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
Abstract
Background: People with Alzheimer’s disease or other types of dementia may experience stigma, which can influence their quality of life. Valid measurement instruments of public dementia-related stigma are lacking.
Objective: We aimed to translate and validate the 16-item Dementia Public Stigma Scale (DePSS) in Dutch.
Materials and Methods: A survey was conducted among a nationally representative sample of the Dutch population (n = 524). A subset (n = 145) completed the DePSS again after one month. Following validation guidelines, floor and ceiling effects, structural validity, internal consistency, and test-retest reliability were assessed. We used open-ended questions to investigate content validity. The responses provided insights into respondents’ perceptions of dementia and their interactions with people with dementia.
Results: Forward-backward translation required minor adaptations. No floor or ceiling effects were observed. Confirmatory factor analysis indicated an acceptable fit (CFI = 0.988, RMSEA = 0.073, SRMR = 0.065). Internal consistency (α = 0.82, ω = 0.79) and test-retest reliability (ICC = 0.82, 95%CI 0.76–0.89) were good, with no significant differences between test and retest scores (t(144) = 0.135, p = .893). Responses to open-ended questions were largely clustered under DePSS items, indicating good content validity. Additional themes were disconnection from present reality; feeling pity for people with dementia; and manifestations of negative emotions.
Conclusions: The Dutch DePSS demonstrated good psychometric properties. Together with other versions, these findings enhance the generalizability of the DePSS across diverse populations. Further validation and application of the DePSS will help deepen our understanding of dementia-related stigma and may also inform stigma reduction interventions.
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
Abstract
Background: Technologies such as assistive devices and social robots show promise in supporting people with dementia and their caregivers. However, their long-term cost-effectiveness remains unclear, and existing health-economic models are limited in capturing the relevant outcomes.
Objective: This study aims to conceptualize a health-economic model to assess the potential impact of care technologies in dementia care on lifetime quality of life and care use.
Materials and Methods: We summarized an impact pathway of three care technologies and conceptualized a health-economic model to estimate the long-term impact on quality of life and care use, drawing on literature and multidisciplinary expert input.
Results: We conceptualized a cohort-based Markov state-transition model simulating states of dementia severity progression (mild, moderate, severe), care setting transitions (no formal care, home care, nursing home), and mortality. Intervention effects are modeled through surrogate outcomes such as functional status and caregiver burden associated to care transitions and quality of life.
Conclusions: This model offers a framework for early health technology assessment of assistive technologies in dementia, supporting extrapolation of effects beyond limited trial data. Future work should focus on developing and operationalizing this model, applying it to establish the value of dementia care technologies.
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
Abstract
Objective: This content analysis study investigates potential biases in image generation by 2 artificial intelligence (AI) tools, DALL-E 3 and Midjourney, in portraying older adults and individuals living with dementia. Despite widespread use of generative AI in various sectors, there is limited research on how these models might perpetuate stereotypes and stigmatization through their images.
Materials and Methods:1056 images were generated using specified prompts categorized into 3 groups: general older adults, dementia-related, and control. Each prompt began with “photorealistic portrait” followed by specific scene descriptions. Four researchers conducted content analysis on each generated image, focusing on factors, such as portrait style, setting, posture, apparent sex of subjects, and emotional affect. The analysis was executed with blinding and randomization protocols to ensure unbiased assessment. Chi-square tests examined the relationship between prompt categories and variables.
Results: Results revealed significant disparities in depictions of older adults and those with dementia compared with control images. Both models more often portrayed subjects in response to dementia-related prompts with negative affect, in less favorable emotional states. However, DALL-E 3 also generated more personas displaying positive affect in response to these prompts. Variations in depiction styles between the 2 AI models were noted, with DALL-E 3 showing a broader diversity of outputs.
Discussion and Conclusions: The findings highlight AI’s potential to reinforce stigmatizing stereotypes through biased image generation. Recommendations include selecting prompts carefully to avoid negative depictions and advocating for greater AI explainability and inclusivity by design. Future research should explore other AI models, other forms of bias, and strategies to mitigate biases.
Keywords: artificial intelligence, ageism, dementia, bias, ethics
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
Abstract
Background: The field of Alzheimer disease (AD) has been moving toward earlier detection, personalized assessment of dementia risk, and dementia prevention. In the near future, a gap is expected between the growing demand for Alzheimer-related health care and a shrinking workforce. Responsibility is increasingly assigned to individuals to take an active role in their own brain health management and dementia prevention. Digital tools are thought to offer support regarding these processes.
Objective: The aim of this scoping review is to create an overview of digital tools published in scientific literature in the context of AD and dementia aimed at people without an AD or dementia diagnosis as primary end users interacting with these digital tools. Additionally, we aim to gain insight into study sample diversity, the stage of maturity and evaluation of these tools, and recommended future directions.
Method: PubMed, IEEE Xplore, Ovid, and Web of Science were searched in January 2023, using terms related to AD and dementia, (pre-)disease stages, digital tools, and various purposes of digital tools. Two independent reviewers screened the titles and abstracts of 2811 records and subsequently 408 full-text articles, based on inclusion and exclusion criteria. Articles on tools targeting those with an AD or dementia diagnosis were excluded. Data extraction included information on the sample characteristics, the digital tool, stage of maturity and evaluation, and future (research) directions.
Results: We included 39 articles, which were aimed at primary prevention (14/39, 36%), secondary prevention (11/39, 28%), daily life support (8/39, 21%), self-administered screening (4/39, 10%), or decision-making (2/39, 5%). Variation in the study sample emerged regarding cognitive abilities (healthy: 11/39, 28%; mild cognitive impairment: 12/39, 31%; [subjective] cognitive impairment: 9/39, 23%; “no dementia”: 1/39, 3%; and variation of cognitive abilities: 6/39, 15%). Less variation was found regarding sex (>50% female: 27/39, 69%), education (>50% high education: 13/39, 33%), and age (>50% >60 y: 23/39, 59%). Few articles reported on ethnicity (12/39, 31%) and digital literacy (11/39, 28%). Most tools were in an early evaluation and maturity stage (31/39, 80%), comprising preprototyping (1/35, 3%), prototyping (15/35, 43%), pilot testing (19/35, 54%), efficacy testing (18/40, 45%), usability testing (12/40, 30%), and feasibility testing (10/40, 25%). Future (research) directions comprised the need for further tool development, attention to diversity, and study advancements, such as large-scale longitudinal studies.
Conclusion: Almost 80% of tools as reported on in academic literature are in early development comprising early stages of maturity and evaluation. Studies and evidence gathered for digital tools developed in the context of AD or dementia aimed at people without an AD or dementia diagnosis are thus preliminary and further development, research, and policy are required before these tools can be implemented for assessing, supporting, and preventing cognitive decline.
Keywords: brain health; digital tools; diversity; eHealth; electronic health; equity; future directions; impact evaluation; implementation; mHealth; mobile health; stage of maturity
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
Abstract
Background: Dementia has an impact on the quality of life (QoL) of people with dementia. Tailored services are crucial for improving their QoL. Advances in artificial intelligence (AI) offer opportunities for personalised care, potentially delaying institutionalisation and enhancing QoL. However, AI’s specific role in approaches to support QoL for people with dementia remains unclear. This scoping review aims to synthesise the scientific evidence and grey literature on how AI can support the QoL of people with dementia.
Method: Following Joanna Briggs Institute guidelines, we searched PubMed, Scopus, ACM Digital Library, and Google Scholar in January 2024. Studies on AI, QoL (using Lawton’s four-domain QoL definition), and people with dementia across various care settings were included. Two reviewers conducted a two-stage screening, and a narrative synthesis identified common themes arising from the individual studies to address the research question.
Results: The search yielded 5.467 studies, after screening, thirty studies were included. Three AI categories were identified: monitoring systems, social robots, and AI approaches for performing activities of daily living. Most studies were feasibility studies, with little active involvement of people with dementia during the research process. Most AI-based approaches were monitoring systems targeting Lawton’s behavioural competence (capacity for independent functioning) domain.
Conclusion: This review highlights that AI applications for enhancing QoL in people with dementia are still in early development, with research largely limited to small-scale feasibility studies rather than demonstrating clinical effectiveness. While AI holds promise, further exploration and rigorous real-world validation are needed before AI can meaningfully impact the daily lives of people with dementia.
Keywords: Artificial intelligence; Dementia; Long-term care; Quality of life; Scoping review.
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
Abstract
Background: Healthcare systems are increasingly investing in artificial intelligence (AI) to address workforce shortages, rising demand, and efficiency pressures. Public policy simultaneously emphasizes the need to scale these innovations. However, scaling is not simply replication; it requires adaptation to heterogeneous local contexts. This creates tensions between standardization and customization in the responsible scaling of healthcare AI.
Objective: This paper examines how tensions between standardization and customization can be reconciled to enable responsible scaling of AI in healthcare. It proposes a configurational perspective to conceptualize this alignment.
Method: The paper develops a conceptual analysis grounded in the notion of socio-technical configurations. AI systems are conceptualized as assemblages of technological and non-technological components that are integrated differently across local contexts. Relevant literature on innovation scaling and socio-technical systems informs the framework.
Results: The analysis demonstrates that standardization and customization are not mutually exclusive but can function synergistically within AI configurations. Standardization at the level of components and interoperability expands configurational possibilities, thereby enabling context-sensitive customization at the architectural level. This configurational approach supports flexible yet coherent scaling strategies.
Conclusion: Responsible scaling of healthcare AI requires deliberate attention to configurability. Policymakers and innovators should promote modularity and interoperability to support local adaptation. Conceptualizing AI ecosystems as socio-technical configurations offers a framework for aligning standardization and customization in heterogeneous healthcare settings.
Keywords: Artificial intelligence · Healthcare · Scaling · Responsible innovation · Configurations · Standardization · Customization