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 Relevant published and unpublished studies were identified in September 2012 via PubMed, EMBASE, PsycINFO, and CINAHL, reference checking and personal communications.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 (β=0.13, 95%CI=0.03;0.22) and PF (β=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.

Exercise interventions, especially when supervised, have statistically significant and small clinical benefit on self-reported QoL and PF in patients with cancer. Unsupervised exercise intervention effects on PF were larger when prescribed at a higher weekly energy expenditure.

ObjectiveThis individual patient data (IPD) meta‐analysis aimed to evaluate the effects of psychosocial interventions (PSI) on quality of life (QoL), emotional function (EF), and social function (SF) in patients with cancer, and to study moderator effects of demographic, clinical, personal, and intervention‐related characteristics.MethodsRelevant studies were identified via literature searches in 4 databases. We pooled IPD from 22 (n = 4217) of 61 eligible randomized controlled trials. Linear mixed‐effect model analyses were used to study intervention effects on the post‐intervention values of QoL, EF, and SF (z‐scores), adjusting for baseline values, age, and cancer type. We studied moderator effects by testing interactions with the intervention for demographic, clinical, personal, and intervention‐related characteristics, and conducted subsequent stratified analyses for significant moderator variables.Results: PSI significantly improved QoL (β = 0.14,95%CI = 0.06;0.21), EF (β = 0.13,95%CI = 0.05;0.20), and SF (β = 0.10,95%CI = 0.03;0.18). Significant differences in effects of different types of PSI were found, with largest effects of psychotherapy. The effects of coping skills training were moderated by age, treatment type, and targeted interventions. Effects of psychotherapy on EF may be moderated by cancer type, but these analyses were based on 2 randomized controlled trials with small sample sizes of some cancer types.ConclusionsPSI significantly improved QoL, EF, and SF, with small overall effects. However, the effects differed by several demographic, clinical, personal, and intervention‐related characteristics. Our study highlights the beneficial effects of coping skills training in patients treated with chemotherapy, the importance of targeted interventions, and the need of developing interventions tailored to the specific needs of elderly patients.

BackgroundEffective interventions to improve quality of life of cancer survivors are essential. Numerous randomized controlled trials have evaluated the effects of physical activity or psychosocial interventions on health-related quality of life of cancer survivors, with generally small sample sizes and modest effects. Better targeted interventions may result in larger effects. To realize such targeted interventions, we must determine which interventions that are presently available work for which patients, and what the underlying mechanisms are (that is, the moderators and mediators of physical activity and psychosocial interventions). Individual patient data meta-analysis has been described as the ‘gold standard’ of systematic review methodology. Instead of extracting aggregate data from study reports or from authors, the original research data are sought directly from the investigators. Individual patient data meta-analyses allow for adequate statistical analysis of intervention effects and moderators of such effects.Here, we report the rationale and design of the Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) Consortium. The primary aim of POLARIS is 1) to conduct meta-analyses based on individual patient data to evaluate the effect of physical activity and psychosocial interventions on the health-related quality of life of cancer survivors; 2) to identify important demographic, clinical, personal, or intervention-related moderators of the effect; and 3) to build and validate clinical prediction models identifying the most relevant predictors of intervention success.Methods/DesignWe will invite investigators of randomized controlled trials that evaluate the effects of physical activity and/or psychosocial interventions on health-related quality of life compared with a wait-list, usual care or attention control group among adult cancer survivors to join the POLARIS consortium and share their data for use in pooled analyses that will address the proposed aims. We are in the process of identifying eligible randomized controlled trials through literature searches in four databases. To date, we have identified 132 eligible and unique trials.DiscussionThe POLARIS consortium will conduct the first individual patient data meta-analyses in order to generate evidence essential to targeting physical activity and psychosocial programs to the individual survivor’s characteristics, capabilities, and preferences.RegistrationPROSPERO: International prospective register of systematic reviews, CRD42013003805

Beneficial effects of exercise on global QoL and physical function in cancer survivors were mediated by increased cardiorespiratory fitness and subsequent reductions in fatigue.

PurposeThis study explored demographic, clinical, and psychological moderators of the effect of a group-based physical exercise intervention on global quality of life (QoL) among cancer survivors who completed treatment.MethodsCancer survivors were assigned to a 12-week physical exercise (n = 147) or a wait-list control group (n = 62). The main outcome measure was global QoL, assessed with the EORTC QLQ-C30 at baseline and 12 weeks later. Potential moderators were age, gender, education level, marital status, employment status, type of treatment, time since treatment, the presence of comorbidities, fatigue, general self-efficacy, depression, and anxiety. Linear regression analyses were used to test effect modification of the intervention by each moderator variable using interaction tests (p ≤ 0.10).ResultsThe physical exercise intervention effect on global QoL was larger for cancer survivors who received radiotherapy (β = 10.3, 95 % confidence interval (CI) = 4.4; 16.2) than for cancer survivors who did not receive radiotherapy (β = 1.8, 95 % CI = −5.9; 9.5, pinteraction = 0.10), larger for cancer survivors who received a combination of chemoradiotherapy (β = 13.0, 95 % CI = 6.0; 20.1) than for those who did not receive this combination of treatments (β = 2.5, 95 % CI = −3.7; 8.7, pinteraction = 0.02), and larger for cancer survivors with higher baseline levels of fatigue (β = 12.6, 95 % CI = 5.7; 19.6) than for those with lower levels (β = 2.4, 95 % CI = −3.9; 8.7, pinteraction = 0.03). No other moderating effects were found.ConclusionsThis study suggests that cancer treatment modality and baseline fatigue levels moderate the effect of a physical exercise program on cancer survivors’global QoL.

BackgroundChronic low back pain (LBP) is the leading cause of pain and disability, resulting in a major socioeconomic impact. The Cochrane Review which examined the effect of spinal manipulative therapy (SMT) for chronic LBP concluded that SMT is moderately effective, but was based on conventional meta-analysis of aggregate data. The use of individual participant data (IPD) from trials allows for a more precise estimate of the treatment effect and has the potential to identify moderators and/or mediators. The aim is (1) to assess the overall treatment effect of SMT for primary and secondary outcomes in adults with chronic LBP, (2) to determine possible moderation of baseline characteristics on treatment effect, (3) to identify characteristics of intervention (e.g., manipulation/mobilization) that influence the treatment effect, and (4) to identify mediators of treatment effects.MethodsAll trials included in the Cochrane Review on SMT for chronic LBP will be included which were published after the year 2000, and the search will be updated. No restrictions will be placed on the type of comparison or size of the study. Primary outcomes are pain intensity and physical functioning. A dataset will be compiled consisting of individual trials and variables included according to a predefined coding scheme. Variables to be included are descriptive of characteristics of the study, treatment, comparison, participant characteristics, and outcomes at all follow-up periods. A one-stage approach with a mixed model technique based on the intention-to-treat principle will be used for the analysis. Subsequent analyses will focus on treatment effect moderators and mediators.DiscussionWe will analyze IPD for LBP trials in which SMT is one of the interventions. IPD meta-analysis has been shown to be more reliable and valid than aggregate data meta-analysis, although this difference might also be attributed to the number of studies that can be used or the amount of data that can be utilized. Therefore, this project may identify important gaps in our knowledge with respect to prognostic factors of treatment effects.Systematic review registration:PROSPERO CRD42015025714 Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-017-0413-y) contains supplementary material, which is available to authorized users.

BackgroundThe utilisation of available cross-European data for secondary data analyses on physical activity, sedentary behaviours and their underlying determinants may benefit from the wide variation that exists across Europe in terms of these behaviours and their determinants. Such reuse of existing data for further research requires Findable; Accessible; Interoperable; Reusable (FAIR) data management and stewardship. We here describe the inventory and development of a comprehensive European dataset compendium and the process towards cross-European secondary data analyses of pooled data on physical activity, sedentary behaviour and their correlates across the life course.MethodsA five-step methodology was followed by the European Determinants of Diet and Physical Activity (DEDIPAC) Knowledge Hub, covering the (1) identification of relevant datasets across Europe, (2) development of a compendium including details on the design, study population, measures and level of accessibility of data from each study, (3) definition of key topics and approaches for secondary analyses, (4) process of gaining access to datasets and (5) pooling and harmonisation of the data and the development of a data harmonisation platform.ResultsA total of 114 unique datasets were found for inclusion within the DEDIPAC compendium. Of these datasets, 14 were eventually obtained and reused to address 10 exemplar research questions. The DEDIPAC data harmonisation platform proved to be useful for pooling, but in general, harmonisation was often restricted to just a few core (crude) outcome variables and some individual-level sociodemographic correlates of these behaviours.ConclusionsObtaining, pooling and harmonising data for secondary data analyses proved to be difficult and sometimes even impossible. Compliance to FAIR data management and stewardship principles currently appears to be limited for research in the field of physical activity and sedentary behaviour. We discuss some of the reasons why this might be the case and present recommendations based on our experience.