Abstract
Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods.Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety.Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study.Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression.Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.
Highlights
Depression and anxiety are leading causes of disability worldwide, with estimated lifetime prevalence rates of 20% [1]
Measurements consisted of baseline, a midpoint and endpoint questionnaire and daily assessed digital phenotyping variables extracted from smartphone data and wearable data (Oura Ring)
With regards to smartphone usage features, we found no significant relationship between smartphone usage duration or smartphone usage frequency and symptoms of depression, anxiety and stress
Summary
Depression and anxiety are leading causes of disability worldwide, with estimated lifetime prevalence rates of 20% [1]. Given the adverse health outcomes and costs associated with untreated conditions and the recent increase in the prevalence of common mental disorders [4,5,6], adequate diagnosis and timely treatment of depression and anxiety has become an urgent priority. Researchers have relied on questionnaire data administered by a clinician or self-reported to assess an individual’s mental health. These methods may be limited in their ability to detect the moment-by-moment changes in psychological factors that is required for preventative measures and rapid interventions. Questionnaires often take place sporadically, with long intervals between them, during which time symptoms may change considerably. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods
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