Key result
Sleep periods are linked to ~17 bpm lower mean pulse rate compared to waking periods.
Why the study?
Reporting wearable pulse metrics as single daily, waking, or nightly averages may mask differences between sleep and wakefulness, temporal variations within a state, and differences in signal retention, leaving it unclear whether single-value averages represent free-living pulse physiology.
Do single-value daily or nightly averages of wearable pulse metrics provide a representative summary of free-living pulse physiology compared to state-specific and temporally resolved measurements?
Observational (n=35)
Do single-value daily or nightly averages of wearable pulse metrics provide a representative summary of free-living pulse physiology compared to state-specific and temporally resolved measurements?
Single-value averages of wearable pulse metrics fail to capture significant physiological variations between and within sleep and wake states as well as substantial differences in signal retention between states.
Answers are grounded in this paper and cite their sources.
Single-value wearable pulse averages may mask sleep-wake differences; leaves open whether granular metrics improve monitoring or risk stratification.
Wearable pulse metrics are typically reported as a single daily, waking, or nightly average. This presentation of the data may mask differences between sleep and wakefulness, temporal variations within a given state, and differences in the amount of signal retained. To determine whether single-value averages provide a representative summary of free-living pulse physiology, we analyzed 28 days of smartwatch and smart-ring data from 35 participants. Sleep and waking periods were identified using the smart-ring sleep data. Pulse rate was obtained from the smartwatch-generated values, and pulse rate variability (PRV) was calculated from the raw photoplethysmography as the root mean square of successive pulse-interval differences (RMSSD) and standard deviation of pulse-to-pulse intervals (SDNN). Analyses compared paired sleep–wake periods and examined temporal variability within both waking and sleep periods. Mean pulse rate was 72.4 beats/min during sleep and 89.1 beats/min during the subsequent waking period (difference: −16.7 beats/min; 95% confidence interval [CI], −23.0 to −10.2). The mean absolute deviation of the five-minute waking pulse-rate measurements from the waking-period mean was 10.89 beats/min (95% CI, 9.75–12.02); on average, 44.6% of waking measurements differed from the mean by more than 10 beats/min. During sleep, the mean absolute deviation from the period mean was 4.30 beats/min (95% CI, 3.17–5.56). From earlier to later sleep, RMSSD increased by 40.8% (95% CI, 18.7–67.5%; q = 0.0001) and SDNN increased by 82.6% (95% CI, 51.4–117.4%; q = 0.0001). Only 8.6% of the waking recordings met the PRV quality criteria, whereas 64.0% of the sleep recordings did. In summary, single-value averages may not capture differences between sleep and wakefulness or temporal structure within states; unequal signal retention may also affect the representativeness of state averages. Therefore, reports of wearable pulse biomarkers should specify sleep–wake state, within-state timing, aggregation method, and signal retention criteria.
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Cai et al. (2026) conducted an observational in Free-living pulse physiology (n=35). Sleep period (smartwatch and smart-ring monitoring) vs. Waking period was evaluated on Mean pulse rate (beats/min) (MD -16.7 beats/min, 95% CI -23.0 to -10.2). Sleep vs waking periods had lower mean pulse rate and higher PRV signal retention.
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