BackgroundArtificial intelligence (AI) has demonstrated superior diagnostic accuracy compared with medical practitioners, highlighting its growing importance in health care. SMART-Pred (Shiny Multi-Algorithm R Tool for Predictive Modeling) is an innovative AI-based application for Alzheimer disease (AD) prediction using handwriting analysis.ObjectiveThis study aimed to develop and evaluate a noninvasive, cost-effective AI tool for early AD detection, addressing the need for accessible and accurate screening methods.MethodsThe study used principal component analysis for dimensionality reduction of handwriting data, followed by training and evaluation of 10 diverse AI models, including logistic regression, naïve Bayes, random forest, adaptive boosting, support vector machine, and neural network. Model performance was assessed using accuracy, sensitivity, precision, specificity, F1-score, and area under the curve (AUC) metrics. The DARWIN (Diagnosis Alzheimer With Handwriting) dataset, comprising handwriting samples from 174 participants (89 patients with AD and 85 healthy controls), was used for validation and testing.ResultsThe neural network classifier achieved an accuracy of 91% (95% CI 0.79‐0.97) and an AUC of 94% on the test set after identifying the most significant features for AD prediction. These performance results surpass those of current clinical diagnostic tools, which typically achieve around 81% accuracy. SMART-Pred’s performance aligns with recent AI advancements in AD prediction, such as Cambridge scientists’ AI tool achieving 82% accuracy in identifying AD progression within 3 years, using cognitive tests and magnetic resonance imaging scans. The variables “air_time” and “paper_time” consistently emerged as critical predictors for AD across all 10 AI models, highlighting their potential importance in early detection and risk assessment. To augment transparency and interpretability, we incorporated the principles of explainable AI, specifically using Shapley Additive Explanations, a state-of-the-art method to emphasize the features responsible for our model’s efficacy.ConclusionsSMART-Pred offers noninvasive, cost-effective, and efficient AD prediction, demonstrating the transformative potential of AI in health care. While clinical validation is necessary to confirm the practical applicability of the identified key variables, the findings of this study contribute to the growing body of research on AI-assisted AD diagnosis and may lead to improved patient outcomes through early detection and intervention.

BackgroundOlder adults make up the largest proportion of nonusers of the internet. With the increasing digitalization of services, it is important to identify what interventions are effective at reducing digital exclusion in older adults.ObjectiveWe aimed to identify what evidence exists on the effectiveness of interventions to address digital exclusion in older adults.MethodsThis rapid review assessed the effectiveness of interventions to address digital exclusion in older adults aged 60 years or older. Searches were conducted in November 2023 across a range of databases and used supplementary search methods. Searches were limited to comparative studies published from 2018 onward in English. Data were analyzed using a narrative synthesis approach.ResultsA total of 21 studies were included that aimed to increase a range of digital literacy skills. Sample sizes ranged from 5 to 381. Intervention approaches varied considerably and were often multicomponent and undertaken in a variety of settings. There is evidence to suggest that a range of interventions can reduce physical, personal, and perceptual barriers and improve older adults’ skills, knowledge, digital literacy, and perceived self-efficacy, reduce technophobia, and increase use of technology. Importantly, findings indicated improvements among a range of subpopulations, including those living in rural areas, at risk of social isolation, who are homebound, of lower socioeconomic groups, and individuals with visual impairment. To achieve improved and sustained digital inclusion in older adults, evidence suggests it may be important to ensure structural barriers, such as access to the internet and affordability of devices, are removed. However, all studies contained methodological limitations and may not be adequately powered to determine effectiveness.ConclusionsThe evidence shows the potential benefits of interventions aimed at improving a range of digital skills and increasing technology use in older adults, which could help to address digital exclusion. The findings of this rapid review can inform the development and delivery of future interventions. However, it is important to consider the context in which the included interventions were used and the lack of certainty of the findings. This review also identified a lack of high-quality evidence, as all studies identified contained methodological limitations and may not have been adequately powered to determine effectiveness. In addition, consideration should also be given to those who do not wish to engage with the online world to ensure they are not left behind.

BackgroundThe medication management process in resident care homes for the elderly (RCHEs) is complex and can be labor intensive. In 2019, a nongovernment organization led by pharmacists with special interest in informatics developed the SafeMed Medication Management System (SMMS), which is a digital web-based system that integrates electronic medical profiles and medication profiles to revamp the traditional manual medication management process in RCHEs in Hong Kong.ObjectiveThis study aimed to assess the effectiveness of the SMMS in improving RCHE staff’s time efficiency and competencies in the medication management process and how this could potentially reduce human resource costs after its implementation.MethodsThis was a pre–post interventional study conducted from September 2022 to August 2024. Time efficiency was evaluated using time–motion analysis. The time spent on each process—preparing the medication doses, checking the prepared doses, and administering the doses to residents—was evaluated in 10-minute blocks. The mean numbers of doses prepared, checked, and administered were calculated for each block. A three-way ANOVA was used to compare the doses before and after the system implementation. Staff competencies and perceived acceptance of the system were evaluated using a structured survey adapted from the technology acceptance model. An exploratory analysis was conducted to estimate the potential financial savings attributable to SMMS implementation using RCHE staff salary data obtained from publicly available governmental sources. Results from the time–motion analysis were used to estimate the cost per dose prepared by RCHE staff before and after the system implementation.ResultsForty-one RCHEs implemented the SMMS, serving a total of 3911 residents. The time–motion analysis (n=6 RCHEs) revealed that the mean (SD) number of doses significantly increased in 10-minute blocks after system implementation (medication preparation: 25, SD 14 to 49, SD 15 doses; medication checking: 21, SD 6 to 85, SD 33 doses; medication administration: 9, SD 1 to 16, SD 6 doses). The overall mean number of doses handled across all processes combined was significantly higher after implementation (18.9 vs 51.9 doses, P=.02). RCHE staff (n=392) reported significantly improved competencies in entering and accessing residents’ records and preparing, checking, and administering medications after the system implementation (all P<.001). The estimated cost of managing one dose of medication dropped substantially from HKD 2.00 (US $0.25) before to HKD 0.74 (US $0.09) after system implementation. If fully implemented in all RCHEs across Hong Kong, the daily human resource cost associated with the medication management process could potentially be reduced from HKD 2,574,000 (US $330,000) to HKD 952,380 (US $122,100).ConclusionsThe time–motion analysis and quantitative survey findings suggest that digital technology combined with automation can improve staff’s time efficiency and competencies and promote human resource cost-saving in the medication management process. Future work should evaluate the long-term impact of this system on medication safety and its cost-effectiveness in RCHEs.

BackgroundDementia is a global health priority. Early identification in asymptomatic or mildly symptomatic individuals (ie, dementia risk detection) is proposed as a clinical solution for early intervention and could support researchers to identify novel neuropathological targets and recruit to clinical trials. Digital biomarkers of behavioral or physiological markers, including sleep, are cited as a potential low-cost, noninvasive, and objective method for dementia risk detection. Understanding perspectives on digital biomarkers, particularly acceptability, from potential end users and clinical staff is required when considering implementation within any clinical service. With emerging evidence of sleep as a risk marker for dementia, the efficacy of the Dementia Research Institute Sleep Index (DRI-SI), based on continuous remote monitoring of sleep patterns detected by a digital sleep mat, for dementia risk detection, is currently being explored by the InSleep46 study.ObjectiveThis qualitative substudy aimed to explore perspectives of potential end users and primary care physicians regarding the use of a digital sleep mat to measure the DRI-SI and its application towards dementia risk detection.MethodsThirty-one potential end users (age: 31-82 years, 11 female and 20 male) from Newcastle and London, United Kingdom, with personal or caregiving experience related to dementia, participated in qualitative focus group workshops. They shared opinions on integrating the sleep mat into their homes, the DRI-SI’s potential for identifying dementia risk, and the necessary information for engagement with related clinical services. Seven primary care physicians from across England participated in semistructured interviews regarding the potential application of the DRI-SI in dementia risk detection and its integration into current clinical practice. Inductive thematic analysis was conducted to identify key themes.ResultsFour key themes emerged from end user focus groups: (1) practical use, (2) prospective acceptability, (3) clinical management, and (4) data concerns. Three main themes came from the semistructured interviews with physicians: (1) prospective acceptability, (2) health care provision, and (3) practical considerations. Common themes were identified in both groups but held differing perspectives. End users were focused on practical aspects of integrating the digital sleep mat within their daily life, the effect of the DRI-SI on clinical care, and privacy concerns regarding data use. Primary care physicians were concerned more broadly with how the DRI-SI and dementia risk detection service would integrate into current clinical practice, the impact on clinical resources and patient well-being, and the need for clinical actionability and guidance on discussing results with patients.ConclusionsEnd users would find the DRI-SI acceptable as part of their clinical care, but primary care physicians require a more robust evidence base. Future research should explore the integration of the DRI-SI into clinical care/research pathways to enhance clinical acceptability. Five key recommendations have been made for further development of digital biomarkers for dementia risk populations.

BackgroundWhile the positive effects of digital technology on cognitive function are established, the specific impacts of different types of technology activities on distinct cognitive domains remain underexplored.ObjectiveThis study aimed to examine the associations between transitions into and out of various technology activities and trajectories of cognitive domains among community-dwelling older adults without dementia.MethodData were drawn from 5566 community-dwelling older adults without dementia who participated in the National Health and Aging Trends Study from 2015 to 2022. Technology activities assessed included online shopping, banking, medication refills, social media use, and checking health conditions online. The cognitive domains measured were episodic memory, executive function, and orientation. Asymmetric effects models were used to analyze the associations between technology activity transitions and cognitive outcomes, adjusting for demographic, socioeconomic, and health-related covariates. Lagged models were applied for sensitivity analysis.ResultsIn the asymmetric effects models, the onset of online shopping (β=.046, P=.02), medication refills (β=.073, P<.001), and social media use (β=.065, P=.01) was associated with improved episodic memory. The cessation of online shopping was associated with faster episodic memory decline (β=−.023, P=.047). In contrast, the cessation of online banking (β=−.078, P=.01) and social media use (β=−.066, P=.003) was associated with decreased episodic memory. The initiation of instrumental, social, and health-related technology activities was associated with slower cognitive decline in orientation. The lagged models further emphasized the effects of stopping online banking and starting online medication refills in relation to episodic memory, as well as the positive associations between online shopping and social media use and orientation. All significant effects were of small magnitude.ConclusionsCombining findings from the main and sensitivity analyses, results suggest that interventions designed to support episodic memory in older adults should emphasize promoting the use of online medication refill services and sustaining engagement with online banking, particularly among those who have already established these habits. To support orientation, strategies should focus on facilitating adoption of online shopping and social media use, helping older adults become comfortable navigating these platforms. Future trials are needed to assess the clinical relevance of targeted interventions for specific cognitive domains, to promote the initiation and maintenance of digital activities to help mitigate domain-specific cognitive decline in aging populations.

BackgroundDigital inclusion has become increasingly important in promoting healthy aging, yet its association with mental health among older adults appears complex and heterogeneous. The role of cognitive function as a moderator and the underlying mechanisms remain understudied.ObjectiveThis study aims to examine cognitive function’s moderating role in the relationship between digital inclusion and depression risk among older adults, and to investigate multiple pathways of association.MethodsUsing data from the 2020 wave of the China Health and Retirement Longitudinal Study, we analyzed 18,673 adults aged 60 years and above (mean age 68.4 y, SD 6.5; 50.8% male participants [n=9486], 49.2% female participants [n=9187]). We constructed interaction effect models to test the moderation hypothesis and employed path analysis with bootstrapped 95% confidence intervals (2000 iterations) to investigate multiple pathways through which digital inclusion is associated with depression.ResultsCognitive function significantly moderated the digital inclusion-depression relationship (β=−.002, P=.03). The association was not statistically significant at low cognitive function (β=−.137, P=.33) but strongly protective at high cognitive function (β=−.517, P<.001), revealing a “cognitive threshold effect.” Path analysis identified 3 significant pathways: direct effects (66.7% of total effect), cognitive enhancement (8.3%), and social participation (8%). Importantly, higher digital inclusion was not found to be associated with increased depression risk at any cognitive function level.ConclusionsOur findings suggest that older adults require adequate cognitive resources to derive mental health benefits from digital participation, though no harmful effects were observed at lower cognitive levels. This asymmetric pattern has important implications for designing cognitive-informed digital inclusion programs that integrate digital skills training with cognitive enhancement strategies for promoting mental health in aging populations.

BackgroundHearing loss and depression are important health issues among the middle-aged and older population.ObjectiveThis study aimed to investigate the associations between hearing loss and depressive symptom trajectories in the Chinese middle-aged and older adult population.MethodsThe survey data of 2011, 2013, 2015, and 2018 waves collected in the China Health and Retirement Longitudinal Study were used for analysis. The latent growth mixture modeling approach was used to explore the trajectories of depressive symptoms. Hearing loss was identified through self-reporting, and depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression scale. The associations between hearing loss and depressive symptom trajectories were examined using logistic regression models.ResultsA total of 4768 participants without depressive symptoms at baseline were included for analysis. Among them, 4 depressive symptom trajectories, including “stable low symptoms” (n=3656, 76.68%), “slowly progressing symptoms” (n=503, 10.55%), “relieved symptoms after progression” (n=467, 9.79%), and “rapidly progressing symptoms” (n=142, 2.98%) were identified. Hearing loss was found to be significantly associated with the trajectory of “rapidly progressing symptoms.”ConclusionsThe trajectories of depressive symptoms in middle-aged and older people have 4 types with distinct patterns. Hearing loss is associated with the progression of depressive symptoms, and its impact is more significant for males, affecting not only symptom severity but also progression speed. These findings indicate that the mental health status of middle-aged and older people with hearing loss requires careful consideration, and timely interventions should be provided.

BackgroundGiven the rapid development of the digital economy and the sustained proliferation of the internet, digital engagement in older adults has garnered mounting attention from the academic community. However, research has yet to systematically examine the impact of digital engagement on sleep in this demographic.ObjectiveThis study aims to examine the association of digital engagement—operationalized as digital access and internet use duration—with the sleep schedules (nocturnal sleep duration, afternoon nap duration, and sleep onset time) of older adults in China, using longitudinal data and robust statistical modeling to explore longitudinal associations and potential mechanisms.MethodsData were derived from 4 waves (2014, 2016, 2018, and 2020) of the China Family Panel Studies, involving 16,784 older adults (≥60 y). We used panel fixed effects models and a random-effects ordered logit model to analyze the effects on continuous outcomes (nocturnal and nap sleep duration), controlling for time-invariant individual characteristics. As sleep onset time is an ordinal variable, a random-effects ordered logit model was used for this outcome. Moderation analyses were conducted by introducing interaction terms (digital engagement×sex and digital engagement×residence) into the models to examine heterogeneity across subgroups (urban or rural, men or women). Mediation analyses were performed using the Sobel test with year-fixed effects and the nonparametric bootstrap method (1000 resamples) to assess the significance of indirect effects via mechanistic pathways (nonfarm employment, protein intake, memory, depressive mood, and instrumental activities of daily living).ResultsThe study included a total of 16,784 older adults, with an average age of 69 (SE 6.946) years, including 9100 (54.22%) women and 7684 (45.78%) men. The results showed that both digital access (β=−.15, 95% CI −.25 to −.06; P=.002) and internet use time (β=−.07, 95% CI −.13 to −.01; P=.027) were significantly associated with significantly shorter sleep duration of older adults. Digital access was significantly associated with a significant reduction in the length of afternoon naps among older adults, while internet use did not have this effect; both digital access and internet use were significantly associated with a significant delay in older adults’ sleep onset time. Digital access was associated with older adults’ sleep schedules through its correlations with nonfarm employment, protein intake, memory, depressive mood, and instrumental activities of daily living. Digital access had a greater and more significant impact on men and urban older adults, while internet use had a greater and more significant impact on women and urban older adults.ConclusionsThe study indicates that digital engagement, such as the use of electronic devices, is associated with a reduction in both daily and nap sleep duration, as well as a delay in sleep onset, among older adults.

BackgroundFirst-line management for hip and knee osteoarthritis includes lifestyle treatments, such as exercise and weight loss (if appropriate), whereas joint replacement surgery is recommended only for severe symptoms after these options have been exhausted. However, many people with osteoarthritis hold misconceptions about the condition, leading to lower acceptance of nonsurgical treatments, such as exercise, and the mistaken belief that surgery is their only option. Novel patient education approaches that address these misconceptions are recommended to improve uptake of lifestyle treatments, reduce unnecessary surgery, and improve outcomes for people with osteoarthritis. We developed a 4-week self-directed consumer e-learning course on osteoarthritis management. In a randomized controlled trial, using the course led to immediate and sustained improvements in osteoarthritis knowledge. However, participants’ perspectives on the course and an understanding of how it impacted osteoarthritis beliefs, treatment choices, and outcomes were unknown.ObjectiveThis study aims to explore how an e-learning course for people with hip and knee osteoarthritis may have impacted their osteoarthritis beliefs, treatment choices, and outcomes.MethodsIn this qualitative study, we conducted semistructured individual interviews (N=20) with randomized controlled trial participants with hip or knee osteoarthritis who accessed a 4-week consumer e-learning course on osteoarthritis and its management. Interviews were audio recorded, transcribed verbatim, and thematically analyzed following a framework approach, which was guided by the common sense model of self-regulation.ResultsFour themes were developed from the interviews: (1) participants reshaped their beliefs and attitudes toward osteoarthritis and its management, (2) participants adopted a proactive approach to management, (3) participants developed a more positive mindset, and (4) the course supported learning and shifts in beliefs.ConclusionsThe e-learning course resulted in shifts in participants’ beliefs and attitudes toward osteoarthritis and its management, increasing their confidence in living with osteoarthritis and resulting in a more optimistic outlook on the future. The e-learning course is freely available and could be a useful resource for people with osteoarthritis to enhance their understanding of the condition and its management.

BackgroundThe global aging population and the high incidence of falls among this population highlight the need for effective preventive strategies. Home-based exercise programs, such as the Otago protocol, have demonstrated efficacy in reducing fall risk but often face barriers related to user adherence. Mobile health (mHealth) apps offer promising tools to support health promotion and enhance autonomy in older adults.ObjectiveThis study aims to develop and validate a prototype mobile app, Mais Equilíbrio (More Balance), designed to guide older adults in performing home-based physical exercises adapted from the Otago protocol.MethodsThis methodological study was conducted in two phases: (1) content validation by 22 experts in physical education and physiotherapy using the Suitability Assessment of Materials (SAM) scale, and (2) usability testing with 24 older adults (aged 60 to 80 y), using the System Usability Scale (SUS). An overall score above 70% on the SAM and above 85 on the SUS were considered indicators of high quality and excellent usability, respectively.ResultsThe Mais Equilíbrio (More Balance) app was developed based on the Otago protocol and tailored for independent home use. A Content Validity Index above 0.95 was observed for all items. An overall average score of 81.20 (SD 15.78) on the SAM scale was found, classifying the material as “superior.” Usability tests with older adults showed an average score of 95.98 (SD 5.58) on the SUS, indicating excellent usability. The highest scores were observed in “ease of use” and “user confidence.”ConclusionsThe Mais Equilíbrio (More Balance) app, distinct for digitally adapting the Otago protocol to the Brazilian context and for its dual validation process with experts and older adults, has proven to be a valid and highly usable tool for guiding home-based physical exercise in older adults, with potential to promote fall prevention and autonomy.