Assessing the feasibility of a sensor-rich gait wearable and advanced behavioural science platform to improve care and rehabilitation outcomes for knee arthroplasty patients

评估富含传感器的步态可穿戴和先进行为科学平台的可行性，以改善膝关节置换术患者的护理和康复结果

• 批准号: 10038058
• 金额: $ 48.95万
• 依托单位: WALK WITH PATH LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10038058
• 关键词: Assessing; feasibility; sensor; rich; gait

Over 125,400 total KAs (TKAs) are carried out in the UK each years, costing the NHS ~£812M pa. Driven in part by an aging population and increase in conditions such as Rheumatoid arthritis (RA), Parkinson's Disease (PD) and Osteoarthritis (OA), the last 10 years have seen a 39% increase in cost, and a 33% increase in surgery delays, dramatically impacting patient's mobility, ability to stay active and quality of life.Path Insight (PIn), is an advanced at-home gait monitoring system that combines a unique and patented, sensor-rich insoles and patient-adapting machine learning to accurately map, metricate and baseline a persons gait symmetry, pronation, supination, pressure distribution, balance, falls risk and significant changes in activity, amongst a range of other clinically accurate metrics.From this, our platform can infer to high-degree of accuracy (validated) a number of clinically-interesting health indicators and biomarkers such as mobility, activity, falls risk assessment and measuring disease progression or therapeutic impact.From this, patients can be stratified and prioritised for surgery or physiotherapy much more accurately, rather than it being on 'first-come, first-serve' basis currently, which leads to delays, creates regional and social inequalities and damages QoL for patients. Target NHS saving = £177m pa (~23% KA-NHS spend;2021).

在英国，每年进行的总KA(TKA)超过12.54万KA，花费NHS~GB 8.12亿PA。部分受人口老龄化和类风湿性关节炎(RA)、帕金森氏病(PD)和骨关节炎(OA)等疾病增加的推动，在过去10年中，成本增加了39%，手术延迟增加了33%，极大地影响了患者的行动能力、保持活动的能力和生活质量。Path Insight(PIN)是一种先进的家庭步态监测系统，它结合了独特的、具有丰富传感器的鞋垫和患者适应的机器学习，以准确绘制、度量和基线一个人的步态对称、旋前、旋后、压力分布、平衡、跌倒风险和活动方面的重大变化，由此，我们的平台可以推断(验证)许多具有临床意义的健康指标和生物标记物，如活动能力、活动、跌倒风险评估和测量疾病进展或治疗效果。由此，可以更准确地对患者进行分层和优先进行手术或物理治疗，而不是目前的“先到先得”的基础，这会导致延误，造成地区和社会不平等，并损害患者的生活质量。NHS节省目标=1.77亿GB pa(约23%KA-NHS支出；2021年)。