Leveraging Wearables for Sleep Metrics in Menopausal Women, With Fiona Baker, PhD

A 28-day observational study using smartwatch sleep tracking found pronounced night-to-night variability in sleep fragmentation among menopausal women with sleep disturbances, while total sleep duration remained relatively stable, according to findings from the ESTeeM study presented at SLEEP 2026.¹

Fiona Baker, PhD, of SRI International, who presented the research, noted the variability pattern is clinically meaningful. Women with sleep disturbances may experience considerable fluctuation in wake time from night to night, with some nights largely unaffected and others substantially disrupted, a pattern obscured by single-timepoint assessments or recall-based measures alone.

ESTeeM Study Design and Smartwatch Sleep Metric Findings

The ESTeeM findings add real-world wearable data to a literature predominantly reliant on self-report and laboratory polysomnography. The 28-day observational study enrolled naturally or surgically menopausal US women aged 40 to 65 years with sleep disturbances.¹

Of 75 women who initiated a Samsung Galaxy Watch6 or Watch6 Classic, 34 met inclusion criteria of at least 11 nights of valid sleep data and were included in person-level monthly summaries. Participants also completed a Daily Morning Diary capturing self-reported total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO), as well as the PROMIS Sleep Disturbance Short Form 8b (PROMIS-SD-SF-8b) at baseline, day 14, and day 28.

Among the participants, the mean age was 54.6 years (SD, 6.0) and 73.5% were White. Participants contributed a median of 16 nights of valid smartwatch data. Smartwatch person-level means were TST 6.4 hours (SD, 0.8), SE 87.6% (SD, 7.0%), WASO 57.9 minutes (SD, 37.9), and number of awakenings 21.0 per night (SD, 10.7).¹

Night-to-night variability, quantified by within-woman coefficients of variation, was relatively low for TST (18%) and SE (6%) but high for WASO (58%) and number of awakenings (29%).¹

Baker described the clinical picture this variability reveals: "You see the variability in the amount of wakefulness, so 20 minutes one night, maybe 40 minutes another night, 5 minutes another night, that kind of variability."

Total sleep time, by contrast, remained anchored within a narrower range, as hour-level changes in sleep duration are less likely to fluctuate dramatically across nights even in women with significant disturbance.

Concordance Between Smartwatch Metrics and Patient-Reported Sleep Outcomes

Spearman correlations between smartwatch and diary-based sleep measures were modest to moderate across all parameters: TST (r = 0.52; 95% CI, 0.22 to 0.73), SE (r = 0.46; 95% CI, 0.14 to 0.69), WASO (r = 0.39; 95% CI, 0.05 to 0.64), and number of awakenings (r = 0.26; 95% CI, -0.09 to 0.55).¹ Test-retest reliability among women with stable diary and PROMIS scores was good to excellent across all measures, with intraclass correlation coefficients ranging from 0.740 to 0.957.

The modest concordance between device-derived and self-reported measures is consistent with patterns observed across sleep research more broadly. Baker noted self-reported sleep is shaped by numerous factors beyond objective wake time, including perception of sleep quality, memory of overnight arousals, and how disturbing any given awakening felt.

"There's so much that goes into how someone reports their sleep," Baker said. "It's not just about the measure, like how much time you were awake isn't the only factor influencing how you feel about your sleep."

The divergence between objective and subjective measures is not a flaw to be resolved but a feature of sleep assessment warranting acknowledgment. Baker emphasized both data streams carry independent value: self-report captures how a patient experiences and perceives sleep disturbance, which is ultimately the clinical target, while wearable tracking provides a more precise and longitudinally dense picture of sleep architecture and fragmentation.

"You always want to ask about how someone feels about their sleep, because you want to improve how someone feels about their sleep," Baker said. "It's also really valuable to be measuring parts of sleep."

Editors’ note: Baker reports relevant disclosures with Bayer.

References

1. Baker F. Night-to-night sleep variability in menopausal women: smartwatch-derived sleep metrics in comparison with patient-reported outcomes from the ESTeeM study. Presented at: SLEEP 2026; June 2026.

2. Kravitz HM, Joffe H. Sleep during the perimenopause: a SWAN story. Obstet Gynecol Clin North Am. 2011;38(3):567-586. doi:10.1016/j.ogc.2011.06.002