Key result
AI identifies gym behaviors linked to performance-enhancing drug use to enable targeted preventive interventions.
Why the study?
Doping is increasing among recreational gym users without medical oversight or risk awareness, creating a public health concern.
The paper proposes using AI and machine learning to analyze gym users' behavioral patterns for early detection of doping risk, shifting from punitive to preventive health strategies.
May inform preventive strategies in fitness settings; leaves open real-world effectiveness pending prospective validation.
Doping is an issue associated with elite sports as athletes attempt to enhance their performance to gain an edge over other athletes. However, the prevalence of doping is continuously increasing among recreational users who try to achieve their physical goals in a gym setting. Doping within a gym setting is an issue of public health concern as most individuals use different substances without a doctor’s prescription or having proper knowledge of the associated risks of the substances. Also, some users buy nutritional supplements that contain steroids without their knowledge. The paper looks into the use of AI and machine learning as a solution to understanding the trends of gym users who are at risk of doping. The use of AI allows for early detection and behavioral pattern analysis, shifting focus from punitive enforcement practices to preventive care, a crucial element in promoting health training habits within the gym setting.
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Damaris Were Ogama (2025) conducted a review in Doping in recreational gym settings. Artificial Intelligence (AI) was evaluated. Artificial intelligence and machine learning can identify behavioral patterns associated with performance-enhancing drug use in gyms, enabling targeted preventive health interventions.
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