Objectives: This study aimed to investigate factors associated with sleep quality in healthcare students and to determine whether depressive and anxiety symptoms may explain some of the associations between sleep quality and self-rated health. Study design: This is a cross-sectional study at wave one. Methods: A total of 637 healthcare students were recruited via a stratified random sampling method in Hangzhou, China. The Sleep Quality Questionnaire (SQQ) and the four-item Patient Health Questionnaire (PHQ-4) were used to assess sleep quality and depressive and anxiety symptoms, respectively. Self-rated health was assessed via a self-developed questionnaire of both physical and psychological health. Structural equation modeling was used to examine the direct and indirect effects of sleep quality on self-rated health through depressive and anxiety symptoms. Results: Students engaged in part-time employment (p = 0.022), with poor perceived employment prospects (p = 0.009), and who did not participate in recreational sports (p = 0.008) had worse sleep quality. Structural equation modeling revealed a significant total effect of sleep quality on self-rated health (b = 0.592, p < 0.001), a significant direct effect of both sleep quality and depressive and anxiety symptoms on self-rated health (b = 0.277, 95% CI: 0.032–0.522), and a significant indirect effect of sleep quality on self-rated health through depressive and anxiety symptoms (b = 0.315, 95% CI: 0.174–0.457). Conclusions: Depressive and anxiety symptoms partially explain the association between sleep quality and self-rated health. Intervening upon sleep quality, depressive, and anxiety symptoms may bolster the self-rated health of healthcare students.
Purpose Stress may relate to an increased risk of psychological and physical disorders. Thus, a brief and efficient measurement instrument for researchers to measure stress is essentially needed. Participants and Methods To assess measurement properties of the validated Chinese version of the Perceived Stress Questionnaire-13 (PSQ-C-13), we conducted a two-wave longitudinal study from September to December, 2021 with a convenient sample of medical students. Results A two-factor (constraint and imbalance) structure showed good fit indices (Comparative Fit Index [CFI] = 0.972, Tucker-Lewis Index [TLI] = 0.966, Root Mean Square Error of Approximation [RMSEA] = 0.062). Spearman correlations with the Chinese Perceived Stress Scale-10 illustrated that convergent validity of the PSQ-C-13 was relatively satisfactory ( r = 0.678 [baseline], 0.753 [follow-up]). Measurement invariance was supported across subgroups (gender, age, home location, single-child status, monthly households’ income, and part-time status) and time points. Internal consistency was sound (Cronbach’s α = 0.908 [baseline], 0.922 [follow-up]; McDonald’s ω = 0.909 [baseline], 0.923 [follow-up]). Stability between time points was good (Intraclass Correlation Coefficient = 0.834). Conclusion The two factors of the PSQ-C-13 including constraint and imbalance may adequately measure the level of stress on participants. The PSQ-C-13 is a convenient and efficient instrument that contains valid and reliable psychometric properties.
BackgroundWith advances in high-throughput computational mining techniques, various quantitative predictive models that are based on ultrasound have been developed. However, the lack of reproducibility and interpretability have hampered clinical use. In this study, we aimed at developing and validating an interpretable and simple-to-use US nomogram that is based on quantitative morphometric features for the prediction of breast malignancy.MethodsSuccessive 917 patients with histologically confirmed breast lesions were included in this retrospective multicentric study and assigned to one training cohort and two external validation cohorts. Morphometric features were extracted from grayscale US images. After feature selection and validation of regression assumptions, a dynamic nomogram with a web-based calculator was developed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness.ResultsThrough feature selection, three morphometric features were identified as being the most optimal for predicting malignancy, and all regression assumptions of the prediction model were met. Combining all these predictors, the nomogram demonstrated a good discriminative performance in the training cohort and in the two external validation cohorts with AUCs of 0.885, 0.907, and 0.927, respectively. In addition, calibration and decision curves analyses showed good calibration and clinical usefulness.ConclusionsBy incorporating US morphometric features, we constructed an interpretable and easy-to-use dynamic nomogram for quantifying the probability of breast malignancy. The developed nomogram has good generalization abilities, which may fit into clinical practice and serve as a potential tool to guide personalized treatment. Our findings show that quantitative morphometric features from different ultrasound machines and systems can be used as imaging surrogate biomarkers for the development of robust and reproducible quantitative ultrasound dynamic models in breast cancer research.
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