Sage Journals: Discover world-class research

Abstract

Background

Prolonged sitting in office environments is a major contributor to musculoskeletal disorders (MSDs), representing a growing concern for occupational health and ergonomics.

Objective

This scoping review aimed to examine the range of methods used to assess sitting postures among office workers, emphasizing their applications, strengths, and limitations.

Methods

A comprehensive search identified 42 studies published between 2000 and December 2023 from an initial pool of 167 articles. Studies were categorized into self-assessment, observational, and instrument-based approaches following PRISMA-ScR guidelines.

Results

Self-assessment methods were the most common (69.05%), capturing subjective reports of discomfort, followed by observational tools (38.09%) for postural risk evaluation and instrument-based approaches (45.24%) utilizing sensor- and vision-based technologies for objective analysis. Several studies combined two or more methods to improve data validity through cross-validation and to achieve a more comprehensive understanding of posture-related risks.

Conclusions

This review synthesizes current approaches for evaluating sitting postures in office settings and highlights methodological trends, gaps, and opportunities to guide future ergonomic research aimed at reducing MSD risks.

Keywords

ergonomics workplace health musculoskeletal disorders posture assessment

Introduction

Over the past few decades, workplace environments have undergone a significant transition, with a marked increase in prolonged sitting, particularly in office-based occupations.^1,2 In the United States alone, occupation-related energy expenditure has decreased by more than 100 kilocalories per day over the last 50 years, contributing significantly to the prevalence of sedentary work patterns.¹ The proportion of office-based jobs increased from approximately 20% in the 1960s to 43% by 2008.¹ Today, office workers spend an estimated 89% of their working hours seated, placing them at elevated risk for a range of adverse health outcomes, including cardiovascular disease mortality, Type 2 diabetes, obesity, and, most notably, musculoskeletal disorders (MSDs).^3–5 These trends highlight the urgent need for targeted interventions aimed at mitigating the health impacts of prolonged sitting. Commonly proposed strategies include ergonomic assessments to identify posture-related and workstation risks, administrative policies that promote scheduled movement breaks, and the integration of active workstations designed to encourage dynamic postural variation and reduce uninterrupted sitting throughout the workday. Among the various health risks associated with prolonged sitting, MSDs remain the most prevalent occupational health concern in office settings, particularly affecting the upper body regions.^3,6,7 Studies report that approximately 37.9% of office workers experience MSDs, with the most commonly affected areas being the neck, shoulders, and back.³ In one investigation involving public service employees who regularly use computers, 50.5% reported shoulder pain, 20.3% experienced discomfort in the elbows, 26.3% reported wrist or hand pain, 44.8% had upper back pain, and 56.1% reported pain in the lower back.⁷ similarly, an Australian study documented that over a one-year period, 76% of office workers experienced neck pain, 71% reported shoulder pain, and 65% suffered from lower back pain.⁶ These findings emphasize the widespread and persistent nature of MSDs in sedentary occupations and underscore the importance of accurate assessment and early intervention strategies.

Beyond the individual health consequences, MSDs impose a significant economic burden on organizations and national healthcare systems.^8–11 According to the Canadian Centre for Occupational Health and Safety (CCOHS), MSDs are the leading cause of lost-time injuries and constitute the primary source of lost-time costs in Canada.⁸ In 2023, there were 10,874 accepted lost-time injury claims related to musculoskeletal and connective tissue disorders, an increase of 6.5% from the previous year, underscoring a growing trend in work-related MSDs.⁹ In the United States, the total annual economic burden of MSDs exceeds $300 billion, encompassing both direct healthcare costs and indirect losses due to reduced productivity and work absenteeism.¹⁰ similarly, across numerous European countries, MSDs are the most frequently reported occupational diseases, driving up expenditures related to workers’ compensation, disability support, and social insurance schemes.¹¹ These statistics underscore the urgent need for proactive workplace interventions. Moving beyond reactive treatment approaches, the adoption of comprehensive ergonomic assessment methods can enable the early identification of risk factors, informing the design of preventive strategies aimed at reducing both the incidence and long-term impact of MSDs in office environments. A variety of posture assessment methods have been employed to analyze office workers’ sitting behavior and identify associated ergonomic risk factors. These approaches are commonly categorized into three primary groups: self-assessment, observational, and instrument-based methods. Each method differs in terms of accuracy, user burden, feasibility, and cost, making the selection of an appropriate tool highly dependent on the study's context and objectives. Self-assessment methods rely on individuals to report perceived discomfort or posture-related issues, typically through standardized questionnaires or surveys.^12–14 These tools are especially practical for large-scale studies due to their low cost and ease of administration. They provide valuable subjective insights into ergonomic concerns as experienced by workers. However, the reliability of self-reported data may be compromised by recall bias, over- or under-reporting, and variability in health literacy or awareness among participants, all of which can affect data accuracy.¹⁵ Observational methods, involve trained evaluators visually inspecting postures using standardized tools like checklists, are widely employed to assess physical workload and monitor ergonomic interventions.^16,17 While they allow for structured evaluations without the need for expensive equipment, these methods depend heavily on the subjective judgment of trained observers, which can introduce variability. Observer training and inter-rater reliability are critical to ensuring consistency and credibility in findings. Nevertheless, their relatively low cost and accessibility contribute to their widespread adoption in both academic and industrial settings.^15,18 Instrument-based methods utilize technological systems, including sensor-based and vision-based techniques, provide continuous and objective measurements, enabling real-time posture analysis.^19–23 Vision-based systems, in particular, can detect joint positions, measure angular deviations, assess ranges of motion, and estimate exerted forces in near real-time, offering a precise and efficient approach to identifying postural risks.^24,25 However, despite their technical advantages, the widespread adoption of such systems may be limited in practice due to cost, setup complexity, and equipment accessibility, especially in smaller organizations or field environments. Sensor-based techniques, such as electromyography (EMG) and motion capture systems, provide detailed biomechanical data by capturing muscle activity or movement patterns. However, they often require body-worn sensors, which may restrict natural movement, cause discomfort during extended wear, and pose challenges for routine workplace implementation—particularly in settings where comfort and usability are paramount.^26–28 In contrast, vision-based systems offer a non-intrusive alternative by leveraging cameras to assess posture; however, their accuracy can be affected by environmental factors such as lighting conditions, occlusions, and camera placement.²⁹

Previous review studies that have addressed ergonomic assessment methods have primarily focused on evaluating the effectiveness of individual tools in isolation.^30–34 A structured search across identified 19 relevant review papers. However, most focused on non-office contexts (e.g., surgical, industrial, construction), where postural demands differ from sedentary office work. While informative, these reviews lacked comparative synthesis across self-assessment, observational, and instrument-based methods and offered limited discussion on how method choice affects study design elements such as sample size, demographics, setting, and duration. Few addressed the specific challenges of prolonged sitting. This scoping review addresses these gaps by synthesizing ergonomic assessment methods used in office environments and examining how they shape study frameworks and MSD-related outcomes. By focusing on sedentary work contexts, it offers a more integrated, contextually relevant perspective for future ergonomic research and practice.

Given the diversity of ergonomic assessment tools and their varied applications, a scoping review was deemed the most suitable approach. It allows for comprehensive mapping of key concepts, methodological trends, and research gaps. Unlike systematic reviews focused on intervention efficacy, scoping reviews accommodate heterogeneous literature and support synthesis across diverse methodologies—making them ideal for emerging or fragmented fields like prolonged sitting and MSD assessment. This approach fosters a more integrated understanding and informs future research directions.

This scoping review aims to:

Catalogue ergonomic assessment methods used in studies of prolonged sitting in office environments, with the goal of generating evidence-based recommendations grounded in the review's findings rather than relying solely on conceptual guidance.

Analyze the impact of assessment methods on experimental design, including their influence on data collection strategies, sample size determination, study settings, and duration.

Provide practical guidance for researchers on selecting appropriate assessment tools to enhance the methodological validity, reliability, and applicability of future ergonomic studies.

Research question

This review is guided by the following research question:

“What types of studies have been conducted regarding office workers’ MSDs, and how does experimental design vary based on the assessment methods used?”

The question was premised on the person, concept and context (PCC) mnemonic: Population – Office workers; Context – Prolonged sitting in office environments and Concept – Ergonomic assessment methods used to evaluate MSDs.

Grounded in this framework, the review aims to synthesize existing literature to enhance methodological transparency in ergonomic research. Specifically, it examines different categories of assessment methods self-assessment, observational, and instrument-based influence research design features such as data collection, participant recruitment, and measurement duration. This synthesis serves to inform future ergonomic evaluations by highlighting trends, methodological considerations, and gaps in the current evidence base.

Methods

This study used a scoping review to systematically map ergonomic assessment methods for evaluating prolonged sitting among office workers. This approach was selected to explore the breadth of existing literature, identify key concepts, synthesize evidence, and highlight research gaps in posture assessment within sedentary work contexts. Unlike systematic reviews, which address narrowly focused questions (e.g., intervention efficacy), scoping reviews are suited for examining diverse literature, refining conceptual boundaries, and informing future research directions.³⁵ The review followed established scoping review methodology and adhered to PRISMA-ScR guidelines.^36,37

Search strategy

A comprehensive search strategy was developed to identify relevant studies examining assessment methods for prolonged sitting in office environments. In this review, prolonged sitting is defined as sedentary behavior lasting two hours or more without substantial physical activity, consistent with established definitions in occupational health literature.³⁸ The search was conducted across four databases to ensure a multidisciplinary approach: (a) IEEE Xplore for a technology perspective, (b) PubMed for a medical perspective, (c) Engineering Village for an engineering perspective, and (d) Web of Science for a cross-disciplinary perspective. These databases were selected based on their relevance to the research topic and their ability to provide access to a wide range of scholarly publications across different disciplines.³⁹

This strategy was customized for each database using carefully selected keywords and search terms. Boolean operators (“AND” for integrating distinct concepts and “OR” for incorporating synonymous terms) were utilized to formulate complex search queries, thereby ensuring exhaustive coverage of pertinent literature. The complete list of search strings applied across different databases is presented in Table 1. The final search was conducted on December 14, 2023.

Table 1.

Search terms used for literature review.

keywords	Search Terms
Office Workers	"Office Workers” OR “White-collar workers” OR “Desk workers” OR “Administrative personnel” OR “Sedentary Lifestyle” OR Telecommuting OR “Data Entry” OR “Call Center Jobs” OR “Computer-Based workers"
	AND
Posture	"Posture detection” OR “Sitting posture” OR “Postural assessment” OR “Corrective posture” OR “Posture monitoring” OR “Postural habits” OR “Pose estimation” OR “Sitting Position” OR “Unhealthy sitting posture detection"
	AND
Ergonomics	Ergonomic OR “Human factors"
	AND
Measurement Methods	"Ergonomic assessment” OR “Rapid Upper Limb Assessment” OR “Rapid Entire Body Assessment” OR “Ovako Working Posture Analysis System” OR “Computer vision” OR “Machine learning” OR “Wearable Device” OR Sensor OR Questionnaire OR “Camera-based systems” OR Biomechanics OR Observation
	AND
Musculoskeletal Disorders	"Musculoskeletal Disorder” OR “Work-related injuries” OR “Workplace discomfort” OR “Repetitive strain injuries” OR “Occupational health hazards” OR “Musculoskeletal discomfort"

Inclusion and exclusion criteria

To ensure the selection of high-quality and relevant studies, predefined inclusion and exclusion criteria were applied. Studies were included if they were peer-reviewed journal articles published between January 2000 and December 2023, written in English, and focused on assessment methods for prolonged sitting in office workers. The year 2000 was chosen as the starting point due to the increasing dominance of computer-based work in office environments during this period, coinciding with the emergence of digital technologies, such as posture-monitoring software and computer vision systems, that began to reshape ergonomic assessment practices. Studies were excluded if they fell into any of the following categories: (1) gray literature, including unpublished works, dissertations, conference abstracts, technical reports, patents, news articles, or book chapters; and (2) review articles, as the aim was to analyze original research. These exclusions ensured a focus on peer-reviewed primary research, which typically adheres to higher methodological standards and editorial rigor. Restricting the review to peer-reviewed articles also supports consistency in study quality, reporting transparency, and the reproducibility of findings.

Study selection

The study selection process commenced with the import of search results into reference management software, Zotero (version 6.0.36). This facilitated the identification and management of duplicate records, ensuring that each unique study was considered only once during the screening process. Duplicate records were identified and removed to streamline the subsequent screening stages. The screening and selection of studies followed a two-stage approach to assess the relevance and eligibility of identified records. In the first stage, titles and abstracts of the retrieved studies were screened against predefined inclusion and exclusion criteria. This initial screening allowed for the rapid identification of potentially relevant studies based on their relevance to the research question and predefined criteria. Studies that did not meet the inclusion criteria or were irrelevant to the research topic were excluded at this stage. In the second stage, full-text articles of the remaining studies were retrieved and assessed for completeness, relevance to the research question, and alignment with the review objectives, in accordance with the scoping review methodology. The PRISMA flow diagram (Figure 1) was utilized to document the study selection process and provide transparency regarding the number of studies identified, screened, assessed for eligibility, and included in the review. The search across IEEE Xplore, PubMed, Engineering Village, and Web of Science yielded 167 studies. After removing 26 duplicates, 142 studies were excluded based on title, abstract, or full-text screening. A total of 42 studies were included in the final review.

Figure 1.

Results of the literature search, screening process, and study classification, according to the PRISMA reporting guidelines.

Data extraction

Data extraction was meticulously carried out using Microsoft Excel to capture pertinent information from the included studies. Extracted data encompassed various aspects, including study characteristics (such as author, year, and location), participant demographics (including age group and gender disparities), assessment methods employed (comprising measurement techniques, observation methods, Instrument-Based Methods, and Self-assessment methods), and key findings pertinent to prolonged sitting postures. For studies involving multiple countries, the country of origin was classified based on the lead institution or the location responsible for primary protocol development.

Data synthesis

Data synthesis combined both quantitative and qualitative approaches to comprehensively analyze the extracted information from the reviewed studies. Qualitatively, an inductive thematic analysis was performed using Microsoft Excel. This process involved a detailed review and annotation of each included study to identify recurring topics such as the types of assessment methods employed, target body regions, and the primary measurement objectives. The identified elements were initially grouped into codes, which were then iteratively refined into broader themes related to ergonomic methodology and data collection practices. To enhance the reliability of the thematic synthesis, careful cross-checking and multiple rounds of refinement were undertaken to ensure internal consistency.

Quantitative synthesis used descriptive statistics (frequencies, means, percentages) to summarize study characteristics such as sample size, gender, age, setting, and data duration. Inferential analyses included a nonparametric aligned rank transform ANOVA (ART ANOVA) for comparing sample sizes, justified by Shapiro-Wilk test results indicating non-normality. Chi-squared tests examined associations between categorical variables, with post hoc analyses using standardized residuals and ART-constrained contrasts. All analyses were conducted in R (v4.5.0), with significance set at p < 0.05.

Results

The reviewed studies span over two decades, with a marked increase in publications beginning around 2011. Only 19% of the studies were published between 2000 and 2010, while approximately 80% were published between 2011 and 2023. This trend reflects the growing recognition of MSD risks associated with prolonged sitting and the increasing research attention directed toward ergonomic assessment in office environments. The studies demonstrate substantial international representation, with contributions from 20 countries. Iran produced the highest number of studies (n = 6), followed by the United States and Israel (n = 5 each). Brazil, Canada, and the Netherlands each contributed three studies, while Australia, Italy, and Norway each contributed two. The remaining countries, China, Hong Kong, India, Indonesia, Japan, Poland, Portugal, Sweden, Switzerland, Thailand, and Turkey, each accounted for a single study. When grouped by region, Asia emerged as the most represented, contributing 18 studies across 9 countries. This global distribution underscores the widespread concern with sedentary behavior and its associated ergonomic risks. It also suggests variation in research priorities, technology adoption, and workplace health regulations across regions. A comprehensive summary of study characteristics, including country- and region-level contributions, is provided in Table 2.

Table 2.

The overall result of reviewing the articles.

Author (year)	Country	Sample Size	Gender		Age (Mean [SD])	Body part	Site	SM	OM	IM
Author (year)	Country	Sample Size	Men	Female	Age (Mean [SD])	Body part	Site	SM	OM	IM
⁵⁹	Norway	90	30	60	42	Upper Body	FE	✓ ✓
⁶⁰	Norway (MNS)	24	0	24	NA	Whole Body	FE	✓ ✓		✓ ✓
⁶¹	Poland	16	0	16	37	Whole Body	FE	✓ ✓		✓ ✓
⁶²	USA	26	0	26	41.02 [9.58]	Whole Body	FE	✓ ✓		✓ ✓
⁶³	Israel	84	57	27	32	Upper Body	FE	✓ ✓	✓ ✓
⁶⁴	Netherlands	84	25	59	39 ¹⁰	Upper Body	FE	✓ ✓
⁶⁵	Turkey	173	14	159	30.5 [2.5]	Upper Body	FE		✓ ✓
⁶⁶	USA	1167	448	719	NA	Upper Body	FE	✓ ✓
⁶⁷	Canada	72	7	65	45.4 [9.1]	Whole Body	FE	✓ ✓	✓ ✓
⁶⁸	Israel	40	28	12	38.48 [9.5]	Whole Body	FE	✓ ✓
⁴²	Isreal	60	22	38	NA	Upper Body	FE	✓ ✓	✓ ✓
²⁸	Netherlands	120	34	86	40	Upper Body	FE	✓ ✓		✓ ✓
¹⁷	Israel	29	13	16	42.1 [10.5]	Whole Body	FE		✓ ✓
⁵⁴	USA	5	0	5	43	Upper Body	FE	✓ ✓
⁶⁹	Canada	24	12	12	23.2 [3.82]	Whole Body	CL	✓ ✓		✓ ✓
²⁰	Canada	14	7	7	22.5 [1.6]	Upper Body	CL			✓ ✓
⁷⁰	Switzerland	20	13	7	45	Trunk/Back	FE	✓ ✓		✓ ✓
⁷¹	Netherlands	45	19	26	43.1 ¹¹	Whole Body	FE	✓ ✓		✓ ✓
⁷²	Brazil	35	24	11	39.9 [10.19]	Whole Body	FE	✓ ✓	✓ ✓
¹²	Portugal	96	38	58	42.1 [9.28]	Whole Body	FE	✓ ✓
⁴¹	Iran	102	42	60	36.4 [7.01]	Upper Body	FE	✓ ✓	✓ ✓
⁷³	Australia	20	7	13	34.7 [8.21]	Whole Body	CL	✓ ✓		✓ ✓
⁴³	Iran	250	121	129	NA	Whole Body	FE	✓ ✓	✓ ✓
¹⁹	Australia	31	5	26	33.9 [11.8]	Upper Body	FE			✓ ✓
¹⁴	Brazil	109	39	70	34.28 [10.93]	Upper Body	FE	✓ ✓
⁷⁴	Iran	188	188	0	32.95 [4.67]	Upper Body	FE	✓ ✓	✓ ✓
⁵⁸	India	400	343	57	NA	Whole Body	FE	✓ ✓	✓ ✓
⁷⁵	Sweden	224	78	146	48 ¹⁰	Upper Body	FE	✓ ✓
²¹	Italy	5	3	2	29.6 [5.96]	Head/Neck	CL			✓ ✓
⁴⁰	Thailand	40	11	29	29 [3.9]	Whole Body	FE	✓ ✓		✓ ✓
⁷⁶	USA	36	18	18	25.78 [4.5]	Whole Body	CL	✓ ✓	✓ ✓
²²	USA	20	12	8	NA	Upper Body	CL		✓ ✓	✓ ✓
⁵²	Israel	35	21	14	NA	Head/Neck	CL			✓ ✓
¹⁶	Iran	26	13	13	35.1 [3.6]	Whole Body	FE		✓ ✓
²⁷	Japan	21	10	11	26 [4.9]	Upper Body	CL			✓ ✓
⁷⁷	Brazil	55	18	37	42.6 [7.5]	Whole Body	FE	✓ ✓	✓ ✓
²³	China	21	12	9	26 [3.82]	Whole Body	CL			✓ ✓
⁷⁸	Italy	10	3	7	27.1 [2.6]	Upper Body	CL			✓ ✓
⁷⁹	Iran	24	24	0	39 [6.7]	Whole Body	FE	✓ ✓	✓ ✓
⁸⁰	Hong Kong	20	NA	NA	NA	Whole Body	CL			✓ ✓
¹³	Iran	40	20	20	28.5 [1.8]	Whole Body	CL	✓ ✓
⁵³	Indonesia	8	3	5	21	Whole Body	CL		✓ ✓	✓ ✓

SM = Self-Assessment Methods, OM = Observational Methods, IM = Instrument-Based Methods, FE = Field Experiments,

CL = Controlled Laboratory, NA = Not Available,

MNS = multinational studies, the country listed refers to the primary coordinating country or the location of the lead research team.

Assessment methods

The reviewed studies employed three primary posture assessment methods: self-assessment, observational, and instrument-based techniques. These methods were not always applied in isolation; several studies combined two or more approaches to enhance data validity and capture complementary aspects of posture and discomfort. As a result, the reported percentages for each method category exceed 100%, reflecting overlapping methodological use rather than mutually exclusive classification.

Self-assessment methods

Among the 42 reviewed studies on office worker postures, 29 (approximately 69.05%) employed self-assessment methods to evaluate posture-related outcomes, including discomfort, pain, fatigue, or musculoskeletal strain localized to specific body regions. Only self-reported instruments targeting these outcomes were included. These methods comprised both standardized, widely recognized questionnaires, such as the Disabilities of the Arm, Shoulder and Hand (DASH) and the Maastricht Upper Extremity Questionnaire (MUEQ), as well as instruments that were developed or adapted by the study authors. A total of 12 distinct self-assessment tools were identified and categorized based on the instrument names reported in the methods sections of the respective articles. When a study did not specify a validated tool but described the use of a self-reported posture or discomfort survey, the method was classified as author-developed. Notably, approximately 38% of the 29 studies employed such author-developed instruments. These included entirely new questionnaires or hybrid tools created by combining elements from established instruments to better align with the specific context and objectives of the study. The validation status of each tool, where reported, is summarized in Table 3.

Table 3.

Self-assessment methods.

Author (year)	MUEQ	DASH	CUDQ	NMQ	MFI	COPSOQ	VPI	BPI	CR-10	UOCC	LMD	VERMA	AD	VS
⁶³				✓										Y
⁵⁹													✓	N
⁶⁰													✓	N
⁶¹													✓	N
⁶²													✓	N
⁶⁴													✓	Y
⁶⁶													✓	Y
⁶⁷			✓											Y
⁶⁸										✓				Y
⁴²		✓					✓						✓	Y
²⁸													✓	PV
⁵⁴			✓										✓	PV
⁶⁹													✓	N
⁷⁰							✓	✓						Y
⁷¹											✓			Y
⁷²	✓													Y
¹²				✓		✓								Y
⁴¹													✓	Y
⁷³				✓										Y
⁴³				✓										Y
¹⁴	✓	✓												Y
⁷⁴				✓	✓									Y
⁵⁸			✓											Y
⁷⁵												✓		Y
⁴⁰									✓					Y
⁷⁶				✓										Y
⁷⁷	✓			✓										Y
⁷⁹				✓										Y
¹³									✓					Y

MUEQ = The Maastricht Upper Extremity Questionnaire, DASH = The Disabilities of the Arm, Shoulder and Hand, CUDQ = The Cornell University Discomfort Questionnaire, NMQ = The Nordic Musculoskeletal Questionnaire, MFI = The Multidimensional Fatigue Inventory, COPSOQ = Copenhagen Psychosocial Questionnaire, VPI = The Von Korff Pain Inventory, BPI = The Brief Pain Inventory questionnaire, CR-10 = The Borg CR-10 Scale, UOCC = The University of California computer usage checklist, VERAM = The Visual Ergonomics Risk Assessment Method, LMD = Local Musculoskeletal Discomfort, AD = Author Design, VS = Validation Status(Y = Yes, N = No, PV = Partial Validation).

Observational methods

Following self-assessment approaches, observational methods were the second most commonly employed posture assessment strategy. Among the 42 reviewed studies, approximately 38.09% utilized observational techniques, relying on four distinct tools: the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), Rapid Office Strain Assessment (ROSA), and the Cumulative Trauma Disorders (CTDs) checklist (Table 4). Among the 16 studies that applied observational methods, RULA was the most frequently used, appearing in 75% (n = 12) of cases. ROSA was employed in 31.25% (n = 5), while REBA and CTDs were each used in a single study (6.25%). These figures underscore RULA's dominance as a preferred observational tool for assessing postural risk in office-based work environments.

Table 4.

Observational methods.

Author (year)	ROSA	RULA	REBA	CTDs
⁶³		✓
⁶⁵				✓
⁴²		✓
⁶⁷	✓
¹⁷		✓
⁷²	✓	✓
⁴¹	✓
⁴³	✓	✓
⁷⁴		✓
⁵⁸		✓
⁷⁶		✓	✓
²²		✓
¹⁶		✓
⁷⁷	✓
⁷⁹		✓
⁵³		✓

Instrument-based methods

Instrument-based methods were employed in approximately 45.24% of the reviewed studies (Table 5), representing the most technologically advanced category of posture assessment. For analytical clarity, these methods were subdivided into two main groups based on the nature of data acquisition: Sensor-Based Recognition and Vision-Based Recognition. Sensor-based recognition methods involve the use of wearable or embedded physical sensors that directly measure biomechanical or physiological signals from the body. These tools capture quantitative data such as muscle activation, body pressure distribution, or joint angles through physical contact with the participant. Vision-Based Recognition, by contrast, utilizes camera systems (e.g., RGB, RGB-D) and computer vision algorithms to infer posture without requiring physical contact. These systems analyze visual data to estimate joint positions, angular deviations, movement patterns, and classify postures.

Table 5.

Instrument-based methods.

	Sensor-Based Recognition	Vision-Based Recognition
Author (year)	MCS	EMG	FBG	PAS	IRA	OMCS	RGB Camera	Depth camera
/RGBD Camera
⁶⁰		✓
⁶¹		✓
⁶²		✓
²⁸		✓
⁶⁹		✓				✓
²⁰						✓
⁷⁰				✓
⁷¹				✓
⁷³	✓	✓
¹⁹	✓
⁴⁰				✓
²¹	✓		✓			✓
²³				✓	✓
²²								✓
⁵²							✓
²⁷	✓
⁵³								✓
⁷⁸			✓
⁸⁰				✓	✓

MCS = Motion Capture Sensors, EMG = Electromyography; FBG = Fiber Bragg Grating; IRA = Infrared Array Sensor; PAS = Pressure array sensors, OMCS = Optoelectronic/Optical Motion Capture Systems.

Sensor-Based Recognition

Sensor-based recognition methods capture postural and biomechanical data using body-worn or embedded sensors. Among the sensor-based techniques, EMG was the most commonly used, appearing in 31.58% of the reviewed studies. EMG sensors monitor electrical activity in muscles and are particularly effective for assessing muscular load and fatigue during prolonged sitting tasks. Motion capture systems were employed in 21.05% of studies, primarily through wearable inertial measurement units (IMUs). These systems track joint angles and movement dynamics in real-time, offering a portable solution for posture monitoring. Optoelectronic motion capture systems, utilized in 15.79% of the studies, use multiple infrared cameras to track reflective markers placed on the body. These systems offer highly accurate three-dimensional data on joint kinematics, although their use is typically restricted to laboratory environments due to setup complexity and cost. Pressure array sensors, featured in 26.31% of studies, were often embedded in seating surfaces to measure pressure distribution across the body. These systems detect postural shifts and provide insight into seat-user interaction, an important factor in ergonomic seating design. Fiber Bragg Grating (FBG) sensors, identified in 10.52% of studies, are optical strain sensors integrated into wearable fabrics or components. They detect subtle mechanical deformations and are valued for their flexibility, lightweight design, and potential for integration into clothing. Lastly, Infrared Array (IRA) sensors, also present in 10.53% of studies, detect thermal radiation or motion patterns. These non-contact sensors offer a passive approach to posture detection, making them suitable for unobtrusive, real-world applications.

Vision-Based Recognition

Vision-based recognition methods leverage image processing and computer vision algorithms to assess posture without requiring physical contact with the subject. RGB cameras, used in 5% of the reviewed studies, capture standard color images or videos. These recordings are processed using pose estimation algorithms to extract key body joint coordinates. While RGB-based systems are cost-effective and easy to deploy, their accuracy can be limited by lighting conditions, occlusions, and background clutter. RGB-D cameras, which combine red-green-blue (RGB) imaging with depth sensing, were employed in 10.52% of the studies. These cameras capture three-dimensional posture data, including joint angles and spatial orientation, enabling more precise assessments of MSD risks. Added depth information improves the robustness of posture detection, particularly for complex sitting behaviors or subtle movements that may not be visible in 2D imagery.

Experimental design of the reviewed studies

Participant recruitment and eligibility criteria

Participant selection criteria varied across the reviewed studies, reflecting efforts to ensure data quality and control for confounding variables. The most frequently reported exclusion criterion was the presence of pre-existing MSDs, chronic pain, upper extremity injuries, systemic illnesses, or cognitive/sensory impairments. This exclusion was applied in 19 studies (45.24%), aiming to isolate posture-related outcomes from pre-existing health conditions that could bias findings.^28,40 To ensure adequate exposure to sedentary work conditions, 9 studies (21.43%) required participants to engage in a minimum of 20 h of computer-based work per week.^28,41 This threshold was intended to capture participants representative of office-based occupations with prolonged sitting behavior. Additionally, 8 studies (19.05%) incorporated minimum job tenure requirements, most commonly at least one year to account for cumulative exposure to prolonged sitting. However, the application of this criterion varied: some studies provided specific justifications, while others offered only approximate durations or lacked clarity in their rationale.^42,43 Although most studies identified their participants as office workers, none explicitly distinguished between remote and in-office work settings.

Sample size distribution across assessment methods

Figures 2 and 3 illustrate the variation in sample sizes associated with different assessment methods and their combinations. Studies utilizing self-assessment methods reported the highest average sample size (206.1), with values ranging from 5 to 1167 participants. This wide range underscores the practicality and scalability of survey-based tools, which are often feasible for large populations due to their low cost and ease of administration. Observational methods demonstrated a more consistent distribution, with an average sample size of 76 and a range between 26 and 173 participants. This moderate scale likely reflects the resource requirements of trained human evaluators and the time-intensive nature of visual assessments. In contrast, instrument-based methods tended to involve the smallest sample sizes, with an average of 19.63 and a range from 5 to 35 participants. This finding is likely attributed to the technical complexity, cost, and setup time associated with sensor-based and computer vision systems.

Figure 2.

Sample size distribution for individual methods.

Figure 3.

Sample size distribution for combined methods.

When assessment methods were used in combination (Figure 3), sample sizes varied accordingly. Studies employing a combination of self-assessment and observational methods had an average sample size of 118.73, ranging from 24 to 400 participants. In contrast, combining self-assessment with instrument-based methods yielded a lower average of 37.22 participants (range: 16 to 120). The smallest sample sizes were observed in studies combining observational and instrument-based methods, with an average of 14 participants and a range from 8 to 20.

To compare sample sizes across six method categories, self-assessment, observational, instrument-based, and their combinations, a nonparametric aligned rank transform ANOVA (ART ANOVA) was conducted due to violations of normality (Shapiro-Wilk test).

The ART ANOVA revealed a significant main effect of assessment method type on sample size, (p = 0.0002), indicating that the type of ergonomic assessment method employed influenced the number of participants included in the reviewed studies. Post hoc pairwise comparisons using ART-constrained contrasts further clarified these differences. Studies using instrument-based methods had significantly smaller sample sizes compared to those using self-assessment methods (p < 0.001) and those combining self-assessment with observational methods (p < 0.001). Additionally, those combining self-assessment with observational methods studies had significantly larger sample sizes than studies that combined observational and instrument-based methods (p < 0.01). Finally, studies with self-assessment methods also had significantly larger sample sizes than studies that combined observational and instrument-based methods (p < 0.01). Other contrasts did not yield statistically significant differences (p > 0.05).

Notably, 21.43% of the studies explicitly cited small sample sizes as a limitation. These studies had an average of 28.55 participants (SD = 32), with sample sizes ranging from 5 to 109 as show in Figure 4. This raises concerns about statistical power and the generalizability of findings, especially in studies using resource-intensive assessment tools.

Figure 4.

Sample size distribution Among studies reporting low sample size.

Gender distribution

An analysis of gender distribution across the studies (see Figure 5) reveals that the majority, approximately 85%, included both male and female participants, while only 15% focused on a single gender. In studies with both male and female participants, the average gender composition was roughly 44% male and 56% female. A Chi-squared goodness-of-fit test was performed to assess whether the overall gender representation differed from an equal distribution. The result was statistically significant (p < 0.001), indicating that female participants were overrepresented across the reviewed literature.

Figure 5.

Gender distribution across the studies.

In the subset of studies that focused solely on one gender, two-thirds (66%) involved only female participants, while the remaining one-third (33%) included only male participants.

Age distribution

In terms of age distribution, approximately 36% of the reviewed studies involved participants with an average age between 30 and 40 years, followed by 26% in the 40–50 age range, and another 26% in the 20–30 range. As illustrated in Figure 6, the majority of studies concentrated on participants aged 20 to 50 years, with the 31–40 age group being the most frequently represented. These findings reflect a focus on the working-age population, particularly those in the prime of their professional careers, who are most likely to be exposed to prolonged sitting in office-based roles.

Figure 6.

Distribution of age groups.

Distribution of study settings

Among the studies reviewed, a substantial majority 69.05% were conducted in field settings, suggesting that the experiments were conducted in real-world environments, such as workplaces, involving office workers within their respective companies or offices. In contrast, 30.95% of the studies were conducted in laboratory settings, where environments were deliberately designed to replicate real-world conditions, typically within academic institutions (Figure 7).

Figure 7.

Distribution of study types (controlled laboratory vs field experiment).

A Chi-squared test of independence was performed to examine the relationship between the type of assessment method and the study setting. The test revealed a statistically significant association between these variables, (p < 0.0001). Post hoc analysis using standardized residuals showed that instrument-based methods were significantly overrepresented in controlled laboratory studies (p < 0.001) and underrepresented in field studies (p < 0.001). Additionally, the studies with combination of observational and instrument-based methods were also more frequently used in laboratory settings (p < 0.05) and less frequently in field settings (p < 0.05). No other method-setting combinations showed statistically significant deviations.

Duration of data collection

The reviewed studies were categorized based on the duration of data collection into two groups: short-term and long-term experiments. Short-term studies, defined as those lasting less than one week, typically involved data collection over several hours or days within a week. In contrast, long-term studies extended beyond one week, in some cases continuing for several months or even years. Results indicated that 26 studies (61.9%) were short-term, while 16 studies (38.1%) were classified as long-term (Figure 8).

Figure 8.

Distribution of short-term vs long-term experiments.

Distribution of focus areas of body parts

Posture assessments in the reviewed literature varied in their anatomical focus (Figure 9). A majority, 52.38%, evaluated posture across the entire body, while 40.47% concentrated specifically on the upper body, typically including the neck, shoulders, upper back, arms, and wrists . A smaller proportion of studies focused exclusively on individual regions: 4.76% examined only the neck, and 2.38% focused solely on the back. This distribution reflects a prevalent emphasis on the upper body in office-based ergonomic research, consistent with the common localization of musculoskeletal discomfort in sedentary workers.

Figure 9.

Distribution of focus areas in studies.

Ergonomic guidelines in defining posture and workstation setup

Several of the reviewed studies referenced established ergonomic guidelines to define optimal posture and appropriate workstation configurations. As summarized in Table 6, these guidelines provide critical recommendations on aspects such as seat height, desk height, monitor viewing angles, and arm positioning. Their application is essential for promoting worker comfort, reducing musculoskeletal strain, and improving postural alignment. By grounding their assessments in these standards, researchers enhance the validity of posture evaluation and ensure alignment with best practices in ergonomic design.^44–51

Table 6.

Some of ergonomic guidelines in defining posture and workstation setup.

	Canada ⁴⁵	USA ⁴⁴	Hong Kong ⁴⁷	⁴⁹	⁴⁶	⁴⁸	⁵¹
Seat requirement and work surface requirements
Saet Hight	Adjustable between 380 and 520 mm	Adjustable between 380 and 560 mm	380–540 mm	380–520 mm	406–520 mm	500–570 mm	410–490 mm
Work surface height	Fixed between 710 and 720 mm Adjustable between 630 and 760 mm	Adjustable between 560 and 720 mm	Adjustable table height preferable	700–800 mm	585–710 mm	700–800 mm	580–710 mm
Vertical viewing angle	Maximum of 45° below the horizontal line of sight	15–25° below the horizontal eye level	15–20° below the horizontal, first line on screen at about or just	15–30°	15–20°	2–26°	10–15°
Viewing distance	400–740 mm	500–1000 mm	350–600 mm	500–700 mm	200–600 mm	610–930 mm	380 mm
Neck
Forward head tilt	-	-	-	0	0–15°	0–20°	0–24°
Trunk
Torso and thighs position	An erect or upright spine, and no twisting of the upper torso (CSA-Z412–00)	Minimum torso-to-thigh angles of 90°	-	torso-to-thigh angles of 90°-105°		Leaning backward 104°	torso-to-thigh angles of 90°-158
Shoulder & Arm
Upper arms and forearms position	Upper arms are approximately vertical, and lower arms are horizontal Elbows close to the body	Elbow angles between70° and 135°	Forearm and arm at about right angle	Elbow angles between90° and 105°		Elbow angles 90°	Elbow angles between 98° and 146°

Canada: CSA (Canadian Standards Association). 1989. A Guideline on Office Ergonomics: A National Standard of Canada (CAN/CSA-Z412-M89). Rexdale: Canadian Standards Association.

USA: ANSI (American National Standards Institute). 2007. American National Standard for Human Factors Engineering of Computer Workstations (ANSI/HFES 100–2007). Santa Monica, CA: The Human Factors Society.

Hong Kong: Labour Department. 2010. A Guide to Work with Computers. Hong Kong: Occupational Safety and Health Branch of Labour Department.

Discussion

This scoping review analyzed 42 studies on prolonged sitting postures in office environments from four major databases.

Assessment methods

The review identifies self-assessment methods as the most frequently employed tools for evaluating prolonged sitting postures, primarily due to their cost-effectiveness and capacity to collect data from large participant samples over extended durations. Among these, the NMQ emerged as the most commonly utilized instrument. However, despite its established validity for general MSD assessment, it lacks the specificity needed to capture the detailed aspects of prolonged sitting behavior, such as posture duration, workstation-specific discomfort, or patterns of discomfort throughout the workday. In contrast, while used in only 10% of the reviewed studies, the MUEQ appears better aligned with the assessment of office workers’ prolonged sitting behavior. The MUEQ explicitly incorporates items related to work posture, workstation ergonomics, and the duration of computer usage, making it a more contextually relevant tool for office-based ergonomic evaluations. Despite its relevance, the limited uptake of the MUEQ in research may stem from lower awareness among researchers, limited availability in multiple languages, or the absence of widespread institutional endorsement, factors that may hinder its broader implementation. This alignment underscores its potential value and highlights the need for greater consideration of the MUEQ in future research. The diversity observed across the 12 distinct self-assessment questionnaires in the reviewed literature reinforces the importance of selecting instruments that closely align with specific research objectives. However, ongoing concerns persist regarding the validity of questionnaires in accurately capturing workplace postures. Many studies rely on assessment tools validated several years ago, raising concerns about their continued applicability. In light of significant technological advancements and changes in work environments, including the rise of hybrid and remote work models, existing questionnaires may no longer fully reflect contemporary office settings or behaviors. Consequently, future research should prioritize revalidation efforts to ensure that these instruments remain reliable and precise in evaluating MSD risks associated with prolonged sitting in office environments.

Observational methods were the second most commonly employed approach in the reviewed studies for assessing prolonged sitting postures among office workers. Among these, RULA was the most frequently utilized tool. RULA's popularity can be attributed to its strong validation history, availability in digital formats, and ease of use. However, it is important to note that RULA was originally developed for use in industrial and manufacturing environments characterized by dynamic, load-bearing tasks. Consequently, while RULA is effective for detecting postural risks in physically demanding jobs, it may be less sensitive to the static and low-force repetitive movements typically associated with prolonged sitting, computer use, and screen-based tasks in office settings. In contrast, ROSA is specifically tailored to evaluate ergonomic risks in office environments. ROSA assesses factors such as workstation setup, seating configuration, and peripheral placement, which are crucial in identifying sources of discomfort during prolonged desk work. However, ROSA's primary limitation lies in its relatively narrow focus on workstation configuration, with limited capacity to capture real-time or dynamic postural variations. Other observational tools, such as REBA and CTD checklist, were used less frequently in the reviewed studies. REBA provides a comprehensive evaluation of full-body postural risks and is particularly valuable for holistic ergonomic assessments. Its limited use in office-based studies may reflect a stronger research emphasis on upper body MSD risks, which are more prevalent in sedentary work. Similarly, CTD assessments were originally designed for industrial applications involving repetitive strain and grip-intensive tasks and are therefore less applicable to the fine motor demands of typical office tasks like typing or mouse use. Despite their varied focus areas, all four tools share a structured, checklist-based framework, which enhances their accessibility and interpretability. These tools require minimal training, making them practical options for both researchers and practitioners in ergonomics. Nevertheless, the suitability of each tool must be evaluated based on its alignment with the task characteristics and movement patterns specific to office work.

The review of sensor-based posture recognition methods reveals that EMG was the most commonly used technique in earlier studies but has not appeared in the reviewed literature since 2018. Recent research increasingly favors advanced technologies such as Fiber Bragg Grating sensors, infrared array sensors, pressure array sensors, and optical motion capture systems, reflecting a shift toward these methods. While sensor-based approaches offer objective and highly accurate data, their application demands substantial time, expertise, and resources. This complexity and resource-intensive nature limit their practicality, particularly in real-world office environments.

In contrast, vision-based recognition methods are gaining traction in recent research due to advancements in depth cameras and real-time posture tracking technologies. These systems leverage computer vision algorithms to estimate joint angles and classify postures without physical contact. Despite their promise, ensuring validation and reliability remains a notable challenge. For instance, one study validated its algorithm using objective goniometry measurements to compare estimated joint angles against gold-standard manual readings.²² Another study combined human observation and self-reported data but lacked formal inter-rater reliability, as video assessments were performed by a single reviewer.⁵² A third study developed a semi-automated RULA tool that used depth camera data to evaluate sitting posture and validated the automated outputs by comparing them with expert-generated scores, thus reducing dependence on manual assessments and increasing the tool's applicability in office settings.⁵³

Several studies adopted multi-method designs. These hybrid approaches were particularly effective in addressing the limitations inherent in single-method studies, such as the subjectivity and recall bias of self-reports or the cost and complexity of sensor-based technologies. By triangulating data sources, subjective perceptions, visual observations, and objective measurements, these multimethod strategies may enhance both the robustness and interpretability of findings. Future studies should systematically analyze the added value of using multiple assessment methods by directly comparing the results of multi-method approaches with those obtained from single-method studies. Such comparisons could help quantify the benefits of methodological integration and guide best practices for ergonomic evaluation in office settings.

Sample size and methodology

Sample sizes across the different assessment methods varied considerably, largely influenced by methodological demands and resource constraints. Self-assessment techniques generally supported larger sample sizes due to their low cost, ease of distribution, and minimal logistical demands. Only one study using a self-assessment tool reported a notably small sample size (n = 5), which was due to its design as a prospective case series evaluating the feasibility, safety, and preliminary effectiveness of a combined ergonomic and chiropractic intervention. This study employed a one-year longitudinal design, categorizing it as a long-term intervention in the MSD domain.⁵⁴ In contrast, observational methods, which require trained assessors to directly monitor participants, typically involved more moderate sample sizes. Their resource-intensive nature—such as the need for trained coders and time-intensive evaluations—limits scalability, thus reducing participant numbers. Instrument-based methods tended to involve the smallest sample sizes. Many studies in this category focused on developing or validating algorithms for posture classification rather than conducting large-scale trials. The technical complexity of these methods, including the need for sensor calibration, specialized equipment, and controlled settings, further restricted the feasibility of recruiting larger samples. However, as these technologies mature, future studies may increasingly apply them in real-world settings with broader participant cohorts, potentially enhancing generalizability.

From a statistical standpoint, the type of assessment method selected has significant implications for feasible sample sizes and overall study design. In ergonomics and human factors research, sample size determination is critical for achieving adequate statistical power and reliabilit.^55–57 Techniques like power analysis, effect size estimation, and calculations based on confidence levels and margins of error are commonly used to determine the minimum sample size needed to detect significant effects, typically targeting a power of 0.8 and a 95% confidence level.^28,41,58 Based on the aggregated findings from the literature and considering studies that acknowledged sample size limitations, and indicative benchmarks for different assessment methods, future ergonomic research should target a sample size of at least 100–200 participants for self-assessment methods to accommodate variability, 50–100 participants for observational methods to maintain sufficient statistical power, and 20–40 participants for instrument-based methods during their development stages. These estimates should be interpreted as general guidelines rather than strict requirements, as the optimal sample size for any study must be determined based on power analysis, study objectives, research design, and available resources. Future ergonomic studies should carefully evaluate their methodological constraints and research questions to determine an appropriate sample size that ensures both statistical robustness and practical applicability.

Gender and age representation

An analysis of gender distribution across the reviewed studies (Figure 5) reveals that approximately 85% of studies included both male and female participants, while 15% focused exclusively on a single gender. However, statistical analysis revealed a significant imbalance, with female participants overrepresented across both mixed- and single-gender study designs. This disparity may stem from female-dominant office environments or recruitment practices tailored to specific populations. Among gender-specific studies, 66% involved only females, and 33% focused solely on males, often reflecting workplace demographics or addressing gender-specific ergonomic concerns. Nonetheless, balanced gender representation is essential for identifying differential ergonomic risks and enhancing the generalizability of findings. Future studies should prioritize proportional gender inclusion to minimize bias and inform the development of equitable ergonomic interventions.

In terms of age distribution (Figure 6), the majority of participants fell within the 30–40 age range, aligning with the demographic typically employed in office-based roles. Fewer studies included participants from younger or older age groups, suggesting a concentration on the core working-age population. This underrepresentation may stem from selection criteria favoring experienced, full-time office workers or excluding age-related variability. While focusing on this demographic may improve internal consistency, it also limits the generalizability of findings across different age groups. To ensure that ergonomic interventions are effective across the lifespan, future research should aim to include a more age-diverse sample reflective of the full working population.

Study settings and duration

As shown in Figure 7, most studies were conducted in real office environments rather than in controlled laboratory settings. Field studies offer greater ecological validity by capturing natural postures and spontaneous in-chair movements in everyday work conditions, thereby enhancing the practical relevance of findings. However, they also introduce variability from factors like inconsistent lighting and diverse workstation setups, which can reduce data precision—particularly for observational and vision-based methods. Laboratory studies, by contrast, offer controlled conditions that support more consistent and accurate posture measurements but may influence participant behavior due to the artificial setting, limiting generalizability.

Statistical analysis revealed a significant association between study setting and assessment method category: instrument-based methods were notably overrepresented in laboratory settings and underrepresented in field studies. This reflects the technical requirements of instrument-based tools, which depend on controlled environments and stable conditions. Field studies, on the other hand, more commonly employed self-assessment and observational methods due to their practicality in real-world settings. These findings highlight the methodological trade-offs in ergonomic research—between precision and control in labs and contextual realism in field studies—which influence the choice and implementation of assessment tools.

Building on the analysis of study settings, the duration of data collection is a key factor that shapes the relevance and depth of research findings. About 38.09% of studies were short-term, often under a week, and focused on immediate postural responses or brief interventions. While manageable and useful for preliminary insights, these studies do not capture the long-term impacts of prolonged sitting, such as sustained musculoskeletal strain or the lasting effects of ergonomic changes. In contrast, long-term studies provide more meaningful insights into chronic discomfort and lasting behavioral shifts but are less common due to their resource demands. This imbalance limits our understanding of how ergonomic interventions perform over time. To address this gap, future research should prioritize longitudinal designs to better evaluate the progression of posture-related discomfort and the enduring effectiveness of ergonomic solutions.

Future direction

Despite the wide range of available assessment tools, many office workers still struggle to maintain proper posture, even after ergonomic training or intervention. This suggests that knowledge alone is insufficient and highlights the need to consider behavioral and motivational factors. Importantly, these factors may not only influence postural habits but also affect the reliability of assessment methods outcomes. For instance, variations in discomfort tolerance, motivation to follow ergonomic guidance, or awareness of one's posture may introduce inconsistencies in subjective reporting. Similarly, engagement levels and perceived burden may influence adherence to posture-related interventions. To address these challenges, future studies should incorporate behavioral science frameworks to better understand the psychological and contextual drivers of postural behavior. Investigating both intrinsic and extrinsic motivational factors, such as the application of gamification strategies, may offer novel insights into promoting sustainable postural improvements.

Another notable gap in the literature is the failure to differentiate between remote and in-office work settings. As hybrid work becomes more common, future studies must examine ergonomic risks unique to home-based environments to improve the relevance of findings.

In terms of ergonomic guidelines, this review highlights regional variability in workstation recommendations, such as seat height, desk dimensions, and screen viewing angles. For instance, Canadian standards propose a seat height of 380–520 mm, whereas U.S. standards suggest a wider range up to 560 mm. Hong Kong recommends even lower ranges (380–540 mm). Viewing angle recommendations also vary, from up to 45° below eye level in Canada to 15–25° in the U.S., and 15–20° in Hong Kong. These discrepancies underscore the need for context-specific ergonomic guidelines that consider regional anthropometric data and workplace norms.

Furthermore, while several reviewed studies simultaneously developed and tested new posture assessment tools, this review did not explicitly separate such tool-development studies from those applying established methods. This limits comparative clarity, especially regarding sample size and validation rigor. Future reviews should distinguish between methodological innovation and applied assessments to enhance methodological transparency and support the evolution of robust evaluation frameworks.

Finally, there is limited comparative analysis across different assessment methods. Future research should prioritize within-sample comparisons of self-report, observational, and instrument-based tools to assess alignment, guide method selection, and support the development of integrated ergonomic assessment systems.

Conclusion

This scoping review evaluated ergonomic tools used to assess prolonged sitting in office environments, highlighting their strengths, limitations, and methodological differences. Three main approaches were identified: self-assessment, observational, and instrument-based methods. Self-assessments were the most common due to their scalability, though limited by subjective bias. Observational tools offered structured evaluations but were prone to inter-rater variability. Instrument-based methods, including sensor- and vision-based systems, provided objective, real-time posture tracking but were constrained by cost, technical complexity, and limited field applicability.

Experimental design varied across studies, with self-assessment methods incorporating larger sample sizes, observational methods requiring expert evaluations, and instrument-based studies involving smaller participant groups due to development constraints. Statistical analysis confirmed significant differences in sample size by method type. Research predominantly focused on short-term assessments, with limited longitudinal studies examining the cumulative effects of prolonged sitting. Additionally, upper-body postures were the primary focus, while whole-body and lower-limb postures remain understudied. Instrument-based methods were overrepresented in lab settings, while field studies favored self-assessment and observational tools. This review underscores the need for continued research on the development, validation, and real-world application of assessment tools, as well as a deeper understanding of behavioral and psychological factors influencing posture adherence.

Footnotes

Ethical approval (name of institute and number)

Not applicable.

Informed consent

Not applicable.

Funding

The research was funded by the NSERC through the Natural Sciences and Engineering Research Council of Canada Discovery Grant [RGPIN-2020-04518]; and University of Windsor through the Start up grant [818140].

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the author(s) used ChatGPT and Grammarly solely to check for grammatical errors. After using this ChatGPT and Grammarly, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

ORCID iDs

Eunsik Kim

Joon Chung

References

Church

Thomas

Tudor-Locke

, et al.

Trends over 5 decades in U.S. Occupation-related physical activity and their associations with obesity

. PLOS ONE 2011 May 25; 6: e19657.

Michalchuk

Lee

Waters

, et al. Systematic review of the influence of physical work environment on office workers’ physical activity behavior. Workplace Health Saf 2022 Feb; 70: 97–119.

Putsa

Jalayondeja

Mekhora

, et al. Factors associated with reduced risk of musculoskeletal disorders among office workers: a cross-sectional study 2017 to 2020. BMC Public Health 2022 Aug 6; 22: 1503.

Patterson

McNamara

Tainio

, et al. Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis. Eur J Epidemiol 2018 Sep 1; 33: 811–829.

Gremaud

Carr

Simmering

, et al. Gamifying accelerometer use increases physical activity levels of sedentary office workers. J Am Heart Assoc 2018 Jul 3; 7: e007735.

Griffiths

Mackey

Adamson

. Behavioral and psychophysiological responses to job demands and association with musculoskeletal symptoms in computer work. J Occup Rehabil 2011 Dec 1; 21: 482–492.

Kaliniene

Ustinaviciene

Skemiene

, et al. Associations between musculoskeletal pain and work-related factors among public service sector computer workers in Kaunas county, Lithuania. BMC Musculoskelet Disord 2016 Oct 7; 17: 420.

Canadian Centre for Occupational Health and Safety. Spread Awareness of Repetitive Strain Injuries [Internet]. 2022 [cited 2024 Dec 20]. Available from: https://www.canada.ca/en/centre-occupational-health-safety/news/2022/02/spread-awareness-of-repetitive-strain-injuries.html.

Canadian Centre for Occupational Health and Safety. Repetitive strain injuries: take action on February 28 [Internet]. 2025. Available from: https://www.canada.ca/en/centre-occupational-health-safety/news/2025/02/repetitive-strain-injuries-take-action-on-february-28.html.

10.

Bitton

. The economic burden of osteoarthritis. Am J Manag Care 2009 Sep 1; 15: S230–S235.

11.

Isusi

Work-related musculoskeletal disorders: facts and figures: synthesis report of 10 EU member states reports. 2020. Available from: https://osha.europa.eu/en/publications/work-related-musculoskeletal-disorders-facts-and-figures-synthesis-report-10-eu-member

12.

Lima

Coelho

. Ergonomic and psychosocial factors and musculoskeletal complaints in public sector administration - A joint monitoring approach with analysis of association. Int J Ind Ergon 2018 Jul; 66: 85–94.

13.

Somayeh

Razeghi

Abdoli-Eramaki

, et al. Recommended maximum holding time of common static sitting postures of office workers. Int J Occup Saf Ergon 2023 Apr 3; 29: 847–854.

14.

Turci

Gorla

Bersanetti

. Assessment of arm, neck and shoulder complaints and scapular static malposition among computer users. Rev Bras Med Trab 2019; 17: 465–472.

15.

Spielholz

Silverstein

Morgan

, et al. Comparison of self-report, video observation and direct measurement methods for upper extremity musculoskeletal disorder physical risk factors. Ergonomics 2001 May; 44: 588–613.

16.

Tahernejad

Choobineh

Razeghi

, et al. Investigation of office workers’ sitting behaviors in an ergonomically adjusted workstation. Int J Occup Saf Ergon 2022 Oct 2; 28: 2346–2354.

17.

Yafa

Lerman

Gefen

, et al. Validity of the modified RULA for computer workers and reliability of one observation compared to six. Ergonomics 2014; 57: 1856–1863.

18.

Lind

Abtahi

Forsman

. Wearable motion capture devices for the prevention of work-related musculoskeletal disorders in ergonomics—an overview of current applications, challenges, and future opportunities. Sensors 2023 Jan; 23: 4259.

19.

Jun

Johnston

McPhail

, et al.

Are measures of postural behavior using motion sensors in seated office workers reliable?

Hum Factors 2019; 61: 1141–1161.

20.

Kingston

Riddell

McKinnon

, et al. Influence of input hardware and work surface angle on upper limb posture in a hybrid computer workstation. Hum Factors 2016 Feb; 58: 107–119.

21.

Presti

Carnevale

DAbbraccio

, et al. A multi-parametric wearable system to monitor neck movements and respiratory frequency of computer workers. Sensors (Switzerland) 2020; 20: 536.

22.

Rodrigues

Xiao

Fukumura

, et al. Ergonomic assessment of office worker postures using 3D automated joint angle assessment. Adv Eng Inf 2022 Apr 1; 52: 101596.

23.

Zhang

Fan

Peng

, et al. A privacy-preserving and unobtrusive sitting posture recognition system via pressure array sensor and infrared array sensor for office workers. Adv Eng Inform 2022 Aug; 53: 101690.

24.

Nayak

Kim

. Development of a fully automated RULA assessment system based on computer vision. Int J Ind Ergon 2021 Nov 1; 86: 103218.

25.

Paudel

Kwon

Kim

, et al. Industrial ergonomics risk analysis based on 3D-human pose estimation. Electronics (Basel) 2022 Jan; 11: 3403.

26.

Yang

, et al. Joint-Level vision-based ergonomic assessment tool for construction workers. J Constr Eng Manag 2019 May 1; 145: 04019025.

27.

Tanghuizi

Iwakiri

Sotoyama

, et al. Relationship between using tables, chairs, and computers and improper postures when doing VDT work in work from home. Ind Health 2022 Jul 31; 60: 307–318.

28.

Jennifer

Bruno

Eijckelhof

BHW

, et al. The effect of over-commitment and reward on trapezius muscle activity and shoulder, head, neck, and torso postures during computer use in the field. Am J Ind Med 2013 Oct; 56: 1190–1200.

29.

Liu

Han

Lee

. Tracking-based 3D human skeleton extraction from stereo video camera toward an on-site safety and ergonomic analysis. Constr Innov 2016 Jul 11; 16: 348–367.

30.

Joshi

Deshpande

. A systematic review of comparative studies on ergonomic assessment techniques. Int J Ind Ergon [Internet] 2019; 74:102865. [cited 2024 Dec 22];74:102865. Available from: https://www.sciencedirect.com/science/article/pii/S0169814119303221

31.

Nadeem

Elbasi

Zreikat

, et al. Sitting Posture Recognition Systems: Comprehensive literature review and analysis. Appl Sci 2024 Sep 1; 14: 8557.

32.

Roggio

Trovato

Sortino

, et al. A comprehensive analysis of the machine learning pose estimation models used in human movement and posture analyses: a narrative review. Heliyon 2024 Nov 15; 10: e39977.

33.

Sabino

Fernandes M do

Cepeda

, et al. Application of wearable technology for the ergonomic risk assessment of healthcare professionals: a systematic literature review. Int J Ind Ergon 2024 Mar 1; 100: 103570.

34.

Yang

Song

Ning

, et al. A systematic review: advancing ergonomic posture risk assessment through the integration of computer vision and machine learning techniques. IEEE Access 2024; 12: 180481–180519.

35.

Munn

Peters

MDJ

Stern

, et al. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol 2018 Nov 19; 18: 143.

36.

Arksey

O’Malley

. Scoping studies: towards a methodological framework. Int J Soc Res Methodol 2005 Feb 1; 8: 19–32.

37.

PRISMA for Scoping Reviews (PRISMA-ScR) [Internet]. PRISMA statement. 2018 [cited 2025 Mar 17]. Available from: https://www.prisma-statement.org/scoping.

38.

Kees

Nicolien

. (vhp human performance, the Netherlands) in cooperation with Alicja Bortkiewicz (Nofer Institute of Occupational Medicine, Łodź, Poland). Prolonged static sitting at work: health effects and good practice advice | Safety and health at work EU-OSHA [Internet]. 2021 [cited 2025 May 12]. Available from: https://osha.europa.eu/en/publications/prolonged-static-sitting-work-health-effects-and-good-practice-advice.

39.

Powers

Bieliaieva

, et al. The human factors and ergonomics of P300-based brain-computer interfaces. Brain Sci 2015; 5: 318–354.

40.

Pooriput

van der Beek

Akkarakittichoke

, et al. Perceived musculoskeletal discomfort and its association with postural shifts during 4-h prolonged sitting in office workers. Appl Ergon 2020 Nov; 89: 103225.

41.

Hormoz

Saffari

Valipour

, et al. The effectiveness of a model-based health education intervention to improve ergonomic posture in office computer workers: a randomized controlled trial. Int Arch Occup Environ Health 2018 Nov; 91: 951–962.

42.

Meirav

T-M

Cwikel

Shapira

, et al. The effectiveness of a training method using self-modeling webcam photos for reducing musculoskeletal risk among office workers using computers. Appl Ergon 2012 Mar; 43: 376–385.

43.

Fariborz

Pourranjbar

Naderi

, et al. Work-related musculoskeletal disorders in Iranian office workers: prevalence and risk factors. J Med Life 2018; 11: 328–333.

44.

ANSI/HFES 100–2007. ANSI/HFES 100–2007Human factors engineering of computer workstations. Hum Fact Ergon Soc 2007; 10: 89.

45.

CAN/CSA-Z412-M89. CAN/CSA-Z412-M89 [Internet]. CSA(Canadian Standards Association) Group. 1989 [cited 2024 Dec 23]. Available from: https://www.csagroup.org/store/product/2410817/.

46.

Chaffin

Andersson

GBJ

Martin

. Occupational biomechanics. New Jersey, USA: John Wiley & Sons, 2006, pp.388.

47.

Government of Hong Kong. GovHK: occupational health for office workers [Internet]. 2010 [cited 2024 Dec 23]. Available from: https://www.gov.hk/en/residents/employment/safety/officeworkers.htm.

48.

GRANDJEAN

NISHIYAMA

HÜNTING

, et al. A laboratory study on preferred and imposed settings of a VDT workstation. Behav Inf Technol 1982 Jul 1; 1: 289–304.

49.

Liu

Zhang

, et al. Healthy human sitting posture estimation in RGB-D scenes using object context. Multimed Tools Appl 2017 Apr; 76: 10721–10739.

50.

White

Lai

CWK

. Ergonomics standards and guidelines for computer workstation design and the impact on users’ health – a review. Ergonomics 2016 Mar 3; 59: 464–475.

51.

Woodson

Tillman

. Human factors design handbook: information and guidelines for the design of systems, facilities, equipment, and products for human use. 2nd ed. McGraw-Hill Education, 1992.

52.

Kronenberg

Kuflik

Shimshoni

. Improving office Workers’ workspace using a self-adjusting computer screen. ACM Trans Interact Intell Syst 2022 Sep; 12: 24.

53.

Romy

Chandra

Oktiarso

, et al. Utilization of depth camera to ease posture-risk assessment of related sitting work. J Eng Sci Technol Rev 2023; 16: 100–108.

54.

Chuck

Johnson

Chester

. Safety, tolerability and effectiveness of an ergonomic intervention with chiropractic care for knowledge workers with upper-extremity musculoskeletal disorders: a prospective case series. Work 2014; 49: 641–651.

55.

Anthoine

Moret

Regnault

, et al. Sample size used to validate a scale: a review of publications on newly-developed patient reported outcomes measures. Health Qual Life Outcomes 2014 Dec 9; 12: 2.

56.

Bell

. New guidance to improve sample size calculations for trials: eliciting the target difference. Trials 2018 Nov 5; 19: 605.

57.

Sapra

. How to calculate an adequate sample size? In: Nundy

Kakar

Bhutta

(eds) How to practice academic medicine and publish from developing countries? A practical guide [Internet]. Singapore: Springer Nature, 2022 [cited 2024 Nov 20], pp.81–93. Available from: 10.1007/978-981-16-5248-6_9

58.

Harbir

Singh

. Musculoskeletal disorders among insurance office employees: a case study. Work 2019; 64: 153–160.

59.

Arne

Horgen

, et al. The effect of an ergonomic intervention on musculoskeletal, psychosocial and visual strain of VDT data entry work: the Norwegian part of the international study. Int J Occup Saf Ergon 2005; 11: 25–47.

60.

Marvin

Aarås

Horgen

, et al. The effect of an ergonomic intervention on musculoskeletal, psychosocial and visual strain of VDT entry work: organization and methodology of the international study. Int J Occup Saf Ergon 2005; 11: 9–23.

61.

Maria

Wolska

Widerszal-Bazyl

, et al. The effect of an ergonomic intervention on musculoskeletal, psychosocial, and visual strain of VDT data entry work: the Polish part of the international study. Int J Occup Saf Ergon 2005; 11: 65–76.

62.

Marvin J

Cohen

BGF

Dainoff

. The effect of an ergonomic intervention on musculoskeletal, psychosocial, and visual strain of VDT data entry work: the United States part of the international study. Int J Occup Saf Ergon 2005; 11: 49–63.

63.

Kerem

Donchin

. Prevalence of upper extremity musculoskeletal symptoms and ergonomic risk factors at a Hi-Tech company in Israel. Int J Ind Ergon 2005; 35: 569–581.

64.

Stefan

Mikkers

Blatter

, et al. Test-retest reliability and concurrent validity of a web-based questionnaire measuring workstation and individual correlates of work postures during computer work. Appl Ergon 2008 Nov; 39: 685–696.

65.

Turhan

Akat

Akyüz

, et al. Ergonomic risk factors for cumulative trauma disorders in VDU operators. Int J Occup Saf Ergon 2008; 14: 417–422.

66.

Jasminka

Cai

, et al. The proactive approach–is it worthwhile? A prospective controlled ergonomic intervention study in office workers. J Occup Environ Med 2009 Oct; 51: 1116–1124.

67.

Sonne

Villalta

Andrews

. Development and evaluation of an office ergonomic risk checklist: ROSA – rapid office strain assessment. Appl Ergon 2012 Jan 1; 43: 98–108.

68.

Levy

Ribak

Badihi

. Remote online ergonomic assessment in the office environment as compared to face-to-face ergonomic assessment. Work 2012; 41: 516–523.

69.

Thomas

Barrett

Callaghan

. A comparison of trunk biomechanics, musculoskeletal discomfort and productivity during simulated sit-stand office work. Ergonomics 2016 Oct; 59: 1275–1287.

70.

Roland

Fliesser

Wippert

, et al. Occupational sitting behaviour and its relationship with back pain - A pilot study. Appl Ergon 2016 Sep; 56: 84–91.

71.

Roossien

Stegenga

Hodselmans

, et al.

Can a smart chair improve the sitting behavior of office workers?

Appl Ergon 2017 Nov; 65: 355–361.

72.

Rodrigues

Leite

RDV

Lelis

, et al. Differences in ergonomic and workstation factors between computer office workers with and without reported musculoskeletal pain. Work 2017; 57: 563–572.

73.

Richelle

Coenen

Howie

, et al. The short term musculoskeletal and cognitive effects of prolonged sitting during office computer work. Int J Environ Res Public Health 2018 Aug; 15: 1678.

74.

Bazazan

Dianat

Feizollahi

, et al. Effect of a posture correction-based intervention on musculoskeletal symptoms and fatigue among control room operators. Appl Ergon 2019 Apr; 76: 12–19.

75.

Heiden

Zetterberg

Lindberg

, et al. Validity of a computer-based risk assessment method for visual ergonomics. Int J Ind Ergon 2019 Jul; 72: 180–187.

76.

Kar

Hedge

. Effect of workstation configuration on musculoskeletal discomfort, productivity, postural risks, and perceived fatigue in a sit-stand-walk intervention for computer-based work. Appl Ergon 2021 Jan; 90: 103211.

77.

Fauzi

EKF

Roberto

dLS

. Telework during the COVID-19 pandemic: ergonomic and psychosocial risks among Brazilian labor justice workers. Work 2022; 71: 395–405.

78.

Zaltieri

Lo Presti

Bravi

, et al. Assessment of a multi-sensor FBG-based wearable system in sitting postures recognition and respiratory rate evaluation of office workers. IEEE Trans Biomed Eng 2023 May; 70: 1673–1682.

79.

Ahmadi Charkhabi

Motamedzade

Dianat

, et al. Investigation of the multi-component ergonomics intervention effects on improving musculoskeletal outcomes and speech communication: A case study in open-plan offices. Work 2023; 76: 275–288.

80.

Zhang

Fan

Peng

, et al. Multimodal data-based deep learning model for sitting posture recognition toward office workers’ health promotion. Sens Actuators, A 2023 Feb 1; 350: 114150.

Prolonged sitting in office environments: A scoping review of assessment methods

Abstract

Background

Objective

Methods

Results

Conclusions

Keywords

Introduction

Research question

Methods

Search strategy

Inclusion and exclusion criteria

Study selection

Data extraction

Data synthesis

Results

Assessment methods

Self-assessment methods

Observational methods

Instrument-based methods

Sensor-Based Recognition

Vision-Based Recognition

Experimental design of the reviewed studies

Participant recruitment and eligibility criteria

Sample size distribution across assessment methods

Gender distribution

Age distribution

Distribution of study settings

Duration of data collection

Distribution of focus areas of body parts

Ergonomic guidelines in defining posture and workstation setup

Discussion

Assessment methods

Sample size and methodology

Gender and age representation

Study settings and duration

Future direction

Conclusion

Footnotes

Ethical approval (name of institute and number)

Informed consent

Funding

Declaration of generative AI and AI-assisted technologies in the writing process

ORCID iDs

References