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