{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T17:49:38Z","timestamp":1793641778173,"version":"4.5.1"},"reference-count":126,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Capital Health Development Research Project","award":["024-2-2069"],"award-info":[{"award-number":["024-2-2069"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82100486, 82270341"],"award-info":[{"award-number":["82100486, 82270341"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"natural science foundation of beijing municipality","doi-asserted-by":"publisher","award":["7232043"],"award-info":[{"award-number":["7232043"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hospital High-Level Research Funding","award":["2024AZB3003"],"award-info":[{"award-number":["2024AZB3003"]}]},{"DOI":"10.13039\/501100005088","name":"beijing municipal health commission","doi-asserted-by":"publisher","award":["02-01"],"award-info":[{"award-number":["02-01"]}],"id":[{"id":"10.13039\/501100005088","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Hospitals Authority\u2019s Ascent Plan","award":["DFL20220603"],"award-info":[{"award-number":["DFL20220603"]}]},{"name":"Beijing Hospitals Authority Youth Programme","award":["QMS20210603"],"award-info":[{"award-number":["QMS20210603"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Clin Med Insights Cardiol"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:p>Cardiovascular diseases (CVDs), which have high morbidity and mortality, have become one of the world\u2019s largest public health concerns. Although primary hospitalization can partially relieve symptoms, many patients continue to have poor prognoses and lowered quality of life after discharge. Cardiac rehabilitation (CR) is an important recommendation for patients undergoing cardiac surgery, as well as those who suffer from severe cardiovascular events or chronic cardiac disease. However, standard, center-based CR options tend to be ineffective and hard to access, leading to low compliance. The development of digital health technologies (DHTs), especially wearables\u2014which can be combined with mobile applications-has enabled home-based CR, that is, remote CR. This model has substantially enhanced CR efficiency and patient adherence. With advancements in artificial intelligence (AI), including machine learning (ML) algorithms and deep learning, large-scale data from wearables and other DHTs can be effectively retrieved and interpreted. Further incorporation of AI into DHTs may provide real-time fitness telemonitoring, accurate risk recognition and prediction, individualized exercise recommendations, and improved patient adherence. In this review, we succinctly highlight several applications of both AI and wearables in remote CR, and evaluate their collective roles in patient CR execution from several perspectives. We hope our review will help advance further integration of AI and DHT into home-based CR, and plan to direct future research toward refining the use of AI in new-era digital CR.<\/jats:p>","DOI":"10.1177\/11795468261421344","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:33:38Z","timestamp":1773218018000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Integrating Artificial Intelligence Into Digital Health-Optimized Therapeutics for the Remote Cardiac Rehabilitation"],"prefix":"10.1177","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4336-2549","authenticated-orcid":false,"given":"Xueqi","family":"Wan","sequence":"first","affiliation":[{"name":"Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limeng","family":"Ge","sequence":"additional","affiliation":[{"name":"Department of Philosophy, East China Normal University, Shanghai, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinfan","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanshou","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingyu","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoci","family":"Geng","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changjiang","family":"Ge","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiantao","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing, P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"issue":"2","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.31083\/j.rcm2302045","article-title":"Mobilizing artificial intelligence to cardiac telerehabilitation","volume":"23","author":"Su J","year":"2022","unstructured":"Su J, Zhang Y, Ke QQ, Su JK, Yang QH. Mobilizing artificial intelligence to cardiac telerehabilitation. Rev Cardiovasc Med. 2022;23(2):45.","journal-title":"Rev Cardiovasc Med"},{"issue":"12","key":"e_1_3_2_3_2","doi-asserted-by":"crossref","DOI":"10.3390\/s20123601","article-title":"Wearable monitoring and interpretable machine learning can objectively track progression in patients during cardiac rehabilitation","volume":"20","author":"De Canni\u00e8re H","year":"2020","unstructured":"De Canni\u00e8re H, Corradi F, Smeets CJP, et al. Wearable monitoring and interpretable machine learning can objectively track progression in patients during cardiac rehabilitation. Sens. 2020;20(12):3601.","journal-title":"Sens"},{"issue":"4","key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1056\/NEJMra2301903","article-title":"Wearable digital health technologies for monitoring in cardiovascular medicine","volume":"390","author":"Spatz ES","year":"2024","unstructured":"Spatz ES, Ginsburg GS, Rumsfeld JS, Turakhia MP. Wearable digital health technologies for monitoring in cardiovascular medicine. N Engl J Med. 2024;390(4):346-356.","journal-title":"N Engl J Med"},{"issue":"4","key":"e_1_3_2_5_2","first-page":"464","article-title":"Cardiac rehabilitation after STEMI","volume":"66","author":"Urbinati S","year":"2018","unstructured":"Urbinati S, Tonet E. Cardiac rehabilitation after STEMI. Minerva Cardioangiol. 2018;66(4):464-470.","journal-title":"Minerva Cardioangiol"},{"key":"e_1_3_2_6_2","volume-title":"Cardiac Rehabilitation","author":"Tessler J","year":"2025","unstructured":"Tessler J, Bordoni B. Cardiac Rehabilitation. StatPearls Publishing LLC; 2025."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.26599\/1671-5411.2024.11.007"},{"issue":"17","key":"e_1_3_2_8_2","first-page":"1","article-title":"Cardiac rehabilitation as secondary prevention. Agency for health care policy and research and national heart, lung, and blood institute","author":"Wenger NK","year":"1995","unstructured":"Wenger NK, Froelicher ES, Smith LK, et al. Cardiac rehabilitation as secondary prevention. Agency for health care policy and research and national heart, lung, and blood institute. Clin Pract Guidel Quick Ref Guide Clin. 1995;(17):1-23.","journal-title":"Clin Pract Guidel Quick Ref Guide Clin"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.106.180945"},{"issue":"1","key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1038\/s44325-024-00017-7","article-title":"Cardiac rehabilitation and secondary prevention of CVD: time to think about cardiovascular health rather than rehabilitation","volume":"1","author":"Redfern J","year":"2024","unstructured":"Redfern J, Gallagher R, Maiorana A, et al. Cardiac rehabilitation and secondary prevention of CVD: time to think about cardiovascular health rather than rehabilitation. NPJ Cardiovasc Health. 2024;1(1):22.","journal-title":"NPJ Cardiovasc Health"},{"issue":"8","key":"e_1_3_2_11_2","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1093\/eurheartj\/ehy417","article-title":"Impact of ambulatory cardiac rehabilitation on cardiovascular outcomes: a long-term follow-up study","volume":"40","author":"Doimo S","year":"2019","unstructured":"Doimo S, Fabris E, Piepoli M, et al. Impact of ambulatory cardiac rehabilitation on cardiovascular outcomes: a long-term follow-up study. Eur Heart J. 2019;40(8):678-685.","journal-title":"Eur Heart J"},{"key":"e_1_3_2_12_2","article-title":"Cardiac rehabilitation","volume":"351","author":"Dalal HM","year":"2015","unstructured":"Dalal HM, Doherty P, Taylor RS. Cardiac rehabilitation. BMJ. 2015;351:h5000.","journal-title":"BMJ"},{"key":"e_1_3_2_13_2","first-page":"31","article-title":"Cardiac rehabilitation availability and density around the globe","volume":"13","author":"Turk-Adawi K","year":"2019","unstructured":"Turk-Adawi K, Supervia M, Lopez-Jimenez F, et al. Cardiac rehabilitation availability and density around the globe. med. 2019;13:31-45.","journal-title":"med"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.4070\/kcj.2023.0242"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.122.061046"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0285801"},{"issue":"1","key":"e_1_3_2_17_2","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1186\/s13102-021-00350-9","article-title":"Effect of personal activity intelligence (PAI) monitoring in the maintenance phase of cardiac rehabilitation: a mixed methods evaluation","volume":"13","author":"Hannan AL","year":"2021","unstructured":"Hannan AL, Hing W, Coombes JS, et al. Effect of personal activity intelligence (PAI) monitoring in the maintenance phase of cardiac rehabilitation: a mixed methods evaluation. BMC Sports Sci Med Rehabil. 2021;13(1):124.","journal-title":"BMC Sports Sci Med Rehabil"},{"issue":"10","key":"e_1_3_2_18_2","article-title":"Home-based versus centre-based cardiac rehabilitation","volume":"10","author":"McDonagh ST","year":"2023","unstructured":"McDonagh ST, Dalal H, Moore S, Clark CE, Dean SG, Jolly K, et al. Home-based versus centre-based cardiac rehabilitation. Cochrane Database Syst Rev. 2023;10(10):Cd007130.","journal-title":"Cochrane Database Syst Rev"},{"issue":"3","key":"e_1_3_2_19_2","article-title":"Comparative assessment of the long-term efficacy of home-based versus center-based cardiac rehabilitation","volume":"14","author":"Nso N","year":"2022","unstructured":"Nso N, Nassar M, Mbome Y, et al. Comparative assessment of the long-term efficacy of home-based versus center-based cardiac rehabilitation. Cureus. 2022;14(3):e23485.","journal-title":"Cureus"},{"issue":"33","key":"e_1_3_2_20_2","doi-asserted-by":"crossref","DOI":"10.1002\/adma.201706910","article-title":"Wearables in medicine","volume":"30","author":"Yetisen AK","year":"2018","unstructured":"Yetisen AK, Martinez-Hurtado JL, \u00dcnal B, Khademhosseini A, Butt H. Wearables in medicine. Adv Mater. 2018;30(33):e1706910.","journal-title":"Adv Mater"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cjca.2024.07.009"},{"issue":"9","key":"e_1_3_2_22_2","article-title":"Cardiac rehabilitation in the modern era: optimizing recovery and reducing recurrence","volume":"15","author":"Zaree A","year":"2023","unstructured":"Zaree A, Dev S, Yaseen Khan I, et al. Cardiac rehabilitation in the modern era: optimizing recovery and reducing recurrence. Cureus. 2023;15(9):e46006.","journal-title":"Cureus"},{"issue":"2","key":"e_1_3_2_23_2","doi-asserted-by":"crossref","DOI":"10.1530\/ERP-18-0081","article-title":"Application of mobile health, telemedicine and artificial intelligence to echocardiography","volume":"6","author":"Seetharam K","year":"2019","unstructured":"Seetharam K, Kagiyama N, Sengupta PP. Application of mobile health, telemedicine and artificial intelligence to echocardiography. Echo Res Pract. 2019;6(2):R41-r52.","journal-title":"Echo Res Pract"},{"issue":"17","key":"e_1_3_2_24_2","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1161\/CIRCULATIONAHA.121.057629","article-title":"The social robots are coming: preparing for a new wave of virtual care in cardiovascular medicine","volume":"145","author":"Bouchard K","year":"2022","unstructured":"Bouchard K, Liu PP, Tulloch H. The social robots are coming: preparing for a new wave of virtual care in cardiovascular medicine. Circulation. 2022;145(17):1291-1293.","journal-title":"Circulation"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","first-page":"1378349","DOI":"10.3389\/fpubh.2024.1378349","article-title":"Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation","volume":"12","author":"Hautala AJ","year":"2024","unstructured":"Hautala AJ, Shavazipour B, Afsar B, Tulppo MP, Miettinen K. Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation. Front Public Health. 2024;12:1378349.","journal-title":"Front Public Health"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2021.633248"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"1371652","DOI":"10.3389\/fspor.2024.1371652","article-title":"Patients\u2019 experiences and perspectives regarding the use of digital technology to support exercise-based cardiac rehabilitation: a qualitative interview study","volume":"6","author":"Zeller A","year":"2024","unstructured":"Zeller A, Gutenberg J, Niebauer J, Crutzen R, Kulnik ST. Patients\u2019 experiences and perspectives regarding the use of digital technology to support exercise-based cardiac rehabilitation: a qualitative interview study. Front Sports Act Living. 2024;6:1371652.","journal-title":"Front Sports Act Living"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcm.2020.11.007"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2018.03.521"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcm.2017.02.005"},{"issue":"4","key":"e_1_3_2_31_2","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1001\/jama.2021.5952","article-title":"Cardiac rehabilitation","volume":"326","author":"Baman JR","year":"2021","unstructured":"Baman JR, Sekhon S, Maganti K. Cardiac rehabilitation. JAMA. 2021;326(4):366.","journal-title":"JAMA"},{"issue":"11","key":"e_1_3_2_32_2","article-title":"Exercise-based cardiac rehabilitation for coronary heart disease","volume":"11","author":"Dibben G","year":"2021","unstructured":"Dibben G, Faulkner J, Oldridge N, et al. Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane Database Syst Rev. 2021;11(11):Cd001800.","journal-title":"Cochrane Database Syst Rev"},{"issue":"10","key":"e_1_3_2_33_2","article-title":"Optimizing cardiac rehabilitation in heart failure: comprehensive insights, barriers, and future strategies","volume":"60","author":"Epelde F","year":"2024","unstructured":"Epelde F. Optimizing cardiac rehabilitation in heart failure: comprehensive insights, barriers, and future strategies. Medicinar. 2024;60(10):1583.","journal-title":"Medicinar"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejheart.2008.08.005"},{"issue":"32","key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"6569","DOI":"10.2174\/0929867328666201218122633","article-title":"Artificial intelligence as a business partner in cardiovascular precision medicine: an emerging approach for disease detection and treatment optimization","volume":"28","author":"Visco V","year":"2021","unstructured":"Visco V, Ferruzzi GJ, Nicastro F, et al. Artificial intelligence as a business partner in cardiovascular precision medicine: an emerging approach for disease detection and treatment optimization. Curr Med Chem. 2021;28(32):6569-6590.","journal-title":"Curr Med Chem"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.hrtlng.2022.04.016"},{"issue":"1","key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s12872-017-0499-0","article-title":"Effectiveness and safety of a home-based cardiac rehabilitation programme of mixed surveillance in patients with ischemic heart disease at moderate cardiovascular risk: a randomised, controlled clinical trial","volume":"17","author":"Bravo-Escobar R","year":"2017","unstructured":"Bravo-Escobar R, Gonz\u00e1lez-Represas A, G\u00f3mez-Gonz\u00e1lez AM, et al. Effectiveness and safety of a home-based cardiac rehabilitation programme of mixed surveillance in patients with ischemic heart disease at moderate cardiovascular risk: a randomised, controlled clinical trial. BMC Cardiovasc Disord. 2017;17(1):66.","journal-title":"BMC Cardiovasc Disord"},{"issue":"6","key":"e_1_3_2_38_2","doi-asserted-by":"crossref","DOI":"10.2196\/jmir.9943","article-title":"Home-based rehabilitation with telemonitoring guidance for patients with coronary artery disease (short-term results of the TRiCH study): randomized controlled trial","volume":"20","author":"Avila A","year":"2018","unstructured":"Avila A, Claes J, Goetschalckx K, et al. Home-based rehabilitation with telemonitoring guidance for patients with coronary artery disease (short-term results of the TRiCH study): randomized controlled trial. J Med Internet Res. 2018;20(6):e225.","journal-title":"J Med Internet Res"},{"issue":"146","key":"e_1_3_2_39_2","doi-asserted-by":"crossref","DOI":"10.3791\/59019","article-title":"A novel digital platform for a monitored home-based cardiac rehabilitation program","author":"Dor-Haim H","year":"2019","unstructured":"Dor-Haim H, Katzburg S, Leibowitz D. A novel digital platform for a monitored home-based cardiac rehabilitation program. J Vis Exp. 2019;(146):e59019.","journal-title":"J Vis Exp"},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","DOI":"10.2196\/49345","article-title":"Outcomes of a remote cardiac rehabilitation program for patients undergoing atrial fibrillation ablation: pilot study","volume":"7","author":"Misra S","year":"2023","unstructured":"Misra S, Niazi K, Swayampakala K, et al. Outcomes of a remote cardiac rehabilitation program for patients undergoing atrial fibrillation ablation: pilot study. JMIR Cardio. 2023;7:e49345.","journal-title":"JMIR Cardio"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0123-z"},{"issue":"19","key":"e_1_3_2_42_2","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph16193718","article-title":"Inclusion of older adults in the research and design of digital technology","volume":"16","author":"Mannheim I","year":"2019","unstructured":"Mannheim I, Schwartz E, Xi W, et al. Inclusion of older adults in the research and design of digital technology. Int J Environ Res Public Health. 2019;16(19):3718.","journal-title":"Int J Environ Res Public Health"},{"key":"e_1_3_2_43_2","unstructured":"Lall P Rees RW Law GCY et al. WHO guidelines approved by the guidelines review committee in WHO guideline recommendations on digital interventions for health system strengthening. 2019. World Health Organization. Geneva."},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106541"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108074"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2020.01.046"},{"issue":"7","key":"e_1_3_2_47_2","doi-asserted-by":"crossref","DOI":"10.3390\/s21072539","article-title":"Smart wearables for cardiac monitoring-real-world use beyond atrial fibrillation","volume":"21","author":"Duncker D","year":"2021","unstructured":"Duncker D, Ding WY, Etheridge S, et al. Smart wearables for cardiac monitoring-real-world use beyond atrial fibrillation. Sens. 2021;21(7):2539.","journal-title":"Sens"},{"issue":"10","key":"e_1_3_2_48_2","article-title":"Realization and technology acceptance test of a wearable cardiac health monitoring and early warning system with multi-channel MCGs and ECG","volume":"18","author":"Lin WY","year":"2018","unstructured":"Lin WY, Ke HL, Chou WC, Chang PC, Tsai TH, Lee MY. Realization and technology acceptance test of a wearable cardiac health monitoring and early warning system with multi-channel MCGs and ECG. Sens. 2018;18(10):3538.","journal-title":"Sens"},{"issue":"20","key":"e_1_3_2_49_2","doi-asserted-by":"crossref","DOI":"10.3390\/s22208002","article-title":"Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review","volume":"22","author":"Huang JD","year":"2022","unstructured":"Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review. Sens. 2022;22(20):8002.","journal-title":"Sens"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2017.03.571"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCRESAHA.122.322389"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-019-0918-5"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejim.2025.04.036"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines13071685"},{"issue":"5","key":"e_1_3_2_55_2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.acvd.2023.04.003","article-title":"Clinical validation of an artificial intelligence algorithm offering cross-platform detection of atrial fibrillation using smart device electrocardiograms","volume":"116","author":"Mannhart D","year":"2023","unstructured":"Mannhart D, Lefebvre B, Gardella C, et al. Clinical validation of an artificial intelligence algorithm offering cross-platform detection of atrial fibrillation using smart device electrocardiograms. Arch Cardiovasc Dis. 2023;116(5):249-257.","journal-title":"Arch Cardiovasc Dis"},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1097\/HCR.0000000000000943","article-title":"Machine learning-based prediction models for healthcare outcomes in patients participating in cardiac rehabilitation: a systematic review","volume":"45","author":"Tieliwaerdi X","year":"2025","unstructured":"Tieliwaerdi X, Manalo K, Abuduweili A, et al. Machine learning-based prediction models for healthcare outcomes in patients participating in cardiac rehabilitation: a systematic review. J Cardiopulm Rehabil Prev. 2025;45:247-257.","journal-title":"J Cardiopulm Rehabil Prev"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.slast.2025.100360"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10880-021-09771-7"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.3390\/app13106120"},{"issue":"3","key":"e_1_3_2_60_2","doi-asserted-by":"crossref","first-page":"508","DOI":"10.3390\/diagnostics13030508","article-title":"Predicting cardiovascular rehabilitation of patients with coronary artery disease using transfer feature learning","volume":"13","author":"Torres R","year":"2023","unstructured":"Torres R, Zurita C, Mellado D, et al. Predicting cardiovascular rehabilitation of patients with coronary artery disease using transfer feature learning. Diagnostics. 2023;13(3):508.","journal-title":"Diagnostics"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2025.3634307"},{"issue":"8","key":"e_1_3_2_62_2","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1016\/j.mayocp.2022.01.016","article-title":"Prediction of mortality in coronary artery disease: role of machine learning and maximal exercise capacity","volume":"97","author":"de Souza E","year":"2022","unstructured":"de Souza E, Silva CG, Buginga GC, de Souza E, Silva EA, et al. Prediction of mortality in coronary artery disease: role of machine learning and maximal exercise capacity. Mayo Clin Proc. 2022;97(8):1472-1482.","journal-title":"Mayo Clin Proc"},{"key":"e_1_3_2_63_2","first-page":"12","article-title":"Understanding risk factors in cardiac rehabilitation patients with random forests and decision trees","volume":"121","author":"Van A","year":"2010","unstructured":"Van A, Gay VC, Kennedy PJ, Barin E, Leijdekkers P. Understanding risk factors in cardiac rehabilitation patients with random forests and decision trees. CRPIT. 2010;121:12.","journal-title":"CRPIT"},{"key":"e_1_3_2_64_2","volume-title":"2016 Computing in Cardiology Conference (CinC)","author":"Lofaro D","year":"2016","unstructured":"Lofaro D, Groccia MC, Guido R, Conforti D, Caroleo S, Fragomeni G. Machine learning approaches for supporting patient-specific cardiac rehabilitation programs. In: 2016 Computing in Cardiology Conference (CinC). Vancouver, BC, Canada, 2016. IEEE."},{"key":"e_1_3_2_65_2","article-title":"Predicting return to work after cardiac rehabilitation using machine learning models","volume":"55","author":"Yuan CJ","year":"2023","unstructured":"Yuan CJ, Varathan KD, Suhaimi A, Ling LW. Predicting return to work after cardiac rehabilitation using machine learning models. J Rehabil Med. 2023;55:jrm00348.","journal-title":"J Rehabil Med"},{"issue":"1","key":"e_1_3_2_66_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3390\/s21010012","article-title":"Digital health in cardiac rehabilitation and secondary prevention: a search for the ideal tool","volume":"21","author":"Falter M","year":"2020","unstructured":"Falter M, Scherrenberg M, Dendale P. Digital health in cardiac rehabilitation and secondary prevention: a search for the ideal tool. Sens. 2020;21(1):12.","journal-title":"Sens"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1177\/2047487320913379"},{"key":"e_1_3_2_68_2","article-title":"Flexible wearable medical devices: from material innovations and data processing to intelligent healthcare applications","author":"Zhou J","year":"2025","unstructured":"Zhou J, Zhang Q, Zhang M, Aikebaier S, Zhu X, Zhou X. Flexible wearable medical devices: from material innovations and data processing to intelligent healthcare applications. J Adv Res. Published online 2025. doi:pii:S2090-1232(25)00651-4.10.1016\/j.jare.2025.08.036","journal-title":"J Adv Res"},{"issue":"6","key":"e_1_3_2_69_2","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1111\/pace.14995","article-title":"Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia","volume":"47","author":"Guo RX","year":"2024","unstructured":"Guo RX, Tian X, Bazoukis G, et al. Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia. Pacing Clin Electrophysiol. 2024;47(6):789-801.","journal-title":"Pacing Clin Electrophysiol"},{"issue":"30","key":"e_1_3_2_70_2","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1093\/eurheartj\/ehx293","article-title":"Making sense of remote monitoring studies in heart failure","volume":"38","author":"Chatterjee NA","year":"2017","unstructured":"Chatterjee NA, Singh JP. Making sense of remote monitoring studies in heart failure. Eur Heart J. 2017;38(30):2361-2363.","journal-title":"Eur Heart J"},{"key":"e_1_3_2_71_2","article-title":"Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: a systematic review and meta-analysis","volume":"63","author":"Lee S","year":"2022","unstructured":"Lee S, Chu Y, Ryu J, Park YJ, Yang S, Koh SB. Artificial intelligence for detection of cardiovascular-related diseases from wearable devices: a systematic review and meta-analysis. Yonsei Med J. 2022;63:S93-s107.","journal-title":"Yonsei Med J"},{"key":"e_1_3_2_72_2","doi-asserted-by":"crossref","first-page":"114825","DOI":"10.1016\/j.bios.2022.114825","article-title":"Wearable artificial intelligence biosensor networks","volume":"219","author":"Zhang Y","year":"2023","unstructured":"Zhang Y, Hu Y, Jiang N, Yetisen AK. Wearable artificial intelligence biosensor networks. Biosens Bioelectron. 2023;219:114825.","journal-title":"Biosens Bioelectron"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.117.030583"},{"issue":"6","key":"e_1_3_2_74_2","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCEP.118.006834","article-title":"Smartwatch performance for the detection and quantification of atrial fibrillation","volume":"12","author":"Wasserlauf J","year":"2019","unstructured":"Wasserlauf J, You C, Patel R, Valys A, Albert D, Passman R. Smartwatch performance for the detection and quantification of atrial fibrillation. Circ Arrhythm Electrophysiol. 2019;12(6):e006834.","journal-title":"Circ Arrhythm Electrophysiol"},{"issue":"10","key":"e_1_3_2_75_2","doi-asserted-by":"crossref","DOI":"10.3390\/s23104805","article-title":"Electrocardiogram monitoring wearable devices and artificial-intelligence-enabled diagnostic capabilities: a review","volume":"23","author":"Neri L","year":"2023","unstructured":"Neri L, Oberdier MT, van Abeelen KCJ, et al. Electrocardiogram monitoring wearable devices and artificial-intelligence-enabled diagnostic capabilities: a review. Sens. 2023;23(10):4805.","journal-title":"Sens"},{"issue":"1","key":"e_1_3_2_76_2","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1186\/s12872-021-02363-1","article-title":"Diagnostic performance of a wearing dynamic ECG recorder for atrial fibrillation screening: the HUAMI heart study","volume":"21","author":"Fu W","year":"2021","unstructured":"Fu W, Li R. Diagnostic performance of a wearing dynamic ECG recorder for atrial fibrillation screening: the HUAMI heart study. BMC Cardiovasc Disord. 2021;21(1):558.","journal-title":"BMC Cardiovasc Disord"},{"issue":"10","key":"e_1_3_2_77_2","doi-asserted-by":"crossref","DOI":"10.2196\/29933","article-title":"Automatic mobile health arrhythmia monitoring for the detection of atrial fibrillation: prospective feasibility, accuracy, and user experience study","volume":"9","author":"Santala OE","year":"2021","unstructured":"Santala OE, Halonen J, Martikainen S, et al. Automatic mobile health arrhythmia monitoring for the detection of atrial fibrillation: prospective feasibility, accuracy, and user experience study. JMIR Mhealth Uhealth. 2021;9(10):e29933.","journal-title":"JMIR Mhealth Uhealth"},{"issue":"22","key":"e_1_3_2_78_2","doi-asserted-by":"crossref","DOI":"10.3390\/s22228588","article-title":"Wearable devices combined with artificial intelligence - a future technology for atrial fibrillation detection?","volume":"22","author":"M\u00e4kynen M","year":"2022","unstructured":"M\u00e4kynen M, Ng GA, Li X, Schlindwein FS. Wearable devices combined with artificial intelligence - a future technology for atrial fibrillation detection? Sensors (Basel). 2022;22(22):8588.","journal-title":"Sensors (Basel)"},{"issue":"2","key":"e_1_3_2_79_2","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1109\/JBHI.2019.2911367","article-title":"LSTM-Based ECG classification for continuous monitoring on personal wearable devices","volume":"24","author":"Saadatnejad S","year":"2020","unstructured":"Saadatnejad S, Oveisi M, Hashemi M. LSTM-Based ECG classification for continuous monitoring on personal wearable devices. IEEE J Biomed Health Inform. 2020;24(2):515-523.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"6","key":"e_1_3_2_80_2","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/TBCAS.2019.2948920","article-title":"ECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices","volume":"13","author":"Amirshahi A","year":"2019","unstructured":"Amirshahi A, Hashemi M. ECG classification algorithm based on STDP and R-STDP neural networks for real-time monitoring on ultra low-power personal wearable devices. IEEE Trans Biomed Circuits Syst. 2019;13(6):1483-1493.","journal-title":"IEEE Trans Biomed Circuits Syst"},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1136\/heartjnl-2017-311198"},{"issue":"2","key":"e_1_3_2_82_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1093\/ehjdh\/ztab029","article-title":"Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram","volume":"2","author":"Chang KC","year":"2021","unstructured":"Chang KC, Hsieh PH, Wu MY, et al. Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram. Eur Heart J Digit Health. 2021;2(2):299-310.","journal-title":"Eur Heart J Digit Health"},{"issue":"2","key":"e_1_3_2_83_2","article-title":"Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study","volume":"4","author":"Bachtiger P","year":"2022","unstructured":"Bachtiger P, Petri CF, Scott FE, et al. Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study. Lancet Digit Health. 2022;4(2):e117-e125.","journal-title":"Lancet Digit Health"},{"issue":"11","key":"e_1_3_2_84_2","article-title":"Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study","volume":"6","author":"Sau A","year":"2024","unstructured":"Sau A, Pastika L, Sieliwonczyk E, et al. Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study. Lancet Digital Health. 2024;6(11):e791-e802.","journal-title":"Lancet Digital Health"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1097\/NPT.0000000000000095"},{"issue":"7","key":"e_1_3_2_86_2","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1111\/jocs.12576","article-title":"Robot-assisted training early after cardiac surgery","volume":"30","author":"Schoenrath F","year":"2015","unstructured":"Schoenrath F, Markendorf S, Brauchlin AE, et al. Robot-assisted training early after cardiac surgery. J Card Surg. 2015;30(7):574-580.","journal-title":"J Card Surg"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2005.848628"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehac747"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1002\/14651858.CD012264"},{"issue":"1","key":"e_1_3_2_90_2","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1186\/s12912-024-02561-0","article-title":"Comparative effectiveness of different interventions on adherence to exercise-based CR among patients after percutaneous coronary intervention: a network meta-analysis of randomized controlled trials","volume":"23","author":"Xia C","year":"2024","unstructured":"Xia C, Zheng Y, Ji L, Liu H. Comparative effectiveness of different interventions on adherence to exercise-based CR among patients after percutaneous coronary intervention: a network meta-analysis of randomized controlled trials. BMC Nurs. 2024;23(1):897.","journal-title":"BMC Nurs"},{"issue":"17","key":"e_1_3_2_91_2","doi-asserted-by":"crossref","DOI":"10.3390\/s20174791","article-title":"Recognition and repetition counting for local muscular endurance exercises in exercise-based rehabilitation: a comparative study using artificial intelligence models","volume":"20","author":"Prabhu G","year":"2020","unstructured":"Prabhu G, O\u2019Connor NE, Moran K. Recognition and repetition counting for local muscular endurance exercises in exercise-based rehabilitation: a comparative study using artificial intelligence models. Sens. 2020;20(17):4791.","journal-title":"Sens"},{"key":"e_1_3_2_92_2","article-title":"Exercise motion classification from large-scale wearable sensor data using convolutional neural networks","author":"Taewoong UT","year":"2016","unstructured":"Taewoong UT, Babakeshizadeh V, Kuli\u0107 D. Exercise motion classification from large-scale wearable sensor data using convolutional neural networks. arXiv e-prints. 2016. arXiv: 1610.07031.","journal-title":"arXiv e-prints"},{"issue":"4","key":"e_1_3_2_93_2","article-title":"Deep learning for sensor-based rehabilitation exercise recognition and evaluation","volume":"19","author":"Zhu ZA","year":"2019","unstructured":"Zhu ZA, Lu YC, You CH, Chiang CK. Deep learning for sensor-based rehabilitation exercise recognition and evaluation. Sens. 2019;19(4):887.","journal-title":"Sens"},{"key":"e_1_3_2_94_2","doi-asserted-by":"crossref","unstructured":"Bingquan H Giggins O Kechadi T Caulfield B. The limb movement analysis of rehabilitation exercises using wearable inertial sensors. Annu Int Conf IEEE Eng Med Biol Soc Orlando FL USA 2016:4686-4689. http:\/\/dx.doi.org\/10.1109\/embc.2016.7591773","DOI":"10.1109\/EMBC.2016.7591773"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.2217\/fca-2021-0010"},{"issue":"1","key":"e_1_3_2_96_2","doi-asserted-by":"crossref","DOI":"10.2196\/mhealth.9865","article-title":"Assessment of physical activity by wearable technology during rehabilitation after cardiac surgery: explorative prospective monocentric observational cohort study","volume":"7","author":"Thijs I","year":"2019","unstructured":"Thijs I, Fresiello L, Oosterlinck W, Sinnaeve P, Rega F. Assessment of physical activity by wearable technology during rehabilitation after cardiac surgery: explorative prospective monocentric observational cohort study. JMIR Mhealth Uhealth. 2019;7(1):e9865.","journal-title":"JMIR Mhealth Uhealth"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22041639"},{"key":"e_1_3_2_98_2","article-title":"Feasibility of a smartphone app for prescribed exercise tutoring in patients with stable coronary heart disease","volume":"9","author":"Xia K","year":"2023","unstructured":"Xia K, Bai C, Ding R, et al. Feasibility of a smartphone app for prescribed exercise tutoring in patients with stable coronary heart disease. Digit Health. 2023;9:20552076231197424.","journal-title":"Digit Health"},{"key":"e_1_3_2_99_2","first-page":"1825","article-title":"Wearable-based assessment of heart rate response to physical stressors in patients after open-heart surgery with frailty","volume":"27","author":"Sokas D","year":"2023","unstructured":"Sokas D, Tamuleviciute-Prasciene E, Beigiene A, et al. Wearable-based assessment of heart rate response to physical stressors in patients after open-heart surgery with frailty. IEEE J Biomed Health Inform. 2023;27:1825-1834.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"e_1_3_2_100_2","first-page":"252","article-title":"Activity monitoring and meal tracking for cardiac rehabilitation patients","volume":"10","author":"Pervaiz U","year":"2018","unstructured":"Pervaiz U, Khawaldeh S, Aleef TA, Minh VH, Hagos YB. Activity monitoring and meal tracking for cardiac rehabilitation patients. Int J Med Eng Inform. 2018;10(3):252-264.","journal-title":"Int J Med Eng Inform"},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.12998\/wjcc.v12.i12.2009"},{"issue":"1","key":"e_1_3_2_102_2","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1186\/s12872-017-0571-9","article-title":"Impaired chronotropic response to physical activities in heart failure patients","volume":"17","author":"Shen H","year":"2017","unstructured":"Shen H, Zhao J, Zhou X, et al. Impaired chronotropic response to physical activities in heart failure patients. BMC Cardiovasc Disord. 2017;17(1):136.","journal-title":"BMC Cardiovasc Disord"},{"issue":"11","key":"e_1_3_2_103_2","doi-asserted-by":"crossref","DOI":"10.2196\/20032","article-title":"Effects of different telemonitoring strategies on chronic heart failure care: systematic review and subgroup meta-analysis","volume":"22","author":"Ding H","year":"2020","unstructured":"Ding H, Chen SH, Edwards I, et al. Effects of different telemonitoring strategies on chronic heart failure care: systematic review and subgroup meta-analysis. J Med Internet Res. 2020;22(11):e20032.","journal-title":"J Med Internet Res"},{"issue":"3","key":"e_1_3_2_104_2","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1080\/17461391.2020.1866081","article-title":"Oxynet: a collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests","volume":"22","author":"Zignoli A","year":"2022","unstructured":"Zignoli A, Fornasiero A, Rota P, et al. Oxynet: a collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests. Eur J Sport Sci. 2022;22(3):425-435.","journal-title":"Eur J Sport Sci"},{"issue":"1","key":"e_1_3_2_105_2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3390\/jcm14010021","article-title":"Predicting heart rate at the anaerobic threshold using a machine learning model based on a large-scale population dataset","volume":"14","author":"Nakayama A","year":"2024","unstructured":"Nakayama A, Iwata T, Sakuma H, Kashino K, Tomoike H. Predicting heart rate at the anaerobic threshold using a machine learning model based on a large-scale population dataset. J Clin Med. 2024;14(1):21.","journal-title":"J Clin Med"},{"issue":"8","key":"e_1_3_2_106_2","doi-asserted-by":"crossref","first-page":"4228","DOI":"10.1109\/JBHI.2022.3163402","article-title":"Using machine learning to identify organ system specific limitations to exercise via cardiopulmonary exercise testing","volume":"26","author":"Portella JJ","year":"2022","unstructured":"Portella JJ, Andonian BJ, Brown DE, et al. Using machine learning to identify organ system specific limitations to exercise via cardiopulmonary exercise testing. IEEE J Biomed Health Inform. 2022;26(8):4228-4237.","journal-title":"IEEE J Biomed Health Inform"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12265-019-09938-6"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1152\/japplphysiol.00519.2015"},{"issue":"2","key":"e_1_3_2_109_2","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1152\/japplphysiol.00299.2017","article-title":"Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models","volume":"124","author":"Beltrame T","year":"2018","unstructured":"Beltrame T, Amelard R, Wong A, Hughson RL. Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models. J Appl Physiol. 2018;124(2):473-481.","journal-title":"J Appl Physiol"},{"key":"e_1_3_2_110_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13102-019-0126-8"},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.4330\/wjc.v16.i2.67"},{"issue":"10","key":"e_1_3_2_112_2","doi-asserted-by":"crossref","DOI":"10.2196\/10754","article-title":"Predicting adherence to internet-delivered psychotherapy for symptoms of depression and anxiety after myocardial infarction: machine learning insights from the U-CARE heart randomized controlled trial","volume":"20","author":"Wallert J","year":"2018","unstructured":"Wallert J, Gustafson E, Held C, et al. Predicting adherence to internet-delivered psychotherapy for symptoms of depression and anxiety after myocardial infarction: machine learning insights from the U-CARE heart randomized controlled trial. J Med Internet Res. 2018;20(10):e10754.","journal-title":"J Med Internet Res"},{"issue":"8","key":"e_1_3_2_113_2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0273815","article-title":"Improving cardiac rehabilitation patient adherence via personalized interventions","volume":"17","author":"Aharon KB","year":"2022","unstructured":"Aharon KB, Gershfeld-Litvin A, Amir O, Nabutovsky I, Klempfner R. Improving cardiac rehabilitation patient adherence via personalized interventions. PLoS One. 2022;17(8):e0273815.","journal-title":"PLoS One"},{"key":"e_1_3_2_114_2","doi-asserted-by":"publisher","DOI":"10.1177\/1741826711422988"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.1177\/02692155211032372"},{"issue":"1","key":"e_1_3_2_116_2","doi-asserted-by":"crossref","DOI":"10.2196\/29434","article-title":"Machine learning for cardiovascular outcomes from wearable data: systematic review from a technology readiness level point of view","volume":"10","author":"Naseri Jahfari A","year":"2022","unstructured":"Naseri Jahfari A, Tax D, Reinders M, van der Bilt I. Machine learning for cardiovascular outcomes from wearable data: systematic review from a technology readiness level point of view. JMIR Med Inform. 2022;10(1):e29434.","journal-title":"JMIR Med Inform"},{"issue":"1","key":"e_1_3_2_117_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s13040-022-00299-6","article-title":"Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data","volume":"15","author":"Brown DE","year":"2022","unstructured":"Brown DE, Sharma S, Jablonski JA, Weltman A. Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data. BioData Min. 2022;15(1):16.","journal-title":"BioData Min"},{"issue":"12","key":"e_1_3_2_118_2","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s12170-023-00731-4","article-title":"Artificial intelligence for risk assessment on primary prevention of coronary artery disease","volume":"17","author":"Chen SF","year":"2023","unstructured":"Chen SF, Loguercio S, Chen KY, et al. Artificial intelligence for risk assessment on primary prevention of coronary artery disease. Curr Cardiovasc Risk Rep. 2023;17(12):215-231.","journal-title":"Curr Cardiovasc Risk Rep"},{"issue":"5","key":"e_1_3_2_119_2","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s11886-021-01482-7","article-title":"Cardiac rehab in the COVID era and beyond: mHealth and other novel opportunities","volume":"23","author":"Epstein E","year":"2021","unstructured":"Epstein E, Patel N, Maysent K, Taub PR. Cardiac rehab in the COVID era and beyond: mHealth and other novel opportunities. Curr Cardiol Rep. 2021;23(5):42.","journal-title":"Curr Cardiol Rep"},{"key":"e_1_3_2_120_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12872-023-03471-w"},{"issue":"1","key":"e_1_3_2_121_2","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1186\/s40001-023-01065-y","article-title":"Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives","volume":"28","author":"Sun X","year":"2023","unstructured":"Sun X, Yin Y, Yang Q, Huo T. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur J Med Res. 2023;28(1):242.","journal-title":"Eur J Med Res"},{"issue":"1","key":"e_1_3_2_122_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s12911-021-01392-2","article-title":"CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases","volume":"21","author":"Ahn I","year":"2021","unstructured":"Ahn I, Na W, Kwon O, et al. CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases. BMC Med Inform Decis Mak. 2021;21(1):29.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"9","key":"e_1_3_2_123_2","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1007\/s10462-024-10873-5","article-title":"Blockchain, artificial intelligence, and healthcare: the tripod of future\u2014a narrative review","volume":"57","author":"Bathula A","year":"2024","unstructured":"Bathula A, Gupta SK, Merugu S, et al. Blockchain, artificial intelligence, and healthcare: the tripod of future\u2014a narrative review. Artif Intell Rev. 2024;57(9):238.","journal-title":"Artif Intell Rev"},{"issue":"2","key":"e_1_3_2_124_2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.artmed.2007.12.002","article-title":"Wearable computing and artificial intelligence for healthcare applications","volume":"42","author":"Lukowicz P","year":"2008","unstructured":"Lukowicz P. Wearable computing and artificial intelligence for healthcare applications. Artif Intell Med. 2008;42(2):95-98.","journal-title":"Artif Intell Med"},{"key":"e_1_3_2_125_2","article-title":"Wearable artificial intelligence for detecting anxiety: systematic review and meta-analysis","volume":"25","author":"Abd-Alrazaq A","year":"2023","unstructured":"Abd-Alrazaq A, AlSaad R, Harfouche M, et al. Wearable artificial intelligence for detecting anxiety: systematic review and meta-analysis. J Med Internet Res. 2023;25:e48754.","journal-title":"J Med Internet Res"},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2019.05.066"},{"issue":"11","key":"e_1_3_2_127_2","article-title":"Blockchain in health information systems: a systematic review","volume":"21","author":"Fons\u00eaca ALA","year":"2024","unstructured":"Fons\u00eaca ALA, Barbalho IMP, Fernandes F, et al. Blockchain in health information systems: a systematic review. Int J Environ Res Public Health. 2024;21(11):1512.","journal-title":"Int J Environ Res Public Health"}],"container-title":["Clinical Medicine Insights: Cardiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/11795468261421344","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/11795468261421344","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/11795468261421344","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:14:02Z","timestamp":1777389242000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/11795468261421344"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":126,"alternative-id":["10.1177\/11795468261421344"],"URL":"https:\/\/doi.org\/10.1177\/11795468261421344","relation":{},"ISSN":["1179-5468","1179-5468"],"issn-type":[{"value":"1179-5468","type":"print"},{"value":"1179-5468","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]},"article-number":"11795468261421344"}}