ArticleScientific reports2025
Understanding intentions to use a multi-component supported self-management platform for long COVID-19: a mixed-methods evaluation in Hong Kong.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A culturally appropriate, multi-component self-management platform was developed to facilitate Long COVID-19 recovery in Hong Kong, comprising a smartphone application, a website, and a customer relationship management (CRM)-based messaging system. Using a mixed-methods design, we evaluated users' intentions to utilise the platform through the behavioural attributes of the Meta-UTAUT (Meta-analysis-based modified Unified Theory of Acceptance and Use of Technology) framework. Structured interviews were conducted to explore themes influencing users' intentions to use the platform, focusing on different attributes. Themes from the interviews were summarised using directed content and thematic analyses. These results informed the design of a cross-sectional survey quantifying the influence of those attributes on users' utilisation of the platform. Multivariate logistic regressions were conducted to investigate the associations between sociodemographic and health characteristics and the likelihood of responses to each attribute. Analysis of 45 interviews identified 17 themes influencing platform use, across six attributes: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived information security, and perceived enjoyment. The survey of 326 users revealed that each of these attributes influenced the intentions of over 80% of participants using the platform. Gender, age, educational attainment, and employment status were significantly associated with their responses to specific attributes. Supported self-management is an emerging intervention for Long COVID-19. While online platforms enhance access to health information, interactions through CRM-based messaging systems may optimise professional and emotional support, thereby improving user engagement. Resources may be directed towards alleviating digital barriers, particularly for older populations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.