World Heart Federation Roadmap for Digital Health in Cardiology

Burden G, Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020; 76: 2982–3021. 

Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020; 396: 1223–1249. DOI: https://doi.org/10.1016/S0140-6736(20)30752-2 

Barber RM, Fullman N, Sorensen RJD, Bollyky T, McKee M, Nolte E, et al. Healthcare access and quality index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990–2015: A novel analysis from the global burden of disease study 2015. Lancet. 2017; 390: 231–266. DOI: https://doi.org/10.1016/S0140-6736(17)30818-8 

Manne-Goehler J, Geldsetzer P, Agoudavi K, Andall-Brereton G, Aryal KK, Bicaba BW, et al. Health system performance for people withdiabetes in 28 low-and middle-incomecountries: A cross-sectional study of nationallyrepresentative surveys. PLoS Med. 2019; 16. DOI: https://doi.org/10.1371/journal.pmed.1002751 

Zhou B, Danaei G, Stevens GA, Bixby H, Taddei C, Carrillo-Larco RM, et al. Long-term and recent trends in hypertension awareness, treatment, and control in 12 high-income countries: an analysis of 123 nationally representative surveys. Lancet. 2019; 394: 639–651. DOI: https://doi.org/10.1016/S0140-6736(19)31145-6 

World Health Organziation (WHO). Physicians (per 1,000 people). Data [Internet]. World Health Organization’s Global Health. Workforce. Statistics; 2016. (4 November 2021). Retrieved from http://data.worldbank.org/indicator/SH.MED.PHYS.ZS?locations=ZM%5Cnhttp://data.worldbank.org/indicator/SH.MED.PHYS.ZS. 

Battineni G, Sagaro GG, Chintalapudi N, Amenta F. The benefits of telemedicine in personalized prevention of cardiovascular diseases (CVD): A systematic review. J Pers Med. 2021; 11. DOI: https://doi.org/10.3390/jpm11070658 

Srinivasapura Venkateshmurthy N, Ajay VS, Mohan S, Jindal D, Anand S, Kondal D, et al. m-Power Heart Project – a nurse care coordinator led, mHealth enabled intervention to improve the management of hypertension in India: study protocol for a cluster randomized trial. Trials. 2018; 19(1): 1–9. DOI: https://doi.org/10.1186/s13063-018-2813-2 

Wilson D, Sheikh A, Görgens M, Ward K. Technology and Universal Health Coverage: Examining the role of digital health. J Glob Health. 2021; 11: 16006. DOI: https://doi.org/10.7189/jogh.11.16006 

Li R, Liang N, Bu F, Hesketh T. The effectiveness of self-management of hypertension in adults using mobile health: Systematic review and meta-analysis. JMIR mHealth uHealth. 2020; 8: e17776. DOI: https://doi.org/10.2196/17776 

Countdown N. NCD Countdown 2030: efficient pathways and strategic investments to accelerate progress towards the Sustainable Development Goal target 3.4 in low-income and middle-income countries. Lancet. 2022; 399: 1266–1278. DOI: https://doi.org/10.1016/S0140-6736(21)02347-3 

Frederix I, Caiani EG, Dendale P, Anker S, Bax J, Böhm A, et al. ESC e-Cardiology Working Group Position Paper: Overcoming challenges in digital health implementation in cardiovascular medicine. Eur J Prev Cardiol. 2019; 26: 1166–1177. DOI: https://doi.org/10.1177/2047487319832394 

Astley CM, Clarke RA, Cartledge S, Beleigoli A, Du H, Gallagher C, et al. Remote cardiac rehabilitation services and the digital divide: Implications for elderly populations during the COVID19 pandemic. Eur J Cardiovasc Nurs. 2021; 20: 521–523. DOI: https://doi.org/10.1093/eurjcn/zvab034 

Lefevre AE, Shah N, Bashingwa JJH, George AS, Mohan Di. Does women’s mobile phone ownership matter for health? Evidence from 15 countries. BMJ Glob Heal. 2020; 5: e002524. DOI: https://doi.org/10.1136/bmjgh-2020-002524 

Pick JB, Azari R. Global digital divide: Influence of socioeconomic, governmental, and accessibility factors on information technology. Inf Technol Dev. 2008; 14: 91–115. DOI: https://doi.org/10.1002/itdj.20095 

Whitelaw S, Pellegrini DM, Mamas MA, Cowie M, Van Spall HGC. Barriers and facilitators of the uptake of digital health technology in cardiovascular care: a systematic scoping review. Eur Hear J – Digit Heal. 2021; 2: 62–74. DOI: https://doi.org/10.1093/ehjdh/ztab005 

Poole L, Ramasawmy M, Banerjee A. Digital first during the COVID-19 pandemic: does ethnicity matter? Lancet Public Health. 2021; 6: e628–e630. DOI: https://doi.org/10.1016/S2468-2667(21)00186-9 

Redfern J, Coorey G, Mulley J, Scaria A, Neubeck L, Hafiz N, et al. A digital health intervention for cardiovascular disease management in primary care (CONNECT) randomized controlled trial. npj Digit Med. 2020; 3. DOI: https://doi.org/10.1038/s41746-020-00325-z 

Taniguchi D, LoGerfo J, Van Pelt M, Mielcarek B, Huster K, Haider M, et al. Evaluation of a multi-faceted diabetes care program including community-based peer educators in Takeo province, Cambodia, 2007–2013. PLoS One. 2017; 12. DOI: https://doi.org/10.1371/journal.pone.0181582 

Steinman L, van Pelt M, Hen H, Chhorvann C, Lan CS, Te V, et al. Can mHealth and eHealth improve management of diabetes and hypertension in a hard-to-reach population? —lessons learned from a process evaluation of digital health to support a peer educator model in Cambodia using the RE-AIM framework. mHealth. 2020; 6: 40–40. DOI: https://doi.org/10.21037/mhealth-19-249 

Indraratna P, Biswas U, Liu H, Redmond SJ, Yu J, Lovell NH, et al. Process Evaluation of a Randomised Controlled Trial for TeleClinical Care, a Smartphone-App Based Model of Care. Front Med. 2022; 8: 3121. DOI: https://doi.org/10.3389/fmed.2021.780882 

Ferreira JP, Kraus S, Mitchell S, Perel P, Piñeiro D, Chioncel O, et al. World Heart Federation Roadmap for Heart Failure. Glob Heart. 2019; 14: 197–214. DOI: https://doi.org/10.1016/j.gheart.2019.07.004 

Jeemon P, Séverin T, Amodeo C, Balabanova D, Campbell NRC, Gaita D, et al. World heart federation roadmap for hypertension – A 2021 update. Glob Heart. 2021; 16: 63. DOI: https://doi.org/10.5334/gh.1066 

Mitchell S, Malanda B, Damasceno A, Eckel RH, Gaita D, Kotseva K, et al. A Roadmap on the Prevention of Cardiovascular Disease Among People Living With Diabetes. Glob Heart. 2019; 14: 215–240. DOI: https://doi.org/10.1016/j.gheart.2019.07.009 

Palafox B, Mocumbi AO, Kumar RK, Ali SKM, Kennedy E, Haileamlak A, et al. The WHF Roadmap for Reducing CV Morbidity and Mortality Through Prevention and Control of RHD. Glob Heart. 2017; 12: 47–62. DOI: https://doi.org/10.1016/j.gheart.2016.12.001 

Grainger Gasser A, Welch C, Arora M, Greenland R, Bhatti L, Sanda L, et al. Reducing Cardiovascular Mortality Through Tobacco Control: A World Heart Federation Roadmap. Glob Heart. 2015; 10: 123–133. DOI: https://doi.org/10.1016/j.gheart.2015.04.007 

Murphy A, Palafox B, O’Donnell O, Stuckler D, Perel P, AlHabib KF, et al. Inequalities in the use of secondary prevention of cardiovascular disease by socioeconomic status: evidence from the PURE observational study. Lancet Glob Heal. 2018; 6: e292–e301. DOI: https://doi.org/10.1016/S2214-109X(18)30031-7 

Freedman B, Hindricks G, Banerjee A, Baranchuk A, Ching CK, Du X, et al. World Heart Federation Roadmap on Atrial Fibrillation – A 2020 Update. Glob Heart. 2021; 16: 41. DOI: https://doi.org/10.5334/gh.1023 

World Health Organization. WHO classification of digital health interventions (DHIs). Res Impact. 2018; 1: 12–18. 

Administration UF and D. Guidances with Digital Health Content [Internet]. US Food Drug Adm; 2020. (4 November 2021); Retrieved from https://www.fda.gov/medical-devices/digital-health-center-excellence/guidances-digital-health-content. 

Gerke S, Stern AD, Minssen T. Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries. NPJ Digit Med. 2020; 3: 1–6. DOI: https://doi.org/10.1038/s41746-020-0306-7 

Herrmann M, Boehme P, Mondritzki T, Ehlers JP, Kavadias S, Truebel H. Digital transformation and disruption of the health care sector: Internet-based observational study. J Med Internet Res. 2018; 20. DOI: https://doi.org/10.2196/jmir.9498 

Whittaker R, Mcrobbie H, Bullen C, Rodgers A, Gu Y. Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev; 2016. DOI: https://doi.org/10.1002/14651858.CD006611.pub4 

Shariful Islam SM, Farmer AJ, Bobrow K, Maddison R, Whittaker R, Pfaeffli Dale LA, et al. Mobile phone text-messaging interventions aimed to prevent cardiovascular diseases (Text2PreventCVD): Systematic review and individual patient data meta-analysis. Open Hear. 2019; 6. DOI: https://doi.org/10.1136/openhrt-2019-001017 

Kario K, Nomura A, Harada N, Okura A, Nakagawa K, Tanigawa T, et al. Efficacy of a digital therapeutics system in the management of essential hypertension: The HERB-DH1 pivotal trial. Eur Heart J. 2021; 42: 4111–4122. DOI: https://doi.org/10.1093/eurheartj/ehab559 

Santo K, Chow CK, Thiagalingam A, Rogers K, Chalmers J, Redfern J. MEDication reminder APPs to improve medication adherence in Coronary Heart Disease (MedApp-CHD) Study: A randomised controlled trial protocol. BMJ Open. 2017; 7. DOI: https://doi.org/10.1136/bmjopen-2017-017540 

Peiris D, Usherwood T, Panaretto K, Harris M, Hunt J, Redfern J, et al. Effect of a computer-guided, quality improvement program for cardiovascular disease risk management in primary health care: The treatment of cardiovascular risk using electronic decision support cluster-randomized trial. Circ Cardiovasc Qual Outcomes. 2015; 8: 87–95. DOI: https://doi.org/10.1161/CIRCOUTCOMES.114.001235 

Smith DM, Duque L, Huffman JC, Healy BC, Celano CM. Text Message Interventions for Physical Activity: A Systematic Review and Meta-Analysis. Am J Prev Med. 2020; 58: 142–151. DOI: https://doi.org/10.1016/j.amepre.2019.08.014 

Skinner R, Gonet V, Currie S, Hoddinott P, Dombrowski SU. A systematic review with meta-analyses of text message-delivered behaviour change interventions for weight loss and weight loss maintenance. Obes Rev. 2020; 21: e12999. DOI: https://doi.org/10.1111/obr.12999 

Adler AJ, Martin N, Mariani J, Tajer CD, Owolabi OO, Free C, et al. Mobile phone text messaging to improve medication adherence in secondary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2017; 2017. DOI: https://doi.org/10.1002/14651858.CD011851.pub2 

Thakkar J, Kurup R, Laba TL, Santo K, Thiagalingam A, Rodgers A, et al. Mobile telephone text messaging for medication adherence in chronic disease a meta-analysis. JAMA Intern Med. 2016; 176: 340–349. DOI: https://doi.org/10.1001/jamainternmed.2015.7667 

Chow CK, Redfern J, Hillis GS, Thakkar J, Santo K, Hackett ML, et al. Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: A randomized clinical trial. JAMA – J Am Med Assoc. 2015; 314: 1255–1263. DOI: https://doi.org/10.1001/jama.2015.10945 

Redfern J, Thiagalingam A, Jan S, Whittaker R, Hackett ML, Mooney J, et al. Development of a set of mobile phone text messages designed for prevention of recurrent cardiovascular events. Eur J Prev Cardiol. 2014; 21: 492–499. DOI: https://doi.org/10.1177/2047487312449416 

Coorey GM, Neubeck L, Mulley J, Redfern J. Effectiveness, acceptability and usefulness of mobile applications for cardiovascular disease self-management: Systematic review with meta-synthesis of quantitative and qualitative data. Eur J Prev Cardiol. 2018; 25: 505–521. DOI: https://doi.org/10.1177/2047487317750913 

Merriel SWD, Andrews V, Salisbury C. Telehealth interventions for primary prevention of cardiovascular disease: A systematic review and meta-analysis. Prev Med (Baltim). 2014; 64: 88–95. DOI: https://doi.org/10.1016/j.ypmed.2014.04.001 

Jin K, Khonsari S, Gallagher R, Gallagher P, Clark AM, Freedman B, et al. Telehealth interventions for the secondary prevention of coronary heart disease: A systematic review and meta-analysis. Eur J Cardiovasc Nurs. 2019; 18: 260–271. DOI: https://doi.org/10.1177/1474515119826510 

Rawstorn JC, Gant N, Direito A, Beckmann C, Maddison R. Telehealth exercise-based cardiac rehabilitation: A systematic review and meta-analysis. Heart. 2016; 102: 1183–1192. DOI: https://doi.org/10.1136/heartjnl-2015-308966 

Chokshi NP, Adusumalli S, Small DS, Morris A, Feingold J, Ha YP, et al. Loss-framed financial incentives and personalized goal-setting to increase physical activity among ischemic heart disease patients using wearable devices: The ACTIVE REWARD randomized trial. J Am Heart Assoc. 2018; 7. DOI: https://doi.org/10.1161/JAHA.118.009173 

Bayoumy K, Gaber M, Elshafeey A, Mhaimeed O, Dineen EH, Marvel FA, et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Rev Cardiol. 2021; 18: 581–599. DOI: https://doi.org/10.1038/s41569-021-00522-7 

Brickwood KJ, Watson G, O’brien J, Williams AD. Consumer-based wearable activity trackers increase physical activity participation: Systematic review and meta-analysis. JMIR mHealth uHealth. 2019; 7. DOI: https://doi.org/10.2196/11819 

Tromp J, Seekings PJ, Hung C-L, Iversen MB, Frost MJ, Ouwerkerk W, et al. Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study. Lancet Digit Heal. 2021; Accepted. DOI: https://doi.org/10.1016/S2589-7500(21)00235-1 

Ghazi L, Yamamoto Y, Riello RJ, Coronel-Moreno C, Martin M, O’Connor KD, et al. Electronic Alerts to Improve Heart Failure Therapy in Outpatient Practice: A Cluster Randomized Trial. J Am Coll Cardiol; 2022. DOI: https://doi.org/10.1016/j.jacc.2022.03.338 

Neubeck L, Lowres N, Benjamin EJ, Freedman SB, Coorey G, Redfern J. The mobile revolution-using smartphone apps to prevent cardiovascular disease. Nat Rev Cardiol. 2015; 12: 350–360. DOI: https://doi.org/10.1038/nrcardio.2015.34 

Steinhubl SR, Waalen J, Edwards AM, Ariniello LM, Mehta RR, Ebner GS, et al. Effect of a home-Based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation the mSToPS randomized clinical trial. JAMA – J Am Med Assoc. 2018; 320: 146–155. DOI: https://doi.org/10.1001/jama.2018.8102 

Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019; 381: 1909–1917. DOI: https://doi.org/10.1056/NEJMoa1901183 

Banerjee A. Big is not always beautiful: the Apple Heart Study – Catalog of Bias [Internet]. (1 July 2022); Retrieved from: https://catalogofbias.org/2019/11/14/big-is-not-always-beautiful-the-apple-heart-study/. 

Expert Training Tool [Internet]. [cited 2022 Apr 12]; Retrieved from: https://www.escardio.org/Education/Practice-Tools/CVD-prevention-toolbox/expert-tool. 

Ouyang D, He B, Ghorbani A, Yuan N, Ebinger J, Langlotz CP, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020; 580: 252–256. DOI: https://doi.org/10.1038/s41586-020-2145-8 

Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation in clinical practice: Feasibility and diagnostic accuracy. Circulation. 2018; 138: 1623–1635. DOI: https://doi.org/10.1161/CIRCULATIONAHA.118.034338 

World Health Organzation (WHO) guideline. Recommendations on digital interventions for health system strengthening. World Health Organization; 2019. 

World Health Organzation (WHO). National eHealth Strategy Toolkit Overview. World Heal Organ Int Telecommun Union. 2012; 9. 

Chowdhury SR, Sunna TC, Ahmed S. Telemedicine is an important aspect of healthcare services amid COVID-19 outbreak: Its barriers in Bangladesh and strategies to overcome. Int J Health Plann Manage. 2021; 36: 4–12. DOI: https://doi.org/10.1002/hpm.3064 

Duggal R, Brindle I, Bagenal J. Digital healthcare: Regulating the revolution. BMJ. 2018; 360. DOI: https://doi.org/10.1136/bmj.k6 

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 2019; 25: 44–56. DOI: https://doi.org/10.1038/s41591-018-0300-7 

World Health Organization. mHealth: new horizons for health through mobile technologies: second global survey on eHealth [Internet]; 2011. (3 May 2022); 3: 1–102. Retrieved from http://apps.who.int/iris/handle/10665/44607. 

Guo C, Ashrafian H, Ghafur S, Fontana G, Gardner C, Prime M. Challenges for the evaluation of digital health solutions—A call for innovative evidence generation approaches. NPJ Digit Med. 2020; 3: 1–14. DOI: https://doi.org/10.1038/s41746-020-00314-2 

Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies in medical imaging. BMJ. 2020; 368. DOI: https://doi.org/10.1136/bmj.m689 

Skivington K, Matthews L, Simpson SA, Craig P, Baird J, Blazeby JM, et al. A new framework for developing and evaluating complex interventions: Update of Medical Research Council guidance. BMJ. 2021; 374. DOI: https://doi.org/10.1136/bmj.n2061 

Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring fairness in machine learning to advance health equity. Ann Intern Med. 2018; 169: 866–872. DOI: https://doi.org/10.7326/M18-1990 

Agarwal S, Lefevre AE, Lee J, L’engle K, Mehl G, Sinha C, et al. Guidelines for reporting of health interventions using mobile phones: Mobile health (mHealth) Evidence reporting and assessment (mERA) checklist. BMJ. 2016; 352. DOI: https://doi.org/10.1136/bmj.i1174 

World Health Organization. Monitoring and evaluating digital health interventions. A practical guide to conducting research and assessment [Internet]. WHO. 2016 (12 April 2022); 1–144. Retrieved from https://www.who.int/reproductivehealth/publications/mhealth/digital-health-interventions/en/%0Ahttp://www.who.int/reproductivehealth/publications/mhealth/digital-health-interventions/en/. 

NICE. Evidence standards framework for digital health technologies [Internet]. 2019 (17 May 2022); Retrieved from https://www.nice.org.uk/about/what-we-do/our-programmes/evidence-standards-framework-for-digital-health-technologies. 

Prabhakaran D, Jha D, Prieto-Merino D, Roy A, Singh K, Ajay VS, et al. Effectiveness of an mHealth-Based Electronic Decision Support System for Integrated Management of Chronic Conditions in Primary Care: The mWellcare Cluster-Randomized Controlled Trial. Circulation. 2019; 139: 380–391. DOI: https://doi.org/10.1161/CIRCULATIONAHA.118.038192 

Glasgow RE, Harden SM, Gaglio B, Rabin B, Smith ML, Porter GC, et al. RE-AIM planning and evaluation framework: Adapting to new science and practice with a 20-year review. Front Public Heal. 2019; 7: 64. DOI: https://doi.org/10.3389/fpubh.2019.00064 

Carolan JE, McGonigle J, Dennis A, Lorgelly P, Banerjee A. Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device. JMIR Med Informatics. 2022; 10: e34038. DOI: https://doi.org/10.2196/34038 

US Food and Drug Administration. Artificial Intelligence and Machine Learning in Software as a Medical Device [Internet]. 2019 [cited 2022 Jul 4]; 1–20. Retrieved from: https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm514737.pdf. 

Li Y. Empirical studies on online information privacy concerns: Literature review and an integrative framework. Commun Assoc Inf Syst. 2011; 28: 453–496. DOI: https://doi.org/10.17705/1CAIS.02828 

Fischer SH, David D, Crotty BH, Dierks M, Safran C. Acceptance and use of health information technology by community-dwelling elders. Int J Med Inform. 2014; 83: 624–635. DOI: https://doi.org/10.1016/j.ijmedinf.2014.06.005 

Fox G, Connolly R. Mobile health technology adoption across generations: Narrowing the digital divide. Inf Syst J. 2018; 28: 995–1019. DOI: https://doi.org/10.1111/isj.12179 

Dhagarra D, Goswami M, Kumar G. Impact of Trust and Privacy Concerns on Technology Acceptance in Healthcare: An Indian Perspective. Int J Med Inform. 2020; 141: 104164. DOI: https://doi.org/10.1016/j.ijmedinf.2020.104164 

Ramdani B, Duan B, Berrou I. Exploring the determinants of mobile health adoption by hospitals in China: Empirical study. JMIR Med Informatics. 2020; 8. DOI: https://doi.org/10.2196/14795 

van Olmen J, Erwin E, García-Ulloa AC, Meessen B, Miranda JJ, Bobrow K, et al. Implementation barriers for mHealth for non-communicable diseases management in low and middle income countries: a scoping review and field-based views from implementers. Wellcome Open Res. 2020; 5. DOI: https://doi.org/10.12688/wellcomeopenres.15581.2 

Leigh S, Ashall-Payne L, Andrews T. Barriers and Facilitators to the Adoption of Mobile Health among Health Care Professionals from the United Kingdom: Discrete Choice Experiment. JMIR mHealth uHealth. 2020; 8: e17704. DOI: https://doi.org/10.2196/17704 

Tiffin N, George A, Lefevre AE. How to use relevant data for maximal benefit with minimal risk: Digital health data governance to protect vulnerable populations in low-income and middle-income countries. BMJ Glob Heal. 2019; 4: e001395. DOI: https://doi.org/10.1136/bmjgh-2019-001395 

GPDR.eu. General Data Protection Regulation (GDPR) Compliance Guidelines [Internet]. Gpdr.Eu.; 2020. (13 April 2022); Retrieved from https://gdpr.eu/. 

Vazirani AA, O’Donoghue O, Brindley D, Meinert E. Blockchain vehicles for efficient Medical Record management. npj Digit Med. 2020; 3: 1–5. DOI: https://doi.org/10.1038/s41746-019-0211-0 

Gagnon MP, Desmartis M, Labrecque M, Car J, Pagliari C, Pluye P, et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst. 2012; 36: 241–277. DOI: https://doi.org/10.1007/s10916-010-9473-4 

Palacholla RS, Fischer N, Coleman A, Agboola S, Kirley K, Felsted J, et al. Provider- and Patient-Related Barriers to and Facilitators of Digital Health Technology Adoption for Hypertension Management: Scoping Review. JMIR Cardio. 2019; 3. DOI: https://doi.org/10.2196/11951 

Broadband Commission Working Group on Digital Health. The Promise of Digital Health: Addressing Non-communicable Diseases to Accelerate Universal Health Coverage in LMICs [Internet]; 2018. (3 May 2022); 1–150. Retrieved from https://broadbandcommission.org/Documents/publications/DigitalHealthReport2018.pdf. 

Wilson K, Gertz B, Arenth B, Salisbury N. The Journey to Scale: Moving Together Past Digital Health Pilots; 2014. 

Kiberu VM, Mars M, Scott RE. Barriers and opportunities to implementation of sustainable e-Health programmes in Uganda: A literature review. African J Prim Heal Care Fam Med. 2017; 9. DOI: https://doi.org/10.4102/phcfm.v9i1.1277 

Labrique AB, Wadhwani C, Williams KA, Lamptey P, Hesp C, Luk R, et al. Best practices in scaling digital health in low and middle income countries. Global Health. 2018; 14: 1–8. DOI: https://doi.org/10.1186/s12992-018-0424-z 

Blandford A, Wesson J, Amalberti R, AlHazme R, Allwihan R. Opportunities and challenges for telehealth within, and beyond, a pandemic. Lancet Glob Heal. 2020; 8: e1364–e1365. DOI: https://doi.org/10.1016/S2214-109X(20)30362-4 

Chen M, Said NM, Rais NCM, Ho F, Ling N, Chun M, et al. Remaining agile in the COVID-19 pandemic healthcare landscape – How we adopted a hybrid telemedicine geriatric oncology care model in an academic tertiary cancer center. J Geriatr Oncol; 2022. DOI: https://doi.org/10.1016/j.jgo.2022.04.006 

Taylor A, Caffery LJ, Gesesew HA, King A, Bassal AR, Ford K, et al. How Australian Health Care Services Adapted to Telehealth During the COVID-19 Pandemic: A Survey of Telehealth Professionals. Front Public Heal. 2021; 9: 121. DOI: https://doi.org/10.3389/fpubh.2021.648009 

Desveaux L, Soobiah C, Bhatia RS, Shaw J. Identifying and overcoming policy-level barriers to the implementation of digital health innovation: Qualitative study. J Med Internet Res. 2019; 21: e14994. DOI: https://doi.org/10.2196/14994 

Zahabi M, Kaber DB, Swangnetr M. Usability and Safety in Electronic Medical Records Interface Design: A Review of Recent Literature and Guideline Formulation. Hum Factors. 2015; 57: 805–834. DOI: https://doi.org/10.1177/0018720815576827 

Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F. Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health. 2021; 21. DOI: https://doi.org/10.1186/s12889-021-11623-w 

Savigny D, Adam T. Systems thinking for health systems strengthening. Alliance for Health Policy and Systems Research & World Health Organization; 2009. 

Fort MP, Mundo W, Paniagua-Avila A, Cardona S, Figueroa JC, Hernández-Galdamez D, et al. Hypertension in Guatemala’s Public Primary Care System: A Needs Assessment Using the Health System Building Blocks Framework. BMC Health Serv Res. 2021; 21: 1–14. DOI: https://doi.org/10.1186/s12913-021-06889-0 

World Health Organization. Monitoring the Building Blocks of Health Systems: a Handbook of Indicators and Their Measurement Strategies [Internet]. World Heal Organization; 2010. (29 April 2022); 35: 1–92. Retrieved from http://www.annualreviews.org/doi/10.1146/annurev.ecolsys.35.021103.105711 

Saparamadu AADNS, Fernando P, Zeng P, Teo H, Goh A, Lee JMY, et al. User-centered design process of an mHealth app for health professionals: Case study. JMIR mHealth uHealth. 2021; 9. DOI: https://doi.org/10.2196/18079 

Mathews SC, McShea MJ, Hanley CL, Ravitz A, Labrique AB, Cohen AB. Digital health: a path to validation. NPJ Digit Med. 2019; 2: 1–9. DOI: https://doi.org/10.1038/s41746-019-0111-3 

W3C Web Accessibility Initiative (www.w3.org). Notes on User Centered Design Process – https://www.w3.org/WAI/redesign/ucd [Internet]. (3 May 2022); Retrieved from https://www.w3.org/WAI/redesign/ucd. 

9241-210 ISO. Ergonomics of human-system interaction — Part 210: Human-centred design for interactive systems [Internet]. Int. Stand.; 2019. (3 May 2022); 2: 1–33. Retrieved from https://www.iso.org/standard/77520.html. 

Hilbert M. The end justifies the definition: The manifold outlooks on the digital divide and their practical usefulness for policy-making. Telecomm Policy. 2011; 35: 715–736. DOI: https://doi.org/10.1016/j.telpol.2011.06.012 

ITU. Measuring digital development. Facts and figures 2020 [Internet]. ITU Publ.; 2020. (3 May 2022); 1–15. Retreived from https://www.itu.int/en/mediacentre/Documents/MediaRelations/ITU Facts and Figures 2019 – Embargoed 5 November 1200 CET.pdf 

Makri A. Bridging the digital divide in health care. Lancet Digit Heal. 2019; 1: e204–e205. DOI: https://doi.org/10.1016/S2589-7500(19)30111-6 

Okediran OO, Sijuade AA, Wahab WB, Oladimeji AI. A Framework for a Cloud-Based Electronic Health Records System for Developing Countries. 2nd Int Conf Electr Commun Comput Eng ICECCE 2020; 2020. DOI: https://doi.org/10.1109/ICECCE49384.2020.9179276 

World Health Organization (WHO). Atlas of eHealth country profiles. The use of eHealth in support of universal health coverage [Internet]. Geneva: WHO; 2016. (3 May 2022); 392. Retreived from www.who.int/%0Awww.who.int/%0Ahttp://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Atlas+of+eHealth+Country+Profiles#0. 

Patel SA, Vashist K, Jarhyan P, Sharma H, Gupta P, Jindal D, et al. A model for national assessment of barriers for implementing digital technology interventions to improve hypertension management in the public health care system in India. BMC Health Serv Res. 2021; 21: 1–11. DOI: https://doi.org/10.1186/s12913-021-06999-9 

ITU-WHO. Digital Health Platform Handbook: Building a Digital Information Infrastructure (Infostructure) for Health; 2017. 

Association AM. Telehealth Implementation Playbook of the American Medical Association [Internet]; 2020. (3 May 2022). Retrieved from https://www.ama-assn.org/practice-management/digital/telehealth-implementation-playbook-overview. 

Celi LA, Cellini J, Charpignon M-L, Dee EC, Dernoncourt F, Eber R, et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review. PLOS Digit Heal. 2022; 1: e0000022. DOI: https://doi.org/10.1371/journal.pdig.0000022 

Collaborative H data. Digital Health & Interoperability [Internet]. (3 May 2022); Retreived from https://www.healthdatacollaborative.org/working-groups/digital-health-interoperability/. 

ODK. ODK – Collect Data Anywhere [Internet]; 2022. (3 May 2022); Retrieved from https://getodk.org/. 

Openmrs. OpenMRS.org [Internet]. (3 May 2022); Retreived from https://openmrs.org/. 

Dimagi. CommCare [Internet]; 2021. (3 May 2022); Retrieved from https://www.dimagi.com/commcare/. 

Surka S, Edirippulige S, Steyn K, Gaziano T, Puoane T, Levitt N. Evaluating the use of mobile phone technology to enhance cardiovascular disease screening by community health workers. Int J Med Inform. 2014; 83: 648–654. DOI: https://doi.org/10.1016/j.ijmedinf.2014.06.008 

Flynn MR, Barrett C, Cosío FG, Gitt AK, Wallentin L, Kearney P, et al. The Cardiology Audit and Registration Data Standards (CARDS), European data standards for clinical cardiology practice. Eur Heart J. 2005; 26: 308–313. DOI: https://doi.org/10.1093/eurheartj/ehi079 

McKinsey. Patients love telehealth–physicians are not so sure [Internet]; 2022. (4 July 2022). Retrieved from: https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/patients-love-telehealth-physicians-are-not-so-sure. 

Asteggiano R, Cowie MR, Richter D, Christodorescu R, Guasti L, Ferrini M. Survey on e-health knowledge and usage in general cardiology of the Council of Cardiology Practice and the Digital Health Committee. Eur Hear J – Digit Heal. 2021; 2: 342–347. DOI: https://doi.org/10.1093/ehjdh/ztab032 

Ware P, Bartlett SJ, Paré G, Symeonidis I, Tannenbaum C, Bartlett G, et al. Using eHealth Technologies: Interests, Preferences, and Concerns of Older Adults. Interact J Med Res. 2017; 6: e3. DOI: https://doi.org/10.2196/ijmr.4447 

Torrent-Sellens J, Díaz-Chao Á, Soler-Ramos I, Saigí-Rubió F. Modelling and predicting eHealth usage in Europe: A multidimensional approach from an online survey of 13,000 European Union Internet Users. J Med Internet Res. 2016; 18. DOI: https://doi.org/10.2196/jmir.5605 

Kontos E, Blake KD, Chou WYS, Prestin A. Predictors of ehealth usage: Insights on the digital divide from the health information national trends survey 2012. J Med Internet Res. 2014; 16. DOI: https://doi.org/10.2196/jmir.3117 

Yao R, Zhang W, Evans R, Cao G, Rui T, Shen L. Inequities in Health Care Services Caused by the Adoption of Digital Health Technologies: Scoping Review. J Med Internet Res. 2022; 24: e34144. DOI: https://doi.org/10.2196/34144 

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