NOVA Information Management School, Universidade Nova de Lisboa, Lisbon, Portugal.
JMIR Mhealth Uhealth. 2020 Jul 9;8(7):e17588. doi: 10.2196/17588.
Mobile health (mHealth) has potential to play a significant role in realizing a reversal of the current paradigm in health care toward a more patient-centric and more collaborative system to improve the outcomes obtained along with the quality and sustainability of health care systems.
The aim of this study was to explore and understand individual mHealth acceptance drivers between two groups of users: those with chronic health conditions and those without.
The extended unified theory of acceptance and usage of technology (UTAUT2) was enhanced with a new health-related framework: behavior intention to recommend and new mediation effects. We applied partial least squares (PLS) causal modeling to test the research model.
We obtained 322 valid responses through an online questionnaire. The drivers of behavior intention with statistical significance were performance expectancy (β=.29, P<.001), habit (β=.39, P<.001), and personal empowerment (β=.18, P=.01). The precursors of use behavior were habit (β= .47, P<.001) and personal empowerment (β=.17, P=.01). Behavior intention to recommend was significantly influenced by behavior intention (β=.58, P<.001) and personal empowerment (β=.26, P<.001). The model explained 66% of the total variance in behavior intention, 54% of the variance in use behavior, and 70% of the variance in behavior intention to recommend.
Our study demonstrates a significant role of personal empowerment, as a second-order construct, in the mHealth acceptance context. The presence of a chronic health condition predicates an impact on acceptance of this technology.
移动医疗(mHealth)有可能在实现当前医疗保健模式的转变方面发挥重要作用,使医疗保健更加以患者为中心,更加协作,从而改善获得的结果,并提高医疗保健系统的质量和可持续性。
本研究旨在探索和理解两组用户(慢性病患者和非慢性病患者)之间的个体 mHealth 接受驱动因素。
扩展的统一技术接受和使用理论(UTAUT2)模型通过一个新的与健康相关的框架得到增强:推荐意愿和新的中介效应。我们应用偏最小二乘法(PLS)因果模型来测试研究模型。
我们通过在线问卷获得了 322 份有效回复。具有统计学意义的行为意向驱动因素是绩效期望(β=.29,P<.001)、习惯(β=.39,P<.001)和个人授权(β=.18,P=.01)。使用行为的前因是习惯(β=.47,P<.001)和个人授权(β=.17,P=.01)。推荐意愿受到行为意向(β=.58,P<.001)和个人授权(β=.26,P<.001)的显著影响。该模型解释了行为意向的 66%、使用行为的 54%和推荐意愿的 70%的总方差。
我们的研究表明,个人授权作为二阶结构,在 mHealth 接受背景下具有重要作用。慢性病的存在预示着对这项技术的接受程度会受到影响。