Sensing behavioral symptoms of mental health and delivering personalized interventions using mobile technologies

Abstract

Unlike most other health conditions, the treatment of mental illness relies on subjective measurement. In addition, the criteria for diagnosing mental illnesses are based on broad categories of symptoms that do not account for individual deviations from these criteria. The increasing availability of personal digital devices, such as smartphones that are equipped with sensors, offers a new opportunity to continuously and passively measure human behavior in situ. This promises to lead to more precise assessment of human behavior and ultimately individual mental health. More refined modeling of individual mental health and a consideration of individual context, assessed through continuous monitoring, opens the way for more precise and personalized digital interventions that may help increase the number of positive clinical outcomes in mental healthcare. In this paper, we provide a conceptual review of such techniques for measuring, modeling, and treating mental illness and maintaining mental health.

Engagement with a cognitive behavioural therapy mobile phone app predicts changes in mental health and wellbeing: MoodMission

“…The ubiquity, portability, and ease of data entry of smartphones makes them ideal tools for health and behaviour change interventions (Aung, Matthews, & Choudhury, 2017;Dogan, Sander, Wagner, Hegerl, & Kohls, 2017;Wendel, 2013). Simultaneously, the need for accessible mental health interventions continues to be dire.…”

“…Conclusions: Engaging with an app that provides CBT strategies can increase mental wellbeing, and coping self-efficacy may mediate effects of the app in individuals experiencing moderate depression or anxiety. The ubiquity, portability, and ease of data entry of smartphones makes them ideal tools for health and behaviour change interventions (Aung, Matthews, & Choudhury, 2017;Dogan, Sander, Wagner, Hegerl, & Kohls, 2017;Wendel, 2013). Simultaneously, the need for accessible mental health interventions continues to be dire.…”

The Application of User Event Log Data for Mental Health and Wellbeing Analysis

“…This was demonstrated in a study by Saeb et al (2015), in which the authors demonstrated significant correlations between the user"s Patient Health Questionnaire scores (PHQ-9; Kroenke & Spencer, 2002) and geo-locational data captured by the user"s smartphone; the metrics used were 24 hour circadian movement, normalised location entropy (i.e. consistency of the user"s presence between routinely visited locations), location variance, phone use duration and frequency maximum (Saeb et al, 2015;Aung, Matthews and Choudhury, 2017).…”

“…For instance, the Diagnostic and Statistical Manual (DSM) outlines the clinical criteria for all known mental illnesses, to which the criteria assess behaviour as a direct symptom of the mental illness itself. A practitioner can see from a user"s event log from a smartphone application that decreased physical activity, irregular sleep patterns and lowered social interactions are signs of depression (American Psychiatric Association, 2014;Aung, Matthews and Choudhury, 2017). This was demonstrated in a study by Saeb et al (2015), in which the authors demonstrated significant correlations between the user"s Patient Health Questionnaire scores (PHQ-9; Kroenke & Spencer, 2002) and geo-locational data captured by the user"s smartphone; the metrics used were 24 hour circadian movement, normalised location entropy (i.e.…”

Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data

“…The fact that they can provide frequent and non-obtrusive recording of PA in a real-world environment offers tremendous opportunities for health and makes them ideal instruments in a large variety of applications including mobile health monitoring (mhealth) [ 16 ], e.g. of chronically ill or elderly patients [ 17 ], sensing behavioural symptoms of mental health [ 18 ], self-monitoring for promoting PA levels [ 19 , 20 ] or studies of sentinel behaviour [ 21 ]. Wearable devices usually contain an accelerometer and, with increased computing power, more functions may be included such as heart rate sensors, ambient light sensors, temperature sensors, altimeters, etc., potentially providing very rich and complex data scenarios.…”