TY  - JOUR
AU  - Koivumäki, Timo
AU  - Pekkarinen, Saara
AU  - Lappi, Minna
AU  - Väisänen, Jere
AU  - Juntunen, Jouni
AU  - Pikkarainen, Minna
PY  - 2017
DA  - 2017/12/22
TI  - Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study
JO  - J Med Internet Res
SP  - e429
VL  - 19
IS  - 12
KW  - health behavior
KW  - consumer behavior
KW  - eHealth
KW  - surveys and questionnaires
KW  - personal health record
KW  - patient-accessible health record
KW  - adoption
KW  - UTAUT
KW  - PHR
AB  - Background: Constantly increasing health care costs have led countries and health care providers to the point where health care systems must be reinvented. Consequently, electronic health (eHealth) has recently received a great deal of attention in social sciences in the domain of Internet studies. However, only a fraction of these studies focuses on the acceptability of eHealth, making consumers’ subjective evaluation an understudied field. This study will address this gap by focusing on the acceptance of MyData-based preventive eHealth services from the consumer point of view. We are adopting the term "MyData", which according to a White Paper of the Finnish Ministry of Transport and Communication refers to "1) a new approach, a paradigmshift in personal data management and processing that seeks to transform the current organization centric system to a human centric system, 2) to personal data as a resource that the individual can access and control."  Objective: The aim of this study was to investigate what factors influence consumers’ intentions to use a MyData-based preventive eHealth service before use. Methods: We applied a new adoption model combining Venkatesh’s unified theory of acceptance and use of technology 2 (UTAUT2) in a consumer context and three constructs from health behavior theories, namely threat appraisals, self-efficacy, and perceived barriers. To test the research model, we applied structural equation modeling (SEM) with Mplus software, version 7.4. A Web-based survey was administered. We collected 855 responses. Results: We first applied traditional SEM for the research model, which was not statistically significant. We then tested for possible heterogeneity in the data by running a mixture analysis. We found that heterogeneity was not the cause for the poor performance of the research model. Thus, we moved on to model-generating SEM and ended up with a statistically significant empirical model (root mean square error of approximation [RMSEA] 0.051, Tucker-Lewis index [TLI] 0.906, comparative fit index [CFI] 0.915, and standardized root mean square residual 0.062). According to our empirical model, the statistically significant drivers for behavioral intention were effort expectancy (beta=.191, P<.001), self-efficacy (beta=.449, P<.001), threat appraisals (beta=.416, P<.001), and perceived barriers (beta=−.212, P=.009). Conclusions: Our research highlighted the importance of health-related factors when it comes to eHealth technology adoption in the consumer context. Emphasis should especially be placed on efforts to increase consumers’ self-efficacy in eHealth technology use and in supporting healthy behavior. 
SN  - 1438-8871
UR  - http://www.jmir.org/2017/12/e429/
UR  - https://doi.org/10.2196/jmir.7821
UR  - http://www.ncbi.nlm.nih.gov/pubmed/29273574
DO  - 10.2196/jmir.7821
ID  - info:doi/10.2196/jmir.7821
ER  -