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A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings
Published on: January 22, 2018
Integration of Ecological Momentary Assessment and Intervention With Wearable-Based Digital Phenotypes to Treat
Hyunsil Song1,2, Yoobin Choi1,2, Jinho Park3
1HAII Corp, Jongno-gu, Republic of Korea.
Background:
Young adults report high levels of anxiety, yet their access to timely treatment remains limited. Mobile-based digital therapeutics, particularly ecological momentary assessment and intervention (EMA/EMI), offer a promising approach to deliver context-responsive care. The integration of wearable-derived digital phenotypes with digital therapeutics may enhance the personalization of feedback in such interventions, but empirical evidence from real-world settings remains scarce.
Objective:
This study examined the real-world applicability and effectiveness of an EMA/EMI-based digital therapeutic, ANZEILAX-Green (HAII Corp), for use in anxiety reduction in young adults and investigated how personalized feedback generated by combining EMA/EMI data with wearable-based digital phenotypes ameliorates symptoms and promotes self-management and user engagement.
Methods:
An 8-week exploratory experimental study was conducted with young adults aged 19-39 years. Participants used a smartphone app in which EMA/EMI modules had been integrated with a smartwatch that continuously collected digital phenotyping data, including heart rate, sleep, and activity. Psychological outcomes were assessed using the Generalized Anxiety Disorder 7-item scale (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) at baseline and weeks 2, 4, 6, and 8, and the Rosenberg Self-Esteem Scale (RSES) at baseline and week 8. Quantitative changes were analyzed using paired-samples t tests and repeated-measures ANOVA, and qualitative interviews were thematically analyzed to identify user experience mechanisms and design implications.
Results:
In all, 29 participants were included in the final analysis. Significant improvements were observed in their GAD-7 (mean difference -4.79, 95% CI -6.56 to -3.02; Cohen d=1.03; P<.001), PHQ-9 (mean difference -3.86, 95% CI -5.79 to -1.93; Cohen d=0.76; P<.001), and RSES scores (mean difference +5.21, 95% CI 3.47 to 6.95; Cohen d=1.14; P<.001). Consistent improvements in GAD-7 scores were observed across the assessment points, with statistically significant reductions from week 4 onward. Participants demonstrated high adherence, completing 80% of the EMA/EMI prompts. The digital phenotype showed trends of increased deep sleep and reduced mobility, and sleep indicators were significantly correlated with depressive symptoms. Analysis of the interview findings revealed 5 mechanisms: enhanced self-awareness, data-driven reflection, behavioral change, intrinsic motivation, and extension of treatment into daily life.
Conclusions:
EMA/EMI-based digital therapeutics integrated with wearable-derived digital phenotypes are feasible and acceptable for use by young adults and were associated with within-group reductions in anxiety, suggesting acceptable efficacy, though this warrants confirmation in a controlled trial. The combination of real-time self-reported assessments of emotional states and passive physiological monitoring enables personalized feedback suited to users' emotional and physiological states. These findings offer empirical and design insights for automated, personalized mental health intervention systems.
