Evidence map›Paper›PMID 31161403›Full record

ArticleSports medicine (Auckland, N.Z.)2019

Causal Mediation Analysis Could Resolve Whether Training-Induced Increases in Muscle Strength are Mediated by Muscle Hypertrophy.

James L Nuzzo, Harrison T Finn, Robert D Herbert

Abstract readLetter
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In one paragraph

Article in Sports medicine (Auckland, N.Z.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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.

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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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

3 authors.

James L NuzzoNeuroscience Research Australia, Barker Street, Randwick, NSW, 2031, Australia. j.nuzzo@neura.edu.au.ORCID 0000-0001-9081-0522
Harrison T FinnNeuroscience Research Australia, Barker Street, Randwick, NSW, 2031, Australia.
Robert D HerbertNeuroscience Research Australia, Barker Street, Randwick, NSW, 2031, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Resistance training increases muscle size (i.e., causes hypertrophy) and muscle strength, particularly in untrained individuals. Hypertrophy is widely believed to be one of the mechanisms (i.e., a mediator) by which resistance training increases strength. However, some researchers have questioned whether training-induced hypertrophy causes increases in strength. One approach to resolving this issue has been to use correlations between training-induced changes in muscle size and strength to infer the effect of hypertrophy on strength. This is problematic because correlations between changes in muscle size and strength may be confounded (i.e., correlation is not causation). Another approach has involved randomizing participants to different volumes of exercise to create different levels of hypertrophy and then comparing the strength increases associated with different levels of hypertrophy. This approach is also problematic because the unit of randomization is exercise volume rather than hypertrophy, and the potential for confounding remains. Thus, a new approach is needed to determine the extent to which hypertrophy increases muscle strength. Here, we introduce resistance training researchers to causal mediation analysis and recommend that it be used to resolve the current debate. Causal mediation analysis potentially provides an unconfounded estimate of the effect of a mediating variable (hypertrophy) on an outcome (strength). This analysis is supplemented by causal maps that help conceptualize research questions and identify potential confounders. In addition to resolving the debate on hypertrophy, causal mediation analysis can be used to answer a host of other questions about mechanisms in the health sciences.

Indexed as

Adaptation, PhysiologicalMuscle StrengthResistance TrainingCausalityConfounding Factors, EpidemiologicHumansMuscle, SkeletalResearch Design

Identifiers

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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.