Toward a Better Understanding of the Intention to Use mHealth Apps: Exploratory Study

Pedro R Palos-Sanchez, Jose Ramon Saura, Miguel Ángel Rios Martin, Mariano Aguayo-Camacho, Pedro R Palos-Sanchez, Jose Ramon Saura, Miguel Ángel Rios Martin, Mariano Aguayo-Camacho

Abstract

Background: An increasing number of mobile health (mHealth) apps are becoming available for download and use on mobile devices. Even with the increase in availability and use of mHealth apps, there has still not been a lot of research into understanding the intention to use this kind of apps.

Objective: The purpose of this study was to investigate a technology acceptance model (TAM) that has been specially designed for primary health care applications.

Methods: The proposed model is an extension of the TAM, and was empirically tested using data obtained from a survey of mHealth app users (n=310). The research analyzed 2 additional external factors: promotion of health and health benefits. Data were analyzed with a PLS-SEM software and confirmed that gender moderates the adoption of mHealth apps in Spain. The explanatory capacity (R2 for behavioral intention to use) of the proposed model was 76.4%. Likewise, the relationships of the external constructs of the extended TAM were found to be significant.

Results: The results show the importance of healthy habits developed by using mHealth apps. In addition, communication campaigns for these apps should be aimed at transferring the usefulness of eHealth as an agent for transforming attitudes; additionally, as more health benefits are obtained, ease of use becomes greater. Perceived usefulness (PU; β=.415, t0.001;4999=3.442, P=.001), attitude toward using (β=.301, t0.01;499=2.299, P=.02), and promotion of health (β=.210, t0.05;499=2.108, P=.03) were found to have a statistically significant impact on behavior intention to use eHealth apps (R2=76.4%). Perceived ease of use (PEOU; β=.179, t0.01;499=2.623, P=.009) and PU (β=.755, t0.001;499=12.888, P<.001) were found to have a statistically significant impact on attitude toward using (R2>=78.2%). Furthermore, PEOU (β=.203, t0.01;499=2.810, P=.005), health benefits (β=.448, t0.001;499=4.010, P<.001), and promotion of health (β=.281, t0.01;499=2.393, P=.01) exerted a significant impact on PU (R2=72.7%). Finally, health benefits (β=.640, t0.001;499=14.948, P<.001) had a statistically significant impact on PEOU (R2=40.9%), while promotion of health (β=.865, t0.001;499=29.943, P<.001) significantly influenced health benefits (R2=74.7%).

Conclusions: mHealth apps could be used to predict the behavior of patients in the face of recommendations to prevent pandemics, such as COVID-19 or SARS, and to track users' symptoms while they stay at home. Gender is a determining factor that influences the intention to use mHealth apps, so perhaps different interfaces and utilities could be designed according to gender.

Keywords: COVID-19; PLS–SEM; TAM; eHealth; mHealth apps; mobile apps; promotion of health.

Conflict of interest statement

Conflicts of Interest: None declared.

©Pedro R Palos-Sanchez, Jose Ramon Saura, Miguel Ángel Rios Martin, Mariano Aguayo-Camacho. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 09.09.2021.

Figures

Figure 1
Figure 1
Research model to explore the influence of health benefits and promotion of health on the mHealth app adoption model. TAM: technology acceptance model.
Figure 2
Figure 2
Analysis results (path coefficient, β, and t statistic are presented). TAM: technology acceptance model.

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