Personalization of Training and Weight Reduction Using Artificial Intelligence: A Scoping Review of Current Evidence and Practical Limitations
Background and Objectives: Artificial intelligence (AI) has rapidly emerged as a promising tool for delivering personalized interventions in physical activity, exercise prescription, and weight management. AI technologies may facilitate individualized recommendations, behavioral support, and lifestyle modification through adaptive digital health solutions, although their effectiveness remains to be established across different populations and settings. However, the current evidence remains heterogeneous, and the practical implementation of AI in personalized training and weight management requires further evaluation. This scoping review aimed to summarize the current evidence regarding the application of artificial intelligence for the personalization of training and weight reduction, with particular emphasis on the types of AI technologies used, their reported outcomes, practical applications, and current limitations. Methods: A scoping review was conducted following a structured literature search of studies investigating AI-supported interventions related to physical activity, exercise, dietary behavior, and weight management. Eight studies involving diverse populations, intervention designs, and AI technologies were included. Data were extracted on study characteristics, AI technologies, intervention characteristics, reported outcomes, and research gaps. The literature search was subject to access-based restrictions, including the use of “Free full text” in PubMed and “Open Access” in Web of Science, as well as language restrictions. Results: The included studies investigated a wide range of AI technologies, including conversational chatbots, natural language processing systems, machine learning algorithms, computer vision applications, knowledge-based systems, and large language models. Selected studies reported favorable or modest changes in exercise adherence, physical activity participation, dietary behaviors, user engagement, and weight-related outcomes; however, the magnitude and consistency of these findings varied across studies. Personalized coaching, real-time feedback, and continuous behavioral support were common features of interventions reporting favorable outcomes. However, considerable heterogeneity existed across study designs, participant populations, intervention protocols, AI technologies, and outcome measures, and evidence regarding long-term effectiveness remains limited. The findings should be interpreted in the context of the adopted search strategy, including access-based restrictions and the inability to retrieve eight of 57 reports sought for retrieval, which may have contributed to availability bias. Conclusions: Current evidence suggests that artificial intelligence may have potential as a tool for supporting the personalization of training and weight management interventions, particularly through individualized behavioral support, feedback, and user engagement. However, the available evidence is heterogeneous and does not yet allow firm conclusions regarding the effectiveness or mechanisms of AI-supported interventions. AI should currently be viewed as a complement rather than a replacement for healthcare and exercise professionals. Future large-scale randomized controlled trials with longer follow-up periods and standardized outcome measures are needed to clarify the effectiveness, sustainability, and practical implementation of AI-supported interventions.