Empowerment through health self-testing apps? Revisiting empowerment as a process.

Empowerment, an already central concept in public health, has gained additional relevance through the expansion of mobile health (mHealth). Especially direct-to-consumer self-testing app companies mobilise the term to advertise their products, which allow users to self-test for various medical conditions independent of healthcare professionals. This article first demonstrates the absence of empowerment conceptualisations in the context of self-testing apps by engaging with empowerment literature. It then contrasts the service these apps provide with two widely cited empowerment definitions by the WHO, which describe the term as a process that, broadly, leads to knowledge and control of health decisions. We conclude that self-testing apps can only partly empower their users, as they, we argue, do not provide the type of knowledge and control the WHO definitions describe. More importantly, we observe that this shortcoming stems from the fact that in the literature on mHealth and in self-testing marketing, empowerment is understood as a goal rather than a process. This characterises a shift in the meaning of empowerment in the context of self-testing and mHealth, one that reveals a lack of awareness for relational and contextual factors that contribute to empowerment. We argue that returning to a process-understanding of empowerment helps to identify these apps' deficits, and we conclude the article by briefly suggesting several strategies to increase self-testing apps' empowerment function.

Towards a Design Toolkit of Informed Consent Models Across Fields: A Systematic Review.

In the 60+ years that the modern concept of informed consent has been around, researchers in various fields of practice, especially medical ethics, have developed new models to overcome theoretical and practical problems. While (systematic) literature reviews of such models exist within given fields (e.g., genetic screening), this article breaks ground by analyzing academic literature on consent models across fields. Three electronic research databases (Scopus, Google Scholar, and Web of Science) were searched for publications mentioning informed consent models. The titles, abstracts, and if applicable, full publications were screened and coded. The resulting data on fields, models, and themes were then analyzed. We scanned 300 sources from three databases to find 207 uniquely named consent models, and created a network visualization displaying which models occur primarily in one field, and which models overlap between fields. This analysis identifies trends in the consent debate in different fields, as well as common goals of consent models. The most frequently occurring consent models are identified and defined. The analysis contributes toward a cross-disciplinary "consent design toolkit" and highlights that there are more interrelationships between models and fields than are acknowledged in the literature. Where some models are designed to solve distinctively field-specific issues and are specific to biomedical ethics, some may be adaptable and applicable for other fields including engineering and design.