Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study

Abstract

Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.

Conflict of interest statement

The authors declare no competing interests.

© 2023. The Author(s).

Figures

Fig. 1. Sample individual participant anomaly detection plot.

The x axis depicts the time in days since the first data point, while the y axis depicts the anomaly detection p value plotted inversely logarithmically. Solid black lines represent relapse events. The dotted gray line represents the p = 0.005 cutoff we chose for anomalies. Blue points represent active data, red points represent passive data, and green points represent data quality.

Fig. 2. Correlation p values between passive data changepoints and symptom changes.

Low p values indicate a strong correlation. A large amount of statistically significant p values illustrates the high association between passive data changepoints and changes in symptomatology.