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. 2022 Apr 13:14:661-674.
doi: 10.2147/NSS.S347419. eCollection 2022.

An Exploration of Self-Reported Sleep Inertia Symptoms Using Network Analysis

Zijuan Ma  1   2 , Yanqiang Tao  3 , Huilin Chen  4 , Yifan Zhang  1   2 , Ye Pan  1   2 , Dongjing Meng  1   2 , Fang Fan  1   2
Affiliations

An Exploration of Self-Reported Sleep Inertia Symptoms Using Network Analysis

Zijuan Ma et al. Nat Sci Sleep. .

Abstract

Purpose: Sleep inertia (SI) is the transitional state accompanied by compromised cognitive and physical performance and sleepiness. Network analysis offers a potential new framework to conceptualize a complex network of symptom-symptom interactions, and the network structure is analyzed to reveal the core characteristics. However, no previous study examined the network structure of SI symptoms. Thus, this study aimed to elucidate characteristics and compare sex differences of SI symptom networks in the general population.

Materials and methods: A total of 1491 participants from China were recruited from 30 May to 17 June, 2021. SI symptoms were assessed by using the Sleep Inertia Questionnaire (SIQ). The network structures were estimated and compared using network analytic methods in the R version 4.1.1.

Results: Centrality properties analysis of the expected influence suggested that symptoms of "Feel sleepy", "Groggy, fuzzy or hazy mind", and "Dread starting your day" exerted greatest influences. The weighted adjacency matrix revealed that the "Dread starting your day" and "Anxious about the upcoming day" edge showed the strongest connection (edge weight value = 0.70). The network comparison test found no significant difference in network global strength (p=0.928), distribution of edge weights (p=0.194) and individual edge weights (all p values >0.05 after Holm-Bonferroni corrections) between males and females.

Conclusion: Symptoms of "Feel sleepy", "Groggy, fuzzy or hazy mind", and "Dread starting your day" were central in the SI symptom network. Intervention, such as the artificial dawn and change in body temperature, for symptoms of "Feel sleepy", "Groggy, fuzzy or hazy mind", and "Dread starting your day" might be crucial to hasten the dissipation of SI in the general population who may need to perform tasks upon waking.

Keywords: Sleep Inertia Questionnaire; general population; network analysis; sleep inertia.

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Conflict of interest statement

The authors declare no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Network structure of the sleep inertia symptoms in the total sample (N= 1240). Blue lines indicate positive associations, and orange lines indicate negative associations. The thickness of an edge represents the association strength, the size of a circle represents the node strength, and the area of red ring indicates the predictability.
Figure 2
Figure 2
Centrality indices of network structure of sleep inertia symptoms, shown as standardized values z-scores.
Figure 3
Figure 3
Stability of centrality indices as assessed using the case-dropping bootstrap method. The x-axis represents the percentage of cases of the original sample used at each step. The y-axis represents the average of correlations between the centrality indices from the original network and the centrality indices from the networks that were re-estimated after excluding increasing percentages of cases. Each line indicates the correlations among betweenness, closeness, and strength, while areas indicate 95% CI.
Figure 4
Figure 4
Estimation of node strength difference by bootstrapped difference test. Bootstrapped difference tests between node strength of factors. Gray boxes indicate nodes that do not significantly differ from one-another. Black boxes represent nodes that differ significantly from one another (α = 0.05). White boxes show the values of node strength.
Figure 5
Figure 5
Estimated network models for sleep inertia symptoms between females and males. Left panel: network structure in females (n = 675); right panel: network structure in males (n = 565).
See this image and copyright information in PMC

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