Machine alarm fatigue is not only a professional concern for hemodialysis nurses, but also a direct threat to the safety of patients undergoing hemodialysis. However, there remains a lack of systematic analyses of its influencing factors and well-established strategies to manage it. This study aimed to systematically investigate the current status and multidimensional influencing factors of machine alarm fatigue among hemodialysis nurses, and to derive potential coping strategies accordingly, so as to provide a reference for clinical practice. This study employed a convergent parallel mixed-methods design, in which quantitative and qualitative data were collected simultaneously with equal priority and integrated during the results and discussion phases. The study was conducted from March to June 2026 across three campuses affiliated with a tertiary hospital in Hubei Province, China. For the quantitative component, 75 hemodialysis nurses were enrolled and surveyed using a general information questionnaire, the Machine Alarm Fatigue Scale, and the Resilience Scale. For the qualitative component, 13 hemodialysis nurses were recruited to participate in semi-structured interviews. Quantitative data were analyzed using descriptive statistics, univariate analyses (independent samples t-tests, one-way ANOVA or Kruskal-Wallis H test, and Spearman correlation), and multiple linear regression, while qualitative data were analyzed using conventional content analysis. In the quantitative phase, multivariate linear regression analysis showed that monthly income (β = 0.185, p = 0.003), night shift (β = 0.247, p = 0.002), rotating CRRT shifts (β = 0.216, p = 0.002), and the total score of the Resilience Scale (β = −0.452, p < 0.001) were independently associated with machine alarm fatigue. For years of experience in hemodialysis, with 1–5 years as the reference category, both the 6–10 year group (β = −0.131, p = 0.040) and the >10 year group (β = −0.155, p = 0.043) had significantly lower alarm fatigue scores. Qualitative interview data revealed four major themes concerning the influencing factors and suggestions for improving machine alarm fatigue, namely: the dynamic association between alarm rhythm and dialysis treatment phases; behavioral differentiation from passive response to active anticipation; attribution of non-actionable alarms to multiple responsible agents; and latent occupational fatigue under seemingly quiet operations. These four themes were further elaborated into 12 sub-themes. Machine alarm fatigue among hemodialysis nurses is associated with multiple factors, including years of working experience, shift type, and psychological resilience, and is associated with individual, organizational, and device-related dimensions. Managers may consider implementing multi-level strategies—including structured knowledge transfer, optimization of staffing allocation, monitoring of psychological resilience, and improvement of device functions, to systematically reduce alarm fatigue and ensure a safe hemodialysis nursing environment.
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wang et al. (2026) studied this question.
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