O USO DO APLICATIVO DE SAÚDE PÚBLICA MÓVEL MEU DIGISUS
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2020 |
| Outros Autores: | , |
| Tipo de documento: | Artigo |
| Idioma: | por |
| Título da fonte: | Revista Valore (Online) |
| Texto Completo: | https://revistavalore.emnuvens.com.br/valore/article/view/379 |
Resumo: | O uso de tecnologias móveis impulsionaram os instituições a transformarem os mecanismo de prestação de serviços para utilizar as potencialidades dos smartphones. Este formato de serviços impõe o desafio da aceitação e uso pelos usuários finais. O objetivo deste artigo é identificar a aceitação do aplicativo público de saúde Meu digiSUS através do Modelo de Aceitação de Tecnologia (TAM) extendido. Para alcançar o objetivo, adotou uma abordagem quantitativa e algumas hipóteses foram estabelecida. As variáveis Uso real, Utilidade percebida, Facilidade de uso percebida, Influência social e Risco percebido foram selecionadas para o modelo. Os resultados mostram que a facilidade percebida e a influência social têm um impacto direto e positivo no uso do Meu digiSUS; o risco percebido tem um impacto negativo no uso do aplicativo. Os desenvolvedores devem se atentar aos inibidores de uso do aplicativo como o risco percebido e a utilidade percebido de uso a fim de manter os usuários existentes e atrair novos usuários. Palavras-Chave: Aceitação de tecnologia; Modelo de Aceitação de Tecnologia; Saúde pública móvel; Meu digiSUS. |
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O USO DO APLICATIVO DE SAÚDE PÚBLICA MÓVEL MEU DIGISUSO uso de tecnologias móveis impulsionaram os instituições a transformarem os mecanismo de prestação de serviços para utilizar as potencialidades dos smartphones. Este formato de serviços impõe o desafio da aceitação e uso pelos usuários finais. O objetivo deste artigo é identificar a aceitação do aplicativo público de saúde Meu digiSUS através do Modelo de Aceitação de Tecnologia (TAM) extendido. Para alcançar o objetivo, adotou uma abordagem quantitativa e algumas hipóteses foram estabelecida. As variáveis Uso real, Utilidade percebida, Facilidade de uso percebida, Influência social e Risco percebido foram selecionadas para o modelo. Os resultados mostram que a facilidade percebida e a influência social têm um impacto direto e positivo no uso do Meu digiSUS; o risco percebido tem um impacto negativo no uso do aplicativo. Os desenvolvedores devem se atentar aos inibidores de uso do aplicativo como o risco percebido e a utilidade percebido de uso a fim de manter os usuários existentes e atrair novos usuários. Palavras-Chave: Aceitação de tecnologia; Modelo de Aceitação de Tecnologia; Saúde pública móvel; Meu digiSUS.FaSFSouza, Rafael de SouzaRibeiro, Wagner Miguel RolimSilva, Priscila Paula da2020-01-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistavalore.emnuvens.com.br/valore/article/view/37910.22408/reva402019379390-406Revista Valore; v. 4 (2019): Caderno Temático - Artigos - II CONEPA - Congresso Nacional de Estudantes e Profissionais de Administração; 390-4062526-043X2525-900810.22408/reva402019reponame:Revista Valore (Online)instname:Faculdade Sul Fluminense (FASF)instacron:FASFporhttps://revistavalore.emnuvens.com.br/valore/article/view/379/324/*ref*/ALI, Eskinder E. et al. Patients perception of app-based educational and behavioural interventions for enhancing oral anticancer medication adherence. Journal of Cancer Education, v. 33, n. 4, 2017./*ref*/ALI, Rahib; ZHANG, Ziqiong; BUX SOOMRO, Muhammad. Smoking-cessation acceptance via mobile health. Human Systems Management, v. 38, n. 3, p. 313-327, 2019/*ref*/ALKHALDI, Ghadah et al. The effectiveness of prompts to promote engagement with digital interventions: a systematic review. Journal of Medical Internet Research, v. 18, n. 1, e6, 2016./*ref*/ANDERSON, Kevin; BURFORD, Oksana; EMMERTON, Lynne. Mobile health apps to facilitate self-care: a qualitative study of user experiences. PLoS ONE, v. 11, n. 5, e0156164, 2016./*ref*/ANTICONA HUAYNATE, Cynthia Fiorella et al. Diagnostics barriers and innovations in rural areas: insights from junior medical doctors on the frontlines of rural care in Peru. BMC Health Services Research, v. 15, n. 454, p. 1-10, 2015./*ref*/AJZEN, Icek. The theory of planned behavior. Organizational behavior and human decision processes, v. 50, n. 2, p. 179-211, 1991./*ref*/BAUER, Raymond A. Consumer Behavior as Risk Taking. In: Marketing classics: a selection of influential articles. Boston: Allyn, Bacon, 1969. p. 119-127./*ref*/BECKER, Dennis. Acceptance of mobile mental health treatment applications. In: THE 6TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH 2016), n. 98, p. 220-227, 2016./*ref*/BÊRNI, Duilio de Avila; FERNANDEZ, Brena Paula Magno. Métodos e Técnicas de Pesquisa: Modelando as ciências empresariais. São Paulo: Saraiva, 2012./*ref*/CAR, Josip et al. eHealth in the future of medications management: personalisation, monitoring and adherence. BMC Medicine, v. 15, n. 1, p. 73, 2017./*ref*/CARLOS, Daniele de A. O. et al. Concepção e avaliação de tecnologia mhealth para promoção da saúde vocal. Revista lbérica de Sistemas e Tecnologias de Informação, n. 9, p. 46-60, 2016./*ref*/CHO, Jaehee et al. Determinants of Adoption of Smartphone Health Apps Among College Students. American Journal of Health Behaviour, v. 38, n. 6, p. 860-870, 2014./*ref*/DAVIS, Fred D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, v. 13, n. 3, p. 319-339, 1989./*ref*/DAVIS, Fred D.; BAGOZZI, Richard P.; WARSHAW, Paul R. User Acceptance of Computer Technology: a comparison of two theoretical models. Management Science, v. 35, n. 8, p. 982-1003, 1989./*ref*/DEEKS, Amanda et aliae. The effects of gender and age on health related behaviors. BMC Public Health, v. 9, n. 1, p. 1-8, 2009./*ref*/DEMIRIS, George. New era for the consumer health informatics. Health Systems, v. 1, n. 1, p. 13-16, 2012./*ref*/DOOCY, Shannon et al. Guidelines and mHealth to Improve Quality of Hypertension and Type 2 Diabetes Care for Vulnerable Populations in Lebanon: Longitudinal Cohort Study. JMIR Mhealth Uhealth, v. 5, n. 10, e158, 2017./*ref*/DOU, Kaili et al. Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test. JMIR mHealth and uHealth, v. 5, n. 12, 2017./*ref*/DWIVEDI, Yogesh K. et al. A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, v. 33, n. 1, p. 174-187, 2016./*ref*/FEATHERMAN, Mauricio S.; PAVLOU, Paul A. Predicting e-services adoption: a perceived risk facet perspective. International Journal of Human-Computer Studies, n. 59, p. 451-474, 2003./*ref*/FISHBEIN, Joel N. et al. Mobile application to promote adherence to oral chemotherapy and symptom management: a protocol for design and development. JMIR Research protocols, v. 6, e62, 2017./*ref*/FISHBEIN, Martin; AJZEN, Icek. Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley, Reading, 1975./*ref*/FORNELL, Claes; LARCKER, David F. Evaluation of structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, v. 18, n. 3, p. 382-388, 1981./*ref*/GAGNON, Marie-Pierre et aliae. m-Health adoption by healthcare professionals: a systematic review. Journal of the American Medical Informatics Association, v. 23, n. 1, p. 212-220, 2016./*ref*/HAIR, Joseph F. et al. A primer on Partial Least Squares Structural Equation Modelling (PLS-SEM). SAGE, 2014./*ref*/______ Análise multivariada de dados. 6. ed. Tradução Adonai Schulp Sant`Anna. Porto Alegre: Bookman, 2009./*ref*/HOSSAIN, Nazmul et al. Factors influencing rural end-users’ acceptance of eHealth in developing countries: A study on Portable Health Clinic in Bangladesh. Telemedicine and e-Health, v. 25, n. 3, p. 221-229, 2018./*ref*/JIMERSON, Brittney H. Improving ergonomic intervention implementation and adoption among caregivers in a healthcare system. 121 f. Dissertation (Doctoral in Industrial and Systems Engineering), North Carolina Agricultural and Technical State University, United States, North Carolina, Greensboro, 2016./*ref*/KEEN, Stefanie M.; ROBERTS, Nicholas. Preliminary evidence for the use and efficacy of mobile health applications in managing posttraumatic stress disorder symptoms. Journal Health Systems, v.6, n. 2, p. 122-129, 2017./*ref*/KLINGBERGM, Anders et al. mHealth for Burn Injury Consultations in a Low-Resource Setting: An Acceptability Study Among Health Care Providers. Telemedicine and e-Health, v. 0, n. 0, 2019./*ref*/KUMAR, Santosh et al. Mobile health technology evaluation: the mHealth evidence workshop. American Journal Preventive Medicine, v. 45, n. 2, p. 228-36, 2013./*ref*/KUMMER, Tyge-F.; RECKER, Jan; BICK, Markus. Technology-induced anxiety: Manifestations, cultural influences, and its effect on the adoption of sensor-based technology in German and Australian hospitals. Information & Management, v. 54, p. 73-89, 2017./*ref*/LEE, Euehun; HAN, Semi. Determinants of adoption of mobile health services. Online Information Review, v. 39, n. 4, p. 556-573, 2015./*ref*/LEUNG, Louis; CHEN, Cheng. E-health/m-health adoption and lifestyle improvements: Exploring the roles of technology readiness, the expectation-confirmation model, and health-related information activities. Telecommunications Policy, v. 43, n. 6, p. 563-575, 2019./*ref*/LIMA, Cíntia C. B. Aplicativos móveis de interesse público: limites e possibilidades para a cidadania no Brasil. 2017. 239f. Dissertação (Mestrado em Comunicação) – Programa de Pós-Graduação em Comunicação, Faculdade de Comunicação, Universidade de Brasília, 2017./*ref*/LOPES, Juliana E.; HEIMANN, Candice. Uso das tecnologias da informação e comunicação nas ações médicas a distância: um caminho promissor a ser investido na saúde pública. Journal of Health Informatics, v. 8, n. 1, 2016./*ref*/MACDONALD, Emma M.; PERRIN, Byron M.; KINGSLEY, Michael I. C. Enablers and barriers to using two-way information technology in the management of adults with diabetes: a descriptive systematic review. Journal of Telemedicine and Telecare, v. 1, n. 1, 2017./*ref*/MARRIE, Ruth A. et al. Use of eHealth and mHealth technology by persons with multiple sclerosis. Multiple Sclerosis and Related Disorders, n. 27, p. 13-19, 2019./*ref*/MATTHEW-MAICH, Nancy et al. Designing, implementing, and evaluating mobile health technologies for managing chronic conditions in older adults: a scoping review. JMIR mHealth and uHealth, v. 4, n. 2, 2016./*ref*/MOORE, Gary C.; BENBASAT, Izak. Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, v. 2, p. 192-222, 1991./*ref*/NG, Denise et al. What Is Being Used and Who Is Using It: Barriers to the Adoption of Smartphone Patient Experience Surveys. JMIR Formative Research, v. 3, n. 1, e9922, 2019./*ref*/PHAM, Quynh; CAFAZZO, Joseph A.; FEIFER, Andrew. Adoption, Acceptability, and Effectiveness of a Mobile Health App for Personalized Prostate Cancer Survivorship Care: Protocol for a Realist Case Study of the Ned App. JMIR Research Protocols, v. 6, n. 10, p. 1-38, 2017./*ref*/PENG, Wei et al. A qualitative study of user perceptions of mobile health apps. BMC Public Health, v. 16, p. 11-58, 2016./*ref*/RAYKOV, Tenko; MARCOULIDES, George A. A method for comparing completely standardized solutions in multiple groups. Structural equation modeling, v. 7, n. 2, p. 292-308, 2000./*ref*/ROCHA, Thiago A. H. et alia. Saúde Móvel: novas perspectivas para a oferta de serviços em saúde. Epidemiololgia e Serviços de Saúde, v. 25, n. 1, p. 159-170, 2016./*ref*/SARNO, Flavio et al. Mobile health e excesso de peso: uma revisão sistemática. Revista Panam Salud Publica, v. 35, n. 5/6, p. 424-431, 2014./*ref*/SCHNALL, Rebecca et al. Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Studies in health technology and informatics, n. 216, p. 467-471, 2015./*ref*/SUN, Yongqiang et al. Understanding the acceptance of mobile health services: a comparison and integration of alternative models. Journal of Electronic Commerce Research, v. 14, n. 2, p. 183-199, 2013./*ref*/STREET, Tamara D.; LACEY, Sarah J. Employee Perceptions of Workplace Health Promotion Programs: Comparison of a Tailored, Semi-Tailored, and Standardized Approach. International journal of environmental research and public health, v. 15, n. 5, p. 332-342, 2018./*ref*/TAYLOR, Shirley; TODD, Peter. Decomposition and crossover effects in the theory of planned behaviour: A study of consumer adoption intentions. International Journal of Research in Marketing, v. 12, n. 2, p. 137-155, 1995./*ref*/VENKATESH, Viswanath et al. User acceptance of information technology: toward a unified view. MIS Quarterly, v. 27, n. 3, p. 425-478, 2003./*ref*/VENKATESH, Viswanath; DAVIS, Fred D. Theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, v. 46, n. 2, p. 186-204, 2000./*ref*/VENKATESH, Viswanath; THONG, James Y. L.; XU, Xin. Unified theory of acceptance and use of technology: a synthesis and the road ahead. Journal of the Association for Information Systems, v. 17, n. 5, p. 328-376, 2016./*ref*/WILLIAMS, Michael D. et al. The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management, v. 28, n. 3, p. 443-488, 2015./*ref*/XUE, Lishan et al. An exploratory study of ageing women's perception on access to health informatics via a mobile phone-based intervention. International Journal of Medical Informatics, v. 81, n. 9, p. 637-648, 2012.Direitos autorais 2020 Revista Valoreinfo:eu-repo/semantics/openAccess2020-01-08T05:28:09Zoai:valore.emnuvens.com.br:article/379Revistahttps://revistavalore.emnuvens.com.br/valore/indexPUBhttps://revistavalore.emnuvens.com.br/valore/oai||revistavalore@gmail.com|| marcus.barbosa1979@gmail.com|| felipetriani@gmail.com2526-043X2525-9008opendoar:2020-01-08T05:28:09Revista Valore (Online) - Faculdade Sul Fluminense (FASF)false |
| dc.title.none.fl_str_mv |
O USO DO APLICATIVO DE SAÚDE PÚBLICA MÓVEL MEU DIGISUS |
| title |
O USO DO APLICATIVO DE SAÚDE PÚBLICA MÓVEL MEU DIGISUS |
| spellingShingle |
O USO DO APLICATIVO DE SAÚDE PÚBLICA MÓVEL MEU DIGISUS Souza, Rafael de Souza |
| title_short |
O USO DO APLICATIVO DE SAÚDE PÚBLICA MÓVEL MEU DIGISUS |
| title_full |
O USO DO APLICATIVO DE SAÚDE PÚBLICA MÓVEL MEU DIGISUS |
| title_fullStr |
O USO DO APLICATIVO DE SAÚDE PÚBLICA MÓVEL MEU DIGISUS |
| title_full_unstemmed |
O USO DO APLICATIVO DE SAÚDE PÚBLICA MÓVEL MEU DIGISUS |
| title_sort |
O USO DO APLICATIVO DE SAÚDE PÚBLICA MÓVEL MEU DIGISUS |
| author |
Souza, Rafael de Souza |
| author_facet |
Souza, Rafael de Souza Ribeiro, Wagner Miguel Rolim Silva, Priscila Paula da |
| author_role |
author |
| author2 |
Ribeiro, Wagner Miguel Rolim Silva, Priscila Paula da |
| author2_role |
author author |
| dc.contributor.none.fl_str_mv |
|
| dc.contributor.author.fl_str_mv |
Souza, Rafael de Souza Ribeiro, Wagner Miguel Rolim Silva, Priscila Paula da |
| description |
O uso de tecnologias móveis impulsionaram os instituições a transformarem os mecanismo de prestação de serviços para utilizar as potencialidades dos smartphones. Este formato de serviços impõe o desafio da aceitação e uso pelos usuários finais. O objetivo deste artigo é identificar a aceitação do aplicativo público de saúde Meu digiSUS através do Modelo de Aceitação de Tecnologia (TAM) extendido. Para alcançar o objetivo, adotou uma abordagem quantitativa e algumas hipóteses foram estabelecida. As variáveis Uso real, Utilidade percebida, Facilidade de uso percebida, Influência social e Risco percebido foram selecionadas para o modelo. Os resultados mostram que a facilidade percebida e a influência social têm um impacto direto e positivo no uso do Meu digiSUS; o risco percebido tem um impacto negativo no uso do aplicativo. Os desenvolvedores devem se atentar aos inibidores de uso do aplicativo como o risco percebido e a utilidade percebido de uso a fim de manter os usuários existentes e atrair novos usuários. Palavras-Chave: Aceitação de tecnologia; Modelo de Aceitação de Tecnologia; Saúde pública móvel; Meu digiSUS. |
| publishDate |
2020 |
| dc.date.none.fl_str_mv |
2020-01-08 |
| dc.type.none.fl_str_mv |
|
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
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https://revistavalore.emnuvens.com.br/valore/article/view/379 10.22408/reva402019379390-406 |
| url |
https://revistavalore.emnuvens.com.br/valore/article/view/379 |
| identifier_str_mv |
10.22408/reva402019379390-406 |
| dc.language.iso.fl_str_mv |
por |
| language |
por |
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https://revistavalore.emnuvens.com.br/valore/article/view/379/324 /*ref*/ALI, Eskinder E. et al. Patients perception of app-based educational and behavioural interventions for enhancing oral anticancer medication adherence. Journal of Cancer Education, v. 33, n. 4, 2017. /*ref*/ALI, Rahib; ZHANG, Ziqiong; BUX SOOMRO, Muhammad. Smoking-cessation acceptance via mobile health. Human Systems Management, v. 38, n. 3, p. 313-327, 2019 /*ref*/ALKHALDI, Ghadah et al. The effectiveness of prompts to promote engagement with digital interventions: a systematic review. Journal of Medical Internet Research, v. 18, n. 1, e6, 2016. /*ref*/ANDERSON, Kevin; BURFORD, Oksana; EMMERTON, Lynne. Mobile health apps to facilitate self-care: a qualitative study of user experiences. PLoS ONE, v. 11, n. 5, e0156164, 2016. /*ref*/ANTICONA HUAYNATE, Cynthia Fiorella et al. Diagnostics barriers and innovations in rural areas: insights from junior medical doctors on the frontlines of rural care in Peru. BMC Health Services Research, v. 15, n. 454, p. 1-10, 2015. /*ref*/AJZEN, Icek. The theory of planned behavior. Organizational behavior and human decision processes, v. 50, n. 2, p. 179-211, 1991. /*ref*/BAUER, Raymond A. Consumer Behavior as Risk Taking. In: Marketing classics: a selection of influential articles. Boston: Allyn, Bacon, 1969. p. 119-127. /*ref*/BECKER, Dennis. Acceptance of mobile mental health treatment applications. In: THE 6TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH 2016), n. 98, p. 220-227, 2016. /*ref*/BÊRNI, Duilio de Avila; FERNANDEZ, Brena Paula Magno. Métodos e Técnicas de Pesquisa: Modelando as ciências empresariais. São Paulo: Saraiva, 2012. /*ref*/CAR, Josip et al. eHealth in the future of medications management: personalisation, monitoring and adherence. BMC Medicine, v. 15, n. 1, p. 73, 2017. /*ref*/CARLOS, Daniele de A. O. et al. Concepção e avaliação de tecnologia mhealth para promoção da saúde vocal. Revista lbérica de Sistemas e Tecnologias de Informação, n. 9, p. 46-60, 2016. /*ref*/CHO, Jaehee et al. Determinants of Adoption of Smartphone Health Apps Among College Students. American Journal of Health Behaviour, v. 38, n. 6, p. 860-870, 2014. /*ref*/DAVIS, Fred D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, v. 13, n. 3, p. 319-339, 1989. /*ref*/DAVIS, Fred D.; BAGOZZI, Richard P.; WARSHAW, Paul R. User Acceptance of Computer Technology: a comparison of two theoretical models. Management Science, v. 35, n. 8, p. 982-1003, 1989. /*ref*/DEEKS, Amanda et aliae. The effects of gender and age on health related behaviors. BMC Public Health, v. 9, n. 1, p. 1-8, 2009. /*ref*/DEMIRIS, George. New era for the consumer health informatics. Health Systems, v. 1, n. 1, p. 13-16, 2012. /*ref*/DOOCY, Shannon et al. Guidelines and mHealth to Improve Quality of Hypertension and Type 2 Diabetes Care for Vulnerable Populations in Lebanon: Longitudinal Cohort Study. JMIR Mhealth Uhealth, v. 5, n. 10, e158, 2017. /*ref*/DOU, Kaili et al. Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test. JMIR mHealth and uHealth, v. 5, n. 12, 2017. /*ref*/DWIVEDI, Yogesh K. et al. A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, v. 33, n. 1, p. 174-187, 2016. /*ref*/FEATHERMAN, Mauricio S.; PAVLOU, Paul A. Predicting e-services adoption: a perceived risk facet perspective. International Journal of Human-Computer Studies, n. 59, p. 451-474, 2003. /*ref*/FISHBEIN, Joel N. et al. Mobile application to promote adherence to oral chemotherapy and symptom management: a protocol for design and development. JMIR Research protocols, v. 6, e62, 2017. /*ref*/FISHBEIN, Martin; AJZEN, Icek. Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley, Reading, 1975. /*ref*/FORNELL, Claes; LARCKER, David F. Evaluation of structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, v. 18, n. 3, p. 382-388, 1981. /*ref*/GAGNON, Marie-Pierre et aliae. m-Health adoption by healthcare professionals: a systematic review. Journal of the American Medical Informatics Association, v. 23, n. 1, p. 212-220, 2016. /*ref*/HAIR, Joseph F. et al. A primer on Partial Least Squares Structural Equation Modelling (PLS-SEM). SAGE, 2014. /*ref*/______ Análise multivariada de dados. 6. ed. Tradução Adonai Schulp Sant`Anna. Porto Alegre: Bookman, 2009. /*ref*/HOSSAIN, Nazmul et al. Factors influencing rural end-users’ acceptance of eHealth in developing countries: A study on Portable Health Clinic in Bangladesh. Telemedicine and e-Health, v. 25, n. 3, p. 221-229, 2018. /*ref*/JIMERSON, Brittney H. Improving ergonomic intervention implementation and adoption among caregivers in a healthcare system. 121 f. Dissertation (Doctoral in Industrial and Systems Engineering), North Carolina Agricultural and Technical State University, United States, North Carolina, Greensboro, 2016. /*ref*/KEEN, Stefanie M.; ROBERTS, Nicholas. Preliminary evidence for the use and efficacy of mobile health applications in managing posttraumatic stress disorder symptoms. Journal Health Systems, v.6, n. 2, p. 122-129, 2017. /*ref*/KLINGBERGM, Anders et al. mHealth for Burn Injury Consultations in a Low-Resource Setting: An Acceptability Study Among Health Care Providers. Telemedicine and e-Health, v. 0, n. 0, 2019. /*ref*/KUMAR, Santosh et al. Mobile health technology evaluation: the mHealth evidence workshop. American Journal Preventive Medicine, v. 45, n. 2, p. 228-36, 2013. /*ref*/KUMMER, Tyge-F.; RECKER, Jan; BICK, Markus. Technology-induced anxiety: Manifestations, cultural influences, and its effect on the adoption of sensor-based technology in German and Australian hospitals. Information & Management, v. 54, p. 73-89, 2017. /*ref*/LEE, Euehun; HAN, Semi. Determinants of adoption of mobile health services. Online Information Review, v. 39, n. 4, p. 556-573, 2015. /*ref*/LEUNG, Louis; CHEN, Cheng. E-health/m-health adoption and lifestyle improvements: Exploring the roles of technology readiness, the expectation-confirmation model, and health-related information activities. Telecommunications Policy, v. 43, n. 6, p. 563-575, 2019. /*ref*/LIMA, Cíntia C. B. Aplicativos móveis de interesse público: limites e possibilidades para a cidadania no Brasil. 2017. 239f. Dissertação (Mestrado em Comunicação) – Programa de Pós-Graduação em Comunicação, Faculdade de Comunicação, Universidade de Brasília, 2017. /*ref*/LOPES, Juliana E.; HEIMANN, Candice. Uso das tecnologias da informação e comunicação nas ações médicas a distância: um caminho promissor a ser investido na saúde pública. Journal of Health Informatics, v. 8, n. 1, 2016. /*ref*/MACDONALD, Emma M.; PERRIN, Byron M.; KINGSLEY, Michael I. C. Enablers and barriers to using two-way information technology in the management of adults with diabetes: a descriptive systematic review. Journal of Telemedicine and Telecare, v. 1, n. 1, 2017. /*ref*/MARRIE, Ruth A. et al. Use of eHealth and mHealth technology by persons with multiple sclerosis. Multiple Sclerosis and Related Disorders, n. 27, p. 13-19, 2019. /*ref*/MATTHEW-MAICH, Nancy et al. Designing, implementing, and evaluating mobile health technologies for managing chronic conditions in older adults: a scoping review. JMIR mHealth and uHealth, v. 4, n. 2, 2016. /*ref*/MOORE, Gary C.; BENBASAT, Izak. Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, v. 2, p. 192-222, 1991. /*ref*/NG, Denise et al. What Is Being Used and Who Is Using It: Barriers to the Adoption of Smartphone Patient Experience Surveys. JMIR Formative Research, v. 3, n. 1, e9922, 2019. /*ref*/PHAM, Quynh; CAFAZZO, Joseph A.; FEIFER, Andrew. Adoption, Acceptability, and Effectiveness of a Mobile Health App for Personalized Prostate Cancer Survivorship Care: Protocol for a Realist Case Study of the Ned App. JMIR Research Protocols, v. 6, n. 10, p. 1-38, 2017. /*ref*/PENG, Wei et al. A qualitative study of user perceptions of mobile health apps. BMC Public Health, v. 16, p. 11-58, 2016. /*ref*/RAYKOV, Tenko; MARCOULIDES, George A. A method for comparing completely standardized solutions in multiple groups. 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Revista Valore; v. 4 (2019): Caderno Temático - Artigos - II CONEPA - Congresso Nacional de Estudantes e Profissionais de Administração; 390-406 2526-043X 2525-9008 10.22408/reva402019 reponame:Revista Valore (Online) instname:Faculdade Sul Fluminense (FASF) instacron:FASF |
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