Introduction
Over recent decades, the prevalence of hypertension has steadily escalated, significantly linked to increased life expectancy and evolving lifestyle choices over time.1 Often, hypertension coexists with multiple chronic diseases, including diabetes, chronic kidney disease, and coronary heart disease. This co-occurrence can increase the cardiovascular risk for individuals suffering from these conditions, placing an additional burden on healthcare systems.2 Consequently, the interplay of risk factors necessitates a collaborative, multidisciplinary approach to assessment and management, which can demand extensive medical resources. In light of the digital information era, researchers are harnessing the potential of transformative technologies, such as robotics and the internet, to optimize the efficiency of medical resource allocation.
The rapid advancement of chat tools has led to the advent of ChatGPT, a sophisticated chatbot that has gained prominence within science, industry, and society, demonstrating rapid growth across various sectors.3 Given ChatGPT’s remarkable capabilities in chronic disease prevention and management,4 scholars are now assessing its viability as a supplemental tool for exercise prescription, a function that has already been validated for its effectiveness.5 Its capabilities extend beyond mere artificial intelligence, as health promotion systems also prove invaluable in enhancing patient care.
The Intelligent Health Promotion System (IHPS) combines wearable devices, data analytics, and AI technology to leverage health indicators and questionnaire data of individuals. This integration facilitates seamless information exchange between Internet of Things (IoT) endpoints and health service infrastructures, thereby generating personalized and comprehensive assessment reports for chronic disease management.6 Numerous researchers have employed exercise prescriptions formulated using this system with middle-aged and elderly residents, leading to improved health outcomes for those involved.7,8
Regular physical exercise is crucial for enhancing the quality of life among chronic disease patients, as well as significantly contributing to long-term disease management strategies.9 Despite recognizing the benefits of regular physical activity, adherence remains suboptimal among chronic disease patients, likely due to exercise prescriptions failing to accommodate individual histories and preferences, as well as potential contraindications.8 Integrating health behavior change theories has been effectively applied to improve outcomes in chronic disease management.10 One particularly useful theory is the Transtheoretical Model (TTM), which stages patient readiness for exercise and aids in crafting personalized exercise prescriptions. This approach empowers practitioners to develop individualized plans focused on implementing and sustaining significant lifestyle changes,11 and provides an opportunity to connect TTM with chatbots in order to further enhance adherence to health behaviors,12 positively influencing health outcomes. However, research comparing the effectiveness of health promotion systems and intelligent chatbots in generating customized exercise prescriptions based on TTM remains underdeveloped and inconclusive.
This paper begins with the Transtheoretical Model (TTM) and delves into health behavior change theories. The focus is on comparing exercise prescriptions provided by health promotion systems and ChatGPT for patients with hypertension comorbidities to evaluate their effectiveness. The study’s objective is to ascertain whether these systems can function as consultation aids for general patients or serve as supplemental resources for professional healthcare providers.
Materials and Methods
Research Objectives and Design
To minimize human error and maintain the authenticity and objectivity of medical records, this study enlisted actual patients to gather medical data. A mixed-methods approach, distinct from traditional qualitative and quantitative research, was adopted to mitigate cognitive biases from experts. As depicted in Figure 1, data were inputted into both ChatGPT 4.0 and the health promotion service system for generating exercise prescriptions. Initially, two specialists in exercise prescriptions evaluated these outputs, followed by comprehensive evaluations from an additional 22 experts. The first author undertook the responsibility of collating and organizing data from both patients and experts.
Participants
The study employed purposive sampling over a three-month period, from December 2023 to February 2024, conducting physical examinations and survey questionnaires on participants meeting the established inclusion and exclusion criteria. These criteria included: (1) community residents providing informed consent; (2) individuals possessing a verified clinical diagnosis of hypertension alongside at least one additional health condition; and (3) individuals with a demonstrated capacity for adequate language comprehension and cognitive function.
Research Tools
The Health Promotion Service System encompasses both hardware devices and a comprehensive software interface designed for health assessment. Key hardware instruments include: Body Composition Analyzer, Ultrasound Bone Density Scanner, Cardiovascular Function Tester, Arteriosclerosis Detector, Spirometer, Handgrip Strength Tester, Flexibility Tester, Reaction Time Tester, and Balance Tester.
The accompanying software component, Intelligent Health Promotion Service System Software (Version: IIM-BSS-100), uses data gleaned from these instruments and user questionnaires to compile fundamental indicators. Questionnaires cover a diverse array of topics, including medical history, presenting symptoms, lifestyle choices, dietary habits, levels of physical activity, and behavioral patterns. This multifaceted assessment of body composition, bone density, cardiovascular function, and lifestyle ultimately facilitates the formulation of an intelligent, personalized lifestyle intervention plan tailored for chronic disease patients.
ChatGPT
Initiate a new conversation window utilizing a singular question format to solicit exercise prescriptions from ChatGPT for 10 distinct patients within one dialogue context. Each prescription is generated through this singular interaction to closely reflect the conditions under which a layperson interacts with the chatbot. This fosters consistency among the sampled group, as all prescribed plans derive from the same interaction.
Several Important Points to Note
- Ensuring Comparability Between Groups: The design ensures comparability between groups, minimizing distinction between AI-generated and system-provided human action details through the exclusion of images in the movement guidance.
- Evidence of Iterative Interaction: Evidence confirms that iterative interactions significantly bolster the accuracy and precision of GPT-generated exercise prescriptions, contingent on the level of expertise demonstrated by the questioner. Based on the “weakest link” principle, exercise prescriptions generated in a single interaction mirror real-world scenarios without professional background consultations. For this investigation, a clear and standardized sentence structure is provided.
- Expert Inquiry: To guarantee comprehensive evaluations, evaluators hail from over ten distinct professional fields, encompassing Sports Science, Human Anatomy, Rehabilitation, Clinical Medicine, General Medicine, Psychology, Nursing, Geriatric Medicine, Computer Science, and Electronic Information Engineering. Each expert has accrued more than a decade of experience in their respective disciplines, contributing to a multidisciplinary, cross-disciplinary panel designed to facilitate collaborative resource sharing and skills integration, leading to innovative problem-solving.
Data Analysis
The authority coefficient (Cr) derived from experts’ self-assessments profoundly influences evaluation reliability, calculated as an average of their judgment basis (Ca) and familiarity with the topic (Cs). A Cr value of 0.7 or higher is indicative of credible research outcomes. The descriptive statistics and consistency measures are analyzed utilizing SPSS 27.0, while visual data analysis is conducted using GraPhad Prism 10.1.2, leading to standardized narrative evaluations.
Results
The average authority coefficient among the experts reached 0.87, surpassing the critical threshold of 0.7, thereby confirming the reliability of the expert opinions rendered. The consistency coefficients presented in Table 1 further elucidate the findings.
GPT achieved an average accuracy score of 4.64 on a 1 to 6 Likert scale, whilst IHPS garnered a score of 4.38. Both systems demonstrated Kendall’s consistency indices exceeding 0.6, showcasing a high degree of agreement among evaluated experts. Detailed exercise prescriptions are documented in the Supplementary Materials; patient-specific information is accessible in Table 2.
Patient Number 1
For diabetic patients, exercise is critical for glycemic control, especially since IHPS emphasizes integrating different exercise types for optimal blood sugar management and complications prevention. For hypertensive patients, ensuring cardiovascular health through exercise is vital, with IHPS providing a broader array of benefits to alleviate patients’ concerns.15 Recommendations guiding exercise frequency and individual perceptions are more descriptive, assisting in adherence to exercise regimens, even as both systems may lack sufficient detail in risk identification.
Patient Number 2
The Japanese Society of Renal Rehabilitation promotes moderated physical activity for patients suffering from chronic kidney diseases. To mitigate cardiovascular risks among the elderly, exercise interventions that enhance stability, gait, and muscle strength are effective fall prevention measures. IHPS facilitates structured recommendations in this regard,41 yet it is crucial for both GPT and IHPS to personalize medication management effectively, particularly for individuals on specific medications.
Patient Number 3
The exercise training principle hinges on progressive overload, allowing organs and systems to adapt, therefore enhancing muscle oxygen delivery and consumption capabilities during exercise.57 Incorporating graded exercise tests into prescriptions for COPD patients is essential for safety and effectiveness.40 IHPS’s subjective intensity perception plays a pivotal role in ensuring patient comprehension, as do both tools regarding intensity measures.
The patient has expressed an intent to exercise but encounters limitations stemming from compromised physical function. IHPS aligns seamlessly with the TTM, advocating gradual strength training introduction to promote adherence and gradually enhance patient functionality, optimizing the trajectory towards improved health outcomes.
Discussion
The relatively low expert consensus on GPT’s applicability rating may arise due to its insufficient consideration of exercise preferences among older Chinese adults, specially in activities such as Pilates and yoga. Moreover, expert divergence in comprehensiveness rating for IHPS hints at potential prescription intensity issues incapable of yielding anticipated health benefits for patients with higher risk profiles.
Moreover, exercise prescriptions generated by GPT often exhibit repetitiveness, regardless of explicit instructions to diversify responses across multiple patients. Such cross-contamination between prescriptions could weaken the uniqueness of individual exercise plans, posing challenges in ensuring tailored healthcare.
In conclusion, AI technologies such as GPT and IHPS have the potential to serve low-risk populations as preliminary consultancy tools in healthcare settings. Nevertheless, the ethical considerations surrounding the use of AI in personal health scenarios must continue to evolve alongside technological advancements.
Conclusion
Despite these strengths, GPT and IHPS may still fall short in addressing complex scenarios and conducting thorough medication management for patients. Further research is warranted to refine their capabilities, especially concerning vulnerable populations prone to chronic condition complications.
Institutional Review Board Statement
This study was performed entirely in accordance with the relevant ChatGPT4.0 terms, generating exercise prescription results without involving human experimentation, adhering to AI usage licenses and ethical frameworks as outlined by the Declaration of Helsinki, with formal ethics approval from the Bengbu Medical University Ethics Committee (No. 2023-253).
Acknowledgments
The authors declare the absence of commercial or financial relationships that could potentially culminate in a conflict of interest.
Disclosure
The authors report no conflicts of interest associated with this study.
References
1. Wang JG, Zhang W, Li Y, Liu L. Hypertension in China: epidemiology and treatment initiatives. Nat Rev Cardiol. 2023;20(8):531–545. doi:10.1038/s41569-022-00829-z
2. Shrestha PL, Shrestha PA, Vivo RP. Epidemiology of comorbidities in patients with hypertension. Current Opinion in Cardiology. 2016;31(4):376–380. doi:10.1097/HCO.0000000000000298
3. Kocoń J, Cichecki I, Kaszyca O, et al. ChatGPT: jack of all trades, master of none. Info Fusion. 2023;99:101861. doi:10.1016/j.inffus.2023.101861
4. Biswas SS. Role of chat gpt in public health. Ann Biomed Eng. 2023;51(5):868–869. doi:10.1007/s10439-023-03172-7
5. Washif J, Pagaduan J, James C, Dergaa I, Beaven C. Artificial intelligence in sport: exploring the potential of using ChatGPT in resistance training prescription. Biol Sport. 2024;41(2):209–220. doi:10.5114/biolsport.2024.132987
6. Sun T, Xu X, Zhu N, Zhang J, Ma Z, Xie H. A Service-Learning Project Based on a Community-Oriented Intelligent Health Promotion System for Postgraduate Nursing Students: mixed Methods Study. JMIR Medical Education. 2023;9(1):e52279. doi:10.2196/52279
7. Muellmann S, Forberger S, Möllers T, Bröring E, Zeeb H, Pischke CR. Effectiveness of eHealth interventions for the promotion of physical activity in older adults: a systematic review. Preventive Med. 2018;108:93–110. doi:10.1016/j.ypmed.2017.12.026
8. Sun T, Xu Y, Xie H, Ma Z, Wang Y. Intelligent personalized exercise prescription based on an ehealth promotion system to improve health outcomes of middle-aged and older adult community dwellers: pretest–posttest study. J Med Inter Res. 2021;23(5):e28221. doi:10.2196/28221
9. Pedersen BK, Saltin B. Exercise as medicine–evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand J Med Sci Sports. 2015;25:1–72. doi:10.1111/sms.12581
10. Hashemzadeh M, Rahimi A, Zare-Farashbandi F, Alavi-Naeini AM, Daei A. Transtheoretical model of health behavioral change: a systematic review. Iranian J Nurs Midwifery Res. 2019;24(2):83–90. doi:10.4103/ijnmr.IJNMR_94_17
11. Gillespie ND, Lenz TL. Implementation of a tool to modify behavior in a chronic disease management program. Adv Prevent Med. 2011;2011:1–5. doi:10.4061/2011/215842
12. Aggarwal A, Tam CC, Wu D, Li X, Qiao S. Artificial intelligence–based chatbots for promoting health behavioral changes: systematic review. J medi Inter rese. 2023;25:e40789. doi:10.2196/40789
13. Alzahrani H, Mackey M, Stamatakis E, Zadro JR, Shirley D. The association between physical activity and low back pain: a systematic review and meta-analysis of observational studies. Sci Rep. 2019;9(1):8244. doi:10.1038/s41598-019-44664-8
14. Bordeleau M, Vincenot M, Lefevre S, et al. Treatments for kinesiophobia in people with chronic pain: a scoping review. Front Behav Neuroscie. 2022;16:933483. doi:10.3389/fnbeh.2022.933483
15. Robroek SJ, Bredt FJ, Burdorf A. The (cost-) effectiveness of an individually tailored long-term worksite health promotion programme on physical activity and nutrition: design of a pragmatic cluster randomised controlled trial. BMC Public Health. 2007;7:1–11. doi:10.1186/1471-2458-7-259
16. Lucas SJ, Cotter JD, Brassard P, Bailey DM. High-intensity interval exercise and cerebrovascular health: curiosity, cause, and consequence. J Cereb Blood Flow Metab. 2015;35(6):902–911. doi:10.1038/jcbfm.2015.49
17. Thompson PD, Franklin BA, Balady GJ, Blair SN, Corrado D, Costa F; American College of Sports Medicine. Exercise and acute cardiovascular events: placing the risks into perspective: a scientific statement from the American Heart Association Council on Nutrition. Phys Act Metab Coun Clin Card Circul. 2007;115(17):2358–2368.
18. Haskell WL, Lee IM, Pate RR, et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation. 2007;116(9):1081.
19. Eijsvogels TM, Molossi S, Lee DC, Emery MS, Thompson PD. Exercise at the extremes: the amount of exercise to reduce cardiovascular events. J Amer Card. 2016;67(3):316–329. doi:10.1016/j.jacc.2015.11.034
20. Domenech-Ximenos B, Garza MSDL, Prat-González S, et al. Exercise-induced cardio-pulmonary remodelling in endurance athletes: not only the heart adapts. Eur J Preven Cardio. 2020;27(6):651–659. doi:10.1177/2047487319868545
21. Mei N, Chang Y. Effect of aerobic exercise on physical function indices in the elderly. Revista Brasileira de Medicina do Esporte. 2022;29:
22. Ozemek C, Laddu DR, Lavie CJ, et al. An update on the role of cardiorespiratory fitness, structured exercise and lifestyle physical activity in preventing cardiovascular disease and health risk. Prog Cardiovasc Diseases. 2018;61(5–6):484–490. doi:10.1016/j.pcad.2018.11.005
23. Christmas C, Andersen RA. Exercise and older patients: guidelines for the clinician. J Am Geriatr Soc. 2000;48(3):318–324. doi:10.1111/j.1532-5415.2000.tb02654.x
24. Scherr J, Wolfarth B, Christle JW, Pressler A, Wagenpfeil S, Halle M. Associations between Borg’s rating of perceived exertion and physiological measures of exercise intensity. Eur J Appl Physiol. 2013;113:147–155. doi:10.1007/s00421-012-2421-x
25. Levine D, Colston MA, Whittle MW, Pharo EC, Marcellin-Little DJ. Sagittal lumbar spine position during standing, walking, and running at various gradients. J Athletic Train. 2007;42(1):29.
26. Chevutschi A, Lensel G, Vaast D, Thevenon A. An electromyographic study of human gait both in water and on dry ground. J Physiothropology. 2007;26(4):467–473. doi:10.2114/jpa2.26.467
27. Kumar T, Kumar S, Nezamuddin M, Sharma VP. Efficacy of core muscle strengthening exercise in chronic low back pain patients. J Back Musculoskrehabil. 2015;28(4):699–707. doi:10.3233/BMR-140572
28. Anemaet WK, Hammerich AS. A framework for exercise prescription. Top Geriatric Rehabil. 2014;30(2):79–101. doi:10.1097/TGR.0000000000000011
29. Nakhleh A, Shehadeh N. Hypoglycemia in diabetes: an update on pathophysiology, treatment, and prevention. World J Diab. 2036;12(12).
30. Patil L, Jadhav A, Borade NG (2013). A Comparative study of heart rate variability during Valsalva maneuver in healthy, hypertensive and hypertensive diabetic.
31. Al-Rubeaan K, Al Derwish M, Ouizi S, et al. Diabetic foot complications and their risk factors from a large retrospective cohort study. PLoS One. 2015;10(5):e0124446. doi:10.1371/journal.pone.0124446
32. Ouyang W, Jia Y, Jin L. Risk factors of diabetic foot ulcer in patients with type 2 diabetes: a retrospective cohort study. Am J Transl Res. 2021;13(8):9554.
33. Rossboth S, Lechleitner M, Oberaigner W. Risk factors for diabetic foot complications in type 2 diabetes—a systematic review. Endocrinol Diab Metabol. 2021;4(1):e00175. doi:10.1002/edm2.175
34. Goetz LH, Schork NJ. Personalized medicine: motivation, challenges, and progress. Fertil Sterility. 2018;109(6):952–963. doi:10.1016/j.fertnstert.2018.05.006
35. Mages M, Shojaa M, Kohl M, et al. Exercise effects on bone mineral density in men. Nutrients. 2021;13(12):4244. doi:10.3390/nu13124244
36. Cao JJ. Effects of obesity on bone metabolism. J Orthopaedic Surg Res. 2011;6:1–7. doi:10.1186/1749-799X-6-30
37. Svarstad E, Myking O, Ofstad J, Iversen BM. Effect of light exercise on renal hemodynamics in patients with hypertension and chronic renal disease. Scand J Urol. 2002;36(6):464–472. doi:10.1080/003655902762467648
38. Taylor-Piliae RE, Haskell WL, Stotts NA, Froelicher ES. Improvement in balance, strength, and flexibility after 12 weeks of Tai chi exercise in ethnic Chinese adults with cardiovascular disease risk factors. Alternative Therapies in Health and Medicine. 2006;12(2):50–59.
39. Yamagata K, Hoshino J, Sugiyama H, et al. Clinical practice guideline for renal rehabilitation: systematic reviews and recommendations of exercise therapies in patients with kidney diseases. Ren Replace Ther. 2019;5(1):1–19. doi:10.1186/s41100-019-0209-8
40. Liguori G; American College of Sports Medicine. ACSM’s Guidelines for Exercise Testing and Prescription. Lippincott Williams & Wilkins; 2020.
41. Sun M, Min L, Xu N, Huang L, Li X. The effect of exercise intervention on reducing the fall risk in older adults: a meta-analysis of randomized controlled trials. Int J Environ Res Public Health. 2021;18(23):12562. doi:10.3390/ijerph182312562
42. Duncan GE, Anton SD, Sydeman SJ, et al. Prescribing exercise at varied levels of intensity and frequency: a randomized trial. Archives of Internal Medicine. 2005;165(20):2362–2369. doi:10.1001/archinte.165.20.2362
43. Benedetti MG, Furlini G, Zati A, Letizia Mauro G. The effectiveness of physical exercise on bone density in osteoporotic patients. Biomed Res Int 2018. doi:10.1155/2018/4840531
44. Daly RM, Dunstan DW, Owen N, Jolley D, Shaw JE, Zimmet PZ. Does high-intensity resistance training maintain bone mass during moderate weight loss in older overweight adults with type 2 diabetes? Osteoporosis Int. 16:1703–1712. doi:10.1007/s00198-005-1906-4
45. Vereijken A, Aerts I, Jetten J, et al. Association between functional performance and return to performance in high-impact sports after lower extremity injury: a systematic review. J Sports Sci Med. 2020;19(3):564.
46. Zhang C, Zhang J, Su J, Liu S, Zhang L. Review of Research on Yoga Intervention and Health Promotion Based on Citespace-A Comparative Analysis between China and the United States. J Asian Society Healt Exe. 2019;1(2):43–70.
47. Brown D, Leledaki A. Eastern movement forms as body-self transforming cultural practices in the West: towards a sociological perspective. Cultural Socio. 2010;4(1):123–154. doi:10.1177/1749975509356866
48. Gautam S, Kiran UV. Clinical Effects of Yoga and Meditational Practices on the Holistic Health of Chronic Kidney Disease Patients: a Systematic Review. Cureus. 2024;16(4).
50. Muhammad M, Nuhu JM, Hassan TM. Therapeutic exercise for hypertension: an update for exercise prescribers. Niger J Cardiol. 17(1):11–20. doi:10.4103/njc.njc_24_19
51. Sims D. Effects of positioning on ankle edema. J Orthop Sports Phys Ther. 1986;8(1):30–33. doi:10.2519/jospt.1986.8.1.30
52. Hays AG, Iantorno M, Schär M, et al. The influence of febuxostat on coronary artery endothelial dysfunction in patients with coronary artery disease: a Phase 4 randomized, placebo-controlled, double-blind, crossover trial. Am Heart J. 2018;197:85–93. doi:10.1016/j.ahj.2017.11.006
53. Schweitzer AM, Gingrich MA, Hawke TJ, Rebalka IA. The impact of statins on physical activity and exercise capacity: an overview of the evidence, mechanisms, and recommendations. Eur J App Physio. 2020;120:1205–1225. doi:10.1007/s00421-020-04360-2
54. Singer E, Couper MP, Fagerlin A, et al. The role of perceived benefits and costs in patients’ medical decisions. Health Expect. 2014;17(1):4–14. doi:10.1111/j.1369-7625.2011.00739.x
55. Vasan RS, Pan S, Larson MG, Mitchell GF, Xanthakis V. Arteriosclerosis, atherosclerosis, and cardiovascular health: joint relations to the incidence of cardiovascular disease. Hypertension. 2021;78(5):1232–1240. doi:10.1161/HYPERTENSIONAHA.121.18075
56. Chen XX, Tian CW, Bai LY, et al. Relationships among body weight, lipids and bone mass in elderly individuals with fractures: a case-control study. World J Orthoped. 2023;14(9):720. doi:10.5312/wjo.v14.i9.720
58. Morris NR, Walsh J, Adams L, Alision J. Exercise training in COPD: what is it about intensity? Respirology. 2016;21(7):1185–1192. doi:10.1111/resp.12864
59. Spruit MA, Singh SJ, Garvey C, et al. An official American Thoracic Society/European Respiratory Society statement: key concepts and advances in pulmonary rehabilitation. Am J Respir Crit Care Med. 2013;188(8):e13–e64. doi:10.1164/rccm.201309-1634ST
60. Maltais F, Simard AA, Simard C, Jobin J, Desgagnés P, LeBlanc P. Oxidative capacity of the skeletal muscle and lactic acid kinetics during exercise in normal subjects and in patients with COPD. Am J Respir Crit Care Med. 1996;153(1):288–293. doi:10.1164/ajrccm.153.1.8542131
61. Whittom FRANÇOIS, Jobin JEAN, Simard PM, et al. Histochemical and morphological characteristics of the vastus lateralis muscle in patients with chronic obstructive pulmonary disease. Med Sci Sports Exercise. 1998;30(10):1467–1474. doi:10.1097/00005768-199810000-00001
62. Fabre C, Chehere B, Bart F, Mucci P, Wallaert B, Grosbois J-M. Relationships between heart rate target determined in different exercise testing in COPD patients to prescribed with individualized exercise training. Int J Chronic Obstr Pulm Dis. 2017;Volume 12:1483–1489. doi:10.2147/COPD.S129889
63. Gloeckl R, Schneeberger T, Jarosch I, Kenn K. Pulmonary rehabilitation and exercise training in chronic obstructive pulmonary disease. Dtsch Arztebl Int. 2018;115(8):117. doi:10.3238/arztebl.2018.0117
64. Cramer H, Haller H, Klose P, Ward L, Chung VC, Lauche R. The risks and benefits of yoga for patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. Clin rehabilitat. 2019;33(12):1847–1862. doi:10.1177/0269215519860551
65. Wong MYC, Ou KL, Chung PK, Chui KYK, Zhang CQ. The relationship between physical activity, physical health, and mental health among older Chinese adults: a scoping review. Front Public Health. 2023;10:914548. doi:10.3389/fpubh.2022.914548
66. O’Donnell DE, Guenette JA, Maltais F, Webb KA. Decline of resting inspiratory capacity in COPD: the impact on breathing pattern, dyspnea, and ventilatory capacity during exercise. Chest. 2012;141(3):753–762. doi:10.1378/chest.11-0787
67. Shei R. Time to move beyond a “one-size fits all” approach to inspiratory muscle training. Front Physiol. 2022;12:766346. doi:10.3389/fphys.2021.766346
68. Zhang Z, Wang B, Wu H, Chai X, Wang W, Peng CK. Effects of slow and regular breathing exercise on cardiopulmonary coupling and blood pressure. Med Biol Eng Comput. 2017;55:327–341. doi:10.1007/s11517-016-1517-6
69. Burbank PM, Reibe D, Padula CA, Nigg C. Exercise and older adults: changing behavior with the transtheoretical model. Orthop Nurs. 2002;21(4):51–63. doi:10.1097/00006416-200207000-00009
70. Kaabi W, Aouinti I, Charfi O, et al. A persistent dyspnea induced by hydrochlorothiazide. Therapies. 2019;74(4):498–499. doi:10.1016/j.therap.2019.03.009
71. Duan RR, Hao K, Yang T. Air pollution and chronic obstructive pulmonary disease. Chronic Diseases and Translational Medicine. 2020;6(04):260–269. doi:10.1016/j.cdtm.2020.05.004
72. Malochet-Guinamand S, Durif F, Thomas T. Parkinson’s disease: a risk factor for osteoporosis. Joint Bone Spine. 2015;82(6):406–410. doi:10.1016/j.jbspin.2015.03.009
73. Xu X, Fu Z, Le W. Exercise and Parkinson’s disease. Inter Rev Neurobi. 2019;147:45–74.
74. Fasano A, Canning CG, Hausdorff JM, Lord S, Rochester L. Falls in Parkinson’s disease: a complex and evolving picture. Mov Disord. 2017;32(11):1524–1536. doi:10.1002/mds.27195
75. Kim R, Jun JS. Impact of overweight and obesity on functional and clinical outcomes of early Parkinson’s disease. J Am Med Directors Assoc. 2020;21(5):697–700. doi:10.1016/j.jamda.2019.11.019
76. Müller J, Myers J. Association between physical fitness, cardiovascular risk factors, and Parkinson’s disease. European J Prevent Card. 2018;25(13):1409–1415. doi:10.1177/2047487318771168
77. Chen J, Zhang C, Wu Y, Zhang D. Association between hypertension and the risk of Parkinson’s disease: a meta-analysis of analytical studies. Neuroepidemiology. 2019;52(3–4):181–192. doi:10.1159/000496977
78. Schenkman M, Moore CG, Kohrt WM, et al. Effect of high-intensity treadmill exercise on motor symptoms in patients with de novo Parkinson disease: a Phase 2 randomized clinical trial. JAMA neurol. 2018;75(2):219–226. doi:10.1001/jamaneurol.2017.3517
79. van der Kolk NM, de Vries NM, Kessels RP, et al. Effectiveness of home-based and remotely supervised aerobic exercise in Parkinson’s disease: a double-blind, randomised controlled trial. Lancet Neurol. 2019;18(11):998–1008. doi:10.1016/S1474-4422(19)30285-6
80. Gomes Neto M, Pontes SS, Almeida LDO, da Silva CM, da Conceição Sena C, Saquetto MB. Effects of water-based exercise on functioning and quality of life in people with Parkinson’s disease: a systematic review and meta-analysis. Clin rehabilitat. 2020;34(12):1425–1435. doi:10.1177/0269215520943660
81. Alberts JL, Rosenfeldt AB, Bloem BR, Brundin P. The universal prescription for Parkinson’s disease: exercise. J Parkinsons Dis. 2020;10(s1):S21–S27. doi:10.3233/JPD-202100
82. Lima LO, Scianni A, Rodrigues-de-Paula F. Progressive resistance exercise improves strength and physical performance in people with mild to moderate Parkinson’s disease: a systematic review. J Physiotherapy. 2013;59(1):7–13. doi:10.1016/S1836-9553(13)70141-3
84. Jeong D, Choi -H-H, Kang J-I, et al. Effect of lumbar stabilization exercise on disc herniation index, sacral angle, and functional improvement in patients with lumbar disc herniation. J Phys Ther Sci. 2017;29(12):2121–2125. doi:10.1589/jpts.29.2121
85. Huot P, Johnston TH, Koprich JB, Fox SH, Brotchie JM, Sibley DR. The pharmacology of L-DOPA-induced dyskinesia in Parkinson’s disease. Pharmacol Rev. 2013;65(1):171–222. doi:10.1124/pr.111.005678
86. Wang Y, Zhou R, Zhong W, Hu C, Lu S, Chai Y. Association of gout with osteoporotic fractures. International Orthopaedics. 2018;42:2041–2047. doi:10.1007/s00264-018-4033-5
87. Jablonski K, Young NA, Henry C, et al. Physical activity prevents acute inflammation in a gout model by downregulation of TLR2 on circulating neutrophils as well as inhibition of serum CXCL1 and is associated with decreased pain and inflammation in gout patients. PLoS One. 2020;15(10):e0237520. doi:10.1371/journal.pone.0237520
88. Jia E, Zhu H, Geng H, et al. The effects of aerobic exercise on body composition in overweight and obese patients with gout: a randomized, open-labeled, controlled trial. Trials. 2022;23(1):745. doi:10.1186/s13063-022-06695-x
89. Hou Y, Ma R, Gao S, et al. The effect of low and moderate exercise on hyperuricemia: protocol for a randomized controlled study. Front Endocrinol. 2021;12:716802. doi:10.3389/fendo.2021.716802
90. Luan L, Bousie J, Pranata A, Adams R, Han J. Stationary cycling exercise for knee osteoarthritis: a systematic review and meta-analysis. Clin rehabilitat. 2021;35(4):522–533. doi:10.1177/0269215520971795
91. Zhang SK, Yang Y, Gu ML, Mao SJ, Zhou WS. Effects of low back pain exercises on pain symptoms and activities of daily living: a systematic review and meta-analysis. Percep Motor Skills. 2022;129(1):63–89. doi:10.1177/00315125211059407
92. Millar AL. Action Plan for Arthritis. Human Kinetics; 2003.
93. Nurhasanah A, Trismiyana E, Pribadi T. Latihan rentang gerak sendi (ROM) untuk menurunkan nyeri sendi pada pasien dengan asam urat tinggi. J Quali Health Rese Case Stu Rep. 2023;3(1):8–14.
94. Saladini F, Mos L, Fania C, Garavelli G, Casiglia E, Palatini P. Regular physical activity prevents development of hypertension in young people with hyperuricemia. J Hypert. 2017;35(5):994–1001. doi:10.1097/HJH.0000000000001271
95. Liu J, Kowal I, Yang Y, et al. Culturally tailored group Qigong exercise in older Chinese immigrants: a feasibility study. Geriatric Nurs. 2023;51:245–252. doi:10.1016/j.gerinurse.2023.03.021
96. Foster C. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15(1):109–115.
97. Hung L, Liu C, Woldum E, et al. The benefits of and barriers to using a social robot PARO in care settings: a scoping review. BMC Geriatr. 2019;19:1–10. doi:10.1186/s12877-019-1244-6
98. Turner NR, Berridge C. How I want technology used in my care: learning from documented choices of people living with dementia using a dyadic decision making tool. Inform Health Soc Care. 2023;48(4):387–401. doi:10.1080/17538157.2023.2252066
The Rise of Hypertension and the Role of ChatGPT: A Comedic Commentary
Introduction: The Hypertension Headache
Well, well, well! Over the last couple of decades, it seems like every other statistic is inflating like my waistline after a hefty meal. Hypertension, the high blood pressure villain, has made its grand entrance, thanks to extended life spans and our ever-loving affection for sedentary lifestyles and questionable food choices. Who needs a heart-healthy salad when there’s a double cheeseburger calling your name? But hold onto your blood pressure cuffs people, because life doesn’t stop there! This sneaky condition often tags along with chronic buddies like diabetes and coronary heart disease. It’s like a bad rom-com where no one gets to leave until the credits roll and tears flow!
And here’s where our knights in shining armor—hypertension management tools come in. Enter: the digital age! We have robots, the Internet, and apparently even chatbots like ChatGPT, trying to save us from ourselves. Let’s put it simply: we’ve got tech tools that are better at managing our health than we are—talk about a plot twist!
Meet Your New Health BFF: The Intelligent Health Promotion System
Ah, the Intelligent Health Promotion System (IHPS)! It’s like that friend who keeps nudging you to go to the gym—except it has data analytics backing it up. This system combines wearable tech and good ol’ AI to craft hand-holding personalized health reports. For example: “Hey there, couch potato! Let’s get moving!” Sounds encouraging, right?
Now, let’s dive into the exercise details. Apparently, being active is great for the long-term management of chronic diseases. Shocking news alert! But here’s the kicker—getting people to stick to their exercise plans is like trying to convince a cat to take a bath. It’s just not happening! But worry not, with concepts like the Transtheoretical Model (TTM)—which sounds like something you’d hear at an over-caffeinated sociology class— we could guide patients into exercising and keeping at it.
Yes, I can literally hear the “wow’s” and “amazing’s” from here. Who knew behavioral change theories could be this riveting?
Study Setup: An Intellectual Smorgasbord
This study takes a dual approach to explore ChatGPT and IHPS in creating exercise prescriptions. You’ve got actual patients putting their health on the line while a squad of experts dissects the results like it’s an ancient fatality from a Roman gladiatorial fight. Mixed-methods is the name of the game folks, because why go simple when you can overcomplicate things?
Diving into the Data: Who Takes the Crown?
Now let’s get down to the nitty-gritty—who produces better exercise prescriptions: ChatGPT or IHPS? Spoiler alert: We conducted an expert panel that probably looked like the Avengers of medical professionals. The results? Both systems had some hiccups. ChatGPT’s efficiency in offering personalized exercise plans was surprisingly better than IHPS on some occasions, but let’s not get too cozy.
As we ventured deeper into this treasure trove of data, it turned out that ChatGPT’s average accuracy came out on top—how surprising! It’s like discovering that your vegan neighbor secretly loves bacon. The moral of the story: AI can wield some influence but doesn’t have the charm of a good doctor yet!
Tackling Patient Predicaments One at a Time
Seven patients were on this journey, each with a unique profile. First up—the back pain guy trying to juggle diabetes and hypertension like a circus performer. Both tools struggled to give him the well-rounded advice he needed, almost like they were playing “guess what hurts." For him, GPT’s more human-like advice on exercise intensity helped paint a clearer picture compared to the clinical tone of IHPS.
Next, we had Mr. Obesity, whose love affair with junk food is just as strong as my affinity for a good Netflix binge. Exercise was promoted, but advice on how to really get him moving often fell short. As much as IHPS touted its knowledge about chronic conditions, the execution (much like some of my cooking attempts) left something to be desired!
What’s With the Exercise Dilemmas?
Now let’s talk specifics—why do exercise regimens not stick? You have your chatbots, your high-tech gadgets, yet folks would rather relax on the couch watching "The Office" for the 18th time. Could it be that the exercise plans sent by these systems are as relatable as Quantum Physics jokes? Or perhaps they just need to remember that the couch-to-5k is way more appealing than a rigorous four-days-of-core plan?
Both GPT and IHPS are stepping up but often miss the mark when it comes to real-life adaptability. For the love of all that’s healthy—exercise plan versatility is key!
Conclusion: AI and Human Collaboration for the Win
In a nutshell, while AI like ChatGPT and systems like IHPS are emerging as helpful allies, they won’t be strutting into your doctor’s office anytime soon dressed as an actual healthcare provider. They are your sidekicks, not your heroes. Feeling like their efficacy lacks a human touch? Well, you’d be right. They suggest, but don’t replace that invaluable human intuition!
If only machines could sprinkle a little humor into play. Now that’s an AI I’d advocate for! Until then, here’s to combining tech with the genuine warmth of human service—a recipe we hope can lead us to a healthier tomorrow!
Final Thoughts: Don’t Forget Your Exercise!
So go ahead, lace up those trainers, and take that initial step—not just for your blood pressure, but for your state of mind. Because let’s face it—if we can chuckle a bit on the way to better health, why the heck not?
Who knew blood pressure could be so entertaining? And remember, if things go awry, blame it on your chatbot and send it a strongly worded roast on social media. Good luck and may your exercise regime be more successful than my attempts at baking sourdough!
Health apps, and even the good ol’ gym instructors, but convincing people to lace up their sneakers is like herding cats. Sure, the benefits are well-documented—reducing hypertension, improving mental health, and enhancing overall quality of life—but getting folks to take that first step is the real challenge. When faced with a choice between sweating it out or binge-watching the latest hit series, let’s be honest: Netflix usually wins.
One of the major sticking points for individuals trying to embrace a more active lifestyle is the daunting nature of exercise itself. For many, the thought of hitting the gym conjures up images of grunting weightlifters and endless treadmill sessions. Instead of feeling inspired, they often feel overwhelmed. Enter the AI and digital tools—seemingly ready to take the reigns and guide us through the process. But the effectiveness of these tools often boils down to the quality of their guidance, and as our study shows, personalization is paramount.
Consider this: a one-size-fits-all exercise plan simply won’t cut it. Just like choosing the perfect pizza topping, preferences vary. Some individuals thrive on high-intensity interval training, while others might prefer a gentle yoga session. This variability is where ChatGPT excelled—it could tailor recommendations based on user input, making exercise feel less like a chore and more like a personal journey.
Yet, navigating the world of physical activity isn’t just about preferences; it’s about tackling barriers too. For those grappling with chronic conditions, any recommendation that lacks specificity may as well be in hieroglyphics. How intensely should one exercise? For how long? And what if the pain medley decides to crash the party mid-session? ChatGPT, in many cases, was able to address these nuances in a more relatable manner, akin to a friend offering support rather than a formal health recommendation that feels detached.
Moreover, motivation plays a massive role. It’s easy to rally behind the benefits of exercise when scrolling through heroically transformed fitness Instagram accounts, but real life is filled with distractions and setbacks. Digital tools being used today, including AI platforms, can provide that much-needed boost, offering reminders and customized goals, each little nudge adding up over time. This promises potential when blended with social accountability—encouraging users to check in with buddies or share progress could amplify motivation exponentially.
as we march forward into an era dominated by technology, pairing human empathy and understanding with artificial intelligence may just create the healthiest combo to combat hypertension and other chronic diseases. While ChatGPT shows remarkable promise in personalizing exercise interventions, the ultimate victory will lie in its ability to work in tandem with healthcare professionals to offer a well-rounded support system. If the future is a landscape where humans and machines work hand-in-hand to promote physical activity and well-being, then we might just find a way to turn those hypertension statistics around—with a hearty laugh and a little more movement along the way! So, let’s buckle up, and maybe share a few more salads along the path to health. Cheers to that!
