The WEAR-BOT checklist: A risk of bias tool for evaluating validity and reliability research in wearable technology

This paper proposes an innovative tool designed to standardize the evaluation of validity and reliability studies in the rapidly evolving field of wearable technology. We introduce the WEArable Technology Risk of Bias and Objectivity Tool (WEAR-BOT), a tool that addresses the need for a comprehensive and systematic way to assess bias in studies examining consumer-grade and research-grade wearable devices. This is the first tool designed to evaluate the risk of bias in validation and reliability studies. Other risk of bias tools like the Cochrane ROB, COSMIN, or the many other risk of bias tools are designed for different study methodologies or are overly broad and largely unnecessary for the specifics of validity/reliability testing studies. The development of the WEAR-BOT involved extensive collaboration among experts, encompassing iterative, open-ended discussions, several rounds of anonymous Delphi-style questionnaires, and pilot testing. The tool comprises detailed checklists for both validity and reliability studies, with subdivisions focusing on study design, methodology, statistical analysis methods, and other critical aspects. The tool balances the need for rigor with ease-of-use. It incorporates a variety of questions to rigorously evaluate the risk of bias in these studies and aims to enhance and standardize methodological approaches in the field. The tool is practical, easily available, and easy to use, as it is built in Microsoft Excel and contains macros that are intuitive and easy to use that allow the user to work more efficiently. The WEAR-BOT represents a significant advancement in the standardization of research methods and statistical analysis in the domain of wearable technology.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2026-02-09
DOI
https://doi.org/10.1371/journal.pone.0338014
Citations
1
Primary Topic
Delphi Technique in Research
Type
article
Field-Weighted Citation Impact
55.57
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The WEAR-BOT checklist: A risk of bias tool for evaluating validity and reliability research in wearable technology

Gregory J. Welk, Brett A. Dolezal, Joel D. Reece, Jennifer A. Bunn et al.
1 citations
PLoS ONE
Delphi Technique in Research
55.57
article

The WEAR-BOT checklist: A risk of bias tool for evaluating validity and reliability research in wearable technology

Gregory J. Welk, Brett A. Dolezal, Joel D. Reece, Jennifer A. Bunn, Bryson Carrier, Chris Eschbach, James W. Navalta
article en
1 citations

Abstract

This paper proposes an innovative tool designed to standardize the evaluation of validity and reliability studies in the rapidly evolving field of wearable technology. We introduce the WEArable Technology Risk of Bias and Objectivity Tool (WEAR-BOT), a tool that addresses the need for a comprehensive and systematic way to assess bias in studies examining consumer-grade and research-grade wearable devices. This is the first tool designed to evaluate the risk of bias in validation and reliability studies. Other risk of bias tools like the Cochrane ROB, COSMIN, or the many other risk of bias tools are designed for different study methodologies or are overly broad and largely unnecessary for the specifics of validity/reliability testing studies. The development of the WEAR-BOT involved extensive collaboration among experts, encompassing iterative, open-ended discussions, several rounds of anonymous Delphi-style questionnaires, and pilot testing. The tool comprises detailed checklists for both validity and reliability studies, with subdivisions focusing on study design, methodology, statistical analysis methods, and other critical aspects. The tool balances the need for rigor with ease-of-use. It incorporates a variety of questions to rigorously evaluate the risk of bias in these studies and aims to enhance and standardize methodological approaches in the field. The tool is practical, easily available, and easy to use, as it is built in Microsoft Excel and contains macros that are intuitive and easy to use that allow the user to work more efficiently. The WEAR-BOT represents a significant advancement in the standardization of research methods and statistical analysis in the domain of wearable technology.

PLoS ONEVol. 21(2)
Brigham Young University–Hawaii (US), Oregon Institute of Technology (US), University of Nevada, Las Vegas (US), North Carolina State University (US), University of California, Los Angeles (US), Iowa State University (US), Sam Houston State University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 0%
Delphi Technique in Research
55.57
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.