Evidence map›Paper›PMID 31254258›Full record

ArticleSports medicine (Auckland, N.Z.)2020

A Method to Stop Analyzing Random Error and Start Analyzing Differential Responders to Exercise.

Scott J Dankel, Jeremy P Loenneke

Abstract read
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In one paragraph

Article in Sports medicine (Auckland, N.Z.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

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

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Scott J DankelDepartment of Health and Exercise Science, Exercise Physiology Laboratory, Rowan University, Glassboro, NJ, USA.
Jeremy P LoennekeDepartment of Health, Exercise Science, and Recreation Management, Kevser Ermin Applied Physiology Laboratory, The University of Mississippi, P.O. Box 1848, University, MS, 38677, USA. jploenne@olemiss.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is commonly stated that individuals respond differently to exercise even when the same exercise intervention is performed. This has led many researchers to conduct exercise interventions and subsequently categorize individuals into different responder categories to determine what causes individuals to respond differently. Some methods by which differential responders are categorized include percentile ranks, standard deviations from the mean, and cluster analyses. Notably, each of these methods will result in the presence of differential responders even in the absence of an exercise intervention, indicating that individuals may be categorized based on the presence of random error as opposed to true differences in the exercise response. Here we propose a method by which differential responders can be classified after accounting for the presence of random error that is quantified from a time-matched control group. Individuals who exceed random error from the mean response of the intervention group can be confidently labelled as high and low responders. Importantly, the number of differential responders will be proportional to the ratio of variance in the exercise and control groups. We provide easy-to-follow steps and examples to demonstrate how this technique can identify differential responders to exercise. We also detail the flaws in other classification methods by demonstrating the number of differential responders who would have been classified using the same data set. Our hope is that this method will help to avoid misclassifying individuals based on random error and, in turn, increase the replicability of differential responder studies.

Indexed as

Adaptation, PhysiologicalData Interpretation, StatisticalExerciseHumansOutcome Assessment, Health CareReproducibility of ResultsResearch Design

Identifiers

PMID31254258

What OpenQuestion holds

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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.