Segmenting Older Adults by Their Acceptance of Digital Health Care Devices: Cross-Sectional Study Using the Augmented Technology Acceptance Model and K-Means Clustering.

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.