Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study

BACKGROUND: Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season. METHODS: Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season. RESULTS: 103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5-7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen's d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small. CONCLUSIONS: It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.The UK Biobank Activity Project and the collection of activity data from participants was funded by the Wellcome Trust (https://wellcome.ac.uk/) and the Medical Research Council (http://www.mrc.ac.uk/). The analysis was supported by the British Heart Foundation Centre of Research Excellence at Oxford (http://www.cardioscience.ox.ac.uk/bhf-centre-of-research-excellence) [grant number RE/13/1/30181 to AD], the Li Ka Shing Foundation (http://www.lksf.org/) [to AD], the UK Medical Research Council (http://www.mrc.ac.uk/) [grant numbers MC_UU_12015/1 and MC_UU_12015/3 to NW and SB], the RCUK Digital Economy Research Hub on Social Inclusion through the Digital Economy (SiDE) (http://www.rcuk.ac.uk/) [EP/G066019/1 to NH], the EPSRC Centre for Doctoral Training in Digital Civics (https://www.epsrc.ac.uk/)[EP/L016176/1 to DJ], and the National Institute for Health Research (http://www.nihr.ac.uk/) [SRF-2011-04-017 to MIT]. The MRC and Wellcome Trust played a key role in the decision to establish UK Biobank, and the accelerometer data collection. No funding bodies had any role in the analysis, decision to publish, or preparation of the manuscript

A randomised trial of observational learning from 2D and 3D models in robotically assisted surgery

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Advances in 3D technology mean that both robotic surgical devices and surgical simulators can now incorporate stereoscopic viewing capabilities. While depth information may benefit robotic surgical performance, it is unclear whether 3D viewing also aids skill acquisition when learning from observing others. As observational learning plays a major role in surgical skills training, this study aimed to evaluate whether 3D viewing provides learning benefits in a robotically assisted surgical task. METHODS: 90 medical students were assigned to either (1) 2D or (2) 3D observation of a consultant surgeon performing a training task on the daVinci S robotic system, or (3) a no observation control, in a randomised parallel design. Subsequent performance and instrument movement metrics were assessed immediately following observation and at one-week retention. RESULTS: Both 2D and 3D groups outperformed no observation controls following the observation intervention (ps < 0.05), but there was no difference between 2D and 3D groups at any of the timepoints. There was also no difference in movement parameters between groups. CONCLUSIONS: While 3D viewing systems may have beneficial effects for surgical performance, these results suggest that depth information has limited utility during observational learning of surgical skills in novices. The task constraints and end goals may provide more important information for learning than the relative motion of surgical instruments in 3D space.This research was supported by an Intuitive Surgical grant awarded to Dr G Buckingha