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Researchers develop app to train post-stroke care

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Lithuanian researchers have developed an app that will train how to take care of a person who has suffered a stroke in a virtual environment.

Statistics indicate that nearly 10 million people in the European Union are living with the severe consequences of a stroke. It is a condition that usually leaves the patient disabled, often requiring constant care. 

Rytis Maskeliūnas, a researcher at KTU’s Faculty of Informatics, Lithuania together with an international team, has developed an app that will teach how to take care of a person who has suffered a stroke in a virtual environment.

A stroke is a devastating experience. Coming back home from the hospital is a second shock since the condition affects not only the stroke victim’s life but also the lives of the family. According to Maskeliūnas, a researcher at the Department of Multimedia Engineering at Kaunas University of Technology (KTU), Lithuania, it is difficult to survive such a change without help.

A teaching app that is a game

‘iTrain’ is an interactive training course designed to support post-stroke care. It consists of a combination of different tools – a brochure, an interactive Massive Open Online Course (MOOC), visual material, and a serious game, which is becoming an increasingly popular form of training.

Many of us have a superficial knowledge of stroke and have been exposed to the disease in our environment in one way or another. However, according to Maskeliūnas, superficial knowledge remains so until life forces us to get to know the problem in more depth. The “iTrain” interactive training package aims to fill this gap.

According to the KTU researcher, the ‘iTrain’ learning tool is both a game and a training programme. It is up to the user to decide which will be more effective.

“The learning takes place in an easy, quick-to-follow mode, somewhat similar to the very popular “EA SIMS” game,” says Maskeliūnas.

Based on real-life data

The ‘iTrain’ game teaches exemplary nursing practice, how to create a safe home environment, allows users to try outpatient care in a virtual environment, and teaches other aspects essential for post-stroke care. How does it work? Clicking on an object or scenario tests the player’s knowledge – he or she has to choose the optimal option that determines the quality of life of the person being cared for.

The situation is constantly changing, as the quality of life is affected by a variety of factors ranging from the very basic ones, such as a person’s need to eat and sleep, to his or her psychological state. All these situations are simulated in the game.

According to the researcher, this form of training is attractive not only to the younger generation; for many, an interactive game is a more enjoyable and less time-consuming activity than reading a brochure or spending several hours on a course.

“Professional medical expertise and real-life evidence gathered during the “iTrain” project have been used for the game’s methodology,” says the researcher.

Medical professionals from Italy, Cyprus, and Sweden interacted with stroke survivors, their families, and professionals in the field. These interviews, complemented by medical practice, formed the basis for the training material on which the game was developed.

“To our knowledge, there are no serious games that teach about stroke care. Even in nursing, it is a rather broad niche, where one of the pioneers was our game, introducing dementia in old age,” says Maskeliūnas.

The effectiveness of the app was measured by a depression scale

Although some patients can live a fulfilling life after rehabilitation – depending on the severity of the stroke and other factors – it is not uncommon for stroke survivors to have their lives changed radically.

According to Maskeliūnas, a researcher at KTU, depression is quite common among people who have had a stroke: “The person realises that his or her life has changed, usually irreversibly.”

Many families do not have the financial means to use the services of a nursing home, so they care for the patient at home, which also disrupts the daily routine of the household: “It is morally difficult to watch a loved one suffer, and the care is time-consuming (with special food, hygiene, exercise, communication) so that there is no time left for oneself.”

For these reasons, depression can also occur in the relatives of patients, whose lives are also changing radically. To investigate the impact of “iTrain” on the course of depression, which can be caused by the burden on carers, researchers used the Geriatric Depression Scale (GDS).

According to Maskeliūnas, the results of the study showed that the ‘iTrain’ game had a significant positive effect on the change in anxiety among carers and stroke victims.

The ‘iTrain’ game is available on Google Play and Apple App Store.

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Opinion: How robots could help to ease the social care crisis

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Robear is an animatronic bear that lifts elders with mobility problems, Paro is a fuzzy robotic seal intended to provide a futuristic form of animal therapy, and Pepper is a humanoid with remote-monitoring capabilities and learning tools.

Pet robots have revolutionised and enhanced the standard of care, improving the wellbeing of care home residents. Here Stephen Hayes, managing director at automation tech firm Beckhoff UK, explores the benefits of integrating robots in social care homes.

There is a wide variety of care robots on the market. Some are aimed at physical care, including machines that can assist with mobility and exercise, feed their owner and help them with hygiene tasks. These could greatly benefit caregivers, freeing their time and preventing them from suffering from long-term health conditions or disability due to the physical effort associated with giving care.

Others play the role of a companion, engaging older people emotionally to reduce and even prevent cognitive decline, providing companionship for lonely older people, and making those with cognitive conditions easier for care staff to manage.

Research by the University of Plymouth, conducted in care homes using these pets found decreased neuropsychiatric symptoms such as delusions, depression, anxiety, apathy and occupational disruptiveness because they provided a sense of responsibility and purpose.

Social care vacancies are higher than before the COVID-19 pandemic, and data shows one in ten social care posts are unfilled in a staffing crisis that could have harmful results for residents. In England, 152,000 social care posts remain empty, according to a report released by Skills for Care.

Steve Barclay, Secretary of State for Health and Social Care commented for The Telegraph earlier this year, stating that robots and AI are key to better supporting patients and reducing demand on social care staff. He said that there was a need to adopt an innovative approach to health and attempt to cut NHS waiting times while improving care for the elderly.

However, these robots present limitations, such as superficiality and lack of personalisation. Also, the content of their conversations can be very limited, making them less entertaining with the pass of time.

This was the case of the humanoid Pepper, for which production ceased in 2021 due to a weak demand as care homes did not see the long-term benefit of his interactions. Nevertheless, robots like Paro, which move and respond to touch, have had a positive impact on the wellbeing of care home residents. However, with a cost of £5,000, care homes are looking for more affordable options.

Japan is a pioneer in developing this kind of technology. The nation is facing a ‘greying’ crisis due to the aging of its population, so the country has invested heavily in developing caretech able to serve and provide emotional support.

In the UK, there are currently almost 12 million people who are aged 65 or over, and the number of people coping with illnesses such as arthritis or dementia is expected to increase. In fact, a recent machine learning study by the Journal of Medical Systems suggests that 135 million people might be affected by dementia by 2050.

To allow for continuous innovation in this field, the UK Government announced its commitment to invest at least 2.4 per cent of GDP in R&D by 2027. The programme supports the UK Government’s Ageing Society Grand Challenge and Future of Mobility Grand Challenge, which will ensure we meet the needs of an ageing society.

With this in mind, there are certain technologies that we are likely to see more of in care homes over the coming years, including robots that can connect to each other and other devices.

This includes devices such as oximeters, thermometers, or even thermal cameras, enabling the elderly to have consultations any time of the day, from home, and send out emergency notices to staff or hospitals. However, for care robots to be a success, state-of-the-art control technology is required.

Beckhoff’s Ethernet-based fieldbus system, EtherCAT, has extension modules that are compatible with third party hardware for integration. This platform process data and transports it directly, has a flexible topology and simple line or tree structure that requires no expensive infrastructure components and includes the environments for programming, diagnostics and configuration.

This global standard for real-time Ethernet communication provides workers with real-time information about elders like location, health condition, or learning progress. This data also allows carers focus their time on other urgent tasks, optimise resources and personalise treatment.

The advancement and implementation of robot pets could improve awareness of preventative care, reduce anxiety on disease and enhance stakeholder relationship.

Further research on caretech would tackle functional problems, making these devices an essential asset for any caregiver. By investing in the right control technology now, social care homes will be better prepared to take care of their residents.

Beckhoff provides PC-based control and EtherCAT to connect caretech systems. See more here.

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New AI program could predict likelihood of Alzheimer’s disease

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By analysing speech patterns, a new machine learning model can predict with a high degree of accuracy whether someone with mild cognitive impairment will develop Alzheimer’s-associated dementia within six years.

Trying to figure out whether someone has Alzheimer’s disease usually involves a battery of assessments—interviews, brain imaging, blood and cerebrospinal fluid tests. But, by then, it’s probably already too late: memories have started slipping away, long established personality traits have begun subtly shifting.

If caught early, new pioneering treatments can slow the disease’s remorseless progression, but there’s no surefire way to predict who will develop the dementia associated with Alzheimer’s.

Now, Boston University researchers say they have designed a promising new artificial intelligence computer program, or model, that could one day help change that—just by analysing a patient’s speech.

Their model can predict, with an accuracy rate of 78.5%, whether someone with mild cognitive impairment is likely to remain stable over the next six years—or fall into the dementia associated with Alzheimer’s disease.

While allowing clinicians to peer into the future and make earlier diagnoses, the researchers say their work could also help make cognitive impairment screening more accessible by automating parts of the process—no expensive lab tests, imaging exams, or even office visits required.

The model is powered by machine learning, a subset of AI where computer scientists teach a program to independently analyse data.

“We wanted to predict what would happen in the next six years—and we found we can reasonably make that prediction with relatively good confidence and accuracy,” says Ioannis (Yannis) Paschalidis, director of the BU Rafik B. Hariri Institute for Computing and Computational Science & Engineering. “It shows the power of AI.”

The multidisciplinary team of engineers, neurobiologists, and computer and data scientists published their findings in Alzheimer’s & Dementia, the journal of the Alzheimer’s Association.

“We hope, as everyone does, that there will be more and more Alzheimer’s treatments made available,” says Paschalidis, a BU College of Engineering Distinguished Professor of Engineering and founding member of the Faculty of Computing & Data Sciences.

“If you can predict what will happen, you have more of an opportunity and time window to intervene with drugs, and at least try to maintain the stability of the condition and prevent the transition to more severe forms of dementia.”

Calculating the probability of Alzheimer’s Disease

To train and build their new model, the researchers turned to data from one of the nation’s oldest and longest-running studies—the BU-led Framingham Heart Study. Although the Framingham study is focused on cardiovascular health, participants showing signs of cognitive decline undergo regular neuropsychological tests and interviews, producing a wealth of longitudinal information on their cognitive well-being.

Paschalidis and his colleagues were given audio recordings of 166 initial interviews with people, between ages 63 and 97, diagnosed with mild cognitive impairment—76 who would remain stable for the next six years and 90 whose cognitive function would progressively decline.

They then used a combination of speech recognition tools—similar to the programs powering your smart speaker—and machine learning to train a model to spot connections between speech, demographics, diagnosis, and disease progression.

After training it on a subset of the study population, they tested its predictive prowess on the rest of the participants.

“We combine the information we extract from the audio recordings with some very basic demographics—age, gender, and so on—and we get the final score,” says Paschalidis.

“You can think of the score as the likelihood, the probability, that someone will remain stable or transition to dementia. It had significant predictive ability.”

Rather than using acoustic features of speech, like enunciation or speed, the model is just pulling from the content of the interview—the words spoken, how they’re structured. And Paschalidis says the information they put into the machine learning program is rough around the edges: the recordings, for example, are messy—low-quality and filled with background noise.

“It’s a very casual recording,” he says. “And still, with this dirty data, the model is able to make something out of it.”

That’s important, because the project was partly about testing AI’s ability to make the process of dementia diagnosis more efficient and automated, with little human involvement. In the future, the researchers say, models like theirs could be used to bring care to patients who aren’t near medical centres or to provide routine monitoring through interaction with an at-home app, drastically increasing the number of people who get screened.

According to Alzheimer’s Disease International, the majority of people with dementia worldwide never receive a formal diagnosis, leaving them shut off from treatment and care.

Rhoda Au, a co-author on the paper, says AI has the power to create “equal opportunity science and healthcare.” The study builds on the same team’s previous work, where they found AI could accurately detect cognitive impairment using voice recordings.

“Technology can overcome the bias of work that can only be done by those with resources, or care that has relied on specialized expertise that is not available to everyone,” says Au, a BU Chobanian & Avedisian School of Medicine professor of anatomy and neurobiology.

For her, one of the most exciting findings was “that a method for cognitive assessment that has the potential to be maximally inclusive—possibly independent of age, sex/gender, education, language, culture, income, geography—could serve as a potential screening tool for detecting and monitoring symptoms related to Alzheimer’s disease.”

A dementia diagnosis from home

In future research, Paschalidis would like to explore using data not just from formal clinician-patient interviews—with their scripted questions and predictable back-and-forth—but also from more natural, everyday conversations.

He’s already looking ahead to a project on if AI can help diagnose dementia via a smartphone app, as well as expanding the current study beyond speech analysis—the Framingham tests also include patient drawings and data on daily life patterns—to boost the model’s predictive accuracy.

“Digital is the new blood,” says Au. “You can collect it, analyse it for what is known today, store it, and reanalyse it for whatever new emerges tomorrow.”

This research was funded, in part, by the National Science Foundation, the National Institutes of Health, and the BU Rajen Kilachand Fund for Integrated Life Science and Engineering.

Republishers are kindly reminded to uphold journalistic integrity by providing proper crediting, including a direct link back to the original source URL.

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UK body calls for more ageing research backing

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The British Society for Research on Ageing (BSRA) is calling for more public backing in the UK for research to help people stay healthier for longer, as an alternative to charities that support research on diseases.

The greatest risk factor for disease is ageing, but we have very little charitable support for research into how to slow ageing, the organisation warns.

Many diseases such as cancers and heart disease tragically shorten lives far too early, or like Alzheimer’s and arthritis, destroy quality of life for patients and carers. There is understandably huge public charitable support for more research. However, the greatest risk factor for those diseases, and even infectious diseases like COVID, is ageing.

Yet in comparison there is currently very little support for research to understand how we can slow ageing to prevent disease. This approach may be more productive in the long term to fight disease. Furthermore, keeping people healthier for longer, or avoiding chronic diseases all together, would be the most favourable outcome.

The UK population is ageing fast, putting pressure on the NHS and the economy. Despite this pressing problem all around us, there is no accessible way for people to support research into ageing in the UK. The BSRA aims to change that.

With a very small budget and almost completely run by volunteers, the BSRA has successfully funded several small research projects but progress needs to be accelerated. More funding is needed because it takes years to see the effects of ageing, so studies are long. Also ageing affects individuals in different ways, meaning that large numbers of people must be studied to make firm conclusions.

Therefore, there is an urgency to get studies funded and the BSRA has decided to launch an ambitious fundraising campaign to boost research into ageing. Initially, the Society aims to fund a series of one year research projects at the Masters degree level at universities across the UK and with plans to raise much more in the future to support longer and more ambitious projects that will impact the lives of the general public.

Chair of the BSRA, Prof David Weinkove from Durham University, says “The time is now to really get behind research into the biology of ageing. We have fantastic researchers across the country, but they are held back by a lack of funding. Evidence-based research is needed to understand how we people can stay healthier for longer, and to then we must make that knowledge available to as many people as possible”.

Dr Jed Lye says “This is a great opportunity for the public to help, for corporations to contribute, or philanthropists wanting a large impact with a relatively small donation; every £20,000 we raise can fund an entire year of research into ageing and longevity, and gets a budding scientist their research qualification.”

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