Behavioral Interventions to Improve Sleep Outcomes in Individuals With Multiple Sclerosis: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Sleep disturbances are common in individuals with multiple sclerosis. The objective of this systematic review was to determine effective behavioral interventions to improve their sleep. METHODS: Literature searches were performed in December 2021 in Ovid MEDLINE, Elsevier Embase, and Web of Science, along with hand searching for grey literature and cited references. Four reviewers independently reviewed titles and abstracts (2 reviewers for each article; n = 830) and the full-text articles (n = 81). Consensus for inclusion was achieved by a fifth reviewer. Thirty-seven articles were eligible for inclusion. Four reviewers extracted relevant data from each study (2 reviewers for each article) using a standard data extraction table. Consensus was achieved for completeness and accuracy of the data extraction table by a fifth reviewer. The same 4 reviewers conducted a quality appraisal of each article to assess the risk of bias and quality of the articles, and consensus was achieved by a fifth reviewer as needed. Descriptive data were used for types of interventions, sleep outcomes, results, and key components across interventions. RESULTS: Overall, the cognitive behavioral therapy for insomnia, cognitive behavioral therapy/psychotherapy, and education/self-management support interventions reported positive improvements in sleep outcomes. Quality appraisal scores ranged from low to high, indicating potential for bias. CONCLUSIONS: Variability in the intervention type, intervention dose, outcomes used, training/expertise of interventionist, specific sample, and study quality made it difficult to compare and synthesize results. Further research is necessary to demonstrate the efficacy of most of the interventions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".