# Social and occupational factors are associated with musculoskeletal pain prevalence in the general population: a population-based cohort study

Florian Bailly, Benjamin Granger, Violaine Foltz, Sofiane Kab, Audrey Petit, Florence Tubach, Bruno Fautrel

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Florian Bailly, Benjamin Granger, Violaine Foltz, Sofiane Kab, Audrey Petit, et al. Social and occupational factors are associated with musculoskeletal pain prevalence in the general population: a population-based cohort study. Joint Bone Spine, 2026, 93 (2), pp.105984. 10.1016/j.jbspin.2025.105984. hal-05295983

HAL Id: hal-05295983

https://hal.science/hal-05295983v1

Submitted on 13 Dec 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

Original article

# Social and occupational factors are associated with musculoskeletal pain prevalence in the general population: A population-based cohort study

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

suppl.
Informations

Florian Bailly<sup>a, *</sup>, Benjamin Granger<sup>b</sup>, Violaine Foltz<sup>c</sup>, Sofiane Kab<sup>d</sup>, Audrey Petit<sup>e, f</sup>, Florence Tubach<sup>b</sup>, Bruno Fautrel<sup>g</sup>

## ARTICLE INFO

**Article history:**
Accepted 23 September 2025
Available online 29 September 2025

**Keywords:**
Musculoskeletal pain
Musculoskeletal diseases
Low back pain
Physical activity
Pain

## ABSTRACT

**Objective:** Musculoskeletal pain (MP) is a leading cause of disability worldwide, affecting individual well-being and public health. However, in the literature, the prevalence of MP varies considerably because of methodological inconsistencies, selection biases, and differences in case definitions. This study aimed to estimate the population-based prevalence of MP in France and identify key demographic, socioeconomic, and occupational factors associated with MP.

**Methods:** This cross-sectional study used baseline data for the CONSTANCES cohort study, a large, population-based epidemiological study with participants representative of the French adult population (18-69 years old). Inverse probability weighting was used to correct for selection bias and to improve the generalizability of prevalence estimates. MP was assessed with the Nordic Musculoskeletal Questionnaire, with significant pain defined as lasting > 30 days in the past 12 months. Multivariate logistic regression models were used to identify factors associated with low back pain, estimating odds ratios (ORs) and 95% confidence intervals (CIs).

**Results:** Among 193,436 participants, 46.2% reported pain in at least one anatomical site. The most affected areas were the low back (26.6% adjusted prevalence), shoulder (21.4%), neck (19.0%), and knee (19.1%). Odds of low back pain was associated with female sex (OR: 1.39 [95% CI: 1.32-1.47]), older age, obesity, depression (1.71 [1.62-1.80]), and comorbidity burden (1.20 [1.15-1.25]). Odds of low back pain was associated with moderate or high occupational physical activity (OR: 1.33 [1.20-1.50] and 1.69 [1.48-1.93]) but was inversely associated with very active leisure-time physical activity (0.82 [0.70-0.96]). Education level but not household income was a significant socioeconomic factor associated with MP.

**Conclusion:** MP imposes a substantial burden on the French population, particularly among individuals with physically demanding jobs and low education levels. These findings highlight the paradox of physical activity associated with MP.

© 2025 The Author(s). Published by Elsevier Masson SAS on behalf of Société Française de Rhumatologie. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

<footnote><sup>a</sup> Sorbonne Université, Inserm, Institut Pierre-Louis d'Épidémiologie et de Santé Publique, Équipe PEPITES, AP-HP, Hôpital Pitié-Salpêtrière, Centre de la douleur, 75013 Paris, France</footnote>

<footnote><sup>b</sup> Sorbonne Université, Inserm, Institut Pierre-Louis d'Épidémiologie et de Santé Publique, Équipe PEPITES, AP-HP, Hôpital Pitié-Salpêtrière, Département de Santé Publique, 75013 Paris, France</footnote>

<footnote><sup>c</sup> Hôpital Pitié-Salpêtrière, Paris(6) Sorbonne Université, AP-HP, Pitié-Salpêtrière Hospital, Department of Rheumatology, Paris, France</footnote>

<footnote><sup>d</sup> Université Paris Cité, Paris Saclay University, Université de Versailles Saint-Quentin-en-Yvelines, Inserm, UMS 011 "Population-based Cohorts Unit", Paris, France</footnote>

<footnote><sup>e</sup> Inserm, EHESP, Irset - UMR_S 1085, 49000 Angers, France</footnote>

<footnote><sup>f</sup> Centre de consultations de pathologies professionnelles, L'Université Nantes Angers Le Mans (L'UNAM), CHU d'Angers, Angers, France</footnote>

<footnote><sup>g</sup> Sorbonne Université, Inserm, Institut Pierre-Louis d'Épidémiologie et de Santé Publique, Équipe PEPITES, AP-HP, Hôpital Pitié-Salpêtrière, Service de Rhumatologie, 75013 Paris, France</footnote>

<footnote>* Corresponding author.
E-mail address: florian.bailly@aphp.fr (F. Bailly).</footnote>
# 1. Introduction

Musculoskeletal pain (MP), including low back pain (LBP), neck pain, osteoarthritis, and shoulder pain, are among the leading causes of disability worldwide. LBP specifically refers to pain localized in the lower back region, with or without radiating symptoms, whereas MP encompass a broader range of conditions affecting muscles, joints, tendons, and bones. By 2050, the prevalence of LBP alone is projected to increase by 36% due to demographic shifts [1].

Beyond their impact on individual mobility and daily activities, MP impose a significant socioeconomic burden, contributing to reduced work productivity, increased disability pensions, early retirement, and increasing healthcare costs. Estimates vary considerably across studies because of differences in methodology, population sampling, and case definitions, which leads to substantial uncertainty. For instance, 1-year prevalence estimates range from 0.8% to 82.5% for LBP (pooled estimate: 38.1%) [2], 4.8% to 79.5% for neck pain (pooled estimate: 23.1%) [2], and 10.8% to 55.2% for shoulder pain (pooled estimate: 16%) [3]. These wide disparities raise concerns about the comparability of prevalence rates and their generalizability to the general population. Many studies focus on specific subpopulations, such as workers, healthcare professionals (e.g., nurses), or patients seeking care for localized pain (e.g., back, knee, or shoulder) and are often limited to specific geographic regions rather than national representation. A meta-analysis of shoulder pain prevalence found that 63% of included studies lacked general population representativeness [3].

Even large-scale nationally representative studies frequently fail to account for selection bias, which affects who participates in epidemiological research, with unadjusted factors distorting prevalence estimates. Meta-analyses of musculoskeletal pain are particularly prone to these issues, exhibiting sampling limitations, uncontrolled confounders, and the absence of standardized adjustment methods. Although some studies attempt to correct for selection bias, only a few have used weighting techniques based on only age, sex, marital status, profession, and household composition to adjust estimates to the general population [4,5]. Those limitations are important to consider because disadvantaged populations, who experience higher levels of MP and comorbidities such as diabetes and obesity than advantaged populations, are often underrepresented in epidemiological studies [6]. The interplay between multimorbidity, low socioeconomic status, and underrepresentation in research likely contributes to the observed discrepancies in prevalence estimates. Moreover, occupational exposure, such as physically demanding jobs, is consistently associated with risk of MP [7].

Several factors are strongly and consistently associated with MP across epidemiological studies. Older age, female sex, and high body mass index (BMI) are among the most robust risk factors [8,9]. Chronic conditions such as diabetes, cardiovascular diseases, sleep disorders, and depression are also closely linked to MP; depression is both a consequence and an aggravating factor, perpetuating a cycle of chronic pain and psychological distress [8,9]. The association of years of education, low economic status, alcohol use [8], and physical activity with MP is more controversial [10,11]. Physical activity is indisputably beneficial when offered individually as a treatment for chronic pain [9,12]. However, the results of systematic reviews of physical activity as a risk factor of MP vary. This variability could be linked to physical activity during leisure time being beneficial but occupational physical activity possibly associated with MP, especially for work involving handling or lifting [13]. Most of the research is of leisure-time physical activity [10]. Occupational physical activity could increase pain due to repeated movements without interruption but also for general reasons such as static posture, insufficient recovery time or increased inflammation [14]. The largest study of leisure-time and occupa-

tional physical activity found opposite effects on pain, but the main limitation of this study was no accounting for possible confounding factors such as socio-professional category [15]. Leisure-time physical activity may be associated with reduced odds of MP and occupational physical activity with increased odds, but disentangling these effects from confounders such as socioeconomic status, smoking, and age is crucial.

Across countries, to our knowledge, no epidemiological study has comprehensively assessed the prevalence and risk factors of MP while addressing key methodological biases, including selection bias and confounding bias. The CONSTANCES cohort study provides a unique opportunity to overcome these limitations because it is a large, population-based epidemiological study designed to capture detailed information on chronic diseases, occupational exposures, and social determinants of health [16]. By linking self-reported data with national health records, the study allowed for an unbiased estimation of the prevalence of MP and factors associated with musculoskeletal pain in the general population. The cohort, designed to be representative of the French adult population aged 18-69 years, includes more than 200,000 participants recruited over 5 years in 22 health screening centers.

This study aimed to (1) estimate the prevalence of MP in the general population and (2) identify independent social and occupational factors associated with MP in body locations. Additionally, we investigated the odds of LBP with physical activity, distinguishing between leisure-time physical activity, which may be protective, and occupational physical activity, which may promote pain.

# 2. Methods

## 2.1. Study design and settings

This study was a cross-sectional analysis of the baseline data from the CONSTANCES cohort study, a large ongoing epidemiological study with participants representative of the French adult population aged 18-69 years [16]. Participants were invited to participate in the cohort according to stratified sampling based on age, sex, socioeconomic status, and geographic region to ensure balanced distribution and reduce participation bias. To take into account non-participation at inclusion and attrition throughout the longitudinal follow-up, a randomly selected representative cohort of 400,000 nonparticipants was set up and is linked to the same national administrative databases as participants to track the same socioeconomic and health data as for the 220,000 volunteers. It allows the analysis of personal, socioeconomic and health factors associated with participation and to develop reweighting techniques to estimate the prevalence of various parameters in the French general population. Inverse probability weighting (IPW) was applied to correct for non-response bias, ensuring that estimates were representative of the general population. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for methodological rigor and reporting transparency (see Supplementary File).

## 2.2. Data source

For the CONSTANCES cohort study, data were collected from multiple sources, combining self-reported questionnaires, clinical examinations, and linkage to the French national health data system. Baseline data were used for this study. Participants completed standardized self-administered questionnaires covering sociodemographic characteristics, occupational status, health-related behaviors, and medical history. They also underwent a comprehensive clinical examination at one of the CONSTANCES
Health Centers, which included measurements of height and weight and a face-to-face interview to assess previous or current diseases.

## 2.3. Study population

The current study included all CONSTANCES cohort participants who completed the Nordic Musculoskeletal Questionnaire (NMQ) at baseline [17]. We excluded participants with a history of active cancer in the past 12 months to avoid heterogeneity in pain etiology and characteristics and those with missing responses on the NMQ because pain status was a required variable for the analyses.

## 2.4. Variables of interest

The primary outcome was MP location during the 12 months before inclusion in the cohort, assessed by the NMQ. The NMQ is a widely used epidemiological tool for identifying MP across specific anatomical sites, including the back, neck, shoulder, elbow/forearm, hand, and knee/leg. Participants were asked whether they had experienced pain in these regions during the past 12 months and if so, whether the pain had lasted more than 30 days or was permanent. We defined clinically significant pain as pain lasting > 30 days. Although chronic pain is conventionally defined as pain persisting for at least 3 months, we opted for a 30-day threshold to capture both subacute and chronic pain phases. This choice ensures a more inclusive classification while limiting misclassification of transient pain episodes. Similar approaches have been used in other large epidemiological studies assessing musculoskeletal pain prevalence [18].

Additional variables at baseline included demographic characteristics (age, sex), socioeconomic status (income, education, occupation), behavioral factors (smoking, alcohol misuse or dependency, cannabis use, sleep duration, physical activity), and clinical variables (BMI, depression using Center for Epidemiologic Studies Depression Scale [19], comorbidities assessed via the Functional Comorbidity Index [20] excluding arthritis-related items). Leisure time physical activity was assessed by 3 questions on regular commuting, sporting activities and Handiwork/gardening/housework. Depending on the amount of time devoted to the activity (less than or more than 2 hours per week, or journeys of more than 15 minutes), each question could score 0, 1 or 2 points, giving a total of 6 points. Occupational physical activity assessed using activity at work such as walking or handling objects less or more than 25 kg, the 25 kg limit having been identified in previous studies as a risk factor for low back pain [21]. The full list of candidate variables is provided in Table S1.

## 2.5. Statistical methods

Missing data were handled by using multiple imputation by chained equations, generating 10 imputed datasets [22]. Missing data for the Nordic Musculoskeletal were not imputed due to the not missing at random hypothesis for this data. Final parameter estimates were pooled across the imputed datasets by using Rubin's rules [22].

The crude prevalence of musculoskeletal pain was calculated as raw proportions for the 193,436 participants included. Adjusted prevalence was computed by using IPW based on data for the participants included in 2017 (25,472 participants) because the coefficients are calculated for a single year of inclusion and cannot be pooled across different years. To control for participation bias, a control population that did not agree to participate in the cohort but allowed for passive data collection from social security reimbursement registers and professional data was used to calculate these weights. We estimated 95% confidence intervals (CIs) by the

bias-corrected and accelerated bootstrap method, which provides better coverage properties than standard exact methods [21].

A multivariate logistic regression model was used to identify factors associated with back pain in the cohort, estimating odds ratios (ORs) and 95% CIs. Low back pain was selected over other pain locations due to its high prevalence. Participants were randomly divided into a training set (two-thirds of the sample) and a validation set (one-third of the sample) to evaluate model performance. This strategy was employed to limit overfitting and enhance the robustness of associations identified, by verifying their consistency in an independent subset of the data. While not primarily aimed at prediction, this approach ensures that reported associations are not driven by idiosyncratic patterns in the full sample. Such internal replication strategies have been encouraged in large epidemiologic studies to support the generalizability of findings and mitigate data-driven bias. The split was completely random and not based on temporal criteria. Variable selection followed a two-step backward elimination process adapted from Heymans et al. [23]:

* initial selection: backward elimination was applied separately to each of the 10 imputed datasets. Variables were retained if they were selected in at least 50% of the models at a significance threshold of $P < 0.05$;

* final selection: the procedure was repeated using only the variables retained in Step 1. A variable was included in the final model if it was present in at least 90% of the models, thus ensuring robust and stable associations across imputations.

The performance of the LBP model was evaluated using the Hosmer-Lemeshow test for calibration and the area under the receiver operating characteristic curve for discrimination. Furthermore, we evaluated potential collinearity among variables, ensuring model stability and interpretability. All statistical analyses involved using R 4.2.2.

## 2.6. Ethical considerations

This study was approved by the Digital Protection Officer of Pitié-Salpêtrière Hospital (BPD-2018-DIAG-008), in compliance with MR-004 regulation of the Commission Nationale Information et Liberté (CNIL). The CONSTANCES International Scientific Advisory Board approved the study on January 8, 2021. Funding of this study was provided by the French Rheumatology Society (grant number 2680) and Inserm. The CONSTANCES cohort is supported and funded by the Caisse nationale d'assurance maladie (CNAM). The CONSTANCES cohort is an "Infrastructure nationale en Biologie et Santé" and benefits from a grant from ANR (ANR-11-INBS-0002) and from the Ministry of Research. CONSTANCES is also partly funded by MSD and L'Oréal. None of these funding sources had any role in the design of the study, collection and analysis of data or decision to publish. Data used in this study are available upon reasonable request from the CONSTANCES Scientific Committee, subject to ethical approval and data-sharing policies. Written informed consent was obtained from study participant. The CONSTANCES cohort was authorized by the French personal data privacy authority (CNIL #910486) and was approved by the Institutional Review Board of Inserm (IRB Inserm #01-011 and 21-842). The statistical analysis code can be provided upon request to ensure transparency and reproducibility.
<figure><img src="image_6.png" /><figcaption>Fig. 1. Flowchart of the participants in the study.</figcaption></figure>

# 3. Results

## 3.1. Description of the study population

A total of 207,221 participants were included in the CON-STANCES cohort. After excluding individuals with a history of cancer in the past 12 months or missing responses to the NMQ (4.9% and 1.9% of the cohort; Fig. 1), the study population comprised 193,436 participants.

The median age was 30 years (interquartile range 19–41), and 54% of the cohort was women. The cohort exhibited diverse socioeconomic and occupational profiles: 21.7% earned <2100 € household income per month and 28% >4200 € income per month. Educational attainment varied: 2.9% had no diploma, 17% a baccalaureate-equivalent education and 24% a Master's degree. In total, 1.9% had never worked, 9.9% were employees, 29% had an intermediate profession and 32% had executive or higher intel-lectual professions. Mean BMI was 25.0 kg/m² (SD 4.5), 22% of participants reported depressive symptoms and 18% were current smokers.

This description is based on the raw dataset before multiple imputation. Overall, 4.6% of variables contained missing data, the proportion ranging from 0.0% to 11.0%. After correction by weight-ing coefficients, the proportion of low-income participants was higher, mean age lower, and proportion of manual workers higher. A detailed comparison of the unweighted and weighted propor-tions is available in Table S2.

## 3.2. Estimation of musculoskeletal pain prevalence

Adjusted estimates of MP prevalence were 26.6% (95% CI: 26.4–26.7) for LBP, 21.4% (21.3–21.6) for knee pain, 19.0% (18.9–19.2) for neck pain, 19.1% (18.9–19.2) for shoulder pain, 17.1% (17.0–17.3) for hand pain, and 10.8% (10.7–10.9) for elbow pain (Table 1).

## 3.3. Factors associated with LBP

Variables were selected with the backward elimination procedure: among the initial set of 14 candidate variables, 12 passed the first elimination step, excluding alcohol misuse or dependency and cannabis use (Table 2). At the second step, all remaining variables

<table><caption>**Table 1**
Prevalence of subacute or chronic pain (> 30 days in the past 12 months) by body location in the French adult population.</caption><thead><tr><th>Location of pain</th><th>Overall population</th><th>Men</th><th>Women</th></tr></thead><tbody><tr><td>Back</td><td>26.6 (26.4–26.7)</td><td>23.7 (23.4–23.9)</td><td>29.0 (28.8–29.3)</td></tr><tr><td>Neck</td><td>19.0 (18.9–19.2)</td><td>14.3 (14.1–14.5)</td><td>23.0 (22.9–23.3)</td></tr><tr><td>Shoulder</td><td>19.1 (18.9–19.2)</td><td>16.2 (16.1–16.5)</td><td>21.5 (21.3–21.7)</td></tr><tr><td>Knee</td><td>21.4 (21.3–21.6)</td><td>20.0 (19.7–20.1)</td><td>22.7 (22.5–22.9)</td></tr><tr><td>Elbow</td><td>10.8 (10.7–10.9)</td><td>9.7 (9.5–9.9)</td><td>11.7 (11.5–11.8)</td></tr><tr><td>Hand</td><td>17.1 (17.0–17.3)</td><td>13.6 (13.4–13.8)</td><td>20.0 (19.9–20.3)</td></tr></tbody></table>

Data are % (95% confidence interval).

were consistently selected across datasets, demonstrating robust associations with LBP.

### 3.3.1. Demographic and health-related risk factors

In the final multivariate model, odds of LBP was associated with female gender (OR: 1.39 [95% CI: 1.32–1.47]) and older age, with 54% higher likelihood of pain for individuals aged 60–70 years than younger adults. Odds of LBP were also associated with increased comorbidity burden (1.20 [1.15–1.25]), depression (1.71 [1.62–1.80]), and short sleep duration (0.94 [0.93–0.95] per hour decrease) as well as current smoking (1.18 [1.09–1.28]).

### 3.3.2. Occupational and lifestyle factors

Odds of LBP was positively associated with moderate to high occupational physical activity (OR: 1.33 [1.20–1.50] and 1.69 [1.48–1.93]) but negatively with high leisure-time physical activity (0.82 [0.70–0.96] for highly active individuals),

### 3.3.3. Socioeconomic factors

Odds of LBP were inversely associated with having a high school diploma or higher degree (OR: 0.77 [0.70–0.86] for Master's degree) and an executive or intellectual profession (0.91 [0.86–0.99]) or never working (0.76 [0.61–0.96]).

## 3.4. Factors associated with musculoskeletal pain at other sites

For pain at other anatomical sites, similar trends were observed, particularly for female sex, depression, sleep deprivation, and occupa-tional physical activity. However, leisure-time physical activity was not consistently associated with pain in other body regions. Table 3 summarize multivariate analysis of factors associated with leisure-time and occupational physical activity by all body location and Table 4 the associated factors with neck, shoulder, knee, elbow or hand pain.

# 4. Discussion

This study highlights the high prevalence of MP in the French adult population, with nearly half of the participants reporting pain in at least one anatomical site. Low back, knee, neck, and shoulder pain were the most frequently reported. These find-ings confirm that MP represent a major public health burden, affecting a substantial proportion of the population. This study provides novel insights into the association between musculoskele-tal pain and social and occupational factors in likelihood of MP. The results identify the physical activity paradox, with occupa-tional physical activity a risk factor for MP but leisure-time physical activity being protective. This differential impact, observed after adjusting for confounders, supports the hypothesis that repeti-tive, high-intensity work-related physical activity contributes to musculoskeletal strain, whereas voluntary physical activity is more beneficial [24]. Furthermore, socioeconomic factors had the great-est strength of association with musculoskeletal pain. Although household income was not significantly associated with pain on
<table><caption>**Table 2**
Bivariate and multivariate analysis of factors associated with subacute and chronic low back pain.</caption><thead><tr><th rowspan="2">Characteristics</th><th colspan="2">Bivariate analysis</th><th rowspan="2">Multivariate analysis<br>OR (95% CI)</th></tr><tr><th>No back pain</th><th>Back pain</th></tr></thead><tbody><tr><td>Gender: female</td><td>52.0%</td><td>58.7%</td><td>1.39 (1.32-1.47)</td></tr><tr><td>Age (years)</td><td></td><td></td><td></td></tr><tr><td>18-30</td><td>13.3%</td><td>9.3%</td><td>Reference</td></tr><tr><td>31-40</td><td>21.6%</td><td>17.4%</td><td>1.12 (0.99-1.29)</td></tr><tr><td>41-50</td><td>23.7%</td><td>22.6%</td><td><strong>1.20 (1.07-1.35)</strong></td></tr><tr><td>51-60</td><td>20.3%</td><td>24.7%</td><td><strong>1.40 (1.26-1.55)</strong></td></tr><tr><td>61-70</td><td>21.1%</td><td>25.9%</td><td><strong>1.54 (1.38-1.73)</strong></td></tr><tr><td>Body mass index (kg/m²)</td><td></td><td></td><td></td></tr><tr><td>&lt;18.5</td><td>3.0%</td><td>2.3%</td><td><strong>0.85 (0.71-0.99)</strong></td></tr><tr><td>18.5-24.9</td><td>56.3%</td><td>48.1%</td><td>Reference</td></tr><tr><td>25-29.9</td><td>29.7%</td><td>32.9%</td><td><strong>1.18 (1.10-1.25)</strong></td></tr><tr><td>30-34.9</td><td>8.2%</td><td>12.0%</td><td><strong>1.13 (1.03-1.23)</strong></td></tr><tr><td>35-39.9</td><td>2.1%</td><td>3.5%</td><td>1.14 (0.98-1.32)</td></tr><tr><td>&gt;40</td><td>0.7%</td><td>1.3%</td><td>1.18 (0.90-1.54)</td></tr><tr><td>Functional Comorbidity Index, mean (SD)</td><td>1.44 (0.70)</td><td>1.65 (0.85)</td><td><strong>1.20 (1.15-1.25)</strong></td></tr><tr><td>Center for Epidemiologic Studies-Depression Scale score</td><td>19.0%</td><td>32.9%</td><td><strong>1.72 (1.63-1.80)</strong></td></tr><tr><td>Sleep (hours), mean (SD)</td><td>6.14 (0.70)</td><td>5.70 (0.85)</td><td><strong>0.94 (0.93-0.95)</strong></td></tr><tr><td>Leisure-time PA</td><td></td><td></td><td></td></tr><tr><td>No PA</td><td>2.4%</td><td>3.1%</td><td>Reference</td></tr><tr><td>Very little PA</td><td>6.8%</td><td>7.7%</td><td>0.97 (0.82-1.14)</td></tr><tr><td>Little PA</td><td>17.1%</td><td>19.4%</td><td>0.96 (0.83-1.12)</td></tr><tr><td>Moderate PA</td><td>21.7%</td><td>20.5%</td><td>0.87 (0.73-1.03)</td></tr><tr><td>Active PA</td><td>24.6%</td><td>24.0%</td><td>0.88 (0.76-1.03)</td></tr><tr><td>Moderately active PA</td><td>16.6%</td><td>14.4%</td><td><strong>0.81 (0.69-0.94)</strong></td></tr><tr><td>Very active PA</td><td>10.8%</td><td>10.8%</td><td><strong>0.82 (0.70-0.96)</strong></td></tr><tr><td>Occupational physical activity</td><td></td><td></td><td></td></tr><tr><td>Sedentary/no work</td><td>64.6%</td><td>61.1%</td><td>Reference</td></tr><tr><td>Slightly active (walking/handling &lt; 10 kg)</td><td>23.2%</td><td>21.0%</td><td>1.02 (0.95-1.09)</td></tr><tr><td>Moderately active (handling 10-25 kg)</td><td>9.2%</td><td>12.4%</td><td><strong>1.33 (1.20-1.50)</strong></td></tr><tr><td>Highly active (handling &gt; 25 kg)</td><td>3.0%</td><td>5.5%</td><td><strong>1.69 (1.48-1.93)</strong></td></tr><tr><td>Smoking</td><td></td><td></td><td></td></tr><tr><td>Non-smoker</td><td>49.1%</td><td>42.7%</td><td>Reference</td></tr><tr><td>Previous smoker</td><td>32.8%</td><td>37.0%</td><td><strong>1.21 (1.15-1.28)</strong></td></tr><tr><td>Current smoker</td><td>18.1%</td><td>20.3%</td><td><strong>1.18 (1.09-1.28)</strong></td></tr><tr><td>Socio-professional category</td><td></td><td></td><td></td></tr><tr><td>Intermediate profession</td><td>28.6%</td><td>28.6%</td><td>Reference</td></tr><tr><td>Craftsman, merchant or business owner</td><td>2.5%</td><td>2.6%</td><td>0.96 (0.79-1.16)</td></tr><tr><td>Executive and higher intellectual profession</td><td>33.4%</td><td>23.5%</td><td><strong>0.91 (0.86-0.99)</strong></td></tr><tr><td>Employee</td><td>23.8%</td><td>29.7%</td><td>0.99 (0.91-1.07)</td></tr><tr><td>Worker</td><td>9.3%</td><td>13.9%</td><td><strong>1.11 (0.99-1.24)</strong></td></tr><tr><td>Never worked</td><td>2.4%</td><td>1.7%</td><td><strong>0.76 (0.61-0.96)</strong></td></tr><tr><td>Education level</td><td></td><td></td><td></td></tr><tr><td>No diploma</td><td>2.4%</td><td>4.3%</td><td>1.19 (0.99-1.42)</td></tr><tr><td>General certificate of secondary education</td><td>4.9%</td><td>7.7%</td><td><strong>1.16 (1.02-1.32)</strong></td></tr><tr><td>Certificate of professional competence</td><td>14.3%</td><td>21.0%</td><td><strong>1.15 (1.01-1.24)</strong></td></tr><tr><td>Baccalaureate</td><td>16.0%</td><td>17.9%</td><td>Reference</td></tr><tr><td>Bachelor degree</td><td>26.6%</td><td>24.7%</td><td><strong>0.91 (0.85-0.99)</strong></td></tr><tr><td>4 years after baccalaureate</td><td>8.9%</td><td>7.3%</td><td><strong>0.83 (0.74-0.94)</strong></td></tr><tr><td>Master's degree</td><td>26.9%</td><td>17.1%</td><td><strong>0.77 (0.70-0.86)</strong></td></tr><tr><td>Household income/month (euros)</td><td></td><td></td><td></td></tr><tr><td>&lt;450</td><td>0.8%</td><td>0.7%</td><td>0.91 (0.66-1.24)</td></tr><tr><td>450-1000</td><td>3.1%</td><td>4.3%</td><td>1.08 (0.91-1.29)</td></tr><tr><td>1000-1500</td><td>6.2%</td><td>8.4%</td><td>Reference</td></tr><tr><td>1500-2100</td><td>10.5%</td><td>12.4%</td><td>0.94 (0.85-1.05)</td></tr><tr><td>2100-2800</td><td>14.3%</td><td>16.6%</td><td>1.00 (0.91-1.10)</td></tr><tr><td>2800-4200</td><td>29.9%</td><td>29.5%</td><td>0.97 (0.89-1.07)</td></tr><tr><td>&gt;4200</td><td>29.6%</td><td>22.2%</td><td>0.92 (0.89-1.03)</td></tr><tr><td>Cannot answer</td><td>1.1%</td><td>1.0%</td><td>0.87 (0.64-1.17)</td></tr><tr><td>Do not want to answer</td><td>4.5%</td><td>5.0%</td><td>0.96 (0.85-1.11)</td></tr></tbody></table>

PA: physical activity; OR: odds ratio; CI: confidence interval; Bold: significant at *P* < 0.05.

multivariate analyses, education level was strongly linked to MP prevalence, which suggests that education level is a relevant marker of health literacy, occupational exposure, and access to preventive measures. Taken together, these findings provide a comprehensive assessment of the populational prevalence of MP in France and factors associated with MP.

Our study provides more accurate prevalences of musculoskeletal pain than many other studies, whose prevalence rates varied widely depending on study methodologies and population characteristics. The Santé publique France survey study reported

musculoskeletal pain affecting the back in 47% of women and 40% of men, with upper limb pain affecting 30% and 27% [25].

The prevalence of MP was relatively high in this study, but acute pain was not included in the definition. This high prevalence is probably linked to the participation in this cohort of people often underrepresented in clinical studies, such as those with low socioeconomic level or manual labor. The method used here, which consists of selecting patients with an over-representation of individuals who usually participate less in clinical studies, and correcting this participation bias using IPW, may contribute to this
<table><caption>**Table 3**
Multivariate analysis of odds of musculoskeletal pain associated with leisure-time and occupational physical activity by body location.</caption><thead><tr><th rowspan="2"></th><th colspan="6">Body location</th></tr><tr><th>Back</th><th>Neck</th><th>Shoulder</th><th>Knee</th><th>Elbow</th><th>Hand</th></tr></thead><tbody><tr><td><strong>Leisure-time PA</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No PA</td><td>Reference</td><td>Reference</td><td>Reference</td><td>Reference</td><td>Reference</td><td>Reference</td></tr><tr><td>Very little PA</td><td>0.97 (0.82–1.14)</td><td>1.01 (0.81–1.27)</td><td>1.04 (0.84–1.27)</td><td>0.95 (0.75–1.21)</td><td>0.99 (0.75–1.3)</td><td>1.05 (0.81–1.36)</td></tr><tr><td>Little PA</td><td>0.96 (0.83–1.12)</td><td>1.04 (0.87–1.25)</td><td>1.05 (0.85–1.31)</td><td>1.00 (0.8–1.24)</td><td>1.03 (0.8–1.33)</td><td>1.11 (0.89–1.38)</td></tr><tr><td>Moderate PA</td><td>0.87 (0.73–1.03)</td><td>0.94 (0.78–1.13)</td><td>1.00 (0.81–1.23)</td><td>0.93 (0.75–1.16)</td><td>0.95 (0.74–1.23)</td><td>1.03 (0.81–1.3)</td></tr><tr><td>Active PA</td><td>0.88 (0.76–1.03)</td><td>0.96 (0.8–1.15)</td><td>1.02 (0.82–1.27)</td><td>1.00 (0.81–1.24)</td><td>1.01 (0.79–1.31)</td><td>1.06 (0.85–1.33)</td></tr><tr><td>Moderately active PA</td><td><strong>0.81 (0.69–0.94)</strong></td><td>0.88 (0.72–1.07)</td><td>0.95 (0.76–1.18)</td><td>0.95 (0.77–1.17)</td><td>0.95 (0.75–1.22)</td><td>0.99 (0.78–1.25)</td></tr><tr><td>Very active PA</td><td><strong>0.82 (0.70–0.96)</strong></td><td>0.9 (0.74–1.1)</td><td>0.96 (0.74–1.23)</td><td>0.99 (0.8–1.23)</td><td>1.00 (0.77–1.3)</td><td>1.09 (0.85–1.4)</td></tr><tr><td><strong>Occupational PA</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sedentary/no work</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>Slightly active (walking/handling &lt; 10 kg)</td><td>1.021 (0.95–1.09)</td><td>0.99 (0.91–1.08)</td><td>1.03 (0.96–1.11)</td><td>1.04 (0.94–1.14)</td><td>1.00 (0.93–1.08)</td><td>0.99 (0.91–1.08)</td></tr><tr><td>Moderately active (handling 10–25 kg)</td><td><strong>1.33 (1.2–1.5)</strong></td><td><strong>1.23 (1.12–1.36)</strong></td><td><strong>1.23 (1.11–1.37)</strong></td><td><strong>1.36 (1.19–1.56)</strong></td><td><strong>1.41 (1.27–1.57)</strong></td><td><strong>1.23 (1.12–1.36)</strong></td></tr><tr><td>Highly active (handling &gt; 25 kg)</td><td><strong>1.69 (1.48–1.93)</strong></td><td><strong>1.56 (1.38–1.78)</strong></td><td><strong>1.43 (1.24–1.65)</strong></td><td><strong>1.66 (1.40–1.96)</strong></td><td><strong>1.67 (1.40–2.00)</strong></td><td><strong>1.56 (1.38–1.78)</strong></td></tr></tbody></table>

Data are odds ratios and 95% confidence intervals. Bold = significant at *P* < 0.05.

Data are adjusted for gender, age, comorbidity score, body mass index, education level, depression, smoking, hours of sleep, work status, socio-professional category and household income. PA: physical activity.

higher but more accurate estimate of the true prevalence in the general population. Our findings confirm the significant burden of MP in a developed country, aligning with previous national estimates. Palazzo et al. highlighted the substantial individual and societal impact of MP, particularly in terms of work disability and healthcare costs [5]. Picavet et al. in the DMC3 study evaluated the prevalence of musculoskeletal pain in the Netherlands, the discrepancies between the respondents of the questionnaire and the whole Dutch population were corrected with weighting factors based on age, sex, region, and marital status [26]. Those weights were estimated using age, sex, type of household, marital status, geographical area of residence and disability. However, in those studies, socio-professional categories were not taken into account, while the prevalence of musculoskeletal pain varies significantly according to participants' occupations.

The key factors we found associated with musculoskeletal pain were consistent with previous research. Female sex, older age, high BMI, comorbidities, and depressive symptoms were all previously found associated with MP prevalence [27]. A novelty of our results among large-scale generalist cohorts was the contrasting effects of physical activity depending on its context. Odds of MP was associated with moderate to high occupational physical activity, whereas high leisure-time physical activity was protective. The “physical activity paradox” has been primarily studied in terms of cardiovascular health, but only a few studies have rigorously evaluated this discrepancy in MP while accounting for associated factors [24]. Several hypotheses can explain this contrast: prolonged exposure to static postures, lack of recovery time, and psychosocial stressors associated with physically demanding jobs [21]. This distinction between leisure and work-related physical activity is needed in longitudinal studies. These results suggest that population-level education initiatives for individuals with high levels of occupational physical activity may complement prevention programs and contribute to reducing musculoskeletal pain prevalence [28].

Regarding socioeconomic factors, educational attainment was strongly associated with MP prevalence, but household income was not a significant predictor after adjustment. Hence, education level may be a more relevant indicator of health literacy, occupational exposure, and access to preventive measures than other socioeconomic factors. Previous studies have similarly found low educational attainment consistently linked to high burden of MP [29].

Several methodological considerations should be acknowledged. Our study defined significant pain as lasting > 30 days in the past 12 months, which does not fully align with the con-

ventional ≥ 3-month chronic pain definition. This threshold was chosen to encompass both subacute and chronic pain phases, allowing for a more inclusive definition while avoiding misclassification of transient pain episodes, as is commonly done in epidemiological studies [30].

The cross-sectional nature of our study prevents causal inference. Although our results highlight strong associations, they do not establish whether risk factors (e.g., occupational physical activity) contribute to the development of MP or whether preexisting MP modify physical activity patterns.

This study highlights the high burden of MP in the French general population, with nearly half of the participants reporting persistent pain. By leveraging a large, population-based cohort with methodological adjustments, our findings provide precise prevalence estimates and identify key demographic, occupational, and socioeconomic risk factors. The distinction between occupational and leisure-time physical activity underscores the complexity of MP determinants, thus reinforcing the need for nuanced preventive strategies.

From a public health perspective, our findings emphasize the need for targeted prevention programs for high-risk populations, particularly those in physically demanding occupations and low educational groups. Given the widespread misconception that occupational physical activity provides the same health benefits as leisure-time physical activity, clear and proactive communication strategies should be developed.

Future research should prioritize longitudinal studies to clarify causality. Additionally, integrating objective measures (e.g., wearable activity monitors, medical diagnoses) with self-reported data could improve exposure assessment and refine risk prediction models. With smartphone-embedded activity trackers, epidemiology is entering a new era, enabling real-time data collection, better differentiation between occupational and leisure-time physical activity, and personalized prevention strategies. By addressing these challenges, future epidemiological studies can contribute to effective prevention policies and reduce the societal burden of MP.

## Authorship and contributorship

F. Bailly, B. Granger, B. Fautrel made substantial contributions to the design and planning of the study, as well as to data management. They also participated in the writing of the article and critical revision for important intellectual content. They approved the final version for publication and agree to be responsible for all aspects
<table><caption>**Table 4**
Factors associated with neck, shoulder, knee, elbow, or hand pain.</caption><thead><tr><th>Location</th><th>Neck pain</th><th>Shoulder</th><th>Knee</th><th>Elbow</th><th>Hand</th></tr></thead><tbody><tr><td>Sex: woman</td><td>1.78 (1.66-1.9)</td><td>1.48 (1.38-1.58)</td><td>1.26 (1.18-1.34)</td><td>1.36 (1.26-1.47)</td><td>1.77 (1.65-1.89)</td></tr><tr><td>Age</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>18-30</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>31-40</td><td><strong>1.19 (1.05-1.35)</strong></td><td><strong>1.38 (1.20-1.58)</strong></td><td><strong>1.09 (0.99-1.20)</strong></td><td><strong>2.0 (1.6-2.5)</strong></td><td><strong>1.49 (1.29-1.72)</strong></td></tr><tr><td>41-50</td><td><strong>1.41 (1.25-1.59)</strong></td><td><strong>2.11 (1.82-2.44)</strong></td><td><strong>1.38 (1.24-1.52)</strong></td><td><strong>4.3 (3.49-5.29)</strong></td><td><strong>2.24 (1.93-2.59)</strong></td></tr><tr><td>51-60</td><td><strong>1.68 (1.49-1.89)</strong></td><td><strong>2.89 (2.57-3.26)</strong></td><td><strong>1.96 (1.77-2.16)</strong></td><td><strong>5.96 (4.93-7.21)</strong></td><td><strong>3.53 (3.05-4.09)</strong></td></tr><tr><td>61-70</td><td><strong>1.60 (1.39-1.84)</strong></td><td><strong>2.89 (2.54-3.28)</strong></td><td><strong>2.3 (2.05-2.58)</strong></td><td><strong>4.43 (3.59-5.45)</strong></td><td><strong>4.59 (3.95-5.34)</strong></td></tr><tr><td>Comorbidity score</td><td><strong>1.2 (1.14-1.26)</strong></td><td><strong>1.18 (1.13-1.23)</strong></td><td><strong>1.18 (1.12-1.24)</strong></td><td><strong>1.16 (1.09-1.22)</strong></td><td><strong>1.16 (1.1-1.22)</strong></td></tr><tr><td>Body mass index</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;18.5</td><td>0.99 (0.83-1.19)</td><td>0.84 (0.71-1.01)</td><td>0.79 (0.66-0.95)</td><td>0.86 (0.67-1.12)</td><td>0.91 (0.74-1.13)</td></tr><tr><td>18.5 to 24.9</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>25 to 29.9</td><td>1 (0.93-1.07)</td><td><strong>1.07 (1.01-1.14)</strong></td><td><strong>1.28 (1.21-1.36)</strong></td><td>1.08 (0.99-1.18)</td><td><strong>1.11 (1.04-1.18)</strong></td></tr><tr><td>30 to 34.9</td><td><strong>0.83 (0.73-0.95)</strong></td><td>0.99 (0.9-1.08)</td><td><strong>1.42 (1.28-1.58)</strong></td><td>1.05 (0.95-1.17)</td><td>1.09 (0.98-1.21)</td></tr><tr><td>35 to 39.9</td><td><strong>0.75 (0.63-0.9)</strong></td><td>0.95 (0.79-1.12)</td><td><strong>1.74 (1.5-2.02)</strong></td><td>1.02 (0.81-1.28)</td><td>1.16 (0.96-1.4)</td></tr><tr><td>&gt; 40</td><td>0.8 (0.58-1.11)</td><td>1.03 (0.77-1.38)</td><td><strong>2.06 (1.59-2.68)</strong></td><td>1.23 (0.86-1.77)</td><td>1.3 (0.97-1.75)</td></tr><tr><td>Diploma</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Without diploma</td><td>0.98 (0.82-1.16)</td><td>1.13 (0.98-1.31)</td><td>1.17 (1.02-1.35)</td><td>1.08 (0.9-1.3)</td><td>1.13 (0.95-1.35)</td></tr><tr><td>General certificate of secondary education</td><td>1.05 (0.91-1.20)</td><td>1.06 (0.94-1.2)</td><td>1.07 (0.93-1.21)</td><td>1.14 (0.97-1.35)</td><td>1.1 (0.98-1.24)</td></tr><tr><td>Certificate of professional competence</td><td>1.09 (1.00-1.20)</td><td>1.1 (1.01-1.19)</td><td>1.06 (0.96-1.17)</td><td>1.13 (1.01-1.25)</td><td>1.09 (0.99-1.2)</td></tr><tr><td>Baccalaureate</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>Bachelor degree</td><td>0.95 (0.85-1.05)</td><td>0.94 (0.87-1.02)</td><td>0.93 (0.84-1.02)</td><td>0.89 (0.79-1)</td><td>0.94 (0.85-1.04)</td></tr><tr><td>4 years after baccalaureate</td><td>0.85 (0.76-0.95)</td><td>0.86 (0.77-0.96)</td><td>0.88 (0.79-0.99)</td><td>0.82 (0.67-1)</td><td>0.86 (0.76-0.98)</td></tr><tr><td>Master's degree</td><td><strong>0.83 (0.74-0.92)</strong></td><td><strong>0.84 (0.76-0.94)</strong></td><td><strong>0.86 (0.77-0.96)</strong></td><td><strong>0.74 (0.63-0.87)</strong></td><td><strong>0.81 (0.72-0.91)</strong></td></tr><tr><td>Depression (CES-D Score)</td><td><strong>1.72 (1.62-1.83)</strong></td><td><strong>1.59 (1.5-1.69)</strong></td><td><strong>1.58 (1.5-1.67)</strong></td><td><strong>1.52 (1.42-1.64)</strong></td><td><strong>1.56 (1.47-1.66)</strong></td></tr><tr><td>Leisure time physical activity (PA)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No PA</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>Very few PA</td><td>1.01 (0.81-1.27)</td><td>1.04 (0.84-1.27)</td><td>0.95 (0.75-1.21)</td><td>0.99 (0.75-1.3)</td><td>1.05 (0.81-1.36)</td></tr><tr><td>Few PA</td><td>1.04 (0.87-1.25)</td><td>1.05 (0.85-1.31)</td><td>1 (0.8-1.24)</td><td>1.03 (0.8-1.33)</td><td>1.11 (0.89-1.38)</td></tr><tr><td>Moderate PA</td><td>0.94 (0.78-1.13)</td><td>1 (0.81-1.23)</td><td>0.93 (0.75-1.16)</td><td>0.95 (0.74-1.23)</td><td>1.03 (0.81-1.3)</td></tr><tr><td>Active PA</td><td>0.96 (0.8-1.15)</td><td>1.02 (0.82-1.27)</td><td>1 (0.81-1.24)</td><td>1.01 (0.79-1.31)</td><td>1.06 (0.85-1.33)</td></tr><tr><td>Rather active for PA</td><td>0.88 (0.72-1.07)</td><td>0.95 (0.76-1.18)</td><td>0.95 (0.77-1.17)</td><td>0.95 (0.75-1.22)</td><td>0.99 (0.78-1.25)</td></tr><tr><td>Very active PA</td><td>0.9 (0.74-1.1)</td><td>0.96 (0.74-1.23)</td><td>0.99 (0.8-1.23)</td><td>1 (0.77-1.3)</td><td>1.09 (0.85-1.4)</td></tr><tr><td>Occupational physical activity</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sedentary/no work</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>Slightly active (walking/handling &lt; 10 kg)</td><td>0.95 (0.87-1.04)</td><td>0.99 (0.91-1.08)</td><td>1.03 (0.96-1.11)</td><td>1.04 (0.94-1.14)</td><td>1 (0.93-1.08)</td></tr><tr><td>Moderately active (handling objects 10-25 kg)</td><td>1.07 (0.95-1.19)</td><td><strong>1.23 (1.12-1.36)</strong></td><td><strong>1.23 (1.11-1.37)</strong></td><td><strong>1.36 (1.19-1.56)</strong></td><td><strong>1.41 (1.27-1.57)</strong></td></tr><tr><td>Highly active (heavy handling &gt; 25 kg)</td><td><strong>1.21 (1.07-1.38)</strong></td><td><strong>1.56 (1.38-1.78)</strong></td><td><strong>1.43 (1.24-1.65)</strong></td><td><strong>1.66 (1.4-1.96)</strong></td><td><strong>1.67 (1.4-2)</strong></td></tr><tr><td>Tobacco</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Non-smoker</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>Previous smoker</td><td><strong>1.19 (1.12-1.26)</strong></td><td><strong>1.19 (1.12-1.26)</strong></td><td><strong>1.15 (1.08-1.22)</strong></td><td><strong>1.19 (1.1-1.28)</strong></td><td><strong>1.21 (1.14-1.29)</strong></td></tr><tr><td>Actual smoker</td><td><strong>1.15 (1.05-1.25)</strong></td><td><strong>1.13 (1.03-1.24)</strong></td><td><strong>1.1 (1.02-1.18)</strong></td><td><strong>1.19 (1.1-1.3)</strong></td><td><strong>1.16 (1.07-1.25)</strong></td></tr><tr><td>Sleep (hours)</td><td><strong>0.95 (0.93-0.96)</strong></td><td><strong>0.95 (0.94-0.96)</strong></td><td><strong>0.94 (0.92-0.95)</strong></td><td><strong>0.94 (0.92-0.96)</strong></td><td><strong>0.93 (0.92-0.95)</strong></td></tr><tr><td>Socio-professional category</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intermediate profession</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>Craftsman, merchant or business owner</td><td>1.08 (0.87-1.33)</td><td>0.99 (0.83-1.18)</td><td>1 (0.81-1.24)</td><td>0.98 (0.78-1.23)</td><td>1.06 (0.87-1.29)</td></tr><tr><td>Executive and higher intellectual profession</td><td>0.93 (0.86-1.01)</td><td>0.95 (0.88-1.02)</td><td><strong>0.91 (0.83-0.99)</strong></td><td>0.92 (0.84-1.02)</td><td><strong>0.88 (0.81-0.96)</strong></td></tr><tr><td>Employee</td><td>0.98 (0.91-1.06)</td><td>1.02 (0.95-1.09)</td><td>0.98 (0.91-1.05)</td><td>1.03 (0.94-1.14)</td><td>1.02 (0.94-1.11)</td></tr><tr><td>Worker</td><td>1.08 (0.96-1.21)</td><td>1.18 (1.05-1.32)</td><td>1.09 (0.98-1.22)</td><td>1.28 (1.14-1.44)</td><td>1.2 (1.06-1.37)</td></tr><tr><td>Never work</td><td>0.81 (0.63-1.03)</td><td>0.84 (0.66-1.08)</td><td>0.88 (0.69-1.12)</td><td>0.88 (0.56-1.37)</td><td>0.79 (0.61-1.02)</td></tr><tr><td>Household income</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 450 euros</td><td>0.92 (0.67-1.26)</td><td>1.06 (0.74-1.52)</td><td>0.98 (0.72-1.34)</td><td>0.96 (0.59-1.57)</td><td>0.97 (0.67-1.41)</td></tr><tr><td>450 to 1000 euros</td><td>1.04 (0.9-1.2)</td><td>1.08 (0.93-1.26)</td><td><strong>1.18 (1.01-1.38)</strong></td><td>1.15 (0.94-1.4)</td><td>1.11 (0.91-1.36)</td></tr><tr><td>1000 to 1500 euros</td><td>0.96 (0.84-1.09)</td><td>0.99 (0.86-1.15)</td><td>0.94 (0.83-1.06)</td><td>0.88 (0.73-1.07)</td><td>0.95 (0.82-1.09)</td></tr><tr><td>1500 to 2100 euros</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>2100 to 2800 euros</td><td>1.01 (0.91-1.13)</td><td>1.03 (0.9-1.19)</td><td>0.96 (0.87-1.07)</td><td>0.95 (0.82-1.11)</td><td>1 (0.88-1.13)</td></tr><tr><td>2800 to 4200 euros</td><td>0.99 (0.89-1.11)</td><td>1.02 (0.89-1.16)</td><td>0.93 (0.85-1.03)</td><td>0.95 (0.84-1.07)</td><td>0.99 (0.88-1.12)</td></tr><tr><td>More than 4200 euros</td><td>0.91 (0.81-1.03)</td><td>0.98 (0.86-1.12)</td><td>0.90 (0.80-1.00)</td><td>0.91 (0.77-1.08)</td><td>0.94 (0.83-1.06)</td></tr><tr><td>Cannot answer</td><td>0.92 (0.69-1.24)</td><td>0.93 (0.66-1.30)</td><td>1.00 (0.71-1.41)</td><td>0.94 (0.57-1.55)</td><td>1.03 (0.72-1.46)</td></tr><tr><td>Do not want to answer</td><td>1.01 (0.87-1.18)</td><td>1.02 (0.87-1.19)</td><td>0.96 (0.83-1.11)</td><td>0.97 (0.8-1.17)</td><td>1.01 (0.86-1.2)</td></tr></tbody></table>

Results are presented with odds ratio and confidence interval (95%). Ref: reference category; CES-D: Center for Epidemiologic Studies-Depression Scale. Bold: *p* < 0.05.

of the work. B. Granger validated the methodological part of this research.

V. Foltz, S. Kab, A. Petit, B. Granger, F. Tubach contributed to the design and planning of the study. They participated in the writing of the article and critical revision for important intellectual content. They approved the final version for publication and agree to be responsible for all aspects of the work.

All authors have reviewed and approved the final manuscript and agree to be responsible for all aspects of the work ensuring that issues related to the accuracy or integrity of any part of the work are properly investigated and resolved.

## Transparency declaration

F. Bailly, the lead author affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

## Disclosure of interest

F. Bailly has received consulting fees from Lilly and Grünenthal.

B. Granger has received consulting fees from Boston Scientific.
B. Fautrel has received research grants from AbbVie, Lilly, MSD and Pfizer, and consultancy fees from AbbVie, Amgen, Biogen, BMS, Celltrion, Chugai, Fresenius Kabi, Galapagos, Janssen, Lilly, Medac, MSD, NORDIC Pharma, Novartis, OWKIN, Pfizer, Roche, Sandoz, Sanofi-Genzyme, SOBI, UCB, and Viatris.

F. Tubach, V. Foltz and S. Kab declare that they have no competing interest.

## Acknowledgments

The authors thank "Population-based Epidemiologic Cohorts Unit", UMS 11 Université de Paris, Inserm, Paris Saclay University, UVSQ, who designed and is in charge of the Constances Project. They also thank the "Caisse nationale de l'Assurance Maladie" (CNAM) and the "Centres d'examen de santé" of the French Social Security, which are collecting a large part of the data, as well as the "Caisse nationale d'assurance vieillesse", ClinSearch, Asqualab and Eurocell in charge of the data quality control.

The authors thanks the French Rheumatology Society for the funding of this project (grant number 2680).

## Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.jbspin.2025.105984.

## References

[1] Ferreira ML, De Luca K, Haile LM, Steinmetz JD, Culbreth GT, Cross M, et al. Global, regional, and national burden of low back pain, 1990–2020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol 2023;5:e316–29.

[2] Hoy DG, Protani M, De R, Buchbinder R. The epidemiology of neck pain. Best Pract Res Clin Rheumatol 2010;24:783–92.

[3] Lucas J, van Doorn P, Hegedus E, Lewis J, van der Windt D. A systematic review of the global prevalence and incidence of shoulder pain. BMC Musculoskelet Disord 2022;23:1073.

[4] Picavet HSJ, Schouten JSAG. Musculoskeletal pain in the Netherlands: prevalences, consequences and risk groups, the DMC3-study. Pain 2003;102:167–78.

[5] Palazzo C, Ravaud J-F, Trinquart L, Dalichampt M, Ravaud P, Poirauede S. Respective contribution of chronic conditions to disability in France: results from the National Disability-Health Survey. PLoS One 2012;7:e44994.

[6] Andrianakos AA, Kontelis LK, Karamitsos DG, Aslanidis SI, Georgountzos AI, Kaziolas GO, et al. Prevalence of symptomatic knee, hand, and hip osteoarthritis in Greece. The ESORDIG study. J Rheumatol 2006;33:2507–13.

[7] Coggon D, Ntani G, Palmer KT, Felli VE, Harari R, Barrero LH, et al. Patterns of multisite pain and associations with risk factors. Pain 2013;154:1769–77.

[8] Creed F. A review of the incidence and risk factors for fibromyalgia and chronic widespread pain in population-based studies. Pain 2020;161:1169–76.

[9] Hartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, et al. What low back pain is and why we need to pay attention. Lancet 2018;391:2356–67.

[10] Sitthipornvorakul E, Janwantanakul P, Purepong N, Pensri P, Van Der Beek AJ. The association between physical activity and neck and low back pain: a systematic review. Eur Spine J 2011;20:677–89.

[11] Heneweer H, Vanhees L, Picavet SJH. Physical activity and low back pain: a U-shaped relation? Pain 2009;143:21–5.

[12] Bailly F, Trouvin A-P, Bercier S, Dadoun S, Deneuville J-P, Faguer R, et al. Clinical guidelines and care pathway for management of low back pain with or without

radicular pain. Joint Bone Spine 2021;88:105227.

[13] Kwon BK, Roffey DM, Bishop PB, Dagenais S, Wai EK. Systematic review: occupational physical activity and low back pain. Occup Med 2011;61:541–8.

[14] Pronk N. Physical activity paradox: providing evidence-based guidance while closing research gaps. Br J Sports Med 2024;58:932–3.

[15] Fjeld MK, Arnes AP, Engdahl B, Morseth B, Hopstock LA, Horsch A, et al. The physical activity paradox: exploring the relationship with pain outcomes. The Tromsø Study 2015–2016. Pain 2024;166:315–27. http://dx.doi.org/10.1097/j.pain.00000000000003344.

[16] Zins M, Goldberg M, CONSTANCES team. The French CONSTANCES population-based cohort: design, inclusion and follow-up. Eur J Epidemiol 2015;30:1317–28.

[17] Descatha A, Roquelaure Y, Chastang J-F, Evanoff B, Melchior M, Mariot C, et al. Validity of Nordic-style questionnaires in the surveillance of upper-limb work-related musculoskeletal disorders. Scand J Work Environ Health 2007;33:58–65.

[18] Evans DW, Liew BXW, Andersen JH, Mose S. Where is the pain? Spatial patterns of pain co-occurrence in a population-based study of 4833 pain drawings incorporating network analysis. Pain 2022, http://dx.doi.org/10.1097/j.pain.00000000000003551.

[19] Radloff LS. The CES-D Scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1977;1:385–401.

[20] Groll D, To T, Bombardier C, Wright J. The development of a comorbidity index with physical function as the outcome. J Clin Epidemiol 2005;58:595–602.

[21] Coenen P, Gouttebarge V, van der Burght ASAM, van Dieën JH, Frings-Dresen MHW, van der Beek AJ, et al. The effect of lifting during work on low back pain: a health impact assessment based on a meta-analysis. Occup Environ Med 2014;71:871–7.

[22] Rubin DB. Multiple imputation for nonresponse in surveys. 1st ed. Wiley; 1987 https://onlinelibrary.wiley.com/doi/book/10.1002/9780470316696.

[23] Heymans MW, van Buuren S, Knol DL, van Mechelen W, de Vet HC. Variable selection under multiple imputation using the bootstrap in a prognostic study. BMC Med Res Methodol 2007;7:33.

[24] Holtermann A, Krause N, Van Der Beek AJ, Straker L. The physical activity paradox: six reasons why occupational physical activity (OPA) does not confer the cardiovascular health benefits that leisure time physical activity does. Br J Sports Med 2018;52:149–50.

[25] SPF. Prévalence de TMS en France, dans la population générale et dans la population des actifs occupés selon la catégorie socioprofessionnelle et le secteur d'activité. Résultats du Baromètre de Santé publique France; 2021 [n.d. https://www.santepubliquefrance.fr/import/prevalence-de-tms-en-france-dans-la-population-générale-et-dans-la-population-des-actifs-occupes-selon-la-categorie-socioprofessionnelle-et-le-sec (accessed March 7, 2025)].

[26] Picavet HSJ. Prevalence of self reported musculoskeletal diseases is high. Ann Rheum Dis 2003;62:644–50.

[27] Leclerc A, Ha C, Roquelaure Y, Goldberg M. La situation épidémiologique des troubles musculo-squelettiques: des définitions et des méthodes différentes, mais un même constat. Bull Epidemiol Hebd 2005;218–9 [1 tabl.] https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url= https://www.santepubliquefrance.fr/content/download/185618/document. file/29659_2494-d1058.pdf&ved=2ahUKEwiXpYqQ5pmQAxU9Q6QEHVsIC 4kQFnoECAsQAQ&usg=AOvVaw2Gq9hlG40rLNjL52Gmty.3.

[28] Moreira-Silva I, Teixeira PM, Santos R, Abreu S, Moreira C, Mota J. The effects of workplace physical activity programs on musculoskeletal pain: a systematic review and meta-analysis. Workplace Health Saf 2016;64:210–22.

[29] Lloyd A, Dugas J, Fouquet N, Geoffroy C, Robert M, Roquelaure Y, et al. Mieux connaître les facteurs de risque de troubles musculo-squelettiques chez les salariés de la santé humaine et de l'action sociale, pour mieux lutter contre. Étude Sumer 2016–2017. [n.d. https://www.santepubliquefrance. fr/import/mieux-connaitre-les-facteurs-de-risque-de-troubles-musculo- squelettiques-chez-les-salaries-de-la-sante-humaine-et-de-l-action-sociale -pour-mieux-l (accessed March 7, 2025)].

[30] Överås CK, Johansson MS, De Campos TF, Ferreira ML, Natvig B, Mork PJ, et al. Distribution and prevalence of musculoskeletal pain co-occurring with persistent low back pain: a systematic review. BMC Musculoskelet Disord 2021;22:91.