Evidence map›Paper›PMID 37365235›Full record

ArticleScientific reports2023

Estimation of maximal lactate steady state using the sweat lactate sensor.

Yuki Muramoto, Daisuke Nakashima, Tsubasa Amano, Tomota Harita, Kazuhisa Sugai, Kyohei Daigo, Yuji Iwasawa, Genki Ichihara, Hiroki Okawara, Tomonori Sawada and 5 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
4.4field-weighted citation impact, top 6% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

15 authors at 2 institutions in 1 country.

Yuki MuramotoInstitute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan.ORCID 0000-0002-9363-2722
Daisuke NakashimaDepartment of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan.ORCID 0000-0003-1105-2669
Tsubasa AmanoKeio University School of Medicine, Tokyo, Japan.
Tomota HaritaKeio University School of Medicine, Tokyo, Japan.
Kazuhisa SugaiSchool of Veterinary Nursing and Technology, Faculty of Veterinary Science, Nippon Veterinary and Life Science University, Tokyo, Japan.
Kyohei DaigoDepartment of Cardiology, Keio University School of Medicine, Tokyo, Japan.
Yuji IwasawaDepartment of Cardiology, Keio University School of Medicine, Tokyo, Japan.
Genki IchiharaDepartment of Cardiology, Keio University School of Medicine, Tokyo, Japan.
Hiroki OkawaraDepartment of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan.ORCID 0000-0002-4211-1759
Tomonori SawadaDepartment of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan.ORCID 0000-0001-9750-8828
Akira KinodaInstitute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan.
Yuichi YamadaInstitute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan.
Takeshi KimuraInstitute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan.
Kazuki SatoInstitute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan.
Yoshinori KatsumataInstitute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan. goodcentury21@keio.jp.ORCID 0000-0001-5576-5789
Keio University · JPNippon Veterinary and Life Science University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A simple, non-invasive algorithm for maximal lactate steady state (MLSS) assessment has not been developed. We examined whether MLSS can be estimated from the sweat lactate threshold (sLT) using a novel sweat lactate sensor for healthy adults, with consideration of their exercise habits. Fifteen adults representing diverse fitness levels were recruited. Participants with/without exercise habits were defined as trained/untrained, respectively. Constant-load testing for 30 min at 110%, 115%, 120%, and 125% of sLT intensity was performed to determine MLSS. The tissue oxygenation index (TOI) of the thigh was also monitored. MLSS was not fully estimated from sLT, with 110%, 115%, 120%, and 125% of sLT in one, four, three, and seven participants, respectively. The MLSS based on sLT was higher in the trained group as compared to the untrained group. A total of 80% of trained participants had an MLSS of 120% or higher, while 75% of untrained participants had an MLSS of 115% or lower based on sLT. Furthermore, compared to untrained participants, trained participants continued constant-load exercise even if their TOI decreased below the resting baseline (P < 0.01). MLSS was successfully estimated using sLT, with 120% or more in trained participants and 115% or less in untrained participants. This suggests that trained individuals can continue exercising despite decreases in oxygen saturation in lower extremity skeletal muscles.

Indexed as

Anaerobic ThresholdLactic AcidAdultBicyclingExercise TestHumansOxygen ConsumptionSweatLactic Acid

Identifiers

PMID37365235
PMCPMC10293173
OpenAlexW4382139320

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.