ArticleNature and science of sleep2021
The Development of a Rapid Classification Scale for Sleep Quality in Community-Dwelling Older Adults - The Yilan Study, Taiwan.
Article in Nature and science of sleep, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Mediating Effect of Depression on Relationship Between Sleep Disturbance and Locomotive Syndrome in Geriatric Cancer Patients.Journal of multidisciplinary healthcare · 2026Article
- Poor subjective sleep reported by people living with HIV is associated with impaired working memory.NeuroImmune pharmacology and therapeutics · 2023Article
- Prediction of sleep quality among university students after analyzing lifestyles, sports habits, and mental health.Frontiers in psychiatry · 2022Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePoor sleep quality is prevailing, deleterious, but heterogeneous in older adults. This study aimed to develop a simplified instrument to screen and classify poor sleep quality in community-dwelling older adults, by which stepped care with needs-based interventions could be implemented.
methodsCohorts of adults aged 65 years and older were used to develop the Rapid Classification Scale for Sleep Quality (RCSSQ). Poor sleep quality was defined with the Pittsburgh Sleep Quality Index (PSQI). Established subgroups of poor sleep quality in the development dataset (n = 2622) were used as the criterion standard. Two independent validation datasets (n = 964 and 193, respectively) were used to examine the external validity. Questions in the PSQI were examined by the stepwise multinomial logistic regressions to determine the optimal numbers of items in the RCSSQ. On the premise of item parsimony and instrument validity, the optimal combination of reduced items was determined.
resultsIn the development dataset, the 4-item RCSSQ (RCSSQ-4) was the optimal predictive model. In terms of internal validity, the accuracy rates to identify PSQI-defined poor sleep quality and its subgroups in the developmental dataset by the RCSSQ-4 were 89.0% and 79.9%, respectively. Meanwhile, the RCSSQ-4 also had good external validity in the validation datasets to detect PSQI-defined poor sleep quality (accuracy rates: 89.1-90.7%). Furthermore, the profiles of PSQI component scores and comorbid conditions for the predicted subgroups in the validation dataset were comparable with the criterion standard.
conclusionThe RCSSQ-4 is a valid instrument for screening and subgrouping poor sleep quality in community-dwelling older adults. The RCSSQ-4 may help guide tailored interventions under the context of stepped care in the community.
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