ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2020
Accelerometer-based prediction of skeletal mechanical loading during walking in normal weight to severely obese subjects.
Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Development and Validation of Accelerometer-Based Machine Learning Models for Classifying Walking, Running, and Jumping Activities.Sensors (Basel, Switzerland) · 2026Article
- Raw Acceleration from Wrist- and Hip-Worn Accelerometers Corresponds with Mechanical Loading in Children and Adolescents.Sensors (Basel, Switzerland) · 2023Article
- Using Raw Accelerometer Data to Predict High-Impact Mechanical Loading.Sensors (Basel, Switzerland) · 2023Article
- Accelerometer-based prediction of ground reaction force in head-out water exercise with different exercise intensity countermovement jump.BMC sports science, medicine & rehabilitation · 2022Article
- Wearable Devices for Physical Activity and Healthcare Monitoring in Elderly People: A Critical Review.Geriatrics (Basel, Switzerland) · 2021Review
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
There is no objective way to monitor mechanical loading characteristics during exercise for bone health improvement. We developed accelerometry-based equations to predict ground reaction force (GRF) and loading rate (LR) in normal weight to severely obese subjects. Equations developed had a high and moderate accuracy for GRF and LR prediction, respectively, thereby representing an accessible way to determine mechanical loading characteristics in clinical settings.
introductionThere is no way to objectively prescribe and monitor exercise for bone health improvement in obese patients based on mechanical loading characteristics. We aimed to develop accelerometry-based equations to predict peak ground reaction forces (pGRFs) and peak loading rate (pLR) on normal weight to severely obese subjects.
methodsSixty-four subjects (45 females; 84.6 ± 21.7 kg) walked at different speeds (2-6 km·h
resultsBody mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration transient rate for pLR prediction. All pGRF equation coefficients of determination were above 0.89, a good agreement between actual and predicted pGRFs, with a mean absolute percent error (MAPE) below 6.7%. No significant differences were observed between actual and predicted pGRFs at each walking speed. Accuracy indices from our equations were better than previously developed equations for normal weight subjects, namely a MAPE approximately 3 times smaller. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF.
conclusionWalking pGRF and pLR in normal weight to severely obese subjects can be predicted with moderate to high accuracy by accelerometry-based equations, representing an easy and accessible way to determine mechanical loading characteristics in clinical settings.
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Registered trials
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