Reducing Coronary Heart Disease Risk Through Treatment of Insomnia Using Web-Based Cognitive Behavioral Therapy for Insomnia: A Methodological Approach.

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Title: Reducing Coronary Heart Disease Risk Through Treatment of Insomnia Using Web-Based Cognitive Behavioral Therapy for Insomnia: A Methodological Approach.
Authors: Javaheri, Sogol (AUTHOR), Reid, Michelle (AUTHOR), Drerup, Michelle (AUTHOR), Mehra, Reena (AUTHOR), Redline, Susan (AUTHOR)
Source: Behavioral Sleep Medicine. May/Jun2020, Vol. 18 Issue 3, p334-344. 11p. 2 Diagrams, 3 Charts.
Subjects: Cognitive therapy, Coronary disease, Heart diseases, Insomnia, Acute coronary syndrome
Abstract: Objective: Observational data demonstrate increased risk of recurrent acute coronary syndrome in patients with comorbid insomnia. We conducted a pragmatic randomized controlled pilot study to address knowledge gaps and inform future large-scale randomized trials to test the impact of Web-based cognitive behavioral therapy for insomnia (wCBT-I) on coronary heart disease (CHD) outcomes. Participants: Thirty-five adults recruited from Brigham and Women's and Cleveland Clinic Hospitals with insomnia, defined by Insomnia Severity Index (ISI) score ≥ 10 and symptoms of at least 3 months, and comorbid CHD identified from medical records. Methods: We randomized 34 patients to either general sleep education coupled with wCBT-I or general sleep education alone followed by an opportunity for treatment after the study (a wait-list control) to evaluate feasibility and uptake of insomnia treatment in patients with heart disease. Participants completed the ISI at baseline and 6 weeks to assess insomnia severity. Results: Twenty-nine adults completed the trial, yielding an 85% retention rate, and adherence rate in the treatment arm was 80%. Mean age was 71.6 ± 9.5 years, 75% were male, and mean body mass index (BMI) was 29 ± 4.5 kg/m2. Baseline ISI scores were 15.6. There was a 6.2 ± 5.3 point reduction in ISI scores in the intervention arm and a 3.3 ± 5.1 reduction in the control arm (p value 0.1). Conclusion: Web-based CBT-I intervention was feasible in an older sample with prevalent CHD and resulted in clinically meaningful improvement in insomnia severity, though statistical significance was limited by lack of power. [ABSTRACT FROM AUTHOR]
Copyright of Behavioral Sleep Medicine is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Reducing Coronary Heart Disease Risk Through Treatment of Insomnia Using Web-Based Cognitive Behavioral Therapy for Insomnia: A Methodological Approach.
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  Data: <searchLink fieldCode="AR" term="%22Javaheri%2C+Sogol%22">Javaheri, Sogol</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Reid%2C+Michelle%22">Reid, Michelle</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Drerup%2C+Michelle%22">Drerup, Michelle</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mehra%2C+Reena%22">Mehra, Reena</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Redline%2C+Susan%22">Redline, Susan</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Behavioral+Sleep+Medicine%22">Behavioral Sleep Medicine</searchLink>. May/Jun2020, Vol. 18 Issue 3, p334-344. 11p. 2 Diagrams, 3 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Cognitive+therapy%22">Cognitive therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Coronary+disease%22">Coronary disease</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+diseases%22">Heart diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Insomnia%22">Insomnia</searchLink><br /><searchLink fieldCode="DE" term="%22Acute+coronary+syndrome%22">Acute coronary syndrome</searchLink>
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  Label: Abstract
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  Data: Objective: Observational data demonstrate increased risk of recurrent acute coronary syndrome in patients with comorbid insomnia. We conducted a pragmatic randomized controlled pilot study to address knowledge gaps and inform future large-scale randomized trials to test the impact of Web-based cognitive behavioral therapy for insomnia (wCBT-I) on coronary heart disease (CHD) outcomes. Participants: Thirty-five adults recruited from Brigham and Women's and Cleveland Clinic Hospitals with insomnia, defined by Insomnia Severity Index (ISI) score ≥ 10 and symptoms of at least 3 months, and comorbid CHD identified from medical records. Methods: We randomized 34 patients to either general sleep education coupled with wCBT-I or general sleep education alone followed by an opportunity for treatment after the study (a wait-list control) to evaluate feasibility and uptake of insomnia treatment in patients with heart disease. Participants completed the ISI at baseline and 6 weeks to assess insomnia severity. Results: Twenty-nine adults completed the trial, yielding an 85% retention rate, and adherence rate in the treatment arm was 80%. Mean age was 71.6 ± 9.5 years, 75% were male, and mean body mass index (BMI) was 29 ± 4.5 kg/m2. Baseline ISI scores were 15.6. There was a 6.2 ± 5.3 point reduction in ISI scores in the intervention arm and a 3.3 ± 5.1 reduction in the control arm (p value 0.1). Conclusion: Web-based CBT-I intervention was feasible in an older sample with prevalent CHD and resulted in clinically meaningful improvement in insomnia severity, though statistical significance was limited by lack of power. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behavioral Sleep Medicine is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/15402002.2019.1584896
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        Text: English
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        Type: general
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              Text: May/Jun2020
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