A Framework for Analyzing and Measuring Usage and Engagement Data (AMUsED) in Digital Interventions: Viewpoint.

behavioral research data analysis health internet patient engagement

Journal

Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882

Informations de publication

Date de publication:
15 02 2019
Historique:
received: 04 05 2018
accepted: 30 10 2018
revised: 17 10 2018
entrez: 16 2 2019
pubmed: 16 2 2019
medline: 18 12 2019
Statut: epublish

Résumé

Trials of digital interventions can yield extensive, in-depth usage data, yet usage analyses tend to focus on broad descriptive summaries of how an intervention has been used by the whole sample. This paper proposes a novel framework to guide systematic, fine-grained usage analyses that better enables understanding of how an intervention works, when, and for whom. The framework comprises three stages to assist in the following: (1) familiarization with the intervention and its relationship to the captured data, (2) identification of meaningful measures of usage and specifying research questions to guide systematic analyses of usage data, and (3) preparation of datasheets and consideration of available analytical methods with which to examine the data. The framework can be applied to inform data capture during the development of a digital intervention and/or in the analysis of data after the completion of an evaluation trial. We will demonstrate how the framework shaped preparation and aided efficient data capture for a digital intervention to lower transmission of cold and flu viruses in the home, as well as how it informed a systematic, in-depth analysis of usage data collected from a separate digital intervention designed to promote self-management of colds and flu. The Analyzing and Measuring Usage and Engagement Data (AMUsED) framework guides systematic and efficient in-depth usage analyses that will support standardized reporting with transparent and replicable findings. These detailed findings may also enable examination of what constitutes effective engagement with particular interventions.

Identifiants

pubmed: 30767905
pii: v21i2e10966
doi: 10.2196/10966
pmc: PMC6396072
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e10966

Informations de copyright

©Sascha Miller, Ben Ainsworth, Lucy Yardley, Alex Milton, Mark Weal, Peter Smith, Leanne Morrison. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.02.2019.

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Auteurs

Sascha Miller (S)

Center for Clinical and Community Applications of Health Psychology, Department of Psychology, University of Southampton, Southampton, United Kingdom.

Ben Ainsworth (B)

Department of Psychology, University of Bath, Bath, United Kingdom.

Lucy Yardley (L)

Center for Clinical and Community Applications of Health Psychology, Department of Psychology, University of Southampton, Southampton, United Kingdom.
School of Psychological Science, University of Bristol, Bristol, United Kingdom.

Alex Milton (A)

School of Psychological Science, University of Bristol, Bristol, United Kingdom.

Mark Weal (M)

Web and Internet Science Group, School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom.

Peter Smith (P)

Department of Social Statistics and Demography, School of Economic, Social and Political Sciences, University of Southampton, Southampton, United Kingdom.

Leanne Morrison (L)

Center for Clinical and Community Applications of Health Psychology, Department of Psychology, University of Southampton, Southampton, United Kingdom.
Primary Care and Population Sciences, School of Medicine, University of Southampton, Southampton, United Kingdom.

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Classifications MeSH