Application progress of artificial intelligence in exercise management for patients with chronic kidney disease_West China Medical Journal

Authors：

CHENG Xin ^1,2,3 , LIU Ke ^1,3,4 , ZHU Yongli ⁵ ,  MU Changping ^3,4 , CUI Lijun ¹

1. School of Nursing, North Sichuan Medical College, Nanchong, Sichuan 637000, P. R. China;
2. Blood Purification Center, Chongqing General Hospital, Chongqing University, Chongqing 401147, P. R. China;
3. Chongqing Academy of Medical Sciences, Chongqing 401147, P. R. China;
4. Department of Radiology, Chongqing General Hospital, Chongqing University, Chongqing 401147, P. R. China;
5. Pediatric Intensive Care Unit, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, P. R. China;

Corresponding?author：

MU Changping, Email: 13657639042@163.com

Keywords：

Artificial intelligence; chronic kidney disease; exercise management; remote follow-up

DOI：

10.7507/1002-0179.202601032

Video：

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The application of artificial intelligence (AI) in exercise management for patients with chronic kidney disease (CKD) has gradually garnered attention. This article reviews the application progress of AI in exercise management for patients with CKD, explores the current status of AI integration with wearable devices, electronic health records, and machine learning models from four aspects: exercise capacity assessment, individualized exercise prescription formulation, exercise-related risk prediction, and remote exercise follow-up management. The aim is to promote the development of CKD exercise management towards precision, continuity, and human-machine collaboration.

Citation： CHENG Xin, LIU Ke, ZHU Yongli, MU Changping, CUI Lijun. Application progress of artificial intelligence in exercise management for patients with chronic kidney disease. West China Medical Journal, 2026, 41(7): 1182-1186. doi: 10.7507/1002-0179.202601032 Copy

1.	魏媛媛, 馬迎春. 慢性腎臟病患者功能評估及康復服務規范[J]. 中華全科醫學, 2021, 19(12): 1983-1988.
2.	王李勝, 童輝, 楊建國, 等. 人工智能在慢性腎臟病應用現狀及展望[J]. 中國血液凈化, 2022, 21(1): 59-62.
3.	上海慢性腎臟病早發現及規范化診治與示范項目專家組. 慢性腎臟病篩查診斷及防治指南[J]. 中國實用內科雜志, 2017, 37(1): 28-34.
4.	Sabanayagam C, Banu R, Lim C, et al. Artificial intelligence in chronic kidney disease management: a scoping review[J]. Theranostics, 2025, 15(10): 4566-4578.
5.	Heiwe S, Jacobson SH. Exercise training for adults with chronic kidney disease[J]. Cochrane Database Syst Rev, 2011, 2011(10): CD003236.
6.	王微平, 馬迎春. 慢性腎臟病患者居家運動康復促進因素與障礙因素的質性研究[J]. 中國康復理論與實踐, 2025, 31(2): 235-241.
7.	臧麗, 王少清. 慢性腎臟病患者運動康復管理策略研究進展[J]. 中國全科醫學, 2020, 23(1): 109-113.
8.	宋欣芫, 張苗苗, 孫瑩, 等. 以自我管理理論為核心的云平臺健康管理在慢性腎臟病患者延續護理中的應用[J]. 天津護理, 2022, 30(5): 532-538.
9.	Tangri N, Ferguson TW. Role of artificial intelligence in the diagnosis and management of kidney disease: applications to chronic kidney disease and acute kidney injury[J]. Curr Opin Nephrol Hypertens, 2022, 31(3): 283-287.
10.	Hamet P, Tremblay J. Artificial intelligence in medicine[J]. Metabolism, 2017, 69S: S36-S40.
11.	Chen HK, Chen FH, Lin SF. An AI-based exercise prescription recommendation system[J]. Appl Sci, 2021, 11(6): 2661.
12.	Ikizler TA, Burrowes JD, Byham-Gray LD, et al. KDOQI clinical practice guideline for nutrition in CKD: 2020 update[J]. Am J Kidney Dis, 2020, 76(Suppl 1): S1-S107.
13.	He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine[J]. Nat Med, 2019, 25(1): 30-36.
14.	Kooman JP, Wieringa FP, Han M, et al. Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?[J]. Nephrol Dial Transplant, 2020, 35(Suppl 2): ii43-ii50.
15.	Sardari S, Sharifzadeh S, Daneshkhah A, et al. Artificial intelligence for skeleton-based physical rehabilitation action evaluation: a systematic review[J]. Comput Biol Med, 2023, 158: 106835.
16.	Canaud B, Kooman J, Davenport A, et al. Digital health technology to support care and improve outcomes of chronic kidney disease patients: as a case illustration, the Withings toolkit health sensing tools[J]. Front Nephrol, 2023, 3: 1148565.
17.	Lunney M, Wiebe N, Kusi-Appiah E, et al. Wearable fitness trackers to predict clinical deterioration in maintenance hemodialysis: a prospective cohort feasibility study[J]. Kidney Med, 2021, 3(5): 768-775.e1.
18.	Song Y, Jeong IC, Ryu S, et al. GAIT-CKD (gait analysis using artificial intelligence for digital therapeutics of patients with chronic kidney disease): design and methods[J]. Kidney Res Clin Pract, 2025, 44(5): 788-801.
19.	王銀璐, 李慧慧, 周司晨, 等. 人工智能技術在運動康復領域的應用研究綜述[J]. 運動與健康, 2024, 3(12): 37-40.
20.	Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine[J]. J Intern Med, 2018, 284(6): 603-619.
21.	張馨月, 劉長梅, 譚文瑞, 等. 人工智能在糖尿病運動管理中應用的研究進展[J]. 預防醫學論壇, 2023, 29(9): 718-720, 封 3.
22.	Li Z, Liu X, Tang Z, et al. TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease[J]. J Am Med Inform Assoc, 2024, 31(11): 2474-2485.
23.	Yanai A, Uchiyama K, Suganuma S. Salt reduction using a smartphone application based on an artificial intelligence system for dietary assessment in patients with chronic kidney disease: a single-center retrospective cohort study[J]. Kidney Dial, 2023, 3(1): 139-151.
24.	馮穎倩, 王夢君, 呂思清, 等. 醫學人工智能在 2 型糖尿病健康管理中的應用[J]. 中華養生保健, 2023, 41(21): 11-16.
25.	Ma J, Wang J, Lu L, et al. Development and validation of a dynamic kidney failure prediction model based on deep learning: a real-world study with external validation[PP/OL]. V2. arXiv(2025-01-25)[2025-10-01]. doi: 10.48550/arXiv.2501.16388.
26.	羅凡, 楊毓彥, 陳淑嬌. 可穿戴技術在老年慢性病管理中的應用進展[J]. 醫學理論與實踐, 2024, 37(12): 2013-2015, 2002.
27.	Yuan Q, Zhang H, Deng T, et al. Role of artificial intelligence in kidney disease[J]. Int J Med Sci, 2020, 17(7): 970-984.
28.	Ali S, Akhlaq F, Imran AS, et al. The enlightening role of explainable artificial intelligence in medical and healthcare domains: a systematic literature review[J]. Comput Biol Med, 2023, 166: 107555.
29.	Dana Z, Naseer AA, Toro B, et al. Integrated machine learning and survival analysis modeling for enhanced chronic kidney disease risk stratification[PP/OL]. arXiv(2024-11-16)[2025-05-20]. doi: 10.48550/arXiv.2411.10754.
30.	Surian NU, Batagov A, Wu A, et al. A digital twin model incorporating generalized metabolic fluxes to identify and predict chronic kidney disease in type 2 diabetes mellitus[J]. NPJ Digit Med, 2024, 7(1): 140.
31.	Yoshizaki Y, Kato K, Fujihara K, et al. Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data[J]. Front Public Health, 2024, 12: 1495054.
32.	Lin P, Lin G, Wan B, et al. Development and validation of prediction model for fall accidents among chronic kidney disease in the community[J]. Front Public Health, 2024, 12: 1381754.
33.	Yan T, Nabi S, Liu X, et al. The association of physical activity with kidney function risk among adults with long working hours[J]. Front Endocrinol (Lausanne), 2024, 15: 1415713.
34.	Kwiendacz H, Huang B, Chen Y, et al. Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project[J]. Cardiovasc Diabetol, 2025, 24(1): 76.
35.	胡佳敏, 邱艷, 任菁菁. AI 在基層醫療慢性病管理中的應用研究進展[J]. 中華全科醫學, 2024, 22(3): 481-485.
36.	徐舒婷, 陶勝茹, 楊濤, 等. 維持性血液透析病人“互聯網+”運動管理的研究進展[J]. 護理研究, 2025, 39(4): 679-684.
37.	Rockenschaub P, Hilbert A, Kossen T, et al. The impact of multi-institution datasets on the generalizability of machine learning prediction models in the ICU[J]. Crit Care Med, 2024, 52(11): 1710-1721.
38.	Valenzuela PL, Castillo-García A, Saco-Ledo G, et al. Physical exercise: a polypill against chronic kidney disease[J]. Nephrol Dial Transplant, 2024, 39(9): 1384-1391.
39.	Letton ME, Tr?n TB, Flower S, et al. Digital physical activity and exercise interventions for people living with chronic kidney disease: a systematic review of health outcomes and feasibility[J]. J Med Syst, 2024, 48(1): 63.
40.	Liu W, Yu X, Wang J, et al. Improving kidney outcomes in patients with nondiabetic chronic kidney disease through an artificial intelligence-based health coaching mobile App: retrospective cohort study[J]. JMIR Mhealth Uhealth, 2023, 11: e45531.
41.	Gabarron E, Larbi D, Rivera-Romero O, et al. Human factors in AI-driven digital solutions for increasing physical activity: scoping review[J]. JMIR Hum Factors, 2024, 11: e55964.
42.	Wu CC, Islam MM, Poly TN, et al. Artificial intelligence in kidney disease: a comprehensive study and directions for future research[J]. Diagnostics (Basel), 2024, 14(4): 397.
43.	Greenwood SA, Young HML, Briggs J, et al. Evaluating the effect of a digital health intervention to enhance physical activity in people with chronic kidney disease (kidney BEAM): a multicentre, randomised controlled trial in the UK[J]. Lancet Digit Health, 2024, 6(1): e23-e32.
44.	應家佩, 戴麗麗, 馬建偉, 等. 慢性腎臟病患者智能健康隨訪管理系統的構建及應用[J]. 護理學雜志, 2024, 39(19): 11-15, 30.
45.	Calota MS, Huang JYC, Chen LL, et al. Assembling the puzzle: exploring collaboration and data sensemaking in nursing practices for remote patient monitoring[C]//Companion publication of the 2024 conference on computer-supported cooperative work and social computing. New York: Association for Computing Machinery, 2024: 296-302.
46.	Mata-Lima A, Paquete AR, Serrano-Olmedo JJ. Remote patient monitoring and management in nephrology: a systematic review[J]. Nefrologia (Engl Ed), 2024, 44(5): 639-667.

1. 魏媛媛, 馬迎春. 慢性腎臟病患者功能評估及康復服務規范[J]. 中華全科醫學, 2021, 19(12): 1983-1988.
2. 王李勝, 童輝, 楊建國, 等. 人工智能在慢性腎臟病應用現狀及展望[J]. 中國血液凈化, 2022, 21(1): 59-62.
3. 上海慢性腎臟病早發現及規范化診治與示范項目專家組. 慢性腎臟病篩查診斷及防治指南[J]. 中國實用內科雜志, 2017, 37(1): 28-34.
4. Sabanayagam C, Banu R, Lim C, et al. Artificial intelligence in chronic kidney disease management: a scoping review[J]. Theranostics, 2025, 15(10): 4566-4578.
5. Heiwe S, Jacobson SH. Exercise training for adults with chronic kidney disease[J]. Cochrane Database Syst Rev, 2011, 2011(10): CD003236.
6. 王微平, 馬迎春. 慢性腎臟病患者居家運動康復促進因素與障礙因素的質性研究[J]. 中國康復理論與實踐, 2025, 31(2): 235-241.
7. 臧麗, 王少清. 慢性腎臟病患者運動康復管理策略研究進展[J]. 中國全科醫學, 2020, 23(1): 109-113.
8. 宋欣芫, 張苗苗, 孫瑩, 等. 以自我管理理論為核心的云平臺健康管理在慢性腎臟病患者延續護理中的應用[J]. 天津護理, 2022, 30(5): 532-538.
9. Tangri N, Ferguson TW. Role of artificial intelligence in the diagnosis and management of kidney disease: applications to chronic kidney disease and acute kidney injury[J]. Curr Opin Nephrol Hypertens, 2022, 31(3): 283-287.
10. Hamet P, Tremblay J. Artificial intelligence in medicine[J]. Metabolism, 2017, 69S: S36-S40.
11. Chen HK, Chen FH, Lin SF. An AI-based exercise prescription recommendation system[J]. Appl Sci, 2021, 11(6): 2661.
12. Ikizler TA, Burrowes JD, Byham-Gray LD, et al. KDOQI clinical practice guideline for nutrition in CKD: 2020 update[J]. Am J Kidney Dis, 2020, 76(Suppl 1): S1-S107.
13. He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine[J]. Nat Med, 2019, 25(1): 30-36.
14. Kooman JP, Wieringa FP, Han M, et al. Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?[J]. Nephrol Dial Transplant, 2020, 35(Suppl 2): ii43-ii50.
15. Sardari S, Sharifzadeh S, Daneshkhah A, et al. Artificial intelligence for skeleton-based physical rehabilitation action evaluation: a systematic review[J]. Comput Biol Med, 2023, 158: 106835.
16. Canaud B, Kooman J, Davenport A, et al. Digital health technology to support care and improve outcomes of chronic kidney disease patients: as a case illustration, the Withings toolkit health sensing tools[J]. Front Nephrol, 2023, 3: 1148565.
17. Lunney M, Wiebe N, Kusi-Appiah E, et al. Wearable fitness trackers to predict clinical deterioration in maintenance hemodialysis: a prospective cohort feasibility study[J]. Kidney Med, 2021, 3(5): 768-775.e1.
18. Song Y, Jeong IC, Ryu S, et al. GAIT-CKD (gait analysis using artificial intelligence for digital therapeutics of patients with chronic kidney disease): design and methods[J]. Kidney Res Clin Pract, 2025, 44(5): 788-801.
19. 王銀璐, 李慧慧, 周司晨, 等. 人工智能技術在運動康復領域的應用研究綜述[J]. 運動與健康, 2024, 3(12): 37-40.
20. Handelman GS, Kok HK, Chandra RV, et al. eDoctor: machine learning and the future of medicine[J]. J Intern Med, 2018, 284(6): 603-619.
21. 張馨月, 劉長梅, 譚文瑞, 等. 人工智能在糖尿病運動管理中應用的研究進展[J]. 預防醫學論壇, 2023, 29(9): 718-720, 封 3.
22. Li Z, Liu X, Tang Z, et al. TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease[J]. J Am Med Inform Assoc, 2024, 31(11): 2474-2485.
23. Yanai A, Uchiyama K, Suganuma S. Salt reduction using a smartphone application based on an artificial intelligence system for dietary assessment in patients with chronic kidney disease: a single-center retrospective cohort study[J]. Kidney Dial, 2023, 3(1): 139-151.
24. 馮穎倩, 王夢君, 呂思清, 等. 醫學人工智能在 2 型糖尿病健康管理中的應用[J]. 中華養生保健, 2023, 41(21): 11-16.
25. Ma J, Wang J, Lu L, et al. Development and validation of a dynamic kidney failure prediction model based on deep learning: a real-world study with external validation[PP/OL]. V2. arXiv(2025-01-25)[2025-10-01]. doi: 10.48550/arXiv.2501.16388.
26. 羅凡, 楊毓彥, 陳淑嬌. 可穿戴技術在老年慢性病管理中的應用進展[J]. 醫學理論與實踐, 2024, 37(12): 2013-2015, 2002.
27. Yuan Q, Zhang H, Deng T, et al. Role of artificial intelligence in kidney disease[J]. Int J Med Sci, 2020, 17(7): 970-984.
28. Ali S, Akhlaq F, Imran AS, et al. The enlightening role of explainable artificial intelligence in medical and healthcare domains: a systematic literature review[J]. Comput Biol Med, 2023, 166: 107555.
29. Dana Z, Naseer AA, Toro B, et al. Integrated machine learning and survival analysis modeling for enhanced chronic kidney disease risk stratification[PP/OL]. arXiv(2024-11-16)[2025-05-20]. doi: 10.48550/arXiv.2411.10754.
30. Surian NU, Batagov A, Wu A, et al. A digital twin model incorporating generalized metabolic fluxes to identify and predict chronic kidney disease in type 2 diabetes mellitus[J]. NPJ Digit Med, 2024, 7(1): 140.
31. Yoshizaki Y, Kato K, Fujihara K, et al. Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data[J]. Front Public Health, 2024, 12: 1495054.
32. Lin P, Lin G, Wan B, et al. Development and validation of prediction model for fall accidents among chronic kidney disease in the community[J]. Front Public Health, 2024, 12: 1381754.
33. Yan T, Nabi S, Liu X, et al. The association of physical activity with kidney function risk among adults with long working hours[J]. Front Endocrinol (Lausanne), 2024, 15: 1415713.
34. Kwiendacz H, Huang B, Chen Y, et al. Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project[J]. Cardiovasc Diabetol, 2025, 24(1): 76.
35. 胡佳敏, 邱艷, 任菁菁. AI 在基層醫療慢性病管理中的應用研究進展[J]. 中華全科醫學, 2024, 22(3): 481-485.
36. 徐舒婷, 陶勝茹, 楊濤, 等. 維持性血液透析病人“互聯網+”運動管理的研究進展[J]. 護理研究, 2025, 39(4): 679-684.
37. Rockenschaub P, Hilbert A, Kossen T, et al. The impact of multi-institution datasets on the generalizability of machine learning prediction models in the ICU[J]. Crit Care Med, 2024, 52(11): 1710-1721.
38. Valenzuela PL, Castillo-García A, Saco-Ledo G, et al. Physical exercise: a polypill against chronic kidney disease[J]. Nephrol Dial Transplant, 2024, 39(9): 1384-1391.
39. Letton ME, Tr?n TB, Flower S, et al. Digital physical activity and exercise interventions for people living with chronic kidney disease: a systematic review of health outcomes and feasibility[J]. J Med Syst, 2024, 48(1): 63.
40. Liu W, Yu X, Wang J, et al. Improving kidney outcomes in patients with nondiabetic chronic kidney disease through an artificial intelligence-based health coaching mobile App: retrospective cohort study[J]. JMIR Mhealth Uhealth, 2023, 11: e45531.
41. Gabarron E, Larbi D, Rivera-Romero O, et al. Human factors in AI-driven digital solutions for increasing physical activity: scoping review[J]. JMIR Hum Factors, 2024, 11: e55964.
42. Wu CC, Islam MM, Poly TN, et al. Artificial intelligence in kidney disease: a comprehensive study and directions for future research[J]. Diagnostics (Basel), 2024, 14(4): 397.
43. Greenwood SA, Young HML, Briggs J, et al. Evaluating the effect of a digital health intervention to enhance physical activity in people with chronic kidney disease (kidney BEAM): a multicentre, randomised controlled trial in the UK[J]. Lancet Digit Health, 2024, 6(1): e23-e32.
44. 應家佩, 戴麗麗, 馬建偉, 等. 慢性腎臟病患者智能健康隨訪管理系統的構建及應用[J]. 護理學雜志, 2024, 39(19): 11-15, 30.
45. Calota MS, Huang JYC, Chen LL, et al. Assembling the puzzle: exploring collaboration and data sensemaking in nursing practices for remote patient monitoring[C]//Companion publication of the 2024 conference on computer-supported cooperative work and social computing. New York: Association for Computing Machinery, 2024: 296-302.
46. Mata-Lima A, Paquete AR, Serrano-Olmedo JJ. Remote patient monitoring and management in nephrology: a systematic review[J]. Nefrologia (Engl Ed), 2024, 44(5): 639-667.

West China Medical Journal

Application progress of artificial intelligence in exercise management for patients with chronic kidney disease

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