Evidence map›Paper›PMID 36173371›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2022

An interactive fitness-for-use data completeness tool to assess activity tracker data.

Sylvia Cho, Ipek Ensari, Noémie Elhadad, Chunhua Weng, Jennifer M Radin, Brinnae Bent, Pooja Desai, Karthik Natarajan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sylvia ChoDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.ORCID 0000-0002-0263-0343
Ipek EnsariDepartment of Artificial Intelligence and Human Health, Icahn School of Medicine, New York, New York, USA.ORCID 0000-0001-7745-8501
Noémie ElhadadDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Chunhua WengDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Jennifer M RadinScripps Research Translational Institute, La Jolla, California, USA.
Brinnae BentDepartment of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
Pooja DesaiDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Karthik NatarajanDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.ORCID 0000-0002-9066-9431

Funding

Enhancing All of Us Data Resources for Nutrition Precision Health: the All of Us Data and Research CenterU2COD023196 · OD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GLAZER, DAVID, HARRIS, PAUL A. · 2016 to 2022
$143.7M
Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
NCATS NIH HHS 1U01TR002062-01NCATS NIH HHS U01 TR002062NIH HHS U2C OD023196
6 · The paper itself

Abstract

objectiveTo design and evaluate an interactive data quality (DQ) characterization tool focused on fitness-for-use completeness measures to support researchers' assessment of a dataset. MATERIALS AND

methodsDesign requirements were identified through a conceptual framework on DQ, literature review, and interviews. The prototype of the tool was developed based on the requirements gathered and was further refined by domain experts. The Fitness-for-Use Tool was evaluated through a within-subjects controlled experiment comparing it with a baseline tool that provides information on missing data based on intrinsic DQ measures. The tools were evaluated on task performance and perceived usability.

resultsThe Fitness-for-Use Tool allows users to define data completeness by customizing the measures and its thresholds to fit their research task and provides a data summary based on the customized definition. Using the Fitness-for-Use Tool, study participants were able to accurately complete fitness-for-use assessment in less time than when using the Intrinsic DQ Tool. The study participants perceived that the Fitness-for-Use Tool was more useful in determining the fitness-for-use of a dataset than the Intrinsic DQ Tool. DISCUSSION: Incorporating fitness-for-use measures in a DQ characterization tool could provide data summary that meets researchers needs. The design features identified in this study has potential to be applied to other biomedical data types.

conclusionA tool that summarizes a dataset in terms of fitness-for-use dimensions and measures specific to a research question supports dataset assessment better than a tool that only presents information on intrinsic DQ measures.

Indexed as

Data AccuracyFitness TrackersExerciseHumansdata qualityfitness trackerspatient-generated health datausability testinguser-centered design

Identifiers

PMID36173371
PMCPMC9667174

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.