BackgroundOlder veterans with anxiety disorders encounter multiple barriers to receiving mental health services, including transportation difficulties, physical limitations, and limited access to providers trained to work with older persons. To address both accessibility and the shortage of available providers, evidence-based treatments that can be delivered via guided self-management modalities are a potential solution.ObjectiveThis study aims to determine the feasibility and acceptability of a randomized controlled trial of 2 guided self-management interventions. This study compared the treatment effects of these 2 interventions (relaxation and health psychoeducation) on anxiety symptom severity and functioning in older veterans with anxiety disorders. Our exploratory aims examined factors related to home practices and treatment engagement and perceptions of the practices.MethodsParticipants were randomized to one of two video-delivered interventions: (1) Breathing, Relaxation, and Education for Anxiety Treatment in the Home Environment (BREATHE)—breathing and progressive relaxation or (2) Healthy Living for Reduced Anxiety—psychoeducation about lifestyle changes. Telephone coaching calls were conducted weekly. Measures of anxiety, depression, and functioning were obtained at baseline, week 4 (end of treatment), week 8, and week 12. Participants completed a semistructured interview at week 12. Analyses included descriptive statistics to summarize measures of intervention engagement; mixed-effects models to characterize symptom change, and qualitative analyses.ResultsOverall, 56 participants (n=48, 86% men; n=23, 41% from ethnic or racial minority groups; mean age 71.36, SD 6.19 y) were randomized. No difference in retention between study arms was found. The Healthy Living group (29/56, 52%) completed significantly more lessons (mean 3.68, SD 0.86) than the BREATHE group (27/56, 48%; mean 2.85, SD 1.43; t53=2.60; P=.01) but did not differ in completion of coaching calls. In the BREATHE group, greater baseline anxiety scores (r=–0.41; P=.03) and greater severity of medical comorbidity (r=–0.50; P=.009) were associated with fewer completed practices. There was no effect of intervention on change in total anxiety scores or functioning. For specific anxiety subtypes, Healthy Living produced a greater decline in somatic anxiety compared with BREATHE. Qualitative analyses found barriers to practicing, including difficulty setting time aside to practice, forgetting, or having other activities that interfered with BREATHE practices. Some participants described adapting their practice routine to fit their daily lives; some also used relaxation skills in everyday situations.ConclusionsThese findings suggest that a larger randomized controlled trial of guided self-management approaches to treating late-life anxiety is feasible; however, BREATHE was not effective in reducing anxiety compared with Healthy Living. Possible contributing factors may have been the reliance on a single technique. Progressive relaxation was reported to be enjoyable for most participants, but maintaining home practices was challenging. Those with milder anxiety severity and fewer health problems were better able to adhere to practices.Trial RegistrationClinicalTrials.gov NCT02400723; https://clinicaltrials.gov/study/NCT02400723

BackgroundFalls pose a significant public health concern, with increasing occurrence due to the aging population, and they are associated with high mortality rates and risks such as multimorbidity and frailty. Falls not only lead to physical injuries but also have detrimental psychological and social consequences, negatively impacting quality of life. Identifying individuals at high risk for falls is crucial, particularly for those aged ≥60 years and living in residential care settings; current professional guidelines favor personalized, multifactorial fall risk assessment approaches for effective fall prevention.ObjectiveThis study aimed to explore the prognostic validity of the Fall Risk Score (FRS), a multifactorial-based metric to assess fall risk (using longitudinal real-world data), and establish the clinical relevance of the FRS by identifying threshold values and the minimum clinically important differences.MethodsThis retrospective cohort study involved 617 older adults (857 observations: 615 of women, 242 of men; mean age 83.3, SD 8.7 years; mean gait speed 0.49, SD 0.19 m/s; 622 using walking aids) residing in German residential care facilities and used the LINDERA mobile health app for fall risk assessment. The study focused on the association between FRS at the initial assessment (T1) and the normalized number of falls at follow-up (T2). A quadratic regression model and Spearman correlation analysis were utilized to analyze the data, supported by descriptive statistics and subgroup analyses.ResultsThe quadratic model exhibited the lowest root mean square error (0.015), and Spearman correlation analysis revealed that a higher FRS at T1 was linked to an increased number of falls at T2 (ρ=0.960, P<.001). Subgroups revealed significant strong correlations between FRS at T1 and falls at T2, particularly for older adults with slower gait speeds (ρ=0.954, P<.001) and those using walking aids (ρ=0.955, P<.001). Threshold values revealed that an FRS of 45%, 32%, and 24% corresponded to the expectation of a fall within 6, 12, and 24 months, respectively. Distribution-based minimum clinically important difference values were established, providing ranges for small, medium, and large effect sizes for FRS changes.ConclusionsThe FRS exhibits good prognostic validity for predicting future falls, particularly in specific subgroups. The findings support a stratified fall risk assessment approach and emphasize the significance of early and personalized intervention. This study contributes to the knowledge base on fall risk, despite limitations such as demographic focus and potential assessment interval variability.

Population aging affects society, with a profound impact on daily activities for those of a low socioeconomic status and with motor impairments. Social assistive robots (SARs) and monitoring technologies can improve older adults' well-being by assisting with and monitoring home activities. This study explored the opinions and needs of older adults, including those with motor difficulties and of a low socioeconomic status, regarding SARs and monitoring technologies at home to promote daily activities and reduce sedentary behaviors. A mixed methods approach was used, with 31 older adults divided into 3 groups: those of a low socioeconomic status, those with motor difficulties, and healthy individuals. Focus groups were conducted, and they were analyzed using thematic analysis. Perceived mental and physical well-being were assessed using the 12-Item Short Form Health Survey, and attitudes toward robots were evaluated using the Multidimensional Robot Attitude Scale. The results identified 14 themes in four key areas: (1) technology use for supporting daily activities and reducing sedentary behaviors, (2) perceived barriers, (3) suggestions and preferences, and (4) actual home technology use. Lower perceived physical well-being was associated with higher levels of familiarity, interest, perceived utility, and control related to SARs. Lower perceived psychological well-being was linked to a more negative attitude, increased concerns about environmental fit, and a preference for less variety. Notably, older adults from the low-socioeconomic status group perceived less control over SARs, whereas older adults with motor difficulties expressed higher perceived utility compared to other groups, as well as higher familiarity and interest compared to the low-socioeconomic status group. Participants indicated that SARs and monitoring technologies could help reduce sedentary behaviors by assisting in the management of daily activities. The results are discussed in the context of these outcomes and the implementation of SARs and monitoring technologies at home. This study highlights the importance of considering the functional and socioeconomic characteristics of older adults as future users of SARs and monitoring technologies to promote widespread adoption and improve well-being within this population.

BackgroundConsidering the growing population of older adults, addressing the influence of loneliness among this demographic group has become imperative, especially due to the link between social isolation and deterioration of mental and physical well-being. Technology has the potential to be used to create innovative solutions to increase socialization and potentially promote healthy aging.ObjectiveThis 6-month study examined the usability and acceptability of a technology-based socialization service and explored how stress and living situation affect older adults’ and their ecosystem’s perceptions of technology, investigating cross-sectional and longitudinal differences among and across user groups.MethodsParticipants were recruited in Tuscany and Apulia (Italy) through a network of social cooperatives and a research hospital, respectively. A total of 20 older adults were provided with the same technology installed on a tablet and on a smart television. The technology has three functionalities: video calling, playing games, and sharing news. Additionally, 20 informal caregivers (IC) and 13 formal caregivers (FC) connected to the older adults were included in the study. After both initial training in the use of the system (T0) and 6 months of using the system (T6), questionnaires on usability, acceptability, and technostress were filled in by older adults, IC, and FC. Nonparametric or parametric tests were conducted to investigate group differences at both time points and changes over time. Additional analyses on older adults were done to assess whether differences in usability and acceptability were related to living situation (ie, alone or with someone). Furthermore, correlation analyses were performed between usability, acceptability, and stress toward technology at T0 and T6.ResultsAt both T0 and T6, older adults had lower usability scores than IC and FC and higher anxiety than IC. Over time, there was a significant decrease in older adults’ attitudes toward technology score, depicting a negative attitude over time (T0 median 4.2, IQR 0.5; T6 median 3.7, IQR 0.8; Cohen d=0.7), while there was no change for IC and FC. At T0, those living alone had lower acceptability than those living with someone but this difference disappeared at T6. People or participants living with someone had a decline in anxiety, attitudes toward technology, enjoyment, and perceived usefulness. Stress toward technology affected usability and acceptability in the older adult group entering the study (ρ=−.85) but this was not observed after 6 months. In the IC group, stress affected trust at T0 (ρ=−.23) but not at T6.ConclusionsAt the start of the study, older adults judged the system to be less usable and more stressful than did the caregivers. Indeed, at first, technostress was correlated with usability and acceptability; however, with repeated use, technostress did not influence the perception of technology. Overall, getting accustomed to technology decreased anxiety and stress toward technology.

BackgroundOlder adults with chronic or acute cognitive impairment, such as dementia or delirium, who are hospitalized face unique barriers to person-centered care and a higher risk for negative outcomes stemming from hospitalizations. There is a need for co-designed interventions adapted for these patients to the hospital setting to improve care and outcomes. Patient life storytelling interventions have demonstrated promise in enhancing person-centered care by improving patient–care team relationships and providing information to enable care tailored to individual needs and values.ObjectiveThis study aims to engage patients, care partners, and clinical stakeholders in a co-design process to adapt an existing life storytelling model for use with older adults with dementia and at risk of delirium in the acute care hospital setting.MethodsWe recruited patients with dementia or at risk of delirium who were hospitalized, their care partners, clinicians, and informaticists. A 3-stage co-design process that used a mixed methods data collection approach including in-depth interviews and surveys was completed. We used content analysis to analyze qualitative data and descriptive statistics to summarize quantitative data.ResultsIn total, 27 stakeholder informants (ie, patients, care partners, and interdisciplinary care team [IDT] members) participated. Stakeholders were unanimously interested in using patient life stories as a tool for hospital care through electronic health record (EHR) integration. Stakeholders shared potential topics for life stories to cover, including social support, information on patients’ key life events, and favorite activities. Participants provided insights into the logistics of integrating life stories into acute care, including interview arrangement, story-sharing methods, and barriers and facilitators. IDT members shared preferences on EHR integration, resulting in 3 co-designed mock-ups of EHR integration options. Stakeholders shared ways to optimize future acceptability and uptake, including engaging with the care team and promoting awareness of life stories, ensuring suitability to the acute environment (eg, distilling information in an easily digestible way), and addressing concerns for patient capacity and privacy (eg, engaging care partners when appropriate). Thoughts on potential impacts of life stories were also elicited, including improving patient- and care partner–IDT member relationships; humanizing patients; increasing clinical team, patient, and caregiver satisfaction; and enabling more specific, tailored care for patients with dementia and at risk of delirium.ConclusionsThis study resulted in a co-designed life storytelling intervention for patients with dementia and at risk for delirium in an acute care hospital setting. Stakeholders provided valuable information to ensure future intervention acceptability and uptake, including potential benefits, facilitators, and challenges in the acute care setting.

BackgroundCase managers for persons with dementia not only coordinate patient care but also provide family caregivers with educational material and available support services. Taiwan uses a government-based information system for monitoring the provision of health care services. Unfortunately, scheduling patient care and providing information to family caregivers continues to be paper-based, which results in a duplication of patient assessments, complicates scheduling of follow-ups, and hinders communication with caregivers, which limits the ability of case managers to provide cohesive, quality care.ObjectiveThis multiphase study aimed to develop an electronic information system for dementia care case managers based on their perceived case management needs and what they would like included in an electronic health care app.MethodsCase managers were recruited to participate (N=63) by purposive sampling from 28 facilities representing two types of community-based dementia care centers in Taiwan. A dementia case management information system (DCMIS) app was developed in four phases. Phase 1 assessed what should be included in the app by analyzing qualitative face-to-face or internet-based interviews with 33 case managers. Phase 2 formulated a framework for the app to support case managers based on key categories identified in phase 1. During phase 3, a multidisciplinary team of information technology engineers and dementia care experts developed the DCMIS app: hardware and software components were selected, including platforms for messaging, data management, and security. The app was designed to eventually interface with a family caregiver app. Phase 4 involved pilot-testing the DCMIS app with a second group of managers (n=30); feedback was provided via face-to-face interviews about their user experience.ResultsFindings from interviews in phase 1 indicated the DCMIS framework should include unified databases for patient reminder follow-up scheduling, support services, a health education module, and shared recordkeeping to facilitate teamwork, networking, and communication. The DCMIS app was built on the LINE (LY Corporation) messaging platform, which is the mobile app most widely used in Taiwan. An open-source database management system allows secure entry and storage of user information and patient data. Case managers had easy access to educational materials on dementia and caregiving for persons living with dementia that could be provided to caregivers. Interviews with case managers following pilot testing indicated that the DCMIS app facilitated the completion of tasks and management responsibilities. Some case managers thought it would be helpful to have a DCMIS desktop computer system rather than a mobile app.ConclusionsBased on pilot testing, the DCMIS app could reduce the growing challenges of high caseloads faced by case managers of persons with dementia, which could improve continuity of care. These findings will serve as a reference when the system is fully developed and integrated with the electronic health care system in Taiwan.

BackgroundCognitive 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.ObjectiveThe 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.MethodsData 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.ResultsOur 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.ConclusionsOur 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.

The duration of sleep plays a crucial role in the development of physiological functions that impact health. However, little is known about the associations between sleep duration and functional disability among older adults in China. This study aimed to explore the associations between sleep duration and functional disabilities in the older population (aged≥65 years) in China. The data for this cross-sectional study were gathered from respondents 65 years and older who participated in the 2018 survey of the China Health and Retirement Longitudinal Study, an ongoing nationwide longitudinal investigation of Chinese adults. The duration of sleep per night was obtained through face-to-face interviews. Functional disability was assessed according to activities of daily living (ADL) and instrumental activities of daily living (IADL) scales. The association between sleep duration and functional disability was assessed by multivariable generalized linear models. A restricted cubic-spline model was used to explore the dose-response relationship between sleep duration and functional disability. In total, 5519 participants (n=2471, 44.77% men) were included in this study with a mean age of 73.67 years, including 2800 (50.73%) respondents with a functional disability, 1978 (35.83%) with ADL disability, and 2299 (41.66%) with IADL disability. After adjusting for potential confounders, the older adults reporting shorter (≤4, 5, or 6 hours) or longer (8, 9, or ≥10 hours) sleep durations per night exhibited a notably increased risk of functional disability compared to that of respondents who reported having 7 hours of sleep per night (all P<.05), which revealed a U-shaped association between sleep duration and dysfunction. When the sleep duration fell below 7 hours, increased sleep duration was associated with a significantly lower risk of functional disability (odds ratio [OR] 0.85, 95% CI 0.79-0.91; P<.001). When the sleep duration exceeded 7 hours, the risk of functional disability associated with a prolonged sleep duration increased (OR 1.16, 95% CI 1.05-1.29; P<.001). Sleep durations shorter and longer than 7 hours were associated with a higher risk of functional disability among Chinese adults 65 years and older. Future studies are needed to explore intervention strategies for improving sleep duration with a particular focus on functional disability.

BackgroundSleep efficiency is often used as a measure of sleep quality. Getting sufficiently high-quality sleep has been associated with better cognitive function among older adults; however, the relationship between day-to-day sleep quality variability and cognition has not been well-established.ObjectiveWe aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults, using accelerometer data and 3 cognitive tests.MethodsWe included older adults aged >65 years with at least 5 days of accelerometer wear time from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer’s Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Sleep efficiency was derived using a data-driven machine learning algorithm. We examined associations between sleep efficiency variability and scores on each cognitive test adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Associations between average sleep efficiency and each cognitive test score were further examined for comparison purposes.ResultsA total of 1074 older adults from the NHANES were included in this study. Older adults with low average sleep efficiency exhibited higher levels of sleep efficiency variability (Pearson r=−0.63). After adjusting for confounding factors, greater average sleep efficiency was associated with higher scores on the DSST (per 10% increase, β=2.25, 95% CI 0.61 to 3.90) and AFT (per 10% increase, β=.91, 95% CI 0.27 to 1.56). Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, β=−3.34, 95% CI −5.33 to −1.34), CERAD-WL (per 10% increase, β=−1.00, 95% CI −1.79 to −0.21), and AFT (per 10% increase, β=−1.02, 95% CI −1.68 to −0.36). In fully adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, β=−2.01, 95% CI −3.62 to −0.40) and AFT (per 10% increase, β=−.84, 95% CI −1.47 to −0.21) scores but not CERAD-WL (per 10% increase, β=−.65, 95% CI −1.39 to 0.08) scores.ConclusionsTargeting consistency in sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.

BackgroundReminiscence, a therapy that uses stimulating materials such as old photos and videos to stimulate long-term memory, can improve the emotional well-being and life satisfaction of older adults, including those who are cognitively intact. However, providing personalized reminiscence therapy can be challenging for caregivers and family members.ObjectiveThis study aimed to achieve three objectives: (1) design and develop the GoodTimes app, an interactive multimodal photo album that uses artificial intelligence (AI) to engage users in personalized conversations and storytelling about their pictures, encompassing family, friends, and special moments; (2) examine the app’s functionalities in various scenarios using use-case studies and assess the app’s usability and user experience through the user study; and (3) investigate the app’s potential as a supplementary tool for reminiscence therapy among cognitively intact older adults, aiming to enhance their psychological well-being by facilitating the recollection of past experiences.MethodsWe used state-of-the-art AI technologies, including image recognition, natural language processing, knowledge graph, logic, and machine learning, to develop GoodTimes. First, we constructed a comprehensive knowledge graph that models the information required for effective communication, including photos, people, locations, time, and stories related to the photos. Next, we developed a voice assistant that interacts with users by leveraging the knowledge graph and machine learning techniques. Then, we created various use cases to examine the functions of the system in different scenarios. Finally, to evaluate GoodTimes’ usability, we conducted a study with older adults (N=13; age range 58-84, mean 65.8 years). The study period started from January to March 2023.ResultsThe use-case tests demonstrated the performance of GoodTimes in handling a variety of scenarios, highlighting its versatility and adaptability. For the user study, the feedback from our participants was highly positive, with 92% (12/13) reporting a positive experience conversing with GoodTimes. All participants mentioned that the app invoked pleasant memories and aided in recollecting loved ones, resulting in a sense of happiness for the majority (11/13, 85%). Additionally, a significant majority found GoodTimes to be helpful (11/13, 85%) and user-friendly (12/13, 92%). Most participants (9/13, 69%) expressed a desire to use the app frequently, although some (4/13, 31%) indicated a need for technical support to navigate the system effectively.ConclusionsOur AI-based interactive photo album, GoodTimes, was able to engage users in browsing their photos and conversing about them. Preliminary evidence supports GoodTimes’ usability and benefits cognitively intact older adults. Future work is needed to explore its potential positive effects among older adults with cognitive impairment.