Trial reportPloS one2014
Ground reaction force estimates from ActiGraph GT3X+ hip accelerations.
Trial report in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis 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
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.
- Does Site Matter? Impact of Inertial Measurement Unit Placement on the Validity and Reliability of Stride Variables During Running: A Systematic Review and Meta-analysis.Sports medicine (Auckland, N.Z.) · 2021Pooled it
- Development and Validation of Accelerometer-Based Machine Learning Models for Classifying Walking, Running, and Jumping Activities.Sensors (Basel, Switzerland) · 2026Article
- Article
- Moderate and Vigorous Physical Activity Intensity Cut-Points for Hip-, Wrist-, Thigh-, and Lower Back Worn Accelerometer in Very Old Adults.Scandinavian journal of medicine & science in sports · 2025Observational
- Estimating braking and propulsion forces during overground running in and out of the lab.PloS one · 2025Article
- Accelerations and EMG Differences Between Isocaloric High-Incline Walking and Level-Grade Jogging.International journal of exercise science · 2025Article
- Handrim Reaction Force and Moment Assessment Using a Minimal IMU Configuration and Non-Linear Modeling Approach during Manual Wheelchair Propulsion.Sensors (Basel, Switzerland) · 2024Article
- Measurement of Sedentary Behavior-The Outcomes of the Angle for Posture Estimation (APE) Method.Sensors (Basel, Switzerland) · 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
- Using Raw Accelerometer Data to Predict High-Impact Mechanical Loading.Sensors (Basel, Switzerland) · 2023Article
- Inertial Sensor Location for Ground Reaction Force and Gait Event Detection Using Reservoir Computing in Gait.International journal of environmental research and public health · 2023Article
- Estimation of horizontal running power using foot-worn inertial measurement units.Frontiers in bioengineering and biotechnology · 2023Article
- The Impact of Footwear on Occupational Task Performance and Musculoskeletal Injury Risk: A Scoping Review to Inform Tactical Footwear.International journal of environmental research and public health · 2022Article
- Accelerometer-based prediction of ground reaction force in head-out water exercise with different exercise intensity countermovement jump.BMC sports science, medicine & rehabilitation · 2022Article
- Determining jumping performance from a single body-worn accelerometer using machine learning.PloS one · 2022Article
- Concurrent validity of the combined HRV/ACC sensor and physical activity diary when monitoring physical activity in university students during free-living days.Frontiers in public health · 2022Article
- Wearable Devices for Physical Activity and Healthcare Monitoring in Elderly People: A Critical Review.Geriatrics (Basel, Switzerland) · 2021Review
- Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running.Human movement science · 2020Article
- Accelerometer-based prediction of skeletal mechanical loading during walking in normal weight to severely obese subjects.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2020Article
- Accelerometer Based Data Can Provide a Better Estimate of Cumulative Load During Running Compared to GPS Based Parameters.Frontiers in sports and active living · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Simple methods to quantify ground reaction forces (GRFs) outside a laboratory setting are needed to understand daily loading sustained by the body. Here, we present methods to estimate peak vertical GRF (pGRFvert) and peak braking GRF (pGRFbrake) in adults using raw hip activity monitor (AM) acceleration data. The purpose of this study was to develop a statistically based model to estimate pGRFvert and pGRFbrake during walking and running from ActiGraph GT3X+ AM acceleration data. 19 males and 20 females (age 21.2 ± 1.3 years, height 1.73 ± 0.12 m, mass 67.6 ± 11.5 kg) wore an ActiGraph GT3X+ AM over their right hip. Six walking and six running trials (0.95-2.19 and 2.20-4.10 m/s, respectively) were completed. Average of the peak vertical and anterior/posterior AM acceleration (ACCvert and ACCbrake, respectively) and pGRFvert and pGRFbrake during the stance phase of gait were determined. Thirty randomly selected subjects served as the training dataset to develop generalized equations to predict pGRFvert and pGRFbrake. Using a holdout approach, the remaining 9 subjects were used to test the accuracy of the models. Generalized equations to predict pGRFvert and pGRFbrake included ACCvert and ACCbrake, respectively, mass, type of locomotion (walk or run), and type of locomotion acceleration interaction. The average absolute percent differences between actual and predicted pGRFvert and pGRFbrake were 8.3% and 17.8%, respectively, when the models were applied to the test dataset. Repeated measures generalized regression equations were developed to predict pGRFvert and pGRFbrake from ActiGraph GT3X+ AM acceleration for young adults walking and running. These equations provide a means to estimate GRFs without a force plate.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.