Evidence map›Paper›PMID 33172425›Full record

ArticleBMC musculoskeletal disorders2020

Intensity of resistance training via self-reported history is critical in properly characterizing musculoskeletal health.

Todd C Shoepe, Joseph W LaBrie, Grant T Mello, Allison G Leggett, Hawley C Almstedt

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC musculoskeletal disorders, 2020. 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
0.2field-weighted citation impact, top 43% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Review
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

5 authors at 1 institution in 1 country.

Todd C ShoepeHealth and Human Performance Laboratory, Department of Health and Human Sciences, Loyola Marymount University, Life Sciences Building 181, 1 LMU Drive, MS 8888, Los Angeles, CA, 90045, USA. Todd.Shoepe@lmu.edu.ORCID https://orcid.org/0000-0001-8010-0930
Joseph W LaBrieDepartment of Psychology, Loyola Marymount University, Los Angeles, CA, USA.
Grant T MelloDepartment of Psychology, Loyola Marymount University, Los Angeles, CA, USA.
Allison G LeggettHealth and Human Performance Laboratory, Department of Health and Human Sciences, Loyola Marymount University, Life Sciences Building 181, 1 LMU Drive, MS 8888, Los Angeles, CA, 90045, USA.
Hawley C AlmstedtHealth and Human Performance Laboratory, Department of Health and Human Sciences, Loyola Marymount University, Life Sciences Building 181, 1 LMU Drive, MS 8888, Los Angeles, CA, 90045, USA.
Loyola Marymount University · US

Funding

Alcohol and Bone Health: Skeletal Effects of Heavy Episodic Drinking in CollegeSignificanceR21AA022942 · NIAAA · LOYOLA MARYMOUNT UNIVERSITY · PI ALMSTEDT, HAWLEY CHASE, LABRIE, JOSEPH W. · 2015 to 2016
$362k
NIAAA NIH HHS R21 AA022942NIAAA NIH HHS R21AA022942
6 · The paper itself

Abstract

backgroundIntensity of resistance training history might be omitted or poorly ascertained in prescreening or data questionnaires involving musculoskeletal health. Failure to identify history of high-versus low-intensity training may overlook higher effect sizes with higher intensities and therefore diminish the precision of statistical analysis with resistance training as a covariate and bias the confirmation of baseline homogeneity for experimental group designation. The purpose was to determine the degree to which a single question assessing participant history of resistance training intensity predicted differences in musculoskeletal health.

methodsIn the first research aim, participants were separated into groups with a history (RT) and no history (NRT) of resistance training. The second research aim evaluated the history of resistance training intensity on muscular strength, lean mass, and bone mineral density (BMD), RT participants were reassigned into a low- (LIRT) or high-intensity resistance training group (HIRT). 83 males and 87 females (19.3 ± 0.6 yrs., 171.1 ± 9.9 cm, 67.1 ± 10.5 kg, 22.9 ± 2.8 BMI, 26.2 ± 7.2% body fat) completed handgrip dynamometry (HG) and dual-energy x-ray absorptiometry scans (DXA) for BMD and bone mineral-free lean mass (BFLM).

resultsA 3-group method (NRT, LIRT, HIRT) reduced type-I error compared with the 2-group method (NRT, RT) in characterizing the likely effects of one's history of resistance training. For the second aim, HIRT had significantly (p < 0.05) greater HG strength (76.2 ± 2.2 kg) and arm BFLM (6.10 ± 0.16 kg) than NRT (67.5 ± 1.3 kg; 4.96 ± 0.09 kg) and LIRT (69.7 ± 2.0 kg; 5.42 ± 0.14 kg) while also showing significantly lower muscle quality (HG/BFLM) than NRT (13.9 ± 0.2 vs. 12.9 ± 0.3). HIRT had greater BMD at all sites compared to NRT (whole body = 1.068 ± 0.008 vs. 1.120 ± 0.014; AP spine = 1.013 ± 0.011 vs. 1.059 ± 0.019; lateral spine = 0.785 ± 0.009 vs. 0.846 ± 0.016; femoral neck = 0.915 ± 0.013 vs. 0.970 ± 0.022; total hip = 1.016 ± 0.012 vs. 1.068 ± 0.021 g/cm

conclusionsRetrospective identification of high-intensity history of resistance training appears critical in characterizing musculoskeletal health and can be ascertained easily in as little as a single, standalone question. Both retrospective-questionnaire style investigations and pre-screening for potential participation in prospective research studies should include participant history of resistance training intensity.

Indexed as

Resistance TrainingAbsorptiometry, PhotonBone DensityFemaleHand StrengthHumansMaleProspective StudiesRetrospective StudiesSelf ReportBMDMuscle qualityOsteoporosisPeak bone massSarcopeniaStrength

Identifiers

PMID33172425
PMCPMC7654594
OpenAlexW3106068606

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.