ReviewFrontiers in digital health2026
Creating customized chatbots with ChatGPT to promote physical activity: a mini review.
Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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0 citing papers in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) chatbots powered by large language models (LLMs) such as ChatGPT offer a promising approach for delivering scalable, personalized physical activity interventions. Despite growing interest in applying these tools to health behaviour change, concerns remain regarding accuracy, safety, hallucinations, privacy, and theoretical grounding. This mini-review summarizes current methods for creating customized ChatGPT-based chatbots for physical activity promotion and outlines approaches for evaluating their performance. A literature search was conducted across five databases, white papers, and OpenAI technical reports. Three primary customization strategies were identified: retrieval-augmented generation (RAG), system prompt engineering, and fine-tuning. RAG enhances accuracy by grounding responses in curated guidelines and behaviour-change frameworks. System prompts define the chatbot's role, tone, and reasoning logic. Fine-tuning adapts the model's communication style using expert-crafted prompt-response pairs. These methods can be implemented independently or in combination, depending on intervention goals. Evaluation of customized chatbots requires both intrinsic model-based testing and extrinsic human-centred assessment. Additional considerations include protecting user privacy by avoiding collecting identifiable data, implementing data-minimization safeguards, and managing token-based operational costs associated with ChatGPT systems. Customized ChatGPT chatbots offer substantial potential for advancing physical activity promotion; however, safe and effective deployment requires thoughtful design, rigorous evaluation, and careful attention to privacy and cost.
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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.