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1.
Somnologie (Berl) ; 25(1): 29-37, 2021.
Article in English | MEDLINE | ID: covidwho-1111349

ABSTRACT

Background: Insomnia is a widespread disease in adults and has a high prevalence rate. As sleep disturbances are a risk factor concerning mental and physical health, prevention and early intervention are necessary. Thus, the aim of this study was to implement a self-learning prevention and early intervention training for university staff members. We adapted an established cognitive behavioral therapy for insomnia (CBT-I) intervention as an online version for use during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) crisis. Methods: Development and adaptation procedure of the internet-based CBT­I (iCBT-I) prevention and early intervention training is described. Sessions and topics are shown in detail. The Online Sleep Prevention and Treatment Acceptance questionnaire (OSTA) and the Online Sleep Prevention and Treatment Feedback questionnaire (OSTF) were used to assess acceptance. Sleep problems of university staff members were assessed using the Pittsburgh Sleep Questionnaire (PSQI). Results: The online-adapted version consisted of seven modules. Contents of sessions and topics were implemented based on video clips. Drawings were added to information regarding sleep and sleep hygiene as well as addressing stress and cognitions. In all, 15 individuals participated in this pilot study. The new iCBT­I self-learning prevention training was well accepted. In addition, participants scored the online version as helpful based on the OSTA. Prior to online training, 89% of the participants reported impaired sleep quality or insomnia symptoms, and 56% had a PSQI score over 10. After training 78% of participants showed reduced sleep problems according to PSQI and 56% reached clinically significant enhancement. In addition, after training 44% were healthy sleepers. Discussion: This is the first iCBT­I prevention and early intervention training for university staff members. The training by participants was very well accepted and they scored the videos as very helpful. Sleep problems decreased after online training. However, further studies with larger samples and more sleep-related assessment strategies, e.g., actigraphy and sleep log, are necessary.

2.
Disaster Med Public Health Prep ; 14(4): 514-520, 2020 08.
Article in English | MEDLINE | ID: covidwho-17725

ABSTRACT

OBJECTIVE: This study compared live instructor-led training with video-based instruction in personal protective equipment (PPE) donning and doffing. It assessed the difference in performance between (1) attending 1 instructor-led training session in donning and doffing PPE at 1 month prior to assessment, and (2) watching training videos for 1 month. METHODS: This randomized controlled trial pilot study divided 21 medical students and junior doctors into 2 groups. Control group participants attended 1 instructor-led training session. Video group participants watched training videos demonstrating the same procedures, which they could freely watch again at home. After 1 month, a doctor performed a blind evaluation of performance using checklists. RESULTS: Nineteen participants were assessed after 1 month. The mean donning score was 84.8/100 for the instructor-led group and 88/100 for the video group; mean effect size was 3.2 (95% CI: -7.5 to 9.5). The mean doffing score was 79.1/100 for the instructor-led group and 73.9/100 for the video group; mean effect size was 5.2 (95% CI: -7.6 to 18). CONCLUSION: Our study found no significant difference in donning and doffing scores between instructor-led and video lessons. Video training could be a fast and resource-efficient method of training in PPE donning and doffing in responding to the COVID-19 pandemic.


Subject(s)
Health Personnel/education , Health Personnel/psychology , Personal Protective Equipment , Teaching/standards , Health Personnel/statistics & numerical data , Humans , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pilot Projects , Teaching/classification , Teaching/statistics & numerical data
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