Background: Step count is an intuitive measure of physical activity frequently quantified in a range of health-related studies; however, accurate quantification of step count can be difficult in the free-living environment, with step counting error routinely above 20% in both consumer and research-grade wrist-worn devices. This study aims to describe the development and validation of step count derived from a wrist-worn accelerometer, and to assess its association with cardiovascular and all-cause mortality in a large prospective cohort study. Methods: We developed and externally validated a hybrid step detection model that involves self-supervised machine learning, trained on a new ground truth annotated, free-living step count dataset (OxWalk, n=39, aged 19 81) and tested against other open-source step counting algorithms. This model was applied to ascertain daily step counts from raw wrist-worn accelerometer data of 75,493 UK Biobank participants without a prior history of cardiovascular disease (CVD) or cancer. Cox regression was used to obtain hazard ratios and 95% confidence intervals for the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders. Findings: The novel step algorithm demonstrated a mean absolute percent error of 12.5% in free-living validation, detecting 98.7% of true steps and substantially outperforming other recent wrist-worn, open-source algorithms. Our data are indicative of an inverse dose-response association, where, for example, taking 6,596 to 8,474 steps per day was associated with a 39% [24-52%] and 27% [16 36%] lower risk of fatal CVD and all-cause mortality, respectively, compared to those taking fewer steps each day. Interpretation: An accurate measure of step count was ascertained using a machine learning pipeline that demonstrates state-of-the-art accuracy in internal and external validation. The expected associations with CVD and all-cause mortality indicate excellent face validity. This algorithm can be used widely for other studies that have utilised wrist-worn accelerometers and an open-source pipeline is provided to facilitate implementation.
Background With more complex primary and revision total knee arthroplasty procedures there is often the need to use more constrained prostheses. This study aims to investigate patient-relevant outcomes following primary and revision rotating-hinged total knee arthroplasty. Methods Electronic searches were performed using four databases from their date of inception to January 2021. Relevant studies were identified, with data extracted and analysed using PRIMSA guidelines. Results Nineteen studies were included, producing a cohort of 568 primary and 413 revision rotating hinge total knee arthroplasties (TKAs). Survival was assessed at 1-, 5-, and 10-year post-implantation. Sensitivity analyses based on person-time incidence ratios (PTIRs) were prespecified for studies not reporting survival at these timepoints. From the primary hinge TKA cohort, the median survival at 1 year was 93.4% and at 10 years it was 87%. The PTIR at long-term follow-up of this primary cohort was 1.07 (95% CI 0.4–1.7) per 100 person-years. From the revision hinge TKA cohort, the median survival at 1 year was 79.6%, and at 10 years it was 65.1%. The PTIR at long term-follow-up of this revision cohort was 1.55 (95% CI 0.9–2.3) per 100 person-years. Post-operative flexion range of motion (ROM) was 110° for primary hinge TKA and 103° for revision hinge TKA. Compared with baseline, the Knee Society Score (KSS) and Knee Society Function Score (KSFS) improved for both groups post-operatively (primary: KSS 17 to 86, KSFS 28 to 58; revision: KSS 37 to 82, KSFS 34 to 61). Conclusion The quality of the evidence for patient-relevant outcomes following hinged knee arthroplasty was limited. While there is the potential for high early revision rates, where successful, large functional benefits may be achieved.
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