Scholarlink

<p>Heart rate variability (HRV) is a clinical marker used to assess autonomic function, and the application of filtering algorithms may significantly influence data quantification and interpretation. Although HRV has been extensively studied in individuals with type 2 diabetes mellitus (T2DM), the impact of artefact correction methods remains underexplored. To evaluate the effects of different artefact correction filters in Kubios software on short-term HRV parameters in individuals with T2DM. This cross-sectional, descriptive, and observational study included adults (≥ 18 years) diagnosed with T2DM. Anthropometric and metabolic data were collected, including fasting blood samples for glucose, insulin, and lipid profiles. HRV indices were analyzed across time-domain, frequency-domain, nonlinear, and global metrics. Statistical analysis was performed using ANOVA or Friedman tests according to data distribution, with significance set at <i>p</i> &lt; 0.05. The sample consisted of 52 individuals (67% male, mean age 52 ± 8 years, mean BMI 29.65 ± 5.50&#xa0;kg/m²). The median duration of T2DM was 3 years (IQR 1.5–10). Median metabolic parameters were insulin 12.50 µU/mL, triglycerides 141.50&#xa0;mg/dL, fasting glucose 149.50&#xa0;mg/dL, and HbA1c 8.65% (IQR 7.20–10.00). Application of the most restrictive artefact correction setting (“very strong”) in Kubios significantly modified overall HRV as well as time-domain, frequency-domain, and nonlinear parameters (<i>p</i> &lt; 0.05), highlighting its influence on HRV quantification. This study demonstrates that artefact correction filters, particularly the “very strong” setting, substantially affect HRV analysis in individuals with T2DM. Excessively restrictive filtering may distort autonomic metrics and potentially bias interpretation. Standardization of artefact correction methods is essential to ensure accurate and reproducible HRV assessment in clinical and research settings.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Threshold-based artefact correction methods influence heart rate variability measurements in individuals with type 2 diabetes mellitus

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月28日

查看全文
  • Daniela Bassi-Dibai,
  • Aldair Darlan Santos-de-Araújo,
  • Daniel Santos Rocha,
  • Lucivalda Viegas de Almeida,
  • José Kléber Figueiredo,
  • Louise Aline Romão Gondim,
  • Marinete Rodrigues de Farias Diniz,
  • Victória Pereira Frutuoso,
  • Mariana Campos Maia,
  • Patrícia Martins Santos,
  • Audrey Borghi-Silva

摘要

Heart rate variability (HRV) is a clinical marker used to assess autonomic function, and the application of filtering algorithms may significantly influence data quantification and interpretation. Although HRV has been extensively studied in individuals with type 2 diabetes mellitus (T2DM), the impact of artefact correction methods remains underexplored. To evaluate the effects of different artefact correction filters in Kubios software on short-term HRV parameters in individuals with T2DM. This cross-sectional, descriptive, and observational study included adults (≥ 18 years) diagnosed with T2DM. Anthropometric and metabolic data were collected, including fasting blood samples for glucose, insulin, and lipid profiles. HRV indices were analyzed across time-domain, frequency-domain, nonlinear, and global metrics. Statistical analysis was performed using ANOVA or Friedman tests according to data distribution, with significance set at p < 0.05. The sample consisted of 52 individuals (67% male, mean age 52 ± 8 years, mean BMI 29.65 ± 5.50 kg/m²). The median duration of T2DM was 3 years (IQR 1.5–10). Median metabolic parameters were insulin 12.50 µU/mL, triglycerides 141.50 mg/dL, fasting glucose 149.50 mg/dL, and HbA1c 8.65% (IQR 7.20–10.00). Application of the most restrictive artefact correction setting (“very strong”) in Kubios significantly modified overall HRV as well as time-domain, frequency-domain, and nonlinear parameters (p < 0.05), highlighting its influence on HRV quantification. This study demonstrates that artefact correction filters, particularly the “very strong” setting, substantially affect HRV analysis in individuals with T2DM. Excessively restrictive filtering may distort autonomic metrics and potentially bias interpretation. Standardization of artefact correction methods is essential to ensure accurate and reproducible HRV assessment in clinical and research settings.