Positive affect and heart rate variability: a dynamic analysis

Tony Beatton, Ho Fai Chan, Uwe Dulleck, Andrea Ristl, Markus Schaffner, Benno Torgler

    Research output: Contribution to journalArticle

    Abstract

    Traditional survey methods can provide noisy data arising from recall, memory and other biases. Technological advances (particularly in neuroscience) are opening new ways of monitoring physiological processes through non-intrusive means. Such dense continuous data provide new and fruitful avenues for complementing self-reported data with a better understanding of human dynamics and human interactions. In this study, we use a survey to collect positive affect (feelings) data from more than 300 individuals over a period of 24 h, and at the same time, map their core activities (5000 recorded activities in total) with measurements of their heart rate variability (HRV). Our results indicate a robust correlation between the HRV measurements and self-reported affect. By drawing on the neuroscience and wellbeing literature we show that dynamic HRV results are what we expect for positive affect, particularly when performing activities like sleep, travel, work, exercise and eating. This research provides new insights into how to collect HRV data, model and interpret it.
    Original languageEnglish
    JournalScientific Reports
    Volume14
    DOIs
    Publication statusPublished - 2024

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