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Record W4396568632 · doi:10.1016/j.jshs.2024.04.012

Associations between health-related fitness and patient-reported symptoms in newly diagnosed breast cancer patients

2024· article· en· W4396568632 on OpenAlexafffundabout
Ki‐Yong An, Fernanda Z. Arthuso, Myriam Filion, Spencer J Allen, Stephanie Ntoukas, Gordon J. Bell, Jessica McNeil, Qinggang Wang, Margaret L. McNeely, Jeff K. Vallance, Lin Yang, S. Nicole Culos‐Reed, Leanne Dickau, John R. Mackey, Christine M. Friedenreich, Kerry S. Courneya

Bibliographic record

VenueJournal of sport and health science/Journal of Sport and Health Science · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsAthabasca UniversityAlberta Health ServicesUniversity of CalgaryAlberta Cancer FoundationUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta InnovatesCanada Research ChairsAlberta Cancer Foundation
KeywordsBreast cancerMedicineQuality of life (healthcare)Affect (linguistics)CancerPhysical therapyInternal medicineOncologyPsychologyNursing

Abstract

fetched live from OpenAlex

• In 1,458 newly diagnosed breast cancer patients, lower relative peak cardiopulmonary fitness was independently associated with higher rates of moderate depression, poor sleep quality, and clinical fatigue. • Lower relative peak cardiopulmonary fitness was also independently associated with a higher likelihood of experiencing any symptom and multiple symptoms. • Both lower relative peak cardiopulmonary fitness and lower relative upper body muscular endurance were independently associated with a higher rate of clinical fatigue. • Relative peak cardiopulmonary fitness appears to be a critical fitness component associated with multiple patient-reported symptoms in newly diagnosed breast cancer patients. Newly diagnosed breast cancer patients experience symptoms that may affect their quality of life, treatment outcomes, and survival. Preventing and managing breast cancer-related symptoms soon after diagnosis is essential. The purpose of this study was to investigate the associations between health-related fitness (HRF) and patient-reported symptoms in newly diagnosed breast cancer patients. This study utilized baseline data from the Alberta Moving Beyond Breast Cancer Cohort Study that were collected within 90 days of diagnosis. HRF measures included peak cardiopulmonary fitness (peak volume of oxygen consumption (VO 2peak )), maximal muscular strength and endurance, flexibility, and body composition. Symptom measures included depression, sleep quality, and fatigue. Adjusted multivariable logistic regression was performed for analyses. Of 1458 participants, 51.5% reported poor sleep quality, 26.5% reported significant fatigue, and 10.4% reported moderate depression. In multivariable-adjusted models, lower relative VO 2peak was independently associated with a greater likelihood of all symptom measures, including moderate depression ( p < 0.001), poor sleep quality ( p = 0.009), significant fatigue ( p = 0.008), any symptom ( p < 0.001), and multiple symptoms ( p < 0.001). VO 2peak demonstrated threshold associations with all symptom measures such that all 3 lower quartiles exhibited similar elevated risk compared to the highest quartile. The strength of the threshold associations varied by the symptom measure with odds ratios ranging from ∼1.5 for poor sleep quality to ∼3.0 for moderate depression and multiple symptoms. Moreover, lower relative upper body muscular endurance was also independently associated with fatigue in a dose-response manner ( p = 0.001), and higher body weight was independently associated with poor sleep quality in an inverted U pattern ( p = 0.021). Relative VO 2peak appears to be a critical HRF component associated with multiple patient-reported symptoms in newly diagnosed breast cancer patients. Other HRF parameters may also be important for specific symptoms. Exercise interventions targeting different HRF components may help newly diagnosed breast cancer patients manage specific symptoms and improve outcomes.

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)0.008
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 score0.031
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.352
Teacher spread0.321 · 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

Citations5
Published2024
Admission routes3
Has abstractyes

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