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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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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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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. 

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.

So what exactly is heart rate variability? How do we measure it? And what can it tell us about our overall health? Let’s break down the intricacies of this emerging physiological measurement.

What Is Heart Rate Variability?

Heart rate variability, or HRV for short, is a measure of the time between each heartbeat. Heart rate by itself is the expression of how many contractions of the heart there are in a given unit of time; however, the rate itself is not constant. There is normal fluctuation of time between heartbeats, in a manner that speeds up and slows down heart rate. Therefore, HRV is a quantifiable measure that assesses these differences. 

Regulated by a fundamental part of our nervous system called the autonomic nervous system (ANS), HRV is one of many functions that occurs without us even having to think about it. HRV has been shown to correlate with emotional and physical stress, sleep, and disease making it a common method for assessing the overall physiological state and the rate of adaptation to stressors. 

Generally, the higher the HRV the better, as high stress and poorer health outcomes have been associated with low values of HRV.

How Do We Measure Heart Rate Variability?

HRV can be measured by an electrocardiogram (ECG) or photoplethysmography (PPG). By referencing a common point in the ECG or PPG waveform, the time between each heart beat can be recorded in milliseconds (ms). Collecting each beat-to-beat interval in ms allows us to compute HRV, most commonly reported as rMSSD (root mean square of successive differences). The rMSSD method of calculation takes each interval, squares the interval, takes the overall mean, and then the square root of that mean. More complex measures of HRV, including frequency domain analysis, can be used to get further information out of heart rate patterns and the state of one’s nervous system.

What Is a Normal Heart Rate Variability?

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. In general, younger individuals, males, and more active individuals tend to have higher heart rate variability, but the inter-subject variability tends to be too high to suggest proper normative ranges.

Focusing On Trends

As previously mentioned, HRV is difficult to interpret and generally nonspecific using data from a single spot check. However, since it is a dynamic measure that responds to various lifestyle factors, tracking HRV over time allows for non-invasive insight into changes in health status or efficacy of certain interventions. In general, since higher HRV is preferable, a greater ability to manage stress results in an increased HRV. The results of the studies demonstrating the relationship between stress and HRV suggest that interventions aimed at reducing mental and physical stress could increase HRV and minimize day-to-day fluctuations (coefficient of variation, CV%). The increase in HRV itself will not reduce risk and improve health over the long term, but rather, it reflects positive adaptations in an individual’s physiology.

For example, if we’re incorporating exercise or meditation into our daily routine, HRV should steadily increase. A downward trend, on the other hand, may be indicative of overtraining, poor sleep, illness, bad eating habits, increased exposure to stress, or failure to hydrate.

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What Factors Influence Heart Rate Variability?

Heart rate variability can be influenced by training, lifestyle, and biological factors.

Training factors that influence HRV include the intensity of a workout, exposure to unfamiliar stimuli, training load, and proper balance between rest days and training days. 

Lifestyle factors that influence HRV include diet and nutrition, stress, sleep habits, and alcohol consumption. Leading a healthy lifestyle that focuses on proper diet and physical fitness, while paying attention to mental health, is a valuable means of improving HRV.

Finally, biological factors such as age, gender, genetics, and health conditions can influence HRV as well. As we age, our HRV tends to decline, and men often have higher HRV than women. Genetics and health conditions such as cardiovascular disease are additional factors that may influence our heart’s ability to operate normally.

Should We Focus on Heart Rate Variability?

Measuring heart rate variability is a valuable form of analysis to monitor healthy individuals or to identify those who should seek improvement. The amount of information we get from HRV is making it a popular health data to assess physiological state, overall well-being and stress adaptation. 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. 

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