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Record W4406050970 · doi:10.1002/alz.084280

Correlation analysis of walking speed with executive function in patients with subjective memory decline

2024· article· en· W4406050970 on OpenAlexaboutno aff
Lei Chen, Jiayu Wang, Linlin Li, Feng Qi, Ziqi Wang, Mingjun Duan

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsCorrelationPsychologyPreferred walking speedFunction (biology)Cognitive psychologyPhysical medicine and rehabilitationMedicineMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: To examine whether walking speed is associated with executive function in patients with subjective memory decline. METHOD: Patients were recruited from the Fourth People's Hospital of Chengdu, including 63 patients with subjective cognitive decline (SCD) and 23 patients with mild cognitive impairment (MCI). Each participant assess global function by the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment-Basic Scale (MoCA-B). Executive function assessments using the Shape Symbol Task A (STT-A) to evaluate visual perception and writing speed, Shape-Tracing Test B (STT-B) to evaluate cognitive flexibility. Animal Fluency Test (AFT) to evaluate semantic organization and retrieval strategies, Symbol Digit Modalities Test (SDMT) to assess processing speed and Digit Span Test (DST) to evaluate information processing processes. 10-m walking test (10MWT) to evaluate walking speed, with time recorded converted to speed (m/s). SPSS 26.0 software was used to analyze the Pearson correlation between walking speed and executive function. RESULT: Significant difference of MMSE, MoCA-B, AFT, STT-A, STT-B and SDMT were found in both individual groups (P <0.05), and associations were independent of Gender, Age, Education and 10MWT. In the total combined group, 10MWT were significantly associated with Education (r = 0.267, p = 0.014), MMSE (r = 0.281, p = 0.009), MoCA-B (r = 0.255, p = 0.019), AFT (r = 0.244, p = 0.024) and SDMT (r = 0.221, p = 0.043), negatively correlated with Age (r = -0.267, p = 0.013). In the SCD group, 10WMT were identified positive association with SDMT (r = 0.259, p = 0.042), negative correlation with Age (r = -0.282, p = 0.026). In the MCI group, 10WMT were identified positive association with Education (r = 0.655, p = 0.001), MMSE (r = 0.528, p = 0.01), MoCA-B (r = 0.655, p = 0.001), AFT (r = 0.426, p = 0.043), negative correlation with STT-A (r = -0.473, p = 0.023). CONCLUSION: There was no difference between two groups in walking speed, but were differences in global cognition and executive functions. In the SCD group, walking speed was correlated with processing speed in executive function. More associations between walking speed and executive function were found in the MCI group, mainly with the ability of organize and extract semantic strategies, visual perception and writing motor speed and also related to global cognitive function. The results revealed that impairment of executive function is a significant feature and has important relation with walking speed in the early stages of Alzheimer's disease.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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 routes1
Has abstractyes

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