GuidelineJournal of biomechanical engineering2015
Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement.
Guideline in Journal of biomechanical engineering, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 270 papers, 4 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
270 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Advances in Musculoskeletal Modeling of the Thoraco-Lumbar Spine: A Comprehensive Systematic Review.Annals of biomedical engineering · 2025Pooled it
- Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review.Frontiers in bioengineering and biotechnology · 2024Pooled it
- Running-Related Biomechanical Risk Factors for Overuse Injuries in Distance Runners: A Systematic Review Considering Injury Specificity and the Potentials for Future Research.Sports medicine (Auckland, N.Z.) · 2022Pooled it
- The effect of modelling parameters in the development and validation of knee joint models on ligament mechanics: A systematic review.PloS one · 2022Pooled it
- Trial
- Subject-specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models.Journal of anatomy · 2020Trial
- Shallow recurrent decoders for neural and behavioural dynamics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Article
- Simulation study of exoskeleton-musculoskeletal coupling for exoskeleton assistance via multi-agent reinforcement learning.Medical & biological engineering & computing · 2026Article
- Ankle Kinematics and Neuromuscular Activity During the Stair-to-Ground Transition in Individuals With Acute Ankle Sprain: A Cross-Sectional Study.Journal of foot and ankle research · 2026Article
- Development and Surface Indentation Assessment of an MRI-Based Subject-Specific Hand-Forearm Finite Element Model: A Preliminary Study.Bioengineering (Basel, Switzerland) · 2026Article
- Methods for assessing core muscle co-activation in the lumbar region: a narrative review.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- Kinematics Limit the Potential to Capitalize on Available Muscle Strength During Sit-to-Stand for Patients Before and After Total Knee Arthroplasty.Annals of biomedical engineering · 2026Article
- Estimating Muscle Parameters via Hierarchical Bayesian Neuromechanics.bioRxiv : the preprint server for biology · 2026Article
- Biomechanical Simulations Informed by Multimodal Measurements Link Symptoms to Glenohumeral Joint Forces Across Healthy and Pathological Rotator Cuffs.Annals of biomedical engineering · 2026Article
- The energetic cost of human standing balance and gait initiation over a range of natural postures.PLoS computational biology · 2026Article
- Reduced metabolic energy cost in bounce squats driven by lower vasti contribution during the eccentric phase.BMC sports science, medicine & rehabilitation · 2026Article
- Ontogenetic changes in femoral morphology and joint loads in children.Bone & joint research · 2026Article
- Biomechanical effects of passive exosuit assistance on tibiofemoral loading and dynamic stability during downhill walking.Journal of neuroengineering and rehabilitation · 2026Article
- Digital range of motion analysis is sensitive to subjective steps in joint model construction.Journal of anatomy · 2026Article
- A study evaluating differences in 3D upper limb kinematics and surface electromyography measures in adults with and without facioscapulohumeral dystrophy.JSES reviews, reports, and techniques · 2026Article
210 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Computational modeling and simulation of neuromusculoskeletal (NMS) systems enables researchers and clinicians to study the complex dynamics underlying human and animal movement. NMS models use equations derived from physical laws and biology to help solve challenging real-world problems, from designing prosthetics that maximize running speed to developing exoskeletal devices that enable walking after a stroke. NMS modeling and simulation has proliferated in the biomechanics research community over the past 25 years, but the lack of verification and validation standards remains a major barrier to wider adoption and impact. The goal of this paper is to establish practical guidelines for verification and validation of NMS models and simulations that researchers, clinicians, reviewers, and others can adopt to evaluate the accuracy and credibility of modeling studies. In particular, we review a general process for verification and validation applied to NMS models and simulations, including careful formulation of a research question and methods, traditional verification and validation steps, and documentation and sharing of results for use and testing by other researchers. Modeling the NMS system and simulating its motion involves methods to represent neural control, musculoskeletal geometry, muscle-tendon dynamics, contact forces, and multibody dynamics. For each of these components, we review modeling choices and software verification guidelines; discuss variability, errors, uncertainty, and sensitivity relationships; and provide recommendations for verification and validation by comparing experimental data and testing robustness. We present a series of case studies to illustrate key principles. In closing, we discuss challenges the community must overcome to ensure that modeling and simulation are successfully used to solve the broad spectrum of problems that limit human mobility.
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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.