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Theory-based habit modeling for enhancing behavior prediction in behavior change support systems.

Chao Zhang1, Joaquin Vanschoren1, Arlette van Wissen2

  • 1Human-Technology Interaction Group, Department of Industrial Engineering and Innovation Sciences, 513, 5600MB Eindhoven, The Netherlands.

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Summary

This study introduces a computational method to automatically calculate habit strength from observable behaviors. This approach aids behavior change systems by predicting actions and assessing intervention effectiveness, improving habit formation research.

Keywords:
Computational modelsDental behavior changeDigital health interventionHabit formationPredictive modeling

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Area of Science:

  • Psychology
  • Behavioral Science
  • Computer Science

Background:

  • Habit formation is key to changing lifestyle behaviors, involving breaking old habits and forming new ones.
  • Habit strength, crucial for behavior prediction in support systems, is currently difficult to measure accurately.
  • Existing self-report measures for habit strength are burdensome for users.

Purpose of the Study:

  • To develop and validate a computational method for intelligent systems to infer habit strength from observable behaviors.
  • To assess the utility of computed habit strength for predicting future behavior in behavior change interventions.
  • To compare the performance of computed habit strength against self-reported measures and past behavior data.

Main Methods:

  • Utilized recent computational models of habit formation to derive habit strength.
  • Collected behavioral data using accelerometers from participants in two dental behavior change studies.
  • Employed a theory-based computational model to predict future brushing behavior based on calculated habit strength.

Main Results:

  • The computational model for habit strength achieved 68.6% accuracy in predicting brushing behavior in Study 1.
  • The model demonstrated higher accuracy in Study 2, reaching 76.1% for predicting future behavior.
  • Computed habit strength outperformed models using self-reported determinants but showed no advantage over models using only past behavior.

Conclusions:

  • A computational approach to estimating habit strength is feasible and useful for behavior prediction.
  • This method offers a less taxing alternative to self-report measures for assessing habit strength.
  • Findings have significant implications for developing more effective behavior change support systems and advancing habit formation research.