Evidence map›Paper›PMID 34831820›Full record

ArticleInternational journal of environmental research and public health2021

Consistency Is Key When Setting a New World Record for Running 10 Marathons in 10 Days.

Nicolas Berger, Daniel Cooley, Michael Graham, Claire Harrison, Georgia Campbell, Russ Best

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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

6 authors.

Nicolas BergerSchool of Health and Life Sciences, Teesside University, Borough Road, Middlesbrough TS1 3BA, UK.ORCID 0000-0001-7276-5246
Daniel CooleySchool of Health and Life Sciences, Teesside University, Borough Road, Middlesbrough TS1 3BA, UK.
Michael GrahamSchool of Health and Life Sciences, Teesside University, Borough Road, Middlesbrough TS1 3BA, UK.ORCID 0000-0002-3178-3226
Claire HarrisonNewcastle Nutrition Community Team, The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle NE7 7AH, UK.
Georgia CampbellSchool of Health and Life Sciences, Teesside University, Borough Road, Middlesbrough TS1 3BA, UK.
Russ BestCentre for Sports Science & Human Performance, WINTEC, Hamilton 3240, New Zealand.ORCID 0000-0001-9017-1513

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe describe the requirements and physiological changes when running 10 consecutive marathons in 10 days at the same consistent pace by a female ultra-endurance athlete.

methodsSharon Gayter (SG) 54 yrs, 162.5 cm, 49.3 kg maximal oxygen uptake (VO

resultsSG broke the previous record by ~2.5 h, with a cumulative completion time of 43 h 51 min 39 s. Over the 10 days, weight decreased from 51 kg to 48.4 kg, bodyfat mass from 9.1 kg to 7.2 kg (17.9% to 14.8%), and muscle mass from 23.2 kg to 22.8 kg. For all marathons combined, exercise intensity was ~60% VO DISCUSSION: Performance and pacing were highly consistent across all 10 marathons without any substantial physiological decrements. Although overall EI did not match EE, leading to a significant ED, resulting in a 2.6 kg weight loss and decreases in bodyfat and skeletal muscle mass, this did not affect performance.

Indexed as

Marathon RunningRunningEnergy MetabolismExercise TestFemaleHeart RateHumansOxygen Consumptionenergy expenditureMarathonpacingrunningultra-enduranceworld record

Identifiers

PMID34831820
PMCPMC8621928

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.