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

Lead Research Organisation: University of Strathclyde
Department Name: Computer and Information Sciences

Abstract

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 - Y1
2 - Review of existing sensor based technology to examine the current accuracy and utility of using sensors to detect and recognise vital signs and symptoms - Y1
3 - 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-Y2
4 - Design and develop demonstrator 'apps' in order to systematically investigate different methods of monitoring symptoms - Y2
5 - 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. - Y4
This 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

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R513349/1 01/10/2018 30/09/2023
2283771 Studentship EP/R513349/1 01/10/2019 30/09/2022 Rachel Sales