ArticlePLOS digital health2024
Infusing behavior science into large language models for activity coaching.
Article in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT06798311 (Reducing Disparities in Urinary Control Symptoms for Minority Women), which is not on this map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Reducing Disparities in Urinary Control Symptoms for Minority Women
Who cites it
13 citing papers in PubMed.
- Cardiology hospital admission risk prediction: training, internal validation and technical implementation in the electronic health record.European heart journal. Digital health · 2026Article
- Machine Learning in Adapted Physical Activity: Clinical Applications, Monitoring, and Implementation Pathways for Personalized Exercise in Chronic Conditions: A Narrative Review.Journal of functional morphology and kinesiology · 2026Review
- The Healthy Environments and Active Living for Translational Health (HEALTH) Platform: A smartphone-based system for geographic ecological momentary assessment research.PLOS digital health · 2025Article
- Systematic review of dynamically tailored eHealth interventions targeting physical activity and healthy diet in chronic disease.NPJ digital medicine · 2025Article
- Reference and Solution Architecture for GenAI- and GIS-Enhanced Physical Activity Interventions: Towards Implementing the AI4Motion Platform.Journal of medical systems · 2025Article
- Evaluation Strategies for Large Language Model-Based Models in Exercise and Health Coaching: Scoping Review.Journal of medical Internet research · 2025Article
- A Behavioral Science-Informed Agentic Workflow for Personalized Nutrition Coaching: Development and Validation Study.JMIR formative research · 2025Article
- Correction: Infusing behavior science into large language models for activity coaching.PLOS digital health · 2025Article
- Fine-tuning Large Language Models in Behavioral Psychology for Scalable Physical Activity Coaching.medRxiv : the preprint server for health sciences · 2025Article
- Fine-tuning LLMs in behavioral psychology for scalable health coaching.NPJ cardiovascular health · 2025Article
- Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges.Sensors (Basel, Switzerland) · 2024Review
- Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Health Care Professionals.Journal of medical Internet research · 2024Article
- Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot: Smoking cessation use case.Digital healthArticle
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have shown promise for task-oriented dialogue across a range of domains. The use of LLMs in health and fitness coaching is under-explored. Behavior science frameworks such as COM-B, which conceptualizes behavior change in terms of capability (C), Opportunity (O) and Motivation (M), can be used to architect coaching interventions in a way that promotes sustained change. Here we aim to incorporate behavior science principles into an LLM using two knowledge infusion techniques: coach message priming (where exemplar coach responses are provided as context to the LLM), and dialogue re-ranking (where the COM-B category of the LLM output is matched to the inferred user need). Simulated conversations were conducted between the primed or unprimed LLM and a member of the research team, and then evaluated by 8 human raters. Ratings for the primed conversations were significantly higher in terms of empathy and actionability. The same raters also compared a single response generated by the unprimed, primed and re-ranked models, finding a significant uplift in actionability and empathy from the re-ranking technique. This is a proof of concept of how behavior science frameworks can be infused into automated conversational agents for a more principled coaching experience.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.