Predicting mHealth Acceptance Using the UTAUT2 Technology Acceptance Model: A Mixed-Methods Approach.

Area of Science:

• Digital Health
• Health Informatics
• Human-Computer Interaction

Background:

• Mobile health (mHealth) apps are vital for chronic disease management, yet user acceptance remains a barrier.
• Existing technology acceptance models like UTAUT2 require adaptation for the specific mHealth context.
• Understanding and improving mHealth app adoption is crucial for effective patient support.

Purpose of the Study:

• To evaluate the predictive power of the UTAUT2 model for mHealth app acceptance.
• To identify and validate additional health-related constructs that enhance UTAUT2's predictive capabilities.
• To refine technology acceptance models for better mHealth user adoption.

Main Methods:

• A mixed-methods approach combining qualitative (literature search, expert/patient interviews) and quantitative (cross-sectional survey) research.
• Involved 413 patients in a quantitative survey to validate findings.
• Utilized qualitative data triangulation to identify relevant constructs.

Main Results:

• Two new constructs, "perceived disease threat" and "trust," were identified as significant predictors of mHealth acceptance.
• These constructs were not originally included in the UTAUT2 model.
• The study validated the relevance of these new factors in the mHealth domain.

Conclusions:

• The UTAUT2 model was successfully extended with "perceived disease threat" and "trust" for the mHealth context.
• This enhanced model offers improved prediction of mHealth app acceptance.
• Findings support the development of more effective mHealth interventions for chronic diseases.