Heart rate variability with photoplethysmography in 8 million individuals: a cross-sectional study
Open accessAbstract
Background Heart rate variability, or the variation in the time interval between consecutive heart beats, is a non-invasive dynamic metric of the autonomic nervous system and an independent risk factor for cardiovascular death. Consumer wrist-worn tracking devices using photoplethysmography, such as Fitbit, now provide the unique potential of continuously measuring surrogates of sympathetic and parasympathetic nervous system activity through the analysis of interbeat intervals. We aimed to leverage wrist-worn trackers to derive and describe diverse measures of cardiac autonomic function among Fitbit device users. Methods In this cross-sectional study, we collected interbeat interval data that are sent to a central database from Fitbit devices during a randomly selected 24 h period. Age, sex, body-mass index, and steps per day in the 90 days preceding the measurement were extracted. Interbeat interval data were cleaned and heart rate variability features were computed. We analysed heart rate variability metrics across the time (measured via the root mean square of successive RR interval differences [RMSSD] and SD of the RR interval [SDRR]), frequency (measured by high-frequency and low-frequency power), and graphical (measured by Poincare plots) domains. We considered 5 min windows for the time and frequency domain metrics and 60 min measurements for graphical domain metrics. Data from participants were analysed to establish the correlation between heart rate variability metrics and age, sex, time of day, and physical activity. We also determined benchmarks for heart rate variability (HRV) metrics among the users. Findings We included data from 8 203 261 Fitbit users, collected on Sept 1, 2018. HRV metrics decrease with age, and parasympathetic function declines faster than sympathetic function. We observe a strong diurnal variation in the heart rate variability. SDRR, low-frequency power, and Poincare S 2 show a significant variation with sex, whereas such a difference is not seen with RMSSD, high-frequency power, and Poincare S 1 . For males, when measured from 0600 h to 0700 h, the mean low-frequency power decreased by a factor of 66·5% and high-frequency power decreased by a factor of 82·0% from the age of 20 years to 60 years. For females, the equivalent factors were 69·3% and 80·9%, respectively. Comparing low-frequency power between males and females at the ages of 40–41 years, measured from 0600 h to 0700 h, we found excess power in males, with a Cohen's d effect size of 0·33. For high-frequency power, the equivalent effect size was −0·04. Increased daily physical activity, across age and sex, was highly correlated with improvement in diverse measures of heart rate variability in a dose-dependent manner. We provide benchmark tables for RMSSD, SDRR, high and low frequency powers, and Poincare S 1 and S 2 , separately for different ages and sex and computed at two times of the day. Interpretation Diverse metrics of cardiac autonomic health can be derived from wrist-worn trackers. Empirical distributions of heart rate variability can potentially be used as a framework for individual-level interpretation. Increased physical activity might yield improvement in heart rate variability and requires prospective trials for confirmation. Funding Fitbit.
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A study on over 8 million fitbit users reported that young people who took 12,000-13,000 steps a day displayed 21.2% (males) and 22.9% (females) greater high frequency HRV than those who took 5,000-6,000 steps.
“For example, a study on over 8 million fitbit users (Natarajan et al., 2020) reported that young people who took 12,000–13,000 steps a day displayed 21.2% (males) and 22.9% (females) greater high frequency HRV than those who took 5,000–6,000 steps.”
From The Complex Construct of Wellbeing and the Role of Vagal Function (2022) · cites this paper as Natarajan et al., 2020
The largest study was conducted in 2019 in North America and included over 8 million participants (75.71%).
“The largest study we identified was conducted in 2019 in North America and included over 8 million participants (75.71%) [153]; the second largest was a European study comprising 742,000 participants (6.85%) [162].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [153]
Some large-scale population-based studies (11/179, 6.1%) relied on secondary data collected with mobile apps or web-based wearable platforms or provided through a wearable company.
“Some large-scale population-based studies (11/179, 6.1%) relied on secondary data collected with mobile apps [87] or web-based wearable platforms [153] or provided through a wearable company [189].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [153]
One study in North America, conducted in 2019 with over 8 million participants, dominated the image of the distribution of participants.
“One study in North America, conducted in 2019 with over 8 million participants [153], dominated the image of the distribution of participants.”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [153]
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- Authors
- Natarajan, A., Pantelopoulos, A., Emir-Farinas, H., & Natarajan, P.
- Affiliations
- Fitbit (United States) (US)
- Broad Institute (US)
- Corresponding author
- Natarajan, Pradeep
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 2
- Issue
- 12
- Pages
- e650-e657
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W3107602031
- Cited by
- 229
- References
- 32
- Citations by year
- 2026: 44 · 2025: 39 · 2024: 41 · 2023: 52 · 2022: 29 · 2021: 22 · 2020: 2
- FWCI
- 16.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- heart rate variability, photoplethysmography, Fitbit, autonomic nervous system, parasympathetic function, sympathetic function, diurnal variation, sex differences, age-related decline, physical activity, Poincaré plot, low-frequency power, cardiovascular risk, wearable health devices, RMSSD, high-frequency power, cardiovascular mortality risk factors
- MeSH terms
- Wearable Electronic Devices, Adolescent, Adult, Age Factors, Aged, Cardiovascular Diseases, Cross-Sectional Studies, Female, Heart Rate, Humans, Male, Middle Aged, Parasympathetic Nervous System, Prospective Studies, Sex Factors, Sympathetic Nervous System, Exercise, Photoplethysmography, Telemedicine, Monitoring, Ambulatory, Young Adult
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