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Record W3091107784 · doi:10.1002/pon.5561

Acute effects of aerobic exercise and relaxation training on fatigue in breast cancer survivors: A feasibility trial

2020· article· en· W3091107784 on OpenAlexaff
Jason D. Cohen, Wendy A. Rogers, Steven J. Petruzzello, Linda Trinh, Sean P. Mullen

Bibliographic record

VenuePsycho-Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBreast cancerAerobic exerciseMedicinePhysical therapyCancerRelaxation (psychology)Cancer-related fatigueTraining (meteorology)OncologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This three-armed randomized controlled feasibility trial tested the acceptability and acute effects of aerobic exercise and technology-guided mindfulness training (relative to standalone interventions) on cancer-related fatigue among breast cancer survivors (BCS). METHODS: BCS recruited from Central Illinois completed pre- and post-testing using established measures and were randomized to one of three groups (combined aerobic exercise with guided-mindfulness relaxation, aerobic exercise only, and relaxation only), conducted in three 90 min sessions over the course of 7 days in a fitness room and research office on a university campus. RESULTS: = 4.56 ± 1.81 as measured by the Piper Fatigue Scale. More favorable post-intervention evaluations were reported by the combined group, compared to aerobic exercise or relaxation only (p < 0.05). Reductions in fatigue favoring the combined group (p = 0.05) showed a modest effect size (Cohen's d = 0.91) compared to aerobic exercise only. CONCLUSIONS: These findings provide preliminary evidence for the feasibility of combining evidence-based techniques to address fatigue among BCS. The combined approach, incorporating mobile health technology, presents an efficacious and well-received design. If replicated in longer trials, the approach could provide a promising opportunity to deliver broad-reaching interventions for improved outcomes in BCS. Preregistered-ClinicalTrials NCT03702712.

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: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.062
GPT teacher head0.368
Teacher spread0.306 · 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 designRandomized 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

Citations23
Published2020
Admission routes1
Has abstractyes

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