Abstract
Background
Although treatment-related adverse effects in children and adolescents with cancer are likely multifactorial, insufficient physical activity (PA) is a major contributor. However, evidence on the association between PA and adverse effects in these population remains limited. We aimed to estimate the effects of PA on health-related outcomes (HrO).
Methods
We searched nine databases from inception to August 5, 2025, for randomized controlled trials evaluating the effects of PA on HrO in children and adolescents with cancer. Data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. Independent reviewers extracted study data, and a random-effects model was used to pool results. Two reviewers independently extracted data, assessed the risk of bias, and evaluated the quality of evidence. The primary outcomes included quality of life (QoL), exercise capacity, and cognitive function indicators, while secondary outcomes encompassed social function and body composition indicators, as well as PA intervention acceptability and safety.
Results
The search identified 3,037 studies, only 24 met the eligibility criteria, including nine articles on children, five on adolescents, and ten on both groups. For primary outcomes, PA significantly improved fatigue (SMD = 0.66), muscle strength (SMD = 1.77), trunk muscle strength (SMD = 4.20), PA behaviour (SMD = 1.04), PA levels (SMD = 0.97), six-minute walk test (SMD = 0.62), cognitive function (SMD = 0.32), and executive function (SMD = 0.47); however, PA had no significant effect on QoL, the QoL scale, upper and lower body muscle strength, balance, flexibility, athletic performance, peak oxygen uptake, and depressive symptoms. Regarding secondary outcomes, PA significantly improved social function (SMD = 0.23) but had no significant effect on bone density, BMI, fat percentage, NK cells, or inflammatory factors. Additionally, moderator analysis showed that PA conditions and participant traits significantly affected the outcomes.
Conclusions
In children and adolescents with cancer, PA can improve HrO, ultimately helping to reduce the disease burden.
Systematic Review Registration
https://www.crd.york.ac.uk/PROSPERO/view/CRD420251018626, identifier CRD420251018626.
Keywords: children and adolescents, oncology, physical activity, psychology, wellness
1. Introduction
In the realm of children and adolescent healthcare, cancer is a formidable adversary (1). Despite the decrease in the incidence of cancer among children and adolescents and a significant increase in 5-year survival rates in recent years, cancer therapies and their adverse events are global challenges (1, 2). Unlike in adults, the low specificity of curative treatments for children and adolescents often leads to long-term and delayed effects due to these treatments impact normal, healthy tissues (3). Compared with their peers without cancer, children and adolescent patients face an increased risk of impaired quality of life (QoL), reduced exercise capacity, and cognitive dysfunction (4). These enduring health deficits not only interfere with daily functioning but also contribute to a substantially higher prevalence of chronic conditions in this population (5, 6). Therefore, identifying strategies to mitigate the negative consequences of cancer therapy in children and adolescents is essential.
Physical activity (PA), defined as any bodily movement generated by skeletal muscles that results in energy expenditure, and exercise—a planned, structured, and repetitive subset of PA aimed at maintaining or improving physical fitness and health—are two closely related yet distinct strategies that are important in cancer care (7). In this meta-analysis, we use the term PA to broadly refer to structured or incidental bodily movements involving energy expenditure (8). Substantial evidence and clinical guidelines support the role of PA in attenuating treatment-related adverse effects (AEs) and improving clinical and patient-reported outcomes in adult oncology populations (9–12). However, these findings cannot be directly extrapolated to children and adolescents (13). This population faces unique challenges, including a higher vulnerability to therapy-induced physical deconditioning and a subsequent vicious cycle of reduced PA, worsened exercise intolerance, and diminished QoL (14–17).
Since the first meta-analysis on this topic was published, several systematic reviews and meta-analyses have assessed the effects of PA interventions on specific health outcomes, including fatigue and cardiopulmonary function, in children and adolescents with cancer (18–24). For instance, Morales et al. (2018) performed a systematic review of eight studies and a meta-analysis of three studies and demonstrated that PA interventions were safe for childhood cancer survivors and did not increase the risk of mortality, recurrence, or other AEs in these population, even during intensive treatment. Moreover, these interventions improved physical function, body composition, PA levels, and QoL (22). Although these preliminary syntheses indicate that PA is safe and potentially beneficial for young patients with cancer, important evidence gaps persist. First, previous reviews have often been restricted to a single outcome domain (e.g., cognitive (21) or cardiopulmonary function (23)) or a specific patient subgroup (e.g., survivors post-treatment (24)), resulting in a fragmented evidence base. Second, and more importantly, comprehensive meta-analytic data quantifying the impact of PA interventions on treatment-related AEs—a central concern for both clinicians and families during active therapy—are lacking. Therefore, this study aims to address these gaps by providing a comprehensive and up-to-date synthesis of randomized controlled trials (RCTs) evidence. We evaluate the effects of PA interventions on a broad range of health-related outcomes (HrO) in children and adolescents across the cancer continuum, including those receiving active treatment. Through this comprehensive synthesis, the review aims to generate more robust, safety-focused evidence to support clinical implementation and guide the development of tailored rehabilitation guidelines for this vulnerable population.
2. Methods
2.1. Search strategy and selection criteria
This study adhered to the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement (25). RCTs on PA interventions for children and adolescents with cancer were identified by searching MEDLINE, Embase, Web of Science, Cochrane Library, CINAHL Plus with Full Text, APA PsycINFO, SPORTDiscus, Educational Resource Information Center, and Scopus from inception to 26 October, 2025, without language restrictions. An experienced medical librarian reviewed and optimised the search terms for each database. The search strategies are detailed in the (Supplementary Files 1). Additional eligible studies were identified by reviewing the references of the included studies and systematic reviews. We included randomized controlled trials investigating the effects of exercise interventions on HrO in children and adolescents aged 6–18 years with cancer. Studies with an intervention period of less than 3 weeks were excluded because cancer is a long-term chronic condition, and physical activity intervention itself was a process that required sustained duration to elicit meaningful physiological and psychological adaptations. A 3-week threshold was therefore established to ensure that the assessed effects of PA were attributable to a substantive intervention period. In addition, short interventions may limit the analysis of clinically meaningful changes and are less likely to predict long-term effects, which are crucial for meeting the ongoing needs of cancer patients and their families. Studies that combined PA with cognitive behavioural therapy, as well as those incorporating additional interventions such as dietary modifications, were excluded to isolate the effects of PA alone, enabling an accurate assessment of its direct effect on psychological and physical function.
To meet the inclusion criteria, intervention groups must be compared with control groups that receive no intervention, a placebo, or a low-intensity intervention serving as a control therapy; this control therapy is not expected to improve physical function but may benefit participants. We assessed the effects of PA on HrO as well as intervention acceptability and safety. HrO included QoL, exercise capacity, cognitive function, social function and body composition indicators. Safety was assessed by identifying adverse events. Acceptability was evaluated by measuring the risk ratio (RR), which was the percentage of patients who withdrew from the study for any reason.
EndNote X9 (Clarivate, Philadelphia, PA, USA) was used to perform de-duplication and to select studies based on titles and abstracts. Data were extracted using Microsoft Excel. Two researchers (HHS and MZ) independently screened the studies by title and abstract. Data extraction and bias risk assessment were performed using the Cochrane Collaboration’s Risk of Bias tool for RCTs (RoB 2) (26). Disagreements in bias risk assessment were resolved by consensus or by a third reviewer. The study protocol was registered with PROSPERO (Registration No. CRD420251018626).
2.2. Data analysis
For studies that met the inclusion criteria, we calculated the means and standard deviations (SDs) of baseline and endpoint measurements in the intervention and control groups. Standard errors were converted to SDs (27). When such data were missing, the means and SDs were calculated from the sample size, medians, interquartile ranges (IQRs), and minimum and maximum values (28). A correlation coefficient of 0.5 was used as recommended by RoB 2 (26). The weight of each study was determined based on the inverse of its variance. Since health outcomes are continuous variables, we used a random-effects model to calculate the effect size of each intervention. Data were presented as standardised mean differences (SMDs) and 95% confidence intervals (CIs) (29, 30).
To correct for small sample bias in effect size estimates, SMDs were adjusted using Hedges’ g. The estimated between-study variance, τ², was calculated using the restricted maximum likelihood method (31). The Knapp–Hartung adjustment was used to account for uncertainties in between-study heterogeneity and reduce the risk of false-positive results (32). SMDs were classified as small (~0.2), moderate (~0.5), and large (~0.8), as described previously (33). We evaluated heterogeneity using Cochran’s Q statistic, Higgins–Thompson I², prediction intervals, and average dispersion in effect sizes τ² (34–36). Publication bias was assessed using funnel plots and Egger’s test (37, 38). The trim-and-fill method was used to assess the effect of publication bias on the interpretation of the results (39).
A sensitivity analysis was performed using the leave-one-out method (40). Subgroup analysis was conducted considering covariates that might influence the intervention effects. A random-effects meta-regression analysis was performed to identify the sources of heterogeneity and examine how continuous covariates moderate intervention outcomes. Acceptability was calculated using a random-effects model rather than a fixed-effects model because of heterogeneity across studies and their small number (41). To exclude zero-event trials without using correction methods, we employed the Mantel–Haenszel method to ensure the robustness and validity of the analysis (42). Data were analysed using R software version 4.3.3 (R Foundation for Statistical Computing, Vienna, Austria) (43). A p-value of less than 0·05 was considered statistically significant.
The quality of evidence for each outcome was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method (44). The reliability of the evidence was rated as high, moderate, low, or very low based on five criteria: risk of bias in studies, inconsistency (I² >90%), indirectness, imprecision, and publication bias (Appendix pages 13 and 14) (45, 46). The quality of evidence was further evaluated according to the standards of the American College of Sports Medicine (ACSM) (13).
3. Results
3.1. Study characteristics
A search of nine databases and two previous studies identified 3,037 studies. After removing 759 duplicates, 2,278 studies were screened by title and abstract, and 90 were screened by full text. Of these, 24 met the eligibility criteria (Figure 1). Excluded studies and the reasons for their exclusion were listed in the Supplementary Files 2. The characteristics of the selected RCTs were shown in Table 1. The RCTs included 1,074 children and adolescents with cancer (607 males and 467 females). Nine studies focused solely on children (median age = 9.59 years, IQR = 9.04–10.5), five on adolescents (median age = 12.6 years, IQR = 12.6–13.25), and ten on both groups (median age = 12.12 years, IQR = 11.75–12.65). Sixteen RCTs included participants in the acute treatment phase, and eight included cancer survivors. Most studies used standard care, but its definition was broad (Supplementary Files 3). Fifteen RCTs had a low risk of bias, and nine had a higher risk (Supplementary Files 4).
Figure 1.
PRISMA flow diagram.
Table 1.
Characteristics of included studies.
| Study | Population | Information of disease | Intervention | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Sample sizes (female %); age (mean ± SD or range) | Country | Types | Treatment stage | Medicine (Y/N) | Length (weeks) | Sessions (per week) | Sessions duration (Min) | Exercise intensity | |
| Masoud (59) | EG: 22 (45.45), 9.04 ± 2.35; CG: 23 (43.48), 9.04 ± 2.47 | Saudi Arabia | ALL | Under | Y | 3 | 2 | 60 | Light ~ Moderate |
| Braam (60) | EG: 22 (45.45), 13.40 ± 3.10; CG: 33 (55.00), 13.10 ± 3.10 | Netherlands | Multiform | Under | Y | 12 | 2 | 45 | Moderate |
| Saultier (61) | EG: 37 (44.00), 11.40 ± 0.60; CG: 25 (41.00), 11.20 ± 0.60 | France | Multiform | Under | Y | 24 | NA | 90 | Light ~ Moderate |
| Ruble (62) | EG: 6 (44.44), 10.30, 8.00 ~ 12.00; CG: 7 (0.00), 9.60, 8.00 ~ 12.00 | American | Multiform | Post | N | 24 | NA | 150 | Light ~ Moderate |
| Tanriverdi (63) | EG: 11 (62.50), 12.90 ± 5.80; CG: 13 (62.50), 12.90 ± 5.80 | Turkey | ALL | Post | N | 12 | 2 | 45 | Moderate |
| Fiuza-Luces (64) | EG: 24 (29.00), 10.00 ± 1.00; CG: 25 (28.00), 11.00 ± 1.00 | Spanish | EST | Under | Y | 20 | 3 | 60 | Light ~ Moderate |
| Caru (65) | EG: 20 (30.00), 13.70 ± 2.40; CG: 20 (40.00), 13.60 ± 2.20 | American | Multiform | Post | NA | 12 | 7 | 60 | Moderate ~ Vigorous |
| Manchola-González (66) | EG: 7 (NA), 11.00 ± 3.70; CG: 12 (NA), 11.80 ± 4.40 | Spanish | ALL | Post | NA | 16 | 3 | 30 | Light ~ Vigorous |
| Lam (67) | EG: 37 (45.90), 12.80 ± 2.50; CG: 33 (54.50), 12.50 ± 2.50 | China | Multiform | Under | Y | 24 | 1-2 | 60 | Light ~ Moderate |
| Tanir (68) | EG: 19 (21.10), 10.21 ± 1.51; CG: 21 (57.10), 10.72 ± 1.51 | Turkey | ALL | Post | NA | 12 | 7 | 60 | Moderate ~ Vigorous |
| Stössel (69) | EG: 13 (37.50), 10.60 ± 5.20; CG: 15 (41.18), 11.40 ± 4.30 | Germany | Multiform | Under | Y | 6-8 | 3 | 45-60 | Light ~ Moderate |
| Hartman (70) | EG: 20 (44.00), 5.30, 5.00 ~ 15.00; CG: 21 (38.00), 6.20, 6.00 ~ 17.00 | Netherlands | ALL | Under | Y | 96 | 7 | NA | Light |
| Müller (71) | EG: 10 (60.00), 15.20 ± 2.00; CG: 11 (54.55), 12.20 ± 2.60 | Germany | Multiform | Under | Y | 24 | 5 | 15-45 | Moderate ~ Vigorous |
| Marchese (72) | EG: 13 (38.46), 7.60, 4.30 ~ 10.60; CG: 15 (20.00), 8.60, 5.10 ~ 15.80 | American | ALL | Under | Y | 12 | 7 | NA | Light |
| Yeh (73) | EG: 12 (50.00), 11.01 ± 3.56; CG: 10 (40.00), 12.48 ± 3.86 | American | ALL | Under | Y | 6 | 3 | 30 | Light |
| Chamorro-Viña (74) | EG: 3 (66.67), 12.67 ± 3.30; CG: 3 (100.00), 13.33 ± 4.50 | Canada | HSCT | Under | Y | 10 | 3 | 50-60 | Light ~ Moderate |
| Fiuza-Luces (75) | EG: 9 (22.22), 11.00 ± 4.00; CG: 11 (45.45), 12.00 ± 4.00 | Spain | EST | Under | Y | 17 | 3 | 60-70 | Light ~ Moderate |
| Li (76) | EG: 103 (46.60), 12.80 ± 2.60; CG: 89 (46.20), 12.40 ± 2.6 | China | Multiform | Post | N | NA | NA | NA | NA |
| Senn-Malashonak (77) | EG: 28 (28.57), 11.00, 5.00 ~ 17.00; CG: 29 (34.29), 12.00, 6.00 ~ 18.00 | Germany | HSCT | Under | Y | NA | NA | NA | NA |
| Elnaggar (78) | EG: 29 (34.50), 13.31 ± 2.61; CG: 29 (44.80), 14.21 ± 2.35 | Saudi Arabia | ALL | Post | N | 12 | 3 | 25-50 | Light ~ Moderate |
| Khodashenas (79) | EG: 10 (40.00), 10.10, 5.00 ~ 12.00; CG: 10 (40.00), 8.80, 5.00 ~ 12.00 | Iran | ALL | Under | Y | 12 | 3 | 60 | Light ~ Moderate |
| Waked (82) | EG: 23 (65.20), 9.26 ± 2.39; CG: 23 (21.70), 9.91 ± 2.09 | Saudi Arabia | ALL | Under | Y | 72 | 1-2 | 30-45 | Light |
| Şahin (80) | EG: 52 (46.20), 12.35 ± 3.43; CG: 41 (48.80), 11.89 ± 3.56 | Turkey | Multiform | Under | Y | 4 | 5 | 40-60 | Light |
| Dubnov-Raz (81) | EG: 10 (60.00), 11.10, 7.80 ~ 13.80; CG: 11 (50.00), 11.80, 9.00 ~ 12.80 | Israel | Multiform | Post | N | 24 | 3 | 60 | Light ~ Vigorous |
ALL, acute lymphoblastic leukaemia; EST, extracranial solid tumour; HSCT, hematopoietic stem cell transplantation; N, no; NA, not available; Post, post treatment; Under, under treatment; Y, yes.
3.2. Meta-analysis results
For primary outcomes, PA interventions significantly improved fatigue (SMD = 0.66; 95% CI = 0.43 to 0.89), muscle strength (SMD = 1.77; 95% CI = 0.72 to 2.83), trunk muscle strength (SMD = 4.20; 95% CI = 1.95 to 6.46), PA behaviour (SMD = 1.04; 95% CI = 0.32 to 1.77), PA levels (SMD = 0.97; 95% CI = 0.10 to 1.84), cardiorespiratory function (SMD = 0.61; 95% CI = 0.06 to 1.17), six-minute walk test (SMD = 0.62; 95% CI = 0.34 to 0.89), cognitive function (SMD = 0.32; 95% CI = 0.18 to 0.46), and executive function (SMD = 0.47; 95% CI = 0.12 to 0.82). Furthermore, the analysis indicated no statistically significant effects for QoL (SMD = 0.32; 95% CI = -0.07 to 0.71), QoL scale (SMD = 0.36; 95% CI = -0.12 to 0.85), lower body muscle strength (SMD = 1.82; 95% CI = -0.71 to 4.36), upper body muscle strength (SMD = -0.01; 95% CI = -0.37 to 0.34), balance (SMD = 0.18; 95% CI = -0.65 to 1.01), flexibility (SMD = 1.04; 95% CI = -4.28 to 6.35), athletic performance (SMD = 0.84; 95% CI = -0.72 to 2.39), peak oxygen uptake (SMD = 0.25; 95% CI = -0.07 to 0.58), depressive symptoms (SMD = 0.21; 95% CI = -0.43 to 0.84) (Figure 2).
Figure 2.
Primary outcomes of included studies. Blue:quality of life indicators, yellow: exercise capacity indicators, red: cognitive function indicators. SMD, standardised mean differences, CI, confidence intervals, PA, physical activity.
The random-effects regression model showed that with regard QoL indicators, differences in length, sessions duration, and disease types all significantly affect the effect of PA. As for exercise capacity indicators, covariates such as age, treatment stage, region, medication, as well as length, intensity, and sessions duration of PA all had significantly effect. Region and length also affected the effect of PA on cognitive function indicators. Furthermore, the results of the subgroup analysis indicated that age, treatment stage, medication, and conditions of PA intervention were significantly associated with improvements in the primary outcomes. Detailed information regarding the regression and subgroup analyses was provided in Supplementary Files 12 and 13.
For secondary outcomes, PA was found to significantly improve social functioning (SMD = 0.23; 95% CI = 0.05 to 0.42), while no significant effects were observed for other outcomes (Figure 3). Furthermore, moderator analysis demonstrated that conditions of PA intervention and disease type significantly affected the effects of PA. Detailed results and forest plots for regression and subgroup analyses on secondary outcomes were shown in the Supplementary Files 12 and 13.
Figure 3.
Secondary outcomes of included studies. Blue:social function indicators, red: body composition indicators. SMD, standardised mean differences, CI, confidence intervals, PA, physical activity.
3.3. Adverse events and acceptance
One RCT reported minor adverse events such as pain and poor mental status, and four reported serious adverse events, including relapse, severe complications, and death. The acceptance of PA interventions was higher in the experimental group than in the control group, though without statistical significance (RR = 0.79, 95% CI = 0.54 to 1.17).
3.4. Sensitivity analysis, publication bias and GRADE
Based on GRADE standards, the overall quality of evidence for outcomes ranged from very low to moderate. The quality of evidence for all outcomes was downgraded by one level because of the risk of bias in allocation concealment in three RCTs (Supplementary Files 9). Sensitivity analysis supports the overall results (Supplementary Files 6). There was publication bias in the effects of PA on muscle strength, cardiopulmonary function, and pro-inflammatory factors. The effects of PA on cardiopulmonary function and pro-inflammatory factors decreased after correcting for publication bias using the trim and fill method (Supplementary Files 8).
4. Discussion
4.1. Evidence summary
This systematic review and meta-analysis of 24 RCTs involving over 1,000 children and adolescents with cancer provided evidence that PA interventions significantly improve fatigue, muscle strength, trunk muscle strength, PA levels, PA behaviour, cardiopulmonary function, SMWT, cognitive function, executive function, and social function compared to control groups. These effects varied depending on disease types, treatment stage, medication, regional economic conditions, and sessions, sessions duration, and intensity of PA. In turn, gender had no significant effect. These results support the implementation of PA interventions to improve the health of children and adolescents with cancer.
Fatigue is highly prevalent during anticancer treatment, impairs daily functioning, and contributes to sedentary behaviour and reduced PA levels (47, 48). However, standardized PA management guidelines to mitigate treatment−related AEs are lacking in children and adolescents with cancer. A previous study demonstrated that supervised exercises reduced fatigue in these population (24). In our meta-analysis, eight RCTs showed that PA interventions had a moderate to large positive effect on fatigue in these population, which is consistent with previous findings (49). Moreover, lower PA levels in these population are strongly associated with cardiovascular disease, osteoporosis, and all-cause mortality (50). Kim et al. (2025) (51) and Langworthy et al. (2023) (52) both provided robust evidence in support of exercise in paediatric cancer populations: Kim et al. conducted a comprehensive meta-analysis of patients and survivors, demonstrating beneficial effects on cardiopulmonary health and functional recovery, whereas Langworthy et al. focused on adolescents undergoing active treatment and reported significant improvements in PA levels and fatigue. In addition, previous reviews treated outcomes such as QoL, muscle strength, and cardiorespiratory function as single−dimensional. In contrast, our review conceptualised these outcomes as composites of multiple dimensions—for example, cardiorespiratory function was assessed as the combined effect of peak oxygen uptake, respiratory exchange ratio, heart rate, and SMWT. The combination of these outcomes increased between-study heterogeneity but provided a more complete and realistic view of the effects of PA on health. Our findings indicated that PA interventions could effectively improve multidimensional indicators, including cardiopulmonary function, muscle strength, and PA behaviour.
We also sought to elucidate PA dose-response relationships for HrO. The intensity, sessions, sessions duration, and length of PA have distinct effects on HrO. Our results indicated that the PA protocols for improving fatigue, muscle strength, physical activity behaviour, cognitive function, and QoL were usually similar, with low-intensity PA, 1-3 sessions per week, and a sessions duration of 30-60 minutes could yield positive effects. To improve cardiopulmonary function, SMWT and PA levels, it seemed that moderate to vigorous intensity PA, higher sessions (7 sessions per week), and a sessions duration of 30-60 minutes were associated with greater effects. The effect of length was roughly similar across HrO, indicating that long-term PA had yielded relatively favourable health improvement effects in children and adolescents with cancer. Furthermore, in addition to the aforementioned sessions, intensity, sessions duration, and length, the PA types also has a significant effect on the effectiveness of PA interventions (53). Seventeen RCTs had focused on the effects of aerobic exercises or resistance training, four on skill-based exercises (e.g., basketball and badminton), two on PA games, and one on functional exercises. Although the types of exercise were similar between the studies, there was considerable variation in outcome measures, and some outcomes were not evaluated. Combining aerobic exercises with resistance training may yield better outcomes in children and adolescents with cancer (54).
We assessed the effects of PA on HrO according to ACSM standards (at least five RCTs, studies with more than 150 participants, and consistent beneficial effects across studies) (13). Most of the primary outcomes met these standards (Supplementary Files 11), which can help inform future clinical guidelines. In addition, there was high between-study heterogeneity in our study, partially explained by regression analysis. High heterogeneity may be due to covariates or measurement errors affecting effect estimates. Outcome assessment methods remain a critical determinant of study validity, irrespective of the overall quality of the intervention design. The included RCTs evaluated body composition, cardiopulmonary function, and muscle strength using objective measures. Furthermore, most indicators of exercise capacity were measured using validated tests, while PA levels were assessed using questionnaires. PA levels were measured using accelerometers in four studies and using questionnaires in five studies. Although the questionnaires were validated against accelerometer data, their accuracy remains relatively low (55). Fatigue and QoL scale also were assessed using questionnaires. Fatigue was measured using the PedsQL™ Multidimensional Fatigue Scale (four studies), the Chinese version of the Fatigue Scale for Children (two studies), or the PROMIS Paediatric Fatigue Scale (one study). However, the single-dimensional nature of the PROMIS Paediatric Fatigue Scale may not fully reflect the complexity of fatigue (56). Most studies (77%) evaluated QoL scale using PedsQL 3.0 and 4.0 (57, 58). Additionally, the interventions received by the control groups varied significantly, increasing the heterogeneity in outcomes. We included 24 RCTs from America, Europe, and Asia, representing various cultural backgrounds.
4.2. Limitations
This review has limitations. First, the poor methodological quality of the reviewed RCTs. Second, included studies on children and adolescents with cancer, representing a broad age range (6 to 18 years). Third, there was a consistent lack of detailed reporting regarding both PA parameters and QoL measures in the primary studies, limiting deeper interpretation. Fourth, PA levels were measured using questionnaires in five studies. Although the questionnaires were validated against accelerometer data, their accuracy remains relatively low. Finally, in children and adolescents with cancer, PA can improve HrO, ultimately helping to reduce the disease burden. However, it is necessary to make a more thorough and serious distinction based on age groups. Teenagers are different from school-age children.
5. Conclusion
In conclusion, PA can effectively reduce treatment-related adverse events in children and adolescents with cancer. Given the profound impact of cancer therapy on physical, psychological, emotional, cognitive, and social functioning, even low-intensity PA interventions—when leading to modest improvements or the maintenance of baseline status—may yield clinically meaningful benefits in HrO.
Acknowledgments
We would like to thank TopEdit (www.topeditsci.com) for English language editing.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Key Research and Development Program of Shaanxi Provincial Science and Technology Department (grant numbers 2024SF-YBXM-680), the Key Laboratory of Exercise for Health Promotion in Shaanxi Provincial Higher Education Institutions, and the Natural Science Research Project of Colleges in Anhui Province (grant number 2023AH051425).
Edited by: Carmen Fiuza-Luces, Research Institute Hospital 12 de Octubre, Spain
Reviewed by: Eugenia Trigoso, Hospital Universitario y Politécnico LA FE, Spain
Abigail Kusi-Amponsah Diji, Kwame Nkrumah University of Science and Technology, Ghana
Jerry Armah, University of Florida Gainesville, United States in collaboration with reviewer AK-AD
Abbreviations: American College of Sports Medicine, ACSM; Adverse effects, AEs; Confidence intervals, CIs; Grading of Recommendations Assessment, Development and Evaluation, GRADE; Health-related outcomes, HrO; Interquartile ranges, IQRs; Physical activity, PA; Quality of life, QoL; Randomised controlled trials, RCTs; Risk of Bias tool for RCTs, RoB 2; Risk ratio, RR; Standard deviations, SDs; Standardised mean differences, SMDs; Six-minute walk test, SMWT.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
Author contributions
HS: Data curation, Software, Writing – original draft. MZ: Writing – review & editing, Writing – original draft. YF: Writing – review & editing. XJ: Data curation, Methodology, Writing – review & editing. WH: Data curation, Methodology, Writing – review & editing. ZL: Writing – review & editing. WC: Writing – review & editing. ST: Writing – review & editing. CH: Writing – review & editing. HZ: Writing – review & editing. PC: Writing – review & editing. CF: Funding acquisition, Writing – review & editing. TL: Conceptualization, Funding acquisition, Resources, Validation, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2026.1773060/full#supplementary-material
References
- 1. Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. (2025) 75:10–45. doi: 10.3322/caac.21871, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Lam CG, Howard SC, Bouffet E, Pritchard-Jones K. Science and health for all children with cancer. Science. (2019) 363:1182–6. doi: 10.1126/science.aaw4892, PMID: [DOI] [PubMed] [Google Scholar]
- 3. Pfister SM, Reyes-Múgica M, Chan JKC, Hasle H, Lazar AJ, Rossi S, et al. A summary of the inaugural who classification of pediatric tumors: transitioning from the optical into the molecular era. Cancer Discov. (2022) 12:331–55. doi: 10.1158/2159-8290.Cd-21-1094, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Phillips SM, Padgett LS, Leisenring WM, Stratton KK, Bishop K, Krull KR, et al. Survivors of childhood cancer in the United States: prevalence and burden of morbidity. Cancer Epidemiol Biomarkers Prev. (2015) 24:653–63. doi: 10.1158/1055-9965.Epi-14-1418, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Ehrhardt MJ, Krull KR, Bhakta N, Liu Q, Yasui Y, Robison LL, et al. Improving quality and quantity of life for childhood cancer survivors globally in the twenty-first century. Nat Rev Clin Oncol. (2023) 20:678–96. doi: 10.1038/s41571-023-00802-w, PMID: [DOI] [PubMed] [Google Scholar]
- 6. Suh E, Stratton KL, Leisenring WM, Nathan PC, Ford JS, Freyer DR, et al. Late mortality and chronic health conditions in long-term survivors of early-adolescent and young adult cancers: A retrospective cohort analysis from the childhood cancer survivor study. Lancet Oncol. (2020) 21:421–35. doi: 10.1016/s1470-2045(19)30800-9, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Bishop DJ, Beck B, Biddle SJH, Denay KL, Ferri A, Gibala MJ, et al. Physical activity and exercise intensity terminology: A joint american college of sports medicine (Acsm) expert statement and exercise and sport science Australia (Essa) consensus statement. Med Sci sports Exercise. (2025) 57:2599–613. doi: 10.1249/mss.0000000000003795, PMID: [DOI] [PubMed] [Google Scholar]
- 8. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. (1985) 100:126–31. [PMC free article] [PubMed] [Google Scholar]
- 9. Fong DY, Ho JW, Hui BP, Lee AM, Macfarlane DJ, Leung SS, et al. Physical activity for cancer survivors: meta-analysis of randomised controlled trials. Bmj. (2012) 344:e70. doi: 10.1136/bmj.e70, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Nader S, Massoud A, Al-Obeidat F, Mohamed WF, Hafez W, Rashid A, et al. Impact of aerobic and resistance training on fatigue, quality of life, and physical activity in prostate cancer patients: A systematic review and meta-analysis. Int J Surg. (2024) 110:6170–81. doi: 10.1097/js9.0000000000000982, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Rock CL, Thomson CA, Sullivan KR, Howe CL, Kushi LH, Caan BJ, et al. American cancer society nutrition and physical activity guideline for cancer survivors. CA Cancer J Clin. (2022) 72:230–62. doi: 10.3322/caac.21719, PMID: [DOI] [PubMed] [Google Scholar]
- 12. Stout NL, Santa Mina D, Lyons KD, Robb K, Silver JK. A systematic review of rehabilitation and exercise recommendations in oncology guidelines. CA Cancer J Clin. (2021) 71:149–75. doi: 10.3322/caac.21639, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Campbell KL, Winters-Stone KM, Wiskemann J, May AM, Schwartz AL, Courneya KS, et al. Exercise guidelines for cancer survivors: consensus statement from international multidisciplinary roundtable. Med Sci sports Exercise. (2019) 51:2375–90. doi: 10.1249/mss.0000000000002116, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. van Brussel M, Takken T, Lucia A, van der Net J, Helders PJ. Is physical fitness decreased in survivors of childhood leukemia? A systematic review. Leukemia. (2005) 19:13–7. doi: 10.1038/sj.leu.2403547, PMID: [DOI] [PubMed] [Google Scholar]
- 15. Berdan CA, Tangney CC, Scala C, Stolley M. Childhood cancer survivors and adherence to the american cancer society guidelines on nutrition and physical activity. J Cancer Surviv. (2014) 8:671–9. doi: 10.1007/s11764-014-0376-0, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Grydeland M, Bratteteig M, Rueegg CS, Lie HC, Thorsen L, Larsen EH, et al. Physical activity among adolescent cancer survivors: the paccs study. Pediatrics. (2023) 152:e2023061778. doi: 10.1542/peds.2023-061778, PMID: [DOI] [PubMed] [Google Scholar]
- 17. Ness KK, Plana JC, Joshi VM, Luepker RV, Durand JB, Green DM, et al. Exercise intolerance, mortality, and organ system impairment in adult survivors of childhood cancer. J Clin Oncol. (2020) 38:29–42. doi: 10.1200/jco.19.01661, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Braam KI, van der Torre P, Takken T, Veening MA, van Dulmen-den Broeder E, Kaspers GJ. Physical Exercise Training Interventions for Children and Young Adults during and after Treatment for Childhood Cancer. Cochrane Database Syst Rev. (2013) 4):Cd008796. doi: 10.1002/14651858.CD008796.pub2, PMID: [DOI] [PubMed] [Google Scholar]
- 19. Mizrahi D, Wakefield CE, Fardell JE, Quinn VF, Lim Q, Clifford BK, et al. Distance-delivered physical activity interventions for childhood cancer survivors: A systematic review and meta-analysis. Crit Rev Oncol Hematol. (2017) 118:27–41. doi: 10.1016/j.critrevonc.2017.08.008, PMID: [DOI] [PubMed] [Google Scholar]
- 20. Wurz A, Brunet J. The effects of physical activity on health and quality of life in adolescent cancer survivors: A systematic review. JMIR Cancer. (2016) 2:e6. doi: 10.2196/cancer.5431, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Bernal JDK, Recchia F, Yu DJ, Fong DY, Wong SHS, Chung PK, et al. Physical activity and exercise for cancer-related cognitive impairment among individuals affected by childhood cancer: A systematic review and meta-analysis. Lancet Child Adolesc Health. (2023) 7:47–58. doi: 10.1016/s2352-4642(22)00286-3, PMID: [DOI] [PubMed] [Google Scholar]
- 22. Morales JS, Valenzuela PL, Rincón-Castanedo C, Takken T, Fiuza-Luces C, Santos-Lozano A, et al. Exercise training in childhood cancer: A systematic review and meta-analysis of randomized controlled trials. Cancer Treat Rev. (2018) 70:154–67. doi: 10.1016/j.ctrv.2018.08.012, PMID: [DOI] [PubMed] [Google Scholar]
- 23. Morales JS, Valenzuela PL, Herrera-Olivares AM, Baño-Rodrigo A, Castillo-García A, Rincón-Castanedo C, et al. Exercise interventions and cardiovascular health in childhood cancer: A meta-analysis. Int J Sports Med. (2020) 41:141–53. doi: 10.1055/a-1073-8104, PMID: [DOI] [PubMed] [Google Scholar]
- 24. Shi Q, Zheng J, Liu K. Supervised exercise interventions in childhood cancer survivors: A systematic review and meta-analysis of randomized controlled trials. Children (Basel). (2022) 9:824. doi: 10.3390/children9060824, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ. (2021) 372:n71. doi: 10.1136/bmj.n71, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. Rob 2: A revised tool for assessing risk of bias in randomised trials. BMJ. (2019) 366:l4898. doi: 10.1136/bmj.l4898, PMID: [DOI] [PubMed] [Google Scholar]
- 27. Hedges LV. Distribution theory for glass’s estimator of effect size and related estimators. J Educ Stat. (1981) 6:107–28. doi: 10.3102/10769986006002107, PMID: 38293548 [DOI] [Google Scholar]
- 28. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Method. (2014) 14:135. doi: 10.1186/1471-2288-14-135, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane handbook for systematic reviews of interventions version 6.5 (Updated August 2024). Cochrane; (2024). Available online at: https://www.cochrane.org/authors/handbooks-and-manuals/handbook (Accessed March 21, 2026). [Google Scholar]
- 30. Berkey CS, Hoaglin DC, Mosteller F, Colditz GA. A random-effects regression model for meta-analysis. Stat Med. (1995) 14:395–411. doi: 10.1002/sim.4780140406, PMID: [DOI] [PubMed] [Google Scholar]
- 31. Viechtbauer W. Bias and efficiency of meta-analytic variance estimators in the random-effects model. J Educ Behav Stat. (2005) 30:261–93. doi: 10.3102/10769986030003261, PMID: 38293548 [DOI] [Google Scholar]
- 32. Knapp G, Hartung J. Improved tests for a random effects meta-regression with a single covariate. Stat Med. (2003) 22:2693–710. doi: 10.1002/sim.1482, PMID: [DOI] [PubMed] [Google Scholar]
- 33. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Hoboken, New Jersey: routledge; (2013). Available online at: https://www.taylorfrancis.com/books/mono/10.4324/9780203771587/statistical-power-analysis-behavioral-sciences-jacob-cohen. [Google Scholar]
- 34. Cochran WG. Some methods for strengthening the common χ 2 tests. Biometrics. (1954) 10:417–51. doi: 10.2307/3001616, PMID: 39964225 [DOI] [Google Scholar]
- 35. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. (2002) 21:1539–58. doi: 10.1002/sim.1186, PMID: [DOI] [PubMed] [Google Scholar]
- 36. IntHout J, Ioannidis JP, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open. (2016) 6:e010247. doi: 10.1136/bmjopen-2015-010247, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Lin L, Chu H, Murad MH, Hong C, Qu Z, Cole SR, et al. Empirical comparison of publication bias tests in meta-analysis. J Gen Intern Med. (2018) 33:1260–7. doi: 10.1007/s11606-018-4425-7, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Easterbrook PJ, Berlin JA, Gopalan R, Matthews DR. Publication bias in clinical research. Lancet. (1991) 337:867–72. doi: 10.1016/0140-6736(91)90201-y, PMID: [DOI] [PubMed] [Google Scholar]
- 39. Weinhandl ED, Duval S. Generalization of trim and fill for application in meta-regression. Res synthesis Methods. (2012) 3:51–67. doi: 10.1002/jrsm.1042, PMID: [DOI] [PubMed] [Google Scholar]
- 40. Viechtbauer W, Cheung MW. Outlier and influence diagnostics for meta-analysis. Res synthesis Methods. (2010) 1:112–25. doi: 10.1002/jrsm.11, PMID: [DOI] [PubMed] [Google Scholar]
- 41. Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to Meta-Analysis. Hoboken, New Jersey: John wiley & sons; (2021). Available online at: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470743386. [Google Scholar]
- 42. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. (1959) 22:719–48. [PubMed] [Google Scholar]
- 43. R Core Team . R: A Language and Environment for Statistical Computing. Vienna, Austria: R foundation for statistical computing; (2013). [Google Scholar]
- 44. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. Grade: an emerging consensus on rating quality of evidence and strength of recommendations. Bmj. (2008) 336:924–6. doi: 10.1136/bmj.39489.470347.AD, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Schünemann. H, Brożek J, Guyatt. G, Oxma. A. Handbook for grading the quality of evidence and the strength of recommendation using the grade approach. (2013). Available online at: https://evidence-impact.org/storage/85/GRADE-Handbook.pdf (Accessed March 21, 2026).
- 46. Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. Grade guidelines: 1. Introduction-grade evidence profiles and summary of findings tables. J Clin Epidemiol. (2011) 64:383–94. doi: 10.1016/j.jclinepi.2010.04.026, PMID: [DOI] [PubMed] [Google Scholar]
- 47. Levesque A, Caru M, Duval M, Laverdière C, Marjerrison S, Sultan S. Cancer-related fatigue in childhood cancer survivors: A systematic scoping review on contributors of fatigue and how they are targeted by non-pharmacological interventions. Crit Rev Oncol Hematol. (2022) 179:103804. doi: 10.1016/j.critrevonc.2022.103804, PMID: [DOI] [PubMed] [Google Scholar]
- 48. van Deuren S, Boonstra A, van Dulmen-den Broeder E, Blijlevens N, Knoop H, Loonen J. Severe fatigue after treatment for childhood cancer. Cochrane Database Syst Rev. (2020) 3:Cd012681. doi: 10.1002/14651858.CD012681.pub2, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Armah J, Alzahid SK, Pei Q, Stacciarini JR, Heldermon C, Starkweather A. Exercise to Manage Fatigue during and after Chemotherapy in Adolescents and Young Adults with Cancer: A Systematic Review and Meta-Analysis. Oncol Nurs Forum. (2025) 52:E77–e92. doi: 10.1188/25.Onf.E77-e92, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50. Florin TA, Fryer GE, Miyoshi T, Weitzman M, Mertens AC, Hudson MM, et al. Physical inactivity in adult survivors of childhood acute lymphoblastic leukemia: A report from the childhood cancer survivor study. Cancer Epidemiol Biomarkers Prev. (2007) 16:1356–63. doi: 10.1158/1055-9965.Epi-07-0048, PMID: [DOI] [PubMed] [Google Scholar]
- 51. Kim BY, Kim HY, Lee DH, Jeon JY. Effects of exercise interventions on physical recovery in paediatric cancer patients and survivors: A meta-analysis. BMC Pediatr. (2025) 25:792. doi: 10.1186/s12887-025-06136-z, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Langworthy E, Gokal K, Kettle VE, Daley AJ. Effects of physical activity interventions on physical activity and health outcomes in young people during treatment for cancer: A systematic review and meta-analysis of randomised controlled trials. BMJ Open Sport Exerc Med. (2023) 9:e001466. doi: 10.1136/bmjsem-2022-001466, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53. Medicine ACoS . Acsm's Guidelines for Exercise Testing and Prescription. Philadelphia, Pennsylvania: Lippincott williams & wilkins; (2013). Available online at: https://acsm.org/education-resources/books/guidelines-exercise-testing-prescription/. [Google Scholar]
- 54. L'Hotta AJ, Randolph SB, Reader B, Lipsey K, King AA. Clinical practice guideline and expert consensus recommendations for rehabilitation among children with cancer: A systematic review. CA Cancer J Clin. (2023) 73:524–45. doi: 10.3322/caac.21783, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55. Götte M, Seidel CC, Kesting SV, Rosenbaum D, Boos J. Objectively measured versus self-reported physical activity in children and adolescents with cancer. PloS One. (2017) 12:e0172216. doi: 10.1371/journal.pone.0172216, PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56. Hinds PS, Nuss SL, Ruccione KS, Withycombe JS, Jacobs S, DeLuca H, et al. Promis pediatric measures in pediatric oncology: valid and clinically feasible indicators of patient-reported outcomes. Pediatr Blood Cancer. (2013) 60:402–8. doi: 10.1002/pbc.24233, PMID: [DOI] [PubMed] [Google Scholar]
- 57. Varni JW, Seid M, Rode CA. The pedsql: measurement model for the pediatric quality of life inventory. Med Care. (1999) 37:126–39. doi: 10.1097/00005650-199902000-00003, PMID: [DOI] [PubMed] [Google Scholar]
- 58. Varni JW, Burwinkle TM, Katz ER, Meeske K, Dickinson P. The pedsql in pediatric cancer: reliability and validity of the pediatric quality of life inventory generic core scales, multidimensional fatigue scale, and cancer module. Cancer. (2002) 94:2090–106. doi: 10.1002/cncr.10428, PMID: [DOI] [PubMed] [Google Scholar]
- 59. Masoud AE, Shaheen AAM, Algabbani MF, et al. Effectiveness of exergaming in reducing cancer-related fatigue among children with acute lymphoblastic leukemia: a randomized controlled trial. Ann Med. (2023) 55:2224048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60. Braam KI, van Dijk-Lokkart EM, Kaspers GJL, et al. Effects of a combined physical and psychosocial training for children with cancer: a randomized controlled trial. BMC Cancer. (2018) 18:1289. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61. Saultier P, Vallet C, Sotteau F, et al. A randomized trial of physical activity in children and adolescents with cancer. Cancers (Basel). (2021) 13. doi: 10.3390/cancers13010121. PMID: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62. Ruble K, Scarvalone S, Gallicchio L, et al. Group physical activity intervention for childhood cancer survivors: a pilot study. J Phys Act Health. (2016) 13:352–9. [DOI] [PubMed] [Google Scholar]
- 63. Tanriverdi M, Cakir E, Akkoyunlu ME, et al. Effect of virtual reality-based exercise intervention on sleep quality in children with acute lymphoblastic leukemia and healthy siblings: a randomized controlled trial. Palliat Support Care. (2022) 20:455–61. [DOI] [PubMed] [Google Scholar]
- 64. Fiuza-Luces C, Padilla JR, Soares-Miranda L, et al. Exercise intervention in pediatric patients with solid tumors: the physical activity in pediatric cancer trial. Med Sci Sports Exerc. (2017) 49:223–30. [DOI] [PubMed] [Google Scholar]
- 65. Caru M, Dandekar S, Gordon B, et al. Implementing a behavioral physical activity program in children and adolescent survivors of childhood cancer: a pilot randomized controlled trial. J Behav Med. (2024) 47:792–803. [DOI] [PubMed] [Google Scholar]
- 66. Manchola-González JD, Bagur-Calafat C, Girabent-Farrés M, et al. Effects of a home-exercise program in childhood survivors of acute lymphoblastic leukemia on physical fitness and physical functioning: results of a randomized clinical trial. Support Care Cancer. (2020) 28:3171–8. [DOI] [PubMed] [Google Scholar]
- 67. Lam KKW, Li WHC, Chung OK, et al. An integrated experiential training program with coaching to promote physical activity, and reduce fatigue among children with cancer: a randomized controlled trial. Patient Educ Couns. (2018) 101:1947–56. [DOI] [PubMed] [Google Scholar]
- 68. Tanir MK, Kuguoglu S. Impact of exercise on lower activity levels in children with acute lymphoblastic leukemia: a randomized controlled trial from Turkey. Rehabil Nurs. (2013) 38:48–59. [DOI] [PubMed] [Google Scholar]
- 69. Stössel S, Neu MA, Wingerter A, et al. Benefits of exercise training for children and adolescents undergoing cancer treatment: results from the randomized controlled MUCKI trial. Front Pediatr. (2020) 8:243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70. Hartman A, te Winkel ML, van Beek RD, et al. A randomized trial investigating an exercise program to prevent reduction of bone mineral density and impairment of motor performance during treatment for childhood acute lymphoblastic leukemia. Pediatr Blood Cancer. (2009) 53:64–71. [DOI] [PubMed] [Google Scholar]
- 71. Müller C, Winter C, Boos J, et al. Effects of an exercise intervention on bone mass in pediatric bone tumor patients. Int J Sports Med. (2014) 35:696–703. [DOI] [PubMed] [Google Scholar]
- 72. Marchese VG, Chiarello LA, Lange BJ. Effects of physical therapy intervention for children with acute lymphoblastic leukemia. Pediatr Blood Cancer. (2004) 42:127–33. [DOI] [PubMed] [Google Scholar]
- 73. Yeh CH, Man Wai JP, Lin US, et al. A pilot study to examine the feasibility and effects of a home-based aerobic program on reducing fatigue in children with acute lymphoblastic leukemia. Cancer Nurs. (2011) 34:3–12. [DOI] [PubMed] [Google Scholar]
- 74. Chamorro-Viña C, Valentín J, Fernández L, et al. Influence of a moderate-intensity exercise program on early NK cell immune recovery in pediatric patients after reduced-intensity hematopoietic stem cell transplantation. Integr Cancer Ther. (2017) 16:464–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75. Fiuza-Luces C, Padilla JR, Valentín J, et al. Effects of exercise on the immune function of pediatric patients with solid tumors: insights from the PAPEC randomized trial. Am J Phys Med Rehabil. (2017) 96:831–7. [DOI] [PubMed] [Google Scholar]
- 76. Li WHC, Ho KY, Lam KKW, et al. Adventure-based training to promote physical activity and reduce fatigue among childhood cancer survivors: a randomized controlled trial. Int J Nurs Stud. (2018) 83:65–74. [DOI] [PubMed] [Google Scholar]
- 77. Senn-Malashonak A, Wallek S, Schmidt K, et al. Psychophysical effects of an exercise therapy during pediatric stem cell transplantation: a randomized controlled trial. Bone Marrow Transplant. (2019) 54:1827–35. [DOI] [PubMed] [Google Scholar]
- 78. Elnaggar RK, Osailan AM, Elbanna MF, et al. Effectiveness of a dose-graded aerobic exercise regimen on cardiopulmonary fitness and physical performance in pediatric survivors of acute lymphoblastic leukemia: a randomized clinical trial. J Cancer Surviv. (2025) 19:1090–101. [DOI] [PubMed] [Google Scholar]
- 79. Khodashenas E, Badiee Z, Sohrabi M, et al. The effect of an aerobic exercise program on the quality of life in children with cancer. Turk J Pediatr. (2017) 59:678–83. [DOI] [PubMed] [Google Scholar]
- 80. Şahin S, Akel BS, Huri M, et al. Investigation of the effect of task-oriented rehabilitation program on motor skills of children with childhood cancer: a randomized-controlled trial. Int J Rehabil Res. (2020) 43:167–74. [DOI] [PubMed] [Google Scholar]
- 81. Dubnov-Raz G, Azar M, Reuveny R, et al. Changes in fitness are associated with changes in body composition and bone health in children after cancer. Acta Paediatr. (2015) 104:1055–61. [DOI] [PubMed] [Google Scholar]
- 82. Waked I, Albenasy K. Bone mineral density, lean body mass, and bone biomarkers following physical exercise in children with acute lymphoblastic leukemia undergoing chemotherapy. Iran J Blood Cancer. (2018) 10:69–75. [Google Scholar]
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Data Availability Statement
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