Depression is a prevalent, debilitating illness characterized by emotional dysregulation and cognitive impairment. Yet, our understanding of depression remains inadequate due in part to features of the illness that are difficult to measure such as disturbances in sleep, circadian disorganization, and atypical moment-to-moment variation in affective state. Actigraphy and ?BiAffect? are well-suited to evaluate these aspects of depression. Actigraphy utilizes accelerometer technology to monitor rest-activity patterns and is validated to estimate naturalistic sleep and circadian timing. Disturbances in sleep, in particular insomnia, and circadian timing (e.g., delayed sleep timing) are highly prevalent in depression and evidence suggests they play a role in depression severity and symptomatology (e.g., emotional dysregulation, cognitive difficulties). BiAffect is an innovative smartphone app comprising a secure virtual keyboard that utilizes dynamic variation in typing behavior that is sensitive to mood and cognitive function dynamics. Separate lines of research provide support for these technologies in the study of depression. Recent pilot data comprising 28+ un-medicated patients with primary or comorbid depression showed more fragmented sleep over the course of 1 week was significantly associated with greater depression level. More fragmented sleep also corresponded with more atypical brain response during emotion processing (e.g., less mid-frontal neural activity during error detection), independent of depression severity. For BiAffect, our published data showed depression was significantly predicted by typing behavior and movement; specifically, greater severity was predicted by more interkey delay, more autocorrect rate, and more accelerometer displacement in 7 patients over a 6-week period suggesting less focus/concentration and/or more psychomotor activity (e.g., agitation) portended depression severity. Pilot data also demonstrated interkey delay dynamics reflected diurnal patterns indicating BiAffect may serve as a proxy of circadian organization. Altogether, findings provide support for the feasibility of BiAffect. The proposed 2-year study endeavors to validate the novel BiAffect app and fill important gaps in the literature. Over the course of 6 weeks we will combine wrist actigraphy with Biaffect in 70 participants with depression, 50 participants with insomnia, and 50 healthy controls. We expect actigraphy and BiAffect data will each prospectively predict weekly depression severity and cognitive function and bi-weekly neurocognitive and brain-behavioral response during emotion processing and emotion regulation. We expect these effects will be more robust in the depressed group relative to the insomnia group, which will be more robust compared to healthy controls.

Public Health Relevance

Passive sensing technologies uniquely capture transitory, dynamic behaviors in real-life settings and as such are well-suited to advance our understanding of disturbances in sleep and circadian organization, which are common in depression. This longitudinal project combines wrist-actigraphy that utilizes rest-activity patterns with BiAffect an innovative mobile smartphone technology that utilizes typing kinematics metadata. These technologies will be used to prospectively predict depression symptomatology.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Exploratory/Developmental Grants (R21)
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Adult Psychopathology and Disorders of Aging Study Section (APDA)
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Leitman, David I
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University of Illinois at Chicago
Schools of Medicine
United States
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