Moderators of Exercise Effects on Cancer-related Fatigue: A Meta-analysis of Individual Patient Data

Jonna K. van Vulpen, Maike G. Sweegers, Petra H.M. Peeters, Kerry S. Courneya, Robert U. Newton, Neil K. Aaronson, Paul B. Jacobsen, Daniel A. Galvão, Mai J. Chinapaw, Karen Steindorf, Melinda L. Irwin, Martijn M. Stuiver, Sandi Hayes, Kathleen A. Griffith, Ilse Mesters, Hans Knoop, Martine M. Goedendorp, Nanette Mutrie, Amanda J. Daley, Alex McConnachieMartin Bohus, Lene Thorsen, Karl-Heinz Schulz, Camille E. Short, Erica L. James, Ronald C. Plotnikoff, Martina E. Schmidt, Cornelia M. Ulrich, Marc van Beurden, Hester S. Oldenburg, Gabe S. Sonke, Wim H. van Harten, Kathryn H. Schmitz, Kerri M. Winters-Stone, Miranda J. Velthuis, Dennis R. Taaffe, Willem van Mechelen, Marie José Kersten, Frans Nollet, Jennifer Wenzel, Joachim Wiskemann, Irma M. Verdonck-de Leeuw, Johannes Brug, Anne M. May, Laurien M. Buffart

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Purpose Fatigue is a common and potentially disabling symptom in patients with cancer. It can often be effectively reduced by exercise. Yet, effects of exercise interventions might differ across subgroups. We conducted a meta-analysis using individual patient data of randomized controlled trials (RCTs) to investigate moderators of exercise intervention effects on cancer-related fatigue.
Methods We used individual patient data from 31 exercise RCTs worldwide, representing 4,366 patients, of whom 3,846 had complete fatigue data. We performed a one-step individual patient data meta-analysis, using linear mixed-effect models to analyze the effects of exercise interventions on fatigue (z-score) and to identify demographic, clinical, intervention- and exercise-related moderators. Models were adjusted for baseline fatigue and included a random intercept on study level to account for clustering of patients within studies. We identified potential moderators by testing their interaction with group allocation, using a likelihood ratio test.
Results Exercise interventions had statistically significant beneficial effects on fatigue (β= -0.17 [95% confidence interval (CI) -0.22;-0.12]). There was no evidence of moderation by demographic or clinical characteristics. Supervised exercise interventions had significantly larger effects on fatigue than unsupervised exercise interventions (βdifference= -0.18 [95%CI -0.28;-0.08]). Supervised interventions with a duration ≤12 weeks showed larger effects on fatigue (β= -0.29 [95% CI -0.39;-0.20]) than supervised interventions with a longer duration.
Conclusions – In this individual patient data meta-analysis, we found statistically significant beneficial effects of exercise interventions on fatigue, irrespective of demographic and clinical characteristics. These findings support a role for exercise, preferably supervised exercise interventions, in clinical practice. Reasons for differential effects in duration require further exploration.
Original languageEnglish
JournalMedicine & Science in Sports & Exercise
Early online date12 Sept 2019
Publication statusE-pub ahead of print - 12 Sept 2019

Keywords / Materials (for Non-textual outputs)

  • exercise
  • fatigue
  • cancer
  • individual patient data meta-analysis


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