Sleep Impairment and Chronic Pain in the Military: A Scoping Review
Bibliographic record
Abstract
This scoping review explores the bidirectional association between chronic pain and sleep disorders in military personnel. It aims to identify gaps in existing studies, offering tools for diagnosing and treating sleep disorders, chronic pain, and their comorbidities. Observational and interventional studies up to 2024 that approached the relationship between chronic pain and sleep disorders were included. Furthermore, PTSD, anxiety, depression, alcohol consumption, suicidal ideation and drug abuse were considered covariables. Cancer-related or acute pain and studies primarily addressing sleep apnoea or traumatic brain injury were excluded. A systematic search was conducted in ScienceDirect, PubMed, Scopus, Embase, Web of Science and Google Scholar until April 2024. Articles were screened using Covidence by two independent researchers, and bias was assessed using the Newcastle Ottawa Scale, ROBINS-I and ROB-2. Sixteen articles analysed data from 15,060 active military personnel or veterans. Overall, studies endorsed the association between sleep quality and chronic pain and their influence on mental health, physical functioning and quality of life. Additionally, behavioural, mind-body and circadian misalignment therapies, along with other nonpharmacologic interventions, positively impacted outcomes related to pain, sleep quality, and psychiatric comorbidities. However, there was heterogeneity in the use of diagnostic tools, non-standardised procedures, and a lack of guidelines in the treatment of these conditions. The construct of sleep disorders, chronic pain and associated comorbidities was shown to improve with nonpharmacologic and integrative interventions that addressed at least one of these conditions.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".