Small Data Approaches to Link Faster Time Scale Engagement Dynamics with Slower Time Scale Outcomes in Biobehavioral Interventions.

Purpose: This study illustrates the application of time series clustering and feature engineering techniques to small data obtained at a fast time-scale from biobehavioral interventions to identify slower time-scale health outcomes.

Methods: Using data from 26 adult kidney stone patients engaged with mini-sip^IT, a month-long digital health intervention targeting increased fluid intake, we identified distinct patterns of engagement with both manual app tracking and automated smart water bottles and examined how those patterns were related to subsequent urine volume.

Results: Time-series based analysis of engagement revealed that manual tracking was significantly associated with increased urine volume, highlighting the potential for active self-monitoring to improve health behaviors. In contrast, differential patterns of engagement with automated tracking were not related to differences in urine volume.

Conclusion: These findings suggest that small data approaches can effectively bridge time scales in behavioral interventions, and that manual engagement methods may be more beneficial than automated ones in fostering behavior change. Absent large datasets to support identification of engagement patterns via deep learning, time series clustering and feature engineering provide valuable tools for linking fast time-scale engagement processes with slow time-scale health outcome processes.

Irb approval: This study was conducted with the approval of the Institutional Review Board (STUDY00015017), granted on 9/22/2021.