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This observational study will employ a multi-modal observation methodology, integrating data from wearable devices and smartphones to establish comprehensive digital biomarkers for identifying symptoms of mental health conditions in university students. The study aims to identify the digital behavioural markers associated with mental health conditions, develop predictive algorithms for mental health states from digital markers, and identify university students at risk for mental health conditions.
Full description
In this study, participants will be monitored over a six-month period to determine the association between digital behavioural markers and mental health symptoms among university students. Participants will be passively monitored to collect digital phenotyping data (e.g., physical activity, sleep activity, heart rate, physiological patterns, sociability indices, finger taps, ambient light, phone states, etc.), using a wearable device and sensors from the smartphone. Participants will also complete self-report questionnaires at Months 0, 1, 3, and 6, to track changes in mental health symptoms and behavioural data. The collected data will be analysed to examine the longitudinal changes in depression, anxiety, and other mental health issues among university students and explore the feasibility of data collection procedures.
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500 participants in 1 patient group
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Zhi Wei Tan, PhD
Data sourced from clinicaltrials.gov
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