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Psychometric evaluation of the Chinese version of the hospital-acquired insomnia scale (HAIS) and analysis of influencing factors

BMC Psychiatry volume 24, Article number: 696 (2024) Cite this article

Abstract

Background

Adequate sleep and rest are essential for patient recovery; however, lack of sleep has become a common problem faced by Chinese patients during hospital stays. Reduced sleep is often associated with a higher risk of disease progression and is strongly associated with increased hospital stay. However, there is no specific tool in China to assess short-term insomnia caused by hospitalization. This study aimed to translate the Hospital-acquired Insomnia Scale (HAIS) into Chinese, test its applicability to Chinese inpatients through reliability and validity indicators, and investigate the potential influencing factors of hospital-acquired insomnia.

Methods

Psychometric analysis from a sample of 679 hospitalized patients to whom the HAIS questionnaire was applied. The structural validity was assessed by exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), and the content validity of the scale was assessed using the content validity index. Cronbach’s alpha coefficient, split-half reliability and test–retest reliability were calculated to evaluate the internal consistency of the scale. Multiple stepwise linear regression analysis was conducted to determine the potential correlates of hospital-acquired insomnia.

Results

EFA supported a four-factor structure with factor loadings for all dimensions greater than 0.40. CFA showed good indicators of model fit. The content validity index of the scale was 0.94. the Cronbach’s alpha of the scale was 0.915, the split-half reliability coefficient was 0.819, and the retest reliability was 0.844. Gender, age, total hours of sleep during the night, medical insurance, length of hospital stay, perceived stress level, and perceptions about sleep explained 46.2% of the variance in hospital-acquired insomnia.

Conclusion

The Chinese version of HAIS has good psychometric characteristics and is an effective instrument for evaluating hospital-acquired insomnia. In addition, hospital-acquired insomnia is more common in women, of younger age, less than 5 h of sleep a night, without medical insurance, stressed, and patients with more misconceptions about sleep.

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Introduction

Insomnia is a common problem in modern society. The Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) defines insomnia as a sleep disorder characterized by frequent and persistent (at least three nights per week for at least three months) difficulty falling asleep and/or maintaining sleep, resulting in insufficient sleep satisfaction. Sleep disturbances that lasted less than three months but satisfied all other diagnostic criteria for insomnia were classified as transitory insomnia [1]. Acute short-term insomnia is a common problem faced by hospitalized patients [2], Patients often have difficulty falling asleep or wake up frequently during the night, experiencing transitory insomnia, such insomnia is defined as hospital-acquired insomnia [3].

A 10-year cohort study in the United States found a significant increase in the prevalence of insomnia among hospitalized patients in recent years [4], and insomnia symptoms were also reported in 32% of older adults with multiple complications in the North India Hospital study [5]. In fact, hospital-acquired insomnia also has been noted in China, the sleep study of hospitalized patients in China found that the incidence of sleep deprivation was as high as 48%, and patients complained that the quality of sleep in the hospital was significantly lower than that at home, and it was difficult to sustain sleep [6]. Notably, nocturnal insomnia not only implies impaired sleep quality and daytime functioning, but also poses some unique disease risks to patients. As a population with elevated sleep needs, sleep deprivation directly affects the immune response of hospitalized patients, increasing the risk of infections, delaying healing, and further prolonging the patient’s hospital stay [7]. In addition, research revealed a strong association between insomnia and the frequency of hospital nighttime incidents, specifically in geriatric units where inadequate sleep negatively impacts senior patients’ cognitive abilities, heightening the risk of falls, delirium, and worsening of nocturnal conditions [8]. Studies have shown that the effects of these sleep deficiencies persist in about three months even after recovery sleep is achieved [9], this finding debunks the idea that “the effects of insomnia can be overcome after discharge“ [10], and that a patient’s insomnia problem is not only accompanied by an elevated risk of disease progression during hospitalization and an increase in hospital costs, but also has a lasting impact on the patient’s life after discharge. Sleep deprivation experienced during hospitalization has been identified as a potential source of harm to patient’s health and well-being. These phenomena suggest the need to further explore the factors influencing hospital-acquired insomnia in inpatients.

Much of the prior clinical research focused on the relationship between surgical methodology, pain, anaesthetic medications, and sleep in hospitalized patients [11,12,13,14]. Gellerstedt believes that patients are more likely to suffer from sleep deprivation because they have already experienced physiological processes such as pain or infection [15]. There are also studies pointed out the impact of the ward environment on patients’ sleep [16,17,18], such as machine noise, light, smell, room heat, night rounds, discomfort with pillows and bedding, and so on. What is certain is that the factors that cause insomnia in hospitalized patients are intricate, but the current exploration of insomnia in hospitalized patients is limited. Peiris attempts to explain the mechanism of insomnia from the perspective of behavioral patterns and neurocognition, and he emphasizes the key role of perceived stress and sleep beliefs [19]. As triggers of insomnia, stress and anxiety alter an individual’s sleep structure by increasing levels of alertness and stimulating the sympathetic nervous system [20]. Negative emotions and misunderstandings about sleep, as a key factor in maintaining insomnia, will exacerbate patients’ anxiety and worry about sleep, causing them to fall into a cycle of insomnia [21, 22]. However, it remains inconclusive whether perceived stress and sleep beliefs are also involved in the development of hospital-acquired insomnia and play a key role in this process.

In the previous several decades, multiple scales to assess sleep disorders have been created, validated and adapted to various cultural settings. These scales included Pittsburgh Sleep Quality Index (PSQI) [23], Insomnia Severity Index (ISI) [24], Epworth Sleepiness Scale (ESS) [25], etc. Whilst there are a number of recognized and commonly used insomnia assessment tools, their content focuses on the measurement of the degree of insomnia and there is no specific tool that focuses on the impact of hospitalization on a patient’s sleep. This has resulted in medical staff failing to identify the causes of hospital-acquired insomnia, with no basis for targeting patients for early prevention and intervention, and hospitals missing out on the opportunity to optimize sleep environments to improve the quality of patients’ sleep during their hospital stay. To remedy this shortcoming, Bahar et al. first developed the Hospital-Acquired Insomnia Scale (HAIS) in Turkey as a professional tool for assessing the severity of a patient’s hospital-acquired insomnia [3]. The 18-item scale provides a comprehensive assessment of factors affecting sleep in hospitalized patients in five areas: physical environment, safety, psychology, socioeconomic and dietary nutrition. The HAIS, a specially developed measurement tool for assessing hospital-acquired insomnia in patients, has been validated in 221 local inpatients with high internal consistency reliabilities, test–retest reliabilities and construct validity, demonstrating that it has good psychometric properties. However, since its development in 2023, it has not been applied to the assessment of hospital-acquired insomnia across cultures in different countries and regions. Given the potential impact of hospital-acquired insomnia on the health of hospitalized patients, especially in countries and regions where there is a relative strain on healthcare resources, such as China. This phenomenon deserves to be evaluated with specialized validated tools and indicators.

The present study translated and cross-culturally adjusted the HAIS scale into Chinese and further assessed its psychometric properties in a Chinese hospitalized patient population. In addition, this study hypothesized that the level of hospital-acquired insomnia may be related to sociodemographic characteristics and that patients’ perceived stress and misconceptions about the existence of beliefs about sleep would have a greater impact on hospital-acquired insomnia. Therefore, we carried a cross-sectional investigation of factors influencing the HAIS.

Materials and methods

Design and participants

In this study, cross-sectional investigation and convenient sampling were used to investigate the inpatients in a tertiary hospital in Jinzhou City, Liaoning Province from November 2023 to February 2024. The admission criteria for this study were :1. Adult patients hospitalized in general wards for more than 3 days; 2. Patients have clear knowledge and understanding of the questionnaire content; 3. Patients who voluntarily participated in this research with their informed consent. The exclusion criteria were: (1) Patients with serious physical diseases who could not complete the questionnaire; (2) Patients undergoing surgery; (3) A history of insomnia before admission (defined as difficulty falling asleep or taking medication for more than 3 days per week within the last month) [1]; (4) Patients with other forms of sleep-related disorders.

Instruments

Questionnaire for general information

The general demographic Characteristics questionnaire included additional information such as sex, age, department of admission, social security, and self-rated sleep during hospitalization.

Hospital-acquired insomnia scale (HAIS)

The HAIS was initially created by Prof. Bahar et al. in 2023 to accurately assess the level of insomnia in currently hospitalized patients [3]. There are five dimensions and eighteen items on the scale: environmental factors, psychological factors, safety factors, socioeconomic factors, and nutritional factors. With a total score of 18 to 90, the scale is graded on a 5-point Likert scale that goes from 1 (never) to 5 (always). The higher the score denotes the more severe the degree of hospital-acquired insomnia. The Turkish version of HAIS has excellent reliability and validity, Cronbach’s alpha coefficient is 0.783. The Cronbach’s α coefficient of this research was 0.915.

Insomnia severity index (ISI)

The ISI was proposed by Bastien et al. in 2001, which consists of 7 items on a 5-point Likert scale to measure the degree of insomnia in individuals, with a final score of 0–28, A higher score implies higher levels of insomnia [26]. The English version has a Cronbach’s alpha coefficient of 0.900, and it was validated in a Chinese population with a Cronbach ‘s alpha coefficient of 0.835 [27]. In this research, the Insomnia Severity Index (ISI) was chosen to measure the level of insomnia in individuals, The Cronbach’s α coefficient of this research was 0.919.

Dysfunctional beliefs and attitudes about sleep scale (DBAS)

The DBAS was developed by Morin in 2000 to assess misconceptions about sleep and insomnia [28]. The 16 items on the measure are rated on a 5-point Likert scale from 1 (strongly disagree) to 4 (strongly agree). More dysfunctional beliefs or misbehaviors toward sleep are indicated by higher scores. The original version has a Cronbach’s alpha coefficient of 0.790 [28], and it was validated in a Chinese population with a Cronbach ‘s alpha coefficient of 0.894 [29]. Cronbach’s α coefficient of the scale in this study was 0.900.

Perceived stress scale (PSS)

The Perceived Stress Scale (PSS) was developed by Cohen et al. in 1983 to assess the level of stress experienced by an individual [30]. The scale consists of 2 dimensions, tension and loss of control, with 7 entries for each dimension, and is based on a 5-point Likert scale, with scores ranging from 0 to 4, from never to always, and a total score of 0 to 56. Higher scores correspond with perceived stress levels that are higher. The original version has a Cronbach’s alpha coefficient of 0.840 [30], and it was validated in a Chinese population with a Cronbach ‘s alpha coefficient of 0.830 [31]. Cronbach’s α coefficient of the scale in this study was 0.904.

Study procedures

Translation process

Prof Bahar provided the English version of the HAIS scale and allowed us to translate it as well as validate it. A forward-backward translation method based on the Brislin translation was applied [32]. First, two bilingual graduate nursing students who were familiar with the background of the study independently translated the HAS into Chinese, and the translation was then discussed and improved by the research team to create a translated version. The translated scale was back-translated by two translators (a university English teacher and an English graduate student) who hadn’t previously seen the original version of the scale and were proficient in English. The back-translated version was developed after group discussion and revision. Controversial items were changed to render the scale better suited for the Chinese context after consultation with the original authors and their team’s evaluations and comments on the scale’s translation. Thirty hospitalized patients participated in a preliminary trial. After receiving the task to finish the scale, they were questioned regarding their comprehension of its entries. They reported no problems with comprehension, and eventually, a definitive Chinese version of the scale was created.

Cultural adaptation of HAIS

After consulting with the original authors, we recognized significant differences in the dietary patterns of hospitalized patients between the two countries. In Turkey, hospitals provide three meals a day for each patient and their relatives. This practice not only addresses the physiological needs of patients but also allows for the control, monitoring, and evaluation of the quantity and timing of their diets. As a result, hospitals can adjust diets promptly and address potential nutritional deficiencies. In contrast, hospital dining arrangements in China are more flexible, enabling patients and their families to select their own meals and adjust meal times according to their preferences. Considering that the nutritional dimension primarily assesses the impact of meal timing on patients’ sleep, and with the original author’s permission, We have made corresponding revisions to items 16 and 17. These changes follow Beaton’s cross-cultural adaptation and testing guidelines [33].

Data collection procedure

In this study, we investigated inpatients at a tertiary hospital in Liaoning Province, from November 2023 to February 2024, and questionnaires were collected in the ward. Patients received thorough explanations of the goal and methods of the research, and data for the study were collected face-to-face after obtaining their consent. The survey procedures strictly adhered to the basic principles of medical research and ensured confidentiality and privacy. The sample size is determined according to the general rules of the factor analysis program, which require at least 10 respondents to be recruited for each item, but a larger sample helps ensure the accuracy of exploratory factor analysis and confirmatory factor analysis [34]. Eventually, 736 questionnaires in all were gathered. 679 valid questionnaires were recovered, and 57 invalid questionnaires with incomplete answers and continuous answers of more than half were excluded [35]. The recovery rate was 92.25%. Considering that patients in the respiratory departments of this hospital have longer hospital stays, after communication with the department, the names and admission numbers (AD) of this population were marked, and retest patients who remain hospitalized after two weeks. This study adhered to the ethical norms of the Helsinki Declaration as well as the Ethics Committee of Jinzhou Medical University (JZMULL2023150).

Data analysis

Categorical variables are expressed as percentage (%) and continuous variables are expressed as mean ± standard deviation (SD). AMOS (V24.0) and SPSS (V26.0) were used to analyze the statistical data. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to determine the potential factor structure of the Chinese version of HAIS. A sample of 679 participants was randomly divided into two groups for EFA (N = 341) and CFA (N = 338). The suitability for factor analysis is determined using the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity. When the KMO value is greater than 0.60 and Bartlett’s test of sphericity is significant (P < 0.05), the data are considered suitable for factor analysis. Factors with eigenvalues greater than 1 were extracted and subjected to varimax orthogonal rotation. Items with loadings of 0.50 or higher were considered for inclusion in separate factors. Factor extraction was guided by eigenvalues, total variance explained, and scree plot. The reliability of the internal consistency of the scale was determined by Cronbach’s alpha coefficient. Cronbach’s alpha coefficient above 0.7 is considered acceptable. Multiple stepwise linear regression analysis was used to investigate the potential determinants of hospital-acquired insomnia. P < 0.05 was considered statistically significant.

Results

Demographic characteristics

A total of 679 hospitalized patients were finally included in the study (Table 1), of which 371 (54.6%) were male and 308 (45.4%) were female. The age of the patients was concentrated at 51–71 years (45.9%). Most of the participants were from the medical ward (57.7%), 43.9% of the patients were hospitalized for 1–2 weeks, the health insurance coverage reached 90.6%, and 41.8% of patients felt that their sleep was affected during their hospitalization. Table 2 illustrates participants’ mean (SD) scores for each item of the revised HAIS.

Table 1 Demographic characteristics (N = 679)
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Table 2 Mean (SD) scores for all participants in the Chinese revised hospital-acquired Insomnia Scale (N = 679)
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Item analysis

Given that item validity is a prerequisite and foundation for the establishment of psychometric indicators for the scale, item analyses were performed before validity and reliability assessments. The total score of the HAIS was ranked from high to low, with the top 27% being the high group and the bottom 27% being the low group. The t values obtained by the t-test of independent samples of high and low subgroups were used as Critical Ratio Decision Value (CR) to evaluate the differences of items [36]. The results showed that the CR values of the 18 items in the scale ranged from 12.454 to 24.647, all showing statistical differences (Table 3), indicating that the Chinese version of HAIS items had good differentiation and could effectively measure the response level of different subjects. Using Pearson correlation analysis, the correlation coefficients between the overall scores on the scale and the scores on each item were determined, to assess the degree of agreement of their measurement attributes [37]. The correlation coefficients between each item and the total score of the scale ranged from 0.430 to 0.672 (P < 0.001). Therefore, all 18 entries were retained (Table 3).

Table 3 Item analysis for Chinese version of the Hospital-acquired Insomnia Scale (N = 679)
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Validity analysis

Content validity

Content Validity Index (CVI) includes item-level content validity index (I-CVI) and universal agreement scale-level content validity index (S-CVI/UA) [38]. Two sleep specialists and five medical experts were invited to rate each item of the scale on a 4-point scale (1–4 for " no association “, " little correlation “, " significant correlation “, and " extremely strong correlation “) [39]. The results showed that the I-CVI value of HAIS was 0.857 ∼ 1.000, and the S-CVI/UA value was 0.94. Most experts believed that the individual and overall contents of the scale were consistent with the measurement objectives, which ensured the objectivity and scientific nature of the scale [40].

Construct validity

Exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA) were used to test the structural validity of HAIS. The 679 samples were randomly divided into two groups: 341 completed EFA and 338 completed CFA.

Exploratory factor analysis (EFA)

The matrix of sample 1 (n = 341) is preliminarily evaluated by factors. The Bartlett test of sphericity had statistical significance (χ2 = 6,610,260, P < 0.05) [41], and the KMO value was 0.914, which exceeded the minimum allowable value of 0.6 [42], suggesting that there was sufficient correlation between the variables to be suitable for factor analysis [43]. Principal component analysis and maximum variance orthogonal rotation method were used for EFA analysis [44], and four factors with feature roots greater than 1 were extracted, which were different from the original 5-factor structure model. The cumulative variance contribution of the four factors supported by gravel blocks is 67.921%. The factor loads of 18 projects ranged from 0.648 to 0.877, and all projects were subject to a single factor load (Table 4). The structure of the four factors was further explained by the weaker decreasing trend of the gravel blocks after point 4 (Fig. 1).

Table 4 Exploratory factor analysis (N = 341)
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Fig. 1
figure 1

Scree plot of exploratory factor analysis for Chinese version of the HAIS (N = 341)

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Confirmatory factor analysis (CFA)

Sample 2 (n = 338) was further analyzed by CFA to confirm the EFA-derived model [45]. After constructing the correlation between the relevant residual variables of item 11 and item 12 according to the modified index, the data fitting of the four-factor 18-item structure is satisfactory: χ2 /df = 2.115, RMSEA = 0.058, GFI = 0.919, NFI = 0.923, TLI = 0.949, CFI = 0.958, indicating that the four-factor model is effective in measuring potential variables [46]. The graphical expression for the CFA result is represented in Fig. 2.

Fig. 2
figure 2

Standard four-factor structural model for the Chinese version of HAIS n = 338. F1 (Physical Environment, five items), F2 (Psychological, seven items). F3 (Socio-economic, three items), F4 (Nutrition, three items)

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Convergent validity and discriminant validity

According to the result of model fitting, the convergence validity of each dimension is calculated. The average variance extracted (AVE) values of F1, F2, F3, and F4 are all greater than 0.5, indicating good convergent validity. The CR values are all greater than 0.7, and the composite reliability is good. In addition, the square root of the AVE value exceeds the inter-factor correlation coefficient (0.430–0.672, P < 0.001), indicating that the model has good discriminant validity [47]. Detailed results are shown in Table 5.

Table 5 Convergent validity and discriminant validity of the Chinese version of the HAIS(N2 = 338)
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Criterion-related validity

In this study, the ISI scale was used as a standard tool and the Pearson correlation was used to analyze the comprehensive scores and total ISI scores of 18 items in Chinese HAIS, and the correlation coefficient was 0.772, indicating that ISI could provide preliminary inference for the validity of HAIS.

Reliability analysis

Internal consistency reliability

Reliability analysis results show that the Chinese version of HAIS (18 items) has good internal consistency. The Cronbach’s alpha coefficient was 0.915, higher than the original Cronbach’s alpha coefficient of 0.783. Table 3 shows that removing any item individually results in an overall decrease in Cronbach’s alpha coefficient. In addition, the split-half reliability is 0.819, indicating that HAIS has high internal consistency [48, 49].

Test-retest reliability

The intraclass correlation coefficient (ICC) was used to evaluate the stability of the scale. Two weeks after the first test, 50 patients whose names and AD had been marked and who were still hospitalized were asked to complete the questionnaire again. Spearman correlation method was used to evaluate the correlation between the two tests, and the correlation coefficient was 0.844, greater than 0.7 [50]. This implies that HAIS is less affected by time and has greater stability.

Multiple regression analysis of hospital-acquired insomnia among Chinese inpatients

The average total HAIS score of 679 Chinese inpatients was 36.43 ± 10.49. Multiple stepwise linear regression analyses revealed that female gender, lack of health insurance, age, longer hospitalization, insufficient total sleep duration at night, perceived higher stress, and misconceptions about sleep were potential factors influencing HAIS. These variables contributed 46.2% to the variance of hospital-acquired insomnia (Table 6).

Table 6 Multiple stepwise linear regression model for overall HAIS score (n=679)
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Discussion

In this study, HAIS was translated into Chinese for the first time and validated after cultural adaptation in 679 Chinese inpatients. The results reveal that the Chinese version of HAIS has good reliability and validity, and HAIS is a practical tool for evaluating patients with hospital-acquired insomnia in China.

In this study, the Cronbach’s α value of the HAIS scale was 0.915, and the coefficients of each dimension ranged from 0.799 to 0.895, which were all higher than the original version. This could be the result of variations in hospital environments and cultural backgrounds between nations. The convenience sample of a single hospital selected in this study may also have influenced the results. Furthermore, following a 2-week interval, the test-retest reliability calculation yielded a value of 0.844, suggesting high temporal stability for the scale.

Four factors were extracted from 18 items through EFA, and the cumulative variance contribution rate reached 67.921%. At the same time, the CFA showed that the four-factor model had a good fit, and showed a good convergent validity, composite reliability, discriminant validity, content validity, and criterion-related validity. It can be seen that this study has changed the five-dimensional structure of the original version. The reasons for the differences are as follows: First, in the translation process, we adjusted the sentences in the original scale that did not conform to the Chinese expression, which had an impact on the project content. Secondly, such differences may be partly due to the cultural background and national psychology of different countries and regions, and the understanding of the two concepts of security and psychology will be greatly affected by cultural factors [51]. Foreign countries believe that safety and psychology are two completely different concepts, and China is more inclined to take safety as one of the criteria to evaluate a person’s psychological state. Maslow also proposed that safety, as a necessary condition for maintaining individual survival and life order, has inherent psychological attributes [52]. Understandably, the “safety” and “psychological” dimensions consistently reflect the psychological state of patients in a hospital setting. Therefore, the third dimension (safety factor) of the original scale is included in the second dimension (psychological factor) in this study. In addition, HAIS was significantly positively correlated with ISI (r = 0.772, P < 0.05), suggesting that it had good concurrent validity. To sum up, the Chinese version of the HAIS scale on the one hand confirms the effectiveness of the HAIS scale in evaluating hospital-acquired insomnia in hospitalized patients, and on the other hand, provides a verified Chinese version of the scale for evaluating the influencing factors of insomnia in Chinese hospitalized patients.

In addition, using the validated Chinese version of HAIS, this study confirmed the hypothesis that hospital-acquired insomnia is associated with sociodemographic characteristics, and that perceived stress and misconceptions about sleep have a greater impact on hospital-acquired insomnia. The existing literature supports the study’s conclusions as well. Several studies on sleep quality have shown that women possess a higher risk of insomnia than men [53,54,55]. On a physiological level, this may be related to female-specific hormonal changes, which lead to an increased probability of insomnia due to factors such as decreased levels of estrogen and melatonin, as well as deprivation of deep rapid eye movement sleep [56]. In addition, a stronger sense of privacy among Chinese may also be a key factor in hospital-acquired insomnia. An earlier study on patients’ perception of privacy in healthcare also revealed that women prefer to choose a single room due to concerns about personal privacy [57], further highlighting the impact of the ward environment on Chinese women’s sleep. Notably, in contrast to the findings of previous studies that found high rates of insomnia in older populations [2, 54, 58], in this study, younger individuals were more likely to experience insomnia in the hospital setting. Studies have shown that younger patients may be more sensitive to light in the ward environment, equipment sounds, and night rounds or nursing operations than older patients with degraded sensory functions, resulting in sleep arousal and disruption [58, 59]. On the other hand, relevant studies have revealed a link between mobile phone addiction and delayed sleep in young people [60], which may lead to shorter night sleep duration in this group. In addition, studies on sleep rhythms and sleep structure in hospitalized patients have shown that bedridden patients show more pronounced compensatory sleep during the day [61], which compensates for their lack of night-time sleep duration.

The study also indicated that lack of medical insurance, higher levels of perceived stress, more misconceptions about sleep, and longer hospitalization were other important risk factors for HAIS. In animal models and human research, stress disrupt various aspects of sleep [62,63,64]. In particular, fragmented non-rapid eye movements (NREM) due to frequent brief arousals (microarousals) disrupts sleep continuity and may cause cognitive impairment and anxiety [13, 65]. Furthermore, REM abnormalities are often observed in patients with insomnia, depression, and post-traumatic stress disorder (PTSD) [66, 67]. For humans, psychosocial stressors are one of the main sources of stress. The inadaptability to the unfamiliar environment, the worry of the disease progression, the absence of family members, and other factors will bring psychological pressure to the hospitalized patients. In rodents, social stress—in particular, a conflict with an aggressive mouse—has been considered to disrupt sleep [68, 69]. These changes in the sleep architecture generated by social stress may result from interactions between stress and sleep regulatory circuits. In addition, a longer hospital stay is not only detrimental to the mental health of patients, but also means that hospitalized patients will bear a heavier economic burden, and economic pressures such as medical tests, drugs, and daily bed costs will also affect the quality of sleep of patients during hospitalization. The multi-level medical security system implemented in China shares part of the hospital expenses for urban residents, workers, and farmers, to some extent easing the financial burden brought to patients by hospitalization. This may well explain the more severe expression of HAIS in uninsured individuals. Also, individuals with more misperceptions about sleep are more likely to experience HAIS. Earlier studies have confirmed that sleep cognitions have a greater impact on insomnia than Physical disorders [70, 71]. Patients with misconceptions about sleep are more inclined to visualize negative events such as the impact of sleep deprivation on disease recovery as catastrophic, and develop a negative bias towards perceived sleep-related threats and perceptions, which prevents them from adopting positive coping strategies during periods of poorer sleep. As an alternative to conventional pharmacological therapies, several researchers have also advocated the application of sleep cognitive behavioral therapy as a first-line therapy for insomnia to enhance sleep quality [72].

Limitations

First, there are flaws of recall bias and extended causal inferences, as in most cross-sectional studies. Second, the conclusion’s generalizability might be somewhat constrained by the handy sample that was chosen by a single hospital, and it is essential to expand the sampling scope and level in subsequent studies to make the research objects more representative. This is the first foreign study to translate and test the validity and reliability of the HAIS, and despite some shortcomings, our study provides a validated Chinese-language scale to measure the effects of hospitalization on patients’ insomnia in a Chinese cultural context.

Conclusions

The Chinese version of the HAIS scale contains 18 items supporting a four-factor structure and shows good validity and reliability, therefore, the validated Hospital Acquired-insomnia Scale is more suitable for the Chinese population. Future studies should further explore its applicability in other population groups and analyze the potential influencing factors of sleep in hospitalized patients. In addition, in the process of developing public health intervention strategies, initial screening and identification of individuals who are female, younger, uninsured, have longer hospitalization, do not sleep long enough at night, and perceive more stress with more misconceptions about sleep can help to identify qualified high-risk groups for early intervention, which is essential to promote good sleep hygiene in general wards in China.

Data availability

The data used in the study will not be shared publicly, as doing so could compromise patient privacy. However, a legitimate request for data may be made to the corresponding author.

References

  1. American Psychiatric Association AP, Association AP. Diagnostic and statistical manual of mental disorders: DSM-IV. vol. 4. American psychiatric association Washington, DC; 1994.

  2. Ho A, Raja B, Waldhorn R, Baez V, Mohammed I. New onset of insomnia in hospitalized patients in general medical wards: incidence, causes, and resolution rate. J Community Hosp Intern Med Perspect. 2017;7:309–13. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/20009666.2017.1374108

    Article  PubMed Central  Google Scholar 

  3. Çiftçi B, Yıldız GN, Yıldız Ö. Hospital-acquired insomnia scale: a validity and reliability study. World J Psychiatry. 2023;13(3):113–25. https://doiorg.publicaciones.saludcastillayleon.es/10.5498/wjp.v13.i3.113

    Article  PubMed Central  Google Scholar 

  4. Ford ES, Wheaton AG, Cunningham TJ, Giles WH, Chapman DP, Croft JB. Trends in Outpatient visits for Insomnia, Sleep Apnea, and prescriptions for Sleep medications among US adults: findings from the National Ambulatory Medical Care Survey 1999–2010. Sleep. 2014;37:1283–93. https://doiorg.publicaciones.saludcastillayleon.es/10.5665/sleep.3914

    Article  PubMed Central  Google Scholar 

  5. Gambhir IS, Chakrabarti SS, Sharma AR, Saran DP. Insomnia in the elderly—A hospital-based study from North India. J Clin Gerontol Geriatr. 2014;5:117–21. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jcgg.2014.05.005

    Article  Google Scholar 

  6. Zheng W, Luo X-N, Li H-Y, Ke X-Y, Dai Q, Zhang C-J, et al. Prevalence of insomnia symptoms and their associated factors in patients treated in outpatient clinics of four general hospitals in Guangzhou, China. BMC Psychiatry. 2018;18:232. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12888-018-1808-6

    Article  PubMed Central  Google Scholar 

  7. Bonnet MH, Arand DL. Hyperarousal and Insomnia: state of the science. Sleep Med Rev. 2010;14:9–15. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.smrv.2009.09.002

    Article  Google Scholar 

  8. Ursin R, Bjorvatn B, Holsten F, Sleep Duration. Subjective sleep need, and Sleep habits of 40- to 45-Year-Olds in the Hordaland Health Study. Sleep. 2005;28:1260–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/sleep/28.10.1260

    Article  Google Scholar 

  9. Arora VM, Stewart N. Sleep in hospitalized older adults 2019. Sleep Med Clin, 13(1), 127–35. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jsmc.2017.09.012

  10. Badr AF, Kurdi S, Alshehri S, McManus C, Lee J. Pharmacists’ interventions to reduce sedative/hypnotic use for insomnia in hospitalized patients. Saudi Pharm J. 2018;26(8):1204–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jsps.2018.07.010

    Article  PubMed Central  Google Scholar 

  11. Rabbitts JA, Zhou C, Narayanan A, Palermo TM. Longitudinal and temporal associations between Daily Pain and sleep patterns after Major Pediatric surgery. J Pain. 2017;18:656–63. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jpain.2017.01.004

    Article  PubMed Central  Google Scholar 

  12. Zheng F, Zheng Z, Wang W, Zhu K, Xing H, Ding L. Association between sleep quality and general anesthesia in teenager under elective surgery: a prospective study. Ir J Med Sci 1971–2022;191:2297–303. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11845-021-02847-1

  13. McCoy JG, Tartar JL, Bebis AC, Ward CP, McKenna JT, Baxter MG, et al. Experimental sleep fragmentation impairs attentional set-shifting in rats. Sleep. 2007;30:52–60. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/sleep/30.1.52

    Article  Google Scholar 

  14. Angarita GA, Emadi N, Hodges S, Morgan PT. Sleep abnormalities associated with alcohol, cannabis, cocaine, and opiate use: a comprehensive review. Addict Sci Clin Pract. 2016;11:9. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13722-016-0056-7

    Article  PubMed Central  Google Scholar 

  15. Gellerstedt L, Medin J, Kumlin M, Rydell Karlsson M. Nursing care and management of patients’ sleep during hospitalisation: a cross-sectional study. J Clin Nurs. 2019;28:3400–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/jocn.14915

    Article  Google Scholar 

  16. Elliott R, Rai T, McKinley S. Factors affecting sleep in the critically ill: an observational study. J Crit Care. 2014;29:859–63. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jcrc.2014.05.015

    Article  Google Scholar 

  17. Fidler AL, Waitt J, Lehmann LE, Solet JM, Duffy JF, Gonzalez BD, et al. Sleep and circadian disruptors: unhealthy noise and light levels for hospitalized pediatric patients. J Hosp Med. 2023;18:999–1003. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/jhm.13218

    Article  Google Scholar 

  18. Lee CY, Le Low LP, Twinn S. Understanding the sleep needs of older hospitalized patients: a review of the literature. Contemp Nurse. 2005;20:212–20. https://doiorg.publicaciones.saludcastillayleon.es/10.5172/conu.20.2.212

    Article  Google Scholar 

  19. Perlis ML, Giles DE, Mendelson WB, Bootzin RR, Wyatt JK. Psychophysiological insomnia: the behavioural model and a neurocognitive perspective. J Sleep Res. 1997;6(3):179–88. https://doiorg.publicaciones.saludcastillayleon.es/10.1046/j.1365-2869.1997.00045.x

    Article  CAS  Google Scholar 

  20. Lo Martire V, Caruso D, Palagini L, Zoccoli G, Bastianini S. Stress & sleep: a relationship lasting a lifetime. Neurosci Biobehav Rev. 2020;117:65–77. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.neubiorev.2019.08.024

    Article  Google Scholar 

  21. McCall WV, Batson N, Webster M, Case LD, Joshi I, Derreberry T, et al. Nightmares and dysfunctional beliefs about Sleep mediate the effect of insomnia symptoms on suicidal ideation. J Clin Sleep Med. 2013;09:135–40. https://doiorg.publicaciones.saludcastillayleon.es/10.5664/jcsm.2408

    Article  Google Scholar 

  22. Chang S, Ma Q, Seow E, Lau YW, Satghare P, Hombali A, et al. Sleep beliefs and attitudes and the association with insomnia among psychiatric outpatients. J Ment Health. 2020;29:33–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/09638237.2018.1466046

    Article  Google Scholar 

  23. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/0165-1781(89)90047-4

    Article  CAS  Google Scholar 

  24. Bastien CH, Vallières A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med. 2001;2:297–307.

    Article  Google Scholar 

  25. Kendzerska TB, Smith PM, Brignardello-Petersen R, Leung RS, Tomlinson GA. Evaluation of the measurement properties of the Epworth sleepiness scale: a systematic review. Sleep Med Rev. 2014;18(4):321–31. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.smrv.2013.08.002

    Article  Google Scholar 

  26. Bastien C. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med. 2001;2:297–307. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S1389-9457(00)00065-4

    Article  Google Scholar 

  27. Yu DS. Insomnia Severity Index: psychometric properties with Chinese community-dwelling older people. J Adv Nurs. 2010;66(10):2350–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/j.1365-2648.2010.05394.x

    Article  Google Scholar 

  28. Morin CM, Vallières A, Ivers H. Dysfunctional beliefs and attitudes about Sleep (DBAS): validation of a brief version (DBAS-16). Sleep. 2007;30:1547–54. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/sleep/30.11.1547

    Article  PubMed Central  Google Scholar 

  29. Wan Y, Xu H, Wang S, Boyda D, Mcfeeters D, Sun Y, et al. Gender differences in the relationship between sleep problems and suicide attempt in adolescents. Front Psychiatry. 2020;11:133. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fpsyt.2020.00133

    Article  PubMed Central  Google Scholar 

  30. Lee E-H. Review of the psychometric evidence of the perceived stress scale. Asian Nurs Res. 2012;6:121–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.anr.2012.08.004

    Article  Google Scholar 

  31. Huang F, Wang H, Wang Z, Zhang J, Du W, Su C, et al. Psychometric properties of the perceived stress scale in a community sample of Chinese. BMC Psychiatry. 2020;20:130. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12888-020-02520-4

    Article  PubMed Central  Google Scholar 

  32. Brislin RW. Back-translation for cross-cultural research[J]. J Cross-Cult Psychol. 1970;1(3):185–216.

    Article  Google Scholar 

  33. Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine. 2000;25:3186–91.

    Article  CAS  Google Scholar 

  34. Yang Z, Chen F, Lu Y, Zhang H. Psychometric evaluation of medication safety competence scale for clinical nurses. BMC Nurs. 2021;20(1):165. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12912-021-00679-z

    Article  CAS  PubMed Central  Google Scholar 

  35. Curran PG. Methods for the detection of carelessly invalid responses in survey data. J Exp Soc Psychol. 2016;66:4–19.

    Article  Google Scholar 

  36. Hosford CC, Siders WA. Felder-Soloman’s index of learning styles: internal consistency, temporal stability, and factor structure. Teach Learn Med. 2010;22:298–303.

    Article  Google Scholar 

  37. Ferketich S. Focus on psychometrics. Aspects of item analysis. Res Nurs Health. 1991;14(2):165–8.

    Article  CAS  Google Scholar 

  38. Polit DF, Beck CT. The content validity index: are you sure you know what’s being reported? Critique and recommendations. Res Nurs Health. 2006;29:489–97.

    Article  Google Scholar 

  39. Hambleton RK, Swaminathan H, Algina J, Coulson DB. Criterion-referenced testing and measurement: a review of technical issues and developments. Rev Educ Res. 1978;48:1–47.

    Article  Google Scholar 

  40. DeVon HA, Block ME, Moyle-Wright P, Ernst DM, Hayden SJ, Lazzara DJ, et al. A psychometric toolbox for testing validity and reliability. J Nurs Scholarsh. 2007;39:155–64. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/j.1547-5069.2007.00161.x

    Article  Google Scholar 

  41. Kaiser HF. An index of factorial simplicity. Psychometrika. 1974;39:31–6. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/BF02291575

    Article  Google Scholar 

  42. Bartlett MS. A note on the multiplying factors for various χ2 approximations. J R Stat Soc Ser B Methodol. 1954;16:296–8.

    Article  Google Scholar 

  43. Heidari Z, Feizi A. Transcultural adaptation and validation of the Persian version of the breast cancer awareness measure (BCAM) questionnaire. Cancer Med. 2018;7:5237–51.

    Article  PubMed Central  Google Scholar 

  44. Shrestha N. Factor analysis as a tool for survey analysis. Am J Appl Math Stat. 2021;9:4–11.

    Article  Google Scholar 

  45. Black WC, Babin BJ, Anderson RE. Multivariate data analysis: a global perspective. Pearson; 2010.

  46. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model Multidiscip J. 1999;6:1–55.

    Article  Google Scholar 

  47. Strauss ME, Smith GT. Construct validity: advances in theory and methodology. Annu Rev Clin Psychol. 2009;5:1–25. https://doiorg.publicaciones.saludcastillayleon.es/10.1146/annurev.clinpsy.032408.153639

    Article  PubMed Central  Google Scholar 

  48. Revelle W, Zinbarg RE. Coefficients alpha, beta, omega, and the glb: comments on Sijtsma. Psychometrika. 2009;74:145–54.

    Article  Google Scholar 

  49. Chakrabartty SN. Best split-half and maximum reliability. IOSR J Res Method Educ. 2013;3:1–8.

    Google Scholar 

  50. Kyriazos TA, Stalikas A. Applied psychometrics: the steps of scale development and standardization process. Psychology. 2018;9:2531–60.

    Article  Google Scholar 

  51. Jorm AF, Ryan SM. Cross-national and historical differences in subjective well-being. Int J Epidemiol. 2014;43:330–40. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/ije/dyt188

    Article  Google Scholar 

  52. Taormina RJ, Gao JH. Maslow and the Motivation Hierarchy: measuring satisfaction of the needs. Am J Psychol. 2013;126:155–77. https://doiorg.publicaciones.saludcastillayleon.es/10.5406/amerjpsyc.126.2.0155

    Article  Google Scholar 

  53. Suh S, Cho N, Zhang J. Sex differences in Insomnia: from Epidemiology and etiology to intervention. Curr Psychiatry Rep. 2018;20:69. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11920-018-0940-9

    Article  Google Scholar 

  54. Vgontzas AN, Liao D, Pejovic S, Calhoun S, Karataraki M, Basta M, et al. Insomnia Short Sleep Durat Mortality: Penn State Cohort. 2010;33:1159–64.

    Google Scholar 

  55. Li RHY, Wing YK, Ho SC, Fong SYY. Gender differences in insomnia—a study in the Hong Kong Chinese population. J Psychosom Res. 2002;53:601–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0022-3999(02)00437-3

    Article  CAS  Google Scholar 

  56. De Zambotti M, Colrain IM, Baker FC. Interaction between Reproductive hormones and physiological sleep in women. J Clin Endocrinol Metab. 2015;100:1426–33. https://doiorg.publicaciones.saludcastillayleon.es/10.1210/jc.2014-3892

    Article  CAS  PubMed Central  Google Scholar 

  57. Gellerstedt L, Medin J, Kumlin M, Rydell Karlsson M. Nurses’ experiences of hospitalised patients’ sleep in Sweden: a qualitative study. J Clin Nurs. 2015;24:3664–73. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/jocn.12985

    Article  Google Scholar 

  58. Liu R, Shao W, Lai JK, Zhou L, Ren M, Sun N. Identification of comprehensive geriatric assessment-based risk factors for insomnia in elderly Chinese hospitalized patients. AGING Med. 2021;4:26–34. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/agm2.12146

    Article  Google Scholar 

  59. Elliott R, McKinley S, Cistulli P, Fien M. Characterisation of sleep in intensive care using 24-hour polysomnography: an observational study. Crit Care. 2013;17:R46. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/cc12565

    Article  PubMed Central  Google Scholar 

  60. Caumo GH, Spritzer D, Carissimi A, Tonon AC. Exposure to electronic devices and sleep quality in adolescents: a matter of type, duration, and timing. Sleep Health. 2020;6:172–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.sleh.2019.12.004

    Article  Google Scholar 

  61. Park KM, Kyong T. Disturbance of sleep and circadian rhythm in hospitalized patients. Chronobiol Med. 2023;5:7–17. https://doiorg.publicaciones.saludcastillayleon.es/10.33069/cim.2023.0003

    Article  Google Scholar 

  62. Harvey AG, Jones C, Schmidt DA. Sleep and posttraumatic stress disorder: a review. Clin Psychol Rev. 2003;23:377–407. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0272-7358(03)00032-1

    Article  Google Scholar 

  63. Kim E-J, Dimsdale JE. The effect of psychosocial stress on sleep: a review of Polysomnographic evidence. Behav Sleep Med. 2007;5:256–78. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/15402000701557383

    Article  PubMed Central  Google Scholar 

  64. Pawlyk AC, Morrison AR, Ross RJ, Brennan FX. Stress-induced changes in sleep in rodents: models and mechanisms. Neurosci Biobehav Rev. 2008;32:99–117. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.neubiorev.2007.06.001

    Article  Google Scholar 

  65. Tartar JL, Ward CP, Cordeira JW, Legare SL, Blanchette AJ, McCarley RW, et al. Experimental sleep fragmentation and sleep deprivation in rats increases exploration in an open field test of anxiety while increasing plasma corticosterone levels. Behav Brain Res. 2009;197:450–3. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.bbr.2008.08.035

    Article  CAS  Google Scholar 

  66. Palagini L, Baglioni C, Ciapparelli A, Gemignani A, Riemann D. REM sleep dysregulation in depression: state of the art. Sleep Med Rev. 2013;17:377–90. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.smrv.2012.11.001

    Article  Google Scholar 

  67. Ross RJ, Ball WA, Dinges DF, Kribbs NB, Morrison AR, Silver SM, et al. Rapid eye movement sleep disturbance in posttraumatic stress disorder. Biol Psychiatry. 1994;35:195–202. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/0006-3223(94)91152-5

    Article  CAS  Google Scholar 

  68. Meerlo P, Turek FW. Effects of social stimuli on sleep in mice: non-rapid-eye-movement (NREM) sleep is promoted by aggressive interaction but not by sexual interaction. Brain Res. 2001;907:84–92. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0006-8993(01)02603-8

    Article  CAS  Google Scholar 

  69. Wells AM, Ridener E, Bourbonais CA, Kim W, Pantazopoulos H, Carroll FI, et al. Effects of Chronic Social Defeat stress on Sleep and Circadian rhythms are mitigated by Kappa-Opioid receptor antagonism. J Neurosci. 2017;37:7656–68. https://doiorg.publicaciones.saludcastillayleon.es/10.1523/JNEUROSCI.0885-17.2017

    Article  CAS  PubMed Central  Google Scholar 

  70. Palagini L, Faraguna U, Mauri M, Gronchi A, Morin CM, Riemann D. Association between stress-related sleep reactivity and cognitive processes in insomnia disorder and insomnia subgroups: preliminary results. Sleep Med. 2016;19:101–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.sleep.2015.10.020

    Article  Google Scholar 

  71. Hantsoo L, Khou CS, White CN, Ong JC. Gender and cognitive–emotional factors as predictors of pre-sleep arousal and trait hyperarousal in insomnia. J Psychosom Res. 2013;74:283–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jpsychores.2013.01.014

    Article  PubMed Central  Google Scholar 

  72. Redeker NS, Yaggi HK, Jacoby D, Hollenbeak CS, Breazeale S, Conley S, et al. Cognitive behavioral therapy for insomnia has sustained effects on insomnia, fatigue, and function among people with chronic heart failure and insomnia: the HeartSleep Study. Sleep. 2022;45:zsab252. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/sleep/zsab252

    Article  Google Scholar 

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Acknowledgements

Thanks to the participants who helped us with this study and Professor Bahar, author of the hospital-Acquired Insomnia Scale, for providing us with tools and encouragement.

Funding

The study did not receive any specific funding from funding organizations in the public, commercial or non-profit sectors.

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Authors and Affiliations

  1. Department of Nursing, Jinzhou Medical University, Jinzhou, 121001, China

    Fan Zhang, Xin Wang, Chunyan Zhang, Kaiyan Xu, Huameng Xu & Qing Chen

  2. Department of Nursing, Huaian Hospital of Huaian City, Huaian, 223200, Jiangsu, China

    Xin Wang

  3. Department of Life and Health, Huzhou College, Huzhou, 313000, Zhejiang, China

    Chunguang Liang

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Contributions

All authors conceived the study. FZ, XW, QC and CL conceived and designed the study. FZ, HX and CL helped with data collection. CZ, QC and KX provided statistical advice on study design and performed data analysis. FZ, QC contributed to manuscript preparation and revision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chunguang Liang.

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Ethics approval and consent to participate

This study adhered to the ethical norms of the Helsinki Declaration as well as the Ethics Committee of Jinzhou Medical University (JZMULL2023150). All participants (or their proxies/legal guardians) provided informed consent to participate in the study. In addition, All the patients included in this study signed the informed consent form.

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All authors approved the final manuscript and the submission to this journal.

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The authors declare no competing interests.

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Zhang, F., Wang, X., Zhang, C. et al. Psychometric evaluation of the Chinese version of the hospital-acquired insomnia scale (HAIS) and analysis of influencing factors. BMC Psychiatry 24, 696 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12888-024-06160-w

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BMC Psychiatry

ISSN: 1471-244X

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