Centralive Blog
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Best Practices for Long-Term Monitoring of HRV Using Frequency Domain Features
Frequency-domain HRV features offer a powerful, non-invasive way to track chronic stress and autonomic balance over time. This post outlines the key metrics—HF power, LF power, and LF/HF ratio—and best practices for consistent long-term monitoring in clinical and research settings.
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From Spikes to Drains: Using HRV to Tell Acute Stress from Chronic Stress
Acute stress shows up as rapid physiological spikes, while chronic stress appears as slow, cumulative drains on the nervous system. In HRV data, fast changing metrics like RMSSD and pNN50 capture immediate reactions, while SDNN and the HRV Triangular Index reflect long term autonomic strain and recovery.
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What Really Breaks HR and HRV in Wearables
Wearable heart rate and HRV sensors are often blamed when data looks wrong, but real world errors are dominated by motion and contact instability. Across everyday use, movement overwhelms the pulse signal far more than skin tone or sunlight. The figures below show why HR and HRV break down in motion, and how modern multi wavelength PPG systems are designed to reduce that damage.
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Why Wearable Studies So Often Miss Real Effects
Wearable data feels inherently powerful. Continuous tracking and long follow up periods create the impression that detecting small effects should be easy. In reality, most wearable studies are underpowered. Not because the effect is impossible, but because we underestimate how much wearable signals move from day to day. The following table shows that different wearable […]
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