Increasing the Value of Digital Phenotyping Through Reducing Missingness: A Retrospective Analysis

ObjectivesDigital phenotyping methods present a scalable tool to realize the potential of personalized medicine. But underlying this potential is the need for digital phenotyping data to represent accurate and precise health measurements. This requires a focus on the data quality of digital phenotyping and assessing the nature of the smartphone data used to derive clinical and health-related features. DesignRetrospective cohorts. Representing the largest combined dataset of smartphone digital phenotyping, we report on the impact of sampling frequency, active engagement with the app, phone type (Android vs Apple), gender, and study protocol features may have on missingness / data quality. SettingmindLAMP smartphone app digital phenotyping studies run at BIDMC between May 2019 and March 2022 Participants1178 people who partook in mindLAMP studies Main outcome measuresRates of missing digital phenotyping data. ResultsMissingness from sensors in digital phenotyping is related to active user engagement with the app. There are small but notable differences in missingness between phone models and genders. Datasets with high degrees of missingness can generate incorrect behavioral features that may lead to faulty clinical interpretations. ConclusionsDigital phenotyping data quality is a moving target that requires ongoing technical and protocol efforts to minimize missingness. Adding run-in periods, education with hands-on support, and tools to easily monitor data coverage are all productive strategies studies can utilize today. Strengths and Limitations of this Study{circ} Methods are informed by a large sample of participants in digital phenotyping studies. {circ}Methods can be replicated by others given the open-source nature of the app and code. {circ}Methods are informed by only mindLAMP studies from one team which is a limitation.