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Aerobic fitness testing plays a significant role in the academy training of law enforcement recruits. There is a lack of normative data for law enforcement recruits and substantial interchangeability for choice of field-based aerobic tests. Therefore, this study aimed to develop normative percentile rankings for the 20-m multistage fitness tests (20MSFT) and 2.4-km run specific to law enforcement recruits; and to analyze the relationship between the 2 tests by linear regression. Data from 5 cohorts of law enforcement recruits completing 20MSFT (n 5 1,536) and 2.4-km run (n 5 1,499) testing during basic training at an Australian Police Academy were retrospectively analyzed. Percentile ranks were calculated (20th, 40th, 50th, 60th, 80th, and 99th) based on the number of shuttles completed and time (seconds) to complete the 2.4-km run. Results were split by sex and age (i.e., ,20 years, 20-29 years, 30-39 years, and 40-49 years). Linear regression analyzed the relationship between the 20MSFT to the 2.4-km run (using both seconds and km·h21), producing predictive equations to translate scores between the 2 assessments. These data presented the first detailed normative values by age and sex for Australian law enforcement recruits. Furthermore, the results demonstrated regression models could explain 65-74% of the variance observed (r2 5 0.649-0.741) between 20MSFT performance and 2.4-km run performance. This study's findings allow for normative performance for the purpose of benchmarking fitness, setting performance goal, and rehabilitation from injury. In addition, a valid conversion through a linear regression affords cross-agency comparisons and temporal changes in test selection/recruit cohorts. [ABSTRACT FROM AUTHOR]

BACKGROUND: Law enforcement is a profession of sedentary physical activity interspersed with physically demanding activity that requires high levels of fitness. It is imperative that agencies training law enforcement recruits maximise their fitness during their time at the academy. OBJECTIVE: The aim of this study was to investigate changes in physical fitness during academy training. METHODS: Retrospective data for 10 academy recruit classes, totalling 715 participants, were collected from a US law enforcement agency. The change in performance on two standardised tests were used as outcome measures. Comparisons were made between percentiles utilising one-way ANOVA and a linear mixed model (LMM). RESULTS: Overall, higher percentiles were found to have smaller improvements in physical fitness than lower percentiles. The results of the LMM support this supposition, showing that lower physical fitness scores resulted in greater improvements in a generalised fitness assessment (value = –0.45, standard error 0.02, p < 0.001) and an occupational assessment (value = –0.49, standard error = 0.02, p < 0.001). CONCLUSION: The results of this study suggest that recruits with lower physical fitness will see greater improvements during academy training. This could be due to a ceiling effect for the more fit but may also be due to recruits of higher physical fitness being under trained during academy. Utilising ability-based training and prescribing an appropriate workload to recruits of higher fitness may improve overall recruit fitness upon graduation. [ABSTRACT FROM AUTHOR]

The 20-m multistage fitness test (20MSFT) is commonly used by law enforcement agencies to measure aerobic fitness and to estimate maximal aerobic consumption (...O2max). These measures are an important occupational variable with aerobic fitness levels linked to employment status, occupational performance, and long-term health in law enforcement officers. There are a multitude of predictive equations used to provide an estimate of ...O2max, with the extent of variability in the estimated ...O2max currently unknown in this population. This has consequences for comparisons between, and across, differing agencies, and in capabilities of deriving normative data. The aim of this investigation was to compare the variability in estimated ...O2max scores derived from different 20MSFT predictive equations. The 20-m multistage fitness test data from 1,094 law enforcement recruits (male n = 741,25.2 ± 6.3 years; female: n = 353, 25.6 ± 5.6 years) from a single agency were retrospectively analyzed. The 20MSFT scores were trans- formed into estimated ...O2max scores using 6 different predictive equations. Significance was set at p < 0.05. Results demonstrated significantly different ...O2max scores between each predictive equation (p < 0.001; d = 0.25-1.53) and between male and female recruits (p < 0.001, r = 0.55). All estimated ...O2max equations showed small to very strong correlations with each other (p < 0.001; r = 0.32-0.99). The findings indicate considerable dispersion of ...O2max scores when using differing equations, suggesting raw 20MSFT variables (e.g., shuttles or distance completed) should be preferred to measure and apply results from aerobic fitness tests if standardized approaches are not developed within law enforcement. [ABSTRACT FROM AUTHOR]

Background: The underdiagnosis of cognitive impairment hinders timely intervention of dementia. Health professionals working in the community play a critical role in the early detection of cognitive impairment, yet still face several challenges such as a lack of suitable tools, necessary training, and potential stigmatization., Objective: This study explored a novel application integrating psychometric methods with data science techniques to model subtle inconsistencies in questionnaire response data for early identification of cognitive impairment in community environments., Methods: This study analyzed questionnaire response data from participants aged 50 years and older in the Health and Retirement Study (waves 8-9, n=12,942). Predictors included low-quality response indices generated using the graded response model from four brief questionnaires (optimism, hopelessness, purpose in life, and life satisfaction) assessing aspects of overall well-being, a focus of health professionals in communities. The primary and supplemental predicted outcomes were current cognitive impairment derived from a validated criterion and dementia or mortality in the next ten years. Seven predictive models were trained, and the performance of these models was evaluated and compared., Results: The multilayer perceptron exhibited the best performance in predicting current cognitive impairment. In the selected four questionnaires, the area under curve values for identifying current cognitive impairment ranged from 0.63 to 0.66 and was improved to 0.71 to 0.74 when combining the low-quality response indices with age and gender for prediction. We set the threshold for assessing cognitive impairment risk in the tool based on the ratio of underdiagnosis costs to overdiagnosis costs, and a ratio of 4 was used as the default choice. Furthermore, the tool outperformed the efficiency of age or health-based screening strategies for identifying individuals at high risk for cognitive impairment, particularly in the 50- to 59-year and 60- to 69-year age groups. The tool is available on a portal website for the public to access freely., Conclusions: We developed a novel prediction tool that integrates psychometric methods with data science to facilitate "passive or backend" cognitive impairment assessments in community settings, aiming to promote early cognitive impairment detection. This tool simplifies the cognitive impairment assessment process, making it more adaptable and reducing burdens. Our approach also presents a new perspective for using questionnaire data: leveraging, rather than dismissing, low-quality data., (©Hongxin Gao, Stefan Schneider, Raymond Hernandez, Jenny Harris, Danny Maupin, Doerte U Junghaenel, Arie Kapteyn, Arthur Stone, Elizabeth Zelinski, Erik Meijer, Pey-Jiuan Lee, Bart Orriens, Haomiao Jin. Originally published in JMIR Formative Research (https://formative.jmir.org), 13.11.2024.)