JMIR formative research · 2026
Behavioral and Contextual Patterns Derived From Ecological Momentary Assessment and Wearable Sensors and Their Associations With Health-Related Outcomes: WEALTH Cross-Sectional Study.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Kose Junko, Bouchan Jerome, Fezeu Léopold K, Gauffeny Nastasia, Buck Christoph, Cimler Richard, Donnelly Alan, Elavsky Steriani, Harrington Janas, Hayes Grainne, Hebestreit Antje, Sigcha Luis, Vetrovsky Tomas, Cardon Greet, Oppert Jean-Michel
Аннотация
BACKGROUND: Physical activity, sedentary behavior, sleep, and eating behavior are recognized as key contributors to physical and mental health. Ecological momentary assessment (EMA) can capture both the temporal variations and contextual correlates of these behaviors.
OBJECTIVE: This study aimed to identify, in adults, (1) multibehavioral clusters, including the social, physical, and psychological contexts as assessed by EMA; and (2) the associations of these behaviors with selected health-related outcomes.
METHODS: A sample of 510 participants from Czechia, Germany, France, and Ireland, with cross-sectional data collected in 2023-2024, was included (WEALTH study; median age 35.5, IQR 25.0-50.0 y; n=289, 56.7% female participants). During a 7-day free-living period, data on sedentary behavior (activPAL), physical activity (ActiGraph), sleep, eating behavior, and contextual characteristics (EMA) were collected. Dietary intake (Food Frequency Questionnaire) was assessed prior to the 7-day period. We used factor analysis of mixed data on 35 variables and then applied hierarchical clustering on principal components. Linear regression models with robust variance were used to examine associations between the clusters and health-related quality of life (36-Item Short Form Health Survey), well-being (5-item World Health Organization Well-Being Index), and handgrip strength. A multinomial logistic regression was used to examine the association between clusters and self-rated health.
RESULTS: Four multibehavioral clusters were identified: unhealthy behavior (n=109, reference group), mixed behavior-healthy diet (n=173), mixed behavior-stable good mood (n=142), and healthy behavior (n=86). The mixed behavior-stable good mood and healthy behavior clusters showed significantly higher average scores on mental health-related quality of life (β=6.32, 95% CI 3.73-8.91 and β=5.61, 95% CI 2.47-8.75, respectively) and well-being (β=11.62, 95% CI 7.86-15.38 and β=9.63, 95% CI 5.17-14.10, respectively) compared with the unhealthy behavior cluster. Participants in these clusters and those in the mixed behavior-healthy diet cluster tended to report better perceived health than those in the unhealthy behavior cluster (odds ratio [OR] 3.19, 95% CI 1.20-8.49; OR 9.85, 95% CI 3.54-27.40; OR 12.18, 95% CI 4.04-36.71, respectively).
CONCLUSIONS: These findings indicate that engaging in multiple healthy lifestyle behaviors is associated with better health outcomes than focusing on a single domain and demonstrate the opportunity to identify meaningful multibehavioral patterns related to health by using EMA combined with body-worn movement sensors.
OBJECTIVE: This study aimed to identify, in adults, (1) multibehavioral clusters, including the social, physical, and psychological contexts as assessed by EMA; and (2) the associations of these behaviors with selected health-related outcomes.
METHODS: A sample of 510 participants from Czechia, Germany, France, and Ireland, with cross-sectional data collected in 2023-2024, was included (WEALTH study; median age 35.5, IQR 25.0-50.0 y; n=289, 56.7% female participants). During a 7-day free-living period, data on sedentary behavior (activPAL), physical activity (ActiGraph), sleep, eating behavior, and contextual characteristics (EMA) were collected. Dietary intake (Food Frequency Questionnaire) was assessed prior to the 7-day period. We used factor analysis of mixed data on 35 variables and then applied hierarchical clustering on principal components. Linear regression models with robust variance were used to examine associations between the clusters and health-related quality of life (36-Item Short Form Health Survey), well-being (5-item World Health Organization Well-Being Index), and handgrip strength. A multinomial logistic regression was used to examine the association between clusters and self-rated health.
RESULTS: Four multibehavioral clusters were identified: unhealthy behavior (n=109, reference group), mixed behavior-healthy diet (n=173), mixed behavior-stable good mood (n=142), and healthy behavior (n=86). The mixed behavior-stable good mood and healthy behavior clusters showed significantly higher average scores on mental health-related quality of life (β=6.32, 95% CI 3.73-8.91 and β=5.61, 95% CI 2.47-8.75, respectively) and well-being (β=11.62, 95% CI 7.86-15.38 and β=9.63, 95% CI 5.17-14.10, respectively) compared with the unhealthy behavior cluster. Participants in these clusters and those in the mixed behavior-healthy diet cluster tended to report better perceived health than those in the unhealthy behavior cluster (odds ratio [OR] 3.19, 95% CI 1.20-8.49; OR 9.85, 95% CI 3.54-27.40; OR 12.18, 95% CI 4.04-36.71, respectively).
CONCLUSIONS: These findings indicate that engaging in multiple healthy lifestyle behaviors is associated with better health outcomes than focusing on a single domain and demonstrate the opportunity to identify meaningful multibehavioral patterns related to health by using EMA combined with body-worn movement sensors.