Evidence map›Paper›PMID 41662393›Full record

ArticlePloS one2026

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

Bryson Carrier, Jennifer A Bunn, Chris Eschbach, Joel D Reece, Gregory J Welk, Brett A Dolezal, James W Navalta

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Bryson CarrierDepartment of Kinesiology, University of Nevada, Las Vegas, Las Vegas Nevada, United States of America.ORCID https://orcid.org/0000-0002-7786-4240
Jennifer A BunnSam Houston State University, College of Health Sciences, Huntsville, Texas, United States of America.
Chris Eschbach12th State Nutrition, Raleigh, North Carolina, United States of America.
Joel D ReeceBrigham Young University Hawaii, Faculty of Sciences, Laie, Hawaii, United States of America.
Gregory J WelkDepartment of Kinesiology, Iowa State University, Ames, Iowa, United States of America.
Brett A DolezalDavid Geffen School of Medicine, University of California, Los Angeles, California, United States of America.
James W NavaltaDepartment of Kinesiology, University of Nevada, Las Vegas, Las Vegas Nevada, United States of America.ORCID https://orcid.org/0000-0002-4445-7614

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ChecklistWearable Electronic DevicesBiasHumansReproducibility of ResultsResearch Design

Identifiers

PMID41662393
PMCPMC12885281

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.