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Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development

Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development

behavior intention in the chronic conditions group, which will be significantly higher when compared to that of the healthy group.MethodsMeasurementAll of the measurement items for each of the constructs described above were adapted from Venkatesh et al [21], Tavares

Tânia Salgado, Jorge Tavares, Tiago Oliveira

JMIR Mhealth Uhealth 2020;8(7):e17588

The Impact of Portal Satisfaction on Portal Use and Health-Seeking Behavior: Structural Equation Analysis

The Impact of Portal Satisfaction on Portal Use and Health-Seeking Behavior: Structural Equation Analysis

Tavares and Oliveira [16] used a derivative of Venkatesh’s UTAUT2 theoretical framework to investigate EHR adoption with an added construct of the patient’s self-perception.

Reginald A Silver, Chandrasekar Subramaniam, Antonis Stylianou

J Med Internet Res 2020;22(3):e16260

Use and the Users of a Patient Portal: Cross-Sectional Study

Use and the Users of a Patient Portal: Cross-Sectional Study

Tavares and Oliveira [21] adapted UTAUT to the eHealth consumer context (UTAUT2) and showed that in addition to performance expectancy and effort expectancy, habit and self-perception (defined as perceived severity of the health complaint) are drivers of the

Bas Hoogenbosch, Jeroen Postma, Janneke M. de Man-van Ginkel, Nicole AM Tiemessen, Johannes JM van Delden, Harmieke van Os-Medendorp

J Med Internet Res 2018;20(9):e262

Factors Affecting Patients’ Use of Electronic Personal Health Records in England: Cross-Sectional Study

Factors Affecting Patients’ Use of Electronic Personal Health Records in England: Cross-Sectional Study

Although the predictive power of the proposed model is comparable with the predictive power of the original UTAUT model (48%), it is higher than the predictive power of models proposed by other studies in the context of ePHRs: Hsieh [90] (42.7%) and Tavares

Alaa Abd-Alrazaq, Bridgette M Bewick, Tracey Farragher, Peter Gardner

J Med Internet Res 2019;21(7):e12373

Patients’ Adoption of Electronic Personal Health Records in England: Secondary Data Analysis

Patients’ Adoption of Electronic Personal Health Records in England: Secondary Data Analysis

This finding is in line with the findings of a study conducted by Tavares and Oliveira, who did not find a significant association between FCs and BI to use ePHRs [40].

Alaa Abd-Alrazaq, Ali Abdallah Alalwan, Brian McMillan, Bridgette M Bewick, Mowafa Househ, Alaa T AL-Zyadat

J Med Internet Res 2020;22(10):e17499

Factors Influencing Patients’ Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey

Factors Influencing Patients’ Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey

Moreover, the Tavares and Oliveira study concerning electronic health record patient portals did not find an association between hedonic motivation and behavioral intention [38].

Yiyu Zhang, Chaoyuan Liu, Shuoming Luo, Yuting Xie, Fang Liu, Xia Li, Zhiguang Zhou

J Med Internet Res 2019;21(8):e15023