IntroductionWhile some evidence exists that real-time remote symptom monitoring devices can decrease morbidity and prevent unplanned admissions in oncology patients, overall, these studies have significant methodological weaknesses. The electronic Symptom Management using the Advanced Symptom Management System (ASyMS) Remote Technology (eSMART) study is designed to specifically address these weaknesses with an appropriately powered, repeated-measures, parallel-group stratified randomised controlled trial of oncology patients.Methods and analysisA total of 1108 patients scheduled to commence first-line chemotherapy (CTX) for breast, colorectal or haematological cancer will be recruited from multiple sites across five European countries.Patients will be randomised (1:1) to the ASyMS intervention (intervention group) or to standard care currently available at each site (control group). Patients in the control and intervention groups will complete a demographic and clinical questionnaire, as well as a set of valid and reliable electronic patient-reported outcome measures at enrolment, after each of their CTX cycles (up to a maximum of six cycles) and at 3, 6, 9 and 12 months after completion of their sixth cycle of CTX. Outcomes that will be assessed include symptom burden (primary outcome), quality of life, supportive care needs, anxiety, self-care self-efficacy, work limitations and cost effectiveness and, from a health professional perspective, changes in clinical practice (secondary outcomes).Ethics and disseminationEthical approval will be obtained prior to the implementation of all major study amendments. Applications will be submitted to all of the ethics committees that granted initial approval.eSMART received approval from the relevant ethics committees at all of the clinical sites across the five participating countries. In collaboration with the European Cancer Patient Coalition (ECPC), the trial results will be disseminated through publications in scientific journals, presentations at international conferences, and postings on the eSMART website and other relevant clinician and consumer websites; establishment of an eSMART website (www.esmartproject.eu) with publicly accessible general information; creation of an eSMART Twitter Handle, and production of a toolkit for implementing/utilising the ASyMS technology in a variety of clinical practices and other transferable health care contexts.Trial registration numberNCT02356081.

The 5th EORTC Quality of Life in Cancer Clinical Trials Conference presented the current state of quality of life and other patient-reported outcomes (PROs) research from the perspectives of researchers, regulators, industry representatives, patients and patient advocates and health care professionals. A major theme was the assessment of the burden of cancer treatments, and this was discussed in terms of regulatory challenges in using PRO assessments in clinical trials, patients' experiences in cancer clinical trials, innovative methods and standardisation in cancer research, innovative methods across the disease sites or populations and cancer survivorship. Conferees demonstrated that PROs are becoming more accepted and major efforts are ongoing internationally to standardise PROs measurement, analysis and reporting in trials. Regulators are keen to collaborate with all stakeholders to ensure that the right questions are asked and the right answers are communicated. Improved technology and increased flexibility of measurement instruments are making PROs data more

No abstract

A comprehensive translational cancer research approach focused on personalized and precision medicine, and covering the entire cancer researchcare-prevention continuum has the potential to achieve in 2030 a 10-year cancer-specific survival for 75% of patients diagnosed in European Union (EU) member states with a well-developed healthcare system. Concerted actions across this continuum that spans from basic and preclinical research through clinical and prevention research to outcomes research, along with the establishment of interconnected high-quality infrastructures for translational research, clinical and prevention trials and outcomes research, will ensure that science-driven and social innovations benefit patients and individuals at risk across the EU. European infrastructures involving comprehensive cancer centres (CCCs) and CCC-like entities will provide researchers with access to the required critical mass of patients, biological materials and technological resources and can bridge research with healthcare systems. Here, we prioritize research areas to ensure a balanced research portfolio and provide recommendations for achieving key targets. Meeting these targets will require harmonization of EU and national priorities and policies, improved research coordination at the national, regional and EU level and increasingly efficient and flexible funding mechanisms. Long-term support by the EU and commitment of Member States to specialized schemes are also needed for the establishment and sustainability of trans-border infrastructures and networks. In addition to effectively engaging policymakers, all relevant stakeholders within the entire continuum should consensually inform policy through evidencebased advice.

Context: Risk profiling of oncology patients based on their symptom experience assists clinicians to provide more personalized symptom management interventions. Recent findings suggest that oncology patients with distinct symptom profiles can be identified using a variety of analytic methods. Objectives:To evaluate the concordance between the number and types of subgroups of patients with distinct symptom profiles using latent class analysis (LCA) and K-modes analysis.Methods: Using data on the occurrence of 25 symptoms from the Memorial Symptom Assessment Scale (MSAS), that 1329 patients completed prior to their next dose of chemotherapy (CTX), Cohen's kappa coefficient was used to evaluate for concordance between the two analytic methods. For both LCA and K-modes, differences among the subgroups in demographic, clinical, and symptom characteristics, as well as quality of life outcomes were determined using parametric and nonparametric statistics.Results: Using both analytic methods, four subgroups of patients with distinct symptom profiles were identified (i.e., All Low, Moderate Physical and Lower Psychological, Moderate Physical and Higher Psychological, All High). The percent agreement between the two methods was 75.32% which suggests a moderate level of agreement. In both analyses, patients in the All High group were significantly younger and had a higher comorbidity profile, worse MSAS subscale scores, and poorer QOL outcomes. Conclusion:Both analytic methods can be used to identify subgroups of oncology patients with distinct symptom profiles. Additional research is needed to determine which analytic methods and which dimension of the symptom experience provides the most sensitive and specific risk profiles.