This individual patient data meta-analysis aimed to evaluate the effects of exercise on quality of life (QoL) and physical function (PF) in patients with cancer, and to identify moderator effects of demographic (age, sex, marital status, education), clinical (body mass index, cancer type, presence of metastasis), intervention-related (intervention timing, delivery mode and duration, and type of control group), and exercise-related (exercise frequency, intensity, type, time) characteristics. Relevant published and unpublished studies were identified in September 2012 via PubMed, EMBASE, PsycINFO, and CINAHL, reference checking and personal communications. Principle investigators of all 69 eligible trials were requested to share IPD from their study. IPD from 34 randomised controlled trials (n=4519 patients) that evaluated the effects of exercise compared to a usual care, wait-list or attention control group on QoL and PF in adult patients with cancer were retrieved and pooled. Linear mixed-effect models were used to evaluate the effects of the exercise on post-intervention outcome values (z-score) adjusting for baseline values. Moderator effects were studies by testing interactions. Exercise significantly improved QoL (β=0.15, 95%CI=0.10;0.20) and PF (β=0.18, 95%CI=0.13;0.23). The effects were not moderated by demographic, clinical or exercise characteristics. Effects on QoL (βdifference_in_effect=0.13, 95%CI=0.03;0.22) and PF (βdifference_in_effect=0.10, 95%CI=0.01;0.20) were significantly larger for supervised than unsupervised interventions. In conclusion, exercise, and particularly supervised exercise, effectively improves QoL and PF in patients with cancer with different demographic and clinical characteristics during and following treatment. Although effect sizes are small, there is consistent empirical evidence to support implementation of exercise as part of cancer care.

Since Exercise and Sports Science Australia (ESSA) first published its position statement on exercise guidelines for people with cancer, there has been exponential growth in research evaluating the role of exercise pre-, during and post-cancer treatment. The purpose of this report is to use the current scientific evidence, alongside clinical experience and exercise science principles to update ESSA's position statement on cancer-specific exercise prescription. Reported in this position statement is a summary of the benefits accrued through exercise following a cancer diagnosis and the strengths and limitations of this evidence-base. An exercise prescription framework is then proposed to enable the application of cancer-specific considerations for individualisation, specificity, safety, feasibility and progression of exercise for all patients. Additional specific exercise prescription considerations are provided for the presence of haematological, musculoskeletal, systemic, cardiovascular, lymphatic, gastrointestinal, genitourinary and neurological disease- and treatment-related concerns, as well as presence of co-morbid chronic disease. Further, we also identify and discuss cancer-specific pragmatic issues and barriers requiring consideration for exercise prescription. While for the majority, multimodal, moderate to high intensity exercise will be appropriate, there is no set prescription and total weekly dosage that would be considered evidence-based for all cancer patients. Targeted exercise prescription, which includes the provision of behaviour change advice and support, is needed to ensure greatest benefit (as defined by the patient) in the short and longer term, with low risk of harm.

Purpose: This study aimed to analyze the effect of resistance training (RT) performed until volitional failure with low, moderate, and high loads on muscle hypertrophy and muscle strength in healthy adults and to assess the possible participant-, design-, and training-related covariates that may affect the adaptations. Methods: Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, MEDLINE, CINAHL, EMBASE, SPORTDiscus, and Web of Science databases were searched. Including only studies that performed sets to volitional failure, the effects of low- (>15 repetitions maximum (RM)), moderate- (9–15 RM), and high-load (≤8 RM) RTs were examined in healthy adults. Network meta-analysis was undertaken to calculate the standardized mean difference (SMD) between RT loads in overall and subgroup analyses involving studies deemed of high quality. Associations between participant-, design-, and training-related covariates with SMD were assessed by univariate and multivariate network meta-regression analyses. Results: Twenty-eight studies involving 747 healthy adults were included. Although no differences in muscle hypertrophy between RT loads were found in overall (P = 0.113–0.469) or subgroup analysis (P = 0.871–0.995), greater effects were observed in untrained participants (P = 0.033) and participants with some training background who undertook more RT sessions (P = 0.031–0.045). Muscle strength improvement was superior for both high-load and moderate-load compared with low-load RT in overall and subgroup analysis (SMD, 0.60–0.63 and 0.34–0.35, respectively; P < 0.001–0.003), with a nonsignificant but superior effect for high compared with moderate load (SMD, 0.26–0.28, P = 0.068). Conclusions: Although muscle hypertrophy improvements seem to be load independent, increases in muscle strength are superior in high-load RT programs. Untrained participants exhibit greater muscle hypertrophy, whereas undertaking more RT sessions provides superior gains in those with previous training experience.

Summary: To systematically review and analyze the effects of resistance‐based exercise programs on body composition, regional adiposity, and body weight in individuals with overweight/obesity across the lifespan. Using PRISMA guidelines, randomized controlled trials were searched in nine electronic databases up to December 2020. Meta‐analyses were performed using random‐effects model. One‐hundred sixteen articles describing 114 trials (n = 4184 participants) were included. Interventions involving resistance training and caloric restriction were the most effective for reducing body fat percentage (ES = −3.8%, 95% CI: −4.7 to −2.9%, p < 0.001) and whole‐body fat mass (ES = −5.3 kg, 95% CI: −7.2 to −3.5 kg, p < 0.001) compared with groups without intervention. Significant results were also observed following combined resistance and aerobic exercise (ES = −2.3% and −1.4 kg, p < 0.001) and resistance training alone (ES = −1.6% and −1.0 kg, p < 0.001) compared with no training controls. Resistance training alone was the most effective for increasing lean mass compared with no training controls (ES = 0.8 kg, 95% CI: 0.6 to 1.0 kg, p < 0.001), whereas lean mass was maintained following interventions involving resistance training and caloric restriction (ES = ~ − 0.3 kg, p = 0.550–0.727). Results were consistently observed across age and sex groups (p = 0.001–0.011). Reductions in regional adiposity and body weight measures were also observed following combined resistance and aerobic exercise and programs including caloric restriction (p < 0.001). In conclusion, this study provides evidence that resistance‐based exercise programs are effective and should be considered within any multicomponent therapy program when caloric restriction is utilized in individuals with overweight or obesity.

This commentary delivers a practical perspective on the current state of subjective training load (TL) monitoring, and in particular sessional ratings of perceived exertion, for performance enhancement and injury prevention. Subjective measures may be able to reflect mental fatigue, effort, stress, and motivation. These factors appear to be important moderators of the relationship TL has with performance and injury, and they also seem to differ between open and closed skill sports. As such, mental factors may affect the interaction between TL, performance, and injury in different sports. Further, modeling these interactions may be limited due to the assumption that an independent signal can adequately account for the performance or injury outcomes. An independent signal model does not accurately reflect training environments where multiple stressors (e.g., mechanical, emotional, nutritional) impact adaptations. Common issues with using subjective TL monitoring, including a lack of differentiation between biomechanical, physiological, and cognitive load, may be overcome by considering psychometric measurement best practices, finer graded scales, and differential ratings of perceived exertion. Methods of calculating TL, including different acute and chronic time periods, may also need to be individualized to different sports and potentially different individuals within the same sport. As TL monitoring is predominately a “chronic” decision-making tool, “acute” decision-making tools, e.g., subjective wellness and autonomic nervous system measures, should be combined in a bespoke multivariate model to aid sports coaches. A call to action is presented for future research on key issues associated with TL monitoring that will have relevance for practitioners in an applied setting.