ClinicalTrials.gg
RecruitingNCT06149273TIPUpdated May 1, 2026

Treatment of Insomnia in Primary Care Study

An interventional study of Sleep School and Treatment as usual in Insomnia Chronic, sponsored by University of Turku. Recruiting at 1 site in Finland. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-05-01.

Sponsored by University of Turku · Not applicable, Interventional, and Treatment

From the registry’s dates

  • Started Dec 2023; still recruiting 2 years 11 months later.
Phase
Not applicable
Study type
Interventional
Enrollment
250
Allocation
Randomized
Ages
18 Years and older
Sex
All
01

Study summary

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.

02

Conditions studied

  • Insomnia Chronic

Keywords

  • Cognitive behavioral therapy for insomnia
  • CBT-I
  • Primary care
  • Insomnia severity index
  • Quality of life
  • Depressive symptoms
03

In context

Sleep Initiation and Maintenance Disorders

2,722 studies on the registry are indexed under Sleep Initiation and Maintenance Disorders; 726 are open to participants now.

This study's planned enrollment of 250 is above the median of 68 across 2,350 interventional studies indexed under Sleep Initiation and Maintenance Disorders.

Browse Sleep Initiation and Maintenance Disorders studies →

Lead sponsor

University of Turku is the lead sponsor of 122 studies on the registry; 16 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • Insomnia severity index (ISI) score at least 8
  • insomnia symptoms present at least for 3 months

Exclusion criteria

Exclusion Criteria:

  • diagnosed dementia based on medical records
  • acute suicidality
  • acute psychotic symptoms
05

Study design

Phase
Not applicable
Primary purpose
Treatment
Allocation
Randomized
Intervention model
Parallel assignment
Masking
None (open label)
Enrollment
250 participants (estimated)

Study arms

  • Experimental
    Sleep School

    Participants attend the Sleep School once a week for six weeks.

    Behavioral: Sleep School

  • Active comparator
    Treatment as usual

    Participants receive short counselling about insomnia at the enrollment visit.

    Behavioral: Treatment as usual

Interventions

  • BehavioralSleep School

    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.

  • BehavioralTreatment as usual

    Oral and written information about improving sleep habits given by an educated nurse.

06

What researchers measure

Primary outcomes

  1. 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.

    Time frame: Baseline and Week 8

Secondary outcomes

  1. 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.

    Time frame: Baseline and Week 8

  2. 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.

    Time frame: Baseline and Week 8

  3. 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.

    Time frame: Baseline and Week 8

Other outcomes

  1. 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.

    Time frame: Baseline

  2. 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.

    Time frame: Baseline

  3. 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.

    Time frame: Baseline

  4. 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.

    Time frame: Week 8

  5. 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.

    Time frame: Week 8

  6. 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.

    Time frame: Week 8

  7. 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.

    Time frame: Week 8 and Week 16

  8. 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.

    Time frame: Baseline and Week 16

  9. 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.

    Time frame: Baseline and Week 16

  10. 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.

    Time frame: Baseline and Week 16

  11. 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.

    Time frame: Week 16

  12. 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.

    Time frame: Week 16

  13. 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.

    Time frame: Week 16

  14. 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.

    Time frame: Week 8 and Week 26

  15. 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.

    Time frame: Baseline and Week 26

  16. 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.

    Time frame: Baseline and Week 26

  17. 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.

    Time frame: Baseline and Week 26

  18. 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.

    Time frame: Week 26

  19. 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.

    Time frame: Week 26

  20. 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.

    Time frame: Week 26

  21. 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.

    Time frame: Week 8 and 12 Months

  22. 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.

    Time frame: Baseline and 12 Months

  23. 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.

    Time frame: Baseline and 12 Months

  24. 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.

    Time frame: Baseline and 12 Months

07

Study locations

1 of 1 sites recruiting
08

References and documents

Publications

  • 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. PubMed 28875581 ↗
  • 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. PubMed 19962922 ↗
  • 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. PubMed 25142569 ↗
  • 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. PubMed 18548833 ↗
  • 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. PubMed 18753000 ↗
  • 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. PubMed 28153671 ↗
  • 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. PubMed 33640704 ↗
  • 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. PubMed 21300408 ↗
  • 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. PubMed 33309524 ↗
  • 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. PubMed 34501836 ↗
  • 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. PubMed 28619248 ↗
  • 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. PubMed 35653951 ↗
  • 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. PubMed 28392168 ↗
  • 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. PubMed 31491656 ↗

Individual participant data

Plan to share: Yes — Data will be available from the study principal investigator upon reasonable request.

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reachedSimilar studies: half by the dot; the band is the middle halfHalf of similar studies reached it by the dot; the band is the middle half, from a quarter to three quarters

Started Dec 2023 (actual). Primary completion is estimated for Dec 2028, 60 months after the start.

Of 1,207 non-industry interventional studies with no phase under Sleep Initiation and Maintenance Disorders, half reached primary completion within 15 months of starting (the median), counting those still running; a quarter within 6 and three quarters within 35. This study's planned 60 months is above that.

Counting every sponsor, half of 1,285 studies reached it within 15 months; a quarter within 6 and three quarters within 34.

How these figures are worked out, and which studies are left out of them.

Registered
Jun 27, 2023
First posted
Nov 28, 2023
StartActual
Dec 2, 2023
Primary completionEst.
Dec 2028
CompletionEst.
Dec 2029
Last update
May 1, 2026
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on May 1, 2026, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT06149273
Collaborators
University of Eastern Finland
Responsible party
Päivi Korhonen (Professor of General Practice, University of Turku) — Principal investigator
First posted
Nov 28, 2023
Start date
Dec 2, 2023
Primary completion
Dec 2028 (estimated)
Completion
Dec 2029 (estimated)
Last update
May 1, 2026

Study contacts

Elina Bergman, PhD
Contact
elkaro@utu.fi
0
Ansa Rantanen, PhD
Contact
atsipp@utu.fi
Päivi Korhonen, PhD
principal investigator · University of Turku

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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