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

Publication date

2025-10

Authors

Buffart, Laurien M
Kenkhuis, Marlou-Floor
Newton, Robert U
May, Anne MORCID 0000-0003-0643-3790
Galvão, Daniel A
Courneya, Kerry S

Editors

Advisors

Supervisors

Document Type

Article

Collections

Open Access logo

License

cc_by

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.

Keywords

Journal Article

Citation

Buffart, L M, Kenkhuis, M-F, Newton, R U, May, A M, Galvão, D A & Courneya, K S 2025, 'Accelerating precision exercise medicine in cancer patients using pooled individual patient data : POLARIS experience', JNCI cancer spectrum, vol. 9, no. 5, pkaf078. https://doi.org/10.1093/jncics/pkaf078