Abstract / Summary
Abstract Background Young and middle-aged adults at high cardiovascular risk face not only cardiovascular risk but also pressures arising from multiple social roles, including work and family responsibilities. Their risk perceptions and self-management mechanisms may therefore be specific to their stage of the life course. Existing research has provided limited in-depth explanation of how this population understands cardiovascular risk, maintains health behaviours, and perceives digital health support. Guided by the Health Belief Model (HBM), this study aimed to explore their health beliefs, facilitators and barriers to self-management behaviours, experiences of using smart health technologies, and needs for related support. Methods A descriptive qualitative design was used. The study was conducted from January to March 2026 at a tertiary hospital in Guangzhou, China. Seventeen young and middle-aged adults at high cardiovascular risk were recruited using purposive sampling combined with a maximum-variation strategy and participated in face-to-face semi-structured interviews. Interview recordings were transcribed verbatim and analysed using directed content analysis, with the six core constructs of the HBM serving as the initial analytical framework; data falling outside this framework were coded inductively. Two researchers coded the data independently and reached consensus through team discussion. Data collection continued until no new themes or concepts emerged. Results Four themes and ten subthemes were identified: (1) coexistence of risk awareness and emotional detachment; (2) behavioural motivation driven by family responsibility; (3) the initiation–maintenance gap in self-efficacy; and (4) barriers to realising the value of smart health technologies. Participants were often able to identify their risk factors but did not consistently experience them as personally relevant threats. Symptoms, illness among peers, and hospitalisation experiences could temporarily increase the perceived reality of risk. Family responsibility was an important motivation for behaviour, whereas long-term maintenance was affected by previous experiences of failure, occupational contexts, and time pressure. Regarding smart health technologies, participants placed greater emphasis on risk interpretation, actionable guidance, and emergency support than on simply increasing data monitoring. Conclusions Self-management among young and middle-aged adults at high cardiovascular risk may be jointly shaped by cognitive, emotional, family-relational, and real-world contextual factors. Beyond risk education, public health and clinical preventive interventions could strengthen contextualised risk communication related to family roles and support behavioural maintenance through low-threshold, staged goals. Digital health technologies should prioritise improved interpretation of abnormal information, actionable recommendations, and safety support, while also considering compatibility with occupational contexts, user autonomy, and privacy and ethical issues. The explanatory pathways identified in this study require further examination in broader populations.