Background: Population aging has become a critical global challenge, with South Korea entering a super-aged society and facing rapidly increasing health care demands. In response, digital health care devices have emerged as promising tools for supporting personalized health management and improving health care accessibility among older adults. However, despite their potential, adoption rates among older adults remain relatively low. Prior research based on the Technology Acceptance Model (TAM) has largely relied on variable-centered approaches, overlooking substantial heterogeneity in acceptance patterns among older adults. A person-centered segmentation approach is therefore needed to identify diverse acceptance profiles. Few studies have integrated the augmented TAM with K-means clustering to identify acceptance-based segments in this population. Objective: This study aims to segment older adults based on their acceptance patterns toward digital health care devices by integrating the TAM framework with data-driven clustering techniques. Methods: A cross-sectional survey was conducted with 349 adults aged 65 years and older who were recruited from older adult welfare centers and community facilities in the Seoul metropolitan area of South Korea. We measured 10 constructs within an augmented TAM framework: 2 core constructs (perceived usefulness, perceived ease of use), 6 extended constructs (compatibility, privacy, self-efficacy, price consciousness, health empowerment, attitude toward digital health care), 1 health-related construct (health threat susceptibility), and intention to use as the outcome. Principal component analysis (PCA) and K-means clustering were used to identify latent segments. The number of components was determined using parallel analysis and the Kaiser criterion, and the optimal number of clusters was validated using the silhouette coefficient. Robustness was further assessed through 100-seed stability analysis and PCA sensitivity tests. Results: We identified 2 principal components, and a 4-cluster solution was selected (K=4, silhouette coeffficient=0.383). The analysis revealed 4 distinct segments: core adopters (57/349, 16.3%), who scored highest across all constructs; potential adopters (64/349, 18.3%), who recognized the value of digital health care devices but exhibited low self-efficacy and perceived ease of use; neutral majority (159/349, 45.6%), who showed near-average scores; and rejecters (69/349, 19.8%), who scored negatively across all dimensions. Robustness checks confirmed high clustering reliability (94%‐99% agreement). Notably, potential adopters represented a critical target group, as their acceptance barriers stemmed from capability constraints rather than lack of motivation. This group combined high perceived usefulness (+0.50) with the lowest self-efficacy (−1.07) and perceived ease of use (−0.83). Conclusions: This study demonstrated that technology acceptance among older adults is heterogeneous rather than uniform and highlights the importance of segment-specific strategies. By integrating theory-driven acceptance constructs with unsupervised machine learning, the study provides a practical framework for identifying actionable user segments and designing tailored diffusion strategies. These findings offer important implications for policymakers, technology developers, and health care professionals seeking to facilitate inclusive adoption of digital health care technologies in aging societies.

OBJECTIVES This cross-national comparative study was conducted to analyze the differences in physical activity-focused policies for older adults among South Korea, the United States, the United Kingdom, and Japan.METHODS The study examined elderly health promotion and physical activity–related legislation in each country through a review of policy documents and official reports issued by public institutions, as well as prior academic literature. A dual-framework approach was employed that integrates comparative policy analysis and framing analysis. In the comparative policy analysis, policies were assessed across six structural dimensions: policy objectives, implementing bodies, financial structure, program components, target populations, and evaluation methods. In the framing analysis, three discursive elements were investigated: how aging-related problems are defined, how policy tools are legitimized, and how perceptions of older adults are constructed within each country’s policy discourse.RESULTS While all four countries aim to extend healthy life expectancy, their policy designs and governance structures differ markedly. South Korea’s centralized approach revealed weak inter-ministerial coordination and regional disparities, along with a tendency to frame older adults as passive welfare recipients. The United States applies federal evidence-based guidelines through multilevel governance involving states and private actors, emphasizing voluntary participation. The United Kingdom promotes emotional well-being and social integration through collaborative efforts between government and community organizations. Japan adopts a scientific and preventive model focused on community sustainability, recognizing older adults as active contributors.CONCLUSIONS The findings suggest that South Korea should reinforce evidence-based and preventive strategies, empower local-level governance, and reframe its perception of older adults as autonomous participants rather than dependent recipients. These shifts are vital to building sustainable and inclusive elderly health promotion policies in the era of super-aged societies.

Facial expression recognition is a pivotal area in computer vision, traditionally focusing on categorical labels such as ‘Happy’, and ‘Sad’. Recent advancements have transitioned towards using the continuous indicators valence and arousal, reflecting the complicated nature of human emotions. This study introduces a method using semi-supervised learning to generate valence and arousal labels for existing categorical datasets, addressing challenges like racial bias. We propose a pseudo-label refinement framework, VAP-Refine (Valence-Arousal Pseudo-label Refinement), which enhances facial expression recognition by combining teacher model predictions and category-level statistics. These predicted labels are adjusted using ground truth category information and combined with actual category labels to train a more robust facial expression recognition model. Our approach improved accuracy from 69.3% to 72.26% and from 62.94% to 67.24% across two datasets. Fine-tuning with a teacher model predicting valence and arousal achieved 75.82% and 91.58% accuracy, with an F1-score of 0.9147, despite data imbalances. These results highlight the potential of semi-supervised learning to enhance facial expression recognition by incorporating continuous emotional indicators, improving model performance and contributing to more accurate affective computing applications. Furthermore, the proposed framework consistently improved performance across various backbone architectures, including ResNet50, SHViT, and DDAMFN++, highlighting its generalizability and versatility.