AI Training Programme vs Evidence Based Programme
AI Programme
The global fitness industry has experienced a paradigm shift with the integration of artificial intelligence into personal training and health coaching. AI-driven fitness programs use algorithms, machine learning, and data analytics to deliver personalised workout plans and nutrition guidance. In contrast, evidence-based fitness programs are developed from clinical trials, valid/ reliable research, and exercise physiology principles.
Popular applications such as Freeletics, Fitbod, and Apple Fitness+ utilize AI to adjust routines based on user progress, preferences, and biometric data (Ai et al., 2021).
Xu et al. (2024) states that users of AI fitness apps reported increased motivation due to instant feedback, and a sense of personalised coaching. The 24/7 availability and low cost of AI fitness programs make them especially attractive to members of the public that may want a cheaper alternative or for individuals who prefer remote workouts.
However, AI systems often lack the nuance of human-led, clinically informed training. McGregor and Tan (2023) state that majority of apps rely on proxy metrics, for example calories burned and steps in a day, which do not necessarily reflect improvements in strength, mobility, or long-term health outcome.
Evidence Based Programme
Evidence-based fitness programs are built on scientific research, including principles from exercise science, biomechanics, and sports medicine. These programs are typically delivered by certified professionals and follow structured protocols based on population-specific needs, such as ACSM guidelines for strength training (Garber et al., 2011).
Evidence-based programs benefit from clinical and individualized assessments. Research shows that evidence based programs are more effective at preventing injuries, improving performance metrics, and adaptable to limitations like hypertension and diabetes (Thyfault & Booth, 2011). these programs also allow for ongoing supervision and modification based on client feedback.
Despite their benefits, evidence-based interventions are often resource-intensive, requiring access to qualified personnel and sometimes expensive facilities. Additionally, adherence rates can be lower due to lack of accessibility or engagement, particularly among younger populations more accustomed to digital solutions (Marcus et al., 2006).
Conclusion
AI-driven fitness programs represent an innovative, scalable approach to physical activity promotion, particularly for technologically inclined users. However, their lack of transparency, reliance on superficial metrics, and limited ability to address complex individual needs raise concerns. Evidence-based fitness interventions remain the gold standard in terms of safety, efficacy, and physiological relevance, though their accessibility and engagement may lag behind AI solutions. Practitioners may benefit by utilising AI to assist their work by significantly reducing their work load whilst providing an hybrid service of the scalability of AI with the scientific integrity of evidence based programming.