Understanding intentions to use a multi-component supported self-management platform for long COVID-19: a mixed-methods evaluation in Hong Kong.

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.