Developing a smartphone application for depression: tracking risk and wellness factors

Abstract

In a time of growing connectivity, technological advances in smartphone capabilities have come to the attention of mental health researchers. Smartphones are now widely used to assess and deliver care for a number of different disorders across the mental health spectrum. However, applications (apps) utilize only a small portion of the data that are collected from these phones. Researchers are beginning to take advantage of the wide array of data collected by smartphones to monitor and predict mental health and behavioral change. The current study assessed the feasibility and utility of an Android-based application designed to measure five areas of functioning related to depression: mood, social functioning, cognitive factors, coping, and lifestyle factors, using both actively inputted and passively collected data. Users were one hundred and fourteen college students who completed daily surveys and allowed the app to collect data over a two-week period. Overall, participants were compliant, with rates of completion ranging from 85% to 93% on the daily, weekly, and morning sleep questionnaires. User feedback also indicated that the app was easy to use (95.6%). Pearson correlations were conducted to examine the associations between depression symptoms, average daily negative and positive mood, mood variability, and the active and passive data collected by the app in the five domains of health and wellness. Overall, correlations indicated strong associations between mood-related variables and items in the domains of social functioning, cognitive factors, and lifestyle factors. However, negative and positive emotion word variables from the Linguistic Inquiry Word Count program and the coping variables performed less well, which may indicate the need to improve or remove these items.

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