Network analysis to estimate central insomnia symptoms among daytime workers at-risk for insomnia

Although insomnia complaints are associated with mental health problems and reduced work productivity, the central insomnia symptoms in workers at-risk for insomnia remain unclear. This study aimed to identify the central insomnia symptoms among daytime workers at risk for insomnia. The participants were 881 Japanese daytime workers at-risk for insomnia with a mean age of 49.33 ± 9.92 years. At-risk for insomnia was defined as an Athens Insomnia Scale score of six or higher. The Athens Insomnia Scale was used as a screening for at-risk insomnia because it has higher sensitivity and specificity than other insomnia screening scales. The Insomnia Severity Index is recommended as a mechanism of insomnia and an outcome measure; therefore, a network analysis was conducted with the seven items of the Insomnia Severity Index. The important variables in the connections between insomnia symptoms were estimated from centrality indices, which were interpretable only for strength. The strength value results suggest that difficulty staying asleep and worry about sleep problems were the central insomnia symptoms. The connections were stronger for difficulty staying asleep and problem waking up too early, difficulty staying asleep and difficulty falling asleep, and interference with daytime functions and noticeable to others. Worry about sleep problems was strongly associated with variables other than nocturnal insomnia symptoms. Therefore, difficulty staying asleep and worry about sleep problems are important variables in daytime workers at-risk for insomnia and are key points for improvement or exacerbation of insomnia symptoms.

performed network analysis of insomnia, depressive, and anxiety symptoms.In a study of adults aged over 18, difficulty staying asleep was estimated to be the central symptom in the symptom-to-symptom association 8 .In another study of adults aged over 18 who met the DSM-5 9 criteria for insomnia disorder and the Sleep Condition Indicator 10 screening for insomnia symptoms, a sense of uncontrolled worry was estimated as a central symptom 11 .A contributing factor to the differences in these network analyses may be whether the participants are from the general population, including healthy individuals, or whether they are only individuals with insomnia symptoms.
Only one study has conducted network analysis of insomnia symptoms alone.Bai et al. 12 used the Insomnia Severity Index (ISI) 13 to estimate central insomnia symptoms in mental health professionals during the COVID-19 pandemic.They reported that daytime dysfunction was the central symptom and that no gender differences were found in the symptom-to-symptom association of insomnia symptoms.However, there are several problems with the findings.For example, they did not establish the criteria for insomnia, included individuals who did not complain of insomnia, and they did not exclude individuals who worked night shifts.These factors skew the results.Insomnia in shift workers is often a circadian rhythm misalignment due to work schedules 14 .In general, insomnia is associated with sleep-related cognitive processes 15 .That is, the causes and maintenance processes of insomnia differ between shift workers and daytime workers.Therefore, shift workers must be excluded.Also, given the possibility that the results of the network analysis may differ depending on whether or not the participants have insomnia complaints 8,11 , it is necessary to study individuals with insomnia complaints to determine the clinically meaningful central symptoms of insomnia.
There are several self-report scales that screen for insomnia symptoms and determine the effectiveness of treatment for insomnia.A meta-analysis of diagnostic accuracy showed that although the Athens Insomnia Scale (AIS) 16 did not indicate statistical differences in diagnostic accuracy compared to other scales, it had the highest values for both sensitivity and specificity 17 .Therefore, the AIS was used for screening for insomnia in this study, and individuals above the cut-off point of the AIS were defined as at-risk for insomnia.ISI is recommended for use in studies of the mechanism/evaluation of insomnia disorder 18 .Therefore, ISI was used for network analysis.This study aimed to estimate the central insomnia symptoms among daytime workers at-risk for insomnia.

Participants and procedure
The present study was conducted as part of an Internet-based cross-sectional survey of insomnia and work productivity among Japanese daytime workers.Part of the data pooled in this survey has already been published 3 .However, Takano et al. 3 did not examine the associations between insomnia symptoms.The survey period was from November 16-18, 2021.Of the 3438 participants who provided informed consent to participate in the previous research, 881 (702 men and 179 women) met all eligibility criteria for this study.Informed consent was obtained from all subjects and/or their legal guardian(s) for this study.The eligibility criteria included working eight hours per day for five days per week (full-time workers), no night shifts, no applicability to inattention detection items, no incomplete responses, no other gender, and a score of six or higher on the Japanese version of the AIS 19 .Inattention detection items are questions (Directed Questions Scale) that identify those who respond to a question without reading it 20 .
The process for selecting the participants for this study is shown in Fig. 1.All procedures performed in the study were in accordance with the ethical standards of the institutional and/ or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.The present study was approved by the Ethics Board of the School of Psychological Science, Health Sciences University of Hokkaido, Japan (Approval number: 22004).

Measurements
This study obtained demographic data of all participants, including age, gender, body mass index (BMI), and occupation.
The Japanese version of the AIS is a self-report scale for assessing insomnia symptoms 19 .The AIS is an eightitem questionnaire with a four-point Likert scale.The Japanese version of the AIS has been confirmed to have concurrent validity with the Japanese version of the ISI (r = 0.85) 19 .Individuals with scores of six or higher on the Japanese version of the AIS 17 were defined as being at-risk for insomnia in this study.
The Japanese version of the ISI is another self-report scale for assessing insomnia symptoms 21 .The ISI is a seven-item questionnaire that uses a five-point Likert scale to rate responses and focuses on nocturnal sleep disturbances such as sleep-onset problems (ISI_1), sleep-maintenance problem (ISI_2), early morning problems (ISI_3), sleep dissatisfaction (ISI_4) , daytime dysfunction such as interference with daily functioning (ISI_5) and noticeability of impairment due to the sleep disturbance (ISI_6), and emotional distress due to sleep disturbance (ISI_7) 13 .

Statistical analysis
R 4.2.2 was used for the statistical analysis.The R packages qgraph (Version: 1.9.2), bootnet (Version: 1.5), and NetworkComparisonTest (NCT, Version: 2.2.1) were used for the network analysis.The visualized network diagram consisted of variables (nodes) connected by lines (edges).In this study, each ISI item was a node.The edges of the network are polychoric correlations of the seven items of the ISI.This study estimated the Gaussian graphical model (GGM) network from the least absolute shrinkage and selection operator and extended Bayesian information criteria.In the GGM, edges can be interpreted as partial correlation coefficients 4 .In the network diagram, the relationship between nodes was represented by the width of the edges and thickness of the color.The stability of the centrality indices was confirmed by the correlation-stability (CS) coefficient.A CS coefficient of 0.25 or less is considered unstable; a CS coefficient of 0.5 or more is recommended 4 .Case-dropping bootstrapping with 2500 samples was used to estimate CS coefficients.This study used three centrality indices-strength, betweenness, and closeness 22 .Strength was estimated as the strength of a node's direct connection to the network.Betweenness was estimated from the concept of the shortest path length connecting any two nodes, and nodes with high distance centrality between them were considered central in connecting other symptoms.Closeness was estimated as how strongly a node is indirectly connected to the network.
Differences in the estimated network of ISI scores between male and female participants were examined using network structure, global strength, and centrality indices.The p-values were adjusted using the Bonferroni method.Network structure is the maximum difference in pairwise edges between the male and female networks.Global strength is the sum of all edges between the male and female networks.
Welch's t-test was used to determine whether there were sex differences in age, BMI, ISI total scores, and each ISI item score.The effect size was calculated as Hedges' g.The R package compute.es(Version: 0.2.5) was used for the effect size.

Comparison of network structure by gender
The male and female ISI network structures did not demonstrate significant differences in terms of network structure (p = 0.17), global strength (p = 0.53), and centrality index values of strength (all p > 0.05).

Discussion
This study's findings suggest that difficulty staying asleep and worry about sleep problems are central insomnia symptoms among workers at-risk for insomnia.Moreover, difficulty staying asleep and worry about sleep problems were more strongly connected with other insomnia symptoms.Thus, the strength values were higher than other insomnia symptoms.Changes in difficulty staying asleep and/or worry about sleep problems may affect other insomnia symptoms, either improving or exacerbating them.The inclusion of worry about sleep problems is consistent with the cognitive model of insomnia 23 .A metaanalysis of cognitive behavioral therapy for insomnia found that it moderately improved worry and difficulty staying asleep 24,25 .CBT-I has been shown to be effective in improving insomnia symptoms in daytime workers 26 .A network analysis comparing the pathways of improvement in insomnia symptoms between Internet-delivered behavior and cognitive therapies showed that behavior therapy improved sleep efficiency, difficulty staying asleep, and dissatisfaction with sleep patterns, while cognitive therapy improved difficulty falling asleep, problems waking up too early, interference with daytime functions, and worry about sleep problems 27 .In Blanken et al. 's 27 network analysis of treatment processes, behavior therapy showed a direct association with difficulty staying asleep at four weeks compared to cognitive therapy, but fundamentally extended to an improvement in other insomnia symptoms via increased sleep efficiency.Cognitive therapy showed a direct association with worry about sleep problems at 10 weeks post-treatment compared to behavior therapy 27 .The results for face-to-face behavior therapy and cognitive therapy were consistent with those of Blanken et al. 27 , except that worry about sleep problems changed during the process of treatment 28 .It is hypothesized that if the central symptoms identified in the network analysis can be addressed, overall symptoms could improve more quickly 5 .The results of this study revealed that the central symptoms of insomnia were difficulty staying asleep and worry about sleep problems.Previous studies have found that these two symptoms changed during the course of treatment 27,28 .Although to our knowledge no studies have focused only on difficulty staying asleep and worry about sleep problems, changes in these two symptoms may contribute significantly to changes in overall insomnia symptoms.Future studies should examine whether there is a difference in difficulty staying asleep and worry about sleep problems between individuals who experienced alleviation of insomnia symptoms at the end of CBT-I and those who did not.This could help determine whether these two symptoms are robust as central symptoms.Bai et al. 12 estimated that daytime dysfunction was a central insomnia symptom.However, our study estimated that the daytime dysfunction was not enough to change the whole network.The independent diagnostic criteria for insomnia disorder include nocturnal sleep problems and daytime dysfunctions 29 .Furthermore, insomnia symptoms have been found to be more persistent over time in the nocturnal sleep problems and psychological distress and/or daytime dysfunction group than in the nocturnal sleep problems, psychological distress, or dysfunction alone group 30 .In individuals who complain of insomnia, difficulty staying asleep and worry about sleep problems are important factors in intervention and assessment.Specifically, the assessment may be important for the early detection of risk for severe insomnia.Moreover, early interventions for people who experience difficulty staying asleep and/or worry about sleep problems may prevent other insomnia symptoms from worsening and, consequently, may decrease the severity of insomnia.
A worldwide epidemiological study during the COVID-19 pandemic estimated that 17.4% of people were likely to have insomnia disorder, and this estimate was 7.9% for people in Japan 31 .Moreover, an epidemiological study in Japan conducted before the COVID-19 pandemic found that 12.2% of men and 14.6% of women had a probability of insomnia disorder 1 .In other words, the probability of insomnia disorder may not have increased much during the COVID-19 pandemic in Japan.Therefore, the results of this study might be applicable after the COVID-19 pandemic.
This study has several limitations.First, because the CS coefficients for closeness and betweenness were below 0.5, the results were interpreted based on strength.However, the order of the nodes with the highest scores of the centrality index of strength does not fully match that of closeness and betweenness.Therefore, central symptoms in this study reflect only the degree of connection with other variables in the network.Second, due to a cross-sectional design, causality could not be determined.In other words, it is unclear whether difficulty staying asleep and worry about sleep problems affect other symptoms in the network or whether other nodes in the network affect difficulty staying asleep and worry about sleep problems.It is also possible that characteristics of the participants' insomnia symptoms at the time of the survey may have been represented.Third, as the participants for this study were recruited through an Internet survey, sampling bias was present; however, the quality of responses was ensured by using items to detect participants who did not read the questionnaire.Fourth, the participants were middle-aged, as the mean age was 49.33 years.Insomnia symptoms were affected by age, as difficulty falling asleep is more frequent in younger than in middle-aged persons, while in middle-age, difficulty staying asleep is more frequent than difficulty falling asleep 32 .The results of our study may be limited to insomnia symptoms in middle-aged daytime workers.Fifth, this study gathered data during the COVID-19 pandemic.This study has not been able to separate essential workers such as medical personnel from other workers.In other words, the study has not been able to consider the stress level of occupations due to the COVID-19 pandemic.Sixth, this study was targeted only to daytime workers at the time of recruiting the participants, which may have caused a selection bias.
Despite these limitations, difficulty staying asleep and worry about sleep problems may be central insomnia symptoms for daytime workers at-risk for insomnia.As insomnia complaints are associated with mental health problems and reduce work productivity, identifying central insomnia symptoms may predict the occurrence of health problems in workers.

Figure 1 .
Figure 1.Flowchart of the participant selection process in this study.

Figure 2 .
Figure 2. Results of the network analysis.The left side presents the network diagram of the Insomnia Severity Index (ISI) results for the study participants.The green edges are positively correlated and the red edges are negatively correlated.The right side displays the resulting centrality indices, ordered by strength.Abbreviations: Difficulty falling asleep (ISI_1), Difficulty staying asleep (ISI_2), Problem waking up too early (ISI_3), Dissatisfaction (ISI_4), Interference with daytime functions (ISI_5), Noticeable to others (ISI_6), and Worry/ Distress (ISI_7).

Figure 3 .
Figure 3. Bootstrapped difference tests (α = 0.05) results between the node strength of the variables in this study.The black boxes indicate a significant difference between the nodes, the gray boxes indicate no significant difference between the nodes.Abbreviations: Difficulty falling asleep (ISI_1), Difficulty staying asleep (ISI_2), Problem waking up too early (ISI_3), Dissatisfaction (ISI_4), Interference with daytime functions (ISI_5), Noticeable to others (ISI_6), and Worry/Distress (ISI_7).

Figure 4 .
Figure 4. Bootstrap confidence intervals were estimated to indicate the magnitude of the edges-weighted between variables in the network structure.The gray ranges are the bootstrap confidence interval.The dots are plotted polychoric correlation coefficient values.Abbreviations: Difficulty falling asleep (ISI_1), Difficulty staying asleep (ISI_2), Problem waking up too early (ISI_3), Dissatisfaction (ISI_4), Interference with daytime functions (ISI_5), Noticeable to others (ISI_6), and Worry/Distress (ISI_7).

Table 1 .
Participants' demographic data and ISI scores.M Mean, SD Standard deviation, BMI Body mass index, ISI Insomnia Severity Index.*p < 0.05.