This study reveals a bidirectional network linking sleep and emotional symptoms in ICU nurses, identifying anxiety and subjective sleep quality as central nodes, with fatigue and sleep disturbances as key bridges.
Key Findings
Results
Tension-Anxiety (POMS1) was identified as the most central node in the sleep-emotional symptom network among ICU nurses.
POMS1 (Tension-Anxiety) had the highest Expected Influence (EI = 1.231) among all nodes in the network.
Centrality measures were used to pinpoint key symptoms within the network.
The study included 498 ICU nurses from 18 tertiary hospitals across Jiangsu, Zhejiang, and Shanghai.
Network analysis was conducted using R version 4.3.3.
Results
Subjective Sleep Quality (PSQI1) was identified as a central node in the symptom network with the second highest expected influence.
PSQI1 (Subjective Sleep Quality) had an Expected Influence of EI = 0.956.
Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI).
The study was conducted as a multicentre cross-sectional analysis from February to March 2025.
PSQI1 was the highest-centrality sleep-related node in the network.
Results
Fatigue-Inertia (POMS3) was identified as the key bridge symptom connecting the emotional and sleep symptom clusters.
POMS3 (Fatigue-Inertia) had the highest Bridge Expected Influence (BEI = 0.161) among all nodes.
Bridge symptoms were identified as those linking otherwise separate symptom communities within the network.
The authors suggest disrupting this fatigue-sleep bridge via circadian-aligned scheduling and fatigue management.
Results
Sleep Disturbance (PSQI5) was identified as the key bridge symptom on the sleep quality side of the network.
PSQI5 (Sleep Disturbance) had a Bridge Expected Influence of BEI = 0.095.
PSQI5 was the highest-BEI node among sleep-related symptoms.
Together, POMS3 and PSQI5 were identified as the primary bridge symptoms linking the emotional and sleep domains.
Results
The strongest edge in the network linked Vigour-Activity (POMS5) and Esteem-Related Affect (POMS7) with a partial correlation of 0.761.
The partial correlation between POMS5 (Vigour-Activity) and POMS7 (Esteem-Related Affect) was 0.761, the highest of any edge in the network.
This edge was within the emotional symptom cluster rather than between sleep and emotional domains.
Mood states were assessed using the Profile of Mood States-Short Form (POMS-SF).
Results
Network predictability was highest for the Depression-Dejection subscale (POMS4).
POMS4 (Depression-Dejection) had the highest node predictability in the network, meaning its variance was best explained by its neighboring nodes.
Node predictability reflects how well a symptom's state can be predicted by its connections to other symptoms in the network.
This finding suggests Depression-Dejection is highly embedded within and influenced by the surrounding symptom network.
Methods
A total of 498 ICU nurses from 18 tertiary hospitals across three Chinese provinces/municipalities participated in the study.
Hospitals were located in Jiangsu, Zhejiang, and Shanghai.
Data were collected from February to March 2025.
The study used a multicentre cross-sectional design.
Both the POMS-SF and PSQI were administered as survey instruments.
What This Means
This research suggests that sleep problems and emotional difficulties in ICU nurses are not independent issues but form an interconnected web of symptoms that reinforce each other. Using a statistical technique called network analysis, the researchers surveyed 498 ICU nurses across 18 hospitals in China and mapped out how 14 different sleep and mood-related symptoms relate to one another. They found that anxiety (specifically 'Tension-Anxiety') and how nurses subjectively perceive their own sleep quality are the most central, or influential, symptoms in this network — meaning these two symptoms have the most connections to other symptoms and thus the most potential to spread distress throughout the system.
The study also identified 'bridge' symptoms — those that link the emotional symptom cluster to the sleep symptom cluster. Fatigue was the most important bridge on the emotional side, and sleep disturbances (such as waking during the night) were the most important bridge on the sleep side. This means that fatigue and sleep disturbances may be critical transmission points through which poor sleep worsens emotional health and vice versa. Interestingly, the single strongest relationship in the entire network was between nurses feeling energetic (vigour) and having positive self-esteem, suggesting these two emotional states are closely tied.
This research suggests that rather than treating sleep and mental health problems in ICU nurses separately, interventions should target the most connected and bridging symptoms together. For example, addressing anxiety through cognitive approaches and improving sleep monitoring could have outsized benefits because of anxiety's central role in the network. Similarly, scheduling practices that account for circadian rhythms and reduce fatigue might help break the link between tiredness and worsening sleep quality. The findings offer a structured way to think about protecting nurse well-being in high-stress clinical environments, which in turn may support patient safety.
Wang Y, Sha S, Lu X, Gu J. (2026). Network Structure of Sleep Quality and Emotional State Among ICU Nurses: A Cross-Sectional Analysis.. Nursing in critical care. https://doi.org/10.1111/nicc.70462