Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning
Clinical Snapshot
PICO Framework
| P — Population | 229 patients with major depressive disorder from multiple centers |
| I — Intervention | Mobile health monitoring including patient-reported outcomes (Immediate Mood Scaler, Altman Self-Rating Mania Scale) and wearable sleep tracking |
| C — Comparator | Baseline characteristics alone vs baseline plus dynamic mHealth features vs ideal clinical visit assessments |
| O — Outcomes | Depression variation trajectory prediction accuracy over 12 weeks using HAMD-17 score changes |
Bottom Line
This multi-center study of 229 patients with major depressive disorder examined whether mobile health measures could predict depression trajectory over 12 weeks. Using functional data analysis to handle sparse mobile data, researchers achieved modest prediction accuracy (55-61%) when combining baseline characteristics with dynamic mHealth features including mood scales and sleep tracking. While this represents improvement over baseline characteristics alone (<50% accuracy), the clinical utility remains questionable given the moderate accuracy rates and acknowledged poor patient adherence. The 12-week follow-up period is relatively short for depression prognosis, and the lack of external validation limits confidence in generalizability. Although the approach shows promise for reducing follow-up burden, current accuracy levels may be insufficient for reliable clinical decision-making. Further research with longer follow-up, improved adherence strategies, and external validation is needed before clinical implementation.
Key Findings
P Value: Not reported
Effect Size: Overall accuracy 54.35-60.87% with mHealth features vs <50% without
Primary Outcome: Depression trajectory prediction accuracy using k-means clustering into 4 classes
Nnt Or Sensitivity: Stable decline patients: 73.68-84.21% accuracy; Non-stable decline: 59.26-70.37% accuracy
Confidence Interval: Not reported
Clinical Application
Limited by patient adherence to mobile health monitoring and modest prediction accuracy Relevant to Australian mental health services using digital health initiatives, though TGA approval for specific mHealth devices would be required. Could complement RACGP depression management guidelines for remote monitoring. Patients with major depressive disorder requiring ongoing monitoring and treatment adjustment
Abstract
BACKGROUND: Continuous follow-up for patients with major depressive disorder (MDD) is essential for treatment decisions and a better prognosis. There remains limited evidence regarding the critical issue of depression variation trajectory prediction using mobile health (mHealth) measures. Moreover, the temporal dynamics of mHealth measures have not been fully modeled in previous studies, and the poor patient adherence to mHealth records poses great challenges to the dynamic feature modeling. OBJECTIVE: This study aimed to examine the contribution of mHealth measures in predicting depression variation trajectory for patients with MDD, with full consideration of the temporal dynamics of mHealth measures. METHODS: A total of 229 patients with MDD from a multiple-center, prospective cohort were included. A 12-week follow-up was conducted involving the collection of the Hamilton Depression Rating Scale (HAMD-17), along with patient-reported outcomes (Immediate Mood Scaler and Altman Self-Rating Mania Scale) via mobile devices and sleep duration through wearable wristbands. We used functional data analysis to extract dynamic features from the sparse mHealth records, rather than aggregating the data to a single scalar summary measure through collapsing over time. Subsequently, 3 machine learning models were applied to predict the depression variation trajectory classes based on the baseline characteristics and these extracted dynamic features. RESULTS: Based on the variation of HAMD-17 scores within 12 weeks, the participants were labeled into 4 classes through the k-means algorithm. The classes included stable decline (n=93), fluctuate decline (n=44), fast decline (n=60), and delayed and fluctuate (n=32), in light of the shape of depression trajectories. With both baseline features and dynamic features of the mHealth measures, accuracy rates for the overall data were 54.35%, 60.87%, and 56.52%, for the stable decline patients were 78.95%, 84.21%, and 73.68%, for the nonstable decline patients were 59.26%, 62.96%, and 70.37% based on the 3 machine learning models, respectively. The results were significantly superior to the prediction obtained without mHealth measures (with an overall accuracy below 50%) and only showed a marginal reduction in accuracy relative to the ideal prediction with assessment obtained from clinical visits. Moreover, in the construction of the most accurate prediction model, dynamic features of the Immediate Mood Scaler, the Altman Self-Rating Mania Scale, and sleep duration emerged as the most influential predictors, ranking first, third, and fourth, respectively, in terms of their relative importance. CONCLUSIONS: Longitudinal mHealth measures show potential in depression variation trajectory monitoring for patients with MDD even under poor patient adherence. Our work provides practical help in alleviating the follow-up burden for patients with MDD and validates the effectiveness of mHealth measures in clinical applications.
References
- 1.Zhong, R., Li, N., Xiao, L., Feng, L., Feng, Y., Wang, G., & Zhu, X. (2026). Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning. JMIR mHealth and uHealth. https://doi.org/10.2196/81397
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