Dirk Steijger, Mark Scheper, Robert Frans van der Willigen, Hannah Christie, Marjolein de Vugt, Hilde Verbeek, Sil Aarts
J Med Internet Res
10.2196/91968
Published: September 15, 2026
What did the researchers do?
This study explored what daily observations can tell us about the quality of life of people living with dementia in nursing homes. The researchers wanted to better understand what people do during the day, how they feel, and how their environment may influence their well-being.
How did the researchers do this?
The researchers analysed more than 5,000 observations of 151 people living with dementia in Dutch nursing homes. Using Artificial Intelligence (AI), they looked for recurring patterns in residents’ activities, emotions, social interactions, and surroundings.
What did we see?
- Many observations described people taking part in quiet, everyday activities, often in shared living spaces.
- Positive emotions, such as smiling and laughing, were frequently observed.
- Social interactions were an important part of residents’ daily lives.
- Quality of life appeared to be influenced by a combination of activities, emotions, relationships, and the environment.
What do we learn from this?
- Technology can support social connectedness, but it cannot replace human relationships.
- Meaningful social interaction depends on the knowledge, skills, and judgement of care professionals.
- Digital tools are most valuable when they help care professionals start conversations, shared activities, and social engagement.
- Technology should be designed to support person-centred care and adapt to individual needs.
What is the relevance for healthcare?
Understanding what contributes to quality of life can help care professionals provide more person-centred care. This study also shows how AI could support the analysis of daily care observations, making it easier to identify patterns that might otherwise be overlooked. Further research is needed before this can be used in routine care.
Teis Arets, Giulia Perugia, Maarten Houben and Wijnand IJsselsteijn
Frontiers in Digital Health
https://doi: 10.3389/fdgth.2026.1944047
Published: September 9, 2026
What did the researchers do?
This study explored how care professionals in dementia care support social connectedness among people with dementia. The researchers also examined how digital technologies are currently used in care settings and what role they can play in helping people stay socially connected.
How did the researchers do this?
The researchers organised five workshops with 15 care professionals from three dementia care organisations. During these workshops, participants discussed how they encourage social interaction, what challenges they face in daily practice, and how they use digital technologies to support residents.
What did we see?
- Social connectedness is not something that happens automatically. Care professionals continuously adapt their approach to each person’s needs, wishes, and situation.
- Digital technologies can help support communication, shared activities, and social interaction.
- Technology is usually used as a supporting tool, rather than as a replacement for human care and relationships.
- Care professionals play a key role in making technology meaningful and useful in practice.
What do we learn from this?
- Technology alone cannot create social connectedness.
- Meaningful social interactions depend on the knowledge, skills, and judgement of care professionals.
- Digital tools are most valuable when they support human relationships rather than replace them
What is the relevance for healthcare?
As pressure on dementia care continues to grow, digital technologies may help care professionals create opportunities for social interaction. However, technologies should be designed and implemented in ways that strengthen person-centred care and support the relationships between people with dementia, their caregivers, and care professionals
Teis Arets, Maarten Houben, Fleur van Haeren, Wijnand IJsselsteijn and Giulia Perugia
Journal of Proceedings of the 2026 CHI Conference on Human Factors in Computing SystemsAlzheimer’s Disease
https://doi.org/10.1145/3772318.3791055
Published: April 13, 2026
What did the researchers do?
This study shows how people with dementia experience the use of generative AI (such as ChatGPT) and how they respond to it. It also describes what types of social interaction and contact emerge when they work with generative AI together.
How did the researchers do this?
In each session, all three participants drew one card, creating a random combination of colours, objects/animals, and environments. The AI models were then used to create stories, images, and music based on these combinations. Participants discussed the outcomes together and adjusted the input.
What did we see?
- People with dementia worked together to decide what input to give to the AI.
- Participants often showed care and attention toward each other.
- At first, participants found it difficult to give instructions to the AI, but over time they became more familiar with how it worked.
What do we learn from this?
- Using AI supports older adults in taking (social) control themselves, rather than relying on care professionals.
- AI helps create a shared reality.
- Using AI leads to longer conversations, where participants no longer need to sustain the conversation on their own.
What is the relevance for healthcare?
This study shows that AI can support people with dementia in conversations, memories, and social contact. Together with residents, care professionals, and relatives, we continue to develop solutions that fit daily practice.
Anne A.C. Kolmans, Sascha R. Bolt, Ruslan Leontjevas, Wijnand A. Ijsselsteijn and Debby L. Gerritsen.
Journal of Alzheimer’s Disease
https://doi.org/10.1177/13872877261419067
Published: February 12, 2026
What did the researchers do?
This study focuses on measuring stigma related to dementia in the Netherlands.
The researchers translated an existing questionnaire and tested whether it works well in the Dutch context.
How did the researchers do this?
A questionnaire was administered to a representative sample of the Dutch population (524 participants). Some participants completed the questionnaire again after one month. In addition, open-ended questions were used to better understand how people think about dementia and how they interact with people with dementia.
What did we see?
- The Dutch version of the questionnaire works well and provides reliable results.
- The answers to the open questions showed that people hold different views about dementia, including feelings of pity and negative emotions.
- It also emerged that some people feel that people with dementia are less connected to reality.
What do we learn from this?
- It is possible to measure stigma related to dementia in the Netherlands in a reliable way.
- The questionnaire helps to better understand how people think about dementia and where stigma originates.
What is the relevance for healthcare?
With this questionnaire, healthcare organizations and researchers can better assess stigma related to dementia. This helps in developing interventions to reduce stigma and improve the quality of life of people with dementia.
Jinjing Fu, Ron Handels, Matthieu Arendse, Teis Arets, Ellis Bartholomeus, Marco Blom, Sascha Bolt, Tibor Bosse, Roel Boumans, Debby Gerritsen, Hans Arnold, Wijnand IJsselsteijn, Anne Kolmans, Henk Herman Nap, Baran Polat, Paul Raingeard de la Blétière, Rebecca S. Schaefer, Dirk Steijger, Sander Osstyn, Marjolein de Vugt, and Erik Buskens
Journal of Alzheimer’s Disease
https://doi.org/10.1177/13872877251415203
Published: January 28, 2026
What did the researchers do?
This study focuses on how technologies, such as assistive devices and social robots, can contribute to the quality of life and care use of people with dementia. The researchers aimed to develop a model to estimate the long-term value of these technologies.
How did the researchers do this?
The researchers used existing knowledge from the literature and input from experts to develop a model. This model includes the progression of dementia (from mild to severe), changes in care situations (home or nursing home), and mortality. It also takes into account outcomes such as the person’s functioning and the burden on informal caregivers.
What did we see?
- A model was developed that shows how care technologies can influence quality of life and care use over the entire course of the disease.
- The model takes into account different stages of dementia and changes in care settings.
- It also shows how the effects of technology can have indirect impacts, for example through functioning and caregiver burden.
What do we learn from this?
- It is possible to better understand the value of care technologies in dementia, including their long-term impact.
- A model can help estimate effects, even when long-term data is still limited.
What is the relevance for healthcare?
This model can help healthcare organizations and policymakers better assess which technologies are valuable to implement. This supports more informed decision-making when adopting and scaling care technologies in practice.
Channah Osinga, MS , Natcha Jintaganon, MS , Dirk Steijger, MS , Marjolein De Vugt, PhD , David Neal, MD, PhD
Journal of the American Medical Informatics Association
https://doi.org/10.1093/jamia/ocaf173
Published: 27 October 2025
What did the researchers do?
This study explored whether AI tools that generate images (such as DALL-E 3 and Midjourney) show bias in how they depict older adults and people with dementia.
The focus was on whether these images reinforce stereotypes or stigmatizing representations.
How did the researchers do this?
The researchers generated 1056 images using different prompts related to older adults, dementia, and neutral situations. Each prompt started with “photorealistic portrait” and described a specific scene. Four researchers analyzed the images, looking at aspects such as facial expression, setting, posture, and apparent gender.
What did we see?
- Images generated with dementia-related prompts more often showed people with negative emotions compared to neutral images.
- Both AI tools showed this pattern, although DALL-E 3 also generated some more positive images.
- There were clear differences between the two tools, with DALL-E 3 producing a wider variety of portrayals.
What do we learn from this?
- AI image generation can reinforce existing stereotypes and stigmatizing views of people with dementia.
- The way prompts are formulated strongly influences how people are depicted.
What is the relevance for healthcare?
Healthcare organizations should be aware that AI-generated images can unintentionally contribute to stigma. Being careful in how AI is used and how prompts are written can help create more inclusive and respectful representations.
de Rijke TJ, Engelsma T, Ng CH, Kaijser KKM, Nap HH, Smets EMA, Visser LNC
Journal of Medical Internet Research
https://doi.org/10.2196/64862
Published: August 2025
What did the researchers do?
This study provides an overview of digital tools that support brain health and dementia prevention for people without a dementia diagnosis. The researchers aimed to understand what kinds of tools exist, how far they are developed, and how well they have been evaluated.
How did the researchers do this?
The researchers reviewed scientific literature and analyzed 39 relevant studies.
They looked at different types of digital tools, such as tools for prevention, daily support, self-screening, and decision-making.
They also examined who participated in these studies and how developed and tested the tools were.
What did we see?
- Most digital tools are still in an early stage of development and testing.
- There is a wide variety of tools, focusing on prevention, support in daily life, and early screening.
- Study populations were often not very diverse, with limited reporting on factors such as ethnicity and digital skills.
What do we learn from this?
- There are many promising digital tools for brain health and dementia prevention, but most are not yet fully developed or validated.
- More research is needed to better understand how these tools work for different groups of people.
What is the relevance for healthcare?
Healthcare organizations should be aware that most digital tools for dementia prevention are still under development. Before large-scale implementation, more evidence and attention to diversity and usability are needed to ensure these tools fit real-world practice.
Dirk Steijger, Hannah Christie, Sil Aarts, Wijnand IJselsteijn, Hilde Verbeek, Marjolein de Vugt
Aging Research Reviews
https://doi.org/10.1016/j.arr.2025.102741
Published: June 2025
What did the researchers do?
This study explored how artificial intelligence (AI) can support the quality of life of people with dementia. The researchers aimed to understand what types of AI applications exist and how they may contribute to different aspects of quality of life.
How did the researchers do this?
The researchers conducted a scoping review of scientific and grey literature.
They analyzed 30 relevant studies that focused on AI, quality of life, and people with dementia across different care settings.
They identified common themes and types of AI applications used in these studies.
What did we see?
- Three main types of AI applications were identified: monitoring systems, social robots, and tools that support daily activities.
- Most studies focused on early feasibility and involved small-scale testing.
- People with dementia were often not actively involved in the design or research process.
What do we learn from this?
- AI has potential to support different aspects of quality of life for people with dementia, especially independence and daily functioning.
- However, most AI applications are still in an early stage and have not yet been tested extensively in real-world settings.
What is the relevance for healthcare?
Healthcare organizations should see AI as a promising but still developing tool to support quality of life in dementia care. More research and real-world validation are needed before these solutions can be widely implemented in daily practice.
Dirk R. M. Lukkien, Henk Herman Nap, Alexander Peine, Mirella M. N. Minkman, Ellen H. M. Moors & Wouter P. C. Boon
Ethics and Information Technology
https://doi.org/10.1007/s10676-025-09842-5
Published: June 2025
What did the researchers do?
This study explored how artificial intelligence (AI) can be scaled responsibly in healthcare.
The focus was on how to balance standardization (doing things in a uniform way) and customization (adapting to local contexts).
How did the researchers do this?
The researchers conducted a conceptual analysis based on existing literature.
They developed a framework in which AI systems are seen as combinations of different elements (both technical and non-technical) that can be applied differently in various healthcare settings.
What did we see?
- Standardization and customization do not have to be in conflict but can work together.
- Standardizing certain components (such as parts of the technology) can actually support flexibility in how AI is used in different contexts.
What do we learn from this?
- It is important to design AI systems in a way that allows for local adaptation.
- Thinking in flexible “building blocks” makes it easier to scale AI while still fitting different healthcare contexts.
What is the relevance for healthcare?
Healthcare organizations and policymakers should focus on flexible and adaptable AI solutions. This helps ensure that AI can be scaled responsibly while still meeting the needs of different teams, organizations, and patient groups.
Hofstede, B., Askari, S., Cuijpers, R., Zhang, C., IJsselsteijn, W. A., & Nap, H. H.
International Journal of Social Robotics
https://doi.org/10.1007/s12369-025-01267-w
Published: June 2025
What did the researchers do?
This study explored how socially assistive robots (SARs) can better support older adults.
The focus was on whether personalising the way these robots communicate can improve their acceptance and effectiveness.
How did the researchers do this?
The research consisted of two studies.
In the first study, researchers explored the communication needs of older adults when interacting with robots.
In the second study, they tested how different forms of personalisation (such as adjusting language and communication style) influenced how people responded to the robots.
What did we see?
- Different aspects of communication, such as tone of voice and message content, can be personalised.
- Personalisation had a positive effect on how persuasive the robot was perceived.
- There were also indications that personalisation may improve acceptance, although this effect was less strong.
What do we learn from this?
- Tailoring the way robots communicate can improve interactions with older adults.
- Personalisation helps make support through technology more relevant and engaging.
What is the relevance for healthcare?
Personalised communication can make social robots more effective in supporting older adults.
This may help address challenges related to ageing, such as supporting independence and daily structure in care settings.
Rucha Khot, Teis Arets, Joel Wester, Franziska Burger, Niels van Berkel, Rens Brankaert, Wijnand IJsselsteijn and Minha Lee
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
https://doi.org/10.1145/3706598.3713727
Published: 25 April 2025
What did the researchers do?
This study explored how chatbots can help people reflect on difficult future scenarios, such as developing dementia.
The focus was on how interacting with different types of chatbots influences people’s attitudes and experiences.
How did the researchers do this?
Participants interacted with a chatbot that was presented either as a regular person or as someone simulating dementia symptoms.
In some cases, the chatbot was framed as the participant’s future self.
The researchers then assessed people’s attitudes toward dementia and their experiences during the interaction.
What did we see?
- The way the chatbot was presented did not change people’s overall attitudes toward dementia.
- However, it did affect how participants experienced the interaction.
- When the chatbot was framed as their future self, participants felt a stronger emotional connection and reflected more on ageing.
- When interacting with a chatbot simulating dementia symptoms as a “stranger,” participants often felt frustrated, especially when they focused on completing a task.
What do we learn from this?
- Chatbots can help people reflect on challenging future situations, such as dementia.
- However, the impact depends on how the interaction is framed and how people expect the chatbot to communicate.
What is the relevance for healthcare?
Chatbots could be used as tools to stimulate reflection and awareness about dementia.
When designing these tools, it is important to carefully consider how the chatbot is presented to users to create meaningful and supportive experiences.
Hofstede, B. M., Askari, S. I., Lukkien, D., Gosetto, L., Alberts, J. W., Tesfay, E., Stal, M. T., van Hoesel, T., Cuijpers, R. H., Vastenburg, M. H., Bevilacqua, R., Amabili, G., Margaritini, A., Benadduci, M., Guebey, J., Trabelsi, M. A., Ciuffreda, I., Casaccia, S., IJsselsteijn, W., Revel, G. M., & Nap, H. H.
Frontiers in Robotics and AI
https://doi.org/10.3389/frobt.2025.1537272
Published: March 2025
What did the researchers do?
This study explored how socially assistive robots (SARs) can better support older adults living at home. The two SARs being used are displayed in the image below.
The focus was on understanding which factors influence how older adults and caregivers experience and accept these robots.
How did the researchers do this?
The researchers conducted a field study with 90 participants in Italy, Switzerland, and the Netherlands.
Robots were placed in the homes of older adults for a period of two to six weeks.
Interviews were held with older adults, formal caregivers, and informal caregivers at different moments during the study.
What did we see?
- Personalisation and interactivity were identified as the most important factors for a positive user experience.
- Additional factors, such as connectedness and dignity, also played an important role.
- The involvement of multiple users (older adults and caregivers) provided valuable insights into how robots are experienced in daily life.
What do we learn from this?
- The design of socially assistive robots should focus on multiple factors, especially personalisation and interactivity.
- Involving all end-users in both research and design is important to better match the needs of real-life care situations.
What is the relevance for healthcare?
Improving the design of social robots can increase their acceptance and use in home care settings.
This may help older adults live independently at home for longer, with support that better fits their needs and daily routines.