scholarly journals Are there sex differences in physiological parameters and reaction time responses to overload in firefighters?

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0249559
Author(s):  
Fabrizio Perroni ◽  
Ludovica Cardinali ◽  
Lamberto Cignitti ◽  
Erica Gobbi ◽  
Federico Grugni ◽  
...  
Keyword(s):  
Reaction Time ◽  
Repeated Measures ◽  
Injury Risk ◽  
Reaction Time Data ◽  
Work Capacity ◽  
Step Test ◽  
Time Data ◽  
Males And Females ◽  
Time Variables ◽  
Post Hoc

Male and female firefighters work side-by-side in the same in strenuous and risky conditions. Anthropometrics, physiological, and reaction time (mean of reaction time -MRT-, and errors made -E) parameters of 12 Female and 13 Male firefighters were compared. Effect of overload (step test with and without equipment) on the MRT and E were analyzed on 3 trials (T1 = 1-1s, T2 = 0.5-1s, T3 = 0.5–0.5s), compared with a pre-test condition (basal). T-test between males and females was applied to assess differences (p<0.05) in all parameters. ANOVA with repeated measures and Bonferroni on 3 conditions of step test between males and females was applied in reaction time variables. Between MRT and E, in T1, T2 and T3 trials and the 3 test conditions, ANCOVA models with interactions were used. Differences (p<0.05) in anthropometric, physiological and reaction time data emerged across groups, and on the 3rd trials (T3 vs T1 and T2) in reaction time parameters of each group. ANCOVA showed differences (p<0.001) in E among trials. Post hoc showed significant differences in T1vsT3 and T1vsT2. MRT x trial interaction was extremely significant (P<0.001). Implementing fitness and reaction time exercise programs is important to decrease the injury risk and increase work capacity in firefighters with reference to female workers.

Modeling Within-Person Variance in Reaction Time Data of Older Adults

GeroPsych ◽  
2011 ◽  
Vol 24 (4) ◽  
pp. 169-176 ◽  
Author(s):  
Philippe Rast ◽  
Daniel Zimprich
Keyword(s):  
Reaction Time ◽  
Cognitive Aging ◽  
Reaction Times ◽  
Reaction Time Task ◽  
Reaction Time Data ◽  
Scale Model ◽  
Time Data ◽  
Time Task ◽  
The Mean ◽  
Second Wave

In order to model within-person (WP) variance in a reaction time task, we applied a mixed location scale model using 335 participants from the second wave of the Zurich Longitudinal Study on Cognitive Aging. The age of the respondents and the performance in another reaction time task were used to explain individual differences in the WP variance. To account for larger variances due to slower reaction times, we also used the average of the predicted individual reaction time (RT) as a predictor for the WP variability. Here, the WP variability was a function of the mean. At the same time, older participants were more variable and those with better performance in another RT task were more consistent in their responses.

Absolute Pitch as a Learned Phenomenon: Evidence Consistent with the Hick-Hyman Law

1994 ◽  
Vol 12 (2) ◽  
pp. 267-270 ◽  
Author(s):  
Jasba Simpson ◽  
David Huron
Keyword(s):  
Reaction Time ◽  
Absolute Pitch ◽  
Reaction Time Data ◽  
Western Music ◽  
Frequency Of Occurrence ◽  
Time Data ◽  
Expected Frequency ◽  
Computer Based ◽  
Additional Support

An analysis of reaction time data collected by Miyazaki (1989) provides additional support for absolute pitch as a learned phenomenon. Specifically, the data are shown to be consistent with the Hick- Hyman law, which relates the reaction time for a given stimulus to its expected frequency of occurrence. The frequencies of occurrence are estimated by analyzing a computer-based sample of Western music. The results are consistent with the view that absolute pitch is acquired through ordinary exposure to the pitches of Western music.

A moving average model for sequenced reaction-time data

1981 ◽  
Vol 23 (2) ◽  
pp. 115-133 ◽  
Author(s):  
Joseph B Kadane ◽  
Jill H Larkin ◽  
Richard H Mayer
Keyword(s):  
Reaction Time ◽  
Moving Average ◽  
Reaction Time Data ◽  
Time Data ◽  
Average Model ◽  
Moving Average Model

Reaction times and burning rates for wind tunnel headfires

2003 ◽  
Vol 12 (2) ◽  
pp. 195 ◽  
Author(s):  
Ralph M. Nelson, Jr.
Keyword(s):  
Reaction Time ◽  
Wind Tunnel ◽  
Wildland Fire ◽  
Mass Loss Rate ◽  
Fire Behavior ◽  
Reaction Times ◽  
Reaction Time Data ◽  
Combustion Regime ◽  
Fuel Particle ◽  
Time Data

Catchpole et al. (1998) reported rates of spread for 357 heading and no-wind fires burned in the wind tunnel facility of the USDA Forest Service's Fire Sciences Laboratory in Missoula, Montana for the purpose of developing models of wildland fire behavior. The fires were burned in horizontal fuel beds with differing characteristics due to various combinations of fuel type, particle size, packing ratio, bed depth, moisture content, and wind speed. In the present paper, fuel particle and fuel bed data for 260 heading fires from that study (plus as-yet unreported combustion efficiency and reaction time data) are used to develop models for predicting fuel bed reaction time and mass loss rate. Reaction time is computed from the flameout time of a single particle and fuel bed structural properties. It is assumed that the beds burn in a combustion regime controlled by the rate at which air mixes with volatiles produced during pyrolysis, and that not all air entering the fuel bed reaction zone participates in combustion. Comparison of reaction time and burning rate predictions with experimental values is encouraging in view of the simplified modeling approach and uncertainties associated with the experimental measurements.

scholarly journals Fatigue-life distributions for reaction time data

2018 ◽  
Vol 12 (3) ◽  
pp. 351-356 ◽  
Author(s):  
Mauricio Tejo ◽  
Sebastián Niklitschek-Soto ◽  
Fernando Marmolejo-Ramos
Keyword(s):  
Fatigue Life ◽  
Reaction Time ◽  
Reaction Time Data ◽  
Time Data ◽  
Life Distributions

An Accurate Measure of Reaction Time can Provide Objective Metrics of Concussion

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Mark Tommerdahl ◽  
Eric Francisco ◽  
Jameson Holden ◽  
Rachel Lensch ◽  
Anna Tommerdahl ◽  
...  
Keyword(s):  
Reaction Time ◽  
Computer Systems ◽  
Reaction Time Data ◽  
Assessment Tools ◽  
Computerized Testing ◽  
Alternative Methods ◽  
Time Variability ◽  
Time Data ◽  
Reaction Time Variability ◽  
Significant Difference

There have been numerous reports of neurological assessments of post-concussed athletes and many deploy some type of reaction time assessment. However, most of the assessment tools currently deployed rely on consumer-grade computer systems to collect this data. In a previous report, we demonstrated the inaccuracies that typical computer systems introduce to hardware and software to collect these metrics with robotics (Holden et al, 2020). In that same report, we described the accuracy of a tactile based reaction time test (administered with the Brain Gauge) as approximately 0.3 msec and discussed the shortcoming of other methods for collecting reaction time. The latency errors introduced with those alternative methods were reported as high as 400 msec and the system variabilities could be as high as 80 msec, and these values are several orders of magnitude above the control values previously reported for reaction time (200-220msec) and reaction time variability (10-20 msec). In this report, we examined the reaction time and reaction time variability from 396 concussed individuals and found that there were significant differences in the reaction time metrics obtained from concussed and non-concussed individuals for 14-21 days post-concussion. A survey of the literature did not reveal comparable sensitivity in reaction time testing in concussion studies using alternative methods. This finding was consistent with the prediction put forth by Holden and colleagues with robotics testing of the consumer grade computer systems that are commonly utilized by researchers conducting reaction time testing on concussed individuals. The significant difference in fidelity between the methods commonly used by concussion researchers is attributed to the differences in accuracy of the measures deployed and/or the increases in biological fidelity introduced by tactile based reaction times over visually administered reaction time tests. Additionally, while most of the commonly used computerized testing assessment tools require a pre-season baseline test to predict a neurological insult, the tactile based methods reported in this paper did not utilize any baselines for comparisons. The reaction time data reported was one test of a battery of tests administered to the population studied, and this is the first of a series of papers that will examine each of those tests independently.  

Assessment and Training of Visuomotor Reaction Time for Football Injury Prevention

2017 ◽  
Vol 26 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Gary B. Wilkerson ◽  
Kevin A. Simpson ◽  
Ryan A. Clark
Keyword(s):  
Reaction Time ◽  
Outcome Measures ◽  
Repeated Measures ◽  
Injury Risk ◽  
Football Players ◽  
Injury Incidence ◽  
Training Methods ◽  
Characteristic Analysis ◽  
Cut Point ◽  
And Training

Context:Neurocognitive reaction time has been associated with musculoskeletal injury risk, but visuomotor reaction time (VMRT) derived from tests that present greater challenges to visual stimulus detection and motor response execution may have a stronger association.Objective:To assess VMRT as a predictor of injury and the extent to which improvement may result from VMRT training.Design:Cohort study.Setting:University athletic performance center.Participants:76 National Collegiate Athletic Association Division-I FCS football players (19.5 ± 1.4 y, 1.85 ± 0.06 m, 102.98 ± 19.06 kg).Interventions:Preparticipation and postseason assessments. A subset of players who exhibited slowest VMRT in relation to the cohort’s postseason median value participated in a 6-wk training program.Main Outcome Measures:Injury occurrence was related to preparticipation VMRT, which was represented by both number of target hits in 60 s and average elapsed time between hits (ms). Receiver operating characteristic analysis identified the optimum cut point for a binary injury risk classification. A nonparametric repeated-measures analysis of ranks procedure was used to compare posttraining VMRT values for slow players who completed at least half of the training sessions (n = 15) with those for untrained fast players (n = 27).Results:A preparticipation cut point of ≤85 hits (≥705 ms) discriminated injured from noninjured players with odds ratio = 2.30 (90% confidence interval, 1.05–5.06). Slow players who completed the training exhibited significant improvement in visuomotor performance compared with baseline (standardized response mean = 2.53), whereas untrained players exhibited a small performance decrement (group × trial interaction effect, L2 = 28.74; P < .001).Conclusions:Slow VMRT appears to be an important and modifiable injury risk factor for college football players. More research is needed to refine visuomotor reaction-time screening and training methods and to determine the extent to which improved performance values can reduce injury incidence.

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