Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review

Depression is highly recurrent and heterogeneous. The unobtrusive, continuous collection of mobile sensing data via smartphones and wearable devices offers a promising approach to monitor and predict individual depression trajectories, distinguish illness states, and anticipate changes in symptom severity.

This systematic review evaluates whether objective data from wearable devices and smartphones can (1) monitor and distinguish different states of depression, (2) predict changes in symptom severity, and (3) identify clinically relevant objective features for tracking and forecasting depression within diagnosed individuals.

We searched PubMed and Web of Science databases for English-language studies (published 2012-2022) that used smartphone or wearable device data, included participants aged ≥14 years with a depression diagnosis, and collected continuous data for at least 12 weeks.

Out of 12,997 peer-reviewed articles, 9 original studies met the inclusion criteria, with sample sizes ranging from 45 to 2200 and durations of 12-52 weeks. Of the 9 studies, 3 used smartphone data, 1 used wearable device data, and 5 used both data types. Commonly collected variables were step count, distance moved, smartphone usage, call logs, sleep, heart rate, light exposure, and speech patterns. One study (11%) successfully differentiated between depressive states (worsening, relapse, or recovery). Six studies (67%) showed that mobile sensing data could predict depressive episodes or symptom severity. Four studies reported the predictive accuracy for depression using mobile sensing data from smartphones and wearable devices, ranging from 81% to 91%. Higher accuracy was achieved with personalized models or multimodal data.

Real-time passive monitoring via wearable devices and smartphones holds promise for personalized self-management, but key gaps remain, such as a lack of longitudinal and long-term studies with data collection for 1 year or longer, studies with confirmatory parameters on an individual level, and studies with a strong correlation between parameters in individual patients to support clinical decision-making. Improvements in reporting standards are highly recommended to provide better-informed insights for clinicians. Throughout this process, there is a clear need to address various other issues, such as limited types of collected data, reliability, user adherence, and privacy concerns.

PROSPERO CRD42022355696; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022355696.