ArticlePloS one2017
Power considerations for the application of detrended fluctuation analysis in gait variability studies.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Assessing the Temporal Organization of Walking Variability: A Systematic Review and Consensus Guidelines on Detrended Fluctuation Analysis.Frontiers in physiology · 2020Pooled it
- Detrended fluctuation analysis of gait dynamics when entraining to music and metronomes at different tempi in persons with multiple sclerosis.Scientific reports · 2020Trial
- Fractal analysis of gait in people with Parkinson's disease: three minutes is not enough.Gait & posture · 2019Trial
- Brain criticality and advanced meditation "cessation" events: An intensively sampled electroencephalography case study.IBRO neuroscience reports · 2026Article
- Hurst-Kolmogorov Process is a More Reliable and Statistically Powerful Alternative to Detrended Fluctuation Analysis for Estimating Hurst in Short Walking Trials.Annals of biomedical engineering · 2026Article
- Ground gradient affects stride-to-stride fluctuations and gait variability in overground walking.European journal of applied physiology · 2026Article
- The Effects of Shoe Sole Thickness on Running Style and Stability During Downhill Running at Different Speeds.European journal of sport science · 2026Article
- Hybrid Framework for Cartilage Damage Detection from Vibroacoustic Signals Using Ensemble Empirical Mode Decomposition and CNNs.Sensors (Basel, Switzerland) · 2025Article
- Precision in Brief: The Bayesian Hurst-Kolmogorov Method for the Assessment of Long-Range Temporal Correlations in Short Behavioral Time Series.Entropy (Basel, Switzerland) · 2025Article
- How Gait Nonlinearities in Individuals Without Known Pathology Describe Metabolic Cost During Walking Using Artificial Neural Network and Multiple Linear Regression.Applied sciences (Basel, Switzerland) · 2024Article
- Article
- Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences.ArXiv · 2023Article
- DFA as a window into postural dynamics supporting task performance: does choice of step size matter?Frontiers in network physiology · 2023Article
- Multifractality in stride-to-stride variations reveals that walking involves more movement tuning and adjusting than running.Frontiers in network physiology · 2023Article
- The temporal pattern and the probability distribution of visual cueing can alter the structure of stride-to-stride variability.Neuroscience letters · 2021Observational
- Motor Synergies Measurement Reveals the Relevant Role of Variability in Reward-Based Learning.Sensors (Basel, Switzerland) · 2021Article
- Significance of trends in gait dynamics.PLoS computational biology · 2020Article
- Static and dynamic postural control deficits in aging fragile X mental retardation 1 (FMR1) gene premutation carriers.Journal of neurodevelopmental disorders · 2019Article
- Article
- Biases in the Simulation and Analysis of Fractal Processes.Computational and mathematical methods in medicine · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The assessment of gait variability using stochastic signal processing techniques such as detrended fluctuation analysis (DFA) has been shown to be a sensitive tool for evaluation of gait alterations due to aging and neuromuscular disease. However, previous studies have suggested that the application of DFA requires relatively long recordings (600 strides), which is difficult when working with clinical populations or older adults. In this paper we propose a model for predicting DFA variance in experimental data and conduct a Monte Carlo simulation to estimate the sample size and number of trials required to detect a change in DFA scaling exponent. We illustrate the model in a simulation to detect a difference of 0.1 (medium effect) between two groups of subjects when using short gait time series (100 to 200 strides) in the context of between- and within-subject designs. We assumed that the variance of DFA scaling exponent arises due to individual differences, time series length, and experimental error. Results showed that sample sizes required to achieve acceptable power of 80% are practically feasible, especially when using within-subject designs. For example, to detect a group difference in the DFA scaling exponent of 0.1, it would require either 25 subjects and 2 trials per subject or 12 subjects and 4 trials per subject using a within-subject design. We then compared plausibility of such power predictions to the empirically observed power from a study that required subjects to synchronize with a persistent fractal metronome. The results showed that the model adequately predicted the empirical pattern of results. Our power simulations could be used in conjunction with previous design guidelines in the literature when planning new gait variability experiments.
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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 OpenQuestion graph.