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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - PreventIT (Early risk detection and prevention in ageing people by self-administered ICT-supported assessment and a behavioural change intervention delivered by use of smartphones and smartwatches)

Teaser

Summary

The European population is ageing. Life expectancy is increasing, but not all extra years come in the guise of healthy years. In order to meet this challenge we need to shift focus from treatment to prevention, increase the use of health technology to alleviate the pressure on health care personnel, and develop tools that empower people to self-manage their own health and function. The PreventIT project aimed to do just this. Technology can be used to activate and motivate people to keep fit and healthy, and is especially powerful when tailored to an individualâ€™s own needs, challenges, and barriers. The aim of PreventIT was to develop a proof of concept mHealth system for the consumer market with seniors between 60 and 70 years of age as target group. The mHealth system was developed to empower older people to take care of their own health and function and included assessment tools and a personalised behaviour change intervention with activities embedded into daily life. The system was developed applying a user centered design process and tested through two pilot studies and a feasibility randomized controlled trial. Results of the three year project demonstrated that, despite immature technology, participants liked the intervention and the system and adhered to the intervention over a period of one year. The healthy sample and a small sample size in the feasibility trial are factors explaining why we were not able to demonstrate different estimates of change between groups. We also developed prototypes of a Risk Screen tool for functional decline, a metric to assess complexity in behavior based on activity, sleep and social data from smartphones, and a self-assessment tool for functional fitness using the sensors in the smartphone to measure and give feedback on performance. To bring the mHealth system or system components to the market, further validation of tools, development of user interfaces and development of the intervention is needed.
PreventIT is a 3-year project that received funding from the European Unionâ€™s Horizon 2020 research and innovation programme under grant agreement No 689238, and was coordinated by the Norwegian University of Science and Technology.

Work performed

PreventIT brought together a strong consortium with partners from five European research institutions (Norwegian University of Science and Technology in Trondheim, Norway; University of Bologna, Italy; Free University of Amsterdam, the Netherlands; University of Manchester, England; Ã‰cole Polytechnique FÃ©dÃ©rale de Lausanne, Switzerland), two hospitals (Robert Bosch Gesellschaft fÃ¼r Medizinische Forschung in Stuttgart, Germany; Local Health Unit Tuscany Centre in Florence, Italy), and two companies (D.o.x.e.e. Spa, Italy; Health Leads BV, the Netherlands).
In PreventIT, we developed iPAS, a proof of concept smartphone and smartwatch mobile health application for assessment and screening of functional fitness and for the delivery of a life style integrated behavioural change intervention focusing on balance, strength and physical activity. Our target group was people between 61 and 70 years of age. We developed and decided on the content of the intervention and motivational aspects to increase uptake over time, and we developed methods to measure complexity in behavior and investigated whether our intervention can prevent loss of complexity. Data from the smartphones were processed and stored on a cloud-based server designed for the project and sent back to the phones in real time for feedback. We applied a user centered design process to develop the technology, and tested it in two pilot studies and a feasibility randomized controlled trial. The feasibility trial aimed at assessing feasibility and usability of the iPAS system compared with the same intervention delivered in a paper-pen format and with a control group. The intervention lasted for six months, and participants were encouraged to continue on their own for another six months. We included in total 180 participants; 60 in each of the two intervention groups and in the control group. Results indicated that the interventions were feasible and safe, with good intervention uptake and acceptable adherence. Participants were aware that the technology was not fully developed, but they liked the concept of lifestyle-integrated activities, managed to change their daily routines towards increased activity, and were positive about the proof-of-concept technologies integrated in the app-based eLiFE. All groups showed small improvements in physical function and complexity throughout the intervention period, without significant differences between the intervention groups and the control group. However, the participants were relatively fit and well-functioning at baseline, indicating that finding those who would benefit the most from an early intervention and behaviour change programme, that is, the group with medium risk for future functional decline, remains an important challenge. A market analysis demonstrated that the proof of concept mHealth system focusing on primary prevention of functional decline needs further development before companies will invest money in the system.

Final results

Through PreventIT, we developed a proof of concept, unobtrusive mobile health system, iPAS, for the consumer market, with young old people as the target group. We used smartphones and smartwatches as frontend technology, and a cloud based server as backend technology. iPAS consists of many building blocks that can be used together or stand alone: 1) A prediction model for functional decline based on existing large epidemiological data sets to predict three and nine years risk of decline in activities of daily living. 2) A web-based risk screen calculator for functional decline. 3) Methods and algorithms for personalising exercise by phenotype. 4) A framework for behavioural change based on behavioural change and motivational theories, including 25 different behavioural change techniques. 5) More than 1300 motivational messages for individualized feedback on exercise behaviour. 6) A database for daily life integrated exercises at different difficulty levels, working on balance, muscle strength, and physical activity. 7) Development of Life Integrated Functional Exercise for young seniors: the aLiFE (adapted LiFE) delivered in a paper-pen format and eLiFE (enhanced LiFE) delivered on smartphones. 8) A complexity metric for assessment of physical behaviour, sleep and social behaviour based on models for complexity in behaviour. 9) Algorithms to assess physical behaviour by use of smartphones and smart watches to allow users to keep track of their goals. 10) Fusion algorithms for exchanging information between smartwatches and smartphones to detect physical behaviour from smartphones when a person carries the phone on the body, and from the smartwatch when the phone is lying still. 11) A smartphone user-interface for delivery and individual feedback of the intervention, designed for older people. 12) A smartphone application for self-assessment of mobility, balance and muscle strength, including realtime vocal feedback on performance of the tests. 14) A secure cloud based platform for storing and processing of data.