Araújo Ivandra, Grilo Ana, Silva Carina
ESTeSL-Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Av. D. João II, Lote 4.69.01., 1990-096 Lisboa, Portugal.
H&TRC-Health & Technology Research Center, ESTeSL-Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Av. D. João II, Lote 4.69.01., 1990-096 Lisboa, Portugal.
Healthcare (Basel). 2023 Jul 3;11(13):1916. doi: 10.3390/healthcare11131916.
The use of technology has proven to be a powerful tool in the fight against COVID-19 and its variants of concern (Gamma, Beta, Alpha, Delta, and Omicron). The urgency of responding to this worldwide pandemic has accelerated the development of monitoring systems and contact tracing applications. Without technology's contribution, the adverse effects on humanity economically, socially, and psychologically would be even more devastating. This study aimed to translate, adapt, and validate the Unified Theory of Acceptance and Use of Technology (UTAUT) model in a Portuguese university population, to evaluate adherence to a mobile application for tracking COVID-19. An observational cross-sectional study was developed using a sample with 1081 participants (71% female, 59.2% with ages between 16 and 24, and 78.2% of the respondent's university students). The Portuguese version model showed a good reliability (Cronbach's α = 0.82) and an acceptable overall adjustment to the sample under study (χ/df = 3.732, CFI = 0.955, TLI = 0.944, RMSEA = 0.05, SRMR = 0.06); however, this model could be improved, as we proved. Since this is a pilot study, more studies are needed. The results indicated that the P-UTAUT can be improved for evaluating adherence to a COVID-19 mobile application.
事实证明，技术的应用是抗击新冠病毒及其关注变种（伽马、贝塔、阿尔法、德尔塔和奥密克戎）的有力工具。应对这一全球大流行的紧迫性加速了监测系统和接触者追踪应用程序的开发。如果没有技术的贡献，对人类在经济、社会和心理方面造成的不利影响将更加具有破坏性。本研究旨在对技术接受与使用统一理论（UTAUT）模型进行翻译、改编和验证，以评估葡萄牙大学人群对一款追踪新冠病毒移动应用程序的依从性。采用了一项观察性横断面研究，样本包括1081名参与者（71%为女性，59.2%年龄在16至24岁之间，78.2%的受访者为大学生）。葡萄牙语版本模型显示出良好的信度（克朗巴哈α系数=0.82），并且对所研究样本的总体适配度可以接受（χ/df = 3.732，CFI = 0.955，TLI = 0.944，RMSEA = 0.05，SRMR = 0.06）；然而，正如我们所证明的，该模型仍可改进。由于这是一项试点研究，因此还需要更多的研究。结果表明，P-UTAUT在评估对新冠病毒移动应用程序的依从性方面可以得到改进。
