RESUMO
The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI's Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model's ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model's potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition-specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback.
RESUMO
INTRODUCTION: Exercise effectively improves mood in major depressive disorder (MDD), but the optimal exercise stimulus to improve depressed mood is unknown. PURPOSE: To determine the dose-response relationship of acute exercise intensity with depressed mood responses to exercise in MDD. We hypothesized that the acute response to exercise would differ between light, moderate, and hard intensity exercise with higher intensities yielding more beneficial responses. METHODS: Once weekly, 24 women (age: 38.6±14.0) diagnosed with MDD underwent a 30-minute session at one of three steady-state exercise intensities (light, moderate, hard; rating of perceived exertion 11, 13 or 15) or quiet rest on a stationary bicycle. Depressed mood was evaluated with the Profile of Mood States before, 10 and 30 minutes post-exercise. RESULTS: Exercise reduced depressed mood 10 and 30 minutes following exercise, but this effect was not influenced by exercise intensity. Participants not currently taking antidepressants (n=10) had higher baseline depression scores, but did not demonstrate a different antidepressant response to exercise compared to those taking antidepressants. CONCLUSIONS: To acutely improve depressed mood, exercise of any intensity significantly improved feelings of depression with no differential effect following light, moderate, or hard exercise. Pharmacological antidepressant usage did not limit the mood-enhancing effect of acute exercise. Acute exercise should be used as a symptom management tool to improve mood in depression, with even light exercise an effective recommendation. These results need to be replicated and extended to other components of exercise prescription (e.g., duration, frequency, mode) to optimize exercise guidelines for improving depression.