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Purpose: Exercise is a vital adjunct therapy for patients with hypertension comorbidities. However, medical personnel and patients face significant obstacles in implementing exercise prescription recommendations. AI has been developed as a beneficial tool in the healthcare field. The performance of intelligent tools such as ChatGPT 4.0 and Intelligent Health Promotion Systems (IHPS) in issuing exercise prescriptions for patients with hypertension comorbidities remains to be verified. Patients and Methods: After collecting patient information through IHPS hardware and questionnaire systems, the data were input into the software terminals of ChatGPT 4.0 and IHPS according to the five stages of the Transtheoretical Model, resulting in exercise prescriptions. Subsequently, experts from various fields scored the accuracy, comprehensiveness, and applicability of each prescription, along with providing professional recommendations based on their expertise. By comparing the performance of both systems, their capability to serve this specific group was evaluated. Results: In most cases, ChatGPT scored significantly higher than IHPS in terms of accuracy, comprehensiveness, and applicability. However, when patients exhibited certain functional movement disorders, GPT's exercise prescriptions involved higher health risks, whereas the more conservative approach of IHPS was advantageous. Conclusion: The path of generating exercise prescriptions using artificial intelligence, whether via ChatGPT or IHPS, cannot achieve a completely satisfactory state.But can serve as a supplementary tool for professionals issuing exercise prescriptions to patients with hypertension comorbidities, especially in alleviating the financial burden of consulting costs. Future research could further explore the performance of AI in issuing exercise prescriptions, harmonize it with physiological indicators and phased feedback, and develop an interactive user experience. [ABSTRACT FROM AUTHOR]

Objective: To explore the trajectories of cognitive function development and predictive factors in disabled middle-aged and older adults., Methods: Utilizing data from 983 disabled middle-aged and older adults in the China Health and Retirement Longitudinal Study (CHARLS) from 2013 to 2020, latent growth mixture models were constructed to analyze the categories of cognitive function development trajectories and their predictive factors., Results: The cognitive function trajectories of the disabled middle-aged and older adults were classified into three categories: rapid decline (32.6%), Slow decline (36.1%), and Stable (31.2%). Multinomial logistic regression analysis identified age, gender, residence, education, marital status, household income, sleep duration, depression, hearing ability, and social participation as predictors of these trajectories., Conclusion: There is heterogeneity in the cognitive function development trajectories among disabled middle-aged and older adults. Healthcare professionals can implement targeted health management based on the characteristics of different groups to prevent the deterioration of cognitive function in this population., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Pang, Xu, Liu, Huang, Li, Ma, Zeng, Ma and Xie.)