%0 Journal Article
%@ 2368-7959
%I JMIR Publications
%V 9
%N 5
%P e35549
%T Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study
%A Braund,Taylor A
%A Zin,May The
%A Boonstra,Tjeerd W
%A Wong,Quincy J J
%A Larsen,Mark E
%A Christensen,Helen
%A Tillman,Gabriel
%A O’Dea,Bridianne
%+ Black Dog Institute, University of New South Wales, Hospital Road, Sydney, 2031, Australia, 61 2 9065 9255, t.braund@blackdog.org.au
%K depression
%K bipolar disorder
%K sensors
%K mobile app
%K circadian rhythm
%K mobile phone
%D 2022
%7 4.5.2022
%9 Original Paper
%J JMIR Ment Health
%G English
%X Background: Mood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode. Objective: Using smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical sample of adults diagnosed with major depressive disorder or bipolar disorder. Methods: A total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline. Results: Although we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants’ social support networks at baseline (r=0.22; P=.03; R2=0.049). In participants with bipolar disorder, circadian rhythm was associated with a change in anxiety from baseline; a higher circadian rhythm was associated with an increase in anxiety and a lower circadian rhythm was associated with a decrease in anxiety at time point 5. Conclusions: Circadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical sample of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders. 
%M 35507385
%R 10.2196/35549
%U https://mental.jmir.org/2022/5/e35549
%U https://doi.org/10.2196/35549
%U http://www.ncbi.nlm.nih.gov/pubmed/35507385