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Record W4406218199 · doi:10.1002/alz.091856

24‐hour activity cycle movement behaviours and brain volume in people with mild cognitive impairment: A compositional and isotemporal substitution analysis

2024· article· en· W4406218199 on OpenAlexaffabout
Guilherme Moraes Balbim, Nárlon Cássio Boa Sorte Silva, Ryan S. Falck, Teresa Liu‐Ambrose

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsWestern UniversityVancouver Coastal Health Research InstituteUniversity of British ColumbiaVancouver Coastal Health
Fundersnot available
KeywordsSubstitution (logic)Cognitive impairmentCognitionMovement (music)PsychologyMedicineNeuroscienceComputer scienceArtAesthetics

Abstract

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Abstract Background People with mild cognitive impairment are at greater risk of Alzheimer’s disease. Physical activity (PA), sedentary behaviour, and sleep are movement behaviours constituting the 24‐hour activity cycle (24‐HAC) and are interactively associated with cognitive and brain health. However, the relationship between 24‐HAC compositions (i.e., time in one behaviour relative to remaining) and brain volume in this population remains underexplored. We aimed to investigate the associations between 24‐HAC compositions and time reallocation of 24‐HAC movement behaviours with brain volume. Method A cross‐sectional study in 110 community‐dwelling adults (55+ years) with mild cognitive impairment (Montreal Cognitive Assessment <26/30). MotionWatch8© assessed 24‐HAC movement behaviours (5‐7 days). Compositional data analysis determined 24‐HAC compositions (four isometric log‐ratio pivot coordinates). FreeSurfer quantified gray matter volume using T1‐weighted magnetic resonance imaging. Linear regressions adjusted for age, sex, intracranial volume, and education determined 24‐HAC compositions and brain volume associations. Compositional isotemporal substitution analysis estimated the difference in brain volume associated with reallocating time between pairs of 24‐HAC movement behaviours. The Benjamini‐Hochberg false‐discovery rate (FDR) adjusted p‐values for multiple comparisons. Result Higher moderate‐to‐vigorous PA composition was associated with greater cortical volume in the inferior temporal gyrus TE2a region (ß = 0.30, 95% CI = 0.15; 0.44, FDR‐adjusted‐p = 0.030). Higher light PA composition was associated with lower cortical volume in the TE2a region (ß = ‐0.45, 95% CI = ‐0.65; ‐0.24, FDR‐adjusted‐p = 0.015). Sedentary behaviour and sleep were not associated with brain volume (FDR‐adjusted‐p>0.05). Reallocating 30 minutes from sedentary behaviour to moderate‐to‐vigorous PA was associated with 2.1% greater volume in the TE2a region (ß = 0.06, 95% CI = 0.02; 0.10, p<0.001). Reallocating 30 minutes from moderate‐to‐vigorous PA to sedentary behaviour was associated with 2.8% lower volume in the TE2a region (ß = ‐0.08, 95% CI = ‐0.14; ‐0.02, p<0.001). Conclusion Greater light PA composition may happen at the expense of lower moderate‐to‐vigorous PA composition, which could explain its associations with lower cortical volume. Greater moderate‐to‐vigorous PA composition and reallocating time from sedentary behaviour to moderate‐to‐vigorous PA may counteract cortical atrophy in an Alzheimer’s disease signature region in people with mild cognitive impairment.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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