Patient Acceptance of Prescribed and Fully Reimbursed mHealth Apps in Germany: An UTAUT2-based Online Survey Study.
Marie UncovskaBettina FreitagSven MeisterLeonard FehringPublished in: Journal of medical systems (2023)
The study aims to (1) investigate current levels of patient acceptance of mHealth in Germany; (2) determine the influencing factors of patients' intention to use, and (3) test the influence of prescription and reimbursement status on patient acceptance. Online survey with 1349 participants, of which 1051 were complete and included for statistical analysis, from a broad cross-section of the German population, addressing both users of mobile health (mHealth) applications and people without prior experience. SEM modeling based on a combination of two theoretical frameworks: the extended Unified Theory of Acceptance and Use of Technology and Health Protective Behavior Theories were used to assess acceptance. Users of mHealth in Germany are mostly patients between the ages of 30 - 50 with mental health or endocrine conditions. General willingness to use mHealth apps / DiGAs (mHealth apps fully reimbursed by social health insurance) is high at 76%, especially if they are governmentally certified, however only 27% of respondents were willing to pay out of pocket. With the exception of a spike in performance expectancy and data security, DiGAs lack a clear differentiation from mHealth apps. Perceived self-efficacy and performance expectancy are significant predictors of willingness to use digital health interventions; with age, attitude, and e-literacy being key demographic predictors. A key takeaway for regulators, providers of mHealth apps/ DiGAs, and other stakeholders involved in mHealth adoption is the importance of addressing negative beliefs early on, targeted communication around effortless usage of mHealth services across age groups and demographics, and focus on highlighting expected benefits of mHealth app/ DiGA usage.
Keyphrases
- mental health
- health insurance
- healthcare
- end stage renal disease
- health information
- ejection fraction
- public health
- newly diagnosed
- chronic kidney disease
- physical activity
- prognostic factors
- cross sectional
- risk assessment
- peritoneal dialysis
- transcription factor
- machine learning
- climate change
- drug delivery
- global health
- affordable care act
- artificial intelligence
- health promotion