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Record W4200503925 · doi:10.1123/japa.2021-0020

Do Older Brazilian Women Who Participate in a Regular Physical Exercise Program Have Higher Habitual Physical Activity Levels? A Cross-Sectional Study Based on Accelerometer Data

2021· article· en· W4200503925 on OpenAlexaff
Kaio César Pinhal, Bruno de Souza Moreira, Renata Alvarenga Vieira, Marcus Alessandro de Alcântara, João Marcos Domingues Dias, Rosângela Corrêa Dias, Renata Noce Kirkwood, Alessandra de Carvalho Bastone

Bibliographic record

VenueJournal of Aging and Physical Activity · 2021
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhysical activityCross-sectional studyPhysical therapyMedicineEnergy expenditureBalance (ability)Physical medicine and rehabilitationGaitPhysical exerciseGerontologyPsychology

Abstract

fetched live from OpenAlex

A cross-sectional study was conducted to compare the habitual physical activity level, measured by accelerometry, gait performance, assessed by the GAITRite® system, handgrip strength, and static balance between older Brazilian women who participate (n = 50; 70.7 ± 5.5 years) and do not participate (n = 50; 70.1 ± 5.6 years) in a regular physical exercise program, and to investigate whether participation in a regular exercise program ensures compliance with physical activity recommendations. Older women who participated in a regular physical exercise program had significantly shorter sedentary activity time (effect size [ES] = 0.54), longer moderate activity time (ES = 0.85), and higher energy expenditure (ES = 0.64), number of steps (ES = 0.82), gait speed (ES = 0.49), and step length (ES = 0.45). However, regular participation in an exercise program did not guarantee compliance with physical activity recommendations. Behavioral changes to increase physical activity levels among older women who do and do not participate in a regular exercise program are necessary.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.141
GPT teacher head0.431
Teacher spread0.290 · 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 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
Published2021
Admission routes1
Has abstractyes

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