TY - JOUR
T1 - Moderators of Exercise Effects on Cancer-related Fatigue
T2 - A Meta-analysis of Individual Patient Data
AU - van Vulpen, Jonna K.
AU - Sweegers, Maike G.
AU - Peeters, Petra H.M.
AU - Courneya, Kerry S.
AU - Newton, Robert U.
AU - Aaronson, Neil K.
AU - Jacobsen, Paul B.
AU - Galvão, Daniel A.
AU - Chinapaw, Mai J.
AU - Steindorf, Karen
AU - Irwin, Melinda L.
AU - Stuiver, Martijn M.
AU - Hayes, Sandi
AU - Griffith, Kathleen A.
AU - Mesters, Ilse
AU - Knoop, Hans
AU - Goedendorp, Martine M.
AU - Mutrie, Nanette
AU - Daley, Amanda J.
AU - McConnachie, Alex
AU - Bohus, Martin
AU - Thorsen, Lene
AU - Schulz, Karl-Heinz
AU - Short, Camille E.
AU - James, Erica L.
AU - Plotnikoff, Ronald C.
AU - Schmidt, Martina E.
AU - Ulrich, Cornelia M.
AU - van Beurden, Marc
AU - Oldenburg, Hester S.
AU - Sonke, Gabe S.
AU - van Harten, Wim H.
AU - Schmitz, Kathryn H.
AU - Winters-Stone, Kerri M.
AU - Velthuis, Miranda J.
AU - Taaffe, Dennis R.
AU - van Mechelen, Willem
AU - Kersten, Marie José
AU - Nollet, Frans
AU - Wenzel, Jennifer
AU - Wiskemann, Joachim
AU - Verdonck-de Leeuw, Irma M.
AU - Brug, Johannes
AU - May, Anne M.
AU - Buffart, Laurien M.
PY - 2019/9/12
Y1 - 2019/9/12
N2 - 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.
AB - 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.
KW - exercise
KW - fatigue
KW - cancer
KW - individual patient data meta-analysis
U2 - 10.1249/MSS.0000000000002154
DO - 10.1249/MSS.0000000000002154
M3 - Article
SN - 0195-9131
JO - Medicine & Science in Sports & Exercise
JF - Medicine & Science in Sports & Exercise
ER -