Person-generated Data in Self-quantification

Kathleen Gray; Fernando J. Martin-Sanchez; Guillermo H. Lopez-Campos; Manal Almalki; Mark Merolli

doi:10.3414/ME15-02-0006

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2017; 56(01): 40-45
DOI: 10.3414/ME15-02-0006

Wearable Therapy

Schattauer GmbH

Person-generated Data in Self-quantification

A Health Informatics Research Program

Authors

Kathleen Gray

¹Health and Biomedical Informatics Centre, The University of Melbourne, Melbourne, Victoria, Australia
Fernando J. Martin-Sanchez

²Department of Healthcare Policy and Research Division of Health Informatics, Weill Cornell Medicine, New York, NY, USA
Guillermo H. Lopez-Campos

¹Health and Biomedical Informatics Centre, The University of Melbourne, Melbourne, Victoria, Australia
Manal Almalki

¹Health and Biomedical Informatics Centre, The University of Melbourne, Melbourne, Victoria, Australia
Mark Merolli

¹Health and Biomedical Informatics Centre, The University of Melbourne, Melbourne, Victoria, Australia

Abstract

Summary

Objectives: The availability of internet-connected mobile, wearable and ambient consumer technologies, direct-to-consumer e-services and peer-to-peer social media sites far outstrips evidence about the efficiency, effectiveness and efficacy of using them in healthcare applications. The aim of this paper is to describe one approach to build a program of health informatics research, so as to generate rich and robust evidence about health data and information processing in self-quantification and associated healthcare and health outcomes.

Methods: The paper summarises relevant health informatics research approaches in the literature and presents an example of developing a program of research in the Health and Biomedical Informatics Centre (HaBIC) at the University of Melbourne. The paper describes this program in terms of research infrastructure, conceptual models, research design, research reporting and knowledge sharing.

Results: The paper identifies key outcomes from integrative and multiple-angle approaches to investigating the management of information and data generated by use of this Centre’s collection of wearable, mobiles and other devices in health self-monitoring experiments. These research results offer lessons for consumers, developers, clinical practitioners and biomedical and health informatics researchers.

Conclusions: Health informatics is increasingly called upon to make sense of emerging self-quantification and other digital health phenomena that are well beyond the conventions of healthcare in which the field of informatics originated and consolidated. To make a substantial contribution to optimise the aims, processes and outcomes of health self-quantification needs further work at scale in multi-centre collaborations for this Centre and for health informatics researchers generally.

Keywords

Autoexperimentation - consumer participation - participatory health - research design - self-quantification - telemedicine

Volltext

Referenzen

References
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