Comparison of Diaphragmatic Breathing Exercises, Volume, and Flow-Oriented Incentive Spirometry on Respiratory Function in Stroke Subjects: A Non-randomized Study

Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI's GPT-4 model

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.

Immediate Effects of Diaphragmatic Breathing with Cervical Spine Mobilization on the Pulmonary Function and Craniovertebral Angle in Patients with Chronic Stroke

Background and Objectives: Patients with stroke have a forward neck posture due to neurological damage and often have impaired pulmonary function. This study investigated the effect of diaphragmatic breathing with cervical mobilization to improve pulmonary function cervical alignments. Materials and Methods: This study used a one-group pre-test-post-test design including 20 patients with stroke. Two types of cervical joint mobilization techniques, consisting of left and right lateral glide mobilization and posterior-anterior mobilization, were utilized. During joint mobilization, the patients performed diaphragmatic breathing. The measurements were performed immediately after the intervention. Pulmonary function was evaluated using a spirometer to measure the forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF). The craniovertebral angle (CVA) was measured using lateral photographs. Results: After diaphragm breathing with cervical joint mobilization, subjects had significantly increased FEV1, FVC, PEF and CVA. Conclusion: Diaphragm breathing with cervical joint mobilization are possible interventions to increase pulmonary function and improve the craniovertebral angle in patients with stroke. However, a complete conclusion can be reached only after a follow-up study has been conducted with a comparison of more subjects and controls.