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Background: Mobile phone sensor technology has great potential in providingbehavioral markers of mental health. However, this promise has not yet beenbrought to fruition. Objective: The objective of our study was to examinechallenges involved in developing an app to extract behavioral markers ofmental health from passive sensor data. Methods: Both technical challenges andacceptability of passive data collection for mental health research wereassessed based on literature review and results obtained from a feasibilitystudy. Socialise, a mobile phone app developed at the Black Dog Institute, wasused to collect sensor data (Bluetooth, global positioning system, and batterystatus) and investigate views and experiences of a group of people with livedexperience of mental health challenges (N=32). Results: On average, sensor datawere obtained for 55% (Android) and 45% (iPhone OS) of scheduled scans. Batterylife was reduced from 21.3 hours to 18.8 hours when scanning every 5 minuteswith a reduction of 2.5 hours or 12%. Despite this relatively small reduction,most participants reported that the app had a noticeable effect on theirbattery life. In addition to battery life, the purpose of data collection,trust in the organization that collects data, and perceived impact on privacywere identified as main factors for acceptability. Conclusions: Based on thefindings of the feasibility study and literature review, we recommend acommitment to open science and transparent reporting and stronger partnershipsand communication with users. Sensing technology has the potential to greatlyenhance the delivery and impact of mental health care. Realizing this requiresall aspects of mobile phone sensor technology to be rigorously assessed.

Using mobile phone sensor technology for mental health research: Integrated analysis to identify hidden challenges and potential solutions