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| Name | Class |
|---|---|
| University of Eastern Finland | OTHER |
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The goal of this clinical trial is to learn about insomnia treatment among primary care patients with chronic insomnia.
The main question it aims to answer is:
• Does Sleep School (a therapy for insomnia) work well to decrease harm of insomnia? Participants will attend a group therapy intervention once a week for six weeks.
Researchers will compare Sleep School to treatment as usual (short counselling by an educated nurse) to see if the Sleep School works better than treatment as usual in decreasing the harm of insomnia.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Sleep School | Experimental | Participants attend the Sleep School once a week for six weeks. |
|
| Treatment as usual | Active Comparator | Participants receive short counselling about insomnia at the enrollment visit. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Sleep School | Behavioral | Sleep school is a structured method for the treatment of insomnia, which is based on cognitive behavioral therapy for insomnia (CBT-I). The central element of the method is a workbook for the patients. The essential parts of the method are strengthening the patient's self-efficacy, introducing the therapeutic exercises, and supporting the continued use of the exercises. The main themes of the Sleep school are information about sleep and the factors affecting it, behavioural components of CBT-I, like restricting the time spent in bed, cognitive components of CBT-I, like constructive worrying -exercise, and the exercises that aim to calm down mind and the autonomic nervous system. The Sleep School is held by an educated nurse. |
| Measure | Description | Time Frame |
|---|---|---|
| Mean Change from Baseline in Insomnia Severity Index (ISI) score at 8 weeks | The ISI is a validated self-report tool for assessing the severity, and impact of current insomnia symptoms. It consists of 7 Likert-scale questions with a total score ranging from 0 to 28 (with higher scores indicating more severe insomnia). Change = Week 8 score - Baseline score. | Baseline and Week 8 |
| Measure | Description | Time Frame |
|---|---|---|
| Mean change from Baseline in Patient Health Questionnaire 9 (PHQ-9) at 8 weeks | PHQ-9 is a validated self-administered instrument assessing each of the 9 Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV criteria for depression as 0 (not at all) to 3 (nearly every day), and the severity of depression. Possible scores range from 0 to 27. Change = Week 8 score - Baseline score. | Baseline and Week 8 |
| Measure | Description | Time Frame |
|---|---|---|
| Sleep Duration at Baseline | Information about sleep duration is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep duration. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep duration. |
Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Elina Bergman, PhD | Contact | 0 | elkaro@utu.fi | |
| Ansa Rantanen, PhD | Contact | atsipp@utu.fi |
| Name | Affiliation | Role |
|---|---|---|
| Päivi Korhonen, PhD | University of Turku | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Turku | Recruiting | Turku | 20014 | Finland |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28875581 | Background | Riemann D, Baglioni C, Bassetti C, Bjorvatn B, Dolenc Groselj L, Ellis JG, Espie CA, Garcia-Borreguero D, Gjerstad M, Goncalves M, Hertenstein E, Jansson-Frojmark M, Jennum PJ, Leger D, Nissen C, Parrino L, Paunio T, Pevernagie D, Verbraecken J, Weess HG, Wichniak A, Zavalko I, Arnardottir ES, Deleanu OC, Strazisar B, Zoetmulder M, Spiegelhalder K. European guideline for the diagnosis and treatment of insomnia. J Sleep Res. 2017 Dec;26(6):675-700. doi: 10.1111/jsr.12594. Epub 2017 Sep 5. | |
| 19962922 |
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Data will be available from the study principal investigator upon reasonable request.
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| ID | Term |
|---|---|
| D007319 | Sleep Initiation and Maintenance Disorders |
| D003863 | Depression |
| ID | Term |
|---|---|
| D020919 | Sleep Disorders, Intrinsic |
| D020920 | Dyssomnias |
| D012893 | Sleep Wake Disorders |
| D009422 | Nervous System Diseases |
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| ID | Term |
|---|---|
| D013812 | Therapeutics |
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|
| Treatment as usual | Behavioral | Oral and written information about improving sleep habits given by an educated nurse. |
|
| Mean change from Baseline in EUROHIS Quality of Life 8-item Index at 8 weeks | EUROHIS Quality of Life 8-item Index is a validated instrument for the assessment of general quality of life. There are altogether eight questions about the general, physical, psychological, social, and environmental aspects of quality of life. Every question is scored from 1 (very poor) to 5 (very good). All scores can be added together and divided by 8 (the sum of the questions) to obtain the EUROHIS-QOL mean score. Change = Week 8 score - Baseline score. | Baseline and Week 8 |
| Mean change from Baseline in Work Ability Score (WAS) at 8 weeks | The WAS is the first item of the Work Ability Index (WAI), a validated instrument for the assessment of work ability. WAS is a single question "What is your current work ability compared to your lifetime best?" It has a 0-10 response scale, where 0 stands for "completely unable to work" and 10 stands for "work ability at its best." The WAS has been shown to have a strong association with the WAI and is reliable in evaluating work ability. Change = Week 8 score - Baseline score. | Baseline and Week 8 |
| Baseline |
| Sleep Stages at Baseline | Information about sleep stages is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep stages. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep stages. | Baseline |
| Sleep Quality at Baseline | Information about objective sleep quality is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep quality. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep quality. | Baseline |
| Sleep Duration at 8 weeks | Information about sleep duration is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep duration. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep duration. | Week 8 |
| Sleep Stages at 8 weeks | Information about sleep stages is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep stages. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep stages. | Week 8 |
| Sleep Quality at 8 weeks | Information about objective sleep quality is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep quality. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep quality. | Week 8 |
| Mean Change from 8 weeks in Insomnia Severity Index (ISI) score at 16 weeks | The ISI is a validated self-report tool for assessing the severity, and impact of current insomnia symptoms. It consists of 7 Likert-scale questions with a total score ranging from 0 to 28 (with higher scores indicating more severe insomnia). Change = Week 16 score - Week 8 score. | Week 8 and Week 16 |
| Mean change from Baseline in Patient Health Questionnaire 9 (PHQ-9) at 16 weeks | PHQ-9 is a validated self-administered instrument assessing each of the 9 DSM-IV criteria for depression as 0 (not at all) to 3 (nearly every day), and the severity of depression. Possible scores range from 0 to 27. Change = Week 16 score - Baseline score. | Baseline and Week 16 |
| Mean change from Baseline in EUROHIS Quality of Life 8-item Index at 16 weeks | EUROHIS Quality of Life 8-item Index is a validated instrument for the assessment of general quality of life. There are altogether eight questions about the general, physical, psychological, social, and environmental aspects of quality of life. Every question is scored from 1 (very poor) to 5 (very good). All scores can be added together and divided by 8 (the sum of the questions) to obtain the EUROHIS-QOL mean score. Change = Week 16 score - Baseline score. | Baseline and Week 16 |
| Mean change from Baseline in Work Ability Score (WAS) at 16 weeks | The WAS is the first item of the Work Ability Index (WAI), a validated instrument for the assessment of work ability. WAS is a single question "What is your current work ability compared to your lifetime best?" It has a 0-10 response scale, where 0 stands for "completely unable to work" and 10 stands for "work ability at its best." The WAS has been shown to have a strong association with the WAI and is reliable in evaluating work ability. Change = Week 16 score - Baseline score. | Baseline and Week 16 |
| Sleep Duration at 16 weeks | Information about sleep duration is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep duration. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep duration. | Week 16 |
| Sleep Stages at 16 weeks | Information about sleep stages is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep stages. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep stages. | Week 16 |
| Sleep Quality at 16 weeks | Information about objective sleep quality is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep quality. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep quality. | Week 16 |
| Mean Change from 8 weeks in Insomnia Severity Index (ISI) score at 26 weeks | The ISI is a validated self-report tool for assessing the severity, and impact of current insomnia symptoms. It consists of 7 Likert-scale questions with a total score ranging from 0 to 28 (with higher scores indicating more severe insomnia). Change = Week 26 score - Week 8 score. | Week 8 and Week 26 |
| Mean change from Baseline in Patient Health Questionnaire 9 (PHQ-9) at 26 weeks | PHQ-9 is a validated self-administered instrument assessing each of the 9 DSM-IV criteria for depression as 0 (not at all) to 3 (nearly every day), and the severity of depression. Possible scores range from 0 to 27. Change = Week 26 score - Baseline. | Baseline and Week 26 |
| Mean change from Baseline in EUROHIS Quality of Life 8-item Index at 26 weeks | EUROHIS Quality of Life 8-item Index is a validated instrument for the assessment of general quality of life. There are altogether eight questions about the general, physical, psychological, social, and environmental aspects of quality of life. Every question is scored from 1 (very poor) to 5 (very good). All scores can be added together and divided by 8 (the sum of the questions) to obtain the EUROHIS-QOL mean score. Change = Week 26 score - Baseline score. | Baseline and Week 26 |
| Mean change from Baseline in Work Ability Score (WAS) at 26 weeks | The WAS is the first item of the Work Ability Index (WAI), a validated instrument for the assessment of work ability. WAS is a single question "What is your current work ability compared to your lifetime best?" It has a 0-10 response scale, where 0 stands for "completely unable to work" and 10 stands for "work ability at its best." The WAS has been shown to have a strong association with the WAI and is reliable in evaluating work ability. Change = Week 26 score - Baseline score. | Baseline and Week 26 |
| Sleep Duration at 26 weeks | Information about sleep duration is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep duration. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep duration. | Week 26 |
| Sleep Stages at 26 weeks | Information about sleep stages is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep stages. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep stages. | Week 26 |
| Sleep Quality at 26 weeks | Information about objective sleep quality is collected with smart watches using a validated method. The method is based on assessment of biological signals with artificial intelligence algorithms. With a smart watch, the participant's heart rate, movements, oxygen saturation, and photoplethysmography signal is measured from the wrist. The photoplethysmography signal measures optically the changes in blood volume, which enables the assessment of the function of the sympathetic and parasympathetic nervous system. Albeit having different units, our previously developed deep learning applications utilize information from heart rate, movements, oxygen saturation, and photoplethysmography signals to assess sleep quality. Thus, regardless of the units of measured signals, information from all of these signals are combined by automatic algorithms to report one single value: sleep quality. | Week 26 |
| Mean Change from 8 weeks in Insomnia Severity Index (ISI) score at 12 months | The ISI is a validated self-report tool for assessing the severity, and impact of current insomnia symptoms. It consists of 7 Likert-scale questions with a total score ranging from 0 to 28 (with higher scores indicating more severe insomnia). Change = 12 Month score - Week 8 score. | Week 8 and 12 Months |
| Mean change from Baseline in Patient Health Questionnaire 9 (PHQ-9) at 12 months | PHQ-9 is a validated self-administered instrument assessing each of the 9 DSM-IV criteria for depression as 0 (not at all) to 3 (nearly every day), and the severity of depression. Possible scores range from 0 to 27. Change = 12 Month score - Baseline score. | Baseline and 12 Months |
| Mean change from Baseline in EUROHIS Quality of Life 8-item Index at 12 months | EUROHIS Quality of Life 8-item Index is a validated instrument for the assessment of general quality of life. There are altogether eight questions about the general, physical, psychological, social, and environmental aspects of quality of life. Every question is scored from 1 (very poor) to 5 (very good). All scores can be added together and divided by 8 (the sum of the questions) to obtain the EUROHIS-QOL mean score. Change = 12 Month score - Baseline score. | Baseline and 12 Months |
| Mean change from Baseline in Work Ability Score (WAS) at 12 months | The WAS is the first item of the Work Ability Index (WAI), a validated instrument for the assessment of work ability. WAS is a single question "What is your current work ability compared to your lifetime best?" It has a 0-10 response scale, where 0 stands for "completely unable to work" and 10 stands for "work ability at its best." The WAS has been shown to have a strong association with the WAI and is reliable in evaluating work ability. Change = 12 Month score - Baseline score. | Baseline and 12 Months |
| Background |
| Kyle SD, Morgan K, Espie CA. Insomnia and health-related quality of life. Sleep Med Rev. 2010 Feb;14(1):69-82. doi: 10.1016/j.smrv.2009.07.004. Epub 2009 Dec 4. |
| 25142569 | Background | Lallukka T, Kaikkonen R, Harkanen T, Kronholm E, Partonen T, Rahkonen O, Koskinen S. Sleep and sickness absence: a nationally representative register-based follow-up study. Sleep. 2014 Sep 1;37(9):1413-25. doi: 10.5665/sleep.3986. |
| 18548833 | Background | Overland S, Glozier N, Sivertsen B, Stewart R, Neckelmann D, Krokstad S, Mykletun A. A comparison of insomnia and depression as predictors of disability pension: the HUNT Study. Sleep. 2008 Jun;31(6):875-80. doi: 10.1093/sleep/31.6.875. |
| 18753000 | Background | Daley M, Morin CM, LeBlanc M, Gregoire JP, Savard J, Baillargeon L. Insomnia and its relationship to health-care utilization, work absenteeism, productivity and accidents. Sleep Med. 2009 Apr;10(4):427-38. doi: 10.1016/j.sleep.2008.04.005. Epub 2008 Aug 26. |
| 28153671 | Background | Javaheri S, Redline S. Insomnia and Risk of Cardiovascular Disease. Chest. 2017 Aug;152(2):435-444. doi: 10.1016/j.chest.2017.01.026. Epub 2017 Jan 30. |
| 33640704 | Background | Johnson KA, Gordon CJ, Chapman JL, Hoyos CM, Marshall NS, Miller CB, Grunstein RR. The association of insomnia disorder characterised by objective short sleep duration with hypertension, diabetes and body mass index: A systematic review and meta-analysis. Sleep Med Rev. 2021 Oct;59:101456. doi: 10.1016/j.smrv.2021.101456. Epub 2021 Jan 23. |
| 21300408 | Background | Baglioni C, Battagliese G, Feige B, Spiegelhalder K, Nissen C, Voderholzer U, Lombardo C, Riemann D. Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. J Affect Disord. 2011 Dec;135(1-3):10-9. doi: 10.1016/j.jad.2011.01.011. Epub 2011 Feb 5. |
| 33309524 | Background | Ong JC, Crawford MR, Wallace DM. Sleep Apnea and Insomnia: Emerging Evidence for Effective Clinical Management. Chest. 2021 May;159(5):2020-2028. doi: 10.1016/j.chest.2020.12.002. Epub 2020 Dec 10. |
| 34501836 | Background | Ragnoli B, Pochetti P, Raie A, Malerba M. Comorbid Insomnia and Obstructive Sleep Apnea (COMISA): Current Concepts of Patient Management. Int J Environ Res Public Health. 2021 Sep 1;18(17):9248. doi: 10.3390/ijerph18179248. |
| 28619248 | Background | Ballesio A, Aquino MRJV, Feige B, Johann AF, Kyle SD, Spiegelhalder K, Lombardo C, Rucker G, Riemann D, Baglioni C. The effectiveness of behavioural and cognitive behavioural therapies for insomnia on depressive and fatigue symptoms: A systematic review and network meta-analysis. Sleep Med Rev. 2018 Feb;37:114-129. doi: 10.1016/j.smrv.2017.01.006. Epub 2017 Feb 7. |
| 35653951 | Background | Alimoradi Z, Jafari E, Brostrom A, Ohayon MM, Lin CY, Griffiths MD, Blom K, Jernelov S, Kaldo V, Pakpour AH. Effects of cognitive behavioral therapy for insomnia (CBT-I) on quality of life: A systematic review and meta-analysis. Sleep Med Rev. 2022 Aug;64:101646. doi: 10.1016/j.smrv.2022.101646. Epub 2022 May 10. |
| 28392168 | Background | van Straten A, van der Zweerde T, Kleiboer A, Cuijpers P, Morin CM, Lancee J. Cognitive and behavioral therapies in the treatment of insomnia: A meta-analysis. Sleep Med Rev. 2018 Apr;38:3-16. doi: 10.1016/j.smrv.2017.02.001. Epub 2017 Feb 9. |
| 31491656 | Background | van der Zweerde T, Bisdounis L, Kyle SD, Lancee J, van Straten A. Cognitive behavioral therapy for insomnia: A meta-analysis of long-term effects in controlled studies. Sleep Med Rev. 2019 Dec;48:101208. doi: 10.1016/j.smrv.2019.08.002. Epub 2019 Aug 12. |
| D001523 |
| Mental Disorders |
| D001526 | Behavioral Symptoms |
| D001519 | Behavior |