Injury Risk Estimation Expertise: Interdisciplinary Differences in Performance on the ACL Injury Risk Estimation Quiz

Background: Simple observational assessment of movement is a potentially low-cost method for anterior cruciate ligament (ACL) injury screening and prevention. Although many individuals utilize some form of observational assessment of movement, there are currently no substantial data on group skill differences in observational screening of ACL injury risk. Purpose/Hypothesis: The purpose of this study was to compare various groups’ abilities to visually assess ACL injury risk as well as the associated strategies and ACL knowledge levels. The hypothesis was that sports medicine professionals would perform better than coaches and exercise science academics/students and that these subgroups would all perform better than parents and other general population members. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A total of 428 individuals, including physicians, physical therapists, athletic trainers, strength and conditioning coaches, exercise science researchers/students, athletes, parents, and members of the general public participated in the study. Participants completed the ACL Injury Risk Estimation Quiz (ACL-IQ) and answered questions related to assessment strategy and ACL knowledge. Results: Strength and conditioning coaches, athletic trainers, physical therapists, and exercise science students exhibited consistently superior ACL injury risk estimation ability (þ2 SD) as compared with sport coaches, parents of athletes, and members of the general public. The performance of a substantial number of individuals in the exercise sciences/sports medicines (approximately 40%) was similar to or exceeded clinical instrument-based biomechanical assessment methods (eg, ACL nomogram). Parents, sport coaches, and the general public had lower ACL-IQ, likely due to their lower ACL knowledge and to rating the importance of knee/thigh motion lower and weight and jump height higher. Conclusion: Substantial cross-professional/group differences in visual ACL injury risk estimation exist. The relatively profound differences in injury risk estimation accuracy and their potential implications for risk screening suggest the need for additional training and outreach

Injury Risk Estimation Expertise Assessing the ACL Injury Risk Estimation Quiz

Background: Available methods for screening anterior cruciate ligament (ACL) injury risk are effective but limited in application as they generally rely on expensive and time-consuming biomechanical movement analysis. A potential efficient alternative to biomechanical screening is skilled movement analysis via visual inspection (ie, having experts estimate injury risk factors based on observations of athletes’ movements). Purpose: To develop a brief, valid psychometric assessment of ACL injury risk factor estimation skill: the ACL Injury Risk Estimation Quiz (ACL-IQ). Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: A total of 660 individuals participated in various stages of the study, including athletes, physicians, physical therapists, athletic trainers, exercise science researchers/students, and members of the general public in the United States. The ACL-IQ was fully computerized and made available online (www.ACL-IQ.org). Item sampling/reduction, reliability analysis, cross-validation, and convergent/discriminant validity analysis were conducted to optimize the efficiency and validity of the assessment. Results: Psychometric optimization techniques identified a short (mean time, 2 min 24 s), robust, 5-item assessment with high reliability (test-retest: r = 0.90) and consistent discriminability (average difference of exercise science professionals vs general population: Cohen d = 1.98). Exercise science professionals and general population individuals scored 74% and 53% correct, respectively. Convergent and discriminant validity was demonstrated. Scores on the ACL-IQ were most associated with ACL knowledge and various cue utilities and were least associated with domain-general spatial/decision-making ability, personality, or other demographic variables. Overall, 23% of the total sample (40% exercise science professionals; 6% general population) performed better than or equal to the ACL nomogram. Conclusion: This study presents the results of a systematic approach to assess individual differences in ACL injury risk factor estimation skill; the assessment approach is efficient (ie, it can be completed in\3 min) and psychometrically robust. The results provide evidence that some individuals have the ability to visually estimate ACL injury risk factors more accurately than other instrument-based ACL risk estimation methods (ie, ACL nomogram). The ACL-IQ provides the foundation for assessing the efficacy of observational ACL injury risk factor assessment (ie, does simple skilled visual inspection reduce ACL injuries?). It also provides a representative task environment that can be used to increase our understanding of the perceptual-cognitive mechanisms underlying observational movement analysis and to improve injury risk assessment performance

Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1

Estimating ToE Risk Level using CVSS

Security management is about calculated risk and requires continuous evaluation to ensure cost, time and resource effectiveness. Parts of which is to make future-oriented, cost-benefit investments in security. Security investments must adhere to healthy business principles where both security and financial aspects play an important role. Information on the current and potential risk level is essential to successfully trade-off security and financial aspects. Risk level is the combination of the frequency and impact of a potential unwanted event, often referred to as a security threat or misuse. The paper presents a risk level estimation model that derives risk level as a conditional probability over frequency and impact estimates. The frequency and impact estimates are derived from a set of attributes specified in the Common Vulnerability Scoring System (CVSS). The model works on the level of vulnerabilities (just as the CVSS) and is able to compose vulnerabilities into service levels. The service levels define the potential risk levels and are modelled as a Markov process, which are then used to predict the risk level at a particular time

A systematic review of software development cost estimation studies

This paper aims to provide a basis for the improvement of software estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) Increase the breadth of the search for relevant studies, 2) Search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) Conduct more studies on estimation methods commonly used by the software industry, and, 4) Increase the awareness of how properties of the data sets impact the results when evaluating estimation methods