Heart rate variability (HRV) measures the variation in time intervals between heartbeats, reflecting complex interactions between the heart and nervous system. HRV is influenced by autonomic nervous system (ANS) activity and is used to assess health, performance, and autonomic balance. HRV can be analyzed using time-domain, frequency-domain, and non-linear metrics. Time-domain metrics include SDNN, RMSSD, and pNN50, which quantify variability in interbeat intervals. Frequency-domain metrics, such as LF and HF power, reflect different aspects of autonomic regulation. Non-linear metrics, like SD1 and SD2, assess the unpredictability and complexity of HRV patterns. HRV norms vary depending on the recording period, subject characteristics, and measurement conditions. Short-term (5 min), ultra-short-term (<5 min), and 24 h HRV measurements are not interchangeable, as they reflect different physiological processes. For example, 24 h HRV is influenced by circadian rhythms, while short-term HRV is affected by respiratory sinus arrhythmia (RSA) and baroreceptor reflexes. Normative values for HRV are essential for clinical and research applications, but they must be interpreted in the context of the recording conditions and subject variables. The authors emphasize the importance of context in HRV interpretation, including recording duration, subject age, sex, and health status. They caution against using 24 h HRV values interchangeably with short-term or ultra-short-term values. Additionally, they highlight the need for researchers to supplement published norms with findings from their own populations. HRV assessment strategies should be tailored to the specific goals of clinical or performance interventions. Non-linear measurements, such as approximate entropy and sample entropy, provide insights into the complexity and unpredictability of HRV patterns. Poincaré plots and detrended fluctuation analysis (DFA) are also used to analyze HRV data. Overall, HRV is a valuable tool for assessing autonomic function, health, and performance, but its interpretation requires careful consideration of the measurement context and physiological mechanisms.Heart rate variability (HRV) measures the variation in time intervals between heartbeats, reflecting complex interactions between the heart and nervous system. HRV is influenced by autonomic nervous system (ANS) activity and is used to assess health, performance, and autonomic balance. HRV can be analyzed using time-domain, frequency-domain, and non-linear metrics. Time-domain metrics include SDNN, RMSSD, and pNN50, which quantify variability in interbeat intervals. Frequency-domain metrics, such as LF and HF power, reflect different aspects of autonomic regulation. Non-linear metrics, like SD1 and SD2, assess the unpredictability and complexity of HRV patterns. HRV norms vary depending on the recording period, subject characteristics, and measurement conditions. Short-term (5 min), ultra-short-term (<5 min), and 24 h HRV measurements are not interchangeable, as they reflect different physiological processes. For example, 24 h HRV is influenced by circadian rhythms, while short-term HRV is affected by respiratory sinus arrhythmia (RSA) and baroreceptor reflexes. Normative values for HRV are essential for clinical and research applications, but they must be interpreted in the context of the recording conditions and subject variables. The authors emphasize the importance of context in HRV interpretation, including recording duration, subject age, sex, and health status. They caution against using 24 h HRV values interchangeably with short-term or ultra-short-term values. Additionally, they highlight the need for researchers to supplement published norms with findings from their own populations. HRV assessment strategies should be tailored to the specific goals of clinical or performance interventions. Non-linear measurements, such as approximate entropy and sample entropy, provide insights into the complexity and unpredictability of HRV patterns. Poincaré plots and detrended fluctuation analysis (DFA) are also used to analyze HRV data. Overall, HRV is a valuable tool for assessing autonomic function, health, and performance, but its interpretation requires careful consideration of the measurement context and physiological mechanisms.