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Updated: Sep 11, 2025

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
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Small Data Approaches to Link Faster Time Scale Engagement Dynamics with Slower Time Scale Outcomes in Biobehavioral

Jingchuan Wu1, Nilam Ram2, James Marks3

  • 1Department of Kinesiology, The Pennsylvania State University, University Park, PA 16802 United States of America.

Chinese Political Science Review
|August 11, 2025
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Summary

Manual self-monitoring in digital health interventions significantly boosts urine volume in kidney stone patients. This highlights active engagement

Keywords:
Biobehavioral InterventionsDigital HealthFeature EngineeringManual TrackingSmall DataTime Series Clustering

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Area of Science:

  • Digital Health Interventions
  • Biobehavioral Science
  • Health Informatics

Background:

  • Digital health interventions (DHIs) offer scalable solutions for chronic disease management.
  • Understanding patient engagement patterns is crucial for optimizing DHI effectiveness.
  • Bridging fast-timescale engagement data with slow-timescale health outcomes remains a challenge.

Purpose of the Study:

  • To apply time series clustering and feature engineering to small, fast-timescale biobehavioral data.
  • To identify distinct patient engagement patterns within a digital fluid intake intervention.
  • To link these engagement patterns to slower-timescale health outcomes, specifically urine volume.

Main Methods:

  • Utilized data from 26 adult kidney stone patients using the mini-sip(IT) digital health intervention.
  • Employed time series clustering and feature engineering on engagement data from manual app tracking and automated smart water bottles.
  • Analyzed the association between identified engagement patterns and subsequent changes in 24-hour urine volume.

Main Results:

  • Manual app tracking engagement patterns were significantly associated with increased urine volume.
  • Automated smart water bottle engagement patterns did not show a significant relationship with urine volume.
  • Active self-monitoring through manual tracking appears more effective in promoting desired health behaviors.

Conclusions:

  • Small data approaches, including time series clustering and feature engineering, can effectively link fast-timescale engagement with slow-timescale health outcomes in DHIs.
  • Manual engagement methods may be superior to automated methods for fostering behavior change in this context.
  • These techniques provide valuable tools for analyzing biobehavioral intervention data when large datasets for deep learning are unavailable.