TY - JOUR

T1 - Effectiveness of an online recovery training for employees exposed to blurred boundaries between work and non-work

T2 - Bayesian analysis of a randomised controlled trial

AU - Brückner, Hanna

AU - Wallot, Sebastian

AU - Horvath, Hanne

AU - Ebert, David Daniel

AU - Lehr, Dirk

N1 - Funding Information: We would like to thank Michael Lee from the University of California, Irvine, for his helpful feedback on the Bayesian analysis. The study was funded by the European Union, project number: EFRE: CCI 2007DE161PR001. Funding Information: The study was funded by the European Union, project number: EFRE: CCI 2007DE161PR001. Publisher Copyright: © Author(s) (or their employer(s)) 2024.

PY - 2024/4/19

Y1 - 2024/4/19

N2 - Background Blurred work–non-work boundaries can have negative effects on mental health, including sleep. Objectives In a randomised control trial, we aimed to assess the effectiveness of an online recovery training programme designed to improve symptoms of insomnia in a working population exposed to blurred boundaries. Methods 128 participants with severe insomnia symptoms (Insomnia Severity Index ≥15) and working under blurred work and non-work conditions (segmentation supplies <2.25) were randomly assigned to either the recovery intervention or a waitlist control group (WLC). The primary outcome was insomnia severity, assessed at baseline, after 2 months (T2) and 6 months (T3). Findings A greater reduction in insomnia was observed in the intervention compared with the WLC group at both T2 (d=1.51; 95% CI=1.12 o 1.91) and T3 (d=1.63; 95% CI=1.23 to 2.03]. This was shown by Bayesian analysis of covariance (ANCOVA), whereby the ANCOVA model yielded the highest Bayes factor (BF10=3.23×e60] and a 99.99% probability. Likewise, frequentist analysis revealed significantly reduced insomnia at both T2 and T3. Beneficial effects were found for secondary outcomes including depression, work-related rumination, and mental detachment from work. Study attrition was 16% at T2 and 44% at T3. Conclusions The recovery training was effective in reducing insomnia symptoms, work related and general indicators of mental health in employees exposed to blurred boundaries, both at T2 and T3. Clinical implications In addition to demonstrating the intervention’s effectiveness, this study exemplifies the utilisation of the Bayesian approach in a clinical context and shows its potential to empower recipients of interventional research by offering insights into result probabilities, enabling them to draw informed conclusions.

AB - Background Blurred work–non-work boundaries can have negative effects on mental health, including sleep. Objectives In a randomised control trial, we aimed to assess the effectiveness of an online recovery training programme designed to improve symptoms of insomnia in a working population exposed to blurred boundaries. Methods 128 participants with severe insomnia symptoms (Insomnia Severity Index ≥15) and working under blurred work and non-work conditions (segmentation supplies <2.25) were randomly assigned to either the recovery intervention or a waitlist control group (WLC). The primary outcome was insomnia severity, assessed at baseline, after 2 months (T2) and 6 months (T3). Findings A greater reduction in insomnia was observed in the intervention compared with the WLC group at both T2 (d=1.51; 95% CI=1.12 o 1.91) and T3 (d=1.63; 95% CI=1.23 to 2.03]. This was shown by Bayesian analysis of covariance (ANCOVA), whereby the ANCOVA model yielded the highest Bayes factor (BF10=3.23×e60] and a 99.99% probability. Likewise, frequentist analysis revealed significantly reduced insomnia at both T2 and T3. Beneficial effects were found for secondary outcomes including depression, work-related rumination, and mental detachment from work. Study attrition was 16% at T2 and 44% at T3. Conclusions The recovery training was effective in reducing insomnia symptoms, work related and general indicators of mental health in employees exposed to blurred boundaries, both at T2 and T3. Clinical implications In addition to demonstrating the intervention’s effectiveness, this study exemplifies the utilisation of the Bayesian approach in a clinical context and shows its potential to empower recipients of interventional research by offering insights into result probabilities, enabling them to draw informed conclusions.

KW - Data Interpretation, Statistical

KW - Depression & mood disorders

KW - Sleep

KW - Psychology

UR - http://www.scopus.com/inward/record.url?scp=85191104200&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/c1cfd58f-8b77-3033-88ae-3941460d0ba4/

U2 - 10.1136/bmjment-2024-301016

DO - 10.1136/bmjment-2024-301016

M3 - Journal articles

C2 - 38642919

VL - 27

JO - BMJ mental health

JF - BMJ mental health

SN - 1362-0347

IS - 1

M1 - e301016

ER -