2,435
Views
2
CrossRef citations to date
0
Altmetric
Review

A Review of the Scope, Future, and Effectiveness of Using Artificial Intelligence in Cardiac Rehabilitation: A Call to Action for the Kingdom of Saudi Arabia

ORCID Icon
Article: 2175111 | Received 31 Oct 2022, Accepted 27 Jan 2023, Published online: 15 Feb 2023

References

  • Aljefree, N., and F. Ahmed. 2015. Prevalence of cardiovascular disease and associated risk factors among adult population in the gulf region: A systematic review. Advances in Public Health 2015:1–683. 2015. doi:10.1155/2015/235101.
  • Al-Jehani, N. B., Z. A. Hawsawi, N. Radwan, and M. Farouk. 2021. Development of artificial intelligence techniques in Saudi Arabia: The impact on COVID-19 pandemic. literature review. Journal of Engineering Science and Technology 16(6):4530–47.
  • Alshaikh, M. K., F. T. Filippidis, J. P. Baldove, A. Majeed, and S. Rawaf. 2016. Women in Saudi Arabia and the prevalence of cardiovascular risk factors: A systematic review. Journal of Environmental and Public Health 2016 2016:1–15. doi:10.1155/2016/7479357.
  • Anderson, L., and R. S. Taylor. 2014. Cardiac rehabilitation for people with heart disease: An overview of cochrane systematic reviews. Cochrane Database System Review 2014 CD011273. doi:10.1002/14651858.CD011273.pub2.
  • Baashar, Y., G. Alkawsi, H. Alhussian, L. F. Capretz, A. Alwadain, A. A. Alkahtani, and M. Almomani. 2022. Effectiveness of artificial intelligence models for cardiovascular disease prediction: Network meta-analysis. Computational intelligence and neuroscience 2022:5849995. doi:10.1155/2022/5849995.
  • Balady, G. J., M. A. Williams, P. A. Ades, V. Bittner, P. Comoss, J. M. Foody, Barry Franklin, Bonnie Sanderson, and Douglas Southard . 2007. Core components of cardiac rehabilitation/secondary prevention programs: 2007 update: A scientific statement from the American heart association exercise, cardiac rehabilitation, and prevention committee, the council on clinical cardiology; the councils on cardiovascular nursing. epidemiology and prevention, and nutrition, physical activity, and metabolism; and the American association of cardiovascular and pulmonary rehabilitation. epidemiology and prevention, and nutrition, physical activity, and metabolism; and the American association of cardiovascular and pulmonary rehabilitation Circulation 115:2675–82. doi:10.1161/CIRCULATIONAHA.106.180945.
  • Barrett, M., J. Boyne, J. Brandts, H. P. Brunner-La Rocca, L. De Maesschalck, K. De Wit, Lana Dixon, Eurlings, Casper Fitzsimons, Donna and Golubnitschaja, Olga . 2019. Artificial intelligence supported patient self-care in chronic heart failure: A paradigm shift from reactive to predictive, preventive and personalised care. The EPMA Journal. 10(4):445–64. doi:10.1007/s13167-019-00188-9.
  • Bhinder, B., C. Gilvary, N. S. Madhukar, and O. Elemento. 2021. Artificial Intelligence in cancer research and precision medicine. Cancer Discovery 11 (4):900–15. doi:10.1158/2159-8290.CD-21-0090.
  • Bindawas, S. M., and V. S. Vennu. 2016. Stroke rehabilitation. a call to action in Saudi Arabia. Neurosciences (Riyadh) 21 (4):297–305. doi:10.17712/nsj.2016.4.20160075.
  • Blease, C., T. J. Kaptchuk, M. H. Bernstein, K. D. Mandl, J. D. Halamka, and C. M. DesRoches. 2019. Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners’ views. Journal of Medical Internet Research 21 (3):e12802. doi:10.2196/12802.
  • Bravo-Escobar, R., A. Gonzalez-Represas, A. M. Gomez-Gonzalez, A. Montiel-Trujillo, R. Aguilar-Jimenez, R. Carrasco-Ruiz, and P. Salinas-Sanchez. 2017. 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 cardiovascular disorders 17 (1):66. doi:10.1186/s12872-017-0499-0.
  • Cao, J. S., Z. Y. Lu, M. Y. Chen, B. Zhang, S. Juengpanich, J. H. Hu, Li, Shi-Jie, Topatana, Win, Zhou, Xue-Yin, and Feng, Xu . 2021. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World Journal of Gastroenterology: WJG. 27(16):1664–90. doi:10.3748/wjg.v27.i16.1664.
  • Case, M. A., H. A. Burwick, K. G. Volpp, and M. S. Patel. 2015. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA 313 (6):625–26. doi:10.1001/jama.2014.17841.
  • Chacin-Suarez, A., S. L. Grace, C. Anchique-Santos, M. Supervia, K. Turk-Adawi, R. R. Britto,Dawn C. Scantlebury, Felipe Gonzalez Graciela, and Benaim, Briseida. 2021. Cardiac rehabilitation availability and characteristics in Latin America and the Caribbean: A global Comparison. American Heart Journal 240:16–27. doi:10.1016/j.ahj.2021.05.010.
  • Chaves, G., K. Turk-Adawi, M. Supervia, C. Santiago, A. de Pio, A. H. Abu-Jeish, T. Mamataz, S. Tarima, F. Lopez Jimenez, and S. L. Grace. 2020. Cardiac Rehabilitation Dose Around the World: Variation and Correlates. Circulation Cardiovascular Quality and Outcomes 13 (1):e005453. doi:10.1161/CIRCOUTCOMES.119.005453.
  • Chen, W., Q. Sun, X. Chen, G. Xie, H. Wu, and C. Xu. 2021. Deep learning methods for heart sounds classification: A systematic review. Entropy (Basel) 23 (6):667. doi:10.3390/e23060667.
  • Chong, M. S., J. W. H. Sit, K. Karthikesu, and S. Y. Chair. 2021. Effectiveness of technology-assisted cardiac rehabilitation: A systematic review and meta-analysis. International Journal of Nursing Studies 124:104087. doi:10.1016/j.ijnurstu.2021.104087.
  • Chowdhury, M., F. A. Heald, J. C. Sanchez-Delgado, M. Pakosh, A. M. Jacome-Hortua, and S. L. Grace. 2021. The effects of maintenance cardiac rehabilitation: A systematic review and Meta-analysis, with a focus on sex. Heart & Lung 50 (4):504–24. doi:10.1016/j.hrtlng.2021.02.016.
  • Christopoulou, F., T. T. Tran, S. K. Sahu, M. Miwa, and S. Ananiadou. 2020. Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods. Journal of the American Medical Informatics Association: JAMIA 27 (1):39–46. doi:10.1093/jamia/ocz101.
  • Davenport, T., and R. Kalakota. 2019. The potential for artificial intelligence in healthcare. Future Healthcare Journal 6 (2):94–98. doi:10.7861/futurehosp.6-2-94.
  • De Canniere, H., F. Corradi, C. J. P. Smeets, M. Schoutteten, C. Varon, C. Van Hoof, S. Van Huffel, W. Groenendaal, and P. Vandervoort. 2020. Wearable monitoring and interpretable machine learning can objectively track progression in patients during cardiac rehabilitation. Sensors (Basel) 20 (12):3601. doi:10.3390/s20123601.
  • De Cannière, H., F. Corradi, C. J. Smeets, M. Schoutteten, C. Varon, C. Van Hoof, S. Van Huffel, W. Groenendaal, and P. Vandervoort. 2020. Wearable monitoring and interpretable machine learning can objectively track progression in patients during cardiac rehabilitation. Sensors 20 (12):3601. doi:10.3390/s20123601.
  • Diciolla, M., G. Binetti, T. Di Noia, F. Pesce, F. P. Schena, A. M. Vagane, R. Bjorneklett, H. Suzuki, Y. Tomino, and D. Naso. 2015. Patient classification and outcome prediction in IgA nephropathy. Computers in Biology and Medicine 66:278–86. doi:10.1016/j.compbiomed.2015.09.003.
  • Duan, Y., J. S. Edwards, and Y. K. Dwivedi. 2019. Artificial intelligence for decision making in the era of big data–evolution, challenges and research agenda. International Journal of Information Management 48:63–71. doi:10.1016/j.ijinfomgt.2019.01.021.
  • Edwards, K., N. Jones, J. Newton, C. Foster, A. Judge, K. Jackson, N. K. Arden, and R. Pinedo-Villanueva. 2017. The cost-effectiveness of exercise-based cardiac rehabilitation: A systematic review of the characteristics and methodological quality of published literature. Health Economics Review 7 (1):37. doi:10.1186/s13561-017-0173-3.
  • Falter, M., W. Budts, K. Goetschalckx, V. Cornelissen, and R. Buys. 2019. Accuracy of apple watch measurements for heart rate and energy expenditure in patients with cardiovascular disease: Cross-sectional study. JMIR Mhealth Uhealth 7 (3):e11889. doi:10.2196/11889.
  • Falter, M., M. Scherrenberg, and P. Dendale. 2020. Digital health in cardiac rehabilitation and secondary prevention: A search for the ideal tool. Sensors (Basel) 21 (1):12. doi:10.3390/s21010012.
  • Francis, T., N. Kabboul, V. Rac, N. Mitsakakis, P. Pechlivanoglou, J. Bielecki, D. Alter, and M. Krahn. 2019. The effect of cardiac rehabilitation on health-related quality of life in patients with coronary artery disease: A meta-analysis. The Canadian Journal of Cardiology 35 (3):352–64. doi:10.1016/j.cjca.2018.11.013.
  • General Authority for Statistics. 2022. Population Estimates. https://www.stats.gov.sa/en (last accessed January 12 2023).
  • Gensini, G. F., C. Alderighi, R. Rasoini, M. Mazzanti, and G. Casolo. 2017. Value of telemonitoring and telemedicine in heart failure management. Card Fail Review 3 (2):116–21. doi:10.15420/cfr.2017:6:2.
  • Gevaert, A. B., V. Adams, M. Bahls, T. S. Bowen, V. Cornelissen, M. Dorr, D. Hansen, H. M. Kemps, P. Leeson, E. M. Van Craenenbroeck, et al. 2020. Towards a personalised approach in exercise-based cardiovascular rehabilitation: How can translational research help? a ’call to action’ from the section on secondary prevention and cardiac rehabilitation of the European Association of preventive cardiology. European Journal of Preventive Cardiology. 27(13):1369–85. doi:10.1177/2047487319877716.
  • Ghisi, G. L., P. Polyzotis, P. Oh, M. Pakosh, and S. L. Grace. 2013. Physician factors affecting cardiac rehabilitation referral and patient enrollment: A systematic review. Clinical cardiology 36 (6):323–35. doi:10.1002/clc.22126.
  • Grace, S. L., K. Kotseva, and M. A. Whooley. 2021. Cardiac rehabilitation: under-utilized globally. Current Cardiology Reports 23 (9):118. doi:10.1007/s11886-021-01543-x.
  • Grace, S. L., K. L. Russell, R. D. Reid, P. Oh, S. Anand, J. Rush, Karen Williamson, Milan Gupta, David A. Alter, and Donna E. Stewart. 2011. Effect of cardiac rehabilitation referral strategies on utilization rates: A prospective, controlled study. Archives of Internal Medicine. 171(3):235–41. doi:10.1001/archinternmed.2010.501.
  • Grace, S. L., S. Shanmugasegaram, S. Gravely-Witte, J. Brual, N. Suskin, and D. E. Stewart. 2009. Barriers to cardiac rehabilitation: Does age make a difference? Journal of Cardiopulmonary Rehabilitation and Prevention 29 (3):183–87. doi:10.1097/HCR.0b013e3181a3333c.
  • Grace, S. L., K. I. Turk-Adawi, A. Contractor, A. Atrey, N. Campbell, W. Derman, Gabriela L Melo Oldridge, Neil Sarkar, Bidyut K Yeo, Tee Joo. 2016. Cardiac rehabilitation delivery model for low-resource settings. Heart. 102(18):1449–55. doi:10.1136/heartjnl-2015-309209.
  • Guha, S., R. Sethi, S. Ray, V. K. Bahl, S. Shanmugasundaram, P. Kerkar, et al. 2017. Cardiological society of India: Position statement for the management of ST elevation myocardial infarction in India. Indian Heart Journal 69(1):S63–97. Suppl. doi:10.1016/j.ihj.2017.03.006.
  • Gunn, A. A. 1976. The diagnosis of acute abdominal pain with computer analysis. Journal of the Royal College of Surgeons of Edinburgh 21 (3):170–72. Retrieved from. https://www.ncbi.nlm.nih.gov/pubmed/781220.
  • Guo, J., and B. Li. 2018. The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity 2 (1):174–81. doi:10.1089/heq.2018.0037.
  • Hibler, B. P., Q. Qi, and A. M. Rossi. 2016. Current state of imaging in dermatology. Seminars in Cutaneous Medicine and Surgery 35 (1):2–8. doi:10.12788/j.sder.2016.001.
  • Huang, K. S., D. X. He, D. J. Huang, Q. L. Tao, X. J. Deng, B. Zhang, G. Mai, and D. Guha-Sapir. 2021. Changes in ischemic heart disease mortality at the global level and their associations with natural disasters: A 28-year ecological trend study in 193 countries. Plos One 16 (7):e0254459. doi:10.1371/journal.pone.0254459.
  • Jiang, F., Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, and Y. Wang. 2017. Artificial intelligence in healthcare: Past, present and future. Stroke Vascular Neurology 2 (4):230–43. doi:10.1136/svn-2017-000101.
  • Jin, K., S. Khonsari, R. Gallagher, P. Gallagher, A. M. Clark, B. Freedman, T. Briffa, A. Bauman, J. Redfern, and L. Neubeck. 2019. Telehealth interventions for the secondary prevention of coronary heart disease: A systematic review and meta-analysis. European Journal of Cardiovascular Nursing: Journal of the Working Group on Cardiovascular Nursing of the European Society of Cardiology 18 (4):260–71. doi:10.1177/1474515119826510.
  • Johnson, K. W., J. Torres Soto, B. S. Glicksberg, K. Shameer, R. Miotto, M. Ali, E. Ashley, and J. T. Dudley. 2018. Artificial intelligence in cardiology. Journal of the American College of Cardiology 71 (23):2668–79. doi:10.1016/j.jacc.2018.03.521.
  • Kalix, P. 1988. Khat: A plant with amphetamine effects. Journal of Substance Abuse Treatment 5 (3):163–69. doi:10.1016/0740-5472(88)90005-0.
  • Kaplan, M. S., J. T. Newsom, B. H. McFarland, and L. Lu. 2001. Demographic and psychosocial correlates of physical activity in late life. American Journal of Preventive Medicine 21 (4):306–12. doi:10.1016/s0749-3797(01)00364-6.
  • Khalsa, R. K., A. Khashkhusha, S. Zaidi, A. Harky, and M. Bashir. 2021. Artificial intelligence and cardiac surgery during COVID-19 era. Journal of Cardiac Surgery 36 (5):1729–33. doi:10.1111/jocs.15417.
  • Kong, G., D. -L. Xu, and J. -B. Yang. 2008. Clinical decision support systems: A review on knowledge representation and inference under uncertainties. International Journal of Computational Intelligence Systems, Cham. Available at: https://link.springer.com/chapter/10.1007/978-3-030-87687-6_36
  • Konstam, M. A., J. A. Hill, R. J. Kovacs, R. A. Harrington, J. A. Arrighi, A. Khera, C, and Academic Cardiology Section Leadership Council of the American College of. 2017. The academic medical system: Reinvention to survive the revolution in health care. Journal of the American College of Cardiology 69 (10):1305–12. doi:10.1016/j.jacc.2016.12.024.
  • Lara, J. S., J. Casas, A. Aguirre, M. Munera, M. Rincon-Roncancio, B. Irfan, E. Senft, T. Belpaeme, and C. A. Cifuentes. 2017. Human-robot sensor interface for cardiac rehabilitation. International Conference on Rehabilitation Robotics (ICORR), London, UK. Available at: https://ieeexplore.ieee.org/document/8009382
  • Leon, A. S., B. A. Franklin, F. Costa, G. J. Balady, K. A. Berra, K. J. Stewart, Thompson, Paul D, Williams, Mark A and Lauer, Michael S. 2005. Cardiac rehabilitation and secondary prevention of coronary heart disease: An American heart association scientific statement from the council on clinical cardiology (subcommittee on exercise, cardiac rehabilitation, and prevention) and the council on nutrition, physical activity, and metabolism (subcommittee on physical activity), in collaboration with the American association of cardiovascular and pulmonary rehabilitation. Circulation. 111(3):369–76. doi:10.1161/01.CIR.0000151788.08740.5C.
  • Leung, Y. W., J. Brual, A. Macpherson, and S. L. Grace. 2010. Geographic issues in cardiac rehabilitation utilization: A narrative review. Health & Place 16 (6):1196–205. doi:10.1016/j.healthplace.2010.08.004.
  • Liang, H., B. Y. Tsui, H. Ni, C. C. S. Valentim, S. L. Baxter, G. Liu, Cai, Wenjia, Kermany, Daniel S, Sun, Xin and Chen, Jiancong. 2019. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nature medicine. 25(3):433–38. doi:10.1038/s41591-018-0335-9.
  • Linder, J. A., J. L. Schnipper, R. Tsurikova, T. Yu, L. A. Volk, A. J. Melnikas, M. B. Palchuk, M. Olsha-Yehiav, and B. Middleton. 2009. Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: A cluster randomised controlled trial. Informatics in Primary Care 17 (4):231–40. doi:10.14236/jhi.v17i4.742.
  • Main, C., T. Moxham, J. C. Wyatt, J. Kay, R. Anderson, and K. Stein. 2010. Computerised decision support systems in order communication for diagnostic, screening or monitoring test ordering: Systematic reviews of the effects and cost-effectiveness of systems. Health Technol Assess 14 (48):1–227. doi:10.3310/hta14480.
  • Mathur, P., S. Srivastava, X. Xu, and J. L. Mehta. 2020. Artificial Intelligence, Machine Learning, and Cardiovascular Disease. Clinical Medicine Insights Cardiology 14:1179546820927404. doi:10.1177/1179546820927404.
  • McGregor, G., R. Powell, P. Kimani, and M. Underwood. 2020. Does contemporary exercise-based cardiac rehabilitation improve quality of life for people with coronary artery disease? A systematic review and meta-analysis. BMJ Open 10 (6):e036089. doi:10.1136/bmjopen-2019-036089.
  • McMahon, S. R., P. A. Ades, and P. D. Thompson. 2017. The role of cardiac rehabilitation in patients with heart disease. Trends in cardiovascular medicine 27 (6):420–25. doi:10.1016/j.tcm.2017.02.005.
  • Medina Quero, J., M. R. Fernandez Olmo, M. D. Pelaez Aguilera, and M. Espinilla Estevez. 2017. Real-time monitoring in home-based cardiac rehabilitation using wrist-worn heart rate devices. Sensors (Basel) 17 (12):2892. doi:10.3390/s17122892.
  • Midence, L., A. Mola, C. M. Terzic, R. J. Thomas, and S. L. Grace. 2014. Ethnocultural diversity in cardiac rehabilitation. Journal of Cardiopulmonary Rehabilitation and Prevention 34 (6):437–44. doi:10.1097/HCR.0000000000000089.
  • Miller, R. A. 1994. Medical diagnostic decision support systems–past, present, and future: A threaded bibliography and brief commentary. Journal of the American Medical Informatics Association: JAMIA 1 (1):8–27. doi:10.1136/jamia.1994.95236141.
  • Murdoch, T. B., and A. S. Detsky. 2013. The inevitable application of big data to health care. JAMA 309 (13):1351–52. doi:10.1001/jama.2013.393.
  • Naci, H., and J. P. Ioannidis. 2015. Comparative effectiveness of exercise and drug interventions on mortality outcomes: Metaepidemiological study. British Journal of Sports Medicine 49 (21):1414–22. doi:10.1136/bjsports-2015-f5577rep.
  • Omura, J. D., S. A. Carlson, P. Paul, K. B. Watson, and J. E. Fulton. 2017. National physical activity surveillance: Users of wearable activity monitors as a potential data source. Preventive Medicle Rep 5:124–26. doi:10.1016/j.pmedr.2016.10.014.
  • Parak, J., M. Salonen, T. Myllymaki, and I. Korhonen. 2021. Comparison of heart rate monitoring accuracy between chest strap and vest during physical training and implications on training decisions. Sensors (Basel) 21 (24):8411. doi:10.3390/s21248411.
  • Patel, V. L., E. H. Shortliffe, M. Stefanelli, P. Szolovits, M. R. Berthold, R. Bellazzi, and A. Abu-Hanna. 2009. The coming of age of artificial intelligence in medicine. Artificial intelligence in medicine 46 (1):5–17. doi:10.1016/j.artmed.2008.07.017.
  • Peláez-Aguilera, M. D., M. Espinilla, M. R. Fernandez Olmo, and J. Medina. 2019. Fuzzy linguistic protoforms to summarize heart rate streams of patients with ischemic heart disease. Complexity 2019:1–11. 2019. doi:10.1155/2019/2694126.
  • Pérez-Robledo, F., A. S. Mendes, L. A. Silva, B. M. Bermejo-Gil, R. Llamas-Ramos, and I. Llamas-Ramos. 2021. CardioSafe: A platform for remote rehabilitation for patients with cardiological problems. International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence, Cham. Available at: https://link.springer.com/chapter/10.1007/978-3-030-87687-6_361
  • Piepoli, M. F., A. W. Hoes, S. Agewall, C. Albus, C. Brotons, A. L. Catapano, M. -T. Cooney, U. Corrà, B. Cosyns, C. Deaton, et al. 2016. 2016 European guidelines on cardiovascular disease prevention in clinical practice: The sixth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of 10 societies and by invited experts)developed with the special contribution of the European Association for cardiovascular prevention & rehabilitation (EACPR). European Heart Journal. 37(29):2315–81. doi:10.1093/eurheartj/ehw106.
  • Pisarchik, A. N., V. A. Maksimenko, and A. E. Hramov. 2019. From novel technology to novel applications: Comment on “An integrated brain-machine interface platform with thousands of channels” by Elon Musk and neuralink. Journal of Medical Internet Research 21 (10):e16356. doi:10.2196/16356.
  • Pouke, M., and J. Hakkila. 2013. Elderly healthcare monitoring using an avatar-based 3D virtual environment. International Journal of Environmental Research and Public Health 10 (12):7283–98. doi:10.3390/ijerph10127283.
  • Rashed, M., N. Theruvan, A. Gad, H. Shaheen, and S. Mosbah. 2020. Cardiac rehabilitation: Future of heart health in Saudi Arabia, a perceptual view. World Journal of Cardiovascular Diseases 10 (09):666–77. doi:10.4236/wjcd.2020.109064.
  • Romiti, S., M. Vinciguerra, W. Saade, I. Anso Cortajarena, and E. Greco. 2020. Artificial intelligence (AI) and cardiovascular diseases: An unexpected alliance. Cardiology Research and Practice 2020:4972346. doi:10.1155/2020/4972346.
  • Roshanov, P. S., N. Fernandes, J. M. Wilczynski, B. J. Hemens, J. J. You, S. M. Handler, Nieuwlaat, Robby, Souza, Nathan M, Beyene, Joseph and Van Spall, Harriette GC. 2013. Features of effective computerised clinical decision support systems: Meta-regression of 162 randomised trials. The BMJ. 346(feb14 1):f657. doi:10.1136/bmj.f657.
  • Roth, G. A., G. A. Mensah, C. O. Johnson, G. Addolorato, E. Ammirati, L. M. Baddour, et al. 2020. Global burden of cardiovascular diseases and risk factors, 1990–2019. Journal of the American College of Cardiology. 76(25):2982–3021. doi:10.1016/j.jacc.2020.11.010.
  • Ruano-Ravina, A., C. Pena-Gil, E. Abu-Assi, S. Raposeiras, A. van ‘t, H. E. Meindersma, E. I. Bossano Prescott, and J. R. Gonzalez-Juanatey. 2016. Participation and adherence to cardiac rehabilitation programs. A systematic review. International Journal of Cardiology 223:436–43. doi:10.1016/j.ijcard.2016.08.120.
  • Samayoa, L., S. L. Grace, S. Gravely, L. B. Scott, S. Marzolini, and T. J. Colella. 2014. Sex differences in cardiac rehabilitation enrollment: A meta-analysis. The Canadian Journal of Cardiology 30 (7):793–800. doi:10.1016/j.cjca.2013.11.007.
  • Santiago de Araujo Pio, C., T. M. Beckie, M. Varnfield, N. Sarrafzadegan, A. S. Babu, S. Baidya, Buckley, John, Chen, Ssu-Yuan, Gagliardi, Anna and Heine, Martin. 2020. Promoting patient utilization of outpatient cardiac rehabilitation: A joint International council and Canadian Association of cardiovascular prevention and rehabilitation position statement. International Journal of Cardiology 298:1–7. doi:10.1016/j.ijcard.2019.06.064.
  • Santiago de Araujo Pio, C., G. S. Chaves, P. Davies, R. S. Taylor, and S. L. Grace. 2019. Interventions to promote patient utilisation of cardiac rehabilitation. Cochrane Database Syst Rev 2: CD007131. doi:10.1002/14651858.CD007131.pub4
  • Santiago de Araujo Pio, C., S. Marzolini, M. Pakosh, and S. L. Grace. 2017. Effect of cardiac rehabilitation dose on mortality and morbidity: A systematic review and meta-regression analysis. Mayo Clinic Proceedings Mayo Clinic 92 (11):1644–59. doi:10.1016/j.mayocp.2017.07.019.
  • Schork, N. J. 2019. Artificial intelligence and personalized medicine. Cancer Treatment and Research 178:265–83. doi:10.1007/978-3-030-16391-4_11.
  • Shajrawi, A., M. Granat, I. Jones, and F. Astin. 2020. Physical activity and cardiac self-efficacy levels during early recovery after acute myocardial infarction: A Jordanian study. The Journal of Nursing Research: JNR 29 (1):e131. doi:10.1097/JNR.0000000000000408.
  • Shameer, K., K. W. Johnson, B. S. Glicksberg, J. T. Dudley, and P. P. Sengupta. 2018. Machine learning in cardiovascular medicine: Are we there yet? Heart 104 (14):1156–64. doi:10.1136/heartjnl-2017-311198.
  • Shanmugasegaram, S., L. Gagliese, P. Oh, D. E. Stewart, S. J. Brister, V. Chan, and S. L. Grace. 2012. Psychometric validation of the cardiac rehabilitation barriers scale. Clinical rehabilitation 26 (2):152–64. doi:10.1177/0269215511410579.
  • Shields, G. E., A. Wells, P. Doherty, A. Heagerty, D. Buck, and L. M. Davies. 2018. Cost-effectiveness of cardiac rehabilitation: A systematic review. Heart 104 (17):1403–10. doi:10.1136/heartjnl-2017-312809.
  • Smith, S. C., Jr., E. J. Benjamin, R. O. Bonow, L. T. Braun, M. A. Creager, B. A. Franklin, Gibbons, Raymond J, Grundy, Scott M, Hiratzka, Loren F and Jones, Daniel W. 2011. AHA/ACCF secondary prevention and risk reduction therapy for patients with coronary and other atherosclerotic vascular disease: 2011 update: A guideline from the American heart association and American college of cardiology foundation. Circulation. 124(22):2458–73. doi:10.1161/CIR.0b013e318235eb4d.
  • Soofi, M. A., and M. A. Youssef. 2015. Prediction of 10-year risk of hard coronary events among Saudi adults based on prevalence of heart disease risk factors. Journal Saudi Heart Association 27 (3):152–59. doi:10.1016/j.jsha.2015.03.003.
  • Sotirakos, S., B. Fouda, N. A. Mohamed Razif, N. Cribben, C. Mulhall, A. O’byrne, B. Moran, and R. Connolly. 2021. Harnessing artificial intelligence in cardiac rehabilitation, a systematic review. Future cardiology 18 (2):154–64. doi:10.2217/fca-2021-0010.
  • Sotirakos, S., B. Fouda, N. A. Mohamed Razif, N. Cribben, C. Mulhall, A. O’byrne, B. Moran, and R. Connolly. 2022. Harnessing artificial intelligence in cardiac rehabilitation, a systematic review. Future cardiology 18 (2):154–64. doi:10.2217/fca-2021-0010.
  • Steinhubl, S. R., and E. J. Topol. 2015. Moving from digitalization to digitization in cardiovascular care: Why is it important, and what could it mean for patients and providers? Journal of the American College of Cardiology 66 (13):1489–96. doi:10.1016/j.jacc.2015.08.006.
  • Stewart, J., J. Lu, A. Goudie, M. Bennamoun, P. Sprivulis, F. Sanfillipo, G. Dwivedi, and G. Bivona. 2021. Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review. Plos One 16 (8):e0252612. doi:10.1371/journal.pone.0252612.
  • Suaya, J. A., D. S. Shepard, S. L. Normand, P. A. Ades, J. Prottas, and W. B. Stason. 2007. Use of cardiac rehabilitation by medicare beneficiaries after myocardial infarction or coronary bypass surgery. Circulation 116 (15):1653–62. doi:10.1161/CIRCULATIONAHA.107.701466.
  • Supervia, M., K. Turk-Adawi, F. Lopez-Jimenez, E. Pesah, R. Ding, R. R. Britto, B. Bjarnason-Wehrens, W. Derman, A. Abreu, A. S. Babu, et al. 2019. Nature of cardiac rehabilitation around the globe. EClinicalMedicine 13:46–56. doi:10.1016/j.eclinm.2019.06.006.
  • Taylor, R. S., H. M. Dalal, and S. T. J. McDonagh. 2022. The role of cardiac rehabilitation in improving cardiovascular outcomes. Nature Reviews Cardiology 19 (3):180–94. doi:10.1038/s41569-021-00611-7.
  • Thomas, R. J., A. L. Beatty, T. M. Beckie, L. C. Brewer, T. M. Brown, D. E. Forman, B. A. Franklin, S. J. Keteyian, D. W. Kitzman, J. G. Regensteiner, et al. 2019. Home-based cardiac rehabilitation: A scientific statement from the American association of cardiovascular and pulmonary rehabilitation, the American heart association, and the American college of cardiology. Circulation. 140(1):e69–89. doi:10.1161/CIR.0000000000000663.
  • Tran, N. K., S. Albahra, L. May, S. Waldman, S. Crabtree, S. Bainbridge, and H. Rashidi. 2021. Evolving applications of artificial intelligence and machine learning in infectious diseases testing. Clinical Chemistry 68 (1):125–33. doi:10.1093/clinchem/hvab239.
  • Turk-Adawi, K., N. Sarrafzadegan, and S. L. Grace. 2014. Global availability of cardiac rehabilitation. Nature Reviews Cardiology 11 (10):586–96. doi:10.1038/nrcardio.2014.98.
  • Turk-Adawi, K., M. Supervia, F. Lopez-Jimenez, E. Pesah, R. Ding, R. R. Britto, B. Bjarnason-Wehrens, W. Derman, A. Abreu, A. S. Babu, et al. 2019. Cardiac rehabilitation availability and density around the globe. EClinicalMedicine 13:31–45. doi:10.1016/j.eclinm.2019.06.007.
  • Visco, V., G. J. Ferruzzi, F. Nicastro, N. Virtuoso, A. Carrizzo, G. Galasso, C. Vecchione, and M. Ciccarelli. 2021. Artificial Intelligence as a business partner in cardiovascular precision medicine: An emerging approach for disease detection and treatment optimization. Current Medicinal Chemistry 28 (32):6569–90. doi:10.2174/0929867328666201218122633.
  • Wang, T. J., B. Chau, M. Lui, G. T. Lam, N. Lin, and S. Humbert. 2020. Physical medicine and rehabilitation and pulmonary rehabilitation for COVID-19. American Journal of Physical Medicine & Rehabilitation / Association of Academic Physiatrists 99 (9):769–74. doi:10.1097/PHM.0000000000001505.
  • Wang, H., Q. Zu, J. Chen, Z. Yang, and M. A. Ahmed. 2021. Application of artificial intelligence in acute coronary syndrome: A brief literature review. Advances in Therapy 38 (10):5078–86. doi:10.1007/s12325-021-01908-2.
  • WHO. 1993. Rehabilitation after cardiovascular diseases, with special emphasis on developing countries: Report of a WHO expert committee. Geneva: World Health Organization.
  • Widmer, R. J., T. G. Allison, R. Lennon, F. Lopez-Jimenez, L. O. Lerman, and A. Lerman. 2017. Digital health intervention during cardiac rehabilitation: A randomized controlled trial. American Heart Journal 188:65–72. doi:10.1016/j.ahj.2017.02.016.
  • Wong, W. P., J. Feng, K. H. Pwee, and J. Lim. 2012. A systematic review of economic evaluations of cardiac rehabilitation. BMC Health Services Research 12 (1):243. doi:10.1186/1472-6963-12-243.
  • Xia, T. L., F. Y. Huang, Y. Peng, B. T. Huang, X. B. Pu, Y. Yang, H. Chai, and M. Chen. 2018. Efficacy of different types of exercise-based cardiac rehabilitation on coronary heart disease: A network meta-analysis. Journal of General Internal Medicine 33 (12):2201–09. doi:10.1007/s11606-018-4636-y.
  • Zhang, Y., H. Cao, P. Jiang, and H. Tang. 2018. Cardiac rehabilitation in acute myocardial infarction patients after percutaneous coronary intervention: A community-based study. Medicine (Baltimore) 97 (8):e9785. doi:10.1097/MD.0000000000009785.
  • Zhao, Y., E. P. Wood, N. Mirin, S. H. Cook, and R. Chunara. 2021. Social determinants in machine learning cardiovascular disease prediction models: A systematic review. American Journal of Preventive Medicine 61 (4):596–605. doi:10.1016/j.amepre.2021.04.016.