Trial of Training to Reduce Driver Inattention in Teens with ADHD.

BACKGROUND: Teens with attention deficit-hyperactivity disorder (ADHD) are at increased risk for motor vehicle collisions. A computerized skills-training program to reduce long glances away from the roadway, a contributor to collision risk, may ameliorate driving risks among teens with ADHD. METHODS: We evaluated a computerized skills-training program designed to reduce long glances (lasting ≥2 seconds) away from the roadway in drivers 16 to 19 years of age with ADHD. Participants were randomly assigned in a 1:1 ratio to undergo either enhanced Focused Concentration and Attention Learning, a program that targets reduction in the number of long glances (intervention) or enhanced conventional driver's education (control). The primary outcomes were the number of long glances away from the roadway and the standard deviation of lane position, a measure of lateral movements away from the center of the lane, during two 15-minute simulated drives at baseline and at 1 month and 6 months after training. Secondary outcomes were the rates of long glances and collisions or near-collisions involving abrupt changes in vehicle momentum (g-force event), as assessed with in-vehicle recordings over the 1-year period after training. RESULTS: During simulated driving after training, participants in the intervention group had a mean of 16.5 long glances per drive at 1 month and 15.7 long glances per drive at 6 months, as compared with 28.0 and 27.0 long glances, respectively, in the control group (incidence rate ratio at 1 month, 0.64; 95% confidence interval [CI], 0.52 to 0.76; P<0.001; incidence rate ratio at 6 months, 0.64; 95% CI, 0.52 to 0.76; P<0.001). The standard deviation of lane position (in feet) was 0.98 SD at 1 month and 0.98 SD at 6 months in the intervention group, as compared with 1.20 SD and 1.20 SD, respectively, in the control group (difference at 1 month, -0.21 SD; 95% CI, -0.29 to -0.13; difference at 6 months, -0.22 SD; 95% CI, -0.31 to -0.13; P<0.001 for interaction for both comparisons). During real-world driving over the year after training, the rate of long glances per g-force event was 18.3% in the intervention group and 23.9% in the control group (relative risk, 0.76; 95% CI, 0.61 to 0.92); the rate of collision or near-collision per g-force event was 3.4% and 5.6%, respectively (relative risk, 0.60, 95% CI, 0.41 to 0.89). CONCLUSIONS: In teens with ADHD, a specially designed computerized simulated-driving program with feedback to reduce long glances away from the roadway reduced the frequency of long glances and lessened variation in lane position as compared with a control program. During real-world driving in the year after training, the rate of collisions and near-collisions was lower in the intervention group. (Funded by the National Institutes of Health; ClinicalTrials.gov number, NCT02848092.).

Enhanced 2D Hand Pose Estimation for Gloved Medical Applications: A Preliminary Model.

(1) Background: As digital health technology evolves, the role of accurate medical-gloved hand tracking is becoming more important for the assessment and training of practitioners to reduce procedural errors in clinical settings. (2) Method: This study utilized computer vision for hand pose estimation to model skeletal hand movements during in situ aseptic drug compounding procedures. High-definition video cameras recorded hand movements while practitioners wore medical gloves of different colors. Hand poses were manually annotated, and machine learning models were developed and trained using the DeepLabCut interface via an 80/20 training/testing split. (3) Results: The developed model achieved an average root mean square error (RMSE) of 5.89 pixels across the training data set and 10.06 pixels across the test set. When excluding keypoints with a confidence value below 60%, the test set RMSE improved to 7.48 pixels, reflecting high accuracy in hand pose tracking. (4) Conclusions: The developed hand pose estimation model effectively tracks hand movements across both controlled and in situ drug compounding contexts, offering a first-of-its-kind medical glove hand tracking method. This model holds potential for enhancing clinical training and ensuring procedural safety, particularly in tasks requiring high precision such as drug compounding.

Examining Patterns and Predictors of ADHD Teens' Skill-Learning Trajectories During Enhanced FOrward Concentration and Attention Learning (FOCAL+) Training.

OBJECTIVE: Examine patterns and predictors of skill learning during multisession Enhanced FOrward Concentration and Attention Learning (FOCAL+) training. BACKGROUND: FOCAL+ teaches teens to reduce the duration of off-road glances using real-time error learning. In a randomized controlled trial, teens with ADHD received five sessions of FOCAL+ training and demonstrated significant reductions in extended glances (>2-s) away from the roadway (i.e., long-glances) and a 40% reduced risk of a crash/near-crash event. Teens' improvement in limiting long-glances as assessed after each FOCAL+ training session has not been examined. METHOD: Licensed teen (ages 16-19) drivers with ADHD (n = 152) were randomly assigned to five sessions of either FOCAL+ or modified standard driver training. Teens completed driving simulation assessments at baseline, after each training session, and 1 month and 6 months posttraining. Naturalistic driving was monitored for one year. RESULTS: FOCAL+ training produced a 53% maximal reduction in long-glances during postsession simulated driving. The number of sessions needed to achieve maximum performance varied across participants. However, after five FOCAL+ training sessions, number of long-glances was comparable irrespective of when teens achieved their maximum performance. The magnitude of reduction in long-glances predicted levels of long-glances during simulated driving at 1 month and 6 months posttraining but not naturalistic driving outcomes. FOCAL+ training provided the most benefit during training to teens who were younger and had less driving experience. CONCLUSION: FOCAL+ training significantly reduces long-glances beginning at the 1st training session. APPLICATION: Providing five FOCAL+ training sessions early on during teen driving may maximize benefit.

Predicting Basketball Shot Outcome From Visuomotor Control Data Using Explainable Machine Learning.

Quiet eye (QE), the visual fixation on a target before initiation of a critical action, is associated with improved performance. While QE is trainable, it is unclear whether QE can directly predict performance, which has implications for training interventions. This study predicted basketball shot outcome (make or miss) from visuomotor control variables using a decision tree classification approach. Twelve basketball athletes completed 200 shots from six on-court locations while wearing mobile eye-tracking glasses. Training and testing data sets were used for modeling eight predictors (shot location, arm extension time, and absolute and relative QE onset, offset, and duration) via standard and conditional inference decision trees and random forests. On average, the trees predicted over 66% of makes and over 50% of misses. The main predictor, relative QE duration, indicated success for durations over 18.4% (range: 14.5%-22.0%). Training to prolong QE duration beyond 18% may enhance shot success.

Barium titanate-enhanced hexagonal boron nitride inks for printable high-performance dielectrics.

Printed electronics have been attracting significant interest for their potential to enable flexible and wearable electronic applications. Together with printable semiconductors, solution-processed dielectric inks are key in enabling low-power and high-performance printed electronics. In the quest for suitable dielectrics inks, two-dimensional materials such as hexagonal boron nitride (h-BN) have emerged in the form of printable dielectrics. In this work, we report barium titanate (BaTiO3) nanoparticles as an effective additive for inkjet-printable h-BN inks. The resulting inkjet printed BaTiO3/h-BN thin films reach a dielectric constant (εr) of âˆ¼16 by adding 10% of BaTiO3nanoparticles (in their volume fraction to the exfoliated h-BN flakes) in water-based inks. This result enabled all-inkjet printed flexible capacitors withC âˆ¼ 10.39 nF cm-2, paving the way to future low power, printed and flexible electronics.

The Effect of Navigation Demand on Decision Making in a Dynamic, Sport-Inspired Virtual Environment.

Athletes commonly make decisions about the passability of closing gaps when navigating sport environments. This study examined whether increased temporal pressure to arrive at a desired location modifies these decisions. Thirty participants navigated toward a waypoint in a virtual, sport-inspired environment. To do so, they had to decide whether they could pass through closing gaps of virtual humans (and take the shortest route) or steer around them (and take a longer route). The decision boundary of participants who were time pressured to arrive at a waypoint was biased toward end gaps of smaller sizes and was less reliably defined, resulting in a higher number of collisions. Effects of temporal pressure were minimized with experience in the experimental task. Results indicate that temporal pressure affects perceptual-motor processes supporting information pickup and shapes the information-action coupling that drives compliance with navigation demands. Theoretical and practical implications are discussed.

From the field of play to the laboratory: Recreating the demands of competition with augmented reality simulated sport.

Biomechanical analysis has typically been confined to a laboratory setting. While attempts have been made to take laboratory testing into the field, this study was designed to assess whether augmented reality (AR) could be used to bring the field into the laboratory. This study aimed to measure knee load in volleyball players through a jump task incorporating AR while maintaining the perception-action couplings by replicating the visual features of a volleyball court. Twelve male volleyball athletes completed four tasks: drop landing, hop jump, spike jump, and spike jump while wearing AR smart glasses. Biomechanical variables included patellar tendon force, knee moment and kinematics of the ankle, knee, hip, pelvis and thorax. The drop landing showed differences in patellar tendon force and knee moment when compared to the other conditions. The hop jump did not present differences in kinetics when compared to the spike conditions, instead of displaying the greatest kinematic differences. As a measure of patellar tendon loading the AR condition showed a close approximation to the spike jump, with no differences present when comparing landing forces and mechanics. Thus, AR may be used in a clinical assessment to better replicate information from the competitive environment.

Visuomotor strategies for collision avoidance in athletes: Insights from virtual reality.

This study explores visuomotor control in athletes for collision avoidance using virtual reality. Thirty-nine athletes navigated dynamic scenarios, pursuing a virtual target while avoiding two to five virtual defenders. Confirmatory Factor Analysis validated a model that captured two features of scanning behavior based on head movements recorded during activity: the overall amount and temporal pattern. Linear mixed models showed that these features significantly differentiated successful from unsuccessful defender avoidance (p < .05), suggesting that efficient environmental scanning is crucial for collision avoidance while highlighting the potential of visuomotor interventions to reduce collision-related sport injuries.

Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking.

Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical component of the ground reaction force (vGRF) in the first 50% of stance is highly associated with biological and patient-reported outcomes linked to knee osteoarthritis symptoms. Monitoring and maintaining typical vGRF profiles may support healthy gait biomechanics and joint tissue loading to prevent the onset and progression of knee osteoarthritis. Yet, the optimal number of sensors and sensor placements for predicting accurate vGRF from accelerometry remains unknown. Our goals were to: 1) determine how many sensors and what sensor locations yielded the most accurate vGRF loading peak estimates during walking; and 2) characterize how prescribing different loading conditions affected vGRF loading peak estimates. We asked 20 young adult participants to wear 5 accelerometers on their waist, shanks, and feet and walk on a force-instrumented treadmill during control and targeted biofeedback conditions prompting 5% underloading and overloading vGRFs. We trained and tested machine learning models to estimate vGRF from the various sensor accelerometer inputs and identified which combinations were most accurate. We found that a neural network using one accelerometer at the waist yielded the most accurate loading peak vGRF estimates during walking, with average errors of 4.4% body weight. The waist-only configuration was able to distinguish between control and overloading conditions prescribed using biofeedback, matching measured vGRF outcomes. Including foot or shank acceleration signals in the model reduced accuracy, particularly for the overloading condition. Our results suggest that a system designed to monitor changes in walking vGRF or to deploy targeted biofeedback may only need a single accelerometer located at the waist for healthy participants.

The Intelligent Phenotypic Plasticity Platform (IP3) for Precision Medicine-Based Injury Prevention in Sport.

The best predictor of future injury is previous injury and this has not changed in a quarter century despite the introduction of evidence-based medicine and associated revisions to post-injury treatment and care. Nearly nine million sports-related injuries occur annually, and the majority of these require medical intervention prior to clearance for the athlete to return to play (RTP). Regardless of formal care, these athletes remain two to four times more likely to suffer a second injury for several years after RTP. In the case of children and young adults, this sets them up for a lifetime of negative health outcomes. Thus, the initial injury is the tipping point for a post-injury cascade of negative sequelae exposing athletes to more physical and psychological pain, higher medical costs, and greater risk of severe long-term negative health throughout their life. This chapter details the technologies and method that make up the automated Intelligent Phenotypic Plasticity Platform (IP3)-a revolutionary new approach to the current standard of post-injury care that identifies and targets deficits that underly second injury risk in sport. IP3 capitalizes on the biological concept of phenotypic plasticity (PP) to quantify an athlete's functional adaptability across different performance environments, and it is implemented in two distinct steps: (1) phenomic profiling and (2) precision treatment. Phenomic profiling indexes the fitness and subsequent phenotypic plasticity of an individual athlete, which drives the personalization of the precision treatment step. IP3 leverages mixed-reality technologies to present true-to-life environments that test the athlete's ability to adapt to dynamic stressors. The athlete's phenotypic plasticity profile is then used to drive a precision treatment that systematically stresses the athlete, via a combination of behavioral-based and genetic fuzzy system models, to optimally enhance the athlete's functional adaptability. IP3 is computationally light-weight and, through the integration with mixed-reality technologies, promotes real-time prediction, responsiveness, and adaptation. It is also the first ever phenotypic plasticity-based precision medicine platform, and the first precision sports medicine platform of any kind.

Developing a Profile of Procedural Expertise: A Simulation Study of Tracheal Intubation Using 3-Dimensional Motion Capture.

BACKGROUND: Improving the assessment and training of tracheal intubation is hindered by the lack of a sufficiently validated profile of expertise. Although several studies have examined biomechanics of tracheal intubation, there are significant gaps in the literature. We used 3-dimensional motion capture to study pediatric providers performing simulated tracheal intubation to identify candidate kinematic variables for inclusion in an expert movement profile. METHODS: Pediatric anesthesiologists (experienced) and pediatric residents (novices) were recruited from a pediatric institution to perform tracheal intubation on airway mannequins in a motion capture laboratory. Subjects performed 21 trials of tracheal intubation, 3 each of 7 combinations of laryngoscopic visualization (direct or indirect), blade type (straight or curved), and mannequin size (adult or pediatric). We used repeated measures analysis of variance to determine whether various kinematic variables (3-trial average for each participant) were associated with experience. RESULTS: Eleven experienced and 15 novice providers performed 567 successful tracheal intubation attempts (9 attempts unsuccessful). For laryngoscopy, experienced providers exhibited shorter path length (total distance traveled by laryngoscope handle; 77.6 ± 26.0 cm versus 113.9 ± 53.7 cm; P = 0.013) and greater angular variability at the left wrist (7.4 degrees versus 5.5 degrees, P = 0.013) and the left elbow (10.1 degrees versus 7.6 degrees, P = 0.03). For intubation, experienced providers exhibited shorter path length of the right hand (mean = 61.1 cm versus 99.9 cm, P < 0.001), lower maximum acceleration of the right hand (0.19 versus 0.14 m/s, P = 0.033), and smaller angular, variability at the right elbow (9.7 degrees versus 7.9 degrees, P = 0.03). CONCLUSIONS: Our study and the available literature suggest specific kinematic variables for inclusion in an expert profile for tracheal intubation. Future studies should include a larger sample of practitioners, actual patients, and measures of the cognitive and affective components of expertise.

Real-time biofeedback integrated into neuromuscular training reduces high-risk knee biomechanics and increases functional brain connectivity: A preliminary longitudinal investigation.

Prospective evidence indicates that functional biomechanics and brain connectivity may predispose an athlete to an anterior cruciate ligament injury, revealing novel neural linkages for targeted neuromuscular training interventions. The purpose of this study was to determine the efficacy of a real-time biofeedback system for altering knee biomechanics and brain functional connectivity. Seventeen healthy, young, physically active female athletes completed 6 weeks of augmented neuromuscular training (aNMT) utilizing real-time, interactive visual biofeedback and 13 served as untrained controls. A drop vertical jump and resting state functional magnetic resonance imaging were separately completed at pre- and posttest time points to assess sensorimotor adaptation. The aNMT group had a significant reduction in peak knee abduction moment (pKAM) compared to controls (p = .03, d = 0.71). The aNMT group also exhibited a significant increase in functional connectivity between the right supplementary motor area and the left thalamus (p = .0473 after false discovery rate correction). Greater percent change in pKAM was also related to increased connectivity between the right cerebellum and right thalamus for the aNMT group (p = .0292 after false discovery rate correction, r2  = .62). No significant changes were observed for the controls (ps > .05). Our data provide preliminary evidence of potential neural mechanisms for aNMT-induced motor adaptations that reduce injury risk. Future research is warranted to understand the role of neuromuscular training alone and how each component of aNMT influences biomechanics and functional connectivity.

Fractal gait patterns are retained after entrainment to a fractal stimulus.

Previous work has shown that fractal patterns in gait can be altered by entraining to a fractal stimulus. However, little is understood about how long those patterns are retained or which factors may influence stronger entrainment or retention. In experiment one, participants walked on a treadmill for 45 continuous minutes, which was separated into three phases. The first 15 minutes (pre-synchronization phase) consisted of walking without a fractal stimulus, the second 15 minutes consisted of walking while entraining to a fractal visual stimulus (synchronization phase), and the last 15 minutes (post-synchronization phase) consisted of walking without the stimulus to determine if the patterns adopted from the stimulus were retained. Fractal gait patterns were strengthened during the synchronization phase and were retained in the post-synchronization phase. In experiment two, similar methods were used to compare a continuous fractal stimulus to a discrete fractal stimulus to determine which stimulus type led to more persistent fractal gait patterns in the synchronization and post-synchronization (i.e., retention) phases. Both stimulus types led to equally persistent patterns in the synchronization phase, but only the discrete fractal stimulus led to retention of the patterns. The results add to the growing body of literature showing that fractal gait patterns can be manipulated in a predictable manner. Further, our results add to the literature by showing that the newly adopted gait patterns are retained for up to 15 minutes after entrainment and showed that a discrete visual stimulus is a better method to influence retention.