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A Stepped Care Model to Deliver CBT-I in Community

The Chinese University of Hong Kong logo

The Chinese University of Hong Kong

Status

Enrolling

Conditions

Insomnia
Sleep Disturbance

Treatments

Behavioral: Cognitive behavioral therapy for insomnia (CBT-I)

Study type

Interventional

Funder types

Other

Identifiers

NCT06109363
20230816

Details and patient eligibility

About

Insomnia is one of the most common sleep disorders and affects approximately 10 - 40% of the population across different age groups in Hong Kong. Cognitive behavioral therapy for insomnia (CBT-I) is the first line treatment for adult insomnia due to its comparable effect to medication in short term but is more sustainable in the long run. However, only a few sufferers have received CBT-I, due to limited accessibility, lack of trained sleep therapists, time costing and geographical limitations. To increase CBT-I accessibility, different formats of CBT-I have been proposed. Empirical evidence including ours consistently suggested that self-help digital CBT-I is effective in improving sleep while its augmentation with a guided approach could further enhance the treatment gain. Previous evidence has suggested that although self-help CBT-I could lead to positive outcomes, the drop out rate is quite high and maybe less effective for patients with comorbidity or high level of distress.

Thus, a stepped-care approach to CBT-I that utilizes online self help and therapist-guided modes of delivery might be a potential way to facilitate efficient dissemination of effective insomnia treatment resources. The effectiveness of the stepped care model will be evaluated in a real world setting using stepped-wedge cluster randomized controlled design. The program will be rolled out to different districts in Hong Kong sequentially in 18 districts over 4 steps with a eqaully spaced time periods.

Full description

This interventional study will be a multicenter, assessor-blinded, pragmatic stepped-wedge cluster randomized controlled trial. A total of 18 districts will be included in the study and the randomization will be carried out based on the districts. The stepped-care CBT-I intervention will be sequentially rolled out to the 4-6 districts per step according to a computer-generated random number while the remaining clusters will continue to stay unexposed to the CBT-I intervention over the control period. Therefore after 4-step exposure, all clusters will receive the stepped-care CBT-I intervention. A total of 3 follow-up assessments (post intervention, 3-month and 12-month follow up) will be conducted to assess the effectiveness and long-term effects of the stepped care model.

Enrollment

1,100 estimated patients

Sex

All

Ages

18 to 70 years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

  1. Chinese adults aged 18-70 years old,
  2. The score of Insomnia Severity Index ≥ 10.

Exclusion criteria

  1. present with psychotic disorders such as bipolar disorder and schizophrenia,
  2. present with severe depression or suicidal ideation,
  3. present with neurodegenerative diseases that prevent participant from completing the intervention (e.g., dementia and Parkinson's disease).
  4. unable to provide consent

Trial design

Primary purpose

Health Services Research

Allocation

Randomized

Interventional model

Crossover Assignment

Masking

None (Open label)

1,100 participants in 2 patient groups

Stepped-care CBT-I group
Experimental group
Description:
A total of 3 steps of CBT-I intervention will be provided, with the objectives to increase the awareness of sleep health, increase sleep literacy, establish good sleep hygiene and treat insomnia. Step 1: self-help digital CBT-I program; Step 2: guided CBT-I program; Step 3: individualized consultation.
Treatment:
Behavioral: Cognitive behavioral therapy for insomnia (CBT-I)
Control group
No Intervention group
Description:
Participants in the control group remain unexposed to the stepped-care CBT-I intervention.

Trial contacts and locations

1

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Central trial contact

Jucheng Yu, PhD; Rachel Ngan Yin Chan, PhD

Data sourced from clinicaltrials.gov

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