1. Latent class analysis of actigraphy within the depression early warning (DEW) longitudinal clinical youth cohort.
- Author
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Sequeira, Lydia, Fadaiefard, Pantea, Seat, Jovana, Aitken, Madison, Strauss, John, Wang, Wei, Szatmari, Peter, and Battaglia, Marco
- Subjects
MENTAL depression risk factors ,RISK assessment ,STATISTICAL correlation ,ACADEMIC medical centers ,RESEARCH funding ,QUESTIONNAIRES ,ACCELEROMETRY ,ACTIGRAPHY ,WEARABLE technology ,STRUCTURAL equation modeling ,DESCRIPTIVE statistics ,LONGITUDINAL method ,SLEEP duration ,TECHNOLOGY ,RESEARCH ,DISEASE relapse ,DATA analysis software ,SLEEP quality ,PSYCHOLOGICAL tests ,PHENOTYPES ,SLEEP disorders ,DISEASE risk factors ,ADOLESCENCE - Abstract
Background: Wearable-generated data yield objective information on physical activity and sleep variables, which, are in turn, related to the phenomenology of depression. There is a dearth of wearable-generated data regarding physical activity and sleep variables among youth with clinical depression. Methods: Longitudinal (up to 24 months) quarterly collections of wearable-generated variables among adolescents diagnosed with current/past major depression. Latent class analysis was employed to classify participants on the basis of wearable-generated: Activity, Sleep Duration, and Sleep efficiency. The Patient Health Questionnaire adapted for adolescents (PHQ-9-A), and the Ruminative Response Scale (RRS) at study intake were employed to predict class membership. Results: Seventy-two adolescents (72.5% girls) were recruited over 31 months. Activity, Sleep Duration, and Sleep efficiency were reciprocally correlated, and wearable-generated data were reducible into a finite number (3 to 4) of classes of individuals. A PHQ-A score in the clinical range (14 and above) at study intake predicted a class of low physical activity (Acceleration) and a class of shorter Sleep Duration. Limitations: Limited power related to the sample size and the interim nature of this study. Conclusions: This study of wearable-generated variables among adolescents diagnosed with clinical depression shows that a large amount of longitudinal data is amenable to reduction into a finite number of classes of individuals. Interfacing wearable-generated data with clinical measures can yield insights on the relationships between objective psychobiological measures and symptoms of adolescent depression, and may improve clinical management of depression. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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