# STOP-BANG questionnaire
The STOP-Bang questionnaire is a yes/no screening tool that estimates a person's risk of obstructive sleep apnea (OSA) from symptom questions and demographic measurements, producing a score rather than a diagnosis. It was developed for preoperative surgical patients and is now used across medical, surgical, dental, pregnancy, and commercial-driver settings. It is self-reportable. The [American Academy of Sleep Medicine](https://www.edgepedia.org/american-academy-of-sleep-medicine)'s clinical practice guidelines recommend against using clinical tools, questionnaires, and prediction algorithms to diagnose OSA, so a positive screen leads to referral for polysomnography (overnight clinical sleep study diagnosing sleep apnea) or a home sleep apnea test, not to treatment.[1](https://link.springer.com/article/10.1007/s11325-021-02299-y) Its strength is ruling out: a low score makes moderate-to-severe and severe OSA unlikely, while a high score cannot confirm the disease.[2](https://www.sciencedirect.com/science/article/pii/S1087079224001114)
| Key fact | Detail |
|---|---|
| Items | 8 yes/no questions: snoring, tiredness, observed apnea, high blood pressure, BMI > 35 kg/m², age > 50 years, neck circumference, male gender[3](https://site.thoracic.org/assemblies/srn/sleep-related-questionnaires/stop-bang) |
| Scoring | 1 point per "yes"; score ≥ 3 = at risk, 0–2 = low risk, 5–8 = high risk[1](https://link.springer.com/article/10.1007/s11325-021-02299-y) |
| Completion time | 1–2 minutes, self-reportable[1](https://link.springer.com/article/10.1007/s11325-021-02299-y) |
| Pooled performance (surgical, cut-off ≥ 3) | Sensitivity 85–90%, specificity 27–47% depending on OSA severity[4](https://link.springer.com/article/10.1186/s12871-022-01912-1) |
| Best use | Ruling out severe OSA; NPV for severe OSA 90–98% in most populations[4](https://link.springer.com/article/10.1186/s12871-022-01912-1) |
| Reliability | Test–retest Kappa 0.923 (CI 0.82–1.0)[3](https://site.thoracic.org/assemblies/srn/sleep-related-questionnaires/stop-bang) |
| Translations | At least 21 languages; use requires the developer's permission[3](https://site.thoracic.org/assemblies/srn/sleep-related-questionnaires/stop-bang) |
## How it works
Each "yes" answer scores 1 point. The four STOP items ask about [Snoring](https://www.edgepedia.org/snoring), Tiredness, Observed apnea, and high blood Pressure, phrased at a fifth-grade reading level.[5](http://www.stopbang.ca/publication/pdf/pub6.pdf) The four Bang items are demographic: BMI greater than 35 kg/m², age over 50 years, neck circumference (given as at least 16 inches in the [American Thoracic Society](https://www.edgepedia.org/american-thoracic-society) record and as more than 40 cm, about 40.6 cm, in scale references), and male gender.[3](https://site.thoracic.org/assemblies/srn/sleep-related-questionnaires/stop-bang) • [6](https://www.med.upenn.edu/cbti/assets/user-content/documents/STOP-Bang.pdf)
A total score of 3 or more places the individual at risk of OSA; 0 to 2 indicates low risk.[6](https://www.med.upenn.edu/cbti/assets/user-content/documents/STOP-Bang.pdf) • [7](https://doi.org/10.1093/bja/aes022) Scores of 5 to 8 carry higher specificity and indicate a patient who should be referred for diagnostic testing.[3](https://site.thoracic.org/assemblies/srn/sleep-related-questionnaires/stop-bang) • [8](https://journals.lww.com/anesthesia-analgesia/fulltext/2020/10000/preoperative_screening_for_obstructive_sleep_apnea.7.aspx) The score also supports rough severity estimation: in a pooled surgical population, the probability of moderate-to-severe OSA rose from 40% at a score of 3 to 48%, 60%, 68%, and 80% at scores of 4, 5, 6, and 7/8.[9](https://journals.lww.com/co-anesthesiology/fulltext/2017/02000/an_update_on_the_various_practical_applications_of.19.aspx)
## How it is done
The questionnaire is completed by the patient, either alone or with staff help, in about 1 to 2 minutes, with response rates of 90 to 100% reported in perioperative settings.[9](https://journals.lww.com/co-anesthesiology/fulltext/2017/02000/an_update_on_the_various_practical_applications_of.19.aspx) • [1](https://link.springer.com/article/10.1007/s11325-021-02299-y) The four symptom questions need no equipment. The Bang items require a measured height and weight for BMI, the patient's age, neck circumference, and recorded gender. Translated versions exist in at least 21 languages, including French, Spanish, Chinese, German, and Turkish.[3](https://site.thoracic.org/assemblies/srn/sleep-related-questionnaires/stop-bang)
## Origin
Frances Chung and colleagues introduced the STOP questionnaire in [Anesthesiology](https://www.edgepedia.org/anesthesiology) in 2008, with the STOP-Bang scoring model presented in the same paper as an appendix; the 2012 British Journal of Anaesthesia paper reported that a high STOP-Bang score indicates a high probability of obstructive sleep apnea.[5](http://stopbang.ca/publication/pdf/pub6.pdf) • [7](https://doi.org/10.1093/bja/aes022) The four STOP questions were derived by factor analysis combining candidate items with items 1 to 10 of the [Berlin Questionnaire](https://www.edgepedia.org/berlin-questionnaire), administered to 278 patients; the Berlin Questionnaire itself was introduced by Netzer, Stoohs, Netzer, Clark, and Strohl in Annals of Internal Medicine in 1999.[5](http://www.stopbang.ca/publication/pdf/pub6.pdf) • [10](https://doi.org/10.7326/0003-4819-131-7-199910050-00002) The STOP questionnaire was validated in surgical patients at preoperative clinics; of 2,467 patients screened, 27.5% were classified as high risk and 211 underwent polysomnography.[5](http://www.stopbang.ca/publication/pdf/pub6.pdf)
The Bang items were added by Chung, Subramanyam, Liao, Sasaki, Shapiro, and Sun in the 2012 paper.[7](https://doi.org/10.1093/bja/aes022) Adding them raised the sensitivity of the original four-item STOP questionnaire from 65.6%, 74.3%, and 79.5% to 83.6%, 92.9%, and 100% at AHI cut-offs of > 5, > 15, and > 30.[5](http://www.stopbang.ca/publication/pdf/pub6.pdf)
## Variants
Chung, Yang, Brown, and Liao proposed alternative scoring models in the Journal of Clinical Sleep Medicine in 2014, including a two-step strategy: scores of 0 to 2 are low risk and 5 to 8 high risk, while midrange scores of 3 to 4 are further stratified by a STOP score ≥ 2 combined with BMI > 35 kg/m², male gender, or neck circumference > 40 cm, or by serum bicarbonate ≥ 28 mmol/L; the combination specificities for moderate-to-severe OSA were 85%, 77%, and 79%.[11](https://doi.org/10.5664/jcsm.4022) • [9](https://journals.lww.com/co-anesthesiology/fulltext/2017/02000/an_update_on_the_various_practical_applications_of.19.aspx) Adding serum bicarbonate ≥ 28 mmol/L to a score ≥ 3 raised specificity to 85.2%, 81.7%, and 79.7% for all, moderate-to-severe, and severe OSA.[2](https://www.sciencedirect.com/science/article/pii/S1087079224001114) However, an external validation in 115 surgical patients found these alternative models, individually and combined, did not improve classification of patients with scores of 3 to 4.[8](https://journals.lww.com/anesthesia-analgesia/fulltext/2020/10000/preoperative_screening_for_obstructive_sleep_apnea.7.aspx)
Population-specific cut-offs have also been proposed. In an Indian population, the optimal BMI threshold was > 27.5 kg/m² and a score ≥ 4 gave the best discrimination.[4](https://link.springer.com/article/10.1186/s12871-022-01912-1) In morbidly obese populations, a cut-off of 4 provides a better sensitivity–specificity balance.[12](http://www.stopbang.ca/publication/pdf/pubchest1.pdf) For lean patients, a modified version using age ≥ 40 years, BMI ≥ 23 kg/m², and neck circumference ≥ 35 cm was proposed, achieving sensitivities of 93.0%, 95.9%, and 97.6% at a score of 3 for AHI ≥ 5, ≥ 15, and ≥ 30, compared with 50.0%, 53.6%, and 56.1% for the original scoring.[13](https://peerj.com/articles/20310/) Combining instruments is another refinement: a 2025 study of 2,208 suspected OSA patients recommended a three-step strategy using a STOP-Bang score ≥ 3, an [Epworth Sleepiness Scale](https://www.edgepedia.org/epworth-sleepiness-scale) score ≥ 9, and the Berlin questionnaire, noting that combining scales lowers sensitivity and negative predictive value while raising specificity and positive predictive value.[14](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1581904/full)
## Applications
Validation has been performed against polysomnography-derived apnea–hypopnea index (AHI) thresholds in multiple populations. In sleep clinic cohorts, pooled sensitivity at a score ≥ 3 was 90%, 94%, and 96% for AHI ≥ 5, ≥ 15, and ≥ 30, with specificities of 49%, 34%, and 25% and negative predictive values of 46%, 75%, and 90%.[15](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0143697) In surgical patients, a 2022 meta-analysis of 10 studies (3,247 participants) found pooled sensitivity of 85% (95% CI 82–88) for all OSA, 88% for moderate-to-severe, and 90% for severe OSA, with specificities of 47%, 29%, and 27%; the negative predictive value for severe OSA was 93.2%.[4](https://link.springer.com/article/10.1186/s12871-022-01912-1) In the general population, sensitivity for moderate-to-severe and severe OSA was 88% and 92%, with negative predictive values of 93% and 98%.[1](https://link.springer.com/article/10.1007/s11325-021-02299-y) In commercial drivers, sensitivity was 91% with specificity of 43%.[1](https://link.springer.com/article/10.1007/s11325-021-02299-y)
The score grades probability as well as classifying risk. In the 2012 validation, the odds ratio for moderate/severe OSA was 4.8 at a score of 5, 6.3 at 6, and 6.9 at scores of 7 and 8; for severe OSA the odds ratios were 10.4, 11.6, and 14.9.[7](https://doi.org/10.1093/bja/aes022) In sleep clinic patients, the probability of severe OSA rose stepwise from 25% at a score of 3 to 75% at 7/8; in surgical patients from 15% to 65%.[15](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0143697)
## Limitations and alternatives
The dominant limitation is low specificity at the conventional cut-off of 3. Raising the threshold from 3 to 5 in surgical patients dropped sensitivity for moderate-to-severe OSA from 88% to 50% while specificity rose from 29% to 78%; a threshold of 6 or greater achieved the highest positive predictive value of 86% with 90% specificity for all OSA.[4](https://link.springer.com/article/10.1186/s12871-022-01912-1) In a high cardiovascular-risk cohort, the cut-off ≥ 3 detected 97% of patients with an REI ≥ 30 events/h but had specificity of only 19%, and none of the compared questionnaires performed well at published cut-offs (AUC < 0.7).[16](https://jcsm.aasm.org/doi/10.5664/jcsm.7536) Because sensitivity exceeds specificity, the instrument minimizes false negatives rather than false positives, and midrange scores of 3 to 4 remain ambiguous; in one cohort, 21% of patients scoring 3 to 4 nevertheless had severe OSA.[2](https://www.sciencedirect.com/science/article/pii/S1087079224001114) • [8](https://journals.lww.com/anesthesia-analgesia/fulltext/2020/10000/preoperative_screening_for_obstructive_sleep_apnea.7.aspx)
Head-to-head comparisons show overlapping performance. In sleep clinic cohorts, the Berlin questionnaire's sensitivity was 85%, 84%, and 89% for AHI ≥ 5, ≥ 15, and ≥ 30 (specificity 43%, 30%, 33%), and the four-item STOP questionnaire's was 90%, 90%, and 95% (specificity 31%, 29%, 21%); summary receiver-operating confidence regions for Berlin, STOP, and STOP-Bang overlapped, suggesting no statistically significant sensitivity difference among the three, and the certainty of the evidence was rated low.[17](https://pmc.ncbi.nlm.nih.gov/articles/PMC8370860/) In surgical cohorts at AHI ≥ 15, pooled sensitivity was 76% for Berlin versus 90% for STOP-Bang, with specificity 47% versus 27%.[17](https://pmc.ncbi.nlm.nih.gov/articles/PMC8370860/) The Epworth Sleepiness Scale behaves differently, trading sensitivity for specificity, and can be combined with STOP-Bang to increase specificity.[1](https://link.springer.com/article/10.1007/s11325-021-02299-y)
Performance varies by population. In midlife women, a score ≥ 3 yielded 77% sensitivity, 45% specificity, and an AUC of 0.67 for moderate-to-severe OSA.[2](https://www.sciencedirect.com/science/article/pii/S1087079224001114) Specificity of Berlin, STOP, and STOP-Bang differed significantly across age groups (20–39, 40–59, ≥ 60 years), while the Epworth Sleepiness Scale showed no age-related differences.[18](https://www.j-rhinology.org/journal/view.php?doi=10.18787%2Fjr.2018.00278) The questionnaire was reported as less useful in veterans and patients with renal failure, and accuracy is affected by variability in neck circumference measurement and by the absence of a bed partner to report observed apneas.[9](https://journals.lww.com/co-anesthesiology/fulltext/2017/02000/an_update_on_the_various_practical_applications_of.19.aspx) In patients with insomnia, sensitivity rises but specificity falls.[19](https://www.dovepress.com/performance-of-four-screening-tools-for-identifying-obstructive-sleep--peer-reviewed-fulltext-article-NSS)
A 2024 comprehensive meta-analysis of 34 studies reported pooled sensitivity of 0.89 and specificity of 0.40 for the STOP-Bang scale, compared with 0.80 and 0.48 for the Berlin questionnaire and 0.48 and 0.73 for the Epworth Sleepiness Scale.[20](https://pubmed.ncbi.nlm.nih.gov/39601864/) Machine-learning models built on STOP-Bang data are the main competitive development. A 2024 study of 262 polysomnography patients found that a K-nearest-neighbors model (K = 11) using the STOP-BANG items achieved sensitivity of 0.94, specificity of 0.85, and accuracy of 0.92, improving specificity from 0.61 in the raw questionnaire data.[21](https://www.mdpi.com/2076-3417/14/7/3117) A 2026 study of 4,036 participants developed an XGBoost-based 15-item questionnaire combining modified Epworth and STOP-Bang items, achieving an AUC of 0.94 for moderate OSA and 0.97 for severe OSA; in the same data the STOP-Bang questionnaire achieved an AUC of 0.68 for moderate-to-severe OSA, and the study reports reduced STOP-Bang accuracy in Asian cohorts (AUC 0.68–0.74 versus 0.78–0.85), attributed to differential craniofacial morphology and adiposity distribution.[22](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1775766/full)
## References
1. [Validation of the STOP-Bang questionnaire for screening of obstructive sleep apnea in the general population and commercial drivers: a systematic review and meta-analysis (Sleep and Breathing, 2021)](https://link.springer.com/article/10.1007/s11325-021-02299-y)
2. [The STOP-Bang questionnaire: A narrative review on its utilization in different populations and settings (Sleep Medicine Reviews, 2024)](https://www.sciencedirect.com/science/article/pii/S1087079224001114)
3. [American Thoracic Society Sleep-Related Questionnaires assembly entry: Stop, Stop Bang](https://site.thoracic.org/assemblies/srn/sleep-related-questionnaires/stop-bang)
4. [Validation of the STOP-Bang questionnaire as a preoperative screening tool for obstructive sleep apnea: a systematic review and meta-analysis (BMC Anesthesiology, 2022)](https://link.springer.com/article/10.1186/s12871-022-01912-1)
5. [STOP questionnaire: a tool to screen patients for obstructive sleep apnea (Chung et al., Anesthesiology 2008)](http://www.stopbang.ca/publication/pdf/pub6.pdf)
6. [STOP-Bang Questionnaire chapter in STOP, THAT and One Hundred Other Sleep Scales (Shahid et al., Springer 2012)](https://www.med.upenn.edu/cbti/assets/user-content/documents/STOP-Bang.pdf)
7. [F. Chung and colleagues (2012). High STOP-Bang score indicates a high probability of obstructive sleep apnoea. British Journal of Anaesthesia.](https://doi.org/10.1093/bja/aes022)
8. [Preoperative Screening for OSA Using Alternative Scoring Models of the STOP-Bang Questionnaire: An External Validation (Anesthesia & Analgesia, 2020)](https://journals.lww.com/anesthesia-analgesia/fulltext/2020/10000/preoperative_screening_for_obstructive_sleep_apnea.7.aspx)
9. [An update on the various practical applications of the STOP-Bang questionnaire in anesthesia, surgery, and perioperative medicine (Current Opinion in Anaesthesiology, 2017)](https://journals.lww.com/co-anesthesiology/fulltext/2017/02000/an_update_on_the_various_practical_applications_of.19.aspx)
10. [Nikolaus C. Netzer and colleagues (1999). Using the Berlin Questionnaire To Identify Patients at Risk for the Sleep Apnea Syndrome. Annals of Internal Medicine.](https://doi.org/10.7326/0003-4819-131-7-199910050-00002)
11. [Frances Chung and colleagues (2014). Alternative Scoring Models of STOP-Bang Questionnaire Improve Specificity To Detect Undiagnosed Obstructive Sleep Apnea. Journal of Clinical Sleep Medicine.](https://doi.org/10.5664/jcsm.4022)
12. [STOP-Bang: A practical review (CHEST publication PDF from stopbang.ca)](http://www.stopbang.ca/publication/pdf/pubchest1.pdf)
13. [Modified STOP-Bang questionnaire for detecting obstructive sleep apnea in individuals with a body mass index below 35 kg/m2 (PeerJ, 2025)](https://peerj.com/articles/20310/)
14. [Improving OSA screening efficiency with subjective questionnaires: integrating STOP-Bang, ESS, and Berlin (Frontiers in Medicine, 2025)](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1581904/full)
15. [Validation of the STOP-Bang Questionnaire as a Screening Tool for Obstructive Sleep Apnea among Different Populations: A Systematic Review and Meta-Analysis (PLOS One, 2015)](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0143697)
16. [Comparison of Commonly Used Questionnaires to Identify Obstructive Sleep Apnea in a High-Risk Population (Journal of Clinical Sleep Medicine)](https://jcsm.aasm.org/doi/10.5664/jcsm.7536)
17. [Diagnostic accuracy of screening questionnaires for obstructive sleep apnoea in adults in different clinical cohorts: a systematic review and meta-analysis (2021)](https://pmc.ncbi.nlm.nih.gov/articles/PMC8370860/)
18. [Are Sleep Questionnaires Valid in All Adult Age Groups as Screening Tools for Obstructive Sleep Apnea? (Japanese Journal of Rhinology, 2018)](https://www.j-rhinology.org/journal/view.php?doi=10.18787%2Fjr.2018.00278)
19. [Performance of Four Screening Tools for Identifying Obstructive Sleep Apnea (Nature and Science of Sleep, 2024/2025)](https://www.dovepress.com/performance-of-four-screening-tools-for-identifying-obstructive-sleep--peer-reviewed-fulltext-article-NSS)
20. [Diagnostic utility of obstructive sleep apnea screening questionnaires: a comprehensive meta-analysis (Sleep and Breathing, published 2024-11-27)](https://pubmed.ncbi.nlm.nih.gov/39601864/)
21. [A Study on Improving Sleep Apnea Diagnoses Using Machine Learning Based on the STOP-BANG Questionnaire (Applied Sciences, 2024)](https://www.mdpi.com/2076-3417/14/7/3117)
22. [Machine learning optimization of obstructive sleep apnea screening: development and validation of a gradient boosting prediction model with a clinical implementation framework (Frontiers in Medicine, 2026)](https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2026.1775766/full)
---
*Topic: Encyclopedia › Life and health › Human health and medicine › Clinical assessment and procedures › Diagnosis and clinical assessment › Diagnostic classification and scoring › Mental health and behavioral assessment scales*
*Initially written Sep 29, 2026 · Reviewed: Sep 30, 2026 · Edited: Sep 30, 2026; Oct 15, 2026 · Last review: Sep 30, 2026*
*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*
License: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license