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SigmaScheduling: Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions.
Asim H Gazi1, Bhanu Teja Gullapalli1, Daiqi Gao1
1Department of Statistics and the School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks
|March 23, 2026
Summary
SigmaScheduling dynamically adjusts decision points for mobile health interventions. This method improves intervention timing for habitual behaviors, increasing effectiveness for individuals with irregular routines.
Area of Science:
- Digital Health
- Behavioral Science
- Machine Learning
Background:
- Effective mobile health (mHealth) interventions require timely decision-making.
- Just-in-time adaptive interventions (JITAIs) use decision points to deliver timely support.
- Current JITAIs often fail for habitual behaviors due to fixed decision point scheduling.
Purpose of the Study:
- To introduce SigmaScheduling, a novel dynamic method for scheduling JITAI decision points.
- To improve the timeliness and effectiveness of mHealth interventions for habitual behaviors.
Main Methods:
- Developed SigmaScheduling to dynamically schedule decision points based on behavior timing uncertainty.
- Evaluated SigmaScheduling using real-world data from a 10-week oral hygiene intervention (Oralytics).
Main Results:
- SigmaScheduling increased the probability of decision points preceding brushing events by over 70%.
- The method demonstrated improved intervention timing for individuals with irregular routines.
- Successfully preserved opportunities for intervention to impact behavior.
Conclusions:
- SigmaScheduling enhances precision mHealth by optimizing decision point timing.
- This approach is particularly beneficial for JITAIs targeting time-sensitive, habitual behaviors.
- Advances the field of adaptive, personalized digital health interventions.
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