Centre for Assistive Technology and Connected Healthcare, University of Sheffield – Written evidence (INQ0026)

 

The Centre for Assistive Technology and Connected Healthcare (CATCH), based in the University of Sheffield, is one of the principal centres in Europe researching technology supporting older people, people with long-term conditions and people with disabilities. The mission of CATCH is to research, develop, evaluate and implement technologies that help people to remain healthy and independent in their own homes. In doing so, we work with the NHS, social care providers, industry, and with service users and their carers, to ensure that technologies we research and develop make a real impact and will be useful, usable and used by the people who need them, and be effective within the services that support them.

 

Responses to the Committee’s Questions

 

Drawing on expertise from across CATCH’s multi-disciplinary team we provide summary responses below to the Committee’s questions on technology (the University’s Healthy Lifespan Institute is responding separately on other questions). We are happy to provide more elaborated responses if requested.

 

5. What technologies will be needed to facilitate treatments for ageing and ageing related diseases, and what is their current state of readiness?

 

We make a distinction between technology for treatment and technology that supports people with age-related disability in their daily lives. Technology for treatment is medical technology and in most cases such technologies is used by professionals and not by older persons themselves. Technology to support people in their daily lives is often referred to as assistive technology (AT). The examples mentioned in the subheadings of this question are a mixture of medical technologies and AT. In our response we focus mainly on AT, although the boundary between medical and assistive technology is not always very well defined.

 

Drug delivery devices, for existing or future treatments

There are fascinating developments in the field of micro-robotics that may in the future enable very local drug delivery or other treatments (e.g. microsurgery) within the body. Other fields that are likely to impact treatment possibilities in the future are non-invasive surgical techniques, ‘robotic’ implants1, ingestible robots2, in-body sensors for monitoring purposes, implanted drug delivery systems (like insulin pumps), and many others. Most of these developments are still quite far from clinical applications, but developments are advancing rapidly. Such technologies will likely be enhanced by artificial intelligence applications. It is hard to predict when such technologies will become widely available.

 

Technologies for monitoring conditions and providing personalised medical advice

The potential to monitor health conditions is rapidly increasing as a result of miniaturisation of sensors, smart and energy-efficient data collection technologies, speech and activity recognition, big data analysis and artificial intelligence. In this field many new possibilities can be expected. There are, however, a number of important concerns when considering this field.

 

Technologies for monitoring healthy living e.g. fitness, diet, etc.

There is conclusive evidence on the factors that are influential in physical and mental health, including diet, physical activity, alcohol, smoking, sleep, sedentary behaviour, and social interaction10. Technologies are already available to monitor most of these factors, however in order to have an effect on healthy living, technologies need to be designed and adopted in a way that leads to long-term health-related behaviour change. There is some evidence that well-designed technologies incorporating behaviour change techniques and persuasive elements11 can lead to health-related behaviour change12,13,14. However, there is little evidence that long-term behaviour change can be achieved, which is key to healthy living. More research is required in this area.

 

6. What technologies will be needed to help people to live independently for longer, with better health and wellbeing?

 

There are many technologies that can support people in their daily lives: this is the field of Assistive Technology (AT). AT can be anything that helps people to live their life as easily and comfortably as possible with their health condition or disability: mobility aids, hearing aids, communication devices, digital agendas, medication dispensers and many more. Very roughly speaking there are two main groups of assistive technologies: technology that help to self-manage (chronic) health conditions like hypertension, physical frailty, diabetes or COPD; and technology that supports people in daily living activities like mobility, communication, feeding, dressing etc. There are thousands of such products available (see website EASTIN: www.eastin.eu) and many more are being developed, but their use is limited for a number of reasons: lack of awareness, lack of information about what is possible and available, lack of knowledge among professionals, financial barriers, lack of training in proper use, etc. More optimal use of the already available AT solutions could have a huge impact on the pressure on our health and social care system. This would in our view be a first priority.

 

Current developments in mobile technologies, robotics, intelligent interfaces and other technology fields will hugely increase the possibilities to develop solutions in the field. A major challenge will be to ensure that these solutions meet the needs of older people with disabilities and support needs. Unfortunately that is not always the case: many new technologies come onto the market without a clear link to real needs. In a current study we have identified the support needs of older people through a literature review and in-depth interviews with older people. In parallel we are creating an overview of emerging technologies. Consequently we will, together with a panel of experts, map these new technologies to the needs identified. This will result in a list of ‘needed technologies’ as well as solutions that may realistically be expected in the next 5-10 years.

 

The study mentioned15 is ongoing but we can present preliminary findings. The most important support needs among older people identified were those in relation to:

 

Five (related) main technology areas that are expected to impact on the field of AT in the next 5-10 years, and make an impact on the needs identified, were:

 

In a related study we are looking at the support needs of older people and their carers. Here we follow a similar approach: mapping current and emerging technologies against needs identified in the literature and during in-depth interviews16. Focusing on dementia, this study shows that people with the condition and their carers face huge challenges to manage their daily lives and that there is a wealth of technological solutions available that might support them, but that these solutions are only rarely used in practice.

 

Looking at the current and expected future challenges in the health and social care system, it is apparent that the field of Assistive Technology has a huge potential to play an important role. To realise this potential, however, a number of preconditions must be satisfied: information about what exists and how it can be used must be available; professionals in the health and social care system must have adequate knowledge about AT; there must be adequate services for provision and follow-up; etc. For the future there should be a much more ‘directed’ development of new technologies, based on the needs of older people and carers who require support. We strongly recommend the establishment of a specialised national centre of expertise (an institute or network of institutes that together fulfil this role) on Assistive Technology. Such a centre (or network) should be responsible for independent information provision and advice, developing and carrying out a needs-driven research and innovation agenda, supporting implementation of new solutions in health and care practice, and playing a role in policy development.

 

What is the current state of readiness of these technologies, and what should be done to help older people to engage with them?

 

As already mentioned there is a wealth of assistive technologies available on the market. For these technologies, the main issue is not the technological readiness, but the limited uptake in practice. For the emerging fields of technology mentioned above our estimate of the technological readiness is as follows:

 

Artificial Intelligence: AI is an area that is surrounded by extremely high expectations. There are indeed very promising applications, especially in the field of diagnostics, but very few in the field of assistive technology. As discussed earlier, however, there are rapid developments, and AI will certainly take an important place in assistive technology. There are plenty of possible applications, but there are also serious risks such as bias, accountability and reliability. The underlying technology is market ready and rapidly expanding, and applications will appear over the next 5 years.

 

Robotics: Worldwide. enormous investments are being made in the development of care robotics. The expectations are huge, but it is also clear that it will take some time before robots will play major roles in healthcare. An exception is the field of social robotics. Especially in the care for people with dementia and care for children with autism, social robots are beginning to belong to the ‘normal’ repertoire of interventions17-27. There are very promising examples of successful use of robots in these areas, but they are still very expensive and not much is known about how optimally to use them. Other relatively well-developed applications can be found in the field of assistive robots, for example robot arms mounted on wheelchairs.

 

Sensors

Sensor technology has seen enormous progress in the past few years. Extremely energy-efficient micro-sensors able to measure physiological and functional aspects, combined with ambient and wearable sensors measuring activity and behaviour have become available. In combination with AI and connectivity solutions this will offer countless possibilities to measure and monitor many aspects of health and daily life.

 

Intelligent interfaces

Speech-enabled ystems like Alexa and Google Home are becoming extremely popular consumer products. They offer unique and very new ways to interact with our (digital and real) environment. Applications are being developed rapidly and there is huge potential to incorporate conversational interfaces into many AT products, but there is also a danger that their development risks excluding people with non-standard voices such as people with dysarthria as a result of stroke, though there is work to remedy this28,29 . Intelligent interfaces thrive on the basis of AI and connectivity.

 

Connectivity: Internet of Things (IoT), edge computing, 5G: The possibilities to transfer data are rapidly increasing. An important development is IoT: the possibility to connect all kinds of devices and ‘things’ like lights, doors, sensors, smart phones, etc. to each other and to collect real time data from these devices. In combination with AI, edge computing and high speed broadband internet access this creates an enormously powerful tool to analyse behaviour and health status and provide smart home control. In industry and the logistics sector this technology is increasingly used. In healthcare the first applications are beginning to appear, but much research is still required before these become routine.

 

7. How can technology be used to improve mental health and reduce loneliness for older people?

There are several technology domains that can play a role in tackling loneliness and related mental health issues: social robots, intelligent interfaces and community IT platforms. Social robots have been discussed above. The first applications in care can already be found22,17,26. Intelligent interfaces like Alexa and Google Home, but also more advanced conversational interfaces like avatars or speech-operated robots have a great potential to easily connect people to others, but also to ‘diagnose’ and monitor mental health problems or to offer psychological support9. Finally, in relation to reducing loneliness, community IT platforms are increasingly being developed and used30,31 .

 

8. What are the barriers to the development and implementation of these various technologies (considered in questions 5-7) and what is needed to overcome these barriers?

 

Barriers to development of technologies include:

 

Barriers to the implementation of one type of digital health technology, telehealth, and recommendations for overcoming these barriers, were the subject of an Innovate UK-funded research study, ’Mainstreaming Assisted Living Technology’. The following summary recommendations resulted from the study, but more detailed information can be found on the project website www.malt.group.shef.ac.uk and in its published papers32-35. While this project considered telehealth, the barriers and recommendations for overcoming them are relevant to many of the technologies considered in questions 5-7. Research carried out in developing the NASSS (Non-adoption or Abandonment of technology by individuals and difficulties achieving Scale-up, Spread and Sustainability) framework36 gives a comprehensive view of barriers over a broader range of technologies, and overseas research identifies similar barriers37.

 

 

In conclusion, we feel there are two parallel priorities to make sure we harness to full potential of technology to support older people. The first is to stimulate the use of the already available solutions. This requires awareness raising, information provision, training of professionals, development of appropriate services and support, etc. The second priority is to develop a more directed national research and development programme, based on a thorough analysis of the current and future support needs of older people.

 

19 September 2019

 

References

  1. Damian D, Arabagi S, Fabozzo A, Ngo P, Jennings R, Manfredi M, Dupont P E, Robotic Implant to Apply Tissue Traction Forces in the Treatment of Esophageal Atresia, 2014 IEEE International Conference on Robotics & Automation (ICRA). Hong Kong, China
  2. Miyashita S, Guitron S, Yoshida K, Li S, Damian D, Rus D, Ingestible, Controllable, and Degradable Origami Robot for Patching Stomach Wounds 2016 IEEE International Conference on Robotics and Automation (ICRA) Stockholm, Sweden
  3. Alessa T, Hawley MS, Hock ES, de Witte L, Smartphone Apps to Support Self-Management of Hypertension: Review and Content Analysis, JMIR Mhealth Uhealth 2019;7(5):e13645
  4. S Al-Hameed, M Benaissa, H Christensen, B Mirheidari, D Blackburn, M Reuber A new diagnostic approach for the identification of patients with neurodegenerative cognitive complaints, PloS one, 2019
  5. Dodge, H.H., Mattek, N.C., Austin, D., Hayes, T.L., Kaye, J.A., 2012. In-home walking speeds and variability trajectories associated with mild cognitive impairment. Neurology 78, 1946–52
  6. Dodge HH, Zhu J, Mattek NC, Austin D, Kornfeld J, Kaye JA (2015) Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials. PLoS ONE 10(9): e0138095.
  7. Powell, L., Parker, J., Martyn-St James, M., Mawson, S. (2016). The effectiveness of lower limb wearable technology for improving activity and participation in adult stroke survivors: a systematic review” Journal of Medical Internet Research. https://www.jmir.org/2016/10/e259
  8. Mawson S, Nasr N, Parker J, Davies R, Zheng H, Mountain G. A Personalized Self-Management Rehabilitation System with an Intelligent Shoe for Stroke Survivors: A Realist Evaluation JMIR Rehabil Assist Technol 2016;3 (1):e1
  9. Easton K, Potter S, Bec R, Bennion M, Christensen H, Grindell C, Mirheidari B, Weich S, de Witte L, Wolstenholme D, Hawley MS, A Virtual Agent to Support Individuals Living With Physical and Mental Comorbidities: Co-Design and Acceptability Testing, J Med Internet Res 2019;21(5):e12996
  10. Sabia S, Singh-Manoux A, Hagger-Johnson G, et al. Influence of individual and combined healthy behaviours on successful aging. CMAJ 2012 184 (18) 1985-1992
  11. Bartlett YK, Webb TL, Hawley MS, Using Persuasive Technology to Increase Physical Activity in People With Chronic Obstructive Pulmonary Disease by Encouraging Regular Walking: A Mixed-Methods Study Exploring Opinions and Preferences, J Med Internet Res 2017;19(4):e124
  12. Verwey R, Weegen S van der, Spreeuwenberg M, Tange H, Weijden T van der, Witte LP de. Technology combined with a counselling protocol to stimulate physical activity of chronically ill patients in primary care. Studies in Health Technology and Informatics, 2014, 201, 264-270.
  13. Verwey R, Weegen S van der, Spreeuwenberg M, Tange H, Weijden T van der, Witte L. de. Upgrading physical activity counselling in primary care in the Netherlands. Health promotion international, 2014; 1:11.
  14. Weegen, S. van der, Verwey, R., Spreeuwenberg, M., Tange, H., Weijden, T. van der, & Witte, L. de. It's LiFe! Mobile and Web-Based Monitoring and Feedback Tool Embedded in Primary Care Increases Physical Activity: A Cluster Randomized Controlled Trial. Journal of medical Internet research, 2015, 17(7), e184.

15. Abdi S, Spann A, Borilovic L, de Witte L, Hawley M, Understanding the care and support needs of older people: a scoping review and categorisation using the WHO, BMC Geriatrics volume 19, Article number: 195 (2019)

  1. Spann A, Vicente J, Allard C, Hawley M, Spreeuwenberg M, de Witte L, Challenges of combining work and unpaid care and ways to overcome them – An international scoping review, Health and Social Care in the Community, forthcoming
  2. Huijnen, C.A.G.J. et al., Mapping Robots to Therapy and Educational Objectives for Children with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders. 2016; 46(6), pp.2100–2114. Available at: http://link.springer.com/10.1007/s10803-016-2740-6
  3. Huijnen, C., Lexis, M., & Witte, L. De. (2016). Matching Robot KASPAR to Autism Spectrum Disorder (ASD) Therapy and Educational Goals. International Journal of Social Robotics. DOI: 10.1007/s12369-016-0369-4
  4. Heuvel, R. van den, Lexis, M., Gelderblom, G. J., Jansens, R., & Witte, L. de. Robots and ICT to support play in children with severe physical disabilities: a systematic review. Disability and Rehabilitation: Assistive Technology, 2015, 1-14. doi: 10.3109/17483107.2015.1079268
  5. Heuvel, R. van den, Lexis, M., Gelderblom, G. J., Jansens, R., et al. Robots and ICT to support play in children with severe physical disabilities: a systematic review. Disability and Rehabilitation: Assistive Technology. 2016b;11(2), 103-116.
  6. Bemelmans R, Gelderblom GJ, Jonker P, Witte L. de. Socially assistive robots in elderly care: A systematic review into effects and effectiveness. Journal of the American Medical Directors Association, 2012; 13(2): 114-20. e1.
  7. Bemelmans, R., Gelderblom, G. J., Jonker, P., & Witte, L. de. Effectiveness of Robot Paro in intramural psychogeriatric care: A multicenter quasi-experimental study. Journal of the American Medical Directors Association, 2015, 16(11), 946-950.
  8. Bemelmans, R., Gelderblom, G. J., Jonker, P., & Witte, L. de. How to use Robot Interventions in intramural Psychogeriatric Care; A Feasibility Study. Applied Nursing Research, 2015, doi: 10.1016/j.apnr.2015.07.003
  9. Bemelmans R, Gelderblom G., Spierts N, Jonker P, Witte LP de. Development of robot interventions for intramural psychogeriatric care. GeroPsych: the journal of gerontopsychology and geriatric psychiatry, 2013, 26(2), 113-120.
  10. Bedaf S, Gelderblom GJ, Syrdal DS, Lehmann H, Michel H, Hewson D, Amirabdollahian F, Dautzenberg K, Witte LP de. Which activities threaten independent living of elderly when becoming problematic: inspiration for meaningful service robot functionality. Disability and Rehabil Assist Technol, 2014 Nov; 9(6):445-52.
  11. Bedaf, S., Gelderblom, G. J., & Witte, L. de. Overview and Categorization of Robots Supporting Independent Living of Elderly People: What Activities Do They Support and How Far Have They Developed. Assistive Technology, 2015, 27(2), 88-100.
  12. Bedaf S, Draper H, Gelderblom G-J, Sorell T, de Witte L. Can a Service Robot Which Supports Independent Living of Older People Disobey a Command? The Views of Older People, Informal Carers and Professional Caregivers on the Acceptability of Robots. International Journal of Social Robotics. 2016;1-12. DOI: 10.1007/s12369-016-0336-0
  13. Hawley, M.S., Enderby, P., Green, P., Cunningham, S.P., Brownsell, S. Carmichael, J., Parker, M., Hatzis, A., O’Neill, P., and Palmer, R., A speech-controlled environmental control system for people with severe dysarthria, Medical Engineering and Physics 29, 5 (2007) 586–593
  14. Hawley MS, Cunningham SP, Green PD, Enderby P, Palmer R, Sehgal S, O’Neill P, A voice-input voice-output communication aid for people with severe speech impairment, IEEE Trans. Neural Systems and Rehabilitation Engineering (2013), 21, 1, pp 23-31
  15. Man, Y. P., Cremers, G., Spreeuwenberg, M., & Witte, L. de. Platform for frail elderly people supporting information and communication. Studies in health technology and informatics, 2015, 217, 311 - 318. doi: 10.3233/978-1-61499-566-1-311
  16. Willard S, Cremers G, Man YP, Rossum E van, Spreeuwenberg M, Witte L de. Development and testing of an online community care platform for frail older adults in the Netherlands: a user-centred design. BMC Geriatrics 2018;18:87
  17. Gorst S L, Armitage C J, Brownsell S, Hawley M S, Home Telehealth Uptake and Continued Use Among Heart Failure and Chronic Obstructive Pulmonary Disease Patients: a Systematic Review, Annals of Behavioral Medicine 48(3):323-336 Dec 2014
  18. Brewster L., Mountain G., Wessels B., Kelly C. & Hawley M. Factors affecting front line staff acceptance of telehealth technologies: a mixed-method systematic review. Journal of Advanced Nursing (2013), 70(1), pp 21-33
  19. Taylor J, Coates E, Brewster L, Mountain G, Wessels B, Hawley M, Examining the use of telehealth in community nursing: identifying the factors affecting frontline staff acceptance and telehealth adoption, Journal of Advanced Nursing 71(2):326-337 01 Feb 2015
  20. Taylor J, Coates E, Wessels B, Mountain G, Hawley MS, Implementing solutions to improve and expand telehealth adoption: participatory action research in four community healthcare settings, BMC Health Services Research 15: 529 01 Dec 2015
  21. Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, Hinder S, Fahy N, Procter R, Shaw S, Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies, J Med Internet Res 2017;19(11):e367
  22. Swinkels ICS, Huygens MWJ, Schoenmakers TM, Oude Nijeweme-D’Hollosy W, Velsen L van, Vermeulen J, Schoone-Harmsen M, Jansen YJFM, Schayck OCP van, Friele R, Witte L de. Lessons learned from a living lab on the broad adoption of eHealth in primary care. JMIR 2018; 20(3):iss.3;e83;1
  23. Ready Steady Go toolkit https://www.e-repository.clahrc-yh.nihr.ac.uk/ready-steady-go-a-telehealth-implementation-toolkit/