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Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test

Kaili Dou, Ping Yu, Ning Deng, Fang Liu, YingPing Guan, Zhenye Li, Yumeng Ji, Ningkai Du, Xudong Lu, Huilong Duan

JMIR Mhealth Uhealth 2017;5(12):e177

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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

influences the behavioral intention of patients to use diabetes management apps.An investigation by Ahadzadeh et al [64] found that perceived health risk and health consciousness influenced perceived usefulness of the health-related internet [64], and a study by Dou

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

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

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Exploring the Usage Intentions of Wearable Medical Devices: A Demonstration Study

This study introduced health consciousness and trust as predictors in the Chinese social context to reflect the health care context, and this result is consisted with the findings in the studies by Dou et al [32] and Andrews et al [33].Implications for PracticeThe

Chiao-Chen Chang

Interact J Med Res 2020;9(3):e19776

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Technology Acceptance in Mobile Health: Scoping Review of Definitions, Models, and Measurement

Dou et al [33] introduced the constructs relationship with doctor and resistance to change. Finally, Cheung et al [34] introduced constructs related to privacy and consumer innovativeness.However, the temporal dimension is missing from these models.

Camille Nadal, Corina Sas, Gavin Doherty

J Med Internet Res 2020;22(7):e17256

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

Dou et al [1] used a combination of TAM2, the dual-factor model, and HBM to study patients’ acceptance of smartphone health technology for chronic disease management, among others.Extended Unified Theory of Acceptance and Usage of TechnologyThe extended version

Tânia Salgado, Jorge Tavares, Tiago Oliveira

JMIR Mhealth Uhealth 2020;8(7):e17588

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Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study

However, EHC had no significant direct effect on IU, which contrasts with the findings of Dou about Chinese patients’ acceptance of mobile phone health technology [31] but is consistent with the work of Kim about consumers’ health behavior IU of health information

Tiantian Ye, Jiaolong Xue, Mingguang He, Jing Gu, Haotian Lin, Bin Xu, Yu Cheng

J Med Internet Res 2019;21(10):e14316

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