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BackgroundSedentary behavior is defined as any waking behavior characterized by an energy expenditure of 1.5 METS or less while in a sitting or reclining posture. This study examines this definition by assessing the energy cost (METs) of common sitting, standing and walking tasks.MethodsFifty one adults spent 10 min during each activity in a variety of sitting tasks (watching TV, Playing on the Wii, Playing on the PlayStation Portable (PSP) and typing) and non-sedentary tasks (standing still, walking at 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, and 1.6 mph). Activities were completed on the same day in a random order following an assessment of resting metabolic rate (RMR). A portable gas analyzer was used to measure oxygen uptake, and data were converted to units of energy expenditure (METs).ResultsAverage of standardized MET values for screen-based sitting tasks were: 1.33 (SD: 0.24) METS (TV), 1.41 (SD: 0.28) (PSP), and 1.45 (SD: 0.32) (Typing). The more active, yet still seated, games on the Wii yielded an average of 2.06 (SD: 0.5) METS. Standing still yielded an average of 1.59 (SD: 0.37) METs. Walking MET values increased incrementally with speed from 2.17 to 2.99 (SD: 0.5 - 0.69) METs.ConclusionsThe suggested 1.5 MET threshold for sedentary behaviors seems reasonable however some sitting based activities may be classified as non-sedentary. The effect of this on the definition of sedentary behavior and associations with metabolic health needs further investigation.

The findings suggest that introducing a sit-to-stand workstation can significantly reduce sedentary time and increase light activity levels during working hours. However, these changes were compensated for by reducing activity and increasing sitting outside of working hours. An intervention of a sit-to-stand workstation should be accompanied by an intervention outside of working hours to limit behavior compensation.

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Back pain is a common form of disability worldwide, and one condition that causes chronic back pain is axial spondyloarthritis (axSpA) which primarily affects spinal joints resulting in pain and joint stiffness. Markerless human motion analysis uses a computer-vision (CV) aided system to automate human movement from videos. In this protocol, the study will aim to estimate criterion validity and reliability of functional movement measurement using a CV-aided system by comparing it to a standard clinical measurement; secondarily, to assess the feasibility of the CV-aided system in the lab and home environments. An index of tests of functional movement, range of motion and posture will be captured on video and measured using the CV-aided system in the lab and home environments. The index of tests will be compared to measurement performed by an experienced physiotherapist. Bland-Altman plots will be used to determine agreement between the methods, and reliability and completion rates will be used to determine the feasibility of the CV-aided system.

Physical Activity and Fatigue in Multiple Sclerosis: Secondary Outcomes from a Double-blinded Randomized Controlled Trial of Cocoa Flavonoid Drinks Maedeh Mansoubi()1,2, Shelly Coe1,2,3, Jo Cossington1, Johnny Collet1,2, Miriam Clegg4, Jacqueline Palace5, Ana Cavey5, Gabriele C DeLuca5, Martin Ovington1 and Helen Dawes1,2,6  1Center for Movement, Occupational and Rehabilitation Sciences, Oxford Institute of Nursing, Midwifery and Allied Health Research, Oxford Brookes University, Oxford, United Kingdom 2Oxford Clinical Allied Technology and Trial Services Unit (OxCATTS), Oxford, United Kingdom 3Oxford Brookes Center for Nutrition and Health, Oxford Brookes University, Oxford, United Kingdom 4Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences, University of Reading, Reading, United Kingdom 5Department of Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom 6Oxford Health NHS Foundation Trust, United Kingdom © The Authors   Abstract Fatigue is a common and pervasive symptom reducing physical activity in people with multiple sclerosis (pwMS). Exercise may reduce fatigue, although evidence to guide optimal prescription is limited. Specifically, supportive evidence for the timing of exercise for fatigue management or the impact of dietary supplements is unavailable. We performed intensive phenotyping of the interrelation of time of day, physical activity levels, and fatigue to evidence exercise prescription in 40 pwMS participating in a six week randomized controlled trial of morning flavonoid intake (n=19) or a control (n=21). Physical activity was measured over seven days by using an accelerometer at baseline, week three and week six. Participants self-reported their fatigue on a 1–10 rating scale at 10 am, 3 pm, and 8 pm daily. Physical activity levels were calculated for 2.5 h before and after fatigue was reported. Generalized estimating equations were used to explore the time of day fatigue profiles, the relationship of physical activity to fatigue, and the effect of morning flavonoids on this relationship. Participants experienced higher fatigue at 8 pm (4.64±2.29) than at 3 pm (4.39±2.28) and 10 am (3.90±2.10) (P<0.001). Higher fatigue was shown to predict subsequent lower physical activity behavior (P=0.015), but physical activity did not predict higher subsequent fatigue (P>0.05). Morning flavonoid cocoa consumption reduced the relationship of fatigue to physical activity (P=0.049) and fatigue to time of the day (P<0.001). Fatigue levels increased during the day and higher fatigue reduced physical activity in pwMS, but physical activity did not increase fatigue. In addition, morning cocoa reduced daytime fatigue and the relationship of fatigue to subsequent physical activity levels. Therefore morning exercise prescription is indicated; in combination with dietary flavonoids, it may optimize exercise and physical activity potential in pwMS. Trial registration: ISRCTN69897291, https://doi.org/10.1186/ISRCTN69897291 Registration name: A study to determine whether the daily consumption of flavonoid-rich pure cocoa has the potential to reduce fatigue in people with relapsing-remitting multiple sclerosis (RRMS). Consort Statement: In this study, we adhered to CONSORT guidelines. As this paper is a secondary analysis, we therefore did not repeat some parts in the methods, results, diagrams, or tables that have been published in the first paper authored by Coe et al. 2019.

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Background The number of wearable technological devices or sensors that are commercially available for gait training is increasing. These devices can fill a gap by extending therapy outside the clinical setting. This was shown to be important during the COVID-19 pandemic when people could not access one-on-one treatment. These devices vary widely in terms of mechanisms of therapeutic effect, as well as targeted gait parameters, availability, and strength of the evidence supporting the claims. Objective This study aimed to create an inventory of devices targeting improvement in gait pattern and walking behavior and identify the strength of the evidence underlying the claims of effectiveness for devices that are commercially available to the public. Methods As there is no systematic or reproducible way to identify gait training technologies available to the public, we used a pragmatic, iterative approach using both the gray and published literature. Four approaches were used: simple words, including some suggested by laypersons; devices endorsed by condition-specific organizations or charities; impairment-specific search terms; and systematic reviews. A findable list of technological devices targeting walking was extracted separately by 3 authors. For each device identified, the evidence for efficacy was extracted from material displayed on the websites, and full-text articles were obtained from the scientific databases PubMed, Ovid MEDLINE, Scopus, or Google Scholar. Additional information on the target population, mechanism of feedback, evidence for efficacy or effectiveness, and commercial availability was obtained from the published material or websites. A level of evidence was assigned to each study involving the device using the Oxford Centre for Evidence-Based Medicine classification. We also proposed reporting guidelines for the clinical appraisal of devices targeting movement and mobility. Results The search strategy for this consumer-centered review yielded 17 biofeedback devices that claim to target gait quality improvement through various sensory feedback mechanisms. Of these 17 devices, 11 (65%) are commercially available, and 6 (35%) are at various stages of research and development. Of the 11 commercially available devices, 4 (36%) had findable evidence for efficacy potential supporting the claims. Most of these devices were targeted to people living with Parkinson disease. The reporting of key information about the devices was inconsistent; in addition, there was no summary of research findings in layperson’s language. Conclusions The amount of information that is currently available to the general public to help them make an informed choice is insufficient, and, at times, the information presented is misleading. The evidence supporting the effectiveness does not cover all aspects of technology uptake. Commercially available technologies help to provide continuity of therapy outside the clinical setting, but there is a need to demonstrate effectiveness to support claims made by the technologies.