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.

Picture3

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!

Jul 21,2016

Published by:Peter Vermeiren

Third PreventIT Workshop in Amsterdam 24th-26th April 2016

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.

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?

Picture1
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).

Apr 12,2016

Published by:Peter Vermeiren

PreventIT project presented at Mobex, Umeå, January 2016

The PreventIT study was presented by Jorunn L. Helbostad and Beatrix Vereijken, on behalf of the PreventIT consortium, at Mobex (Mobility and Exercise in the Elderly) meeting in Umeå, Sweden, January 2016.
The title of the presentation was Early risk detection and prevention in young older adults – The protocol of the PreventIT study.

Mobex picture 2016

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.

Picture3

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

 

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.

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?

Picture1
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).

Apr 12,2016

Published by:Peter Vermeiren

PreventIT project presented at Mobex, Umeå, January 2016

The PreventIT study was presented by Jorunn L. Helbostad and Beatrix Vereijken, on behalf of the PreventIT consortium, at Mobex (Mobility and Exercise in the Elderly) meeting in Umeå, Sweden, January 2016.
The title of the presentation was Early risk detection and prevention in young older adults – The protocol of the PreventIT study.

Mobex picture 2016