Published by:Beatrix Vereijken
The European Commission’s Innovation Radar is an instrument to discover the best and most promising innovations in high quality EU-funded projects. The latest Innovation Radar includes no less than four PreventIT results that address the needs of existing markets. PreventIT was a Horizon 2020 project (2016-2019) on advancing active and healthy ageing with ICT. PreventIT aimed at early risk detection of physical functional decline in ageing people by self- administered ICT-supported assessment and a behavioural change intervention delivered by use of smartphones and smartwatches. The highlighted innovations are a screening tool to identify seniors at risk of age-related functional decline, an innovative exercise programme integrated in daily life to improve strength, balance and physical activity in at-risk seniors, a profiling tool to tailor the digital version of the exercise programme, and a system to automatically personalize the exercise program to individual needs and preferences. These innovations are crucial elements in the development of future health services for an ageing population by offering personalized and effective interventions to prevent functional decline and empower older people to change behaviour towards a healthy lifestyle. Further development and implementation of all four innovations are included in the recently submitted Horizon2020 research and innovation proposals HAPPIER and co-SINAPSE.
PreventIT in a nutshell
Lifestyle, disease and biology put older adults at risk of accelerated functional decline, leading to falls, cognitive impairment, frailty, and negative consequences for quality of life. PreventIT developed and tested an ICT-based mHealth system (iPAS) for the consumer market, that enabled early identification of risk of age-related functional decline and engendered behavioural change in those at risk to adopt a healthy, active lifestyle. An integrated system of smartphone/watch functioned as frontend technology and a protected cloud-based platform for handling personal data as backend technology. We developed online instruments for risk-screening and self-testing, a novel complexity metric, a tool to measure motivation for behavioural change, and a method to personalise exercise by phenotype, based on currently available large epidemiological studies. The ICT-based intervention was delivered on a smartphone/watch with exercises integrated in daily life and underpinned by a behaviour change programme. We evaluated the feasibility of using technology and behavioural change theories in risk prevention by performing a multi-national feasibility RCT, the results of which allowed for recommendations for the next version of the personalised self-administered activity system, designed to empower ageing people to control their own health and function.
The innovations in more detail
The first highlighted innovation, risk screening tool, predicts future functional decline in the ageing population. We pooled the data of nearly 800 participants from large, ongoing cohort studies in Europe and analysed whether they could perform several activities of daily living independently over a time span of 9 years. For men and women separately, we identified three distinct subgroups with different trajectories of functional decline and the variables that predicted a person’s risk for functional decline. We translated these results into a web-based application, in which baseline values on the predictors can be entered and the computer automatically estimates a person’s risk. This web-based risk screening tool was subsequently tested with data collected during the feasibility randomised controlled trial. A further step was the development of self-administrated smartphone-based tests for physical function to allow people to follow their own functional trajectories over time.
The second listed innovation is the development of an intervention program appropriate for relatively fit young seniors that integrates small bouts of activities and exercises in daily life in order to improve strength and balance, decrease sedentariness and increase physical activity.
The third highlighted innovation is a profiling tool that was included in the iPAS and operated through a smartphone to give personalised intervention advice in those domains where an individual is most at risk and suggest the most effective exercises to combat this risk. The phenotype-based tool takes into consideration personal characteristics, function in seven different domains, and individual preferences.
The fourth identified innovation was a tool to individually tailor lifestyle-integrated functional activities in the domains of balance, strength, and physical activity. The tool assesses the appropriate starting level of all activities ranked in the profiling tool from very easy to more challenging, thus offering a personalised rank list of activities that help a user to choose those activities that benefit him or her the most.
The road ahead
PreventIT developed a proof of concept e-health system that empowers ageing people to adopt a healthy lifestyle to prevent or delay functional decline. To be commercialised and implemented in routine practice, the tools need a next step of development and testing. PreventIT partners have taken the concept of lifestyle-integrated functional activities a step further and are currently testing it in a group setting. Furthermore, PreventIT partners have submitted two new project proposals under Horizon2020. HAPPIER is a DHT-02 project proposal building on results from PreventIT and other successful EU projects by the consortium, aiming at developing and piloting a service model for prevention of physical, mental and social functional decline in the ageing population through use of digital tools, with focus on increasing health literacy and facilitating behavioural change in the target population. The second proposal, co-SINAPSE, is a DHT-04 collaborate project between Europe and Canada focusing on smart living environments for ageing people and transition of care in the post-COVID era. Future healthcare for the ageing population needs to shift focus from treatment to prevention, be digitalised to be sustainable and widely accessible, and empower people to take informed decisions about their own health and functioning. The identification of the four innovations from PreventIT among the most promising innovations from EU projects confirms that we are on the right track.
Published by:Beatrix Vereijken
We are very proud to announce that the eLiFE innovation that we developed in the PreventIT EU project is nominated for the Radar Innovation Prize in the Technology for Society category! If you agree that prevention and healthy ageing are important, please support us and give eLiFE your vote here:
https://ec.europa.eu/futurium/en/tech-society-2018/health-leads
The eLiFE program is recognized as an impactful innovation that can help to prevent age-related functional decline. Within the PreventIT project, the successful original LiFE program was adapted to a younger age group between 60-70 years (aLiFE, adapted Life). The program was then implemented on a smartphone-smartwatch combination in the form of an app that delivers a personalized exercise program that is aligned with the individual’s own goals, tracks daily activities, and motivates users to change their daily routines into a more active lifestyle (eLiFE).
Within PreventIT, we have developed technologies for predicting and preventing functional decline in the young elderly. Our eLiFE intervention uses smartphones and smartwatches for delivery and provides timely encouraging messages and feedback. Instructions are provided through videos and texts within the PreventIT app and users can monitor their strength, balance, and physical activities. Furthermore, we developed a Risk Screening Tool within the project that offers an objective and standardized prediction model for the markers of functional decline.
Our 1-year feasibility study showed that users were very positive about the program and reported sustained behavioral change towards a more active lifestyle. This is the first time a program like this is tested over a longer period of time. Further research and development of the system and eventually a market introduction is being investigated and projected.
Published by:Beatrix Vereijken
The health benefits of physical activity are well known, yet few of us achieve the 150 minutes of moderate intensity physical activity per week recommended by the World Health Organization (World Health Organization [WHO], 2010). In addition to walking more and sitting less, older adults should also be working on their strength and balance in order to prevent age-related functional decline (National Health Service [NHS], 2015). We know that tailored interventions can be successful, so researchers in the PreventIT project are working on developing and trialling two behaviour change interventions, targeting risk factors for functional decline, that are tailored to the needs and preferences of the individual. The interventions have been designed to change behaviour, supporting young older adults to form long term physical activity habits.
The European Horizon 2020 Project ‘PreventIT’ (Grant Agreement Number: 689238) has adapted the Lifestyle-integrated Functional Exercise (LiFE) programme, which reduced falls in people 75 years and over (Clemson et al., 2012), for a younger cohort (aLiFE). The aLiFE programme incorporates more challenging strength and balance/agility tasks, as well as specific recommendations for increasing physical activity in the target group, aged 60-70 years. Personalised advice is given on how to integrate strength, balance and physical activities into daily life, in a way which should not be time consuming. Participants plan and monitor their strength, balance and physical activities, with support from a Trainer making home visits, using a paper-based manual. aLiFE has been operationalised to be delivered using smartphones and smartwatches (eLiFE), providing the opportunity to send timely encouraging messages and real-time feedback to the user. Guidance and instruction is provided through videos and text within the PreventIT app and participants plan and monitor their strength, balance and physical activities using the app.
Smartphones and smartwatches are used by an increasing number of people, with thousands of smartphone applications available to promote healthy lifestyles. However, few of these applications are evidence based, meaning that their contribution to overcoming the challenges presented by an ageing population is limited. PreventIT has taken the original LiFE concept and further developed the behaviour change elements, explicitly relating and mapping them to Social Cognitive Theory (Schwarzer, 2008) and specific Behaviour Change Techniques (Michie et al., 2013). Goal setting, planning, prompts and real-time feedback are used to deliver a person-centred experience for participants in the intervention.
The PreventIT mHealth intervention (eLiFE) focusses on behaviour change from initiation to long-term maintenance, addressing the different phases of adopting a healthier lifestyle. As such, it makes a strong contribution to the developing field of evidence-based mHealth. The interventions (aLiFE and eLiFE) are currently being trialled in a three-arm feasibility randomised controlled trial in Norway, the Netherlands and Germany, with results eagerly awaited!
Source: Health Psychology in Public Network
An overview of the project can be viewed on YouTube: https://www.youtube.com/watch?v=upAfGHbNvdU
Email: elisabeth.boulton@manchester.ac.uk
References
Clemson, L., Fiatarone Singh, M. A., Bundy, A., Cumming, R. G., Manollaras, K., O’Loughlin, P., & Black, D. (2012). Integration of balance and strength training into daily life activity to reduce rate of falls in older people (the LiFE study): randomised parallel trial. BMJ (Clinical Research Ed.), 345e4547. doi:10.1136/bmj.e4547
Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., & … Wood, C. E. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals Of Behavioral Medicine, 46(1), 81-95. doi:10.1007/s12160-013-9486-6
National Health Service. (2015). Physical activity guidelines for older adults. Retrieved from: https://www.nhs.uk/Livewell/fitness/Pages/physical-activity-guidelines-for-older-adults.aspx
Schwarzer, R. (2008). Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology: An International Review, 57(1), 1-29. doi:10.1111/j.1464-0597.2007.00325.x
World Health Organization. (2010). Recommendations on Physical Activity for Health. Retrieved from: http://apps.who.int/iris/bitstream/10665/44399/1/9789241599979_eng.pdf
Dr Lis Boulton, Research Associate, PreventIT Project, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester
Email: elisabeth.boulton@manchester.ac.uk
Published by:Beatrix Vereijken
June 17-22, 2018 – University Residential Centre of Bertinoro, Italy
International Summer School for Early Career Researchers*
Date: 17th of June – 22nd of June, 2018
Place: University Residential Centre of Bertinoro, Italy
Aims and topic
Emerging smart, mobile technologies and behavioural intervention programmes provide the focus for understanding the role of technology in changing behaviours in adults towards an active and healthy lifestyle and preserving physical function and quality of life at older age. PhD students and early career researchers* from a variety of disciplines will be exposed to relevant topics such as how to:
*PhD students and post docs within 5 years after completing their PhD
Confirmed list of lecturers
Elisabeth Boulton, School of Health Sciences, University of Manchester, United Kingdom
Luca Pietrantoni, Department of Psychology, University of Bologna, Italy
Giuseppe Mincolelli, Industrial Design, University of Ferrara, Italy
Yuan Lu, Department of Industrial Design, Eindhoven University of Technology, The Netherlands Sonia Bergamaschi, Department of Engineering, University of Modena and Reggio Emilia, Italy Paolo Paolini, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Italy Antonella Frisiello, Centre for Applied Research on ICT, Istituto Superiore Mario Boella, ItalySimone Maccaferri, Research Development Manager, University of Bologna, Italy
Marco Pieterse, Health Leads B.V., healthcare innovation, The Netherlands
Mirko Orsini, DataRiver S.R.L., management and processing of clinical data, Italy
Sabato Mellone, Electrical, Electronic, and Information Engineering, University of Bologna, Italy
Call for applications and selection of students
Participants are expected to be early career researchers* working on a project or study which aims to change behaviour.
Students are invited to send their CV (max 2 pages) and a letter outlining their motivation for applying (max 1 page), no later than the 5th of March, to: summerschool@preventit.eu
Applicants will be informed of their acceptance by the 19th of March. The number of students will be max 55 and the Summer School aims to include participants from various countries and cultures.
General information
Travel has to be paid by the participants. Dedicated shuttle buses will be available on Sunday the 17th from Bologna airport “Guglielmo Marconi” to the venue and on Friday the 22nd from the venue to Bologna airport.
Course fee: 500€
The course fee covers participation in the course, accommodation, meals, and social activities.
Admitted students will need to register and pay the course fee by the 20th of April 2018.
*PhD students and post docs within 5 years after completing their PhD
Published by:Beatrix Vereijken
Interview with Professors Jorunn Helbostad and Beatrix Vereijken who are the coordinators of the PreventIT project, which will use wearable technology to measure and prevent function decline in older age groups. Here they explain how they will use the information to develop strategies to keep people as healthy as possible, for as long as possible.
Read the full article here: PreventIT _IMPACTPUBLICATION_Brochure_Final
Published by:Beatrix Vereijken
SAVE THE DATE
“New Technologies and Changing Behaviours”
Summer School
June 17-22, 2018 – University Residential Centre of Bertinoro, Italy
Concept: Emerging smart and mobile technologies and behavioural intervention programmes provide the focus for understanding the role of technology in changing behaviours and preserving health and quality of life. PhD stu- dents and junior researchers from a variety of disciplines (e.g. behavioural science, computer science, engineering, human-computer interaction) will be exposed to relevant topics such as how to: • Apply evidence and theory in development and evaluation • Engage users, without which the most impressive technologies will be of little use • Evaluate the quality and ethics of products • Implement digital technologies at scale to maximise their potential in improving global health. More information will follow soon.
Published by:Beatrix Vereijken
Evaluating new technologies for promotion of healthy active ageing: using smartphone apps and sensors to promote activity- acceptability and adherence measurement?
This PhD will investigate the acceptability of smartphones and sensors to young older people (61-70) and how these can be designed to be attractive to this age group. The literature and our own experience 
(Waterman et al 2016) reveal that there can be mismatch between activity data from sensors and report data. Whilst at first sight one is tempted to argue the sensor data must be correct and self-report in some way biased, sensors can misclassify or miss activity because of e.g. gait characteristics.
Published by:Beatrix Vereijken
Published by:Beatrix Vereijken
Several PreventIT partners met in Lausanne in Switzerland June 6-7, 2016, to discuss and put together pieces of work required for the electronic version of the LiFE intervention (eLiFE) to work as intended. The coordinator of PreventIT, Jorunn L Helbostad, described the work performed by the consortium as a ‘cog wheel’: Each of the elements of the work performed has to work together in order for the mHealth system to work as intended. And, we seem to be on track! An exercise database has been developed and instructions translated. The development of the smartphone application delivering the intervention has made good progress, and we have made important decisions on which behavioural change components to use to facilitate good uptake and long term change in behaviour. We are in the process of identifying important risk factors for functional decline that will later be used to screen people for functional fitness and for personalising the intervention. We are also in the process of developing a metric to assess complexity in behaviour. One pilot study testing out the PreventIT exercise concept (aLiFE) has already been performed. Based on the good progress in PreventIT so far, we find it realistic to run the eLiFE pilot study as planned in October 2016 in Trondheim, Amsterdam, and Stuttgart. This will be the first real test on how the mobile health system we are developing is working in real life!
Published by:Beatrix Vereijken
The Amsterdam workshop (24th – 26th April) was the third workshop in a series of workshops (workshop #1 Stuttgart, workshop #2 Bologna) focusing on the development of the adapted Lifestyle-integrated Functional Exercise Programme (aLiFE). The aLiFE development and related workshops were coordinated by Work Package 4 (RBK Stuttgart). The aLiFE programme has been adapted to the PreventIT target populations of young older adults aged 60-70. The Amsterdam workshop focused on teaching aLiFE trainers and assessors for a pilot study aiming to evaluate the new aLiFE programme. RBK taught the components of the aLiFE programme including a new exercise framework focusing on challenging balance and strength exercises, specific agility tasks, multi-task exercises, and a standardized assessment tool for assessing baseline performance and tailoring the exercises according to the capacity of the participant. Another innovation of the aLiFE programme is a standardised approach for increasing a participant’s PA level. Workshop attendees learned how to administer the aLiFE programme within the 4-week pilot study by using a newly developed trainers and participants manual. The workshop included teaching of theoretical knowledge and practical skills for the aLiFE trainers. The aLiFE programme is a behavioural change exercise programme. This implies that the new aLiFE exercises are included in activities and movements which are part of daily routine. This concept is different compared to a traditional exercise programme. The long term goal of aLiFE is that the new exercises become habitual. Principles for how to change habits and how to change behaviour were therefore another focus of the workshop. Furthermore, a motor assessment battery consisting of challenging tests was taught to the assessors of the aLiFE pilot study.
During the workshop, we managed to go through the complete aLiFE teaching procedure and performance-based testing, and we really look forward to starting the aLiFE pilot study. The main goal of the aLiFE pilot study is to evaluate whether the adapted components of the aLiFE programme including the exercise framework, manuals, and assessment tool are appropriate for the target population of PreventIT (young older adults). After the aLiFE study, we will be able to judge whether we need to further modify the aLiFE exercise paradigm, manuals, and assessment. Apart from practicing the aLiFE intervention and gaining experience on the tests, the workshop provided good opportunities to discuss all procedures in detail and ensure that the pilot will be implemented similarly across the three study sites. The “Preventers” hope to include 30 participants into the aLiFE pilot study which will be conducted in Stuttgart, Amsterdam, and Trondheim. We hope that the participants will provide extensive feedback about the aLiFE programme in order to adapt the programme components and exercises to target populations as best as possible.
Published by:Beatrix Vereijken
The European Commission’s Innovation Radar is an instrument to discover the best and most promising innovations in high quality EU-funded projects. The latest Innovation Radar includes no less than four PreventIT results that address the needs of existing markets. PreventIT was a Horizon 2020 project (2016-2019) on advancing active and healthy ageing with ICT. PreventIT aimed at early risk detection of physical functional decline in ageing people by self- administered ICT-supported assessment and a behavioural change intervention delivered by use of smartphones and smartwatches. The highlighted innovations are a screening tool to identify seniors at risk of age-related functional decline, an innovative exercise programme integrated in daily life to improve strength, balance and physical activity in at-risk seniors, a profiling tool to tailor the digital version of the exercise programme, and a system to automatically personalize the exercise program to individual needs and preferences. These innovations are crucial elements in the development of future health services for an ageing population by offering personalized and effective interventions to prevent functional decline and empower older people to change behaviour towards a healthy lifestyle. Further development and implementation of all four innovations are included in the recently submitted Horizon2020 research and innovation proposals HAPPIER and co-SINAPSE.
PreventIT in a nutshell
Lifestyle, disease and biology put older adults at risk of accelerated functional decline, leading to falls, cognitive impairment, frailty, and negative consequences for quality of life. PreventIT developed and tested an ICT-based mHealth system (iPAS) for the consumer market, that enabled early identification of risk of age-related functional decline and engendered behavioural change in those at risk to adopt a healthy, active lifestyle. An integrated system of smartphone/watch functioned as frontend technology and a protected cloud-based platform for handling personal data as backend technology. We developed online instruments for risk-screening and self-testing, a novel complexity metric, a tool to measure motivation for behavioural change, and a method to personalise exercise by phenotype, based on currently available large epidemiological studies. The ICT-based intervention was delivered on a smartphone/watch with exercises integrated in daily life and underpinned by a behaviour change programme. We evaluated the feasibility of using technology and behavioural change theories in risk prevention by performing a multi-national feasibility RCT, the results of which allowed for recommendations for the next version of the personalised self-administered activity system, designed to empower ageing people to control their own health and function.
The innovations in more detail
The first highlighted innovation, risk screening tool, predicts future functional decline in the ageing population. We pooled the data of nearly 800 participants from large, ongoing cohort studies in Europe and analysed whether they could perform several activities of daily living independently over a time span of 9 years. For men and women separately, we identified three distinct subgroups with different trajectories of functional decline and the variables that predicted a person’s risk for functional decline. We translated these results into a web-based application, in which baseline values on the predictors can be entered and the computer automatically estimates a person’s risk. This web-based risk screening tool was subsequently tested with data collected during the feasibility randomised controlled trial. A further step was the development of self-administrated smartphone-based tests for physical function to allow people to follow their own functional trajectories over time.
The second listed innovation is the development of an intervention program appropriate for relatively fit young seniors that integrates small bouts of activities and exercises in daily life in order to improve strength and balance, decrease sedentariness and increase physical activity.
The third highlighted innovation is a profiling tool that was included in the iPAS and operated through a smartphone to give personalised intervention advice in those domains where an individual is most at risk and suggest the most effective exercises to combat this risk. The phenotype-based tool takes into consideration personal characteristics, function in seven different domains, and individual preferences.
The fourth identified innovation was a tool to individually tailor lifestyle-integrated functional activities in the domains of balance, strength, and physical activity. The tool assesses the appropriate starting level of all activities ranked in the profiling tool from very easy to more challenging, thus offering a personalised rank list of activities that help a user to choose those activities that benefit him or her the most.
The road ahead
PreventIT developed a proof of concept e-health system that empowers ageing people to adopt a healthy lifestyle to prevent or delay functional decline. To be commercialised and implemented in routine practice, the tools need a next step of development and testing. PreventIT partners have taken the concept of lifestyle-integrated functional activities a step further and are currently testing it in a group setting. Furthermore, PreventIT partners have submitted two new project proposals under Horizon2020. HAPPIER is a DHT-02 project proposal building on results from PreventIT and other successful EU projects by the consortium, aiming at developing and piloting a service model for prevention of physical, mental and social functional decline in the ageing population through use of digital tools, with focus on increasing health literacy and facilitating behavioural change in the target population. The second proposal, co-SINAPSE, is a DHT-04 collaborate project between Europe and Canada focusing on smart living environments for ageing people and transition of care in the post-COVID era. Future healthcare for the ageing population needs to shift focus from treatment to prevention, be digitalised to be sustainable and widely accessible, and empower people to take informed decisions about their own health and functioning. The identification of the four innovations from PreventIT among the most promising innovations from EU projects confirms that we are on the right track.
Published by:Beatrix Vereijken
We are very proud to announce that the eLiFE innovation that we developed in the PreventIT EU project is nominated for the Radar Innovation Prize in the Technology for Society category! If you agree that prevention and healthy ageing are important, please support us and give eLiFE your vote here:
https://ec.europa.eu/futurium/en/tech-society-2018/health-leads
The eLiFE program is recognized as an impactful innovation that can help to prevent age-related functional decline. Within the PreventIT project, the successful original LiFE program was adapted to a younger age group between 60-70 years (aLiFE, adapted Life). The program was then implemented on a smartphone-smartwatch combination in the form of an app that delivers a personalized exercise program that is aligned with the individual’s own goals, tracks daily activities, and motivates users to change their daily routines into a more active lifestyle (eLiFE).
Within PreventIT, we have developed technologies for predicting and preventing functional decline in the young elderly. Our eLiFE intervention uses smartphones and smartwatches for delivery and provides timely encouraging messages and feedback. Instructions are provided through videos and texts within the PreventIT app and users can monitor their strength, balance, and physical activities. Furthermore, we developed a Risk Screening Tool within the project that offers an objective and standardized prediction model for the markers of functional decline.
Our 1-year feasibility study showed that users were very positive about the program and reported sustained behavioral change towards a more active lifestyle. This is the first time a program like this is tested over a longer period of time. Further research and development of the system and eventually a market introduction is being investigated and projected.
Published by:Beatrix Vereijken
The health benefits of physical activity are well known, yet few of us achieve the 150 minutes of moderate intensity physical activity per week recommended by the World Health Organization (World Health Organization [WHO], 2010). In addition to walking more and sitting less, older adults should also be working on their strength and balance in order to prevent age-related functional decline (National Health Service [NHS], 2015). We know that tailored interventions can be successful, so researchers in the PreventIT project are working on developing and trialling two behaviour change interventions, targeting risk factors for functional decline, that are tailored to the needs and preferences of the individual. The interventions have been designed to change behaviour, supporting young older adults to form long term physical activity habits.
The European Horizon 2020 Project ‘PreventIT’ (Grant Agreement Number: 689238) has adapted the Lifestyle-integrated Functional Exercise (LiFE) programme, which reduced falls in people 75 years and over (Clemson et al., 2012), for a younger cohort (aLiFE). The aLiFE programme incorporates more challenging strength and balance/agility tasks, as well as specific recommendations for increasing physical activity in the target group, aged 60-70 years. Personalised advice is given on how to integrate strength, balance and physical activities into daily life, in a way which should not be time consuming. Participants plan and monitor their strength, balance and physical activities, with support from a Trainer making home visits, using a paper-based manual. aLiFE has been operationalised to be delivered using smartphones and smartwatches (eLiFE), providing the opportunity to send timely encouraging messages and real-time feedback to the user. Guidance and instruction is provided through videos and text within the PreventIT app and participants plan and monitor their strength, balance and physical activities using the app.
Smartphones and smartwatches are used by an increasing number of people, with thousands of smartphone applications available to promote healthy lifestyles. However, few of these applications are evidence based, meaning that their contribution to overcoming the challenges presented by an ageing population is limited. PreventIT has taken the original LiFE concept and further developed the behaviour change elements, explicitly relating and mapping them to Social Cognitive Theory (Schwarzer, 2008) and specific Behaviour Change Techniques (Michie et al., 2013). Goal setting, planning, prompts and real-time feedback are used to deliver a person-centred experience for participants in the intervention.
The PreventIT mHealth intervention (eLiFE) focusses on behaviour change from initiation to long-term maintenance, addressing the different phases of adopting a healthier lifestyle. As such, it makes a strong contribution to the developing field of evidence-based mHealth. The interventions (aLiFE and eLiFE) are currently being trialled in a three-arm feasibility randomised controlled trial in Norway, the Netherlands and Germany, with results eagerly awaited!
Source: Health Psychology in Public Network
An overview of the project can be viewed on YouTube: https://www.youtube.com/watch?v=upAfGHbNvdU
Email: elisabeth.boulton@manchester.ac.uk
References
Clemson, L., Fiatarone Singh, M. A., Bundy, A., Cumming, R. G., Manollaras, K., O’Loughlin, P., & Black, D. (2012). Integration of balance and strength training into daily life activity to reduce rate of falls in older people (the LiFE study): randomised parallel trial. BMJ (Clinical Research Ed.), 345e4547. doi:10.1136/bmj.e4547
Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., & … Wood, C. E. (2013). The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals Of Behavioral Medicine, 46(1), 81-95. doi:10.1007/s12160-013-9486-6
National Health Service. (2015). Physical activity guidelines for older adults. Retrieved from: https://www.nhs.uk/Livewell/fitness/Pages/physical-activity-guidelines-for-older-adults.aspx
Schwarzer, R. (2008). Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology: An International Review, 57(1), 1-29. doi:10.1111/j.1464-0597.2007.00325.x
World Health Organization. (2010). Recommendations on Physical Activity for Health. Retrieved from: http://apps.who.int/iris/bitstream/10665/44399/1/9789241599979_eng.pdf
Dr Lis Boulton, Research Associate, PreventIT Project, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester
Email: elisabeth.boulton@manchester.ac.uk
Published by:Beatrix Vereijken
The European Commission’s Innovation Radar is an instrument to discover the best and most promising innovations in high quality EU-funded projects. The latest Innovation Radar includes no less than four PreventIT results that address the needs of existing markets. PreventIT was a Horizon 2020 project (2016-2019) on advancing active and healthy ageing with ICT. PreventIT aimed at early risk detection of physical functional decline in ageing people by self- administered ICT-supported assessment and a behavioural change intervention delivered by use of smartphones and smartwatches. The highlighted innovations are a screening tool to identify seniors at risk of age-related functional decline, an innovative exercise programme integrated in daily life to improve strength, balance and physical activity in at-risk seniors, a profiling tool to tailor the digital version of the exercise programme, and a system to automatically personalize the exercise program to individual needs and preferences. These innovations are crucial elements in the development of future health services for an ageing population by offering personalized and effective interventions to prevent functional decline and empower older people to change behaviour towards a healthy lifestyle. Further development and implementation of all four innovations are included in the recently submitted Horizon2020 research and innovation proposals HAPPIER and co-SINAPSE.
PreventIT in a nutshell
Lifestyle, disease and biology put older adults at risk of accelerated functional decline, leading to falls, cognitive impairment, frailty, and negative consequences for quality of life. PreventIT developed and tested an ICT-based mHealth system (iPAS) for the consumer market, that enabled early identification of risk of age-related functional decline and engendered behavioural change in those at risk to adopt a healthy, active lifestyle. An integrated system of smartphone/watch functioned as frontend technology and a protected cloud-based platform for handling personal data as backend technology. We developed online instruments for risk-screening and self-testing, a novel complexity metric, a tool to measure motivation for behavioural change, and a method to personalise exercise by phenotype, based on currently available large epidemiological studies. The ICT-based intervention was delivered on a smartphone/watch with exercises integrated in daily life and underpinned by a behaviour change programme. We evaluated the feasibility of using technology and behavioural change theories in risk prevention by performing a multi-national feasibility RCT, the results of which allowed for recommendations for the next version of the personalised self-administered activity system, designed to empower ageing people to control their own health and function.
The innovations in more detail
The first highlighted innovation, risk screening tool, predicts future functional decline in the ageing population. We pooled the data of nearly 800 participants from large, ongoing cohort studies in Europe and analysed whether they could perform several activities of daily living independently over a time span of 9 years. For men and women separately, we identified three distinct subgroups with different trajectories of functional decline and the variables that predicted a person’s risk for functional decline. We translated these results into a web-based application, in which baseline values on the predictors can be entered and the computer automatically estimates a person’s risk. This web-based risk screening tool was subsequently tested with data collected during the feasibility randomised controlled trial. A further step was the development of self-administrated smartphone-based tests for physical function to allow people to follow their own functional trajectories over time.
The second listed innovation is the development of an intervention program appropriate for relatively fit young seniors that integrates small bouts of activities and exercises in daily life in order to improve strength and balance, decrease sedentariness and increase physical activity.
The third highlighted innovation is a profiling tool that was included in the iPAS and operated through a smartphone to give personalised intervention advice in those domains where an individual is most at risk and suggest the most effective exercises to combat this risk. The phenotype-based tool takes into consideration personal characteristics, function in seven different domains, and individual preferences.
The fourth identified innovation was a tool to individually tailor lifestyle-integrated functional activities in the domains of balance, strength, and physical activity. The tool assesses the appropriate starting level of all activities ranked in the profiling tool from very easy to more challenging, thus offering a personalised rank list of activities that help a user to choose those activities that benefit him or her the most.
The road ahead
PreventIT developed a proof of concept e-health system that empowers ageing people to adopt a healthy lifestyle to prevent or delay functional decline. To be commercialised and implemented in routine practice, the tools need a next step of development and testing. PreventIT partners have taken the concept of lifestyle-integrated functional activities a step further and are currently testing it in a group setting. Furthermore, PreventIT partners have submitted two new project proposals under Horizon2020. HAPPIER is a DHT-02 project proposal building on results from PreventIT and other successful EU projects by the consortium, aiming at developing and piloting a service model for prevention of physical, mental and social functional decline in the ageing population through use of digital tools, with focus on increasing health literacy and facilitating behavioural change in the target population. The second proposal, co-SINAPSE, is a DHT-04 collaborate project between Europe and Canada focusing on smart living environments for ageing people and transition of care in the post-COVID era. Future healthcare for the ageing population needs to shift focus from treatment to prevention, be digitalised to be sustainable and widely accessible, and empower people to take informed decisions about their own health and functioning. The identification of the four innovations from PreventIT among the most promising innovations from EU projects confirms that we are on the right track.
Published by:Beatrix Vereijken
We are very proud to announce that the eLiFE innovation that we developed in the PreventIT EU project is nominated for the Radar Innovation Prize in the Technology for Society category! If you agree that prevention and healthy ageing are important, please support us and give eLiFE your vote here:
https://ec.europa.eu/futurium/en/tech-society-2018/health-leads
The eLiFE program is recognized as an impactful innovation that can help to prevent age-related functional decline. Within the PreventIT project, the successful original LiFE program was adapted to a younger age group between 60-70 years (aLiFE, adapted Life). The program was then implemented on a smartphone-smartwatch combination in the form of an app that delivers a personalized exercise program that is aligned with the individual’s own goals, tracks daily activities, and motivates users to change their daily routines into a more active lifestyle (eLiFE).
Within PreventIT, we have developed technologies for predicting and preventing functional decline in the young elderly. Our eLiFE intervention uses smartphones and smartwatches for delivery and provides timely encouraging messages and feedback. Instructions are provided through videos and texts within the PreventIT app and users can monitor their strength, balance, and physical activities. Furthermore, we developed a Risk Screening Tool within the project that offers an objective and standardized prediction model for the markers of functional decline.
Our 1-year feasibility study showed that users were very positive about the program and reported sustained behavioral change towards a more active lifestyle. This is the first time a program like this is tested over a longer period of time. Further research and development of the system and eventually a market introduction is being investigated and projected.
Published by:Beatrix Vereijken
Blog by Ronny Bergquist, PhD student in the Geriatrics, Movement and Stroke (GeMS) research group at the Department of Neuromedicine and Movement Science at NTNU, Norway, and partner in the EU PreventIT consortium.
ronny.bergquist@ntnu.no
A switch from treatment to prevention is needed to meet the increasing pressure on the healthcare system that follows a growing older population and higher life expectancy.
Objective assessment of physical function can be an important step in empowering people to take responsibility of their own health and function, as it can create awareness of risk of functional decline and deficiencies in physical function with ageing.
Commonly used clinical tests of physical function at older age are the Timed Up and Go (TUG), Standing tandem, and Five times sit-to-stand. One challenge with these tests is that the measurement outcome, the time it takes to perform these tests, does not seem to be sensitive enough for detecting early decline in physical function in younger seniors. By instrumenting these tests with wearable sensors, such as those found in smartphones, one can extract movement features that provide more detailed information about test performance than total time alone [1].
Figure 1: Tets set-up for the Instrumented TUG (iTUG)
Figure 2: Sensor acceleration signals from a smartphone worn over the lower back, derived from the instrumented TUG. The figure shows rising from a chair (1), individual steps during walking back and forth 3 meters (2), and turning and sitting down on a chair (3).
These features include duration of specific phases (walking, turning, or sit-to-stand transitions), and one can derive further features such as velocity, sway, and jerkiness of movements, which inform about the speed, stability, and smoothness of the movements. Alice Coni, a PhD student at the University of Bologna in Italy, performed a study in which she found that features from an instrumented version of TUG and the ‘30 second chair stand’-test were more sensitive than total time alone for distinguishing between people with low and high function in activities of daily life.
The aim of my PhD is to develop – and assess the usability of – an app that allows people to administer and perform these instrumented tests on their own. We started out with a prototype-version of iTUG and Standing tandem developed by associate professor Sabato Mellone and his research group at the University of Bologna. The self-tests were included in the test battery that all participants in the PreventIT feasibility trial went through during the baseline assessment. Participants received a written instruction on how to perform the self-tests themselves, while the assessors observed without assisting.
Figure 3: Self-test of TUG
The assessor recorded any errors that the participants performed with respect to both the use of the system, as well as the performance of the test. Based on the results from the testing during baseline, the tests were upgraded in order to enhance the user-friendliness, as well as adding a prototype for a self-test version of the ‘Five times sit-to-stand’-test, known from the Short Physical Performance Battery [2]. Now, 12 months later, the participants are using the upgraded version of the three self-tests as part of the follow-up test battery. The instructions for the tests are now included as video demonstrations in the app. The assessors will observe but not assist, and we hope to see that the upgrades we made have made the app more user-friendly, as indicated by fewer errors in administering and performing the tests. While doing these self-tests as well as the instrumented assessor-based tests, the participants wear a smartphone attached to their lower back, and sensor signals from these two tests will be compared for the two tasks.
Later this year we will arrange a usability workshop in Trondheim to which we will invite potential end-users of the self-test app. The goal of this workshop is to explore the end-users’ opinions on the usability of the app. With this information, we will continue to improve the app and continue make it more reliable and user friendly. In the end, the aim is to have a valid and reliable app-based test battery that is available for use in both a clinical- and the home-setting.
[1] Sabato Mellone, Carlo Tacconi, Lorenzo Chiari; Validity of a Smartphone-based instrumented Timed Up and Go, Gait and Posture, Volume 36, Issue 1, May 2012, Pages 163-165, https://doi.org/10.1016/j.gaitpost.2012.02.006
[2] Jack M. Guralnik, Eleanor M. Simonsick, Luigi Ferrucci, Robert J. Glynn, Lisa F. Berkman, Dan G. Blazer, Paul A. Scherr, Robert B. Wallace; A Short Physical Performance Battery Assessing Lower Extremity Function: Association With Self-Reported Disability and Prediction of Mortality and Nursing Home Admission, Journal of Gerontology, Volume 49, Issue 2, 1 March 1994, Pages M85–M94, https://doi.org/10.1093/geronj/49.2.M85