Abstract
Background: Emerging evidence has suggested lifestyle and behavioral factors such as physical activity, diet, and sleep may be associated with cardiometabolic health outcomes in US adults. However, these studies typically have limited temporal or spatial coverage. The current study aimed to fill in this gap by applying smartphone-based digital phenotyping to collect dynamic, high temporal resolution measures of lifestyle and behavioral factors. Methods: Participants (N = 2,127 as of Sep 7 th , 2022) of the Beiwe Smartphone Sub-study of Nurses’ Health Study 3 (NHS3) and Growing Up Today Study (GUTS) completed microsurveys delivered by the Beiwe smartphone application for a year each. Starting July 2021, a microsurvey was delivered every 10 days, covering one of the 12 different topics, including emotions, stress, physical activity, green space, pets, diet, sleep, and sitting. These questionnaires aimed to measure various aspects of participants’ key health behaviors, to combine with objectively assessed high-resolution GPS and accelerometer data that participants provided during the same period. Results: Between July 28 th , 2021, and Sep 7 th , 2022, 2,127 participants completed 22,039 microsurveys delivered by the Beiwe application. The mean follow-up for participants was 116 days (SD=124, range=221). During this period, each participant on average submitted 10 microsurveys (SD=10, range=39). The topic completed most consistently (N=1,955 responses) was pets (the first microsurvey), and 43.3% of responses from participants reported a park visit in the past week. Additionally, 71.2% of responses from participants reported they spent at least a few minutes walking for exercise in the past week. Furthermore, during the same period, participants on average provided 177 days (SD=123, range=218) of GPS data and 181 days (SD=181, range=224) of accelerometer data. Conclusions: In this study, the smartphone-based digital phenotyping technology was implemented to collect intensive longitudinal data on lifestyle and behavioral factors in two well-established prospective cohorts. The efforts so far have resulted in a rich dataset on health behaviors, which can be linked to locations and future cardiometabolic health outcomes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
