Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study

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Servia-Rodríguez, S 
Rachuri, KK 
Rentfrow, PJ 
Lathia, N 

Measuring mental well-being with mobile sensing has been an increasingly active research topic. Pervasiveness of smartphones combined with the convenience of mobile app distribution platforms (e.g., Google Play) provide a tremendous opportunity to reach out to millions of users. However, the studies at the confluence of mental health and mobile sensing have been longitudinally limited, controlled, or confined to a small number of participants. In this paper we report on what we believe is the largest longitudinal in-the-wild study of mood through smartphones. We describe an Android app to collect participants’ self-reported moods and system triggered experience sampling data while passively measuring their physical activity, sociability, and mobility via their device’s sensors. We report the results of a large-scale analysis of the data collected for about three years from 18; 000 users. The paper makes three primary contributions. First, we show how we used physical and software sensors in smartphones to automatically and accurately identify routines. Then, we demonstrate the strong correlation between these routines and users’ personality, well-being perception, and other psychological variables. Finally, we explore predictability of users’ mood using their passive sensing data. Our findings show that, especially for weekends, mobile sensing can be used to predict users’ mood with an accuracy of about 70%. These results have the potential to impact the design of future mobile apps for mood/behavior tracking and interventions.

46 Information and Computing Sciences, 4608 Human-Centred Computing, Basic Behavioral and Social Science, Mental Health, Clinical Research, Behavioral and Social Science, Networking and Information Technology R&D (NITRD), Mental health, 3 Good Health and Well Being
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26th International World Wide Web Conference
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Association for Computing Machinery
Engineering and Physical Sciences Research Council (EP/I032673/1)
Engineering and Physical Sciences Research Council (EP/K019392/1)
This work was supported by the EPSRC through Grants UBHAVE (EP/I032673/1) and GALE (EP/K019392).