Wearable Data Decoder

What Wearable Health Metrics Actually Measure

Consumer wearables from Oura, WHOOP, Apple Watch, and Garmin have brought continuous physiological monitoring into everyday life, but the raw data is only useful if you understand what each metric actually reflects at a biological level. Heart rate variability (HRV) is the most biologically informative metric most wearables track. HRV measures the millisecond-to-millisecond variation in time between heartbeats — counterintuitively, a heart that beats with irregular timing (high HRV) is healthier than one that beats with metronomic regularity (low HRV). High HRV reflects robust autonomic nervous system regulation, with the parasympathetic (rest-and-digest) branch capable of quickly modulating heart rate. Low HRV indicates sympathetic dominance — the nervous system stuck in a state of chronic stress or physiological strain.

Resting heart rate (RHR) is a lagging indicator of cardiovascular fitness: it reflects the heart's stroke volume, which determines how much blood is pumped per beat. Elite endurance athletes have RHRs in the 40s because their hearts are so efficient that fewer beats per minute are needed to maintain cardiac output. For most people, an RHR below 60 indicates good cardiovascular fitness; values above 80 are associated with increased cardiovascular disease risk in prospective studies. Week-to-week changes in RHR are particularly useful: a RHR elevated 3–5 beats above your normal baseline often precedes or accompanies illness, overtraining, or significant psychological stress by 12–24 hours.

Sleep Staging and Its Limitations

Wearables estimate sleep stages (light, deep, REM) using a combination of heart rate data, movement (accelerometry), and in some devices, skin temperature and blood oxygen. Lab-grade sleep staging requires polysomnography with EEG, EMG, EOG, and respiratory sensors — a standard that consumer wearables cannot match. Validation studies comparing Oura, WHOOP, and Apple Watch sleep staging against simultaneous polysomnography show that these devices are reasonably accurate for distinguishing sleep from wakefulness (85–90% accuracy) and for detecting REM sleep (70–80%), but less reliable for distinguishing light from deep NREM sleep. The absolute numbers should be treated as directional estimates rather than precise measurements.

That said, consistent patterns across nights are informative even if absolute accuracy is imperfect. Consistently low deep sleep percentages (below 15% of total sleep time) may indicate sleep apnoea, alcohol consumption, sedative use, or poor sleep timing relative to your chronotype. Consistently low REM percentages often follow sleep restriction, alcohol, certain antidepressants (particularly SSRIs), or beta-blockers. Tracking these trends over weeks and correlating them with recovery scores, mood, and cognitive performance gives you a personalised signal that is more useful than any single night's data.

How to Act on Your Readiness and Recovery Scores

Readiness scores (Oura), Recovery scores (WHOOP), and Body Battery (Garmin) are composite metrics that combine HRV, RHR, sleep duration, sleep staging, and activity levels to produce a daily recommendation about training intensity. These scores are most useful as guardrails at the extremes: on days when your score is very low (Oura readiness below 60, WHOOP recovery below 30%), research from WHOOP's internal studies suggests that training hard produces less fitness adaptation and higher injury risk than on high-recovery days. The physiological basis is clear — training when HRV is significantly suppressed adds cumulative allostatic load to a system already under strain. On high-readiness days, the same training produces greater cardiovascular adaptation. This decoder translates your weekly averages into plain-English context, explains which metrics are most and least reliable, and flags values that warrant attention or lifestyle adjustment.