Preliminary Evidence for the Clinical Utility of Tactile Somatosensory Assessments of Sport-Related mTBI

Objectives: To evaluate the clinical utility of tactile somatosensory assessments to assist clinicians in diagnosing sport-related mild traumatic brain injury (SR-mTBI), classifying recovery trajectory based on performance at initial clinical assessment, and determining if neurophysiological recovery coincided with clinical recovery.Research Design: Prospective cohort study with normative controls. Methods: At admission (n = 79) and discharge (n = 45/79), SR-mTBI patients completed the SCAT-5 symptom scale, along with the following three components from the Cortical Metrics Brain Gauge somatosensory assessment (BG-SA): temporal order judgement (TOJ), TOJ with confounding condition (TOJc), and duration discrimination (DUR). To assist SR-mTBI diagnosis on admission, BG-SA performance was used in logistic regression to discriminate cases belonging to the SR-mTBI sample or a healthy reference sample (pooled BG-SA data for healthy participants in previous studies). Decision trees evaluated how accurately BG-SA performance classified SR-mTBI recovery trajectories. Results: BG-SA TOJ, TOJc, and DUR poorly discriminated between cases belonging to the SR-mTBI sample or a healthy reference sample (0.54–0.70 AUC, 47.46–64.71 PPV, 48.48–61.11 NPV). The BG-SA evaluated did not accurately classify SR-mTBI recovery trajectories (> 14-day resolution 48%, ≤14–day resolution 54%, lost to referral/follow-up 45%). Mann-Whitney U tests revealed differences in BG-SA TOJc performance between SR-mTBI participants and the healthy reference sample at initial clinical assessment and at clinical recovery ( 14-day resolution 48%, ≤14–day resolution 54%, lost to referral/follow-up 45%). Mann-Whitney U tests revealed differences in BG-SA TOJc performance between SR-mTBI participants and the healthy reference sample at initial clinical assessment and at clinical recovery (p Conclusions: BG-SA TOJ, TOJc, and DUR appear to have limited clinical utility to assist clinicians with diagnosing SR-mTBI or predicting recovery trajectories under ecologically valid conditions. Neurophysiological abnormalities persisted beyond clinical recovery given abnormal BG-SA TOJc performance observed when SR-mTBI patients achieved clinical recovery

Is it really the result of a concussion? Lessons from a case study

Abstract Background Within the last two decades, attitudes have shifted from considering sports-related concussion as an insignificant minor injury with no long-term repercussions to a potentially serious brain injury garnering attention from media, clinicians, researchers, and the general public. Objectives To conduct a case study to determine the underlying cause of persistent issues suspected to be associated with a history of sports-related concussion. Protocol Participant A underwent neurophysiological testing following the Neary protocol (assessment of cerebrovascular and cardiovascular variables), comprehensive concussion assessment at a dedicated sports concussion clinic (history, neurological assessment, cervical spine screening, vestibulo-ocular screening, SCAT-5, and exercise testing), referral to a neurologist, structural MRI scan, and referral for specialised assessment at a dedicated dizziness and balance centre. Results Despite a history of multiple sports-related concussions, Participant A’s persistent symptom reports were associated with peripheral vestibular dysfunction and otolithic dysfunction seemingly unrelated to his concussion history. Discussion Lessons from Participant A’s case study showed that on-going symptoms that patients may associate with the effects of concussions may instead be due to unrelated causes that share similar symptomology. Conclusion This research exemplifies the importance of a multi-disciplinary assessment using a repeated testing protocol

A Novel Method to Assist Clinical Management of Mild Traumatic Brain Injury by Classifying Patient Subgroups Using Wearable Sensors and Exertion Testing: A Pilot Study

Although injury mechanisms of mild traumatic brain injury (mTBI) may be similar across patients, it is becoming increasingly clear that patients cannot be treated as one homogenous group. Several predominant symptom clusters (PSC) have been identified, each requiring specific and individualised treatment plans. However, objective methods to support these clinical decisions are lacking. This pilot study explored whether wearable sensor data collected during the Buffalo Concussion Treadmill Test (BCTT) combined with a deep learning approach could accurately classify mTBI patients with physiological PSC versus vestibulo-ocular PSC. A cross-sectional design evaluated a convolutional neural network model trained with electrocardiography (ECG) and accelerometry data. With a leave-one-out approach, this model classified 11 of 12 (92%) patients with physiological PSC and 3 of 5 (60%) patients with vestibulo-ocular PSC. The same classification accuracy was observed in a model only using accelerometry data. Our pilot results suggest that adding wearable sensors during clinical tests like the BCTT, combined with deep learning models, may have the utility to assist management decisions for mTBI patients in the future. We reiterate that more validation is needed to replicate the current results

Effect of biological movement variability on the performance of the golf swing in high and low handicapped players

The purpose of this study was to examine the role of neuromotor noise on golf swing performance in high- and low-handicap players. Selected two-dimensional kinematic measures of 20 male golfers (n = 10 per high- or low-handicap group) performing 10 golf swings with a 5-iron club was obtained through video analysis. Neuromotor noise was calculated by deducting the standard error of the measurement from the coefficient of variation obtained from intra-individual analysis. Statistical methods included linear regression analysis and one-way analysis of variance using SPSS. Absolute invariance in the key technical positions (e.g., at the top of the backswing) of the golf swing appears to be a more favorable technique for skilled performance

Are anthropometric, flexibility, muscular strength, and endurance variables related to clubhead velocity in low and high handicap golfers?

The present study assessed the anthropometric profile (International Society for the Advancement of Kinanthropometry protocol), flexibility, muscular strength, and endurance of 20 male golfers. These data were collected in order to determine: a) the relationship between these kinanthropometric measures and clubhead velocity; and b) if these measures could distinguish low-handicap (LHG) and high-handicap (HHG) golfers. Ten LHG (handicap of 0.3 ± 0.5) and 10 HHG (handicap of 20.3 ± 2.4) performed 10 swings for maximum velocity and accuracy with their own 5-iron golf club at a wall-mounted target. LHG hit the target significantly more (115%) and had a 12% faster clubhead velocity than HHG (p < 0.01). The LHG also had significantly (28%) greater golf swing-specific cable woodchop (GSCWC) strength (p < 0.01) and tendencies for greater (30%) bench press strength and longer (5%) upper am and total arm (4%) length and less (24%) right hip internal rotation than HHG (0.01 < p < 0.05). GSCWC strength was significantly correlated to clubhead velocity (p < 0.01), with bench press and hack squat strength as well as upper arm and total arm length also approaching significance (0.01 < p < 0.05). Golfers with high GSCWC strength and perhaps greater bench press strength and longer arms may therefore be at a competitive advantage, as these characteristics allow the production of greater clubhead velocity and resulting ball displacement. Such results have implications for golf talent identification programs and for the prescription and monitoring of golf conditioning programs. While golf conditioning programs may have many aims, specific trunk rotation exercises need to be included if increased clubhead velocity is the goal. Muscular hypertrophy development may not need to be emphasized as it could reduce golf performance by limiting range of motion and/or increasing moment of inertia

The Stroke RiskometerTM App: Validation of a data collection tool and stroke risk predictor

Background: The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the 'mass' approach), the 'high risk' approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke RiskometerTM, has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. Methods: 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke RiskometerTM) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R2 statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. Results: The Stroke RiskometerTM performed well against the FSRS five-year AUROC for both males (FSRS=75·0% (95% CI 72·3%-77·6%), Stroke RiskometerTM=74·0(95% CI 71·3%-76·7%) and females [FSRS=70·3% (95% CI 67·9%-72·8%, Stroke RiskometerTM=71·5% (95% CI 69·0%-73·9%)], and better than QStroke [males - 59·7% (95% CI 57·3%-62·0%) and comparable to females=71·1% (95% CI 69·0%-73·1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0·51-0·56, D-statistic ranging from 0·01-0·12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P<0·006). Conclusions: The Stroke RiskometerTM is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke RiskometerTM will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors. International Journal of Strok