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BMJ Open
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BMJ Open
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10.1136/bmjopen-2023-082851
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Original Research
Public Health
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Modulatory effect of sedentary behaviour on obesity and chronic low back pain: a cross-sectional study using data from the National Health and Nutrition Examination Survey
Liu Lu lucia2lau@foxmail.com
1
Wang Bihan echo19881116@163.com
1
Wen Huaneng whn18270096305@126.com
12
Yang Cheng yc19890418@163.com
1
http://orcid.org/0009-0002-6697-6244
Wang Bingshui wangbingshui0825@126.com
1
1 Department of Rehabilitation, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, China
2 Southern Medical University, Guangzhou, Guangdong, China
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None declared.

Dr; wangbingshui0825@126.com
2024
26 8 2024
14 8 e08285105 12 2023
09 8 2024
Copyright © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

Abstract

Objective

To explore whether sedentary behaviour could modulate the association between overweight or obesity and chronic low back pain (CLBP).

Design

A retrospective cross-sectional study.

Setting and participants

A total of 4289 participants in the US cohort from the National Health and Nutrition Examination Survey were included.

Primary and secondary outcome measures

CLBP was the outcome.

Results

After adjusting for confounding factors, an increased risk of CLBP was identified in people who were overweight (OR 1.41, 95% CI 1.13 to 1.76) and obesity (OR 1.48, 95% CI 1.01 to 2.18). No significant association between sedentary behaviour time and CLBP was observed. In body mass index (BMI)<25 kg/m2 vs BMI≥30 kg/m2 group, sedentary behaviour time showed a modulatory effect on obesity and CLBP (p=0.047). In the sedentary behaviour time >4.5 hours group, the risk of CLBP was increased as BMI elevation, indicating sedentary behaviour time >4.5 hours played a modulatory role in the relationship between obesity and CLBP.

Conclusion

Obesity was significantly associated with an increased risk of CLBP, and sedentary behaviour time modulated the association between obesity and CLBP. The findings might provide a reference for the lifestyle modifications among individuals with obesity and reducing sedentary behaviour is recommended for this population.

Obesity
Chronic Pain
REHABILITATION MEDICINE
Shenzhen Science and Technology Program JCYJ20190814114207451
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pmcSTRENGTHS AND LIMITATIONS OF THIS STUDY

This study used a large sample size and employed a complex multistage probabilistic sampling method to ensure the generalisability of the results to other populations.

This was a cross-sectional study, limiting the ability to establish causality between sedentary behaviour, obesity and chronic low back pain (CLBP).

Some data were obtained through questionnaires, which might be subject to recall bias.

The severity of CLBP was not captured in the database and further research is needed to explore how sedentary behaviour modulates the association between obesity and CLBP across different levels of severity.

Introduction

Low back pain (LBP) is a very prevalent condition and continues to be the most common disorder all over the world.1 The National Institutes of Health Task Force on Research Standards for Chronic LBP (CLBP) indicated that CLBP was defined as a back pain problem that has persisted for at least 3 months and has resulted in pain on at least half the days in the past 6 months.2 LBP was reported to influence about 520 000 000 people in America and 5680 000 000 people globally.3 LBP has become the leading cause of years lived with disability from 1990 to 2019 all through the world.3 4 A former meta-analysis revealed that one-third of people with LBP experienced persistent pain and disability 3 months after symptom onset, and these individuals were unlikely to recover completely within 1 year.5 LBP was associated with changes in lifestyle, which seriously affects the quality of life of patients.6 It is important to explore additional factors related to the LBP.

Previously, multiple studies have shown that excessive body mass index (BMI) or waist/waist-to-hip ratio can significantly increase the risk of CLBP.7 8 These suggested that overweight and obesity might be crucial risk factors for CLBP. Further evidence also showed that a sedentary lifestyle is a risk factor for CLBP.9 10 Citko et al found that a sedentary lifestyle significantly increased the incidence of recurring LBP.11 There was evidence indicating the positive risk association between elevated rates of sedentary behaviour and physical inactivity in individuals with obesity.12 Another study conducted on public school teachers also revealed that high sedentary behaviour was associated with abdominal obesity.13 A ELSA-Brasil cohort study depicted that reducing sedentary behaviour time might contribute to the prevention of abdominal obesity.13 Increasing studies have provided evidence of the association between sedentary behaviour and increased risk of LBP.9 14 15 Physical inactivity and obesity might affect the risk of CLBP by increasing the level of inflammation, and insufficient physical activity may affect obesity through systemic inflammation and lead to the occurrence of CLBP.15

Additionally, another study demonstrated that the daily proportion of sedentary time was associated with pain-related disability in a 1-month accelerometer measurement in adults with overweight or obesity.16 Sedentary time was associated with deficits in response inhibition among adults with overweight and obesity.17 Physical inactivity and obesity might affect the risk of CLBP by increasing the level of inflammation, and insufficient physical activity may affect obesity through systemic inflammation and lead to the occurrence of CLBP.15 Therefore, we suspected that sedentary behaviour might be related to the association between obesity and CLBP.

In the present study, we evaluated the associations of BMI, and sedentary time with CLBP based on the data from the National Health and Nutrition Examination Survey (NHANES). Whether sedentary time could modulate the association between overweight or obesity and CLBP was also analysed. Subgroup analysis was also performed in different age groups and patients with depression, hypertension, diabetes, dyslipidaemia or cardiovascular disease (CVD).

Methods

Patient and public involvement

No patients were involved.

Study design and population

In this cross-sectional study, 6527 participants’ records were extracted from the NHANES if they met the following criteria: (1) ≥18 years; (2) measurement of height and weight; (3) measurement of sedentary behaviour time and (4) answer to the question for diagnosis of CLBP. As a cross-sectional survey of the National Center for Health Statistics, NHANES collects the information of participants by combining the way of interviews and physical examinations, aiming to assess the health and nutritional status of adults and children in the USA.18 The excluded criteria of this study were as follows: (1) pregnant (RHD143-yes) or breast feeding a child (RHQ200-yes); (2) with cancer or malignancy (MCQ220-yes) and (3) missing important covariables. Finally, 4289 participants were included.

Chronic low back pain

CLBP was the outcome, which was defined based on the answer of ‘yes’ to variable LBP, and ‘no’ to variables ARQ024D (Had LBP 3 months in a row?) and ARQ022AD (Still have LBP?).

Sedentary behaviour

The sedentary behaviour time (hours) was one of the main variables in the current study. Sedentary behaviour was defined as activities that do not increase energy expenditure above the resting level (ie, <1.5 metabolic equivalents (METs)) and included time spent on activities such as sitting and lying down during waking hours, working on a computer, watching TV and engaging in other forms of screen-based entertainment.19 The duration of SB was calculated using the self-reported time usually spent sitting on a typical day, ranging from 0 to 1320 min per day, which was divided by 60 to convert into hours. This duration of sedentary behaviour was also divided into ≤4.5 hours and >4.5 hours according to the median value.

Body mass index

BMI <18.5 kg/m2 was underweight, 18.5 kg/m2≤BMI<25 kg/m2 was normal weight, 25 kg/m2≤BMI and <30 kg/m2 was overweight and BMI ≥30 kg/m2 was obese.20 In the present study, the sample size of people who were underweight was small (n=68), and they were combined with normal weight.

Confounding variables

Age (years), gender, race (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black or other race-including multiracial), poverty income ratio (≤1.0, 1.0–2.0 or >2.0), education (less than 9th grade, 9th–11th grade (includes 12th grade with no diploma), high school graduate/general equivalent diploma or equivalent, or some college or AA degree/college graduate or above), marriage (married, never married or others), cotinine (ng/mL), drink (never, <once a week or ≥once a week), physical activity (<450 MET×min/week or ≥450 MET×min/week), total spine bone mineral density (≤1.04 gm/cm2 or >1.04 g/cm2), arthritis (yes or no), depression (yes or no), disorder (yes or no), hypertension (yes or no), diabetes (yes or no), dyslipidaemia (yes or no), CVD (yes or no), C reactive protein (mg/dL), neutrophils (1000 cell/µL), white cell count (10ˆ9/L), topical non-steroidal anti-inflammatories (yes or no) and glucocorticoids (yes or no) were potential covariates analysed.

Arthritis was identified based on variable MCQ191 (Which type of arthritis was it?), d04418-GLUCOSAMINE (osteoarthritis) or 105-192-ANTIRHEUMATICS (rheumatoid arthritis). Depression was defined based on the Patient Health Questionnaire-9 (DPQ010, DPQ020, DPQ030, DPQ040, DPQ050, DPQ060, DPQ070, DPQ080 and DPQ090). The score was 0–3 for each question. The total score of nine questions was calculated and ≥10 was regarded as depression.21 The antidepressant codes 242 PSYCHOTHERAPEUTIC AGENTS-249 ANTIDEPRESSANTS were also applied to identify patients with depression. Sleep disorder was defined based on variable SLQ060 or drug code 57–67. Hypertension was defined based on SBP≥140 and or DBP≥90, self-reported hypertension (variable BPQ020) or taking blood pressure medications (BPQ040A or drug code 40-CARDIOVASCULAR AGENTS-42, 47, 48, 49, 482 and 55). Never drinking was defined based on the answer of ‘no’ to variable ALQ101. And those who answered ‘yes’ were divided into <once a week or ≥once a week based on variables ALQ120Q and ALQ120U. Physical activity was converted into energy expenditure according to the report on ‘PAQ’, in which, each participant was asked about vigorous work activity/vigorous recreational activities, moderate work activity/moderate recreational activities and walk/bicycle information.22 The definition of vigorous work activity encompasses typical activities that consistently elicit significant increases in breathing or heart rate for a minimum duration of 10 min, such as the exertion involved in hauling or lifting heavy weights, excavating or engaging in construction work. Vigorous recreational activities are characterised by high-intensity sports, fitness pursuits or leisurely pastimes that result in notable elevations in respiration or heart rate, such as jogging or playing basketball. Moderate work activity was defined as any activity that elicits minor increases in breathing or heart rate, such as brisk walking or carrying light loads for at least 10 min continuously. Moderate leisure activities were defined as those that induce a slight elevation in breathing or heart rate for at least 10 min continuously, such as brisk walking, cycling, swimming or playing volleyball. Walking or using a bicycle was defined as a means of transportation to school/work or for shopping for at least 10 min continuously to commute between places. The weekly MET was calculated based on NHANES recommended MET scores of 8 for vigorous work activity/vigorous recreational activities and 4 for moderate work activity/moderate recreational activities as well as walking/bicycling.23 Energy expenditure (MET×min)=recommended MET×exercise time of corresponding activity (min), which can be converted into weekly energy expenditure. Physical activity was divided into <450 MET×min/week, ≥450 MET×min/week and unknown groups. Total spine bone mineral density was defined based on variable DXXOSBMD, which was divided into ≤1.04 g/cm2, >1.04 g/cm2 and unknown based on the median.

Statistical analysis

Kolmogorov-Smirnov was used to conduct normality test for measurement data. Normal-distributed measurement data were described as mean (SE) (mean (SE)). Independent sample t-test was used for comparison between the two groups. The non-normally distributed measurement data were described by median and quartiles (M (Q1, Q3)), and the Mann-Whitney U rank sum test was used for comparison between groups. Enumeration data were described by the number and percentage of cases (n (%)), and χ2 test was used for comparison between groups. Categorical data were described by the number and percentage of cases (n (%)), and χ2 test was used for comparison between groups. All the data were subjected to a weighted manner using sdmvstra (the masked variance unit pseudostratum), sdmvpsu (the masked variance unit pseudoprimary sampling units), SDMVSTRA (the CI applied for evaluating the reliability of an estimate) and WTMEC2YR (full sample 2-year MEC exam weight). The variables with missing values were manipulated based on the stochastic forest chain equation multiple interpolation method using the miceforest package in python (online supplemental table 1). Sensitivity analysis was performed to compare the data before and after missing values manipulation (online supplemental table 2). All variables were included in a weighted univariate logistic regression model, and significant variables combined with age, gender and race were subjected to backward stepwise regression method to explore the potential confounding factors associated with CLBP. Variables were eliminated step-by-step backwards according to maximum p value to the minimum. The eliminations were then repeated sequentially until all the variables in the model were significant (online supplemental table 3). CLBP was the outcome variable, and weighted logistic regression models were established. Sedentary behaviour time was stratified into two groups (>4.5 hours and ≤4.5 hours) to further explore the modulatory effect of sedentary behaviour time on obesity and CLBP. The issue of multicollinearity was determined via the colldiag function in the perturb package (V.2.10). Condition indexes and variance decomposition proportions were calculated in order to test for collinearity among the independent variables of a regression model and identify the sources of collinearity if present. If the largest condition index (the condition number) is large (Read and Belsley suggest ≥30), then there may be collinearity problems. All large condition indexes may be worth investigating.24 The result was exhibited in online supplemental table 4, and no multicollinearity was identified. The ORs and 95% CIs were applied as effect size. All statistical tests were performed by two-sided test with a test level of α=0.05. Python V.3.9 and SAS V.9.4 (SAS Institute) were used for statistical analysis, and R V.4.0 was used for graph drawing.

Results

The characteristics of participants with and without CLBP

In total, the data of 6527 subjects were extracted from the NHANES database. Among them, participants without height and weight measurements (n=241), sedentary behaviour time (n=15) and those who did not answer the question regarding CLBP diagnosis (n=1628) were excluded. The pregnant women (n=44), mothers breast feeding a child (n=29) and patients with cancer or malignancy (n=281) were excluded. Finally, 4289 participants were included. The flow chart of the screening process is exhibited in online supplemental figure 1.

The mean BMI (30.41 (0.47) kg/m2 vs 28.41 (0.16) kg/m2), age (44.99 (0.67) years vs 42.06 (0.35) years) and cotinine (78.53 (9.52) ng/mL vs 58.61 (4.00) ng/mL) in the CLBP group were higher than the non-CLBP group. The percentages of participants in different physical activity groups were statistically different between the CLBP group and the non-CLBP group. The percentages of subjects who had arthritis (18.17% vs 7.98%), depression (32.21% vs 12.32%), sleep disorder (23.18% vs 9.68%), hypertension (36.75% vs 23.40%), diabetes (15.53% vs 9.96%), dyslipidaemia (74.81% vs 67.91%) or CVD (19.47% vs 10.52%) in the CLBP group were higher than the non-CLBP group (online supplemental table 5).

Association among BMI, sedentary behaviour time and CLBP

The potential covariates included age, race, poverty income ratio, marriage, cotinine, total spine bone mineral density, arthritis, depression, sleep disorder, hypertension, diabetes, dyslipidaemia and CVD. The backward stepwise regression method was performed, and the covariates were identified including age, gender, race, marriage, arthritis, depression, sleep disorder and hypertension (online supplemental table 6). In the unadjusted model, overweight (OR 1.46, 95% CI 1.19 to 1.80) and obesity (OR 1.91, 95% CI 1.34 to 2.73) might increase the risk of CLBP. After adjusting for confounding factors, increased risk of CLBP was identified in people who were overweight (OR 1.41, 95% C: 1.13 to 1.76) and obesity (OR 1.48, 95% CI 1.01 to 2.18). No significant association between sedentary behaviour time and CLBP was observed (table 1).

Table 1 The associations among BMI, sedentary behaviour time and CLBP

Variables	Model 1*	Model 2†	Model 3‡	
OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value	
BMI							
 <25 kg/m2	Ref		Ref		Ref		
 25–30 kg/m2	1.46 (1.19 to 1.80)	0.001	1.42 (1.14 to 1.76)	0.004	1.41 (1.13 to 1.76)	0.005	
 ≥30 kg/m2	1.91 (1.34 to 2.73)	0.001	1.79 (1.24 to 2.59)	0.004	1.48 (1.01 to 2.18)	0.048	
Sedentary behaviour time							
 ≤4.5 hours	Ref		Ref		Ref		
 >4.5 hours	0.97 (0.72 to 1.30)	0.823	0.95 (0.70 to 1.30)	0.746	0.88 (0.65 to 1.18)	0.365	
* Model 1 Univariable logistical regression model adjusting no factor.

† Model 2 Multivariable logistical regression model adjusting for age, gender and race.

‡ Model 3 Multivariable logistical regression model adjusting for age, gender, race, marriage, arthritis, depression, sleep disorder and hypertension.

BMI, body mass index; CLBP, chronic low back pain; Ref, reference

Modulatory effect of sedentary behaviour time on the association between overweight or obesity and CLBP

In BMI<25 kg/m2 vs 25 kg/m2≤BMI<30 kg/m2 group, the modulatory effect of sedentary behaviour time on the association between overweight and CLBP was evaluated via the interaction term overweight×sedentary behaviour time. The data revealed that there was no significant modulatory effect of sedentary behaviour time on the association between overweight and CLBP (p=0.795) (table 2). In the sedentary behaviour time ≤4.5 hours, the risk of CLBP presented an upward trend with the increase of BMI, and the same upward trend was observed in the sedentary behaviour time >4.5 hours group, which indicated that no interaction between overweight and sedentary behaviour time on CLBP was found (figure 1).

Table 2 Modulatory effect of sedentary behaviour time on the association between overweight or obesity and CLBP

Variables	Model 1*	Model 2†	Model 3‡	
OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value	
BMI<25 kg/m2 vs 25 kg/m2≤BMI <30 kg/m2	
 Overweight	1.42 (0.94 to 2.14)	0.092	1.33 (0.86 to 2.08)	0.187	1.45 (0.92 to 2.27)	0.100	
 Sedentary behaviour time	0.74 (0.49 to 1.10)	0.127	0.71 (0.47 to 1.07)	0.096	0.71 (0.46 to 1.10)	0.117	
 Overweight×sedentary behaviour time	1.06 (0.55 to 2.06)	0.848	1.06 (0.54 to 2.06)	0.856	0.91 (0.44 to 1.90)	0.795	
BMI<25 kg/m2 vs BMI≥30 kg/m2	
 Obesity	1.42 (0.83 to 2.44)	0.184	1.40 (0.81 to 2.43)	0.211	1.15 (0.68 to 1.96)	0.581	
 Sedentary behaviour time	0.74 (0.49 to 1.10)	0.127	0.74 (0.49 to 1.13)	0.153	0.72 (0.45 to 1.15)	0.158	
 Obesity×sedentary behaviour time	1.71 (1.03 to 2.85)	0.040	1.65 (1.01 to 2.73)	0.049	1.65 (1.01 to 2.72)	0.047	
* Model 1 Univariable logistical regression model adjusting no factor.

† Model 2 Multivariable logistical regression model adjusting for age, gender and race.

‡ Model 3 Multivariable logistical regression model adjusting for age, gender, race, marriage, arthritis, depression, sleep disorder and hypertension.

BMI, body mass index; CLBP, chronic low back pain

Figure 1 The interaction map of overweight and different sedentary behaviour time on the risk of CLBP. CLBP, chronic low back pain.

In BMI<25 kg/m2 vs BMI≥30 kg/m2 group, the interaction term of obesity×sedentary behaviour time was used to verify whether sedentary behaviour time mediated the relationship between obesity and CLBP. After adjusting for confounding factors, sedentary behaviour time showed a modulatory effect on obesity and CLBP (p=0.047) (table 2). In the sedentary behaviour time ≤4.5 hours, the risk of CLBP exhibited a relatively stable downward trend with the increase of BMI while in the sedentary behaviour time >4.5 hours group, the risk of CLBP was increased with the elevation of BMI (figure 2). These suggested that sedentary behaviour time played a modulatory role in the relationship between obesity and CLBP. Obesity was associated with an increased risk of CLBP in people with sedentary behaviour time >4.5 hours (p=0.002) (table 3). When BMI and sedentary time were dealt with as continuous variables, increased BMI was associated with elevated the risk of CLBP in people with elevated sedentary behaviour time (OR 1.01, 95% CI 1.01 to 1.01) (online supplemental table 7).

Table 3 Modulatory effect of different sedentary behaviour time on the association between obesity and CLBP

Variables	Model 1 *	Model 2†	Model 3‡	
OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value	
Sedentary behaviour time ≤4.5 hours	Ref		Ref		Ref		
 BMI<25 kg/m2	Ref		Ref		Ref		
 BMI≥30 kg/m2 (obese)	1.42 (0.83 to 2.44)	0.184	1.28 (0.67 to 2.44)	0.425	1.00 (0.52 to 1.93)	0.992	
Sedentary behaviour time >4.5 hours	Ref		Ref		Ref		
 BMI<25 kg/m2	Ref		Ref		Ref		
 BMI≥30 kg/m2 (obese)	2.44 (1.81 to 3.29)	<0.001	2.45 (1.82 to 3.28)	<0.001	2.09 (1.36 to 3.21)	0.002	
* Model 1 Univariable logistical regression model adjusting no factor.

† Model 2 Multivariable logistical regression model adjusting for age, gender and race.

‡ Model 3 Multivariable logistical regression model adjusting for age, gender, race, marriage, arthritis, depression, sleep disorder and hypertension.

BMIbody mass indexCLBP, chronic low back pain; Ref, reference

Figure 2 The interaction map of obesity and different sedentary behaviour time on the risk of CLBP. CLBP, chronic low back pain.

Subgroup analysis of the modulatory effect of sedentary behaviour time on the association between overweight or obesity and CLBP

Sedentary behaviour time modulated the association between obesity and CLBP in people ≥45 years (p=0.017). The modulatory role of sedentary behaviour time on the association between obesity and CLBP was observed in those without depression (p=0.026) or dyslipidaemia (p=0.002). Also, sedentary behaviour time might modulate the association between obesity and CLBP in people without hypertension (p=0.053), diabetes (p=0.054) or CVD (p=0.051) (online supplemental table 8). In people ≥45 years (OR 3.28, 95% CI 1.78 to 6.05), or those without depression (OR 2.23, 95% CI 1.36 to 3.65), dyslipidaemia (OR 4.50, 95% CI 2.21 to 9.15), hypertension (OR 2.34, 95% CI 1.30 to 4.19), diabetes (OR 2.10, 95% CI 1.28 to 3.44) or CVD (OR 2.05, 95% CI 1.29 to 3.25), obesity increased the risk of CLBP in sedentary behaviour time >4.5 hours group (online supplemental table 9).

Discussion

The present study assessed whether sedentary behaviour could modulate the association between overweight or obesity and CLBP. Subgroup analysis was conducted in different age groups and patients with or without depression, hypertension, diabetes, dyslipidaemia or CVD. The data delineated that overweight and obesity were associated with an increased risk of CLBP. Sedentary behaviour time could moderate the association between obesity and CLBP. Subgroup analysis indicated that sedentary behaviour time modulated the association between obesity and CLBP in people ≥45 years, those without depression or dyslipidaemia. The findings might provide a reference for the lifestyle modifications among individuals with obesity and reducing sedentary behaviour is recommended for this population.

Previously, increasing evidence demonstrated that obesity was associated with various musculoskeletal conditions and contributed to serious disability and impaired quality of life.25 Another systematic review and meta-analysis identified positive cross-sectional associations between increased body fat and widespread and single-site joint pain in the low back.26 Additionally, data on the association between obesity and LBP were frequently reported.27 28 A former meta-analysis depicted that obesity was a risk factor for LBP.7 These findings gave support to the results of our study, which revealed that people who were overweight and obese were associated with an increased risk of CLBP. In previous studies, sedentary behaviour was widely identified to be associated with LBP.9 Sedentary behaviour was associated with a higher risk of musculoskeletal pain including LBP in occupational and non-occupational settings.29 Cho et al found that non-sedentary lifestyle played a protective role in LBP in people without allergies.30 Although no significant association between sedentary behaviour time and CLBP was identified in this study, we observed that sedentary behaviour time modulated the association between obesity and CLBP. Smuck et al demonstrated that physical activity affected the association between obesity and LBP.31 Hashem et al found that there might be inter-relationships between obesity, physical inactivity and LBP,32 which suggested that there might be association among sedentary behaviour time, obesity and CLBP.

Researchers found that physical inactivity and obesity might modulate the risk of CLBP through the elevation of inflammation levels.33 Insufficient physical activity might influence obesity by regulating systemic inflammation and further lead to the occurrence of CLBP.34 Evidence confirmed that more exercise was beneficial for CLBP in people who were overweight and obese.35 Lifestyle interventions using technologies, social facilitation, motivational counselling and self-monitoring to reduce sedentary behaviour should be advocated.36 Subgroup analysis indicated that in people without diseases including depression, dyslipidaemia, hypertension, diabetes or CVD, these might be because the inflammation states of those with diseases might be more complex,3739 and the modulatory role of sedentary behaviour time on obesity and CLBP might be not significant. The findings of our study suggest that reducing sedentary behaviour is important for improving specific health conditions such as obesity and quality of life, including lowering the risk of CLBP. Further research should focus on identifying potential causal mechanisms underlying these relationships. Additionally, more longitudinal and cluster-randomised controlled trials are needed to assess the dose-response effect of physical activity and sedentary behaviour on obesity and CLBP.

This study used a large sample size and employed complex multistage probabilistic sampling method to ensure the generalisability of the results to other populations. Several limitations were identified in our study. First, this was a cross-sectional study, limiting the ability to establish causality between sedentary behaviour, obesity and CLBP. Second, some data were obtained through questionnaires, which might be subject to recall bias. Third, the severity of CLBP was not captured in the database and further research is needed to explore how sedentary behaviour modulates the association between obesity and CLBP across different levels of severity.

Conclusions

The current study aimed to investigate the potential modulatory effect of sedentary behaviour on the relationship between overweight or obesity and CLBP. Our findings revealed that obesity was significantly associated with an increased risk of CLBP, and sedentary behaviour time modulated the association between obesity and CLBP. The findings recommended that individuals with obesity should avoid prolonged sedentary behaviour and engage in regular physical activity.

supplementary material

10.1136/bmjopen-2023-082851 online supplemental file 1

10.1136/bmjopen-2023-082851 online supplemental file 2

Data availability statement

Data are available on reasonable request.

Review Process File
26 08 2024

Funding: This study was supported by Shenzhen Science and Technology Programme (No. JCYJ20190814114207451).

Prepublication history and additional supplemental material for this paper are available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2023-082851 ).

Provenance and peer review: Not commissioned; externally peer reviewed.

Patient consent for publication: Consent obtained directly from patient(s).

Ethics approval: The requirement of ethical approval for this was waived by the Institutional Review Board of Shenzhen Hospital, Southern Medical University because the data were accessed from NHANES (a publicly available database). The need for written informed consent was waived by the Institutional Review Board of Shenzhen Hospital, Southern Medical University due to retrospective nature of the study.

Data availability free text: Not applicable.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
==== Refs
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