Factors associated with intention to use an educative mHealth application among high-risk target groups in health prevention.

Abstract

Background: An unhealthy and sedentary lifestyle is widespread in industrialized countries, increasing chronic disease risks and potentially leading to a systemic crisis in nursing and elder care. Mobile health (mHealth) applications offer a promising solution by promoting health literacy and supporting holistic, health-oriented lifestyles. However, little is known about the factors influencing the intended use of such applications among individuals with an urgent need for preventive action. This study therefore aims to investigate behavioral intention (BI) to use a chatbot-based mHealth application designed to enhance health literacy and support balanced approaches to physical activity, nutrition, and stress management. Specifically, it seeks to identify key determinants influencing intention to use the application among individuals with high prevention needs, in order to inform the design of engaging and sustainable digital health interventions.

Methods: A quantitative survey design based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model was applied to examine determinants of BI to use the Digital Health Companion (DHC), a chatbot-based mHealth application. The model variables included performance expectancy (PE), hedonic motivation (HM), social influence (SI), effort expectancy (EE), and facilitating conditions (FC). In addition, potential moderating effects of age, gender, prior mHealth experience, educational status, and health literacy were assessed using supplementary questionnaires.

Results: Data from 105 participants (58 female; 38.4 years) with need for action in the area of physical activity, nutrition, or stress management were analyzed. Results indicate that PE and HM significantly influence BI (P<0.001), while SI shows the least influence (P=0.26) as well as the lowest score overall and therefore has the highest potential to strengthen BI as social functions were enhanced. Factors such as age, gender, prior experience with mHealth applications, or educational status did not influence BI.

Conclusions: These findings underscore the need for engaging, user-friendly mHealth applications that motivate preventive health measures, reducing future care dependency. Users must be convinced of the personal health benefits (PE) of the application and experience a certain degree of enjoyment (HM) while using it. The integration of social features however, especially in cooperation with health insurance companies, is restricted by stringent data protection regulations.