Search Results (8)

Cardiac deaths account for the largest share of on-duty firefighter deaths. To help ensure duty fitness and minimize injury risk, many fire departments require the passing of an annual physical ability test, consisting of a battery of simulated fire suppression activities (sFSAs). The purpose of the study was to determine the relationship of sFSA performance to acute cardiac and respiratory events (ACREs) and the effect that estimated VO₂max has on sFSA performance. The study was retrospective. As part of an annual physical ability test, five timed sFSAs were performed, summed for a composite time, and categorized into three performance levels (fast, moderate, and slow). Estimated VO₂max was determined using the Forestry Step Test. A significant (p = 0.023) linear trend was observed with higher sFSA performance times being associated with a higher proportion of firefighters going on to suffer an ACRE. The estimated VO₂max was significantly (p < 0.001) higher in the fast group compared to the slow group. There was not a significant (p = 0.70) difference in estimated VO₂max between the moderate and slow groups. Estimated VO₂max performance and sFSA performance were significantly correlated, with r_s(488) = −0.272 and p < 0.001. Poorer sFSA performance was found to be associated with a higher proportion of ACREs. The results suggest that sFSA performance may be a valid indicator of ACRE injury risk and aerobic capacity. Full article

The characteristics of an individual patient experiencing out-of-hospital cardiac arrest who recovered spontaneous circulation with the assistance of witnesses and paramedics were examined. The analysis of bystander cardiopulmonary resuscitation (CPR) and the professional first aid efforts of paramedics in the pre-hospital environment is pivotal to enhancing the survival rate of out-of-hospital cardiac arrest patients. The data used in this study were extracted from the Korea Centers for Disease Control and Prevention (KCDC) nationally recognized statistics, Acute Heart Failure big data survey. Out-of-hospital cardiac arrest (OHCA) customer data were collected from the Gangwon Fire Headquarters public information database as social management data. The data were analyzed using SPSS 24. The study’s results emphasized the significance of offering basic CPR training to the public. This is evident from the fact that 90.5% of the first witnesses in the study performed CPR on OHCA patients, resulting in the recovery of spontaneous circulation (ROSC). The majority of patients with ROSC were male, with the highest age group being 41–50 years. Heart disease, hypertension, and diabetes were common medical conditions. The rate of witnessing cardiac arrest was high. Among the first witnesses, about 78.4% were of cardiac arrest incidents involving family members, co-workers, or acquaintances; 12.2% were on-duty medical healthcare personnel; and 9.5% were off-duty healthcare personnel. Cardiac arrest was treated in 83.8% of cases, with 90% of witnesses performing CPR. The percentage of witnesses that used an automated external defibrillator (AED) was 13.5%. In this study, the rates of ECG monitoring, CPR performance, and defibrillation performed by paramedics were high, but intravascular access and drug administration had a lower rate of performance. The time elapsed depended on the patient’s physical fitness. The study found that paramedics had the highest CPC restoration rate in patients with cardiac arrest, followed by EMTs and nurses. Significant differences were observed in cerebral performance scores after care by these paramedics and nurses. To increase the performance of AEDs, more AEDs should be installed in public spaces so that the public can access them conveniently in cases of emergency. In addition, it is necessary to improve the quality of professional first aid physical activity services performed by first-class paramedics. Full article

A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic idea of this study is to forecast important work-related risk variables linked to WMSDs in bus drivers using machine learning approaches. A total of 400 full-time male bus drivers from the east and west zone depots of Bengaluru Metropolitan Transport Corporation (BMTC), which is based in Bengaluru, south India, took part in this study. In total, 92.5% of participants responded to the questionnaire. The Modified Nordic Musculoskeletal Questionnaire was used to gather data on symptoms of WMSD during the past 12 months (MNMQ). Machine learning techniques including decision tree, random forest, and naïve Bayes were used to forecast the important risk factors related to WMSDs. It was discovered that WMSDs and work-related characteristics were statistically significant. In total, 66.75% of subjects reported having WMSDs. Various classifiers were used to derive the simulation results for the frequency of pain in the musculoskeletal systems throughout the last 12 months with the important risk variables. With 100% accuracy, decision tree and random forest algorithms produce the same results. Naïve Bayes yields 93.28% accuracy. In this study, through a questionnaire survey and data analysis, several health and work-related risk factors were identified among the bus drivers. Risk factors such as involvement in physical activities, frequent posture change, exposure to vibration, egress ingress, on-duty breaks, and seat adaptability issues have the highest influence on the frequency of pain due to WMSDs among bus drivers. From this study, it is recommended that drivers get involved in physical activities, adopt a healthy lifestyle, and maintain proper posture while driving. For any transport organization/company, it is recommended to design driver cabins ergonomically to mitigate the WMSDs among bus drivers. Full article

Physical inactivity, coupled with increasing obesity levels, in firefighters plays a key role in aggregated cardiovascular events. The purpose of this study was to investigate device-measured physical activity (PA) for firefighters while on- and off-duty to have a clearer understanding of their overall PA level. Methods: Twenty-nine career firefighters participated in this non-experimental, within-subjects study by wearing an accelerometer to assess PA intensities and step-count. Obesity was classified using body mass index (BMI). Dependent t-tests were used to examine mean differences in PA intensities when on- and off-duty. Pearson product-moment correlations were used to assess the association between PA intensities when on and off-duty. Results: According to the World Health Organization BMI categorizations, 20 firefighters were overweight, 9 were obese, and, thus, none were normal weight. Only light PA (LPA) was statistically significant (p = 0.026) for on- and off-duty days with a small-to-medium effect size (d = 0.47), meaning that on average, firefighters performed more minutes of LPA when on-duty compared to off. There was a significant difference between on- (9060.2 ± 2636.4) and off-duty (7495.3 ± 2835.8) daily step counts (p = 0.011). Conclusion: As the results demonstrate, there is a dire need for increased PA levels in firefighters while on- and off-duty. Full article

Prolonged ocean voyages constrain the regular physical activity and sleep patterns of seafarers. However, there is a lack of information on seafarers’ physical activity and sleep behavior. The purpose of this study was to systematically examine physical activity and sleep patterns among seafarers using a single wrist-worn accelerometer. Fifty-one senior maritime students (mean age = 22.8 years; 80.0% male) in a university navigation department participated in the study. Data were collected from participants on three sea voyages in the Asia-Pacific region. Indicators of moderate to vigorous intensity physical activity (MVPA) and sleep patterns were compared between several conditions: (1) moored versus sailing, (2) on-navigation duty and off- navigation duty, and (3) day versus night navigation duty. Regardless of conditions, low levels of physical activity and short sleep durations were observed. Independent sample t-tests revealed that time spent doing MVPA was significantly higher when participants were off-duty than when they were on-duty (p < 0.001). Physical activity did not significantly differ between the other two conditions. While total sleep duration was not significantly different between mooring and sailing, the results showed that participants awakened more frequently (p = 0.007) and their sleep was more restless (p < 0.001) while sailing. The results demonstrated that developing effective programs to promote physical activity should be a public health priority for the seafaring population, and serious consideration is required to mitigate sleep disruption during sailing. Full article

To prevent transmission of the coronavirus, we established the campus protection measures for coronavirus disease 2019 (COVID-19) (CPMCV-19) and analyzed the effectiveness and cost in practice. This project was set in Taiwan. We organized an anti-epidemic task force team from multidisciplinary co-workers to establish the CPMCV-19. The essential components were as follows: no close contact communication, sterilization, temperature control, social distancing, activity restrictions, personal hygiene control, and situational awareness. During 100 days of operation, the mean time spent for frontal temperature measuring was 2.7 ± 0.3 s per person. The mean on-duty time for individual personnel to control the gate and measure temperature was 3.5 h per day. In total, 31 persons with loss of taste/smell or fever were detected on campus and sent to hospital for screening within 1 h. A total of 6 persons were instructed to observe self-health management due to possible contact or travel history, and none were diagnosed with COVID-19 infection. A total budget of USD 27,100 was used for CMPCV-19 in this period. The established campus protection measures for COVID-19 were practical and might be effective. They can be used as reference for schools in a pandemic, such as COVID-19. Full article

While working on fire ground, firefighters risk their well-being in a state where any incident might cause not only injuries, but also fatality. They may be incapacitated by unpredicted falls due to floor cracks, holes, structure failure, gas explosion, exposure to toxic gases, or being stuck in narrow path, etc. Having acknowledged this need, in this study, we focus on developing an efficient portable system to detect firefighter’s falls, loss of physical performance, and alert high CO level by using a microcontroller carried by a firefighter with data fusion from a 3-DOF (degrees of freedom) accelerometer, 3-DOF gyroscope, 3-DOF magnetometer, barometer, and a MQ7 sensor using our proposed fall detection, loss of physical performance detection, and CO monitoring algorithms. By the combination of five sensors and highly efficient data fusion algorithms to observe the fall event, loss of physical performance, and detect high CO level, we can distinguish among falling, loss of physical performance, and the other on-duty activities (ODAs) such as standing, walking, running, jogging, crawling, climbing up/down stairs, and moving up/down in elevators. Signals from these sensors are sent to the microcontroller to detect fall, loss of physical performance, and alert high CO level. The proposed algorithms can achieve 100% of accuracy, specificity, and sensitivity in our experimental datasets and 97.96%, 100%, and 95.89% in public datasets in distinguishing between falls and ODAs activities, respectively. Furthermore, the proposed algorithm perfectly distinguishes between loss of physical performance and up/down movement in the elevator based on barometric data fusion. If a firefighter is unconscious following the fall or loss of physical performance, an alert message will be sent to their incident commander (IC) via the nRF224L01 module. Full article

In a randomly deployed and large scale wireless sensor network, coverage-redundant nodes consume much unnecessary energy. As a result, turning off these redundant nodes can prolong the network lifetime, while maintaining the degree of sensing coverage with a limited number of on-duty nodes. None of the off-duty eligibility rules in the literature, however, are sufficient and necessary conditions for eligible nodes. Hence redundancy or blind points might be incurred. In this paper we propose a complete Eligibility Rule based on Perimeter Coverage (ERPC) for a node to determine its eligibility for sleeping. ERPC has a computational complexity of O(N²log(N)), lower than the eligibility rule in the Coverage Control Protocol (CCP), O(N³), where N is the number of neighboring nodes. We then present a Coverage Preserving Protocol (CPP) to schedule the work state of eligible nodes. The main advantage of CPP over the Ottawa protocol lies in its ability to configure the network to any specific coverage degree, while the Ottawa protocol does not support different coverage configuration. Moreover, as a localized protocol, CPP has better adaptability to dynamic topologies than centralized protocols. Simulation results indicate that CPP can preserve network coverage with fewer active nodes than the Ottawa protocol. In addition, CPP is capable of identifying all the eligible nodes exactly while the CCP protocol might result in blind points due to error decisions. Quantitative analysis and experiments demonstrate that CPP can extend the network lifetime significantly while maintaining a given coverage degree. Full article