mlsanityData to 7 Oct 2026

A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention

arXiv 2607.21403 ↗First posted 23 Jul 2026cs.LG stat.MEcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Mobile-health interventions increasingly use online learning and decision making algorithms to personalize when to nudge users toward healthier behavior, but a poorly designed algorithm can burden and disengage participants. New algorithm design decisions should therefore be vetted against realistic simulated users before each real-life deployment. We propose a method to develop ``JITAI-Twins'': digital twins of a target subpopulation for comparing candidate online algorithms before a just-in-time adaptive intervention (JITAI) deployment. The method builds on a conditional time-series diffusion model that is temporally consistent (future actions do not affect the generated past), and it supports repeated updating from three sources of information, in three steps: pre-training on a large observational dataset, fine-tuning on small prior intervention deployments in related populations, and inference-time calibration to the next target population from domain-scientist expertise. We validate the twin at each pre-deployment stage of the long-running HeartSteps series (v2 through v4) of physical-activity suggestion intervention deployments, treating each successive deployment as an upcoming study. The proposed method reproduces the target subpopulation's temporal and between-participant structure better than simpler simulators. These results suggest that our twin can be used to simulate a target deployment before it runs, the prerequisite for testing and informing online algorithm design decisions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 22 of its 45 references that are in the map. First posted on arXiv 23 Jul 2026.

Builds on

22 of 45 references.

Closest ancestors · 2

Same direction, newest first

  1. 2026Steering diffusion models with quadratic rewards: a fine-grained analysis18 Feb
  2. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 20

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
  4. 2017A Tutorial on Thompson Samplingcited by 420
  5. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 332
  6. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 195
  7. 2023Diffusion Models for Time Series Applications: A Surveycited by 72
  8. 2019Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activitycited by 70
  9. 2021Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Healthcited by 43
  10. 2024Time Weaver: A Conditional Time Series Generation Modelcited by 35
  11. 2020Offline Policy Selection under Uncertaintycited by 31
  12. 2017Non-Stationary Bandits with Habituation and Recovery Dynamicscited by 23
8 more
  1. 2024A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trialcited by 11
  2. 2022No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RLcited by 6
  3. 2026Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directionscited by 5
  4. 2022Towards Data-Driven Offline Simulations for Online Reinforcement Learningcited by 2
  5. 2024MiWaves Reinforcement Learning Algorithmcited by 2
  6. 2025SigmaScheduling: Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventionscited by 1
  7. 2025Reproducible workflow for online AI in digital healthcited by 1
  8. 2026Learning When to Intervene on Habitual Behaviors: A Case Study in Oral Health Carecited by 1

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  1. 2023Adaptive Interventions with User-Defined Goals for Health Behavior Change
  2. 2026LifeLong Digital Twin: A Unified Modeling Paradigm and Agent Harness for Event-Driven Lifelong Health State Trajectories
  3. 2023RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions
  4. 2026Wearable Foundation Models Should Go Beyond Static Encoders
  5. 2018A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions
  6. 2026Toward Personalized Digital Twins for Cognitive Decline Assessment: A Multimodal, Uncertainty-Aware Framework
  7. 2026Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translation
  8. 2026Simulating clinical interventions with a generative multimodal model of human physiology