Literature DB >> 27782291

Person-generated Data in Self-quantification. A Health Informatics Research Program.

Kathleen Gray1, Fernando J Martin-Sanchez, Guillermo H Lopez-Campos, Manal Almalki, Mark Merolli.   

Abstract

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.

Entities:  

Keywords:  Autoexperimentation; consumer participation; participatory health; research design; self-quantification; telemedicine

Mesh:

Year:  2016        PMID: 27782291     DOI: 10.3414/ME15-02-0006

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  5 in total

1.  Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

Authors:  Ying Kuen Cheung; Pei-Yun Sabrina Hsueh; Min Qian; Sunmoo Yoon; Laura Meli; Keith M Diaz; Joseph E Schwartz; Ian M Kronish; Karina W Davidson
Journal:  Methods Inf Med       Date:  2018-02-10       Impact factor: 2.176

2.  Research data management in health and biomedical citizen science: practices and prospects.

Authors:  Ann Borda; Kathleen Gray; Yuqing Fu
Journal:  JAMIA Open       Date:  2019-12-09

3.  Health Observation App for COVID-19 Symptom Tracking Integrated With Personal Health Records: Proof of Concept and Practical Use Study.

Authors:  Keiichi Yamamoto; Tsubasa Takahashi; Miwa Urasaki; Yoichi Nagayasu; Tomonari Shimamoto; Yukiko Tateyama; Keiichi Matsuzaki; Daisuke Kobayashi; Satoshi Kubo; Shigeyuki Mito; Tatsuya Abe; Hideo Matsuura; Taku Iwami
Journal:  JMIR Mhealth Uhealth       Date:  2020-07-06       Impact factor: 4.773

4.  Development and Validation of a Taxonomy for Characterizing Measurements in Health Self-Quantification.

Authors:  Manal Almalki; Kathleen Gray; Fernando Martin-Sanchez
Journal:  J Med Internet Res       Date:  2017-11-03       Impact factor: 5.428

5.  A Brief Survey on Six Basic and Reduced eHealth Indicators in Seven Countries in 2017.

Authors:  Reinhold Haux; Elske Ammenwerth; Sabine Koch; Christoph U Lehmann; Hyeoun-Ae Park; Kaija Saranto; C P Wong
Journal:  Appl Clin Inform       Date:  2018-09-05       Impact factor: 2.342

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.