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Record W2321817763 · doi:10.1055/s-0031-1275741

Acute Effects of Exercise on Posture in Arthritic Patients

2011· article· en· W2321817763 on OpenAlexaff
Chiho Fukusaki, Kei Masani, Kimitaka Nakazawa

Bibliographic record

VenueInternational Journal of Sports Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsToronto Rehabilitation Institute
Fundersnot available
KeywordsMedicinePosturographyBalance (ability)Physical therapyFrequency domainCenter of pressure (fluid mechanics)Physical medicine and rehabilitationCardiologyInternal medicineMathematicsPhysics

Abstract

fetched live from OpenAlex

In this study, we aimed to investigate the acute effects of exercise on postural measures in arthritic patients. We obtained posturographic measurements of 8 women with lower extremity arthritis for 30 s before and after a 60-min aquatic exercise. The center of pressure (COP) was recorded while the volunteers were in an upright position with their eyes open. The time domain measures and the frequency domain measures of the COP time series in the anterior-posterior (AP) and medial-lateral (ML) directions were calculated. In addition, the frequency domain measures were calculated for the COP velocity time series. A paired T-test revealed no significant differences in any time domain measures between pre- and post-exercise; however, there were significant decreases in the 95% power frequency of the COP in the AP direction (0.834 ± 0.296 to 0.627 ± 0.230 Hz, p=0.027) for the frequency domain measures. For the velocity time series, the mean power frequency in both the AP (1.47 ± 0.528 to 1.22 ± 0.360 Hz, p=0.047) and ML (1.28 ± 0.245 to 1.13 ± 0.151 Hz, p=0.022) directions, and the 95% power frequency in the ML direction (3.41 ± 0.653 to 2.96 ± 0.468 Hz, p=0.038) decreased significantly in the post-exercise condition. This study reports that a single session of exercise has a subtle but detectable acute effect on postural balance in arthritic patients.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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