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The sleep patterns and their associations with mental health among nursing home residents: a latent profile approach

BACKGROUND: Nursing home residents commonly experience poor sleep conditions. However, few studies have explored the potential sleep patterns among nursing home residents. This study aimed to identify the sleep patterns in nursing home residents, compare residents’ characteristics across sleep patte...

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Autores principales: Mou, Huanyu, Xu, Dongjuan, Zhu, Shanshan, Zhao, Meng, Wang, Yaqi, Wang, Kefang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401828/
https://www.ncbi.nlm.nih.gov/pubmed/37537539
http://dx.doi.org/10.1186/s12877-023-04124-5
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author Mou, Huanyu
Xu, Dongjuan
Zhu, Shanshan
Zhao, Meng
Wang, Yaqi
Wang, Kefang
author_facet Mou, Huanyu
Xu, Dongjuan
Zhu, Shanshan
Zhao, Meng
Wang, Yaqi
Wang, Kefang
author_sort Mou, Huanyu
collection PubMed
description BACKGROUND: Nursing home residents commonly experience poor sleep conditions. However, few studies have explored the potential sleep patterns among nursing home residents. This study aimed to identify the sleep patterns in nursing home residents, compare residents’ characteristics across sleep patterns, and examine the relationships between sleep patterns and residents’ mental health (i.e., depressive and anxiety symptoms). METHODS: This cross-sectional study was conducted in 27 nursing homes in Jinan, China, from March to June 2018. In total, 353 participants were recruited via convenience sampling, and of which, 326 completed the survey. A latent profile analysis was performed to identify sleep patterns based on the seven dimensions of the Pittsburgh Sleep Quality Index. Bivariate analyses were conducted to compare residents’ characteristics among the sleep patterns. Mixed-effects logistic regression analyses were adopted to investigate the relationships between sleep patterns and residents’ mental health. RESULTS: Three sleep patterns were identified, including ‘good sleepers’, ‘poor sleepers without hypnotic use’, and ‘poor sleepers with hypnotic use’. Residents’ gender, education, pain, instrumental activities of daily living, and number of chronic conditions were significantly differentiated across the sleep patterns. Compared with ‘good sleepers’, ‘poor sleepers without hypnotic use’ were significantly associated with more depressive symptoms (OR = 3.73, 95% CI = 2.09, 6.65, p < 0.001), but not with anxiety symptoms (OR = 2.04, 95% CI = 0.97, 4.29, p = 0.062); whereas ‘poor sleepers with hypnotic use’ had significantly more depressive (OR = 5.24, 95% CI = 2.54, 10.79, p < 0.001) and anxiety symptoms (OR = 5.02, 95% CI = 2.13, 11.83, p < 0.001). CONCLUSIONS: This study reveals three distinct sleep patterns in nursing home residents and their significant associations with residents’ mental health. These findings can inform future research to develop appropriate and tailored intervention strategies for improving sleep and promoting mental health for nursing home residents. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04124-5.
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spelling pubmed-104018282023-08-05 The sleep patterns and their associations with mental health among nursing home residents: a latent profile approach Mou, Huanyu Xu, Dongjuan Zhu, Shanshan Zhao, Meng Wang, Yaqi Wang, Kefang BMC Geriatr Research BACKGROUND: Nursing home residents commonly experience poor sleep conditions. However, few studies have explored the potential sleep patterns among nursing home residents. This study aimed to identify the sleep patterns in nursing home residents, compare residents’ characteristics across sleep patterns, and examine the relationships between sleep patterns and residents’ mental health (i.e., depressive and anxiety symptoms). METHODS: This cross-sectional study was conducted in 27 nursing homes in Jinan, China, from March to June 2018. In total, 353 participants were recruited via convenience sampling, and of which, 326 completed the survey. A latent profile analysis was performed to identify sleep patterns based on the seven dimensions of the Pittsburgh Sleep Quality Index. Bivariate analyses were conducted to compare residents’ characteristics among the sleep patterns. Mixed-effects logistic regression analyses were adopted to investigate the relationships between sleep patterns and residents’ mental health. RESULTS: Three sleep patterns were identified, including ‘good sleepers’, ‘poor sleepers without hypnotic use’, and ‘poor sleepers with hypnotic use’. Residents’ gender, education, pain, instrumental activities of daily living, and number of chronic conditions were significantly differentiated across the sleep patterns. Compared with ‘good sleepers’, ‘poor sleepers without hypnotic use’ were significantly associated with more depressive symptoms (OR = 3.73, 95% CI = 2.09, 6.65, p < 0.001), but not with anxiety symptoms (OR = 2.04, 95% CI = 0.97, 4.29, p = 0.062); whereas ‘poor sleepers with hypnotic use’ had significantly more depressive (OR = 5.24, 95% CI = 2.54, 10.79, p < 0.001) and anxiety symptoms (OR = 5.02, 95% CI = 2.13, 11.83, p < 0.001). CONCLUSIONS: This study reveals three distinct sleep patterns in nursing home residents and their significant associations with residents’ mental health. These findings can inform future research to develop appropriate and tailored intervention strategies for improving sleep and promoting mental health for nursing home residents. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04124-5. BioMed Central 2023-08-03 /pmc/articles/PMC10401828/ /pubmed/37537539 http://dx.doi.org/10.1186/s12877-023-04124-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Mou, Huanyu
Xu, Dongjuan
Zhu, Shanshan
Zhao, Meng
Wang, Yaqi
Wang, Kefang
The sleep patterns and their associations with mental health among nursing home residents: a latent profile approach
title The sleep patterns and their associations with mental health among nursing home residents: a latent profile approach
title_full The sleep patterns and their associations with mental health among nursing home residents: a latent profile approach
title_fullStr The sleep patterns and their associations with mental health among nursing home residents: a latent profile approach
title_full_unstemmed The sleep patterns and their associations with mental health among nursing home residents: a latent profile approach
title_short The sleep patterns and their associations with mental health among nursing home residents: a latent profile approach
title_sort sleep patterns and their associations with mental health among nursing home residents: a latent profile approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401828/
https://www.ncbi.nlm.nih.gov/pubmed/37537539
http://dx.doi.org/10.1186/s12877-023-04124-5
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