Applying an Extended UTAUT2 Model to Explain User Acceptance of Lifestyle and Therapy Mobile Health Apps: Survey Study.

BACKGROUND: Mobile health (mHealth) care apps are a promising technology to monitor and control health individually and cost-effectively with a technology that is widely used, affordable, and ubiquitous in many people's lives. Download statistics show that lifestyle apps are widely used by young and healthy users to improve fitness, nutrition, and more. While this is an important aspect for the prevention of future chronic diseases, the burdened health care systems worldwide may directly profit from the use of therapy apps by those patients already in need of medical treatment and monitoring. OBJECTIVE: We aimed to compare the factors influencing the acceptance of lifestyle and therapy apps to better understand what drives and hinders the use of mHealth apps. METHODS: We applied the established unified theory of acceptance and use of technology 2 (UTAUT2) technology acceptance model to evaluate mHealth apps via an online questionnaire with 707 German participants. Moreover, trust and privacy concerns were added to the model and, in a between-subject study design, the influence of these predictors on behavioral intention to use apps was compared between lifestyle and therapy apps. RESULTS: The results show that the model only weakly predicted the intention to use mHealth apps (R2=0.019). Only hedonic motivation was a significant predictor of behavioral intentions regarding both app types, as determined by path coefficients of the model (lifestyle: 0.196, P=.004; therapy: 0.344, P<.001). Habit influenced the behavioral intention to use lifestyle apps (0.272, P<.001), while social influence (0.185, P<.001) and trust (0.273, P<.001) predicted the intention to use therapy apps. A further exploratory correlation analysis of the relationship between user factors on behavioral intention was calculated. Health app familiarity showed the strongest correlation to the intention to use (r=0.469, P<.001), stressing the importance of experience. Also, age (r=-0.15, P=.004), gender (r=-0.075, P=.048), education level (r=0.088, P=.02), app familiarity (r=0.142, P=.007), digital health literacy (r=0.215, P<.001), privacy disposition (r=-0.194, P>.001), and the propensity to trust apps (r=0.191, P>.001) correlated weakly with behavioral intention to use mHealth apps. CONCLUSIONS: The results indicate that, rather than by utilitarian factors like usefulness, mHealth app acceptance is influenced by emotional factors like hedonic motivation and partly by habit, social influence, and trust. Overall, the findings give evidence that for the health care context, new and extended acceptance models need to be developed with an integration of user diversity, especially individuals' prior experience with apps and mHealth.

Bare necessities? How the need for care modulates the acceptance of ambient assisted living technologies.

BACKGROUND: Steadily increasing numbers of older people and people in need of care represent critical challenges for today's society. In the last years, diverse (health-related) technologies have been developed to facilitate living at home for older people but also to support (professional) care personnel in their daily care efforts. Ambient Assisted Living (AAL) technologies have the potential to enhance safety, support medical therapy, or facilitate everyday chores and social life. With the huge range and variety of technical opportunities, the question arises what influences (potential) users' decisions for the right technology in their individual conditions and situations. In particular with regard to the fragility of the care situation, it is unknown which technologies are desired for different care needs and diverse situations. OBJECTIVES: The present study investigates (1) personal care needs as a potential influencing parameter for technology acceptance and (2) the selection of specific technologies. METHOD: In an online questionnaire (including n = 162 people of all ages), technology acceptance and the selection of specific technologies was assessed, using two scenarios differing in their personal care needs (low care needs vs. moderate care needs) in two situational contexts (emergency detection vs. medical reminders). RESULTS: Personal care needs influence the perception of benefits, barriers, and general acceptance of assisting technologies, independent from situational context. Higher needs for care lead to higher acknowledgements of the technology's benefits, lower agreements or, in parts, higher rejections of potential barriers and higher acceptance. The two care situations differ regarding the participants' preferences for technologies: For emergency detection, smart watches and emergency buttons are clearly accepted. In contrast, cameras are consistently rejected. For situations in which medical reminders are used, smartphone and smartwatches are most wanted, whereas audio assistants and smart TV were rather rejected. CONCLUSIONS: The results provide insights into users' preferences for specific technologies for the purpose of emergency detection and medical reminders as well as for the important influence of personal care needs. These insights can be used to derive user-tailored solutions of technology configurations for specific care needs and situations.