Development and validation of a nomogram for screening poor sleep quality in night-shift nurses: a public health tool for occupational health management

Sleep is the quiet engine of human health, a biological necessity that restores the mind and body after the demands of the day. For most people, this cycle is governed by the sun, a natural rhythm that tells the body when to wake and when to rest. But for a specific group of workers, this rhythm is constantly broken. Night-shift nurses, who care for patients while the world sleeps, face a unique and relentless challenge. Their work requires them to be alert during hours when their bodies are biologically programmed to shut down, a conflict that often leads to a state of poor sleep quality. This is not merely a matter of feeling tired; it is a significant occupational health issue that can erode a nurse's well-being and, by extension, compromise the safety of the patients they serve. When sleep is consistently disrupted, the risks of burnout, medical errors, and long-term health problems rise sharply, making the identification of those at highest risk a critical public health priority.

In a recent study conducted across four hospitals in Qingdao, China, researchers set out to solve a specific problem: how to quickly and accurately identify which night-shift nurses are struggling with poor sleep. While doctors and managers know that sleep problems are common in this profession, they often lack a simple, visual tool to pinpoint who is most at risk before a crisis occurs. To address this, the team surveyed 595 night-shift nurses over a three-month period in early 2026. They gathered detailed information about the nurses' lives, including their age, marital status, work schedules, and habits like drinking coffee or tea. They also measured their levels of anxiety and physical exhaustion using standard, well-validated questionnaires designed to screen for these specific conditions. The goal was not just to list these factors, but to weave them together into a single, easy-to-use prediction tool.

The researchers divided the group of nurses into two sets: a larger group to build the model and a smaller group to test it. By analyzing the data, they discovered that eight specific factors were strongly linked to poor sleep quality. These included being over forty years old, being married, working in high-pressure departments like emergency rooms or intensive care units, and working more than forty hours a week. The frequency of night shifts was particularly telling; nurses who worked more than eight night shifts in a month were significantly more likely to suffer from poor sleep. Other contributors included drinking coffee or tea, experiencing generalized anxiety, and reporting high levels of physical or mental fatigue. Interestingly, the study found that simply being female or having a certain level of education did not make a difference, nor did smoking or alcohol use, which are often assumed to be major culprits in sleep issues.

Using these eight factors, the team constructed a nomogram, which is essentially a visual calculator. Imagine a chart with several horizontal lines, each representing one of the risk factors. A user would find the nurse's specific situation on each line—such as "married" or "working ten night shifts"—and draw a line up to a points scale. Adding up all the points gives a total score, which then translates directly into a percentage chance that the nurse is experiencing poor sleep quality. This tool allows hospital managers or occupational health professionals to take a nurse's basic profile and instantly see their risk level without needing complex statistical software.

The performance of this new tool was rigorously tested. When applied to the group of nurses used to build it, the model achieved an AUC of 0.86. When tested on the separate group of nurses used for validation, it maintained a similar AUC of nearly 0.85. The researchers also checked how well the tool's predictions matched reality, finding that the estimated risks aligned closely with the actual sleep quality reported by the nurses. Furthermore, an analysis of the tool's clinical value showed that using it would provide a genuine benefit in identifying at-risk individuals, outperforming the strategy of either ignoring the problem or assuming every nurse has a sleep issue.

The study highlights that poor sleep among nurses is not just an individual failing but a result of specific, measurable workplace conditions. The findings suggest that the intensity of the work environment, the frequency of night shifts, and the psychological burden of the job are the primary drivers of sleep disruption. While the tool is a significant step forward, the authors note that it was developed using data from a single region and requires further testing in different hospitals and countries to confirm its universal usefulness. They also emphasize that because the study looked at a single point in time, it cannot prove that these factors cause the sleep problems, only that they are strongly associated with them. Nevertheless, this visual screening tool offers a practical, immediate way for healthcare institutions to identify vulnerable staff and intervene early, potentially protecting both the health of the nurses and the safety of the patients they care for.