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Machine Learning in Adapted Physical Activity: Clinical Applications, Monitoring, and Implementation Pathways for

Gianpiero Greco1, Alessandro Petrelli1,2, Luca Poli1

  • 1Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, 70124 Bari, Italy.

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Summary

Machine learning (ML) enhances adapted physical activity (APA) by improving assessment and personalized exercise for chronic conditions. Responsible governance is key for integrating ML decision support into APA practice.

Keywords:
decision supportdigital biomarkersfunctional assessmenthuman-centered AIremote monitoringtele-exercisewearable technologies

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Area of Science:

  • Exercise Science
  • Biomedical Engineering
  • Rehabilitation Medicine

Background:

  • Machine learning (ML) is transforming movement and exercise assessment and delivery.
  • Its specific applications and impact within adapted physical activity (APA) for chronic conditions remain underexplored.
  • APA populations often exhibit functional heterogeneity and varied exercise responses, highlighting the need for advanced tools.

Purpose of the Study:

  • To synthesize and critically evaluate current ML applications in APA practice.
  • To identify how ML supports core professional processes in APA.
  • To discuss the potential and challenges of ML integration in APA settings.

Main Methods:

  • A structured narrative review was performed.
  • Searches included major biomedical databases (PubMed/MEDLINE, Scopus, Web of Science) and engineering-focused sources.
  • The review focused on contemporary ML applications in human movement and exercise research up to January 2026.

Main Results:

  • ML applications in APA encompass markerless motion/gait analysis, wearable sensor data processing, and balance/fall-risk assessment.
  • Predictive and adaptive models enable individualized exercise prescription, intensity regulation, and remote monitoring.
  • ML is applied across diverse conditions including oncology, cardiometabolic, respiratory, and neuromuscular diseases, as well as adapted sports.

Conclusions:

  • ML offers a valuable decision-support tool for APA, enhancing assessment, personalization, and monitoring.
  • Ethical considerations like algorithmic bias, data privacy, and accountability are crucial for equitable implementation.
  • Effective integration requires validation, interpretability, and responsible governance to augment professional judgment in APA.