Evidence map›Paper›PMID 38878117›Full record

ReviewSports medicine (Auckland, N.Z.)2024

N of 1: Optimizing Methodology for the Detection of Individual Response Variation in Resistance Training.

Zac P Robinson, Eric R Helms, Eric T Trexler, James Steele, Michael E Hall, Chun-Jung Huang, Michael C Zourdos

Abstract readReview
PubMed Publisher
In one paragraph

Review in Sports medicine (Auckland, N.Z.), 2024. 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
–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

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

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

7 authors.

Zac P RobinsonDepartment of Exercise Science and Health Promotion, Muscle Physiology Laboratory, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA.
Eric R HelmsDepartment of Exercise Science and Health Promotion, Muscle Physiology Laboratory, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA.
Eric T TrexlerDepartment of Exercise Science and Health Promotion, Muscle Physiology Laboratory, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA.
James SteeleDepartment of Exercise Science and Health Promotion, Muscle Physiology Laboratory, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA.
Michael E HallDepartment of Exercise Science and Health Promotion, Muscle Physiology Laboratory, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA.
Chun-Jung HuangDepartment of Exercise Science and Health Promotion, Muscle Physiology Laboratory, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA.
Michael C ZourdosDepartment of Exercise Science and Health Promotion, Muscle Physiology Laboratory, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA. mzourdos@fau.edu.ORCID 0000-0003-3265-7683

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most resistance training research focuses on inference from average intervention effects from observed group-level change scores (i.e., mean change of group A vs group B). However, many practitioners are more interested in training responses (i.e., causal effects of an intervention) on the individual level (i.e., causal effect of intervention A vs intervention B for individual X). To properly examine individual response variation, multiple confounding sources of variation (e.g., random sampling variability, measurement error, biological variability) must be addressed. Novel study designs where participants complete both interventions and at least one intervention twice can be leveraged to account for these sources of variation (i.e., n of 1 trials). Specifically, the appropriate statistical methods can separate variability into the signal (i.e., participant-by-training interaction) versus the noise (i.e., within-participant variance). This distinction can allow researchers to detect evidence of individual response variation. If evidence of individual response variation exists, researchers can explore predictors of the more favorable intervention, potentially improving exercise prescription. This review outlines the methodology necessary to explore individual response variation to resistance training, predict favorable interventions, and the limitations thereof.

Indexed as

Research DesignResistance TrainingHumans

Identifiers

What OpenQuestion holds

Textmetadata
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.