JMIR Aging最新文献

Background: Digital technologies are increasingly central to supporting autonomy, health, and social participation in later life. However, disparities persist in the ability to keep up with technological developments, affecting individuals' opportunities to benefit from digital health and social innovations.

Objective: This study aimed to investigate factors associated with individuals' self-reported ability to keep up with technological developments, focusing on generational differences, attitudes toward digital tools, and sociodemographic characteristics.

Methods: We conducted a national cross-sectional online survey in Sweden with 2121 respondents aged 30 to 39 years, 50 to 59 years, and 70 to 79 years. Logistic regression analyses were used to identify associations between self-reported ability to keep up with technology and independent variables, including attitudes toward information and communication technology, gender, education, self-rated economic situation, and general health.

Results: Most respondents reported being able to keep up with technological developments. Compared to the oldest generation (70-79 years), participants aged 30 to 39 years had 188% higher odds (odds ratio [OR] 2.88, 95% CI 1.84-4.53) of reporting they kept up with technology developments, and women had lower odds than men (OR 0.52, 95% CI 0.39-0.70). Positive attitudes toward information and communication technology being user-friendly (OR 1.81, 95% CI 1.21-2.73), timesaving (OR 2.03, 95% CI 1.44-2.87), and increasing independence (OR 1.99, 95% CI 1.33-2.96) were also significantly associated with keeping up.

Conclusions: These findings suggest that digital inclusion in aging societies is shaped by complex and intersecting factors that go beyond age. Promoting equitable digital engagement requires addressing attitudinal, economic, and gender-related barriers and fostering inclusive technology design and support systems for both current and future generations of older adults.

Background: Voice-based digital health technologies are highly feasible and acceptable tools for supporting older adults. However, their development has rarely focused on caregiving needs, and it is often poorly integrated with existing care services, thereby limiting their sustained effect.

Objective: This study aimed to evaluate the feasibility and effectiveness of a comprehensive voice-based remote care program developed in partnership with a local public health center.

Methods: A single-center, single-arm clinical study involving community-dwelling, socioeconomically vulnerable older adults was conducted using a Clinical Frailty Scale of 4-5. Participants received a 6-month voice-based care program comprising smart speaker daily check-ins, an emergency response system, and artificial intelligence-driven well-being check calls. These components were integrated with the public health center for continuous monitoring. The primary outcome was caregiver burden, assessed using the Korean version of the Zarit Burden Interview. Secondary outcomes include depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder-7), and quality of life (Korean version of the Control, Autonomy, Self-realization, and Pleasure scale).

Results: Among 100 enrolled participants, 96 (96%) completed the program. The caregiver burden slightly decreased from 17.1-16.2 points (mean difference -1, 95% CI -2.17 to 0.24; P=.12). However, caregivers reported a significant reduction in their perception of being the sole support provider (P=.003). Among older adults, significant improvements were observed in depression (Patient Health Questionnaire-9; P<.001), anxiety (Generalized Anxiety Disorder-7; P=.008), and quality of life (Korean version of the Control, Autonomy, Self-Realization, and Pleasure scale; P =.048).. Program adherence was high, with participants engaging for a median of 184 (IQR 154-203; 186/214, 87%) days.

Conclusions: Whereas the voice-based remote care program did not significantly reduce the overall caregiver burden, it significantly reduced the perception of the caregivers as being the sole support system. Furthermore, it influenced the psychological well-being of older adults by reducing depression and anxiety and enhancing their quality of life. High adherence and engagement enhance the feasibility and acceptability of scalable digital health interventions for vulnerable older adults in rural settings.

Background: Social media engagement among older adults has surged worldwide, with China's older users exceeding 120 million in 2023. However, research remains disproportionately focused on youth. Critically, the dose-response relationship between use intensity and mental health in this population is poorly quantified, especially in rapidly aging societies such as China, where 23% of the population will be aged ≥65 years by 2035.

Objective: This study aimed to outline the social media use status among retired older adults and explore the association between social media use, including time spent on social media and social media addiction, and mental health status.

Methods: A cross-sectional survey was conducted in Shanghai, China, in 2024. A total of 15,986 retired participants were recruited via universities for older adults and primary health care institutions. Short versions of anxiety (the 2-item Generalized Anxiety Disorder scale) and depression (the 2-item Patient Health Questionnaire) scales were used to minimize the required time to complete the questionnaires for older adults. Logistic regressions were used to examine the associations between social media use and mental health after controlling for covariates. Subgroup analysis was conducted considering sex, age, marital status, urbanicity, and socioeconomic status.

Results: The participants had an average age of 68.49 (SD 7.6) years, with most (13,854/15,986, 86.7%) being married and living with their spouse and approximately half (8155/15,986, 51.0%) being male. Our research indicated that over 98% of retired older individuals (15,807/15,986, 98.88%) had used social media, with WeChat, Douyin, and Kuaishou being the most common platforms. Among them, 52.3% (8361/15,986) spent 2 to 3 hours a day on social media, 32.29% (5162/15,986) spent >4 hours a day, and 20.34% (3253/15,986) were addicted to social media. Older adults with ≥6 hours of daily social media use time exhibited higher rates of anxiety (odds ratio [OR] 1.44, 95% CI 1.20-1.72; P<.001) and depression (OR 1.50, 95% CI 1.25-1.79; P<.001) compared with those who used social media for ≤1 hour per day. Older adults addicted to social media had higher odds of anxiety (OR 2.81, 95% CI 2.57-3.08; P<.001) and depression (OR 2.51, 95% CI 2.30-2.75; P<.001). Subgroup analyses revealed stronger associations for women, people aged 49-75 years, those with a lower educational level and income, urban residents, and non-solo dwellers.

Conclusions: Retired older adults in Shanghai are an active group of social media users. Using social media for over 6 hours a day and social media addiction were significantly associated with anxiety and depression. Future social media research should pay more attention to older adults and explore these longitudinal relationships.

Background: Depressive symptoms, sleep disturbances, and functional disability are interrelated. However, the bidirectional pathways between depression, sleep disturbances, and disability in instrumental activities of daily living (IADLs) remain underexplored in China.

Objective: We aimed to examine the bidirectional longitudinal relationships between depression and disability in IADLs among older Chinese adults, with a focus on elucidating the mediating role of sleep disturbances in this dynamic interplay.

Methods: The study encompassed 2677 older adults who provided complete data at T1 (2015), T2 (2018), and T3 (2020) for the China Health and Retirement Longitudinal Study (CHARLS). Depressive symptoms were assessed using the 10-item Center for Epidemiological Studies Depression (CESD-10) scale, and a 6-item scale was used to measure disability in IADLs. Sleep disturbances were self-reported. Temporal associations between depressive symptoms and disability in IADLs as well as the longitudinal mediating effect of sleep disturbances were examined using a cross-lagged panel model.

Results: Prior depression significantly predicted subsequent disability in IADLs at T2 (β=0.070, P<.001) and T3 (β=0.074, P<.001), and prior disability in IADL predicted subsequent depression at T2 (β=0.094, P<.001) and T3 (β=0.100, P<.001). Additionally, the indirect effect of prior disability in IADLs on subsequent depression via sleep disturbances was statistically significant (β=0.062, SE=0.010, P<.001), with the mediation effect accounting for 50.41% of the total effect. In contrast, after accounting for this mediation, the direct effect of prior depression on subsequent disability in IADLs was not significant (β=0.009, SE=0.018, P=.61). Consequently, the impact of depression on disability in IADLs was fully mediated through sleep disturbances in this cohort of older Chinese adults.

Conclusions: Depressive symptoms and disability in IADLs are bidirectionally linked, and sleep disturbances play a longitudinal mediating role in the bidirectional relationship among older Chinese adults. The potential longitudinal bidirectionality highlights the importance of sleep health for interventions on depression and functional disability in older adults.

Background: The prevalence of dementia has led to a growing interest in wearable technologies to assist dementia care. Despite their potential, these technologies face low adoption rates, often attributed to poor aesthetic design and insufficient consideration of user experience.

Objective: This study aims to (1) explore how the aesthetic design of wearable devices relates to their adoption and user experience in dementia care and (2) critically examine the ways in which aesthetic elements shape people with dementia's perceptions of acceptability and inform future design considerations.

Methods: A critical interpretive synthesis with a systematic search was conducted across 2 databases, namely Web of Science and Scopus on August 22, 2024. Studies were included if they reported on the current use of wearable technologies in dementia care or provided value in qualitative studies addressing attitudes from people with dementia and their caregivers toward the wearable product. Two authors independently screened the abstracts and full texts to extract data, and additional studies were included from web searches, owing to their conceptual contributions to offering insights into the emergence of wearable technology, including the factors driving its commercial value and appraisal.

Results: A total of 63 studies were included in this study. Findings suggest that aesthetically considered designs are preferred by users when concerning their acceptance toward wearable devices, particularly when devices symbolize empowerment and support personal engagement. The objects that evoke comfort, emotional connection, and personal meaning are more likely to be accepted by people with dementia. Improved aesthetics may also support caregivers through more consistent and effective data collection.

Conclusions: This study uncovers a significant gap in the aesthetic design of wearable technologies for dementia care, limiting user acceptance and emotional engagement. By synthesizing key themes focusing on the interaction between user and product, this review proposes a conceptual framework for dementia care, emphasizing the importance of aesthetics in enabling more meaningful, inclusive, and human-centered design.

Background: AI has demonstrated superior diagnostic accuracy compared to medical practitioners, highlighting its growing importance in healthcare. SMART-Pred (Shiny Multi-Algorithm R Tool for Predictive Modeling) is an innovative AI-based application for Alzheimer's disease (AD) prediction using handwriting analysis.

Objective: To develop and evaluate a non-invasive, cost-effective AI tool for early AD detection, addressing the need for accessible and accurate screening methods.

Methods: The study employed Principal Component Analysis (PCA) for dimensionality reduction of handwriting data, followed by training and evaluation of ten diverse AI models, including logistic regression, Naïve Bayes, random forest, AdaBoost, Support Vector Machine (SVM), and neural network. Model performance was assessed using accuracy, sensitivity, specificity, F1-score, and ROC-AUC metrics. The DARWIN dataset, comprising handwriting samples from 174 participants (89 AD patients, 85 healthy controls) was used for validation.

Results: The Neural Network classifier achieved an accuracy of 91% with a 95% CI ranging from 0.79-0.97 and an AUC of 94%, on the test set after identifying the most significant features for AD prediction. These results surpass current clinical diagnostic tools, which typically achieve around 81% accuracy. SMART-Pred's performance aligns with recent AI advancements in AD prediction, such as the Cambridge scientists' AI tool achieving 82% accuracy in identifying AD progression within three years using cognitive tests and MRI scans. The variables "air_time" and "paper_time" consistently emerged as critical predictors for AD across all ten 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 (SHAP) values, a state-of-the-art method to emphasize the features responsible for our model's efficacy.

Conclusions: SMART-Pred offers non-invasive, cost-effective, and efficient AD prediction, demonstrating the transformative potential of AI in healthcare. While clinical validation is necessary to confirm the practical applicability of the identified key variables, this study contributes to the growing body of research on AI-assisted AD diagnosis and may lead to improved patient outcomes through early detection and intervention.

Clinicaltrial:

Background: First-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.

Objective: This 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.

Methods: In 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.

Results: Four 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.

Conclusions: The 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.