Wearables for longevity: what your data actually means

Heart rate variability (HRV): the most useful wearable metric

HRV — the variation in time between heartbeats — is the most information-rich signal available from consumer wearables. High resting HRV reflects strong parasympathetic (vagal) tone, cardiovascular health, and physiological stress resilience. Low HRV correlates with elevated sympathetic nervous system activity, inflammation, poor sleep quality, inadequate recovery, overtraining, illness, and excessive alcohol consumption. A 2023 meta-analysis in the Journal of the American Heart Association confirmed that low HRV is independently associated with increased risk of cardiovascular disease and all-cause mortality.

Critical interpretation note: HRV is highly individual. Population norms are nearly meaningless for self-tracking purposes — a 65-year-old's 'normal' HRV may be 28ms; a trained 35-year-old's may be 75ms. The clinically useful practice is tracking your own baseline over weeks and months, and using acute deviations (drops of 15%+ below your rolling 30-day average) as early warning signals of physiological stress. Devices differ in accuracy: research-grade validation exists for Oura (ring PPG), Polar chest straps (gold standard), and WHOOP. Wrist-based optical sensors on standard smartwatches have lower accuracy for HRV, particularly during the day.

Resting heart rate: a simple but powerful metric

Resting heart rate (RHR) is the simplest useful metric available from any consumer wearable. It is a direct proxy for cardiovascular fitness and autonomic function. Longitudinal studies consistently show that lower RHR is associated with lower all-cause mortality across a wide range of values. A 2013 Lancet study of 576,000 participants across 46 countries found a linear relationship between RHR and mortality for values above 45 bpm. For most adults, a progressive downward trend in RHR over months of consistent aerobic training is one of the most reliable signals of improving cardiovascular health.

Interpretation: a sudden unexplained increase in resting heart rate (5–10+ bpm above baseline) often precedes subjective illness by 24–48 hours and is a sensitive early signal for infection, overtraining, or cardiovascular stress.

Sleep staging and sleep quality

Modern wearables track sleep stages (light, deep/slow-wave, REM) using accelerometry and photoplethysmography (PPG). The accuracy varies significantly between devices and individuals. A 2022 study in SLEEP journal compared 5 consumer wearables against polysomnography (the clinical gold standard) and found that all devices overestimated light sleep, with variable accuracy for deep sleep and REM. Oura Ring performed best among consumer devices, with approximately 75% accuracy for deep sleep staging.

What to monitor: total sleep time consistency (a more reliable metric than staging accuracy); sleep efficiency (time asleep / time in bed × 100% — below 85% suggests insomnia or sleep disruption); timing regularity (consistent sleep and wake times are independently associated with lower mortality per the UK Biobank data). Don't obsess over deep sleep and REM percentages — the staging accuracy is insufficient for precision optimisation.

Continuous glucose monitoring (CGM)

CGMs — historically restricted to diabetics — are now available to general consumers via products such as Levels, Dexterity, and Freestyle Libre (used off-label). They measure interstitial glucose every 5–15 minutes via a small sensor inserted under the skin, providing a real-time view of metabolic responses to food, exercise, stress, and sleep.

The evidence for CGM use in non-diabetic individuals as a longevity intervention is genuinely limited — there are no RCTs showing that CGM-guided behaviour change in healthy non-diabetics improves long-term outcomes. However, the device has strong utility for identifying personal glycaemic responses to specific foods (which vary widely between individuals), catching post-meal glucose spikes that standard HbA1c testing misses, and motivating dietary changes. A 2023 Nature Medicine study (n=1,000 non-diabetics) found that standard dietary patterns produced dramatically different glucose responses between individuals — a key argument for personalised dietary guidance.

Australian note: Freestyle Libre 2 sensors are TGA-approved; in Australia they cost approximately $90 per 14-day sensor. Medicare subsidises CGMs for Type 1 diabetics but not for general health monitoring. The 14-day CGM period twice per year is a reasonable starting point for Australians who want periodic metabolic insight without continuous monitoring costs.

VO2 max estimation: the metric worth tracking most

VO2 max — maximal oxygen uptake, the gold standard measure of cardiorespiratory fitness — can be estimated by most modern Garmin, Apple Watch, and Polar devices using submaximal heart rate data. The estimates are meaningfully less accurate than laboratory VO2 max testing but track changes over time with reasonable fidelity. Given that VO2 max is arguably the strongest single modifiable predictor of all-cause longevity, having a directional trend indicator is highly valuable even if the absolute number is imprecise.

Targets: the JAMA Network Open study (Mandsager et al., 2018) defined VO2 max fitness categories with associated mortality risks. Moving from 'low' to 'below average' was associated with 50%+ mortality risk reduction — larger than eliminating any conventional risk factor. Most modern GPS sports watches provide VO2 max estimates in a comparable format (ml/kg/min). The trend over 3–6 month training blocks is the most actionable signal.

Making sense of the data

Wearable data is most useful as a trend indicator, not a diagnostic tool. The practical framework: track HRV as your primary recovery and stress indicator; use RHR as a cardiovascular fitness proxy; prioritise sleep consistency over staging metrics; use VO2 max estimates to track aerobic fitness progression; and consider periodic CGM use to understand personal glucose responses to diet. Avoid 'metric anxiety' — reviewing data obsessively and making multiple daily decisions based on it. Weekly reviews and monthly trend analysis are more productive than reactive daily adjustments.

References

1. Journal of the American Heart Association (2023). HRV and cardiovascular/all-cause mortality: meta-analysis.
2. The Lancet (2013). Resting heart rate, cardiovascular disease, and longevity: 576,000 participants, 46 countries.
3. de Zambotti, M., et al. (2022). Accuracy of consumer wearable sleep staging vs. polysomnography. SLEEP.
4. Windred, D. P., et al. (2024). Sleep regularity and mortality: UK Biobank. SLEEP.
5. Korem, T., et al. (2023). Personalised glycaemic responses to diet: n=1,000 non-diabetic study. Nature Medicine.
6. Mandsager, K., et al. (2018). VO2 max fitness categories and mortality. JAMA Network Open.
7. TGA (2024). Freestyle Libre 2 regulatory status in Australia.