The recent arrival of smartphone-sensing methods has made it possible to objectively track consequential everyday health-related behaviors rather than rely on self-reports. To evaluate the viability of using sensing methods to monitor such behaviors in detail, the present research used a smartphone-sensing application to describe the patterns of stability and change that characterize a cohort of students' activity and sociability behaviors over the course of a 10-week academic term. Data were collected from 48 students using a smartphone-sensing application, StudentLife, which was designed to track daily durations of activity (via the accelerometer sensor) and sociability (via the microphone sensor). Results showed stability estimates were moderate to high for activity (rmean = 0.66) and sociability (rmean = 0.72) across the 10 weeks. Students started the term with generally healthy levels of activity (M = 1.87 h) and sociability (M = 4.99 h), which then dropped (activity by 0.42 h, sociability by 0.90 h) over the first half of the term (i.e., before midterm exams). Over the second half of the term, activity levels did not change but sociability increased (by 0.88 h). Students’ ethnicity and academic class predicted variation in the activity and sociability trajectories. Discussion focuses on the implications of our results for designing mHealth interventions to address consequential student outcomes (e.g., mental health, physical health).
['Gabriella M. Harari', 'Samuel D. Gosling', 'Rui Wang', 'Fanglin Chen', 'Zhenyu Chen', 'Andrew T. Campbell']
Patterns of behavior change in students over an academic term: A preliminary study of activity and sociability behaviors using smartphone sensing methods