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Examining the antecedents and health outcomes of health apps and wearables use

An integration of the technology acceptance model and communication inequality

Bibliographic Data

ID21452214
AuthorsHuanyu Bao (0000-0003-4740-4603, Nanyang Technological University, corresponding author), Edmund W J Lee (0000-0003-0536-2898, Nanyang Technological University)
Year2024
Volume43
Issue4
Pages695-716
Publication date2024-03-11
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueBehaviour and Information Technology (JOURNAL)
Journal identifiersISSN: 0144-929X • E-ISSN: 1362-3001
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/0144929x.2023.2183062
OpenAlexW4321787778
LanguageEN
Citations received10
References cited140

While health apps and wearables are often touted to promote individuals’ physical and mental health, a fundamental question that remains unanswered is how individuals from low socioeconomic status (SES) groups benefit from using these digital health technologies. This study draws upon two theoretical frameworks – an extended technology acceptance model and communication inequality to answer this research question. A nationally representative sample of 906 Singaporeans was administered, and data were analysed using hierarchical multiple regression. First, the results showed that factors at both the technological- (e.g. perceived usefulness and design aesthetics) and societal-level (e.g. descriptive and injunctive norms) contributed to closing the gap in health apps and wearables use between higher and lower SES groups. Second, using health apps and wearables was positively associated with physical and mental health and they can also bridge gaps in social well-being between the higher and lower SES groups; Third, the use of health apps and wearables was driven by multifaceted factors across technological-, individual-, and societal-levels, with hedonic motivation having the strongest positive association with both intention and actual use. Finally, theoretical and practical implications were discussed

Embedded system · Health equity · Inequality · Public health · Sociology · Wearable computer · Wearable technology · Computer Science · Mathematics · Medicine · Mobile Health and mHealth Applications · Nursing · Psychology · Technology Adoption and User Behaviour · Technology Use by Older Adults

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Unique citing works10
Citations per year3,33
Citation span2023 - 2026 (4)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 10
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