Aug 30,2018

Published by:Peter Vermeiren

Using a smartphone app to measure your physical function

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

Ronny

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].

RonnysBlog_figure1

Figure 1: Tets set-up for the Instrumented TUG (iTUG)

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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.

RonnysBlog_figure3

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

Mar 27,2018

Published by:Peter Vermeiren

Motivating 60-70 year olds to be more physically active: The PreventIT Project

Dr. Lis Boulton, research associate. PreventIT project, school of health sciences, faculty of biology, medicine and health, University of Manchester
Dr. Lis Boulton, research associate. PreventIT project, School of Health Sciences, Faculty of biology, medicine and health, University of Manchester.

 

 

 

 

 

 

 

 

 

 

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 Medicine46(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 Review57(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

 

Feb 7,2018

Published by:Peter Vermeiren

Summerschool: New technologies and changing behaviours

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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:

  •   Apply theory and evidence in the development and evaluation of interventions
  •   Engage users, without which the most impressive technologies will be of little use
  •   Evaluate the quality and ethical aspects of technological solutions
  •   Implement digital technologies to maximise their potential in improving health, function and well-beingOrganisationMultiple experts in a variety of fields will present state-of-the-art solutions, current trends, and novel approaches in the areas of behaviour change theory and taxonomy, co-design, user engagement, digital technologies, service models, and sensitive data management. Students will be engaged through workshops, group work, and panel discussions.CertificationA certificate of attendance will be delivered to each participant.Scientific Organizing CommitteeJorunn L Helbostad and Beatrix Vereijken, Norwegian University of Science and Technology, Norway Sabato Mellone and Lorenzo Chiari, University of Bologna, Italy
    Elisabeth Boulton, University of Manchester, United Kingdom
    Michael Schwenk, Robert-Bosch-Krankenhaus, Germany

*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

Nov 13,2017

Published by:Peter Vermeiren

Interview with PreventIT coordinators prof. Jorunn Helbostad and prof. Beatrix Vereijken

Healthy ageing Impact Hub article

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

Jul 17,2017

Published by:Peter Vermeiren

Save the Date: June 17-22, 2018 Summerschool “New Technologies and Changing Behaviours”

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.

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Jun 16,2017

Published by:Peter Vermeiren

Making a day more complex might slow down functional decline

Blog by: Dr. Wei Zhang, Dr. Anisoara Ionescu and Prof. Kamiar Aminian. Laboratory of Movement Analysis and Measurements, EPFL Zurich, Switzerland.

 

Life expectancy is higher than ever, making it crucial that we empower older adults to take care of their own health and function as much and as long as possible. Physical activity plays an important role in healthy living. Thanks to the advancement of welfare technologies, such as mobile health (mHealth) technology, we are one step further in delivering services to assist older adults to stay physically active and adopt healthy behaviour in their daily life.

What is physical activity and how can it be measured by a smart phone or smart watch?

Physical activity[1]is any bodily movement that works your muscles and requires more energy than resting. Walking, running, cycling but also house cleaning and gardening are examples of physical activities performed in daily life. Physical activity can be quantified by its duration and intensity by monitoring activity by inertial motion sensors, typically accelerometers and gyroscopes, which are embedded in a portable device, such as a smart mobile phone or a smart watch. For example, the intensity of the movement is indicated by the magnitude of the accelerometer signals. A flat accelerometer signal at around 0 indicates that a person remains inactive (Figure 1).

fig 1 blog epfl

Figure 1. Postures and movements can be identified based on the accelerometer signal features.

With advanced pattern recognition techniques, the types of different activities can be distinguished, thereby allowing to detect activities the person is performing or to estimate the energy consumed by performing the activities or the number of steps taken during a day.

What is physical behaviour and how can it be measured by mHealth technologies?

Physical behaviour is an umbrella term, which includes the behaviour of a person in terms of body postures, movements, and daily activities in his/her own environment[2]. In recent years, multi-modal sensing and sensor fusion techniques have enabled new developments in mHealth. Beyond the different activities identified by the motion sensors, more information provided by the smart phones and smart watches can be used to deduce the context in which the physical activities are performed. For example, a mHealth system can tell whether a person has walked outdoors and the distance walked by sampling the GPS sensor data. Such data provides additional insight into different contexts of the same types of physical activities (indoors or outdoors). Moreover, this context information can be used to deduce other aspects of a person’s behaviour, such as social interaction with the outside world.

How to quantify one’s physical behaviour?

There are many different ways to describe physical activity and behaviour. Some typical examples are percentage of time spent on different postures and movements in a day, energy expenditure in the form of burnt calories, the number of steps or the distance walked, etc. This information is usually illustrated using a pie chart or a list of numerical values. In PreventIT, we developed a new way to register and illustrate the physical behaviour that incorporates the multi-dimensional nature of physical activity in the daily environment by using a colour-coded barcode. The colour changes from cold (dark blue) to hot (dark red) when the person is active and thus the barcode changes from a lower (that is, more passive) to a higher (that is, more active) state. The barcode illustrates each posture or movement bout in a given state based on its type, duration and intensity in a time sequence. A physical activity bout is assigned to a higher state than a sedentary bout. The longer the physical activity lasts or the higher the intensity is, the higher the barcode state will be and hence, the ‘hotter’ the colour is in the barcode. The barcode maps physical activities with useful details to a time sequence of the monitored period. It directly shows the distribution of different physical activities and the dynamic change between the postures and movements. From the barcode example in Figure 2, we can tell the difference between the behaviours of the two persons. Person A is a patient suffering from chronic pain, whose barcode shows mostly bluish colours, which indicates that the person spent most of the time being inactive and had several sustained sedentary periods. On the other hand, the age-matched healthy person B’s barcode is filled with more reddish states, where he/she was engaging in high intensity physical activity.

fig 2 blog epfl

Figure 2. Two examples of barcode illustration of physical activity of a chronic pain patient (A) and an age-matched health person (B) [3]

From Figure 2, we can also tell that the healthy person B had more bouts of physical activities performed with various length and intensity throughout the monitored period, reflecting a more varied or complex pattern throughout his/her day. In contrast, chronic pain patient A had fewer physical activity bouts with little variation (few colours), reflecting a rather simple day. The barcode suggests that the chronic pain patient was not able to dynamically alternate between various body movements or activities, probably because of pain or other factors such as fear of movement and activity avoidance3.

In order to have a numerical signature of the pattern illustrated by the barcode, the entropy of the barcode can be computed to quantify the level of complexity of the physical activity of the monitored period. The entropy, which is based on the Lempel-Ziv algorithm, captures unique sub-patterns or combinations of postures and movements encoded in the barcode. The more sub-patterns and combinations found, the higher the entropy is. It means that the more variety or alternation between postures and movements the person performs, the more complex the person’s physical behaviour is.

Adopt a healthy behaviour by engaging in physical activity in everyday life

Reduced variation in behaviour or complexity does not only appear in patients suffering from chronic conditions. With ageing, reduced complexity in physical behaviour reflects functional decline (Figure 3). Reduced complexity at old age implies that the person might be in a down-spiral circle. This circle leads the person to reduced adaptability and being vulnerable to the fluctuations and perturbations in the daily living environment.

Fig 3 blog epfl

Figure 3. Complexity of physical behaviour reduces at high age.

To assist older adults in maintaining their physical function as long as possible, PreventIT proposes to add extra strength and balance challenges to daily activities, break up sedentary periods, and increase length and intensity of active periods. For example, one suggestion can be to break long sitting periods and take a walk; while walking, trying to take a longer path and increasing the speed on the way to the destination. Even such small changes will fill up a day with more variety in physical activities (Figure 4) and thereby allow the person to adopt and maintain a complex physical behaviour. The higher complexity one manages to maintain at old age, the better equipped one is to meet daily challenges.

fig 4 blog epfl

Figure 4. Increase the complexity by breaking up long sitting and walking longer and faster

[1] Paraschiv-Ionescu A, Perruchoud C, Buchser E, Aminian K. Barcoding Human Physical Activity to Assess Chronic Pain Conditions. PLOS ONE. 2012 Feb 23;7(2):e32239.

[2] J. B. J. Bussmann and R. J. G. van den Berg-Emons, “To total amount of activity and beyond: perspectives on measuring physical behavior,” Front. Psychol., vol. 4, no. JUL, pp. 1–6, 2013.

[3] Paraschiv-Ionescu A, Perruchoud C, Buchser E, Aminian K. Barcoding Human Physical Activity to Assess Chronic Pain Conditions. PLOS ONE. 2012 Feb 23;7(2):e32239.

Jun 1,2017

Published by:Peter Vermeiren

Interested in technology and healthy ageing? Our Manchester team is looking for a PhD student!

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 g8ZYR9e5_400x400(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.

Find out more here

 

Sep 8,2016

Published by:Peter Vermeiren

International Physical Therapy Day

Today, the 8th of September is the international physical therapy day, which this year focuses on healthy ageing, and “adding life to years”.  This important topic is exactly also the main focus of PreventIT. Through PreventIT we will empower people to stay healthy and active over time, by using technology embedded in smartphones and smartwatches to monitor and give individualised feedback on healthy behaviour.

Jul 21,2016

Published by:Peter Vermeiren

Development of eLife intervention in Lausanne June 6-7 2016

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!

Mar 27,2018

Published by:Peter Vermeiren

Motivating 60-70 year olds to be more physically active: The PreventIT Project

Dr. Lis Boulton, research associate. PreventIT project, school of health sciences, faculty of biology, medicine and health, University of Manchester
Dr. Lis Boulton, research associate. PreventIT project, School of Health Sciences, Faculty of biology, medicine and health, University of Manchester.

 

 

 

 

 

 

 

 

 

 

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 Medicine46(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 Review57(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

 

Feb 7,2018

Published by:Peter Vermeiren

Summerschool: New technologies and changing behaviours

Picture3

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:

  •   Apply theory and evidence in the development and evaluation of interventions
  •   Engage users, without which the most impressive technologies will be of little use
  •   Evaluate the quality and ethical aspects of technological solutions
  •   Implement digital technologies to maximise their potential in improving health, function and well-beingOrganisationMultiple experts in a variety of fields will present state-of-the-art solutions, current trends, and novel approaches in the areas of behaviour change theory and taxonomy, co-design, user engagement, digital technologies, service models, and sensitive data management. Students will be engaged through workshops, group work, and panel discussions.CertificationA certificate of attendance will be delivered to each participant.Scientific Organizing CommitteeJorunn L Helbostad and Beatrix Vereijken, Norwegian University of Science and Technology, Norway Sabato Mellone and Lorenzo Chiari, University of Bologna, Italy
    Elisabeth Boulton, University of Manchester, United Kingdom
    Michael Schwenk, Robert-Bosch-Krankenhaus, Germany

*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

Nov 13,2017

Published by:Peter Vermeiren

Interview with PreventIT coordinators prof. Jorunn Helbostad and prof. Beatrix Vereijken

Healthy ageing Impact Hub article

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

Aug 30,2018

Published by:Peter Vermeiren

Using a smartphone app to measure your physical function

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

Ronny

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].

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Figure 1: Tets set-up for the Instrumented TUG (iTUG)

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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.

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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

Jun 16,2017

Published by:Peter Vermeiren

Making a day more complex might slow down functional decline

Blog by: Dr. Wei Zhang, Dr. Anisoara Ionescu and Prof. Kamiar Aminian. Laboratory of Movement Analysis and Measurements, EPFL Zurich, Switzerland.

 

Life expectancy is higher than ever, making it crucial that we empower older adults to take care of their own health and function as much and as long as possible. Physical activity plays an important role in healthy living. Thanks to the advancement of welfare technologies, such as mobile health (mHealth) technology, we are one step further in delivering services to assist older adults to stay physically active and adopt healthy behaviour in their daily life.

What is physical activity and how can it be measured by a smart phone or smart watch?

Physical activity[1]is any bodily movement that works your muscles and requires more energy than resting. Walking, running, cycling but also house cleaning and gardening are examples of physical activities performed in daily life. Physical activity can be quantified by its duration and intensity by monitoring activity by inertial motion sensors, typically accelerometers and gyroscopes, which are embedded in a portable device, such as a smart mobile phone or a smart watch. For example, the intensity of the movement is indicated by the magnitude of the accelerometer signals. A flat accelerometer signal at around 0 indicates that a person remains inactive (Figure 1).

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Figure 1. Postures and movements can be identified based on the accelerometer signal features.

With advanced pattern recognition techniques, the types of different activities can be distinguished, thereby allowing to detect activities the person is performing or to estimate the energy consumed by performing the activities or the number of steps taken during a day.

What is physical behaviour and how can it be measured by mHealth technologies?

Physical behaviour is an umbrella term, which includes the behaviour of a person in terms of body postures, movements, and daily activities in his/her own environment[2]. In recent years, multi-modal sensing and sensor fusion techniques have enabled new developments in mHealth. Beyond the different activities identified by the motion sensors, more information provided by the smart phones and smart watches can be used to deduce the context in which the physical activities are performed. For example, a mHealth system can tell whether a person has walked outdoors and the distance walked by sampling the GPS sensor data. Such data provides additional insight into different contexts of the same types of physical activities (indoors or outdoors). Moreover, this context information can be used to deduce other aspects of a person’s behaviour, such as social interaction with the outside world.

How to quantify one’s physical behaviour?

There are many different ways to describe physical activity and behaviour. Some typical examples are percentage of time spent on different postures and movements in a day, energy expenditure in the form of burnt calories, the number of steps or the distance walked, etc. This information is usually illustrated using a pie chart or a list of numerical values. In PreventIT, we developed a new way to register and illustrate the physical behaviour that incorporates the multi-dimensional nature of physical activity in the daily environment by using a colour-coded barcode. The colour changes from cold (dark blue) to hot (dark red) when the person is active and thus the barcode changes from a lower (that is, more passive) to a higher (that is, more active) state. The barcode illustrates each posture or movement bout in a given state based on its type, duration and intensity in a time sequence. A physical activity bout is assigned to a higher state than a sedentary bout. The longer the physical activity lasts or the higher the intensity is, the higher the barcode state will be and hence, the ‘hotter’ the colour is in the barcode. The barcode maps physical activities with useful details to a time sequence of the monitored period. It directly shows the distribution of different physical activities and the dynamic change between the postures and movements. From the barcode example in Figure 2, we can tell the difference between the behaviours of the two persons. Person A is a patient suffering from chronic pain, whose barcode shows mostly bluish colours, which indicates that the person spent most of the time being inactive and had several sustained sedentary periods. On the other hand, the age-matched healthy person B’s barcode is filled with more reddish states, where he/she was engaging in high intensity physical activity.

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Figure 2. Two examples of barcode illustration of physical activity of a chronic pain patient (A) and an age-matched health person (B) [3]

From Figure 2, we can also tell that the healthy person B had more bouts of physical activities performed with various length and intensity throughout the monitored period, reflecting a more varied or complex pattern throughout his/her day. In contrast, chronic pain patient A had fewer physical activity bouts with little variation (few colours), reflecting a rather simple day. The barcode suggests that the chronic pain patient was not able to dynamically alternate between various body movements or activities, probably because of pain or other factors such as fear of movement and activity avoidance3.

In order to have a numerical signature of the pattern illustrated by the barcode, the entropy of the barcode can be computed to quantify the level of complexity of the physical activity of the monitored period. The entropy, which is based on the Lempel-Ziv algorithm, captures unique sub-patterns or combinations of postures and movements encoded in the barcode. The more sub-patterns and combinations found, the higher the entropy is. It means that the more variety or alternation between postures and movements the person performs, the more complex the person’s physical behaviour is.

Adopt a healthy behaviour by engaging in physical activity in everyday life

Reduced variation in behaviour or complexity does not only appear in patients suffering from chronic conditions. With ageing, reduced complexity in physical behaviour reflects functional decline (Figure 3). Reduced complexity at old age implies that the person might be in a down-spiral circle. This circle leads the person to reduced adaptability and being vulnerable to the fluctuations and perturbations in the daily living environment.

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Figure 3. Complexity of physical behaviour reduces at high age.

To assist older adults in maintaining their physical function as long as possible, PreventIT proposes to add extra strength and balance challenges to daily activities, break up sedentary periods, and increase length and intensity of active periods. For example, one suggestion can be to break long sitting periods and take a walk; while walking, trying to take a longer path and increasing the speed on the way to the destination. Even such small changes will fill up a day with more variety in physical activities (Figure 4) and thereby allow the person to adopt and maintain a complex physical behaviour. The higher complexity one manages to maintain at old age, the better equipped one is to meet daily challenges.

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Figure 4. Increase the complexity by breaking up long sitting and walking longer and faster

[1] Paraschiv-Ionescu A, Perruchoud C, Buchser E, Aminian K. Barcoding Human Physical Activity to Assess Chronic Pain Conditions. PLOS ONE. 2012 Feb 23;7(2):e32239.

[2] J. B. J. Bussmann and R. J. G. van den Berg-Emons, “To total amount of activity and beyond: perspectives on measuring physical behavior,” Front. Psychol., vol. 4, no. JUL, pp. 1–6, 2013.

[3] Paraschiv-Ionescu A, Perruchoud C, Buchser E, Aminian K. Barcoding Human Physical Activity to Assess Chronic Pain Conditions. PLOS ONE. 2012 Feb 23;7(2):e32239.

May 25,2016

Published by:Peter Vermeiren

Activity app for an ageing population

Bloggers:

Jorunn L. Helbostad and Beatrix Vereijken
Professors at the Department of Neuroscience, NTNU Norway,
and coordinators of the EU-project PreventIT

Jorunn        Beatrix

Prof. Jorunn Helbostad                Prof. Beatrix Vereijken

Almost daily, new mobile technology becomes available to help us get or remain in shape, such as fitness apps, heart rate monitors, and fitness trackers. Most of this technology is aimed at young adults and is developed to help them achieve specific training goals. Can we use this new mobile technology to create solutions that can help older adults to become more active in their everyday life?

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Can mobile technology help older adults to become more active?
We investigate this in the PreventIT project.
Picture credit: Thor Nielsen/NTNU

Most European countries face a major change in the composition of the population, with a steadily increasing number and proportion of older adults. As a result, it is both a national and an international goal to facilitate an active late adulthood, with good health and quality of life, that allows older adults to be more self-reliant in everyday life for as long as possible.

We coordinate a European research project, PreventIT, that uses smartphones and smart watches to collect data about physical function and social behaviour in newly retired seniors. These data will allow us to detect very early signs of increased risk for functional decline in later life, and tailor everyday activities for the individual person, in order to achieve the goal of active and healthy ageing.

TOMORROW’S OLDER ADULTS

People in their seventies today have much better health and function than those 20 years ago. We live longer and, by 2060, life expectancy will be close to 90 years for women and over 85 years for men in many European countries.

At the same time, the proportion of people above 70 years of age will almost double from just over 10% today to just below 20% in 2060. The largest increase will be in the oldest age groups, and we expect that 10% of the European population will be 80 years or older in 2060. Unfortunately, not all the additional years we can look forward to will be years spent in good health. On the contrary, we need to count on having to live an increased number of years with disease and reduced functional ability.

Because of increased life expectancy and fewer births, the number of employees per retired person will go down from 5-6 employees today to about 2 for every retired person in 20150. In most European countries, the increase in number of older adults will be most noticeable from about 2020. That means that the time is now to plan future services for older adults!

OLDER ADULTS IN THE RISK ZONE

With the coming demographic changes, it is more important than ever that national and international authorities aim at facilitating active ageing, in which the older adults themselves are empowered to take care of their own health and function as much as possible for as long as possible.

A good late adulthood should also contribute to good life quality and give older adults the opportunity to live independent lives for longer. Health promotion and disease prevention will therefore be more important than ever. It is crucial to catch people at risk for functional decline at a very early stage, before functional disability becomes a reality.

To order to achieve this, we need better knowledge of the earliest signs of functional decline and development of countermeasures that can reverse the loss or maintain current functional levels for longer.

WELFARE TECHNOLOGY AS SOLUTION

Welfare technology is one of the solutions that should help ensure that older adults can live a good and independent life for as long as possible. Welfare technology is defined as technological assistance that contributes to improved safety, security, social participation, mobility, and physical and cultural activity, and strengthens the ability of individuals to fend for themselves in everyday life despite illness and social, mental, or physical disabilities.

Mobile health technology is used to describe welfare technology solutions that are based on the use of wearable technology, such as smart phones or smart watches, which in principle are modern computers. Many people use such technology already for training and health purposes, and there is a steadily increasing number of smart products that can register type, intensity, and localization of activities throughout the day, as well as sleep patterns and quality during the night.

In order for these systems to be suitable for older adults, it is necessary to develop systems specifically designed for older adults, taking account of their needs, barriers, and motivations. Moreover, to be useful for health purposes, systems must be based on research, which is rarely the case today.

ACTIVITY IN DAILY LIFE CAN BE THE BEST TRAINING

Our society moves steadily in the direction of less activity and more sedentary behaviour. Tasks that used to be performed manually by people are more and more automatized and performed by machines. The technology that surrounds us makes us move less and less. While there is a tendency for more people to take up structured exercise, everyday activity levels go down. Most adult Europeans spend about 9 hours of their waking time sitting down.

Moreover, older adults are more inactive than younger adults. But there is good evidence that exercise and an active daily life improve health and function in all age groups, including the oldest adults.

To have an effect over time, people need to change their behaviour towards a more active lifestyle. There are numerous studies that have shown that training effects diminish shortly after a training period, probably because structured exercise is difficult to maintain and often does not lead to a lasting change in activity levels and patterns in daily life. It seems therefore plausible that exercise that is integrated in daily life more easily leads to a change in behaviour that can last over time.

There is an existing training programme for older adults where exercises are integrated in daily life, the LiFE program, which has shown good training effects over time on strength and balance and reduced falls in older adults. This programme entices the user to make daily life a little more complex by doing many activities during the day in a slightly more difficult or challenging way. Examples are to balance on one leg while brushing your teeth, to bend knees and hips rather than the back and hips when emptying the dishwasher, or to get off the bus a stop earlier and walk the last part home. This is a different kind of training concept that should be tested at a larger scale, as an alternative to traditional forms of exercising, when the goal is to bring about a change in lifestyle.

THE PreventIT STUDY

PreventIT is a 3-year project financed by the EU HORIZON 2020 programme. We will build further on the LiFE training concept and adapt this to older adults who are in the transition of becoming retired. We will develop mobile phone applications that will enable older adults to map their own functional level in different domains, and give personalized advice on activities in daily life.

The necessary technology and training programme will be developed during 2016 and tested in a feasibility study in 2017. The last year of the project will be used to further develop the technology so that it can be used by younger older adults, so that they can influence their own health and function. During the life of the project, different parts of the intervention and technologies will be tested out in Germany, the Netherlands, and Norway.

PreventIT is also on LinkedIn (preventit), Twitter (@PreventitEU), and Facebook (Preventit.eu).