A data-driven approach to the “Everesting” cycling challenge

Simple item page
dc.contributor.authorSeo, Junhyeonen
dc.contributor.authorRaeymaekers, Barten
dc.date.accessioned2025-02-17T17:37:11Zen
dc.date.available2025-02-17T17:37:11Zen
dc.date.issued2023-02-08en
dc.description.abstractThe “Everesting” challenge is a cycling activity in which a cyclist repeats a hill until accumulating an elevation gain equal to the elevation of Mount Everest in a single ride. The challenge experienced a surge in interest during the COVID-19 pandemic and the cancelation of cycling races around the world that prompted cyclists to pursue alternative, individual activities. The time to complete the Everesting challenge depends on the fitness and talent of the cyclist, but also on the length and gradient of the hill, among other parameters. Hence, preparing an Everesting attempt requires understanding the relationship between the Everesting parameters and the time to complete the challenge. We use web-scraping to compile a database of publicly available Everesting attempts, and we quantify and rank the parameters that determine the time to complete the challenge. We also use unsupervised machine learning algorithms to segment cyclists into distinct groups according to their characteristics and performance. We conclude that the power per unit body mass of the cyclist and the tradeoff between the gradient of the hill and the distance are the most important considerations when attempting the Everesting challenge. As such, elite cyclists best select a hill with gradient > 12%, whereas amateur and recreational cyclists best select a hill with gradient < 10% to minimize the time to complete the Everesting challenge.en
dc.description.versionPublished versionen
dc.format.extent8 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier2269 (Article number)en
dc.identifier.doihttps://doi.org/10.1038/s41598-023-29435-wen
dc.identifier.eissn2045-2322en
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.orcidRaeymaekers, Bart [0000-0001-5902-3782]en
dc.identifier.other10.1038/s41598-023-29435-w (PII)en
dc.identifier.pmid36755051en
dc.identifier.urihttps://hdl.handle.net/10919/124600en
dc.identifier.volume13en
dc.language.isoenen
dc.publisherNature Publishing Groupen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/36755051en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subject.meshHumansen
dc.subject.meshExerciseen
dc.subject.meshAlgorithmsen
dc.subject.meshBicyclingen
dc.subject.meshPandemicsen
dc.subject.meshCOVID-19en
dc.titleA data-driven approach to the “Everesting” cycling challengeen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dcterms.dateAccepted2023-02-03en
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Engineeringen
pubs.organisational-groupVirginia Tech/Engineering/Mechanical Engineeringen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/COE T&R Facultyen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A data-driven approach to the Everesting cycling challenge.pdf
Size:
2.97 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Plain Text
Description:
Download

Collections

All Faculty Deposits
Scholarly Works, Mechanical Engineering