Intraday Activity Levels May Better Reflect the Differences Between Major Depressive Disorder and Bipolar Disorder Than Average Daily Activity Levels

Abstract

It is important to establish an objective index to differentiate mood disorders (i.e., bipolar disorder; BD and major depressive disorder; MDD). The present study focused on the pattern of changes of physical activity in the amount of activity intraday, and examined the relationship between activity patterns and mood disorders. One hundred and eighteen inpatients with MDD or BD in a depressive state provided the activity data by using wearable activity trackers for 3 weeks. In order to illuminate the characteristic patterns of intraday activities, Principal Component Analysis (PCA) was adopted to extract the main components of intraday activity changes. We found that some of the PCs reflected the differences between the types of mood disorder. BD participants showed high activity pattern in the morning and low activity pattern in evenings. However, MDD showed the opposite. Our results suggest that activity tracking focused on daytime activity patterns may provide objective auxiliary diagnostic information.

Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone

“…A significantly low level of daytime activity was observed in the depression group, similar to the findings of previous research [45]. A study [15] using actigraphy reported that patients with depression displayed less daytime motor activity than did individuals without depression.…”

“…Our study findings confirmed group differences in daytime activity, light exposure in the late afternoon, and night-time sleep. Thus, we suggest the need to collect data throughout the day and identify features closely related to depression [12,45]. Third, we suggest using sensor data, such as Actiwatch or activity tracking, for monitoring purpose.…”

Opportunities for digital health technology: identifying unmet needs for bipolar misdiagnosis and depression care management

“…To reduce burden, tasks can be designed to be lightweight and strategically scheduled. For example, effective assessment scheduling for BD could leverage known sleep-wake rhythmicity (32,(56)(57)(58)(59) and apply an adaptive sampling algorithm to up-sample assessment frequency during suspected periods of mood change. Passive and active monitoring each have their respective trade-offs-the optimal solution is likely juxtaposition of the two data streams (60).…”

IoT-Based Technologies for Addressing the Unique Healthcare Needs of the Elderly Population

“…Multiple studies have been done (Aljehani et al, 2018;Ha et al, 2020) that have used IoT-based technology concerning these conditions. Tanaka et al (2018), Faedda et al (2016), O'Brien et al (2017 have used IoT-based wrist-worn accelerometers to measure the physical activities of participants with major depressive disorders, bipolar disorders, borderline personality disorders, attention deficit hyperactivity disorders, and healthy controls to distinguish between the activity patterns. The results helped understand how differences in activity models could be used to make IoT-based wearable devices for diagnosis and motivating individuals with mental disorders to engage more with the world and not let depressive thoughts kick in.…”