JMIR Publications

ABSTRACT

Background:

Digital health technologies offer promising opportunities to support physical health. However, their acceptance, use and associated benefits are not equally distributed across society. While existing research has mainly focused on traditional socioeconomic indicators, broader sociological influences, including economic, cultural, social and person capital, may provide a more comprehensive understanding of these inequalities. Yet, too little is currently known about how different subgroups, based on their economic, cultural, social and person capital, relate to intentions to accept and use digital health technologies.

Objective:

This study aimed to: (1) identify distinct subgroups of individuals based on their acceptance and use of digital technologies to support their physical health; and (2) examine how these subgroups differ in terms of economic, cultural, social and person capital.

Methods:

We used cross-sectional data from the LISS panel (Longitudinal Internet Studies for the Social Sciences), including data from the LISS Core Study on health, economic situation, and social integration and leisure. To supplement this data, we conducted an additional online survey in November 2023 via the LISS panel to assess participants' acceptance and use of digital technologies to support their physical health. The final sample included 1,096 participants. We applied three-step Latent Class Analysis to identify subgroups based on constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT). Post hoc comparisons were used to characterize the subgroups based on 22 indicators of economic, cultural, social and person capital.

Results:

Five subgroups were identified: neutral users (43.8%), uninterested users (21.4%), engaged users (20.6%), resistant users (9.3%) and enthusiastic users (4.8%). The largest group, neutral users, neither fully adopted nor rejected digital technologies to support their physical health. Higher levels of economic, cultural and social capital were generally associated with greater acceptance and use of digital health technologies. However, person capital showed a different pattern: neutral users reported low self-confidence despite moderate use, while resistant users reported high self-image despite low acceptance and use. This suggests that person capital does not always align with traditional predictors of digital engagement.

Conclusions:

Inequalities in digital health engagement extend beyond socioeconomic factors and reflect broader differences in economic, cultural, social, and person capital. The distinct user types that were identified reveal that combinations of different types of capital can influence acceptance and use in unexpected ways. Addressing these multidimensional disparities is crucial for designing targeted and equitable strategies to enhance digital health participation across diverse populations.