List of Titles

Schema : an open-source, distributed mobile platform for deploying mHealth research tools and interventions

Authors: Shatte, Adrian , Teague, Samantha
Date: 2020
Type: Text , Journal article
Relation: BMC Medical Research Methodology Vol. 20, no. 1 (2020), p.
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Description: Background: Mobile applications for health, also known as 'mHealth apps', have experienced increasing popularity over the past ten years. However, most publicly available mHealth apps are not clinically validated, and many do not utilise evidence-based strategies. Health researchers wishing to develop and evaluate mHealth apps may be impeded by cost and technical skillset barriers. As traditionally lab-based methods are translated onto mobile platforms, robust and accessible tools are needed to enable the development of quality, evidence-based programs by clinical experts. Results: This paper introduces schema, an open-source, distributed, app-based platform for researchers to deploy behavior monitoring and health interventions onto mobile devices. The architecture and design features of the platform are discussed, including flexible scheduling, randomisation, a wide variety of survey and media elements, and distributed storage of data. The platform supports a range of research designs, including cross-sectional surveys, ecological momentary assessment, randomised controlled trials, and micro-randomised just-in-time adaptive interventions. Use cases for both researchers and participants are considered to demonstrate the flexibility and usefulness of the platform for mHealth research. Conclusions: The paper concludes by considering the strengths and limitations of the platform, and a call for support from the research community in areas of technical development and evaluation. To get started with schema, please visit the GitHub repository: Https://github.com/schema-app/schema. © 2020 The Author(s).

Emerging point of care devices and artificial intelligence : prospects and challenges for public health

- Stranieri, Andrew, Venkatraman, Sitalakshmi, Minicz, John, Zarnegar, Armita, Firmin, Sally, Balasubramanian, Venki, Jelinek, Herbert

Authors: Stranieri, Andrew , Venkatraman, Sitalakshmi , Minicz, John , Zarnegar, Armita , Firmin, Sally , Balasubramanian, Venki , Jelinek, Herbert
Date: 2022
Type: Text , Journal article
Relation: Smart Health Vol. 24, no. (2022), p.
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Description: Risk assessments for numerous conditions can now be performed cost-effectively and accurately using emerging point of care devices coupled with machine learning algorithms. In this article, the case is advanced that point of care testing in combination with risk assessments generated with artificial intelligence algorithms, applied to the universal screening of the general public for multiple conditions at one session, represents a new kind of in-expensive screening that can lead to the early detection of disease and other public health benefits. A case study of a diabetes screening clinic in a rural area of Australia is presented to illustrate its benefits. Universal, poly-aetiological screening is shown to meet the ten World Health Organisation criteria for screening programmes. © Elsevier Inc.

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