Using digital phenotyping to understand health-related outcomes: A scoping review

• Published in: International journal of medical informatics (Shannon, Ireland) Ireland), 2023-06, Vol.174, p.105061-105061, Article 105061
• Main Authors: Lee, Kyungmi, Lee, Tim Cheongho, Yefimova, Maria, Kumar, Sidharth, Puga, Frank, Azuero, Andres, Kamal, Arif, Bakitas, Marie A., Wright, Alexi A., Demiris, George, Ritchie, Christine S., Pickering, Carolyn E.Z., Nicholas Dionne-Odom, J.
• Format: Article
• Language: English
• Subjects: Algorithms; Data Collection; Databases, Factual; Digital phenotyping; Exercise; Health outcomes; Humans; PubMed; Scoping review; Smartphones
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/j.ijmedinf.2023.105061

Digital phenotyping may detect changes in health outcomes and potentially lead to proactive measures to mitigate health declines and avoid major medical events. While health-related outcomes have traditionally been acquired through self-report measures, those approaches have numerous limitations, such as recall bias, and social desirability bias. Digital phenotyping may offer a potential solution to these limitations. The purpose of this scoping review was to identify and summarize how passive smartphone data are processed and evaluated analytically, including the relationship between these data and health-related outcomes. A search of PubMed, Scopus, Compendex, and HTA databases was conducted for all articles in April 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines. A total of 40 articles were included and went through an analysis based on data collection approaches, feature extraction, data analytics, behavioral markers, and health-related outcomes. This review demonstrated a layer of features derived from raw sensor data that can then be integrated to estimate and predict behaviors, emotions, and health-related outcomes. Most studies collected data from a combination of sensors. GPS was the most used digital phenotyping data. Feature types included physical activity, location, mobility, social activity, sleep, and in-phone activity. Studies involved a broad range of the features used: data preprocessing, analysis approaches, analytic techniques, and algorithms tested. 55% of the studies (n = 22) focused on mental health-related outcomes. This scoping review catalogued in detail the research to date regarding the approaches to using passive smartphone sensor data to derive behavioral markers to correlate with or predict health-related outcomes. Findings will serve as a central resource for researchers to survey the field of research designs and approaches performed to date and move this emerging domain of research forward towards ultimately providing clinical utility in patient care.