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- Original Article Relationship between obstructive sleep apnea risk and low back pain among shift workers in a tire manufacturing factory
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Sunjin Jung1
, Seunghyeon Cho2, Suwhan Kim1, Kyung Wook Kang3, JiHwan Kim1, Won-Ju Park1,* -
Annals of Occupational and Environmental Medicine 2026;38:e15.
DOI: https://doi.org/10.35371/aoem.2026.38.e15
Published online: May 12, 2026
1Department of Occupational and Environmental Medicine, Chonnam National University Medical School and Chonnam National University Hwasun Hospital, Hwasun, Korea
2Department of Occupational and Environmental Medicine, Chonnam National University Hospital, Gwangju, Korea
3Department of Neurology, Chonnam National University Medical School and Chonnam National University Hospital, Gwangju, Korea
- *Corresponding author: Won-Ju Park Department of Occupational and Environmental Medicine, Chonnam National University Medical School and Chonnam National University Hwasun Hospital, 322 Seoyang-ro, Hwasun 58128, Korea E-mail: wonjupark@jnu.ac.kr
• Received: January 31, 2026 • Revised: April 29, 2026 • Accepted: May 2, 2026
© 2026 Korean Society of Occupational & Environmental Medicine
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
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Background Low back pain (LBP) is highly prevalent among industrial workers, and obstructive sleep apnea (OSA) has been increasingly recognized as a factor influencing pain modulation. This study evaluated the association between OSA risk, assessed by the STOP-Bang questionnaire, and LBP among shift workers in a tire manufacturing factory.
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Methods A total of 976 male shift workers from a tire manufacturing factory were analyzed. OSA risk was assessed using the STOP-Bang questionnaire and classified as low, moderate, or high. LBP and musculoskeletal pain were defined as self-reported symptoms occurring within the preceding 6 months. Multivariable logistic regression analyses were performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs).
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Results Compared with workers at low OSA risk, those at moderate and high risk had significantly higher odds of LBP (OR: 1.50; 95% CI: 1.02–2.20; p = 0.038; and OR: 1.75; 95% CI: 1.20–2.55; p = 0.004, respectively). Similarly, moderate and high OSA risk were independently associated with increased odds of musculoskeletal pain (OR: 1.86; 95% CI: 1.26–2.73; p = 0.002; and OR: 1.84; 95% CI: 1.26–2.68; p = 0.002, respectively).
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Conclusions Among male shift workers, elevated OSA risk is independently associated with a higher prevalence of LBP and musculoskeletal pain. Systematic workplace screening for OSA risk using the STOP-Bang questionnaire may support occupational health assessments by identifying shift workers with elevated OSA risk who are more likely to report pain-related morbidity.
BACKGROUND
Low back pain (LBP) is a prevalent musculoskeletal disorder and represents a major occupational health concern among workers in industrial settings. Systematic reviews have reported a mean overall prevalence of LBP of 31.0%, irrespective of the prevalence period assessed, while the prevalence of chronic LBP in the general population has been estimated to range from 4% to 25%, depending on the assessment method and demographic characteristics of the study population.1,2 Among industrial workers, the burden appears substantially higher, with one study reporting an LBP prevalence of 61.6%.3 In addition to its high prevalence, LBP is a leading cause of sickness absence and reduced productivity, contributing to considerable socioeconomic costs.4
Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurrent episodes of upper airway obstruction during sleep, resulting in intermittent hypoxia and sleep fragmentation.5 Epidemiological studies have estimated that the overall prevalence of OSA, defined by an apnea–hypopnea index (AHI) ≥ 5, ranges from 9% to 38% in the general population, with evidence of an increasing trend over time.6,7 OSA is associated with metabolic dysregulation, endothelial dysfunction, and systemic inflammation, thereby increasing the risk of comorbid conditions such as cardiovascular disease, metabolic syndrome, and chronic obstructive pulmonary disease.8,9 In addition, intermittent hypoxia, systemic inflammation, and altered opioid receptor activity are suggested as potential mechanisms contributing to heightened pain sensitivity in patients with OSA.10
Consistent with these mechanisms, accumulating clinical and epidemiologic evidence suggests that OSA is associated with a greater burden of pain outcomes. A systematic review reported an overall association between OSA and pain-related outcomes in adults, although the included studies were heterogeneous in design, study population, and pain assessment methods.11 Among patients with moderate-to-severe OSA, both the prevalence and severity of pain were reported to be high, and pain was frequently observed at musculoskeletal sites, including the back.12 In addition, higher OSA risk scores based on the STOP-Bang questionnaire were associated with nonspecific chronic LBP in adults, suggesting a potential link between OSA risk and chronic LBP.13 Nevertheless, evidence specifically focused on LBP remains limited.
Despite the substantial prevalence and clinical burden of both LBP and OSA, the potential relationship between these conditions has not been adequately investigated, particularly in populations exposed to shift work. Shift workers may be especially vulnerable due to circadian rhythm disruption, insufficient sleep, and sleep disturbance, which could amplify both sleep-related disorders and pain outcomes. Therefore, this study aimed to evaluate the association between OSA risk, assessed using the STOP-Bang questionnaire, and LBP among shift workers employed at a tire manufacturing factory.
METHODS
This cross-sectional study analyzed a subset of male workers employed at a tire manufacturing factory who underwent special health examinations at a tertiary university hospital between 2018 and 2019. Of the 1,238 workers who completed the examinations, 107 individuals with missing data and 10 female workers were excluded. Because the study focused specifically on shift work, an additional 145 daytime workers were excluded. Rotating shift workers followed a four-team, three-shift system operating on a 20-day cycle, consisting of five consecutive day shifts (07:00–15:00) followed by 1 day off; five consecutive night shifts (23:00–07:00) followed by 2 days off; and five consecutive evening (swing) shifts (15:00–23:00) followed by 2 days off. After application of these exclusion criteria, a total of 976 male rotating shift workers were included in the final analysis.
Sociodemographic characteristics, including age, smoking status, alcohol consumption, and regular exercise, as well as occupational factors such as working period and work schedule, were collected using a structured questionnaire. Smoking status was classified as non-smoker (fewer than 100 cigarettes smoked in a lifetime) or smoker (100 or more cigarettes smoked in a lifetime). Alcohol consumption was assessed as never, less than once per week, once or twice per week, or three or more times per week; participants who reported drinking alcohol less than once per week were classified as non-alcohol drinkers. Exercise status was categorized as less than once per week, once or twice per week, or three or more times per week. Blood samples were obtained after a 12-hour fasting period to measure fasting glucose levels and lipid profiles. Anthropometric and clinical measurements, including height, weight, neck circumference, and blood pressure, were obtained for all participants. Neck circumference was measured just below the laryngeal prominence, perpendicular to the long axis of the neck. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²). Blood pressure was measured on the right arm in the seated position using a digital blood pressure monitor under stable conditions. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or current use of antihypertensive medication. Diabetes mellitus was defined as a fasting blood glucose level ≥ 126 mg/dL or current use of antidiabetic medication. Dyslipidemia was defined as the presence of at least one abnormal lipid parameter, including total cholesterol ≥ 240 mg/dL, triglycerides ≥ 200 mg/dL, low-density lipoprotein cholesterol ≥ 160 mg/dL, or current use of lipid-lowering medication.
LBP and musculoskeletal pain were assessed using a structured questionnaire consisting of two items: (1) a question on LBP, “Have you experienced LBP within the past 6 months?” and (2) a question on musculoskeletal pain excluding LBP, “The following question pertains to musculoskeletal pain excluding LBP. Have you experienced musculoskeletal pain within the past 6 months?”
In this study, we assessed screening-based risk of OSA using the STOP-Bang questionnaire rather than polysomnography. The STOP-Bang questionnaire is a concise screening tool for OSA developed for preoperative evaluation.14,15 The questionnaire consists of eight dichotomous items addressing established risk factors for OSA, including (1) snoring, (2) daytime tiredness, (3) observed apnea, (4) high blood pressure, (5) BMI (≥ 35 kg/m²), (6) age (≥ 50 years), (7) neck circumference ≥ 43 cm in men, and (8) male sex. Participants were classified into OSA risk categories based on total STOP-Bang scores, with scores of 0–2 indicating low risk, 3–4 indicating moderate risk, and 5–8 indicating high risk.
Statistical analyses were conducted to compare participant characteristics according to LBP status. Continuous variables were expressed as arithmetic means ± standard deviations and compared using the Student’s t-test. Categorical variables were presented as frequencies and percentages (%) and compared using Pearson’s χ² test. Multivariable logistic regression analyses were performed to evaluate the association between OSA risk and pain outcomes. Two adjusted models were constructed. Model 1 was adjusted for smoking status, alcohol consumption, exercise status, and working period. Because age, BMI, and hypertension are components of the STOP-Bang questionnaire, these variables were not included in the primary adjusted model. Model 2 additionally included diabetes mellitus, dyslipidemia, age, BMI, and hypertension as a sensitivity analysis to assess the robustness of the findings. Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). A p-value < 0.05 was considered statistically significant. All statistical analyses were performed using IBM SPSS Statistics for Windows version 29.0 (IBM Corp., Armonk, NY, USA).
This study was conducted through a retrospective review of participants’ medical records. All participant data were anonymized and securely stored in the Chonnam National University Hospital Clinical Data Warehouse. Data utilization complied with the Personal Information Protection Act. Informed consent for data collection and use was obtained prior to the medical checkup. The study protocol was approved by the Institutional Review Board of Chonnam National University Hwasun Hospital (CNUHH-2016-150).
RESULTS
The general characteristics of the study participants are summarized in Table 1. A total of 976 individuals were included in the analysis, of whom 466 (47.7%) reported LBP and 510 (52.3%) did not. The mean age was 49.5 ± 6.3 years in the LBP group and 48.8 ± 6.9 years in the non-LBP group. The proportion of smokers was significantly higher among participants with LBP than among those without LBP (76.2% vs. 69.6%, p = 0.021). With respect to OSA risk categories, participants with LBP were less likely to be in the low OSA risk group (57.1% vs. 69.2%, p < 0.001) and more likely to be in the high OSA risk group (22.5% vs. 15.1%, p = 0.003). The proportion of participants in the moderate-risk group was also higher in the LBP group, although this difference was not statistically significant (20.4% vs. 15.7%, p = 0.056). Overall, Table 1 indicates that LBP was more prevalent among workers with a higher OSA risk and among smokers.
Table 2 presents the associations between individual components of the STOP-Bang questionnaire and the presence of LBP. Among the eight components, snoring, daytime tiredness, and observed apnea were significantly more prevalent among participants with LBP than among those without. Specifically, snoring was reported by 26.6% of participants with LBP compared with 19.4% of those without LBP (p = 0.007). Daytime tiredness was reported by 34.3% of the LBP group and 23.3% of the non-LBP group (p < 0.001). Observed apnea was reported by 13.5% of participants with LBP compared with 6.3% of participants without LBP (p < 0.001). No statistically significant differences were observed between the two groups for the remaining STOP-Bang components (Table 2). Thus, Table 2 indicates that symptom-based STOP-Bang components, including snoring, daytime tiredness, and observed apnea, were more strongly associated with LBP than the other components.
Table 3 summarizes the ORs for the association between OSA risk and LBP among shift workers, with the low OSA risk group serving as the reference category. In the unadjusted model, both moderate OSA risk (OR: 1.58; 95% CI: 1.13–2.21; p = 0.008) and high OSA risk (OR: 1.81; 95% CI: 1.30–2.53; p < 0.001) were associated with significantly higher odds of LBP. After adjustment for smoking status, alcohol consumption, exercise status, and working period (model 1), these associations remained statistically significant for both moderate (OR: 1.55; 95% CI: 1.09–2.21; p = 0.015) and high OSA risk (OR: 1.76; 95% CI: 1.25–2.47; p = 0.001). In model 2, which additionally included diabetes mellitus, dyslipidemia, age, BMI, and hypertension, the associations persisted for moderate OSA risk (OR: 1.50; 95% CI: 1.02–2.20; p = 0.038) and high OSA risk (OR: 1.75; 95% CI: 1.20–2.55; p = 0.004) (Table 3). Taken together, Table 3 indicates that both moderate and high OSA risk were independently associated with increased odds of LBP across all models compared with low OSA risk.
Table 4 presents the associations between OSA risk and musculoskeletal pain among shift workers. Using the low OSA risk group as the reference, both moderate (OR: 1.82; 95% CI: 1.30–2.56; p = 0.001) and high OSA risk (OR: 1.87; 95% CI: 1.34–2.61; p < 0.001) were associated with increased odds of musculoskeletal pain in the unadjusted model. These associations remained significant after adjustment for smoking status, alcohol consumption, exercise status, and working period (model 1) for moderate OSA risk (OR: 1.93; 95% CI: 1.35–2.76; p < 0.001) and high OSA risk (OR: 1.87; 95% CI: 1.33–2.62; p < 0.001). In model 2, which additionally included diabetes mellitus, dyslipidemia, age, BMI, and hypertension, moderate OSA risk (OR: 1.86; 95% CI: 1.26–2.73; p = 0.002) and high OSA risk (OR: 1.84; 95% CI: 1.26–2.68; p = 0.002) remained significantly associated with musculoskeletal pain (Table 4). Overall, Table 4 indicates that moderate and high OSA risk were consistently associated with increased odds of musculoskeletal pain, even after multivariable adjustment.
DISCUSSION
This study examined the association between OSA risk and LBP among male shift workers employed at a tire manufacturing factory. The findings demonstrate that both moderate and high OSA risk were associated with a higher prevalence of LBP and musculoskeletal pain. Importantly, these associations remained statistically significant after adjustment for multiple potential confounders, supporting the robustness of the observed relationships. Because musculoskeletal pain was defined separately from LBP, the observed association may also reflect pain in body regions other than the lower back.
OSA has increasingly been recognized as a contributor to altered pain perception and the development or exacerbation of chronic pain conditions. A cross-sectional study by Aytekin et al. reported that 55% of patients with OSA experienced chronic widespread musculoskeletal pain, highlighting the substantial burden of pain in this population.16 Similarly, a large cross-sectional analysis of a veteran cohort found that young adults with OSA were more likely to report moderate to severe pain intensity than those without OSA, even after adjustment for demographic and clinical factors.17 In addition, a systematic review of 12 studies concluded that OSA is generally associated with increased pain intensity or reduced pain tolerance, although the magnitude and direction of these associations varied across studies, likely reflecting differences in the relative contributions of hypoxemia and sleep fragmentation among OSA phenotypes.11
Several biological mechanisms have been proposed to explain the association between OSA and pain. Intermittent hypoxia, systemic inflammation, and altered opioid receptor activity are thought to contribute to heightened pain sensitivity.10 OSA is associated with increased systemic inflammation, characterized by elevated levels of pro-inflammatory cytokines such as interleukin (IL)-6, tumor necrosis factor α, and IL-8, which are linked to sleep fragmentation and nocturnal hypoxia.18-23 Furthermore, sympathetic nervous system overactivation resulting from recurrent airway obstruction may further amplify cytokine release and exacerbate pain perception in individuals with OSA.24-26 Emerging evidence also suggests that hypoxia-related biomarkers elevated in OSA, including hypoxia-inducible factor 1α and insulin-like growth factor binding proteins, may play a role in pain modulation. However, the precise mechanisms through which these factors influence pain regulation in OSA remain incompletely understood.27-29
Clinical and interventional studies support the biological plausibility of a relationship between OSA and pain modulation. A randomized crossover study by Onen et al. demonstrated that continuous positive airway pressure (CPAP) treatment significantly improved pain tolerance in elderly patients with OSA, suggesting a potential analgesic effect of effective OSA treatment.30 In addition, a within-subject study of patients with severe OSA showed that CPAP therapy reduced pain sensitivity, with pain sensitivity re-emerging following CPAP withdrawal, indicating a reversible and sleep-dependent mechanism.31 These findings underscore the clinical relevance of identifying individuals at high risk for OSA, while further prospective studies are needed to determine whether treatment of confirmed OSA influences pain outcomes.
Beyond biological mechanisms, shift work may further intensify the relationship between OSA and pain. Previous studies have shown that diurnal polysomnography performed after night shift work is associated with a higher AHI than nocturnal polysomnography, suggesting increased OSA severity during daytime sleep.32 Such exacerbation of OSA during post-shift sleep may amplify its adverse physiological effects. Moreover, several studies have reported associations between shift work and pain outcomes, including LBP, indicating that circadian disruption and occupational stressors may influence pain susceptibility.33,34 From an occupational health perspective, these findings suggest that systematic screening for OSA using the STOP-Bang questionnaire among shift workers may help identify workers who are more likely to report pain-related outcomes and may warrant further clinical evaluation.
This study has several limitations. First, because of the cross-sectional study design, we cannot determine the temporal direction of the observed associations between OSA risk and pain outcomes. Chronic pain itself may worsen sleep quality and daytime functioning, potentially increasing STOP‑Bang scores and perceived OSA risk, which raises the possibility of reverse causation rather than a purely unidirectional effect of OSA risk on pain.35,36 In addition, because the STOP-Bang questionnaire includes symptom-based items such as daytime tiredness, bidirectional relationships cannot be excluded. Second, LBP was assessed using a single self-reported question regarding the presence or absence of symptoms within the preceding 6 months. The absence of information on pain duration, severity, and clinical characteristics limits the depth of clinical interpretation. Such self‑reported measures are susceptible to recall bias and non‑differential misclassification, which may attenuate the true strength of associations and limit direct comparability with studies using standardized, validated pain instruments. Accordingly, future studies incorporating standardized pain assessment instruments and detailed evaluations of underlying musculoskeletal conditions are needed. Third, OSA risk was assessed using the STOP-Bang questionnaire alone. Although polysomnography remains the gold standard for diagnosing OSA, its high cost and logistical complexity limit its feasibility in large occupational studies. Therefore, this study used the STOP-Bang questionnaire as a practical screening tool, and the findings should be interpreted in relation to screening-defined OSA risk rather than clinically confirmed OSA.14 Given the Asian study population, we additionally performed sensitivity analyses using a modified STOP-Bang questionnaire with an Asian-specific BMI cutoff of 30 kg/m², and the results were consistent with the primary findings.37 Fourth, occupational factors such as work posture, frequency of heavy load handling, and other ergonomic exposures were not comprehensively evaluated. Although working period was included in the adjusted models as a proxy for cumulative occupational exposure, detailed ergonomic factors were not available. Therefore, residual confounding by occupational physical workload cannot be excluded. Fifth, psychological factors, including depression, anxiety, sleep duration, and job stress, were not assessed, raising the possibility of residual confounding. Finally, our study included only male shift workers from a single tire manufacturing factory, which limits external validity. Sex‑specific differences in both OSA and musculoskeletal pain, as well as differences in ergonomic and organizational factors across industries, may restrict the generalizability of our findings to female workers and to other occupational settings. Future longitudinal studies incorporating objective sleep measurements and more detailed occupational and psychosocial assessments are needed to clarify the temporal relationship between OSA risk and pain outcomes.
Despite these limitations, this study has several notable strengths. First, this is the first study to examine the association between OSA risk and LBP specifically among shift workers. Given the potential influence of shift work on both sleep disorders and pain-related outcomes, these findings provide important insights for occupational health management. Second, restricting the analysis to shift workers minimized confounding related to work schedule variability, which can substantially affect sleep patterns and sleep-related disorders. Third, the study population was relatively homogeneous in terms of work schedule and work environment, which may have strengthened internal validity, although this also limits external generalizability. Finally, by examining both LBP and musculoskeletal pain more broadly, this study suggests that elevated OSA risk may be associated with multiple pain conditions. Collectively, these findings suggest that systematic screening for OSA risk may be considered as part of comprehensive occupational health assessments for workers reporting musculoskeletal symptoms.
CONCLUSIONS
In this study, moderate and high OSA risk were independently associated with an increased prevalence of both LBP and musculoskeletal pain among male shift workers in a tire manufacturing factory, and these associations remained statistically significant after adjustment for multiple confounders. These findings suggest that screening for OSA risk using the STOP-Bang questionnaire may be considered in occupational health assessments of shift workers reporting LBP or musculoskeletal symptoms, particularly as a means of identifying workers who may warrant further clinical evaluation. Further longitudinal studies using objective sleep measurements and more comprehensive occupational, clinical, and psychosocial assessments are needed to clarify the temporal relationship between OSA risk and pain outcomes.
Abbreviations
AHI
apnea-hypopnea index
BMI
body mass index
CI
confidence interval
CPAP
continuous positive airway pressure
LBP
low back pain
OR
odds ratio
OSA
obstructive sleep apnea
OSA risk
STOP-Bang-defined risk of obstructive sleep apnea
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Competing interests
Prof. Won-Ju Park has been a member of the editorial board of the Annals of Occupational and Environmental Medicine since 2021. He was not involved in the review process. Otherwise, no other potential conflict of interest relevant to this article was reported.
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Author contributions
Conceptualization: Jung S, Cho S. Data curation: Cho S. Investigation: Kim S. Methodology: Kang KW, Kim J. Writing - original draft: Jung S, Park WJ. Writing - review & editing: Jung S, Cho S, Kang KW, Kim S, Kim J, Park WJ.
NOTES
Table 1.
General characteristics of participants according to low back pain
Table 2.
Association between specific STOP-Bang questionnaire components and low back pain
Table 3.
Odds ratios for low back pain according to screening-defined obstructive sleep apnea risk
Model 1 was adjusted for smoking, alcohol consumption, exercise status, and working period.
Model 2 was adjusted for smoking, alcohol consumption, exercise status, working period, age, body mass index, hypertension, diabetes mellitus, and dyslipidemia.
OSA risk: STOP-Bang-defined risk of obstructive sleep apnea.
Table 4.
Odds ratios for musculoskeletal pain according to screening-defined obstructive sleep apnea risk
Model 1 was adjusted for smoking, alcohol consumption, exercise status, and working period.
Model 2 was adjusted for smoking, alcohol consumption, exercise status, working period, age, body mass index, hypertension, diabetes mellitus, and dyslipidemia.
OSA risk: STOP-Bang-defined risk of obstructive sleep apnea.
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XML DownloadRelationship between obstructive sleep apnea risk and low back pain among shift workers in a tire manufacturing factory
Relationship between obstructive sleep apnea risk and low back pain among shift workers in a tire manufacturing factory
| Variable | Low back pain | p-value | |
|---|---|---|---|
| Yes (n = 466) | No (n = 510) | ||
| Age (years) | 49.5 ± 6.3 | 48.8 ± 6.9 | 0.108 |
| BMI (kg/m2) | 24.9 ± 3.1 | 24.7 ± 2.9 | 0.165 |
| Hypertension | 89 (19.1) | 80 (15.7) | 0.159 |
| Diabetes mellitus | 65 (13.9) | 62 (12.2) | 0.406 |
| Dyslipidemia | 189 (40.6) | 216 (42.4) | 0.570 |
| Smoking | 355 (76.2) | 355 (69.6) | 0.021 |
| Alcohol consumption | 330 (70.8) | 349 (68.4) | 0.419 |
| Regular exercise | 191 (41.0) | 215 (42.2) | 0.711 |
| Working period (months) | 283.0 ± 89.5 | 278.7 ± 93.8 | 0.473 |
| OSA risk | |||
| Low | 266 (57.1) | 353 (69.2) | <0.001 |
| Moderate | 95 (20.4) | 80 (15.7) | 0.056 |
| High | 105 (22.5) | 77 (15.1) | 0.003 |
| Variable | Low back pain | p-value | |
|---|---|---|---|
| Yes (n = 466) | No (n = 510) | ||
| Snoring | 124 (26.6) | 99 (19.4) | 0.007 |
| Tiredness | 160 (34.3) | 119 (23.3) | <0.001 |
| Observed apnea | 63 (13.5) | 32 (6.3) | <0.001 |
| Hypertension | 89 (19.1) | 80 (15.7) | 0.159 |
| BMI ≥ 35 kg/m2 | 1 (0.2) | 3 (0.6) | 0.631 |
| Age ≥ 50 years | 248 (53.2) | 258 (50.6) | 0.411 |
| Neck circumference ≥ 43 cm | 10 (2.1) | 8 (1.6) | 0.503 |
| Variable | Unadjusted | p-value | Model 1 | p-value | Model 2 | p-value |
|---|---|---|---|---|---|---|
| OSA risk | ||||||
| Low | 1 | 1 | 1 | |||
| Moderate | 1.58 (1.13–2.21) | 0.008 | 1.55 (1.09–2.21) | 0.015 | 1.50 (1.02–2.20) | 0.038 |
| High | 1.81 (1.30–2.53) | < 0.001 | 1.76 (1.25–2.47) | 0.001 | 1.75 (1.20–2.55) | 0.004 |
| Variable | Unadjusted | p-value | Model 1 | p-value | Model 2 | p-value |
|---|---|---|---|---|---|---|
| OSA risk | ||||||
| Low | 1 | 1 | 1 | |||
| Moderate | 1.82 (1.30–2.56) | 0.001 | 1.93 (1.35–2.76) | < 0.001 | 1.86 (1.26–2.73) | 0.002 |
| High | 1.87 (1.34–2.61) | < 0.001 | 1.87 (1.33–2.62) | < 0.001 | 1.84 (1.26–2.68) | 0.002 |
Table 1. General characteristics of participants according to low back pain
Values are presented as number (%), arithmetic mean ± standard deviation, or BMI: body mass index; OSA risk: STOP-Bang-defined risk of obstructive sleep apnea.
Table 2. Association between specific STOP-Bang questionnaire components and low back pain
Values are presented as number (%) or BMI: body mass index.
Table 3. Odds ratios for low back pain according to screening-defined obstructive sleep apnea risk
Model 1 was adjusted for smoking, alcohol consumption, exercise status, and working period. Model 2 was adjusted for smoking, alcohol consumption, exercise status, working period, age, body mass index, hypertension, diabetes mellitus, and dyslipidemia. OSA risk: STOP-Bang-defined risk of obstructive sleep apnea.
Table 4. Odds ratios for musculoskeletal pain according to screening-defined obstructive sleep apnea risk
Model 1 was adjusted for smoking, alcohol consumption, exercise status, and working period. Model 2 was adjusted for smoking, alcohol consumption, exercise status, working period, age, body mass index, hypertension, diabetes mellitus, and dyslipidemia. OSA risk: STOP-Bang-defined risk of obstructive sleep apnea.
