measure. Br J Sports Med. 2011;45(3):203\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, et al. Stan: A probabilistic programming language. J Stat Softw. 2017;76:1\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFieuws S, Verbeke G. Pairwise fitting of mixed models for the joint modeling of multivariate longitudinal profiles. Biometrics. 2006;62(2):424\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerbeke G. Linear mixed models for longitudinal data. Linear mixed models in practice: A SAS-oriented approach. Springer; 2000. pp. 63\u0026ndash;153.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Post-Traumatic Stress, Longitudinal Studies, ST Elevation Myocardial Infarction, Quality of Life","lastPublishedDoi":"10.21203/rs.3.rs-8508163/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8508163/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePsychosocial recovery after ST-segment elevation myocardial infarction (STEMI) is not well described, especially regarding how perceived stress and health-related quality of life (HRQoL) change together over time. To jointly characterize the trajectories of stress and HRQoL after first-time STEMI and identify the demographic and behavioral determinants of these trajectories.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn a multicenter cohort of 1,730 STEMI survivors, perceived stress and HRQoL were assessed 12, 24, and 36 months after discharge. A bivariate Student-t mixed-effects model estimated joint trajectories and within-person associations, adjusting for age, sex, marital status, education, physical activity, coping, and sleep quality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 1,730 patients (mean age 56.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 years; 81.9% men), mean stress increased from 1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85 at year 1 to 4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.43 at year 2 and then decreased to 3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13 at year 3, whereas HRQoL changed modestly from 32.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39 to 32.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.72 and 33.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82. Stress and HRQoL were moderately negatively correlated (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.39). Poor sleep quality was associated with higher stress (β\u0026thinsp;=\u0026thinsp;0.85; 95% CI 0.56\u0026ndash;1.14) and lower HRQoL (β = \u0026minus;2.68; 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;3.33\u0026ndash;\u0026minus;2.03), while more adaptive coping was associated with lower stress (β = \u0026minus;0.86; 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1.02\u0026ndash;\u0026minus;0.70) and higher HRQoL (β\u0026thinsp;=\u0026thinsp;3.45; 95% CI 3.05\u0026ndash;3.84).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDespite contemporary acute care, psychosocial recovery remains incomplete, with stress and HRQoL tied to sociodemographic and behavioral factors. These joint trajectories identified vulnerable subgroups and highlight the need for psychosocial assessment, coping support, and exercise-based rehabilitation into routine follow-up.\u003c/p\u003e","manuscriptTitle":"Joint Longitudinal Trajectories of Perceived Stress and Health-Related Quality of Life after First-Time ST-Segment Elevation Myocardial Infarction: A Multicenter Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 19:19:08","doi":"10.21203/rs.3.rs-8508163/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-17T17:19:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-28T07:20:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T03:13:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-27T03:13:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-01-03T16:25:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba93fd65-6ddd-4c70-8b9e-4a449813a72d","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-20T19:19:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 19:19:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8508163","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8508163","identity":"rs-8508163","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}