Evaluation of Reaction Time on Karate Athletes UKM Syiah Kuala University

The purpose of the study. Reaction time is a determining factor in sports, especially in martial arts. The purpose of this study is to evaluate the reaction time of UKM Karate athletes at Syiah Kuala University. Materials and methods. The approach in this study uses a quantitative approach. The sampling technique used purposive sampling, which means this technique is used to achieve certain goals. The samples in this study were athletes from UKM karate at Syiah Kuala University. The data collection technique was carried out by using the whole body reaction test to measure the reaction time of the athlete's arms and legs. Results. results of the research in the form of a whole body reaction test, the results using the formula for the average value and the total percentage can be concluded: (1) The arm reaction time test with the total average value for male athletes is 0.169 seconds and female athletes are 0.173, and the highest percentage is in the very good category, amounting to 87% for male and female athletes, 75% in the very good category and (2) the limb reaction time test has an average value of 0.269 seconds for male athletes and female athletes of 0.361 seconds, with the highest percentage value amounted to 100% in the good category for male athletes and for female athletes it was in the sufficient category with a percentage of 50%. Conclusions. the analysis show that most athletes have been able to maintain and increase reaction time well, to be able to improve and maintain it, regular and continuous training is needed

Feasibility of Cognitive-Motor Exergames in Geriatric Inpatient Rehabilitation: A Pilot Randomized Controlled Study

Objective: The aim of this pilot randomized clinical trial was to test the feasibility and efficacy of an exergame-based cognitive-motor training program in geriatric inpatients.Methods: The study participants were randomly allocated to either the exergame intervention group or the control group. The control group received the standard rehabilitation treatment offered in the clinic. In addition to the standard rehabilitation program, the intervention group conducted supervised exergame training on 5 days per week using the Dividat Senso, an exergame system specifically designed for older adults. The primary outcome was feasibility, as measured by e.g., adherence rate, attrition rate, occurrence of adverse events, System Usability Scale (SUS) and NASA-TLX score. Secondary outcomes included measures of physical and cognitive functioning such as comfortable walking speed, maximal walking speed, dual task walking speed, Short Physical Performance Battery (SPPB), Timed Up and Go test (TUG), Color-Word Interference test (D-KEFS), Trail Making test A and B (TMT), Go/No-Go test and Step Reaction Time test (SRTT). All secondary outcome measures were assessed pre- and post-intervention.Results: Thirty-nine persons were included in the study. Average adherence rate was 99%, there were no intervention-related dropouts and no adverse events. The mean System Usability Scale (SUS) score was 83.6 and the mean NASA-TLX score 45.5. Significant time-group interaction effects were found for the dual task walking speed, the Go/No-Go test and Step Reaction Time test (SRTT).Conclusion: Exergaming is a feasible, safe and effective cognitive-motor training approach in inpatient rehabilitation of geriatric patients. Incorporating exergaming in the rehabilitation program of geriatric patients offers potential to reduce fall risk factors and to increase patients’ exercise motivation and rehabilitation success.

The Physical Activity Level and Reaction Time During Covid 19 Pandemic

The outbreak of the COVID-19 pandemic has a huge global impact in terms of public health, economic activities, employment, psychological and social life. The educational system had to adapt to the conditions imposed by avoiding the spread of coronavirus among pupils and students. The purpose of this study is to find out if the level of physical activity influence the reaction time using dominant and non-dominant hand. We collected data from 83 students (age ranged between 15–24 years; M = 49, f = 34) who participated to the study voluntarily. To measure physical activity index we have used three items about type of physical activity performed, frequency and duration. In addition, to measure reaction time we have used Reaction Time Test and Aim Trainer Test. Each student completed and assessed himself the questionnaire and the tests online. Data were recorded and analysed in SPSS (version 20.0). The level of physical activity relieved that 18,1% of subjects are very active, 26,5% are active, 20,5% have an acceptable level of physical activity, 12% are insufficient active, and 22,9% are sedentary. The values of reaction time for both tests are fastest for subjects with insufficient level of physical activity for dominant hand. The sedentary person are fasted reaction time with non-dominant hand in Reaction Time Test, and the very active subjects are fasted reaction time in Aim Trainer Test. The results has limitations, conducted by speed of Internet connection, and type of devise used in performing tests.

Comparison of gait speed reserve, usual gait speed, and maximum gait speed of adults aged 50+ in Ireland using explainable machine learning

Gait speed is a measure of general fitness. Changing from usual (UGS) to maximum (MGS) gait speed requires a general effort across many body systems. The difference, MGS − UGS, is defined as gait speed reserve (GSR). In the present study, using 3925 participants aged 50+ from Wave 3 of The Irish Longitudinal Study on Ageing (TILDA), we used a gradient boosted trees-based stepwise feature selection pipeline for the discovery of clinically relevant predictors of GSR, UGS, and MGS using a shortlist of 88 features across 5 categories (socio-demographics/anthropometrics/medical history; cardiovascular system; physical strength; sensory; and cognitive/psychological). The TreeSHAP explainable machine learning package was used to analyse the input-output relationships of the three models. The mean R2adj (SD) from 5-fold cross validation on training data and the R2adj score on test data for the models are: 0.38 (0.04) and 0.41 for UGS; 0.45 (0.04) and 0.46 for MGS; and 0.19 (0.02) and 0.21 for GSR. Features selected for the UGS model were: age, chair stands time, body mass index, grip strength, number of medications, resting state pulse interval, mean motor reaction time in the choice reaction time test, height, depression score, sit-to-stand difference in diastolic blood pressure, and left visual acuity. The features selected for the MGS model were: age, grip strength, repeated chair stands time, body mass index, education, mean motor reaction time in the choice reaction time test, number of medications, height, the standard deviation of the mean reaction time in the sustained attention to response task, mean heart rate at resting state, fear of falling, MOCA errors, orthostatic intolerance during active stand, smoking status, total heart beat power during paced breathing, the root mean square of successive differences between heartbeats during paced breathing, and visual acuity. Finally, the features chosen for the GSR model were: mean motor reaction time in the choice reaction time test, grip strength, education, chair stands time, MOCA errors, accuracy proportion in the sound induced flash illusion (two beeps and one flash with stimulus-onset asynchrony of +150 ms), fear of falling, height, age, sex, orthostatic intolerance, MMSE errors, and number of cardiovascular conditions. MGS and UGS were more explainable than GSR. All three models contain features from all five categories. There were common features to all three models (age, grip strength, chair stands time, mean motor reaction time in the choice reaction time test, and height), but also some features unique to each of them. Overall, findings on all three models were clinically plausible and support a network physiology approach to the understanding of predictors of performance-based tasks. By employing an explainable machine learning technique, our observations may help clinicians gain new insights into the multisystem predictors of gait speed and gait speed reserve in older adults.

Cognitive and Motor Cortical Activity During Cognitively Demanding Stepping Tasks in Older People at Low and High Risk of Falling

Background: Choice stepping reaction time tasks are underpinned by neuropsychological, sensorimotor, and balance systems and therefore offer good indices of fall risk and physical and cognitive frailty. However, little is known of the neural mechanisms for impaired stepping and associated fall risk in older people. We investigated cognitive and motor cortical activity during cognitively demanding stepping reaction time tasks using functional near-infrared spectroscopy (fNIRS) in older people at low and high fall risk.Methods: Ninety-five older adults [mean (SD) 71.4 (4.9) years, 23 men] were categorized as low or high fall risk [based on 12-month fall history (≥2 falls) and/or Physiological Profile Assessment fall risk score ≥1]. Participants performed a choice stepping reaction time test and a more cognitively demanding Stroop stepping task on a computerized step mat. Cortical activity in cognitive [dorsolateral prefrontal cortex (DLPFC)] and motor (supplementary motor area and premotor cortex) regions was recorded using fNIRS. Stepping performance and cortical activity were contrasted between the groups and between the choice and Stroop stepping conditions.Results: Compared with the low fall risk group (n = 71), the high fall risk group (n = 24) exhibited significantly greater DLPFC activity and increased intra-individual variability in stepping response time during the Stroop stepping task. The high fall risk group DLPFC activity was greater during the performance of Stroop stepping task in comparison with choice stepping reaction time. Regardless of group, the Stroop stepping task elicited increased cortical activity in the supplementary motor area and premotor cortex together with increased mean and intra-individual variability of stepping response times.Conclusions: Older people at high fall risk exhibited increased DLPFC activity and stepping response time variability when completing a cognitively demanding stepping test compared with those at low fall risk and to a simpler choice-stepping reaction time test. This increased hemodynamic response might comprise a compensatory process for postural control deficits and/or reflect a degree of DLPFC neural inefficiency in people with increased fall risk.

The Effect of Preference of Music on Reaction Time

A series of 4 collective, exploratory case studies were conducted in order to determine the effect of different genres on reaction time. The Brain Gauge System was used to measure raw reaction time via a tactile reaction time test, testing three different conditions: no music, preferred genre, classical music. The subjects listened to 10 minute increments of music on noise-cancelling headphones and took the tactile reaction time test once before listening and twice (2 minute and 8 minute mark) while the music was playing. Results indicated that there was a general trend of increased reaction time (i.e., decreased performance) with music playing in the background. An ANOVA test was performed, with a resulting p-value of 0.411. While statistical analysis proved the results to be insignificant, the trends found in the case studies indicate that listening to music worsens your reaction time. Furthermore, preferred types of music do not have a significant effect on reaction time. Consistent with literature, this indicates that music in general is a form of distraction, regardless of preference and genre. Further in-depth studies need to be conducted with a larger sample size in order to expand upon these preliminary findings.

Pre-pandemic cognitive function and COVID-19 mortality: prospective cohort study

Poorer performance on standard tests of pre-morbid cognitive function is related to an elevated risk of death from lower respiratory tract infections but the link with coronavirus (COVID‑19) mortality is untested. Participants in UK Biobank, aged 40 to 69 years at study induction (2006–10), were administered a reaction time test, an indicator of information processing speed, and also had their verbal-numeric reasoning assessed. Between April 1st and September 23rd 2020 there were 388 registry-confirmed deaths (138 women) ascribed to COVID-19 in 494,932 individuals (269,602 women) with a reaction time test result, and 125 such deaths (38 women) in the subgroup of 180,198 people (97,794 women) with data on verbal-numeric reasoning. In analyses adjusted for age, sex, and ethnicity, a one standard deviation slower reaction time was related to a higher rate of death from COVID-19 (hazard ratio; 95% confidence interval: 1.18; 1.09, 1.28), as was a one standard deviation disadvantage on the verbal-numeric reasoning test (1.32; 1.09, 1.59). While there was some attenuation in these relationships after adjustment for additional covariates which included socio-economic status and lifestyle factors, the two pre-pandemic indicators of cognitive function continued to be related to COVID-19 mortality.

PERBANDINGAN MENGONSUMSI BERBAGAI JENIS MINUMAN BERKAFEIN (KOPI, TEH HIJAU, DAN MINUMAN BERENERGI) TERHADAP WAKTU REAKSI

Generally, the aim of this research to know the comparison of caffeine drinks through the result of reaction time after consumption coffee, green tea, and energy drink by the same amount of caffeine. The way to gather the data is using purposive sampling, the population that chosen by the researcher is 10 respondents’ college students of Ilmu Keolahragaan batch 2016. For data analysis, using SPSS 21 program for windows and Statistical calculation that used is One-Way ANOVA continues with Post Hoc Tukey. The average result from time reaction on Speed Anticipation Reaction is 1.263 (placebo), 1,257 (green tea), 1.271 (energy drink), and 1.358 (black coffee). For Body Reaction Time Test (visual) shows the average around 1.239 (placebo), 0.245 (green tea), 0.257 (green tea), and 0.244 (black coffee). While on Whole Body Reaction Time Test (auditory) shows the average0.239 (placebo), 0.239 (green tea), 0.274 (energy drink), and 0.263 (black coffee). The signification value from three-time reaction test are (Speed Anticipation Reaction Sig. 0.706 > 0.05, Whole Body Reaction (visual) Sig. 0.877 > 0.05, and Whole Body Reaction (auditory) Sig. 0.348 > 0.05) all of the result above 0.05. Hence, it can be concluded there is no significant difference through time reaction result after consumption kind of caffeine drinks with the same concentration.

Auditory, Visual and Postural Reaction Time Among Middle Aged Type 2 Diabetics and Healthy Individuals – A Cross-Sectional Study

Objective: Study aims to compare Auditory, Visual and Postural RT among middle aged type 2 diabetics and healthy individuals. Materials and Methods: A Cross-Sectional Study which included 200 Participants that met inclusion and exclusion criteria, were divided into 2 groups. Diabetic group included 100 participants who were clinically diagnosed with type 2 Diabetes and healthy group insldued 100 healthy participants. Both the groups performed Auditory Reaction Time test (ART) and Visual reaction time test (VRT) using Inquisit 4.0 computer software, Ruler drop test (RDT), Wobble Board (WB) Test and Timed up and go test (TUGT) was performed. Results: Statistical analysis (independent sample T-test) revealed a significant delay in VRT (P-value= 0.001), ART (P-value= 0.001), Wobble Board Test (P-value= 0.001) and TUGT (P value= 0.001) among diabetic group compared to healthy group. There is no significant difference in Ruler drop test (P-value= 0.919) among both the groups. Conclusion: There was is a significant delay in RT among middle aged type 2 diabetic participants when compared to healthy participants. This is associated with reduced sensory stimuli from various systems and resulting in late response in diabetic group.