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PA 05-3-0690 Mavie-lab sports: a mhealth for injury prevention and risk management in sport
  1. Madelyn Yiseth Rojas Castro1,2,
  2. Marina Travanca1,2,
  3. Marta Avalos Fernandez1,2,3,
  4. Ludivine Orriols1,2,
  5. David Valentin Conesa4,
  6. Emmanuel Lagarde1,2
  1. 1Université de Bordeaux, Bordeaux, France
  2. 2INSERM, Bordeaux, France
  3. 3INRIA, Talence, France
  4. 4Universitat de València, Burjassot, Spain


Computational advances in smart-phone technology and the development of expert systems has been an opportunity to devise the MAVIE-Lab an innovative Mobile Health Application (mHealth) for primary prevention of Home, Leisure and Sport Injuries (HLIs). Here, we present MAVIE-Lab Sports, the first module of the application focused on sports injuries.

MAVIE-Lab was developed in the framework of the MAVIE project. A large web-based cohort launched with the objective of prospectively collecting data related to HLIs. A sample size of 26 000 volunteers have been already enrolled in this cohort and the ultimate goal is to recruit 1 00 000 participants in France. As a first step, the MAVIE-Lab will be available for MAVIE volunteer’s only.

MAVIE-Lab Sports is a decision support system (DSS) aimed to enable the self-management the potential risk of injury and to facilitate the choice of preventive measures. The App first allows participants to compare the overall injury risk between different sports. It then provides an estimation of their personal injury risk. Finally, the user is invited to experiment their potential risk change when opting for a set of proposed behavioral changes, protective devices, equipment or sport practice environments.

The MAVIE-Lab algorithms were developed using detailed MAVIE cohort data related to participant’s health, demographics, training practices and the occurrence of injuries, there causes, consequences and severity. The model was constructed to predict the injury risk using probabilistic reasoning and graphical modelling through Bayesian Networks. This approach combines qualitative and quantitative modelling, allowing the combination of MAVIE data evidences and prior expert’s information about risk, protection factors and causal relations between them.

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