Mobile Sensing at the Service of Mental Well-being

Abstract

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.

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

“…Several research groups chose to present their correlation results in absolute values [9,49,89]. This is a problem because the directionality of the correlation is lost, and the only information left is a measure of the strength of the relation.…”

“…We observed very different behaviors among users, having in some cases positive correlations, in others, negative ones and in others no correlation at all [89]. …”

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

“…Several research groups chose to present their correlation results in absolute values [9,50,76]. This is a problem since the directionality of the correlation is lost, and the only information left is a measure of the strength of the relation.…”

“…Canzian & Musolesi [9] clearly visualized this problem in several histogram plots representing each subject correlation values which almost resembles a normal distribution around zero, but with a tail towards one of the directions. Raw correlation values were provided when requested from one of three studies [9] while the other two commented on their choice of reporting absolute values of the correlation: "We observed very different behavior among users, having in some cases positive correlations, in others negative ones and in others no correlation at all" [76]. However, as Figure 3 and 4 shows, there are several consistent correlations between features and mood assessments.…”

Designing Effective Movement Digital Biomarkers for Unobtrusive Emotional State Mobile Monitoring

“…Likamwa et al proposed the use of mobile sensing and interaction logs (such as SMS, email, phone call, application usage, web browsing, and location) for predicting users' daily average mood [7]. In [20] Servia et al present a longitudinal study based on data collected by means of a smartphone application investigating the relation between user's activity and sociability and a variety of psychological dimensions, such as perception of health, life satisfaction, and connectedness.…”