scholarly journals The feasibility of using an electronic system to collect health and exposure data in Australian Olympic combat sports (Preprint)

2017 ◽  
Author(s):  
Sally Bromley ◽  
Michael Drew ◽  
Scott Talpey ◽  
Andrew McIntosh ◽  
Caroline Finch

BACKGROUND Electronic methods are increasingly being used to manage health-related data amongst sporting populations. Collection of such data permits analysis of injury and illness trends, improves early detection of injuries and illnesses, collectively referred to as health problems, and provides evidence to inform prevention strategies. The Athlete Management System (AMS) has been employed across a range of sports to monitor health. Australian combat athletes train across the country without dedicated national medical/sports science teams to monitor and advocate for their health. Employing an internet-based system, such as the AMS, may provide an avenue to increase visibility of health problems experienced by combat athletes, and deliver key information to stakeholders detailing where prevention programs may be targeted. OBJECTIVE The objectives of this paper are to: 1) report on the feasibility of utilising the AMS to collect longitudinal injury and illness data of combat sport athletes, and 2) describe the type, location, severity and recurrence of injuries and illnesses that the cohort of athletes experience across a 12-week period. METHODS Twenty-six elite and developing athletes from four Olympic combat sports (boxing, judo, taekwondo and wrestling) were invited to participate in this study. Engagement with the AMS system was measured and collected health problems (injuries/illnesses) were coded using Orchard Sport Injury Classification System (OSICS, version 10.1) and International Classification of Primary Care (version 2). RESULTS Despite over 160 contacts, athlete engagement with online tools was poor with only 13% compliance across the 12 week period. No taekwondo or wrestling athletes were compliant. Despite low overall engagement, a large number of injuries/illness were recorded across the 11 athletes who entered data: 22 unique injuries, 8 unique illnesses, 30 recurrent injuries and two recurrent illnesses. The most frequent injuries were to the knee in boxing (n=41) and thigh in judo (n=9). In this cohort, judo players experienced more severe, but less frequent, injuries than did boxers, yet sustained more illnesses. In 97% of cases, athletes in this cohort continued to train irrespective of their health problems. CONCLUSIONS Amongst athletes who reported injuries, many reported multiple conditions indicating that there is a need for health monitoring in Australian combat sport. A number of factors may have influenced engagement with the AMS, including access to internet, the design of the system, coach views on the system, previous experiences with the system and the existing culture within Australian combat sports. To increase engagement, there may need to be a requirement for sports staff to provide relevant feedback on data entered into the system. Until the barriers are addressed, it is not feasible to implement the system in its current form across a larger cohort of combat athletes.

2015 ◽  
Author(s):  
William E. Hammond ◽  
Vivian L. West ◽  
David Borland ◽  
Igor Akushevich ◽  
Eugenia M. Heinz

2021 ◽  
Vol 13 (6) ◽  
pp. 3572
Author(s):  
Lavinia-Maria Pop ◽  
Magdalena Iorga ◽  
Iulia-Diana Muraru ◽  
Florin-Dumitru Petrariu

A busy schedule and demanding tasks challenge medical students to adjust their lifestyle and dietary habits. The aim of this study was to identify dietary habits and health-related behaviours among students. A number of 403 students (80.40% female, aged M = 21.21 ± 4.56) enrolled in a medical university provided answers to a questionnaire constructed especially for this research, which was divided into three parts: the first part collected socio-demographic, anthropometric, and medical data; the second part inquired about dietary habits, lifestyle, sleep, physical activity, water intake, and use of alcohol and cigarettes; and the third part collected information about nutrition-related data and the consumption of fruit, vegetables, meat, eggs, fish, and sweets. Data were analysed using SPSS v24. Students usually slept M = 6.71 ± 1.52 h/day, and one-third had self-imposed diet restrictions to control their weight. For both genders, the most important meal was lunch, and one-third of students had breakfast each morning. On average, the students consumed 1.64 ± 0.88 l of water per day and had 220 min of physical activity per week. Data about the consumption of fruit, vegetables, meat, eggs, fish, sweets, fast food, coffee, tea, alcohol, or carbohydrate drinks were presented. The results of our study proved that medical students have knowledge about how to maintain a healthy life and they practice it, which is important for their subsequent professional life.


2021 ◽  
Author(s):  
Ben Philip ◽  
Mohamed Abdelrazek ◽  
Alessio Bonti ◽  
Scott Barnett ◽  
John Grundy

UNSTRUCTURED Our objective is to better understand health-related data collection across different mHealth app categories. This would help in developing a health domain model for mHealth apps to facilitate app development and data sharing between these apps to improve user experience and reduce redundancy in data collection. We identified app categories listed in a curated library which was then used to explore the Google Play Store for health/medical apps that were then filtered using our inclusion criteria. We downloaded and analysed these apps using a script we developed around the popular AndroGuard tool. We analysed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects/generates health data. We retrieved 3,251 applications meeting our criteria, and our analysis showed that only 10.7% of these apps requested permission for Bluetooth access. We found 50.9% of the Bluetooth Service UUIDs to be known in these apps, with the remainder being vendor specific. The most common health-related services using the known UUIDs were Heart Rate, Glucose and Body Composition. App permissions show the most used device module/sensor to be the camera (20.57%), closely followed by GPS (18.39%). Our findings are consistent with previous studies in that not many health apps were found to use built-in sensors or peripherals for collecting health data. The use of more peripherals and automated data collection along with integration with other apps could increase usability and convenience which would eventually also improve user experience and data reliability.


Curationis ◽  
1999 ◽  
Vol 22 (1) ◽  
Author(s):  
P Jagananen

This study assessed whether community participation in health related activity was a reality or just popular development rhetoric.Using action research methodology, focus group discussions and informal contacts were made with farm workers consisting of twelve families in Umkomaas, south of Durban in the province of Kwa-Zulu Natal. The aim was to establish whether this community could be actively involved in all aspects of community participation. The level of participation was described using Rifkin’s model (Rifkin et al, 1988). Results of this study revealed that the community was able to identify their own health problems, prioritize them and plan appropriate strategies to meet the needs identified.


Author(s):  
Sotiris Diamantopoulos ◽  
Dimitris Karamitros ◽  
Luigi Romano ◽  
Luigi Coppolino ◽  
Vassilis Koutkias ◽  
...  

Author(s):  
Kerina H Jones ◽  
Arron S Lacey ◽  
Brian L Perkins ◽  
Mark I Rees

ABSTRACTObjectivesData safe havens can bring together and combine a rich array of anonymised person-based data for research and policy evaluation within a secure setting. To date, the majority of available datasets have been structured micro-data derived from routine health-related records. Possibilities are opening up for the greater reuse of genomic data such as Genome Wide Association studies (GWAS) and Whole Exome/Genome Sequencing (WES or WGS). However, there are considerable challenges to be addressed if the benefits of using these data in combination with health-related data are to be realized safely. ApproachWe explore the benefits and challenges of using genomic datasets with health-related data, and using the Secure Anonymised Information Linkage (SAIL) system as a case study, the implications and way forward for Data Safe Havens in seeking to incorporate genomic data for use with health-related data. ResultsThe benefits of using GWAS, WES and WGS data in conjunction with health-related data include the potential to explore genetics at a population level and open up novel research areas. These include the ability to increasingly stratify and personalize how medical indications are detected and treated through precision medicine by understanding rare conditions and adding socioeconomic and environmental context to genomic data. Among the challenges are: data availability, computing capacity, technical solutions, legal and regulatory frameworks, public perceptions, individual privacy and organizational risk. Many of the challenges within these areas are common to person-based data in general, and often Data Safe Havens have been designed to address these. But there are also aspects of these challenges, and other challenges, specific to genomic data. These include issues due to the unknown clinical significance of genomic information now or in the future, with corresponding risks for privacy and impact on individuals. ConclusionGenomic data sets contain vast amounts of valuable information, some of which is currently undefined, but which may have direct bearing on individual health at some point. The use of these data in combination with health-related data has the potential to bring great benefits, better clinical trial stratification, epidemiology project design and clinical improvements. It is, therefore, essential that such data are surrounded by a properly-designed, robust governance framework including technical and procedural access controls that enable the data to be used safely.


2020 ◽  
Author(s):  
Charles Bernick ◽  
Tucker Hansen ◽  
Winnie Ng ◽  
Vernon Williams ◽  
Margaret Goodman ◽  
...  

AbstractObjectivesDetermine, through video reviews, how often concussions occur in combat sport matches, how well non-medical personnel can be trained to recognize concussions and how often fights are judged to continue too long.MethodsThis is a retrospective video analysis by an 8 person panel of 60 professional fights (30 boxing and 30 mixed martial arts). Through video review, medical and non-medical personnel recorded details about each probable concussion and determined if and when they would have stopped the fight compared to the official stoppage time.ResultsA concussion was recorded in 47/60 fights. The fighter that sustained the first concussion ultimately lost 98% of the time. The physician and non-physician raters had 86% agreement regarding the number of concussions that occurred to each fighter per match. The mean number of concussions per minute of fight time was 0.08 (0.06 for boxers and 0.10 for MMA). When stratifying by outcome of the bout, the mean number of concussion per minute for the winner was 0.01 compared to the loser at 0.15 concussions per minute. The physician raters judged that 24 of the 60 fights (11 boxing [37%]; 13 MMA [43 %]) should have been stopped sooner than what occurred.ConclusionRecognizing that the losing fighter almost always is concussed first and tends to sustain more concussions during the fight, along with the demonstration that non-physician personnel can be taught to recognize concussion, may guide policy changes that improve brain health in combat sports.


10.2196/16879 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16879 ◽  
Author(s):  
Christophe Olivier Schneble ◽  
Bernice Simone Elger ◽  
David Martin Shaw

Tremendous growth in the types of data that are collected and their interlinkage are enabling more predictions of individuals’ behavior, health status, and diseases. Legislation in many countries treats health-related data as a special sensitive kind of data. Today’s massive linkage of data, however, could transform “nonhealth” data into sensitive health data. In this paper, we argue that the notion of health data should be broadened and should also take into account past and future health data and indirect, inferred, and invisible health data. We also lay out the ethical and legal implications of our model.


2018 ◽  
Vol 5 (4) ◽  
pp. e27 ◽  
Author(s):  
Sally Bromley ◽  
Michael Drew ◽  
Scott Talpey ◽  
Andrew McIntosh ◽  
Caroline Finch

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