Evidence map›Paper›PMID 40795083›Full record

ArticleJNCI cancer spectrum2025

Accelerating precision exercise medicine in cancer patients using pooled individual patient data: POLARIS experience.

Laurien M Buffart, Marlou-Floor Kenkhuis, Robert U Newton, Anne M May, Daniel A Galvão, Kerry S Courneya

Abstract read
In one paragraph

Article in JNCI cancer spectrum, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Laurien M BuffartDepartment of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0002-8095-436X
Marlou-Floor KenkhuisDepartment of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0002-4199-4326
Robert U NewtonExercise Medicine Research Institute, Edith Cowan University, Joondalup, WA, Australia.ORCID 0000-0003-0302-6129
Anne M MayJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0003-0643-3790
Daniel A GalvãoExercise Medicine Research Institute, Edith Cowan University, Joondalup, WA, Australia.ORCID 0000-0002-8209-2281
Kerry S CourneyaFaculty of Kinesiology, Sport, and Recreation, University of Alberta, Edmonton, Canada.ORCID 0000-0002-9677-3918

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Numerous exercise oncology trials have been completed, greatly informing exercise recommendations for patients with cancer. Exercise medicine can be administered in various types, doses, and schedules at various time points. Advancing precision exercise medicine requires understanding of how the effects of different exercise interventions vary by characteristics of individual patients. The Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study provides an international infrastructure and shared database to perform pooled analyses of individual patient data (IPD) from multiple randomized controlled trials. This commentary aims to highlight the value of pooled IPD analyses, summarize key findings from published pooled IPD analyses on the effects of physical exercise on various outcomes, and provide guidance to advance precision exercise medicine for patients with cancer. POLARIS currently includes IPD from 52 exercise trials. Findings to date indicate that exercise interventions in patients with cancer have beneficial effects on physical fitness, fatigue, health-related quality of life, self-reported cognition (posttreatment), sleep disturbances, and symptoms of anxiety and depression. Additionally, it was determined that the exercise effects varied by characteristics of the patients, including the initial value of the outcome, age, marital status, and education level, and by characteristics of the intervention, including exercise supervision and specificity. Future research opportunities to advance precision exercise medicine for patients with cancer include pooling of trial data from understudied populations, data on clinical outcomes, and biomarkers, as well as applying machine learning models for identifying combinations of covariables that modify intervention effects and predictions of individual treatment effects.

Indexed as

Exercise TherapyNeoplasmsPrecision MedicineAnxietyCognitionDatabases, FactualDepressionExerciseFatigueHumansPhysical FitnessQuality of LifeRandomized Controlled Trials as Topic

Identifiers

PMID40795083
PMCPMC12401491

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.