JMIR Publications

ABSTRACT

Background:

The COVID-19 pandemic has intensified mental health issues globally, highlighting the urgent need for remote mental health monitoring. Digital phenotyping using smart devices has emerged as a promising approach, but it remains unclear which features are essential for predicting depression and anxiety.

Objective:

This systematic review aimed to identify the types of features collected through smart packages—integrated systems combining smartphones with wearable devices such as Actiwatches, smartbands, and smartwatches—and to determine which features should be considered essential for mental health monitoring based on the type of device used.

Methods:

A systematic review was conducted. Searches were performed across Web of Science, PubMed, and Scopus on February 5, 2025. Inclusion criteria comprised quantitative studies involving adults (≥19 years) using smart devices to predict depression or anxiety based on passive data collection. Studies focusing solely on smartphones or qualitative designs were excluded. Risk of bias was assessed using the Mixed Methods Appraisal Tool and Quality Criteria Checklist. Data were synthesized descriptively, and the relative contribution of each feature was further assessed by calculating coverage (proportion of studies using a feature) and importance among used (proportion identifying it as important when used). These metrics were visualized in quadrant-based scatter plots to identify consistently important features across devices.

Results:

From 1,382 records, 22 studies across 11 countries were included. The overall synthesis identified a core feature package—accelerometer (ACC), steps, Heart Rate (HR), and sleep. Device-specific analyses revealed further nuances: In Actiwatch studies, ACC and activity were consistently important, but sleep features were rarely examined. In smartbands, HR, steps, sleep, and phone usage were essential, while Global Positioning System (GPS), Electrodermal Activity (EDA), and skin temperature (TEMP) showed high importance when used, suggesting opportunities for broader adoption. In smartwatch studies, sleep and HR emerged as core features, whereas steps and ACC were widely used but often not identified as important.

Conclusions:

This systematic review identified a core feature package comprising ACC, steps, HR, and sleep that consistently contributes to mood disorder prediction across devices. At the same time, device-specific differences were observed: Actiwatch studies mainly emphasized ACC and activity but underutilized sleep features; smartbands highlighted HR, steps, sleep, and phone usage, with EDA, TEMP, and GPS showing additional promise; and smartwatches most reliably leveraged sleep and HR, while steps and ACC were widely used yet less effective. These findings suggest that while a shared core set of features exists, optimizing digital phenotyping requires tailoring feature selection to the characteristics of each device type. To advance this field, improving data accessibility, particularly in smartwatch ecosystems, and adopting standardized reporting frameworks will be essential to enhance comparability, reproducibility, and future meta-analytic integration. Clinical Trial: This review was registered in the Open Science Framework.