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Record W2800578468 · doi:10.1177/2333721418770333

The Impact of Blood Pressure Dipping Status on Cognition, Mobility, and Cardiovascular Health in Older Adults Following an Exercise Program

2018· article· en· W2800578468 on OpenAlexafffund
Nárlon Cássio Boa Sorte Silva, Michael A. Gregory, Dawn P. Gill, Cheri L. McGowan, Robert J. Petrella

Bibliographic record

VenueGerontology and Geriatric Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsUniversité de MontréalLawson Health Research InstituteUniversity of WindsorWestern University
FundersCanadian Institutes of Health Research
KeywordsMedicineBlood pressureCognitionPhysical therapyAerobic exerciseCardiovascular healthGaitInternal medicinePhysical medicine and rehabilitationDiseasePsychiatry

Abstract

fetched live from OpenAlex

Objectives: To determine whether a dual-task gait and aerobic exercise intervention differentially impacted older adults with normal blood pressure (BP) dipping status (dippers) compared to those with nondipping status (nondippers). Methods: This study was a secondary analysis involving participants (mean age = 70.3 years, 61% women) who attended a laboratory-based exercise intervention over a 6-month period (40 min/day and 3 days/week). Participants were assessed in measures of cognition, mobility, and cardiovascular health at baseline, 3, 6, and 12 months (after a 6-month no-contact follow-up). Results: We observed improvements in cognition in both groups at 6 and 12 months, although no between-group differences were seen. Nondippers demonstrated superior improvements in usual gait velocity and step length after the exercise intervention compared to dippers. Dippers reduced daytime systolic BP at 6 and 12 months to a greater extent than nondippers. Discussion: BP dipping status at baseline did not influence exercise benefits to cognition but did mediate changes in mobility and cardiovascular 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.464

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 integrity0.0000.000
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 head0.015
GPT teacher head0.338
Teacher spread0.322 · 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.

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

Citations5
Published2018
Admission routes2
Has abstractyes

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