Journal of Safety Research最新文献

{"title":"Factors influencing pedestrian injury severity in Chile: A hierarchical probit ordered model approach","authors":"Margareth Gutiérrez ,&nbsp;Raúl Ramos ,&nbsp;Jose J. Soto ,&nbsp;Felisa Córdova","doi":"10.1016/j.jsr.2024.11.021","DOIUrl":"10.1016/j.jsr.2024.11.021","url":null,"abstract":"<div><div>Introduction: Traffic crashes remain a leading cause of fatalities worldwide, with higher fatality and injury rates in non-developed countries. Understanding the relationship among variables influencing traffic crashes and its outcome, measured as crash severity, is crucial for developing effective and targeted countermeasures to mitigate this problem. Method: In this study, we analyze traffic crashes involving pedestrians in Chile from 2022 to 2023. This allowed us to consider the entire country rather than a specific urban area, which is the first of its kind for a Latin American country. A Hierarchical Ordered Probit (HOPIT) model was estimated to model both risk propensity and severity of pedestrian and vehicle crashes while maintaining an ordered threshold structure. Findings reveal that pedestrian and driver characteristics significantly influence crash severity. Results: Male drivers have a higher probability of being involved in more severe crashes. Meanwhile, older pedestrians present a higher risk of severe and fatal injuries. Crash severity is significantly influenced by variables related to vehicle type and environmental factors. Pedestrians hit by heavy-duty vehicles have a 60% and 30% higher chance of suffering fatal or severe injuries, respectively. Highways exhibit a 421% higher chance of fatal injuries, followed by crashes at night and crashes in rural areas with 380% and 267%, respectively. Practical Applications: This research indicates the need for targeted safety measures addressing pedestrian and driver demographics and behavior, vehicle types, and environmental factors to effectively reduce pedestrian injury severity.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 272-282"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Charge combinations and conviction rates among alcohol-influenced drivers involved in motor vehicle crashes in Iowa","authors":"Cara J. Hamann ,&nbsp;Stephanie Jansson ,&nbsp;Linder Wendt ,&nbsp;Michelle Reyes ,&nbsp;Jon Davis ,&nbsp;Joseph E. Cavanaugh ,&nbsp;Corinne Peek-Asa","doi":"10.1016/j.jsr.2024.12.008","DOIUrl":"10.1016/j.jsr.2024.12.008","url":null,"abstract":"<div><h3>Introduction</h3><div>Alcohol impairment is a major contributor to road traffic crashes and has increased across the United States in recent years. In 2022, over 13,000 people were killed in drunk driving crashes. Enforcement of impaired driving laws is an essential strategy to reduce alcohol-impaired driving and subsequent crashes. However, little is known about conviction outcomes related to alcohol-involved crashes. The aim of this study is to examine the association between charge combinations and conviction rates among alcohol-influenced drivers involved in crashes.</div></div><div><h3>Methods</h3><div>Data for this study included 2016–2019 Iowa Department of Transportation crash data linked to charges and convictions from the Iowa Court Information System. The study sample included drivers with reported BAC ≥ 0.08 g/dl and/or driver condition reported as under influence of alcohol. Charges were divided into three categories: alcohol, moving, and administrative/miscellaneous. Two logistic regression models were built with any conviction and alcohol conviction as the outcomes. The main predictor was charge combination.</div></div><div><h3>Results</h3><div>The study sample included 8,238 alcohol-impaired drivers, of whom 6,846 (83.1%) were charged with any type of traffic offense and 6,253 (75.8%) were charged with alcohol-related traffic offenses. Among charged drivers, 96.2% were convicted on any traffic charge and 87.7% were convicted on an alcohol charge. Drivers with a combination of alcohol, administrative, and moving violation charges had higher odds of any conviction (OR = 2.6, 95% CI = 1.7–4.3) compared to drivers with only alcohol charges.</div></div><div><h3>Conclusions</h3><div>Charging impaired drivers with multiple types of charges was associated with increased odds of conviction on any charge but not on alcohol charges, which had high conviction rates overall.</div></div><div><h3>Practical Applications</h3><div>Results from this study can help guide law enforcement to ensure appropriate charges are made in all relevant categories and optimal combinations of charges are administered to impaired drivers to increase odds of conviction.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 375-384"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying individual- and residence-based equity measures to characterize disparities in crash outcomes","authors":"Kristina B. Metzger ,&nbsp;Romario Smith ,&nbsp;Sara A Freed ,&nbsp;Emma Sartin ,&nbsp;Melissa R. Pfeiffer ,&nbsp;Lauren O’Malley ,&nbsp;Allison E. Curry","doi":"10.1016/j.jsr.2025.01.006","DOIUrl":"10.1016/j.jsr.2025.01.006","url":null,"abstract":"<div><div><em>Introduction:</em> Transportation safety priorities emphasize the importance of incorporating equity into efforts to reduce deaths and injuries. Using integrated data, we investigated relationships between individual- and residence-based measures of equity and rates of crash involvement in New Jersey, 2016–2019. <em>Methods</em>: We used statewide integrated data that includes linked crash reports, hospital discharge data, and residence-based equity measures. We calculated crash rates among drivers involved in and injured in a crash by residential census tract. Using generalized Poisson regression, we estimated rate ratios and 95% confidence intervals (aRR, 95% CI) in separate models for race and ethnicity categories and for six previously developed, multi-dimensional equity measures, controlling for driver sex and age. <em>Results:</em> We identified 1,629,219 drivers involved in crashes of whom 8.3% were injured. Hispanic and non-Hispanic Black drivers had higher rates of crash involvement than non-Hispanic White drivers (aRR, 1.67 [95% CI, 1.65–1.68] and aRR, 1.78 [95% CI, 1.77–1.80], respectively). For community equity measures, drivers who resided in census tracts with poorest equity scores had higher crash rates than those living in census tracts with most favorable equity scores (e.g., Index of Concentration at the Extremes: aRR, 2.10 [95% CI, 2.07–2.12]). We observed similar results for injury crash rates. Model fit improved for both all crashes and injury crashes models after adding each equity measure to baseline. <em>Conclusions:</em> Rates of all crashes and injury crashes were consistently higher among drivers of minoritized race and ethnicity groups and among those who lived in less equitable communities. Associations among crash rates and different equity measures provided similar evidence that disparities in traffic safety outcomes are related to inequity. <em>Practical Applications:</em> The usefulness of individual and residence-based equity measures lies in the opportunity to identify communities with higher crash risks for tailored intervention to improve traffic safety and to reduce disparities.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 522-531"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pipeline to enhance animal vehicle collision analysis in crash report dataset","authors":"Boshra Besharatian,&nbsp;Sattar Dorafshan","doi":"10.1016/j.jsr.2024.12.002","DOIUrl":"10.1016/j.jsr.2024.12.002","url":null,"abstract":"<div><div><em>Introduction</em>: Animal vehicle collisions (AVCs) are a global safety concern, requiring analysis and predictive models for understanding and mitigation. Police crash report data are one of the main sources of AVC data globally. However, they are prone to reporting policy change and other inconsistencies, particularly in rural areas, hindering the development of predictive models. Through development of a robust approach for data cleaning, quality control, feature selection, and contribution level identification, this study proposes a pipeline to address this shortcoming. <em>Method:</em> North Dakota crash data set is used as a case study due to high rates on AVC in this rural region and its diverse wildlife ecosystem. Theil’s U association index, and chi-square tests were implemented in the pipeline to evaluate the proposed pipeline effectiveness. The pipeline detects and removes skewed proportion samples, while addressing data collection inconsistency, low variance, and duplicated features. <em>Results:</em> Pipeline imposed 3.5% sample size and 88.9% feature size reduction on the original crash data over 20 years. Observation on the modified dataset revealed year, day, and driver features had the lowest while hour, county, and speed limit had the highest statistical contribution to the AVC. Light, hour, and month were lumped in daily solar cycle and represented as a single temporal feature that can be used effectively to develop predictive model. Finally, presented pipeline increased spatiotemporal integrity while reducing the runtime by 92.46% for the association analysis.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 245-261"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conditional Generative Adversarial Network-Based roadway crash risk prediction considering heterogeneity with dynamic data","authors":"Nuri Park ,&nbsp;Juneyoung Park ,&nbsp;Chris Lee","doi":"10.1016/j.jsr.2024.12.001","DOIUrl":"10.1016/j.jsr.2024.12.001","url":null,"abstract":"<div><div><em>Introduction</em>: Roadway crash data are very rare and occur randomly, therefore there are several challenges to developing a crash prediction model for real-time traffic safety management. Recently, to resolve the problem of crash data sample size, researchers have conducted studies on crash data augmentation using machine learning techniques for developing safety evaluation models. However, it’s important to incorporate the specific characteristics of crash data into augmentation and crash risk assessment, as these characteristics vary depending on spatial and temporal conditions. <em>Method:</em> Therefore, this study developed a real-time crash risk model in three stages. First, crash data were clustered to define heterogeneous crash risk situations and then, key variables were derived by the ensemble and explainable artificial intelligence techniques, Boruta-SHAP. Second, augmentation of each clustered crash data was performed using oversampling techniques including Conditional Generative Adversarial Network (CGAN), which can consider each crash risk cluster’s characteristics. Finally, crash risk models were developed and compared with other crash risk models developed by using binary logistic regression model (BLM), Random Forest (RF), extreme gradient boosting (XGBoost), and Support Vector Machine (SVM). <em>Results:</em> The results showed that the CGAN-based XGBoost model has the best performance and the variable of the temporal speed difference at 10-minute intervals and the precipitation variable have a large impact on crash risk prediction. This paper emphasizes that crash risk characteristics must be distinguished in crash risk prediction and provides new insights into addressing the imbalance data issue within crash and non-crash datasets.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 217-229"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Naturalistic driving study data applied to road infrastructure: A systematic review","authors":"Fletcher J. Howell,&nbsp;Azhaginiyal Arularasu,&nbsp;David B. Logan,&nbsp;Sjaan Koppel","doi":"10.1016/j.jsr.2024.11.022","DOIUrl":"10.1016/j.jsr.2024.11.022","url":null,"abstract":"<div><h3>Introduction:</h3><div>Naturalistic driving studies (NDS) have great potential to characterize the road infrastructure factors influencing everyday driving. A systematic review was undertaken to evaluate the objectives, data processing, and analyses in best-practice applications of NDS data to road infrastructure. <em>Method:</em> Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, a systematic search of seven databases was conducted on 27 June 2023 (PROSPERO CRD42023434948). Fifty-three English-language, peer-reviewed studies were analyzed on the basis of the primary infrastructure category reflected in the research aims. <em>Results:</em> Studies described curves (14), turns at intersections (8), intersections (6), multi-modal treatments (6), ramps (4), work zones (4), charging (2), and other factors (9). Each study was assessed for the risk of methodological bias using amended National Heart, Lung, and Blood Institute templates for Quality Assurance. 74% of studies were assessed to be of ’Good’ quality, 13% of ‘Fair’ quality, and 13% of ‘Poor’ quality. Road infrastructure was characterized by external video (38%) complemented by non-NDS sources including satellite imagery (21%) and government data (19%). Data preparation was required in 91% of studies to extract meaningful variables (e.g. manual video coding) and/or link multiple datasets. Analysis predominantly determined correlations between aspects of driver behavior (speed, trajectory, etc.) and infrastructure factors (geometry, lane configuration, etc.). Conclusions: The methods employed were broadly applicable, but required considerable subject-specific adaptation for non-NDS datasets and/or time-consuming video coding. The incorporation of road infrastructure factors in NDS research can continue to be improved by reducing the computational cost of sample processing.Practical Applications: Encouraged by the adaptability of the identified methods, NDS research has the potential to benefit from the consideration of road infrastructure factors in a Safe System context. The analytical requirements for all components of the Safe System should be considered when planning future NDS data collections and/or analysis.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 346-374"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing the perceptions of ethnic minority construction workers and management staff on the factors affecting safety communications","authors":"Sainan Lyu ,&nbsp;Carol K.H. Hon ,&nbsp;Albert P.C. Chan ,&nbsp;Ran Gao ,&nbsp;Martin Skitmore ,&nbsp;Xin Hu","doi":"10.1016/j.jsr.2024.11.029","DOIUrl":"10.1016/j.jsr.2024.11.029","url":null,"abstract":"<div><div><em>Introduction</em>: Communications is of great importance to the workplace health and safety. This study explores safety communication perceptions of ethnic minority construction workers (EMCWs) and managers to address the vulnerability of EMCWs and overcome communication barriers in ensuring their safety. <em>Method:</em> A questionnaire survey of 134 EMCWs and 95 management staff in the Hong Kong and Australian construction industries is analyzed by the mean score ranking technique, Kendall’s concordance test, Spearman’s rank correlation test, and the Mann-Whitney <em>U</em> test. <em>Results:</em> The main finding is that “Adequacy of language ability of workers” is the most important factor for effective safety communication. EMCWs also prioritize “Personality characteristics of workers” and “Adequacy of workers’ construction experience” for understanding safety information. Management staff emphasize the importance of “Adequacy of time when communicating with workers” and the “Appropriateness of communication style of management” for effective communication. Significant differences exist between EMCWs and management staff, with EMCWs considering 23 out of 36 factors as more important, particularly regarding cultural sensitivity and workers’ understanding of the host country’s culture. <em>Conclusions:</em> A fresh perspective is provided on safety communication factors, revealing significant differences in perceptions between EMCWs and management staff, highlighting communication gaps requiring attention. The prevailing organizational-centric approach is challenged by emphasizing EMCWs’ prioritization of worker-related factors like language ability and personality traits, emphasizing the need to address worker-specific issues. Cultural sensitivity emerges as a significant factor, rated higher by EMCWs, emphasizing the importance of recognizing and addressing cultural differences in communication. A research gap is filled by examining safety communication issues specific to EMCWs, providing insights for interventions and strategies to enhance safety practices and protect their well-being. <em>Practical Applications:</em> The research findings highlight the importance of addressing language barriers, considering personality traits and construction experience, allocating adequate communication time, and promoting cultural sensitivity in safety communication between EMCWs and management staff.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 511-521"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical diagnosis groups developed to bridge the ICD-9-CM to ICD-10-CM coding transition and monitor trends in workers’ compensation claims — Ohio, 2011–2018","authors":"Alysha R. Meyers ,&nbsp;Tara N. Schrader ,&nbsp;Edward Krieg ,&nbsp;Steven J. Naber ,&nbsp;Chih-Yu Tseng ,&nbsp;Michael P. Lampl ,&nbsp;Brian Chin ,&nbsp;Steven J. Wurzelbacher","doi":"10.1016/j.jsr.2024.12.007","DOIUrl":"10.1016/j.jsr.2024.12.007","url":null,"abstract":"<div><div><em>Introduction:</em> This study aimed to develop a set of broad clinical diagnosis (ClinDx) groups relevant to occupational safety and health. The ClinDx groups are necessary for analysis and interpretation of longitudinal health data that include injury and disease codes from the Ninth and Tenth Revision of the International Classification of Disease, Clinical Modification (ICD-9-CM, ICD-10-CM). <em>Methods:</em> Claims data were analyzed for Ohio Bureau of Workers’ Compensation insured employers from 2011 to 2018. We used interrupted time series regression models to estimate level (frequency) and slope (trend) changes to the percentage of each ClinDx group in October 2015. We created ClinDx groups aligned with ICD-10-CM structure and coding principles. Each ClinDx group was counted once per claim (distinct groups). Monthly percentages were calculated based on the injury date. When present, seasonality was assessed separately for each outcome using an autoregressive-moving average model. <em>Results:</em> The final set of ClinDx groups included 57 mutually exclusive and exhaustive groups. The study population included 661,684 claims, with 959,322 distinct ClinDx groups. Among all claims, 96.27% included injury code(s) and 11.77% included disease(s) codes. At the transition to ICD-10-CM, 33 ClinDx groups lacked any statistically significant (P &lt; 0.05) changes between periods. We observed level changes for 17 ClinDx groups and slope changes for nine groups. Eight ClinDx groups had ≥ 20% (+/-) level changes. <em>Conclusion:</em> While the transition to ICD-10-CM is a break in series, about two-thirds of disease groups and half of injury groups were relatively stable across the transition. These findings also underscore the need for characterizing both injury and disease outcomes when analyzing workers’ compensation data. <em>Practical Applications:</em> The 57 ClinDx groups created in this study may be a practical starting point for other occupational epidemiologic analyses that include a mixture of ICD-9-CM and ICD-10-CM data.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 408-419"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying built environment factors influencing driver yielding behavior at unsignalized intersections: A naturalistic open-source dataset collected in Minnesota","authors":"Tianyi Li ,&nbsp;Joshua Klavins ,&nbsp;Te Xu ,&nbsp;Niaz Mahmud Zafri ,&nbsp;Raphael Stern","doi":"10.1016/j.jsr.2024.12.006","DOIUrl":"10.1016/j.jsr.2024.12.006","url":null,"abstract":"<div><div><em>Introduction</em>: Many factors influence the yielding result of driver–pedestrian interactions, including traffic, vehicle, roadway, pedestrian attributes, and more. While researchers have examined the individual influence of these factors on interaction outcomes, there is a noticeable absence of comprehensive, naturalistic studies in current literature, particularly those investigating the impact of the built environment on driver-yielding behavior. <em>Method</em>: To address this gap, our study introduces an extensive open-source dataset, compiled from video data at 18 unsignalized intersections across Minnesota. Documenting more than 3000 interactions, this dataset provides a detailed view of driver–pedestrian interactions and over 50 distinct contextual variables. The data, which covers individual driver–pedestrian interactions and contextual factors, is made publicly available at <span><span>https://hdl.handle.net/11299/254556</span><svg><path></path></svg></span>. Using logistic regression, we developed a classification model that predicts driver yielding based on the identified variables. <em>Results</em>: Our analysis indicates that vehicle speed, the presence of parking lots, proximity to parks or schools, and the width of major road crossings significantly influence driver yielding at unsignalized intersections. <em>Conclusions</em>: Through our findings and by publishing one of the most comprehensive driver–pedestrian datasets in the United States, our study will support communities across Minnesota and the United States in their ongoing efforts to improve road safety for pedestrians and be helpful for automated vehicle design. <em>Practical Applications</em>: We have compiled a dataset on driver–pedestrian interactions at 18 unsignalized intersections in Minnesota, making it one of the most extensive datasets available in the United States. This dataset can be utilized by researchers and local agencies to enhance intersection safety and walkability. Furthermore, our study proposes recommendations for increasing pedestrian safety at intersections, providing valuable insights that local governments can use as guidance for designing future intersections.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 331-345"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A predictive equation for maximum acceptable efforts based on duty cycle in repetitive back-involved tasks","authors":"Niromand Jasimi Zindashti ,&nbsp;Karla Beltran Martinez ,&nbsp;Ali Golabchi ,&nbsp;Mahdi Tavakoli ,&nbsp;Hossein Rouhani","doi":"10.1016/j.jsr.2024.12.003","DOIUrl":"10.1016/j.jsr.2024.12.003","url":null,"abstract":"<div><div><em>Introduction:</em> There are many different tasks involved within a workplace, and assessing the required efforts for performing them depends on factors such as the human’s body posture, task nature, and number of repetitions. This study aims to develop an equation for back-involved repetitive tasks that relates the maximum acceptable effort (MAE), the maximum acceptable efforts that an individual can sustain for a specific task and is expressed as a percentage of the maximum strength, to the duty cycle, the amount of time an individual is engaged in a task relative to the total time. The equation was derived based on psychophysical data collected from previous studies on lifting, lowering, and carrying tasks. The literature search identified studies reporting maximum acceptable loads (e.g., forces and toques) for back-involved tasks. <em>Method:</em> Data analysis was done by calculating duty cycles and for each task. Statistical tests were conducted to compare the results across different parameters, such as sex, task nature, lifting box size, box distance from the body, and population percentages. <em>Results:</em> The results showed a strong negative relationship between duty cycle and MAE. This relationship shows that by increasing the duty cycle, MAE should be decreased to be acceptable and prevent worker’s fatigue. The developed equation was compared to existing equations for upper-limb tasks and demonstrated a close resemblance. Additionally, statistical analysis indicated that the proposed equation eliminated the effects of various parameters. The proposed equation provides an individual-specific approach for estimating MAEs and can contribute to preventing workers’ fatigue and injury and reducing their associated costs.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 504-510"},"PeriodicalIF":3.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}