MétaCan
Menu
Back to cohort
Record W4312104844 · doi:10.1093/geroni/igac059.1309

WHAT CAN DUAL-TASK WALKING AND TAPPING TELL US ABOUT SUBJECTIVE COGNITIVE DECLINE? AN FNIRS STUDY

2022· article· en· W4312104844 on OpenAlexaff
Talia Salzman, Hannah Perreault, Farah Farhat, Diana Tobón Vallejo, Sarah Fraser

Bibliographic record

VenueInnovation in Aging · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFinger tappingTappingCognitionTask (project management)GaitCognitive declinePhysical medicine and rehabilitationEffects of sleep deprivation on cognitive performanceDual (grammatical number)MedicineAudiologyPsychologyNeuroscienceInternal medicineDementia

Abstract

fetched live from OpenAlex

Abstract Older adults who pass standard cognitive tests but report subjective cognitive decline (SCD) may be identifying early changes in cognition at a stage when intervening can prevent further declines. Changes may be subtle highlighting the need for novel approaches, such as divided attention tasks, to distinguish between those with and without SCD. This pilot study examined 15 older women (9 SCD, 6 non-SCD) completing dual-task walking and tapping. Brain (cerebral oxygenation) and behavioural (gait and tap speed, accuracy, and vocal response) measures were assessed during single and dual-tasks. Older adults with SCD were marginally less accurate during dual-task tapping (p < .06) and had greater cerebral oxygenation than the non-SCD group (p = .01). SCD did not moderate gait speed from single to dual-task while non-SCD did (p = .02). Findings suggest that challenging dual-task paradigms may help identify different behavioural and brain activity markers of SCD and intervention targets.

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)0.005
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.027
GPT teacher head0.351
Teacher spread0.324 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInnovation in AgingSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207