Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling
| Autor: | Stefanie Nickels, Matthew D Edwards, Sarah F Poole, Dale Winter, Jessica Gronsbell, Bella Rozenkrants, David P Miller, Mathias Fleck, Alan McLean, Bret Peterson, Yuanwei Chen, Alan Hwang, David Rust-Smith, Arthur Brant, Andrew Campbell, Chen Chen, Collin Walter, Patricia A Arean, Honor Hsin, Lance J Myers, William J Marks Jr, Jessica L Mega, Danielle A Schlosser, Andrew J Conrad, Robert M Califf, Menachem Fromer |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | JMIR Mental Health, Vol 8, Iss 8, p e27589 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2368-7959 96998024 |
| DOI: | 10.2196/27589 |
| Popis: | BackgroundAlthough effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care. ObjectiveThis study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity. MethodsA total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9). ResultsA total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|