Periodic Reporting for period 3 - STARR (Decision SupporT and self-mAnagement system for stRoke survivoRs)
Reporting period: 2018-08-01 to 2019-10-31
Stroke is a leading cause of death and disability, with an estimated total cost of approximately €64 billion per year in Europe.
Recurrent stroke carries with it a greater risk than first-ever stroke for death and disability. In the same time, secondary stroke prevention has proved not very successful in the general population. One of the main reasons for these poor results is the fact that quality healthcare outcomes depend upon patients' adherence to recommended treatments. This adherence remains a challenge, since people do not always understand or remember well enough what they are supposed to do to follow the treatment or to improve their general health status. Furthermore, they do not feel actively involved in a collaborative decision-making process with their physician(s). On the other end of the patient-healthcare professionals relationship, healthcare professionals need to have an understanding of why, how, and when patients do not engage in optimal self-management behaviours in order to engage in a fruitful collaboration with their patients and co-manage more efficiently a person’s health condition.
Thus, better results in the prevention of stroke could be achieved if we improved patients’ adherence to treatments, the management and self-management of stroke risk factors (e.g. high blood pressure, unhealthy diet, alcohol consumption, physical inactivity) and the collaboration between patients and healthcare professionals. This is the main objective of the STARR project. We developed a modular, affordable, and easy-to-use system, which informs stroke survivors about the relation between their daily activities (e.g. medication intake, physical and cognitive exercise, diet, social contacts) and the risk of having a secondary stroke. The STARR system is based on an existing computational predictive model of stroke risk factors; a number of connected objects integrating off-the-shelf sensors for real-time sensing of proprioceptive functions and simple movements; a vision-based sensing platform for measuring the execution of more complex rehabilitation tasks, as well as for evaluating the stroke survivor’s emotional state; a Decision-Support System (DSS) integrating and processing all this information, evaluating progress towards the achievement of given rehabilitation and lifestyle change goals, and providing the basis for personalised diagnosis and prognosis of the stoke survivor’s health status and of a secondary stroke; a number of cloud services assuring the relations with informal and formal carers, peers and medical staff; a processing unit collecting and distributing the information from the sensors to the different modules; self-management services for stroke survivors giving recommendations and support for improving the adherence to prescribed treatments and adopting a healthier lifestyle.
Recurrent stroke carries with it a greater risk than first-ever stroke for death and disability. In the same time, secondary stroke prevention has proved not very successful in the general population. One of the main reasons for these poor results is the fact that quality healthcare outcomes depend upon patients' adherence to recommended treatments. This adherence remains a challenge, since people do not always understand or remember well enough what they are supposed to do to follow the treatment or to improve their general health status. Furthermore, they do not feel actively involved in a collaborative decision-making process with their physician(s). On the other end of the patient-healthcare professionals relationship, healthcare professionals need to have an understanding of why, how, and when patients do not engage in optimal self-management behaviours in order to engage in a fruitful collaboration with their patients and co-manage more efficiently a person’s health condition.
Thus, better results in the prevention of stroke could be achieved if we improved patients’ adherence to treatments, the management and self-management of stroke risk factors (e.g. high blood pressure, unhealthy diet, alcohol consumption, physical inactivity) and the collaboration between patients and healthcare professionals. This is the main objective of the STARR project. We developed a modular, affordable, and easy-to-use system, which informs stroke survivors about the relation between their daily activities (e.g. medication intake, physical and cognitive exercise, diet, social contacts) and the risk of having a secondary stroke. The STARR system is based on an existing computational predictive model of stroke risk factors; a number of connected objects integrating off-the-shelf sensors for real-time sensing of proprioceptive functions and simple movements; a vision-based sensing platform for measuring the execution of more complex rehabilitation tasks, as well as for evaluating the stroke survivor’s emotional state; a Decision-Support System (DSS) integrating and processing all this information, evaluating progress towards the achievement of given rehabilitation and lifestyle change goals, and providing the basis for personalised diagnosis and prognosis of the stoke survivor’s health status and of a secondary stroke; a number of cloud services assuring the relations with informal and formal carers, peers and medical staff; a processing unit collecting and distributing the information from the sensors to the different modules; self-management services for stroke survivors giving recommendations and support for improving the adherence to prescribed treatments and adopting a healthier lifestyle.
We collected future users' and stakeholders' needs at 4 locations in Europe (France, Spain, Sweden and the UK). In total, 141 stroke suvivors and more than 40 stakeholders representatives were involved in the studies. We produced substantial information to design the STARR platform.
In the beginning of the project, we also did a critical analysis of the existing clinical literature on models of stroke risk. We chose 2 models as applicable for the design of STARR (Zuum and the Stroke Riskometer). Zuum was implemented in the DSS. It was combined with one model for motion analysis using a 3D skeleton representation. We also developed a psychological model for behavioural change to help sustaining patients' motivation to adopt a healthier lifestyle. As for sensing, we chose easy-to-install, reliable, low-cost and energy- and performance-efficient sensors (e.g. Kinect Version 2; inertial and pressure sensors integrated in an insole).
The STARR system has been intergrated, and iterative usability tests were done. Afterwards, a 5-month pilot in Spain was also done. Its objective was to assess the usefulness and usability of the STARR system, as well as to measure the impact on general health, quality of life, and number of readmissions to hospital. 18 people used the STARR system at home throughout the 5-month period. 18 other followed a traditional protocol for home-based care. The STARR system users were very satisfied with the system. It also proved to be very effective for the control of hypertension and for improving a number of indicators of quality of life. Using the system and exchanging with medical professionals also enhaced stroke survivors' and caregivers' knowledge about their condition. With the STARR system, there were less readmissions in the emergency department and in hospital in general. Also, the consultations with general practitioners or specialisits were more timely and efficient because they were supported by objective data on stroke suvivors' daily activities.
Throught the project, an analysis of the data flows from a data protection perspective was done. The purpose was to ensure that the data flows, which underlie the STARR architecture, comply with the EU data protection legal provisions. Attention was paid that the stroke survivor retains control over the processing of his data and is thus able to see, control and decide what it is they want to share with anyone else.
As for exploitation and dissemination, it was very successful. We have 39 publications, a number of participations in relevant events, 2 technology transfers and 2 patents filed.
In the beginning of the project, we also did a critical analysis of the existing clinical literature on models of stroke risk. We chose 2 models as applicable for the design of STARR (Zuum and the Stroke Riskometer). Zuum was implemented in the DSS. It was combined with one model for motion analysis using a 3D skeleton representation. We also developed a psychological model for behavioural change to help sustaining patients' motivation to adopt a healthier lifestyle. As for sensing, we chose easy-to-install, reliable, low-cost and energy- and performance-efficient sensors (e.g. Kinect Version 2; inertial and pressure sensors integrated in an insole).
The STARR system has been intergrated, and iterative usability tests were done. Afterwards, a 5-month pilot in Spain was also done. Its objective was to assess the usefulness and usability of the STARR system, as well as to measure the impact on general health, quality of life, and number of readmissions to hospital. 18 people used the STARR system at home throughout the 5-month period. 18 other followed a traditional protocol for home-based care. The STARR system users were very satisfied with the system. It also proved to be very effective for the control of hypertension and for improving a number of indicators of quality of life. Using the system and exchanging with medical professionals also enhaced stroke survivors' and caregivers' knowledge about their condition. With the STARR system, there were less readmissions in the emergency department and in hospital in general. Also, the consultations with general practitioners or specialisits were more timely and efficient because they were supported by objective data on stroke suvivors' daily activities.
Throught the project, an analysis of the data flows from a data protection perspective was done. The purpose was to ensure that the data flows, which underlie the STARR architecture, comply with the EU data protection legal provisions. Attention was paid that the stroke survivor retains control over the processing of his data and is thus able to see, control and decide what it is they want to share with anyone else.
As for exploitation and dissemination, it was very successful. We have 39 publications, a number of participations in relevant events, 2 technology transfers and 2 patents filed.
STARR is scoping the extent to which existing risk factor management, modelling and technology could be applied to secondary stroke prevention. The originality of the approach lies in the work on the extension of existing models to support independent decision-making by the stroke survivor. Also, all these different models are enriched with real-time data on stroke survivors’ daily activities. Another original aspect of the project is the feedback given to the user when sensing their motions. This feedback and the associated algorithms for vision sensing have been described in a number of peer-reviewed publications.
A highly scalable DSS has been developped. The original features of the DSS implementation and validation are related to two key areas: (1) PaaS layer for the predictive model execution, which provides easy adaptation and upgrade of predictive models; (2) scalability, allowing the support for growing number of users by means of straightforward (re)configuration.
A number of self-management services (i.e. mobile applications, serious games) have also been developped. They allow stroke survivors to adopt a healthier lifetyle and practice their rehabilitation alone or with a therapist. The general impact of the STARR system on stroke consequences was evaluated during a 5-month pilot. The STARR system proved to be very effective for the control of hypertension and for improving quality of life. With STARR, there were less readmissions in the emergency department. Also, the consultations with general practitioners or specialisits were more timely and efficient because supported by objective data on daily activities. In this sense, we can affirm that, when put in the market, the STARR system could be a useful companion for stoke survivors and their carers. It could also help save money by reducing the number of readmissions in hospitals.
A highly scalable DSS has been developped. The original features of the DSS implementation and validation are related to two key areas: (1) PaaS layer for the predictive model execution, which provides easy adaptation and upgrade of predictive models; (2) scalability, allowing the support for growing number of users by means of straightforward (re)configuration.
A number of self-management services (i.e. mobile applications, serious games) have also been developped. They allow stroke survivors to adopt a healthier lifetyle and practice their rehabilitation alone or with a therapist. The general impact of the STARR system on stroke consequences was evaluated during a 5-month pilot. The STARR system proved to be very effective for the control of hypertension and for improving quality of life. With STARR, there were less readmissions in the emergency department. Also, the consultations with general practitioners or specialisits were more timely and efficient because supported by objective data on daily activities. In this sense, we can affirm that, when put in the market, the STARR system could be a useful companion for stoke survivors and their carers. It could also help save money by reducing the number of readmissions in hospitals.