PDF(Pubmed)
Abstract：
UNASSIGNED: Sleep efficiency is often used as a measure of sleep quality. Getting sufficiently high-quality sleep has been associated with better cognitive function among older adults; however, the relationship between day-to-day sleep quality variability and cognition has not been well-established.
UNASSIGNED: We aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults, using accelerometer data and 3 cognitive tests.
UNASSIGNED: We included older adults aged >65 years with at least 5 days of accelerometer wear time from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer\'s Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Sleep efficiency was derived using a data-driven machine learning algorithm. We examined associations between sleep efficiency variability and scores on each cognitive test adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Associations between average sleep efficiency and each cognitive test score were further examined for comparison purposes.
UNASSIGNED: A total of 1074 older adults from the NHANES were included in this study. Older adults with low average sleep efficiency exhibited higher levels of sleep efficiency variability (Pearson r=-0.63). After adjusting for confounding factors, greater average sleep efficiency was associated with higher scores on the DSST (per 10% increase, β=2.25, 95% CI 0.61 to 3.90) and AFT (per 10% increase, β=.91, 95% CI 0.27 to 1.56). Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, β=-3.34, 95% CI -5.33 to -1.34), CERAD-WL (per 10% increase, β=-1.00, 95% CI -1.79 to -0.21), and AFT (per 10% increase, β=-1.02, 95% CI -1.68 to -0.36). In fully adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, β=-2.01, 95% CI -3.62 to -0.40) and AFT (per 10% increase, β=-.84, 95% CI -1.47 to -0.21) scores but not CERAD-WL (per 10% increase, β=-.65, 95% CI -1.39 to 0.08) scores.
UNASSIGNED: Targeting consistency in sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.
UNASSIGNED: We aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults, using accelerometer data and 3 cognitive tests.
UNASSIGNED: We included older adults aged >65 years with at least 5 days of accelerometer wear time from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer\'s Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Sleep efficiency was derived using a data-driven machine learning algorithm. We examined associations between sleep efficiency variability and scores on each cognitive test adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Associations between average sleep efficiency and each cognitive test score were further examined for comparison purposes.
UNASSIGNED: A total of 1074 older adults from the NHANES were included in this study. Older adults with low average sleep efficiency exhibited higher levels of sleep efficiency variability (Pearson r=-0.63). After adjusting for confounding factors, greater average sleep efficiency was associated with higher scores on the DSST (per 10% increase, β=2.25, 95% CI 0.61 to 3.90) and AFT (per 10% increase, β=.91, 95% CI 0.27 to 1.56). Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, β=-3.34, 95% CI -5.33 to -1.34), CERAD-WL (per 10% increase, β=-1.00, 95% CI -1.79 to -0.21), and AFT (per 10% increase, β=-1.02, 95% CI -1.68 to -0.36). In fully adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, β=-2.01, 95% CI -3.62 to -0.40) and AFT (per 10% increase, β=-.84, 95% CI -1.47 to -0.21) scores but not CERAD-WL (per 10% increase, β=-.65, 95% CI -1.39 to 0.08) scores.
UNASSIGNED: Targeting consistency in sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.
摘要：
■睡眠效率通常用作睡眠质量的量度。获得足够高质量的睡眠与老年人更好的认知功能有关；然而，日常睡眠质量变异性与认知之间的关系尚未得到很好的证实.
■我们旨在确定老年人的日常睡眠效率变异性与认知功能之间的关系，使用加速度计数据和3项认知测试。
■我们包括来自国家健康和营养检查调查（NHANES）的65岁以上的老年人，他们完成了数字符号替代测试（DSST），至少有5天的加速度计佩戴时间，建立阿尔茨海默病单词学习子测试(CERAD-WL)注册表的联盟，动物流畅度测试（AFT）。使用数据驱动的机器学习算法得出睡眠效率。我们检查了睡眠效率变异性与每个认知测试得分之间的关系，性别,教育,家庭收入,婚姻状况,抑郁症状,糖尿病,吸烟习惯,酒精消费,关节炎,心脏病,之前的心脏病发作,先前的中风，日常生活活动,和日常生活的工具性活动。为了比较目的，进一步检查了平均睡眠效率和每个认知测试得分之间的关联。
■本研究共纳入了来自NHANES的1074名老年人。平均睡眠效率较低的老年人表现出更高水平的睡眠效率变异性（Pearsonr=-0.63）。在调整混杂因素后，更高的平均睡眠效率与DSST上更高的分数相关(每增加10%，β=2.25，95%CI0.61至3.90)和AFT(每增加10%，β=.91，95%CI0.27至1.56）。基于DSST，更大的睡眠效率变异性与更差的认知功能(每增加10%，β=-3.34，95%CI-5.33至-1.34），CERAD-WL(每增加10%,β=-1.00，95%CI-1.79至-0.21），和AFT(每增加10%，β=-1.02，95%CI-1.68至-0.36）。在完全调整的模型中，更大的睡眠效率变异性仍然与更低的DSST相关(每增加10%，β=-2.01，95%CI-3.62至-0.40)和AFT(每增加10%，β=-.84，95%CI-1.47至-0.21)分数，但不是CERAD-WL(每增加10%，β=-.65，95%CI-1.39至0.08）评分。
■针对睡眠质量的一致性可能对寻求保护老年人认知功能的干预措施有用。
■我们旨在确定老年人的日常睡眠效率变异性与认知功能之间的关系，使用加速度计数据和3项认知测试。
■我们包括来自国家健康和营养检查调查（NHANES）的65岁以上的老年人，他们完成了数字符号替代测试（DSST），至少有5天的加速度计佩戴时间，建立阿尔茨海默病单词学习子测试(CERAD-WL)注册表的联盟，动物流畅度测试（AFT）。使用数据驱动的机器学习算法得出睡眠效率。我们检查了睡眠效率变异性与每个认知测试得分之间的关系，性别,教育,家庭收入,婚姻状况,抑郁症状,糖尿病,吸烟习惯,酒精消费,关节炎,心脏病,之前的心脏病发作,先前的中风，日常生活活动,和日常生活的工具性活动。为了比较目的，进一步检查了平均睡眠效率和每个认知测试得分之间的关联。
■本研究共纳入了来自NHANES的1074名老年人。平均睡眠效率较低的老年人表现出更高水平的睡眠效率变异性（Pearsonr=-0.63）。在调整混杂因素后，更高的平均睡眠效率与DSST上更高的分数相关(每增加10%，β=2.25，95%CI0.61至3.90)和AFT(每增加10%，β=.91，95%CI0.27至1.56）。基于DSST，更大的睡眠效率变异性与更差的认知功能(每增加10%，β=-3.34，95%CI-5.33至-1.34），CERAD-WL(每增加10%,β=-1.00，95%CI-1.79至-0.21），和AFT(每增加10%，β=-1.02，95%CI-1.68至-0.36）。在完全调整的模型中，更大的睡眠效率变异性仍然与更低的DSST相关(每增加10%，β=-2.01，95%CI-3.62至-0.40)和AFT(每增加10%，β=-.84，95%CI-1.47至-0.21)分数，但不是CERAD-WL(每增加10%，β=-.65，95%CI-1.39至0.08）评分。
■针对睡眠质量的一致性可能对寻求保护老年人认知功能的干预措施有用。
