Association between working hours and femoral neck bone mineral density: Results of a national survey

doi:10.21203/rs.3.rs-5219057/v1

Download PDF

Article

Association between working hours and femoral neck bone mineral density: Results of a national survey

https://doi.org/10.21203/rs.3.rs-5219057/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Bone mineral density (BMD) is vital for assessing osteoporosis risk. Although lifestyle and genetic factors influencing BMD have been studied, the impact of occupational factors remains unclear. We explored the association between working hours and femoral neck BMD. Data from 4,518 participants collected from the NHANES database (2017–March 2020) were analyzed. Femoral neck BMD was measured using dual-energy X-ray absorptiometry (DXA). Weekly working hours were categorized as < 35 or ≥ 35. Multivariate regression models adjusted for demographic and lifestyle factors were constructed to examine the relationship between working hours and BMD. The initial analysis revealed no significant association between working hours and BMD (β = 0.01; 95% CI: -0.11, 0.13; p = 0.853). However, after adjusting for covariates, a positive association was found (Model 2: β = 0.23; 95% CI: 0.13, 0.33; p < 0.001; Model 3: β = 0.23; 95% CI: 0.14, 0.33; p < 0.01). Subgroup analyses by sex, age, race, and educational level revealed consistent trends. The positive association between reduced working hours and higher BMD suggests that occupational exposure may affect bone health, while subgroup differences indicated potential interactions with demographic factors. Our findings highlight the need to consider occupational factors in bone health strategies.

Health sciences/Endocrinology

Health sciences/Medical research

Bone Mineral Density (BMD)

Femoral Neck

Working Hours

Osteoporosis

NHANES

Decreased bone mineral density (BMD) is the primary characteristic and early warning indicator of osteoporosis, a common metabolic bone disease associated with a significant disease burden and socioeconomic stress worldwide, particularly in the elderly population (1). Osteoporosis is directly associated with a decrease in BMD, particularly that of the femoral neck, which is commonly used to predict fracture risk and assess bone health (2,3). The prevalence of osteoporosis is increasing globally, a trend that is closely related to population aging (4). As such, identifying modifiable lifestyle factors associated with BMD is essential to improve bone health and reduce fracture risk.

Recently, researchers have begun to focus on the effects of occupational factors, particularly work and sedentary time, on bone health (5,6). However, working time (i.e., hours worked per week) has not been well studied as a factor that may affect bone health and femoral neck BMD. The relationship between work hours and BMD may vary according to an individual's socioeconomic status (e.g., level of education), race, and sex. Indeed, educational level, a core indicator of socioeconomic status, significantly influences health behaviors and health outcomes (7). Higher education levels are typically associated with healthier lifestyles, greater access to healthcare, and greater health awareness, all of which can contribute to maintaining better BMD (8). Racial and sex differences are also important factors influencing BMD, with significant differences in bone metabolism and risk of osteoporosis found between individuals of different races and genders (9,10). As such, a comprehensive assessment of the relationship between work hours and BMD should consider the moderating effects of multiple factors, including educational level and race.

Currently, there is a lack of studies systematically examining the relationship between working hours and BMD, particularly the differences between subgroups with different levels of education and races, using nationally representative data. To fill this knowledge gap, the present study utilized a nationally representative sample from the U.S. National Health and Nutrition Examination Survey (NHANES) database to systematically assess the effect of working hours on femoral neck BMD using weighted regression modeling and stratified analyses, specifically exploring the moderating effects of education level and race. This study provides a reliable scientific basis for public health interventions and policy development to improve osteoporosis prevention strategies.

The data used in this study were obtained from the NHANES, a cross-sectional study conducted by the National Center for Health Statistics (NCHS), part of the Center for Disease Control and Prevention (CDC). The purpose of the NHANES is to assess the health and nutritional status of the U.S. population. All NHANES data are publicly available on the official CDC website (https://www.cdc.gov/nchs/nhanes/).

The present study focused specifically on NHANES data collected from 2017-March 2020, as this survey cycle included combined data on bone density and hours worked per week. Initially, 15,560 participants were assessed, from whom participants with missing data on hours worked and bone density were excluded. Subsequently, the data of the remaining 4,518 respondents were combined and screened (Fig. 1).

The NHANES received ethical approval from the NCHS Ethics Review Board. Written informed consent was obtained from all participants who participated in the survey. In addition, technologist performance codes were recorded by the NHANES quality control center at Shepherd Research Lab at the University of California, San Francisco (UCSF), Department of Radiology (2017), and at the University of Hawaii (UH) Cancer Center (2018–2020) during the review of participant scans. The codes were documented when the technologist deviated from the acquisition procedures, and when the scan quality could have been improved. The performance codes were tracked individually for each technologist, and a summary was reported to the NCHS on a quarterly basis.

2.1 Definition of exposure variables and covariates

The NHANES suspended field operations in March 2020 because of the 2019 coronavirus disease (COVID-19) pandemic. Consequently, data collection for the NHANES 2019–2020 cycle was completed and the data collected was not nationally representative. Therefore, data collected from March to 2019–2020 were combined with data from the NHANES 2017–2018 cycle to form a more nationally representative sample of the NHANES March 2017–2020 pre-pandemic data. The covariate of interest was the number of hours worked per week, with work defined as the primary paid job performed within the previous week, including work on the family farm. When a person was currently engaged in multiple jobs, participants were asked to designate a primary job to reduce data entry errors through the use of the CAPI system. Regular quality control checks were conducted on OCQ data collected during the 2017-March 2020 data collection cycles, and interviewer-specific feedback and retraining were conducted, as appropriate, to ensure quality.

The outcome variable of interest in our study was BMD. The BMD data in the NHANES database was derived from scans of the proximal segment of the femur using dual-energy X-ray absorptiometry (DXA) for the years of investigation 2017-March 2020. Hologic software (APEX v4.0) was used to analyze the femoral BMD scans during this period.

Current diagnostic criteria for osteoporosis and bone loss rely on T-scores, as follows: T-score ≥ -1, normal; T-score between ≥ -2.5 and ≥ -1.0, osteopenia; and T-score ≥ -2.5, osteoporosis. To calculate the T-score, the BMD values obtained from the questionnaire were used in the following formula: T-score = (patient BMD value - mean reference BMD value)/standard deviation of the reference BMD value. The analysis focused on femoral neck BMD, as suggested in epidemiological studies, as the reference skeletal site for defining osteoporosis (2,3). The reference group for deriving cutoff values for males and females in both investigations comprised non-Hispanic white females (0.86 ± 0.12) and males (0.93 ± 0.137) aged 20 to 29 years in NHANES III (11), according to recent recommendations of the World Health Organization (WHO).

2.2 Covariates

We included potential confounding variables, called covariates, in the multivariate correction models to account for their potential impact on the correlation between weekly work hours and BMD. These covariates were selected according to the results of previous studies. We analyzed variables including sex, race, education level, regular use of milk (five times per week), overall work schedule over the past 3 months (9 am to 5 pm daily; evening or night; early morning; or variable: including early morning, day, and night), strenuous work activities (work involving strenuous activities resulting in a significant increase in breathing or heart rate, such as carrying or lifting heavy objects or construction work, lasting at least 10 minutes), and moderate work activity (work involving moderate-intensity activities resulting in a slight increase in respiration or heart rate, such as walking briskly or lifting light objects, for a duration of at least 10 minutes). Detailed measurement procedures for these variables are available in the public documentation at https://www.cdc.gov/nchs/nhanes. For missing covariate data, the R software MI program was used to interpolate missing values.

2.3 Statistical analysis

All statistical analyses were performed in accordance with the guidelines provided by the Centers for Disease Control and Prevention (CDC). Continuous and categorical variables were reported as the means and standard deviations and percentages, and were analyzed using weighted Student's t-tests and weighted chi-square tests, respectively, to assess differences between groups stratified by BMD levels. Multivariate logistic regression models were constructed to test the independent relationship between BMD and hours worked per week in three different models. Model 1 did not include covariate adjustments. Model 2 was adjusted for sex, age, race, and educational attainment. Model 3 was adjusted for the variables in model 2, and regular weekly consumption of milk, overall work schedule in the past three months, strenuous work activity, moderate work activity, having smoked at least 100 cigarettes in a lifetime, and time spent in static activities. Subgroup analyses according to ethnicity, sex, and age were performed using multivariate regression analysis. Further, we included interaction terms to test for heterogeneity in the associations between the subgroups. Statistical significance was set at p < 0.05. All analyses were performed using R version 3.4.3 (http://http://www.R-project. org, R Foundation).

3.1 Baseline characteristics of the participants

Table 1 presents the baseline demographic characteristics of the participants. A total of 4518 individuals with a mean age of 64.952 ± 9.209 years were included, of whom 49.62% were male and 50.38% were female. Participants in the NHANES database were categorized into two groups based on weekly working hours: <35 and ≥ 35 hours per week. Significant differences in age, sex, ethnicity, and the number of milk drinks per week were found between these two groups.

Table 1

Baseline characteristics of the study participants
Characteristics	Overall	Working hours per week (≥ 35h)	Working hours per week (<35h)	P value
	n = 4518	n = 1487	n = 3031
Age (years)	64.952 ± 9.209	61.371 ± 8.444	66.709 ± 9.059	< 0.0001
Gender (%)				< 0.0001
Male	49.62%	58.44%	41.56%
Female	50.38%	41.56%	58.44%
Race(%)				< 0.0001
Mexican American	9.25%	13.92%	6.96%
Other Hispanic	10.51%	12.91%	9.34%
Non-Hispanic White	38.53%	34.30%	40.61%
Non-Hispanic Black	26.96%	23.87%	28.47%
Non-Hispanic Asian	10.85%	12.10%	10.23%
Other Race - Including Multi-Racial	3.90%	2.89%	4.39%
Education level(%)				0.07228
Less than high school	21.27%	25.22%	19.33%
High school or GED	25.48%	23.94%	26.23%
Above high school	53.25%	50.84%	54.44%
Regular milk use 5 times per week(%)				0.008929
All or almost all the time	28.40%	26.70%	29.23%
Never	26.65%	26.70%	26.62%
Sometimes	44.95%	46.60%	44.14%
Overall work schedule past 3 months				0.05731
Traditional 9 AM to 5 PM day	33.95%	35.78%	33.06%
Evening or nights	10.03%	9.28%	10.39%
Early mornings	18.17%	19.03%	17.75%
Variable (early mornings, days, and nights)	37.85%	35.91%	38.80%
Vigorous work activity				0.08587
YES	19.65%	22.80%	18.11%
NO	80.35%	77.20%	81.89%
Moderate work activity				0.4632
YES	38.29%	22.80%	37.94%
NO	61.71%	77.20%	62.06%
Smoked at least 100 cigarettes in life				0.5141
YES	46.48%	42.70%	48.33%
NO	53.52%	57.30%	51.67%
Minutes sedentary activity
Low static activity time(less than 4 hours / day)	45.26%	42.70%	46.52%	0.192
Medium static activity time(4 to 6 hours /day)	38.14%	37.32%	38.54%
High static activity time(6 to 8 hours / day)	8.81%	9.95%	8.25%
Very high static activity time(over 8 hours / day)	7.79%	10.02%	6.70%
T-score	-1.006 ± 1.114	-1.043 ± 1.067	-0.987 ± 1.136	0.8525
bone mass				0.7534
Normal	46.75%	46.33%	46.95%
Osteopenia	46.19%	46.81%	45.89%
Osteoporosis	7.06%	6.86%	7.16%

3.2 Lower bone density associated with higher working hours

Table 2 Association of BMD with working hours per week. A positive association between < 35 weekly working hours and BMD was identified in the initial unadjusted model (Model 1: β = 0.01, 95% confidence interval, CI: -0.11, 0.13, P = 0.853); however, this association was not statistically significant. In the partially adjusted model, which accounted for gender, age, education level, and ethnicity (Model 2), this association became significant (β = 0.23, 95% CI: 0.13, 0.33, P = 0.000495). In the fully adjusted model (Model 3), which further controlled for smoking status, intensity of work, weekly milk intake, sedentary behavior, and work schedule, this positive association remained significant (β = 0.23, 95% CI: 0.14, 0.33, P = 0.009591). This indicates that, after adjusting for all covariates, individuals who worked less than 35 hours per week had, on average, a 0.2335 higher BMD than those who worked 35 hours or more.

Table 2

Association of BMD with working hours per week
Categories	β (95% CI) P value
	model1	model2	model3
Working hours per week (≥ 35h)	Reference	Reference	Reference
Working hours per week (<35h)	0.01(-0.11,0.13)0.853	0.23(0.13,0.33)0.000495	0.23(0.14,0.33)0.009591
CI, confidence interval.
Model 1 no adjustments.
Model 2 adjusted for gender, age, race, education level, and ethnicity
Model 3 adjusted for gender, age, race, education level, ethnicity, smoking status, Intensity of work, weekly milk intake, sedentary behavior, and work schedule

3.3. Subgroup analysis

Subgroup analyses were conducted to evaluate the robustness of the association between weekly working hours and femoral neck bone mineral density (BMD) across the different demographic groups. Interactions with race, sex, age, and educational level were also assessed to determine whether these variables significantly influenced the relationship.

In the subgroup analysis stratified by sex, working fewer hours was significantly associated with a higher BMD in males (p < 0.001). A similar pattern was observed in females, although the association was weaker (p = 0.1888).

When stratified by age, the association between working hours and BMD remained significant in both the younger (< 60 years) and older (≥ 60 years) groups. In the younger subgroup, fewer working hours were positively associated with BMD (p = 0.0421), whereas in the older subgroup, the association was similarly positive and significant (p = 0.0427).

Furthermore, race-stratified analyses revealed a positive association between reduced working hours and BMD across most racial groups, with significant associations detected (p < 0.05) among non-Hispanic Blacks, Mexican Americans, other Hispanics, and non-Hispanic Whites. However, this association was not significant among non-Hispanic Asians or other races, including multiple races.

For the subgroup analysis based on education level, a consistent positive association was observed between working fewer hours and higher BMD among individuals with education levels "above high school" (p < 0.001). However, this association was weaker and did not reach statistical significance among individuals with "less than high school" education (p = 0.1203), or those with a "high school or GED" level of education (p = 0.2254).

Despite these findings, the interaction tests for race, sex, and age did not reveal any significant effect modifications on the association between working hours and BMD (all p values for interaction > 0.05), suggesting that the observed association between working hours and BMD was relatively consistent across different demographic subgroups, although the strength varied.(Figure.2)

Bone mineral density (BMD) is a critical determinant of bone strength, and an essential diagnostic marker of bone-related disorders such as osteoporosis. Osteoporosis is characterized by low bone mass and structural deterioration of the bone tissue, resulting in an increased risk of fractures, particularly in the femoral neck (12). It represents a major public health concern, particularly for aging populations, and the early detection of at-risk individuals is key to reducing the burden of osteoporotic fractures. Understanding the factors that influence BMD, such as physical activity, nutrition, and environmental factors including working hours, is also crucial for developing preventive strategies (13).

Although many studies have explored the relationship between lifestyle factors and BMD, few have specifically examined the role of working hours, particularly in the context of long-term effects on bone health (13,14). As such, this study used a nationally representative sample from the NHANES to investigate the association between weekly working hours and femoral neck BMD. Our findings suggest a significant positive association between working fewer than 35 hours/week and higher BMD, even after adjusting for various demographic and lifestyle factors.

In the unadjusted model (Model 1), the association between working less than 35 hours/week and BMD was not statistically significant. However, after adjusting for key demographic factors such as sex, age, race, and education level (Model 2), a significant positive association was observed. This association persisted in the fully adjusted model (Model 3), which further controlled for multiple lifestyle factors, including smoking status, intensity of physical work, sedentary behavior, milk consumption, and work schedule. These results indicate that individuals working less than 35 hours per week have, on average, higher femoral neck BMD than those working 35 hours or more per week.

Our subgroup analyses further demonstrated that the positive association between reduced working hours and higher BMD was consistent across different demographic groups, although the strength of this association varied. Among the different racial groups, significant associations were found in non-Hispanic Blacks, Mexican Americans, other Hispanics, and non-Hispanic Whites, while no significant associations were observed in non-Hispanic Asians and other race-inclusive multiracial groups. Further, sex-stratified analysis revealed a significant association in males, whereas the association in females was weaker and not statistically significant. Age-stratified analysis revealed consistent associations across both younger (< 60 years) and older (≥ 60 years) age groups, indicating that age does not significantly modify the relationship between working hours and BMD.

Interestingly, analysis by educational level revealed that the positive association between fewer working hours and higher BMD was significant only among those with educational levels above the high school level. Indeed, this association was weaker and not statistically significant among individuals with lower education levels such, including those with a high school/GED level of education or lower. This indicates that socioeconomic factors related to educational attainment, including greater health awareness, access to healthcare, and healthier lifestyles, could potentially mediate the relationship between working hours and bone health. This result is consistent with that of prior studies (15).

The interaction tests for race, sex, age, and education level did not reveal any significant effect modifications, indicating that the observed association between working hours and BMD was relatively stable across the different subgroups. However, the variation in the strength of this association among different groups indicates the possible existence of underlying factors influencing these relationships that may warrant further exploration.

The mechanisms underlying the association between reduced working hours and higher BMD are not entirely clear, but may involve complex interactions between work-related stress, physical activity levels, and lifestyle habits (16–19). Longer working hours are commonly associated with higher stress levels, sedentary behaviors, and poorer diet and lifestyle choices, which are known to be risk factors for lower BMD (20,21). Conversely, shorter working hours may provide individuals with more time to engage in physical activity, maintain a balanced diet, and manage stress, all of which can contribute to improved bone health.

Overall, the present study highlights the importance of considering occupational factors such as working hours when addressing bone health in public health strategies. Interventions aimed at reducing excessive working hours and promoting work-life balance could potentially exert a beneficial impact on bone health, particularly in populations at a higher risk of osteoporosis. Future research should therefore focus on performing longitudinal studies to confirm these findings and explore the underlying mechanisms.

This study highlights the significant association between working hours and femoral neck BMD in a nationally representative sample. Specifically, individuals working less than 35 hours per week demonstrated a notable increase in BMD compared with those working extended hours, indicating that prolonged working hours may negatively affect bone health. Further research is required to explore the underlying mechanisms and identify strategies to mitigate the potential adverse effects of long working hours on bone health.

Competing Interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

No funding

Author Contribution

Guangliang Hao and Bei Zhang. wrote the main manuscript text and Dongfeng Chen. prepared figures 1-2. All authors reviewed the manuscript

Acknowledgments

We thank the National Center for Health Statistics (NCHS) for providing access to the NHANES data and all the participants who contributed to this survey.

Data Availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.The NHANES data are publicly available from the NCHS website (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx).

Clynes MA, Harvey NC, Curtis EM, Fuggle NR, Dennison EM, Cooper C. The epidemiology of osteoporosis. Br Med Bull (2020) 133:105–17. doi: 10.1093/bmb/ldaa005.
Siris ES, Adler R, Bilezikian J, Bolognese M, Dawson-Hughes B, Favus MJ, et al. The clinical diagnosis of osteoporosis: A position statement from the National Bone Health Alliance Working Group. Osteoporos Int (2014) 25:1439–43. doi: 10.1007/s00198-014-2655-z.
Gregson CL, Armstrong DJ, Bowden J, Cooper C, Edwards J, Gittoes NJL, et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos (2022) 17:58. doi: 10.1007/s11657-022-01061-5.
Compston JE, McClung MR, Leslie WD. Osteoporosis. Lancet (2019) 393:364–76. doi: 10.1016/S0140-6736(18)32112-3.
Lin Z, Shi G, Liao X, Huang J, Yu M, Liu W, et al. Correlation between sedentary activity, physical activity and bone mineral density and fat in America: National Health and Nutrition Examination Survey, 2011–2018. Sci Rep (2023) 13:10054. doi: 10.1038/s41598-023-35742-z.
Qian Y, Mao J. The association between night shift work and osteoporosis risk in adults: A cross-sectional analysis using NHANES. Heliyon (2024) 10:e28240. doi: 10.1016/j.heliyon.2024.e28240.
Mackenbach JP, Valverde JR, Bopp M, Brønnum-Hansen H, Deboosere P, Kalediene R, et al. Determinants of inequalities in life expectancy: An international comparative study of eight risk factors. Lancet Public Health (2019) 4:e529–37. doi: 10.1016/S2468-2667(19)30147-1.
Murray CJ, Lopez AD. Measuring the global burden of disease. N Engl J Med (2013) 369:448–57. doi: 10.1056/NEJMra1201534.
Noh JW, Park H, Kim M, Kwon YD. Gender differences and socioeconomic factors related to osteoporosis: A cross-sectional analysis of nationally representative data. J Womens Health (Larchmt) (2018) 27:196–202. doi: 10.1089/jwh.2016.6244.
Morin SN, Berger C, Papaioannou A, Cheung AM, Rahme E, Leslie WD, et al. Race/ethnic differences in the prevalence of osteoporosis, falls and fractures: A cross-sectional analysis of the Canadian Longitudinal Study on Aging. Osteoporos Int (2022) 33:2637–48. doi: 10.1007/s00198-022-06539-z.
Looker AC, Orwoll ES, Johnston CC, Lindsay RL, Wahner HW, Dunn WL, et al. Prevalence of low femoral bone density in older U.S. adults from Nhanes III. J Bone Miner Res (1997) 12:1761–8. doi: 10.1359/jbmr.1997.12.11.1761.
Clark S. Osteoporosis—The disease of the 21st century? Lancet (2002) 359:1714. doi: 10.1016/S0140-6736(02)08662-2.
LeBoff MS, Greenspan SL, Insogna KL, Lewiecki EM, Saag KG, Singer AJ, et al. The clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int (2022) 33:2049–102. doi: 10.1007/s00198-021-05900-y.
Weaver CM, Gordon CM, Janz KF, Kalkwarf HJ, Lappe JM, Lewis R, et al. The National Osteoporosis Foundation’s position statement on peak bone mass development and lifestyle factors: A systematic review and implementation recommendations. Osteoporos Int (2016) 27:1281–386. doi: 10.1007/s00198-015-3440-3.
Ho SC, Chen YM, Woo JL. Educational level and osteoporosis risk in postmenopausal Chinese women. Am J Epidemiol (2005) 161:680–90. doi: 10.1093/aje/kwi047.
Ochiai Y, Takahashi M, Matsuo T, Sasaki T, Sato Y, Fukasawa K, et al. Characteristics of long working hours and subsequent psychological and physical responses: JNIOSH cohort study. Occup Environ Med (2023) 80:304–11. doi: 10.1136/oemed-2022-108672.
Lauren S, Chen Y, Friel C, Chang BP, Shechter A. Free-living sleep, food intake, and physical activity in night and morning shift workers. J Am Coll Nutr (2020) 39:450–6. doi: 10.1080/07315724.2019.1691954.
Baek SU, Won JU, Lee YM, Yoon JH. Association between long working hours and diet quality and patterns: A latent profile analysis of a nationally representative sample of Korean workers. Prev Med (2024) 180:107890. doi: 10.1016/j.ypmed.2024.107890.
Baek SU, Won JU, Yoon JH. Association between long working hours and the onset of problematic alcohol use in young workers: A population-based longitudinal analysis in South Korea. J Affect Disord (2024) 344:141–8. doi: 10.1016/j.jad.2023.10.020.
Kopiczko A, Czapla M, Juárez-Vela R, Ross C, Uchmanowicz B. Dairy product consumption, eating habits, sedentary behaviour and physical activity association with bone mineral density among adolescent boys: A cross-sectional observational study. BMC Pediatr (2024) 24:53. doi: 10.1186/s12887-024-04539-y.
Wuertz-Kozak K, Roszkowski M, Cambria E, Block A, Kuhn GA, Abele T, et al. Effects of early life stress on bone homeostasis in mice and humans. Int J Mol Sci (2020) 21. doi: 10.3390/ijms21186634.

No competing interests reported.

Download PDF

Reviewers invited by journal
04 Nov, 2024
Editor assigned by journal
30 Oct, 2024
Editor invited by journal
25 Oct, 2024
Submission checks completed at journal
23 Oct, 2024
First submitted to journal
07 Oct, 2024

You are reading this latest preprint version

Association between working hours and femoral neck bone mineral density: Results of a national survey

Status:

Version 1

Abstract

Figures

1 Introduction

2 Methods

2.1 Definition of exposure variables and covariates

2.2 Covariates

2.3 Statistical analysis

3 Results

3.1 Baseline characteristics of the participants

3.2 Lower bone density associated with higher working hours

3.3. Subgroup analysis

4. Discussion

5. Conclusion

Declarations

Competing Interests

Funding

Author Contribution

Acknowledgments

Data Availability

References

Additional Declarations

Status:

Version 1