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Whilst existing quantitative research identifies outcomes believed to be important by researchers and clinicians, it may neglect outcomes that are meaningful to patients. This study aimed to explore the outcomes of exercise that are important to people with lung cancer and their carers. Data collection involved a qualitative methodology including semi-structured interviews and focus groups. Question guide development was informed by the International Classification of Functioning (ICF) framework. Data were analyzed by two researchers with NVivo (v12) software using a conventional content analysis process, followed by directed content analysis to map outcomes to the ICF. Conduct and reporting adhered to COREQ guidelines. Fifteen participants provided data. Most participants had received their diagnoses 24 months prior to study involvement ( n = 9), and one-third had completed treatment ( n = 5). Important outcomes were reported by participants across all domains of the ICF: activity and participation ( n = 24), body function ( n = 19), body structure ( n = 5), environmental factors ( n = 5), and personal factors ( n = 1). Additional code categories pertained to the impacts of non-cancer factors such as age, frailty, and comorbidities; identifying barriers to exercise; and individualizing outcome measures. Clinicians and researchers should consider selecting outcomes from all relevant domains of the ICF, with a focus on the activity and participation domain, in addition to non-cancer factors such as ageing, frailty, and co-morbidities. Feedback should be provided to patients following outcome measures collection and reassessment.

Liquid-chromatography mass spectrometry is commonly used to identify and quantify metabolites from biological samples to gain insight into human physiology and pathology. Metabolites and their abundance in biological samples are labile and sensitive to variations in collection conditions, handling and processing. Variations in sample handling could influence metabolite levels in ways not related to biology, ultimately leading to the misinterpretation of results. For example, anticoagulants and preservatives modulate enzyme activity and metabolite oxidization. Temperature may alter both enzymatic and non-enzymatic chemistry. The potential for variation induced by collection conditions is particularly important when samples are collected in remote locations without immediate access to specimen processing. Data are needed regarding the variation introduced by clinical sample collection processes to avoid introducing artifact biases. In this study, we used metabolomics and lipidomics approaches paired with univariate and multivariate statistical analyses to assess the effects of anticoagulant, temperature, and time on healthy human plasma samples collected to provide guidelines on sample collection, handling, and processing for vaccinology. Principal component analyses demonstrated clustering by sample collection procedure and that anticoagulant type had the greatest effect on sample metabolite variation. Lipids such as glycerophospholipids, acylcarnitines, sphingolipids, diacylglycerols, triacylglycerols, and cholesteryl esters are significantly affected by anticoagulant type as are amino acids such as aspartate, histidine, and glutamine. Most plasma metabolites and lipids were unaffected by storage time and temperature. Based on this study, we recommend samples be collected using a single anticoagulant (preferably EDTA) with sample processing at <24 h at 4 °C.