Managing Insomnia Using Lucid Dreaming Training: A Pilot Study
Bibliographic record
Abstract
Objectives/Background: Despite Cognitive Behavioral Therapy for Insomnia (CBT-I) being considered the first-line treatment for insomnia, it is not without its challenges. As such it is worthwhile to consider, and test, alternative or adjuvant management options.Methods/Participants: The aim of the present study was to examine whether Lucid Dreaming Training for insomnia (LDT-I) impacted on insomnia, depressive and anxious symptomology in an open label trial of 48 adults with Insomnia Disorder. Participants completed the Insomnia Severity Index, General Anxiety Disorder-7 and Patient Health Questionnaire at baseline then one month following LDT-I. Training consisted of four modules delivered over a period of two consecutive weeks.Results: The results suggest, albeit preliminarily, that LDT-I may have a place within the non-pharmacological management of insomnia, as there were significant reductions in insomnia severity (t(46) = 8.16,p <.001), anxious symptomology (t(46) = 4.75,p <.001) and depressive symptomology (t(46) = 5.87,p <.001). Further, the effect size in terms of pre-post reductions on ISI scores was large (dz 1.17).Conclusions: Whilst the results are promising, further testing of LDT-I is needed to inform its place amongst the non-pharmacological treatments for insomnia.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".