Investigating the effectiveness of electronically delivered cognitive behavioural therapy (e-CBTi) compared to pharmaceutical interventions in treating insomnia: Protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Insomnia is one of the most prevalent sleep disorders characterized by an inability to fall or stay asleep. Available treatments include pharmacotherapy and cognitive behavioural therapy for insomnia (CBTi). Although CBTi is the first-line treatment, it has limited availability. Therapist-guided electronic delivery of CBT for insomnia (e-CBTi) offers scalable solutions to enhance access to CBTi. While e-CBTi produces comparable outcomes to in-person CBTi, there is a lack of comparison to active pharmacotherapies. Therefore, direct comparisons between e-CBTi and trazodone, one of the most frequently prescribed medications for insomnia, is essential in establishing the effectiveness of this novel digital therapy in the health care system. OBJECTIVE: The aim of this study is to compare the effectiveness of a therapist-guided electronically-delivered cognitive behavioural therapy (e-CBTi) program to trazodone in patients with insomnia. METHODS: Patients (n = 60) will be randomly assigned to two groups: treatment as usual (TAU) + trazodone and TAU + e-CBTi for seven weeks. Each weekly sleep module will be delivered through the Online Psychotherapy Tool (OPTT), a secure, online mental health care delivery platform. Changes in insomnia symptoms will be evaluated throughout the study using clinically validated symptomatology questionnaires, Fitbits, and other behavioural variables. RESULTS: Participant recruitment began in November 2021. To date, 18 participants have been recruited. Data collection is expected to conclude by December 2022 and analyses are expected to be completed by January 2023. CONCLUSIONS: This comparative study will improve our understanding of the efficacy of therapist-guided e-CBTi in managing insomnia. These findings can be used to develop more accessible and effective treatment options and influence clinical practices for insomnia to further expand mental health care capacity in this population. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05125146).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.042 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.010 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.082 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".