Miles&Methods

Insights are AI-generated summaries of research studies, intended for education—not medical advice. Always consult the original sources.

RecoveryAI
npj Digital Medicine·Han Wu et al.

Researchers developed machine learning models to predict weekly injury risk in competitive endurance runners by analyzing multiple types of risk factors simultaneously. They tracked 142 runners for a full year, collecting data on genetics, training history, strength, movement patterns, body composition, nutrition, and weekly training loads to see how well computer algorithms could forecast when injuries might occur.

Key Findings

  • Machine learning models achieved moderate accuracy in predicting weekly injury risk using multidisciplinary data
  • Random forest algorithms performed best among the computational approaches tested
  • Including broader risk factors improved prediction only for certain modeling methods
For RunnersThis research highlights how injury risk emerges from the complex interplay of many factors rather than single causes that runners often focus on. The moderate prediction accuracy suggests that even comprehensive data collection captures only part of what determines when injuries develop.

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RecoveryAI
Journal of Medical and Biological Engineering·Ray Ban Chuan Loh et al.

Researchers investigated whether video-based analysis of running movement patterns could reliably distinguish between runners with and without running-related injuries. The study explored how visible gait characteristics might correlate with injury status in running populations.

Key Findings

  • Video analysis showed some capacity to differentiate between injured and uninjured runners
  • Certain observable movement patterns appeared associated with injury presence
  • The discriminant ability varied across different types of running-related injuries
For RunnersThis work suggests that subtle movement differences may exist between injured and healthy runners, though these patterns might not be obvious to casual observation. The findings underscore how injury status could potentially manifest in measurable but nuanced ways during running.

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RecoveryAI
Physical Therapy in Sport·Matthew Klein et al.

Researchers examined whether a simple single-leg hopping test could predict the forces and mechanics that masters runners generate during actual running, comparing those with and without Achilles tendon issues. The study explored potential connections between how runners perform in a controlled hopping assessment and their running movement patterns.

Key Findings

  • Single-leg horizontal hopping mechanics showed relationships with running force production patterns in masters runners
  • Both runners with and without Achilles tendinopathy were included in the analysis
  • The study focused specifically on propulsive forces during both hopping and running movements
For RunnersThis research highlights how movement patterns in simple tests might reflect what happens during running itself. Runners could interpret this as evidence that how their body produces force in basic movements may connect to their running mechanics, though the specific relationships remain to be fully understood.

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Recovery190AI
The American Journal of Sports Medicine·Zoe Y. S. Chan et al.

Researchers examined whether teaching novice runners to modify their footfalls — specifically to land with less impact force — could reduce their likelihood of getting injured over a full year. They compared runners who received visual feedback to adjust their gait against those who trained similarly but without the feedback.

Key Findings

  • Two weeks of visual feedback training successfully reduced impact loading in novice runners
  • Runners with modified gait patterns experienced substantially fewer injuries over 12 months
  • The gait changes achieved through brief retraining appeared to persist long-term
For RunnersThe connection between how forcefully a runner's foot contacts the ground and their injury risk may be more direct than previously understood. What feels like a subtle adjustment in landing technique could potentially influence injury patterns over extended periods.

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Recovery224AI
The American Journal of Sports Medicine·Stephen P. Messier et al.

Researchers followed 300 recreational runners over two years to identify which baseline characteristics distinguished those who remained injury-free from those who developed overuse injuries. They measured everything from training history and biomechanics to psychological factors, then tracked who got injured and what predicted it.

Key Findings

  • Two-thirds of runners sustained at least one injury, with women experiencing higher injury rates than men
  • Greater knee stiffness emerged as the primary predictor of injury risk in comprehensive analysis
  • Commonly assumed risk factors like flexibility, arch height, and previous injury showed no predictive relationship
For RunnersThis research highlights how injury risk may connect to biomechanical properties like joint stiffness rather than the flexibility or structural factors runners often worry about. The findings suggest that what feels rigid in the knee joint during movement could signal vulnerability, though individual experiences of stiffness vary considerably.

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Recovery190AI
British Journal of Sports Medicine·Steven Duhig et al.

Researchers examined the relationship between high-speed running volumes and hamstring injuries in professional Australian Football League players over two seasons. They found that players who performed unusually high amounts of high-speed running relative to their own typical patterns faced substantially greater odds of hamstring injury in the following weeks.

Key Findings

  • Higher than typical high-speed running volumes preceded hamstring injuries with strongest association in the week immediately before injury
  • Perceived exertion ratings and total running distances showed no meaningful differences between injured and uninjured players
  • More experienced players demonstrated lower hamstring injury risk regardless of running patterns
For RunnersThis research highlights how departures from individual baseline patterns, rather than absolute workload amounts, may signal elevated injury vulnerability. The disconnect between physical load and perceived effort suggests that runners might not always sense when their high-intensity exposure has shifted into riskier territory.

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Recovery224AI
Journal of science and medicine in sport·Shane Malone et al.

Researchers examined the relationship between training workloads, high-intensity running exposure, and injury occurrence in elite Gaelic football players. They found that athletes maintaining higher chronic training loads and regular exposure to maximal velocity running experienced fewer injuries compared to those with lower workloads and less high-speed running exposure.

Key Findings

  • Higher chronic training loads were associated with reduced injury risk in elite athletes
  • Regular exposure to maximal velocity running correlated with fewer injury occurrences
  • The protective effect appeared strongest when both high loads and speed exposure were present
For RunnersThis research suggests that consistent exposure to demanding training, rather than conservative load management, might prepare tissues and movement patterns for competitive demands. The relationship between regular high-speed running and injury reduction may reflect adaptation-specific protection that develops through repeated exposure to maximal efforts.

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