Earticle

초록

영어

Digital therapeutics deliver clinically validated treatments autonomously, yet behavioral mechanisms sustaining long-term change remain underexplored. This study examines how user interaction patterns with a mobile insomnia app influence treatment adherence and sleep outcomes. Analyzing two randomized controlled trials using fixed-effects regression, generalized random forest, and Hidden Markov Models, we find significant improvements in sleep efficiency, total sleep time, and sleep onset latency. Early-stage gains were driven by baseline depressive and anxiety symptoms, while later improvements depended on behavioral engagement patterns like bedtime regularity and routine adherence. HMM analysis identified three behavioral profiles—normative-timing, highly routinized, and disorganized—with users transitioning toward structured routines. Critically, disorganized participants showed strongest recovery following external intervention prompts, highlighting the importance of human-mediated support. These findings reframe digital therapeutics as dynamic behavioral infrastructures scaffolding self-regulation, offering design implications for adaptive, user-centered interventions.

목차

Abstract
Introduction
Context and Settings
Research Context
Empirical Settings
Empirical Analysis
Additional Analysis
Hidden Markov Model
Conclusion
References

저자

• 윤석채 [ 한국과학기술원 경영공학부 ]
• 고서우 [ 한국과학기술원 경영공학부 ]
• 오원석 [ 한국과학기술원 경영공학부 ]

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