Trends in Age and Socioeconomic Inequalities in Telemedicine Adoption in Japan, 2020-2024: Longitudinal Panel Study
Journal of Medical Internet Research ·
Background: Theoretical perspectives offer contrasting predictions regarding how inequalities in health technology adoption evolve over time. While classical diffusion of innovations theory suggests that early adoption gaps may narrow as technologies such as telemedicine become widespread, the inverse equity hypothesis posits that such disparities are likely to persist or even widen. Longitudinal evidence on the long-term evolution of individual telemedicine adoption, however, remains scarce. Objective: This study aims to assess changes in age and socioeconomic inequalities in telemedicine adoption in Japan from 2020 to 2024. Methods: We used data from a nationwide, internet-based panel survey of the general population in Japan. Participants aged 18-75 years who had completed both the 2020 baseline and the 2024 follow-up surveys were included. The primary outcome was self-reported telemedicine adoption, harmonized as cumulative ever use at each survey wave. For each age and socioeconomic indicator, we fitted a separate inverse probability-weighted multivariable logistic regression model incorporating the exposure, survey wave, and their interaction, and adjusted for baseline demographic, socioeconomic, and health-related characteristics. Predictive margins were used to estimate adjusted prevalence and probability-scale difference-in-differences (DIDs). Results: The study included 10,818 participants (mean age 49.7, SD 16.8 years; 50.6% [unweighted n/N 5011/10,818] women). In the unweighted counts, 282 (2.6%) participants had adopted telemedicine by 2020, with this figure increasing to 758 (7%) participants by 2024. Telemedicine adoption was generally less prevalent in older age groups in 2020. Although adoption increased across all age groups, the increase was smaller among older participants (70-75 years: +1.0 percentage point [PP] vs 18-29 years: +13.1 PP; DID −12.1 PP, 95% CI −18.2 to −5.9). The increase was also smaller among participants who were unemployed at baseline than among upper nonmanual workers (+2.8 vs +5.7 PP; DID −2.9 PP, 95% CI −4.6 to −1.2). DIDs for educational attainment, urbanicity, and household income were not statistically distinguishable from zero. The findings remained largely unchanged after several sensitivity analyses. Conclusions: Between 2020 and 2024, telemedicine adoption inequalities by age and socioeconomic status showed no evidence of narrowing, and age-related inequalities widened. These findings suggest that the broader diffusion of telemedicine has not necessarily been accompanied by equitable adoption, and highlight the potential importance of targeted assistance, inclusive platform design, and support for digital access and literacy among populations facing barriers to telemedicine adoption.
Background: Theoretical perspectives offer contrasting predictions regarding how inequalities in health technology adoption evolve over time. While classical diffusion of innovations theory suggests that early adoption gaps may narrow as technologies such as telemedicine become widespread, the inverse equity hypothesis posits that such disparities are likely to persist or even widen. Longitudinal evidence on the long-term evolution of individual telemedicine adoption, however, remains scarce. Objective: This study aims to assess changes in age and socioeconomic inequalities in telemedicine adoption in Japan from 2020 to 2024. Methods: We used data from a nationwide, internet-based panel survey of the general population in Japan. Participants aged 18-75 years who had completed both the 2020 baseline and the 2024 follow-up surveys were included. The primary outcome was self-reported telemedicine adoption, harmonized as cumulative ever use at each survey wave. For each age and socioeconomic indicator, we fitted a separate inverse probability-weighted multivariable logistic regression model incorporating the exposure, survey wave, and their interaction, and adjusted for baseline demographic, socioeconomic, and health-related characteristics. Predictive margins were used to estimate adjusted prevalence and probability-scale difference-in-differences (DIDs). Results: The study included 10,818 participants (mean age 49.7, SD 16.8 years; 50.6% [unweighted n/N 5011/10,818] women). In the unweighted counts, 282 (2.6%) participants had adopted telemedicine by 2020, with this figure increasing to 758 (7%) participants by 2024. Telemedicine adoption was generally less prevalent in older age groups in 2020. Although adoption increased across all age groups, the increase was smaller among older participants (70-75 years: +1.0 percentage point [PP] vs 18-29 years: +13.1 PP; DID −12.1 PP, 95% CI −18.2 to −5.9). The increase was also smaller among participants who were unemployed at baseline than among upper nonmanual workers (+2.8 vs +5.7 PP; DID −2.9 PP, 95% CI −4.6 to −1.2). DIDs for educational attainment, urbanicity, and household income were not statistically distinguishable from zero. The findings remained largely unchanged after several sensitivity analyses. Conclusions: Between 2020 and 2024, telemedicine adoption inequalities by age and socioeconomic status showed no evidence of narrowing, and age-related inequalities widened. These findings suggest that the broader diffusion of telemedicine has not necessarily been accompanied by equitable adoption, and highlight the potential importance of targeted assistance, inclusive platform design, and support for digital access and literacy among populations facing barriers to telemedicine adoption.