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Comparison of Aerobic Exercise Intensity Prescription Methods in Breast Cancer Patients and Survivors

2011· article· en· W4238363003 on OpenAlexaff
Amy A. Kirkham, Kristin L. Campbell, Donald C. McKenzie

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineExercise prescriptionRating of perceived exertionIntensity (physics)Heart rateBreast cancerExercise intensityMedical prescriptionTreadmillPopulationPhysical therapyAerobic exerciseRepeated measures designAnalysis of varianceInternal medicineCardiologyCancerBlood pressureMathematicsStatistics

Abstract

fetched live from OpenAlex

It is unknown how intensities achieved by different aerobic exercise intensity prescription methods compare in any population. This complicates the interpretation and comparison of studies necessary to create evidence-based exercise guidelines, which are currently a high priority for the cancer population. The accuracy of prescription methods in achieving the prescribed intensity is also unknown for the cancer population. Methods that are inaccurate could be unsafe or ineffective. PURPOSE:To compare the achieved intensity (AI) and accuracy (AC) of four common methods of intensity prescription within and between breast cancer patients recently finished chemotherapy (n=10), survivors finished treatment (n=10) and healthy controls (n=10). METHODS: In randomized order, 1) the ACSM's metabolic equation for treadmill walking (MET equation), 2) heart rate reserve (HRR), 3) direct heart rate (direct HR) and 4) rating of perceived exertion (RPE) methods were used to prescribe an intensity of 60% of oxygen consumption reserve (VO2R) in separate 10-minute treadmill bouts with expired gas analysis to measure AI, AC was defined as: [60%VO2R - AI]. Comparisons of AI and AC within each group were made with one-way ANOVA. The interaction between group and AC was analyzed with 3 x 4 ANOVA. RESULTS: In ranked order from most accurate, the average AI (%VO2R), and AC (+/- percentage points (pp)) in the patient group were: HRR: 61%, 3 pp; MET equation: 56%, 4 pp; direct HR: 60%, 8 pp; RPE: 53%, 9 pp. RPE differed significantly from the HRR method in both AI (p=.02) and AC (p=.05), and the direct HR method in AI (p<.01). In the survivor group results were: MET equation: 59%, 3 pp; HRR: 63%, 5 pp; direct HR: 64%, 5 pp; RPE: 47%, 13 pp. RPE differed significantly in AI (all at p<.01) and AC (all at p<.01) from all three methods. There was a significant interaction effect (p=.04) between group and AC. CONCLUSIONS: The four methods of intensity prescription do not achieve equivalent intensities, and vary in AC in prescribing 60 %VO2R within the 3 groups. The AC of the four methods is not equivalent among breast cancer patients, survivors and healthy controls. These results have implications for selecting an aerobic exercise intensity prescription method in this clinical population. Supported by CIHR & MSFHR.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.039
GPT teacher head0.348
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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
Published2011
Admission routes1
Has abstractyes

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