Learning When to Intervene on Habitual Behaviors: A Case Study in Oral Health Care

2026-07-10

AI Summary

This study addresses the limited effectiveness of fixed-time digital health interventions, which often fail due to misalignment with users’ habitual behavioral rhythms. To overcome this, the authors propose an online sequential decision-making framework that dynamically integrates intervention timing into the decision process, enabling real-time adaptation to individual temporal shifts in toothbrushing behavior. By combining online learning algorithms, a coverage-based evaluation metric, and a hybrid analysis of real-world and simulated behavioral data, the approach facilitates a paradigm shift from static scheduling to dynamic personalization. Compared to fixed strategies based solely on initial user input, the proposed adaptive intervention significantly improves temporal alignment with actual brushing events. Preliminary evidence from an ongoing randomized controlled trial supports its efficacy.

Abstract

A central challenge for digital health interventions aimed at improving habitual behaviors is deciding when to deliver an intervention prompt. For many daily habits, such as tooth brushing or eating, individuals tend to act around a usual time of day, but this timing is not fixed and can shift as routines evolve. When intervention timing is selected in advance and held constant throughout a study, it can gradually become misaligned with behavior, causing interventions to potentially arrive after the behavior has already occurred or too early to be effective. In this work, we address this habitual timing misalignment in digital health interventions by proposing an online decision-making framework that continuously adapts intervention timing as individual behavior patterns change. Rather than treating intervention timing as a static design choice, our framework adapts it over time and integrates it into a sequential process that determines both when and whether to deliver an intervention. Using data from a deployed oral health intervention trial as a case study, we evaluate our approach using both observed data and simulated settings to assess how well different intervention timing strategies align with the timing of brushing events. Across these evaluations, we measure performance using a coverage-based metric that captures whether an intervention is delivered sufficiently close to a subsequent brushing event. We find that adaptive intervention timing consistently improves coverage compared to fixed intervention times based on user-provided input. The proposed framework is currently deployed in an ongoing randomized controlled trial of a digital oral health intervention, with preliminary results that are consistent with and further support our prior evaluations.

Problem

• habitual behaviors
• intervention timing
• digital health interventions
• timing misalignment
• oral health care

Innovation

• adaptive intervention timing
• habitual behavior
• digital health
• online decision-making
• sequential intervention