Gholamhosseini Hamid, Baig Mirza, Maratas Joseph, Mirza Farhaan, Lindén Maria
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand.
School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
Stud Health Technol Inform. 2019;261:91-96.
There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions such as obesity is increasing to the point that requires effective interventions and advancements to reduce the burden of the healthcare. This research focuses on the early risk assessment of overweight/obesity using wearable technology. We establish an individualised health profile that identifies the level of activity and current health status of an individual using real-time activity and vital signs. We developed an algorithm to assess the risk of obesity using the individual's current activity and calorie expenditure. The algorithm was deployed on a smartphone application to collect wearable device data, and user reported data. Based on the collected data, the proposed application assesses the risk of obesity/overweight, measures the current activity level and recommends an optimized calorie plan.
全球肥胖率及其相关长期病症呈上升趋势，这凸显了立即应对这一现代全球健康生活流行病的必要性。此外，用于肥胖等长期或慢性护理病症的医疗保健支出不断增加，已达到需要有效干预和进步以减轻医疗负担的程度。本研究聚焦于使用可穿戴技术对超重/肥胖进行早期风险评估。我们建立了一个个性化健康档案，通过实时活动和生命体征来确定个人的活动水平和当前健康状况。我们开发了一种算法，利用个人当前的活动和卡路里消耗来评估肥胖风险。该算法部署在一个智能手机应用程序上，以收集可穿戴设备数据和用户报告的数据。基于收集到的数据，所提出的应用程序评估肥胖/超重风险，测量当前活动水平，并推荐优化的卡路里计划。
