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INNOVAge PROJECT
Improvement of the effectiveness of regional
development policies in eco-INNovation for smart
hOme and independent liVing to increase the
quality of life of Ageing people
INNOVAge
Cross-SWOT Report
Jan 2013
By 2020, around a quarter of the EU
population will be over 65 and the
number of people over 80 will more
than double. INNOVAge aims to
increase the effectiveness of regional
development policies in the field of eco-
innovation applied to independent
living for elderly by networking and
mentoring activities at regional and
interregional level. Ageing is considered
part of an overall strategy of mutually
reinforced INNOVATION policies and
regional competitiveness. All
organizations has called for
commitments to mainstream ageing
into all relevant EU policies. Public,
research and business stakeholders
have to work closely, in partnership
implementing new joint solutions and
initiatives that put at the beginning of
any discussion the end-user. Ageing
poses significant challenges to regions
dependent on traditional policy division
of competencies and traditional
industries. Thus, INNOVAge invites 14
EU regions to explore the
Research&Innovation Driven Cluster
model as efficient way to strengthen
creative interaction in the knowledge
triangle(business-private-research, to
accelerate research, development and
market deployment of innovations to
tackle major societal challenges, pool
expertise and resources and boost
the competitiveness of EU industry,
starting with the area of healthy
ageing
This document reports on the SWOT data collection and inter-regional cross analysis performed as
part of the INNOVAge project funded under the last call of INTERREG IVC. The data is in a separate
appendix to this report. The analysis identified important areas of activity for the next stage of the
project such as the training and mentoring scheme and makes recommendations for policy makers,
for cluster development and for future actions.
1. Introduction to the INNOVAge project
INNOVAge (Improvement of the effectiveness of regional development policies in eco-INNovation for
smart hOme and independent liVing to increase the quality of life of Ageing people) aims to increase
the effectiveness of regional development policies in the field of eco-independent living for the
elderly by networking and mentoring activities at regional and interregional level.
Ageing poses significant challenges to regions dependent on a traditional policy division of
competencies and traditional industries: demographic trends demand an innovative policy approach,
which strengthens creative interaction in the knowledge triangle (Care providers-businesses-
academic research). From an economic standpoint, intelligent home solutions offers the opportunity
to deploy cutting edge technology and a system-level approach to design new eco-friendly smart
housing.
The INNOVAge project focuses on:
1.Independent Living: aimed at helping elderly people to live independently for longer in their
homes, increasing their autonomy and assisting them in carrying out their daily activities
2.Eco-innovation applied to Smart and Sustainable : by encouraging the adoption of smart solutions,
homes becomes more accessible and comfortable for the elderly, with a valuable contribution to
minimize the environmental impact of daily life.
Despite the potential of independent living and related eco-innovation solutions, (which have been
demonstrated throughout Europe in pilot trials) its benefits and technical maturity are still limited.
The objective of INNOVAge is to spread innovation and best practice through the partnership of 14
participating organisations: Marche Regional Authority (IT), SEHTA (UK), Medic@Alps (France),
Culminatum Innovation Oy Ltd (Fi), Sofia Municipality (Bg), Region of Central Macedonia (EL),
Regional Management of Social Services- Junta de Castilla y Leon (ES) Geroskipou Municipality (CY),
Development Centre Litija (SI), Lithuania Innovation Centre (Lt), Intras Foundation (Es), Regional
Development Agency of South Bohemia (Cz), Rzeszow Regional Development Agency (Pl),
Netherlands Organisation for applied scientific research TNO (NL), Blekinge Institute of Technology
(SE).
The first step in this process was to map the capability and capacity for eco-innovation for smart
home and independent living to increase the quality of life of Ageing people in each of the 14
participating regions and then to compare them. The quantitative and qualitative data collected was
enriched through feedback rounds and the inclusion of EU policy and Horizon2020 ideas. This report
describes how this was done, what the comparison revealed and makes recommendations on next
steps.
2. Methodology
The SWOT (Strengths, Weaknesses, Opportunities and Threats) methodology was selected as
providing an appropriate template through which to view this data. A central tenet of the InnovAge
project is that Europe has an ageing society and that this will place a burden on the statutory care
sector. This burden could be reduced by adopting technology to help deliver and to support the
elderly to living independently for longer – referred to as Assisted Living (AL). The widescale
implementation and use of AL would be easier if houses were built or were able to be easily
retrofitted with the necessary communications systems – so called Smart Homes. InnovAge aims to
bring together 14 regions of EU to stimulate the development of AL, of Smart Homes and the
convergence of the two by exchanging good practice (GP), transferring ideas for one to another and
starting a debate on how best to influence policy and uptake.
Initial development of the SWOT approach consisted of discussions and debate relating to the data
required in order to carry out the work. Data had to be collected which was sufficiently robust and
comprehensive in order to validate the analysis, but it was agreed that absolute figures were less
important than identifying trends and future developments and that experience and expertise in
each region should also be used to complement data. It was also recognised that datasets would vary
from country to country depending upon availability of robust national data sources. Attempts were
made by all clusters to collect data relating to the following:
Category Detailed Requirements
Socio-demographic framework population, density, population of over 65 years, life
expectancy, marital status, technology usage, household
incomes and old-age support ratio
Knowledge base number and value of projects related to in eco-innovation
smart and independent living, patents publications and
training option
Industry providers of technology related to econ-innovation smart
and independent living
total number of companies ,turnover, employees number
of projects and funding
Policy national and regional policies in favor of eco-innovation
applied to independent living and smart homes
Good Practice Examples of good practice and regional centers of
excellence
As a basis for this process quantitative data on predictions for the growth in the size of the ageing
population in each region was gathered from EU databases. A refinement on the ageing demographic
is how the overall population is changing due to migration or the emergence of a higher working age
population. The capacity and capability of each region to cope with the situation in terms of its
industrial and intellectual base is also indicated from the same database sources (See Appendix 1). In
terms of the SWOT template these are the “Opportunities and Strengths”
Qualitative data was gathered directly by the regions. Whilst confirming the demographic data it
offered an opportunity for the regions (in narrative form) their perception of the current situation. In
terms of the SWOT template these are the “Weaknesses and Threats”.
The methodology used was therefore based around first a quantitative analysis and also a qualitative
analysis done by the regions, followed by a number of joint workshops where group discussions lead
to draw common conclusions and recommandations.
After inital comparisons it was soon detected that the quality of the data from region to region
made comparisons difficult. Drawing from available and publlic database (ESPON) an overview over
the 14 regions was created on some essential indicators that provide information for policy makers.
In Appendix 1 this information is presented for all regions, indicating the fragmented picture across
the 14 regions in the are of demographic trends, science & technology base and regional cluster
networking.
2.1 Results of the SWOT data collection
The initial compilation of the data collected by the regions under the SWOT template revealed a rich
but complex picture, (See Appendix 2). What some regions report as a strength, others report as a
weakness. This could lead to opportunities for cross fertilisation of ideas and perhaps exchanges of
good practice .
Following receipt of this data from the regions an attempt was made to try and explain this diversity
at a project meeting. It became clear that the regions are starting from very different positions.
Broadly it is possible to divide the 14 regions into those that have some history of addressing the rise
in the number of elderly people and the demands they make on the care services and those that
haven’t for whatever reason, yet started. These reasons are often as much social as technical for
instance in Cyprus. In some regions the social infrastructure is still adequate to cater for the needs.
Whilst it is clear that these regions will have the problem in the future it isn’t necessarily true that
the solutions developed in regions who have addressed the problem are suitable for them. There is a
need for knowledge transfer but at the same time it must be accompanied by a dialogue. These two
broad groups are referred to as the ‘mentor group regions’ and the ‘learning group regions’ but in
some aspects the learning group have lessons to learn for the mentor group!
Training was identified by all regions as a need. The granularity of the data was not sufficient to
indicate at this stage who needed to be trained or what the subject of the training should be.
However, other studies of AL have indicated that a range of stakeholders including patients, informal
carers, care professionals, equipment installers and system developers need to be generally aware of
the benefits of AL and of their role in the AL service supply chain in particular. An understanding of
the benefits of AL and how to incorporate them in smart home design and build needs to be
extended to architects, construction companies and those responsible for development of policy in
the field of sustainable housing. Further discussion on this topic and a training programme will be
part of the next semester activities in Innovage.
Whilst the majority of regions reported some political support for the need to resolve the issues
surrounding elderly care most also thought that policy development was not very advanced. It can be
suggested that these two issues- policy development and training – are linked as funding for training
would only become available if there was a strong policy. Only two regions mentioned smart homes
which suggest that there is little activity (beyond research) in the area of smart homes in
combination with elderly care (in the 14 regions).
The results were presented for the mentor group and the learning group separately. This shows
more starkly the differences between the two groups. The most noticeable difference is in the area
of infrastructure – both the industry/research base and the care provision. When there is no
“market” for assisted living (either driven by policy or users), no companies can or will become
involved. Creating a mechanism whereby the market makers (the elderly, informal carers, clinicians,
policy makers) can have a constructive dialogue with solution providers (companies and academics)
is key to starting this process. The EU PPI mechanism and the UK SBRI & Dutch SBIR funding
programmes are a move in this direction.
The presentation of the data through the SWOT template has revealed gaps between the two
groups. There is obviously much more data that can be gathered but it is not clear if this would shed
any more light on the subject. In practical terms though the SWOT analysis has shown that there is a
lot to be gained by:
• Sharing good practice in cluster formation and development and cataloguing it
• Sharing experiences on influencing policy makers
• Sharing the results of pilot activity (good and bad)
• The learning group regions to identify closely in what areas they want help
• The mentor group to respond in a coordinated way to this learning group request
3. Conclusions and Recommendations
The need to do something about the care burden that the ageing population poses seems to be
univerally acknowledged. The timescale on which regions and national governments are reacting is
not clear, though. The concept of assisted living which may contribute to coping with the situation is
familiar to every regional care authority. However the implementation and use of assisted living
products and services is not always supported by regional policies. The reasons for this are many and
varied and can be cultural and organisational as well as technical.
Similarly, the concept of smart homes is on the agenda of government housing departments and
housing associations but few people live in smart homes. The housing construction industry does not
seem to see a commercial benefit in building new smart homes and there is no market to turn the
homes where the majority of people live into smart homes.
it is the external perspective that is dominant (Opportunities & Threats) and not the internal view
(Strength and Weaknesses) as knowledge, people and products are freely flowing through Europe.
The marketplace for eco-innovations is wider than a specific area and bespoke solutions are usually
not developed unless heavily subsidized. There are many examples of heavily subsidised programmes
that fail to be adopted in the marketplace as the business case is overly reliant on technology push
instead of market pull and users needs.
The development of both assisted living and smart homes requires progress on a number technical,
financial and organisational fronts at the same time. This requires policy makers in health, social care
and housing to work together with a shared objective and a common goal.
� Recommendation 1: Integrate policy making across health, social care and housing
It is not sufficient to rely on policy makes to stimulate change but other stakeholders such as
industry, care professionals and end users must be involved in the design of policy and new types of
services.
� Recommendation 2: Implement mechanisms to ensure that all stakeholders are involved
In this area of eco-innovation, industry includes device manufacturers, telecommunications,
architects and builders. The assisted living and smart homes sectors already have their special
interest groups (SIGs). The role of the regional innovation cluster should be to build on existing SIGs
and networks
� Recommendation 3: The RDCs should avoid duplication and build on existing innovation
infrastructure
Even if the regional innovation clusters bring all the stakeholders together to focus on user need,
industry will be reluctant to invest unless the market is large and there is a reasonable expectation of
a good return on their investment. Clusters should be examining their regions need for “care” as
widely as possible such as combatting social isolation and thus meeting a basic need.
� Recommendation 4: Cluster innovation policy should be to identify large markets.
Meeting a basic need might be common to several regions which could lead not only to the sharing
of good practice and the exchange of ideas but to the identification of a bigger market.
� Recommendation 5: Interregional activities should be explored.
The next phase of the Innovage project will allow the ‘learning group regions’ to access some of the
models in place in the ‘mentoring regions’ and also those Good Practices identified through the GP
collection and catalogue. Not one model fits all, and the transfer of knowledge will be tailored to each
regional scenario having learnt from the GP but also the ‘mistakes’ of others.
Appendix 1
Introduction
In this paragraph data is presented with regards to aging of regions of project partners and
information with regards to the industry base per region in tables and figures. A selection of data
sources have been used from the ESPON1 database and Eurostat
2. Demographic data have been used
from the DEMIFER-project3 in which four future scenario’s with regards to demographic change are
compiled and averaged.
Data have been collected on the level of NUTS-2 region of the project partners and are identified by
their geographical name. This makes comparisons on the basis of these databases possible as the
data validity is high.
1 Espon database 2005/ 2006. In general, the ESPON Database supplies different users with data, indicators and tools that can be used for
European territorial development and cohesion policy formulation, application and monitoring at different geographical levels. The data
included in the ESPON Database is mainly coming from European institutions such as EUROSTAT and EEA, and from all ESPON projects.
2 Eurostat is the Statistical Office of the European Union. Its mission is to provide the European Union with high quality statistical
information. For that purpose, it gathers and analyses figures from the national statistical offices across Europe and provides comparable
and harmonised data for the European Union to use in the definition, implementation and analysis of Community policies.
3 DEMIFER Demographic and Migratory Flows affecting European Regions and Cities ESPON, Applied Research 2013/1/3, Final Report |
Version 30/09/2010
Demographics
The starting point has been the potential end-user and the market for the smart home product. A
description of the demographic trends is shown below.
Figure 4: Elderly density in 2011 and 2025 per region and the EU average
The bars represent the density of elderly in 2011 and 2025 for the regions of the project partners.
The elderly density is the 65+ population as a part of the total population ((population 65+/ total
population*100%)).
Ageing will affect all regions. From 2011 to 2025, the ageing population will increase in all individual
partner countries. The EU Average for the elderly density in 2025 (22%) will be higher than in 2011
(18%).
The elderly density in 2011 ranged from 13% in Kypros and Podkarpackie to 23% in Castillia y León. In
2025, it is estimated that elderly density will range from 19% in Utrecht to 26% in Castillia y León.
Among the presented regions in this figure, Jihozápad is the most rapidly ageing region: from 2011
tot 2025 the elderly density increases with 9%. In terms of increases in the (absolute) number of
elderly person the top five regions are:
1. Rhones-Alpes + 437.000
2. Etelä Suomi + 194.000
3. Podkarpackie + 130.000
4. Surrey, East & west Sussex + 105.000
5. Utrecht + 89.000
In 2011, the density of elderly in the regions Surrey, East and West Sussex, Castillia y León, Marche,
Sydsverige and Kentriki Makedonia were above the EU average, while in 2025 it is estimated that the
regions Castillia y León, Marche, Kentriki Makedonia and Jihozápad will be higher than the EU
average.
Another indicator that is relevant is the life-expectancy for males and females. It is not only that
amount of 65+ that is relevant, but also their expected lifespan.
Figure 5: life expectancy males and females at birth
This figure represents the life expectancy for females (blue bars) and males (green bars) at birth4. The
EU averages are expressed in the blue (female, age 80,86) and the green (male, age 74,74) lines. It is
clear that that life expectancy for females at birth in the period 2005-2010 was higher than for males.
The life expectancy for males in the mentioned period ranged from age 64,88 in Lietuva to age 79,70
in Surrey, East and West Sussex. For women the range was between 76,21 in Yugozapaden to 84,28
in Marche.
Life expectancy for females was higher than the EU average for all regions except Kentriki
Makedonia, Lietuva, Podkarpackie, Yugozapaden and Jihozápad.
Life expectancy for males was higher than the EU average for all regions except Lietuva,
Podkarpackie, Zahodna Slovenija, Yugozapaden and Jihozápad.
Based on the parameters in the database: a selection of data have been taken to represent the
demographic situation (now and in the future). Regions can be classified on the bases of the
population type, giving an early indication of where potential challenges have to be addressed from a
policy point of view or where the application of eco innovation in smart homes has a chance to be
successful. It helps to establish a baseline for the Need per region.
There are 7 different population types:
� Type 1 is coming close to the overall average of the ESPON area with respect to the
indicators used in the cluster analysis. However, the age structure is slightly older than the average.
Overall, a stagnating natural population balance and a positive net migration rate are prevalent.
These regions are mainly found in Northern and Western Europe.
� Type 2 features a high share of population in young working ages and a slight population
decline, driven by a negative natural population development. These regions are mainly situated in
Eastern Europe and in some peripheral areas in Southern Europe.
� Type 3 has a slightly younger than average age structure and high natural population
increases, as well as a positive net migration rate. Several regions in Northern and Western Europe
belong to this type.
� Type 4 is characterized by older populations and natural population decreases. Nevertheless,
the overall population size is still increasing due to a strong net migration surplus. This is a rather
Southern European type.
� Type 5 is shaped by a negative natural population balance, as well as a negative migratory
balance. In consequence, this leads to depopulation accompanied by demographic ageing. This type
of region is situated in Eastern Europe, including Eastern Germany.
4 Source: Eurostat database, 2005-2010.
� Type 6 features a young age structure, a positive natural population increase, as well as a
strong migratory surplus. These regions are mainly found in Spain.
� Type 7 is featuring considerable high shares in the young ages and by far the lowest share of
elder population. The strong natural population increase is more than counterbalancing the negative
migratory balance. This type of regions consists of the French Overseas Territories and the Spanish
exclaves of Ceuta and Melilla.
Population type per region
Population
type
1
Euro
Standard
2
Challenge of
Labour force
3
Family
potentials
4
Challenge of
ageing
6
Young
potentials
Regions
Surrey, East
and West
Sussex
Sydsverige
Jihozápad
Kentriki
Makedonia
Lietuva
Podkarpackie
Yugozapaden
Zahodna
Slovenija
Etelä-Suomi
Rhône-Alpes
Utrecht
Castilla y León
Marche
Kypros
Table 1: population type per region
Table 1 presents the population type per region.
Type 5 and 7 are not included in the table because none of the participating regions belong to these
types.
An interesting observation is that although the regions that have a large increase in the number of
elderly (top-five); three of these regions are classified as a population type 3 (Family potentials). It is
likely to assume that the overall population is growing rapidly and balanced (as the density is growing
alongside the average growth rate (Etelä-Suomi) or even below (Rhône-Alpes & Utrecht).
Industry and R&D base per region
Indicators have been selected that identify the scientific and technology type per region. This
indicator is composed in the ESPON database and represents the categorization of NUTS-2 region in
four classes from scientific region (1) to human capital intensive region (4) . The background data is
compiled from regions with research activities (e.g. R&D expenditure per region, R&D Employment
and patents) and human capital level (e.g. share of population by highest level of education).
Table 2 presents the scientific and technology type per region. Most regions belong to type 1, which
is a scientific region. Type 3 is a region with no specialization in knowledge activities while type 4
represents a human capital intensive region. Type 2, the research intensive region, is excluded from
this table because none of the regions belong to this type.
Scientific and technology type per region
Scientific and technology
type 1 scientific region
3 region with no
specialization in
knowledge
activities
5 human capital
intensive region
Region
Etelä-Suomi Castilla y León Kypros
Rhône-Alpes Jihozápad Lietuva
Surrey, East and West
Sussex
Kentriki
Makedonia
Yugozapaden
Sydsverige Marche
Utrecht Podkarpackie
Zahodna Slovenija
Table 2: Scientific and technology type per region
Although number of patents and patents in the high tec sector are incorporated in the previous
indicator, we show them here as well for the year (2005-2006, latest figures in ESPON database).
Figure 3: all patents and high patents per region
The number of all patents per region is diverse. Rhône-Alpes owns the largest number of patents
(1.363) while Podkarpackie owns only one. Etelä-Suomi owns the highest number (335) of high tech
patents, while Lietuva and Jihozápad don’t have any high tech patents on their name.
The number of total patents in 2005/ 20065 ranged from 1 in Podkarpackie to 1.363 in Rhône-Alpes.
The number of high patents in 2005/ 2006 ranged from 0 in Lietuva and Jihozápad to 335 in Etelä-
Suomi.
5 Source: Espon database 2005/ 2006.
Knowledge networking regions6
When defining Knowledge Networking Regions the idea is followed that knowledge is created within
some crucial nodes (i.e. firms and universities) which tend to co-locate in specific places. Knowledge
is then diffused and exchanged either through a diffusive pattern in which
spatial proximity is essential or according to intentional relations based on a-spatial networks.
Translating these ideas to the regional level, knowledge networking regions can be
understood as regions that rely on external sources of knowledge and on facilitating interactive
learning and interaction in innovation. This knowledge diffusion can take place through diffusive
patterns based on spatial proximity (henceforth “spatial linkages”) and/or through intentional
relations based on a-spatial networks or non-spatially mediated mechanisms (“aspatial linkages”).
A combination of different measures is used to assess the degree of regional a-spatial linkages,
namely:
Co-patents with other ESPON regions: number of patents co-authored with inventors
from outside the region.
Inflows: number of inflows of inventors coming from other regions (from where they bring
knowledge, brain gain).
Cross-regional patent citations: number of citations made to patents of other regions.
Knowledge networking regions are those European regions showing for both synthetic indicators, on
spatial and spatial linkages, values greater than the European average. Regions showing values
greater than the average for spatial linkages indicator but lower than the average for a-spatial
linkages are labelled Clustering regions. On the contrary, regions characterized by values lower than
the average for spatial linkages but higher for a-spatial linkages are indicated as Globalizing regions.
Finally, regions showing values lower than the average for both indicators are Non-interactive
regions.
Knowledge networking
regions Clustering region Networking region
Non interactive
region
Region
Marche Sydsverige
Surrey, East and West
Sussex
Utrecht
Rhône-Alpes
Etelä-Suomi
Kypros
Lietuva
Yugozapaden
Kentriki
Makedonia
Jihozápad
6 Source KIT Interim report 2013, section 3.3
Zahodna Slovenija Castilla y León
Podkarpackie
Table 3: knowledge networking type per region . Although the above classification gives an indication where regions are it should be
noticed that the data used range back to the period 2002-2004.
Appendix 2
INNOVAge SWOT Data
This appendix contains the data that was collected by regional partners on the basis of best available
data in Phases 1 & 2 and used to populate the SWOT template
Contents
Table Description
1 Compilation of regional strengths
2 Compilation of regional weaknesses
3 Compilation of regional opportunities
4 Compilation of regional threats
5 Phase 2 data compilation for mentor group
6 Phase 2 data compilation for learning group
7 Criteria used for completing tables 5 & 6
Table 1: Strengths
Region
Factor
Marche Rhone-
Alpes
Etela-
Suomi Yugozapaden
Kentriki
Makedonia
Castilla
y Leon Kypros
Surrey,
E & W
Sussex
Zahodna
Slovenija
Lietuva Jihozapad Podkarpacckie Utrecht Sydsverige
Experience
and
competence
X X X X X
Networks+
partnerships X X X X X
High income X X X
Research X X X X X X X X X
Science
parks X X X
Training X X
Industry/
X X X X X X X X
technology
Construction
projects X X
Clustering X X
Political
support X X X X X
Innovation
activity X X
Increasing R
and D spend X X
Integration
user
networks
X X X
Cheap
healthcare X X
Good
healthcare X X
Market
potential X X
Tech.savy
older
population
X X X
Social
support X X X
Table 2; Weaknesses
Region
Factor
Marche Rhone-
Alpes
Etela-
Suomi Yugozapaden
Kentriki
Makedonia
Castilla
y Leon Kypros
Surrey,
E & W
Sussex
Zahodna
Slovenija
Lietuva Jihozapad Podkarpacckie Utrecht Sydsverige
Skills migration X
Income X X
Communications
infrastructure X X
Networks and
collaboration X X X X
Investment X
Social services
and healthcare X X X X
Technology
usage X X X X X
Training X X
Undeveloped
market X X X X X X
Undeveloped
policy X X X X X X X
Housing
infrastructure X X X X
Regional
variation X X X
Government
cut-backs X X
Innovation and
adoption culture X X X X
Research base X X
R and D spend X X X
Small
population X
Public sector
procurement X X
Table 3: Opportunities
Region
Factor
Marche Rhone-
Alpes
Etela-
Suomi Yugozapaden
Kentriki
Makedonia
Castilla
y Leon Kypros
Surrey,
E & W
Sussex
Zahodna
Slovenija
Lietuva Jihozapad Podkarpacckie Utrecht Sydsverige
Experience X
Market
(demographics) X X X X X X X X X
Market
X X
(location)
Access to
technology X X
Investment in
research X X X X X X X X X
Cluster
development X X X
Integration
health + social
services
X X X X
International
partnerships X X
Investment in
technology X X X X X X
New centres
excellence X X
Quality of city life X
Smart home
construction X X
Test bed X X
Integration 3rd
sector X X X
Retrofit easy X
Policy X X X X
Smart home
products/services X
Smart home
infrastructure X
Green agenda X X X
Table 4: Threats
Region
Factor
Marche Rhone-
Alpes
Etela-
Suomi Yugozapaden
Kentriki
Makedonia
Castilla
y Leon Kypros
Surrey,
E & W
Sussex
Zahodna
Slovenija
Lietuva Jihozapad Podkarpacckie Utrecht Sydsverige
Pensions X X X
Construction law
and policies X X X
Low/variable
income elderly X X X
Public funding X X
Elderly
expensive low
priority
X X X
Undeveloped
business models X
Infrastructure X
Interoperability
+standards X X
Increase cost
health and social
services
X X
National/regional
policy crisis X X
Lack private
funding X X
Cost Smart
homes X X X
Current market
size X X X
Competition
from large
companies
X X
International
cultural barriers X
Workforce X X
Cost devices and
services X X X
Political
resistance X X
Migration skilled X X
workforce
Limited
investment in
research
X X
Global economy X X X X
Barriers to
adoption
+support by
users
X
Re-location
cheaper X
Table 5: Phase 2 Mentor Regions
Region
Criteria
Surrey, E &
W Sussex
Rhone-
Alpes
Castilla y
León Utrecht Marche Sydsverige Etela-Suomi
The Need
Demographics 17 16 14 22 22
Care provision 7 7 7 7 5 8
Policy on AL 3 3 7 6 5 7
The Market
User income 7 7 9 5
User acceptability/use of IT 60 30 75 30 71 53
The Infrastructure
Policy on AL & smart homes 3 1 2 5 5
Community alarm service 8 4 1
Construction industry
involvement 2 2 6 2
Telecomms industry involvement 5 5 2
Smart metering programme 5 1
Training 2 5 3 5 3
The Research
Projects & Expenditure 4 4 6 6 4 6
Centres of Excellence 3 3 2 3 5
Networks/Science Parks 4 3 2 6
Table 6: Phase 2 Learning Group partners
Region
Criteria
Kypros Lietuva Podkarpackie Zahodna
Slovenija Yugozapaden Jihozapad
The Need
Demographics 9 13 16 17
Care provision 7 7 4 6 5
Policy on AL 1 1 3 5
The Market
User income 2 2 2
User acceptability/use of IT 0 23 6 2
The Infrastructure
Policy on AL & smart homes 1 1 1 1 1
Community alarm service 4
Construction industry involvement 1 1 1 1
Telecomms industry involvement 2 2 2 2
Smart metering programme
Training
The Research
Projects and Expenditure 5 2 3 3
Centres of Excellence 4 1 4
Networks/Science Parks 2 2 2 3 2
Table 7: Phase 2 Scoring System
Criteria Description Example
The Need
This section is about the elderly and
those with long term chronic conditions
and how well their care is managed by
the existing care services
Demographics The number of people over 65 as a
percentage of the population
Give actual percentage to nearest whole
number
Care provision
How well do the care services (both
statutory and private) cater for the
elderly
1 = Very poorly, 5= Average, 10 =very
well
Policy on AL Does the government have a policy on
the use of assisted living
1 = None, 3 = High level discussion, 5 =
strategic goals set, 10 = clear policy and
some implementation
The Market This section is about the capability and
capacity of users to adopt new IT-based
services that allow them to live more
independently
User income What is the average income of
individuals over 65
1 = well below EU average, 5 = EU
average, 10 = well above EU average
User acceptability/use of IT What is percentage of people over 65
have access to Internet
Give actual percentage to nearest whole
number
The Infrastructure
This section is about how well
government, existing care services and
industry are positioned to introduce new
IT-based services that allow users to live
more independently
Policy on AL & smart homes Is there government policy on smart
homes and assisted living
1 = None, 3 = High level discussion, 5 =
strategic goals set, 10 = clear policy and
some implementation
Community alarm service
Does the region have a service allowing
people to get assistance (other then the
emergency service). Sometimes called
Telecare Service
1 = No such service, 5= well used, 10 =
well used and expanded to include other
alerts and alarms eg fire, bogus caller
Construction industry involvement Are private sector house builders building
smart or eco-homes
1 = very little, 5= some new build is
smart, 10 = routine availability
Telecomms industry involvement
Are the telecommunications suppliers
involved in providing assisted living
(telecare and telehealth services
1 = not at all, 5= involvement in projects,
10 = routine availability
Smart metering programme Is there a national programme to install
smart meters in houses
1 = No plans, 3 = High level discussion, 5
= plans, 10 = ongoing implementation
Training Are there organisations that offer
relevant training courses 1 = None, 5 = Some, 10 = Many
The Research
This section is about the relevant
research and innovation capacity in the
region
Projects and R&D Expenditure
Is the research community undertaking
activity in this area (as a percentage of
the available resources)
1 = None, 5 = Some activity , 10 = High
level of activity
Centres of Excellence Are there demonstration sites in the 1 = None, 5 = Some, 10 = Many
region
Networks/Science Parks Are there academic/industry networks in
operation in the region 1 = None, 5 = Some, 10 = Many