Consumer Behaviour in Mobile Health Applications (mHealth)

General description of the project

The use of mobile health applications (mHealth) has experienced remarkable growth in recent years, establishing itself as an increasingly prevalent tool in the promotion of healthy habits and self-care. This phenomenon is part of a context in which, both at regional and European level, health promotion and disease prevention are strategic priorities, as pointed out by international organisations such as the World Health Organisation.

mHealth applications have significant potential to address global public health issues, such as sedentary lifestyles, which are considered one of the main risk factors associated with mortality. However, despite their widespread availability and initial adoption, important questions remain about the factors that influence their sustained use, user satisfaction, and perceived usefulness. The lack of systematic knowledge on these aspects limits both the design of more effective applications and their informed integration into health recommendations.

Objectives

The objective of this research is to comprehensively analyse the factors that influence consumer adoption, use and satisfaction with mobile health applications (mHealth), in order to generate knowledge that can guide both the design of these applications and the development of innovative strategies for personalised health monitoring. In particular, the project seeks to understand why users decide to use this type of application and what elements determine their experience and perception of value over time.

The research aims to explore the role played by motivations for use, both hedonic (related to enjoyment, motivation and entertainment) and utilitarian (linked to functionality, ease of use and perceived usefulness), as well as the influence of perceived risks on user satisfaction. It also aims to analyse how these factors vary depending on the type of health application, such as those focused on monitoring physical activity, step counting, nutrition or rest, and how they contribute to explaining positive or negative user ratings.

This analysis will provide an understanding of which attributes, functional benefits and emotional benefits influence the construction of positive or negative perceptions, and how these perceptions affect the acceptance and evaluation of the applications.

Contribution of NAIR Center

NAIR Center has carried out a high-value methodological and technological contribution to the project, making significant progress in the use of artificial intelligence for the analysis of consumer’s behaviour in digital health. Its contribution has focused on designing a holistic approach capable of analysing user-generated content on a large scale, combining advanced natural language processing and deep learning models tailored to the mHealth application domain.

The development of specialised models has made it possible to automatically and accurately identify motivations for using applications, user perceptions and key dimensions of brand image, as well as to extract relevant semantic patterns from large volumes of text. The integration of state-of-the-art language models with expert validation has facilitated scalable and reliable analysis, capable of capturing the complexity of user opinions while maintaining methodological rigour.

As a result, the project has established a robust and reproducible analytical framework for the systematic study of health applications from the user's perspective, while also generating valuable labelled data resources for future research.

Funding

Project funded by the ‘Programa MRR Investigo’, an initiative supported by the European Union under the Next Generation programme, which facilitates the recruitment of research staff.

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