Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

Taylor A Braund; May The Zin; Tjeerd W Boonstra; Quincy J J Wong; Mark E Larsen; Helen Christensen; Gabriel Tillman; Bridianne O'Dea

doi:10.2196/35549

Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

JMIR Ment Health. 2022 May 4;9(5):e35549. doi: 10.2196/35549.

Authors

Taylor A Braund^{1

2}, May The Zin², Tjeerd W Boonstra^{1

2

3}, Quincy J J Wong^{1

4}, Mark E Larsen^{1

2}, Helen Christensen^{1

2}, Gabriel Tillman⁵, Bridianne O'Dea^{1

2}

Affiliations

¹ Black Dog Institute, University of New South Wales, Sydney, Australia.
² Faculty of Medicine and Health, University of New South Wales, Sydney, Australia.
³ Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
⁴ School of Psychology, Western Sydney University, Sydney, Australia.
⁵ School of Science, Psychology and Sport, Federation University, Ballarat, Australia.

PMID: 35507385
PMCID: PMC9118091
DOI: 10.2196/35549

Abstract

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.

Keywords: bipolar disorder; circadian rhythm; depression; mobile app; mobile phone; sensors.

©Taylor A Braund, May The Zin, Tjeerd W Boonstra, Quincy J J Wong, Mark E Larsen, Helen Christensen, Gabriel Tillman, Bridianne O’Dea. Originally published in JMIR Mental Health (https://mental.jmir.org), 04.05.2022.