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Record W2920643337 · doi:10.1177/1545968319832605

Synergistic Benefits of Combined Aerobic and Cognitive Training on Fluid Intelligence and the Role of IGF-1 in Chronic Stroke

2019· article· en· W2920643337 on OpenAlexafffund
Michelle Ploughman, Gail A. Eskes, Liam Kelly, Megan C. Kirkland, Augustine Joshua Devasahayam, Elizabeth M. Wallack, Beraki Abraha, S. M. Mahmudul Hasan, Matthew B. Downer, Laura Keeler, G. Wilson, Elaine Skene, Ishika Sharma, Arthur R. Chaves, Marie E. Curtis, Emily Bedford, George S. Robertson, Craig S. Moore, Jason McCarthy, Marilyn MacKay-Lyons

Bibliographic record

VenueNeurorehabilitation and neural repair · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsDalhousie UniversityMemorial University of Newfoundland
FundersCanada Research ChairsCanada Foundation for InnovationNova Scotia Health Research Foundation
KeywordsAerobic exercisePsychologyRandomized controlled trialCognitive trainingCognitionPhysical therapyCogPsychological interventionPhysical medicine and rehabilitationRaven's Progressive MatricesMedicineInternal medicineNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

Background. Paired exercise and cognitive training have the potential to enhance cognition by “priming” the brain and upregulating neurotrophins. Methods. Two-site randomized controlled trial. Fifty-two patients >6 months poststroke with concerns about cognitive impairment trained 50 to 70 minutes, 3× week for 10 weeks with 12-week follow-up. Participants were randomized to 1 of 2 physical interventions: Aerobic (>60% VO 2peak using <10% body weight–supported treadmill) or Activity (range of movement and functional tasks). Exercise was paired with 1 of 2 cognitive interventions (computerized dual working memory training [COG] or control computer games [Games]). The primary outcome for the 4 groups (Aerobic + COG, Aerobic + Games, Activity + COG, and Activity + Games) was fluid intelligence measured using Raven’s Progressive Matrices Test administered at baseline, posttraining, and 3-month follow-up. Serum neurotrophins collected at one site (N = 30) included brain-derived neurotrophic factor (BDNF) at rest (BDNF resting ) and after a graded exercise test (BDNF response ) and insulin-like growth factor–1 at the same timepoints (IGF-1 rest , IGF-1 response ). Results. At follow-up, fluid intelligence scores significantly improved compared to baseline in the Aerobic + COG and Activity + COG groups; however, only the Aerobic + COG group was significantly different (+47.8%) from control (Activity + Games −8.5%). Greater IGF-1 response at baseline predicted 40% of the variance in cognitive improvement. There was no effect of the interventions on BDNF resting or BDNF response ; nor was BDNF predictive of the outcome. Conclusions. Aerobic exercise combined with cognitive training improved fluid intelligence by almost 50% in patients >6 months poststroke. Participants with more robust improvements in cognition were able to upregulate higher levels of serum IGF-1 suggesting that this neurotrophin may be involved in behaviorally induced plasticity.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.014
GPT teacher head0.251
Teacher spread0.238 · 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 designObservational
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

Citations74
Published2019
Admission routes2
Has abstractyes

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