# Detecting Receptivity for mHealth Interventions in the Natural Environment

arXiv: https://arxiv.org/abs/2011.08302 · HTML: https://mlsanity.com/paper/2011.08302/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 8 of 8)

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- [WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch](https://mlsanity.com/paper/2502.05783/) (2025)
- [Imputation Matters: A Deeper Look into an Overlooked Step in Longitudinal Health and Behavior Sensing Research](https://mlsanity.com/paper/2412.06018/) (2024)
- [Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Study](https://mlsanity.com/paper/2407.11612/) (2024)
- [Real-World Receptivity to Adaptive Mental Health Interventions: Findings from an In-the-Wild Study](https://mlsanity.com/paper/2508.02817/) (2025)
- [PULSE: Agentic Investigation with Passive Sensing for Proactive Affective Intervention in Cancer Survivorship](https://mlsanity.com/paper/2605.17679/) (2026)

## Abstract (arXiv)

JITAI is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach~-- Ally~-- that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally~app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a textitstatic model/ that was built before the study started and remained constant for all participants and an textitadaptive model/ that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a textitcontrol model/ that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study.