57 research outputs found

    Smartphone Application to Quantify Sport‐Related Injury Risk of Individual Athletes

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    Context: Following sport‐related concussion (SRC), risk for musculoskeletal injury may be approximately 2 times greater, and risk for another SRC may be 3 to 5 times greater. Preparticipation screening methods are needed that can accurately identify athletes who possess elevated injury risk. Methods: Occurrences of SRC and core or lower extremity injury (CLEI) were documented throughout a high school football season for a cohort of 92 players who performed a pre‐participation perceptual‐motor test and provided survey responses. A smartphone flanker test app presented displays of incongruent (\u3c\u3c\u3e\u3c\u3c or \u3e\u3e\u3c\u3e\u3e) or congruent (\u3c\u3c\u3c\u3c\u3c or \u3e\u3e\u3e\u3e\u3e) arrows arrows that required determination of a right versus left manual tilt of the device to register a response. Reaction time and response accuracy measures were used to derive conflict effect (CE), inverse efficiency index (IEI), and inverse efficiency ratio (IER) metrics. Any post‐concussion symptoms (Sx) were quantified by the Overall Wellness Index (OWI) and any persisting effects of previous musculoskeletal injuries were quantified by the Sport Fitness Index (SFI). Results: History of SRC (HxSRC) was reported by 15% of the players. Factors that provided strongest discrimination of HxSRC cases from players who denied such history (NoSRC) were OWI Sx ≥4 and CE ≥52, and, with OR=13.9 for both versus 0 or 1 positive. Prospective predictors of CLEI among players with HxSRC included SFI score ≤92 (OR=33.8), IER ≥2.0 (OR=17), and OWI Sx ≥3 (OR=15). Prospective predictors of SRC for the full cohort included OWI Sx ≥7 (OR=36.4), SFI score ≤78 (OR=16.8), OWI score ≤76 (OR=12.8), HxSRC (OR=10), and IER ≥1.7 (OR=3.8). Conclusion: Our results suggest the combination of OWI and SFI survey responses with perceptual‐motor performance metrics derived from the smartphone flanker test provides an effective means for pre‐participation identification of high school football players who possess elevated risk for subsequent SRC or CLEI occurrence

    Sports Injury Prevention Screen (SIPS): Design and Architecture of an Internet of Things (IoT) Based Analytics Health App

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    At present, technology-based injury risk screening methods are typically utilized by large and well-funded athletic programs at both the professional and collegiate levels. Such screening is not available to athletes who participate in most scholastic and amateur programs, due to the high cost of testing equipment and the need for oversight by medical professionals who possess the appropriate level of expertise. However, a mobile health app for injury risk screening can eliminate these obstacles, thereby facilitating the availability of systematic injury prevention initiatives to a much larger population of athletes. This study describes the design and architecture of a mobile health app for Sports Injury Prevention Screening (SIPS). SIPS enables athletic programs with limited funding and personnel the ability to conduct individualized injury risk assessments and deploy personalized injury prevention plans that are currently available only at collegiate and professional levels. Even for well funded athletic programs, typical injury screening methods are restricted to assessing either an athlete’s musculoskeletal coordination or neurocognitive abilities; assessing both simultaneously is only possible in the laboratory environment. SIPS introduces a novel, dual-task assessment by using two devices simultaneously to measure an athlete’s neuro-mechanical responsiveness without the requirements of a laboratory and expert-level domain knowledge. Single device tests, designed specifically to replicate established injury screening techniques using just a mobile phone, are also available in SIPS. Data is collected for all of these tests from devices’ motion sensors and is synchronized using Bluetooth® technology. Ongoing work is integrating various predictive analytics algorithms for providing real time feedback to the athlete, medical director and coaches

    Daily Heart Rate Variability before and after Concussion in an American College Football Player

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    This case report demonstrates the effects of sport-related concussion (SRC) on heart rate variability (HRV) in an American college football player. Daily measures of resting, ultra-short natural logarithm of the root mean square of successive differences (LnRMSSD), subjective wellbeing, and Player Load were obtained each training day throughout a 4-week spring camp and 4 weeks of preseason training. SRC occurred within the first 2 weeks of the preseason. During spring camp and preseason pre-SRC, the athlete demonstrated minimal day-to-day fluctuations in LnRMSSD, which increased post-SRC (LnRMSSD coefficient of variation pre-SRC ≤ 3.1%, post-SRC = 5.8%). Moderate decrements in daily-averaged LnRMSSD were observed post-SRC relative to pre-SRC (Effect Size ± 90% Confidence Interval = −1.12 ± 0.80), and the 7-day rolling average fell below the smallest worthwhile change for the remainder of the preseason. LnRMSSD responses to SRC appeared similar to trends associated with stress and training fatigue. Therefore, performance and sports medicine staff should maintain regular communication regarding player injury and fatigue status so that HRV can be interpreted in the appropriate context. Detection and monitoring of autonomic dysregulation post-SRC may require near-daily assessment, as LnRMSSD showed greater daily fluctuations rather than chronic suppression following the head injury

    Prediction modeling for Board of Certification exam success for a professional master’s athletic training program

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    Introduction: The Commission on Accreditation of Athletic Training Education mandates accredited athletic training programs have a minimum, three-year aggregate, first-attempt pass rate on the Board of Certification (BOC) examination of 70%. No studies have examined first-attempt BOC exam success for students enrolled in a professional master’s athletic training program (PMATP). Purpose: The purpose of this study was to identify factors associated with first-attempt success on the BOC examination for PMATP students. Methods: This cohort designed study used common application data from subjects’ university and PMATP applications to create prediction models to identify those factors that predict first-attempt success on the BOC exam. Results: A four-factor model was produced to predict first-attempt BOC exam success. Both models demonstrated a student with two, three or more predictors had an odds ratio of 16.0 or greater, a relative frequency of success of 1.45 or greater, and correctly predicted first-attempt success on the BOC exam over 92% of the time. Conclusions: It is possible to predict success on the BOC exam for students from a PMATP based on common application data. Recommendations: Although this project involved predicting success on the athletic training certification exam, the procedures and methods used could be adapted to any academic program

    Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma

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    SummaryWe describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers

    Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer

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    Invasive lobular carcinoma (ILC) is the second most prevalent histologic subtype of invasive breast cancer. Here, we comprehensively profiled 817 breast tumors, including 127 ILC, 490 ductal (IDC), and 88 mixed IDC/ILC. Besides E-cadherin loss, the best known ILC genetic hallmark, we identified mutations targeting PTEN, TBX3 and FOXA1 as ILC enriched features. PTEN loss associated with increased AKT phosphorylation, which was highest in ILC among all breast cancer subtypes. Spatially clustered FOXA1 mutations correlated with increased FOXA1 expression and activity. Conversely, GATA3 mutations and high expression characterized Luminal A IDC, suggesting differential modulation of ER activity in ILC and IDC. Proliferation and immune-related signatures determined three ILC transcriptional subtypes associated with survival differences. Mixed IDC/ILC cases were molecularly classified as ILC-like and IDC-like revealing no true hybrid features. This multidimensional molecular atlas sheds new light on the genetic bases of ILC and provides potential clinical options

    Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

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    Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts
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