JMIR Publications

ABSTRACT

Background:

With increasing digitalization of the health sector, more and more mobile health (mHealth) applications are coming onto the market to continuously collect and process sensitive health data for the benefit of patients and providers. These technologies open up new opportunities to make the healthcare system more efficient and save costs, but also pose potential threats such as loss of data or finances.

Objective:

The empirical review and adaptation of the extended privacy calculus model to the mHealth domain and to understand what factors influence intended usage of mHealth technologies.

Methods:

A survey study was conducted to empirically validate our model, using a case vignette as cover story. Data were collected from N = 250 German participants and analyzed using a covariance-based structural equation model (CB-SEM).

Results:

The model explains R²=86.3% of the variance in intention to use. The three main factors (social norms, attitude to privacy, and perceived control over personal data) influenced the intention to use mHealth apps, albeit partially indirectly. The intention to use mHealth apps is driven by perceived benefits of the technology, trust in the provider, and social norms. Privacy concerns have no bearing on the intention to use. The attitude to privacy has a large inhibiting effect on perceived benefits, as well as on trust in the provider. Perceived control over personal data clearly dispels privacy concerns and supports the relationship of trust between user and provider.

Conclusions:

Based on the privacy calculus, our model explains the intention to use mHealth applications better than previous ones. The findings allow healthcare providers to improve their products and to increase usage by targeting specific user groups. Ultimately, if more mHealth applications are used, their potential to improve care delivery may be realized.