Tell me your apps and I will tell you your mood

Abstract

Bipolar Disorder is a disease that is manifested with cycling periods of polar episodes, namely mania and depression. Depressive episodes are manifested through disturbed mood, psychomotor retardation, behaviour change, decrease in energy levels and length of sleep. Manic episodes are manifested through elevated mood, psychomotor acceleration and increase in intensity of social interactions. In this paper we report results of a clinical trial with bipolar patients that amongst other aspects, investigated whether changes in general behaviour of patients due to onset of a bipolar episode, can be captured through the analysis of smartphone usage. We have analysed changes in smartphone usage, specifically app usage and how these changes correlate with the self-reported patient state. We also used psychiatric evaluation scores provided by the clinic to understand correlation of the patient smartphone behaviour before the psychiatric evaluation and after the psychiatric evaluation. The results show that patients have strong correlation of patterns of app usage with different aspects of their self-reported state including mood, sleep and irritability. While, on the other hand, the patients' application usage shows discernable changes in the period before and after psychiatric evaluation.

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

“…Several mobile solutions have been proposed to utilize a self-monitoring and intervention-based treatment of depression [2-5]. One particular research approach adopted by many research groups has been to investigate how objectively measured behavioral features such as “location” and “social interaction” correlate with depression; using this approach, they have tried to differentiate euthymic and depressed states [6-11]. For example, using a mobile phone app passively recording information from sensors in the phone, Saeb et al [7] could show a statistically significant correlation between 6 different objective features, including mobile phone usage frequency and self-assessed mood using the Patient Health Questionnaire-9 (PHQ-9) scale [12] in nonclinical samples.…”

“…Many studies have found statistically significant correlations between objective behavioral features collected from mobile and wearable devices and mood symptoms in nonclinical samples of participants without psychiatric illnesses [14-17] as well as in clinical samples of patients diagnosed with psychiatric disorders [11,18-20]. …”

Structural Ageism in Big Data Approaches

“…Smartphone logs are key input for research aimed at understanding different dimensions of mobile communication, from smartphone use (Böhmer et al, 2011) or general communication behaviours (Wagner et al, 2013) to the way in which given contexts shape them (Karikoski & Soikkeli, 2013). At the intersection of social sciences and computer sciences, mobile logs help to predict selected human behaviour dimensions, including human stress (Ferdous et al, 2015), bipolar disorder states (Alvarez-Lozano et al, 2014) and other personality traits (De Montjoye et al, 2013). In the following, we describe the biases identified in this area.…”

Methodological Strategies to Understand Smartphone Practices for Social Connectedness in Later Life

“…Shared use is difficult to grasp, and it becomes more relevant when logs are used for psychometric predictions, e.g. [76][77][78], as they refer to a single user. Therefore, similarly to the questioning of self-reported use not being 'objective data,' tracked use also faces interpretive challenges as it is a proxy of usage not fully representing actual human use.…”