Journal of Behavioral Medicine最新文献

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.

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.

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.

Hispanic individuals experience significant health disparities related to smoking. Research focused on the methods employed to quit smoking among the Hispanic population is needed to better understand how to increase engagement with evidence-based smoking cessation guidelines and mitigate smoking-related health disparities. The present investigation sought to: (1) document smoking cessation methods used in previous quit attempts, including Nicotine Replacement Therapy (NRT), and (2) test group differences (NRT use vs not) in smoking-related vulnerability processes (i.e., cigarette dependence, perceived barriers for smoking cessation, severity of problems when trying to quit, and number of prior failed quit attempts). Participants were recruited nationally throughout the United States via Qualtrics Panels and were 302 Hispanic adults (38.1% female, M_age = 35.70, SD = 8.63) who endorsed daily cigarette smoking and a prior quit attempt. Results indicated that the most common method of quitting was 'cold turkey' (65.6%), but other methods were also employed (e.g., gradual reduction of cigarettes, enlisting social support). Moreover, there was a substantial number (57.9%) who used NRT in the form of nicotine gum or patch. Additionally, across each of the criterion variables studied, those who had used NRT demonstrated greater cigarette dependence, perceived barriers for smoking cessation, severity of problems when trying to quit, and number of prior failed quit attempts when compared to persons with no such history of NRT use. Overall, these data suggest that history of NRT use may identify a subgroup of Hispanic persons in need of more intensive smoking cessation treatment.

HIV-related stigma experiences impede healthcare engagement and adversely impact people living with HIV. However, the mechanisms that account for the association between stigma and poor health are not fully known. Grounded in contemporary theories of stigma and health, we conducted a 16-month prospective cohort study of 435 younger people living with HIV (ages 18-36), with 352 (81%) retained over 16 months. We tested two primary hypotheses: (a) in cross-sectional analyses, perceived social support would mediate for the association between HIV stigma experiences and missing recent HIV care appointments; and (b) in prospective analyses, the association between stigma and HIV viral load would be serially mediated by perceived social support and missed care appointments. We tested these associations for both past stigma experiences (e.g., enacted stigma) and expectancies for future stigma (e.g., anticipated stigma). Results confirmed both hypotheses: perceived social support mediated the association between both enacted and anticipated stigma and recently missed care appointments; and the association between both enacted and anticipated stigma and viral load were serially mediated by perceived social support and missed care appointments. These findings affirm that diminished social support may explain the association between HIV-related stigma and poor health outcomes in younger people living with HIV. Results are discussed in the context of interventions designed to mitigate the adverse effects of stigma by building sustainable support networks among younger people living with HIV.

Background: Healthy lifestyle technologies (HLT), including (i) exergaming systems; (ii) fitness trackers; (iii) fitness apps; (iv) interactive exercise equipment; (v) smart apparel; and (vi) heart rate monitors are highly commercialized, but it is unclear whether their volitional use promotes physical activity (PA). The first objective was to describe the average association between use of different types of HLT and PA levels. The second objective was to assess if changes in use of HLT predict changes in PA.

Methods: We used four yearly cycles of data from the MATCH longitudinal study, when participants were 20, 21, 22 and 23 years on average, respectively. Participants self-reported their use of HLT and their PA level at every cycle. General estimating equations were used to estimate (1) the odds of reporting any PA, (2) the association between use of HLT and PA level of participants reporting PA, and (3) the association between change in use of HLT and change in PA.

Results: Among the 522 participants retained, approximately 40% reported using HLT. The most used HLT were fitness trackers (30%), followed by fitness apps (9%), interactive equipment (9%), heart rate monitors (7%), smart apparel (5%), and exergaming (4%). Whereas HLT use was not associated with the odds of reporting some PA (OR 1.2, 95% CI 0.9 -1.7), participants using at least one HLT reported 21% (95% CI 7-36%) higher levels of PA than non-users. Over the years, the adoption of HLT was not related to a change in PA, but discontinuing the use of HLT was associated with a 40% reduction in PA.

Conclusion: Although HLT users report higher PA than non-users, the use of HLT is not associated with a different likelihood of taking part in MVPA. The volitional use of HLT does not lead to an uptake in PA.