The Benefits of Technology for Engaging Aging Adults: Findings From the PRISM 2.0 Trial.

Background and Objectives: Technology has potential for providing support for aging adults. This study evaluated the Personal Reminder Information and Social Management 2.0 (PRISM 2.0) software, in terms of enhancing social engagement and quality of life, and decreasing loneliness among older adults. Research Design and Methods: The randomized field trial conducted in diverse living contexts (rural locations, senior housing, and assisted living communities [ALC]). Two hundred and forty-five adults, aged 64 to 99 years, were randomly assigned to the PRISM 2.0 (integrated software system designed for aging through an iterative design process) or a Standard Tablet (without PRISM) Control condition, where participants received the same amount of contact and training as those in the PRISM 2.0 condition. Primary outcomes included measures of loneliness, social support, social connectedness, and quality of life. Secondary outcomes included measures of social isolation, mobile device proficiency, and technology readiness. Data were collected at baseline and 6 and 9 months postrandomization. This article focuses on the 6-month outcomes due to coronavirus disease 2019-related data challenges at 9 months. Results: Contrary to our hypothesis, participants in rural locations and senior housing in both conditions reported less loneliness and social isolation, and greater social support and quality of life at 6 months, and an increase in mobile device proficiency. Participants in the ALCs in both conditions also evidenced an increase in mobile device proficiency. Improvements in quality of life and health-related quality of life were associated with decreases in loneliness. Discussion and Implications: This study provides compelling evidence about the benefits of technology for older adults in terms of enhancing social outcomes and quality of life. However, the findings also underscore that for technology applications to be successful, they need to be adapted to the abilities and needs of the user group and instructional support needs to be provided. Clinical Trials Registration #: NCT03116399.

Are Virtual Assistants Trustworthy for Medicare Information: An Examination of Accuracy and Reliability.

BACKGROUND AND OBJECTIVES: Advances in AI-based virtual assistants provide a potential opportunity for older adults to use this technology in the context of health information-seeking. Meta-analysis on trust in AI shows that users are influenced by the accuracy and reliability of the AI trustee. We evaluated these dimensions for responses to Medicare queries. RESEARCH DESIGN AND METHODS: During the summer of 2023, we assessed the accuracy and reliability of, Alexa, Google Assistant, Bard, and ChatGPT-4, on Medicare terminology and general content from a large, standardized question set. We compared the accuracy of these AI systems to that of a large representative sample of Medicare beneficiaries who were queried twenty years prior. RESULTS: Alexa and Google Assistant were found to be highly inaccurate when compared to beneficiaries' mean accuracy of 68.4% on terminology queries and 53.0% on general Medicare content. Bard and ChatGPT-4 answered Medicare terminology queries perfectly and performed much better on general Medicare content queries (Bard = 96.3%, ChatGPT-4 = 92.6%) than the average Medicare beneficiary. About one month to a month-and-a-half later, we found that Bard and Alexa's accuracy stayed the same, while ChatGPT-4's performance nominally decreased, and Google Assistant's performance nominally increased. DISCUSSION AND IMPLICATIONS: LLM-based assistants generate trustworthy information in response to carefully phrased queries about Medicare, in contrast to Alexa and Google Assistant. Further studies will need to determine what factors beyond accuracy and reliability influence the adoption and use of such technology for Medicare decision-making.

Adherence Promotion With Tailored Motivational Messages: Proof of Concept and Message Preferences in Older Adults.

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.

Terms of Engagement: Understanding the Motivations, Preferences, and Attitudes of Older Adults Toward Mobile Cognitive Assessment and Training.

BACKGROUND AND OBJECTIVES: The future of cognitive assessment is likely to involve mobile applications for smartphones and tablets; cognitive training is also often delivered in these formats. Unfortunately, low adherence to these programs can hinder efforts at the early detection of cognitive decline and interfere with examining cognitive training efficacy in clinical trials. We explored factors that increase adherence to these programs among older adults. RESEARCH DESIGN AND METHODS: Focus groups were conducted with older adults (N = 21) and a younger adult comparison group (N = 21). Data were processed using reflexive thematic analysis with an inductive, bottom-up approach. RESULTS: Three primary themes related to adherence were developed from the focus group data. Switches of engagement reflects factors that must be present; without them, engagement is unlikely. Dials of engagement reflects a cost-benefit analysis that users undergo, the outcome of which determines whether a person will be more or less likely to engage. Bracers of engagement reflects factors that nudge users toward engagement by minimizing barriers associated with the other themes. Older adults in general were more sensitive to opportunity costs, preferred more cooperative interactions, and were more likely to mention technology barriers. DISCUSSION AND IMPLICATIONS: Our results are important for informing the design of mobile cognitive assessment and training apps for older adults. These themes provide guidance about ways apps could be modified to increase engagement and adherence, which in turn can more effectively facilitate the early detection of cognitive impairment and the evaluation of cognitive training efficacy.

Adaptação transcultural do Mobile Proficiency Questionnaire (Questionário de Proficiência em Dispositivos Móveis) e validade de conteúdo para o português brasileiro / Cross-cultural adaptation of the Mobile Device Proficiency Questionnaire and content validity for Brazilian Portuguese

Resumo Objetivo Apresentar o processo de adaptação transcultural do Mobile Device Proficiency Questionnaire na população idosa brasileira e sua validade de conteúdo. Método Trata-se de um estudo de validação. Foram realizadas a tradução, retrotradução, análise do comitê de especialistas e pré-teste com 32 pessoas idosas para avaliar a equivalência semântica e cultural. Resultados A versão brasileira do instrumento manteve as categorias da versão original, avaliando de tarefas básicas a avançadas, divididas em oito subescalas avaliativas: funções básicas, comunicação, armazenamento de dados e arquivos, internet, calendário, entretenimento, privacidade e soluções de problemas e gerenciamento de software. Nesses domínios são investigadas a facilidade de uso e a experiência com as funções do dispositivo móvel. Conclusão A adaptação transcultural mostrou que o instrumento avaliativo é uma ferramenta útil e com alto potencial para a avaliação da proficiência de pessoas idosas no uso de dispositivos móveis. O instrumento permite estabelecer um panorama das competências digitais na população idosa, além de favorecer no processo de programação dos serviços de inclusão digital.

Abstract Objective To present the process of cross-cultural adaptation of the Mobile Device Proficiency Questionnaire in the Brazilian older population and its content validity. Method This is a validation study. Translation, back-translation, analysis by an expert committee, and also a pre-test with 32 older adults were conducted to assess semantic and cultural equivalence. Results The Brazilian version of the instrument maintained the categories of the original version, evaluating tasks ranging from basic to advanced, divided into eight evaluative subscales: basic functions, communication, data and file storage, internet usage, calendar, entertainment, privacy, and problem-solving and software management. Within these domains, the ease of use and experience with mobile device functions are examined. Conclusion The cross-cultural adaptation demonstrated that the evaluative instrument is a useful tool with high potential for assessing the proficiency of older adults in using mobile devices. The instrument enables the establishment of a panorama of digital competencies in the older population, facilitating the planning of digital inclusion services.

Comparing the effectiveness of two theory-based strategies to promote cognitive training adherence.

This study compared the effectiveness of two theory-based strategies to promote cognitive training adherence among older adults (Mage = 70 years, SD = 4.42, range = 64-84). Strategies incorporated either (a) elements of implementation intention formation or (b) positive message framing, both of which have been found to promote adherence to health behaviors in other domains. Participants (N = 120) were asked to engage in technology-based cognitive training at home comprised of seven gamified neuropsychological tasks. In Phase 1 (structured), participants were provided a schedule that required engagement in 1 hr of cognitive training 5 days each week over 2 months. In Phase 2 (unstructured), participants were instructed to engage with the intervention as much as they desired for 1 month. Contrary to expectations, neither the implementation intention nor positive message framing produced greater adherence relative to control as measured by the total number of training sessions completed in each phase. However, exploratory analysis indicated a greater likelihood of intervention engagement for participants assigned to the implementation intention condition on many days of the intervention, though the trajectory of engagement decline was similar for all three groups. Measures of cognition, attitudes/personality, and technology proficiency also did not predict adherence over either phase. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Understanding Barriers to the Collection of Mobile and Wearable Device Data to Monitor Health and Cognition in Older Adults: A Scoping Review.

Advances in technology have made continuous/remote monitoring of digital health data possible, which can enable the early detection and treatment of age-related cognitive and health declines. Using Arksey and O'Malley's methodology, this scoping review evaluated potential barriers to the collection of mobile and wearable device data to monitor health and cognitive status in older adults with and without mild cognitive impairment (MCI). Selected articles were US based and focused on experienced or perceived barriers to the collection of mobile and wearable device data by adults 55 years of age or older. Fourteen articles met the study's inclusion criteria. Identified themes included barriers related to usability, users' prior experiences with health technologies, first and second level digital divide, aesthetics, comfort, adherence, and attitudinal barriers. Addressing these barriers will be crucial for effective digital data-collection among older adults to achieve goals of improving quality of life and reducing care costs.

Predicting Older Adults' Continued Computer Use After Initial Adoption.

Background and Objectives: Sustained computer and internet use have the potential to help older adults in various aspects of their lives, making predicting sustained use a critical goal. However, some factors related to adoption and use (e.g., computer attitudes) change over time and with experience. To understand these dynamics, the current study modeled changes in constructs related to computer use after initial computer adoption and examined whether these changes predict continued use. Research Design and Methods: We used data from the computer arm (N = 150, MAge = 76.15) of a 12-month field trial examining the potential benefits of computer use in older adults. Individual differences identified in the technology acceptance literature (perceived usefulness, ease of use, computer interest, computer self-efficacy, computer anxiety, quality of life, social isolation, and social support) were measured before (baseline), during (Month 6), and after the intervention (post-test). Univariate and bivariate latent change score models examined changes in each predictor and their potential causal relationship with use. Results: Results demonstrated large interindividual differences in the change patterns of individual difference factors examined. Changes in perceived usefulness, perceived ease of use, computer interest, computer self-efficacy, and computer anxiety were correlated with but not predictive of change in use. Discussion and Implications: Our findings demonstrate the limitation of popular constructs in technology acceptance literature in predicting continued use and point out important gaps in knowledge to be targeted in future investigations.

Using Immersive Virtual Reality to Enhance Social Interaction Among Older Adults: A Cross-Site Investigation.

Background and Objectives: Virtual reality (VR) applications are increasingly being targeted toward older adults as a means to maintain physical and cognitive skills and to connect with others, especially during the coronavirus disease 2019 era. Our knowledge about how older adults interact with VR is limited, however, since this is an emerging area and the related research literature is still rather slim. The current study focused specifically on older adults' reactions to a social-VR environment, examining participant's views about the possibility of meaningful interactions in this format, the impacts of social-VR immersion on mood and attitude, and features of the VR environment that affected these outcomes. Research Design and Methods: The researchers designed a novel social-VR environment with features intended to prompt conversation and collaborative problem-solving among older adults. Participants were recruited from 3 diverse geographic locations (Tallahassee, FL; Ithaca, NY; and New York City, NY), and were randomly assigned to a partner from one of the other sites for social-VR interaction. The sample consisted of 36 individuals aged 60 and older. Results: Reactions to the social VR were quite positive. Older adults reported high levels of engagement in the environment and perceived the social VR to be enjoyable and usable. Perceived spatial presence was found to be a central driver of positive outcomes. A majority of the participants indicated a willingness to reconnect with their VR partner in the future. The data also identified important areas for improvement that were of concern to older adults, such as the use of more realistic avatars, larger controllers more suited to aging hands, and more time for training/familiarization. Discussion and Implications: Overall, these findings suggest that VR can be an effective format for social engagement among older adults.

Understanding older adults' attitudes toward mobile and wearable technologies to support health and cognition.

The use of technology to facilitate remote patient monitoring and virtual care is desirable due to the challenges of providing healthcare during the COVID-19 pandemic and the need for more efficient and effective methods to care for the expanding older adult population. Further, the collection and sharing of patient generated health data (PGHD) through these technologies holds promise with respect to improving outcomes and reducing the cost of care by facilitating the early detection and treatment of cognitive and health problems. Despite the potential benefits of these technologies, their promise might be hampered by low rates of acceptance and adoption among older adults. In an online survey, we assessed community-dwelling older adults' (N = 92) attitudes towards the use of wearable and mobile technologies for (1) predicting cognitive decline, (2) assisting with adherence to healthy activities, and (3) collecting self-report data to understand current and predict future health states. Participants generally agreed hypothetical technology solutions would be useful (M = 4.20, SD = 0.70 on a 1-5 agreement scale; 5 = "strongly agree"), that they were interested in learning more about these technologies (M = 4.04, SD = 0.74), and that they would be willing to adopt these technologies (M = 3.83, SD = 0.93), though attitudes varied. Although participants were generally positive toward these technologies, they were relatively neutral in terms of their agreement that privacy of generated data was a concern (M = 2.92, SD = 1.02). Privacy concerns were associated with lower interest and willingness to adopt. More positive general technology attitudes and higher mobile device proficiency were associated with greater acceptance and willingness to adopt these technologies. Finally, poorer self-rated health was related to negative attitudes toward these technologies. These findings highlight barriers and potential targets for intervention to increase uptake of these and similar technologies among older adults who may be reluctant to adopt remote monitoring technologies.

Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy.

As the population ages, the number of older adults experiencing mild cognitive impairment (MCI), Alzheimer's disease, and other forms of dementia will increase dramatically over the next few decades. Unfortunately, cognitive changes associated with these conditions threaten independence and quality of life. To address this, researchers have developed promising cognitive training interventions to help prevent or reverse cognitive decline and cognitive impairment. However, the promise of these interventions will not be realized unless older adults regularly engage with them over the long term, and like many health behaviors, adherence to cognitive training interventions can often be poor. To maximize training benefits, it would be useful to be able to predict when adherence lapses for each individual, so that support systems can be personalized to bolster adherence and intervention engagement at optimal time points. The current research uses data from a technology-based cognitive intervention study to recognize patterns in participants' adherence levels and predict their future adherence to the training program. We leveraged the feature learning capabilities of deep neural networks to predict patterns of adherence for a given participant, based on their past behavior. A separate, personalized model was trained for each participant to capture individualistic features of adherence. We posed the adherence prediction as a binary classification problem and exploited multivariate time series analysis using an adaptive window size for model training. Further, data augmentation techniques were used to overcome the challenge of limited training data and enhance the size of the dataset. To the best of our knowledge, this is the first research effort to use advanced machine learning techniques to predict older adults' daily adherence to cognitive training programs. Experimental evaluations corroborated the promise and potential of deep learning models for adherence prediction, which furnished highest mean F-scores of 75.5, 75.5, and 74.6% for the Convolution Neural Network (CNN), Long Short-Term Memory (LSTM) network, and CNN-LSTM models respectively.

Investigating the behavioral mechanisms of action video game effects in a complex transfer task.

Over the past few decades, researchers have argued that playing action video games can substantially improve cognitive abilities and enhance learning. However, consensus has not been reached regarding the mechanisms through which action game experience facilitates superior performance on untrained perceptually and cognitively demanding transfer tasks. We argue that analysis of behaviors engaged in during transfer task performance may provide key insights into answering this question. In the current investigation, we examined potential action game effects in the context of a complex psychomotor task, the Space Fortress (SF) game, that allows for the detailed examination of player behaviors beyond aggregate score reports. Performance (game score) was compared between action video game players (VGPs) and non-gamers (nVGPs) in two different control interface conditions (keyboard or joystick), followed by analyses of behaviors associated with superior performance. Against expectations, VGPs displayed superior performance only in the keyboard condition, suggesting that the action gamer advantage may not generalize to less-familiar control interfaces. Performance advantages were specifically associated with more efficient ship control behaviors by VGPs. Findings highlight how process-tracing approaches may provide insight into the nature of, and mechanisms producing, action gamers' advantages on learning untrained tasks.

A Machine-Learning Based Approach for Predicting Older Adults' Adherence to Technology-Based Cognitive Training.

Adequate adherence is a necessary condition for success with any intervention, including for computerized cognitive training designed to mitigate age-related cognitive decline. Tailored prompting systems offer promise for promoting adherence and facilitating intervention success. However, developing adherence support systems capable of just-in-time adaptive reminders requires understanding the factors that predict adherence, particularly an imminent adherence lapse. In this study we built machine learning models to predict participants' adherence at different levels (overall and weekly) using data collected from a previous cognitive training intervention. We then built machine learning models to predict adherence using a variety of baseline measures (demographic, attitudinal, and cognitive ability variables), as well as deep learning models to predict the next week's adherence using variables derived from training interactions in the previous week. Logistic regression models with selected baseline variables were able to predict overall adherence with moderate accuracy (AUROC: 0.71), while some recurrent neural network models were able to predict weekly adherence with high accuracy (AUROC: 0.84-0.86) based on daily interactions. Analysis of the post hoc explanation of machine learning models revealed that general self-efficacy, objective memory measures, and technology self-efficacy were most predictive of participants' overall adherence, while time of training, sessions played, and game outcomes were predictive of the next week's adherence. Machine-learning based approaches revealed that both individual difference characteristics and previous intervention interactions provide useful information for predicting adherence, and these insights can provide initial clues as to who to target with adherence support strategies and when to provide support. This information will inform the development of a technology-based, just-in-time adherence support systems.

Older Adults' concerns regarding Hurricane-Induced evacuations during COVID-19: Questionnaire findings.

The COVID-19 pandemic has drastically affected our day-to-day life in the last few years. This problem becomes even more challenging when older adults are considered due to their less powerful immune system and vulnerability to infectious diseases, especially in Florida where 4.5 million people aged 65 and over reside. With its long coastline, large and rapidly growing of older adult population, and geographic diversity, Florida is also uniquely vulnerable to hurricanes, which significantly increases the associated risks of COVID-19 even further. This study investigates older adults' evacuation-related concerns during COVID-19 using statistical analysis of a questionnaire conducted among 389 older adult Florida residents. The questionnaire includes questions concerning demographic information and older adults' attitudes toward hurricane-induced evacuations during the COVID-19 pandemic. Ordered Probit regression models were developed to investigate the impacts of demographic parameters on older adults' tendencies toward evacuating as well as their preferences to stay at home or shelter during the pandemic. The model results reveal that male participants felt safer to evacuate compared to females. Also, any decrease in the level of income was associated with an increase in the need for help for evacuation by 18%. Findings indicated that the participants who found the evacuation safe normally also had a positive attitude toward staying in their vehicle, hotel, or even shelters if maintaining social distance was possible. Emergency management policies can utilize these findings to enhance hurricane preparations for dealing with the additional health risks posed by the pandemic for older adults, a situation that could be exacerbated by the upcoming hurricane season in Florida.

A Grand Challenge for Psychology: Reducing the Age-related Digital Divide.

World-wide population aging and rapid diffusion of digital technology have converged to produce an age-related digital divide in technology adoption, as seen in use of the internet and ownership of smartphones. Given the centrality of these technologies for full participation in modern society, reducing that gap is an important challenge for psychologists. We outline more and less malleable factors associated with technology adoption. We argue that interventions that can change both the aging user and the design of products will be necessary. Adaptive technology systems that incorporate artificial intelligence and extended reality represent promising new approaches to reducing the age-related digital divide.

Enhancing neurocognitive health, abilities, networks, & community engagement for older adults with cognitive impairments through technology: a scoping review introducing the ENHANCE Center.

PURPOSE: Age associated cognitive impairments (CIs) result in difficulties with a wide range of daily activities. As the older adult population increases, so does the importance of understanding and supporting the needs and disabilities of those with CIs. The Enhancing Neurocognitive Health, Abilities, Networks, and Community Engagement (ENHANCE) Centre was established with a focus on developing assistive technology to support the socialisation, transportation, and prospective memory needs of older adults with CIs. The extent to which relevant literature in these domains existed was unknown. MATERIALS AND METHODS: A scoping review identified existing research meeting the following criteria: participants aged 50+ years classified as having a CI due to mild cognitive impairment (MCI), traumatic brain injury (TBI), or stroke; and a focus on technology-based support for socialisation, transportation, and/or prospective memory activities. RESULTS: Qualitative and quantitative data are reported from relevant studies. In addition to few studies available, it was common for studies to include 20 or fewer participants. Most assessed technology interactions at one time and few studies examined longitudinal use and benefit. While each paper examined one aspect of user-centred design, no technologies were reported that underwent all stages of the user-centred design process, from needs assessment to iterative design and usability testing, to efficacy trial. CONCLUSIONS: Gaps highlight notable directions for future research and the important role ENHANCE can play in addressing the needs and disabilities of older adults with CIs. IMPLICATIONS FOR REHABILITATIONAlthough older adults with common cognitive impairments often experience challenges in the domains of socialisation, transportation, and/or prospective memory, few studies exist that examine assistive technology solutions in these domains.Additional research with more robust designs and larger sample sizes is needed to discover and assess assistive technology solutions for the needs and disabilities of older adults with mild cognitive impairment (MCI), traumatic brain injury (TBI), and post-stroke cognitive impairment (PSCI).The ENHANCE Centre will employ a systematic process of needs assessments, user testing, and efficacy tests to develop novel technology solutions to support the needs of older adults with common cognitive impairment.

Applying aspects of deliberate practice to help low performers improve manual control in a complex task.

Investigations of expert performers across many domains have suggested the accumulation of deliberate practice over many years is a key component of developing expertise. Researchers propose deliberate practice is effective for improving performance in the long term because it includes several structured elements that facilitate learning. However, the effects of such structured practice across shorter training periods are less understood. The current study examined the acquisition of manual control skill in the experimental video game Space Fortress. Video demonstrations and continuous auditory feedback were combined in a 90-min intervention session designed to discourage behaviors correlated with low performance and encourage behaviors associated with high levels of skill. After 4 h of gameplay, the initially lower-performing participants who received the intervention displayed significantly greater manual control performance than the initially lower-performing participants who received undirected training. This performance difference corresponded with durable improvements in behaviors targeted by the intervention once feedback was removed. These findings suggest training that incorporates elements of deliberate practice can be successfully applied in short-term skill acquisition settings.

Using a Nature-Based Virtual Reality Environment for Improving Mood States and Cognitive Engagement in Older Adults: A Mixed-Method Feasibility Study.

Background and Objectives: Exposure to nature and nature-based imagery has been shown to improve mood states and stave off cognitive decline in older adults. Even "micro-doses" of natural scenery can provide beneficial effects in situations where more extensive interactions with nature are not feasible. In the current study, we evaluated the use of virtual reality (VR) for delivering interactive nature-based content with the goal of prompting active engagement and improving mood states in older adults. Research Design and Methods: The researchers developed a novel VR environment that combined 360-degree videos of natural areas and botanical gardens with interactive digital features that allowed users to engage with aspects of the environment. We recruited 50 older adults to try out this VR environment and measured changes in mood states and attitudes toward VR from before versus after the sessions. We controlled for variables such as age, education level, and exposure to nature in everyday life, and we looked for differences in responses to the VR among participants with cognitive impairments (CIs) versus without, and participants with physical disabilities versus without. Results: The findings indicated significant improvements in "good" mood and "calm" mood dimensions after exposure to the VR, as well as improvements in attitudes toward the technology. These positive outcomes were significantly greater for participants with physical disabilities compared to those without disabilities. No differences were found in the responses of participants with CIs versus those without. Exit interviews provided a variety of helpful suggestions about ways to improve the VR equipment design and content to meet the needs of an older adult population. Discussion and Implications: The study demonstrates that VR can provide a cost-effective, noninvasive, and nonpharmaceutical approach for improving the lives of older adults in both clinical and recreational settings, particularly when real-world access to nature is limited.

Boldness moderates cognitive performance under acute threat: Evidence from a task-switching paradigm involving cueing for shock.

Understanding factors that influence behavioral performance in high-pressure contexts is relevant to critical occupations such as first responders, military personnel, and frontline medical workers. A recent study by Yancey et al. (2019) demonstrated an association between boldness, a biobehavioral trait reflecting social dominance and fearlessness, and enhanced task-switching performance during threat of shock relative to a no-shock (safe) condition. This study used a sustained threat manipulation in which cues signaling possible shock were present throughout blocks of multiple task trials. Here, we extended this work by evaluating the relationship between boldness and task-switching performance under acute threat of shock conditions, in which cues signaling possible shock occurred during individual task trials, intermingled with safe trials. Participants (N = 79) completed a task-switching procedure involving acute threat of shock in which unwarned noise probes were presented to elicit blink-startle responses. Boldness was associated with better switching performance under threat versus safe conditions, with high-bold participants who exhibited low startle potentiation during threat showing the best performance. These findings provide additional evidence that dispositional boldness is a meaningful individual difference characteristic related to effective performance in high-pressure situations and have implications for personnel selection and assignment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Motivation to Engage in Aging Research: Are There Typologies and Predictors?

BACKGROUND AND OBJECTIVES: Study recruitment and retention of older adults in research studies is a major challenge. Enhancing understanding of individual differences in motivations to participate, and predictors of motivators, can serve the dual aims of facilitating the recruitment and retention of older adults, benefiting study validity, economy, and power. RESEARCH DESIGN AND METHODS: Older adults (N = 472) past and potential participants were surveyed about motivations to participate in research, demographic, and individual difference measures (e.g., health status, cognitive difficulties). Latent class and clustering analyses explored motivation typologies, followed by regression models predicting individual motivators and typologies. RESULTS: Older adults endorsed a diversity of research motivations, some of which could be predicted by individual difference measures (e.g., older participants were more motivated by the desire to learn new technology, participants without a college education were more motivated by financial compensation, and participants with greater self-reported cognitive problems were more likely to participate to gain cognitive benefit). Clustering analysis revealed 4 motivation typologies: brain health advocates, research helpers, fun seekers, and multiple motivation enthusiasts. Cognitive difficulties, age, employment status, and previous participation predicted membership in these categories. DISCUSSION AND IMPLICATIONS: Results provide an understanding of different participant motivations beyond differences between younger and older adults and begin to identify different classes of older adults motivated to participate in research studies. Results can provide guidance for targeted recruitment and retention strategies based on individual differences in stated or predicted motivations.