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Record W2808541049 · doi:10.1016/j.dcn.2018.05.001

Aerobic-Exercise and resistance-training interventions have been among the least effective ways to improve executive functions of any method tried thus far

2018· letter· en· W2808541049 on OpenAlexaff
Adele Diamond, Daphne S. Ling

Bibliographic record

VenueDevelopmental Cognitive Neuroscience · 2018
Typeletter
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Drug Abuse
KeywordsPsychologyMindfulnessPsychological interventionAerobic exerciseCognitionExecutive functionsCognitive psychologyResistance (ecology)Applied psychologyClinical psychologyPhysical therapyMedicineNeuroscience

Abstract

fetched live from OpenAlex

We appreciate that our colleagues, Hillman et al. (2018), would like to conclude that aerobic exercise improves executive functions (EFs).We, too, would like to conclude that.However, the facts thus far indicate that aerobic exercise interventions (with greater or lesser cognitive and motor skill demands), resistance training, and yoga have produced the weakest results for improving EFs of any method tried.We refer to that evidence briefly below and discuss how physical activity (in ways that researchers have largely ignored) may indeed help to improve EFs.All of this is discussed in far greater depth in Diamond and Ling (in press), which systematically reviews 179 studies reported across 193 papers.We would like to mention three important caveats: First, "weakest" evidence does not mean "no" evidence; 44% of aerobic-exercise studies and 25% of resistance-training studies have found at least suggestive evidence of EF benefits.Thus, some studies have demonstrated EF benefits from these activities.Compare that, however, to 79% of Cogmed ® studies and 100% of studies of taekwondo, t'ai chi, Chinese mind-body practices, and Quadrato motor training (which can all be considered mindfulness practices involving movement) finding at least suggestive evidence of EF benefits (see Table 1 below).Second, our focus is exclusively on EF outcomes.We are not saying that physical activity has shown weak benefits across all domains; we are saying that physical activity interventions have thus far shown weak benefits specifically for EFs.Ours was never meant to be a review of the whole exercise-cognition literature nor a review of the physical fitness, health, or neural benefits of exercise.Third, we are not saying that physical activity does not benefit EFs.There are reasons to think it does.We are saying that interventions used to try to prove that have generally met with disappointing results.As scientists we need to set the record straight.We show below that almost all of the many criticisms leveled by Hillman et al. (2018) of the summary of our review presented in Diamond and Ling (2016) are wholly incorrect or at best misguided.It does not advance science to mischaracterize what we said.We acknowledge, however, that two of the criticisms leveled by Hillman et al. are well-taken; we apologize for those errors.Correcting those errors, though, does not change our conclusions.1.The overwhelming preponderance of evidence is that resistance training and aerobic exercise interventions have thus far generally not been successful in improving EFs Diamond and Ling (2016) was part of a special issue presenting invited addresses from the Flux International Society for Integrative Developmental Cognitive Neuroscience Meeting in 2014.Both that paper, and the invited address on which it was based, were explicitly a brief summary of the initial findings of the systematic review by Diamond and Ling (in press).Diamond and Ling (in press) is an especially comprehensive and extensive review of interventions, programs, and approaches that have tried to improve EFs: "Previous reviews have focused on the large literature on cognitive training approaches to improving EFs or the large literature on physical activity approaches to improving EFs, often concentrating only on studies in children or adults.This review looks at all the different methods that have been tried for improving EFs (including cognitive training and physical exercise, but also all the other approaches) and at all ages (not only children or only the elderly)" (Diamond and Ling, in press) To locate studies for review, "we searched PubMed and PsycNET for all publications that had any keyword, or word in the title or abstract, from both of the following sets (Set 1: evaluate, evaluation, intervention, program, randomized control trial, train, or training; Set 2: attention (apart from Attention Deficit Hyperactivity Disorder [ADHD]), cognitive control, cognitive flexibility, EF, inhibition, inhibitory control, fluid intelligence, mental flexibility, reasoning, self-control, self-regulation, set shifting, task switching, or WM)" (Diamond and Ling, in press).Initially that search was limited to papers published by 2014.(That search did not pick up some important papers, such as the seminal one by Kramer et al. (1999), since none of our search terms was in its title, "Ageing, fitness and neurocognitive function," and since it had no abstract or keyword list, where terms included in our search might have appeared.)Publication of Diamond and Ling (in press) had been expected in early 2016.When that was delayed we used the time to ( a) systematically investigate the references cited in papers that had met our search criteria for still more studies meeting our 11 inclusion criteria (hence Kramer et al. (1999) appears in Diamond & Ling (in press)) and

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.068
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0050.004
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.312
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations130
Published2018
Admission routes1
Has abstractyes

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