Determinants of the Mobile Health Continuance Intention of Elders with Chronic Diseases: An Integrated Framework of ECM-ISC and UTAUT

Abstract

With the deepening of population aging in China, chronic diseases are a major public health concern that threatens the life and health of nationals. Mobile health or mHealth can effectively monitor chronic diseases, which holds vital significance to the alleviation of social pressure caused by aging. To patients with chronic diseases, mHealth cannot give full play to its value, only when it is used in the long term. However, there is not yet research exploring mHealth continuance intention from the perspective of elders with chronic diseases. So, this research represents the first attempt to empirically analyze mHealth continuance intention from the perspective of elders with chronic diseases. The purpose of this research is to make up the research gap of the mHealth field and to put forward theoretical and practical implications based on research results. To obtain research data, a questionnaire was conducted. A total of 926 copies were collected online and 527 copies were collected offline. The structural equation model (SEM) was used for data analysis. Research results suggest that confirmation can significantly influence satisfaction, performance expectancy and effort expectancy. Meanwhile, confirmation and performance expectancy can significantly influence satisfaction. Additionally, effort expectancy, performance expectancy, social influence and facilitating conditions can directly and significantly influence continuance intention. Among them, performance expectancy can directly influence continuance intention in the most significant way. This research provides solid evidence for the validity of the integrated model of ECM-ISC and UTAUT in the mHealth field, which can be a theoretical basis for mHealth operators’ product R&D.

The behavioral intention to adopt mobile health services: The moderating impact of mobile self-efficacy

“…To our surprise, the result of the study has shown that both the main constructs of the UTAUT, performance expectancy and effort expectancy were both not significant predictors of the intention to use mobile health services. The non-significant impact of performance and effort expectancy on the behavioral intention to use does not support previous studies which have indicated the positive effect of these two factors (PE and EE) on the adoption of mobile health services ( 38 , 39 , 41 , 66 , 90 , 91 ). Other studies, however, have corroborated our findings of the non-significant impact of performance expectancy and effort expectancy on the intention to adopt ( 51 , 92 ).…”

“…This integration is expected to contribute to the mobile health literature by demonstrating the moderating impact of mobile self-efficacy on the relationship between both the performance and effort expectancy of mobile health services and the behavioral intention to adopt mobile health services. While studies have shown the direct impact of performance expectancy and effort expectancy on the behavioral intention to use mobile health services (37)(38)(39)(40)(41)(42), no studies to the best of our knowledge have explored the moderating impact of mobile self-efficacy on these two relationships. This is the first attempted set of contributions to this study.…”

Factors Influencing Expectant Mothers' Continued Use of Digital Health Information

“…This study is novel as it combined constructs from social support, UTAUT, and PMT to examine expectant mothers' continued utilization of digital health information. It is also significantly distinct from previous works in continuance intention (Alsyouf, Ishak and Abdullah, 2018;Imlawi and Gregg, 2020;Yang and Jong, 2021;Tian and Wu, 2022).…”

“…The model was assessed using 18 measurement items, excluding all demographic variables. The ESPSM items were adapted from the work of Lin, Wang and Hajli (2019), the SMHIU items were from Venkatesh et al (2003) and Puspitasari and Firdauzy (2019). Additionally, the SMHIC items were developed based on items from Chiu, Cho and Chi (2020), while the PS and PV were adapted from Jun, Park and Cho (2019) and Shatta et al (2020).…”

Research on the Factors Affecting the Adoption of Smart Aged-Care Products by the Aged in China: Extension Based on UTAUT Model

“…The UTAUT model has a wide range of applications in the field of smart aging [ 12 , 23 , 24 , 25 , 26 , 27 ]. Since smart aged-care products are a relatively new type of product, an integrated technology acceptance model can help us explore the key factors affecting the adoption of smart aged-care products by the aged.…”