JMIR Publications

ABSTRACT

Background:

Population aging has become a critical global challenge, with South Korea entering a super-aged society and facing rapidly increasing healthcare demands. In response, digital healthcare devices have emerged as promising tools for supporting personalized health management and improving healthcare 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 that assume a homogeneous population, thereby overlooking the substantial heterogeneity in acceptance patterns among older adults. Therefore, a person-centered, data-driven segmentation approach is needed to better understand diverse adoption profiles.

Objective:

This study aims to segment older adults based on their acceptance patterns toward digital healthcare devices by integrating the TAM framework with data-driven clustering techniques.

Methods:

Survey data were collected from 349 adults aged 65 and older at senior centers and community facilities in the Seoul metropolitan area of South Korea. Ten constructs—health threat susceptibility, perceived usefulness, perceived ease of use, compatibility, privacy, self-efficacy, price consciousness, health empowerment, attitude toward digital healthcare, and intention to use—were measured. Principal Component Analysis (PCA) and K-means clustering were applied 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:

Two principal components were identified, and a four-cluster solution was selected (K = 4, Silhouette = 0.383). The analysis revealed four distinct segments: Core Adopters (16.3%), who scored highest across all constructs; Potential Adopters (18.3%), who recognized the value of digital healthcare devices but exhibited low self-efficacy and perceived ease of use; Passive Non-adopters (45.6%), who showed near-average scores; and Rejecters (19.8%), who scored negatively across all dimensions. Robustness checks confirmed high clustering reliability (94–99% agreement). Notably, Potential Adopters represent a critical target group, as their adoption barriers stem from capability constraints rather than lack of motivation.

Conclusions:

This study demonstrates 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 healthcare professionals seeking to facilitate inclusive adoption of digital healthcare technologies in aging societies.