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Record W2989921554 · doi:10.3233/nre-192861

Accessible exercise equipment and individuals with multiple sclerosis: Aerobic demands and preferences

2019· article· en· W2989921554 on OpenAlexaff
Kaitlyn Jg Snyder, Eleni Patsakos, John White, David S. Ditor

Bibliographic record

VenueNeurorehabilitation · 2019
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsBrock University
Fundersnot available
KeywordsMultiple sclerosisAerobic exercisePhysical medicine and rehabilitationPsychologyPhysical therapyMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Although exercise training has benefits for individuals with multiple sclerosis (MS), research regarding the type of exercise equipment that requires the greatest aerobic demand, and consumer-based preferences, is lacking. OBJECTIVE: To determine the aerobic demands of various pieces of accessible exercise equipment and consumer-based preferences on several domains. METHODS: Ten individuals with moderate-severity MS had their VO2 measured during 10 minutes of moderate-intensity arm ergometry (AE), body-weight support treadmill training (BWSTT), recumbent arm-leg exercise (NuStep), FES-arm exercise (RT300), FES-leg exercise (RT300) and FES arm-leg exercise (RT200). VO2peak test was also measured on the NuStep and the RT200. Equipment preferences were determined by questionnaire after moderate exercise sessions. RESULTS: AE required a lower VO2 compared to the NuStep (p = 0.02), and FES-arm exercise required a lower VO2 compared to the NuStep (p = 0.01) and FES arm-leg exercise (p = 0.04). There was no difference in VO2peak when using the NuStep or FES arm-leg exercise. AE was perceived as safer than BWSTT, but otherwise there were no preferences for any equipment. CONCLUSIONS: For individuals with moderate-severity MS, arm-only exercise requires less aerobic demands than combined arm-leg exercise at a moderate intensity. Perceived risks may be greater when exercise requires a transfer, upright positioning, or assistance.

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 score0.034
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.046
GPT teacher head0.294
Teacher spread0.248 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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