Diagnosis of depressive symptoms using mobile devices: a systematic review
DOI:
https://doi.org/10.37914/riis.v8i2.439Keywords:
systematic review, depression disorder, remote sensors, mHealthAbstract
Background: depression is a widespread mental health issue, often underdiagnosed and undertreated due to reliance on subjective self-reports and limited access to care. Mobile health (mHealth) technologies, utilizing smartphone and wearable sensors, offer innovative solutions for objective and scalable diagnostics. Objectives: this review examines the effectiveness of mobile device sensors in diagnosing depression, identifying relevant biosignals, and exploring diagnostic methods. Methodology: a systematic search following PRISMA-DTA guidelines was conducted in MEDLINE/PubMed and Embase, focusing on diagnostic accuracy of mHealth sensors validated against gold standards like DSM-5 or PHQ-9. Results: eleven studies showed that accelerometers and heart rate monitors are key in detecting movement, activity, and physiological patterns linked to depression. Machine learning algorithms, especially random forests, achieved high diagnostic accuracy. Conclusion: mHealth technologies hold promise for depression diagnostics, but improvements in methodological consistency, sample size, and external validation are necessary for broader clinical use.
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