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Published on: August 8, 2019
Automatic detection of social rhythms in bipolar disorder
Saeed Abdullah1, Mark Matthews2, Ellen Frank3
1Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA sma249@cornell.edu.
Smartphone sensing can automatically assess the Social Rhythm Metric (SRM) for individuals with bipolar disorder (BD). This technology offers a feasible way to monitor rhythmicity, a key indicator of wellbeing.
Area of Science:
- Digital health
- Psychiatry
- Wearable technology
Background:
- The Social Rhythm Metric (SRM) is a validated measure of stability and rhythmicity in bipolar disorder (BD).
- Traditional SRM assessment relies on manual patient input, which can be burdensome and infrequent.
- Objective, continuous monitoring of social rhythms is needed for better BD management.
Purpose of the Study:
- To determine if passively collected smartphone data can accurately assess the SRM in individuals with BD.
- To explore the feasibility of using automated sensing for inferring behavioral and contextual patterns relevant to BD.
Main Methods:
- Seven patients with BD participated in a 4-week study.
- Smartphones passively collected sensor data (accelerometer, microphone, location, communication).
- Participants also completed daily SRM entries via a smartphone app.
Main Results:
- Automated sensing successfully inferred SRM scores, with a generalized model achieving an RMSE of 1.40.
- Personalized models further enhanced accuracy, yielding a mean RMSE of 0.92.
- Classifiers accurately predicted stable (SRM ≥3.5) and unstable (SRM <3.5) states (precision: 0.85, recall: 0.86).
Conclusions:
- Passive smartphone sensing is a feasible method for inferring rhythmicity in individuals with BD.
- This approach can provide objective, continuous monitoring of a key marker for wellbeing.
- Digital health tools show promise for improving the management of bipolar disorder.
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