Interobserver and intraobserver reliabilities of determining the ventilatory thresholds in subjects with a lower limb amputation and able-bodied subjects during a peak exercise test on the combined arm-leg (Cruiser) ergometer

The first (VT1) and second ventilator (VT2) (anaerobic) thresholds are used to individually prescribe exercise training programs. The purpose of this research was to analyze inter- and intraobserver reliabilities of determining VT1 and VT2 in subjects with lower limb amputation (LLA) and able-bodied (AB) subjects during a peak exercise test on the arm-leg (Cruiser) ergometer. Previously published data of exercise tests on the Cruiser ergometer of subjects with LLA (n = 17) and AB subjects (n = 30) were analyzed twice by two observers. The VT1 and VT2 were determined based on ventilation plots. Differences in determining the VT1 and VT2 between the observers for the first and second analyses were analyzed. To quantify variation in measurement a variance component analysis was performed. Bland-Altmann plots were made, and limits of agreement were calculated. The number of observations in which thresholds could not be determined differed significantly between observers and analysis. Variation in VT1 between and within observers was small (0-1.6%) compared with the total variation, for both the subjects with an LLA and AB subjects. The reliability coefficient for VT1 was more than 0.75, and the limits of agreement were good. In conclusion, based on the results of this study on a population level, VT1 can be used to prescribe exercise training programs after an LLA. In the current study, the determination of VT2 was less reliable than VT1. More research is needed into the clinical application of VT1 and VT2 during a peak exercise test on the Cruiser ergometer

Gender differences in the electrocardiogram screening of athletes

Objectives: Gender-related differences are frequently used in medicine. Electrocardiograms are also subject to such differences. This study evaluated gender differences in ECG parameters of young athletes, discussing the possible implications of these differences for ECG criteria used in the cardiovascular screening of young athletes. Design: Observational cross-sectional study. Methods: In 2013 and 2014 all the ECGs from the cardiovascular screenings performed at University Sports Medical Centre in Groningen of the student athletes who wanted to participate in a college sports program were collected. The ECG characteristics were scored using computer-based measurements and the Seattle ECG criteria. Results: The study population included 1436 athletes, of which 72% were male. Male athletes were older (19.3 years vs. 18.6 years), participated in sports more frequently (4.0/week vs. 3.8/week) and spent more hours per week practising sports (6.4 h/week vs. 5.8 h/week) than female athletes. Male athletes had significantly higher PR intervals (149 ms vs. 141 ms), lead voltages and QRS duration (98 ms vs. 88 ms). Female athletes had significantly higher resting heart rates (69/min vs. 64/min) and QTc intervals (407 ms vs. 400 ms). Male athletes also had significantly higher amounts of sinus bradycardia (38.3% vs. 23.0%), incomplete RBBB (15.0% vs. 3.7%), early repolarisation (4.5% vs. 1.0%) and isolated QRS voltage criteria for LVH (26.3% vs. 4.6%). All P-values were Conclusions: ECGs of young athletes demonstrate gender-related differences. These differences could be considered in their cardiovascular screening. For the Seattle ECG criteria we advise additional research into the clinical implications of using gender-based cut-off values for the QRS duration in the intraventricular conduction delay criterion. (C) 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved

The electrocardiographic manifestations of athlete's heart and their association with exercise exposure

Objective: The aim of this study was to define the minimum amount of exercise per week (current exposure') and the total amount of exercise (lifetime exposure') needed to lead to the electrocardiographic changes fitting athlete's heart. Methods: All the pre-participation screenings (including electrocardiograms (ECGs)) from collegiate athletes performed at University Sports Medical Center in 2013 and 2014 were collected. Data on height, weight, sex, age, current sport(s) participation and lifetime sport(s) participation were collected. Current exposure was categorised into 0-3, 3-6, 6-10 and >10 hours/week. Lifetime sport exposure was divided into five categories: 0-1000, 1001-2000, 2001-3000, 3001-4000 and >4000 hours. Results: The study population consisted of 1229 athletes (current exposure) and 1104 athletes (lifetime exposure). Current sport exposure: There was a significant increase in training-related ECG changes in the category 3-6 vs. 10 hours/week).Lifetime sport exposure: There was an increase in training-related ECG changes that reached significance at an exposure >3000 hours. When looking at individual parameters, we found an association with a significant difference in sinus bradycardia (0-1000 vs. 2001-3000), QRS voltage (0-1000 vs. 3001-4000) and first-degree AV-block (0-1000 vs. >4000). Conclusion: A minimum of 3 hours/week of current exposure and a lifetime exposure of >3000 hours is needed to lead to the electrocardiographic changes fitting athlete's heart

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

BACKGROUND: Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant’s goal achievement. METHODS: We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy. RESULTS: From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69–0.73) and (0.71 95%CI 0.67–0.76), respectively, followed by cannabis (0.67 95%CI 0.59–0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success. DISCUSSION: Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

Background: Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant’s goal achievement. Methods: We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy. Results: From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69–0.73) and (0.71 95%CI 0.67–0.76), respectively, followed by cannabis (0.67 95%CI 0.59–0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success. Discussion: Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention