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Objectives: Physical activity is promoted in patients with hip or knee osteoarthritis (OA), yet little is known about its relationship with symptoms, functional limitations and Quality of Life (QoL). We investigated if OA-associated pain, functional limitations and QoL are associated with objectively measured physical activity in patients with end-stage hip/knee OA.Methods: Cross-sectional study including patients scheduled for primary total hip/knee arthroplasty. Patients wore an accelerometer (Activ8) with physical activity assessed over waking hours, and expressed as number of activity daily counts (ADC) per hour, %time spent on physical activity i.e. walking, cycling or running (%PA), and %time spent sedentary (%SB). Pain, functional limitations and joint-specific and general QoL were assessed with the Hip disability/Knee Injury and Osteoarthritis Outcome Score (HOOS/KOOS) and the Short Form (SF)-12. Multivariate linear regression models with the three to Z-scores transformed parameters of physical activity as dependent variables and adjusted for confounding, were conducted. Results: 49 hip and 48 knee OA patients were included. In hip and knee OA patients the mean number of ADC, %PA and %SB were 18.79 +/- 7.25 and 21.19 +/- 6.16, 14 +/- 6.4 and 15 +/- 5.0, and 66 +/- 10.5 and 68 +/- 8.7, respectively. In hip OA, better joint-specific and general QoL were associated with more ADC, (13 0.028; 95%CI:0.007-0.048, 130.041; 95%CI:0.010- 0.071). Also, better general QoL was associated with the %PA (13 0.040, 95%CI:0.007- 0.073). No other associations were found.Conclusion: Whereas QoL was associated with physical activity in hip OA, pain and functional limitations were not related to objectively measured physical activity in patients with end-stage hip or knee OA.(c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Summary There are many challenges associated with ground modelling studies; as with any other sub-surface modelling study, there are a plethora of multi-disciplinary data that characterise the sub-surface, including geophysics, geology, GIS/geospatial, geotechnical. All must be incorporated to create an integrated, holistic ground model. As offshore wind turbines increase in size to yield greater output, the areal extent of wind farm projects grows accordingly, creating large shallow sub-surface areas that must be characterised. Large offshore wind farm developments require significant investment decisions at early stages, at which point there may be limited geotechnical information available. One key challenge affecting offshore wind farm teams is disparate data and disjointed applications, creating siloed outputs and reducing productivity of data, people and capital. Leapfrog can create a ground model at the inception of a wind farm project which can be automatically updated in a repeatable and standardised way as more data is acquired. Combining an automated modelling workflow with cloud-hosted data and model management enables geotechnical teams to collaborate around a single source of truth and track temporal changes throughout projects. This paper presents an integrated ground modelling workflow using multi-disciplinary data from the Hollandse Kust Zuid Wind Farm by the Netherlands Enterprise Agency.