ReviewAdvances in experimental medicine and biology2026
The Evolution of Machine Learning Algorithms and Their Contribution to Physical Activity Management.
Review in Advances in experimental medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
2 authors.
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Abstract
The evolution of society has redefined people's needs and expanded the possibilities available through technology to manage daily activities, including physical activity. The purpose of this research was to study the extent to which machine learning algorithms can contribute to the personalized suggestion of physical activity programs and the prediction of specific fitness goals. It is recognized that people's daily lives are characterized by complex situations, such as the high prevalence of sedentary lifestyles, the methods of transport used in daily activities, attitudes toward physical activity, and more. Gaps in the literature focus on the lack of individualized recommendations for physical activity. It is further concluded that machine learning algorithms can model data governed by dynamic relationships, such as human behavior. The literature review shows that some machine learning algorithm models demonstrate high prediction accuracy, and that the choice of the appropriate algorithm is guided by the features given to the model.
Indexed as
Identifiers
41252014What 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.