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Record W4312088129 · doi:10.1002/alz.061616

The SYNERGIC Trial: A Randomized Controlled Trial Assessing Multimodal Interventions to Improve Cognition in Mild Cognitive Impairment in Older Adults

2022· article· en· W4312088129 on OpenAlexaff
Manuel Montero‐Odasso, Quincy J. Almeida, Amer M. Burhan, Richard Camicioli, Karen Li, Teresa Liu‐Ambrose, Laura E. Middleton, Julien Doyon, Sarah Fraser, Susan Hunter, Bill McIlroy, José Morais, Frederico Pieruccini‐Faria, J. Kevin Shoemaker, Mark Speechley, Akshya Vasudev, G Y Zou, Nicolas Berryman, Maxime Lussier, Leanne Vanderhaeghe, Louis Bherer

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsSt Joseph's Health CareUniversité de MontréalBishop's UniversityLawson Health Research InstituteInstitut Universitaire de Gériatrie de MontréalMcGill UniversityUniversity of OttawaMcGill University Health CentreParkwood InstituteWilfrid Laurier UniversityUniversity of WaterlooUniversity of British ColumbiaOntario Shores Centre for Mental Health SciencesWestern UniversityConcordia UniversityUniversity of Alberta
Fundersnot available
KeywordsPlaceboMedicineRandomized controlled trialCognitionCognitive trainingPsychological interventionDementiaPhysical therapyPhysical medicine and rehabilitationCogCognitive declineInternal medicinePsychiatryPathology

Abstract

fetched live from OpenAlex

Abstract Background Older adults with Mild Cognitive Impairment (MCI) have increased risk of dementia. Physical exercise, cognitive training, and vitamin D supplementation are emerging interventions for improving cognition. However, the potential synergism of combining them to improve cognition in MCI has not been yet tested. Methods The SYNERGIC trial (SYNnchronizing Exercises, Remedies in Gait and Cognition)— a multi‐site, double‐blinded, controlled, quasi‐factorial trial—assessed the efficacy and synergistic effect of combining bi‐modal exercises (aerobic + progressive resistance training = E), cognitive training (CT) and, vitamin D supplementation (D = 10000IU 3xweekly) to improve cognition in MCI. Personalized interventions were delivered for 20 weeks in 5 arms: Arm 1 received 3 active interventions (E+CT+D), Arm 2 received E+CT+placebo D, Arm 3 received E+ sham CT+ D, Arm 4 received E+ sham CT and placebo D, and Arm 5 received all control interventions (Control E‐balance and toning‐, sham CT and placebo D). Change in ADAS‐Cog‐13 was the primary outcome assessed with linear mixed effects models adjusting for age, sex, education, and number of comorbidities. Results 170 MCI participants were randomized. As compared to the control group (Arm 5), multimodal intervention (Arm 1) had significant improvements in ADAS‐Cog‐13 scores (difference = ‐2.3 ADAS‐Cog‐13 points; 95% CI: ‐1.06 ‐3.54). Following analyses guidelines for factorial trials, interactions of intervention across arms were analyzed and no significant interactions were found for vitamin D supplementation. Therefore, we collapsed Arms 1 and 2 (combined E + CT), and we collapsed Arms 3 and 4 (E), and compared them to Arm 5 (control). Relative to the control group (Arm 5), the combined exercise and cognitive intervention (Arms 1+2) had significantly improved cognitive function (difference = ‐2.2 ADAS‐Cog‐13 points; 95% CI: ‐0.70, ‐3.72). In contrast, the exercise only intervention (Arms 3+4) did not have significantly improved cognition as compared to the control group (difference = ‐1.24 ADAS‐Cog‐13 points; 95% CI: ‐0.21, 2.69). Conclusion A synergistic effect of combined bi‐modal exercise with sequential computerized cognitive training was found to improve cognition in older adults with MCI with a large effect size = 0.62. Addition of vitamin D supplementation did not improve the effect.

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)0.002
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: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.029
GPT teacher head0.356
Teacher spread0.327 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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