Remote Monitoring using Smart Watches to Monitor and Manage Cardio Vascular Disease (CVD)

有据可查的人口老龄化现象带来了挑战和机遇。人们的寿命越来越长，因此往往要管理多种疾病，并且越来越期望或希望在医院和养老院之外管理自己的健康和福祉。新兴的数字健康技术，如手机应用、可穿戴设备和智能手表，为我们提供了主动监测和管理健康的新方法（例如通过Strava或Fitbit等应用程序）。例如，智能手表等可穿戴设备不仅为我们提供了跟踪和监控我们的运动（例如通过加速度计）的机会，还可以监测血压、心率等生命体征。随着通过手表的互动变得越来越普遍，我们还可以探索如何实时捕捉自我报告的结果（患者如何报告他们的感受）以及患者/用户的体验。生成的大量数据（包括自我报告的和感知的）可用于识别模式和事件，这些模式和事件可用于决定何时在正确的时间和正确的地点使用量身定制的健康信息进行干预，从而有助于更好地监测和/或防止一系列症状恶化。该项目的目的将是使用大规模快速原型和“野外”研究来实证调查智能手表技术的使用，以支持对人群的远程监测（针对健康人群和长期患有呼吸道疾病或糖尿病等疾病的人群）。这将包括探索(i)使用手表中的传感器可以可靠地检测到哪些新的生命体征和症状；（ii）通过现代智能手表可以有意义地检测到哪些活动模式（例如久坐行为或身体活动），（iii）通过此类可穿戴设备实时远程监测患者报告结果的最佳方式（体验采样、日记记录、语音），以及（iv）如何根据（i-iv）收集的数据模式向用户提供最佳的定制通知（模式、时间、背景）。学生将：1 -回顾文献，研究智能手表和传感器技术如何在健康和HCI（人机交互）文献中得到有效使用- Y12 -回顾现有的基于传感器的技术，研究当前使用传感器检测和识别生命体征和症状的准确性和实用性- Y13 -定性地探索患者，消费者，临床医生关于通过手表进行症状监测的接受度和可用性（例如信任、隐私、可用性）- Y1-Y24 -设计和开发演示“应用程序”，以便系统地研究监测症状的不同方法- Y25 -通过智能手表进行远程症状监测的大规模用户试验，这将允许：- Y3 - (a)系统评估通过智能手表进行远程症状监测的各种不同方法的性能和偏好- (b)评估反馈模式和方法（通过智能手表向用户/患者提供基于健康的通知/消息的方式）。6 -为健康从业者和智能手表开发者提供建议和指南。该博士研究数据科学和健康领域，这是英国和苏格兰政府投资的关键领域（例如通过SFC资助的数字健康与护理研究所和DataLab）。这个博士学位与广泛的健康和护理专家（包括NHS）和决策者以及苹果（手表）等大玩家以及越来越多的小型传感器和可穿戴公司相关

The well documented phenomenon of an increasing ageing population brings with it both challenges and opportunities. People are living longer and as a result are often managing multiple morbidities and increasingly expected to, or wanting to manage their health and wellbeing out of hospitals and care homes. The promise of emerging digital health technologies such as mobile phone apps, wearables and smart watches offers us novel ways to proactively monitor and manage our health and wellness (for example via applications such as Strava or Fitbit). Wearables such as smart watches for example offer opportunity for us to track and monitor not just our movements (via accelerometers for example) but also to monitor vital signs such as blood pressure, heart rate etc. As interaction via watches becomes more common, we can also explore how to capture self-reported outcomes (how the person is reporting they feel) and experiences of the patient/user in real time and on the go. This wealth of generated data (both self-reported and sensed) could be used to identify patterns, and events, that can be used to decide when to intervene with tailored health information at the right time, and the right place in ways that can help to better monitor and/or prevent a range of symptoms from exacerbating.The aim of this project will be to use large scale rapid prototyping and studies 'in the wild' to empirically investigate the use of smart watch technology to support the remote monitoring of the population (targeting both healthy populations and sub sets of people with long term conditions such as respiratory disease or diabetes for example). This will include exploring (i) what new vital signs and symptoms can be reliably detected using sensors available in the watch; (ii) what patterns of activity (e.g. sedentary behaviour or physical activity) can be meaningfully detected via modern smart watches, (iii) the best ways (experience sampling, diary entries, speech) to remotely monitor patient reported outcomes via such wearable devices in real time and (iv) how to best deliver (modality, timing, context) tailored notifications to the users based on patterns of data collected from (i-iv).The student will:1 - Review the literature to examine how smart watch and sensor technology has been used effectively in both the health and HCI (human computer interaction) literature - Y12 - Review of existing sensor based technology to examine the current accuracy and utility of using sensors to detect and recognise vital signs and symptoms - Y13 - Qualitatively explore the perceptions and attitudes of either (or both of) patients, consumers, clinicians with regards to the acceptance and usability of such symptom monitoring via watches (e.g. trust, privacy, usability) - Y1-Y24 - Design and develop demonstrator 'apps' in order to systematically investigate different methods of monitoring symptoms - Y25 - Conduct a large scale user trial of remote symptom monitoring via smart watches which will allow: - Y3 - (a) systematic evaluation of both the performance of, and preference for a variety of different methods for remote symptom monitoring via smart watches - (b) evaluation of feedback modalities and methods (ways of delivering health based notifications/messages) to the user/patient via the smart watch.6 - Produce recommendations and guidelines for both health and wellness practitioners and also smartwatch developers. - Y4This PhD is in the domain of data science and health, key areas of investment for both UK and Scottish governments (e.g. via the SFC funded Digital Health and Care Institute and DataLab.This PhD is relevant to a wide range of health and care specialists (inc. NHS) and decision makers and to both big players such as Apple (watch) as well as an increasing number of smaller sensor and wearable companies