A significant decrease in anxiety symptoms (ß=-.41, 95% CI -0.50 to -0.33 P≤.001) was revealed during the period of 1-6 weeks, which was maintained during the period of 7-16 weeks. Coach interaction significantly moderated the reduction in depression symptoms during the period of weeks 1-6 (ß=-.03, 95% CI -0.05 to -0.001 P=.02). RESULTS: Analysis revealed a significant decrease in depression symptoms (ß=-.37, 95% CI -0.46 to 0.28 P≤.001) during the period of weeks 1-6 of app use, which was maintained during the period of 7-16 weeks. Finally, simple slope analysis was used for the interpretation of the interactions probing the moderators: coaching sessions and breathing exercises in both depression and anxiety cohorts. A piecewise mixed-effects model was applied to model the trajectories of the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7 mean scores in 2 segments (1-6 weeks and 7-16 weeks). Levels of depression (Patient Health Questionnaire-9) and anxiety (Generalized Anxiety Disorder-7) were tracked throughout the first 16 weeks. This retrospective real-world analysis of users on a mobile platform-based treatment followed two cohorts of people: (1) users who started with moderate levels of depression and completed at least 2 depression assessments (n=519) and (2) users who started with moderate levels of anxiety and completed at least 2 anxiety assessments (n=474). METHODS: Depression and general anxiety symptoms were evaluated in real-world data cohorts using the digital health platform for digital intervention and monitoring change. We hypothesized that participation in coaching sessions and breathing exercises would be associated with a reduction in depression and anxiety. OBJECTIVE: The aim of the study is to investigate the contribution of specific digital engagement tools to mental health conditions. However, there is a limited understanding of the association between digital platform components and clinical outcomes. A common goal of digital health apps is to increase user digital engagement to improve outcomes. Digital therapeutic solutions using cognitive behavioral therapy have demonstrated efficacy in depression and anxiety. Conclusions: This analytical framework offers an opportunity for investigating the personalized efficacy of digital therapeutics for pain management, taking into account users' characteristics and boosting interpretability and can benefit from including more users' characteristics.īACKGROUND: Depression and anxiety are the main sources of work and social disabilities as well as health-related problems around the world. Train duration increased during the first 3 weeks only for older users, whereas all the users decreased the training duration during the next 5 weeks. The age of the users moderated the time fluctuations in pain levels, whereas age and gender interactively moderated the trajectories in the posture quality. Results: Pain levels and posture quality demonstrated strong improvement during the first 3 weeks of the training, followed by a sustained pattern. We developed personalized models for nonlinear time-related fluctuations of pain levels, posture quality, and weekly training duration using age, gender, and body mass index as potential moderating factors. The users reported their pain levels and posture quality. Methods: We demonstrated the implementation of the model with posture biofeedback training data of 3610 users collected during 8 weeks. Objectives: This study presents an analytical framework for personalized pain management using piecewise mixed-effects model trees, considering the data dependencies, nonlinear trajectories, and boosting model interpretability. Moreover, classical ML models are not adapted for the longitudinal nature of the DT follow-up data, which may also include nonlinear fluctuations. However, the ML model accuracy is mainly associated with reduced clinical interpretability. Machine learning (ML) approaches provide a great opportunity for personalizing the efficacy of DT. However, the efficacy of such approaches demonstrates substantial heterogeneity. Introduction: Digital therapeutics (DT) emerged and has been expanding rapidly for pain management.
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