Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach.
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Abstract
Background Bipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent costly hospitalizations. Although several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood for one or more days. Objective This study aimed to examine the feasibility of forecasting daily subjective mood scores based on daily self-assessments collected from patients with bipolar disorder via a smartphone-based system in a randomized clinical trial. Methods We applied hierarchical Bayesian regression models, a multi-task learning method, to account for individual differences and forecast mood for up to seven days based on 15,975 smartphone self-assessments from 84 patients with bipolar disorder participating in a randomized clinical trial. We reported the results of two time-series cross-validation 1-day forecast experiments corresponding to two different real-world scenarios and compared the outcomes with commonly used baseline methods. We then applied the best model to evaluate a 7-day forecast. Results The best performing model used a history of 4 days of self-assessment to predict future mood scores with historical mood being the most important predictor variable. The proposed hierarchical Bayesian regression model outperformed pooled and separate models in a 1-day forecast time-series cross-validation experiment and achieved the predicted metrics, R2=0.51 and root mean squared error of 0.32, for mood scores on a scale of −3 to 3. When increasing the forecast horizon, forecast errors also increased and the forecast regressed toward the mean of data distribution. Conclusions Our proposed method can forecast mood for several days with low error compared with common baseline methods. The applicability of a mood forecast in the clinical treatment of bipolar disorder has also been discussed.
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12 of the 17 included studies had sample sizes smaller than 100.
“12 of the 17 included studies had sample sizes smaller than 100 [28–31,33–35,37–39,41,42].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [37]
There were a large variety of study types among the 17 studies, including pilot randomised controlled trials, observational studies, model evaluations, and a qualitative study.
“There were a large variety of study types among the 17 studies, including pilot randomised controlled trials (RCTs) [28–30], observational studies [31–35], model evaluations [36,37], and a qualitative study [38].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [37]
The most common use of artificial intelligence in the mental health apps was prediction (of mood, stress, and risk) (8/17 studies).
“The most common uses of artificial intelligence in the mental health apps were prediction (of mood, stress, and risk) (8/17 studies) [31–37,41]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [37]
3 of the 17 studies focused on predicting mood in people with mood disorders.
“3 studies each focused on predicting mood in people with mood disorders [34,35,37]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [37]
Only 2 out of the 17 studies reported used boosting algorithms.
“Only 2 out of the 17 studies reported used boosting algorithms [31,37].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [37]
Depressive mood has a strong autocorrelation.
“As depressive mood has a strong autocorrelation [48], we considered a baseline hierarchical Bayesian linear regression model with the last observed PHQ-8 score and demographics as predictors.”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [48]
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From Prediction to Personalization
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- Authors
- Busk, J., Faurholt-Jepsen, M., Frost, M., Bardram, J. E., Vedel Kessing, L., & Winther, O.
- Affiliations
- Technical University of Denmark (DK)
- University of Copenhagen (DK)
- Corresponding author
- Busk, Jonas
- Venue
- JMIR mhealth and uhealth
- Publisher
- JMIR Publications Inc.
- Published
- 2020-04-01
- Volume
- 8
- Issue
- 4
- Pages
- e15028
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC7367518
- OpenAlex
- W2996627455
- Cited by
- 58
- References
- 40
- Citations by year
- 2026: 11 · 2025: 8 · 2024: 10 · 2023: 6 · 2022: 11 · 2021: 6 · 2020: 6
- FWCI
- 6.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- Funders
- Innovationsfonden
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- bipolar disorder, Bayesian hierarchical model, multi-task learning, time series cross-validation, forecast horizon, regression to the mean, individual differences
- MeSH terms
- Smartphone, Affect, Bayes Theorem, Bipolar Disorder, Forecasting, Humans
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