Digital technology holds a promise to improve older adults’ well-being and promote ageing in place. However, there seems to be a discrepancy between digital technologies that are developed and what older adults actually want and need. Ageing is stereotypically framed as a problem needed to be fixed, and older adults are considered to be frail and incompetent. Not surprisingly, many of the technologies developed for the use of older adults focus on care. The exclusion of older adults from the research and design of digital technology is often based on such negative stereotypes. In this opinion article, we argue that the inclusion rather than exclusion of older adults in the design process and research of digital technology is essential if technology is to fulfill the promise of improving well-being. We emphasize why this is important while also providing guidelines, evidence from the literature, and examples on how to do so. We unequivocally state that designers and researchers should make every effort to ensure the involvement of older adults in the design process and research of digital technology. Based on this paper, we suggest that ageism in the design process of digital technology might play a role as a possible barrier of adopting technology.

Background and Objectives: Involving older persons in the design process of digital technology (DT) promotes the development of technologies that are appealing, beneficial, and used. However, negative discourse on aging and ageism are potential underlying factors that could influence which and how DTs are designed and how older persons are involved in the design process. This scoping review investigates the explicit and implicit manifestations of ageism in the design process of DT.Research Design and Methods: Seven databases were screened for studies reporting on the design of DT with older persons between January 2015 and January 2020. Data regarding study and DT characteristics, discourse about older persons, and their involvement in the design process were extracted, coded, and analyzed using critical discourse analysis.Results: Sixty articles met the inclusion criteria and were included in the analysis. Various forms of exclusion of older persons from the design process were identified, such as no or low involvement, upper-age limits, and sample biases toward relatively “active,” healthy and “tech-savvy” older persons. Critical discourse analysis revealed the use of outdated language, stereotypical categorizations, and/or design decisions based on ageism in 71.7% of the studies.Discussion and Implications: A discrepancy was found between an “ideal” discourse regarding the involvement of older persons throughout the design process and actual practice. Manifestations of ageism, errors, and biases of designing DT with older persons are discussed. This article calls for more authentic inclusion of older persons and higher awareness toward the implications of ageism in the design process of DT.

Background: Chronic stress responses can lead to physical and behavioural health problems, often experienced and observed in the care of people with intellectual disabilities or people with dementia. Electrodermal activity (EDA) is a bio-signal for stress, which can be measured by wearables and thereby support stress management. However, the how, when and to what extent patients and healthcare providers can benefit is unclear. This study aims to create an overview of available wearables enabling the detection of perceived stress by using EDA. Methods: Following the PRISMA-SCR protocol for scoping reviews, four databases were included in the search of peer-reviewed studies published between 2012 and 2022, reporting detection of EDA in relation to self-reported stress or stress-related behaviours. Type of wearable, bodily location, research population, context, stressor type and the reported relationship between EDA and perceived stress were extracted. Results: Of the 74 included studies, the majority included healthy subjects in laboratory situations. Field studies and studies using machine learning (ML) to predict stress have increased in the last years. EDA is most often measured on the wrist, with offline data processing. Studies predicting perceived stress or stress-related behaviour using EDA features, reported accuracies between 42% and 100% with an average of 82.6%. Of these studies, the majority used ML. Conclusion: Wearable EDA sensors are promising in detecting perceived stress. Field studies with relevant populations in a health or care context are lacking. Future studies should focus on the application of EDA-measuring wearables in real-life situations to support stress management.

Background: Digital technologies (DTs) for older adults focus mainly on health care and are considered to have the potential to improve the well-being of older adults. However, adoption rates of these DTs are considered low. Although previous research has investigated possible reasons for adoption and acceptance of DT, age-based stereotypes (eg, those held by health care professionals) toward the abilities of older adults to use DTs have yet to be considered as possible barriers to adoption.Objective: The aim of this study was to investigate the influencing role of ageism in the context of health care professionals attitudes toward older adults’ abilities to use health care DT. A further goal was to examine if social comparison and stereotype activation affect and moderate this association.Methods: A new measurement to assess health care professionals’ attitudes toward older adults using technology (ATOAUT-10) was developed and used in 2 studies. Study 1 involved the development of the ATOAUT-10 scale using a principal component analysis and further examined health care professionals’ attitudes toward the use of health care DTs and correlations with ageism. Study 2 further explored the correlation between ageism and ATOAUT in an experimental design with health care professionals.Results: In study 1, physiotherapists (N=97) rated older adults as young as 50 years as less able to use health care DT compared to younger adults (P<.001). A multiple regression analysis revealed that higher levels of ageism, beyond other predictors, were predictive of more negative ATOAUT, (β=.36; t=3.73; P<.001). In study 2, the salience of age was manipulated. Health care professionals (N=93) were randomly assigned to rate the abilities of a young or old person to use health care DT. Old age salience moderated the correlation between ageism and ATOAUT (R2=0.19; F6,85=3.35; P=.005), such that higher levels of ageism correlated with more negative ATOAUT in the old age salient condition, but not the young condition. Stereotype activation accounted for health care professionals’ attitudes more than did the experience of working with older patients or the professionals’ age.Conclusions: Negative and ageist attitudes of health care professionals can potentially affect how older adults are viewed in relation to DT and consequently might influence actual use and adoption of technology-based treatment. Future studies should broaden the validation of the ATOAUT-10 scale on more diverse samples and focus on the discriminatory aspect of ageism and self-ageism of older adults. This study calls for a focus on ageism as a determinant of adoption of DT.

Algorithmic technologies and (large) data infrastructures, often referred to as Artificial Intelligence (AI), have received increasing attention from gerontological research in the last decade. Although there is much literature that dissects and explores the development, application, and evaluation of AI relevant to gerontology, this study makes a novel contribution by critically engaging with the theorizing in this growing field of research. We observe that gerontology’s engagement with AI is shaped by an interventionist logic that situates AI as a black box for gerontological research. We demonstrate how this black box logic has neglected many aspects of AI as a research topic for gerontology and discuss three classical concepts in gerontology to show how they can be used to open various black boxes of aging and AI in the areas: (a) the datafication of aging, (b) the political economy of AI and aging, and (c) everyday engagements and embodiments of AI in later life. In the final chapter, we propose a model of the co-constitution of aging and AI that makes theoretical propositions to study the relational terrain between aging and AI and hence aims to open the black box of AI in gerontology beyond interventionist logic.

Both rapid technological changes and (self-)ageism are pervasive challenges of the 21st century, potentially affecting older adults’ everyday functioning, health, and well-being. This systematic literature review aimed to synthesize scholarly evidence to determine the associations between everyday information and communication technology (EICT) usage and (self-)ageism as well as potential moderators. A systematic search was performed in 8 academic databases, covering the time frame from January 1995 to January 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a total of 15 articles met the inclusion criteria and were involved in the analysis. The standardized National Heart, Lung, and Blood Institute’s quality assessment tools were used for risk bias. Several studies demonstrated significant associations between EICT usage and stereotype embodiment (n = 8), stereotype threat (n = 2), and age discrimination (n = 3). Age (group), gender, and motivation were examined as potential moderators. This review provides initial evidence on the associations between (self-)ageism and EICT usage. It highlights the importance of positive subjective aging perceptions for active EICT usage in older adults, but also emphasizes the detrimental consequences of ageism in EICT learning settings and technology design on older persons’ willingness and ability to use EICT. Further ecologically valid and methodologically sound research is needed to better understand both the nature and direction of the association between EICT usage and (self-)ageism.