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Age and gender as influencing factors of heart rate variability

Why knowing your personal values is important and how age and gender impact HRV

Heart rate variability (HRV) is a metric that offers valuable insights into many physiological processes, such as the state of our cardiovascular system and its readiness to take on and manage stress, our general or mental wellbeing, and many more. HRV is directly linked to the autonomic nervous system (ANS) and therefore can be used as a good indicator of how the ANS is functioning. Recently, HRV measuring became more accessible, thanks to technological advancements in digital health, delivered through different wearable devices and smartphone applications. In this scenario, HRV is set off to be a convenient, affordable, and easily accessible metric for health assessment.

HRV may be easy to record, but this metric is very sensitive and affected by many internal and external factors. It is not surprising that the heart activity needs to adjust to different situations and challenges and it does so thanks to complex control networks. As a measure of the variation in such activity, HRV also measures indirectly how well/fast body functions can adapt to the circumstances. In general, a low HRV rating (low adaptability) can indicate an imbalance in the ANS due to acute or chronic (e.g. disease) conditions and might even correlate to a worse prognosis in certain illnesses. In contrast, a high HRV is normally a good sign and suggests a well balanced ANS activity and the body’s readiness to take on and manage stress.

However, many basic factors, like age, gender, lifestyle, and others contribute to determining HRV. Taking them into account is essential for a correct interpretation of each measurement.

To correctly recognize imbalances in the ANS, HRV requires some normative or reference values, for example for different age and gender groups. Establishing such reference ranges is not an easy task, mainly because the list of factors that affect HRV is long and include the respiratory function, physical activity, body weight, and – among many others – the specific heart rate (HR) during the measurement itself. We should always keep in mind how close the correlation between HRV and HR is, as discussed in a previous article.

Tracking HRV has been proposed as a marker of several conditions, and an assessment metric for the risk of cardiac morbidity and mortality. Using HRV as a predictor in clinical settings must take into consideration the impact of specific physiological factors that influence its values. Age and gender are considered to be the most prominent factors that affect HRV metrics. Several physiological or pathological processes have a potential role in determining age and gender influence on HRV. For example, due to ageing or age-related impairments, structural or functional changes occur (e.g. loss of sinoatrial pacemaker cells or of arterial distensibility, vagal tone loss) that result in a reduction of the overall HRV. This has been identified as a predisposing factor for cardiovascular disease development. Furthermore, when we look at gender, the picture becomes even more complicated due to the role played by sex hormones. Both sex hormones and differences in the development are responsible for different functionality of the ANS, which ultimately explains different HRV ranges for the two gender in a specific age group. Hormone levels may also produce differences between pre- and post-menopausal women and amongst pre-menopausal women at different phases of the menstrual cycle. Taken altogether, these concepts reveal how complex the interpretation of HRV metrics really is and indicate the need for extensive data analysis and continuous research.

HRV fluctuation and age

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To better analyze how HRV changes (i.e. decreases) with age and at which rate, research uses several established indexes, like the SDNN (the standard deviation of NN interval time series, in ms), rMSSD (square root of the mean squared differences of successive NN intervals, in ms) and pNN50 (percentage derived by dividing the number of interval differences of successive NN intervals > 50 ms by the total number of NN intervals, in %). All these metrics show that HRV clearly declines over time (over age decades). Research using SDNN showed that the most prominent drop is recorded between the second (20-29 yo) and third (30-39 yo) decade. After that, this index continues decreasing but at a slower rate. After the age of 80, these HRV metrics dropped at an increased rate, reaching 60% of the HRV baseline in the tenth decade, compared to the recorded HRV values in the second decade.

When having a look at other parameters, like rMSSD or pNN50, HRV metrics appear to decline with age as well, but more rapidly than with SDNN. All methods of recording indicate that HRV decreases over time and with normal/healthy ageing, although at different rates depending on the index used for recording. Altogether this indicates how complex it is to use lowered HRV metrics to predict cardiovascular disease, as a function of age, gender and HRV indexes.

HRV fluctuation and gender

Photo by Marcus Aurelius from Pexels

When it comes to the differences in HRV according to gender, several studies indicate that women have lower HRV metrics compared to men, for example among groups of 10-29 years old. However, gender differences tend to balance out over time, with women showing more comparable HRV values to those of men at an older age. Importantly, the data in this context also depend on the HRV index considered (e.g. SDNN or rMSSD) and, of course, on how the data is collected. It appears to be crucial to consider thoroughly both age and gender, due to the influence of sex hormones and their dependency on age. The essential implications of these studies, especially concerning the predisposition to certain health conditions, the responses to stress or ageing prompt research to focus on age and gender as main “players” in HRV science.

Establishing solid knowledge on this topic determines the accuracy of HRV as a predictor and diagnostic tool for several conditions in the clinical setting.

About KENKOU: Kenkou works across multiple platforms, solutions and technology sources to coherently integrate cardiovascular vital data measurement into existing health solutions. We provide an SDK (software development kit) for easy integration of HRV-measurement across a spectrum of use-cases. To find out how cardiovascular vital data can boost your app, contact us at [email protected] and arrange a meeting.

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