A. Basic Sleep Science V. Arousal and Behaviorlike daytime napping. Elite sport might, therefore, may select for those with more robust sleep, and a propensity to sleep 'on demand'. Neither construct has been systematically explored in elite athletes. The present study, therefore, examines sleep reactivity and daytime sleep tendency in elite athletes and healthy non-athlete controls. Introduction: Athletes may use daytime napping to supplement their night time sleep; however the time to optimal performance after waking from a nap is not known. The aim of this study was to examine sprint ability and reaction time at 30, 60, 90 and 120 minutes after waking from a daytime nap. Methods: Twelve well-trained soccer players (18.3 ± 1.0 years) completed two conditions in a randomised order. In one condition, participants had nine hours time in bed (22:00-07:00h) without napping the next day, and in the other condition participants had seven hours time in bed (00:00-07:00h) with a two-hour nap the next day (14:00-16:00h). Sleep was assessed using polysomnography. Each day, participants completed four 30-minute test sessions (every 30 min starting at 16:15h) that included a seven-minute warm up, two 10-metre sprints, and a 90-second reaction time task. Total sleep time was compared between conditions using a paired t-test. The effect of condition (no nap vs. nap) and test session (30, 60, 90, 120 min) on fastest sprint, and mean reaction time were assessed by separate repeated ANOVAs. Results: Total sleep time was similar between conditions (no nap 8.1 ± 0.7 h vs. nap 8.0 ± 1.0 h, p=0.87). There was a main effect of test session (p=0.02) on reaction time, but no effect of condition (p=0.84) and no interaction between condition and session (p=0.26). Reaction time was faster at 120 minutes (211.3 ± 20.0 ms) vs. 30 minutes (219.5 ± 20.5 ms, p=0.01) and 60 minutes (219.8 ± 20.8 ms, p=0.01).
several measures including one assessing sleep quality (Pittsburgh Sleep Quality Index; PSQI) and the other insomnia severity (Insomnia Severity Index; ISI). Results: The group of menopausal women reported significantly poorer sleep quality than non-menopausal women (PSQI means of 7.44 vs. 5.49, p<0.001), although both groups exceeded the threshold of 5 typically used to define poor sleep quality. Menopausal women also reported significantly more severe insomnia than non-menopausal women (ISI means of 9.64 vs. 8.07, p<0.001), and both groups also exceeded clinical threshold on this variable. There was no significant group difference on either sleep quality or insomnia severity between menopausal women with or without HRT. Conclusion: These findings suggest that menopausal women report poorer sleep quality and more severe insomnia than non-menopausal women. HRT does not seem to have a significant impact on these indices of sleep impairments. The development of therapies other than HRT is therefore warranted to reduce the impact of sleep disturbances on the quality of life of menopausal women. For the machine learning approach, we analyzed 154 continuous and categorical variables from the ASQ related to insomnia. Sequential forward feature selection was applied to identify a subset of variables. Bagged decision trees were used as a classification model to accommodate missing values and categorical data.Two regression models were created. The first approach included 15 potential variables selected using clinical judgement. The second, hybrid model, included the initial 15 variables plus 4 additional variables identified in the machine learning approach and considered clinically relevant. Backward elimination stepwise selection was used to build the regression models.Performance was evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operator curve (AUC). Optimal diagnosis point threshold was selected by weighting sensitivity and specificity equally. Results: 5701 patients (3239 males, 2462 females) were analyzed and 944 met insomnia criteria. Machine learning selected 35 features using a learning dataset, resulting in Sensitivity=73.2%, Specificity=73.1%, PPV=35.0%, NPV=93.3%, and AUC=0.80 when applied to the test data. The original ASQ model included 8 covariates with Sensitivity=71.1%, Specificity=69.6%, PPV=29.5%, NPV=92.8%, and AUC=0.78. The hybrid model contained 13 covariates with Sensitivity=75.1%, Specificity=73.0%, PPV=35.7%, NPV=93.6%, and AUC=0.81. Conclusion: Although the machine learning approach had slightly higher specificity and sensitivity compared to the original regression model, it took longer to build and process the data. The hybrid regression model, with covariates selected using a combination of clinical judgement and machine learning, had the best overall performance. Support (If Any): Philips Respironics Foundation grant, the Stanford Center for Sleep Sciences and Medicine, and gift funds.
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