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Updated: Jan 20, 2026
Hypertension V: Nursing Management
Published on: June 19, 2025
Acceptance of mHealth Apps for Self-Management Among People with Hypertension
Bernhard Breil1, Lisanne Kremer1, Severin Hennemann2
1Hochschule Niederrhein, University of Applied Sciences.
Background:
Mobile health applications (mHealth apps) have the potential to help patients with chronic conditions such as hypertension by supporting self-management activities in daily life. However, the uptake of mHealth apps remains poor among patients. To improve the utilization of mHealth apps for hypertension, the analysis of the behavioral intention to use such applications must consider personality traits and illness-related perceptions.
Method:
Adults with hypertension in Germany and Austria filled out a self-administered questionnaire in a cross-sectional study based on the UTAUT-model in order to identify potential predictors for the behavioral intention to use mHealth applications as an indicator for their early acceptance. Beyond the four core determinants of acceptance of the UTAUT (performance expectancy, effort expectancy, social influence and facilitating conditions), self-efficacy, openness to experience and perceived health threat were analyzed as predictors.
Results:
145 participants (mean age 52.51 years, SD 14.33; 60% female) completed the survey. Acceptance was moderate on average (M = 3.26, SD = 1.07, min 1 to max 5). In a multiple hierarchical regression, performance expectancy and effort expectancy were confirmed as significant predictors of acceptance (step 1, R2 = .57, p < .001), while self-efficacy could not be confirmed (step 2, p = .87). In addition, perceived health threat (β = .12, p < .05) and openness to experience (β = .22, p < .001) had a significant influence on acceptance of mHealth apps for hypertension (step 3, overall model with R2 = .62). Age showed a negative association with the intention to use (β = .22, p = .005) while no influence of gender could be found (p = .06).
Conclusion:
Above expectations regarding effectiveness and usability, openness to experience and perceived health threat make a significant contribution in predicting the acceptance of mHealth solutions in the field of chronic diseases.
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