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Record W2771990314 · doi:10.5432/ijshs.201706

Effect of a Concurrent Well-Rounded Exercise Training Using a Floor-Based Exercise Station in Older Women

2017· article· en· W2771990314 on OpenAlexaff
Aiko Imai, Naoko Sengoku, Daisuke Koizumi, Yukiko Kitabayashi, Aiko Naruse, Michael E. Rogers, Nobuo Takeshima

Bibliographic record

VenueInternational Journal of Sport and Health Science · 2017
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsActive Aging Canada
Fundersnot available
KeywordsCircuit trainingAerobic exercisePhysical therapyBalance (ability)Physical medicine and rehabilitationResistance trainingMedicinePsychology

Abstract

fetched live from OpenAlex

To evaluate the effects of a 12-wk circuit exercise training program using the floor-based exercise station (FBES) on aerobic fitness, strength, and balance in older women. Participants were divided into: FBES exercise group (EX: n=22; 68.1±6.5 yr) and a non-exercise control group (CN: n=18; 68.2±5.7 yr). EX participated in a 12-wk circuit training program, 3 d/wk for 50 min/d, consisting of warm-up exercise (10 min), circuit training (30 min), and cool-down/relaxation exercise (10 min). Twelve strength and balance exercises and 12 aerobic dance exercises were performed alternatively for 30s each with a heart rate of 100-110 bpm. CN continued normal physical activity patterns. After 12-wk, all measurements were repeated in both groups. Compared to CN, EX increased (p<0.05) arm curl (12.6%), timed up-and-go (-8.7%), 12-min walk (5.4%), predict VO2max (6.3%), sit-and-reach (24.3%), and back scratch (271.5%), but not balance parameters. Incorporating both aerobic and resistance training via the FBES improves multiple aspects of fitness but not balance. These results are similar to those from larger, more expensive hydraulic exercise machines. This study supports the efficacy of the FBES for older adults and implementation is feasible in multiple settings as it is relatively inexpensive and requires little space.

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)0.006
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 score0.089
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.064
GPT teacher head0.457
Teacher spread0.393 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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