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Record W4300080919

Effects of combined physical and cognitive training on fitness and neuropsychological outcomes in healthy older adults

2016· article· en· W4300080919 on OpenAlexaboutno aff
L Desjardins-Crépeau, N Berryman, L Bosquet, L Bherer

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsNeuropsychologyPhysical fitnessCognitionPsychologyTraining (meteorology)Cognitive trainingPhysical medicine and rehabilitationGerontologyClinical psychologyPhysical therapyMedicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Laurence Desjardins-Crépeau,1,2 Nicolas Berryman,2,3 Sarah A Fraser,4 Thien Tuong Minh Vu,5,6 Marie-Jeanne Kergoat,2,6 Karen ZH Li,7 Laurent Bosquet,8 Louis Bherer2,7 1Department of Psychology, University of Quebec at Montreal, Montreal, QC, Canada; 2Research Center, Institut universitaire de gériatrie de Montréal, Montreal, QC, Canada; 3Department of Sports Studies, Bishop’s University, Sherbrooke, QC, Canada; 4Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, ON, Canada; 5Research Center, Centre hospitalier de l’Université de Montréal, Montreal, QC, Canada; 6Faculty of Medicine, Université de Montréal, Montreal, QC, Canada; 7Department of Psychology and PERFORM Centre, Concordia University, Montréal, QC, Canada; 8Faculté des sciences du sport, Université de Poitiers, Poitiers, France Purpose: Physical exercise and cognitive training have been shown to enhance cognition among older adults. However, few studies have looked at the potential synergetic effects of combining physical and cognitive training in a single study. Prior trials on combined training have led to interesting yet equivocal results. The aim of this study was to examine the effects of combined physical and cognitive interventions on physical fitness and neuropsychological performance in healthy older adults.Methods: Seventy-six participants were randomly assigned to one of four training combinations using a 2×2 factorial design. The physical intervention was a mixed aerobic and resistance training program, and the cognitive intervention was a dual-task (DT) training program. Stretching and toning exercises and computer lessons were used as active control conditions. Physical and cognitive measures were collected pre- and postintervention.Results: All groups showed equivalent improvements in measures of functional mobility. The aerobic–strength condition led to larger effect size in lower body strength, independently of cognitive training. All groups showed improved speed of processing and inhibition abilities, but only participants who took part in the DT training, independently of physical training, showed increased task-switching abilities. The level of functional mobility after intervention was significantly associated with task-switching abilities.Conclusion: Combined training did not yield synergetic effects. However, DT training did lead to transfer effects on executive performance in neuropsychological tests. Both aerobic-resistance training and stretching-toning exercises can improve functional mobility in older adults. Keywords: aging, combined intervention, physical performance, cognitive performance, dual task, executive functions

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.164
GPT teacher head0.548
Teacher spread0.384 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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

Citations0
Published2016
Admission routes1
Has abstractyes

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