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Measuring Psychological Well-Being and Behaviors Using Smartphone-Based Digital Phenotyping: An Intensive
Li Yi1,2, Claudia Trudel-Fitzgerald3,4,5, Cindy R Hu6
1Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, MA, 02215, United States.
Background:
Intensive measures of well-being and behaviors in large epidemiologic cohorts have the potential to enhance health research in these areas. Yet, little is known regarding the feasibility of using mobile technology to collect intensive data in the "natural" environment in the context of ongoing large cohort studies.
Objective:
We examined the feasibility of using smartphone digital phenotyping to collect highly resolved psychological and behavioral data from participants in a pilot study with participants in Nurses' Health Study II, a nationwide prospective cohort of women.
Methods:
In this pilot study, an 8-day intensive smartphone protocol was implemented using the "Beiwe" smartphone app. Participants (n=181) completed a baseline survey on day 1 and answered ecological momentary assessment (EMA) surveys twice daily (days 2-8; early afternoon and evening) using their smartphone and provided minute-level accelerometer and GPS data. A feedback survey at the end of Substudy queried participants' experience with the app and data collection process. We assessed adherence to the protocol by examining completion on EMA surveys and completeness of accelerometer and GPS data at the participant, participant day, and prompt levels.
Results:
Our pilot study demonstrated modest overall compliance with smartphone-based surveys: the baseline survey completion rate was high (156/181, 86.2%), but average daily EMA response rates during the 7-day period were lower with 55.6% (SD 3.9%) for early afternoon and 54.7% (SD 3.2%) for evening. We also observed good average daily completeness of smartphone accelerometer (mean 62.0%, SD 4.5%) data and GPS data (mean 57.7%, SD 3.1%). The feedback survey revealed that the participants found "the app easy to use" (median 85.0 on a scale of 1-100) and were "willing to repeat similar studies" (median 85.0 on a scale of 1-100). Although participants reported feeling their participation was a positive experience (median 64.0 on a scale of 1-100), they also identified some important issues, including user fatigue due to repetitive daily surveys.
Conclusions:
We observed modest compliance with smartphone surveys and completeness of smartphone passive sensing data in this pilot study compared with similar studies in the past. However, this was not unexpected, given our participants were older (aged 57-75 years, with more than 3 decades of follow-up at the time of the substudy) and may encounter more technological barriers, not to mention that the indication of willingness to participate in such studies again was fairly high. Our findings also highlight that the success and data quality of efforts to obtain daily measures may vary depending on data type and emphasize the need to improve the design of the EMA survey to improve or sustain participant engagement over the study period. Overall, our findings suggest smartphone-based digital phenotyping as a promising technology when embedding in large epidemiological cohorts to collect intensive longitudinal observation data.
