Early detection of atrial fibrillation is important for timely initiation of anticoagulation. Hand-held single-lead electrocardiogram devices are recommended for atrial fibrillation screening, but limited evidence is available for its use in Chinese population.
To investigate the diagnostic performance of a hand-held single-lead ECG device (MyDiagnostick) in atrial fibrillation screening and its potential in the opportunistic screening of atrial fibrillation in the community.
Diagnostic performance of the MyDiagnostick was examined among 296 hospitalized patients with atrial fibrillation, using 12-lead electrocardiograms as the gold standard. Sensitivity, specificity, and kappa value were calculated. A single-time point opportunistic screening for atrial fibrillation was conducted using the MyDiagnostick in 1 577 community dwellings. Detection rate of atrial fibrillation was calculated.
The sensitivity and specificity of the MyDiagnostick for diagnosing atrial fibrillation were 0.945 and 0.947, respectively. The Kappa value was 0.892. Among 1 577 community dwellings, 60 cases of atrial fibrillation were detected (3.80%), among which 33 cases were new-onset atrial fibrillation (2.13%). Male gender (OR=2.553, 95%CI=1.504-4.333) and a history of stroke/transient ischemic attack/thrombosis (OR=2.840, 95%CI=1.330-6.064) were risk factors for atrial fibrillation in the community population.
The handheld single-lead electrocardiogram device of MyDiagnostick has a high accuracy in atrial fibrillation diagnosis and shows the potential to be used in atrial fibrillation screening in the community.
In China, the structural contradiction between surging health management demands and an insufficient high-quality supply has driven the need for smart health management services. By leveraging information and communication technology (ICT) alongside artificial intelligence (AI), these services integrate the entire process of health monitoring, assessment, and intervention, emerging as a critical new area of demand.
To systematically analyze the current usage, demands, and influencing factors of smart health management services and devices among adult residents in China, thereby providing an evidence base and strategic recommendations for their sustainable development.
An explanatory sequential mixed-methods design was employed. Quantitative data were collected from September to December 2023 using a stratified cluster random sampling method. Adults aged 18 and above were surveyed via the Wenjuanxing platform. The questionnaire captured demographic characteristics and utilized standardized scales alongside self-developed items to measure eHealth literacy, media motivation, attitude toward the behavior, subjective norms, perceived behavioral control, as well as the demands, behavioral intentions, and actual usage behaviors regarding smart health management services. Structural equation modeling (SEM) was applied to analyze the influencing factors. Qualitative data were gathered from May 2024 to March 2025 through one-on-one, semi-structured interviews. The interviews explored participants' basic profiles, personalized demands, usage experiences with smart health management monitoring devices, service model preferences, and feedback. Finally, an explanatory sequential mixed-methods framework was utilized to integrate and interpret the quantitative and qualitative results.
A total of 2 900 questionnaires were distributed, yielding 2 786 valid responses (effective recovery rate: 96.1%). Thirteen individuals participated in the qualitative interviews. Findings revealed: (1) The usage rate of smart health management monitoring devices among adult residents in China was 37.7% (1 051/2 786), demonstrating a declining trend with age, with significantly lower usage among the elderly. (2) User demands exhibited multi-level and age-stratified characteristics. Overall demands were concentrated on basic functions such as health monitoring and answering health queries; qualitative research further revealed a progressive demand model ranging from basic life management to empowered self-actualization. Younger groups preferred basic prevention and lifestyle optimization, whereas older groups focused more on practical functions like disease management. (3) The intention to use was at a moderately high level (62.68±20.65). SEM results indicated that attitude toward the behavior (β=0.568, P<0.001) was the strongest predictor of intention, while subjective norms (β=0.103, P<0.001), and media motivation (β=0.089, P<0.001) also had significant positive effects. eHealth literacy indirectly influenced both intention (β=0.045, P<0.001) and usage behavior (β=0.051, P<0.001) through media motivation. Perceived behavioral control indirectly affected intention (β=0.014, P<0.001) and usage behavior (β=0.016, P<0.001). Urban residence independently and positively affected intention (β=0.056, P<0.001) and usage behavior (β=0.125, P<0.001), and having medical insurance significantly promoted intention (β=0.039, P=0.008). Qualitative findings identified that usage barriers included objective factors (e.g., high costs, product quality issues) and perceived obstacles (e.g., discomfort in wearing, privacy concerns). User attitudes were significantly polarized; positive or negative evaluations stemmed directly from usage experience, perceived benefits, and product intelligence, whereas individuals with neutral attitudes were prone to abandonment due to a lack of clear perceived value. Furthermore, personal beliefs and cultural backgrounds profoundly influenced technological decision-making.
This study reveals that smart health management services among Chinese adults are characterized by high intention but low usage, with age-stratified differences in current usage and demand structures. Regarding influencing mechanisms, attitude toward behavior and subjective norms are the core drivers of intention. eHealth literacy and media motivation exert a significant serial mediating effect, while perceived barriers constitute the primary resistance to behavioral transformation. Future promotion of smart health services should emphasize age-friendly design and precision implementation, while leveraging the synergistic role of primary healthcare to bridge the intention-behavior gap. This approach will enhance adults' proactive health management capabilities and facilitate the goal of healthy aging.
