The Definitions of Health Apps and Medical Apps From the Perspective of Public Health and Law: Qualitative Analysis of an Interdisciplinary Literature Overview.

BACKGROUND: The terms health app and medical app are often used interchangeably but do not necessarily mean the same thing. To better understand these terms and better regulate such technologies, we need distinct definitions of health and medical apps. OBJECTIVE: This study aimed to provide an overview of the definitions of health and medical apps from an interdisciplinary perspective. We summarized the core elements of the identified definitions for their holistic understanding in the context of digital public health. METHODS: The legal frameworks for medical device regulation in the United States, the European Union, and Germany formed the basis of this study. We then searched 6 databases for articles defining health or medical apps from an interdisciplinary perspective. The narrative literature review was supported by a forward and backward snowball search for more original definitions of health and medical apps. A qualitative analysis was conducted on the identified relevant aspects and core elements of each definition. On the basis of these findings, we developed a holistic definition of health and medical apps and created a decision flowchart to highlight the differences between the 2 types. RESULTS: The legal framework showed that medical apps could be regulated as mobile medical devices, whereas there is no legal term for health apps. Our narrative literature review identified 204 peer-reviewed publications that offered a definition of health and medical apps. After screening for original definitions and applying the snowball method, 11.8% (24/204) of the publications were included in the qualitative analysis. Of these 24 publications, 22 (88%) provided an original definition of health apps and 11 (44%) described medical apps. The literature suggests that medical apps are a part of health apps. To describe health or medical apps, most definitions used the user group, a description of health, the device, the legal regulation, collected data, or technological functions. However, the regulation should not be a distinction criterion as it requires legal knowledge, which is neither suitable nor practical. An app's intended medical or health use enables a clear differentiation between health and medical apps. Ultimately, the health aim of an app and its main target group are the only distinction criteria. CONCLUSIONS: Health apps are software programs on mobile devices that process health-related data on or for their users. They can be used by every health-conscious person to maintain, improve, or manage the health of an individual or the community. As an umbrella term, health apps include medical apps. Medical apps share the same technological functions and devices. Health professionals, patients, and family caregivers are the main user groups. Medical apps are intended for clinical and medical purposes and can be legally regulated as mobile medical devices.

The digital rainbow: Digital determinants of health inequities.

The widely used socioecological rainbow model from Dahlgren and Whitehead specifies determinants of health inequity on multiple hierarchical levels and suggests that these determinants may interact both within and between levels. At the time of its inception, digital determinants only played a minor role in tackling inequities in public health and were therefore not specifically considered. This has dramatically changed: From today's perspective, health inequities increasingly depend on digital determinants. In this article, we suggest adapting the Dahlgren-Whitehead model to reflect these developments. We propose a model that allows formulating testable hypotheses, interpreting research findings, and developing policy implications against the background of the global spread of digital technologies. This may facilitate the development of a new line of research and logic models for public health interventions in the digital age. Using the COVID-19 pandemic as a case study, we illustrate how the digitization of all aspects of life affects the different levels of determinants of health inequities in the Dahlgren-Whitehead model. In doing so, we deliberately argue for not introducing a separate digital sphere in its own right, but for understanding digitization as a phenomenon that permeates all levels of determinants of health inequities. As a result, we present a digital rainbow model that integrates Dahlgren and Whitehead's 1991 model with digital environments to identify current health promotion and research issues without changing the rainbow model's initial structure.