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Record W4411531685 · doi:10.3389/fnagi.2025.1605128

Investigating the effects of a randomized, double-blinded aerobic, resistance, and cognitive training clinical trial on neurocognitive function in older adults with cardiovascular risk factors: the ACTIONcardioRisk protocol

2025· article· en· W4411531685 on OpenAlexafffund
Louis Bherer, Tudor Vrinceanu, Emma Gabrielle Dupuy, Mathieu Gayda, Thomas Vincent, P.-É. Magnan, Hânieh Mohammadi, Claudine Gauthier, Christine Gagnon, Simon Duchesne, Kirk I. Erickson, Daniel Gagnon, Frédéric Lesage, Sonia Lupien, Judes Poirier, Marie‐Pierre Dubé, Éric Thorin, Martin Juneau, Juliana Breton, Sylvie Belleville, Guylaine Ferland, Flavie Gaudreau-Majeau, Caroll‐Ann Blanchette, Paolo Vitali, Anil Nigam

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityConcordia UniversityUniversité LavalUniversité de MontréalInstitut Universitaire de Gériatrie de MontréalMontreal Heart Institute
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsNeurocognitiveRandomized controlled trialResistance trainingCognitionMedicineCognitive trainingProtocol (science)Aerobic exercisePhysical therapyPsychologyPhysical medicine and rehabilitationInternal medicinePsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

Background: Lifestyle factors like exercise and cognitive stimulation might help improve cognitive performance in older adults. However, studies investigating this, reported mixed results. Most of the data supporting the benefit of exercise comes from cross-sectional studies, cohort studies, or short intervention studies of 3-6 months with poorly designed control groups. Meta-analyses suggest that longer intervention studies of around 1 year are more likely to show cognitive improvements and changes in brain biomarkers. Moreover, the type and content and optimal dose of the training program that best predict improvement in cognition is still poorly understood. Latest studies suggest that combining cognitive training with exercise training might have an added benefit. Moreover, functional and structural cerebral mechanisms involved are still poorly documented. Finally, few studies have systematically investigated the potential impact that cardiovascular risk factors (CVRF) progression might have on training neurocognitive outcomes. Methods: 159 seniors over the age of 60 with CVRF and no contraindications to exercise will be assigned to one of the three 1-year training programs: (1) Physical exercise intervention (aerobic and resistance exercises); (2) Multidomain intervention (combined cognitive training with aerobic and resistance exercises); or (3) Active control (stretching and toning exercises). All interventions take place 3 times a week, are supervised and individualized to each participant's profile. Assessments will be administered before, half-way and after the intervention: cognition (primary outcome), cerebral imaging with a focus on cerebrovascular mechanisms (secondary outcomes), and exploratory outcomes (genetic profile, chronic stress biomarkers, metabolic function, inflammation markers, mood, sleep, and diet). Discussion: The present design uses a 12-month intervention period to maximize the likelihood of identifying the cerebrovascular markers involved in exercise training effects on cognitive performance in individuals with CVRF. Moreover, we measure a series of exploratory outcomes that could also play a role in modulating the effect of the multidomain training on cognition. This will allow an investigation of their potential mediating role on the primary outcomes. Clinical trial registration: [https://clinicaltrials.gov/] identifier [NCT04962061].

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)0.007
metaresearch head score (Gemma)0.007
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: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0180.002

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 head0.031
GPT teacher head0.338
Teacher spread0.307 · 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
GenreProtocol

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

Citations1
Published2025
Admission routes2
Has abstractyes

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