Sociodemographic characteristics of missing data in digital phenotyping

• Published in: Scientific reports Vol. 11; no. 1; pp. 15408 - 11
• Main Authors: Kiang, Mathew V., Chen, Jarvis T., Krieger, Nancy, Buckee, Caroline O., Alexander, Monica J., Baker, Justin T., Buckner, Randy L., Coombs, Garth, Rich-Edwards, Janet W., Carlson, Kenzie W., Onnela, Jukka-Pekka
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.07.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/174; 692/700/478; Accelerometry - methods; Adolescent; Adult; Bayes Theorem; Bayesian analysis; Black People; Child; Cognition; Data collection; Data Collection - methods; Education; Environment; Feasibility Studies; Female; Follow-Up Studies; Geographic Information Systems; Global positioning systems; GPS; Humanities and Social Sciences; Humans; Male; Mathematical models; Middle Aged; Missing data; multidisciplinary; Natural environment; Nervous system; Phenotyping; Science; Science (multidisciplinary); Sensitivity analysis; Sensors; Smartphone - instrumentation; Smartphones; Social Behavior; Sociodemographics; Sociological Factors; Young Adult
• DOI: 10.1038/s41598-021-94516-7
• ISSN: 2045-2322

The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for “digital phenotyping,” the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.