Impact and prevention of sedentary behavior on musculoskeletal disorders in the workplace: a meta-analysis and LDA topic model

Abstract

Background

Sedentary behavior is a significant risk factor for musculoskeletal disorders. This study aimed to comprehensively investigate the relationship between sedentary behavior and MSDs in the workplace, identifying affected body regions and exploring potential prevention strategies.

Methods

A systematic search of six public databases (Scopus, Web of Science, IEEE, PubMed, Cochrane, and Embase) was conducted following predefined inclusion and exclusion criteria. After duplicate removal and multi-stage screening of titles, abstracts, and full texts, 33 studies were included in the meta-analysis. In parallel, 75 eligible full-text articles were analyzed using Latent Dirichlet Allocation (LDA) topic modeling to identify thematic patterns related to prevention strategies.

Results

Various industry types entail different risks of musculoskeletal disorders and varying degrees of harm to body regions. Among indoor occupations, the prevalence rates were as follows: handicraft industry, 54.5% (95% CI: 43.7–65.1); service industry, 28.8% (95% CI: 24.4–33.4); manufacturing industry, 23.9% (95% CI: 17.6–30.9); and medical industry, 37.9% (95% CI: 26.4–50.1). Outdoor occupations exhibited a prevalence rate of 53.8% (95% CI: 41.6–65.7). Effective prevention identified included workplace improvements, sitting posture assessments, torso tilting, intermittent standing or exercise, professional ergonomic training, and personal health awareness initiatives.

Conclusion

These findings offer valuable insights into musculoskeletal disorders and provide practical prevention recommendations for workers across various industries who engage in prolonged sitting.

Introduction

Sedentary behavior is a significant public health concern linked to various chronic diseases, including musculoskeletal disorders (MSDs) [1–4]. In occupational settings, MSDs remain among the most prevalent health problems at both industry and national levels [5–9]. Evidence indicates that adults in the United States spend an average of approximately 9.5 h per day sitting, with a substantial proportion of this time occurring in the workplace [10]. MSDs can affect multiple anatomical structures, including joints, the spine, muscles, ligaments, and intervertebral discs, ultimately leading to reduced work productivity [11], impaired daily functioning [12], and, in severe cases, paralysis [13] or disability [14]. Existing research on sedentary behavior further suggests that musculoskeletal risks vary across occupational groups. Elevated risks have been reported among drivers [15], handicraft workers [16], and manufacturing workers [17], largely attributable to occupation-specific exposures such as repetitive manual tasks, prolonged screen-based sitting, and combined sitting-standing work patterns [18–20]. Given the substantial occupational and clinical burden associated with prolonged sitting, this study aims to identify the body regions most affected, examine differences in MSDs prevalence across occupational and industrial groups, and synthesize evidence on potential prevention strategies.

We therefore focus on occupations and workplace settings characterized by prolonged static sitting. International systematic reviews have consistently reported a high prevalence and substantial occupational burden of work-related MSDs in specific occupational groups [21–23], particularly in sectors requiring sustained static postures or operating within constrained work environments [18, 24]. This burden is especially evident in handicraft industries [25] and among healthcare professionals [26], including operating room personnel [23, 27–29], surgeons [29], physiotherapists [30–31], and dentists [32–34]. Collectively, this body of evidence indicates that sedentary behavior is associated with musculoskeletal pain in both occupational and non-occupational contexts, with high work intensity, repetitive tasks, and poor ergonomic postures [35, 36] contributing to injuries of the lower back, neck-shoulder region, and upper extremities. Similarly, occupation-specific studies further demonstrate elevated MSDs prevalence among professional drivers exposed to prolonged sitting [15], sewing machine operators in handicraft industries characterized by static and repetitive work [23], automobile manufacturing workers experiencing constrained postures and restricted movement [17], and healthcare workers who maintain sustained static postures in clinical environments [27–29].

However, current analyses of sedentary behavior and its association with MSDs remain largely confined to homogeneous occupational settings [27–33], limiting their comprehensiveness. Although many studies have identified associations between upper-body sedentary exposure and MSDs [18, 29–34], these effects may extend to other body regions [15–21, 24, 37, 38]. In addition, although workplace interventions aimed at reducing sedentary behavior have been extensively studied, high-quality evidence supporting their effectiveness in alleviating musculoskeletal symptoms remains limited [39]. This limitation suggests that reducing sitting time alone may be insufficient and underscores the need to better understand how sedentary behavior relates to musculoskeletal disorders across different occupational contexts and affected body regions. Moreover, the similarities and differences in sedentary behavior-related MSDs patterns across industries remain poorly understood, and universally applicable prevention strategies have yet to be established [36]. While previous studies have examined MSDs prevalence stratified by body region, sex, age, underlying conditions [26, 39], and specific occupational categories, it remains unclear whether sedentary behavior-related MSDs patterns are consistent across occupational domains. Consequently, existing evidence provides insufficient guidance for sedentary workers to develop a systematic understanding of musculoskeletal disorders, thereby constraining the development of comprehensive and occupation-specific prevention strategies.

This study employed two complementary approaches to comprehensively explore MSDs resulting from sedentary behavior. First, we conducted a meta-analysis to provide a quantitative synthesis of existing studies, enabling a more precise estimation of sedentary behavior’s effects on different body regions and its varying impacts across industries. This approach allows for a clearer understanding of effect sizes and potential moderating factors. Additionally, we utilized LDA topic modeling to analyze thematic patterns in relevant literature. This method facilitates the extraction of key research topics and emerging trends, providing novel insights into the field. By integrating these methodologies, we aim to develop a more nuanced understanding of MSDs related to sedentary behavior, ultimately informing the development of targeted prevention strategies across different occupational sectors.

This research provides novel insights into the differential impacts of prolonged sitting on MSDs across diverse occupational sectors, including handicrafts, healthcare, services, and outdoor work. By elucidating previously overlooked risk factors within specific industries, our findings enable occupational health professionals to develop tailored, evidence-based prevention strategies. Moreover, this study offers a comprehensive overview of sedentary behavior patterns among workers across various workplace settings and delineates effective interventions to mitigate MSDs risk. These findings have the potential to inform targeted occupational health policies and practices, ultimately enhancing the musculoskeletal health and overall well-being of workers engaged in sedentary occupations.

Methods

Meta-analysis

Meta-analysis is a statistical method utilized to examine and synthesize data from multiple independent studies on a specific research question. This approach involves the aggregation of findings from individual studies to generate a single, comprehensive estimate of the effect size or outcome of interest [40]. The process of identifying overlaps, contradictions, and improvements in the cognitive process forms the basis for deepening our understanding of the causes of MSDs in different locations due to prolonged sitting in the workplace. As a powerful research tool, meta-analysis plays a pivotal role in the hypothesis testing of single-study results, thereby increasing the reliability of findings. This approach can be invaluable in gaining insights into the complex factors contributing to MSDs in occupational settings [41].

This study aims to systematically consolidate and reanalyze empirical research on the impact of sedentary behavior on workplace MSDs. By synthesizing findings from multiple studies, we seek to draw novel conclusions regarding the broader population, work-related factors, affected body regions, and underlying contributors to these disorders. Meta-analysis is identified as the appropriate method for this task, given its ability to facilitate secondary analysis of data and contribute to a deeper understanding of the broader population context, work-related factors, specific affected body regions, and underlying contributors to these disorders. Ultimately, this approach promises to provide more comprehensive and robust research conclusions.

LDA Topic Model

Latent Dirichlet Allocation (LDA), an unsupervised algorithm introduced by Blei et al. [42], is used for topic modeling. It operates based on a three-level hierarchical Bayesian model, facilitating the identification of potential topics within documents. This is achieved by estimating the probability of topics based on document distribution and the probability of words based on topic distribution [43]. LDA topic modeling employs a “bag-of-words” approach, simplifying individual documents into word vectors and conducting a term vocabulary by assessing word frequency through the algorithm. The model then characterizes topics by examining the probability distribution of words and assigns documents to topics based on these probabilities [44]. The LDA topic modeling process comprises the following steps:

1. Choose a document d_i, based on the prior probability p(d_i).

2. Generate the topic distribution θ_i of document d_i by sampling from the Dirichlet distribution α.

3. Generate the topic z_ij for the j^th word of document d_i by sampling from the Dirichlet distribution of topics θ_i.

4. Generate the word distribution ϕz_ij corresponding to topic z_ij from the Dirichlet distribution β.

5. Sample. from the multinomial distribution of words ϕz_ij to ultimately generate words ω_ij.

The LDA topic model is recognized for its versatile application potential [44]and ability to provide in-depth insights across diverse domains [45]. It has been extensively utilized for extracting relevant topics from texts in fields, including social science [46], energy policy [47], big data [48], Industry 4.0 [49], and ergonomics [45]. While existing literature offers a broad scope for reviewing affected body regions and preventions, it lacks a comprehensive perspective to fully understand the research focus and common prevention measures for MSDs stemming from sedentary behavior in the workplace. This study aims to bridge this gap by providing a unique perspective through the implementation of the LDA topic model.

Data collection

This study presents a systematic review and meta-analysis of the prevalence of MSDs among sedentary employees in workplaces from 2013 to the present. The study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines.

Selection criteria

Scopus, Web of Science, Institute of Electrical and Electronics Engineers (IEEE), PubMed, Cochrane, and Embase databases were searched using the search terms listed in Table 1.

Table 1.

Search terms per database

Database	Search Terms	Filers
Scopus	(TITLE-ABS-KEY (“Sedentary time” OR “sitting”) AND TITLE-ABS-KEY (“Work-related musculoskeletal”))	Full text,2013-, In English, on Humans
Web of Science	“Work related musculoskeletal“(Topic) AND “sedentary time” OR “sitting“(Topic)	Full text,2013-, In English, on Humans
IEEE	(“All Metadata”: work-related musculoskeletal) AND (“All Metadata”: “sedentary time” OR “All Metadata”: “sitting”)	Full text,2013-, In English, on Humans
PubMed	(Musculoskeletal disorders [Title/Abstract]) AND (sitting [Title/Abstract] OR Sedentary Time [Title/Abstract])	Full text,2013-, In English, on Humans
Cochrane	Work-related musculoskeletal in Title Abstract Keyword AND “sedentary time” OR “sitting” in Title Abstract Keyword (Word variations have been searched)	Full text,2013-, In English, on Humans
Embase	work-related musculoskeletal’ AND (‘sedentary time’ OR ‘sitting’)	Full text,2013-, In English, on Humans

The list of references included in the article was also manually searched and professors with expertise in the subject area were approached to identify other relevant research. We screened all titles and abstracts identified articles that fit the study through electronic databases and excluded articles that did not meet the inclusion criteria based on the published before 2013 (2013-present), removing those that were not easy to obtain, no experimental methods appeared in the paper, lack of data, and reviews. Eligible articles progress to full-text screening to determine their inclusion or exclusion. A total of 633 articles were identified after the systematic search. Once the screening and selection process was complete, 33 articles were still included in the systematic review. An overview of article identification, screening, qualification assessment, and selection is shown in Fig. 1.

Fig. 1.

Literature Data Collection Process

Quality assessment

The evaluation criteria, based on the Joanna Briggs Institute checklist, attest to the high quality of all the studies we selected. Two authors (XIONG and LI) independently reviewed the data, including their critical appraisal, the two authors discussed any inconsistencies among the review authors, and carefully analyzed and resolved any remaining differences.

Statistical analysis

After screening the database according to the search formula, we excluded 147 duplicate publications. Additionally, to ensure greater relevance to current developments, we limited our selection to studies published within the last decade. We excluded a total of 325 articles that did not contain experiments, did not offer open access, or were reviewed literature. Furthermore, 86 articles were eliminated as they did not meet the specified criteria based on their topic, abstract, and full-text content. During the data collation for the meta-analysis, 42 articles were discarded due to incomplete data. As a result, the literature screening process reduced the number of articles from 633 to 33 [50–81].

To comprehensively understand the characteristics of MSDs among sedentary employees in the workplace, we classified the characteristics of occupations that typically involve prolonged sitting, and thoroughly examined the impact of sedentary work on the employees’ musculoskeletal health. The meta-analysis results aimed to draw extensive and conclusive insights to enhance the general understanding of these common issues and establish a foundational data set for the subsequent disease prevention analysis. This study employed Stata 17 for conducting the meta-analysis. The meta-analysis included all studies with comparable results which also calculated the event rate and the respective 95% confidence interval (CI) for each body part. Heterogeneity and inconsistency among studies were assessed using the I² statistic, and a random effects model was employed when heterogeneity was confirmed (I² > 50%). Eventually, we used Egger’s test to assess publication bias for each result. The bias susceptibility of each cluster was scored according to whether the sum of assigned items was high (< 50%), medium or unclear (50%-99%), or low (100%). The good quality of the study indicates a low risk of bias and a moderate or predisposed risk. Poor study quality indicates a significant risk of bias, and inclusion in such studies needs to be considered.

Data extraction

Data were extracted for the following categories from the included studies: (1) publication characteristics (author(s), year), (2) study characteristics (occupations and their classification), and (3) sample characteristics (sample size, sex, age). The specific analysis is shown in Table 2.

Table 2.

Literature statistics data

Id	study	occupation	occupation classification	gender	age(years)	samples	The body regions involved
1	Tesfaye 2020	tailors	handicraftsman	382male/27female	range = 18–60	422	neck; shoulder
2	Bukhari 2022	office workers	service	212male/333female	range = 20–59	545	neck; shoulder; lower back
3	Xiaowen 2023	manufacturing workers	manufacturing		mean = 35	367	neck; shoulder; elbows; wrist/hands; upper back; lower back; knees
4	Kibria 2023	computer workers	service	172male/99female	range = 23–65	271	neck; shoulder; elbows; wrist/hands; feet
5	Richelle 2018	office workers	service	7male/13female	range = 18–65	20	neck; shoulder; elbows; wrist/hands; upper back; lower back; knees
6	Getasew 2024	car drivers	outside	413male/9female	range = 20–60	422	lower back
7	Desta 2023	weavers	handicraftsman	419male/1female	range = 18–60	420	neck; shoulder; upper back; lower back
8	Dan 2023	operation and maintenance workers	manufacturing	1158male/185female	39.42 ± 9.89	1343	neck; shoulder
9	Peifang 2023	Dentists	medical	296male/307female	range = 18–60	520	neck
10	Xiaowen 2023	manufacturing workers	manufacturing	351male/67female	mean = 35	418	neck; shoulder; elbows; wrist/hands; upper back; lower back; knees
11	Coates 2023	farmers	outside	99male/51female	40.96 ± 13.49	150	lower back; knees
12	Fatima 2020	cab drivers	outside		range = 18–55	30	neck
13	Dagne 2020	Bank workers	service	372male/383female	range = 20–60	838	neck; shoulder; elbows; wrist/hands; upper back; lower back; knees; feet
14	Ranisha 2020	teachers	service	91males/164females	36 ± 7.4	262	neck; shoulder; elbows; wrist/hands; upper back; lower back; knees
15	Mohammad 2018	RMG workers	manufacturing	46Male/186female	31.3 ± 7	232	neck; shoulder; wrist/hands
16	Amitava 2017	Jari workers	manufacturing	156 female	range = 18–60	156	neck; shoulder; elbows; wrist/hands; upper back; lower back; knees; feet
17	Anne 2017	dental professionals	medical	231male/160female	range = 25–60	391	wrist/hands; feet
18	Fariborz 2018	architects; banker; secretaries	service		range = 20–45	35	neck; wrist/hands; lower back
19	Abate 2023	sewing machine operators	handicraftsman	11male/168female	range = 19–60	179	neck; shoulder; elbows; wrist/hands; upper back; lower back; knees; feet
20	Zhang 2016	sonographers	medical	128male/439female	36.9 ± 7.6	440	neck; shoulder; elbows; wrist/hands; upper back; lower back
21	Thacker 2023	dentists	medical	120female	30.7 ± 7.6	120	neck; shoulder; wrist/hands; upper back; lower back
22	Moreira 2021	office workers	service	85male/female 117	37 ± 11	18	neck; shoulder; wrist/hand; lower back
23	Aziz 2014	general workers	manufacturing	35male/56female	range = 18–49	83	neck; shoulder; elbows; wrist/hands; upper back; lower back; knees; feet
24	Alam 2023	nurses	medical	50male/49female	female 29.01 ± 9.46 Male 30.58 ± 12.03	91	neck; shoulder; elbows; lower back
25	Navidi 2022	computer workers	service	9male/10female	39.26 ± 5.56	19	Neck; shoulder; elbows; wrist/hands; upper back; lower back; knees
26	Das 2022	handicraft workers	handicraftsman		33 ± 6	334	neck; lower back; knees
27	Ogunlana 2019	health workers	medical	153male/224female		377	neck; shoulder; elbows; wrist/hands; upper back; lower back; knees; feet
28	Chowdhury 2018	computer workers	service			72	lower back
29	Celik 208	office workers	service	258male/270female	38.55 ± 9.79	528	neck; upper back; lower back
30	Hakim 2017	drivers	outside	180male	37.5 ± 9.2	180	lower back
31	Ojoawo 2016	office workers	service	206male/214female	45.76 ± 10.22	420	neck; lower back; knees
32	Sy 2016	hair Braiders	handicraftsman	101female	mean = 34.3	101	neck; shoulder; wrist/hands; upper back; lower back; knees
33	Alghadir 2015	dental professionals	medical	24male/17female	range = 18–60	41	neck; shoulder; elbows; wrist/hands; upper back; lower back; feet

Results

Results of the meta-analysis

We systematically reviewed and summarized various sedentary occupations to investigate the association between specific occupational disorders and bodily ailment locations in the workplace. It is important to note that while some of the affected regions identified in the study results are based on self-reported pain areas from employees, not all can be verified through direct pathology reports. Thus, the prevalence of these disorders is not only confined to those confirmed through medical diagnosis.

After conducting a preliminary analysis of the data obtained from the screening by meta-analysis, we investigated the influence of a sedentary state on employees in different occupations and various parts of the body. The act of sitting is a pervasive condition in the workplace, including a wide range of occupational types, such as fully sedentary occupations like textile workers, and drivers, and indirect sedentary occupations like teachers and dentists. We categorized these occupations into indoor and outdoor work based on the natural environment of the work, and further divided indoor work into four categories based on work content and industry type (Table 2). These categories include the handicraft industry (tailors, weavers, handicraft workers, hair braiders), the service industry (office workers, computer workers, bank clerks, secretaries, teachers), the manufacturing industry (operation and maintenance workers, RMG workers, Jari workers, general workers) and the medical profession (sonographers, dentists, dental professionals, nurses, health workers). To assess heterogeneity among the studies, we employed the I² statistic, which indicated its significance, necessitating the use of a random effects model for meta-analysis.

According to the results of a meta-analysis comprising 33 studies, the prevalence of MSDs in employees working in a sedentary state was found to have a total effect size of 0.353 (95% CI: 0.310–0.397). The meta-analysis further revealed the prevalence of sedentary work in different industries: handicraft industry (0.545, 95% CI: 0.437–0.651), service industry (0.288, 95% CI: 0.244–0.334), manufacturing industry (0.239, 95% CI: 0.176–0.309), and medical industry (0.379, 95% CI: 0.264–0.501). Moreover, outdoor workers exhibited a prevalence of 0.538 (95% CI: 0.416–0.657). Additionally, the meta-analysis identified the effects of sedentary work on MSDs in various body regions, with the lower back (0.501, 95% CI: 0.402–0.601), neck (0.420, 95% CI: 0.324–0.520), and shoulders (0.323, 95% CI: 0.225–0.429) being the sites with the greatest impact. Additionally, injuries of varying degrees were observed in the upper back, elbows, knees, wrists, and feet (Table 3).

Table 3.

Meta-analysis of the prevalence of musculoskeletal disorders in different occupations and different body regions in the sedentary behavior

Meta-analysis	ES	[95% Conf. Interval]
overall	0.353	0.310 0.397
occupation
manufacturing	0.239	0.176 0.309
service	0.288	0.244 0.334
medical	0.379	0.264 0.501
outside	0.538	0.416 0.657
handicraftman	0.545	0.437 0.651
body part
elbows	0.187	0.083 0.319
knees	0.26	0.174 0.357
feet	0.291	0.167 0.433
wrist/hands	0.301	0.197 0.416
shoulder	0.323	0.225 0.429
upper back	0.38	0.226 0.547
neck	0.42	0.324 0.520
lower back	0.501	0.402 0.601

Secondly, to further understand the relationship between type of occupation and location of disease in sedentary status, we used subgroup analyses to detail the prevalence of musculoskeletal conditions in each occupational type. The prevalence of various body regions in the study participants is presented as follows:

Workers in the handicraft industry maintain specific sitting postures and perform complex hand movements. MSDs have a significant impact on this industry, with an overall prevalence of 0.545 (95% CI: 0.437–0.651). These disorders particularly affect the upper back, lower back, wrists/hands, and feet, with prevalence rates of 0.608 (CI 95%: 0.011-0.1.00), 0.746 (CI 95%: 0.424–0.966), 0.663 (CI 95%: 0.269–0.969), 0.609 (CI 95%: 0.350–0.841) respectively (Fig. 2).

Fig. 2.

Prevalence of musculoskeletal disorders in handicraft industry

Workers in the service industry experience prolonged periods of sedentary work, often maintaining the same position while handling paperwork or using a computer. The overall prevalence of MSDs in this industry is 0.288 (95% CI: 0.244–0.334). These disorders have a more significant impact on the lower back, neck, and upper back, with prevalence rates of 0.411 (CI 95%: 0.308–0.519), 0.345 (CI 95%: 0.268–0.457), and 0.334 (CI 95%: 0.219–0.450), respectively (Fig. 3).

Fig. 3.

Prevalence of musculoskeletal disorders in service industries

According to Fig. 3, the study indicated that sedentary behavior has a significant impact on MSDs in the manufacturing sector, with an overall effect size of 0.239 (95% CI: 0.017–0.309). The prevalence rates of lower back, neck, and shoulder disorders were 0.363 (95% CI: 0.073–0.723), 0.293 (95% CI: 0.112–0.517), and 0.241 (95% CI: 0.113–0.399), respectively (Fig. 4).

Fig. 4.

Prevalence of musculoskeletal disorders in the manufacturing industry

According to Fig. 5, for medical industry workers, the overall effect of sedentary time on MSDs was 0.379 (CI 95%: 0.254–0.501). The prevalence of lower back, neck, shoulder, and upper back was 0.548 (95%CI: 0.253–0.817), 0.553 (95%CI: 0.270–0.820), and 0.441 (95%CI: 0.135–0.773), and 0.399 (95%CI: 0.091–0.759), respectively.

Fig. 5.

Prevalence of musculoskeletal disorders in medical industry

According to Fig. 6, We found that compared to outdoor occupations, sedentary roles are less common indoors. Nevertheless, our pooled analysis revealed that the prevalence of MSDs due to sedentary work in outdoor industries is substantial, with an overall rate of 0.538 (95% CI: 0.416–0.657), and employees are particularly susceptible to back injuries, with a prevalence of 0.453 (95% CI: 0.372–0.537).

Fig. 6.

Musculoskeletal Disorders on the Outside

The results of the meta-analysis in this study are succinctly summarized in Table 4, vividly illustrating the varying impact of different occupations on distinct body regions within a sedentary workplace. The study employed a funnel plot to assess publication bias among the included studies in the meta-analysis, followed by an Egger test to further validate the absence of bias (p = 0.589). In summary, sedentary workplaces were strongly associated with a higher incidence of back injuries. Workers in the handicraft industry exhibited a higher likelihood of experiencing MSDs, with those in the medical industry following closely. Additionally, employees in sedentary settings were more prone to developing MSDs compared to their counterparts in outdoor environments. This study seeks to comprehensively review the relevant literature from the past decade using the LDA topic model, to delineate the primary research areas, and to propose targeted strategies for prevention and intervention.

Table 4.

Summary of meta-analysis results

Occupation classification	Specific occupations included	The prevalence of musculoskeletal disorders in the sedentary state
		serious injuries	ES	[95% CI]
handicraftman	tailors; weavers; sewing machine operators; handicraft workers; hair braiders	overall	0.545	0.437–0.651
		lower back	0.746	0.424–0.966
		wrist/hands	0.663	0.269–0.969
		upper back	0.608	0.011-0.1.00
		feet	0.609	0.350–0.841
service	office workers; computer workers; bank workers; teachers; architects; secretaries	overall	0.288	0.244–0.334
		lower back	0.411	0.308–0.519
		neck	0.345	0.268–0.457
		upper back	0.334	0.219–0.450
manufacturing	manufacturing workers; RMG workers; Ari workers; general workers	overall	0.239	0.017–0.309
		lower back	0.363	0.073–0.723
		neck	0.293	0.112–0.517
		shoulder	0.241	0.113–0.399
medical	dental professionals; health workers; nurses; sonographers	overall	0.379	0.254–0.501
		lower back	0.548	0.253–0.817
		neck	0.553	0.270–0.820
		shoulder	0.441	0.135–0.773
		upper back	0.399	0.091–0.759
outside	car drivers; farmers	overall	0.538	0.416–0.657
		lower back	0.560	0.424–0.692

Results of the LDA topic model

The LDA topic model effectively extracted information on pain locations in the body and the prevention of MSDs in the workplace. To extract comprehensive literature research content in this study, we continued to extract abstracts, titles, and keywords from 79 [1–31], 26– [23, 50–97] studies that meet the requirements. Among these studies, there also exist studies related to the treatment of MSDs through intervention measures such as device design and course training in these studies. This facilitated a deeper understanding of current research on MSDs and the search for universal preventive measures. The process of document synthesis entailed condensing the title, abstract, and keywords of the literature into a cohesive document. Subsequently, the document content underwent an extensive cleaning process to remove less relevant terms and words. Specifically, a series of data preprocessing steps were conducted for subsequent topic modeling, which encompassed data acquisition, cleaning, retention of user-defined words, and removal of stopwords. Furthermore, in pursuit of our objective to investigate preventive measures, we broadened the list of stopwords. In the context of identifying causes and preventing MSDs in the workplace, we excluded words such as MSDs, WMSD, disease, work, study, questionnaire (main research method), and musculoskeletal.

This study applies LDA to extract topics and generate “topic vocabulary” and “document topic” matrices from the cleaned dataset with the Python package “gensim”. And consider both topic perplexity and coherence to determine the optimal number of topics. Based on current studies [83–86], perplexity serves as a metric for assessing the training model’s quality that can help identify the appropriate number of topics and it measures how well the model is generated from the corpus which indicates the lower the better. Furthermore, LDA configurations that demonstrate stronger thematic coherence not only enhance the overall lexical appropriateness of the model but also gauge the similarity of sentences within each topic in the dataset. Thus, higher coherence indicates better quality.

According to Fig. 7, we have initially selected 6 topics as the turning point of the model’s confusion. Additionally, the overall coherence of the model ranges from 0.81 to 0.84, and the overall fluctuation trend is relatively stable. Therefore, we identified 6 topics and displayed 10 related keywords.

Fig. 7.

LDA Topic model perplexity and coherence trend

After establishing the number of LDA topic models, we utilized Python to explore 10 words associated with each topic and determined the weight of these words. The conclusive findings have been presented in Table 5.

Table 5.

LDA Topic Words and Weight Distribution

LDA topic word weight distribution
Topic 1	0.104*"office” + 0.079*"computer” + 0.069*"training” + 0.058*"industry” + 0.036*"assessment” + 0.037*"stress” + 0.029*"level” + 0.024*"exposure” + 0.021*"floor” + 0.026*"china”
Topic 2	0.186*"neck” + 0.153*"posture” + 0.053*"torso” + 0.031*"stand” + 0.030*"awkward” + 0.027*"position” + 0.021*"employees” + 0.018*"low” + 0.017*"prevention” + 0.017*"shoulders”
Topic 3	0.095*"lower” + 0.066*"areas” + 0.049*"workstation” + 0.040*"public” + 0.036*"sensors” + 0.024*"bank” + 0.028*"professionals” + 0.021*"staff” + 0.021*"interventions” + 0.022*"measurement”
Topic 4	0.081*"ergonomics” + 0.080*"higher” + 0.074*"long” + 0.044*"standing” + 0.042*"visual” + 0.041*"years” + 0.038*"nurses” + 0.022*"behaviors” + 0.017*"jari” + 0.021*"maintenance”
Topic 5	0.091*"shoulder” + 0.061*"high” + 0.058*"drivers” + 0.068*"health” + 0.048*"dental” + 0.039*"female” + 0.024*"rest” + 0.028*"limbs” + 0.021*"prevent” + 0.022*"operation”
Topic 6	0.252*"workers” + 0.143*"back” + 0.125*"ergonomic” + 0.039*"workplace” + 0.020*"regions” + 0.021*"medical” + 0.013*"male” + 0.012*"hospitals” + 0.010*"load” + 0.009*"intensity”

Topic 1: This topic provides a comprehensive analysis of research examining sedentary behavior as an occupational exposure leading to MSDs. Office workers and computer industry professionals exemplify typical subjects within the service sector, while other service industries, such as community and customer service roles, have received comparatively less attention. However, given the similarities in occupational characteristics and workplace environments, indoor service employees may benefit from relevant recommendations derived from these studies. Industry-level research predominantly emphasizes the importance of conducting ergonomic evaluations of indoor workstation design as an efficacious preventive measure. Psychological factors stemming from work-related stress and physiological factors arising from improper sitting posture also play crucial roles in the etiology of various MSDs. Furthermore, for these sedentary professions, socioeconomic factors, such as living standards, emerge as potential exacerbating factors, while higher educational attainment appears to mitigate symptom severity. Improper sitting posture is a significant factor leading to MSDs in professions that require sitting on the floor, such as handicrafts. Notably, a substantial proportion of research samples originate from China, a populous and rapidly developing country, suggesting that findings from these studies may hold broader implications for other regions experiencing similar developmental trajectories.

Topic 2: This topic presents a comprehensive overview of the impact of prolonged sitting on MSDs and the contributing factors. From a holistic research perspective, the neck, shoulders, and lower back emerge as the three regions most susceptible to injury from prolonged sitting. Awkward postures and maintaining fixed or twisted positions are significant contributing factors. Consequently, maintaining a scientifically sound sitting posture is crucial for prevention. The findings suggest that proper standing can mitigate the effects of incorrect sitting postures and adjusting the angle of the torso while sitting is also important for prevention. For instance, reclining the torso by 6.5 to 6.9 degrees for individuals with small and large body frames, respectively, contributes to overall body comfort and may reduce the risk of MSD development.

Topic 3: This topic offers an overview of relevant research concerning lower back issues, which are particularly prevalent among individuals engaged in prolonged sitting, notably in the service industry. Occupations such as public service and banking are specifically highlighted due to their characteristic requirements for extended periods of sedentary work. Recent studies have focused on developing effective strategies for addressing lower back discomfort and enhancing workstation design. Specifically, the utilization of wearable sensors for postural assessment enables real-time monitoring of workers’ postures, providing valuable data for identifying and mitigating risk factors associated with prolonged sitting. Evaluation of workstation attributes, with a particular focus on chair design, with studies investigating the optimal angle of inclination for lumbar support can suggest more ergonomic design evaluations to improve workstation design.

Topic 4: This topic highlights that maintaining a fixed posture for extended periods and advancing age are significant factors exacerbating sedentary-related ailments. This concern extends to professionals such as nurses and “Jari” workers, particularly within the medical and handicraft industries. Effective preventive measures identified in current research include imparting awareness regarding detrimental behaviors, promoting proper ergonomic practices, particularly regarding sitting postures, incorporating intermittent standing intervals during prolonged sitting sessions, and conducting biomechanical assessments of sitting postures.

Topic 5: Drivers, dentists, women, and those with higher education exhibit an increased propensity for sustaining injuries to their shoulders and limbs. This may be attributed to the repetitive actions required for driving and the extended surgeries performed by dentists. Similarly, professionals engaging in analogous behaviors, such as surgeons, internal medicine specialists, chefs, and decorators, necessitate enhanced shoulder care. Effective preventive measures encompass properly scheduled rest intervals, tailored care provision, and the promotion of physical well-being. Furthermore, to elucidate the term “high,” lexical positioning was implemented to explicate its context. On one hand, “high” signifies the elevated prevalence of MSDs in the existing research survey samples, while on the other, it underscores its relevance to the education and medical sectors, implying a heightened susceptibility to illness among highly educated personnel.

Topic 6: For employees in the medical and manufacturing industries, workplace conditions, workload, and work intensity are pivotal factors influencing the onset of MSDs attributed to prolonged sitting, particularly among male workers. The back, being a susceptible area, requires particular attention in disease prevention. Hence, effective preventive strategies entail both enhancing the working environment and fostering workers’ awareness of ergonomics. Simultaneously, engaging in low-to-moderate intensity exercises during leisure time also serves as an effective intervention.

In conclusion, the analysis of the LDA topic model results reveals that MSDs stemming from prolonged sitting are specifically influenced by the working environment, working posture, work intensity, work pressure, age, work experience (qualifications), and other factors. Thus, our preventive measures are categorized into the following aspects. Firstly, it is essential to improve the working environment, which involves enhancing ergonomically designed seats, utilizing scientifically sound and user-friendly tools, and configuring comfortable workstation designs. Additionally, developing employee ergonomic awareness, conducting a sitting biomechanical assessment, focusing on sitting trunk tilt (6.5^。 -6.9^。), and integrating standing postures within sitting intervals (between half an hour to an hour) can serve as highly effective preventive measures. Moreover, in industries significantly impacted by prolonged sitting, such as the service, medical, and handicraft sectors, managers need to conscientiously implement intervention measures, pay attention to employees’ self-reports of bodily discomfort, regulate work intensity, schedule appropriate rest periods, provide ergonomic posture training courses, and offer reminders for body care to aid in preventing MSDs caused by prolonged sitting. Finally, employees should vigorously attend to their health by engaging in low-to-medium-intensity exercises during work breaks, improving their sitting or standing posture every half an hour, and managing their health during vacations.

Discussion

This study moves beyond prior ergonomics research that has predominantly focused on the living environment [82–83] by examining the association between sedentary behavior and MSDs in occupational settings. Going beyond a purely physiological perspective, a meta-analysis of 33 eligible studies was conducted to quantify the effects of sedentary behavior on different body regions across occupational sectors, complemented by LDA topic modeling to synthesize evidence related to risk factors and prevention strategies.

According to the overall meta-analysis, the musculoskeletal effects of prolonged sitting are not confined to the trunk or cervical regions but also involve the knees, feet, and hands, particularly among workers exposed to passive sitting and constrained postures [11–14, 90–91]. This finding partially corroborates existing systematic reviews and meta-analyses in healthcare-related occupations [22, 26], including operating room personnel [27], surgeons [29], nurses [22], and physiotherapists [30–31], which consistently identify the lower back, neck, and shoulders as the most frequently affected body regions under sustained static postures and high work intensity [27–30, 37, 84]. Large-scale meta-analyses among nurses in China and global reviews of healthcare professionals further demonstrate that these high-prevalence body zones remain stable across sex, age groups, and geographic regions, suggesting a reproducible pattern of MSDs distribution in medical settings [22]. Importantly, our findings extend this evidence by demonstrating that sedentary-related MSDs exhibit distinct body-region profiles across occupational sectors. Subgroup analyses reveal that in handicraft industries-where prolonged sitting is combined with repetitive manual tasks-MSDs prevalence is disproportionately concentrated in the wrists/hands, feet, and lower back. This pattern is consistent with prior occupation-specific studies among sewing machine operators and informal craft workers [50, 56, 77]. In service industries characterized by prolonged screen-based work, neck-related MSDs emerge as the dominant concern [19–24, 37, 90–92], aligning with evidence from office-based and administrative populations [35–38, 87, 90]. Manufacturing workers exhibit comparatively lower overall MSDs prevalence, with symptoms primarily affecting the lower back, neck, and shoulders, possibly reflecting greater task variability and intermittent movement reported in ergonomic research [81–82, 87]. In contrast, healthcare workers show elevated MSDs prevalence across multiple body regions, particularly the back, shoulders, and neck, supporting international meta-analytic evidence on the combined effects of static postures, prolonged standing or sitting, and high workload in clinical environments [23, 27]. Outdoor workers demonstrate pronounced vulnerability in the lower back, which may be attributable to sustained postures, uneven terrain, and limited ergonomic support [55, 78].

By integrating occupation-specific subgroup meta-analyses with LDA topic modeling, this study methodologically complements and extends prior meta-analytic research that has largely focused on single occupational groups or a limited set of parameters. The LDA-based thematic analysis indicates that preventive measures primarily cluster around several key dimensions, including the working environment [24, 93], such as ergonomic improvements to workstations and seating [85, 87]; working posture [88, 94], including trunk inclination and uninterrupted sitting duration; interventions [95], such as physical exercise, intermittent standing, and ergonomic training; work intensity [96]; and individual factors [97], including age and psychological stress. More specifically, workers in handicraft industries should prioritize protection of the wrists, feet, and lower back; service-sector workers should focus on alleviating neck-related strain; manufacturing workers should address exposure of the lower back, neck, and shoulders; healthcare workers require comprehensive interventions targeting the back, shoulders, and neck; and outdoor workers should prioritize protection of the lower back.

This study employs a novel combination of meta-analysis and LDA topic modeling to offer a more nuanced understanding of the complex relationship between sedentary behavior and MSDs. This methodological pairing, not commonly observed in prior studies, was selected based on several considerations. Firstly, meta-analysis serves as a crucial tool for constructing systematic insights, frequently employed in literature reviews to elucidate the strength of influencing factors and the degree of correlation between variables. Our meta-analysis provides a quantitative overview of the strength of associations between various factors and MSDs across different body regions. Secondly, while meta-analysis excels in evaluating the strength of associations, it is not suited for exploring the broader understanding of causes - affected body regions - prevention, necessitating consideration of interdisciplinary knowledge structures. The LDA topic model proves effective in this regard, facilitating the exploration of scientific topics, particularly within fuzzy domains [44, 96]. As a big data analysis method, LDA deeply mines text data and can be integrated with techniques like social network analysis to uncover connections between elements [45, 97]. The results of both methods mutually verify and complement each other, with that meta-analysis quantifying the impact of various factors on MSDs across different body regions, while LDA identified underlying themes and potential prevention strategies. The synergy between these two methodologies contributes to the provision of more rigorous and scientifically grounded analysis compared to traditional literature reviews.

The study exhibits certain limitations that may have constrained the research outcomes. First, the cross-sectional nature of the included studies precludes a definitive analysis of the temporal progression of MSDs. While the LDA topic model provides insights into research trends, it does not account for changes in research focus over time. Second, we advise that future research can focus on some niche occupational workers or expand the scope, such as indoor office workers, and expand the sample of outdoor workers impacted to achieve broader conclusions. This research could help design workplace interventions and promote effective prevention during leisure time. Simultaneously, we advocate for the pursuit of generalizable conclusions through quasi-natural or rehabilitation experiment designs.

Conclusion

MSDs exhibit considerable variability across diverse occupational settings, hindering the development of universally applicable preventions. This study provides a comprehensive analysis of the relationship between sedentary behavior and MSDs within the working population. Through the implementation of meta-analysis and LDA topic modeling, this investigation offers significant insights into the research field of MSDs by identifying affected body regions and prevention strategies for MSDs associated with sedentary behavior. A meta-analysis of 33 studies revealed the primary body regions affected by prolonged sitting in the workplace and elucidated industry-specific variations in affected areas, while the LDA topic modeling analysis of 75 related studies attempted to extract effective prevention topics. These findings hold practical implications for mitigating workplace-related MSDs caused by prolonged sitting.

Abbreviations

LDA

Latent Dirichlet Allocation

CI

Confidence interval

MSDs

Musculoskeletal disorders

Authors’ contributions

ZD, JH, and YS conceptualized and designed the study. CH and TL collected, analyzed, and interpreted the data. CH and TL drafted the initial manuscript. All authors revised the drafted manuscript.All authors read and approved the final manuscript.

Funding

No Funding.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.