Search Results (83)

AI-powered health assistants offer promising opportunities to enhance health management among older adults. However, real-world uptake remains limited, not only due to individual hesitation, but also because of complex interactions among users, platforms, and public policies. This study investigates the dynamic behavioral mechanisms behind adoption in aging populations using a tripartite evolutionary game model. Based on replicator dynamics, the model simulates the strategic behaviors of older adults, platforms, and government. It identifies evolutionarily stable strategies, examines convergence patterns, and evaluates parameter sensitivity through a Jacobian matrix analysis. Results show that when adoption costs are high, platform trust is low, and government support is limited, the system tends to converge to a low-adoption equilibrium with poor service quality. In contrast, sufficient policy incentives, platform investment, and user trust can shift the system toward a high-adoption state. Trust coefficients and incentive intensity are especially influential in shaping system dynamics. This study proposes a novel framework for understanding the co-evolution of trust, service optimization, and institutional support. It emphasizes the importance of coordinated trust-building strategies and layered policy incentives to promote sustainable engagement with AI health technologies in aging societies. Full article

With the increase in the global aging population, the demand for elderly-friendly game products is growing rapidly. To address existing limitations, particularly in user demand extraction and design parameter setting, this study proposed a design framework integrating the BTM–AHP–AD–TOPSIS methods. The goal was to accurately identify the core needs of elderly users and translate them into effective design solutions. User reviews of elderly-friendly game products were collected from e-commerce platforms using Python 3.8-based web scraping. The Biterm Topic Model (BTM) was employed to extract user needs from review texts. These needs were prioritized using the Analytic Hierarchy Process (AHP) and translated into specific design parameters through Axiomatic Design (AD). Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to comprehensively evaluate multiple design schemes and select the optimal solution. The results demonstrate that the proposed design path offers a holistic method for progressing from need extraction to design evaluation. It effectively overcomes previous limitations, including inefficient need extraction, limited scope, unclear need weighting, and unreasonable design parameters. This method enhances user acceptance and satisfaction while establishing rigorous design processes and scientific evaluation standards, making it well suited for developing elderly-friendly products. Full article

Background: The ageing process is associated with a decline in cognitive functions, including eye–hand coordination, attention concentration, and psychomotor reaction time. This study aimed to assess the effectiveness of virtual reality–based therapy in enhancing cognitive functions in seniors. Methods: This study was conducted on 38 cases (29 women and 9 men) with a mean age of 87.2 years, who were divided into two groups: a VR group (with a 4-week, three-time-week training program using the game “Beat Saber”) and a control group (with a standard 4-week exercise program). Assessments of eye–hand coordination and attentional concentration were conducted at the beginning (T₀) and the end (T₁) of the training. Results: Analysis of eye–hand coordination and attentional concentration showed significant improvement in both groups (T₀ vs. T₁: p = 0.0002 for the intervention group and p = 0.007 for the control group). However, the effect in the VR group was almost three times greater than in the control group (1.689 vs. 0.615 in D effect). Moreover, in the VR group, an analysis of “good cuts” indicated improvements in both parameters after 4 weeks of VR training. The percentage of correctly received stimuli increased significantly across sessions (p < 0.00001). Furthermore, 84.3% of participants experienced a twofold improvement in performance over the 12 VR sessions (42% vs. 80% accuracy in successful hits). The distribution of results also suggests a positive subjective impact of VR therapy in maintaining mental activity. Conclusions: The findings indicate that VR-related training can support elderly individuals in recovering cognitive function, potentially enhancing their independence and life quality. Full article

As Taiwan is about to enter a super-elderly society (the elderly over 65 years old account for more than 20% of the total population), how to delay disability and dementia in the elderly has become an important issue. Increasing the use of toys and games for the elderly delays their disability. Therefore, we developed three 3D interactive games for the elderly using mixed-reality (MR) technology. The three games were designed for hand-eye coordination, including digital cognition, object shape recognition, and color recognition ability. The reaction time was recorded to analyze the elderly’s reaction ability when playing the games. The questionnaire survey results showed that more than 83% of the elderly were satisfied with the use of MR equipment. More than 87% of the elderly accepted the interactive mechanism of MR while more than 80% felt that the three games improved their reaction ability. Full article

Amid accelerating population aging and the rapid evolution of digital technologies, the digital transformation of rural elderly care services has become a pivotal strategy for restructuring the rural elderly care system. This study identified the local government, rural elderly care service centers, and the elderly population as the principal stakeholders, and developed a tripartite evolutionary game-theory model to examine the dynamic strategic interactions among these actors under the influence of digital technologies. The model further investigated the evolutionary trajectories and equilibrium conditions of their behavioral strategies. Numerical simulations conducted via MATLAB were employed to validate and visualize the model outcomes. The findings revealed the following. (1) The evolutionary equilibrium of digital elderly care service development in rural areas is jointly determined by the strategic choices of the three parties, with its stability shaped by a complex interplay of cost structures, incentive mechanisms, and utility outcomes. (2) Cost factors exhibit heterogeneous effects across stakeholders. Specifically, excessive regulatory costs diminish the performance incentives of local governments, digital infrastructure and operational expenditures influence service centers’ capacity for precision-oriented service delivery, and the participation of the elderly is constrained by affordability thresholds. (3) Local government behavior demonstrates a pronounced sensitivity to incentives. In particular, rewards and social reputation conferred by higher-level governmental bodies exert a significantly stronger influence than punitive measures. (4) Government subsidies for digital transformation enhance cross-stakeholder synergy through dual transmission channels. Nonetheless, excessive subsidies may escalate fiscal risk, while moderately calibrated penalty mechanisms effectively curb moral hazard within service centers. This study advances theoretical understanding of multi-stakeholder coordination in the context of digitally enabled rural elderly care and provides actionable insights for policymakers aiming to formulate interest-aligned strategies and construct resilient, intelligent governance systems for elderly care. Full article

Recent studies have increasingly focused on using virtual reality (VR) exercise video games (exergames) to boost motivation for physical activity among the elderly. However, there is little discussion about the factors influencing the performance and effort expectancy of immersive exergames in older adults. The study was based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. UTAUT model was tested and extended by investigating two predicted factors—Physical Safety (PPS) and Physical Postures (PP)—of immersive exergames among older adults. PP, PPS, Performance Expectancy (PE), and Effort Expectancy (EE) relationships were analyzed. In this study, 40 healthy older adults were divided into 2 groups to play an immersive exergame for at least 5 min, either sitting or standing. Then, a list of measurement items based on the extended UTAUT model was completed by one-on-one interviews. The t-test results showed that PP was significantly correlated with PPS (t = −6.598, p < 0.001) and PE (t = 6.465, p < 0.001). The path analysis showed that PPS as a negative correlation was verified as a factor of PE (β = −0.438, p < 0.01). Both PP and PPS had no significant effect on EE. Overall, valuable insights and theoretical guidelines for older adults accepting immersive exergames are supported in this study. Full article

The aging population increases the need for accessible interventions for cognitive training of the elderly to preserve cognitive health. Serious games have been widely used for this purpose, with many existing applications leveraging virtual reality (VR) technology. In contrast, this study explores the potential of augmented reality (AR) for cognitive training. The literature review shows that cognitive training interventions typically employ supermarket-themed serious games that are used extensively in such interventions. MarketMind AR is a supermarket-themed serious game that was created to train memory, attention, and executive function using mobile phone sensors such as cameras, accelerometers, and gyroscopes to interact and recognize the environment. Fifteen older adults participated in a three-attempt trial and completed the System Usability Scale (SUS), the in-game Game Experience Questionnaire (iGEQ), and an adapted version of the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaires. Qualitative interviews and in-game data (e.g., completion times, PIN recall) were also examined. The results indicated that participants had a positive experience, confirming ease of use, immersive appeal, and perceived cognitive benefits, despite some difficulties with environment scanning and object detection. The results provide evidence that an AR supermarket game leveraging mobile sensors has the potential to be an effective cognitive training tool for older adults. Full article

This research explored the integration of the real-time machine learning classification of motor imagery data with a brain–machine interface, leveraging prefabricated exoskeletons and an EEG headset integrated with virtual reality (VR). By combining these technologies, the study aimed to develop practical and scalable therapeutic applications for rehabilitation and daily motor training. The project showcased an optimized system designed to assess and train cognitive–motor functions in elderly individuals. Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. Comparative analyses were conducted with traditional models such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. The performance was further enhanced through a random hyperparameter search, optimizing feature extraction and learning parameters to achieve high classification accuracy (89.23%). A novel VR fishing game was developed to dynamically respond to EEG outputs, enabling the performance of interactive motor imagery tasks in coordination with upper limb exoskeleton arms. While clinical testing is ongoing, the system demonstrates potential for increasing ERD/ERS polarization rates in alpha and beta waves among elderly users after several weeks of training. This integrated approach offers a tangible step forward in creating effective, user-friendly solutions for motor function rehabilitation. Full article

In low- and middle-income countries (LMICs), where healthcare resources may be limited, the elderly are especially vulnerable to the adverse effects of cardiovascular diseases (CVDs). The aging population in these regions presents unique challenges, highlighting the urgent need for effective, accessible, and culturally appropriate interventions to address this cardiovascular health challenge in older adults. We aimed to evaluate the impact of traditional dance and games on cardiovascular health outcomes in LMICs through a scoping review of existing literature. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. PubMed, Scopus, ProQuest, EBSCO, SPORT Discuss, Web of Science, and the grey literature were searched from 2000 to 20 September 2024. Two reviewers independently screened the titles, abstracts, and full texts and conducted data extraction. All conflicts were resolved with a third reviewer. A total of 3465 records were identified, of which 12 full-text articles were included in the review. The studies, five randomised clinical trials and seven non-randomised clinical trials, included varied age groups and populations including healthy, sedentary and obese participants. The interventions were traditional dance and games interventions with some extension to nutrition education. All the interventions were short term, with less than 6 months follow-up. Any traditional dance styles and games that involve physical performance can induce positive health outcomes. Undertaking traditional dance and games (TDGs) is equally effective on cardiovascular, functional and metabolic adaptations, leading to comparable improvements in older adults as for other forms of structured exercise. Collaboration of health practitioners, legislators, non-governmental agencies and local communities in LMICs in using TDGs may reduce the burden of CVDs. Full article

Background: Early cognitive training is important to prevent cognitive decline in patients with mild cognitive impairment (MCI) or dementia. Therefore, developing an application that can provide evidence-based cognitive training is necessary for patients with MCI or dementia. Method: This study aimed to develop and evaluate Smart Brain, an evidence-based application that provides comprehensive cognitive training tailored to this population. The application was developed using an ADDIE (analysis, design, development, implementation, and evaluation) model. A systematic review of databases, including Ovid-MEDLINE, Ovid-EMBASE, Cochrane Library, and CINAHL, was conducted up to April 15, 2021, to identify key content areas. Additionally, a survey of 100 participants highlighted the need for features such as cognitive games, health notes, social networking services, and goal achievement. Result: The application was developed with distinct user and administrator interfaces to support engagement and monitoring. Usability testing involved 7 experts and 11 elderly individuals with MCI or dementia from a daycare center. Based on usability feedback, features such as the time limits for cognitive games were refined. The final application integrates cognitive games, physical exercises, emotional support, and health management tools to address user needs comprehensively. Conclusion: Smart Brain holds significant potential to improve the quality of life and cognitive health of elderly individuals with MCI or dementia. Its usability and functionality make it a promising tool for community-based interventions. Full article

In the era where digital technologies are becoming increasingly prevalent, it is anticipated that a majority of the global population will have at least a basic understanding of informatics. However, empirical evidence suggests that a significant portion of the global population remains digitally illiterate. This phenomenon is particularly pronounced in the case of the senior adult population. In light of the aforementioned challenges, this work integrates Computer Science Unplugged exercises, based on games and recreational activities without the use of computers, and L2T2L, a learning-by-teaching methodology whereby university students learn and then, in turn, teach that learning to other populations in a cascading manner. A case study was conducted in Lima, Peru, with the participation of 140 volunteers from centres for the elderly. Thirty-five students and one teacher from the Universidad Científica del Sur were responsible for initiating the transfer of knowledge from the university to the senior citizens, with the assistance of twelve individuals responsible for their care. The results demonstrate that the participants attained a commendable level of comprehension when attempting to complete all of the assigned tasks. Furthermore, the efficacy of L2T2L is evident in its adaptability and suitability for scenarios beyond those for which it was originally designed. Full article

With the aging population rising, the decline in spatial cognitive ability has become a critical issue affecting the quality of life among the elderly. Electroencephalogram (EEG) signal analysis presents substantial potential in spatial cognitive assessments. However, conventional methods struggle to effectively classify spatial cognitive states, particularly in tasks requiring multi-class discrimination of pre- and post-training cognitive states. This study proposes a novel approach for EEG signal classification, utilizing Permutation Conditional Mutual Information (PCMI) for feature extraction and a Multi-Scale Squeezed Excitation Convolutional Neural Network (MSSECNN) model for classification. Specifically, the MSSECNN classifies spatial cognitive states into two classes—before and after cognitive training—based on EEG features. First, the PCMI extracts nonlinear spatial features, generating spatial feature matrices across different channels. SENet then adaptively weights these features, highlighting key channels. Finally, the MSCNN model captures local and global features using convolution kernels of varying sizes, enhancing classification accuracy and robustness. This study systematically validates the model using cognitive training data from a brain-controlled car and manually operated UAV tasks, with cognitive state assessments performed through spatial cognition games combined with EEG signals. The experimental findings demonstrate that the proposed model significantly outperforms traditional methods, offering superior classification accuracy, robustness, and feature extraction capabilities. The MSSECNN model’s advantages in spatial cognitive state classification provide valuable technical support for early identification and intervention in cognitive decline. Full article

Stroke, amyotrophic lateral sclerosis (ALS), and Parkinson’s disease are some of the conditions that can lead to neuromotor disabilities requiring rehabilitation. To address the socio-economic burden that is amplified by the rapidly increasing elderly population, traditional rehabilitation techniques have recently been complemented by technological advancements, particularly Robot-Assisted Therapy (RAT). RAT enhances motor learning by improving both accuracy and consistency. This study proposes an innovative rehabilitation system that combines serious gaming and augmented reality (AR) with the LegUp parallel robot, developed for the spatial rehabilitation of the hip, knee, and ankle in bed-ridden patients. The system aims to improve patient outcomes and actively involve patients in their therapy. Electro-goniometers and a HoloLens 2 device are used to provide immediate feedback about the position of the patient’s joints, forming the basis of an interactive game in which the patient moves their leg to reach various targets. Two game modes were developed, each targeting different aspects of neuromotor rehabilitation, such as coordination, strength, and flexibility. Preliminary findings suggest that combining RAT with augmented reality-based serious gaming can increase patient motivation and engagement. Furthermore, the personalized and interactive nature of the therapy holds the potential to improve rehabilitation outcomes by fostering sustained engagement and effort. Full article

The rapid aging of the global population has highlighted the urgent need for age-friendly technological solutions. However, our review of the existing studies revealed significant gaps between elderly users’ needs and the current smart technology products. While previous research has explored various aspects of elderly technology design, our systematic analysis indicated limitations, in terms of integrated frameworks, empirically validated interaction models, and long-term effectiveness evaluation. This study proposes a seamless technology framework for the development of elderly-centered interactive systems—named Pillow Fight—which we validated through a mixed-methods study of an innovative intergenerational gaming system. This research was conducted in two phases across 34 sites, with 1997 participants. Phase one (n = 659) established the framework’s effectiveness through systematic field testing, while phase two (n = 1338) validated its scalability and long-term benefits. The results highlight significant improvements in system usability scores (SUS), from individual use (77.85) to group activities (85.32), with intergenerational interaction achieving the highest scores (92.12). The integration of health monitoring features further enhanced learnability (94.12) usability (82.15). This study contributes to the design of technology for the elderly through (1) establishing an integrated theoretical framework for seamless technology integration, (2) developing and validating innovative intergenerational interaction models, and (3) providing empirical evidence through systematic field studies. These contributions could advance both theoretical understanding and practical applications while maintaining high user-friendliness. Full article

Background/Objectives: This study was conducted to develop information and communication technology (ICT)-based oral functional rehabilitation exercise (OFRE) program content to effectively improve the oral function of the elderly people. Methods: After selecting evidence-based effective OFRE items through systematic review, the final items were constructed through the validity evaluation of detailed items through an expert Delphi survey. The items were composed in a simple content form that can be performed directly and applied to ICT-based mobile applications. Results: The final content items consisted of an oral functional motor-ability measurement, oral Pilates videos, and games. The first is to measure the maximum opening amount before and after exercise, and the opening amount was designed to be measured by eating the fruit displayed on the screen by opening and closing the lips. The second one consisted of eight exercises in the video, and each exercise was to be performed at least three times a day, with a total of two sets. The third is a salivary secretion function exercise that stimulates the salivary glands to stimulate the user’s interest and enable them to perform oral movements on their own. It consists of a lip and respiratory muscle exercise that inflates the cheeks and bursts a balloon, and the image disappears when the word in the image presented on the screen is pronounced correctly. It consists of pronunciation exercises. Conclusions: This content development attempt can be expanded into new convergence research linked to ICT and can be used as basic data when developing related content as part of digital care for the elderly in the future. Full article