The Development of a Rapid Classification Scale for Sleep Quality in Community-Dwelling Older Adults – The Yilan Study, Taiwan

Chen, Hsi‐Chung; Hsu, Nan‐Yung; Pan, Po‐Jung; Kuo, Po-Hsiu; Chien, Meng-Yueh; Chou, Pesus

doi:10.2147/nss.s324928

Cited by 4 publications

(6 citation statements)

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“…In addition, the prediction model is gradually applied to evaluate sleep quality. For example, Chen et al ( 16 ) developed the rapid classification scale for sleep quality to screen and subgroup poor sleep quality using four main variables, including sleep quality, hypnotic use, sleep onset, and lacking enthusiasm. But Chen’s scale was designed specifically for older adults, and the young were not enrolled, which meant that this scale was not applicable to the young population.…”

Section: Discussionmentioning

confidence: 99%

“…The PSQI score was the indicator variable to cluster participants with poor or good sleep quality. In detail, poor sleep quality was defined as participants with a total PSQI score of six points or above, while good sleep quality was defined as participants with a total PSQI score of less than six points ( 16 , 22 ).…”

Section: Methodsmentioning

confidence: 99%

“…However, these variables could not accurately calculate the risk probability of having poor sleep quality, and thus early detection was difficult. Accordingly, this might result in not only inadequate diagnosis and therapeutic interventions but also overtreatment or improper administration of hypnosedatives ( 16 ). Thus, a valid tool that was able to cluster sleep quality was warranted before we could perform individualized healthcare.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, a model to evaluate the probability of poor sleep quality would be of great help to early detect the status, stratify risk, and treat this negative outcome individually. The currently available prediction models to predict the quality of sleep were particularly designed for elderly patients ( 16 ), rescuers ( 17 ), and caregivers ( 18 ). Nonetheless, data on prediction models for calculating the risk of poor sleep quality were extremely limited, especially among university students.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of sleep quality among university students after analyzing lifestyles, sports habits, and mental health

et al. 2022

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The aim of this study was to develop and validate a prediction model to evaluate the risk of poor sleep quality. We performed a cross-sectional study and enrolled 1,928 college students from five universities between September and November 2021. The quality of sleep was evaluated using the Chinese version of the Pittsburgh Sleep Quality Index (PSQI). Participants were divided into a training (n = 1,555) group and a validation (n = 373) group. The training group was used to establish the model, and the validation group was used to validate the predictive effectiveness of the model. The risk classification of all participants was performed based on the optimal threshold of the model. Of all enrolled participants, 45.07% (869/1,928) had poor sleep quality (PSQI score ≧ 6 points). Multivariate analysis showed that factors such as older age, a higher grade, previous smoking, drinking, midday rest, chronic disease, anxiety, and stress were significantly associated with a higher rate of poor sleep quality, while preference for vegetables was significantly associated with better sleep quality, and all these variables were included to develop the prediction model. The area under the curve (AUC) was 0.765 [95% confidence interval (CI): 0.742–0.789] in the training group and 0.715 (95% CI: 0.664–0.766) in the validation group. Corresponding discrimination slopes were 0.207 and 0.167, respectively, and Brier scores were 0.195 and 0.221, respectively. Calibration curves showed favorable matched consistency between the predicted and actual probability of poor sleep quality in both groups. Based on the optimal threshold, the actual probability of poor sleep quality was 29.03% (317/1,092) in the low-risk group and 66.03% (552/836) in the high-risk group (P < 0.001). A nomogram was presented to calculate the probability of poor sleep quality to promote the applicationof the model. The prediction model can be a helpful tool to stratify sleep quality, especially among university students. Some intervention measures or preventive strategies to quit smoking and drinking, eat more vegetables, avoid midday rest, treat chronic disease, and alleviate anxiety and stress may be considerably beneficial in improving sleep quality.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of sleep quality among university students after analyzing lifestyles, sports habits, and mental health

et al. 2022

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“…Polysomnography studies are the gold standard for evaluation of sleep architecture, but requirements for overnight participation and complex data analyses procedures have historically precluded su ciently-sampled evaluations [cf., 9, 10, 11]. The Pittsburgh Sleep Quality Index (PSQI) is a well-validated measure of subjective sleep quality [12][13][14] that has been used in a variety of populations [e.g., 12,15,[16][17][18][19] with a global score ≥ 5 distinguishing good from poor sleepers [20]. Although subjective sleep indices such as the PSQI do not necessarily align with polysomnography measures [e.g., 21], they may nevertheless screen relevant aspects of sleep [e.g., 22,23].…”

Section: Introductionmentioning

confidence: 99%

Poor subjective sleep reported by people living with HIV is associated with impaired working memory

Zahr

Sullivan

Pfefferbaum

2022

Preprint

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Background. Poor sleep can undermine the health of people in general and may be especially disruptive to those with chronic conditions including HIV infection. Methods. Here, clinically well-described people living with HIV infection [PLWH] (74 men, 35 women) and healthy control (38 men, 35 women) participants were administered the Pittsburgh Sleep Quality Index (PSQI), which is a validated measure of subjective sleep with a global score ≥ 5 able to distinguish good from poor sleepers. In addition, participants completed neuropsychological tests assessing executive functioning, working memory, and learning and memory. Results. PLWH (6.8±3.7) had higher global PSQI scores than those of healthy controls (4.1±2.8): 39.7% of uninfected controls and 68.8% of PLWH had a PSQI ≥ 5 indicative of poor sleep. There were no relations between the global PSQI score and any evaluated variables among healthy-control individuals or with demographic or HIV-related variables in PLWH. Instead, a higher global PSQI score among PLWH was associated with worse “Quality of Life” measures including scores on the Global Assessment of Functioning (GAF, p = .0007), the Medical Outcomes Study survey (21-item short form, SF-21, p < .0001), and Activities of Daily Living-Instrumental (ADL-I, p = .0041); and more depressive symptoms as determined by the Beck Depression Index (BDI, p < .0001). Further, in PLWH, a higher global PSQI score was associated with poor performance on a working memory task, the digit backward span (p = .0036). In PLWH, the 5 variables together explained 32.3% of the global PSQI score variance; only 3 variables – the SF-21, BDI, and digit backward scores – explained 30.6% of the variance. Conclusions. To the extent that poor subjective sleep contributes to impaired working memory in HIV, we speculate that this impairment may be ameliorated by improved sleep health.

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Artificial neural network–based model for sleep quality prediction for frontline medical staff during major medical assistance

Chen,

Zhu

et al. 2024

DIGITAL HEALTH

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Background: The sleep quality of medical staff was severely affected during COVID-19, but the factors influencing the sleep quality of frontline staff involved in medical assistance remained unclear, and screening tools for their sleep quality were lacking. Methods: From June 25 to July 14, 2022, we conducted an Internet-based cross-sectional survey. The Pittsburgh Sleep Quality Index (PSQI), a self-designed general information questionnaire, and a questionnaire regarding the factors influencing sleep quality were combined to understand the sleep quality of frontline medical staff in Fujian Province supporting Shanghai in the past month. A chi-square test was used to compare participant characteristics, and multivariate unconditional logistic regression analysis was used to determine the predictors of sleep quality. Stratified sampling was used to divide the data into a training test set ( n = 1061, 80%) and an independent validation set ( n = 265, 20%). Six models were developed and validated using logistic regression, artificial neural network, gradient augmented tree, random forest, naive Bayes, and model decision tree. Results: A total of 1326 frontline medical staff were included in this survey, with a mean PSQI score of 11.354 ± 4.051. The prevalence of poor sleep quality was 80.8% ( n = 1072, PSQI >7). Six variables related to sleep quality were used as parameters in the prediction model, including type of work, professional job title, work shift, weight change, tea consumption during assistance, and basic diseases. The artificial neural network (ANN) model produced the best overall performance with area under the curve, accuracy, sensitivity, specificity, precision, F1 score, and kappa of 71.6%, 68.7%, 66.7%, 69.2%, 34.0%, 45.0%, and 26.2% respectively. Conclusions: In this study, the ANN model, which demonstrated excellent predictive efficiency, showed potential for application in monitoring the sleep quality of medical staff and provide some scientific guidance suggestions for early intervention.

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The Development of a Rapid Classification Scale for Sleep Quality in Community-Dwelling Older Adults – The Yilan Study, Taiwan

Cited by 4 publications

References 55 publications

Prediction of sleep quality among university students after analyzing lifestyles, sports habits, and mental health

Prediction of sleep quality among university students after analyzing lifestyles, sports habits, and mental health

Poor subjective sleep reported by people living with HIV is associated with impaired working memory

Artificial neural network–based model for sleep quality prediction for frontline medical staff during major medical assistance

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