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

Abstract

BackgroundChronic 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.ObjectiveThe 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.MethodsMultiple 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.ResultsThe 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.ConclusionsThis 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.

Effective Factors in Adoption of Mobile Health Applications between Medical Sciences Students Using the UTAUT Model

“…This is not consistent with previous research findings and the study by Hoque that gender was strongly associated with the adoption and use of mHealth in developing countries. 38,39 Venkatesh et al 31 suggested that age has received very little attention in the technology acceptance research literature, and this is consistent with the finding of this research. Zhao et al showed that different age groups have specific moderating effects on mHealth services adoption.…”

Barriers and Enablers to Using a Patient-Facing Electronic Questionnaire: A Qualitative Theoretical Domains Framework Analysis

“…Some participants also noted that their own previous experience with poor health outcomes acted as facilitators by enabling them to recognize the importance of completing the questionnaire (reinforcement). Correspondingly, previous work has shown that perceived health threats predict patient intentions to use smartphone-based health technology [ 34 ]. This facilitator may be particularly relevant in diseases that are characterized by episodic flares, such as asthma [ 35 ], chronic obstructive pulmonary disease [ 36 ], and congestive heart failure [ 37 ] and can also be leveraged in patient-facing messaging to drive usage.…”

Patient’s Intention to Use Mobile Health App

“…TAM has been widely used in studies of technology acceptance to predict users' intention to adopt a mobile health app (Dou et al, 2017;Sun et al, 2013). Sun et al (2013) formulated a model combining TAM theory with other models to explain consumers' acceptance of health technology (Sun et al, 2013); Hung and Jen also applied the TAM model to assess the students' intention to use mobile technology to manage their personal health (Hung and Jen, 2012).…”

“…Specially designed for the adoption of mobile health apps, it is believed that the intention of patients to use the app will depend greatly on the important others' suggestion, especially healthcare professionals as well as friends or relatives who recommend it to patients. The reason for the subjective norm on behavioral intention is that people will be more likely to adopt a particular behavior when the behavior is recommended by people who are important referents, even if the behaviors are not favored by the patients' belief structure, but patients are sufficiently motivated to conform to referees' perception if they think they need to adopt certain behaviours (Deng et al, 2014;Dou et al, 2017).…”

“…The behavioral intention to adopt a new technology is a dependent variable widely used in most research related to the acceptance of new technologies. Typically, this is a measurement variable designed to examine the intention to quickly adopt new technology or the continued use of a technology (Dou et al, 2017). In this current study, the concern for behavioral intention to use is seen as the intention of patients who were previously non-users to use the mobile health app, as well as the intention of adopters to continue to use the mobile health app.…”