E-HEALTH TECHNOLOGY ACCEPTANCE: A META-ANALYSIS: a meta-analysis

Diego Fettermann; Luiz  Philipi Calegari

doi:10.18225/ci.inf.v52i2.7089

Autores

Diego Fettermann UFSC
Luiz Philipi Calegari UFSC

DOI:

https://doi.org/10.18225/ci.inf.v52i2.7089

Palavras-chave:

healthcare 4.0, smart health, internet of health things, technology acceptance, Information Technology

Resumo

Despite the potential benefits of e-health systems in sharing health information, the relationship between technology providers and potential users is inherently complex. This study aims to elucidate the factors driving the acceptance of new technologies among users by synthesizing results on the adoption of e-health technologies using the constructs and relationships outlined in the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Additionally, the impact of moderating variables—including gender, age group, presence of illness, user types, technological application, and publication year—was examined through meta-regression. Significant effects were observed for “Performance Expectancy,” “Effort Expectancy,” and “Social Influence” on “Behavioral Intention,” as well as the influence of “Behavioral Intention” and “Facilitating Conditions” on “Usage Behavior.” Among the tested moderating variables, all except for “age group” demonstrated significant moderation effects in various relationships. This research provides detailed estimates of the factors influencing the acceptance of new health technologies and offers strategic directions for the development of e-health systems, considering user acceptance. It contributes to a deeper understanding of the complex interplay between e-health systems and their users, highlighting the importance of tailored approaches to enhance technology adoption.

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Referências

ADAPA, A.; NAH, F. F.; HALL, R. H.; SIAU, K.; SMITH, S. N. Factors Influencing the Adoption of Smart Wearable Devices. International Journal of Human–Computer Interaction, [s. l.], v. 34, n. 5, p. 399–409, May 2017. DOI https://doi.org/10.1080/10447318.2017.1357902.

AL-KHAFAJIY, M.; THAR BAKER; CHALMERS, C.; ASIM, M.; KOLIVAND, H.; FAHIM, M.; WARAICH, A. Remote health monitoring of elderly through wearable sensors. Multimedia Tools and Applications, [s. l.], v. 78, p. 24681–24706, Jan. 2019. DOI https://doi.org/10.1007/s11042-018-7134-7.

ALSSWEY, A.; AL-SAMARRAIE, H. Elderly users’ acceptance of mHealth user interface (UI) design-based culture: the moderator role of age. Journal on Multimodal User Interfaces, [s. l.], v. 14, n. 1, p. 49–59, Mar. 2020. DOI https://doi.org/10.1007/s12193-019-00307-w.

AN, J. Y. Theory development in health care informatics: Information and communication technology acceptance model (ICTAM) improves the explanatory and predictive power of technology acceptance models. Studies in Health Technology and Informatics, [s. l.], v. 122, p. 63–67, Jun. 2006. Disponível em: https://www.scopus.com/inward/record.uri?eid=2-s2.0-39049191130 andpartnerID=40 andmd5=660d46f738142e34585767358c50dfb5. Acesso em: 5 maio de 2020.

ATASEVEN, C.; NAIR, A. Assessment of supply chain integration and performance relationships: a meta-analytic investigation of the literature. International Journal of Production Economics, v. 185, p. 252–265, Mar. 2017. DOI https://doi.org/10.1016/j.ijpe.2017.01.007.

BABA, N. M.; BAHARUDIN, A. S.; ALOMARI, A. S. Determinants of users’ intention to use smartwatch. Journal of Theoretical and Applied Information Technology, v. 97, n. 18, p. 4738–4750, Set. 2019. Disponível em: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075540758 andpartnerID=40 andmd5=08eda88454b587327c8e8bec2afa2a2b. Acesso em: 11 maio 2020.

BANNA, S.; OTTESEN, A. Health solutions in developing countries: case of Kuwait. 2018, Bangkok. In: IEEE International Conference on Innovative Research and Development, ICIRD 2018, Bangkok. Conference […]. Bangkok: Institute of Electrical and Electronics Engineers Inc., Jun. 2018. p. 1–6. DOI https://doi.org/10.1109/ICIRD.2018.8376316.

BEH, P. K.; GANESAN, Y.; IRANMANESH, M.; FOROUGHI, B. Using smartwatches for fitness and health monitoring: the UTAUT2 combined with threat appraisal as moderators. Behaviour and Information Technology, [s. l.], v. 40, n. 3, p. 282-299, Nov. 2021. DOI https://doi.org/10.1080/0144929X.2019.1685597.

BEN HASSEN, H.; DGHAIS, W.; HAMDI, B. An E-health system for monitoring elderly health based on Internet of Things and Fog computing. Health information science and systems, v. 7, n. 24, p. 1-9, Out. 2019.

BHATTACHERJEE, A.; HIKMET, N. Reconceptualizing organizational support and its effect on information technology usage: evidence from the health care sector. Journal of Computer Information Systems, [s. l.], v. 48, n. 4, p. 69–76, Jun. 2008. DOI 10.1080/08874417.2008.11646036.

BORENSTEIN, M.; HEDGES, L. V.; HIGGINS, J. P.; ROTHSTEIN, H. R. Introduction to Meta-Analysis. Reino Unido: Weley, 2011. 421 p. ISBN: 978-0-470-05724-7.

BREWSTER, L.; MOUNTAIN, G.; WESSELS, B.; KELLY, C.; HAWLEY, M. Factors affecting front line staff acceptance of telehealth technologies: a mixed-method systematic review. Journal of Advanced Nursing, [s. l.], v. 70, n. 1, p. 21–33, Jan. 2014. DOI https://doi.org/10.1111/jan.12196.

BUDRIONIS, A.; BELLIKA, J. G. The Learning Healthcare System: where are we now? A systematic review. Journal of Biomedical Informatics, [s. l.], v. 64, p. 87–92, Dec. 2016. DOI https://doi.org/10.1016/j.jbi.2016.09.018.

CALEGARI, L. P.; FETTERMANN, D. C. A review of e-health technologies applications. International Journal of Bioinformatics Research and Applications, [s. l.], v. 18, n. 4, p. 318-357, Oct. 2022.

CALEGARI, L. P.; BARBOSA, J.; MARODIN, G. A.; FETTERMANN, D. C. A conjoint analysis to consumer choice in Brazil: defining device attributes for recognizing customized foods characteristics. Food research international, [s. l.], v. 109, p. 1-13, July 2018.

CANHOTO, A. I.; ARP, S. Exploring the factors that support adoption and sustained use of health and fitness wearables. Journal of Marketing Management, [s. l.], v. 33, n. 1–2, p. 32–60, Oct. 2016. DOI https://doi.org/10.1080/0267257X.2016.1234505.

CARD, N. A. Applied Meta-Analysis for Social Science Research. New York: The Guilford Press, 2012. ISBN 978-1-60918-499-5.

CARACCIOLO, A. L. Mobile screening units for the early detection of breast cancer and cardiovascular disease: a pilot telemedicine study in southern italy. Telemedicine and e-Health, [s. l.], v. 26, n. 3, p. 286–293, Mar. 2020. DOI https://doi.org/10.1089/tmj.2018.0328.

CAVALCANTE, R. B.; PINHEIRO, M. M. K.; WATANABE, Y. J. Á.; SILVA, C. J. D. Grupo técnico de informação em saúde e populações: contribuições para a política nacional de informação e informática em saúde. Perspectivas em Ciência da Informação, Belo Horizonte, v. 20, n. 1, p. 92-119, jan./mar. 2015. DOI https://doi.org/10.1590/1981-5344/1905.

CHANG, Y. T.; CHAO, C. M.; YU, C. W.; LIN, F. C. Extending the Utility of UTAUT2 for Hospital Patients’ Adoption of Medical Apps: Moderating Effects of e-Health Literacy. Mobile Information Systems, [s. l.], v. 2021, p. 1-10, 2021.

CHAU, K. Y.; LAM, M. H. S.; CHEUNG, M. L.; TSO, E. K. H.; FLINT, S. W.; BROOM, D. R.; TSE, G.; LEE, K.Y. Smart technology for healthcare: exploring the antecedents of adoption intention of healthcare wearable technology. Health Psychology Research, [s. l.], v. 7, n. 1, p. 80–99, Mar. 2019. DOI https://doi.org/10.4081/hpr.2019.8099.

CHAUHAN, S.; JAISWAL, M. A meta-analysis of e-health applications acceptance: moderating impact of user types and e-health application types. Journal of Enterprise Information Management, [s. l.], v. 30, n. 2, p. 295–319, 2017. Disponível em: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014037761 anddoi=10.1108%2FJEIM-08-2015-0078 andpartnerID=40 andmd5=b5c6248f4bd66e4ea1975cf644ccb4c5. Acesso em: 5 maio 2020.

CHEN, K.; CHAN, A. H. S. Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics, [s. l.], v. 57, n. 5, p. 635–652, Mar. 2014. DOI https://doi.org/10.1080/00140139.2014.895855.

CIMPERMAN, M.; MAKOVEC BRENČIČ, M.; TRKMAN, P. Analyzing older users’ home telehealth services acceptance behavior-applying an Extended UTAUT model. International Journal of Medical Informatics, [s. l.], v. 90, p. 22–31, Jun. 2016. DOI https://doi.org/10.1016/j.ijmedinf.2016.03.002.

DA COSTA, C. A.; PASLUOSTA, C. F.; ESKOFIER, B.; SILVA, D. B.; ROSA RIGHI, R. Internet of Health Things: toward intelligent vital signs monitoring in hospital wards. Artificial Intelligence in Medicine, [s. l.], v. 89, p. 61–69, Jul. 2018. DOI https://doi.org/10.1016/j.artmed.2018.05.005.

DAI, B; LARNYO, E.; TETTEH, E. A.; ABOAGYE, A. K.; MUSAH, A. A.I. Factors affecting caregivers’ acceptance of the use of wearable devices by patients with dementia: an extension of the unified theory of acceptance and use of technology model. American Journal of Alzheimer’s Disease and other Dementias, v. 2019, n. 35, p. 1-11, 2019. Disponível em: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074693399 anddoi=10.1177%2F1533317519883493 andpartnerID=40 andmd5=c5c2056b537b5139942da30fd45d4576. Acesso em: 9 maio 2020.

DAVIS, F. D.; BAGOZZI, R.; WARSHAW, P. User acceptance of computer technology: a comparison of two theoretical models. Management science, v. 5, n. 8, p. 982–1003, Aug. 1989.

DEBAUCHE, O.; MAHMOUDI, S.; MANNEBACK, P.; ASSILA, A. Fog iot for health: a new architecture for patients and elderly monitoring. In: The 9th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, 9., 2019, Coimbra. Conference […]. Coimbra: Elsevier, 2019. p. 289–297. DOI https://doi.org/10.1016/j.procs.2019.11.087.

DROSATOS, G.; KALDOUDI, E. Blockchain applications in the biomedical domain: a scoping review. Computational and Structural Biotechnology Journal, v. 2019, n. 17, p. 229–240, Jan. 2019. DOI https://doi.org/10.1016/j.csbj.2019.01.010.

DUTTA, B.; PENG, M. H.; SUN, S. L. Modeling the adoption of personal health record (PHR) among individual: the effect of health-care technology self-efficacy and gender concern. Libyan Journal of Medicine, [s. l.], v. 13, n. 1, Jan. 2018. DOI https://doi.org/10.1080/19932820.2018.1500349.

ECHEVESTE, M. E. S.; ROZENFELD, H.; FETTERMANN, D. C. Customizing practices based on the frequency of problems in new product development process. Concurrent Engineering, [s. l.], v. 25, n. 3, p. 245-261, 2017. DOI https://doi.org/10.1177/1063293X166861.

ENAIZAN, O.; ZAIDAN, A. A.; ALWI, N. H. M.; ZAIDAN, B. B.; ALSALEM, M A; ALBAHRI, O. S.; ALBAHRI, A S. Electronic medical record systems: decision support examination framework for individual, security and privacy concerns using multi-perspective analysis. Health and Technology, [s. l.], v. 10, n. 3, p. 795–822, May 2020. Disponível em: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081328173 anddoi=10.1007%2Fs12553-018-0278-7 andpartnerID=40 andmd5=be0791658e15edec3e5aeff62287797d. Acesso em: 5 maio 2020.

EVERETT, R. Diffusion of innovations. 3. ed. New York: the free press, 1995. 453 p.

FETTERMANN, D. C.; BORRIELLO, A.; PELLEGRINI, A.; CAVALCANTE, C. G.; ROSE, J. M.; BURKE, P. F. Getting smarter about household energy: the who and what of demand for smart meters. Building Research and Information, [s. l.], v. 49, n. 1, p. 100-112, Aug. 2020. DOI https://doi.org/10.1080/09613218.2020.1807896.

FETTERMANN, D. C.; CAVALCANTE, C. G. S.; AYALA, N. F.; AVALONE, M. C. Configuration of a smart meter for Brazilian customers. Energy Policy, [s. l.], v. 2020, n. 139, p. 111309, Apr. 2020. DOI https://doi.org/10.1016/j.enpol.2020.111309.

GUIMARÃES, E. M. P.; ÉVORA, Y. D. M. Sistema de informação: instrumento para tomada de decisão no exercício da gerência. Ciência da Informação, Brasília, v. 33, p. 72-80, jan./abr. 2004.

GUO, X.; ZHANG, X.; SUN, Y. The privacy-personalization paradox in mHealth services acceptance of different age groups. Electronic Commerce Research and Applications, [s. l.], v. 16, p. 55–65, Mar. 2016. DOI https://doi.org/10.1016/j.elerap.2015.11.001.

HENNEMANN, S.; BEUTEL, M. E.; ZWERENZ, R. Drivers and barriers to acceptance of web-based aftercare of patients in inpatient routine care: a cross-sectional survey. Journal of Medical Internet Research, [s. l.], v. 18, n. 12, p. 337, Dec. 2016. DOI https://doi.org/10.2196/jmir.6003.

HOQUE, M. R.; BAO, Y.; SORWAR, G. Investigating factors influencing the adoption of e-Health in developing countries: a patient’s perspective. Informatics for Health and Social Care, [s. l.], v. 42, n. 1, p. 1–17, Feb. 2016. DOI https://doi.org/10.3109/17538157.2015.1075541.

HUNTER, J. E.; SCHMIDT, F. L. Methods of meta-analysis: correcting error and bias in research findings. 3. ed. New York: SAGE Publications, 2014. 672 p.

IFINEDO, P. Applying uses and gratifications theory and social influence processes to understand students’ pervasive adoption of social networking sites: perspectives from the Americas. International Journal of Information Management, [s. l.], v. 36, n. 2, p. 192–206, Apr. 2016. DOI https://doi.org/10.1016/j.ijinfomgt.2015.11.007.

JANG, W. J.; JANG, W. A study on current status and prospects of global food-tech industry. Journal of the Korea Convergence Society, [s. l.], v. 11, n. 4, p. 247–254, 2020. DOI: https://doi.org/10.15207/JKCS.2020.11.4.247.

JAYASEELAN, R.; KOOTHOOR, P.; PICHANDY, C. Index terms ICT, E-Health, UTAUT, Health Communication, Health Management, Medical Doctors. Medical Doctors Article in International Journal of Scientific and Technology Research, [s. l.], v. 9, n. 1, 2020. Disponível em: www.ijstr.org. Acesso em: 15 Jun. 2021.

KAMAL, S. A.; SHAFIQ, M.; KAKRIA, P. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, [s. l.], v. 2020, n. 60, p. 101212, Nov. 2019. DOI https://doi.org/10.1016/j.techsoc.2019.101212.

KAO, Y.-S.; NAWATA, K.; HUANG, C.-Y. An exploration and confirmation of the factors influencing adoption of IoT-basedwearable fitness trackers. International Journal of Environmental Research and Public Health, [s. l.], v. 16, n. 18, Sept. 2019. DOI 10.3390/ijerph16183227.

KARPOVA, E. V.; KARYAKINA, E. E.; KARYAKIN, A. A. Wearable non-invasive monitors of diabetes and hypoxia through continuous analysis of sweat. Talanta, [s. l.], v. 215, p. 120922, Aug. 2020. DOI: https://doi.org/10.1016/j.talanta.2020.120922.

KEMP, A.; PALMER, E.; STRELAN, P. A taxonomy of factors affecting attitudes towards educational technologies for use with technology acceptance models. British Journal of Educational Technology, [s. l.], v. 50, n. 5, p. 2394–2413, Sept. 2019. DOI: https://doi.org/10.1111/bjet.12833.

KHALIFA, M.; LIU, V. The state of research on information system satisfaction. journal of information technology theory and Application, v. 5, n. 4, p. 37-49, 2004.

KHAN, I.; XITONG, G.; AHMAD, Z.; SHAHZAD, F. Investigating factors impelling the adoption of e-health: a perspective of african expats in China. SAGE Open, [s. l.], v. 9, n. 3, p. 1–12, Jul. 2019. DOI: https://doi.org/10.1177/2158244019865803.

KIJSANAYOTIN, B.; PANNARUNOTHAI, S.; SPEEDIE, S. M. Factors influencing health information technology adoption in Thailand’s community health centers: applying the UTAUT model. International Journal of Medical Informatics, [s. l.], v. 78, n. 6, p. 404–416, June 2009. DOI: https://doi.org/10.1016/j.ijmedinf.2008.12.005.

KOCH, S. Home telehealth: current state and future trends. International Journal of Medical Informatics, [s. l.], v. 75, n. 8, p. 565–576, Aug. 2006. DOI: https://doi.org/10.1016/j.ijmedinf.2005.09.002.

KONONOVA, O., PROKUDIN, D., TIMOFEEVA, A., MATROSOVA, E. In: ZARAMENSKIKH, E., FEDOROVA, A. Digital Transformation and New Challenges. Lecture Notes in Information Systems and Organisation. [S. l.]: Springer, 2021. v. 45. p. 265-286.

LACERDA, F.; LIMA-MARQUES, M. Da necessidade de princípios de arquitetura da informação para a internet das coisas. Perspectivas em Ciência da Informação, Belo Horizonte, v. 20, n. 2, p. 158–171, abr./jun. 2015.

LAPÃO, L. V. Artificial intelligence: is it a friend or foe of physicians? Einstein, São Paulo, v. 17, n. 2, p. 1-2, 2019. DOI: https://doi.org/10.31744/einstein_journal/2019ED4982.

LI, J.; MA, Q; CHAN, A H; MAN, S S. Health monitoring through wearable technologies for older adults: smart wearables acceptance model. Applied Ergonomics, [s. l.], v. 75, p. 162–169, 2019. Disponível em: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055573600 anddoi=10.1016%2Fj.apergo.2018.10.006 andpartnerID=40 andmd5=1044438afc955b49b48e2996788918bb. Acesso em: 6 maio 2020.

LIM, S.; XUE, L.; YEN, C. C.; CHANG, L.; CHAN, H. C.; TAI, B. C.; DUH, H. B. L.; CHOOLANI, M. A study on Singaporean women’s acceptance of using mobile phones to seek health information. International Journal of Medical Informatics, [s. l.], v. 80, n. 12, p. e189–e202, Dec. 2011. DOI https://doi.org/10.1016/j.ijmedinf.2011.08.007.

LIN, S. P.; HSIEH, C. Y.; HO, T. M. Innovative Healthcare Cloud Service Model, Applied Mechanics and Materials, [s. l.], v. 543, p. 4511–4513, Mar. 2014. DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.4511.

LIU, I.; NI, S.; PENG, Kaiping. Happiness at your fingertips: assessing mental health with smartphone photoplethysmogram-based heart rate variability analysis. Telemedicine and e-Health, [s. l.], v. 26, n. 12, p. 1–9, Feb. 2020. DOI https://doi.org/10.1089/tmj.2019.0283.

LOPES, I. L. Novos paradigmas para avaliação da qualidade da informação em saúde recuperada na Web. Ciência da Informação, Brasília, v. 33, p. 81-90, jan./abril. 2004.

MACDONALD, E. M.; PERRIN, B. M.; HYETT, N.; KINGSLEY, M. I.C. Factors influencing behavioural intention to use a smart shoe insole in regionally based adults with diabetes: a mixed methods study. Journal of Foot and Ankle Research, [s. l.], v. 12, n. 1, p. 1–9, May. 2019. DOI: https://doi.org/10.1186/s13047-019-0340-3.

MAGALHÃES, J. L.; Hartz, Z.; Menezes, M. S.; Quoniam, L. Big Data e a saúde negligenciada em dengue, zika e chicungunha: uma análise translacional da tríplice ameaça no século 21. Ciência da Informação, Brasília, v. 45, n. 3, p. 234 – 250, set./dez. 2016.

MARINO, M. M.; RIENZO, M.; SERRA, N.; MARINO, N.; RICCIOTTI, R.; MAZZARIELLO, L.; LEONETTI, C. A.; CERALDI, M. P.; CASAMASSIMI, A.; CAPOCELLI, F.; MARTONE, G.; MARITSCH, M.; FÖLL, S.; LEHMANN, V.; BÉRUBÉ, C.; KRAUS, M.; FEUERRIEGEL, S.; KOWATSCH, T.; ZÜGER, T.; STETTLER, C.; FLEISCH, E.; WORTMANN, F. Towards wearable-based hypoglycemia detection and warning in diabetes. In: CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 20., 2020. New York. Anais […]. New York: Association for Computing Machinery (ACM), 2020. p. 1–8. DOI: https://doi.org/10.1145/3334480.3382808.

MARTINS, A. Q.; PERES, A. M.; DYNIEWICZ, A. M.; TONIOLO, R. M.; GONÇALVES, L. S.; NETO, P. P. Integração da informação na Rede de Urgência e Emergência: percepção dos profissionais sobre o E-Saúde. Ciência da Informação, Brasília, v. 49, n. 1, p. 92-105, jan./abr. 2020. DOI: 10.18225/ci.inf.v49i1.4804.

MARTINS, T. G. S.; COSTA, A. L. F. A.; MARTINS, T. G. S. Big Data use in medical research. Einstein, São Paulo, v. 16, n. 3, p. 1–2, Sept. 2018. DOI: https://doi.org/10.1590/S1679-45082018ED4087.

MATASSA, A.; RIBONI, D. Reasoning with smart objects’ affordance for personalized behavior monitoring in pervasive information systems. Knowledge and Information Systems, [s. l.], v. 62, n. 4, p. 1255-1278, Mar. 2020.

MATHIESON, K. Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, [s. l.], v. 2, n. 3, p. 173–191, Sept. 1991. DOI https://doi.org/10.1287/isre.2.3.173.

MENACHEMI, N.; COLLUM, T. H. Benefits and drawbacks of electronic health record systems. Risk Management and Healthcare Policy, [s. l.], v. 2011, n. 4, p. 47–55, 2011. DOI: https://doi.org/10.2147/RMHP.S12985.

MONTAGNI, I.; TZOURIO, C.; COUSIN, T.; SAGARA, J. A.; BADA-ALONZI, J.; HORGAN, A. Mental health-related digital use by university students: a systematic review. Telemedicine and e-Health, [s. l.], v. 26, n. 2, p. 131–146, Feb. 2020. DOI https://doi.org/10.1089/tmj.2018.0316.

MORESI, E. A. D.; LOPES, M. C.; MORAIS, M. A. A. T. O cidadão como sensor inteligente. Ciência da Informação, Brasília, v. 45, n. 3, 2018. DOI 10.18225/ci.inf.v45i3.4047.

NADLIFATIN, Reny; MIRAJA, Bobby Ardiansyah; PERSADA, Satria Fadil; BELGIAWAN, Prawira Fajarindra; REDI, A.A.N Perwira; LIN, Shu-Chiang. The measurement of university students’ intention to use blended learning system through technology acceptance model (tam) and theory of planned behavior (tpb) at developed and developing regions: lessons learned from taiwan and indonesia. International Journal of Emerging Technologies in Learning (iJET), [s. l.], v. 15, n. 9, p. 219–230, 2020. DOI 10.3991/ijet.v15i09.11517.

NAIR, A. Meta-analysis of the relationship between quality management practices and firm performance-implications for quality management theory development. Journal of Operations Management, [s. l.], v. 24, n. 6, p. 948–975, Dec. 2006. DOI: https://doi.org/10.1016/j.jom.2005.11.005.

NASCIMENTO, D. R.; TORTORELLA, G. L.; FETTERMANN, D. Association between the benefits and barriers perceived by the users in smart home services implementation. Kybernetes, [s. l.], v. 52, n. 12, p. 6179-6202, 2022. DOI https://doi.org/10.1108/K-02-2022-0232.

NAYAK, L.; LEE, P.; WHITE, A. P. An application of the technology acceptance model to the level of Internet usage by older adults. Universal Access in the Information Society, [s. l.], v. 9, n. 4, p. 367–374, Nov. 2010.

NILSSON, L.; HELLSTRÖM, A.; WENNERBERG, C.; EKSTEDT, M.; EKSTEDT, M.; EKSTEDT, M.; SCHILDMEIJER, K. Patients’ experiences of using an e-Health tool for self-management support after prostate cancer surgery: a deductive interview study explained through the FITT framework. BMJ Open, [s. l.], v. 10, n. 6, p. e035024, June 2020. DOI https://doi.org/10.1136/bmjopen-2019-035024.

ONO, H.; ZAVODNY, M. Gender and the internet. Social Science Quarterly, [s. l.], v. 84, n. 1, p. 111–121, Mar. 2003. DOI: https://doi.org/10.1111/1540-6237.t01-1-8401007.

OR, C. K. L.; KARSH, B. T.; SEVERTSON, D. J.; BURKE, L. J.; BROWN, R. L.; BRENNAN, P. F. Factors affecting home care patients’ acceptance of a web-based interactive self-management technology. Journal of the American Medical Informatics Association, [s. l.], v. 18, n. 1, p. 51–59, Jan. 2011. DOI: https://doi.org/10.1136/jamia.2010.007336.

PAL, D.; FUNILKUL, S.; CHAROENKITKARN, N.; KANTHAMANON, P. Internet-of-Things and smart homes for elderly healthcare: an end user perspective. IEEE Access, [s. l.], v. 6, p. 10483–10496, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2808472.

PAL, D.; ARPNIKANONDT, C.; FUNILKUL, S.; CHUTIMASKUL, W. The adoption analysis of voice based smart IoT products. IEEE Internet of Things Journal, [s. l.], v. 7 n. 1, p.10852 –10867, Nov. 2020. DOI https://doi.org/10.1109/jiot.2020.2991791.

PIOTROWICZ, E. The management of patients with chronic heart failure: the growing role of e-Health. Expert Review of Medical Devices, [s. l.], v. 14, n. 4, p. 271–277, Apr. 2017. DOI: https://doi.org/10.1080/17434440.2017.1314181.

PITTALIS, M. Extending the technology acceptance model to evaluate teachers’ intention to use dynamic geometry software in geometry teaching. International Journal of Mathematical Education in Science and Technology, [s. l.], v. 52, n. 9, p. 1–20, May 2021. DOI: https://doi.org/10.1080/0020739X.2020.1766139.

PIWEK, L.; ELLIS, D. A.; ANDREWS, S.; JOINSON, A. The rise of consumer health wearables: promises and barriers. PLOS Medicine, San Francisco, v. 13, n. 2, Feb. 2016. DOI https://doi.org/10.1371/journal.pmed.1001953.

RAZMAK, J.; BÉLANGER, C. H.; FARHAN, W. Development of a techno-humanist model for e-health adoption of innovative technology. International Journal of Medical Informatics, [s. l.], v. 120, p. 62–76, Dec. 2018. DOI: https://doi.org/10.1016/j.ijmedinf.2018.09.022.

REEDER, B.; DAVID, A. Health at hand: A systematic review of smart watch uses for health and wellness. Journal of Biomedical Informatics, [s. l.], v. 63, p. 269–276, Oct. 2016. DOI: https://doi.org/10.1016/j.jbi.2016.09.001.

SADOUGHI, F.; BEHMANESH, A.; SAYFOURI, N. Internet of things in medicine: a systematic mapping study. Journal of Biomedical Informatics, [s. l.], v.103, p. 1- 20, Mar. 2020. DOI https://doi.org/10.1016/j.jbi.2020.103383.

SAFI, S.; DANZER, G.; SCHMAILZL, K. J. G. Empirical research on acceptance of digital technologies in medicine among patients and healthy users: questionnaire study. Journal of Medical Internet Research, [s. l.], v. 21, n. 11, Oct. 2019. Disponível em: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076128742 anddoi=10.2196%2F13472 andpartnerID=40 andmd5=7af447c1ed2be1efbb3ac5f2dbc04e4f. Acesso em: 11 maio 2020.

SALGADO, T.; TAVARES, J.; OLIVEIRA, T. Drivers of mobile health acceptance and use from the patient perspective: survey study and quantitative model development. JMIR mHealth and uHealth, [s. l.], v. 8, n. 7, Jul. 2020. DOI: https://doi.org/10.2196/17588.

SCHMIDT, F. L. History and development of the Schmidt-Hunter meta-analysis methods. Research Synthesis Methods, [s. l.], v. 6, n. 3, p. 232–239, Sept. 2015. DOI: https://doi.org/10.1002/jrsm.1134.

SERGUEEVA, K.; SHAW, N.; LEE, S. H. Understanding the barriers and factors associated with consumer adoption of wearable technology devices in managing personal health. Canadian Journal of Administrative Sciences, [s. l.], v. 37, n. 1, p. 45–60, Mar. 2020. DOI: https://doi.org/10.1002/cjas.1547.

SHARMA, S. K.; AHMED, N.; RATHINASAMY, R. S. E-healthcare: a model on the offshore healthcare delivery for cost saving. International Journal of Healthcare Technology and Management, [s. l.], v. 6, n. 3, p. 331–351, Mar. 2005. DOI: https://doi.org/10.1504/IJHTM.2005.006540.

SHEMESH, T.; BARNOY, S. Assessment of the intention to use mobile health applications using a technology acceptance model in an israeli adult population. Telemedicine and e-Health, [s. l.], v. 26, n. 9, p. 1–9, Jan. 2020. DOI: https://doi.org/10.1089/tmj.2019.0144.

SUN, S.; LEE, P. C.; LAW, R.; ZHONG, L. The impact of cultural values on the acceptance of hotel technology adoption from the perspective of hotel employees. Journal of Hospitality and Tourism Management, [s. l.], v. 44, p. 61–69, Sept. 2020. DOI: https://doi.org/10.1016/j.jhtm.2020.04.012.

TALUKDER, M.; CHIONG, R.; BAO, Y.; MALIK, B. H. Acceptance and use predictors of fitness wearable technology and intention to recommend: an empirical study. Industrial Management and Data Systems, [s. l.], v. 119, n. 1, p. 170–188, Feb. 2019. DOI: https://doi.org/10.1108/IMDS-01-2018-0009.

TALUKDER, M. S.; SORWAR, G.; BAO, Y.; AHMED, J. U.; PALASH, M. Predicting antecedents of wearable healthcare technology acceptance by elderly: a combined SEM-Neural Network approach. Technological Forecasting and Social Change, [s. l.], v. 150, p. 1-13, Jan. 2020. DOI: https://doi.org/10.1016/j.techfore.2019.119793.

TAVARES, J.; OLIVEIRA, T. Electronic Health Record Portal Adoption: a cross country analysis. BMC Medical Informatics and Decision Making, [s. l.], v. 17, n. 1, p. 1–17, Jul. 2017. DOI: https://doi.org/10.1186/s12911-017-0482-9.

TSAI, T.; LIN, W.; CHANG, Y.; CHANG, P.; LEE, M. Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults. PLOS ONE, [s. l.], v. 15, n. 1, Jan. 2020. DOI: https://doi.org/10.1371/journal.pone.0227270.

VAHDAT, A.; ALIZADEH, A.; QUACH, S.; HAMELIN, N. Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Marketing Journal, [s. l.], v. 29, n. 2, Jan. 2020. DOI: https://doi.org/10.1016/j.ausmj.2020.01.002.

VÄISÄNEN, J. Consumer acceptance of future my data based preventive ehealth services. Orientador: Koivumäki T.; Lappi M. 2015. 48 f. Dissertação (Master in Marketing) - OULU BUSINESS SCHOOL, Finlândia, 2015.

VAN DER KAMP, M. R.; KLAVER, E. C.; SPECTRUM, M.; BERNARD, T.; THIO, J.; JEAN, T.; DRIESSEN, M. M.; TWENTE, Z.; TABAK, M.; RESEARCH, R.; VAN DER PALEN, J. HERMESNS, H. J. WEARCON: Wearable home monitoring in children with asthma reveals a strong association with hospital based assessment of asthma control. Research square, [s. l.], p. 1–23, Jun. 2020. DOI: https://doi.org/10.21203/rs.3.rs-15928/v2.

VAN SLYKE, C.; CONCA, C.; TRIMMER, K.; Requirements for SME Information Technology. In: HARVIE, C.; LEE, B. C. (ed.). Globalisation and SMEs in East Asia. [S. l.]: Elgar, 2002. p. 158-189.

VENKATESH, V.; BROWN, S. A. A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly: Management Information Systems, Minnesota, v. 25, n. 1, p. 71–98, Mar. 2001. DOI: https://doi.org/10.2307/3250959.

VENKATESH, V.; MORRIS, M. G. Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly: Management Information Systems, v. 24, n. 1, p. 115–136, 2000. DOI: https://doi.org/10.2307/3250981.

VENKATESH, V.; MORRIS, M. G.; DAVIS, G. B.; DAVIS, F. D. User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, Minnesota, v. 27, n. 3, p. 425–478, Mar. 2003-0. DOI https://doi.org/10.2307/30036540.

VISHWAKARMA, P.; MUKHERJEE, S.; DATTA, B. Impact of cashback usage restriction exemption on travel booking: a goal-directed approach. Tourism Recreation Research, [s. l.], v. 45, n. 2, p. 218–230, Apr. 2020. DOI: https://doi.org/10.1080/02508281.2019.1683687.

WANG, H.; TAO, D.; YU, N.; QU, X. Understanding consumer acceptance of healthcare wearable devices: an integrated model of UTAUT and TTF. International Journal of Medical Informatics, [s. l.], v. 139, p. 1-10, July 2020. DOI https://doi.org/10.1016/J.IJMEDINF.2020.104156.

GAO, Y.; HE, L.; LUO, Y. An empirical study of wearable technology acceptance in healthcare. Industrial Management and Data Systems, [s. l.], v. 115, n. 9, p. 1704–1723, Oct 2015. DOI https://doi.org/10.1108/IMDS-03-2015-0087.

WANG, Y.; XUE, H.; HUANG, Y.; HUANG, L.; ZHANG, D. A systematic review of application and effectiveness of mhealth interventions for obesity and diabetes treatment and self-management. Advances in Nutrition: an international review journal, [s. l.], v. 8, n. 3, p. 449–462, May 2017. DOI: https://doi.org/10.3945/an.116.014100.

WIEGARD, R.; GUHR, N.; KRYLOW, S.; BREITNER, M. H. Analysis of wearable technologies’ usage for pay-as-you-live tariffs: recommendations for insurance companies. Zeitschrift fur die gesamte Versicherungswissenschaft, [s. l.], v. 108, n. 1, p. 63–88, Feb. 2019. DOI: https://doi.org/10.1007/s12297-019-00431-2.

WU, B.; CHEN, X. Continuance intention to use MOOCs: integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, [s. l.], v. 67, p. 221–232, Feb. 2017. DOI: https://doi.org/10.1016/j.chb.2016.10.028.

WU, J.; LI, He; CHENG, S.; LIN, Z. The promising future of healthcare services: when big data analytics meets wearable technology. Information and Management, [s. l.], v. 53, n. 8, p. 1020–1033, Dec. 2016. DOI: https://doi.org/10.1016/j.im.2016.07.003.

XU, L.; PENG, X.; PAVUR, R.; PRYBUTOK, V. Quality management theory development via meta-analysis. International Journal of Production Economics, [s.l.], v. 229, p. 1-16, Nov. 2020. DOI: https://doi.org/10.1016/j.ijpe.2020.107759.

YARBROUGH, A. K.; SMITH, T. B. Technology acceptance among physicians: a new take on TAM. Medical care research and review: MCRR, Nova Iorque, v. 64, n. 6, p. 650–72, Dec. 2007. DOI: https://doi.org/10.1177/1077558707305942.

YEN, P.; MCALEARNEY, A. S.; SIECK, C. J.; HEFNER, J. L.; HUERTA, T. R. Health Information Technology (HIT) Adaptation: refocusing on the journey to successful hit implementation. JMIR medical informatics, [s. l.], v. 5, n. 3, Sept. 2017. DOI: https://doi.org/10.2196/medinform.7476.

ZHARKIKH, E. V.; LOKTIONOVA, Y. I.; KOZLOV, I. O.; ZHEREBTSOVA, A. I.; SIDOROV, V. V.; ZHEREBTSOV, E. A.; DUNAEV, A. V.; RAFAILOV, E. U. Wearable laser Doppler flowmetry for the analysis of microcirculatory changes during intravenous infusion in patients with diabetes mellitus. PROCEEDINGS OF SPIE, [s. l.], v. 11363. p. 57. Apr. 2020. DOI: https://doi.org/10.1117/12.2552464.

ZOLAIT, A.; RADHI, N.; ALHOWAISHI, M. M.; SUNDRAM, V. P. K.; ALDOSERI, L. M. Can Bahraini patients accept e-health systems? International Journal of Health Care Quality Assurance, [s. l.], v. 32, n. 4, p. 720–730, 2019. DOI: https://doi.org 10.1108/IJHCQA-05-2018-0106.