Explaining intention and use of Mhealth with the unified theory of acceptance and use of technology

BACKGROUND: mHealth can alleviate the pressure on healthcare. However, its adoption is limited, especially among low socioeconomic groups. Identifying factors that influence the intention to and actual use of mHealth is crucial to increase adoption.

OBJECTIVE: To identify factors influencing intention and use of mHealth apps among Dutch adults, focusing on various socioeconomic populations using the Unified Theory of Acceptance and Use of Technology.

METHOD AND SAMPLE: A web based survey recruited 242 Dutch adults via panels and convenience sampling. Two multiple linear regressions were performed to explain behavioral intention and the use of mHealth.

RESULTS: Of the participants, 65.0 % were low educated and 10.0 % had a low income. A significant model fit explaining 82.5 % of the variance was found for the outcome behavioral intention to use mHealth. Within this model, five significant correlating factors were found: habit (β = 0.23), compatibility (β = 0.57), Age (β = 0.10), performance expectancy (β = 0.15) and price value (β = -0.08). A significant model fit for the outcome use of mHealth was found and explained 57.9 % of variance. Factors that correlated significantly with the outcome were: habit (β = 0.21), education (β = 0.17), innovativeness (β = 0.12) and behavioral intention (β = 0.58). Low-income participants did not use mHealth less. High education, personal innovativeness, and older age were linked to greater mHealth use and intention.

CONCLUSION: To increase mHealth use, the results of this study suggest raising awareness of its benefits and compatibility. Additionally, encouraging habitual use can boost both intention and usage.