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Differential Effects of Exercise on Cancer-Related Fatigue During and Following Treatment
Differential Effects of Exercise on Cancer-Related Fatigue During and Following Treatment A Meta-Analysis Timothy W. Puetz, PhD, Matthew P. Herring, PhD Context: Exercise-induced improvements in cancer-related fatigue may be moderated differentially in patients during and following treatment. These effects have not been reviewed systematically. In accordance with PRISMA guidelines, the population effect size for exercise training on cancerrelated fatigue during and following treatment was estimated and the extent to which the effect is differentiated across the time course of treatment and recovery was determined. Evidence acquisition: Articles published before August 2011 were retrieved using Google Scholar, MEDLINE, PsycINFO, PubMed, and Web of Science databases. Seventy studies involving 4881 cancer patients during or following treatment were selected. Articles included a cancer-related fatigue outcome measured at baseline and post-intervention and randomized allocation to exercise or non-exercise comparison. From August to October 2011, Hedges’ d effect sizes were computed, study quality was evaluated, and random effects models were used to estimate sampling error and population variance. Evidence synthesis: Exercise signifıcantly reduced cancer-related fatigue by a mean effect ⌬ (95% CI) of 0.32 (0.21, 0.43) and 0.38 (0.21, 0.54) during and following cancer treatment, respectively. During treatment, patients with lower baseline fatigue scores and higher exercise adherence realized the largest improvements. Following treatment, improvements were largest for trials with longer durations between treatment completion and exercise initiation, trials with shorter exercise program lengths, and trials using wait-list comparisons. Conclusions: Exercise reduces cancer-related fatigue among patients during and following cancer treatment. These effects are moderated differentially over the time course of treatment and recovery. Exercise has a palliative effect in patients during treatment and a recuperative effect post-treatment. (Am J Prev Med 2012;43(2):e1– e24) © 2012 American Journal of Preventive Medicine
Introduction
T
he National Cancer Institute estimates that nearly 12 million Americans with a history of cancer were alive in January 2008.1 Continued increases in cancer diagnoses and issues of survivorship are important public health problems, particularly because approximately 1.6 million new cancer diagnoses are expected in 2012.1
From the Department of Behavioral Sciences and Health Education (Puetz), Rollins School of Public Health, Emory University, Atlanta, Georgia; and the Department of Epidemiology (Herring), University of Alabama at Birmingham, Birmingham, Alabama Address correspondence to: Timothy W. Puetz, PhD, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta GA 30322. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2012.04.027
Cancer-related fatigue is a persistent, subjective sense of tiredness related to cancer or cancer treatment that interferes with usual functioning.2 Approximately 50%– 90% of cancer patients undergoing cancer treatment experience cancer-related fatigue.3 For a substantial number of these patients, cancer-related fatigue persists after treatment is completed.4 For example, 40% of cancer survivors have reported at least 2 weeks of fatigue in the previous month, with more than 33% of survivors reporting fatigue despite being approximately 5 years posttreatment.5 Exercise has been proposed as an effective, nonpharmacologic intervention to promote psychological well-being during and following cancer treatment. Exercise effects on cancer-related fatigue among patients both during and following treatment consistently have been positive, but the magnitude of the effect has varied substantially.6 – 8 Cancer-related fatigue occurs
© 2012 American Journal of Preventive Medicine • Published by Elsevier Inc.
Am J Prev Med 2012;43(2):e1– e24
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both as a consequence of cancer and as a side effect of cancer treatment.4 Both the time course and contributing factors of cancer-related fatigue may differ before,9 during,10 and after11 the initiation of cancer treatment. Understanding the characteristics of cancer-related fatigue across the time course of the disease can be helpful in the development and implementation of exercise interventions to reduce fatigue during and following treatment. Previous meta-analyses6 – 8 have not examined directly the effects of exercise on cancer-related fatigue in patients both during and following treatment or conducted a moderator analysis despite the heterogeneity of effects.12 These issues have led to diffıculties both in estimating the effect of exercise interventions on cancer-related fatigue and in identifying potential differentiating effects of exercise on cancer-related fatigue across the time course of treatment and recovery and the variables that may moderate these effects. Such information is important in guiding clinical decisions on exercise prescription across the time course of cancer and treatment. To address previous limitations, the primary objective was to review systematically RCTs examining the effects of exercise interventions on cancer-related fatigue in patients during and following treatment in order to determine the extent to which the effect is differentiated across the time course of treatment and recovery. Heterogeneity of study results was examined to determine how potential moderating variables may influence the effıcacy of exercise found in patients during and following cancer treatment.
Evidence Acquisition This review was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines.13 Analyses were conducted in August to October 2011. Articles published before August 2011 were located with searches of Google Scholar, MEDLINE, PsycINFO, PubMed, and Web of Science databases using the keywords cancer, exercise, fatigue, physical activity, and randomized controlled trial. Searches of reference lists from retrieved articles were performed manually. Publication language was not restricted.
Study Selection Inclusion criteria were (1) cancer patients currently undergoing treatment (e.g., chemotherapy, radiation therapy, hormone therapy) or cancer patients post-treatment; (2) randomization to either exercise training or a non-exercise comparison; and (3) a cancer-related fatigue outcome measured before and during and/or after exercise training (Appendix A).14 Exclusion criteria were (1) compared exercise only with an active therapy (e.g., pharmacotherapy, another mode of exercise); (2) examined the effect of acute exercise on cancer-related fatigue; and/or (3) used education or promotion interventions aimed at
434 records screened 425 exercise and CRF in patients during and following treatment 9 from other sources 109 RCTs of exercise and CRF in patients during and following treatment
325 excluded 154 narrative reviews and metaanalyses 171 non-RCTs and/or animal studies 23 excluded (no primary data)
86 studies assessed for eligibility
70 studies included in quantitative synthesis 43 studies of cancer patients during active treatment 27 studies of cancer patients following active treatment
16 excluded 7 CRF not reported 4 inadequately measured physical activity interventions 2 poor exercise adherence; comparison group contamination 2 convenience sample; no comparison condition 1 effect size could not be calculated
Figure 1. Flowchart of study selection CRF, cancer-related fatigue
increasing physical activity but failed to show increased physical activity. A flowchart of study selection is presented in Figure 1.
Data Extraction and Quality Assessment Authors independently extracted data, and discrepancies were resolved by consensus judgment. Effect sizes were calculated by subtracting the mean change in the comparison from the mean change in the exercise condition and dividing the difference by the pooled SD of pre-intervention scores.15 Effect sizes were adjusted using Hedges’ small-sample bias correction and calculated so that decreases in cancer-related fatigue resulted in positive effect sizes.15 Multiple effects within a trial were averaged such that each trial contributed only one effect to analysis.16 When precise means were not reported, effect sizes were estimated17 from t-tests,18 exact p-values,19,20 or fıgures.21–23 When precise SDs were not reported,24 –27 the SD was drawn from published norms or the largest other study using the same measure.
Study Quality Assessment Authors independently assessed the methodologic quality of each study using a 15-item scale that addressed randomization, sample selection, quality of outcome measures, and statistical analysis.28 Quality assessment showed high concordance between authors (ICC [3, 2]⫽0.96, 95% CI⫽0.89, 0.98).29 Using the Bland and Altman limits-of-agreement procedure, the average disagreement (M, 95% CI) was close to zero (0.40 [0.10, 0.70]), suggesting no systematic bias between reviewers.30,31 Quality scores were not used as weights or moderators in the analysis because of the potential disparity in results that depends on the specifıc quality scale employed.32
Data Synthesis and Analysis Statistical analyses initially were performed on the basis of an overall model examining patients both during and following treatment. Because analyses revealed differential effects among patients during and following treatment, separate regression models for during and post-treatment were tested to better understand exercise effects on cancer-related fatigue over the time course of treatment and to identify variables that moderate the effect. www.ajpmonline.org
Puetz and Herring / Am J Prev Med 2012;43(2):e1– e24 Using SPSS macros (MeanES, MetaReg), random effects models were employed to aggregate mean effect size (⌬), 95% CI, and the sampling error variance and to test for variation in effects according to moderator variables.33 Heterogeneity and consistency were evaluated with the Q statistic and the I2 statistic, respectively.34 Heterogeneity was examined relative to observed variance and was indicated if the sampling error accounted for less than 75% of the observed variance.15 Potential publication bias was addressed by inspection of funnel plots35 and quantifıed with rank correlation and regression methods.35,36
Primary Moderators and Analysis To provide focused research hypotheses about variation in effect size,37 primary moderator variables were selected a priori for each model based on logical, theoretic, or prior empirical relationship to cancer-related fatigue during and following treatment.4,38 Three moderator variables were selected for the overall model: treatment status (i.e., patient undergoing treatment or patient post-treatment); percentage fatigue reduction (i.e., percentage change in fatigue for the exercise condition minus the percentage change in fatigue for control condition); and the treatment status by percentage fatigue reduction interaction. Three moderator variables were selected for the duringtreatment model: adherence rate, baseline fatigue T-scores, and the adherence by baseline fatigue score interaction. Three moderator variables were selected for the post-treatment model: duration post-treatment, exercise program length, and type of comparison (i.e., wait-list control or other comparison conditions). Primary moderator variables for each model were included in a weighted least-squares multiple linear regression analysis with maximum-likelihood estimation.15,33 Tests of the regression model (QR) and its residual error (QE) are reported. Signifıcant categoric moderators were decomposed using a random effects model to compute mean effect sizes and 95% CIs.33 The JohnsonNeyman procedure was conducted to identify the critical point in signifıcant interactions of categoric and continuous variables in order to defıne signifıcance regions.39
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0⫽2.34, t(25)⫽2.97, p⫽0.006, models. Sensitivity analyses of study quality (i.e., removing studies in the 25th quartile) and exercise adherence (i.e., removing studies with ⬍80% adherence rates) examined the robustness of the conclusions of the meta-analysis.99 Sensitivity analyses for all models related to quality and adherence reinforced the results of the meta-analysis.
Overall Model Sixty-two of 70 (88.6%) effects were greater than zero (Figure 2). The mean effect size ⌬ (95% CI) was 0.34 (k⫽70, 95% CI⫽0.25, 0.43; z⫽7.290, p⬍0.001). The effect was heterogeneous, QT(69)⫽143.49, p⬍0.001. Sampling error accounted for 54.7% of the observed variance. The effect was moderately consistent across studies (I2⫽52.6%, 95% CI⫽45.6%, 58.7%).
Primary Moderator Analysis The overall multiple regression model was related to effect size, QR(3)⫽74.12; p⬍0.0001, R2⫽0.54; QE(63)⫽63.03, p⫽0.48. The interaction of treatment status and percentage fatigue reduction (Appendix D) was independently related to effect size (⫽0.009, z⫽2.96, p⫽0.003). The Johnson-Neyman procedure yielded a critical point for percentage fatigue reduction at ⫺37.4% (⫽⫺0.19, t⫽2.00, p⫽0.05). Further decomposition revealed (1) greater mitigation of cancer-related fatigue symptoms among exercising patients compared to controls during treatment (⫺4.2% vs 29.1%) and (2) larger reductions among exercising patients compared to controls post-treatment (⫺20.5% vs 1.3%; Appendix E).
During Treatment Secondary Moderators and Analysis Secondary moderator variables were selected for descriptive, univariate analyses based on a logical, theoretic, or prior empirical relationship with cancer-related fatigue (Appendix B). Mean effect sizes (⌬) and 95% CIs were computed for continuous and categoric variables using a random effects model.33
Evidence Synthesis Characteristics of the trials of patients during18,21–27,40 –74 and following treatment19,20,75–98 that were included in the meta-analysis and study quality assessment results are presented in Table 1. Examination of funnel plots and statistical tests for funnel-plot asymmetry suggested potential small-study bias for all models (Appendix C). Begg’s rank correlation was signifıcant for the overall, (69)⫽⫺0.33, p⬍0.001; during treatment, (42)⫽⫺0.32, p⫽0.003; and post-treatment, (26)⫽⫺0.43, p⫽0.025, models. Egger’s regression test was signifıcant for the overall, 0⫽1.85, t(68)⫽3.57, p⫽0.001; during treatment, 0⫽1.75, t(41)⫽3.01, p⫽0.004; and post-treatment, August 2012
Thirty-nine of the 43 effects (94.3%) were greater than zero (Figure 2). Exercise training signifıcantly reduced cancer-related fatigue (⌬⫽0.32, 95% CI⫽0.21, 0.43; z⫽5.74, p⬍0.001). The effect was heterogeneous, QT(42)⫽79.44, p⫽0.004. Sampling error accounted for 59.4% of the observed variance. The effect was moderately consistent across studies (I2⫽48.4%, 95% CI⫽38.2%, 56.8%).
Primary Moderator Analysis The multiple regression model for patients during treatment was signifıcantly related to effect size, QR(3)⫽22.09, p⫽0.0001, R2⫽0.45; QE(27)⫽27.08, p⫽0.46. The interaction of baseline fatigue and exercise adherence (Appendix F) was independently related to effect size (⫽0.19, z⫽3.14, p⫽0.002).
Post-Treatment Twenty-three of the 27 effects (94.3%) were greater than zero (Figure 2). Exercise training signifıcantly improved
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cancer-related fatigue (⌬⫽0.38, 95% CI⫽0.21, 0.54; z⫽4.44, p⬍0.0001). The effect was heterogeneous, QT(26)⫽ 63.62, p⫽0.0001. Sampling error accounted for 46.8% of the observed variance. The effect was moderately consistent across studies (I2⫽60.7%, 95% CI⫽ 51.3%, 68.3%).
Primary and Secondary Moderator Analysis The multiple regression model for patients post-treatment was signifıcantly related to effect size, QR(3)⫽22.36, p⫽0.0001, R2⫽0.50; QE(23)⫽22.56, p⫽ 0.49. Duration posttreatment (⫽0.01, z⫽ 2.21, p⫽0.0271); exercise program length (⫽⫺0.03, z⫽⫺2.86, p⫽0.0042); and comparison type (⫽0.44, z⫽3.90, p⫽0.0013) were independently related to effect size. Signifıcantly larger effects (⌬, 95% CI) were found for studies that used a wait-list comparison (⌬⫽ 0.66, 95% CI⫽0.42, 0.90) compared with the average effect for other comparison types (⌬⫽0.19, 95% CI⫽0.00, 0.37), QB(1)⫽9.74, p⫽0.002. The number of effects (k), mean effect ⌬, 95% CI, p-value, and I2 for each level of each moderator for each model are presented in Appendixes G and H).
Puetz and Herring / Am J Prev Med 2012;43(2):e1– e24
Table 1. Characteristics of included studies and quality assessment, % or M (SD) unless otherwise noted
Characteristics Total sample (N)
During treatment (k⫽43) 3235
Post-treatment (k⫽27) 1646
Age (years)
52.0 [10.2]
55.0 [5.5]
Women
68.0
87.0
BMI (kg/m )
26.8 [2.2]
27.2 [1.8]
Aerobic capacity (VO2max, ml/kg/min)
21.1 [6.5]
24.2 [4.5]
2
Cancer site Blood
13.3
3.7
Brain
0.9
0.2
Breast
58.3
73.7
Colon
1.3
8.3
Gastrointestinal
2.6
0.9
Gynecologic
1.3
3.7
Head and neck
1.2
2.8
Lung
1.0
1.6
Prostate
18.0
0.3
Testicular
0.7
1.1
Other
1.5
3.7
Chemotherapy
58.9
38.2
Radiation
29.3
42.4
Hormone Therapy
11.9
19.4
50.3 [6.3]
41.4 [10.7]
Cancer treatment
Baseline fatigue (T-score) Duration post-treatment (months, M [range])
N/A
16.3 [1.0–75.0]
Exercise setting Home-based
37.0
29.6
Supervised
63.0
70.4
Exercise frequency (days/week)
3.4 [1.3]
2.9 [1.3]
Exercise session duration (minutes)
42.3 [21.1]
49.6 [27.0]
Exercise program length (weeks)
11.7 [6.9]
12.6 [6.5]
Exercise intensity (% aerobic power)
55.0 [14.4]
53.3 [10.9]
Exercise
89.0 [64.0–100]
86.5 [59.0–100]
Control
87.5 [50.0–100]
90.3 [60.0–100]
Adherence (M % [range])
78.5 [58.0–100]
87.4 [34.0–98.0]
Study quality (rating 0-15)
10.9 [2.1]
11.1 [1.9]
Retention rate (median % [range])
N/A, not applicable
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During treatment studies
Hedges' d (95% CI)
Segal (2001)65 Shang (2009)68 Courneya (2008)49 Brown (2006)22 Courneya (2007)50 Crowley (2003)24 Mutrie (2007)64 Dimeo (1999)54 Hacker (2011)58 Courneya (2007)51 Moadel (2007)60 Cohen (2004)47 Cheville (2010)46 Caldwell (2009)43 Musan (2009)63 Culos-Reed (2010)53 Drouin (2002)56 Mock (2005)61 Adamsen (2009)40 Yeh (2011)74 Dodd (2010)55 Coleman (2003)48 Segal (2003)66 Mock (1997)27 Galvao (2010)57 Courneya (2009)52 Segal (2009)67 Jarden (2009)59 Hwang (2008)23 Wang (2011)71 Yang (2011)73 Wiskemann (2011)72 Vito (2007)69 Windsor (2004)21 Vadiraja (2009)70 Mock (1994)26 MacVicar (1987)25 Campbell (2005)44 Headley (2004)18 Chang (2008)45 Barfoot (2005)41 Monga (2007)62 Baaglini (2004)42
-0.27 (-0.64, 0.10) -0.14 (-0.49, 0.21) -0.12 (-0.65, 0.41) -0.02 (-0.41, 0.37) 0.01 (-0.30, 0.32) 0.04 (-0.80, 0.88) 0.04 (-0.23, 0.31) 0.10 (-0.41, 0.61) 0.10 (-0.86, 1.06) 0.11 (-0.20, 0.42) 0.12 (-0.25, 0.49) 0.13 (-0.50, 0.76) 0.15 (-0.24, 0.54) 0.19 (-0.65, 1.03) 0.21 (-0.44, 0.86) 0.21 (-0.32, 0.74) 0.22 (-0.68, 1.12) 0.24 (-0.15, 0.63) 0.25 (0.00, 0.50) 0.27 (-0.57, 1.12) 0.28 (-0.15, 0.71) 0.29 (-1.28, 1.86) 0.31 (0.00, 0.62) 0.33 (-0.26, 0.92) 0.33 (-0.20, 0.86) 0.41 (0.04, 0.78) 0.46 (0.01, 0.91) 0.52 (-0.11, 1.15) 0.54 (-0.13, 1.21) 0.56 (0.09, 1.03) 0.56 (0.09, 1.03) 0.57 (0.12, 1.02) 0.59 (-0.21, 1.39) 0.63 (0.12, 1.14) 0.66 (0.19, 1.13) 0.67 (-0.47, 1.81) 0.73 (-0.58, 2.04) 0.76 (-0.18, 1.70) 0.96 (0.23, 1.69) 0.97 (0.09, 1.85) 1.67 (0.71, 2.63) 1.90 (0.94, 2.86) 2.12 (1.14, 3.10)
Paent mean Δ
I2 = 48.4% (38.2%, 56.8%)
0.32 (0.21, 0.43)
Post-treatment studies
Hedges' d (95% CI)
Thorsen (2005)96 Cadmus (2009)79 Eyigor (2010)86 Dimeo (2004)85 Cadmus (2009)79 Courneya (2003)82 Berglund (1994)76 McNeely (2008)91 Fillion (2008)87 Liman (2011)89 Sprod (2010)95 Courneya (2003)81 Malec (2002)90 Bourke (2011)77 Pinto (2005)94 van Weert (2010)97 Lee (2010)88 Daley (2007)83 Courneya (2003)80 Pinto (2003)93 Danhauer (2009)84 Yuen (2007)98 Burnham (2002)78 Milne (2008)92 McKenzie (2003)19 Carson (2009)20 Banasik (2011)75
-0.47 (-0.82,-0.12) -0.32 (-0.87, 0.23) -0.03 (-0.66, 0.60) 0.00 (-0.47, 0.47) 0.04 (-0.41, 0.49) 0.06 (-0.37, 0.49) 0.11 (-0.18, 0.40) 0.15 (-0.40, 0.70) 0.23 (-0.20, 0.66) 0.27 (-0.25, 0.80) 0.33 (-0.20, 0.86) 0.36 (-0.05, 0.77) 0.39 (-0.41, 1.19) 0.47 (-0.46, 1.41) 0.52 (0.07, 0.97) 0.57 (0.22, 0.92) 0.58 (-0.22, 1.38) 0.59 (0.12, 1.06) 0.71 (0.14, 1.28) 0.75 (-0.09, 1.59) 0.76 (0.13, 1.39) 0.77 (-0.29, 1.83) 0.84 (-0.18, 1.86) 0.96 (0.41, 1.51) 1.18 (0.06, 2.30) 1.42 (0.71, 2.13) 1.49 (0.30, 2.67)
Survivor mean Δ
-4.0
-3.0
I2 = 60.7% (51.3%, 68.3%)
-2.0
-1.0
0.38 (0.21, 0.54)
0.0
1.0
2.0
3.0
4.0
5.0
Favors intervenon
Favors control Hedges’ d (95% CI)
Figure 2. Effects of exercise intervention versus comparison condition in meta-analyses of RCTs of cancer patients during treatment (n⫽43) and post-treatment (n⫽27)
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Discussion The cumulative evidence reviewed here supports previous reports that exercise training reduces cancer-related fatigue among patients both during and following cancer treatment. However, this is the fırst analysis to concurrently examine cancer patients during and following treatment and to identify variables that discriminately modify the effect during specifıc points in the time course of treatment and recovery. To date, only one RCT has examined exercise effects on cancer-related fatigue from diagnosis, through hospital admission and treatment, and into post-treatment follow-up.72 The present fındings support the evidence from that trial, bolstering the argument of differential effects of exercise on cancerrelated fatigue across the time course of treatment and recovery. The magnitude of the overall mean effect for patients during treatment (⌬⫽0.32) and post-treatment (⌬⫽0.38) is comparable to the effect of (1) exercise interventions on related outcomes in cancer patients, including depression,100 anxiety,101 and quality of life100; (2) individual or group therapy on cancer-related fatigue102; and (3) pharmacotherapy on cancer-related fatigue.103 Expressed as a binomial effect size,104 the effect of exercise training is equivalent to a clinical effect of 15.8% and 18.6% beyond chance among exercising patients during and posttreatment, respectively. The reduction in cancer-related fatigue found among exercising patients undergoing and following treatment is also equivalent to a number needed to treat105 of approximately 3 (1.6 – 4.2) and 4 (2.0 –15.7), respectively.
Overall Model: Treatment Status X Percentage Fatigue Reduction Interaction Among the combined sample of patients undergoing treatment and patients post-treatment, cancer-related fatigue reductions varied according to an interaction between treatment status and percentage reduction in cancer-related fatigue. For studies with larger percentage reductions in fatigue, the magnitude of the effect of exercise on cancer-related fatigue was greater among patients post-treatment compared with patients during treatment. However, for studies with smaller percentage reductions in fatigue, patients during treatment realized larger reductions in cancer-related fatigue than patients post-treatment. Exercise interventions appear to have a greater effect in reducing cancer-related fatigue in patients post-treatment than in patients during treatment when percentage reductions in fatigue are below ⫺37.4%. For studies with percentage reductions above ⫺37.4%, there was insuffıcient evidence to conclude whether the effect of exercise in reducing cancer-related fatigue was
signifıcantly different between patients during and following treatment. The interaction is likely related to the differential response of cancer patients to exercise and control conditions during and post-treatment. Cancer-related fatigue is mitigated in exercising patients compared to nonexercising patients during treatment (⫺4.2% vs 29.1%), whereas cancer-related fatigue is reduced in exercising patients compared to non-exercising patients posttreatment (⫺20.5% vs ⫺1.3%). These fındings suggest that exercise has a palliative effect in patients undergoing cancer treatment and a recuperative effect in patients following treatment. This evidence should assist clinicians when prescribing exercise.
During Treatment: Baseline Fatigue X Exercise Adherence Interaction Improvement in cancer-related fatigue for patients during treatment varied according to the patient’s baseline fatigue scores and exercise adherence rates. Patients with lower baseline fatigue scores and higher intervention adherence realized the largest improvements. This fınding should be interpreted with caution. It is plausible that patients with lower levels of cancer-related fatigue were able to better tolerate exercise than those with higher levels of cancer-related fatigue during treatment and therefore experienced greater protective effects. However, baseline fatigue severity was not associated with exercise adherence in a previous study of breast cancer patients receiving chemotherapy. As exercise exposure increased in that study, the intensity of cancer-related fatigue decreased across all baseline levels of fatigue.106 Cancer-related fatigue also was not a predictor of exercise adherence in an RCT of breast cancer patients undergoing chemotherapy; however, aerobic fıtness (i.e., VO2peak) was a predictor of adherence.107 This cumulative evidence suggests that fatigue during cancer treatment likely is maintained at pre-treatment levels through the palliative effects of exercise. The present fındings also provide evidence to recommend exercise before cancer treatment to increase fıtness, which may mediate the relationship of cancer-related fatigue and adherence.
Following Treatment: Post-Treatment Duration, Exercise Program Length, and Comparison Condition Following treatment greater effects were seen for trials with a longer duration between treatment completion and exercise program initiation, exercise interventions with shorter program lengths, and trials using wait-list comparisons. Unlike with patients undergoing treatment, cancer-related fatigue is a predictor of exercise adherence in patients following treatment.108 Exercise www.ajpmonline.org
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levels among patients decrease from pre-diagnosis to active treatment and then slowly increase from active treatment to post-treatment, but usually not to pre-diagnostic levels.109 Thus, a longer post-treatment duration will increase the natural progression toward exercise in cancer patients following treatment.110 Exercise program length also could be related to this phenomenon. Larger effects associated with shorter exercise interventions may be related to the reduction in cancer-related fatigue that naturally occurs over time in controls and/or with the exercise contamination effects seen in longer clinical trials.111–113 Baseline exercise stage of change and past exercise are predictors of exercise contamination in comparison groups.111–113 Unlike other types of comparisons, wait-list controls may provide a viable active treatment in post-treatment cancer patients’ natural progression toward exercise such that it serves as a pre-contemplation or contemplation stage in the Transtheoretical Model of Behavior Change.114 In any case, clinicians should consider prescribing exercise at cancer diagnosis in order to help mitigate the deleterious effects of active treatment that reduce the physical activity levels of patients post-treatment and ultimately compound cancer-related fatigue.
Limitations Limitations in the quality and reporting of the included trials are notable. Many studies lacked adequate information regarding features of the exercise intervention (e.g., intensity, mode, duration, frequency), appropriateness of comparisons, and under-reporting of adherence levels, medication use, and cancer sites. The inconsistency observed in study quality is disappointing, as is the fact that approximately 10% of the included trials did not include a well-validated cancer-related fatigue outcome measure.14 These limitations emphasize the importance of adoption of and compliance with reporting guidelines to improve the quality of future trials.
Future Research For a better understanding of exercise effects on cancerrelated fatigue, well-designed RCTs should (1) seek to better characterize the features of the exercise stimulus (i.e., frequency, intensity, session duration, program length, mode); (2) examine exercise effects on specifıc neurobiologic and psychological measures of cancerrelated fatigue; (3) examine relationships of exercise with cancer-related fatigue and related mood states, including anxiety, depression, and quality of life; and (4) investigate the mechanistic similarities, differences, and interactions among various exercise training protocols, psychosocial interventions, and pharmacologic treatments employed to reduce cancer-related fatigue. Such investigations will August 2012
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help defıne appropriate exercise prescription across the time course of cancer treatment and survivorship and offer important insight into the biopsychosocial mechanisms of cancer-related fatigue.
Conclusion Exercise reduces cancer-related fatigue among patients both undergoing and following cancer treatment, but these effects are differentially moderated over the time course of treatment and recovery. Exercise has a palliative effect in patients undergoing treatment and a restorative effect following treatment. These fındings provide evidence for prescribing exercise during and following cancer treatment as a potentially low-risk, adjuvant therapy for cancer-related fatigue. However, clinicians should recognize the differential effects of exercise on cancerrelated fatigue when prescribing exercise. No fınancial disclosures were reported by the authors of this paper.
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Appendix E: Cancer patients and survivors by exercise and control intervention F G H
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Percent Change in Fague
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10
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0 -4.2
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-20.5
-20
-30 Paents During Treatment
Paents Post-Treatment
Cancer Paents and Survivors by Exercise and Control Intervenon
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Introduction
T
he National Cancer Institute estimates that nearly 12 million Americans with a history of cancer were alive in January 2008.1 Continued increases in cancer diagnoses and issues of survivorship are important public health problems, particularly because approximately 1.6 million new cancer diagnoses are expected in 2012.1
From the Department of Behavioral Sciences and Health Education (Puetz), Rollins School of Public Health, Emory University, Atlanta, Georgia; and the Department of Epidemiology (Herring), University of Alabama at Birmingham, Birmingham, Alabama Address correspondence to: Timothy W. Puetz, PhD, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta GA 30322. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2012.04.027
Cancer-related fatigue is a persistent, subjective sense of tiredness related to cancer or cancer treatment that interferes with usual functioning.2 Approximately 50%– 90% of cancer patients undergoing cancer treatment experience cancer-related fatigue.3 For a substantial number of these patients, cancer-related fatigue persists after treatment is completed.4 For example, 40% of cancer survivors have reported at least 2 weeks of fatigue in the previous month, with more than 33% of survivors reporting fatigue despite being approximately 5 years posttreatment.5 Exercise has been proposed as an effective, nonpharmacologic intervention to promote psychological well-being during and following cancer treatment. Exercise effects on cancer-related fatigue among patients both during and following treatment consistently have been positive, but the magnitude of the effect has varied substantially.6 – 8 Cancer-related fatigue occurs
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both as a consequence of cancer and as a side effect of cancer treatment.4 Both the time course and contributing factors of cancer-related fatigue may differ before,9 during,10 and after11 the initiation of cancer treatment. Understanding the characteristics of cancer-related fatigue across the time course of the disease can be helpful in the development and implementation of exercise interventions to reduce fatigue during and following treatment. Previous meta-analyses6 – 8 have not examined directly the effects of exercise on cancer-related fatigue in patients both during and following treatment or conducted a moderator analysis despite the heterogeneity of effects.12 These issues have led to diffıculties both in estimating the effect of exercise interventions on cancer-related fatigue and in identifying potential differentiating effects of exercise on cancer-related fatigue across the time course of treatment and recovery and the variables that may moderate these effects. Such information is important in guiding clinical decisions on exercise prescription across the time course of cancer and treatment. To address previous limitations, the primary objective was to review systematically RCTs examining the effects of exercise interventions on cancer-related fatigue in patients during and following treatment in order to determine the extent to which the effect is differentiated across the time course of treatment and recovery. Heterogeneity of study results was examined to determine how potential moderating variables may influence the effıcacy of exercise found in patients during and following cancer treatment.
Evidence Acquisition This review was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines.13 Analyses were conducted in August to October 2011. Articles published before August 2011 were located with searches of Google Scholar, MEDLINE, PsycINFO, PubMed, and Web of Science databases using the keywords cancer, exercise, fatigue, physical activity, and randomized controlled trial. Searches of reference lists from retrieved articles were performed manually. Publication language was not restricted.
Study Selection Inclusion criteria were (1) cancer patients currently undergoing treatment (e.g., chemotherapy, radiation therapy, hormone therapy) or cancer patients post-treatment; (2) randomization to either exercise training or a non-exercise comparison; and (3) a cancer-related fatigue outcome measured before and during and/or after exercise training (Appendix A).14 Exclusion criteria were (1) compared exercise only with an active therapy (e.g., pharmacotherapy, another mode of exercise); (2) examined the effect of acute exercise on cancer-related fatigue; and/or (3) used education or promotion interventions aimed at
434 records screened 425 exercise and CRF in patients during and following treatment 9 from other sources 109 RCTs of exercise and CRF in patients during and following treatment
325 excluded 154 narrative reviews and metaanalyses 171 non-RCTs and/or animal studies 23 excluded (no primary data)
86 studies assessed for eligibility
70 studies included in quantitative synthesis 43 studies of cancer patients during active treatment 27 studies of cancer patients following active treatment
16 excluded 7 CRF not reported 4 inadequately measured physical activity interventions 2 poor exercise adherence; comparison group contamination 2 convenience sample; no comparison condition 1 effect size could not be calculated
Figure 1. Flowchart of study selection CRF, cancer-related fatigue
increasing physical activity but failed to show increased physical activity. A flowchart of study selection is presented in Figure 1.
Data Extraction and Quality Assessment Authors independently extracted data, and discrepancies were resolved by consensus judgment. Effect sizes were calculated by subtracting the mean change in the comparison from the mean change in the exercise condition and dividing the difference by the pooled SD of pre-intervention scores.15 Effect sizes were adjusted using Hedges’ small-sample bias correction and calculated so that decreases in cancer-related fatigue resulted in positive effect sizes.15 Multiple effects within a trial were averaged such that each trial contributed only one effect to analysis.16 When precise means were not reported, effect sizes were estimated17 from t-tests,18 exact p-values,19,20 or fıgures.21–23 When precise SDs were not reported,24 –27 the SD was drawn from published norms or the largest other study using the same measure.
Study Quality Assessment Authors independently assessed the methodologic quality of each study using a 15-item scale that addressed randomization, sample selection, quality of outcome measures, and statistical analysis.28 Quality assessment showed high concordance between authors (ICC [3, 2]⫽0.96, 95% CI⫽0.89, 0.98).29 Using the Bland and Altman limits-of-agreement procedure, the average disagreement (M, 95% CI) was close to zero (0.40 [0.10, 0.70]), suggesting no systematic bias between reviewers.30,31 Quality scores were not used as weights or moderators in the analysis because of the potential disparity in results that depends on the specifıc quality scale employed.32
Data Synthesis and Analysis Statistical analyses initially were performed on the basis of an overall model examining patients both during and following treatment. Because analyses revealed differential effects among patients during and following treatment, separate regression models for during and post-treatment were tested to better understand exercise effects on cancer-related fatigue over the time course of treatment and to identify variables that moderate the effect. www.ajpmonline.org
Puetz and Herring / Am J Prev Med 2012;43(2):e1– e24 Using SPSS macros (MeanES, MetaReg), random effects models were employed to aggregate mean effect size (⌬), 95% CI, and the sampling error variance and to test for variation in effects according to moderator variables.33 Heterogeneity and consistency were evaluated with the Q statistic and the I2 statistic, respectively.34 Heterogeneity was examined relative to observed variance and was indicated if the sampling error accounted for less than 75% of the observed variance.15 Potential publication bias was addressed by inspection of funnel plots35 and quantifıed with rank correlation and regression methods.35,36
Primary Moderators and Analysis To provide focused research hypotheses about variation in effect size,37 primary moderator variables were selected a priori for each model based on logical, theoretic, or prior empirical relationship to cancer-related fatigue during and following treatment.4,38 Three moderator variables were selected for the overall model: treatment status (i.e., patient undergoing treatment or patient post-treatment); percentage fatigue reduction (i.e., percentage change in fatigue for the exercise condition minus the percentage change in fatigue for control condition); and the treatment status by percentage fatigue reduction interaction. Three moderator variables were selected for the duringtreatment model: adherence rate, baseline fatigue T-scores, and the adherence by baseline fatigue score interaction. Three moderator variables were selected for the post-treatment model: duration post-treatment, exercise program length, and type of comparison (i.e., wait-list control or other comparison conditions). Primary moderator variables for each model were included in a weighted least-squares multiple linear regression analysis with maximum-likelihood estimation.15,33 Tests of the regression model (QR) and its residual error (QE) are reported. Signifıcant categoric moderators were decomposed using a random effects model to compute mean effect sizes and 95% CIs.33 The JohnsonNeyman procedure was conducted to identify the critical point in signifıcant interactions of categoric and continuous variables in order to defıne signifıcance regions.39
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0⫽2.34, t(25)⫽2.97, p⫽0.006, models. Sensitivity analyses of study quality (i.e., removing studies in the 25th quartile) and exercise adherence (i.e., removing studies with ⬍80% adherence rates) examined the robustness of the conclusions of the meta-analysis.99 Sensitivity analyses for all models related to quality and adherence reinforced the results of the meta-analysis.
Overall Model Sixty-two of 70 (88.6%) effects were greater than zero (Figure 2). The mean effect size ⌬ (95% CI) was 0.34 (k⫽70, 95% CI⫽0.25, 0.43; z⫽7.290, p⬍0.001). The effect was heterogeneous, QT(69)⫽143.49, p⬍0.001. Sampling error accounted for 54.7% of the observed variance. The effect was moderately consistent across studies (I2⫽52.6%, 95% CI⫽45.6%, 58.7%).
Primary Moderator Analysis The overall multiple regression model was related to effect size, QR(3)⫽74.12; p⬍0.0001, R2⫽0.54; QE(63)⫽63.03, p⫽0.48. The interaction of treatment status and percentage fatigue reduction (Appendix D) was independently related to effect size (⫽0.009, z⫽2.96, p⫽0.003). The Johnson-Neyman procedure yielded a critical point for percentage fatigue reduction at ⫺37.4% (⫽⫺0.19, t⫽2.00, p⫽0.05). Further decomposition revealed (1) greater mitigation of cancer-related fatigue symptoms among exercising patients compared to controls during treatment (⫺4.2% vs 29.1%) and (2) larger reductions among exercising patients compared to controls post-treatment (⫺20.5% vs 1.3%; Appendix E).
During Treatment Secondary Moderators and Analysis Secondary moderator variables were selected for descriptive, univariate analyses based on a logical, theoretic, or prior empirical relationship with cancer-related fatigue (Appendix B). Mean effect sizes (⌬) and 95% CIs were computed for continuous and categoric variables using a random effects model.33
Evidence Synthesis Characteristics of the trials of patients during18,21–27,40 –74 and following treatment19,20,75–98 that were included in the meta-analysis and study quality assessment results are presented in Table 1. Examination of funnel plots and statistical tests for funnel-plot asymmetry suggested potential small-study bias for all models (Appendix C). Begg’s rank correlation was signifıcant for the overall, (69)⫽⫺0.33, p⬍0.001; during treatment, (42)⫽⫺0.32, p⫽0.003; and post-treatment, (26)⫽⫺0.43, p⫽0.025, models. Egger’s regression test was signifıcant for the overall, 0⫽1.85, t(68)⫽3.57, p⫽0.001; during treatment, 0⫽1.75, t(41)⫽3.01, p⫽0.004; and post-treatment, August 2012
Thirty-nine of the 43 effects (94.3%) were greater than zero (Figure 2). Exercise training signifıcantly reduced cancer-related fatigue (⌬⫽0.32, 95% CI⫽0.21, 0.43; z⫽5.74, p⬍0.001). The effect was heterogeneous, QT(42)⫽79.44, p⫽0.004. Sampling error accounted for 59.4% of the observed variance. The effect was moderately consistent across studies (I2⫽48.4%, 95% CI⫽38.2%, 56.8%).
Primary Moderator Analysis The multiple regression model for patients during treatment was signifıcantly related to effect size, QR(3)⫽22.09, p⫽0.0001, R2⫽0.45; QE(27)⫽27.08, p⫽0.46. The interaction of baseline fatigue and exercise adherence (Appendix F) was independently related to effect size (⫽0.19, z⫽3.14, p⫽0.002).
Post-Treatment Twenty-three of the 27 effects (94.3%) were greater than zero (Figure 2). Exercise training signifıcantly improved
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cancer-related fatigue (⌬⫽0.38, 95% CI⫽0.21, 0.54; z⫽4.44, p⬍0.0001). The effect was heterogeneous, QT(26)⫽ 63.62, p⫽0.0001. Sampling error accounted for 46.8% of the observed variance. The effect was moderately consistent across studies (I2⫽60.7%, 95% CI⫽ 51.3%, 68.3%).
Primary and Secondary Moderator Analysis The multiple regression model for patients post-treatment was signifıcantly related to effect size, QR(3)⫽22.36, p⫽0.0001, R2⫽0.50; QE(23)⫽22.56, p⫽ 0.49. Duration posttreatment (⫽0.01, z⫽ 2.21, p⫽0.0271); exercise program length (⫽⫺0.03, z⫽⫺2.86, p⫽0.0042); and comparison type (⫽0.44, z⫽3.90, p⫽0.0013) were independently related to effect size. Signifıcantly larger effects (⌬, 95% CI) were found for studies that used a wait-list comparison (⌬⫽ 0.66, 95% CI⫽0.42, 0.90) compared with the average effect for other comparison types (⌬⫽0.19, 95% CI⫽0.00, 0.37), QB(1)⫽9.74, p⫽0.002. The number of effects (k), mean effect ⌬, 95% CI, p-value, and I2 for each level of each moderator for each model are presented in Appendixes G and H).
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Table 1. Characteristics of included studies and quality assessment, % or M (SD) unless otherwise noted
Characteristics Total sample (N)
During treatment (k⫽43) 3235
Post-treatment (k⫽27) 1646
Age (years)
52.0 [10.2]
55.0 [5.5]
Women
68.0
87.0
BMI (kg/m )
26.8 [2.2]
27.2 [1.8]
Aerobic capacity (VO2max, ml/kg/min)
21.1 [6.5]
24.2 [4.5]
2
Cancer site Blood
13.3
3.7
Brain
0.9
0.2
Breast
58.3
73.7
Colon
1.3
8.3
Gastrointestinal
2.6
0.9
Gynecologic
1.3
3.7
Head and neck
1.2
2.8
Lung
1.0
1.6
Prostate
18.0
0.3
Testicular
0.7
1.1
Other
1.5
3.7
Chemotherapy
58.9
38.2
Radiation
29.3
42.4
Hormone Therapy
11.9
19.4
50.3 [6.3]
41.4 [10.7]
Cancer treatment
Baseline fatigue (T-score) Duration post-treatment (months, M [range])
N/A
16.3 [1.0–75.0]
Exercise setting Home-based
37.0
29.6
Supervised
63.0
70.4
Exercise frequency (days/week)
3.4 [1.3]
2.9 [1.3]
Exercise session duration (minutes)
42.3 [21.1]
49.6 [27.0]
Exercise program length (weeks)
11.7 [6.9]
12.6 [6.5]
Exercise intensity (% aerobic power)
55.0 [14.4]
53.3 [10.9]
Exercise
89.0 [64.0–100]
86.5 [59.0–100]
Control
87.5 [50.0–100]
90.3 [60.0–100]
Adherence (M % [range])
78.5 [58.0–100]
87.4 [34.0–98.0]
Study quality (rating 0-15)
10.9 [2.1]
11.1 [1.9]
Retention rate (median % [range])
N/A, not applicable
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During treatment studies
Hedges' d (95% CI)
Segal (2001)65 Shang (2009)68 Courneya (2008)49 Brown (2006)22 Courneya (2007)50 Crowley (2003)24 Mutrie (2007)64 Dimeo (1999)54 Hacker (2011)58 Courneya (2007)51 Moadel (2007)60 Cohen (2004)47 Cheville (2010)46 Caldwell (2009)43 Musan (2009)63 Culos-Reed (2010)53 Drouin (2002)56 Mock (2005)61 Adamsen (2009)40 Yeh (2011)74 Dodd (2010)55 Coleman (2003)48 Segal (2003)66 Mock (1997)27 Galvao (2010)57 Courneya (2009)52 Segal (2009)67 Jarden (2009)59 Hwang (2008)23 Wang (2011)71 Yang (2011)73 Wiskemann (2011)72 Vito (2007)69 Windsor (2004)21 Vadiraja (2009)70 Mock (1994)26 MacVicar (1987)25 Campbell (2005)44 Headley (2004)18 Chang (2008)45 Barfoot (2005)41 Monga (2007)62 Baaglini (2004)42
-0.27 (-0.64, 0.10) -0.14 (-0.49, 0.21) -0.12 (-0.65, 0.41) -0.02 (-0.41, 0.37) 0.01 (-0.30, 0.32) 0.04 (-0.80, 0.88) 0.04 (-0.23, 0.31) 0.10 (-0.41, 0.61) 0.10 (-0.86, 1.06) 0.11 (-0.20, 0.42) 0.12 (-0.25, 0.49) 0.13 (-0.50, 0.76) 0.15 (-0.24, 0.54) 0.19 (-0.65, 1.03) 0.21 (-0.44, 0.86) 0.21 (-0.32, 0.74) 0.22 (-0.68, 1.12) 0.24 (-0.15, 0.63) 0.25 (0.00, 0.50) 0.27 (-0.57, 1.12) 0.28 (-0.15, 0.71) 0.29 (-1.28, 1.86) 0.31 (0.00, 0.62) 0.33 (-0.26, 0.92) 0.33 (-0.20, 0.86) 0.41 (0.04, 0.78) 0.46 (0.01, 0.91) 0.52 (-0.11, 1.15) 0.54 (-0.13, 1.21) 0.56 (0.09, 1.03) 0.56 (0.09, 1.03) 0.57 (0.12, 1.02) 0.59 (-0.21, 1.39) 0.63 (0.12, 1.14) 0.66 (0.19, 1.13) 0.67 (-0.47, 1.81) 0.73 (-0.58, 2.04) 0.76 (-0.18, 1.70) 0.96 (0.23, 1.69) 0.97 (0.09, 1.85) 1.67 (0.71, 2.63) 1.90 (0.94, 2.86) 2.12 (1.14, 3.10)
Paent mean Δ
I2 = 48.4% (38.2%, 56.8%)
0.32 (0.21, 0.43)
Post-treatment studies
Hedges' d (95% CI)
Thorsen (2005)96 Cadmus (2009)79 Eyigor (2010)86 Dimeo (2004)85 Cadmus (2009)79 Courneya (2003)82 Berglund (1994)76 McNeely (2008)91 Fillion (2008)87 Liman (2011)89 Sprod (2010)95 Courneya (2003)81 Malec (2002)90 Bourke (2011)77 Pinto (2005)94 van Weert (2010)97 Lee (2010)88 Daley (2007)83 Courneya (2003)80 Pinto (2003)93 Danhauer (2009)84 Yuen (2007)98 Burnham (2002)78 Milne (2008)92 McKenzie (2003)19 Carson (2009)20 Banasik (2011)75
-0.47 (-0.82,-0.12) -0.32 (-0.87, 0.23) -0.03 (-0.66, 0.60) 0.00 (-0.47, 0.47) 0.04 (-0.41, 0.49) 0.06 (-0.37, 0.49) 0.11 (-0.18, 0.40) 0.15 (-0.40, 0.70) 0.23 (-0.20, 0.66) 0.27 (-0.25, 0.80) 0.33 (-0.20, 0.86) 0.36 (-0.05, 0.77) 0.39 (-0.41, 1.19) 0.47 (-0.46, 1.41) 0.52 (0.07, 0.97) 0.57 (0.22, 0.92) 0.58 (-0.22, 1.38) 0.59 (0.12, 1.06) 0.71 (0.14, 1.28) 0.75 (-0.09, 1.59) 0.76 (0.13, 1.39) 0.77 (-0.29, 1.83) 0.84 (-0.18, 1.86) 0.96 (0.41, 1.51) 1.18 (0.06, 2.30) 1.42 (0.71, 2.13) 1.49 (0.30, 2.67)
Survivor mean Δ
-4.0
-3.0
I2 = 60.7% (51.3%, 68.3%)
-2.0
-1.0
0.38 (0.21, 0.54)
0.0
1.0
2.0
3.0
4.0
5.0
Favors intervenon
Favors control Hedges’ d (95% CI)
Figure 2. Effects of exercise intervention versus comparison condition in meta-analyses of RCTs of cancer patients during treatment (n⫽43) and post-treatment (n⫽27)
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Discussion The cumulative evidence reviewed here supports previous reports that exercise training reduces cancer-related fatigue among patients both during and following cancer treatment. However, this is the fırst analysis to concurrently examine cancer patients during and following treatment and to identify variables that discriminately modify the effect during specifıc points in the time course of treatment and recovery. To date, only one RCT has examined exercise effects on cancer-related fatigue from diagnosis, through hospital admission and treatment, and into post-treatment follow-up.72 The present fındings support the evidence from that trial, bolstering the argument of differential effects of exercise on cancerrelated fatigue across the time course of treatment and recovery. The magnitude of the overall mean effect for patients during treatment (⌬⫽0.32) and post-treatment (⌬⫽0.38) is comparable to the effect of (1) exercise interventions on related outcomes in cancer patients, including depression,100 anxiety,101 and quality of life100; (2) individual or group therapy on cancer-related fatigue102; and (3) pharmacotherapy on cancer-related fatigue.103 Expressed as a binomial effect size,104 the effect of exercise training is equivalent to a clinical effect of 15.8% and 18.6% beyond chance among exercising patients during and posttreatment, respectively. The reduction in cancer-related fatigue found among exercising patients undergoing and following treatment is also equivalent to a number needed to treat105 of approximately 3 (1.6 – 4.2) and 4 (2.0 –15.7), respectively.
Overall Model: Treatment Status X Percentage Fatigue Reduction Interaction Among the combined sample of patients undergoing treatment and patients post-treatment, cancer-related fatigue reductions varied according to an interaction between treatment status and percentage reduction in cancer-related fatigue. For studies with larger percentage reductions in fatigue, the magnitude of the effect of exercise on cancer-related fatigue was greater among patients post-treatment compared with patients during treatment. However, for studies with smaller percentage reductions in fatigue, patients during treatment realized larger reductions in cancer-related fatigue than patients post-treatment. Exercise interventions appear to have a greater effect in reducing cancer-related fatigue in patients post-treatment than in patients during treatment when percentage reductions in fatigue are below ⫺37.4%. For studies with percentage reductions above ⫺37.4%, there was insuffıcient evidence to conclude whether the effect of exercise in reducing cancer-related fatigue was
signifıcantly different between patients during and following treatment. The interaction is likely related to the differential response of cancer patients to exercise and control conditions during and post-treatment. Cancer-related fatigue is mitigated in exercising patients compared to nonexercising patients during treatment (⫺4.2% vs 29.1%), whereas cancer-related fatigue is reduced in exercising patients compared to non-exercising patients posttreatment (⫺20.5% vs ⫺1.3%). These fındings suggest that exercise has a palliative effect in patients undergoing cancer treatment and a recuperative effect in patients following treatment. This evidence should assist clinicians when prescribing exercise.
During Treatment: Baseline Fatigue X Exercise Adherence Interaction Improvement in cancer-related fatigue for patients during treatment varied according to the patient’s baseline fatigue scores and exercise adherence rates. Patients with lower baseline fatigue scores and higher intervention adherence realized the largest improvements. This fınding should be interpreted with caution. It is plausible that patients with lower levels of cancer-related fatigue were able to better tolerate exercise than those with higher levels of cancer-related fatigue during treatment and therefore experienced greater protective effects. However, baseline fatigue severity was not associated with exercise adherence in a previous study of breast cancer patients receiving chemotherapy. As exercise exposure increased in that study, the intensity of cancer-related fatigue decreased across all baseline levels of fatigue.106 Cancer-related fatigue also was not a predictor of exercise adherence in an RCT of breast cancer patients undergoing chemotherapy; however, aerobic fıtness (i.e., VO2peak) was a predictor of adherence.107 This cumulative evidence suggests that fatigue during cancer treatment likely is maintained at pre-treatment levels through the palliative effects of exercise. The present fındings also provide evidence to recommend exercise before cancer treatment to increase fıtness, which may mediate the relationship of cancer-related fatigue and adherence.
Following Treatment: Post-Treatment Duration, Exercise Program Length, and Comparison Condition Following treatment greater effects were seen for trials with a longer duration between treatment completion and exercise program initiation, exercise interventions with shorter program lengths, and trials using wait-list comparisons. Unlike with patients undergoing treatment, cancer-related fatigue is a predictor of exercise adherence in patients following treatment.108 Exercise www.ajpmonline.org
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levels among patients decrease from pre-diagnosis to active treatment and then slowly increase from active treatment to post-treatment, but usually not to pre-diagnostic levels.109 Thus, a longer post-treatment duration will increase the natural progression toward exercise in cancer patients following treatment.110 Exercise program length also could be related to this phenomenon. Larger effects associated with shorter exercise interventions may be related to the reduction in cancer-related fatigue that naturally occurs over time in controls and/or with the exercise contamination effects seen in longer clinical trials.111–113 Baseline exercise stage of change and past exercise are predictors of exercise contamination in comparison groups.111–113 Unlike other types of comparisons, wait-list controls may provide a viable active treatment in post-treatment cancer patients’ natural progression toward exercise such that it serves as a pre-contemplation or contemplation stage in the Transtheoretical Model of Behavior Change.114 In any case, clinicians should consider prescribing exercise at cancer diagnosis in order to help mitigate the deleterious effects of active treatment that reduce the physical activity levels of patients post-treatment and ultimately compound cancer-related fatigue.
Limitations Limitations in the quality and reporting of the included trials are notable. Many studies lacked adequate information regarding features of the exercise intervention (e.g., intensity, mode, duration, frequency), appropriateness of comparisons, and under-reporting of adherence levels, medication use, and cancer sites. The inconsistency observed in study quality is disappointing, as is the fact that approximately 10% of the included trials did not include a well-validated cancer-related fatigue outcome measure.14 These limitations emphasize the importance of adoption of and compliance with reporting guidelines to improve the quality of future trials.
Future Research For a better understanding of exercise effects on cancerrelated fatigue, well-designed RCTs should (1) seek to better characterize the features of the exercise stimulus (i.e., frequency, intensity, session duration, program length, mode); (2) examine exercise effects on specifıc neurobiologic and psychological measures of cancerrelated fatigue; (3) examine relationships of exercise with cancer-related fatigue and related mood states, including anxiety, depression, and quality of life; and (4) investigate the mechanistic similarities, differences, and interactions among various exercise training protocols, psychosocial interventions, and pharmacologic treatments employed to reduce cancer-related fatigue. Such investigations will August 2012
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help defıne appropriate exercise prescription across the time course of cancer treatment and survivorship and offer important insight into the biopsychosocial mechanisms of cancer-related fatigue.
Conclusion Exercise reduces cancer-related fatigue among patients both undergoing and following cancer treatment, but these effects are differentially moderated over the time course of treatment and recovery. Exercise has a palliative effect in patients undergoing treatment and a restorative effect following treatment. These fındings provide evidence for prescribing exercise during and following cancer treatment as a potentially low-risk, adjuvant therapy for cancer-related fatigue. However, clinicians should recognize the differential effects of exercise on cancerrelated fatigue when prescribing exercise. No fınancial disclosures were reported by the authors of this paper.
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Appendix E: Cancer patients and survivors by exercise and control intervention F G H
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40 Exercise Intervenon Control Intervenon
30
Percent Change in Fague
20 29.1
10
-1.3
0 -4.2
-10
-20.5
-20
-30 Paents During Treatment
Paents Post-Treatment
Cancer Paents and Survivors by Exercise and Control Intervenon
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