The digital health capital framework: cross-cutting digital health inequality and smart-aging in post-COVID Hong Kong

The COVID-19 pandemic has accelerated the adoption of e-health technologies while simultaneously amplifying existing digital health disparities. To identify the digital health divide, we propose the Digital Health Capital (DHC) framework, an integrated multidimensional measure including usage, literacy and transformative capacity, to scrutinize the digital health divide in the emerging technology landscape. Utilizing a mixed-mode dataset that combines a quota-sampled online survey with an offline sample of elderly participants, we apply this framework to examine the digital health capital divide in post-COVID Hong Kong. Our data reveals three findings. First, consistent with previous studies, substantial disparities in DHC scores correlated with socioeconomic status (SES): individuals with higher education, wealthier, and employment consistently exhibit stronger digital health capacities. Second, the impacts of SES on the divide vary by different dimensions of DHC, revealing that the mechanisms driving inequalities in transformative capacity differ significantly from those affecting usage and literacy. Third, the findings challenge the stereotype of the homogeneity of digital disadvantage among the elderly and highlight the overlooked disadvantage faced by the middle-aged ‘sandwiched generation.’ These findings call for more refined policies for smart aging initiatives, emphasizing the need for targeted strategies to bridge the digital health capital divide when developing techcare for diverse demographic groups.

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Publication Details

Journal
Information Communication & Society
Published
2026-06-29
DOI
https://doi.org/10.1080/1369118x.2026.2689041
Primary Topic
COVID-19 Digital Contact Tracing
Type
article
Field-Weighted Citation Impact
0.00

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article

The digital health capital framework: cross-cutting digital health inequality and smart-aging in post-COVID Hong Kong

Yulu Ouyang, X J Wang, Pei Zhi, Yi‐Hui Christine Huang et al.
Information Communication & Society
COVID-19 Digital Contact Tracing
article

The digital health capital framework: cross-cutting digital health inequality and smart-aging in post-COVID Hong Kong

Yulu Ouyang, X J Wang, Pei Zhi, Yi‐Hui Christine Huang, Fen Lin, Bo Wen
article en

Abstract

The COVID-19 pandemic has accelerated the adoption of e-health technologies while simultaneously amplifying existing digital health disparities. To identify the digital health divide, we propose the Digital Health Capital (DHC) framework, an integrated multidimensional measure including usage, literacy and transformative capacity, to scrutinize the digital health divide in the emerging technology landscape. Utilizing a mixed-mode dataset that combines a quota-sampled online survey with an offline sample of elderly participants, we apply this framework to examine the digital health capital divide in post-COVID Hong Kong. Our data reveals three findings. First, consistent with previous studies, substantial disparities in DHC scores correlated with socioeconomic status (SES): individuals with higher education, wealthier, and employment consistently exhibit stronger digital health capacities. Second, the impacts of SES on the divide vary by different dimensions of DHC, revealing that the mechanisms driving inequalities in transformative capacity differ significantly from those affecting usage and literacy. Third, the findings challenge the stereotype of the homogeneity of digital disadvantage among the elderly and highlight the overlooked disadvantage faced by the middle-aged ‘sandwiched generation.’ These findings call for more refined policies for smart aging initiatives, emphasizing the need for targeted strategies to bridge the digital health capital divide when developing techcare for diverse demographic groups.

Information Communication & Society
City University of Hong Kong (HK), University of Macau (MO)
City University of Hong Kong
Openalex Percentile: Top 4%
COVID-19 Digital Contact Tracing
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