Monitoring work-related physical activity and estimating lower-limb loading: a proof-of-concept study.

BACKGROUND: Physical activity (PA) is important to general health and knee osteoarthritis (OA). Excessive workplace PA is an established risk factor for knee OA however, appropriate methods of measurement are unclear. There is a need to examine and assess the utility of new methods of measuring workplace PA and estimating knee load prior to application to large-scale, knee OA cohorts. Our aims, therefore, were to monitor workplace PA and estimate lower-limb loading across different occupations in health participants. METHODS: Twenty-four healthy adults, currently working full-time in a single occupation (≥ 35 h/week) and free of musculoskeletal disease, comorbidity and had no history of lower-limb injury/surgery (past 12-months) were recruited across New South Wales (Australia). A convenience sample was recruited with occupations assigned to levels of workload; sedentary, light manual and heavy manual. Metrics of workplace PA including tasks performed (i.e., sitting), step-count and lower-limb loading were monitored over 10 working days using a daily survey, smartwatch, and a smartphone. RESULTS: Participants of light manual occupations had the greatest between-person variations in mean lower-limb load (from 2 to 59 kg*m/s3). Lower-limb load for most participants of the light manual group was similar to a single participant in heavy manual work (30 kg*m/s3) and was at least three times greater than the sedentary group (2 kg*m/s3). The trends of workplace PA over working hours were largely consistent, per individual, but rare events of extreme loads were observed across all participants (up to 760 kg*m/s3). CONCLUSIONS: There are large interpersonal variations in metrics of workplace PA, particularly among light and heavy manual occupations. Our estimates of lower-limb loading were largely consistent with pre-conceived levels of physical demand. We present a new approach to monitoring PA and estimating lower-limb loading, which could be applied to future occupational studies of knee OA.

Validity of load rate estimation using accelerometers during physical activity on an anti-gravity treadmill.

INTRODUCTION: A simple tool to estimate loading on the lower limb joints outside a laboratory may be useful for people who suffer from degenerative joint disease. Here, the accelerometers on board of wearables (smartwatch, smartphone) were used to estimate the load rate on the lower limbs and were compared to data from a treadmill force plate. The aim was to assess the validity of wearables to estimate load rate transmitted through the joints. METHODS: Twelve healthy participants (female n = 4, male n = 8; aged 26 ± 3 years; height: 175 ± 15 cm; body mass: 71 ± 9 kg) carried wearables, while performing locomotive activities on an anti-gravity treadmill with an integrated force plate. Acceleration data from the wearables and force plate data were used to estimate the load rate. The treadmill enabled 7680 data points to be obtained, allowing a good estimate of uncertainty to be examined. A linear regression model and cross-validation with 1000 bootstrap resamples were used to assess the validation. RESULTS: Significant correlation was found between load rate from the force plate and wearables (smartphone: R 2 = 0.71 ; smartwatch: R 2 = 0.67 ). CONCLUSION: Wearables' accelerometers can estimate load rate, and the good correlation with force plate data supports their use as a surrogate when assessing lower limb joint loading in field environments.

Black-box calibration for complex-system simulation.

Predicting or measuring the output of complex systems is an important and challenging part of many areas of science. If multiple observations are required for parameter studies and optimization, accurate, computationally intensive predictions or expensive experiments are intractable. This paper looks at the use of Gaussian-process-based correlations to correct simple computer models with sparse data from physical experiments or more complex computer models. In essence, physics-based computer codes and experiments are replaced by fast problem-specific statistics-based codes. Two aerodynamic design examples are presented. First, a cheap two-dimensional potential-flow solver is calibrated to represent the flow over the wing of an unmanned air vehicle. The rear wing of a racing car is then optimized using rear-wing simulations calibrated to include the effects of the flow over the whole car.

The influence of strut-connectors in stented vessels: a comparison of pulsatile flow through five coronary stents.

The design of coronary stents has evolved significantly over the past two decades. However, they still face the problem of in-stent restenosis, formation of neointima within 12 months of the implant. The biological response after stent implantation depends on various factors including the stent geometry which alters the hemodynamics. This study takes five different coronary stent designs, used in clinical practice, and explores the hemodynamic differences arising due to the difference in their design. Of particular interest is the design of the segments (connectors) that connect two struts. Pulsatile blood flow analysis is performed for each stent, using 3-D computational fluid dynamics (CFD), and various flow features viz. recirculation zones, velocity profiles, wall shear stress (WSS) patterns, and oscillatory shear indices are extracted for comparison. Vessel wall regions with abnormal flow features, particularly low, reverse, and oscillating WSS, are usually more susceptible to restenosis. Unlike previous studies, which have tried to study the effect of design parameters such as strut thickness and strut spacing on hemodynamics, this work investigates the differences in the flow arising purely due to differences in stent-shape, other parameters being similar. Two factors, the length of the connectors in the cross-flow direction and their alignment with the main flow, are found to affect the hemodynamic performance. This study also formulates a design index (varying from 18.81% to 24.91% for stents used in this study) that quantifies the flow features that could affect restenosis rates and which, in future, could be used for optimization studies.