ArticleJournal of biomechanics2018
Accelerometer-based prediction of running injury in National Collegiate Athletic Association track athletes.
Article in Journal of biomechanics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Methods of Monitoring Internal and External Loads and Their Relationships with Physical Qualities, Injury, or Illness in Adolescent Athletes: A Systematic Review and Best-Evidence Synthesis.Sports medicine (Auckland, N.Z.) · 2023Pooled it
- Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Exploring the Role of Wearable Technology in Sport Kinematics and Kinetics: A Systematic Review.Sensors (Basel, Switzerland) · 2019Pooled it
- An Accelerometer-Equipped Running Watch Provides a Valid Estimate of Peak Vertical Ground Reaction Force and Cumulative Weighted Damage: A Cross-Sectional Laboratory Study.European journal of sport science · 2026Article
- The Use of Machine Learning to Estimate Ground Reaction Forces During Running: A Scoping Review of the Current Practices.Sensors (Basel, Switzerland) · 2026Article
- Do Training Load Metrics Agree? A Comparison of Session Rate of Perceived Exertion, Physiological and Biomechanical Load in Outdoor Running.Sports medicine - open · 2026Article
- Use of Technologies for the Acquisition and Processing Strategies for Motion Data Analysis.Biomimetics (Basel, Switzerland) · 2025Review
- Cumulative Impact Loading and Cartilage Synthesis Biomarkers May Be Associated With Injury Risk in Female Collegiate Basketball Players.Arthroscopy, sports medicine, and rehabilitation · 2024Article
- Identification of Pain through Actigraphy-Recorded Patient Movement: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Definitions and surveillance methods of running-related injuries: A scoping review.European journal of sport science · 2024Article
- Sex- and age-related differences in kinetics and tibial accelerations during military-relevant movement tasks in U.S. Army trainees.European journal of sport science · 2024Article
- A 0.05 m Change in Inertial Measurement Unit Placement Alters Time and Frequency Domain Metrics during Running.Sensors (Basel, Switzerland) · 2024Article
- The virtual pivot point concept improves predictions of ground reaction forces.Frontiers in bioengineering and biotechnology · 2024Article
- Acceleration-Based Estimation of Vertical Ground Reaction Forces during Running: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns.Sensors (Basel, Switzerland) · 2023Article
- The Effects of a Simple Sensor Reorientation Procedure on Peak Tibial Accelerations during Running and Correlations with Ground Reaction Forces.Sensors (Basel, Switzerland) · 2023Article
- Aetiological Factors of Running-Related Injuries: A 12 Month Prospective "Running Injury Surveillance Centre" (RISC) Study.Sports medicine - open · 2023Article
- Unsupervised Gait Event Identification with a Single Wearable Accelerometer and/or Gyroscope: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns.Sensors (Basel, Switzerland) · 2023Article
- Joint regression modelling of intensity and timing of accelerometer counts.Statistics in medicine · 2023Article
- Estimation of gait events and kinetic waveforms with wearable sensors and machine learning when running in an unconstrained environment.Scientific reports · 2023Article
- Gait Alteration in Individual with Limb Loss: The Role of Inertial Sensors.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Running-related injuries (RRI) may result from accumulated microtrauma caused by combinations of high load magnitudes (vertical ground reaction forces; vGRFs) and numbers (strides). Yet relationships between vGRF and RRI remain unclear - potentially because previous research has largely been constrained to collecting vGRFs in laboratory settings and ignoring relationships between RRI and stride number. In this preliminary proof-of-concept study, we addressed these constraints: Over a 60-day period, each time collegiate athletes (n = 9) ran they wore a hip-mounted activity monitor that collected accelerations throughout the entire run. Accelerations were used to estimate peak vGRF, number of strides, and weighted cumulative loading (sum of peak vGRFs weighted to the 9th power) across the entirety of each run. Runners also reported their post-training pain/fatigue and any RRI that prevented training. Across 419 runs and >2.1 million strides, injured (n = 3) and uninjured (n = 6) participants did not report significantly different pain/fatigue (p = 0.56) or mean number of strides per run (p = 0.91). Injured participants did, however, have significantly greater peak vGRFs (p = 0.01) and weighted cumulative loading per run (p < 0.01). Results from this small but extensively studied sample of elite runners demonstrate that loading profiles (load magnitude-number combinations) quantified with activity monitors can provide valuable information that may prove essential for: (1) testing hypotheses regarding overuse injury mechanisms, (2) developing injury-prediction models, and (3) designing and adjusting athlete- and loading-specific training programs and feedback.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.