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The multiple uses of artificial intelligence in exercise programs: a narrative review.

Alberto Canzone^1,2, Giacomo Belmonte¹, Antonino Patti¹

¹Sport and Exercise Sciences Research Unit, Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, Italy.

Frontiers in Public Health

|February 17, 2025

Summary

Artificial intelligence (AI) and machine learning show promise in promoting physical activity and improving health outcomes. These technologies are used for communication, prediction, control, data collection, and guided training across diverse populations.

Keywords:

deep-learning health machine-learning physical activity sport wellbeing wellness “artificial intelligence and movement”

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Area of Science:

Computer Science
Health Sciences
Sports Science

Background:

Artificial intelligence (AI) algorithms enable machines to perform human-like tasks.
Machine learning (ML), a subset of AI, empowers autonomous learning from data.
AI offers innovative solutions across various fields, including health and physical activity.

Purpose of the Study:

To review the utilization of AI in promoting physical activity, training, exercise, and health outcomes.
To address the gap in understanding the practical applications of AI in these domains.
To elucidate the role of AI in fostering healthier lifestyles.

Main Methods:

Narrative review of Randomized Controlled Trials (RCTs) published in English from January 1, 2014.
Inclusion criteria focused on RCTs using AI for programming, supervising, managing, or assisting physical activity, training, exercise, or health programs.
Exclusion of studies involving robot-assisted, robot-supported, or robotic training.

Main Results:

15 studies met the eligibility criteria after screening 1772 initial studies.
AI shows promise in promoting physical activity across diverse populations: children, adolescents, adults, older adults, and individuals with disabilities.
AI applications were identified for user communication, predictive analysis, gesture recognition, control systems, data collection, and guided training.

Conclusions:

AI, ML, and deep learning techniques are effectively applied in various capacities within physical activity and health programs.
These AI applications include automated messaging, predictive modeling, posture recognition, system control, data management, and personalized training guidance.
AI holds significant potential for enhancing physical activity engagement and improving health outcomes for a wide range of users.