Identifying the most important, frequent, and physically demanding tasks of Brazilian firefighters

This study aimed to identify the most important, frequently performed, and physically demanding tasks performed by Brazilian firefighters and to identify tasks that could be used to assess physical fitness. A subjective task analysis was conducted. Five hundred twenty-four firefighters (84% male; 16% females) responded to an online survey and rated 37 tasks across three domains (most important, most frequent, and most physically demanding). A dichotomous decision analysis was used to inform the proposed physical fitness tests. Wildland firefighting tasks presented the highest overall mean rate. Traffic control was considered the most important and frequently performed task. Lifeguard rescue was considered the most physically demanding task. The dichotomous analysis identified 14 essential tasks (seven structural firefighting and seven automobile accidents). The tasks identified may be helpful in developing criterion physical fitness tests and training programs related to firefighters' demands.Practitioner summary: The unpredictability, variability, and dangerousness of firefighting make it challenging to observe the physical demands imposed on firefighters. A subjective task analysis was conducted to identify essential tasks performed by Brazilian firefighters. Wildland firefighting, lifeguard rescue, automobile accidents, and structural firefighting tasks were the most important, frequent, and physically demanding.

Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test

Physical fitness is a pillar of U.S. Air Force (USAF) readiness and ensures that Airmen can fulfill their assigned mission and be fit to deploy in any environment. The USAF assesses the fitness of service members on a periodic basis, and discharge can result from failed assessments. In this study, a 21-feature dataset was analyzed related to 223 active-duty Airmen who participated in a comprehensive mental and social health survey, body composition assessment, and physical performance battery. Graphical analysis revealed pass/fail trends related to body composition and obesity. Logistic regression and limited-capacity neural network algorithms were then applied to predict fitness test performance using these biomechanical and psychological variables. The logistic regression model achieved a high level of significance (p < 0.01) with an accuracy of 0.84 and AUC of 0.89 on the holdout dataset. This model yielded important inferences that Airmen with poor sleep quality, recent history of an injury, higher BMI, and low fitness satisfaction tend to be at greater risk for fitness test failure. The neural network model demonstrated the best performance with 0.93 accuracy and 0.97 AUC on the holdout dataset. This study is the first application of psychological features and neural networks to predict fitness test performance and obtained higher predictive accuracy than prior work. Accurate prediction of Airmen at risk of failing the USAF fitness test can enable early intervention and prevent workplace injury, absenteeism, inability to deploy, and attrition.

"Fitness and Fatness" in Children and Adolescents: An Italian Cross-Sectional Study

Children with obesity tend to have lower level of physical activity compared to non-obese peers. In fact, sedentary behaviors are prevalent in obese children causing difficulties to perform motor tasks and engaging in sport activities. This, in turn, has direct repercussions on adiposity and related comorbidities. The aim of the study was to investigate several components of fitness and their relationship with the degree of fatness in children. We considered 485 Italian schoolchildren (9.5 ± 1.12 years). BMI and prediction modelling outputs of fat mass were employed as markers of body fatness. Physical fitness (PF) was assessed by the 9-item test battery (explosive power, leg muscle power, arm muscle power, upper body power, coordination, agility, speed and endurance). Differences between groups in the PF tests (p < 0.05) were noted. A similar pattern was reflected in both genders. The relationship between anthropometrics' characteristics and PF tests showed that weight and fat mass had a high level of correlation with different PF tests. Our findings highlight the importance of investigating the degree of fatness in relation with different components of fitness, in children and adolescents. This combination of proxies may cover an unexpectedly helpful screening of the youth population, for both health and performance.

Pathway linking physical activity, sleep duration, and breakfast consumption with the physical/psychosocial health of schoolchildren

The relationship between certain lifestyle habits and schoolchildren's health has previously been reported on, but the exact pathway of the effects lifestyle habits have on physical/psychosocial health (PPH) has not been investigated nor has the relative influence of different habits on schoolchildren's health. In this study, schoolchildren were recruited from a primary school in Toyama Prefecture, Japan (n = 576), and the relevant data were collected in June/July 2017. Path analysis was used to examine the relationships of lifestyle habits and physical fitness with PPH among schoolchildren in grades 1-4 and 5-6. Body weight and total fitness scores were found to be not related to the children's PPH. The pathway via which lifestyle habits influenced PPH was determined successfully. Among children in grades 1-4, sex (p < .05), age (p < .01), and breakfast intake (p < .05) were related to PPH. Among schoolchildren in grades 5-6, the duration of sleep (p < .05) was related to PPH. Thus, factors related to schoolchildren's PPH vary by school grade. The identification of the predictors of the PPH of schoolchildren should inform the design of tailored, grade-specific health promotion interventions in Japanese elementary schools.

Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests

AIM

The purpose of this study was to develop a machine learning prediction model for successful aging (SA) based on physical fitness tests.

METHODS

A total of 3657 community-dwelling adults aged ≥60 years from Nanchang city were recruited in this study. A 3-year follow-up test was carried out for all the participants to determine whether they turn to non-SA. Developed questionnaires and physical fitness tests were used to obtain overall health condition, balance, agility, speed, reactions and gait. Four machine learning models (logistic regression, deep learning, random forest and gradient boosting decision tree) were applied to develop the prediction models, the analyzed sample was 890.

RESULTS

The baseline prevalence of successful aging was 26.99%, The average annual incidence rate of SA to non-SA was 11.04%. There were significant differences between the SA and non-SA groups for all physical fitness tests at baseline. The accuracy and area under the curve of all four machine learning models was >85%, the positive predictive value and sensitivity was >75%, and the specificity was >86% on the average. The deep learning model outperformed the other model, with area under the curve 90.00%, accuracy 89.3%, positive predictive value 85.8% and specificity 93.1%, respectively. Compared with other models, the logistic regression model performed best in sensitivity. Age, arm curl, 30-s sit-to-stand and reaction time were important predictors in all models.

CONCLUSION

The deep learning model is ideal in the prediction of SA maintenance, and the corresponding physical fitness interventions are essential to ensuring SA. Geriatr Gerontol Int 2020; ••: ••-••.

A database of predictor test sex bias for development of military physical employment standards

BACKGROUND

Hesitation to employ females for physically demanding jobs is often due to sex related physical abilities. A physical employment standard (PES) identifies individuals who are physically capable for work.

OBJECTIVE

A database containing 300 + sources of physical performance tests (PFTs) will inform potential sex bias for PES development.

METHODS

Weighted means and probability density curves illustrate the percentage overlap between male and female performance on PFT data from the armed forces of 11 countries and the open literature. Where female training data were available, the change in percentage overlap illustrates the potential for reduction in sex-related differences.

RESULTS

PFTs demonstrating the extremes of sex disparity were bench press (11 sources) and sit-ups (14 sources) with 9% and 93% overlap in performance, respectively. Training for bench press; pull ups; VO2max; and upright pull improved female performance by 12%, 22%, 35%, and 23% respectively. This translated into narrowing the gap between male and female mean performance by 1%, 4%, 5%, and 10% respectively.

CONCLUSIONS

The ability of PFT to predict performance is essential; however, PFTs with more overlap will facilitate development of PES with reduced sex bias. PFTs with the greatest potential for improvement in females are identified here.