Accident Factors Research Articles

Risk of myocardial infarction in Parkinson's disease: A systematic review and meta-analysis.

Previous studies investigating cardiovascular disorders in patients with Parkinson's disease (PD) showed heterogeneous results regarding whether there is a higher or lower risk of myocardial infarction (MI) in these patients compared to the general population. Because of the inconsistency in findings, herein the aim was to perform a systematic review and meta-analysis to investigate the risk of MI in patients with PD. A comprehensive literature search was performed using four databases, PubMed, Web of Science, Scopus and Embase, in June 2022. Peer-reviewed observational studies comprising case-controls, cohort, cross-sectional and longitudinal studies that reported MI in the PD population were included. After the screening, 20 studies with a total of 80,441 patients with PD and 802,857 controls were included in our qualitative and quantitative synthesis. The pooled estimated odds ratio for MI in PD patients compared to controls was 0.80 (95% confidence interval [CI] 0.56-1.05) which indicates that there is no association. The pooled prevalence of MI was 5% (95% CI 3%-7%) with a range of 1%-20% amongst patients with PD. The men (6%, 95% CI 1%-13%) and women (6%, 95% CI 1%-14%, Q = 29.27, I2 = 98.50%, p < 0.001) had similar MI prevalence. This comprehensive systematic review and meta-analysis provide compelling evidence that PD is associated with a reduced risk of MI. Whilst the exact mechanism underlying this association remains to be fully elucidated, it is clear that certain risk factors for cardiac events appear to be less present in PD patients, which may serve as a protective factor. However, given the reports of increased risk for cerebrovascular events in PD patients, it is possible that the major risk factors for MI and cardiovascular accidents in this population differ. These findings have important implications for clinical management and further research in this area is warranted.

Driver Predicting Behavior Based on Accelerometer and Gyroscope Sensors with K-Means Algorithm Method

The transportation with an increasing population level in Indonesia will also affect the smoothness of traffic. However, with the existence of transportation that is owned by someone, it is often misused in driving. Aggressive driving behavior is a major factor in traffic accidents. As reported AAA Foundation for Traffic Safety, 106,727 dangerous accidents – 55.7 percent of the over past four year period involved drivers engaging in one or more acts of aggressive driving. Therefore, to predict driver behavior, the K-Means algorithm used the Orange Application. Using the driver behavior dataset that has been recorded with Accelerometer and Gyroscope Sensor-based applications, the results obtained where the k value is the number of cluster 1 there are 814 items, cluster 2 has 997 items, cluster 3 has 1273 items. From this test, it was found that proportion of predicted driver behavior with Aggressive qualifications was 96.4%, Normal was 92.2% and from driver behavior with Low qualifications was 74.3%. The accuracy rate of research using the K-Means algorithm to determine predictions is 0.861 or 86.1%. The results of predictions can help prevent accidents or other risks that will occur in the future.

Prioritizing risk factors of hazardous material road transportation accidents using the fuzzy AHP method.

Annually, large amounts of hazardous materials (hazmat) are transported through the roads and this movement causes various accidents. Identifying the causes of these accidents is a critical issue for all public governments. This study aimed to identify the effective risk factors for hazmat road transport accidents and determine their relative weight using the fuzzy analytical hierarchy process (AHP) method. This qualitative study was conducted in 2021 in Iran and included four steps, i.e., the identification (using literature review and semi-structured interview), determination (according to the expert panel opinion), classification, and prioritization of effective factors in hazmat road transportation accidents. To prioritize and determine the relative weight of the effective factors, the fuzzy AHP technique was used. In total, 159 risk factors were identified, which were classified into six factors (including road, transportation management, vehicle, cargo, driver, and weather conditions) and 24 sub-factors. The main factor (greatest relative weight) with the highest priority was the driver (0.181). The road (0.167), cargo (0.166), vehicle (0.169), transportation management (0.161), and weather conditions (0.159) were the next priorities, in that order. The results demonstrated that the driver is the most important factor in causing accidents when transporting hazmat by road. The findings of this study might have the potential to decrease the frequency and consequence of accidents caused by the road transport of hazmat.

Risk Analysis of Airplane Upsets in Flight: An Integrated System Framework and Analysis Methodology

Generally, airplane upsets in flight are considered a precursor to loss of control in flight (LOC-I) accidents, and unfortunately LOC-I is classified as the leading cause of fatal accidents. To further explore the risk factors, causal relationships, and coupling mechanism of airplane upsets, this study proposed a risk analysis model integrating the Interpretative Structural Modeling (ISM) and Bayesian Network (BN). Seventeen key risk factors leading to airplane upsets were identified through the analysis of typical accident cases and the literature. The ISM approach was used to construct the multi-level interpretative structural model of airplane upsets, which could reveal the causal relationship among various risk factors and risk propagation paths. Then, taking 286 accident/incident investigation data as training samples, a data-driven BN model was established using machine learning for dependency intensity assessment and inference analysis. The results reveal that the interaction among risk factors of fatal accidents caused by airplane upsets is more significant than that of non-fatal accidents/incidents. Risk factors such as pilot-induced oscillations/airplane-pilot coupling and non-adherence to Standard Operating Procedures (SOPs)/neglect of cross-validation have a significant effect on airplane upsets in flight among seventeen risk factors. Moreover, this study also identifies the most likely set of risk factors that lead to fatal accidents caused by airplane upsets. The research results have an important theoretical significance and application value for preventing airplane upsets risk.

The Risk Factors for Acute Cerebrovascular Accident (Stroke) in Patients with Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2).

Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) patients may experience an acute ischemic stroke; however, risk factors, in-hospital deaths, and outcomes have not been thoroughly investigated. This study investigates the risk factors, comorbidities, and outcomes in patients with SARS-VoV-2 infection and acute ischemic stroke compared to patients without these conditions. The present retrospective study was conducted in the King Abdullah International Medical Research Centre (KAIMRC), Ministry of National Guard, Health Affairs, Riyadh, Saudi Arabia, during the period from April 2020 to February 2022. This study investigates the risk variables among the individuals who were diagnosed with either SARS-CoV-2 with stroke or patients with stroke alone. A total of 42,688 COVID-19 patients were registered, 187 cases of strokes were listed in COVID-19 patients, however, 5395 cases with stroke without SARS-CoV-2 infection. The results revealed that factors including age, hypertension, deep vein thrombosis, and ischemic heart disease are associated with an increased risk of ischemic stroke. The results also displayed an elevated frequency of in-hospital deaths in COVID-19 patients with acute ischemic stroke. The results also showed that SARS-CoV-2 together predicts the probability of stroke and death in the study sample. The study findings conclude that ischemic strokes were infrequent in patients with SARS-CoV-2 and usually occur in the presence of other risk factors. The risk factors of ischemic strokes in patients with SARS-CoV-2 are old age, male gender, hypertension, hyperlipidaemia, DVT, ischemic heart disease, and diabetes mellitus. Furthermore, the results showed a higher frequency of in-hospital deaths in COVID-19 patients with stroke compared to COVID-19 patients without stroke.

Leveraging the GIDAS Database for the Criticality Analysis of Automated Driving Systems

A safe introduction of automated driving systems on urban roads requires a thorough understanding of the traffic conflicts and accidents. This understanding is paramount to constructively safeguard these systems, i.e., to design a system that exhibits an adequate performance even in critical situations. In this work, we present an approach to gather knowledge by analyzing the German In-Depth Accident Study (GIDAS) database, which is representative of all German traffic accidents, along with the influencing factors that are hypothesized to be associated with increased criticality in relation to automated driving. In order to gain an insight into the risk associated with these factors in real-world accidents, we determine their presence in the database’s accident cases within a selected operational domain, enabled by translation from a natural language description to the database scheme employed by GIDAS. This initial catalog as well as the subsequent statistical considerations is motivated by analyzing the criticality for automated driving systems in urban areas. Based on this catalog, our work delineates a method for quantification of risk associated with such influencing factors in a given operational domain based on real-world accident data. This quantification can subsequently be used in decompositional, scenario-based risk assessment before system design and for the embedding safety argumentation. This paper, therefore, provides a blueprint of how the matured field of traffic accident research studies and its results, in particular accident databases, can be leveraged for risk assessment of the operational domain of automated driving systems.

Exploring hazardous chemical explosion accidents with association rules and Bayesian networks

Hazardous chemical accidents pose a severe threat to human and economic activities. To explore the causes and paths of hazardous chemical explosion accidents (HCEAs) in China, a qualitative and quantitative model was proposed in this study. Firstly, 129 accident cases were collected from the official website of the Ministry of Emergency Management of the People's Republic of China, and the accident factors were screened based on association rule (AR). Then, the fault tree (FT) structure of HCEA was developed by the AR, and the qualitative model was transformed into a quantitative Bayesian network (BN) model. Finally, the accident path, the importance size of the basic events, and the sensitivity of the direct cause of the accident were uncovered based on the BN model. The results show that improper temperature control due to operating procedures is the most likely route to accidents. Among the basic events, process management and operating procedures issues ranked the first in importance, with the most significant impact on HCEA. The sensitivity of improper temperature control far exceeds other direct causes and is most likely to induce accidents directly. According to the study results, some feasible measures are put forward to improve the safety management of hazardous chemical production.

A Study of the Effect Factor of Unexpected Accidents on Expressways

A Study of the Effect Factor of Unexpected Accidents on Expressways

Penentuan Prioritas Utama Faktor Kecelakan Kerja dan Alternatif Pencegahannya Menggunakan Metode AHP

Occupational health and safety is a set of activities to prevent injuries and health problems for workers and to provide a safe and healthy workplace. PT PLN (Persero) ULP SIbolga Kota is a sub-unit under UP3 SIbolga that helps manage customer service and distribution electricity network service. Electrical distribution work is one of the jobs that have a high risk. Some of the injuries that often occur during the distribution of electricity are electric shock, burns, and falls from a height. The purpose of this study was to determine the main priority factors causing work accidents and alternative prevention using the Analytical Hierarchy Process (AHP) method. Data collection from expert respondents was carried out by distributing questionnaires. After analyzing the data, the consistency value (CR) of all risk criteria was determined to be less than 0.1, and this indicates that the decisions made by the experts are consistent. The main priority of the work accident factor at the criterion level is Discipline, with a weight of 0.372, the main priority of the work accident factor at the sub-criteria level is bad weather conditions, with a weight of 0.818, and the main priority of preventing work accidents in the alternative level is equipment maintenance with a weight of 0.298.

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach.

In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study's objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions.

ISM-MICMAC based safety risk sources analysis and control measures for underground engineering of urban rail transit projects

Vigorously developing urban rail transit projects lowers the growing pressure on public transport in densely populated cities. However, accidents frequently occur due to the complex construction environment and diverse risk source factors emerging in urban rail transit construction. Existing research on the causes of urban rail transit construction accidents is mostly limited to the analysis of individual factors. This has negatively affected risk control of projects where risk factors for accidents have mutual influence. This paper adopted an integrated ISM-MICMAC approach to analyze and control urban rail transit construction accident risks. Through the analysis of China’s urban rail transit construction accidents (N = 134) from 2001 to 2019, this study 1) identified 18 risk source factors and their influencing relationships, 2) established a five-level Interpretive Structure Model (ISM) with 38 causal paths, and 3) classified the risk source factors using the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) procedure. All causal paths starting with the driver factors were then analyzed. Truncation factors and control measures were proposed to prevent accidents. Globally, China’s urban rail transit projects are large in number and size and cover a wide range of geological conditions. The analysis and control of accident risks in these projects provide valuable insights into risk management of urban rail transit construction in other countries.

DEMATEL, AISM, and MICMAC-Based Research on Causative Factors of Self-Build Housing Fire Accidents in Rural Areas of China

In recent years, the fire safety problems in self-build housing in China’s vast rural areas have become increasingly prominent. We analyzed the interaction of causative factors and logical structure of self-build housing fire accidents (SBHFAs) to find their key causes and reduce their occurrence. Using the 24Model, 30 SBHFA investigation reports were analyzed, and 44 SBHFA causative factors and 97 causal relationship codes were obtained. The causality and centrality degree of causative factors were analyzed using the decision-making trial and evaluation laboratory (DEMATEL) method to obtain the causal attribute and importance of causative factors. An adversarial hierarchical topology model of causative factors was conducted using the adversarial interpretive structural modeling (AISM) method, and the causal hierarchical relationships were obtained. Using the Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis, the causative factors’ dependency degree and driving force were calculated. Combining and comparing the results of DEMATEL, AISM, and MICMAC analyses, we found that the adversarial hierarchical topology model of causative factors was reasonable, and key direct causative factors, key transitional causative factors, and key root causative factors were mined. Controlling the key causative factors could effectively reduce the occurrence of SBHFAs and guide the fire safety management of self-build housings in rural areas of China.

A holistic view of maritime navigation accidents and risk indicators: examining IMO reports from 2011 to 2021

This paper investigated the risk indicators in maritime accidents and how they are considered within the reporting of maritime accidents, drawing on ten years of International Maritime Organisation (IMO) (2011–2020) accident reports. It highlighted the lack of consistent findings in studies exploring the role of vessel characteristics in maritime accidents, which often result from different methods, databases, techniques and motivations used by each respective study for gathering and analysing data. Furthermore, as human error continues to be highlighted as the top-cited cause of accidents, this study examined the qualitative content of IMO accident reports in-depth to broaden our understanding of maritime accident risk factors. Using a data-driven approach, statistical (ANOVA) and advanced text-mining techniques (using IRAMUTEQ software) were applied to extract meaning from the semi-structured and unstructured narrative descriptions that constitute most of the national administrations’ investigation reports to the IMO. Building on the text analysis of the IMO accident data, we proposed the Accident Maritime Ecosystem framework, which incorporates individuals, the ship organisation (on board), the internal ship ecosystem (on board and onshore), the external ship ecosystem (external factors) and the global maritime ecosystem (policies and regulations); moreover, it identifies these entities as risk factors in maritime accidents. The findings illustrate how accident reporting is largely human-centric and that as maritime transportation is becoming increasingly complex, there is a need for policy and organisational decision-makers to incorporate a broader scope of actors when considering maritime risk factors, which can be achieved by using the AME framework as a guideline.

Prediction of severity of aviation landing accidents using support vector machine models

The purpose of this study was to apply support vector machine (SVM) models to predict the severity of aircraft damage and the severity of personal injury during an aircraft approach and landing accident and to evaluate and rank the importance of 14 accident factors across 39 sub-categorical factors. Three new factors were introduced using the theory of inattentional blindness: The presence of visual area surface penetrations for a runway, the Federal Aviation Administration’s (FAA) visual area surface penetration policy timeframe, and the type of runway approach lighting. The study comprised 1,297 aircraft approach and landing accidents at airports within the United States with at least one instrument approach procedure. Support vector machine models were developed in using the linear, polynomial, radial basis function (RBF), and sigmoid kernels for the severity of aircraft damage and additional SVM models were developed for the severity of personal injury.The SVM models using the RBF kernel produced the best machine learning models with a 96% accuracy for predicting the severity of aircraft damage (0.94 precision, 0.95 recall, and 0.95 F1-score) and a 98% accuracy for predicting the severity of personal injury (0.99 precision, 0.98 recall, and 0.99 F1-score). The top predictors across both models were the pilot’s total flight hours, time of the accident, pilot’s age, crosswind component, landing runway number, single-engine land certificate, and any obstacle penetration. This study demonstrates the benefit of SVM modeling using the RBF kernel for accident prediction and for datasets with categorical factors.

Driver Drowsiness Detection Using Face Recognition

Abstract: Every year, hundreds of people die in traffic accidents all over the world. According to national statistics, human factors play a determining role in 90-95% of car accidents in Iran. Globally, 25% of accidents are caused by driver fatigue and around 60% of road accidents result in death or serious injury. In a National Transportation Research Institute (NTSRB) study of 107 randomly selected car crashes, fatigue accounted for 58% of all crashes. Drowsy driving is a major factor in serious road accidents that claim thousands of lives every year. The application of intelligent systems in automobiles has developed considerably in recent years. These systems use wireless sensor networks to monitor and transmit car and driver status. Smart cars that use software technology to control engine speed, steering, transmission, braking, etc. Management and control, the quality of management has been greatly improved. Ad hoc networks are the first systems to develop automatic navigation in cars. A notable weakness of these systems is that their response to changes in the environment is not in real time. This is especially important in driving, where time is a key factor in a driver's decision. On the other hand, another way to check driver fatigue is to monitor the physical condition and facial expression of the driver, which cannot be processed and transmitted accurately enough by wireless sensor networks. Driver fatigue is a major factor in a large number of road accidents. Recent statistics estimate that 1,200 deaths and 76,000 injuries are attributable to fatigue-related accidents each year.

A Systematic Review of Pattern and Prevalence of Cannabis Use among Transporters and Associated Road Traffic Accidents

Background: Cannabis (marijuana), while being prohibited in many countries, is the most commonly used illicit drug worldwide (WHO, 1997). "Cannabis-impaired driving" refers to the impairment brought on by 9-tetrahydrocannabinol's (THC) cognitive and psychomotor effects, which have a negative impact on a driver of a motor vehicle after THC ingestion. In contrast, a "cannabis-positive driver" is a person who operates a motor vehicle while exhibiting driving impairments due to any measurable THC concentration in blood, oral fluid, or urine. A driver is considered to be "driving under the influence of cannabis" (DUIC) if their cognitive or psychomotor abilities are significantly impaired and their blood, oral fluid, or urine contains a specific amount of THC.&#x0D; Aim: This study aimed at conducting a systematic review of pattern and prevalence of cannabis use among transporters and the associated road traffic accidents.&#x0D; Design: A systematic review.&#x0D; Data Sources: Systematic search for worldwide published literature from PubMed, PsycINFO, EMBASE, Google Scholar, Science Direct, Cochrane Library and Medline databases.&#x0D; Study eligibility criteria: These studies provided techniques and/or measurements of the frequency and pattern of cannabis use among transporters and the related traffic incidents.&#x0D; Data extraction: The first reviewer extracted the data, and the second reviewer verified it. Both reviewers individually critiqued each of the identified papers. Data was extracted from each eligible study (including author, title, year, and study setting) following the Preferred Reporting Items on Systematic Reviews and Meta-Analysis (PRISMA). These data abstraction forms were examined, and studies that satisfied the criteria were added to the meta-analysis.&#x0D; Findings: Only 10 studies met all of the inclusion criteria out of the 2,251 papers that were recovered after looking at the titles and abstracts (where an abstract was not available, the article was still counted).&#x0D; Conclusion: The study emphasizes the significance of education, law enforcement, and routine drug testing, reducing bribery among federal prosecutors, and influencing the accessibility of cannabis and other psychiatric substances as assessments for preventing substance cruising and restricting road traffic accidents. It also highlights areas where the study's findings and the scientific literature on risk factors for traffic accidents and preventative measures overlap. To increase acceptance and improve results, it is crucial to incorporate lay perspectives into road safety policies and initiatives.

Identifying relevant factors about work accidents in the road transport sector and the deaths relation in this scenario

Workers’ health and safety are a major concern in society, since work accidents have a major impact on productivity and economy. In Brazil, the accidents are officially reported through Work Accident Communication and they are available to the public. Thus, this study analyzed a balanced dataset containing 1,206 records of deaths caused by work accidents related to the transport sector. Its aim was analyzing how the deaths in the transport sector are related with the other work accident factors. To achieve this goal, twelve performance data mining techniques are compared, through five performance metrics, regarding the predictive capacity of the occurrence of deaths caused by work accidents. In this context, the XGBoost and Naïve Bayes algorithms showed the best predictive capacity. The explanatory analysis indicates that work accidents followed by death in road transport are predictable due to the severity of the injuries and vital parts of the body are affected.

A Comprehensive Approach through Robust Regression and Gaussian/Mixed-Markov Graphical Models on the Example of Maritime Transportation Accidents: Evidence from a Listed-in-NYSE Shipping Company

The main objective of this article is to determine the internal factors of maritime transportation accidents using a comprehensive approach through robust regression and Gaussian/mixed-Markov graphical models. Globally, this could be a strong incentive for the employees to negotiate higher compensation and for the insurance companies to impose higher premiums to cover the risk for these kinds of accidents. The article uses a dataset consisting of 166 real cases (human injuries) in the period 2014–2022 in different ships owned by a shipping company indexed in the New York Stock Exchange. The results of the study support the hypotheses as have been set in the article, connecting the internal factors with the injuries of any type. The practical implementation of the study is its ability to be used by policy makers in shipping to compensate employees depending on the risk of their work on board and at the same time to calculate the insurance premiums in a more accurate way. The originality of the research lies in the fact that this is a unique study in maritime transportation related to human accidents and not on ship or cargo casualties. The idea came from the results of another study conducted on a bibliometric analysis of the factors related to maritime transportation accidents. The findings of the current study can provide valuable insights to stakeholders and shipping planners in formulating effective policies for better wage packages and insurance premiums.

The Effect of Onang-Onang Instrumental Music Therapy on Elderly With Insomnia

Sleep disorders that are often experienced in the elderly, namely insomnia. Sleep disturbance (insomnia) will cause drowsiness throughout the day which will affect daily activities and general health. Drowsiness is a risk factor for accidents, this condition also causes various bad effects, including stress, emotional disturbances, which will lead to a decrease in the quality of life in old age. The purpose of this study was to determine the effect of onang-onang music on insomnia in lanisa. This type of research is quantitative and the research design used a Quasi Experiment with a pre-test-post design. The population in this study were all elderly in the working area of Pijorkoling Health Center, Padangsidimpuan City. The sample in this study was determined by using the Accidental Sampling technique so that the results were 17 respondents. The statistical test used is the Wilcoxon test. The results showed that there was an effect of onang - onang music therapy on decreasing the insomnia scale after the intervention with a P-value of 0.000, the result was &lt;0.05. The results of this study are expected to be used as an alternative therapy for the elderly with insomnia.&#x0D; Keywords: Music therapy; Onang-onang instrumental; Insomnia

Work Accident at Sugar Farmers in Banyumas Regency

A work accident is an accidental event in the employment relationship, including diseases of the working relationship, the accident that happened on the way go to work, and coming home in the usual way. Work accidents can be experienced by sugar farmers. In Banyumas Regency, there are about 26.580 Sugar farmers. Based on Kesra Setda Banyumas Regency, from 2017 to November 2019 there were 323 cases of sugar farmers accidents, with 236 disabilities and 87 deaths. The purpose is to analyze the risk factors of work accidents consisting of behavioral and environmental factors. The method is an analytical survey with a cross-sectional approach. The sample was 200 people in Cilongok Districts. Data analysis includes univariate, bivariate, and multivariate. The results show that behavioral factors significantly associated with work accidents are breakfast status (p = 0,010) and unhealthy conditions (p = 0,002). Environmental factors significant to work accidents are past medical history (p = 0.000) and protein adequacy (p = 0,000). Recommendations for this research are socialization about the importance of breakfast and paying attention to the body condition before climbing, also training energy consumption diet of protein corresponding to the workload.