Up to 2500 participants will be enrolled to develop algorithms for predicting depression and anxiety using smartphone and smartwatch data.
The study aims to identify individuals at risk for depressive and anxiety disorders through .
Data collection will involve both active self-reports and passive smartphone usage metrics over a 4-week period.
techniques will be employed to analyze digital biomarkers for predicting levels of depression and anxiety.
Features such as physical activity, location, and sleep patterns may serve as potential markers for mental health assessment.
The study will explore the feasibility and acceptability of using mobile devices for collecting mental health data.
Simplified
INTRODUCTION: Depression and anxiety are highly prevalent mental health conditions that significantly affect quality of life and cause societal burdens. However, their detection and diagnosis rates remain low owing to the limitations of the current screening methods. With rapid technological advancements and the proliferation of consumer-grade wearable devices and smartphones, their integration into research has enabled the unobtrusive screening for depression and anxiety in natural settings. The Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety study aims to develop prediction algorithms to identify individuals at risk for depressive and anxiety disorders, as well as those with mild-to-severe levels of either condition or both. By collecting comprehensive data using smartphones and smartwatches, this study aims to facilitate the translation of artificial intelligence-based early detection research into clinical impact, thereby potentially enhancing patient care through more accurate and timely interventions.
METHODS AND ANALYSIS: This cross-sectional observational study will enrol up to 2500 participants (at least 1000) aged 19-59 years from South Korea via social media outreach and clinical referrals. The eligible participants must use a compatible smartphone. Each participant will be followed up for 4 weeks. Data will be collected using a custom-developed smartphone application called PixelMood. Active data collection will include daily, weekly and monthly self-report questionnaires incorporating validated scales, such as the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7. Passive data from smartphones include information on physical activity, location, ambient light and smartphone usage patterns. Optionally, participants using the Apple Watch or Galaxy Watch devices can provide additional data on physiological responses and sleep health. The primary outcome will be the development of algorithms to predict depression and anxiety based on these digital biomarkers. We will employ various machine-learning techniques, including random forest, support vector machine and deep-learning models. The secondary outcomes will include the association between digital biomarkers and clinical measures, and the feasibility and acceptability of data collection methods. Various features characterising mobile usage behaviours, physical/social activity, sleep patterns, resting physiological states and circadian rhythms will be exploited to serve as potential digital phenotyping markers. Advanced machine-learning and deep-learning techniques will be applied to multimodal data for model generation.
ETHICS AND DISSEMINATION: This study protocol was reviewed and approved by the Institutional Review Board of the Korea University Anam Hospital (approval number: 2023AN0506). The results of this study will be disseminated via multiple channels. The findings will be presented at local, national and international conferences in relevant fields, such as psychiatry, psychology and digital health. Manuscripts detailing the study results will be submitted to peer-reviewed journals for publication.
TRIAL REGISTRATION NUMBER: The present study was registered with the Clinical Research Information Service (CRIS, https://cris.nih.go.kr; identifier: KCT0009183).
Key numbers
2500
Participant Enrollment Target
Up to 2500 participants will be recruited for the study.
1000
Minimum Enrollment Requirement
At least 1000 participants are required for the study.
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