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

doi:10.2196/mhealth.7886

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

JMIR Mhealth Uhealth. 2017 Dec 6;5(12):e177. doi: 10.2196/mhealth.7886.

Authors

Kaili Dou¹, Ping Yu², Ning Deng¹, Fang Liu³, YingPing Guan³, Zhenye Li³, Yumeng Ji¹, Ningkai Du¹, Xudong Lu¹, Huilong Duan¹

Affiliations

¹ The Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
² School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, Australia.
³ General Hospital of Ningxia Medical University, Yinchuan, China.

Abstract

Background: Chronic disease patients often face multiple challenges from difficult comorbidities. Smartphone health technology can be used to help them manage their conditions only if they accept and use the technology.

Objective: The aim of this study was to develop and test a theoretical model to predict and explain the factors influencing patients' acceptance of smartphone health technology for chronic disease management.

Methods: Multiple theories and factors that may influence patients' acceptance of smartphone health technology have been reviewed. A hybrid theoretical model was built based on the technology acceptance model, dual-factor model, health belief model, and the factors identified from interviews that might influence patients' acceptance of smartphone health technology for chronic disease management. Data were collected from patient questionnaire surveys and computer log records about 157 hypertensive patients' actual use of a smartphone health app. The partial least square method was used to test the theoretical model.

Results: The model accounted for .412 of the variance in patients' intention to adopt the smartphone health technology. Intention to use accounted for .111 of the variance in actual use and had a significant weak relationship with the latter. Perceived ease of use was affected by patients' smartphone usage experience, relationship with doctor, and self-efficacy. Although without a significant effect on intention to use, perceived ease of use had a significant positive influence on perceived usefulness. Relationship with doctor and perceived health threat had significant positive effects on perceived usefulness, countering the negative influence of resistance to change. Perceived usefulness, perceived health threat, and resistance to change significantly predicted patients' intentions to use the technology. Age and gender had no significant influence on patients' acceptance of smartphone technology. The study also confirmed the positive relationship between intention to use and actual use of smartphone health apps for chronic disease management.

Conclusions: This study developed a theoretical model to predict patients' acceptance of smartphone health technology for chronic disease management. Although resistance to change is a significant barrier to technology acceptance, careful management of doctor-patient relationship, and raising patients' awareness of the negative effect of chronic disease can negate the effect of resistance and encourage acceptance and use of smartphone health technology to support chronic disease management for patients in the community.

Keywords: chronic disease; disease management; hypertension; mobile health; patients; smartphone.

©Kaili Dou, Ping Yu, Ning Deng, Fang Liu, YingPing Guan, Zhenye Li, Yumeng Ji, Ningkai Du, Xudong Lu, Huilong Duan. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 06.12.2017.