Observational studyScientific reports2024
Impact of applying different levels of threshold-based artifact correction on the processing of heart rate variability data in individuals with temporomandibular disorder.
Observational study in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- An innovative method of evaluating the association of heat exposure and heart rate variability in a panel of agricultural workers with small and lightweight personal sensors.Journal of exposure science & environmental epidemiology · 2026Article
- Threshold-based artefact correction methods influence heart rate variability measurements in individuals with type 2 diabetes mellitus.Scientific reports · 2026Observational
- Association of body mass index and visceral fat with heart rate variability in university students: a BMI-stratified analysis.Frontiers in physiology · 2026Article
- Resting heart rate variability as a predictor of exercise response in mild post-COVID: insights from a six-minute step test.Biomedical engineering online · 2025Article
- Blood metabolome shows signatures of metabolic dysregulation in obese and overweight subjects that can be predicted by machine learning applied to heart rate variability.Frontiers in molecular biosciences · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although heart rate variability (HRV) is a valid method to evaluate the behavior of the autonomic nervous system in individuals with temporomandibular disorder (TMD), the measurement can easily be biased by factors involving the analysis methodology, such as the removal of artifacts. Therefore, the objective of this investigation is to evaluate the impact of using different levels of threshold-based artifact correction to process HRV data in individuals with TMD. This cross-sectional observational study. Adults aged 18 to 55 years old with a diagnosis of myogenic TMD, score ≥ 50 on the Fonseca Anamnestic Index (FAI) and pain ≥ 3 on the Numerical Pain Scale (NPS) participated. The HRV was registered in the supine position (short-term) using a Polar S810i. Kubios software was used for HRV analysis using all filters. One-way ANOVA with Tukey-Kramer post-hoc was used to test the differences in HRV using the different Kubios Software artifact correction filters. The effect size was calculated based on the Cohen d. The very strong filter was statistically different (p < 0.05) compared to the no filter in all overview and time domain variables. In the frequency domain, the variables VLF, LF, HF and Total Power showed statistical differences (p < 0.05) when using the very strong filter. The same occurred with the variables SD1, SD2 and DFA α2 of the non-linear analysis (p < 0.05). The most restrictive filter of the Kubios software (very strong) significantly impacts the quantification of HRV parameters in individuals with TMD.
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