Artificial Intelligence and Machine Learning—Powerful Yet Underutilized Tools and Algorithms in Physical Activity and Sedentary Behavior Research

Recent years have seen a significant rise in the development and implementation of advanced artificial intelligence (AI) and machine learning (ML) tools and algorithms in health care settings and medicine. 1,2For instance, a large language model (LLM), is a form of AI trained extensively on vast volumes of natural language texts, capable of generating human-like conversations. 3 Although still controversial, 4 preliminary evidence strongly indicates that these conversational AI models, if not yet surpassing, exhibit performance levels at least comparable to human capabilities, particularly in medical applications.For instance, they can generate more empathetic responses to patient queries than physicians 5 and even provide patient diagnoses as accurately as a trained doctor. 6owever, despite their great promise, the full potential of AI and ML remains largely underutilized in physical activity (PA) and sedentary behavior research.Here, we highlight the capabilities of AI and ML as alternative methods and approaches possessing the complexity required to further advance the study of these naturally complex behaviors.Now, the Question Is as Follows: Why Have AI and ML Not Yet Been Widely Used in Research on PA and Sedentary Behaviors?Implementing AI and ML techniques is typically more timeconsuming than performing other forms of statistical analyses and may also require substantial computing power.In addition, while AI and ML show promise in advancing medical and health research, the ethical issues surrounding these techniques can be complex. 25For instance, individuals whose data are utilized must have the right to determine ownership of their data, specify where it should be stored, and dictate its permissible uses.Hence,