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Record W4393032338 · doi:10.1101/2024.03.20.24302631

Brief Physical Activity Selectively Modulates the Performance of Serial Subtract 7 in Young Adults – A Wearable Sensor-based, Randomized, Control Study

2024· preprint· en· W4393032338 on OpenAlexafffund
Xin Ran Chu, Tanmoy Newaz, Elbert Tom, Allison Yang, Taylor Chomiak, Bin Hu

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsWearable computerControl (management)Randomized controlled trialComputer sciencePsychologyEmbedded systemMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract OBJECTIVE This study explores the effects of physical activities on cognitive performance in healthy subjects, specifically evaluating Serial Subtract 7 Test (SST) performance during a cognitive-stepping dual task influenced by the 6-Minute Walking Test (6MWT) with and without music. METHODS A controlled experiment was conducted using the Ambulosono device to standardize walking exercises. 54 high school students participated, undergoing the 6MWT in different scenarios: Verbal 6-Minute Walking Test (6MWT) or Music-Guided Walking (MU). Final data from 43 students was used in the analysis. The SST measured cognitive changes in both single-task and dual-task conditions. RESULTS The 6MWT significantly enhanced cognitive performance in both single and dual-task conditions. However, the addition of music did not show a substantial improvement in cognitive performance. The findings indicated the positive impact of 6MWT on cognitive abilities, irrespective of musical accompaniment. CONCLUSIONS This research contributes to the understanding of how physical exercises can modulate cognitive functions in healthy individuals. It highlights the potential of 6MWT in enhancing cognitive performance, suggesting further exploration into the role of physical activity in cognitive health.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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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