We use a novel method (cross-lagged hidden Markov models) to identify which combinations of job demands and resources occur among workers, how often, and how these affect mental health and vice versa. Hidden Markov models (HMM) are a longitudinal extension of latent class analysis (LCA), which can be used to measure concepts that are not directly observable. As in LCA, indicator variables are used to measure such concepts. We use twelve indicators of JDR, and five indicators of mental health. HMMs group individuals with similar response patterns on the indicators in categories of the latent variable and analyse how individuals move between these categories. Additionally, predictors can be added to the model to investigate which factors influence transitions between the identified states. We used this model to study the cross-lagged relations between JDR and mental health: how JDR in time point [Formula: see text] affects mental health in time point t and mental health in time point [Formula: see text] affects JDR in time point t. We used yearly data from the Dutch Longitudinal Internet Social Survey (LISS) from 2016 to 2023. Our sample includes respondents who were employees in 2016 and for whom we had data on their JDR and mental health for at least four years. We identified six JDR states, ranging from 'Tough job' (high demands and few resources) to 'Dream job' (moderate demands and very high resources). We also identified three mental health states: poor, moderate, and good. Among those in moderate health, transitions to good health were more common for respondents in the 'Dream job' state and less common for respondents in the 'Tough job' state. Our results suggest a healthy worker effect: transitions from states with a disadvantageous combination of JDR to better states were more common among employees in moderate or good mental health. Our study shows how HMMs can improve our knowledge on the empirical predictions of widely studied theories such as the JDR model and its interplay with mental health. This is relevant for scholars and practitioners alike.

The purpose of this study was to examine the relations between barriers, self-efficacy, and daily moderate-to-vigorous physical activity (MVPA) among adults within underresourced communities using 7-day accelerometry wear. A total of 84 adult staff from 24 underresourced afterschool programs (ASPs) completed the Self-Efficacy for PA Scale (perceived barriers) and Self-Efficacy for Exercise Questionnaire (self-efficacy). The results indicated no differences in the predominant types of PA barriers by race for adults, but European American adults reported slightly more PA barriers than African American adults within these underrecourced communities. Perceived barriers were negatively related to daily MVPA. Self-efficacy (the mediator variable) was significantly and positively related with daily MVPA. Contrary to what was expected, perceived barriers were positively related with self-efficacy. In the full mediation model, self-efficacy served as a significant mediator between barriers of PA on staff MVPA. This study highlights the negative impact of barriers on ASP staff MVPA that can be attenuated by self-efficacy and suggests that addressing barriers of culture and environmental factors, promoting self-efficacy, and exploring effective model characteristics continues to be an important research direction for future ASP staff health initiatives.Trial registration Connect Through PLAY: A Staff-based Physical Activity Intervention for Middle School Youth (Connect). https://clinicaltrials.gov/ct2/show/NCT03732144 . Registered 11/06/2018. Registration number: NCT03732144.

Behavioral science and health psychology researchers often strive to investigate treatment effects using traditional statistical approaches, such as repeated measures ANOVA. However, these methods often fall short in addressing complexities like measurement error, intraindividual variability, and change processes over time. This study introduces the Two-Wave Latent Change Score Model (2W-LCSM; Henk & Castro-Schilo, 2016) as a robust alternative for modeling treatment-induced change and its long-term behavioral consequences. We demonstrate an illustrative example using data from individuals convicted of sexual crimes, incarcerated, and completing psychotherapy programs based on cognitive behavioral therapy. Our findings highlight the utility of 2W-LCSM in capturing both within-person change and its predictive relationship with recidivism. Results indicate a significant reduction in cognitive distortions post-treatment, with latent change scores emerging as a significant predictor of reduced sexual crime recidivism. These findings underscore the value of 2W-LCSM in behavioral medicine research, offering insights for tailoring interventions and advancing statistical methodologies in the field.

This study introduces an innovative approach for analyzing longitudinal behavioral data with hidden patterns in mean (location) and intraindividual variability (scale) trajectories, using location-scale regressions with latent classes in both the location and scale parts of the model. A full Bayesian approach using Stan is adopted for the estimation of the model parameters. Using simulation studies, we demonstrate that our latent class model yields more precise and informative results, especially regarding the scale, in data exhibiting hidden patterns. Simulation results also show that our model can achieve unbiased parameter estimates as well as a high correct classification rate without over-identifying latent classes in data lacking hidden heterogeneity. Our study equips researchers with a practical tool for subgrouping subjects based on both mean and within-subject variability trajectories of longitudinal outcomes. As an illustration, the latent class model is applied to calorie intake data from a weight loss management study. The integration of latent classes into intraindividual variability trajectories of calorie intake facilitates an understanding of dietary behavior consistency, aiding in personalized weight management interventions.

Sleep health disparities are well documented, whereas racial differences in treatment response to sleep interventions, are not. This single arm sleep intervention study explored treatment-response differences in sleep behaviors, quality of life, well-being, depressive symptoms, and daytime sleepiness between White and Underrepresented racial groups, as well as racial differences in pre-treatment sleep-relevant characteristics. Middle-aged adults at risk for the metabolic syndrome with short sleep duration (N = 41; 49% Underrepresented racial group [n = 20], 51% White [n = 21]) participated in a virtually-delivered, 12-week personalized systematic sleep time extension informed by cognitive behavioral therapy for insomnia. Sleep behaviors were estimated using wrist actigraphy. Quality of life, emotional well-being, daytime sleepiness, chronotype preference, daytime sleepiness, depressive symptoms, quality of life, and well-being were assessed using validated surveys. Sleep environment, race, and socio-demographic characteristics were self-reported. Underrepresented participants had a greater increase in fragmentation indexes and a greater improvement in emotional well-being from pre to post-intervention compared to their White counterparts of medium and medium-to-large magnitude, respectively. Within each racial group, statistically and clinically significant improvements in sleep duration and daytime sleepiness were found. Within the Underrepresented group, the sleep regularity index increased and sleep onset times advanced significantly. These exploratory findings suggest that future studies with larger samples should investigate the modulating effects of chronotype on sleep intervention treatment response for Underrepresented racial groups and the upstream contextual and systemic factors impacting sleep.Trial registration numberTrial registration number ClincalTrials.gov NCT03596983.

Longitudinal mixture modeling allows for estimation of person-level patterns when there is heterogeneity in how people change over time. We demonstrate two modeling approaches: latent class growth analysis/growth mixture modeling (LCGA/GMM) and repeated measures latent profile analysis (RMLPA). The data originated from a randomized trial examining mechanisms of exercise behavior maintenance. We previously reported that average affective response remained stable during exercise training. The present study tests whether affective response over time could be best described through the estimation of latent subpopulations. Secondary analysis of women (n = 201, mean age = 37.4; baseline mean BMI = 29.3) recruited for a 16-week randomized trial of exercise intensity/duration. Affective response was measured within exercise bout (minutes 0, 10, 20, 30, and 40) over four waves (weeks 1, 4, 8, and 16). LCGA/GMM was the primary approach for average-bout affective response (4 time points; "wave-level"), where a 3-class solution emerged of "stable," "high, increasing," and "decreasing" affective response patterns over time. RMLPA was used for minute-interval analyses where a four-class solution emerged. Weighted analyses examined theoretical outcomes (e.g., change in VO2max, posttest Theory of Planned Behavior constructs) of latent class membership. Person-centered methodologies demonstrated heterogeneity in affective response over time and within specific exercise bouts. The rich longitudinal data structure facilitated illustration and comparison between methods in terms of: (1) assumptions about functional form, missing data, and random effects; (2) consideration of across wave versus within bout changes; and (3) implications of modeling choice on theory development. Supplemental materials include annotated MPlus and R code for data visualization and model estimation.

We conducted a preregistered experiment testing the comparative efficacy of five common communication strategies (fear, disgust, values affirmation/gain frame, disrupt-then-reframe, or self-efficacy) in people who do and do not avoid colorectal cancer (CRC) information. Participants (N = 274; 45-74 years, not CRC screening adherent, no history of CRC, 49% CRC information avoiders) were randomly assigned to view one of five brief CRC screening intervention videos or an attentional control video, after which they completed assessments of affect and screening intentions followed by manipulation checks. Participants who watched the fear, disgust, disrupt-then-reframe and self-efficacy videos, rather than the control video, had stronger intentions to talk to a doctor about screening and those who watched fear and self-efficacy videos had stronger intentions to be screened. Video effects on intentions did not vary between avoiders and nonavoiders, but affective responses did. Nonavoiders reported more positive affect if they viewed CRC videos rather than the control video, but the trend reversed for avoiders. There were indirect effects of avoidance on intentions through positive affect. Depressed positive affect in response to threatening information may be part of an avoidance affective signature that could undermine motivation to engage in health behaviors such as cancer screening.

The Random-Intercept Cross-Lagged Panel Model (RICLPM) has gained popularity in longitudinal research due to its ability to disaggregate within- and between-subjects variance. This approach more accurately depicts processes over time compared to traditional Cross-Lagged Panel Models (CLPMs). While RICLPMs are increasingly used, their application to data from behavioral interventions still is underexplored. This study aims to address this gap by demonstrating the application of RICLPM using data from a clinical trial of the digital Unified Protocol (iUP), a transdiagnostic cognitive-behavioral intervention applicable to mental and physical health comorbidities. We focus specifically on how RICLPM can be used to examine dynamic psychological processes during treatment, a central yet under-addressed question in behavioral medicine. We provide a methodological tutorial on adapting the model to intervention outcomes data, compare model fit statistics from an RICLPM and a traditional CLPM, and interpret results specifically in the context of psychological processes during a cognitive-behavioral intervention. Our findings show that RICLPM offers superior fit and more precise estimates of within-subject processes, underscoring its value in clinical research. We argue that adopting RICLPM in behavioral medicine research can help accurately identify psychological mechanisms and processes during behavioral interventions in health settings, aiding intervention personalization. The tutorial offers a resource for researchers interested in using RICLPM for more robust longitudinal analyses of behavioral intervention outcomes.