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Background:
Systematic Review

From Evidence to Practice: A Systematic Review and Meta-Analysis on the Effects of Supervised Exercise on Fatigue in Breast and Prostate Cancer Survivors

by
Arturo Cano-Uceda
1,2,
Pablo García-Fernández
1,*,
Blanca Peuyadé-Rueda
3,4,
Ana María Cañuelo-Marquez
2,
Cristian Solís-Mencía
5,
Carmen Lucio-Allende
6,
Luis De Sousa-De Sousa
1,7 and
José Luis Maté-Muñoz
1
1
Faculty of Nursing, Physiotherapy and Podiatry, Complutense University of Madrid, 28040 Madrid, Spain
2
Faculty of Health Sciences, Alfonso X El Sabio University, 28691 Madrid, Spain
3
HM Faculty of Health Sciences, Camilo José Cela University, Villanueva de la Cañada, 28692 Madrid, Spain
4
Instituto de Investigación Sanitaria HM Hospitales, 28015 Madrid, Spain
5
Department of Medicine, Faculty of Health Sciences, University of Deusto, 48007 Bilbao, Spain
6
Physiotherapy, Occupational Therapy and Speech Therapy Unit, Infanta Leonor University Hospital, Vallecas, 28031 Madrid, Spain
7
School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(15), 8399; https://doi.org/10.3390/app15158399
Submission received: 1 May 2025 / Revised: 2 June 2025 / Accepted: 20 June 2025 / Published: 29 July 2025
(This article belongs to the Special Issue Recent Advances in Exercise-Based Rehabilitation)

Abstract

Background: Breast and prostate cancer represent a significant global public health burden. Among the adverse effects of oncological treatments, fatigue is one of the most prevalent, persistent, and disabling symptoms. Therapeutic exercise has been shown to be effective for its management, with supervision identified as a key factor that may enhance adherence, safety, and intensity control. This systematic review and meta-analysis aimed to compare the effects of supervised exercise programs versus usual care on cancer-related fatigue in patients with breast or prostate cancer. Methods: A systematic search (September–December 2024) was conducted in six databases (PubMed, Web of Science, Scopus, Cochrane, PEDro, Scielo), selecting RCTs from the past 10 years in English or Spanish. Studies compared supervised exercise with unsupervised exercise or usual care in stage I–III breast or prostate cancer patients within five years post-treatment. Methodological quality was assessed with the PEDro scale and risk of bias with Cochrane’s RoB 2.0. A random-effects model was used to calculate pooled effect sizes (ES, 95% CI), with heterogeneity (I2), sensitivity, subgroup, and publication bias analyses. Results: A total of 25 interventions from 19 randomized controlled trials involving over 2200 participants were included. Supervised exercise significantly reduced cancer-related fatigue compared to usual care (effect size = 0.34; 95% CI: 0.22–0.47; p < 0.001; I2 = 56%). Sensitivity analyses supported the robustness of the findings. Subgroup analyses revealed greater effects in combined exercise programs, in men, and in patients with prostate cancer. No evidence of publication bias was observed. While 73.7% of studies were rated as having good methodological quality, the risk of bias was often unclear or high. Conclusions: Supervised therapeutic exercise programs are effective and safe for reducing fatigue in breast and prostate cancer survivors. These interventions should be incorporated into comprehensive care plans, with individualization based on patients’ clinical and demographic characteristics. Further research is needed to identify the most effective and sustainable strategies for different patient subgroups.

1. Introduction

Cancer is one of the leading causes of morbidity and mortality worldwide [1], representing a significant public health challenge on a global scale [2]. In 2020, it was responsible for approximately one in six deaths globally [3]. Breast and prostate cancer, in particular, represent a significant burden to global health: breast cancer is the most commonly diagnosed malignancy and the leading cause of cancer-related death in women across more than 100 countries, while prostate cancer is the most frequent among men. Together, they account for 11.7% and 7.3% of new cases, respectively [4,5]. In 2018, the World Cancer Research Fund estimated that approximately 18% of all cancer cases in the United States could be attributed to physical inactivity, poor diet, and/or excess adiposity [6]. In this context, adopting a healthy lifestyle is considered an important protective factor and an effective tool in cancer prevention and control [7].
Cancer treatment is typically multimodal. In the case of breast cancer, conservative surgery followed by radiotherapy is common and has been associated with a 21.7% reduction in local recurrence at 10 years, a 5.4% reduction in breast cancer mortality, and a 5.3% reduction in all-cause mortality at 15 years [8]. For prostate cancer, prostatectomy is a frequent intervention, often combined with radiotherapy [9]. Chemotherapy is also commonly used to prevent recurrence in patients with stage I–III cancer, alongside immunotherapy in certain cases [10,11]. However, these treatments can induce multiple adverse effects, including fatigue, pain, cognitive impairment, sarcopenia, osteoporosis, and cardiotoxicity, among others [12,13,14,15].
Fatigue is among the most prevalent and debilitating symptoms experienced by cancer patients [16], often persisting for years after treatment completion [17]. Cancer-related fatigue is defined by the National Comprehensive Cancer Network as “a distressing, persistent, and subjective sense of physical, emotional, and/or cognitive tiredness or exhaustion related to cancer or its treatment, which is not proportional to recent activity and interferes with usual functioning” [18]. It affects physical, emotional, and cognitive domains; tends to be disproportionate to the level of exertion; and significantly interferes with patients’ daily functioning [19]. Fatigue may even occur prior to treatment initiation and is frequently exacerbated during chemotherapy [20], radiotherapy [21], or hormone therapy [22]. This condition negatively impacts functional capacity, activities of daily living, and emotional well-being, and may predict lower overall survival and recurrence-free survival [19,23].
Given the high prevalence and impact of cancer-related fatigue, numerous non-pharmacological interventions have been studied to alleviate it [24]. Among these, therapeutic exercise has been shown to be an effective strategy with a strong benefit–risk profile [18,24,25]. Therapeutic exercise programs have demonstrated positive effects in reducing cancer-related fatigue [24,25], as well as additional benefits for quality of life, strength, and overall physical function [26].
Among the variables influencing exercise program outcomes, supervision stands out as a key factor [27]. Supervised programs provide continuous professional guidance, which can enhance adherence and improve intervention effectiveness [28,29,30]. They also ensure proper technique, reduce the risk of injury [31], and allow for tailored adjustments of intensity and exercise type based on the patient’s physical condition [30]. The American Society of Clinical Oncology (ASCO) also supports supervised exercise, highlighting that both aerobic and combined aerobic–resistance training are effective in reducing fatigue among cancer patients [32]. In contrast, unsupervised programs, while offering greater flexibility and accessibility, may compromise adherence, increase perceived exertion, and reduce safety [28,29,30,31], which can ultimately diminish the program’s benefit–risk balance.
Various exercise modalities (aerobic, resistance, or combined) have been evaluated for fatigue management in patients with breast and prostate cancer [33,34,35,36], yielding variable results depending on the type of intervention applied. However, many systematic reviews have focused exclusively on one cancer type [37,38], potentially introducing sex-related biases. Furthermore, the umbrella review by Zhou et al. found no systematic reviews meeting high methodological quality standards [39]. Some reviews fail to distinguish between supervised and unsupervised programs [38,40], or evaluate unrelated outcomes such as sleep parameters [41]. Others include overly heterogeneous populations, limiting the generalizability of their findings [42,43,44,45]. A notable example is the meta-analysis by Van Vulpen et al. [40], which included multiple cancer types and both supervised and unsupervised interventions, finding significantly greater effects in the supervised ones. Similarly, in 2011, Cramp and Byron-Daniel reported benefits only from aerobic programs, not from resistance training [46].
Considering these discrepancies and the need for more detailed evaluation, the present systematic review and meta-analysis aims to analyze and compare the effects of supervised therapeutic exercise programs (based on aerobic, resistance, stretching, or combined training) versus usual care in reducing fatigue among patients treated for breast and prostate cancer.

2. Methods

2.1. Search Strategy

This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [47] and followed the recommendations of the Cochrane Handbook for Systematic Reviews [48]. This systematic review and meta-analysis was registered in the Open Science Framework (OSF) under the digital object identifier https://doi.org/10.17605/OSF.IO/E23RT.
A systematic search was carried out from September to December 2024 to identify studies evaluating the effect of exercise interventions on fatigue in cancer patients. The search was conducted across the following databases: Web of Science (WOS), PubMed, PEDro, Scielo, Cochrane, and Scopus. A PICO-based strategy was employed, using both controlled vocabulary and natural language terms, which were reviewed by a health sciences librarian. The search included the following terms: “Breast cancer”; “Prostate cancer”; “supervised exercise”; “strength”; “aerobic”; “resistance”; “stretching”; and “Fatigue”. The goal was to gather all available scientific evidence on the effects of supervised therapeutic exercise programs on cancer-related fatigue compared to usual care or unsupervised exercise. The complete search strategy is presented in Table 1.

2.2. Eligibility Criteria

Two independent reviewers (A.C.U. and P.G.F.) screened the titles and abstracts of the retrieved articles to identify studies meeting the inclusion criteria. Articles with titles and abstracts deemed relevant to the review’s objective were further evaluated by accessing or requesting the full text. Studies were included if they met the following criteria: (1) published within the past 10 years; (2) written in English or Spanish; (3) randomized controlled trials with a usual care control group, in which the intervention group received supervised therapeutic exercise (strength training, aerobic/cardiorespiratory training, stretching, or any combination thereof); (4) included patients with breast or prostate cancer who began the exercise program no more than 5 years after completing cancer treatment; (5) cancer stages I–III with no comorbid conditions; (6) exercise interventions not combined with other treatments (e.g., pharmacological, dietary, psychological, or supplemental therapies), aside from standard oncological care; and (7) outcomes included cancer-related fatigue.
Exclusion criteria were as follows: (1) animal studies, (2) systematic reviews, and (3) non-RCTs (including pilot studies, single-arm trials, non-randomized or uncontrolled studies, retrospective or cross-sectional studies).

2.3. Data Extraction and Quality Assessment

Using a customized data extraction table based on the Cochrane “Data Collection Form for Intervention Reviews: RCTs and non-RCTs,” two authors (A.C.U. and P.G.F.) independently extracted the following information from each included study: author, year, country, cancer type, sample size, age, sex, intervention characteristics, duration of intervention, fatigue assessment tool, and main results. Disagreements were resolved by a third reviewer (J.L.M.M.). Duplicate records were removed using Mendeley Reference Manager (version 2.100).
Risk of bias was assessed using the Cochrane risk of bias tool (RoB 2.0) [49], which evaluates five domains: (1) bias arising from the randomization process, (2) bias due to deviations from intended interventions (including blinding of participants and personnel), (3) bias due to missing outcome data, (4) bias in outcome measurements (including blinding of outcome assessors), and (5) bias in the selection of the reported results. As with study selection and data extraction, risk of bias assessments were performed independently by two reviewers (A.C.U. and P.G.F.), with any disagreements resolved by a third reviewer (J.L.M.M.).
The methodological quality of the included trials was assessed using the PEDro scale [50], which includes 11 items covering external validity (item 1), internal validity (items 2–9), and statistical reporting (items 10–11). Items are scored as “yes” or “no,” and only clearly satisfied criteria receive a “yes.” The total score is based on items 2 through 11, with a maximum possible score of 10. Studies scoring below 4 points were rated as “poor,” scores of 4–5 were “fair,” 6–8 as “good,” and 9–10 were “excellent.”

2.4. Data Analysis

From each included study, pre- and post-intervention data were extracted to calculate individual effect sizes (ESs) as standardized mean differences (SMDs), along with their corresponding 95% confidence intervals (95% CI) and standard errors (SEs) [51]. ESs were considered positive when favoring the intervention group and negative when favoring the control group. For the quantitative synthesis, we estimated pooled effect sizes and their 95% confidence intervals using a random-effects model based on the DerSimonian and Laird method [52].
The overall effect size for exercise interventions compared to control groups was calculated by assessing the direction and magnitude of the observed change. Between-study heterogeneity was assessed using the I2 statistic, which quantifies the proportion of variability across studies due to true heterogeneity rather than chance. I2 values were interpreted as follows: 0–30% (low), >30–50% (moderate), >50–80% (substantial), and >80–100% (considerable). Associated p-values and confidence intervals for I2 were also reported for a more precise interpretation of heterogeneity [53]
In studies including multiple exercise intervention arms, effect sizes were calculated separately for each modality to avoid data duplication in the pooled synthesis. To assess the robustness of the overall findings, a sensitivity analysis was conducted by sequentially removing each study to detect potential outliers or influential studies.
Subgroup analyses were performed based on participant sex, exercise modality, cancer type, and intervention duration to explore potential sources of heterogeneity and variations in effect magnitude across clinical and methodological characteristics.
Finally, publication bias was assessed through visual inspection of funnel plots and Egger’s regression test, which detects asymmetry related to study size [54]. All statistical analyses were conducted using SPSS software (version 30.0.0.0, IBM Corp.).

3. Results

3.1. Systematic Review

3.1.1. Study Selection

The search strategy identified 6492 potentially eligible studies. Of these, 19 were included in the final systematic review. The selection process is illustrated in Figure 1, following the PRISMA 2020 guidelines.

3.1.2. Study and Intervention Characteristics

Table 2 provides a summary of the results from the studies included in the final review, detailing information on participants, experimental conditions, and the measurement tools used.

3.1.3. Characteristics of Included Studies

The randomized controlled trials included in this systematic review evaluated the effects of supervised exercise programs compared to unsupervised exercise or usual care in patients diagnosed with breast or prostate cancer. These studies were conducted in a variety of countries including the United States, Germany, Mexico, Denmark, Belgium, Brazil, Canada, and the Netherlands, thus offering an international perspective.
A total of 19 randomized controlled trials were analyzed in this review, encompassing more than 2200 participants overall. Mean participant ages varied across studies but generally ranged from 40 to 70 years, reflecting the broad age spectrum at which these types of cancer may occur.
Supervised exercise interventions included aerobic training, resistance training, High-Intensity Interval Training (HIIT), and combined programs that incorporated aerobic and resistance exercise components. The duration of interventions also varied, ranging from 8 to 52 weeks, with training frequencies typically between two and three sessions per week, although some studies applied frequencies as low as once or as high as five times per week. Although the inclusion criteria allowed for studies with interventions conducted up to five years after cancer treatment, most of the included studies implemented exercise during active oncologic treatment (Supplementary Table S1).
Cancer-related fatigue was assessed using various validated measurement tools, including the Piper Fatigue Scale (PFS), Fatigue Assessment Questionnaire (FAQ), Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), Functional Assessment of Cancer Therapy: Fatigue (FACT-F), and the Multidimensional Fatigue Inventory (MFI).

3.1.4. Quality Assessment and Risk of Bias

Methodological quality was evaluated using the PEDro scale, with 5 of the 19 studies (26.3%) rated as “fair” and the remaining 14 studies (73.7%) rated as “good” (Table 3).
Risk of bias was assessed using the Cochrane Risk of Bias Tool (RoB 2.0). Among the 19 included studies, 7 studies (36.8%) were found to have a high risk of bias, 11 studies (57.9%) showed some concerns, and 1 study (5.3%) was rated as low risk of bias (Figure 2 and Figure 3).

3.2. Data Synthesis

3.2.1. Meta-Analysis

The pooled effect size for the impact of therapeutic exercise versus the CG on fatigue was 0.34 (95% CI: 0.22–0.47; I 2 = 56%, p = 0.00) (Figure 4).

3.2.2. Sensitivity and Subgroup Analyses

The sensitivity analysis showed that removing individual studies from the meta-analysis did not substantially alter the pooled effect size estimate for therapeutic exercise vs. control group (Table 4).
Subgroup analyses based on exercise type, intervention duration, sex, and cancer type modified the pooled effect size estimate but did not affect the statistical significance of the comparison between therapeutic exercise and the control group (Table 5, Table 6, Table 7 and Table 8).

3.2.3. Publication Bias

No significant publication bias was found in the studies comparing therapeutic exercise to the control group, as indicated by funnel plot symmetry (Figure 5) and the results of Egger’s test (p = 0.672) (Table 9).

4. Discussion

This systematic review and meta-analysis was conducted to determine the effectiveness of supervised therapeutic exercise on fatigue in oncology patients with breast or prostate cancer.
A total of 25 interventions from 19 randomized controlled trials were included, all demonstrating a positive effect of therapeutic exercise on fatigue in cancer patients. Although the magnitude of the effect falls within the range considered small by conventional standards, its clinical relevance is notable given that cancer-related fatigue is a highly prevalent, debilitating symptom that is difficult to manage pharmacologically. These results support the inclusion of therapeutic exercise as a safe and effective strategy to reduce fatigue, with the potential to improve quality of life, functional capacity, and recovery after cancer treatment. The consistency observed across studies also suggests that these programs can be flexibly adapted to different clinical profiles without compromising effectiveness.
These findings align with those reported in previous studies [37,38,46]. The improvements observed may be explained by exercise’s protective effect against the decline in maximal oxygen uptake, which may help alleviate cancer-related fatigue [55]. In addition, physical training promotes progressive improvements in exercise tolerance and muscle strength, which may delay fatigue onset. Another relevant mechanism is the anti-inflammatory effect of physical activity, which, together with its cognitive benefits, could help counteract some treatment-related side effects, including fatigue [69].
These benefits are further enhanced by professional supervision, which helps patients overcome common barriers such as a lack of knowledge about exercise benefits or perceiving fatigue as a limitation. Supervision by trained professionals improves understanding of exercise utility, thereby increasing adherence and intervention effectiveness [36]. Importantly, no serious adverse events or clinical complications directly attributable to the supervised exercise programs were reported. This finding, consistent with prior systematic reviews [24], reinforces the favorable risk–benefit profile of exercise prescription in cancer patients. The absence of major adverse effects strongly supports the integration of these non-pharmacological strategies into personalized care plans.
Beyond its direct impact on patients’ daily functioning, cancer-related fatigue has been commonly associated with lower treatment adherence, reduced quality of life, and worse clinical outcomes [17]. Groenvold et al. [23] also demonstrated that fatigue is one of the most debilitating symptoms in cancer patients, significantly affecting overall health status and functional capacity, particularly in advanced or palliative stages. Moreover, persistent fatigue may reduce patients’ ability to complete chemotherapy, radiotherapy, or hormonal therapy regimens, thereby compromising therapeutic efficacy and survival. Rock et al. [70] reported that modifiable lifestyle factors such as physical inactivity, poor diet, and obesity accounted for over 18% of cancer cases in the U.S. in 2014. Altogether, these data support the clinical importance of interventions targeting fatigue and related behaviors, as improvements may not only enhance patient well-being but also promote better adherence, treatment tolerance, and long-term clinical outcomes.
Subgroup analyses by exercise type showed that all supervised modalities were effective in reducing cancer-related fatigue, with statistically significant effect sizes. Combined aerobic and resistance training showed the largest effect size, although with considerable heterogeneity, suggesting variability in how these interventions are implemented. This could result from the combination of strength training—which affects fatigue through inflammatory and metabolic pathways and helps preserve muscle mass, function, and body composition [15,60,71,72], and aerobic training, which improves cardiorespiratory fitness and cardiovascular efficiency, thereby reducing fatigue perception [60]. HIIT programs, like resistance training (though with a slightly smaller effect size), also demonstrated consistent and significant effects with no heterogeneity, indicating a more uniform response to these interventions. HIIT has been shown to reduce inflammatory markers such as interleukins, improve body composition, and enhance cardiorespiratory fitness, notably increasing IL-15 levels, which may contribute to reduced fatigue and improved overall health in cancer survivors [73]. It is also highly effective in improving VO2 max, a key indicator of cardiorespiratory fitness [74], and provides significant benefits in a shorter time frame, allowing for higher training loads and improved adherence [75]. Moreover, HIIT positively impacts mitochondrial function, muscle capillarization, and fat oxidation capacity, all of which may lower fatigue during daily activities [76,77]. Conventional aerobic training showed a smaller but still significant effect, with low heterogeneity. These results suggest that while all exercise modalities are beneficial, combined interventions may offer the greatest clinical impact, although they require more standardized implementation. On the other hand, the consistent effects of HIIT and resistance training may facilitate their use in resource-limited clinical settings due to their structured format and more predictable outcomes.
Subgroup analysis based on program duration showed that all durations evaluated (≤12 weeks, >12–24 weeks, and >24 weeks) were associated with significant reductions in fatigue, with moderate effect sizes. The greatest effect was seen in programs longer than 24 weeks, followed by those ≤12 weeks, while programs lasting 12–24 weeks had the smallest effect. Notably, substantial heterogeneity was observed among studies with ≤12-week programs, possibly reflecting variability in intensity, exercise type, or participant characteristics. In contrast, the absence of heterogeneity in longer-duration groups indicates more consistent outcomes, strengthening the evidence in favor of extended programs. The superior effectiveness of >24-week programs may be due to better physiological and psychological adaptation occurring over time, resulting in more sustained benefits and fatigue reduction. These programs may also enhance adherence, which is crucial for long-term effects [78]. While brief interventions also show benefits, the high heterogeneity suggests the need for further research to optimize their effectiveness.
Subgroup analyses revealed differences in the magnitude of therapeutic exercise’s effects on fatigue based on sex and cancer type. Among men, predominantly with prostate cancer, the effect size was notably higher, with moderate heterogeneity. Among women, primarily with breast cancer, the effect was more modest but more consistent across studies. These findings may reflect physiological or psychological differences or differences in exercise protocol implementation between men and women. Men in prostate cancer trials often received strength- or HIIT-focused interventions, while women with breast cancer typically participated in mixed programs with more varied frequencies or intensities. Clinically, these results suggest that therapeutic exercise is beneficial for both groups but may require tailoring to individual patient profiles to maximize effectiveness. Additionally, the greater heterogeneity in male-focused studies highlights the need to better standardize prostate cancer interventions, while the consistency in female-focused studies strengthens current recommendations in this population.
One of the main methodological challenges in exercise oncology trials is the lack of uniformity in the definition of control groups and the variability in intervention timing. In this meta-analysis, control conditions ranged from inactivity to light interventions, and most studies implemented exercise during active treatment, with few conducting it in later phases. These inconsistencies, together with the limited number of studies in certain categories and the absence of clear trends in the forest plot, precluded formal subgroup analyses. Altogether, these findings highlight the need for more precise and standardized reporting of both control group composition and intervention timing in future trials.
The analysis was performed using a random-effects model, appropriate for accounting for variability in study design, exercise type, intervention duration, and population characteristics. The heterogeneity observed was moderate, suggesting some variability in the results but not enough to compromise the validity of the pooled effect.
Sensitivity analyses demonstrated a high degree of stability in the exercise effect on cancer-related fatigue, with consistently significant results. Notably, heterogeneity dropped substantially when the study by Hojan et al. [62] was excluded, suggesting that this study may contribute to overall variability, possibly due to its large reported effect size. Nevertheless, the consistent effect across studies confirms the robustness and clinical applicability of therapeutic exercise, regardless of modality.
Potential publication bias was assessed via visual inspection of the funnel plot and Egger’s test, with no significant asymmetry detected. The symmetrical distribution of studies and non-significant standard error coefficient support this conclusion. The comprehensive search strategy further reduces the risk of omitting relevant studies, reinforcing the validity and robustness of the meta-analysis and the reliability of the observed effects of therapeutic exercise on cancer-related fatigue.
This meta-analysis has some methodological limitations that should be considered. First, the impossibility of blinding participants in exercise interventions may introduce performance and detection biases. Additionally, several studies did not adequately describe the randomization process, limiting risk-of-bias assessment. Moderate statistical heterogeneity was observed, likely due to differences in participant characteristics, study designs, interventions, and fatigue measurement tools. This methodological variability affects comparability and external validity. While subgroup analyses by exercise type, cancer type, and sex were performed, the limited number of studies in each subgroup may have affected the robustness of these results. Lastly, the scarcity of long-term follow-up data limits our understanding of the sustainability of exercise benefits. Therefore, although the results are promising, they should be interpreted with caution within the context of these limitations.

5. Conclusions

This meta-analysis confirms that supervised therapeutic exercise is an effective and safe intervention for reducing fatigue in patients with breast and prostate cancer, yielding clinically meaningful effects, with effect sizes exceeding established thresholds of clinical relevance. All exercise modalities were found to be beneficial, with combined interventions showing the greatest impact, and resistance or high-intensity protocols demonstrating the most consistent results. Despite moderate heterogeneity, the findings were robust, with no evidence of publication bias or adverse events. These results support the integration of therapeutic exercise into oncological care, with personalized interventions based on patient characteristics. Further studies are needed to identify the most effective and sustainable program components, particularly in relation to cancer type, sex, and clinical status.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app15158399/s1, Table S1: Timing of Exercise Interventions in Breast and Prostate Cancer Studies.

Author Contributions

Conceptualization, A.C.-U. and P.G.-F.; methodology, A.C.-U., J.L.M.-M. and P.G.-F.; software, A.C.-U. and P.G.-F.; validation, A.C.-U., P.G.-F. and J.L.M.-M.; formal analysis, A.C.-U., P.G.-F. and L.D.S.-D.S.; investigation, A.C.-U., P.G.-F., B.P.-R., A.M.C.-M., C.S.-M., C.L.-A. and L.D.S.-D.S.; resources, P.G.-F., C.L.-A. and J.L.M.-M.; data curation, A.C.-U., P.G.-F., C.S.-M. and A.M.C.-M.; writing—original draft preparation, A.C.-U. and P.G.-F.; writing—review and editing, A.C.-U., P.G.-F., B.P.-R., L.D.S.-D.S. and J.L.M.-M.; visualization, P.G.-F., A.C.-U., J.L.M.-M. and L.D.S.-D.S.; supervision, P.G.-F. and J.L.M.-M.; project administration, P.G.-F.; funding acquisition, not applicable. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used and/or analyzed during the current review and meta-analysis are available from the corresponding author upon reasonable request.

Conflicts of Interest

All authors state that they have no interests which might be perceived as posing a conflict with or bias with respect to the content of this article. This includes no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this article.

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Figure 1. Flowchart of article selection.
Figure 1. Flowchart of article selection.
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Figure 2. Risk of bias assessment: summary of individual studies.
Figure 2. Risk of bias assessment: summary of individual studies.
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Figure 3. Risk of bias assessment: aggregate appraisal results.
Figure 3. Risk of bias assessment: aggregate appraisal results.
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Figure 4. Meta-analysis for therapeutic exercise vs. control group (pooled ES analysis).
Figure 4. Meta-analysis for therapeutic exercise vs. control group (pooled ES analysis).
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Figure 5. Funnel Plot.
Figure 5. Funnel Plot.
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Table 1. Search strategy.
Table 1. Search strategy.
Search StrategyNatural Terms and EquationsResults Obtained
Pubmed
#1(“Breast cancer” [Title/Abstract] OR “Prostate cancer” [Title/Abstract])
#2(“supervised exercise” [Title/Abstract] OR “strength” [Title/Abstract] OR “aerobic” [Title/Abstract] OR “resistance” [Title/Abstract] OR “stretching” [Title/Abstract]))
#3(“Fatigue” [Title/Abstract])
#4#1 AND #2 AND #3848
Scielo
#1(ab:(“Breast cancer” OR “Prostate cancer”))
#2(ab:(“ Supervised exercise” OR ab: “strength” OR ab:”aerobic” OR ab:”resistance” OR ab:”stretching”))
#3(ab:(“Fatigue”))
#4#1 AND #2 AND #35
Web of Science
#1(“Breast cancer” OR “Prostate cancer”)
#2(“supervised exercise” OR aerobic OR strength OR resistance OR strecthing)
#3(“Fatigue”)
#4Tittle/abstract: #1 AND #2 AND #31215
Cochrane
#1(“Breast cancer” OR “Prostate cancer”)
#2(“supervised exercise” OR aerobic OR strength OR resistance OR stretching)
#3(“Fatigue”)
#4Tittle/Abstract/Keywords #1 AND #2 AND #31945
Scopus
#1(TITLE-ABS-KEY (“breast cancer”) OR TITLE-ABS-KEY (“Prostate cancer”))
#2(TITLE-ABS-KEY (“supervised exercise”) OR TITLE-ABS-KEY (aerobic) OR TITLE-ABS-KEY(strength) OR TITLE-ABS-KEY(resistance) OR TITLE-ABS-KEY(stretching))
#3(TITLE-ABS-KEY(“Fatigue”)
#4#1 AND #2 AND #32407
PEDro
SubdisciplineOncology
Body Part#1 Chest #2 Perineum or genito urinary system
Therapy#3 fitness training #4 strength training
#1 + #3; #1 + #4; #2 + #3; #2 + #472
TOTALPubmed +Scielo + Web of Science + Cochrane + Scopus + PEDro6492
Table 2. Included studies.
Table 2. Included studies.
Author/YearCountryCancer Type Sample SizeAge (SD)SexIntervention CharacteristicsDuration of InterventionMeasurement ToolResults
Al-Majid et al.,
2015 [55]
USABreastIG: 7
CG: 7
IG: 47.9 (10.4)
CG: 52.7 (10.7)
WIG: AT
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
12 weeks
2 days/week
PFSIG Pre: 3.0 ± 0.7
Post: 3.0 ± 0.8
CG Pre: 0.8 ± 0.5
Post: 4.6 ± 0.9
Schmidt et al.,
2015 [56]
GermanyBreastIG: 49
CG: 46
ITT
IG: 52.2 (9.9)
CG: 53.3 (10.2)
WIG: RT
CG: Active Structured Controls (stretching)
12 weeks
2 days/week
FAQIG Pre: 36.4 ± 19.2
Post: 36.1 ± 20.6
CG Pre: 41.0 ± 21.1
Post: 44.8 ± 21.0
Schmidt et al.,
2015 [57]
GermanyBreastGRT: 21
GET: 20
CG: 26
RT: 53 (12.55)
ET: 56 (10.15)
CG: 54 (11.19)
WRT: RT
ET: AT
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
12 weeks
2 days/week
MFIGRT Pre: 9.25 ± 3.09
Post: 10.55 ± 3.22
GET Pre: 8.76 ± 4.31
Post: 12.35 ± 4.37
CG Pre: 9.54 ± 3.35
Post: 12.38 ± 3.50
Travier et al.,
2015 [58]
NetherlandsBreastIG: 102
CG: 102
IG: 49.7 (8.2)
CG: 49.5 (7.9)
WIG: Mixed
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
18 weeks
2 days/week
MFIIG Pre: 10.1 ± 4.3 Δ change IC95% 1.9 [1.0 to 2.8]
CG Pre: 10.6 ± 4.1 Δ change IC95% 2.3 [1.4 to 3.3]
Van Waart et al.,
2015 [59]
NetherlandsBreastIG: 76
CG: 77
IG: 49.9 (8.4)
CG: 51.6 (8.8)
WCI: Mixed
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
20 weeks
2 days/week
MFI y FQLIG Pre: 10.6 ± 4.1
Post: 13.1 ± 3.9
CG Pre: 11.7 ± 4.4
Post: 14.7 ± 4.2
Mijwel et al.,
2018 [60]
SwedenBreastGRT/HIIT: 74
GAT/HIIT: 72
CG: 60
RT/HIIT: 52.7 (10.3)
AT/HIIT: 54.4 (10.3)
CG: 52.6 (10.2)
WGRT/HIIT + RT
GAT/HIIT +AT
CG: Usual Care with General Recommendations (ACSM, WHO, healthy lifestyle advice)
16 weeks
2 days/week
PFSGRT/HIIT Pre: 3.09 ± 3.17
Post: 3.16 ± 2.9
GAT/HIIT Pre: 2.10 ± 2.63
Post: 3.16 ± 2.6
CG Pre: 2.30 ± 2.81
Post: 3.94 ± 2.95
Ammitzbøll et al.,
2019 [29]
DenmarkBreastIG: 82
CG: 76
IG: 53 (33–73)
CG: 52 (30–74)
WIG: RT
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
20 weeks 2 days/weekFACIT-FIG Δ change IC95% 2.71 [−0.1 to 5.5]
CG Δ change IC95% 0.00 Reference
Baglia et al.,
2019 [61]
USABreastIG: 61
CG: 60
IG: 61.2 (7.09
CG: 62.0 (7.0)
WIG: RT + AT
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
52 weeks
2 days/week
Facit-FIG Pre: 37.9 ± 10.6
Post: 43.6 ± 10.6
CG Pre: 36.2 ± 10.8
Post: 36.7 ± 10.8
Mijwel et al.,
2019 [36]
SwedenBreastGRT/HIIT: 74
GAT/HIIT: 72
CG: 60
GRT/HIIT: 52.7 (10.3)
GAT/HIIT: 54.4 (10.3)
CG: 52.6 (10.2)
WRT: HIIT + RT
AT HIIT: HIIT + AT
CG: Usual Care with General Recommendations (ACSM, WHO, healthy lifestyle advice)
16 weeks
2 days/week
PFSGRT/HIIT Pre: 3.09 ± 3.17 Post: 3.12 ± 3.03
GAT/HIIT Pre: 2.10 ± 2.63 Post: 3.18 ± 2.77
CG Pre: 2.30 ± 2.81
Post: 3.98 ± 3.05
Hojan et al.,
2020 [62]
PolandProstateIG: 36
CG: 36
ITT
IG: 65.7 (6.2)
CG: 67.9 (4.9)
MIG: Mixed
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
8 weeks
5 days/week
FACT-FIG Pre: 113.4 ± 3.5
Post: 117.9 ± 9.7
CG Pre: 112.9 ± 3.9
Post: 81.5 ± 9.7
Ndjavera et al.,
2020 [63]
UKProstateIG: 24
CG: 26
ITT
IG: 71.4 (5.4)
CG: 72.5 (4.2)
MIG: Mixed
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
12 weeks
2 days/week
Facit-FIG Pre: 41.8 ± 10.2
Post: 41.8 ± 11.2
CG Pre: 42.9 ± 8.4
Post: 38.5 ± 11.9
Pereira et al.,
2020 [64]
Mexico BreastGMICT: 80
GHIIT: 70
CG: 66
GMICT: 51 (4)
GHIIT: 55 (5)
CG: 53 (7)
WMICT: Mixed
HIIT: HIIT + RT
CG: Usual Care with General Recommendations (ACSM, WHO, healthy lifestyle advice)
36 weeks
3 days/week
FACT-FGMICT Pre: 18.6 ± 9.5
Post: 8.0 ± 4.2
GHIIT Pre: 20.4 ± 5.6
Post: 5.1 ± 3.6
CG Pre: 16.6 ± 5.6
Post: 16.9 ± 4.6
Piraux et al.,
2020 [65]
BelgiumProstateGHIIT: 24
GRES: 24
CG: 24
GHIIT: 67.4 (8.9)
GRES: 67.9 (7.1)
CG: 71.9 (8.1)
MHIIT: HIIT
RT: RT
CG: Usual Care with General Recommendations (ACSM, WHO, healthy lifestyle advice)
5–8 weeks
3 days/week
Facit-FGHIIT Pre: 43.1 ± 6.9
Post: 42.1 ± 10.3
GRES Pre: 41.2 ± 7.7
Post: 40.5 ± 9.8
CG Pre: 41.1 ± 9.0
Post: 40.5 ± 9.8
Scott et al.,
2020 [66]
USABreastGLET: 58
GNLET: 59
CG: 57
GLET: 58 (9)
GNLET: 59 (9)
CG: 58 (9)
WLET: AT
NLET: AT + HIIT
CG: Active Structured Controls (stretching)
16 weeks
3–4 days/week
Facit-FGLET Pre: 36.7 ± 11.9
Post: 39.5 ± 12.2
GNLET Pre: 42.8 ± 8.9
Post: 44.8 ± 9.0
CG Pre: 39.6 ± 10.9
Post: 39.9 ± 10.7
Gal et al.,
2021 [67]
NetherlandsBreastIG: 68
CG: 114
IG: 58.0 (9.8)
CG: 58.3 (9.5)
WIG: Mixed
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
12 weeks
2 days/week
MFI IG Pre: 11.6 ± 4.7 Δ change IC95% −1.0 [−1.8 to −0.1]
CG Pre: 10.6 ± 4.3 Δ change IC95% −0.3 [−1.0 to 0.4]
Moraes et al.,
2021 [68]
BrazilBreastIG: 13
CG: 13
IG: 55.0 (5.8)
CG: 54.3 (5.2)
WIG: RT
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
8 weeks
1 day/week
PFSIG Pre: 5.1 ± 2.7
Post: 2.3 ± 1.4
CG Pre: 3.9 ± 2.0
Post: 3.0 ± 2.4
Harrison et al.,
2022 [34]
USAProstateIG: 13
CG: 13
IG: 65.7 (8.1)
CG: 64.4 (8.3)
MIG: Mixed
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
16 weeks
3 days/week
Facit-FIG vs. CC Δ change IC95% +4.0 [−3.2 to 11.1]
Kang et al.,
2022 [35]
CanadaProstateIG: 26
CG: 26
IG-CG: 63.4 (7.1)MIG: HIIT
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
12 weeks
3 days/week
FACT-FIG Pre: 43.6 ± 6.6
Post: 46.0 ± 4.3
CG Pre: 45.3 ± 4.7
Post: 44.6 ± 6.0
Koevoets et al.,
2022 [69]
NetherlandsBreastIG: 91
CG: 90
IG: 52.1 (8.6)
CG: 52.5 (8.7)
WIG: Mixed
CG: Passive Usual Care (no exercise/maintain habitual activity/no specific advice)
6 months
4 days/week
MFIIG vs. CC Δ change IC95% +2.22 [1.11 to 3.32]
IG = Intervention Group; CG = Control Group; PFS = Piper Fatigue Scale; Facit-F = Functional Assessment of Chronic Illness Therapy-Fatigue; FACT-F = Functional Assessment of Cancer Therapy: Fatigue; MFI = Multidimensional Fatigue Inventory; RT = Resistance Training; AT = Aerobic Training; HIIT = High-Intensity Interval Training; MICT = Moderate-Intensity Continuous Training; FAQ = Fatigue Assessment Questionnaire; ET = Endurance Training; LET = Linear Exercise Therapy; NLET = Non-Linear Exercise Therapy; FQL = Fatigue Quality List; M = Men; UC = Usual Care; W = WomenGeneral.
Table 3. Methodological quality as measured by PEDro scale.
Table 3. Methodological quality as measured by PEDro scale.
Study1234567891011Total
Al-Madjid et al., 2015 [55]++-+---+-++5
Schmidt et al., 2015 [56]+++------++5
Schmidt et al., 2015 [57]++-+---++++6
Travier et al., 2015 [58]++++--+++++8
Van Waart et al., 2015 [59]++-+---++++6
Mijwel et al., 2018 [60]++-+-----++4
Ammitzbøll et al., 2019 [29]++-+----+++5
Baglia et al., 2019 [61]++++---++++7
Mijwel et al., 2019 [36]++-+---++++6
Hojan 2020 [62]++++---++++7
Ndjavera et al., 2020 [63]++++--+-+++7
Pereira et al., 2020 [64]++++--+--++6
Piraux et al., 2020 [65]-+-+---++++6
Scott et al., 2020 [66]-+-+---++++6
Gal et al., 2021 [67]++-+----+++5
Moraes et al., 2021 [68]++-+---++++6
Harrison et al., 2022 [34]-+-+--++-++6
Kang et al., 2022 [35]++-+---++++6
Koevoets et al., 2022 [69]-+-+---++++6
1. Eligibility criteria and source (this item does not contribute to the total score). 2. Random allocation. 3. Concealed allocation. 4. Baseline comparability. 5. Blinding of participants. 6. Blinding of therapists. 7. Blinding of assessors. 8. Adequate follow-up (>85%). 9. Intention-to-treat analysis. 10. Between-group statistical comparisons. 11. Reporting of point measures and measures of variability.
Table 4. Sensitivity analyses.
Table 4. Sensitivity analyses.
Author, YearESLLULI2
Ammitzbøll et al., 2019 [29] R0.350.220.4856.7
Harrison et al., 2022 [34] C0.340.210.4755.8
Kang et al., 2022 [35] H0.340.210.4757.6
Mijwel 2019 [36] R + HIIT0.330.200.4757.2
Mijwel 2019 [36] A + HIIT0.350.210.4857.4
Al Majid et al., 2015 [55] A0.330.200.4555.7
Schmidt el al. 2015 [56] A0.340.220.4757.3
Schmidt el al. 2015 [57] R0.350.220.4857.2
Schmidt et al., 2015 [57] R0.350.220.4856.5
Travier et al., 2015 [58] C0.360.230.4954.8
Van Waart et al., 2015 [59] C0.340.220.4757.6
Mijwel 2018 [60] A + HIIT0.350.220.4856.1
Mijwel 2018 [60] R + HIIT0.350.210.4856.9
Baglia et al., 2019 [61] C0.330.200.4656.9
Hojan et al., 2019 [62] C0.280.190.360.0
Ndjavera et al., 2020 [63] C0.340.210.4757.7
Pereira et al., 2020 [64] A0.330.200.4757.5
Pereira et al., 2020 [64] H0.340.200.4759.5
Piraux et al., 2020 [65] H0.340.210.4757.6
Piraux et al., 2020 [65] R0.340.210.4757.6
Scott et al., 2020 [66] A C0.350.220.4856.3
Scott et al., 2020 A I [66]0.350.220.4855.9
Gal et al., 2021 [67] C0.360.230.4853.3
Moraes et al., 2021 [68] R0.330.210.4657.4
Koevoets et al., 2022 [69] C0.340.210.4757.6
ES: Effect size; LL: lower limit; UL: upper limit.
Table 5. Subgroup analysis by type of exercise.
Table 5. Subgroup analysis by type of exercise.
ExerciseESLLULI2
Endurance0.2580.0520.51826.4
Combined0.4210.0950.74780.0
HIIT0.3210.1750.4680.0
Resistance0.2500.0620.4390.0
ES: Effect size; LL: lower limit; UL: upper limit; HIIT: High-Intensity Interval Training.
Table 6. Subgroup analysis by duration of the exercise program.
Table 6. Subgroup analysis by duration of the exercise program.
DurationESLLULI2
≤12 weeks0.4070.1310.68272.7
>12–24 weeks0.2340.1310.6820.0
>24 weeks0.4740.2900.6790.0
ES: Effect size; LL: lower limit; UL: upper limit; HIIT: High-Intensity Interval Training.
Table 7. Subgroup analysis by gender.
Table 7. Subgroup analysis by gender.
GenderESLLULI2
Men0.5550.2540.85555.0
Women0.2740.1900.3620.0
ES: Effect size; LL: lower limit; UL: upper limit.
Table 8. Subgroup analysis by cancer type.
Table 8. Subgroup analysis by cancer type.
Cancer TypeESLLULI2
Prostate0.5550.2540.85555.0
Breast0.2740.1860.3620.0
ES: Effect size; LL: lower limit; UL: upper limit.
Table 9. Publication bias by Egger’s test.
Table 9. Publication bias by Egger’s test.
Exercise vs. Control GroupCoefficientp-Value
0.2840.672
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Cano-Uceda, A.; García-Fernández, P.; Peuyadé-Rueda, B.; Cañuelo-Marquez, A.M.; Solís-Mencía, C.; Lucio-Allende, C.; De Sousa-De Sousa, L.; Maté-Muñoz, J.L. From Evidence to Practice: A Systematic Review and Meta-Analysis on the Effects of Supervised Exercise on Fatigue in Breast and Prostate Cancer Survivors. Appl. Sci. 2025, 15, 8399. https://doi.org/10.3390/app15158399

AMA Style

Cano-Uceda A, García-Fernández P, Peuyadé-Rueda B, Cañuelo-Marquez AM, Solís-Mencía C, Lucio-Allende C, De Sousa-De Sousa L, Maté-Muñoz JL. From Evidence to Practice: A Systematic Review and Meta-Analysis on the Effects of Supervised Exercise on Fatigue in Breast and Prostate Cancer Survivors. Applied Sciences. 2025; 15(15):8399. https://doi.org/10.3390/app15158399

Chicago/Turabian Style

Cano-Uceda, Arturo, Pablo García-Fernández, Blanca Peuyadé-Rueda, Ana María Cañuelo-Marquez, Cristian Solís-Mencía, Carmen Lucio-Allende, Luis De Sousa-De Sousa, and José Luis Maté-Muñoz. 2025. "From Evidence to Practice: A Systematic Review and Meta-Analysis on the Effects of Supervised Exercise on Fatigue in Breast and Prostate Cancer Survivors" Applied Sciences 15, no. 15: 8399. https://doi.org/10.3390/app15158399

APA Style

Cano-Uceda, A., García-Fernández, P., Peuyadé-Rueda, B., Cañuelo-Marquez, A. M., Solís-Mencía, C., Lucio-Allende, C., De Sousa-De Sousa, L., & Maté-Muñoz, J. L. (2025). From Evidence to Practice: A Systematic Review and Meta-Analysis on the Effects of Supervised Exercise on Fatigue in Breast and Prostate Cancer Survivors. Applied Sciences, 15(15), 8399. https://doi.org/10.3390/app15158399

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