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Large Language Model-Based Agents for Physical Activity and Cognitive Training: Scoping Review.

Alessandro Silacci1,2, Benedetta Giachetti3,4, Leonardo Angelini2,3

  • 1Department of Information Systems, Faculty of Business and Economics, University of Lausanne, Quartier Centre, Lausanne, 1015, Switzerland, 41 21 692 11 11.

JMIR AI
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PubMed
Summary
This summary is machine-generated.

Large language model (LLM) agents show promise for physical activity and cognitive training interventions. However, current research is limited, highlighting a need for more rigorous and transparent evaluations of these digital health tools.

Keywords:
cognitive trainingconversational agentslarge language modelsphysical activityprompt engineeringreproducibilityscoping review

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Area of Science:

  • Digital Health
  • Artificial Intelligence in Healthcare
  • Human-Computer Interaction

Background:

  • Large language model (LLM)-based conversational agents are increasingly used in digital health.
  • Their application in physical activity (PA) and cognitive training, critical well-being domains, requires systematic mapping.
  • These domains need personalized, adaptive support and conversational engagement, making them suitable for LLM agent deployment.

Purpose of the Study:

  • To systematically map the scope, characteristics, and design practices of LLM-based conversational agents for PA and cognitive training.
  • To analyze application contexts, social roles, and technological features of these agents.
  • To understand the current landscape of LLM applications in these well-being domains.

Main Methods:

  • A scoping review following PRISMA-ScR guidelines.
  • Searched major academic databases (Web of Science, Scopus, PubMed, ACM, IEEE Xplore) from January 2018 to December 2024.
  • Included studies describing LLM agents for PA or cognitive training, using descriptive synthesis and framework analysis.

Main Results:

  • 10 studies met eligibility criteria (7 PA, 3 cognitive training).
  • Agents primarily served coaching roles for PA and companion/scaffolding roles for cognitive training.
  • Proprietary LLMs (GPT-3.5, GPT-4, Bard) dominated; prompt engineering was key but inconsistently reported; outcomes focused on perceived usefulness and engagement, not behavioral changes.

Conclusions:

  • LLM agents show early promise for PA and cognitive training support, but evidence is exploratory and limited.
  • Challenges include inconsistent prompt reporting, reliance on proprietary models, and lack of standardized outcome measures.
  • More rigorous, transparent evaluations are needed to strengthen evidence and guide future LLM-based digital health development.