Psychometric analysis of a Chinese version of the Sleep Hygiene Index in nursing students in China: a cross-sectional study

The Long COVID Symptoms and Severity Score: Development, Validation, and Application

OBJECTIVES

The primary focus of this research is the proposition of a methodological framework for the clinical application of the long COVID symptoms and severity score (LC-SSS). This tool is not just a self-reported assessment instrument developed and validated but serves as a standardized, quantifiable means to monitor the diverse and persistent symptoms frequently observed in individuals with long COVID.

METHODS

A 3-stage process was used to develop, validate, and establish scoring standards for the LC-SSS. Validation measures included correlations with other patient-reported measures, confirmatory factor analysis, Cronbach's α for internal consistency, and test-retest reliability. Scoring standards were determined using K-means clustering, with comparative assessments made against hierarchical clustering and the Gaussian Mixture Model.

RESULTS

The LC-SSS showed correlations with EuroQol 5-Dimension 5-Level (rs = -0.55), EuroQol visual analog scale (rs = -0.368), Patient Health Questionnaire-9 (rs = 0.538), Beck Anxiety Inventory (rs = 0.689), and Insomnia Severity Index (rs = 0.516), confirming its construct validity. Structural validity was good with a comparative fit index of 0.969, with Cronbach's α of 0.93 indicating excellent internal consistency. Test-retest reliability was also satisfactory (intraclass correlation coefficient 0.732). K-means clustering identified 3 distinct severity categories in individuals living with long COVID, providing a basis for personalized treatment strategies.

CONCLUSIONS

The LC-SSS provides a robust and valid tool for assessing long COVID. The severity categories established via K-means clustering demonstrate significant variation in symptom severity, informing personalized treatment and improving care quality for patients with long COVID.

A comparison between perceived rurality and established geographic rural status among Indiana residents

The study assessed the association and concordance of the traditional geography-based Rural-Urban Commuting Area (RUCA) codes to individuals' self-reported rural status per a survey scale. The study included residents from rural and urban Indiana, seen at least once in a statewide health system in the past 12 months. Surveyed self-reported rural status of individuals obtained was measured using 6 items with a 7-point Likert scale. Cronbach's alpha was used to measure the internal consistency between the 6 survey response items, along with exploratory factor analysis to evaluate their construct validity. Perceived rurality was compared with RUCA categorization, which was mapped to residential zip codes. Association and concordance between the 2 measures were calculated using Spearman's rank correlation coefficient and Gwet's Agreement Coefficient (Gwet's AC), respectively. Primary self-reported data were obtained through a cross-sectional, statewide, mail-based survey, administered from January 2018 through February 2018, among a random sample of 7979 individuals aged 18 to 75, stratified by rural status and race. All 970 patients who completed the survey answered questions regarding their perceived rurality. Cronbach's alpha value of 0.907 was obtained indicating high internal consistency among the 6 self-perceived rurality items. Association of RUCA categorization and self-reported geographic status was moderate, ranging from 0.28 to 0.41. Gwet's AC ranged from -0.11 to 0.26, indicating poor to fair agreement between the 2 measures based on the benchmark scale of reliability. Geography-based and self-report methods are complementary in assessing rurality. Individuals living in areas of relatively high population density may still self-identify as rural, or individuals with long commutes may self-identify as urban.

Cross-cultural adaptation and validation of the Chinese version of the Sleep Health Index

OBJECTIVE

To generate the Chinese Sleep Health Index (SHI-C) in Mandarin with cross-cultural adaptations and test its psychometric properties.

METHODS

This study used a cross-sectional design. Health science students were included (N = 271) and a sub-set (n = 74) was invited for the re-test. Cross-cultural adaptation of the SHI-C was performed prior to formal validation. The SHI-C, Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Bedtime Procrastination Scale, and Sleep Hygiene Index were used to measure variables of interest. Exploratory factor analysis was used to evaluate the structure validity. Bivariate analyses were used to evaluate the construct validity.

RESULTS

Exploratory factor analysis identified 3 factors (ie, sleep quality, sleep duration, and disordered sleep) accounting for 55.6% of the total variance. The SHI-C total and sleep quality sub-index scores were significantly associated with both PSQI global score (r = -0.132, p < .05; r = -0.182, p < .01, respectively) and ISI score (r = -0.655, p < .05; r = -0.820, p < .05, respectively). SHI-C total, sleep quality sub-index, and sleep duration sub-index scores were significantly associated with Bedtime Procrastination Scale and Sleep Hygiene Index scores (r = -0.238 to -0.368, p < .05). Students with insomnia (ISI > 9) or poor sleep quality (PSQI > 5) had significantly lower SHI-C scores than those without (73.5 vs. 89.0, p < .01; 84.1 vs. 86.7, p < .05, respectively). SHI-C showed good internal consistency (Cronbach's alpha = 0.73) and test-retest reliability (intraclass correlation coefficient = 0.82).

CONCLUSIONS

The SHI-C demonstrated good validity and adequate reliability in a Chinese sample of health science students. It could be used to measure sleep health in future research and practice. Psychometric properties of the SHI-C among other Chinese populations remain to be confirmed.

Psychometric properties of the Sleep Hygiene Index in a large Italian community sample

OBJECTIVE/BACKGROUND

Poor sleep hygiene is considered an exacerbating and perpetuating factor of sleep disturbances and is also associated with poor mental health. The Sleep Hygiene Index (SHI) is a self-report measure assessing adherence to sleep hygiene practices. The aim of this study was to estimate the psychometric properties of the SHI in an Italian representative sample of the general population, following a formative measurement approach.

PATIENTS/METHODS

Participants (n = 6276; M = 33.62, SD = 13.45) completed the SHI alongside measures of sleep disturbance, depression, anxiety, and stress. To consider the item formative nature, sets of item-composites weighted by means of canonical correlation analysis was created and a confirmatory factor analysis (CFA) was implemented. Factorial invariance tests were computed considering both presence of sleep problems and presence of emotional distress symptoms as grouping variables.

RESULTS AND CONCLUSIONS

CFA confirmed the unidimensional structure of SHI. Internal consistency was acceptable (ω = 0.752). Test-retest reliability at 8-10 months presented an ICC of 0.666. SHI significantly correlated with sleep, depression, anxiety and stress symptoms (r range from 0.358 to 0.500). Configural and metric invariance were reached for both grouping variables. Partial scalar invariance was obtained only across emotional distress groups. People with emotional symptoms reported higher latent means on the sleep hygiene dimension. Findings support the validity and reliability of the Italian version of the SHI. Importantly, the SHI showed robust psychometric properties both in healthy individuals and in individual reporting mental health symptoms. Thus, it is advisable to use this version of the SHI in both research and clinical practice.

The relationship of sleep quality among internship nurses with clinical learning environment and mental stress: a cross-sectional survey

BACKGROUND

Previous evidence has supported an association between sleep quality and psychological stress. However, the association between internship nurses' sleep status and its relevant factors is poorly understood.

OBJECTIVE

The aim of this study was to investigate sleep quality and its related factors in clinical learning environment and mental stress.

METHODS

A cross-sectional survey was conducted by three instruments: Clinical Learning Environment, Supervision, and Nurse Teacher Evaluation Scale (CLES + T), Stress Rating Scale for practicing nurses (SRS) and Pittsburgh Sleep Quality Index (PSQI).

RESULTS

A total of 508 (91.86%) of 553 students experienced poor sleep quality. The structural equation model showed a correlation of the PSQI with the CLES + T (r = -0.21, p < 0.001), a correlation of the PSQI with the SRS (r = 0.32, p < 0.001), and a correlation of the SRS with the CLES + T (r = -0.22, p < 0.001). Linear regression analysis showed that education (B = -0.56, p < 0.001), willingness to engage in nursing after graduation (B = -0.75, p < 0.001), pedagogical atmosphere in the ward (B = -0.05, p < 0.001) measured by the CLES + T, workload (B = 0.11, p = 0.01), interpersonal relationships (B = -0.12, p = 0.03), and conflicts between study and work (B = 0.12, p < 0.001) on the SRS were significant factors influencing the PSQI.

CONCLUSIONS

Poor sleep quality is common among internship nurses and it's affected by clinical environment and mental stress. It's necessary to apply more tailored education programs to promote nursing development.