Research

We're building our Intelligent Health platform based on decades of scientifically validated research and literature. Explore some of the scientific foundations we are using to shape the future of health.
Profile Logo
Posted by Chris Li, PHD
Last updated 3 months ago
Monitoring Athlete Training Loads: Consensus Statement

In 2016, a multidisciplinary panel of international experts in athlete management participated in a conference to review methods for athlete monitoring. This paper discusses and summarizes principles and concepts of contemporary cutting-edge applied research in the science of athlete management in elite sport.

Logit.AI draws upon these principles to design a revolutionary product that is in line with cutting-edge research and practices.

Profile Logo
Posted by Chris Li, PHD
Last updated 3 months ago
The Mental Health of Elite Athletes: A Narrative Systematic Review

Congruent with our design philosophy here at Logit.AI, we incorporate components of mental health to improve our designs for establishing stress-reducing and recovery-promoting behaviours for early-intervention.

Profile Logo
Posted by Chris Li, PHD
Last updated 3 months ago
Psychosocial Factors and Sports Injuries: Meta-analyses for Prediction and Prevention

A critical meta-analysis of psychosocial factors (the interaction between individual psychological factors and social environment) reveals a strong positive association between injury risk and high-levels of sustained stress. Furthermore, psychosocial-based intervention studies demonstrate a significant reduction in injuries in comparison to control groups. This article provides compelling evidence for the incorporation of psychosocial-based intervention and management when designing injury prevention programs.

Profile Logo
Posted by Chris Li, PHD
Last updated 3 months ago
Has the athlete trained enough to return to play safely? The acute chronic workload ration permits clinicians to quantify a player's risk of subsequent injury.

The return-to-play decision is the subject of intense research. Often, sports medicine clinicians and coaches will have to decide between returning a player to play too early and risking injury recurrence while trying to maintain adequate roster sizes.

In this systematic review of published literature, a comprehensive checklist of criterion is provided. In addition, a set of practices designed to assess components of the checklist are given. Among these practices include rigorous psychosocial assessment.

In line with these standards, Logit.AI is building a sophisticated and easy-to-use platform for coaches and sport medicine practitioners to perform the necessary assessments required to make informed return-to-play decisions to promote athlete health and performance.

Profile Logo
Posted by Chris Li, PHD
Last updated 3 months ago
The athlete monitoring cycle: a practical guide to interpreting and applying training monitoring data

In this editorial, a group of sport medicine clinicians from a variety of professional sporting bodies including the NFL, NBA, and Champions League, provide an expert opinion of effective athlete monitoring.

A key component of their recommended practices includes the assessment of athlete perceptual well-being.

Research Icon

Interested in Our Research?

We're always looking for brilliant data scientists, researchers, and advisors to join our team.

Apply Now

Join Our Newsletter

Keep up to date with everything great happening at Logit AI. We'll do our part to keep you in the loop with the latest and greatest in health tech.

Required Field
Intercom logo