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Record W2024629875 · doi:10.1519/jsc.0b013e3181918a83

Effects of Unstable Surface Training on Measures of Balance in Older Adults

2009· article· en· W2024629875 on OpenAlexaff
Brian K. Schilling, Michael J. Falvo, Robyn E. Karlage, Lawrence W. Weiss, Corey A. Lohnes, Loren Z.F. Chiu

Bibliographic record

VenueThe Journal of Strength and Conditioning Research · 2009
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Alberta
FundersUniversity of Memphis
KeywordsBalance (ability)Confidence intervalPhysical medicine and rehabilitationBalance testDynamic balancePhysical therapyEyes openPsychologyBerg Balance ScaleMedicineInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this investigation was to examine the effects of a 5-week, low-cost unstable surface balance training program in sexagenarians. Nineteen men and women (60-68 years; 83.7 +/- SD kg) were randomly assigned to a control or training group. The training group performed various balance activities on air-filled rubber disks for 5 weeks. Each thrice-weekly session was supervised, and progression was based on proficiency. While in an upright position, static balance (length of path [LOP] of the center of pressure) was assessed in both eyes-open and eyes-closed states for each leg separately as well as for both legs. Participants also performed the timed up-and-go (TUG) test and completed the Activity-specific Balance Confidence (ABC) questionnaire. A significant group x time effect for the ABC questionnaire was found (p = 0.04). Tukey post hoc analysis indicates that the balance training program increased self-perceived balance confidence (p < 0.01). No significant group x time interactions were noted for TUG or LOP. Because no objective measure of balance or function was changed, the increase in ABC may be spurious. Unstable surface training may not be effective in improving balance among persons for whom balance is not problematic. However, the large number of acute training variables in such a program leaves opportunity for further research in this paradigm.

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)0.003
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 score0.832
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.390
Teacher spread0.343 · 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

Citations36
Published2009
Admission routes1
Has abstractyes

Explore more

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