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Insomnia with physiological hyperarousal is associated with hypertension
Abstracts/Sleep Medicine 16 (2015) S2–S199
Results: A majority of the sample was White (81.1%), married (53.7%), and had an average of 16 years of education (SD = 3.2). Overall, 60.4% reported a past or current medical condition, 87.4% reported visiting a health professional in the past year (Mage = 4.1, SD = 6.2; range 0–52), 66.1% reported a history of hospitalization (Mage = 1.5, SD = 1.6; range 0–7), and 37.8%, 18.6%, and 4.3% reported the use of prescription, over-the counter medication, and natural product use, respectively. After controlling for age, sex, body mass index, medication use, and comorbidity, no significant differences were found for past or current medical conditions or history of hospitalizations as a function of objective sleep duration. However, compared with individuals with insomnia with long sleep duration, individuals with insomnia with short sleep duration had an odds ratio of 1.20 (95% CI 1.20, 9.14) of visiting a health professional in the past year. Additionally, insomnia with short sleep duration was associated with an odds ratio of 1.06 (95% CI 1.15, 7.22) of past or current use of prescription medication as compared with their counterparts, all while controlling for multiple covariates. No significant differences were observed for over-the-counter medication or natural product use between groups. Conclusion: Results suggest that insomnia with objective short sleep duration is associated with more physician visits and medication use compared with insomnia with long sleep duration. Knowledge of such health maintenance behaviors is important to identify at-risk individuals, as this may help develop more targeted prevention and intervention strategies for insomnia. Acknowledgements: This study was funded by the National Institute of Mental Health Grant (R01MH60413) and by the Canadian Institutes of Health Research (B0512201).
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examine the association of insomnia with hypertension based on different levels of hyperarousal measured by MSLT values. Results: After controlling for age, sex, BMI, AHI, diabetes, smoking, alcohol and caffeine use, insomnia was associated with a nearly significant increased odds for hypertension compared with normal sleepers (OR = 2.17, 95% CI = 0.98–4.84). Insomnia combined with MSLT > 14 min increased the odds of hypertension by 300% (OR = 3.27, 95% CI = 1.20–8.96) whereas insomnia combined with MSLT > 17 min increased even further the odds of hypertension by 400% (OR = 4.33, 95% CI = 1.48–12.68) compared with normal sleepers with MSLT ≤ 14 min. In contrast, in insomniacs with MSLT ≤ 14 min, the odds of hypertension were not significantly increased compared with normal sleepers with MSLT ≤ 14 min (OR = 1.17, 95% CI = 0.40– 3.43). The odds of hypertension of normal sleepers with MSLT > 14 min were not significantly increased compared with normal sleepers with MSLT ≤ 14 min (OR = 0.87, 95% CI = 0.19– 3.90). Even after excluding the participants who were using antihypertensive medication, the linear relationship between blood pressure and MSLT values were still significant (systolic BP, p-linear = 0.04, diastolic BP, p-linear = 0.003). Conclusion: Insomnia associated with physiological hyperarousal is associated with a significant risk of hypertension. Long MSLT values may be a reliable index of the physiological hyperarousal and biological severity of chronic insomnia. Acknowledgements: The work was performed at the Sleep Medicine Center at the West China Hospital, Sichuan University, and our technical staff (Fei Lei and Lina Du) is especially commended for their efforts. http://dx.doi.org/10.1016/j.sleep.2015.02.025
http://dx.doi.org/10.1016/j.sleep.2015.02.024
Insomnia with physiological hyperarousal is associated with hypertension Y. Li 1,2, A. Vgontzas 2, J. Fernandez-Mendoza 2, E. Bixler 2, Y. Sun 1, J. Zhou 1, R. Ren 1, X. Tang 1 1 West China Hospital, Sichuan University, China 2 Pennsylvania State University College of Medicine, USA
Introduction: Insomnia with objective short sleep duration is associated with a higher risk of hypertension and it has been speculated that the underlying mechanism is physiological hyperarousal. In this study, we tested whether insomnia with physiological hyperarousal measured by Multiple Sleep Latency Test (MSLT), is associated with increased risk of hypertension. Materials and methods: During the January 2010 to July 2014 recruitment period, 844 consecutive insomniacs and 117 normal sleepers were studied in the sleep laboratory. We excluded insomniacs and normal sleepers who had (1) a current major psychiatric condition; (2) current and past use of hypnotics, anxiolytics, antidepressants, and any other psychotropic medication; (3) evidence of sleep disordered breathing disorder, (4) evidence of sleeprelated movement disorder and (5) evidence of a hypersomnia disorder. After an overnight polysomnography followed by a standard MSLT study, 219 insomniacs and 96 normal sleepers met selection criteria for the present study. Chronic insomnia was defined based on standard diagnostic criteria with symptoms lasting ≥6 months. We used the median mean MSLT value (i.e., >14 min) and the 75th percentile of mean MSLT value (i.e., >17 min) to define hyperarousal. Hypertension was defined based either on blood pressure measures or on diagnosis treatment by a physician. Logistic regression was used to assess the independent association of insomnia with hypertension compared with normal sleep and to
Data from the Internet: New methods for automated insomnia interventions H. Lord 1, F. Thorndike 1, C. Morin 2, L. Gonder-Frederick 1, M. Quigg 1, K. Ingersol 1, L. Ritterband 1 1 University of Virginia, USA 2 Laval University, Canada
Introduction: The utilization of Internet interventions for insomnia is quickly increasing for individuals with insomnia. This new approach results in Big Data issues requiring new methods of data management and analysis in order to process and summarize data. We propose a methodology for managing large and automated influxes of sleep-related data. Materials and methods: Three hundred three participants provided data as part of a national RCT to evaluate the effectiveness of SHUTi, an Internet intervention for insomnia based on cognitive behavioral therapy for insomnia (CBT-I). Subjects were randomly assigned to SHUTi or a Patient Education website. All provided daily sleep dairies as well as other data, including demographics, psychological measures, quality of life, and website usage. The collection of data was completely automated and resulted in larger quantities of data than in previous studies of CBT-I. Before data could be analyzed, multiple processes of data integration and transformation were necessary, including joining data, creating structured fields, and checking for data accuracy, consistency, corruption, and completeness on a large scale. Nineteen thousand seven hundred sixtyseven diary entries were examined for impossible, extreme and inconsistent values. Algorithms were created for selecting diary entries to represent sleep outcomes at four time points. Diaries were then aggregated for final data quality checks and for analysis. Results: Automated data collection resulted in data quality challenges while also enabling considerable improvements. Challenges
Results: A majority of the sample was White (81.1%), married (53.7%), and had an average of 16 years of education (SD = 3.2). Overall, 60.4% reported a past or current medical condition, 87.4% reported visiting a health professional in the past year (Mage = 4.1, SD = 6.2; range 0–52), 66.1% reported a history of hospitalization (Mage = 1.5, SD = 1.6; range 0–7), and 37.8%, 18.6%, and 4.3% reported the use of prescription, over-the counter medication, and natural product use, respectively. After controlling for age, sex, body mass index, medication use, and comorbidity, no significant differences were found for past or current medical conditions or history of hospitalizations as a function of objective sleep duration. However, compared with individuals with insomnia with long sleep duration, individuals with insomnia with short sleep duration had an odds ratio of 1.20 (95% CI 1.20, 9.14) of visiting a health professional in the past year. Additionally, insomnia with short sleep duration was associated with an odds ratio of 1.06 (95% CI 1.15, 7.22) of past or current use of prescription medication as compared with their counterparts, all while controlling for multiple covariates. No significant differences were observed for over-the-counter medication or natural product use between groups. Conclusion: Results suggest that insomnia with objective short sleep duration is associated with more physician visits and medication use compared with insomnia with long sleep duration. Knowledge of such health maintenance behaviors is important to identify at-risk individuals, as this may help develop more targeted prevention and intervention strategies for insomnia. Acknowledgements: This study was funded by the National Institute of Mental Health Grant (R01MH60413) and by the Canadian Institutes of Health Research (B0512201).
S11
examine the association of insomnia with hypertension based on different levels of hyperarousal measured by MSLT values. Results: After controlling for age, sex, BMI, AHI, diabetes, smoking, alcohol and caffeine use, insomnia was associated with a nearly significant increased odds for hypertension compared with normal sleepers (OR = 2.17, 95% CI = 0.98–4.84). Insomnia combined with MSLT > 14 min increased the odds of hypertension by 300% (OR = 3.27, 95% CI = 1.20–8.96) whereas insomnia combined with MSLT > 17 min increased even further the odds of hypertension by 400% (OR = 4.33, 95% CI = 1.48–12.68) compared with normal sleepers with MSLT ≤ 14 min. In contrast, in insomniacs with MSLT ≤ 14 min, the odds of hypertension were not significantly increased compared with normal sleepers with MSLT ≤ 14 min (OR = 1.17, 95% CI = 0.40– 3.43). The odds of hypertension of normal sleepers with MSLT > 14 min were not significantly increased compared with normal sleepers with MSLT ≤ 14 min (OR = 0.87, 95% CI = 0.19– 3.90). Even after excluding the participants who were using antihypertensive medication, the linear relationship between blood pressure and MSLT values were still significant (systolic BP, p-linear = 0.04, diastolic BP, p-linear = 0.003). Conclusion: Insomnia associated with physiological hyperarousal is associated with a significant risk of hypertension. Long MSLT values may be a reliable index of the physiological hyperarousal and biological severity of chronic insomnia. Acknowledgements: The work was performed at the Sleep Medicine Center at the West China Hospital, Sichuan University, and our technical staff (Fei Lei and Lina Du) is especially commended for their efforts. http://dx.doi.org/10.1016/j.sleep.2015.02.025
http://dx.doi.org/10.1016/j.sleep.2015.02.024
Insomnia with physiological hyperarousal is associated with hypertension Y. Li 1,2, A. Vgontzas 2, J. Fernandez-Mendoza 2, E. Bixler 2, Y. Sun 1, J. Zhou 1, R. Ren 1, X. Tang 1 1 West China Hospital, Sichuan University, China 2 Pennsylvania State University College of Medicine, USA
Introduction: Insomnia with objective short sleep duration is associated with a higher risk of hypertension and it has been speculated that the underlying mechanism is physiological hyperarousal. In this study, we tested whether insomnia with physiological hyperarousal measured by Multiple Sleep Latency Test (MSLT), is associated with increased risk of hypertension. Materials and methods: During the January 2010 to July 2014 recruitment period, 844 consecutive insomniacs and 117 normal sleepers were studied in the sleep laboratory. We excluded insomniacs and normal sleepers who had (1) a current major psychiatric condition; (2) current and past use of hypnotics, anxiolytics, antidepressants, and any other psychotropic medication; (3) evidence of sleep disordered breathing disorder, (4) evidence of sleeprelated movement disorder and (5) evidence of a hypersomnia disorder. After an overnight polysomnography followed by a standard MSLT study, 219 insomniacs and 96 normal sleepers met selection criteria for the present study. Chronic insomnia was defined based on standard diagnostic criteria with symptoms lasting ≥6 months. We used the median mean MSLT value (i.e., >14 min) and the 75th percentile of mean MSLT value (i.e., >17 min) to define hyperarousal. Hypertension was defined based either on blood pressure measures or on diagnosis treatment by a physician. Logistic regression was used to assess the independent association of insomnia with hypertension compared with normal sleep and to
Data from the Internet: New methods for automated insomnia interventions H. Lord 1, F. Thorndike 1, C. Morin 2, L. Gonder-Frederick 1, M. Quigg 1, K. Ingersol 1, L. Ritterband 1 1 University of Virginia, USA 2 Laval University, Canada
Introduction: The utilization of Internet interventions for insomnia is quickly increasing for individuals with insomnia. This new approach results in Big Data issues requiring new methods of data management and analysis in order to process and summarize data. We propose a methodology for managing large and automated influxes of sleep-related data. Materials and methods: Three hundred three participants provided data as part of a national RCT to evaluate the effectiveness of SHUTi, an Internet intervention for insomnia based on cognitive behavioral therapy for insomnia (CBT-I). Subjects were randomly assigned to SHUTi or a Patient Education website. All provided daily sleep dairies as well as other data, including demographics, psychological measures, quality of life, and website usage. The collection of data was completely automated and resulted in larger quantities of data than in previous studies of CBT-I. Before data could be analyzed, multiple processes of data integration and transformation were necessary, including joining data, creating structured fields, and checking for data accuracy, consistency, corruption, and completeness on a large scale. Nineteen thousand seven hundred sixtyseven diary entries were examined for impossible, extreme and inconsistent values. Algorithms were created for selecting diary entries to represent sleep outcomes at four time points. Diaries were then aggregated for final data quality checks and for analysis. Results: Automated data collection resulted in data quality challenges while also enabling considerable improvements. Challenges