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At what point do dementia patients need 24-hour care?



The decision to move a family member with dementia into a care home is not easy. Agetech World explores when it’s the right time.

Being a family member of someone with dementia, it may become difficult to decide at what point they need 24-hour care.

A person with dementia will need more care and support as their condition progresses but it can be hard to know when the time is right and who should make this decision. The main thing to think about is whether the dementia patient’s needs are met at home; is moving into a care home in their best interest?

However, moving the person into a care home is not the only option: there are other options that would enable them to live at home while being assisted. 

Here are some signs that may aid in deterring the right time for 24-hour care:

Aggressive behaviours 

Aggressive behaviours should not be taken personally when it comes to dementia patients. This kind of behaviour is the response of the patient to the false signals generated in their brain or sometimes simply an attempt to communicate.

Aggressive behaviours can be one of the first signs for full-time care as it can become difficult for a singular carer or family member to control these behaviours on their own.

Patient’s safety

With dementia, judgement and memory become poor and the person becomes easily exposed to domestic accidents. To prevent this, patients start needing constant monitoring and 24-hour care becomes integral for home safety to such an extent that a person cannot even identify common hazards.

Caregiver stress 

Alzheimer’s caregivers frequently report experiencing high levels of stress as it can be overwhelming to take care of a loved one with Alzheimer’s or other dementia.

It is not unusual that a dementia caregiver forgets to set boundaries, creates unrealistic and impractical expectations, and ends up burnt out. In addition to this, the overwhelming needs of the loved ones can make the caregiver frustrated and stressed.

The main symptoms of caregiver stress are denial, anger, social withdrawal, anxiety, depression, exhaustion, sleeplessness, irritability, lack of concentration and health problems.

Mobility issues

Dementia patients tend to be more mobile than in many other medical conditions. The effects of dementia can cause wandering which is defined as a clinical symptom characterised by frequent, repetitive, temporally confused behaviour manifesting as random.

But, dementia is likely to have a big physical impact on the person in the later stages of the condition. They may gradually lose their ability to walk, stand or get themselves up from chair or bed. They may also be more likely to fall.

Therefore, deciding when 24-hour care is needed is not an easy task and it differs from patient to patient. The decision must be made as a consequence of the advancing symptoms of the condition, keeping in mind the best interests of the patient, of the carer and of the family. 


The role of AI in early detection of dementia



By Mike Battista, Director, Science and Research, Creyos

Dementia is a debilitating condition affecting millions all over the world, characterised by a decline in cognitive functions such as memory, reasoning, and communication.

Early detection is advantageous in planning for and managing diseases that cause dementia, as early detection can identify mild issues with cognition while pathology in the brain is still minimal, and potential interventions have the greatest chance of slowing decline.

Early modification of lifestyle and medical risk have been estimated to prevent 40 per cent of dementia cases.

Artificial Intelligence (AI) is emerging as a ubiquitous tool in healthcare, offering new hope for early dementia detection through advanced data analysis and pattern recognition.

Dementia consists of a range of symptoms that can include memory loss, confusion, atypical behaviour, and difficulty completing everyday activities.

Mike Battista

Diagnosing dementia early is challenging with traditional methods, as so many of the most common diagnostic tools rely on significant cognitive decline.

However, early detection can lead to better management of dementia slowing the progression of symptoms and improving the overall quality of life for patients.

With these benefits in mind, innovative uses of AI are needed to create more sensitive tools that can detect the most subtle signs of dementia earlier than ever before.

AI in Identifying Dementia Markers

In relation to dementia, AI can search extensive datasets to find markers indicative of the condition.

Then, by analysing massive amounts of data, AI is advancing healthcare capabilities by identifying patterns indicative of the condition that clinicians might miss.

Understanding these data markers is imperative for accurate detection.

For instance, AI can analyse cognitive performance, language use, and even facial expressions to differentiate between healthy individuals and those showing early signs of dementia.

A strong understanding of these markers comes from reviewing scientific literature and adopting a data-driven approach.

For researchers, AI systems can be trained on large datasets to help discover the subtle differences in performance and behaviour associated with early dementia.

Clinicians can make use of the models trained by researchers to accurately identify patients with early signs of dementia.

Data from clinicians can also be used to further train AI models, improving the accuracy of early detection over time.

The Importance of High-Quality Data

High-quality, clean data are essential for AI to function effectively in detecting dementia.

These data must be accurate and comprehensive, containing the “truth” about individuals’ health status.

One of the biggest challenges in healthcare AI is obtaining this level of data, as it involves building a repository of information that includes both individuals with dementia and healthy individuals.

To overcome this challenge, there are various initiatives to gather diverse data.

These include gathering diagnostic information from patients and creating data partnerships between healthcare practices and research organisations.

These data partnerships are not only interesting for research purposes but also help gather high-quality real-world data from patient populations that can be used to train AI systems for practical clinical application.

Longitudinal datasets are also key to achieving early diagnosis.

They are required to answer research questions about which early signs progress into dementia, which lifestyle factors slow progression over time, and more.

However, gathering these datasets is a complex process.

It requires collecting data from a large group of healthy individuals at a specific time and tracking who develops conditions like mild cognitive impairment or dementia over time.

This involves at least two time points with good follow-up, minimal dropout, and accurate diagnoses, making it a complicated dataset to get right.

Additionally, there is a great deal of selection bias in the patients who are repeatedly tested.

That is because these patients are likely more proactive about addressing their health, and/or more likely to already have issues and seek medical attention because of concerns about their cognition.

What is needed are large scale, randomised controlled studies where people are recruited ahead of time and followed throughout their health journey with state-of-the-art tools to assist in the process.

For example, the Maintain Your Brain trial followed thousands of older participants over three years to examine the effectiveness of lifestyle interventions meant to slow cognitive decline.

They used online cognitive testing from Creyos to ensure that testing was easy for participants, but provided high-quality data for the researchers.

normative dataset from healthy individuals allows researchers to compare individuals with a known condition to the general population, ensuring AI models can accurately distinguish the two groups.

Building and maintaining these datasets is a complex process but is critical for the success of AI applications.

An example of successful implementation of machine learning is the Creyos dementia screener, which used data from thousands of neurology clinic patients and a health normative database to train a model that accurately detects individuals with subtle cognitive impairments.

Current Success and Future Potential

Several success stories illustrate AI’s potential in early dementia detection.

For instance, pilot programmes using AI have shown promising results in identifying early cognitive decline through routine health assessments and patient interactions.

Examples of how AI recorded observations include:

  • How the patient moved the computer mouse while performing assessments
  • The time taken by the patient to read the tutorial
  • Passive data gathering such as device motion data (noting a person’s gait) handwriting, virtual reality integration (to test for driving ability), facial expression and speech pattern analysis.

These pilot programmes demonstrate novel ways to exploit AI’s capability to provide real-time monitoring and personalised treatment plans, tailored to individual patients’ needs.

It’s clear that the future potential of AI in dementia care is immense.

As technology advances, AI could enable even more precise and individualised healthcare, offering the earliest interventions and continuous monitoring that could further slow the progression of dementia.

This proactive approach could revolutionise how we manage and treat dementia, improving patient outcomes.

Ethical Considerations and Challenges

While AI offers great potential, it also raises ethical considerations and challenges.

Privacy concerns regarding patient data must be addressed, ensuring that data is securely handled, and patients’ confidentiality is maintained.

Additionally, the bias that can get baked into some AI systems must be scrutinised to maintain accuracy, and avoid false positives and negatives, which could lead to misdiagnosis and inappropriate treatments.

Ethical use of AI in healthcare involves balancing technological advancements with patient rights.

Furthermore, AI cannot replace human decision-making. Machine learning models and AI software can enhance human judgment, but a physicians must always make the final call when it comes to diagnosing dementia

AI holds tremendous promise in the early detection of dementia, offering tools that can analyse complex data and identify subtle signs of disruptions to cognition.

To fully realise this potential, continued research and investment in AI technologies and high-quality data collection are essential.

About the Author – Mike Battista, Director, Science and Research, Creyos 

Mike Battista is the Director of Science and Research at Creyos.

His interests and science communication focus on brain health, cognition, and neuropsychological testing.

He received his PhD in personality and measurement psychology at Western University in 2010 and has been exploring the intersection of science and technology ever since.

For more information about Creyos Health visit 

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



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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How AI speech analysis could predict dementia onset



AI could be used to predict whether a person will develop Alzheimer’s-associated dementia by simply analysing their speech, scientists believe.

Researchers at Boston University are exploring how analysis of speech patterns via a machine learning model could detect with a high degree of accuracy whether someone with mild cognitive impairment will develop Alzheimer’s-associated dementia within six years

They say their model can predict, with an accuracy rate of 78.5 percent, 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 make earlier diagnoses, the researchers say their work could also help make cognitive impairment screening more accessible by automating parts of the process;  with no expensive lab tests, imaging exams, or office visits required.

Ioannis (Yannis) Paschalidis, director of the Boston University Rafik B. Hariri Institute for Computing and Computational Science & Engineering, says: “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.

“We hope, as everyone does, that there will be more and more Alzheimer’s treatments made available.

“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.”

The project involves a multidisciplinary team of engineers, neurobiologists, and computer and data scientists.

“We hope, as everyone does, that there will be more and more Alzheimer’s treatments made available,” says Paschalidis.

“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.”

To train and build their new model, the researchers turned to data from one of the oldest and longest-running studies in the US —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 centers 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 coauthor 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.

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.”

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