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Record W7116884771 · doi:10.1002/alz70860_097971

Sleep moderates the effects of resistance training on cognition among older adults with subischemic vascular cognitive impairment: Secondary results of a randomized clinical trial

2025· article· en· W7116884771 on OpenAlexaff
Ryan S. Falck, Ryan G Stein, Rachel A. Crockett, Roger Tam, Teresa Liu‐Ambrose

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaVancouver Coastal Health
Fundersnot available
KeywordsCognitionRandomized controlled trialSleep (system call)Resistance trainingCognitive trainingEffects of sleep deprivation on cognitive performance

Abstract

fetched live from OpenAlex

BACKGROUND: Subcortical ischaemic vascular cognitive impairment (SIVCI) is the most common cause of vascular cognitive impairment. Resistance training (RT) is a promising strategy to promote cognitive function among adults with SIVCI. Emerging evidence suggests sleep may be a key moderator of the effects of exercise on cognitive function. Thus, we examined whether sleep moderates the effects of RT on cognitive function in individuals with SIVCI. METHOD: A 12-month, parallel group, secondary analysis of a randomized controlled trial (RCT) among community-dwelling adults with SIVCI, aged 55+ years. Participants were randomly allocated to receive 12 months of either 1) twice-weekly progressive RT or 2) twice-weekly balance and tone (BAT). At baseline, device-measured sleep duration and efficiency were indexed using wrist-worn actigraphy; self-reported sleep quality was measured by Pittsburgh Sleep Quality Index (PSQI). Participants were classified at baseline as having good or poor device-measured duration, device-measured efficiency, or self-reported quality based on PSQI. Cognitive function was indexed using the 13-item Alzheimer's Disease Assessment Cognitive Subscale (ADAS-Cog 13) at baseline, 6 months (midpoint), and 12 months (end of intervention). We examined if baseline sleep categorizations (i.e., good/poor) moderated the effects of RT on ADAS-Cog 13. RESULT: Eighty-nine participants were randomized (Figure 1; RT=44; BAT=45). Participants are described in Table 1. Mean age was 75 years (SD=6 years) and 65.2% were female. At baseline, mean sleep duration was 385 minutes/night (SD=67 minutes/night) and mean efficiency was 81.40% (SD=6.98%); average ADAS-Cog 13 score was 13.71 (SD=5.38). Table 2 describes our moderation analysis. Compared with BAT participants with poor sleep duration, RT participants with poor sleep duration had better ADAS-Cog 13 performance following the intervention (estimated mean difference: -2.72; 95% CI:[-4.84, -0.61]; p = 0.012). RT participants with poor sleep efficiency had better ADAS-Cog 13 performance following the intervention (estimated mean difference: -2.63; 95% CI:[-4.97, -0.28]; p = 0.029), compared with BAT participants with poor sleep efficiency. There were no effects of RT on ADAS-Cog 13 for participants with good sleep duration or efficiency. Self-reported sleep quality did not moderate intervention effects. CONCLUSION: RT appears to be particularly beneficial for cognitive function in adults with SIVCI and poor sleep.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.006
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)0.0080.001

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 head0.013
GPT teacher head0.291
Teacher spread0.278 · 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
Published2025
Admission routes1
Has abstractyes

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