MétaCan
Menu
Back to cohort
Record W2891196398 · doi:10.1136/bjsports-2018-099191

Effects and moderators of exercise on muscle strength, muscle function and aerobic fitness in patients with cancer: a meta-analysis of individual patient data

2018· review· en· W2891196398 on OpenAlexaff
Maike G. Sweegers, Teatske M. Altenburg, Johannes Brug, Anne M. May, Jonna K. van Vulpen, Neil K. Aaronson, Martin Bohus, Kerry S. Courneya, Amanda Daley, Daniel A. Galvão, Rachel Garrod, Kathleen A. Griffith, Wim H. van Harten, Sandra C. Hayes, F. Herrero, Marie José Kersten, Alejandro Lucía, Alex McConnachie, Willem van Mechelen, Nanette Mutrie, Robert U. Newton, Frans Nollet, Karin Potthoff, Martina E. Schmidt, Kathryn H. Schmitz, Karl‐Heinz Schulz, Gabe S. Sonke, Karen Steindorf, Martijn M. Stuiver, Dennis R. Taaffe, Lene Thorsen, Jos W. R. Twisk, Miranda J. Velthuis, Jennifer Wenzel, Kerri M. Winters‐Stone, Joachim Wiskemann, Mai J. M. Chinapaw, Laurien M. Buffart

Bibliographic record

VenueBritish Journal of Sports Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAerobic exerciseMedicinePhysical therapyPsycINFOPsychological interventionPhysical fitnessCINAHLMeta-analysisConfidence intervalPhysical medicine and rehabilitationMEDLINEInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To optimally target exercise interventions for patients with cancer, it is important to identify which patients benefit from which interventions. DESIGN: We conducted an individual patient data meta-analysis to investigate demographic, clinical, intervention-related and exercise-related moderators of exercise intervention effects on physical fitness in patients with cancer. DATA SOURCES: We identified relevant studies via systematic searches in electronic databases (PubMed, Embase, PsycINFO and CINAHL). ELIGIBILITY CRITERIA: We analysed data from 28 randomised controlled trials investigating the effects of exercise on upper body muscle strength (UBMS) and lower body muscle strength (LBMS), lower body muscle function (LBMF) and aerobic fitness in adult patients with cancer. RESULTS: Exercise significantly improved UBMS (β=0.20, 95% Confidence Interval (CI) 0.14 to 0.26), LBMS (β=0.29, 95% CI 0.23 to 0.35), LBMF (β=0.16, 95% CI 0.08 to 0.24) and aerobic fitness (β=0.28, 95% CI 0.23 to 0.34), with larger effects for supervised interventions. Exercise effects on UBMS were larger during treatment, when supervised interventions included ≥3 sessions per week, when resistance exercises were included and when session duration was >60 min. Exercise effects on LBMS were larger for patients who were living alone, for supervised interventions including resistance exercise and when session duration was >60 min. Exercise effects on aerobic fitness were larger for younger patients and when supervised interventions included aerobic exercise. CONCLUSION: Exercise interventions during and following cancer treatment had small effects on UBMS, LBMS, LBMF and aerobic fitness. Demographic, intervention-related and exercise-related characteristics including age, marital status, intervention timing, delivery mode and frequency and type and time of exercise sessions moderated the exercise effect on UBMS, LBMS and aerobic fitness.

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)0.035
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.069
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0200.080
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.292
Teacher spread0.252 · 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 designMeta-analysis
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

Citations108
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of Sports MedicineSame topicCancer survivorship and careFrench-language works237,207