The relationships between insomnia, nightmares, and dreams: A systematic review
Bibliographic record
Abstract
Insomnia and nightmares are both prevalent and debilitating sleep difficulties. The present systematic review aims to document the relationships between insomnia and nightmares in individuals without a concomitant psychopathology. The relationships between insomnia and dreams are also addressed. PsycINFO and Medline were searched for papers published in English or French from 1970 to March 2023. Sixty-seven articles were included for review. Most results support positive relationships between insomnia variables and nightmare variables in individuals with insomnia, individuals with nightmares, the general population, students, children and older adults, and military personnel and veterans. These positive relationships were also apparent in the context of the COVID-19 pandemic. Some psychological interventions, such as Imagery Rehearsal Therapy, might be effective in alleviating both nightmares and insomnia symptoms. Regarding the relationships between insomnia and dreams, compared with controls, the dreams of individuals with insomnia are characterized by more negative contents and affects. The results show that insomnia and nightmares are connected and may be mutually aggravating. A model is proposed to explain how insomnia might increase the likelihood of experiencing nightmares, and how nightmares can in turn lead to sleep loss and nonrestorative sleep.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".