Evidence map›Paper›PMID 40547055›Full record

ArticleFrontiers in digital health2025

Does digital device software lead to exclusion? Investigating a portable metabolic analysis system and the input of sex data on physiological parameters.

James W Navalta, Olivia R Perez, Michael W H Wong, Dustin W Davis

Abstract read
In one paragraph

Article in Frontiers in digital health, 2025. 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

4 authors.

James W NavaltaExercise Physiology Laboratory, Department of Kinesiology and Nutrition Sciences, University of Nevada, Las Vegas, Las Vegas, NV, United States.
Olivia R PerezExercise Physiology Laboratory, Department of Kinesiology and Nutrition Sciences, University of Nevada, Las Vegas, Las Vegas, NV, United States.
Michael W H WongExercise Physiology Laboratory, Department of Kinesiology and Nutrition Sciences, University of Nevada, Las Vegas, Las Vegas, NV, United States.
Dustin W DavisExercise Physiology Laboratory, Department of Kinesiology and Nutrition Sciences, University of Nevada, Las Vegas, Las Vegas, NV, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Digital health devices have enhanced healthcare accessibility, but their design may unintentionally exclude gender diverse people. This study examines whether the input of binary sex data in a portable metabolic analysis system (COSMED K5) impacts the accuracy of physiological measurements during self-paced exercise. Methods: Twenty adult participants (10 females, 10 males) completed two identical self-paced walking and running protocols with sex data alternately input as female or male in the device software. Key metabolic and pulmonary variables, including VO Results: No differences were observed in any measured variables between the female and male conditions during walking or running ( Conclusion: The COSMED K5 device does not utilize binary sex input to alter physiological outputs, confirming that these data remain unaffected by this demographic variable. However, the limitation of binary sex options in the device software represents a barrier to inclusivity for gender diverse people. Device manufacturers are encouraged to update software with more inclusive options, aligning with recommendations for equitable research practices and addressing existing knowledge gaps in sport and exercise science.

Indexed as

digital healthexercise physiologygender inclusionmetabolic analysiswearable technology

Identifiers

PMID40547055
PMCPMC12180134

What OpenQuestion holds

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LicenceCC BY
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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.