Differential Effects of Exercise on Cancer-Related Fatigue During and Following Treatment

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Differential Effects of Exercise onCancer-Related Fatigue During and

Following TreatmentA Meta-Analysis

Timothy W. Puetz, PhD, Matthew P. Herring, PhD

Context: Exercise-induced improvements in cancer-related fatiguemay bemoderated differentiallyin patients during and following treatment. These effects have not been reviewed systematically. Inaccordance with PRISMA guidelines, the population effect size for exercise training on cancer-related fatigue during and following treatment was estimated and the extent to which the effect isdifferentiated across the time course of treatment and recovery was determined.

Evidence acquisition: Articles published before August 2011 were retrieved using GoogleScholar, MEDLINE, PsycINFO, PubMed, and Web of Science databases. Seventy studies involving4881 cancer patients during or following treatment were selected. Articles included a cancer-relatedfatigue outcome measured at baseline and post-intervention and randomized allocation to exerciseor 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 andpopulation variance.

Evidence synthesis: Exercise signifıcantly reduced cancer-related fatigue by amean 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 realizedthe largest improvements. Following treatment, improvements were largest for trials with longerdurations between treatment completion and exercise initiation, trials with shorter exercise programlengths, and trials using wait-list comparisons.

Conclusions: Exercise reduces cancer-related fatigue among patients during and following cancertreatment. 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

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Introduction

TheNational Cancer Institute estimates that nearly12 million Americans with a history of cancerwere alive in January 2008.1 Continued increases

n cancer diagnoses and issues of survivorship are impor-ant public health problems, particularly because approx-mately 1.6 million new cancer diagnoses are expected in012.1

From the Department of Behavioral Sciences and Health Education(Puetz), Rollins School of Public Health, Emory University, Atlanta, Geor-gia; and theDepartment of Epidemiology (Herring),University ofAlabamaat Birmingham, Birmingham, Alabama

Address correspondence to: Timothy W. Puetz, PhD, Rollins School ofPublic Health, Emory University, 1518 Clifton Road, Atlanta GA 30322.E-mail: [email protected].

v0749-3797/$36.00http://dx.doi.org/10.1016/j.amepre.2012.04.027

© 2012 American Journal of Preventive Medicine • Published by Elsev

Cancer-related fatigue is a persistent, subjective senseof tiredness related to cancer or cancer treatment thatinterferes with usual functioning.2 Approximately 50%–0% of cancer patients undergoing cancer treatment ex-erience cancer-related fatigue.3 For a substantial num-er of these patients, cancer-related fatigue persists afterreatment is completed.4 For example, 40% of cancerurvivors have reported at least 2 weeks of fatigue in thereviousmonth, withmore than 33%of survivors report-ng fatigue despite being approximately 5 years post-reatment.5 Exercise has been proposed as an effective,onpharmacologic intervention to promote psychologi-al well-being during and following cancer treatment.Exercise effects on cancer-related fatigue among pa-

ients both during and following treatment consistentlyave been positive, but the magnitude of the effect has
aried substantially.6–8 Cancer-related fatigue occurs
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both as a consequence of cancer and as a side effect ofcancer treatment.4 Both the time course and contributingactors of cancer-related fatigue may differ before,9 dur-ing,10 and after11 the initiation of cancer treatment. Un-derstanding the characteristics of cancer-related fatigueacross the time course of the disease can be helpful in thedevelopment and implementation of exercise interven-tions to reduce fatigue during and following treatment.Previous meta-analyses6–8 have not examined directly

the effects of exercise on cancer-related fatigue in patientsboth during and following treatment or conducted amoderator analysis despite the heterogeneity of effects.12

These issues have led to diffıculties both in estimating theeffect of exercise interventions on cancer-related fatigueand in identifying potential differentiating effects of ex-ercise on cancer-related fatigue across the time course oftreatment and recovery and the variables that may mod-erate these effects. Such information is important in guid-ing clinical decisions on exercise prescription across thetime course of cancer and treatment.To address previous limitations, the primary objective

was to review systematically RCTs examining the effectsof exercise interventions on cancer-related fatigue in pa-tients during and following treatment in order to de-termine the extent to which the effect is differentiatedacross the time course of treatment and recovery. Het-erogeneity of study results was examined to determinehow potential moderating variables may influence theeffıcacy of exercise found in patients during and fol-lowing cancer treatment.

Evidence AcquisitionThis review was conducted in accordance with the PreferredReporting Items for Systematic reviews and Meta-Analyses(PRISMA) guidelines.13 Analyses were conducted in August toOctober 2011.Articles published before August 2011 were located with

searches of Google Scholar, MEDLINE, PsycINFO, PubMed, andWeb of Science databases using the keywords cancer, exercise,fatigue, physical activity, and randomized controlled trial. Searchesof reference lists from retrieved articles were performed manually.Publication language was not restricted.

Study Selection

Inclusion criteria were (1) cancer patients currently undergoingtreatment (e.g., chemotherapy, radiation therapy, hormonetherapy) or cancer patients post-treatment; (2) randomizationto either exercise training or a non-exercise comparison; and(3) a cancer-related fatigue outcome measured before and dur-ing and/or after exercise training (Appendix A).14

Exclusion criteriawere (1) compared exercise onlywith an activetherapy (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
ncreasing physical activity but failed to show increased physicalctivity. A flowchart of study selection is presented in Figure 1.

Data Extraction and Quality Assessment

Authors independently extracted data, and discrepancies were re-solved by consensus judgment. Effect sizes were calculated bysubtracting the mean change in the comparison from the meanchange in the exercise condition and dividing the difference by thepooled SD of pre-intervention scores.15 Effect sizes were adjustedusing Hedges’ small-sample bias correction and calculated sothat decreases in cancer-related fatigue resulted in positive ef-fect sizes.15 Multiple effects within a trial were averaged suchthat each trial contributed only one effect to analysis.16 Whenrecise means were not reported, effect sizes were estimated17

from t-tests,18 exact p-values,19,20 or fıgures.21–23 When preciseDs were not reported,24–27 the SD was drawn from publishednorms or the largest other study using the same measure.

Study Quality Assessment

Authors independently assessed the methodologic quality of eachstudy using a 15-item scale that addressed randomization, sampleselection, 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 andltman limits-of-agreement procedure, the average disagreementM, 95% CI) was close to zero (0.40 [0.10, 0.70]), suggesting noystematic bias between reviewers.30,31 Quality scores were notsed as weights or moderators in the analysis because of the poten-ial disparity in results that depends on the specifıc quality scalemployed.32

Data Synthesis and Analysis

Statistical analyses initially were performed on the basis of anoverall model examining patients both during and following treat-ment. Because analyses revealed differential effects among patientsduring and following treatment, separate regression models forduring and post-treatment were tested to better understand exer-cise effects on cancer-related fatigue over the time course of treat-

325 excluded 154 narrative reviews and meta-

analyses171 non-RCTs and/or animal

studies

434 records screened425 exercise and CRF in

patients during and following treatment

9 from other sources

23 excluded (no primary data)

86 studies assessed for eligibility 16 excluded7 CRF not reported4 inadequately measured physical

activity interventions2 poor exercise adherence;

comparison group contamination2 convenience sample; no

comparison condition1 effect size could not be

calculated

70 studies included in quantitative synthesis

43 studies of cancer patients during active treatment

27 studies of cancer patients following active treatment

109 RCTs of exercise and CRF in patients during and following treatment

Figure 1. Flowchart of study selectionCRF, cancer-related fatigue

ment and to identify variables that moderate the effect.

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Using SPSS macros (MeanES,MetaReg), random effects modelswere employed to aggregate mean effect size (�), 95% CI, and thesampling error variance and to test for variation in effects accord-ing to moderator variables.33 Heterogeneity and consistency werevaluated with the Q statistic and the I2 statistic, respectively.34

Heterogeneity was examined relative to observed variance and wasindicated if the sampling error accounted for less than 75% of theobserved variance.15 Potential publication bias was addressed bynspection of funnel plots35 and quantifıed with rank correlationand regression methods.35,36

Primary Moderators and Analysis

To provide focused research hypotheses about variation in effectsize,37 primary moderator variables were selected a priori for eachodel based on logical, theoretic, or prior empirical relationship toancer-related fatigue during and following treatment.4,38 Threeoderator variables were selected for the overall model: treatmenttatus (i.e., patient undergoing treatment or patient post-treatment);ercentage fatigue reduction (i.e., percentage change in fatigue for thexercise conditionminus the percentage change in fatigue for controlondition); and the treatment status by percentage fatigue reductionnteraction. Three moderator variables were selected for the during-reatment model: adherence rate, baseline fatigue T-scores, and thedherence by baseline fatigue score interaction. Three moderatorariables were selected for the post-treatment model: durationost-treatment, exercise program length, and type of comparisoni.e., wait-list control or other comparison conditions).Primary moderator variables for each model were included in aeighted least-squares multiple linear regression analysis withaximum-likelihood estimation.15,33 Tests of the regression

model (QR) and its residual error (QE) are reported. Signifıcantategoric moderators were decomposed using a random effectsodel to compute mean effect sizes and 95% CIs.33 The Johnson-

Neyman procedure was conducted to identify the critical point insignifıcant interactions of categoric and continuous variables inorder to defıne signifıcance regions.39

Secondary Moderators and Analysis

Secondary moderator variables were selected for descriptive, uni-variate analyses based on a logical, theoretic, or prior empiricalrelationship with cancer-related fatigue (Appendix B). Mean effectsizes (�) and 95%CIs were computed for continuous and categoricvariables using a random effects model.33

Evidence SynthesisCharacteristics of the trials of patients during18,21–27,40–74

and following treatment19,20,75–98 that were included inthemeta-analysis and study quality assessment results arepresented in Table 1. Examination of funnel plots andstatistical tests for funnel-plot asymmetry suggested po-tential 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 theoverall, �0�1.85, t(68)�3.57, p�0.001; during treat-
ment,�0�1.75, t(41)�3.01, p�0.004; andpost-treatment, z
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�0�2.34, t(25)�2.97, p�0.006,models. Sensitivity analysesf study quality (i.e., removing studies in the 25th quartile)nd exercise adherence (i.e., removing studies with �80%dherence rates) examined the robustness of the conclu-ionsof themeta-analysis.99 Sensitivity analyses for allmod-els related to quality and adherence reinforced the results ofthe meta-analysis.

Overall ModelSixty-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 ef-fect was heterogeneous, QT(69)�143.49, p�0.001. Sam-ling error accounted for 54.7% of the observed variance.he effect was moderately consistent across studiesI2�52.6%, 95% CI�45.6%, 58.7%).

Primary Moderator AnalysisThe overall multiple regression model was related to effectsize, QR(3)�74.12; p�0.0001, R2�0.54; QE(63)�63.03,p�0.48. The interaction of treatment status and percent-ge fatigue reduction (Appendix D) was independentlyelated to effect size (��0.009, z�2.96, p�0.003). Theohnson-Neyman procedure yielded a critical point forercentage fatigue reduction at �37.4% (��0.19,

t�2.00, p�0.05). Further decomposition revealed (1) greateritigation of cancer-related fatigue symptoms among exercis-

ngpatients compared to controls during treatment (�4.2%vs9.1%) and (2) larger reductions among exercising patientsompared to controls post-treatment (�20.5% vs 1.3%;ppendixE).

During TreatmentThirty-nine of the 43 effects (94.3%) were greater thanzero (Figure 2). Exercise training signifıcantly reducedcancer-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 for9.4% of the observed variance. The effect was moderatelyonsistent across studies (I2�48.4%, 95% CI�38.2%,6.8%).

Primary Moderator AnalysisThe multiple regression model for patients during treat-mentwas signifıcantly related to effect size,QR(3)�22.09,p�0.0001, R2�0.45; QE(27)�27.08, p�0.46. The inter-action of baseline fatigue and exercise adherence(Appendix F) was independently related to effect size(��0.19, z�3.14, p�0.002).

Post-TreatmentTwenty-three of the 27 effects (94.3%) were greater than
ero (Figure 2). Exercise training signifıcantly improved

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cancer-related fatigue(��0.38, 95% CI�0.21,.54; z�4.44, p�0.0001).he effect was het-rogeneous, QT(26)�63.62, p�0.0001. Sam-pling error accountedfor 46.8% of the ob-served variance. Theeffect was moderatelyconsistent across studies(I2�60.7%, 95% CI�1.3%, 68.3%).

Primary andSecondaryModeratorAnalysisThe multiple regres-sion model for patientspost-treatment was sig-nifıcantly related to ef-fect size, QR(3)�22.36,�0.0001, R2�0.50;E(23)�22.56, p�.49. Duration post-reatment (��0.01, z�2.21, p�0.0271); exer-cise program length(��0.03, z��2.86,p�0.0042); and com-parison type (��0.44,�3.90, p�0.0013)ere independently re-ated to effect size. Sig-ifıcantly larger effects�, 95%CI) were foundor studies that used aait-list comparison(��.66,95%CI�0.42, 0.90)omparedwith the aver-ge effect for other com-arison types (��0.19,5% CI�0.00, 0.37),B(1)�9.74, p�0.002.he number of effectsk), mean effect �,5% CI, p-value, and2 for each level ofach moderator forach model are pre-ented in Appendixes

Table 1. Characteristics of inotherwise noted

Characteristics

Total sample (N)

Age (years)

Women

BMI (kg/m2)

Aerobic capacity (VO2max, ml/

Cancer site

Blood

Brain

Breast

Colon

Gastrointestinal

Gynecologic

Head and neck

Lung

Prostate

Testicular

Other

Cancer treatment

Chemotherapy

Radiation

Hormone Therapy

Baseline fatigue (T-score)

Duration post-treatment (mont

Exercise setting

Home-based

Supervised

Exercise frequency (days/week

Exercise session duration (min

Exercise program length (week

Exercise intensity (% aerobic po

Retention rate (median % [rang

Exercise

Control

Adherence (M % [range])

Study quality (rating 0-15)

N/A, not applicable

and H).

cluded studies and quality assessment, % or M (SD) unless

During treatment(k�43)

Post-treatment(k�27)

3235 1646

52.0 [10.2] 55.0 [5.5]

68.0 87.0

26.8 [2.2] 27.2 [1.8]

kg/min) 21.1 [6.5] 24.2 [4.5]

13.3 3.7

0.9 0.2

58.3 73.7

1.3 8.3

2.6 0.9

1.3 3.7

1.2 2.8

1.0 1.6

18.0 0.3

0.7 1.1

1.5 3.7

58.9 38.2

29.3 42.4

11.9 19.4

50.3 [6.3] 41.4 [10.7]

hs, M [range]) N/A 16.3 [1.0–75.0]

37.0 29.6

63.0 70.4

) 3.4 [1.3] 2.9 [1.3]

utes) 42.3 [21.1] 49.6 [27.0]

s) 11.7 [6.9] 12.6 [6.5]

wer) 55.0 [14.4] 53.3 [10.9]

e])

89.0 [64.0–100] 86.5 [59.0–100]

87.5 [50.0–100] 90.3 [60.0–100]

78.5 [58.0–100] 87.4 [34.0–98.0]

10.9 [2.1] 11.1 [1.9]

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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 Mus�an (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 Ba�aglini (2004)42

Pa�ent mean Δ

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 Li�man (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

Survivor mean Δ

Hedges' d (95% CI)

-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)

0.32 (0.21, 0.43)

Hedges' d (95% CI)

-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)

0.38 (0.21, 0.54)

-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0

I2 = 48.4% (38.2%, 56.8%)

I2 = 60.7% (51.3%, 68.3%)

Hedges’ d (95% CI)

Favors control Favors interven�on

During treatment studies

Post-treatment studies

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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DiscussionThe cumulative evidence reviewed here supports previ-ous reports that exercise training reduces cancer-relatedfatigue among patients both during and following cancertreatment. However, this is the fırst analysis to concur-rently examine cancer patients during and followingtreatment and to identify variables that discriminatelymodify the effect during specifıc points in the time courseof treatment and recovery. To date, only one RCT hasexamined exercise effects on cancer-related fatigue fromdiagnosis, through hospital admission and treatment,and into post-treatment follow-up.72 The present fınd-ings support the evidence from that trial, bolstering theargument of differential effects of exercise on cancer-related fatigue across the time course of treatment andrecovery.The magnitude of the overall mean effect for patients

during treatment (��0.32) andpost-treatment (��0.38) isomparable to the effect of (1) exercise interventions onelated outcomes in cancer patients, including depres-ion,100 anxiety,101 and quality of life100; (2) individual orroup therapy on cancer-related fatigue102; and (3) phar-acotherapy on cancer-related fatigue.103 Expressed as ainomial effect size,104 the effect of exercise training isquivalent to a clinical effect of 15.8% and 18.6% be-ond chance among exercising patients during and post-reatment, respectively. The reduction in cancer-related fa-igue found among exercising patients undergoing andollowing treatment is also equivalent to a numbereeded to treat105 of approximately 3 (1.6–4.2) and 42.0–15.7), respectively.

Overall Model: Treatment Status XPercentage Fatigue Reduction InteractionAmong the combined sample of patients undergoingtreatment and patients post-treatment, cancer-related fa-tigue reductions varied according to an interaction be-tween treatment status and percentage reduction incancer-related fatigue. For studies with larger percentagereductions in fatigue, the magnitude of the effect of exer-cise on cancer-related fatigue was greater among patientspost-treatment compared with patients during treat-ment. However, for studies with smaller percentage re-ductions in fatigue, patients during treatment realizedlarger reductions in cancer-related fatigue than patientspost-treatment. Exercise interventions appear to have agreater effect in reducing cancer-related fatigue in pa-tients post-treatment than in patients during treatmentwhen 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 fol-lowing treatment.The interaction is likely related to the differential re-

sponse of cancer patients to exercise and control condi-tions during and post-treatment. Cancer-related fatigueis mitigated in exercising patients compared to non-exercising patients during treatment (�4.2% vs 29.1%),whereas cancer-related fatigue is reduced in exercisingpatients compared to non-exercising patients post-treatment (�20.5% vs �1.3%). These fındings suggesthat exercise has a palliative effect in patients undergoingancer treatment and a recuperative effect in patientsollowing treatment. This evidence should assist clini-ians when prescribing exercise.

During Treatment: Baseline Fatigue XExercise Adherence InteractionImprovement in cancer-related fatigue for patients dur-ing treatment varied according to the patient’s baselinefatigue scores and exercise adherence rates. Patients withlower baseline fatigue scores and higher intervention ad-herence realized the largest improvements. This fındingshould be interpreted with caution. It is plausible thatpatients with lower levels of cancer-related fatigue wereable to better tolerate exercise than those with higherlevels of cancer-related fatigue during treatment andtherefore experienced greater protective effects. How-ever, baseline fatigue severity was not associated withexercise adherence in a previous study of breast cancerpatients receiving chemotherapy. As exercise exposureincreased in that study, the intensity of cancer-relatedfatigue decreased across all baseline levels of fatigue.106

Cancer-related fatigue also was not a predictor of ex-ercise adherence in an RCT of breast cancer patientsundergoing chemotherapy; however, aerobic fıtness (i.e.,VO2peak) was a predictor of adherence.

107 This cumula-ive evidence suggests that fatigue during cancer treat-ent likely is maintained at pre-treatment levels through

he palliative effects of exercise. The present fındings alsorovide evidence to recommend exercise before cancerreatment to increase fıtness, which may mediate the re-ationship of cancer-related fatigue and adherence.

Following Treatment: Post-TreatmentDuration, Exercise Program Length, andComparison ConditionFollowing treatment greater effects were seen for trialswith a longer duration between treatment completionand exercise program initiation, exercise interventionswith shorter program lengths, and trials using wait-listcomparisons. Unlike with patients undergoing treat-ment, cancer-related fatigue is a predictor of exercise
adherence in patients following treatment.108 Exercise
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levels among patients decrease from pre-diagnosis to ac-tive treatment and then slowly increase from active treat-ment to post-treatment, but usually not to pre-diagnosticlevels.109 Thus, a longer post-treatment duration will in-crease the natural progression toward exercise in cancerpatients following treatment.110

Exercise program length also could be related to thisphenomenon. Larger effects associatedwith shorter exer-cise interventions may be related to the reduction incancer-related fatigue that naturally occurs over time incontrols and/or with the exercise contamination effectsseen in longer clinical trials.111–113 Baseline exercise stageof change and past exercise are predictors of exercisecontamination in comparison groups.111–113 Unlikeother types of comparisons, wait-list controls may pro-vide a viable active treatment in post-treatment cancerpatients’ natural progression toward exercise such that itserves as a pre-contemplation or contemplation stage inthe Transtheoretical Model of Behavior Change.114 Inny case, clinicians should consider prescribing exerciset cancer diagnosis in order to help mitigate the deleteri-us effects of active treatment that reduce the physicalctivity levels of patients post-treatment and ultimatelyompound cancer-related fatigue.

LimitationsLimitations in the quality and reporting of the includedtrials are notable.Many studies lacked adequate informa-tion regarding features of the exercise intervention (e.g.,intensity, mode, duration, frequency), appropriateness ofcomparisons, and under-reporting of adherence levels,medication use, and cancer sites. The inconsistency ob-served in study quality is disappointing, as is the fact thatapproximately 10%of the included trials did not include awell-validated cancer-related fatigue outcomemeasure.14

These limitations emphasize the importance of adoptionof and compliance with reporting guidelines to improvethe quality of future trials.

Future ResearchFor a better understanding of exercise effects on cancer-related fatigue, well-designed RCTs should (1) seek tobetter characterize the features of the exercise stimulus(i.e., frequency, intensity, session duration, programlength, mode); (2) examine exercise effects on specifıcneurobiologic and psychological measures of cancer-related fatigue; (3) examine relationships of exercise withcancer-related fatigue and related mood states, includinganxiety, depression, and quality of life; and (4) investigatethe mechanistic similarities, differences, and interactionsamong various exercise training protocols, psychosocialinterventions, and pharmacologic treatments employed
to reduce cancer-related fatigue. Such investigations will
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help defıne appropriate exercise prescription across thetime course of cancer treatment and survivorship andoffer important insight into the biopsychosocial mecha-nisms of cancer-related fatigue.

ConclusionExercise reduces cancer-related fatigue among patientsboth undergoing and following cancer treatment, butthese effects are differentially moderated over the timecourse of treatment and recovery. Exercise has a palliativeeffect in patients undergoing treatment and a restorativeeffect following treatment. These fındings provide evi-dence for prescribing exercise during and following can-cer treatment as a potentially low-risk, adjuvant therapyfor cancer-related fatigue. However, clinicians shouldrecognize the differential effects of exercise on cancer-related fatigue when prescribing exercise.

No fınancial disclosures were reported by the authors of thispaper.

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