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The advent of digital health and research has has opened up new frontiers in healthcare delivery, diagnosis, treatment, and disease prevention. One central element that fuels this digital revolution is data. Data has become the lifeblood of digital health and research, driving insights, innovation, and improved patient outcomes. However, the potential of data can only be fully realized when there is transparency.

What is data transparency

Data transparency refers to the ability of all stakeholders to have access to and understand the data that is being used. It is about making the data freely available and clear for everyone to see, without hidden agendas or misuse. This transparency is critical to foster trust, enable collaboration, and ensure accountability in the digital health landscape.

They can see where the data comes from, how it is processed, and how it is used. This transparency empowers everyone to make informed decisions, enhances collaboration, and fosters trust.

The importance of data transparency

The value of data transparency in digital health and research cannot be overstated. It forms the foundation of trust between patients, healthcare providers, and researchers. When there is transparency, patients are more likely to share their data, knowing that it will be used responsibly and for their benefit. This trust is crucial for the success of digital health initiatives.

Data transparency also leads to better research outcomes. When researchers have access to transparent data, they can conduct more robust analyses, develop novel algorithms, make accurate predictions, and develop innovative solutions and even discover new biomarkers.

That’s why transparency is one of the core values of Biostrap in how it provides access to data that’s captured by its wearable, Kairos. This transparency enables collaboration, as researchers can share their data and findings, leading to greater scientific progress.

Moreover, data transparency can help to ensure accountability in the digital health space. It allows for the tracking and auditing of data, ensuring that it is used responsibly and ethically.

This accountability can help to prevent data breaches and misuse, protecting the privacy and rights of patients.

The missing link: data transparency in wearables

Wearables, including fitness trackers, smartwatches, and health monitors, collect a wealth of data about our bodies and lifestyles. This data can provide valuable insights into our health and well-being, enabling personalized healthcare and proactive disease prevention.

However, there is a missing link that threatens to undermine their value: data transparency. Many wearable companies operate in a black box, with little transparency about how they collect, process, and use the data.

This lack of transparency raises concerns about the privacy, security, and ethical use of the data.

That’s where Biostrap’s team wanted to bridge the gap and be a fully transparent digital health solution. At Biostrap, we believe that transparency is key to building trust and fostering collaboration in the digital health space.

Hence, we are committed to providing our enterprise clients with clear, understandable information about how the data is collected, processed, and used. It also gives them control over the data, allowing them to decide who can access it and for what purpose.

It can also enable collaboration among researchers, leading to better research outcomes. Especially so because Biostrap also collaborates with researchers, sharing anonymized data to support health research.

Challenges to data transparency in digital health and research

While the benefits of data transparency are clear, there are several challenges to achieving it in digital health and research. These challenges include privacy concerns, data security risks, and the complexity of health data.

Privacy is a major concern in the digital health space. Many individuals are wary of sharing their health data, fearing that it could be misused or fall into the wrong hands. To address this concern, there is a need for strong privacy protections and clear communication about how the data will be used.

Data security is another significant challenge. With the increasing volume of health data being collected, the risk of data breaches and cyberattacks is high. This risk can be mitigated through robust data security measures and regular audits.

To address these challenges, Biostrap has implemented high-quality data protection measures, built a secure ecosystem that’s fully HIPAA-compliant, and ensures that user and patient data is safe and can never be accessed by a third party.

Reach out to our team to discuss how Biostrap can help get your research project off the ground, or how we can help best implement a successful remote health monitoring program in your practice.

Click on the “Partner with us” button at the top of THIS page to book a meeting with a team member.

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In today’s fast-paced world, stress has become a pervasive problem that affects our physical and mental well-being. Chronic stress can have detrimental effects on our health, leading to various disorders and increasing the risk of cardiovascular problems and mental health issues. For effective stress monitoring and management, it is crucial to accurately measure its impact on our bodies.

Traditional methods of stress assessment, such as physical tests and questionnaires, have limitations in terms of subjectivity and accuracy.

However, advancements in wearable biosensors have paved the way for real-time, continuous monitoring of stress biomarkers, providing valuable insights for clinical diagnoses and personal stress management.

Unraveling the complexities of stress: a holistic approach

Stress, as an intricate and multifaceted physiological response to external demands, triggers the orchestrated release of cortisol, adrenaline, and noradrenaline – molecular protagonists underpinning the “fight-or-flight” reaction.

However, prolonged exposure to stress ushers in a cascade of physiological changes, resulting in disruptions to the harmonious equilibrium orchestrated by the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic adrenal medullary (SAM) axis. These disruptions contribute substantively to the pathogenesis of anxiety, depression, and cardiovascular morbidity.

Beyond traditional metrics: the futility of conventional stress assessment

Traditionally, stress has been measured through physical tests and questionnaires. The Trier Social Stress Test (TSST) is a commonly used test that assesses acute stress levels by subjecting individuals to public speaking and arithmetic tasks. Saliva, blood, psychophysiological, and cognitive measures are then analyzed to evaluate stress levels.

While these tests provide valuable information, they are not without limitations. Variability in test conditions and the subjective nature of self-reporting can impact the reliability and reproducibility of results.

Another widely used method is the Perceived Stress Scale (PSS), a survey that assesses an individual’s overall stress levels based on their perceived life experiences. Similarly, the Kessler Psychological Distress Scale (K10) measures mental distress levels. These tools provide insights into an individual’s subjective experience of stress but do not offer objective measures of physiological responses.

Wearable biosensors: a panacea for stress monitoring

Recent advancements in stress monitoring focus on the quantification of stress biomarkers, which are molecules or biometrics, or physiological indicators, that provide insight into an individual’s nervous system state. Biomarkers can be detected in various bodily fluids, such as blood, saliva, urine, and sweat. Sweat biomarkers, in particular, have gained attention due to the non-invasiveness and ease of collection.

Sweat contains a wide range of metabolites, electrolytes, and minerals that can serve as indicators of stress levels. Cortisol, a glucocorticoid hormone, is considered the gold standard for evaluating the activity of the HPA axis. Other stress biomarkers include epinephrine, norepinephrine, alpha-amylase, and interleukin-6. Electrochemical and colorimetric transduction methods have been developed to detect and quantify these biomarkers in sweat.

Additionally, the Biostrap Kairos, introduces a novel way to assessing autonomic nervous system balance including sympathetic and parasympathetic branch quantifications. Utilizing raw PPG, Kairos captures relevant biometrics including beat-to-beat heart rate data, heart rate variability (HRV) and respiratory rate to allow for in-depth objective data analysis.

Such real-time, seamless, and unobtrusive collection of stress data is highly valuable for simultaneously prioritizing user comfort and feasibility during diverse physiological parameters.

Challenges of sweat measurements

While sweat analysis have shown great potential in stress monitoring, there are still challenges to overcome. The correlation between sweat analyte (or chemical undergoing analysis) concentrations and blood concentrations is complex, and factors such as sweat rate and analyte distribution can affect the accuracy of measurements. Extracting interstitial fluid (ISF) analytes in a non-invasive manner also presents challenges, as extraction efficiency and skin surface contamination can impact accuracy.

Further research is needed to validate the clinical utility of sweat as a diagnostic biofluid for stress monitoring. Improvements in sampling methods and analyte monitoring techniques are necessary to enhance the reliability and accuracy of wearable stress sensors. Integration of multiple sensing arrays and the development of multiplexed wearable sensing platforms hold promise for comprehensive stress assessment.

Innovations toward a new trajectory

Advancements in wearable biosensors have revolutionized the field of stress monitoring. These devices provide real-time, continuous data on stress biomarkers, allowing for personalized stress management and clinical diagnoses. While sweat sensors offer a non-invasive and convenient method for stress assessment, there are challenges that are yet to be overcome.

By harnessing the power of wearable biosensors, like Biostrap Kairos, we can gain valuable insights into our stress levels in real time and any given time of the day and take proactive steps to improve the state of our nervous system.

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Understanding Heart Rate Variability (HRV)

There are a number of metrics we can use to get a snapshot of our health and well-being. From blood pressure to heart rate, doctors and researchers are more interested in our physiological data than ever before, especially heart rate variability in healthy individuals. 

There is one marker for resilience and well-being that researchers have just begun to utilize over the past two decades. It’s called Heart Rate Variability, or HRV. This metric, once measured primarily in athletes and those with abnormal heart rhythms, has since become a key piece of data for individuals wanting insight into the state of their physiology and nervous system.

HRV provides profound insights into the autonomic nervous system’s regulation and overall physiological resilience. This comprehensive guide delves into the multifaceted nature of HRV, exploring its significance as a biomarker for health, fitness, and stress management. With advancements in wearable technology and data analytics, HRV has transitioned from a niche research interest to a mainstream tool for individuals and healthcare professionals alike. By examining the factors influencing HRV, such as age, lifestyle, and environmental stressors, this guide aims to equip readers with the knowledge to interpret HRV data effectively. Furthermore, it highlights the potential applications of HRV in optimizing athletic performance, enhancing mental well-being, and monitoring chronic health conditions. As we navigate the complexities of modern life, understanding HRV offers a window into the body’s adaptive capacity, empowering individuals to make informed decisions for improved health outcomes. Let’s dive in!

What is Heart Rate Variability and Why is it Important?

Heart Rate Variability (HRV) measures the variation in time between consecutive heartbeats and reflects how well the autonomic nervous system (ANS) regulates the body. The ANS consists of two branches:

  1. Sympathetic Nervous System (SNS): The “fight or flight” system that increases heart rate in response to stress.
  2. Parasympathetic Nervous System (PNS): The “rest and digest” system that slows the heart rate to promote recovery.

HRV reflects the balance between these systems. A higher HRV indicates better adaptability and a healthy response to stress, while lower HRV can signal fatigue or imbalance.

Unlike a metronome, which beats at perfectly regular intervals, a healthy heart varies its time between beats. This variation, known as the inter-beat interval (IBI), shows the heart’s flexibility in responding to different demands. A metronome-like heart rhythm (with no variability) could indicate stress or impaired health, whereas HRV reflects a heart that adapts dynamically to changing conditions.

Heart Rate Variability (HRV) is a measure of the variation in time (in milliseconds) between consecutive heart beats, known as the heart period. It is an important indicator of the autonomic nervous system’s regulation of the heart and is used in the analysis of heart rate and blood pressure. A high HRV is generally associated with a healthy heart and indicates a robust autonomic response, whereas a lower heart rate variability can be a sign of stress or potential health issues, such as congestive heart failure.

Heart Rate Variability (HRV) Illustration with time between each peaks in milliseconds

What Is a Normal Heart Rate Variability (HRV)?

HRV has a large individual component that has yet to be understood clinically, and therefore is more often used to assess changes in health over time. HRV can fluctuate day-to-day based on exposure to stress, sleep quality, diet, exercise, and more. This leads to low repeatability, and therefore makes normative data difficult to collect. 

The table below indicates age ranges and genders and their corresponding average HRV (RMSSD) ranges. Note that these are general guidelines and individual HRV can vary significantly based on factors like fitness level, stress, and overall health.

20-29 50-100 55-105
30-39 45-95 45-95
40-49 40-85 40-85
50-59 35-75 35-75
60-69 30-65 25-60
70+ 25-60 25-65
Age Range Men (RMSSD) Women (RMSSD)

Key points

  1. HRV generally decreases with age for both men and women
  2. Men tend to have slightly higher HRV than women, especially in younger age groups.
  3. The difference in HRV between genders becomes less pronounced in older age group.
  4. The most common HRV for men is around 40 ms, while for women it’s around 37 ms.
  5. A normal HRV for adults can range from below 20 to over 200 milliseconds, emphasizing the high degree of individual variation.

Long-term vs Short-term Trends

HRV can be difficult to interpret from a single reading because it is influenced by many factors and fluctuates throughout the day. However, tracking both short-term and long-term HRV trends provides valuable insights into your health and well-being.

Short-term Trends:

In the short term, HRV can fluctuate due to immediate factors like stress, sleep quality, hydration, or even meals. A daily spot check might show variability based on these conditions, but such short-term changes can help identify immediate stressors or signs of recovery. For example, a temporary dip in HRV after a hard workout or poor night’s sleep is normal, as the body is dealing with physical stress. However, frequent day-to-day fluctuations can indicate poor recovery or chronic stress, especially if there’s a high coefficient of variation (CV%).

Long-term Trends:

Long-term trends, on the other hand, provide a more stable and meaningful picture. Tracking HRV over weeks or months allows for non-invasive insight into overall health and the effectiveness of lifestyle changes. In general, higher HRV over time indicates better stress management and physiological adaptability. Positive lifestyle habits like regular exercise, meditation, and good sleep hygiene should lead to a gradual increase in HRV. Conversely, a downward trend in heart rate variability may signal issues like overtraining, poor sleep, illness, or excessive stress.

While an increase in HRV itself won’t directly improve health, it reflects healthier adaptations in the body’s ability to handle stress over time.

Understanding HRV and its Role in Health

Understanding heart rate variability (HRV) and its role in health is crucial for both clinicians and patients. Heart rate variability is associated with the autonomic nervous system’s regulation of the heart, reflecting how efficiently the heart is beating. A high heart rate variability is generally indicative of good cardiovascular health, while a reduced HRV may signal underlying health issues. The relationship between heart rate variability and overall health is complex, as various factors can affect your heart rate variability, including stress, sleep, and physical activity.

In clinical settings, heart rate variability analysis is often used to assess the health status of patients with heart failure and other cardiac conditions. Studies have found that HRV can be a predictive marker for adverse outcomes in patients with heart conditions. The analysis of heart rate variability involves measuring changes in heart rate over time, often using heart rate monitors. These devices can track low frequency and other components of HRV to provide insights into a person’s health.

To improve your HRV, practices such as heart rate variability biofeedback and lifestyle modifications can be beneficial. Establishing a baseline HRV and monitoring average HRV can help individuals understand their normal HRV levels and make informed decisions about their health. By maintaining a healthy lifestyle, individuals can potentially enhance their HRV, contributing to better overall health and well-being.

The Connection Between HRV and the Autonomic Nervous System

The importance of HRV lies in its ability to reflect the balance within the autonomic nervous system, particularly between the sympathetic and parasympathetic branches. Heart rate variability indexes the oscillations in the heart and provides insights into how many times your heart beats in response to various stimuli. The role of heart rate variability is crucial in understanding the rate variability and cardiac vagal tone, which is a key component of heart rate variability.

How to Measure Heart Rate Variability Effectively?

It’s important to use a reliable device and ensure that the context of your measurement is consistent. Biostrap, as an example, offers advanced, clinical-grade sensors that can capture accurate HRV readings, making it an excellent choice for monitoring HRV.

  1. Use a Reliable Device: Devices like Biostrap are equipped with precise sensors that measure HRV through photoplethysmography (PPG). Accurate readings from devices like this help ensure that the data you’re collecting is meaningful and reflective of your true autonomic function.
  2. Measure During Rest: The context of when and how you measure HRV is critical. To get accurate readings, measure HRV when your body is calm, such as after waking up or during a relaxed state. Biostrap’s Spot Check feature can be used during these times to capture a stable HRV reading, giving insight into your body’s resting state and parasympathetic activity.
  3. Consistency is Key: HRV can fluctuate throughout the day based on factors like stress, activity, and hydration. For consistent results, take measurements at the same time every day, in similar conditions. For example, using Biostrap each morning after a good night’s sleep provides a reliable baseline, whereas measuring during a stressful moment might lead to an artificially low HRV.
  4. Track HRV Trends Over Time: Short-term fluctuations are normal, but the power of HRV measurement comes from observing long-term trends. Devices like Biostrap allow you to monitor your HRV data over weeks or months, giving you a better understanding of your overall heart health and how factors like exercise, diet, and stress management affect your autonomic balance.
  5. Context Matters: HRV is highly influenced by context, such as whether you’re in a resting state or during physical activity. Measuring HRV during or right after a workout can show a low HRV, as your sympathetic nervous system is activated. However, measuring at rest, such as using Biostrap when relaxed, will provide a more accurate assessment of your parasympathetic recovery state.
  6. Analyze with HRV Metrics: The Biostrap app offers a comprehensive analysis of your HRV, including metrics like RMSSD and SDNN. These metrics are important for understanding your short-term and long-term HRV, providing insights into how well your body is managing stress and recovery.

Heart Rate Variability Parameters

Various methods exist to measure HRV using different analytical approaches, each providing unique insights into the variability and heart rate dynamics. The methods are listed below.

Time Domain

Time domain measures focus on the variation in time intervals between successive heartbeats, known as R-R intervals or NN intervals (normal-to-normal heartbeats). These are the simplest HRV measurements and are widely used to assess the overall level of autonomic activity, particularly the parasympathetic nervous system’s influence on heart rate.

Common time domain metrics:

  • SDNN: Standard deviation of all NN intervals, indicating overall HRV. Higher values reflect greater variability and a healthier balance between the sympathetic and parasympathetic nervous systems.
  • RMSSD: Root mean square of successive differences between NN intervals, primarily reflecting short-term HRV influenced by the parasympathetic nervous system.
  • pNN50/NN50: Percentage or count of successive NN intervals that differ by more than 50 ms, indicative of short-term vagal (parasympathetic) activity.

Higher time domain values generally indicate better heart health, higher parasympathetic activity, and better stress recovery. Lower values can signal stress, fatigue, or chronic conditions.

Frequency Domain

Frequency domain analysis breaks down the R-R intervals into different frequency bands using spectral analysis (usually via Fast Fourier Transform or autoregressive modeling). Each frequency band is associated with specific components of autonomic regulation.

Common frequency domain metrics:

  • VLF (Very Low Frequency, <0.04 Hz): Represents slow regulatory mechanisms, possibly reflecting thermoregulation and long-term regulation.
  • LF (Low Frequency, 0.04-0.15 Hz): Represents both sympathetic and parasympathetic activity, though it is often linked more closely with sympathetic modulation.
  • HF (High Frequency, 0.15-0.4 Hz): Reflects parasympathetic or vagal activity, often associated with respiratory cycles (respiratory sinus arrhythmia).
  • LF/HF Ratio: A commonly used measure to assess the balance between sympathetic (LF) and parasympathetic (HF) activity. A higher ratio may indicate increased sympathetic nervous system dominance, while a lower ratio indicates parasympathetic dominance.

Frequency domain measures provide a deeper look into the balance of autonomic activity over time. High HF values suggest strong parasympathetic activity, while high LF values suggest either increased sympathetic or both sympathetic and parasympathetic activity. The LF/HF ratio is often used to assess autonomic balance, but its interpretation is context-dependent.

Non-linear Domain

Non-linear analysis looks at HRV from the perspective of chaos theory and complexity. It assesses the unpredictability and variability in heart rate patterns over time, providing insights into the autonomic nervous system’s flexibility in adapting to stressors.

  • Common non-linear domain metrics:
    • Poincaré Plot: A graphical representation of the relationship between successive R-R intervals, showing both short-term and long-term variability.
      • SD1: Measures short-term HRV, corresponding to parasympathetic activity.
      • SD2: Measures long-term HRV, associated with the overall variability.
    • Sample Entropy (SampEn) and Approximate Entropy (ApEn): Measures the complexity and irregularity of heart rate fluctuations. Higher entropy reflects greater variability and adaptability.
    • DFA (Detrended Fluctuation Analysis): Assesses fractal-like correlation properties in HRV, reflecting long-term regulatory mechanisms.

Non-linear metrics provide a deeper understanding of how adaptive and flexible the autonomic nervous system is. Higher complexity (e.g., high entropy) suggests a robust, adaptive cardiovascular system, while lower values can indicate poor adaptability and increased risk for cardiovascular problems.

Geometric Domain

Geometric methods assess HRV by creating geometrical representations of R-R interval distributions, typically through histograms or other graphical plots.

  • Common geometric domain metrics:
    • HRV Triangular Index: Measures the total number of NN intervals divided by the height of the frequency distribution of all NN intervals. It reflects overall HRV and is sensitive to both short- and long-term changes in variability.
    • TINN (Triangular Interpolation of NN Interval Histogram): The width of the base of the RR interval histogram, indicating overall variability.

Geometric methods give a broad view of HRV, emphasizing long-term trends in variability. These metrics are particularly useful in clinical settings for assessing overall heart health and risk stratification.

Which HRV method is better?

The choice of method for measuring HRV varies depending on the specific aspect of autonomic function being investigated. Each method offers distinct advantages and limitations, contributing to a holistic understanding of heart rate variability among different populations and clinical conditions.

While RMSSD has taken the stage as the most popular HRV parameter, there are over 50 various HRV measurements used in clinical psychology as shown in the table below.

HRV Parameter Description Domain
Mean RR Average time between successive R-R intervals Time
SDNN Standard deviation of NN intervals Time
RMSSD Root mean square of successive differences between adjacent RR intervals Time
pNN50 Percentage of successive RR intervals differing by more than 50 ms Time
NN50 Number of successive RR intervals differing by more than 50 ms Time
SDANN Standard deviation of the average of NN intervals in 5-minute segments Time
HR Max – HR Min Difference between maximum and minimum heart rate Time
Triangular Index Integral of the density distribution of RR intervals divided by the height of the histogram Geometric
TINN Baseline width of the RR interval histogram Geometric
LF Power in low-frequency range (0.04-0.15 Hz) Frequency
HF Power in high-frequency range (0.15-0.4 Hz) Frequency
VLF Power in very low-frequency range (0.0033-0.04 Hz) Frequency
ULF Power in ultra-low-frequency range (<0.0033 Hz) Frequency
LF/HF Ratio Ratio of LF to HF power, indicating sympathovagal balance Frequency
Total Power Total power of all spectral components Frequency
ApEn Approximate entropy, measures the complexity or irregularity in data Non-linear
SampEn Sample entropy, an improvement over ApEn for measuring irregularity Non-linear
DFA Detrended fluctuation analysis, assesses fractal correlations in RR intervals Non-linear
Baevsky Stress Index A geometric measure of HRV that reflects the stress experienced by the cardiovascular system Geometric
Poincaré SD1 Short-term HRV, ellipse perpendicular to the line of identity in the Poincaré plot Geometric
Poincaré SD2 Long-term HRV, ellipse along the line of identity in the Poincaré plot Geometric
Shannon Entropy Measures the unpredictability of RR intervals Non-linear
LFnu Normalized low-frequency power Frequency
HFnu Normalized high-frequency power Frequency
CVRR Coefficient of variation of RR intervals Time
CSI (Cardiac Sympathetic Index) A ratio indicating sympathetic activity Geometric
CVI (Cardiac Vagal Index) A ratio indicating parasympathetic activity Geometric
RR Triangular Index Measure of overall HRV based on the distribution of RR intervals Geometric
HF Peak Frequency Frequency at which the peak power of the HF band occurs Frequency
LF Peak Frequency Frequency at which the peak power of the LF band occurs Frequency
ULF Peak Frequency Frequency at which the peak power of the ULF band occurs Frequency
VLF Peak Frequency Frequency at which the peak power of the VLF band occurs Frequency
Lyapunov Exponent Measure of the rate of separation of infinitesimally close trajectories Non-linear
Correlation Dimension Estimates the dimensionality of RR intervals Non-linear
Multiscale Entropy Measures complexity across multiple scales Non-linear
Fractal Dimension Measures self-similarity in HRV data Non-linear
SDRR Standard deviation of RR intervals Time
NN20 Number of successive RR intervals differing by more than 20 ms Time
pNN20 Percentage of successive RR intervals differing by more than 20 ms Time
Skewness Measures asymmetry in the RR interval distribution Non-linear
Kurtosis Measures the “tailedness” of the RR interval distribution Non-linear
Log Power Logarithmic transformation of total power Frequency
Relative LF Power LF power relative to total power Frequency
Relative HF Power HF power relative to total power Frequency
Relative VLF Power VLF power relative to total power Frequency
Deceleration Capacity Measures deceleration in heart rate, associated with vagal tone Time/Non-linear

Using a Heart Rate Variability Monitor for Accurate Results

Utilizing a reliable and clinical-grade Heart Rate Variability Monitor, such as the Biostrap Kairos, offers a sophisticated method for obtaining accurate results in assessing cardiovascular health.

To obtain accurate R-R intervals from PPG data for HRV analysis, it’s essential to filter out noise and artifacts that can interfere with signal quality. Motion artifacts, sensor placement, and external interference can cause disruptions in PPG signals, so applying band-pass filtering helps isolate the frequency range associated with heartbeats. Advanced noise reduction techniques, like adaptive noise cancellation or Kalman filtering, are used to remove unwanted noise from movement or environmental factors. Additionally, accurate peak detection is necessary to identify true heartbeats and avoid confusing features like the dicrotic notch in the pulse waveform.

Once the signal is processed, artifact correction plays a crucial role in ensuring clean data. Motion or stress-induced outliers can be filtered out using statistical techniques, and signal reconstruction methods, such as spline interpolation, can fill in gaps where noise has corrupted the signal. Using adaptive algorithms that adjust to dynamic conditions, such as changes in physical activity or body movement, ensures consistency in R-R interval detection across different states.

Lastly, it’s important to measure HRV in appropriate contexts, such as during rest, to avoid artificially low readings caused by sympathetic nervous system activation during exercise or stress. By applying these filtering techniques and carefully managing the measurement context, PPG-derived HRV data can be made more reliable and accurate, comparable to ECG in many cases.

You can track your HRV with clinical reliability with the Biostrap wrist-worn device and keep an eye on your nocturnal HRV as well as weekly, monthly and yearly trends.

What Factors Influence Heart Rate Variability?

Several factors influence heart rate variability, including age, physical fitness, and lifestyle choices. As individuals age, there is a natural decline in HRV, which may be attributed to the reduced elasticity of the heart and blood vessels, as well as changes in autonomic function. Conversely, regular physical activity and a healthy lifestyle can enhance HRV by improving cardiovascular efficiency and autonomic balance.

Psychological stress and emotional well-being also significantly impact heart rate variability. Chronic stress and anxiety can lead to a reduction in HRV, indicating a dominance of the sympathetic nervous system over the parasympathetic system. This imbalance may result in increased heart rate and reduced adaptability to environmental changes. Conversely, practices such as meditation and mindfulness can enhance HRV by promoting relaxation and parasympathetic activation.

Moreover, sleep quality and duration are vital determinants of heart rate variability. Poor sleep can lead to low HRV, suggesting impaired recovery and heightened stress levels. Differences in heart rate during sleep cycles are essential for restoring autonomic balance and ensuring optimal cardiovascular health.

The Impact of the Sympathetic Nervous System on HRV

The sympathetic nervous system plays a significant role in reducing heart rate variability (HRV) by activating the body’s “fight or flight” response during times of stress. When the sympathetic system is dominant, it increases heart rate and reduces the body’s ability to relax, leading to a lower HRV.

How the Parasympathetic Nervous System Affects HRV

The parasympathetic nervous system has a positive effect on heart rate variability (HRV) by promoting relaxation and recovery in the body. When the parasympathetic system is active, it slows down the heart rate and allows the body to enter a calm state, which increases HRV.

External Factors that Can Influence HRV

Several external factors can significantly influence heart rate variability (HRV).

  1. Stress: Activates the sympathetic nervous system, reducing HRV.
  2. Physical Activity: Regular exercise improves HRV long-term, but intense exercise can temporarily lower HRV.
  3. Sleep Quality: Poor sleep decreases HRV, while good sleep boosts parasympathetic nervous system activity and increases HRV.
  4. Diet:
    • Alcohol and caffeine can lower HRV by increasing sympathetic nervous system activity.
    • A diet rich in omega-3 fatty acids supports higher HRV.
  5. Hydration: Dehydration reduces HRV by straining cardiovascular function.
  6. Temperature Extremes: Exposure to heat or cold can stress the body and temporarily reduce HRV.
  7. Emotional State: Positive emotions increase HRV, while negative emotions and anxiety can lower HRV.
  8. Medication: Some medications, especially those affecting the cardiovascular or autonomic systems, can impact HRV levels.

How to Increase Heart Rate Variability for Better Health?

Techniques to Increase HRV Naturally

Here are several science-backed techniques that have been shown to increase Heart Rate Variability (HRV) naturally.

1. Mindfulness Meditation
Mindfulness meditation promotes relaxation by focusing on the present moment, which activates the parasympathetic nervous system. Regular practice has been shown to reduce stress and increase HRV, helping balance the autonomic nervous system by reducing sympathetic dominance and enhancing vagal tone.

2. Deep Breathing (Slow and Diaphragmatic)
Slow, controlled breathing, especially at a rate of around 5-6 breaths per minute, stimulates the vagus nerve, improving parasympathetic activity. This method naturally increases HRV by helping the body transition from a stressed state to a relaxed, restorative state.

3. Physical Exercise
Moderate, consistent aerobic exercise boosts cardiovascular health and enhances autonomic balance, leading to long-term increases in heart rate variability and improved influence on HRV. Exercise strengthens parasympathetic modulation and reduces sympathetic nervous system overactivity, particularly when combined with proper recovery.

4. Good Sleep Hygiene
High-quality sleep is essential for autonomic recovery and improving HRV. Ensuring sufficient, uninterrupted sleep allows the body to restore balance between the sympathetic and parasympathetic nervous systems, leading to higher HRV during waking hours.

5. Cold Exposure
Cold showers or ice baths can stimulate the vagus nerve, activating the parasympathetic system and increasing HRV. Short, controlled exposure to cold can enhance autonomic resilience and improve the body’s ability to switch between sympathetic and parasympathetic states.

6. Balanced Diet with Omega-3 Fatty Acids
A diet rich in omega-3 fatty acids (found in fish, nuts, and seeds) supports heart health and reduces inflammation, leading to improvements in HRV. Omega-3s help balance the autonomic nervous system by enhancing parasympathetic tone.

7. Yoga and Tai Chi
These practices combine movement, breathing, and mindfulness, which together increase parasympathetic activity and HRV. Regular participation in yoga or tai chi helps the body maintain a balanced autonomic nervous system, reducing stress and improving heart function.

8. Biofeedback Training
Biofeedback devices allow individuals to monitor and consciously control their HRV. Through guided breathing and relaxation techniques, users can learn to regulate their autonomic nervous system, improving their HRV by promoting parasympathetic activation.

9. Reducing Alcohol and Caffeine Intake
Alcohol and caffeine can reduce HRV by overstimulating the sympathetic nervous system. Reducing consumption of these substances helps maintain autonomic balance, allowing parasympathetic activity to recover and HRV to increase naturally.

10. Positive Social Connections
Meaningful interactions with friends and loved ones can reduce stress and improve HRV. Positive social bonds promote emotional well-being, which activates the parasympathetic nervous system, helping to enhance heart rate variability.

11. Laughter and Positive Emotions
Experiencing joy and laughter activates the parasympathetic nervous system, reducing stress and increasing HRV. Positive emotions contribute to greater autonomic balance, helping the body recover from stress more efficiently.

Each of these methods has been scientifically shown to support autonomic health, helping to increase HRV naturally by promoting parasympathetic activity and reducing the effects of stress.

12. Hydration
Dehydration can strain the heart, increase sympathetic activity, and reduce HRV by making it harder for the body to regulate temperature, blood flow, and overall physiological balance. Staying well-hydrated helps the body maintain a healthy balance between sympathetic and parasympathetic activity, supporting optimal heart function and improving HRV. Drinking enough water throughout the day is a simple yet effective way to promote autonomic balance and enhance overall well-being.

The Role of HRV Biofeedback in Health Improvement

HRV biofeedback is a powerful tool for improving health by providing real-time insights into the balance between the sympathetic and parasympathetic nervous systems. By using biofeedback devices, individuals can monitor their heart rate variability (HRV) and learn to control their breathing, stress responses, and autonomic function. Through this process, users can increase high heart rate variability, promoting a healthier resting heart rate and better regulation of heart rate and blood pressure. Studies show that HRV biofeedback can reduce stress, enhance emotional resilience, and improve cardiovascular health, making it particularly beneficial for those at risk of coronary heart disease or heart failure. Over time, regular HRV biofeedback training can lead to lasting improvements in overall well-being and heart function, as it helps individuals strengthen their parasympathetic nervous system and improve their body’s response to stress.

Understanding the Benefits of a Higher HRV

A higher Heart Rate Variability (HRV) is a strong indicator of a healthy, adaptable heart and balanced autonomic nervous system, reflecting the balance between the sympathetic nervous system (fight or flight) and the parasympathetic nervous system (rest and digest). When HRV is high, it suggests that the heart is responsive to changes in stress, activity, and rest, leading to better regulation of heart rate and blood pressure. Conversely, low HRV or low heart rate variability is often linked to health risks such as coronary heart disease, heart failure, and poor cardiovascular resilience. By increasing HRV through healthy lifestyle choices, individuals can promote heart health, reduce the risk of heart failure, and improve overall well-being.

What are the Implications of Low Heart Rate Variability?

Low Heart Rate Variability (HRV) indicates an imbalance in the autonomic nervous system, with the sympathetic nervous system often dominating over the parasympathetic nervous system. This can suggest that the heart is less adaptable to stress, making it harder to regulate heart rate and blood pressure effectively.

Health Risks Associated with Low Heart Rate Variability

A low heart rate variability is linked to several serious health risks, particularly involving the heart. It is a strong predictor of congestive heart failure, coronary heart disease, and increased mortality. When the HRV index is consistently low, the body’s ability to regulate stress is compromised, leading to poor long-term health outcomes. Heart rate variability in patients with chronic conditions tends to decrease, signaling deteriorating autonomic function. This can further complicate recovery and increase the likelihood of experiencing cardiovascular events like heart attacks or strokes.

Frequently Asked Questions

1. What is a good heart rate variability?

Heart Rate Variability values are highly individual, and what’s considered “good” varies from person to person. Marco Altini points out that it’s more important to focus on personal trends rather than comparing absolute HRV values with others. A higher HRV generally indicates better autonomic nervous system balance and cardiovascular health for an individual, but personal baselines are key to meaningful interpretation.

2. Can tracking HRV help in managing stress and improving recovery?

Yes, monitoring HRV can be a valuable tool for managing stress and enhancing recovery. Andrew Flatt emphasizes that fluctuations in HRV reflect changes in autonomic nervous system activity. A decrease in HRV may signal increased stress or inadequate recovery, suggesting the need for rest or stress-reduction strategies. By keeping an eye on HRV trends, individuals can make informed decisions to adjust their lifestyle, optimize recovery, and maintain overall well-being.

3. How does HRV change with age and fitness level?

HRV generally decreases with age due to reduced autonomic nervous system flexibility. Marco Altini notes that regular physical activity can help maintain higher HRV levels, as fitness enhances cardiovascular health and autonomic function. Physically active individuals often exhibit higher HRV compared to sedentary peers.

4. Can HRV be used to detect overtraining in athletes?

Yes, HRV monitoring can identify signs of overtraining. Andrew Flatt emphasizes that a consistent drop in HRV may indicate inadequate recovery and accumulated fatigue. By tracking HRV, athletes can adjust their training intensity to prevent overtraining and reduce the risk of injury.

5. What factors can influence HRV measurements?

Various factors affect HRV, including stress, sleep quality, hydration, and time of day. Marco Altini advises measuring HRV under consistent conditions, preferably in the morning, to obtain reliable data. Being aware of these factors helps in accurately interpreting HRV trends.

6. Is HRV monitoring useful for mental health management?

HRV is linked to stress and emotional regulation. Andrew Flatt suggests that tracking HRV can provide insights into an individual’s stress levels and psychological well-being. Higher HRV is associated with better stress resilience, while lower HRV may signal increased stress or anxiety.

7. How can HRV biofeedback improve health outcomes?

HRV biofeedback training involves exercises that enhance autonomic control and increase HRV. Marco Altini explains that such practices can reduce stress, improve heart health, and promote relaxation. Biofeedback is a tool that can support overall well-being when incorporated into regular routines.

8. What is the relationship between HRV and sleep quality?

Quality sleep positively impacts HRV. Andrew Flatt notes that sufficient and restful sleep promotes autonomic balance, leading to higher HRV readings. Poor sleep can decrease HRV, indicating heightened stress and inadequate recovery.

9. Can HRV help in managing chronic diseases?

Monitoring HRV may assist in managing conditions like hypertension and diabetes. Marco Altini points out that lower HRV is often associated with chronic diseases due to autonomic dysfunction. Regular HRV tracking can help assess disease progression and the effectiveness of treatments.

10. Are wearable devices accurate for measuring HRV?

Wearable devices have made HRV monitoring more accessible, but accuracy varies. Andrew Flatt recommends using validated devices and following standardized protocols for consistency. While convenient, it’s important to interpret wearable HRV data cautiously and consider potential limitations.

11. How high can heart rate variability go?

In adults, heart rate variability (HRV) typically spans from less than 20 milliseconds to in excess of 200 milliseconds. Nonetheless, HRV is subject to individual differences and is affected by numerous factors, such as age, gender, physical fitness, and genetic predispositions.

 

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Summary algorithms are metrics designed to aggregate and simplify multiple physiological and behavioral measurements into three easy-to-interpret outcomes. These scores are presented on a 0 to 100 scale, where 100 is the ‘best’ attainable score. By presenting data in a reader-friendly format, less experience and time is required to interpret each individual’s status and progress over time, which helps both the end-user as well as any care-takers or data monitors.

Below are the descriptions of each of three main summary scores presented by Biostrap: Activity, Recovery, and Sleep Scores.

Activity Score

Physical activity is a metric that is correlated with numerous health outcomes and diseases. Activity is not exclusive to exercise bouts, and sedentary behavior has also been shown to be associated with health outcomes.

Therefore, Biostrap calculates activity score using the activity distribution over the course of a 24-hour window, emphasizing consistent physical activity of 500 steps per hour during 12 unique hours. Additionally, energy expenditure relative to the user’s goal contributes to the activity score. The energy expenditure goal, or workout calories, can be modified in the Settings tab on the user application.

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Recovery Score

The recovery score is computed based on sleep data, with weighted inputs including relative resting heart rate and heart rate variability values. The Biostrap Recovery Score assesses a user’s daily value compared to a personal 5 to 30-day baseline to better understand an individual’s physiological recovery and readiness to perform.

Measures of various sleep parameters, such sleep duration, sleep latency, and the number of sleep disruptions also contribute to the overall Recovery Score calculation.

Sleep Score

The Biostrap Sleep Score includes a comprehensive analysis of over a dozen sleep parameters, including but not limited to nocturnal biometrics, sleep duration, sleep quality, awakenings, and movement.

The Sleep Score incorporates a global and individualized penalty system for calculating the score; for example, if an individual has oxygen saturation values below 90%, the algorithm will apply a global penalty. However, if an individual has an oxygen saturation within the normal range but just slightly below the trailing average over the last 30 days, they will receive a minor ‘relative’ penalty.

Ready to start tracking your Sleep and Recovery? Join our Biostrap family and get started with our Recover Set.

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What is it

Sleep latency is the term given to describe how long it takes to fall asleep. Sleep latency can vary greatly due to behaviors before bedtime, such as alcohol, medications, exercise, diet, and blue light exposure, among others.

However, tracking sleep latency can provide additional insight to help reflect on health, behavior, and intervention changes.

How is it measured

Sleep latency is measured in minutes from the time an individual attempts to fall asleep to the time when the individual enters the first stage of sleep.

Tracking changes in physiological metrics through photoplethysmography (PPG) and accelerometry provides improved insight as individuals may have difficulty reporting the time of initial sleep onset. By tracking metrics such as heart rate, heart rate variability, respiration rate, and limb movements, a good understanding of bedtime and onset of sleep can be made.

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Correlations to health conditions

It is important to note that directionality and magnitude of latency may or may not have clinical relevance based on an individual’s situation. For example, long sleep latencies can be indicative of disorders, particularly related to stress or insomnia. However, shortening sleep latency may not reflect positive changes, as high amounts of sleep debt decrease sleep latency. Further, substances such as alcohol may reduce sleep latency but may lead to lesser quality of sleep.

Many of the correlations between latency and health are drawn in anxiety and depression. These psychological disorders are relatively common and affect sleep and sleep latency. However, sleep latency is associated with decreased total sleep, where less sleep causes more anxiety and depression.

Thus, it can be essential to monitor sleep latency changes to catch trends before they become problematic.

Normal or acceptable ranges

The National Sleep Foundation acknowledges up to 30 minutes of sleep latency, regardless of age, as appropriate. Sleep latency of 31-45 minutes is listed as ‘uncertain,’ which could be due to individual trends. It stands to reason that very short sleep latency (<5 minutes) could indicate problems with fatigue and sleep deprivation; however, more research is needed on normative values in this range.

Interpreting trends

Although the clinical recommendations remain unclear, tracking sleep latency could benefit most individuals. This metric, inversely associated with total sleep duration, could provide insight into behavioral changes and how they affect sleep architecture.

Should sleep latency trend negatively for an individual, behavioral interventions could be suggested to correct sleep latency and potentially increase total sleep duration.

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What is it?

Sleep duration is simply the amount of time an individual is asleep. This measure is essential to quantify, as it directly impacts physiological and psychological parameters in the short and long term, impacting health, performance, and longevity.

What does it measure?

Sleep duration is the total sum of time spent asleep, regardless of sleep stage. Using combinations of heart rate, heart rate variability, breathing, motion, and pulse waveform data, approximating sleep versus awake time is possible.

Biostrap uses inputs from all the listed measurements to estimate light sleep, deep sleep, and time spent awake; therefore, the reflected sleep duration is the sum of light and deep sleep.

Correlation with health conditions

Total sleep duration is a commonly reported metric and highly correlates with health outcomes. Sleep is vital to regulating biological processes, allowing adaptation, recovery, and preparation. Many repair processes occur during sleep, with surges in growth hormones and reduction in stress hormones.

Physiologically, increased sleep duration has been shown to reduce stress, improve cardiovascular markers (e.g. heart rate, heart rate variability, and arterial stiffness), reduce weight gain, improve immune function, and lower risk of all cause mortality and varying diseases. As such, sleep appears to improve physiological pathways robustly.

In addition to physiological effects, increased sleep has many cognitive benefits, including improved memory, problem-solving, and reaction speed.

Normal or acceptable range

The American Academy of Sleep Medicine recommends at least 7 hours of sleep per night for adults aged 18-60 years. The National Sleep Foundation recommends supplementing this recommendation with 7-9 hours of sleep per night for adults aged 65 years and older.

Biostrap records users’ sleep each night, and from this data, we can gather average values of distinct populations.

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Interpreting Trends

Considering the broad health implications associated with sleep duration, tracking sleep duration over time is recommended, so individuals may notice trends in their behavior. Including sleep duration into longitudinal metrics can either explain or rule out other physiological trends and therefore is included in Biostrap biometrics, allowing users and remote monitors to have a broader view of individual health.

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Respiratory rate is one of the main vital signs that measure a person’s health. Alongside body temperature, blood pressure, heart rate, respiratory rate is an essential measurement for determining well-being. 

Respiratory rate is the rate at which a full breathing cycle occurs, both inhalation and exhalation. While this is something that can be taken over by voluntary control, the majority of respiratory rate is an autonomic process, which occurs as a result of many inputs, including the respiratory center of the brain, which sends physiological sensory information throughout the body.

Age, weight and certain illnesses can all influence a person’s respiration rate. That’s why your normal respiratory rate may differ from the next person’s. If you learn to measure your respiratory rate, you’ll gain a better understanding of what is normal for you.

What Is a Normal Respiratory Rate?

The normal ranges for respiratory rate vary depending on a number of factors. For example, people who regularly practice deep breathing exercises, or those who play woodwind instruments, may take less breaths per minute, whereas people who are less physically fit may take more breaths per minute.

“We don’t really believe in comparing someone to a normal curve,” explains Kevin Longoria, MSc, a clinical exercise physiologist and the Chief Science Officer for Biostrap. “More importantly, we believe in comparing them to themself. We can see what someone’s normal respiratory rate may be and establish a trend. Then, you look at changes in the trend.” This allows you to determine what is a normal respiratory rate and what is abnormal for you as an individual.

In general, an abnormal respiratory rate is when the number of breaths per minute is under 12 or over 25 — but again, this can vary for every individual. A high respiratory rate means that the respiratory system is being overworked when it circulates oxygen through the blood and body. Conditions like asthma or sudden episodes, like an anxiety attack, can increase the number of times a person breathes per minute, resulting in a high respiratory rate.

In contrast, a child’s breathing rate would be different. “The normal respiratory rate changes significantly throughout the first several years of a child’s development,” says Longoria. For example, a baby up to 6-month old will usually have a breathing rate between 30 and 60 breaths per minute. A child between one and five will usually have a breathing rate between 20 and 30. By the time a child is 12, their breathing rate will usually be under 20.

According to Longoria, “abnormally high respiratory rates in children may be a sign of fever, dehydration, or conditions including bronchiolitis or pneumonia. Children may also experience rapid respiratory rates similar to adults due to other medical conditions including acidosis (with diabetes) and asthma.” And these are just a few of the factors that influence respiratory rate.

Factors That Influence Respiratory Rate

There are a number of common conditions that influence a person’s respiratory rate. For example, having anxiety and suffering from panic attacks can result in shallow breathing patterns and an acutely high respiratory rate for a short period of time (also referred to as hyperventilation).

“What’s really important when you look at respiratory rate is understanding autonomic control,” says Longoria. “When we look at the autonomic nervous system, it’s controlling all these voluntary processes like heart rate, respiratory rate, response to exercise, stress hormones, inflammatory processes — essentially things we don’t have to think about.” Factors such as exercise, stress, or changes in heart rate can influence respiratory rate involuntarily.

Shallow breaths or hyperventilation could be caused by the following conditions.

Lung and Airway Diseases

Lung diseases cause reduced oxygen uptake and prevent the lungs from working properly. For example, emphysema, severe/refractory asthma, and chronic bronchitis block a person’s airflow and contribute to an increasing sense of breathlessness.

These diseases are classified under the term Chronic Obstructive Pulmonary Disorder (COPD). Symptoms include shortness of breath, wheezing, chest tightness and chronic cough with and without mucus. COPD is primarily caused by smoking cigarettes but can also be associated with genetic conditions.

When it comes to factors that influence respiratory rate, “smoking is an obvious one,” notes Longoria. “If you’re smoking, then you’re getting more tar buildup and essentially causing what’s called a respiratory restriction.” Tar buildup in the lungs will damage your air sacs (alveoli) where gas diffusion takes place. “If we’re limiting the surface area of our lungs, our body will have to compensate with a higher respiratory rate.”

Having COPD is a risk indicator of cardiac arrest, in which the heart suddenly stops beating. Sudden cardiac arrest is a medical emergency that requires immediate treatment with a defibrillator. According to Harvard Medical School, more than 13 million Americans have COPD, and it tends to coexist with other conditions, like heart disease. Yet, many people are unaware of COPD.

Anyone who suspects they have COPD can be diagnosed by a spirometry test. This is a simple non-invasive process that evaluates your exhalation to see how well your lungs are working.

Sleep Disorders

Sleep disorders are another leading cause of airway obstruction and respiratory irregularities. During non-REM sleep, the part of our sleep cycle in which we spend 80% of the night, it’s normal to breathe slowly and steadily.

Breathing normally increases and decreases through different sleep stages, and the number of breaths a person takes will vary. However, having a sleep disorder can cause irregular sleep patterns and contribute to respiratory abnormalities. One example is sleep apnea, a common sleep disorder where a person stops breathing for as much as several seconds in their sleep. 

Heart Conditions

Heart problems can also affect a person’s respiratory rate. For example, atrial fibrillation (marked by irregular and rapid heart rate) can cause shortness of breath. When your heart beats irregularly due to atrial fibrillation, you may experience a tight chest and shortness of breath because the heart’s electrical signals don’t fire properly.

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Cardiovascular Fitness

Cardiovascular fitness is defined as the ability of your heart and lungs to deliver oxygen-rich blood to muscles throughout the body. “If you are frequently exercising, you tend to maintain better lung volume, resting lung capacity, and you have more arterial elasticity,” says Longoria. “Per breath, you’re consuming more oxygen than the next guy.” As a result, your respiratory rate will be lower because it will require fewer breaths to consume the same amount of oxygen.

How to Measure Respiratory Rate

Understanding your respiratory rate can make you more familiar with your body and help you monitor changes as you age. Plus, it can ensure that you recognize when it might be time to consult a medical professional about any changes to your body if changes to your respiratory rate occur.

Respiratory rate can be measured through photoplethysmography (PPG) by measuring the baseline shifts that occur with breathing. The baselines move up and down in an oscillatory pattern, which corresponds to the breath cycle.

A way to use PPG to measure is through a wearable that tracks vital biometrics for both sleep and fitness like Biostrap. “Biostrap is the only clinically-validated sensor system out there,” says Longoria. Wearable trackers can measure your oxygen saturation to indicate how much oxygen is pumping through the blood. It also helps you monitor the effectiveness of your workouts to better understand how blood oxygen levels could be increased through exercise, diet, deep breathing, and other healthy lifestyle changes.

Knowing Your Respiratory Rate

Your respiratory rate tells how much oxygen is flowing through your blood, but it also provides deeper clues to your health. For example, an abnormal respiratory rate can shed light on potential sleep disorders, lung disease, and heart conditions. “It’s an extremely important biometric,” Longoria stresses. “But it’s almost more important to know how and when you’re measuring it.”

Respiratory rate helps us understand changes in our own bodies, especially as we age or try out new approaches to fitness. Measuring your respiratory rate with a wearable that measures and tracks vital biometrics for both sleep and fitness can ensure that you’re always in tune with your body and the breath that keeps it alive.

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