In the last decade, evidence-based medical practice has been supported on a large scale by computerized decision support tools, aiming to reduce diagnostic and therapeutic uncertainty, complementing the actions of the health professional. With technological developments, it is now possible to consider these systems as part of clinical intervention, both for the diagnosis and treatment of diseases. The literature has described the implementation of e-health tools, that is, technological innovations in the health area such as software, applications, serious games, among others, as a strategy to improve the process and adherence to treatment. However, there is still no standardized instrument in Brazil that can be used to guide the development, from the research phase, and the implementation of these tools as a health intervention, also impacting patient outcomes. With the objective of investigating a new therapeutic and preventive form, based on intervention with a computerized system, this work proposes the creation of guidelines for the registration and implementation of e-health tools as a clinical intervention. The proposal aims to be able to assist in the reporting standardization from the development stage to the application of the e-health tool helping in the treatment of diseases, registering all the experience lived in the research and applying it in different contexts of health.
References
[1]
Budd, J., Miller, B.S., Manning, E.M., et al. (2020) Digital Technologies in the Public-Health Response to COVID-19. Nature Medicine, 26, 1183-1192.
https://doi.org/10.1038/s41591-020-1011-4
[2]
Gostic, K., Gomez, A.C.R., Mummah, R.O., Kucharski, A.J. and Lloyd-Smith, J.O. (2020) Estimated Effectiveness of Symptom and Risk Screening to Prevent the Spread of COVID-19. eLife, 9, e55570. https://doi.org/10.7554/eLife.55570
[3]
Lester, R.T., et al. (2010) Effects of a Mobile Phone Short Message Service on Antiretroviral Treatment Adherence in Kenya (WelTel Kenya1): A Randomised Trial. The Lancet, 376, 1838-1845. https://doi.org/10.1016/S0140-6736(10)61997-6
[4]
Tetzlaff, R. and Bruening, A. (2012) Memristor Technology in Future Electronic System Design. Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, 12-16 March 2012, 592-592.
https://doi.org/10.1109/DATE.2012.6176541
[5]
Vodopivec-Jamsek, V., et al. (2012) Mobile Phone Messaging for Preventive Health Care. Cochrane Database of Systematic Reviews, 12, CD007457.
https://doi.org/10.1002/14651858.CD007457.pub2
[6]
Belisario, J.S.M., et al. (2013) Smartphone and Tablet Self-Management Apps for Asthma. The Cochrane Database of Systematic Reviews, 2013, CD010013.
https://doi.org/10.1002/14651858.CD010013.pub2
[7]
Liu, L.-M. (2013) A New Software Development Methodology for Clinical Trial Systems. Advances in Software Engineering, 2013, Article ID: 796505.
https://doi.org/10.1155/2013/796505
[8]
Sarno, F., Canella, D.S. and Bandoni, D.H. (2014) Mobile health e excesso de peso: Uma revisao sistemática. Revista Panamericana de Salud Pública, 35, 424-431.
https://www.scielosp.org/pdf/rpsp/v35n5-6/18.pdf
[9]
Oliveira, J.A., et al. (2017) Impact of Telephone Monitoring on Patients with Heart Failure: A Randomized Clinical Trial. Acta Paulista de Enfermagem, 30, 333-342.
https://doi.org/10.1590/1982-0194201700050
[10]
Yan, J., et al. (2017) Development and Effectiveness of a Mobile Phone Application Conducting Health Behavioral Intervention among Men Who Have Sex with Men, a Randomized Controlled Trial: Study Protocol. BMC Public Health, 17, Article No. 355. https://doi.org/10.1186/s12889-017-4235-6
[11]
Bakker, J.P., et al. (2019) A Systematic Review of Feasibility Studies Promoting the Use of Mobile Technologies in Clinical Research. NPJ Digital Medicine, 2, Article No. 47. https://doi.org/10.1038/s41746-019-0125-x
[12]
Dorst, M.T., Anders, S.H., Chennupati, S., Chen, Q. and Purcell, J.G. (2019) Health Information Technologies in the Support Systems of Pregnant Women and Their Caregivers: Mixed-Methods Study. Journal of Medical Internet Research, 21, e10865.
https://doi.org/10.2196/10865
[13]
Geoghegan, C., et al. (2020) Learning from Patient and Site Perspectives to Develop Better Digital Health Trials: Recommendations from the Clinical Trials Transformation Initiative. Contemporary Clinical Trials Communications, 19, Article ID: 100636.
https://doi.org/10.1016/j.conctc.2020.100636
[14]
Inan, O.T., Tenaerts, P., Prindiville, S.A., et al. (2020) Digitizing clinical trials. NPJ Digital Medicine, 3, Article No. 101. https://doi.org/10.1038/s41746-020-0302-y
[15]
Cheng, L., et al. (2021) The Effect of Digital Health Technologies on Managing Symptoms across Pediatric Cancer Continuum: A Systematic Review. International Journal of Nursing Sciences, 8, 22-29. https://doi.org/10.1016/j.ijnss.2020.10.002
[16]
Mantua, V., et al. (2021) Digital Health Technologies in Clinical Trials for Central Nervous System Drugs: An EU Regulatory Perspective. Nature Reviews Drug Discovery, 20, 83-84.
https://media.nature.com/original/magazine-assets/d41573-020-00168-z/d41573-020-00168-z.pdf
[17]
Radin, J.M., Quer, G., Ramos, E., et al. (2021) Assessment of Prolonged Physiological and Behavioral Changes Associated with COVID-19 Infection. JAMA Network Open, 4, e2115959. https://doi.org/10.1001/jamanetworkopen.2021.15959
[18]
Wang, P., Nair, M.S., Liu, L., et al. (2021) Antibody Resistance of SARS-CoV-2 Variants B.1.351 and B.1.1.7. Nature, 593, 130-135.
https://doi.org/10.1038/s41586-021-03398-2
[19]
Eysenbach, G. and Consort-Ehealth Group (2011) CONSORT-Ehealth: Improving and Standardizing Evaluation Reports of Web-Based and Mobile Health Interventions. Journal of Medical Internet Research, 13, e126.
https://doi.org/10.2196/jmir.1923
[20]
Liu, X., Cruz Rivera, S., Moher, D., et al. (2020) Reporting Guidelines for Clinical Trial Reports for Interventions Involving Artificial Intelligence: The CONSORT-AI Extension. Nature Medicine, 26, 1364-1374.
https://doi.org/10.1038/s41591-020-1034-x
[21]
Cruz Rivera, S., et al. (2020) Guidelines for Clinical Trial Protocols for Interventions Involving Artificial Intelligence: The SPIRIT-AI Extension. Nature Medicine, 26, 1351-1363. https://doi.org/10.1038/s41591-020-1037-7
[22]
BRAZIL, Ministry of Health. TELESUS (2020).
https://www.gov.br/ebserh/pt-br/hospitais-universitarios/regiao-sul/hu-furg/saude/covid-19/materiais/ministerio-da-saude/telesus.pdf/view
[23]
Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G. and PRISMA Group (2009) Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLOS Medicine, 6, e1000097. https://doi.org/10.1371/journal.pmed.1000097
[24]
MacPherson, H., Altman, D.G., Hammerschlag, R., et al. (2010) Revised Standards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA): Extending the CONSORT Statement. PLOS Medicine, 7, e1000261.
https://doi.org/10.1371/journal.pmed.1000261
[25]
Chan, A., Tetzlaff, J.M., GAtzsche, P.C., Altman, D.G., Mann, H., Berlin, J.A., et al. (2013) SPIRIT Explanation and Elaboration: Guidance for Protocols of Clinical Trials. BMJ, 346, e7586. https://doi.org/10.1136/bmj.e7586
[26]
Hoffmann, T.C., Glasziou, P.P., Boutron, I., Milne, R., Perera, R., Moher, D., et al. (2014) Better Reporting of Interventions: Template for Intervention Description and Replication (TIDieR) Checklist and Guide. BMJ, 348, g1687.
https://doi.org/10.1136/bmj.g1687
[27]
Moher, D., Cook, D.J., Susan Eastwood, E.L., Olkin, I., et al. (1999) Improving the Quality of Reports of Meta-Analyses of Randomised Controlled Trials: The QUOROM Statement Quality of Reporting of Meta-Analyses. The Lancet, 354, 1896-1900.
https://doi.org/10.1016/S0140-6736(99)04149-5
[28]
Kostoulas, P., et al. (2017) STARD-BLCM: Standards for the Reporting of Diagnostic Accuracy Studies That Use Bayesian Latent Class Models. Preventive Veterinary Medicine, 138, 37-47. https://doi.org/10.1016/j.prevetmed.2017.01.006
[29]
Rodrigues, L.M.L., et al. (2018) Towards a Standardized Protocol for Conducting Randomized Clinical Trial for Software. Procedia Computer Science, 138, 125-130.
https://doi.org/10.1016/j.procs.2018.10.018
[30]
Dalkey, N. and Helmer, O. (1963) An Experimental Application of the DELPHI Method to the Use of Experts. Management Science, 9, 351-515.
https://doi.org/10.1287/mnsc.9.3.458
[31]
Brouwers, M.C., Kho, M.E., Browman, G.P., et al. (2010) AGREE II: Advancing Guideline Development, Reporting and Evaluation in Health Care. CMAJ, 182, E839-E842.
https://doi.org/10.1503/cmaj.090449
[32]
Hyrkas, K., Appelqvist-Schmidlechner, K. and Oksa, L. (2003) Validating an Instrument for Clinical Supervision Using an Expert Panel. International Journal of Nursing Studies, 40, 619-625. https://doi.org/10.1016/S0020-7489(03)00036-1
[33]
Polit, D.F. and Beck, C.T. (2006) The Content Validity Index: Are You Sure You Know What’s Being Reported? Critique and Recommendations. Research in Nursing and Health, 29, 489-497. https://doi.org/10.1002/nur.20147
[34]
Lobato, M. (2005) O Saci/Monteiro Lobato. Brasiliense, Sao Paulo.
https://www.fortaleza.ce.gov.br/images/Cultura/Monteiro_Lobato_-_O_Saci.pdf
[35]
Hollis, S. and Campbell, F. (1999) What Is Meant by Intention to Treat Analysis? Survey of Published Randomised Controlled Trials. BMJ, 319, 670-674.
https://doi.org/10.1136/bmj.319.7211.670