Mobile health monitoring to characterize depression symptom trajectories in primary care

Background: Classification of depression severity can guide treatment decisions. This study examined whether using repeated mobile health assessments to determine symptom trajectories is a potentially useful method for classifying depression severity. Methods: 344 primary care patients with depression were identified and recruited as part of a program of mobile health symptom monitoring and self-management support. Depression symptoms were measured weekly via interactive voice response (IVR) calls using the Patient Health Questionnaire (PHQ-9). Trajectory analysis of weekly IVR PHQ-9 scores from baseline through week 6 was used to subgroup patients according to similar trajectories. Multivariable linear regression was used to determine whether the trajectories predicted 12 week PHQ-9 scores after adjusting for baseline and 6-week PHQ-9 scores. Results: The optimal trajectory analysis model included 5 non-intersecting trajectories. The subgroups of patients assigned to each trajectory had mean baseline PHQ-9s of 197, 145, 9.5, 5.0, and 2.0, and respective mean decreases in PHQ-9s over six weeks of .3, 2.0, 3.6, 2.3, and 1.9. In regression analyses, each trajectory significantly predicted 12-week PHQ-9 scores (using the modal trajectory as a reference) after adjusting for both baseline and 6-week PHQ-9 scores. Limitations: Treatment history was unknown, findings may not be generalizable to new episodes of treatment. Conclusions: Depression symptom trajectories based on mobile health assessments are predictive of future depression outcomes, even after accounting tot typical assessments at baseline and a single follow-up time point. Approaches to classify patients disease status that involve multiple repeated assessments may provide more accurate and useful information for depression management compared to lower frequency monitoring. Published by Elsevier B.V.