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Digital (consumer) services, such as ticket machines, self-checkout, and online reservations, have become increasingly important in modern society. Studies on adoption of these services and openness to using future public digital services (e.g., online voting, online taxes, electronic patient records) have mostly focused on younger adults or nonrepresentative samples among older adults. Therefore, two important questions remain that can best be addressed with representative sampling: To what extent do older adults use or are willing to use current and future digital services in their everyday lives? How do older adults evaluate the ease of use of these services?. The study included data on use of current and future digital services among a large Swiss sample of 1149 people age 65 years and older (mean age: 74.1 years, SD: 6.69). Descriptive and multivariate analyses showed that (a) established services such as cash machines were used more often than new services, such as self-checkout apps or machines. (b) Perceived ease of use is related to age, socioeconomic status, health, and interest in technology. (c) Only 8.9% had an overall positive attitude toward these digital services, and this attitude was predicted by age, gender, socioeconomic status, and interest in technology. (d) Participants were more often open to filing taxes online than voting online, and openness was predicted by age, income, and interest in technology. Today, mainly older adults with a high interest in technology use digital services. Nevertheless, potential for greater use is evident.

In 2014, two groups of scientists published open letters on the efficacy of brain-training interventions, or \"brain games,\" for improving cognition. The first letter, a consensus statement from an international group of more than 70 scientists, claimed that brain games do not provide a scientifically grounded way to improve cognitive functioning or to stave off cognitive decline. Several months later, an international group of 133 scientists and practitioners countered that the literature is replete with demonstrations of the benefits of brain training for a wide variety of cognitive and everyday activities. How could two teams of scientists examine the same literature and come to conflicting \"consensus\" views about the effectiveness of brain training? In part, the disagreement might result from different standards used when evaluating the evidence. To date, the field has lacked a comprehensive review of the brain-training literature, one that examines both the quantity and the quality of the evidence according to a well-defined set of best practices. This article provides such a review, focusing exclusively on the use of cognitive tasks or games as a means to enhance performance on other tasks. We specify and justify a set of best practices for such brain-training interventions and then use those standards to evaluate all of the published peer-reviewed intervention studies cited on the websites of leading brain-training companies listed on Cognitive Training Data (www.cognitivetrainingdata.org), the site hosting the open letter from brain-training proponents. These citations presumably represent the evidence that best supports the claims of effectiveness. Based on this examination, we find extensive evidence that brain-training interventions improve performance on the trained tasks, less evidence that such interventions improve performance on closely related tasks, and little evidence that training enhances performance on distantly related tasks or that training improves everyday cognitive performance. We also find that many of the published intervention studies had major shortcomings in design or analysis that preclude definitive conclusions about the efficacy of training, and that none of the cited studies conformed to all of the best practices we identify as essential to drawing clear conclusions about the benefits of brain training for everyday activities. We conclude with detailed recommendations for scientists, funding agencies, and policymakers that, if adopted, would lead to better evidence regarding the efficacy of brain-training interventions.

This study aims to develop a machine learning-based approach to predict adherence to gamified cognitive training using a variety of baseline measures (demographic, attitudinal, and cognitive abilities) as well as game performance data. We aimed to: (1) identify the cognitive games with the strongest adherence prediction and their key performance indicators; (2) compare baseline characteristics and game performance indicators for adherence prediction, and (3) test ensemble models that use baseline characteristics and game performance data to predict adherence over ten weeks. Using machine learning algorithms including logistic regression, ridge regression, support vector machines, classification trees, and random forests, we predicted adherence from weeks 3 to 12. Predictors included game performance metrics in the first two weeks and baseline measures. These models' robustness and generalizability were tested through five-fold cross-validation. The findings indicated that game performance measures were superior to baseline characteristics in predicting adherence. Notably, the games \"Supply Run,\" \"Ante Up,\" and \"Sentry Duty\" emerged as significant adherence predictors. Key performance indicators included the highest level achieved, total game sessions played, and overall gameplay proportion. A notable finding was the negative correlation between initial high achievement levels and sustained adherence, suggesting that maintaining a balanced difficulty level is crucial for long-term engagement. Conversely, a positive correlation between the number of sessions played and adherence highlighted the importance of early active involvement. The insights from this research inform just-in-time strategies to promote adherence to cognitive training programs, catering to the needs and abilities of the aging population. It also underscores the potential of tailored, gamified interventions to foster long-term adherence to cognitive training.

Clinical studies, especially randomized, controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long‐standing concern when applying trial results to real‐world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic review to understand the practice of generalizability assessment. We identified 187 relevant articles and systematically organized these studies in a taxonomy with three dimensions: (i) data availability (i.e., before or after trial (a priori vs. a posteriori generalizability)); (ii) result outputs (i.e., score vs. nonscore); and (iii) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but < 30% of studies reported positive generalizability results. As a priori generalizability can be assessed using only study design information (primarily eligibility criteria), it gives investigators a golden opportunity to adjust the study design before the trial starts. Nevertheless, < 40% of the studies in our review assessed a priori generalizability. With the wide adoption of electronic health records systems, rich real‐world patient databases are increasingly available for generalizability assessment; however, informatics tools are lacking to support the adoption of generalizability assessment practice.

Older drivers are at greatest risk for injury or death as a result of a car crash. In this mini-review, we outline the normative age-related changes to perceptual, cognitive, and motor abilities that contribute to increased crash risk and decreased comfort with driving, and highlight specific driving scenarios and conditions that are particularly challenging for aging road users. Adopting a person-environment fit framework, we discuss how the roadway environment can be modified to better match the abilities of the aging driver. We also review evidence for the efficacy of training interventions that aim to change the abilities and strategies of the aging driver to better match the demands of the driving environment. Evidence suggests that specific changes to the roadway and driver training strategies can bring the abilities of the older driver back into alignment with the demands of the driving task. A focus on both approaches will help ensure the safety of all road users as the number of aging drivers greatly increases over the next few decades.

Older adults tend to have insufficient health literacy, which includes eHealth literacy-the ability to access, assess, and use digital health information. Interventions using methods such as collaborative learning (CL) and individualistic learning (IL) may be effective in addressing older adults' low eHealth literacy, but little is known about the short- and long-term effects of CL versus IL on older adults' eHealth literacy. The objective of this study was to use a 3 × 2 × 3 mixed factorial design to examine older adults' learning with CL versus IL for eHealth literacy. Older adults (N=466; mean age 70.5, SD 7.2; range 60-96 years) from diverse racial and ethnic groups were randomly assigned to either the CL or IL group (233/466, 50% in each). The intervention consisted of 4 weeks of training in 2-hour sessions held twice a week. Using ANOVA and multiple regression, we focused on the main effects of learning condition and interaction between learning condition and previous computer experience. Learning method (CL or IL) and previous computer experience (experienced, new, or mixed) were between-subject variables, and time of measurement (pretest measurement, posttest measurement, and 6-month follow-up) was the within-subject variable. Primary outcome variables were eHealth literacy efficacy, computer and web knowledge, basic computer and web operation skills, information-seeking skills, and website evaluation skills. Control variables were age, sex, education, health status, race and ethnicity, income, primary language, and previous health literacy. eHealth literacy efficacy, computer and web knowledge, basic computer and web operation skills, information-seeking skills, and website evaluation skills improved significantly (P<.001 in all cases) from before to after the intervention. From postintervention measurement to 6-month follow-up, there was a significant interaction between learning condition and previous computer experience based on 1 outcome measure, computer and web operation skills (F =3.69; P=.03). To maintain computer and web operation skills 6 months after the intervention, it was more effective for people with little to no previous computer experience to learn individually, whereas for people with more previous computer experience, it was more effective to learn collaboratively. From postintervention measurement to 6-month follow-up, statistically significant decreases were found in 3 of the 5 outcome measures: eHealth literacy efficacy, computer and web knowledge, and basic computer and web operation skills (P<.001 for all 3 cases). Older adults' eHealth literacy can be improved through effective intervention, and the IL or CL condition may have little effect on short-term outcomes. However, to maintain long-term benefits, it may be best to learn collaboratively with others who have similar previous computer experience. eHealth literacy is multidimensional, with some components retained better over time. Findings suggest a need for resources to provide continuous training or periodic boosting to maintain intervention gains.

When we think of technology-savvy consumers, older adults are typically not the first persons that come to mind. The common misconception is that older adults do not want to use or cannot use technology. But for an increasing number of older adults, this is not true (Pew Internet and American Life Project 2003 ). Older adults do use technologies similar to their younger counterparts, but perhaps at different usage rates. Previous research has identified that there may be subgroups of older adults, “Silver Surfers”, whose adoption patterns mimic younger adults (Pew Internet and American Life Project 2003 ). Much of the previous research on age-related differences in technology usage has only investigated usage broadly—from a “used” or “not used” standpoint. The present study investigated age-related differences in overall usage of technologies, as well as frequency of technology usage (i.e., never, occasional, or frequent). The data were gathered through a questionnaire from younger adults ( N  = 430) and older adults ( N  = 251) in three geographically separate and ethnically diverse areas of the United States. We found that younger adults use a greater breadth of technologies than older adults. However, age-related differences in usage and the frequency of use depend on the technology domain. This paper presents technology usage and frequency data to highlight age-related differences and similarities. The results provide insights into older and younger adults’ technology-use patterns, which in turn provide a basis for expectations about knowledge differences. Designers and trainers can benefit from understanding experience and knowledge differences.

There are many products, tools, and technologies available that could provide support for older adults. However, their success requires that they are designed with older adults in mind by being aware of, and adhering to, design principles that recognize the needs, abilities, and preferences of diverse groups of older adults. Achieving good design is a process facilitated by seeing principles and guidelines in action. Design success requires understanding how to use the methods and tools available to evaluate initial ideas and prototypes. The goal of this book is to provide illustrative \"case studies\" of designing for older adults based on real design challenges faced by the researchers of the Center for Research and Education on Aging and Technology Enhancement (CREATE) over the past two decades. These case studies exemplify the use of human factors tools and user-centered design principles to understand the needs of older adults, identify where existing designs failed older users, and examine the effectiveness of design changes to better accommodate the abilities and preferences of the large and growing aging population. Features Reviews important design considerations for older adults and presents a framework for design Provides a series of real-world case studies to ground design principles and guidelines Offers a unique set and broad array of design challenges, from the design of healthcare devices, to computer systems and apps, to transportation systems and robots Gives an overview of emerging technologies, their potential benefits to older adults, anticipated design considerations, and new and emerging approaches to evaluating design Covers these topics with designers in mind, providing the most up-to-date recommendations based on the scientific literature but in an accessible, easy-to-understand, non-technical manner

Cognitive impairment and dementia pose a significant challenge to the aging population, impacting the well-being, quality of life, and autonomy of affected individuals. As the population ages, this will place enormous strain on health care and economic systems. While computerized cognitive training programs have demonstrated some promise in addressing cognitive decline, adherence to these interventions can be challenging. The objective of this study is to improve the accuracy of predicting adherence lapses to ultimately develop tailored adherence support systems to promote engagement with cognitive training among older adults. Data from 2 previously conducted cognitive training intervention studies were used to forecast adherence levels among older participants. Deep convolutional neural networks were used to leverage their feature learning capabilities and predict adherence patterns based on past behavior. Domain adaptation (DA) was used to address the challenge of limited training data for each participant, by using data from other participants with similar playing patterns. Time series data were converted into image format using Gramian angular fields, to facilitate clustering of participants during DA. To the best of our knowledge, this is the first effort to use DA techniques to predict older adults' daily adherence to cognitive training programs. Our results demonstrated the promise and potential of deep neural networks and DA for predicting adherence lapses. In all 3 studies, using 2 independent datasets, DA consistently produced the best accuracy values. Our findings highlight that deep learning and DA techniques can aid in the development of adherence support systems for computerized cognitive training, as well as for other interventions aimed at improving health, cognition, and well-being. These techniques can improve engagement and maximize the benefits of such interventions, ultimately enhancing the quality of life of individuals at risk for cognitive impairments. This research informs the development of more effective interventions, benefiting individuals and society by improving conditions associated with aging.

This study examined the feasibility of using tailored text messages to promote adherence to longitudinal protocols and determined what facets of text message tone influence motivation. Forty-three older adults (Mage = 73.21, SD = 5.37) were recruited to engage in video-game-based cognitive training for 10 consecutive days. Participants received encouraging text messages each morning that matched their highest or lowest ranking reasons for participating in the study, after which they rated how effective each message was in motivating them to play the games that day. After 10 days, participants rated all possible messages and participated in semi-structured interviews to elicit their preferences for these messages. Results showed that messages matching participants’ reasons for participating were more motivating than mismatched messages. Further, participants preferred messages that were personalized (i.e., use second person voice) and in formal tones. Messages consistent with these preferences were also rated as more motivating. These findings establish the feasibility of using message tailoring to promote adherence to longitudinal protocols and the relevance of tailoring messages to be personal and formal.