{
  "abstract": "Heart rate variability (HRV) reflects autonomic nervous system functioning and is commonly examined using statistical methods that compare individual HRV indices across demographic groups. Such approaches, however, may overlook group differences in how HRV components relate to one another. This study applies a network approach to delineate gender differences in the conditional relationships among short-term HRV indices and demographic characteristics in a Singaporean adult sample ($N = 2147$). Conventional regression analyses identified gender differences in average heart rate and low-frequency/high-frequency (LF/HF) ratio, whereas differences in root mean square of successive differences (RMSSD) and standard deviation of normal-to-normal intervals (SDNN) were minimal and largely confined to specific age groups. In contrast, mixed graphical models revealed more pronounced gender differences in the conditional organisation of HRV. Among males, age was negatively associated with SDNN, SDNN was strongly associated with RMSSD, and weaker inverse relationships emerged between age and average heart rate and LF/HF ratio. The female network was substantially sparser, with an association observed primarily between ethnicity and LF/HF ratio, and no association between SDNN and RMSSD. These findings demonstrate that network analysis can reveal structural differences across gender that may be overlooked in traditional analyses. More broadly, they highlight the utility of psychological network analysis as an exploratory, hypothesis-generating tool for characterising multivariate conditional structures, even when the network theory underlying the approach is not assumed to hold.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Ho Han Sheng"
    },
    {
      "name": "Emily Ortega"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a7s8y6sje0kr9.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/uxb4s_v1",
  "fulltext_url": null,
  "id": "a7s8y6sje0kr9",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/uxb4s_v1/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Physiology"
    ],
    [
      "bepress",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Quantitative Psychology"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://osf.io/uxb4s/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a7s8y6sje0kr9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:17Z",
      "native_id": "uxb4s",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/uxb4s_v1/"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "bepress:Physiology",
      "subfield": "physiology"
    }
  ],
  "title": "A Network Approach to Analysing Heart-Rate Variability",
  "title_text": "A Network Approach to Analysing Heart-Rate Variability",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:17Z",
  "url": "https://research.draconitic.com/paper/a7s8y6sje0kr9",
  "venue": "PsyArXiv",
  "versions": []
}