Overnight Temperature Regulation Improves Circadian Rhythm and Cardiovascular Recovery in Postmenopausal Women and Age-Matched Men

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Jacobsen, Megan L. Holm, Emma R. Cary, Breanne C. Wilhite, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8652742/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sleep quality declines during menopause, yet the associated changes in body temperature and circadian rhythm during sleep remain poorly understood. In 90 postmenopausal women and age-matched men (1408 nights; mean ± SD: 56 ± 6 y), we examined how the menopausal transition alters the circadian rhythm of core temperature (T C ), and whether these changes relate to sleep composition and cardiovascular recovery (i.e., heart rate and heart rate variability). Additionally, we evaluated whether sleeping on an active temperature-regulated mattress cover (ATR) could improve circadian rhythm and sleep. Men and women demonstrated a blunted core temperature rhythm during sleep, which was restored when sleeping with ATR as a result of increased amplitude and lowered mesor of the T C . These T C changes significantly related to improvements in cardiovascular recovery during sleep (3% lower heart rate and 11% higher heart rate variability, on average). Although improvements in T C and cardiovascular recovery did not uniformly translate to changes in sleep composition, restoring the U-shaped T C curve has been linked to benefits in metabolic flexibility and cognition. Together, these findings support ATR as a promising non-pharmacological strategy to restore T C rhythmicity and improve cardiovascular recovery during sleep in older adults, including postmenopausal women. Physiology core body temperature skin temperatures sleep composition heart rate heart rate variability women's health active temperature regulation hormone replacement therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Sleep is essential for optimal health. Insufficient or poor-quality sleep is associated with increased risk of all-cause mortality as a result of higher cardiovascular (CV) and metabolic disease incidence, cognitive decline, and obesity 1 , 2 . Sleep quality declines during the menopausal transition, with over 50% of menopausal women experiencing sleep disorders like insomnia, obstructive sleep apnea, and early morning awakenings, which impact daily function and quality of life 3 , 4 . These changes in sleep quality contribute to the heightened disease risk with menopause, where CV disease, Type II diabetes, and cognitive decline become more prevalent in women vs. men 5 . By 2030, it is projected that over 1.2 billion women globally will be menopausal 6 , highlighting the importance of understanding menopause-related sleep changes. To date, there is minimal, yet conflicting research about how estrogen and progesterone, the primary hormones that decline during menopause 7 , modify sleep composition. In particular, it is unclear how menopause-related hormonal changes modify two main drivers of optimal sleep quality: circadian rhythm and body temperature regulation during sleep. Despite worsening subjective sleep, there is currently no consensus on how sleep composition changes with menopause (i.e., time spent in light, deep, rapid-eye-movement (REM) sleep, and wake) 8 – 10 . Some studies report more deep sleep and higher sleep efficiency in postmenopausal women 11 , whereas others report less deep sleep and lower sleep efficiency despite similar total sleep time 10 . These inconsistencies likely reflect differences in reference groups (which often confound aging with hormone status) and varying sample sizes 9 , 11 – 13 . A major gap is that no studies have directly compared postmenopausal women to age-matched men when evaluating sleep composition, a comparison that could help separate menopause-related hormonal effects from the generally negative effects of aging on sleep composition 14 . Similarly, although postmenopausal women have shown reduced HRV and elevated HR during sleep, relative to premenopausal women 15 , 16 , it remains unclear whether these differences reflect menopause-related hormonal changes or declining CV recovery associated with aging more broadly 17 . During healthy sleep, core body temperature (T C ) follows a U-shape, where T C declines approximately 30 min before sleep onset 18 , and continues to drop throughout the first half of the night, reaching its lowest point around 4 AM 19,20 . The majority of deep sleep occurs during the first half of the night 21 , and cooler T C during this period has been linked to more deep sleep 22 , 23 . Additionally, a blunted decline in T C pre-sleep has been associated with worse CV recovery during sleep, as reflected by a higher sleeping heart rate (HR) and lower sleeping heart rate variability (HRV) 24 . During the second half of the night, REM sleep dominates 21 , as T C rises until waking 21 . Generally, the body is more sensitive to environmental temperature disturbances during this time, as thermoregulatory function is compromised 25 . Temperature regulation during sleep also involves how heat is distributed across the skin. T C is reduced leading up to sleep onset partially as a result of sending blood flow to the periphery (e.g. hands and feet) 18 . Once sleep is initiated, maintaining a more uniform skin temperature across the body helps with sleep continuity 26 . Overall, the coordination of these core and skin temperature rhythms support stable sleep, and disruption of these rhythms has been linked to poorer sleep quality 18 , 26 , 27 . With aging and menopause, there is some evidence that the rhythm of body temperatures are blunted 28 , 29 , which may contribute to poor quality sleep reported with menopause. However, studies and sample sizes are limited. Very little is known about how the hormonal transition of menopause, independent of aging, impacts temperature regulation and sleep. We know that postmenopausal women not taking hormone replacement therapy (HRT) have a lower average T C during sleep and throughout a full 24-hour cycle 30 vs. premenopausal women. Yet it is unclear whether the circadian rhythm of T C is modified with menopause. Evidence from one paper suggests that the circadian rhythm of skin temperature is blunted with menopause, showing a lower daily amplitude (smaller temperature range) and an earlier timing (approximately a one-hour phase advance; i.e. earlier nadir) in postmenopausal compared with premenopausal women 28 . Together, these findings suggest that menopause-related hormonal changes can reshape both core and skin temperature curves; however, none of the aforementioned research explored how these changes in circadian rhythm and body temperature impacted sleep. It remains unclear how estrogen and progesterone affect circadian rhythm and temperature regulation during sleep, and how these changes influence sleep composition and CV recovery. Thus, one aim of our study is to compare how circadian rhythm and body temperatures are altered in postmenopausal women taking vs. not taking HRT, and how these potential changes may impact sleep and CV recovery. One approach taken to improve sleep quality in postmenopausal women is to prescribe HRT 31 , 32 , however, as a lot of women report significant side-effects 33 , the need for other, potentially non-pharmacological, interventions is pressing. Additionally, using hormone replacement therapy (HRT) increases T C 34 , potentially due to estrogen decreasing heat dissipation mechanisms and increasing thermogenesis 35 . Yet it is unclear how this altered temperature regulation may benefit or harm temperature regulation during sleep. Given the outlined importance of circadian rhythmicity and body temperature shifts for sleep onset and continuity, temperature-based interventions during sleep may provide a solution to improve sleep quality in menopausal women. As evidence of this concept, one study in postmenopausal not using HRT (nHRT) utilizing a high-heat capacity mattress (HHCM), which passively absorbs body heat and reduces T C , showed improved deep sleep and sleeping HR 22 . At the same time, HHCM approaches are typically limited in their ability to deliver individualized, time-varying temperature control across the full night (i.e. they absorb body heat during the first half of the night and do not adapt to sleep-stage needs or individual thermoneutral ranges). As a result, it may be that Active Temperature Regulation (ATR) throughout the night, via a smart mattress cover, may further enhance sleep and CV recovery in postmenopausal women. Altogether, critical questions remain about how temperature regulation and circadian rhythm during sleep impact sleep and CV recovery with the menopausal transition, and whether ATR could be a key non-pharmacological solution to improve sleep and reduce disease risk during this critical time. Therefore, the goals of this study are to understand (1) how the menopausal transition (independent of aging) impacts body temperature and circadian rhythm during sleep, and therefore sleep composition and CV recovery, and (2) whether changing body temperatures and circadian rhythm via ATR throughout the night can improve sleep and CV recovery in these populations. Results Sleep Composition and Body Temperatures Differed by Cohort Independent of ATR condition (i.e. across ATR ON and ATR OFF nights), we observed some differences in sleep composition, CV recovery, and body temperatures among cohorts (see Table 1 ). When exploring cohort-differences independent of ATR condition, women using HRT had slightly better sleep composition than age-matched men, observed through 2.1 ± 1.0% less WASO ( p = 0.040), 4.8 ± 1.5% less light sleep ( p = 0.006), and 2.8 ± 1.1% more REM sleep ( p = 0.011). However, there were no differences in sleep composition between age-matched men and women not taking HRT, or women taking vs. not taking HRT (both p > 0.05). Independent of ATR conditions, there were no differences among cohorts for overnight CV recovery, deep sleep, total sleep time, sleep onset latency, or sleep efficiency (all p > 0.05). Independent of ATR condition, women using HRT had slightly 0.10 ± 0.04°C higher nightly T C values than age-matched men ( p = 0.028), but there were no statistical differences in T C between age-matched men vs. women not taking HRT, or between women taking vs. not taking HRT (both p > 0.05). However, no main effect of cohort was evident for weighted mean skin temperature (T SK ) or body temperature (T B ) (all p > 0.05). All cosinor comparisons below for T C and HR are independent of ATR condition. Men had a significantly lower T C mesor compared to both women using HRT (-0.54 ± 0.03°C) and women not using HRT (-0.17 ± 0.03°C; both p < 0.001), indicating a lower 24-hour mean T C in age-matched men vs. women. Women not taking HRT had a significantly higher T C amplitude than both women using HRT (+ 0.11 ± 0.03°C; p = 0.002) and age-matched men (+ 0.15 ± 0.03°C; p < 0.001), suggesting a larger overall range of T C over a 24-hour period for women not using HRT. Age-matched men had a significantly lower HR mesor compared to both women taking HRT (-4.7 ± 1.5 bpm, p = 0.009) and not taking HRT (-3.9 ± 1.5 bpm, p = 0.031), suggesting that men had a lower mean 24-hour HR than either female cohort. There were no differences among cohorts for HR amplitude, T C acrophase or HR acrophase (all p > 0.05). Table 1 Differences in sleep composition, cardiovascular (CV) recovery, core temperature (T C ), body temperature (T B ), and skin temperatures by cohort and active temperature regulation condition (ATR, ON or OFF) Metric Women using HRT Women nHRT Age-matched men Sleep composition and cardiovascular (CV) recovery (mean ± SE) Wake after sleep onset (WASO) (%) ‡ OFF = 10.2 ± 0.8 ON = 9.8 ± 0.8 OFF = 11.8 ± 0.8 ON = 11.4 ± 0.8 OFF = 12.6 ± 0.8 ON = 12.2 ± 0.8 Rapid eye movement (REM) sleep (%) ‡ OFF = 19.9 ± 0.7 ON = 20.0 ± 0.7 OFF = 18.9 ± 0.7 ON = 18.9 ± 0.7 OFF = 17.1 ± 0.7 ON = 17.2 ± 0.7 Light sleep (%) ‡ OFF = 52.9 ± 0.9 ON = 53.4 ± 0.9 OFF = 53.8 ± 0.9 ON = 54.3 ± 0.9 OFF = 55.4 ± 0.9 ON = 55.9 ± 0.9 Deep sleep (%) OFF = 13.2 ± 0.6 ON = 13.2 ± 0.6 OFF = 12.0 ± 0.6 ON = 12.0 ± 0.6 OFF = 11.3 ± 0.6 ON = 11.2 ± 0.6 Sleeping HR (bpm) * OFF = 63.9 ± 1.4 ON = 62.0 ± 1.4 OFF = 62.4 ± 1.4 ON = 60.6 ± 1.4 OFF = 59.8 ± 1.4 ON = 58.0 ± 1.4 Sleeping HRV (ms) * OFF = 32.2 ± 2.4 ON = 35.9 ± 2.4 OFF = 33.4 ± 2.4 ON = 37.0 ± 2.4 OFF = 29.8 ± 2.4 ON = 33.4 ± 2.4 Total sleep time (hours) OFF = 7.0 ± 0.1 ON = 7.0 ± 0.1 OFF = 7.1 ± 0.1 ON = 7.1 ± 0.1 OFF = 6.7 ± 0.1 ON = 6.8 ± 0.1 Sleep onset latency (min) OFF = 20.9 ± 1.6 ON = 19.7 ± 1.6 OFF = 20.3 ± 1.6 ON = 19.2 ± 1.6 OFF = 20.3 ± 1.6 ON = 19.2 ± 1.5 Sleep efficiency (%) OFF = 86.0 ± 0.8 ON = 86.5 ± 0.8 OFF = 84.7 ± 0.8 ON = 85.2 ± 0.8 OFF = 83.8 ± 0.8 ON = 84.3 ± 0.8 Overnight core temperature (T C ) and skin temperature (mean ± SE) T C (°C) * ‡ OFF = 36.70 ± 0.03 ON = 36.55 ± 0.03 OFF = 36.62 ± 0.03 ON = 36.46 ± 0.03 OFF = 36.59 ± 0.03 ON = 36.46 ± 0.03 Mean weighted skin temperature (T SK , °C) OFF = 33.9 ± 0.1 ON = 34.0 ± 0.1 OFF = 33.7 ± 0.1 ON = 33.7 ± 0.1 OFF = 33.6 ± 0.1 ON = 33.8 ± 0.1 Body temperature (T B , °C) * OFF = 36.42 ± 0.03 ON = 36.29 ± 0.03 OFF = 36.34 ± 0.03 ON = 36.21 ± 0.03 OFF = 36.32 ± 0.03 ON = 36.19 ± 0.03 Circadian rhythm measurements (i.e. cosinor analysis) of T C and heart rate (HR, mean ± SE) T C mesor (°C) * ‡ ⧻ OFF = 36.99 ± 0.03 ON = 36.94 ± 0.02 OFF = 37.01 ± 0.02 ON = 36.94 ± 0.02 OFF = 36.85 ± 0.03 ON = 36.77 ± 0.03 T C amplitude (°C) * ✝ ⧻ OFF = 0.36 ± 0.03 ON = 0.44 ± 0.03 OFF = 0.47 ± 0.02 ON = 0.55 ± 0.02 OFF = 0.33 ± 0.03 ON = 0.39 ± 0.03 T C acrophase (hours after sleep onset) OFF = 15.2 ± 0.4 ON = 15.1 ± 0.3 OFF = 15.3 ± 0.3 ON = 15.6 ± 0.3 OFF = 15.6 ± 0.4 ON = 15.8 ± 0.4 HR mesor (bpm) ‡ ⧻ OFF = 73.8 ± 1.0 ON = 73.9 ± 1.0 OFF = 73.2 ± 1.0 ON = 72.9 ± 1.0 OFF = 69.6 ± 1.2 ON = 68.7 ± 1.2 HR amplitude (bpm) * OFF = 13.0 ± 1.0 ON = 14.9 ± 1.0 OFF = 14.3 ± 0.9 ON = 15.4 ± 1.0 OFF = 11.9 ± 1.2 ON = 13.6 ± 1.2 HR acrophase (hours after sleep onset) OFF = 15.8 ± 0.3 ON = 15.7 ± 0.3 OFF = 15.6 ± 0.2 ON = 15.9 ± 0.3 OFF = 15.8 ± 0.3 ON = 16.2 ± 0.3 Note: Model estimated means ± standard error (SE) of core temperature ( T C ), skin temperature and sleep composition including heart rate (HR) and HR variability (HRV) by Active Temperature Regulation (ART, ON/OFF) and cohort (women using hormone replacement therapy (HRT), women not using HRT (nHRT), and age-matched men). For a complete overview of sample sizes, see Supplementary Information table S1 . The mesor indicates the mean value around which T C or HR oscillates, the amplitude reflects the half-width of 24-hour max-min, and acrophase represents the hours since sleep onset where amplitude peaks . The following symbols indicate statistical significance (p < 0.05) : * = main effect of ATR , ‡ = difference between women using HRT vs. age-matched men, independent of ATR condition , ✝ = difference between women using HRT vs. women nHRT, independent of ATR condition , ⧻ = difference between age-matched men vs. women nHRT, independent of ATR condition, using linear mixed-effects modeling. Active Temperature Regulation Improves Cardiovascular Recovery and Lowers Body Temperatures Independent of cohort, ATR ON compared to OFF was associated with significantly better CV recovery, as observed through an average nightly decrease of 1.8 ± 0.3 bpm (-3%) in sleeping HR and an average nightly increase of 3.6 ± 0.6 ms (+ 11%) in sleeping HRV (both p < 0.001). ATR ON was also significantly linked to cooler core and body temperatures, observed through an average reduction in T B of 0.13 ± 0.02°C and T C of 0.16 ± 0.01°C across nights and cohorts. The extent to which T C was reduced was significantly dependent on the participant’s ATR OFF T C values, with reductions estimated to occur by 0.55 ± 0.11°C for each 1°C in ATR OFF T C above 36.34°C, meaning that the warmer a participant’s mean overnight T C during ATR OFF, the greater the reduction in T C with ATR ON (all comparisons p < 0.001). For visualizations, see Supplementary Information, Fig. S1. The larger the decrease in T C from ATR OFF to ON, the greater the reduction in HR with ATR ON (Fig. 1a, r = 0.34, p = 0.037). Moreover, colder ATR ON temperatures during the second half of the night were significantly correlated with larger improvements in nightly HRV from ATR OFF to ON (Fig. 1b, r = -0.31, p = 0.004). Overall, regardless of ATR condition, lower T B was linked to significantly greater improvements in CV recovery, such that each 1°C decrease in T B predicted a 8.8 ± 1.4 bpm decrease in HR ( p < 0.001) and a 10.60 ± 3.82 ms increase in HRV ( p = 0.010). In addition to the overall decrease in T C and HR with ATR ON, changes were also observed in the circadian rhythm of T C and HR (via cosinor analysis). ATR ON (vs. OFF) was significantly associated with a 0.07 ± 0.01°C decrease in mesor and a 0.07 ± 0.02°C increase in T C amplitude. Together, these changes indicate a larger range of T C during the 24-hour period (both p < 0.001). Notably, these two changes indicate a similar maximal (peak) T C value between ATR conditions, but a reduced minima (i.e. nadir, Fig. 2a). We also observed a significant 11.6% increase in the HR amplitude (1.5 ± 0.4 bpm, p < 0.001) with ATR ON, which is largely a result of the lower sleeping HR (i.e. nadir) vs. a higher peak HR during wake (Fig. 2b). Lastly, ATR-related changes in T C and HR amplitude were significantly positively correlated, indicating that larger increases in T C amplitude with ATR ON were correlated with larger increases in HR amplitude ( r = 0.46, p = 0.045). Visualizations of the T C and HR 24-hour changes with ATR are presented in Fig. 2. Active Temperature Regulation Balances Sleep Composition For those with below average sleep in a specific sleep stage with ATR OFF, sleeping with ATR ON helped rebalance sleep composition. In general, participants with lower percentages of deep or REM sleep during ATR OFF achieved higher percentages with ATR ON, while participants with higher percentages of light sleep or WASO during ATR OFF showed reductions with ATR ON. Specifically, sleeping with ATR ON improved sleep for those with abnormal amounts of light sleep (i.e. >65% light sleep; r=-0.28; p = 0.007), deep sleep (< 13.7% deep sleep; r=-0.30; p = 0.004), REM sleep (9.5% WASO; r=-0.26; p = 0.014) with ATR OFF. In total, when observing changes in sleep composition with ATR conditions across the cohorts, we observed that 26% of participants had a 2+% reduction in the percentage of light sleep (n = 23), 18% of participants had a 2+% increase in the percentage of deep and REM sleep (n = 16), and 19% of participants had a 2+% reduction in WASO % (n = 17) with ATR ON. Mean Weighted Skin Temperature Differs Across Sleep Stages T SK varied significantly by sleep stage, independent of cohort and ATR condition. During REM sleep, T SK was the warmest, being 0.08 ± 0.02°C warmer than in deep sleep ( p = 0.014) and 0.11 ± 0.02°C warmer than in light sleep (Fig. 3a, p < 0.001). During WASO, T SK was significantly the coldest, with temperatures 0.51 ± 0.02°C lower than in REM sleep, 0.40 ± 0.02°C lower than light sleep, and 0.43 ± 0.02°C lower than deep sleep (Fig. 3a, all p 0.05 ) . When exploring the relationship between T SK and sleep stages from the Initial to Final phases, women using HRT exhibited a significant 0.38 ± 0.15°C higher T SK during WASO in the Final phase (Fig. 3b, p = 0.018), compared to the Initial phase. No other phase- or cohort-related T SK differences were found across sleep stages or cohorts (all p > 0.05). T SK also predicted sleep stage probabilities, supporting the findings that a colder T SK linked to WASO and a warmer T SK linked to REM (Fig. 4a). Relative to deep sleep, each 1°C T SK increase was associated with a 59% decrease in the likelihood of being in WASO (OR = 0.41, 95% CI [0.31, 0.53]), with WASO being more likely below 32.9°C (CI’s non-overlapping at 32.5°C). Additionally, each 1°C T SK increase was associated with a 17% increase in the likelihood of being in REM sleep (OR = 1.17, 95% CI [0.99, 1.38]), with REM being more likely above 33.8°C (however, note overlapping CI’s, making this prediction more uncertain). No clear differences in cohorts were observed due to overlapping CI’s (Fig. 4b). However, the T sk where deep sleep and WASO were equally likely was 33.0°C for women using HRT, 33.2°C for women nHRT, and 32.5°C for age-matched men, suggesting men may require ~ 0.5–0.7°C colder skin temperatures for wake to predominate. Discussion We examined how body temperatures and circadian rhythm during sleep are modified by the menopausal transition, and how these physiological changes impact sleep and CV recovery. In general, we found that the circadian rhythm of T C was blunted during sleep across all cohorts, including women taking HRT and age-matched men. Next, we explored whether sleeping on a mattress cover with ATR throughout the night would improve circadian rhythm of T C and therefore sleep composition and CV recovery in older adults. Across all cohorts we found that sleeping with ATR ON improved the circadian rhythm of T C and HR (i.e. reinstated the U-shape), which improved CV recovery (lower sleeping HR and higher sleeping HRV). Yet despite the improvement in circadian rhythmicity of T C and CV recovery, we did not see a uniform improvement in sleep composition with T C cooling as previously seen in younger individuals. Instead, we found that sleeping with ATR ON primarily rebalanced sleep composition in individuals with sub-optimal sleep composition with ATR OFF. Our findings suggest that age and sex influence the circadian rhythm of core temperature, but not necessarily in the way that we expected or that is seen in younger adults. Notably, our data do not align with previous research indicating that more favorable T C dynamics (i.e. a U-shape during sleep, reflecting a larger pre-sleep drop in T C ) leads to better sleep composition, especially increased deep sleep 18 , 24 . For example, non-HRT women had a larger T C drop with ATR ON, yet this did not result in more deep sleep or better CV recovery than the other cohorts (see Table 1 and Fig. 2). Conversely, women using HRT exhibited higher T C and an opposite circadian rhythm response (i.e. inverted U-shape) with ATR OFF, yet had a slightly more favorable sleep composition than age-matched men (Table 1 and Fig. 2). These patterns suggest that with age (independent of hormone status) there may be a decoupling of body temperature with sleep composition from that which is often observed in younger populations 18 , 24 . We expected that as T C decreased, we would see an increase in deep sleep; however, we did not see any significant relationship between T C and deep sleep in our cohorts. Furthermore, we found that in some cases sleep composition can remain relatively favorable even when T C is higher throughout the night and circadian rhythm does not mimic the expected U-shape (e.g. HRT women; Fig. 2). As very little is known about how body temperature and sleep composition are mechanistically related (e.g. the neuronal pathways), more research is needed to understand why this decoupling might occur from a cellular/molecular perspective with age. This apparent dissociation between body temperature and sleep with age begs the question: If T C and sleep composition are not tightly coupled in older adults, can an external thermal intervention still drive improvements through a lower T C and restoration of circadian rhythm? More specifically, when ATR restores the 24-hour T C rhythm, does this translate into meaningful improvements in sleep composition, CV recovery, or both? We found that ATR produced consistent improvements across cohorts in reinstating the U-shape of T C circadian rhythm during sleep. ATR was linked to a nightly decrease in T C across cohorts, along with lower T C mesor and higher T C amplitude across 24 hours, indicative of enhanced T C circadian waveform. These findings align with previous work showing that nocturnal cooling lowers T C 22 , though to our knowledge we are the first to show that ATR can restore the U-shaped circadian rhythm of T C during sleep in older adults (Fig. 2a). Although the improvements in circadian rhythm of T C did not lead to uniform improvements in sleep composition (as seen in data on younger adults), it may be that enhancing the U-shape of the T C curve during sleep resulted in metabolic, cognitive or emotional processing improvements that we did not measure. Previous research shows there are strong links between the T C rhythm during sleep and metabolic flexibility, cognitive flexibility and emotional processing 36 – 38 . We observed that ATR-related reductions in T C were positively correlated with reductions in sleeping HR across the night, indicating that a larger decrease in T C from ATR OFF to ON was associated with a greater reduction in sleeping HR (Fig. 1a). While decreases in HR have been observed with T C reductions before 22 , 24 , to our knowledge we are the first to demonstrate a direct relationship between the two. We also observed that cooler ATR temperatures in the second half of the night were related to larger increases in HRV, suggesting that the magnitude of cooling may play an important role in parasympathetic activity during sleep 39 . Finally, ATR-related increases in T C amplitude were accompanied by increases in HR amplitude, largely driven by the nadir of T C and HR decreasing in tandem with ATR ON. Together, these findings support the conclusion that T C decreases via ATR can meaningfully improve CV recovery during sleep in older adults. For sleep composition, ATR's effects were selective rather than universal. ATR did not produce uniform sleep stage increases; rather, it rebalanced sleep composition by improving time spent in sleep stages where individuals were initially below- or above-average. This pattern is particularly relevant for older adults, given that sleep composition commonly shifts toward more light sleep and less deep/REM sleep with age 21 . Although we did not observe a direct link between changes in T C with sleep stages from ATR OFF to ON, we did observe clear differences in T SK based on sleep stage, similar to previous research 40 , 41 . REM sleep was associated with the warmest T SK , whereas WASO was associated with the coldest T SK . Moreover, lower T SK values were associated with higher likelihood of wake relative to deep sleep. These findings partly support the notion that aging does not fundamentally alter the relationship between T SK and sleep-wake states, as lower T SK during WASO than during light sleep has also been reported in young men and women 40 . However, Zhang et al.’s study in younger women 40 did not observe higher T SK during REM relative to light or deep sleep stages, and their absolute T SK values were approximately 0.2–0.6°C higher than those observed in our study. It is unclear whether the discrepancies reflect changes to skin temperature during sleep with aging or methodological differences in how T SK was computed (7-site estimate versus our 3-site estimate). Nevertheless, studies comparing younger and older populations’ skin temperatures during wake at rest similarly found T SK was lower by 0.3°C for the older group 42 . Although the lower T SK was not statistically significant, this supports the theory that skin temperatures may change with age during sleep. Further work using similar T SK estimates across age groups during sleep is needed to confirm these differences. Limitations . Although our data suggest that the blunted circadian rhythm and the decline in sleep quality and cardiovascular recovery observed during the menopausal transition are largely attributable to natural aging, confirming that these changes are driven solely by aging would require inclusion of additional reference groups, such as premenopausal women and younger age-matched men. Additionally, HRT formulation or dose was not standardized across the HRT women in the study, which resulted in a wide range of progesterone values in the HRT group (SD = 8.0 µg/mL, see Supplementary Information, Table S2). As progesterone impacts temperature regulation and sleep 43 , 44 , it may be that our HRT results would have been modified had we ensured everyone was on similar HRT dosing and formulations. However, doing so would have severely limited our sample sizes and applicability of results to all women taking HRT. Moreover, the generalizability of these results is somewhat limited by (1) the exclusion of individuals with chronic conditions, thus not adequately reflecting the disease burden present in the overall population at this age 45 , and (2) only including participants that already owned an Eight Sleep Pod. Pod-owners can be presumed to be more invested in their sleep and general health, thus reflecting a selection bias in our data. However, participants’ average sleep metrics with ATR OFF were below-average, as expected with this age group (Table 1 ) indicating that sleep composition in these individuals matched that of the general older adult population. Lastly, because T C was scheduled for one night per ATR condition mid-week, we considered whether the T C differences between ATR ON vs. OFF could reflect short-term adaptation to ATR rather than an acute thermal effect. Several observations argue against this. First, physiological cold acclimation typically requires repeated exposures over ~ 1 week or longer 46 , making it unlikely that one to two nights of mild surface cooling would produce meaningful acclimation. Second, the GI pill often remained in the system for more than 24 hours, allowing us to capture ≥ 2 nights of T C data from 70.4% of participants. When we evaluated the T C cosinor metrics from night 1 to night 2, there was no difference between the first night and subsequent nights, suggesting stable within-person rhythms across repeated measurements. Finally, with ATR ON, T C declined progressively across the sleep period and reached its nadir ~ 4–5 hours after sleep onset (Fig. 2a), consistent with an ongoing, real-time cooling influence rather than a fixed acclimation shift. In conclusion, we found that aging blunts (i.e. flattens) the circadian rhythm of T C during sleep, independent of menopausal status. However, sleeping on a mattress cover with ATR reinstated the circadian rhythm of T C and significantly improved CV recovery during sleep. Although the improvements in circadian rhythm of T C did not lead to uniform improvements in sleep composition (as seen in data on younger adults), enhancing the U-shape of the T C curve during sleep has been shown to improve metabolic flexibility, cognitive flexibility, and emotional processing 36 – 38 . Overall, these findings support ATR as a promising non-pharmacological strategy to restore circadian rhythm of T C , improve CV recovery during sleep, and to selectively improve sleep composition in older adults with sub-optimal sleep quality, including postmenopausal women experiencing disturbed sleep. Future research should examine whether the ATR-induced improvements in T C and CV recovery lead to long-term reductions in CV, metabolic, and cognitive disease risk. Methods Participant Characteristics In total, 90 participants completed the experimental protocol, which comprised three cohorts of n = 30 each: 1) postmenopausal women using combination HRT (i.e. estrogen and progesterone), 2) postmenopausal nHRT, and 3) age-matched men not taking any supplement hormones (like testosterone). On average, participants weighed 161 ± 29 lb, were 5′7″ ± 3″ tall, and were 56 ± 6 years old. Additional participant characteristics are provided in Supplementary Information, Table S2. Urinary hormone levels of pregnanediol glucuronide, luteinizing hormone, follicle-stimulating hormone, and estrone-3-glucuronide were assessed the morning after Night 3 on each ATR condition (Mira Clarity Kit, Miracare, Pleasanton, California, US; see Supplementary Information, Table S2 for values). Experimental Design All participants slept on their Eight Sleep Pod for 14 consecutive nights: one week with ATR disabled (ATR OFF, i.e. temperatures turned off) and one week with ATR enabled at their preferred temperature settings (ATR ON, i.e. temperatures turned on). The order of these conditions was randomized across participants, with half of participants starting with ATR ON and half of participants with ATR OFF. Temperature selections during the ATR ON week were recorded for each participant in Eight Sleep’s database (see Supplementary Information, Table S3, for mean Pod temperature values). Throughout the study, participants were instructed to maintain their normal routines regarding exercise, diet, alcohol consumption, and sleep habits to maximize ecological validity. During the 14-day study and during the 2 nights before, participants continuously wore wearable rings (Generation 4, Oura Health, Oulu, Finland) to track their HR (both during sleep and across 24 hours), sleeping HRV, and sleep composition, including the duration of time spent in deep, light, and REM sleep stages, time awake, sleep onset latency, sleep efficiency, and total sleep time. Both weeks followed a similar 7-night structure, differing only in whether ATR was ON or OFF (Fig. 5). On Nights 2–4 of each week, participants recorded skin temperatures during sleep at three sites (forehead, left chest, and left foot) using iButton temperature loggers (Thermochron iButtons, Model DS1922L; Maxim Integrated, San Jose, USA). On Night 3 of each week, participants recorded their T c during sleep via gastrointestinal pills (e-Celsius Performance, BodyCAP, Hérouville Saint-Clair, France) (see the ‘Physiological data’ section in Methods for details). Participant Exclusion Criteria In total, 90 participants were enrolled and completed the study. All participants provided written informed consent in accordance with the Declaration of Helsinki. The study protocol was approved by the Sterling Institutional Review Board in June 2025 (IRB: 14003). Participants were excluded if they were under 45 years of age, transgender, did not own a Pod, unable to sleep on their Pod for 14 consecutive days, unwilling to have ATR OFF for 7 nights, could not comply with the testing protocol, or reported regularly sleeping less than 4 hours per night for more than half of the week. Participants were also excluded if they had CV disease, sleep disorders (e.g., insomnia, sleep apnea, narcolepsy), and/or one or more chronic diseases known to affect estrogen, progesterone, testosterone, glucose regulation, thermoregulation, and/or sleep quality and regularity. These exclusions minimized individual variability that could confound results. Postmenopausal women confirmed through self-report that they had not had a menstrual period for at least 12 consecutive months 47 . Postmenopausal women were excluded if they had changed their HRT status (i.e. started or stopped taking HRT) in the 3 months prior to the study. Women in the HRT cohort were required to use combination estrogen and progesterone HRT, but there was no restriction on the dose of these hormones or in the application (e.g. oral pill, topical gel, etc). Women were also excluded if they had a current diagnosis of polycystic ovarian syndrome (PCOS) or endometriosis, and/or had an oophorectomy at any time in their life. These exclusion parameters were implemented to ensure a similar hormonal profile within each cohort. To minimize risks associated with taking the gastrointestinal pill, participants were excluded according the manufacturers' instructions: body weight below 88 lbs (40 kg) or a BMI exceeding 44.6, current or previous gastrointestinal disorders (e.g., gastroparesis, diverticulitis, Crohn’s disease, or recent gastrointestinal surgery), those unable to swallow a capsule-sized pill, those with implanted pacemakers or other electromagnetic medical devices, and/or a scheduled MRI during or within one week after the study period. Physiological Data The Eight Sleep Pod’s Active Temperature Regulation The Eight Sleep Pod is a temperature-regulated mattress cover that modulates bed surface temperature in real-time based on the biometric signals it captures throughout the night (e.g. HR, HRV, sleep stages). The system consists of a Hub, positioned beside the bed, which has a water reservoir that also heats and cools the water temperature flowing through the ATR cover. The temperature is controlled by the participant via the Eight Sleep app. Importantly, the Pod Cover allows independent temperature control for each side of the bed. Participants can set discrete temperatures in the Eight Sleep App (allowable range from 13–43°C) at three time points: (1) Bedtime phase, from bed entry until ~ 15 minutes after sleep onset; (2) Initial phase, covering the subsequent four hours; and (3) Final phase, from four hours post-sleep onset to wake. Within the Initial and Final phases, the Pod automatically adjusts temperatures based on the individual’s current sleep stage, their biological sex, and age. Generally, the Pod will cool slightly during deep and light sleep stages, and warm during REM sleep, to help maximize time in each sleep stage 22 , 26 . The mean ± SD temperature settings the cohorts chose are provided in Supplementary Information, Table S3. Ambient room temperature was slightly higher with ATR ON vs. OFF by 0.3 ± 0.1°C ( p < 0.001); however, this small change in room temperature has not been shown to impact temperature regulation 48 . Two functional modes were implemented: ATR ON and ATR OFF. During ATR ON, the Pod actively regulated bed surface temperature according to each participant’s programmed thermal profile with automatic adjustments based on the current sleep stage, while collecting continuous biometric data. During ATR OFF, temperature regulation was disabled, allowing only biometric data collection. Note that none of the CV recovery or sleep stage metrics reported in this paper are from the Pod – they are all from the wearable ring. Pod-derived sleep staging was only used to align T SK to sleep stages, as the Pod was able to detect when participants left the bed. Sleep and Cardiovascular Recovery Metrics The Oura Ring (Generation 4, Oura Health, Oulu, Finland) is a validated wearable device that continuously records physiological and behavioral parameters through photoplethysmography (PPG), skin temperature sensing, and a tri-axial accelerometer 49 . For this study, Oura data were used to characterize both nightly sleep composition and associated CV recovery patterns. Four data summaries were downloaded from the Oura API: (1) night-level summary data, providing a single observation per night, (2) epoch-level (i.e. 30 s) sleep stage data, detailing the timing and duration of light, deep, and REM sleep, as well as awake periods throughout each sleep episode, (3) continuous HR and HRV estimates sampled every 5 minutes during sleep, and (4) continuous 24-hour HR data, sampled irregularly every 5–60 seconds depending on signal stability and participant motion 50 . The nightly summaries included mean or total estimates of the following: average HR (bpm), average HRV (ms), sleep efficiency (%), time spent awake (min), as well as stage-specific durations for light, deep, and REM sleep. Additional variables included total sleep time, sleep onset latency (min), bedtime start, and bedtime end. The sleep window was defined as the period from sleep onset (calculated as the time elapsed after bedtime start to sleep onset latency) until bedtime end, reflecting total sleep time. Oura’s HRV values are derived from interbeat interval (IBI) data collected during sleep, calculated as the root mean square of successive differences (RMSSD) between consecutive heartbeats. Skin Temperature Measurements Skin temperature was wirelessly recorded using iButtons (Thermochron iButtons, Model DS1922L; Maxim Integrated, San Jose, USA), a validated methodology for acquiring skin temperature 51 , every 5 minutes at three sites during sleep on Nights 2–4. Participants wore iButtons on their forehead, left chest, and top of left foot. These locations allowed us to calculate the most accurate mean-weighted skin temperature that has been shown to reflect thermal sensation during sleep 52 . Devices were attached using 3M Transpore™ medical tape at least 30 minutes prior to sleep onset and were removed immediately upon waking. This protocol timing ensured thermal equilibration before sleep and minimized post-awakening artifacts. Skin temperature data were stored internally on the iButtons and then downloaded after participants shipped back the units. Core Body Temperature Monitoring T C was continuously monitored every minute during sleep via a gastrointestinal pill (e-Celsius Performance, BodyCAP, Hérouville Saint-Clair, France). This pill is a wireless, single-use device approximately the size of a standard vitamin capsule, designed to measure internal temperature from the intestines. The gastrointestinal pill has been validated against both esophageal and rectal temperatures and deemed reliable and comparable to these other T C locations 53 . When taking the pill each week, participants were instructed to ingest the pill at least 4 hours before their bedtime on Night 3 to ensure that the pill traveled to the small intestines and temperature readings were normal before sleep onset. Once swallowed, the pill recorded T C and transmitted data in real time via bluetooth to a lightweight external monitor kept within a one-meter range of the participant. The monitor stored the temperature data locally and after participants shipped back the unit, temperatures were downloaded to a secure application for processing and analysis. Data Preprocessing and Filtering Data preprocessing and filtration were performed using Python (version 3.13.5) in Visual Studio Code (version 1.102.3). All data types were aligned to each participant’s local timezone. All data types were summarized not only as nightly means, but also separately for the first (Initial) and second (Final) portions of the night to assess time-of-night effects. For sleep, CV, and body temperature metrics, these phases were defined using each participant’s average sleep midpoint from the prior three months: Initial spanned sleep onset to the average midpoint, and Final spanned the average midpoint to sleep offset. Unless otherwise specified, “Initial/Final” refers to this midpoint-based segmentation. In contrast, ATR temperature phases followed the Pod’s built-in schedule: Initial corresponds to the first ~ 4 hours of sleep and Final corresponds to the period from ~ 4 hours post–sleep onset until waking. Because data were collected in free-living conditions across multiple sensors, filtration was required to remove artifacts, ensuring that analyses reflected typical sleep and thermophysiology. For all datatypes, nights/periods with extreme sleep duration ( 14 h) were excluded. Sleep and CV outcomes were filtered by participant-relative outliers. This retained 1,408 nights from 90 participants for cohort comparisons of sleep composition and CV recovery (including 1,230 nights within the ATR ON/OFF experimental weeks). Skin temperature data were filtered to remove nights indicative of sensor detachment, poor-quality recordings, or partial detachment episodes identified by rapid non-physiological shifts (with minor trimming where appropriate), resulting in 552 nights from 89 participants. T C data were filtered to remove non-physiological periods (shipping and pre-ingestion), retain only stable physiological readings, remove beverage-related artifacts, and exclude nights with 4 h missing data (with limited manual inclusions when gaps still allowed cosinor fitting), resulting in 279 24-h T C periods from 78 participants. Similarly, 24-h HR windows were excluded for gaps > 4 h, or insufficient data density for cosinor fitting, resulting in 669 24-h HR periods from 54 participants. Full filtration criteria and justification are provided in ‘Data Filtration’ in Supplementary Information. For a visual overview of the filtration pipeline, see Supplementary Information, Fig. S2. Sleep Metrics and Cardiovascular Recovery The proportions of deep, light, and REM sleep were derived by dividing the duration of each stage (in min) by the total sleep time (in min) and multiplying by 100 to obtain a percentage of time in each sleep stage. WASO percentage was computed as the time spent awake after falling asleep until waking in the morning, divided by total time in bed minus sleep onset latency, and expressed as a percentage. All other metrics (HR, HRV, sleep efficiency, sleep onset latency, total sleep time) were pulled directly from the Oura API. To calculate sleep composition in the Initial phase, the total time in light, deep and REM sleep from sleep onset to the night’s midpoint was summed both within and across each sleep stage to calculate the percent time in each sleep stage (time in each sleep stage divided by total sleep time in the Initial phase, multiplied by 100). Percent WASO in the Initial phase was calculated as the time awake divided by the time from sleep onset to the average midpoint multiplied by 100. HR and HRV mean estimates for the Initial phase were similarly determined by taking a mean of the continuously sampled (every 5 min) HR and HRV from sleep onset to the average midpoint. The same approach was used to obtain sleep composition, WASO, and CV mean estimates in the Final phase by using the same computations from the average sleep midpoint to sleep offset. For overview of the calculations for each sleep phase, see Fig. 6. To evaluate how ATR ON influenced sleep relative to their ATR OFF sleep composition, we calculated participant-level means for each sleep stage during ATR OFF and ON, and then computed the mean difference from ATR OFF to ON. Similarly, we computed ATR ON-induced changes in HRV and HR relative to ATR OFF. Percent change in HRV and HR was defined as (ON − OFF)/OFF×100, using each participant’s mean nightly values in each of the two ATR conditions. Skin Temperatures Mean weighted skin temperature (T SK ) was calculated at the data-point level (sampled every 5 minutes) using the following equation to reflect thermal sensation 52 : $$\:{T}_{SK\:}\:=\:0.4\:\times\:\:{T}_{chest\:}+0.4\:\times\:\:{T}_{forehead}+\:0.2\:\times\:\:{T}_{foot}$$ The nightly means for T SK were calculated by taking the average T SK from the Pod-derived sleep onset to the Pod-derived offset (after filtration). To get a mean T SK for each sleep stage, across and within cohorts, each T SK datapoint was aligned with the sleep stage its timestamp aligned with according to the Pod’s sleep staging algorithm, then averaged within each sleep stage across the night. WASO episodes when participants left the bed during the night were excluded, as T SK values might have been altered due to movement and/or changing environments. Sleep-stage specific T SK was also calculated separately for the Initial and Final phases by taking the average T SK values within each sleep stage across the time-windows of the two phases. Core Temperature T C was sampled every minute and averaged nightly, using Pod-estimated sleep onset/offset timestamps after filtration. Body temperature (T B ) was calculated as 0.9×T C + 0.1×T SK 54 using the mean nightly T C and T SK values for each participant during each condition. T C changes from ATR OFF to ATR ON were calculated together with sleeping HR changes on a participant level. Mean nightly T C changes between conditions were calculated for nights with HR data available. Similarly, HR percent change was calculated as (ON − OFF)/OFF×100 using mean nightly HR values where T C data were available. Circadian Rhythm Data for Core Temperature and Heart Rate T C data for circadian rhythm analysis required continuous 24-hour periods for cosinor analysis (see ‘Data Analysis’). For each participant, the 24-hour windows started at the individual’s mean sleep onset time, calculated as the circular mean to account for the wrap-around from 24:00 to 00:00, across all valid nights (i.e. nights not removed during T C filtration) in the study. For visualization, a secondary 24-hour window starting one hour prior to sleep onset was created to better show the T C drop at sleep onset 18 . Because the sampling times varied slightly (by < 1 minute) across and within T C pills, we rounded each datapoint timestamp to the nearest minute to standardize timing across pills and enable minute-by-minute averaging for visualization. The 24-hour HR data also underwent cosinor analysis, with 24-hour periods starting at each participant’s mean sleep onset across the study. HR data were binned in 5-minute increments to prevent high frequency sampling during physical activity from biasing the cosinor fit. For visualization, 24-hour HR was plotted alongside T C , starting one hour before sleep onset, utilizing the same HR data as for the cosinor analysis. Statistical Analysis Analyses included only nights with complete data across all required variables. Therefore, statistical models comparing T sk and sleep metrics included 527 nights from 89 participants. Models including T C and T sk to predict T B included 273 nights from 85 participants, and the model predicting HR from T B included 258 nights from 85 participants. For ATR-related changes, differences from ATR OFF to ATR ON were calculated at the participant level. Comparisons of ATR-related changes in sleep composition based on ATR OFF estimates included 90 participants. Comparisons of ATR-related changes in T C and HR included 70 participants, while comparisons of ATR temperatures to HRV percent change included 90 participants. An a priori power analysis, based on parameter estimates from published literature 52 , 55 , 56 , indicated that ~ 30 participants per cohort would provide 80% power to detect group-level differences in HRV and T C . Statistical analyses were performed using R in R Studio (version 2025.05.1 + 513). Given that most dependent variables were measured on multiple nights within both ATR conditions (within-participant predictors), the data was primarily analyzed on a nightly basis utilizing linear mixed effects models (LMMs) with participant ID as a random intercept. The model uses the individual observations (nights) to make the overall estimates, with each participant having their own intercept for expected values in the dependent variable. LMMs were used for all comparisons except those involving participant-level mean estimates for ATR OFF and/or ATR-related changes. For cohort and ATR status comparisons, estimated marginal means contrasts were created for pairwise comparisons of the LMMs using the emmeans R-package. These contrasts had Tukey adjustments for multiple comparisons for between-cohort comparisons and Holm adjustments for within-cohort comparisons. As LMMs are robust to normality violations 57 , heteroscedasticity and influential clusters were the primary focus of assumption testing. All LMMs and pairwise comparisons were run with CR2-adjusted estimates to account for potential heteroscedasticity and influential participants biasing the results. For participant-level ATR-related changes (e.g. comparing changes in sleep composition based on ATR OFF mean values), linear models with HC3-adjusted estimates were utilized to similarly prevent issues induced by heteroscedasticity and influential clusters. For correlation plots based on participant mean values, Pearson correlation coefficient ( r ) and associated p-values were reported. Cosinor analysis with a single harmonic was used to estimate 24-hour T C and HR dynamics. Cosinor fitting was conducted as a linear model using ordinary least squares (OLS) in base R to estimate \(\:{B}_{0}\) , \(\:{B}_{1}\) , and \(\:{B}_{2}\) following the equation below 58 , 59 . The mesor (24-hour average), amplitude (half-width of 24-hour max − min) and acrophase (hours since sleep onset where amplitude peaks) were extracted from the cosinor function 59 and analyzed for each day and subject using LMMs. $$\:y\left(t\right)\:=\:{B}_{0}+{B}_{1}sin\left(wt\right)+{B}_{2}cos\left(wt\right),\:where\:w\:=\frac{2\pi\:}{24}\:and\:t\:=\:time$$ $$\:Mesor\:={B}_{0}\:,\:Amplitude\:=\:\sqrt{{B}_{1}^{2}+{B}_{2}^{2}},\:Acrophase\:=\frac{atan2({B}_{1},{B}_{2})\:mod\:2\pi\:}{w}\:$$ To examine how mean T SK predicted sleep stage and WASO probability, a Bayesian multinomial logistic mixed-effects model was fitted using a categorical logit link with deep sleep as the reference category. The model was estimated across four Markov chains with 4,000 iterations per chain (first 2,000 for warm-up). Default brms priors were applied. The model outputs consisted of the log odds and credible intervals (CIs), which both converted to odds ratio (OR) and the CIs of OR for interpretability. These estimates represent the expected change in the odds of being in a given sleep stage (REM sleep, light sleep, or WASO) relative to deep sleep (model reference category), per 1°C increase in T SK . We wanted to identify the T sk at which the model predicts people are more likely to be in one sleep stage versus another. To do this, posterior predicted probabilities were computed across a continuous temperature range to visualize how sleep stage likelihoods varied with T SK . From these predictions, we derived equal-likelihood temperatures (where two sleep stages were equally probable) and CI-overlap temperatures (where 95% CIs of adjacent sleep stages intersected). These crossing points reflect nonlinear transformations of the model estimates calculated from predicted probabilities rather than raw log-odds coefficients. Consequently, the reported crossing T sk represents values on the posterior prediction surface, where population-level probabilities of two sleep stages converge, rather than direct translations of the underlying logit-scale effects. Declarations Competing interests & funding This study was funded by Eight Sleep, Inc. Authors S.S.J., M.L.H., E.R.C., B.C.W., D.D.H., and N.E.M. are employees of Eight Sleep, Inc. and hold, or may be eligible to hold, equity in the company. They declare no other competing interests. Author T.M. declares support from the Canada Research Chairs Program (CRC-2022-00245); otherwise no other competing interests. Materials & Correspondence All correspondence and requests for materials should be addressed to corresponding author Nicole E. Moyen. Author contributions Conceptualization: S.S.J., M.L.H., D.D.H., N.E.M.; Data collection: M.L.H., E.R.C., S.S.J.; Data storage: E.R.C.; Data analysis and visualization: S.S.J., N.E.M; Data interpretation: S.S.J., N.E.M., M.L.H., T.M.; Writing (original draft): S.S.J., M.L.H., E.R.C., B.C.W., D.D.H., T.M., N.E.M. Acknowledgements We thank the study participants for their time and participation. We also thank the Eight Sleep team, particularly Natasha G. Ragland, for technical assistance and equipment preparation, and Jonathan Taso for firmware development that allowed us to turn ATR off. Data availability The datasets generated for this study are not publicly available due to proprietary restrictions. Code availability The underlying code for this study is not publicly available for proprietary reasons but may be made available to qualified researchers on reasonable request from the corresponding author. 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Methods Ecol Evol 11:1141–1152 Doyle MM et al (2022) Enhancing Cosinor Analysis of Circadian Phase Markers Using the Gamma Distribution. Sleep Med 92:1–3 Cornelissen G (2014) Cosinor-based rhythmometry. Theor Biol Med Model 11:16 Byrne C, Lim CL (2007) The ingestible telemetric body core temperature sensor: a review of validity and exercise applications. Br J Sports Med 41:126–133 Sun WM, Houghton LA, Read NW, Grundy DG, Johnson AG (1988) Effect of meal temperature on gastric emptying of liquids in man. Gut 29:302–305 Additional Declarations The authors declare potential competing interests as follows: This study was funded by Eight Sleep, Inc. Authors S.S.J., M.L.H., E.R.C., B.C.W., D.D.H., and N.E.M. are employees of Eight Sleep, Inc. and hold, or may be eligible to hold, equity in the company. They declare no other competing interests. Author T.M. declares support from the Canada Research Chairs Program (CRC-2022-00245); otherwise no other competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8652742","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578081097,"identity":"411c8141-9625-4485-897e-d9e5a81d7285","order_by":0,"name":"Sofie S. Jacobsen","email":"","orcid":"https://orcid.org/0000-0002-2689-4212","institution":"Eight Sleep, Inc.","correspondingAuthor":false,"prefix":"","firstName":"Sofie","middleName":"S.","lastName":"Jacobsen","suffix":""},{"id":578081098,"identity":"09d944e6-e9ca-4f55-bb05-7cadd7bf6ca8","order_by":1,"name":"Megan L. Holm","email":"","orcid":"","institution":"Eight Sleep, Inc.","correspondingAuthor":false,"prefix":"","firstName":"Megan","middleName":"L.","lastName":"Holm","suffix":""},{"id":578081099,"identity":"17e32575-dd87-406d-9d56-a59729b6dbff","order_by":2,"name":"Emma R. Cary","email":"","orcid":"","institution":"Eight Sleep, Inc.","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"R.","lastName":"Cary","suffix":""},{"id":578081100,"identity":"0c245f3e-fd42-4918-aef4-b5bcbe33d9bf","order_by":3,"name":"Breanne C. Wilhite","email":"","orcid":"https://orcid.org/0009-0002-5281-6776","institution":"Eight Sleep, Inc.","correspondingAuthor":false,"prefix":"","firstName":"Breanne","middleName":"C.","lastName":"Wilhite","suffix":""},{"id":578081101,"identity":"4e831c8c-d5a1-4c3d-b87f-5aa3fcd69271","order_by":4,"name":"David D. 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Each datapoint represents a single participant mean (n=70). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Mean heart rate variability (HRV) percent change (%) during Final phase from ATR OFF to ON was negatively associated with mean Final phase ATR temperature selection. Each datapoint represents a single participant mean (n=90). The black line represents the line of best fit (r = 0.34 and r = -0.31), and the blue shaded region indicates SE.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"HRHRVtemp.png","url":"https://assets-eu.researchsquare.com/files/rs-8652742/v1/0d45c366ef926d2df1480cc2.png"},{"id":101185866,"identity":"809ede36-4108-4c76-86a5-6977179fdcf8","added_by":"auto","created_at":"2026-01-27 06:05:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2411337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e24-hour core temperature \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(T\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e and heart rate (HR) for Active Temperature Regulation (ATR) ON vs. OFF\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) Mean T\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e and (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) mean HR for all cohorts across 24-hours beginning 1 hour before mean sleep onset until 1 hour before the next mean sleep onset, and split by ATR condition (ON and OFF). Estimates were based on 279 T\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e time-period and 669 HR time-periods. 24-hour mean core temperature by ATR condition for each cohort: \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(c)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Women using hormone replacement therapy (HRT), (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) women not using HRT (nHRT), and \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(e) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eage-matched men. Women using HRT contributed 87 nights, women nHRT contributed 124 nights, and age-matched men contributed 68 nights. Gray shading indicates the mean sleep window based on mean nightly sleep duration across participants and average participant-specific sleep onset time. Values were first obtained for each 24-hour period based on participant-specific average sleep onset. Then, the mean ± SE (shading) was calculated for each 24 hour period for T\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e and HR (see Methods for more detail).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"corehroverallpluscohorts.png","url":"https://assets-eu.researchsquare.com/files/rs-8652742/v1/f5b8f8b0ecddef4beb0f3858.png"},{"id":101206861,"identity":"06a4f432-2f8b-4636-9de4-e8a9602e70a4","added_by":"auto","created_at":"2026-01-27 09:56:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":297824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMean weighted skin temperature (T\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003eSK\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u003cstrong\u003e) predicted by sleep stage\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003ea)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Model-estimated mean T\u003c/em\u003e\u003csub\u003e\u003cem\u003eSK\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e predicted by sleep stage (light, deep, REM or WASO) across all cohorts for Active Temperature Regulation (ATR) ON (gray), OFF (blue), and overall (black). REM sleep was associated with warmer T\u003c/em\u003e\u003csub\u003e\u003cem\u003eSK\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e than light and deep sleep overall, while wake after sleep onset (WASO) was linked to lower T\u003c/em\u003e\u003csub\u003e\u003cem\u003eSK\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e than light and deep sleep overall. Estimates were based on 527 nights. * = p \u0026lt; 0.05, *** = p \u0026lt; 0.001. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Model-estimated mean T\u003c/em\u003e\u003csub\u003e\u003cem\u003eSK\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e predicted by sleep stages stratified by the Initial/Final phase and cohort (women using hormone replacement therapy (HRT, red), women not using HRT (nHRT, green), and age-matched men (blue)). * = p \u0026lt; 0.05, observed for women using HRT show higher Final phase T\u003c/em\u003e\u003csub\u003e\u003cem\u003eSK\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e during WASO than during the Initial phase. Women using HRT contributed 180 nights, women nHRT contributed 189 nights, and age-matched men contributed 183 nights. Error bars represent SE.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"skintemppublicationpanels.png","url":"https://assets-eu.researchsquare.com/files/rs-8652742/v1/c994e718a4c692af1afd3f27.png"},{"id":101207194,"identity":"e470d767-82b0-47aa-9a61-4c6f8c383d05","added_by":"auto","created_at":"2026-01-27 09:58:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":595916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSleep stages predicted by mean weighted skin temperature (T\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003eSK\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u003cstrong\u003e) values. a)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Mean posterior predicted probabilities for T\u003c/em\u003e\u003csub\u003e\u003cem\u003eSK\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e across cohorts split by sleep stages: light sleep (green), deep sleep (red), REM sleep (blue) or wake after sleep onset (WASO, purple). Lower skin temperatures (below 32.9°C) are more likely to be associated with wake after sleep onset (WASO) than deep sleep. Temperatures above 33.8°C are marginally more likely to be associated with REM than deep sleep. Estimates were based on 527 nights. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eb)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Mean posterior predicted probabilities for T\u003c/em\u003e\u003csub\u003e\u003cem\u003eSK\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e by cohort and sleep stages. Cohorts consisted of women using hormone replacement therapy (HRT, left), women not using HRT (nHRT, middle), and age-matched men (right). The relationship between T\u003c/em\u003e\u003csub\u003e\u003cem\u003eSK\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e and sleep stage probabilities did not differ across cohorts, based on 180, 189, and 183 nights from HRT women, nHRT women, and men, respectively. Vertical dashed lines on the left and right side of each subplot indicate the T\u003c/em\u003e\u003csub\u003e\u003cem\u003eSK \u003c/em\u003e\u003c/sub\u003e\u003cem\u003eat which deep sleep and WASO (left) or deep and REM sleep (right) are equally likely. Shading surrounding the mean posterior predicted probabilities reflects credible intervals (CIs).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"sleepstageskinpredictionparticiapnt.png","url":"https://assets-eu.researchsquare.com/files/rs-8652742/v1/d984e44726993d0d8f60bb29.png"},{"id":101185861,"identity":"230fdecc-de03-4389-9c40-48c4d6ae83fc","added_by":"auto","created_at":"2026-01-27 06:05:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":111011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eExperimental Design Schematic\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Participants (n=90) spent 14 consecutive nights sleeping with active temperature regulation (ATR) ON or OFF, in a randomized order. Sleep and cardiovascular (CV) metrics (pink bars) were collected continuously across all 7 nights of each week with a wearable ring. Skin temperature at 3 sites (blue bars) was collected on nights 2-4, and core temperature (T\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) (purple boxes) was collected on night 3 of each week. Hormone levels (orange boxes) were measured on the morning after Night 3.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Expdesign.png","url":"https://assets-eu.researchsquare.com/files/rs-8652742/v1/1e3e5db48b161d206ca870d7.png"},{"id":101206685,"identity":"ee78050c-7272-4887-862a-04e4a262c354","added_by":"auto","created_at":"2026-01-27 09:56:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":78209,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCalculation of phase-specific sleep composition and cardiovascular recovery metrics.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Gray boxes denote the start of the sleep episode (“Time asleep”) and the end of the sleep episode (“Time awake”). Each night was divided into an Initial phase (sleep onset to the individual’s mean sleep midpoint) and a Final phase (mean sleep midpoint to sleep offset). Within each phase, sleep-stage composition (percent time in light, deep, and REM sleep) was calculated as stage minutes divided by total minutes asleep in that phase, and WASO% was calculated as minutes awake divided by total minutes in that phase. Heart rate and heart rate variability for each phase were computed as the mean of continuous measurements within the corresponding phase window. Brackets/arrows indicate the time windows and the quantities derived from each phase.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Phasecalculation.png","url":"https://assets-eu.researchsquare.com/files/rs-8652742/v1/95e6131eca9718c9e6e4a3d6.png"},{"id":102298535,"identity":"160b4b33-dd28-4f6c-af7c-900e00afb12d","added_by":"auto","created_at":"2026-02-10 10:44:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5928815,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8652742/v1/476ca35e-2f55-4a2d-8ee5-202ece649037.pdf"},{"id":101185859,"identity":"c03ed020-b278-492c-948a-34884faef0dc","added_by":"auto","created_at":"2026-01-27 06:05:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":491621,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8652742/v1/7292cd39b65968d974b9bfec.docx"}],"financialInterests":"The authors declare potential competing interests as follows: This study was funded by Eight Sleep, Inc. Authors S.S.J., M.L.H., E.R.C., B.C.W., D.D.H., and N.E.M. are employees of Eight Sleep, Inc. and hold, or may be eligible to hold, equity in the company. They declare no other competing interests. Author T.M. declares support from the Canada Research Chairs Program (CRC-2022-00245); otherwise no other competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eOvernight Temperature Regulation Improves Circadian Rhythm and Cardiovascular Recovery in Postmenopausal Women and Age-Matched Men\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSleep is essential for optimal health. Insufficient or poor-quality sleep is associated with increased risk of all-cause mortality as a result of higher cardiovascular (CV) and metabolic disease incidence, cognitive decline, and obesity\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Sleep quality declines during the menopausal transition, with over 50% of menopausal women experiencing sleep disorders like insomnia, obstructive sleep apnea, and early morning awakenings, which impact daily function and quality of life\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. These changes in sleep quality contribute to the heightened disease risk with menopause, where CV disease, Type II diabetes, and cognitive decline become more prevalent in women vs. men\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. By 2030, it is projected that over 1.2\u0026nbsp;billion women globally will be menopausal\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, highlighting the importance of understanding menopause-related sleep changes. To date, there is minimal, yet conflicting research about how estrogen and progesterone, the primary hormones that decline during menopause\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, modify sleep composition. In particular, it is unclear how menopause-related hormonal changes modify two main drivers of optimal sleep quality: circadian rhythm and body temperature regulation during sleep.\u003c/p\u003e \u003cp\u003eDespite worsening subjective sleep, there is currently no consensus on how sleep composition changes with menopause (i.e., time spent in light, deep, rapid-eye-movement (REM) sleep, and wake)\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Some studies report more deep sleep and higher sleep efficiency in postmenopausal women\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, whereas others report less deep sleep and lower sleep efficiency despite similar total sleep time\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These inconsistencies likely reflect differences in reference groups (which often confound aging with hormone status) and varying sample sizes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. A major gap is that no studies have directly compared postmenopausal women to age-matched men when evaluating sleep composition, a comparison that could help separate menopause-related hormonal effects from the generally negative effects of aging on sleep composition\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Similarly, although postmenopausal women have shown reduced HRV and elevated HR during sleep, relative to premenopausal women\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, it remains unclear whether these differences reflect menopause-related hormonal changes or declining CV recovery associated with aging more broadly\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDuring healthy sleep, core body temperature (T\u003csub\u003eC\u003c/sub\u003e) follows a U-shape, where T\u003csub\u003eC\u003c/sub\u003e declines approximately 30 min before sleep onset\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and continues to drop throughout the first half of the night, reaching its lowest point around 4 AM\u003csup\u003e19,20\u003c/sup\u003e. The majority of deep sleep occurs during the first half of the night\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and cooler T\u003csub\u003eC\u003c/sub\u003e during this period has been linked to more deep sleep\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Additionally, a blunted decline in T\u003csub\u003eC\u003c/sub\u003e pre-sleep has been associated with worse CV recovery during sleep, as reflected by a higher sleeping heart rate (HR) and lower sleeping heart rate variability (HRV)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. During the second half of the night, REM sleep dominates\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, as T\u003csub\u003eC\u003c/sub\u003e rises until waking\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Generally, the body is more sensitive to environmental temperature disturbances during this time, as thermoregulatory function is compromised\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Temperature regulation during sleep also involves how heat is distributed across the skin. T\u003csub\u003eC\u003c/sub\u003e is reduced leading up to sleep onset partially as a result of sending blood flow to the periphery (e.g. hands and feet)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Once sleep is initiated, maintaining a more uniform skin temperature across the body helps with sleep continuity\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Overall, the coordination of these core and skin temperature rhythms support stable sleep, and disruption of these rhythms has been linked to poorer sleep quality\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. With aging and menopause, there is some evidence that the rhythm of body temperatures are blunted\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, which may contribute to poor quality sleep reported with menopause. However, studies and sample sizes are limited.\u003c/p\u003e \u003cp\u003eVery little is known about how the hormonal transition of menopause, independent of aging, impacts temperature regulation and sleep. We know that postmenopausal women not taking hormone replacement therapy (HRT) have a lower average T\u003csub\u003eC\u003c/sub\u003e during sleep and throughout a full 24-hour cycle\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e vs. premenopausal women. Yet it is unclear whether the circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e is modified with menopause. Evidence from one paper suggests that the circadian rhythm of skin temperature is blunted with menopause, showing a lower daily amplitude (smaller temperature range) and an earlier timing (approximately a one-hour phase advance; i.e. earlier nadir) in postmenopausal compared with premenopausal women\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Together, these findings suggest that menopause-related hormonal changes can reshape both core and skin temperature curves; however, none of the aforementioned research explored how these changes in circadian rhythm and body temperature impacted sleep. It remains unclear how estrogen and progesterone affect circadian rhythm and temperature regulation during sleep, and how these changes influence sleep composition and CV recovery. Thus, one aim of our study is to compare how circadian rhythm and body temperatures are altered in postmenopausal women taking vs. not taking HRT, and how these potential changes may impact sleep and CV recovery.\u003c/p\u003e \u003cp\u003eOne approach taken to improve sleep quality in postmenopausal women is to prescribe HRT\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, however, as a lot of women report significant side-effects\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, the need for other, potentially non-pharmacological, interventions is pressing. Additionally, using hormone replacement therapy (HRT) increases T\u003csub\u003eC\u003c/sub\u003e\u003csup\u003e34\u003c/sup\u003e, potentially due to estrogen decreasing heat dissipation mechanisms and increasing thermogenesis\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Yet it is unclear how this altered temperature regulation may benefit or harm temperature regulation during sleep. Given the outlined importance of circadian rhythmicity and body temperature shifts for sleep onset and continuity, temperature-based interventions during sleep may provide a solution to improve sleep quality in menopausal women. As evidence of this concept, one study in postmenopausal not using HRT (nHRT) utilizing a high-heat capacity mattress (HHCM), which passively absorbs body heat and reduces T\u003csub\u003eC\u003c/sub\u003e, showed improved deep sleep and sleeping HR\u003csup\u003e22\u003c/sup\u003e. At the same time, HHCM approaches are typically limited in their ability to deliver individualized, time-varying temperature control across the full night (i.e. they absorb body heat during the first half of the night and do not adapt to sleep-stage needs or individual thermoneutral ranges). As a result, it may be that Active Temperature Regulation (ATR) throughout the night, via a smart mattress cover, may further enhance sleep and CV recovery in postmenopausal women.\u003c/p\u003e \u003cp\u003eAltogether, critical questions remain about how temperature regulation and circadian rhythm during sleep impact sleep and CV recovery with the menopausal transition, and whether ATR could be a key non-pharmacological solution to improve sleep and reduce disease risk during this critical time. Therefore, the goals of this study are to understand (1) how the menopausal transition (independent of aging) impacts body temperature and circadian rhythm during sleep, and therefore sleep composition and CV recovery, and (2) whether changing body temperatures and circadian rhythm via ATR throughout the night can improve sleep and CV recovery in these populations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSleep Composition and Body Temperatures Differed by Cohort\u003c/h2\u003e \u003cp\u003eIndependent of ATR condition (i.e. across ATR ON and ATR OFF nights), we observed some differences in sleep composition, CV recovery, and body temperatures among cohorts (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When exploring cohort-differences independent of ATR condition, women using HRT had slightly better sleep composition than age-matched men, observed through 2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0% less WASO (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040), 4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5% less light sleep (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), and 2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1% more REM sleep (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). However, there were no differences in sleep composition between age-matched men and women not taking HRT, or women taking vs. not taking HRT (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Independent of ATR conditions, there were no differences among cohorts for overnight CV recovery, deep sleep, total sleep time, sleep onset latency, or sleep efficiency (all \u003cem\u003ep\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIndependent of ATR condition, women using HRT had slightly 0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u0026deg;C higher nightly T\u003csub\u003eC\u003c/sub\u003e values than age-matched men (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), but there were no statistical differences in T\u003csub\u003eC\u003c/sub\u003e between age-matched men vs. women not taking HRT, or between women taking vs. not taking HRT (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, no main effect of cohort was evident for weighted mean skin temperature (T\u003csub\u003eSK\u003c/sub\u003e) or body temperature (T\u003csub\u003eB\u003c/sub\u003e) (all \u003cem\u003ep\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAll cosinor comparisons below for T\u003csub\u003eC\u003c/sub\u003e and HR are independent of ATR condition. Men had a significantly lower T\u003csub\u003eC\u003c/sub\u003e mesor compared to both women using HRT (-0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u0026deg;C) and women not using HRT (-0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u0026deg;C; both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a lower 24-hour mean T\u003csub\u003eC\u003c/sub\u003e in age-matched men vs. women. Women not taking HRT had a significantly higher T\u003csub\u003eC\u003c/sub\u003e amplitude than both women using HRT (+\u0026thinsp;0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u0026deg;C; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and age-matched men (+\u0026thinsp;0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u0026deg;C; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting a larger overall range of T\u003csub\u003eC\u003c/sub\u003e over a 24-hour period for women not using HRT. Age-matched men had a significantly lower HR mesor compared to both women taking HRT (-4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 bpm, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) and not taking HRT (-3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 bpm, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), suggesting that men had a lower mean 24-hour HR than either female cohort. There were no differences among cohorts for HR amplitude, T\u003csub\u003eC\u003c/sub\u003e acrophase or HR acrophase (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifferences in sleep composition, cardiovascular (CV) recovery, core temperature (T\u003csub\u003eC\u003c/sub\u003e), body temperature (T\u003csub\u003eB\u003c/sub\u003e), and skin temperatures by cohort and active temperature regulation condition (ATR, ON or OFF)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWomen using HRT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen nHRT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAge-matched men\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSleep composition and cardiovascular (CV) recovery (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWake after sleep onset (WASO) (%) \u003cb\u003e\u0026Dagger;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRapid eye movement (REM) sleep (%) \u003cb\u003e\u0026Dagger;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;18.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;18.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLight sleep (%) \u003cb\u003e\u0026Dagger;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;52.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;53.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;54.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;55.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;55.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeep sleep (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;12.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;12.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleeping HR (bpm) \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;62.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;62.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;60.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleeping HRV (ms) \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;32.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;35.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal sleep time (hours)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep onset latency (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep efficiency (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;86.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;86.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;84.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;85.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;83.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;84.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOvernight core temperature (T\u003c/b\u003e\u003csub\u003e\u003cb\u003eC\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e) and skin temperature (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003csub\u003eC\u003c/sub\u003e (\u0026deg;C) \u003cb\u003e* \u0026Dagger;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;36.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;36.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;36.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;36.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;36.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;36.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean weighted skin temperature (T\u003csub\u003eSK\u003c/sub\u003e, \u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;33.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;34.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;33.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;33.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;33.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;33.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody temperature (T\u003csub\u003eB\u003c/sub\u003e, \u0026deg;C) \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;36.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;36.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;36.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;36.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;36.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;36.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCircadian rhythm measurements (i.e. cosinor analysis) of T\u003c/b\u003e\u003csub\u003e\u003cb\u003eC\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eand heart rate (HR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003csub\u003eC\u003c/sub\u003e mesor (\u0026deg;C) \u003cb\u003e*\u003c/b\u003e\u003cb\u003e\u0026Dagger;\u003c/b\u003e ⧻\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;36.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;36.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;37.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;36.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;36.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;36.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003csub\u003eC\u003c/sub\u003e amplitude (\u0026deg;C) \u003cb\u003e*\u003c/b\u003e\u003cb\u003e✝\u003c/b\u003e ⧻\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003csub\u003eC\u003c/sub\u003e acrophase (hours after sleep onset)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR mesor (bpm) \u003cb\u003e\u0026Dagger;\u003c/b\u003e ⧻\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;73.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;73.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;73.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;69.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;68.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR amplitude (bpm) \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;14.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR acrophase (hours after sleep onset)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;15.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOFF\u0026thinsp;=\u0026thinsp;15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003cp\u003eON\u0026thinsp;=\u0026thinsp;16.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: Model estimated means\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003estandard error (SE) of core temperature (\u003c/em\u003eT\u003csub\u003eC\u003c/sub\u003e\u003cem\u003e), skin temperature and sleep composition including heart rate (HR) and HR variability (HRV) by Active Temperature Regulation (ART, ON/OFF) and cohort (women using hormone replacement therapy (HRT), women not using HRT (nHRT), and age-matched men). For a complete overview of sample sizes, see Supplementary Information table S1\u003c/em\u003e. \u003cem\u003eThe mesor indicates the mean value around which T\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eor HR oscillates, the amplitude reflects the half-width of 24-hour max-min, and acrophase represents the hours since sleep onset where amplitude peaks\u003c/em\u003e. \u003cem\u003eThe following symbols indicate statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e: \u003cb\u003e*\u003c/b\u003e\u003cem\u003e= main effect of ATR\u003c/em\u003e, \u003cb\u003e\u0026Dagger;\u003c/b\u003e\u003cem\u003e= difference between women using HRT vs. age-matched men, independent of ATR condition\u003c/em\u003e, \u003cb\u003e✝\u003c/b\u003e\u003cem\u003e= difference between women using HRT vs. women nHRT, independent of ATR condition\u003c/em\u003e, ⧻ \u003cem\u003e= difference between age-matched men vs. women nHRT, independent of ATR condition, using linear mixed-effects modeling.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eActive Temperature Regulation Improves Cardiovascular Recovery and Lowers Body Temperatures\u003c/h3\u003e\n\u003cp\u003eIndependent of cohort, ATR ON compared to OFF was associated with significantly better CV recovery, as observed through an average nightly decrease of 1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 bpm (-3%) in sleeping HR and an average nightly increase of 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 ms (+\u0026thinsp;11%) in sleeping HRV (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eATR ON was also significantly linked to cooler core and body temperatures, observed through an average reduction in T\u003csub\u003eB\u003c/sub\u003e of 0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u0026deg;C and T\u003csub\u003eC\u003c/sub\u003e of 0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u0026deg;C across nights and cohorts. The extent to which T\u003csub\u003eC\u003c/sub\u003e was reduced was significantly dependent on the participant\u0026rsquo;s ATR OFF T\u003csub\u003eC\u003c/sub\u003e values, with reductions estimated to occur by 0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u0026deg;C for each 1\u0026deg;C in ATR OFF T\u003csub\u003eC\u003c/sub\u003e above 36.34\u0026deg;C, meaning that the warmer a participant\u0026rsquo;s mean overnight T\u003csub\u003eC\u003c/sub\u003e during ATR OFF, the greater the reduction in T\u003csub\u003eC\u003c/sub\u003e with ATR ON (all comparisons \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For visualizations, see Supplementary Information, Fig. S1.\u003c/p\u003e \u003cp\u003eThe larger the decrease in T\u003csub\u003eC\u003c/sub\u003e from ATR OFF to ON, the greater the reduction in HR with ATR ON (Fig.\u0026nbsp;1a, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). Moreover, colder ATR ON temperatures during the second half of the night were significantly correlated with larger improvements in nightly HRV from ATR OFF to ON (Fig.\u0026nbsp;1b, \u003cem\u003er\u003c/em\u003e = -0.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Overall, regardless of ATR condition, lower T\u003csub\u003eB\u003c/sub\u003e was linked to significantly greater improvements in CV recovery, such that each 1\u0026deg;C decrease in T\u003csub\u003eB\u003c/sub\u003e predicted a 8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 bpm decrease in HR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a 10.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82 ms increase in HRV (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010).\u003c/p\u003e \u003cp\u003eIn addition to the overall decrease in T\u003csub\u003eC\u003c/sub\u003e and HR with ATR ON, changes were also observed in the circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e and HR (via cosinor analysis). ATR ON (vs. OFF) was significantly associated with a 0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u0026deg;C decrease in mesor and a 0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u0026deg;C increase in T\u003csub\u003eC\u003c/sub\u003e amplitude. Together, these changes indicate a larger range of T\u003csub\u003eC\u003c/sub\u003e during the 24-hour period (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, these two changes indicate a similar maximal (peak) T\u003csub\u003eC\u003c/sub\u003e value between ATR conditions, but a reduced minima (i.e. nadir, Fig.\u0026nbsp;2a). We also observed a significant 11.6% increase in the HR amplitude (1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 bpm, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with ATR ON, which is largely a result of the lower sleeping HR (i.e. nadir) vs. a higher peak HR during wake (Fig.\u0026nbsp;2b). Lastly, ATR-related changes in T\u003csub\u003eC\u003c/sub\u003e and HR amplitude were significantly positively correlated, indicating that larger increases in T\u003csub\u003eC\u003c/sub\u003e amplitude with ATR ON were correlated with larger increases in HR amplitude (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). Visualizations of the T\u003csub\u003eC\u003c/sub\u003e and HR 24-hour changes with ATR are presented in Fig.\u0026nbsp;2.\u003c/p\u003e\n\u003ch3\u003eActive Temperature Regulation Balances Sleep Composition\u003c/h3\u003e\n\u003cp\u003eFor those with below average sleep in a specific sleep stage with ATR OFF, sleeping with ATR ON helped rebalance sleep composition. In general, participants with lower percentages of deep or REM sleep during ATR OFF achieved higher percentages with ATR ON, while participants with higher percentages of light sleep or WASO during ATR OFF showed reductions with ATR ON.\u003c/p\u003e \u003cp\u003eSpecifically, sleeping with ATR ON improved sleep for those with abnormal amounts of light sleep (i.e. \u0026gt;65% light sleep; \u003cem\u003er=-0.28; p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), deep sleep (\u0026lt;\u0026thinsp;13.7% deep sleep; \u003cem\u003er=-0.30; p\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.004), REM sleep (\u0026lt;\u0026thinsp;21.3% REM sleep; \u003cem\u003er=-0.27\u003c/em\u003e; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.012), or WASO sleep (i.e. \u0026gt;9.5% WASO; \u003cem\u003er=-0.26; p\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.014) with ATR OFF. In total, when observing changes in sleep composition with ATR conditions across the cohorts, we observed that 26% of participants had a 2+% reduction in the percentage of light sleep (n\u0026thinsp;=\u0026thinsp;23), 18% of participants had a 2+% increase in the percentage of deep and REM sleep (n\u0026thinsp;=\u0026thinsp;16), and 19% of participants had a 2+% reduction in WASO % (n\u0026thinsp;=\u0026thinsp;17) with ATR ON.\u003c/p\u003e\n\u003ch3\u003eMean Weighted Skin Temperature Differs Across Sleep Stages\u003c/h3\u003e\n\u003cp\u003eT\u003csub\u003eSK\u003c/sub\u003e varied significantly by sleep stage, independent of cohort and ATR condition. During REM sleep, T\u003csub\u003eSK\u003c/sub\u003e was the warmest, being 0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u0026deg;C warmer than in deep sleep (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and 0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u0026deg;C warmer than in light sleep (Fig.\u0026nbsp;3a, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). During WASO, T\u003csub\u003eSK\u003c/sub\u003e was significantly the coldest, with temperatures 0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u0026deg;C lower than in REM sleep, 0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u0026deg;C lower than light sleep, and 0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u0026deg;C lower than deep sleep (Fig.\u0026nbsp;3a, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences in T\u003csub\u003eSK\u003c/sub\u003e were observed between light and deep sleep (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.500) or between ATR ON and OFF in any sleep stage (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eWhen exploring the relationship between T\u003csub\u003eSK\u003c/sub\u003e and sleep stages from the Initial to Final phases, women using HRT exhibited a significant 0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u0026deg;C higher T\u003csub\u003eSK\u003c/sub\u003e during WASO in the Final phase (Fig.\u0026nbsp;3b, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), compared to the Initial phase. No other phase- or cohort-related T\u003csub\u003eSK\u003c/sub\u003e differences were found across sleep stages or cohorts (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eT\u003csub\u003eSK\u003c/sub\u003e also predicted sleep stage probabilities, supporting the findings that a colder T\u003csub\u003eSK\u003c/sub\u003e linked to WASO and a warmer T\u003csub\u003eSK\u003c/sub\u003e linked to REM (Fig.\u0026nbsp;4a). Relative to deep sleep, each 1\u0026deg;C T\u003csub\u003eSK\u003c/sub\u003e increase was associated with a 59% decrease in the likelihood of being in WASO (OR\u0026thinsp;=\u0026thinsp;0.41, 95% CI [0.31, 0.53]), with WASO being more likely below 32.9\u0026deg;C (CI\u0026rsquo;s non-overlapping at 32.5\u0026deg;C). Additionally, each 1\u0026deg;C T\u003csub\u003eSK\u003c/sub\u003e increase was associated with a 17% increase in the likelihood of being in REM sleep (OR\u0026thinsp;=\u0026thinsp;1.17, 95% CI [0.99, 1.38]), with REM being more likely above 33.8\u0026deg;C (however, note overlapping CI\u0026rsquo;s, making this prediction more uncertain). No clear differences in cohorts were observed due to overlapping CI\u0026rsquo;s (Fig.\u0026nbsp;4b). However, the T\u003csub\u003esk\u003c/sub\u003e where deep sleep and WASO were equally likely was 33.0\u0026deg;C for women using HRT, 33.2\u0026deg;C for women nHRT, and 32.5\u0026deg;C for age-matched men, suggesting men may require\u0026thinsp;~\u0026thinsp;0.5\u0026ndash;0.7\u0026deg;C colder skin temperatures for wake to predominate.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe examined how body temperatures and circadian rhythm during sleep are modified by the menopausal transition, and how these physiological changes impact sleep and CV recovery. In general, we found that the circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e was blunted during sleep across all cohorts, including women taking HRT and age-matched men. Next, we explored whether sleeping on a mattress cover with ATR throughout the night would improve circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e and therefore sleep composition and CV recovery in older adults. Across all cohorts we found that sleeping with ATR ON improved the circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e and HR (i.e. reinstated the U-shape), which improved CV recovery (lower sleeping HR and higher sleeping HRV). Yet despite the improvement in circadian rhythmicity of T\u003csub\u003eC\u003c/sub\u003e and CV recovery, we did not see a uniform improvement in sleep composition with T\u003csub\u003eC\u003c/sub\u003e cooling as previously seen in younger individuals. Instead, we found that sleeping with ATR ON primarily rebalanced sleep composition in individuals with sub-optimal sleep composition with ATR OFF.\u003c/p\u003e \u003cp\u003eOur findings suggest that age and sex influence the circadian rhythm of core temperature, but not necessarily in the way that we expected or that is seen in younger adults. Notably, our data do not align with previous research indicating that more favorable T\u003csub\u003eC\u003c/sub\u003e dynamics (i.e. a U-shape during sleep, reflecting a larger pre-sleep drop in T\u003csub\u003eC\u003c/sub\u003e) leads to better sleep composition, especially increased deep sleep\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For example, non-HRT women had a larger T\u003csub\u003eC\u003c/sub\u003e drop with ATR ON, yet this did not result in more deep sleep or better CV recovery than the other cohorts (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;2). Conversely, women using HRT exhibited higher T\u003csub\u003eC\u003c/sub\u003e and an opposite circadian rhythm response (i.e. inverted U-shape) with ATR OFF, yet had a slightly more favorable sleep composition than age-matched men (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;2). These patterns suggest that with age (independent of hormone status) there may be a decoupling of body temperature with sleep composition from that which is often observed in younger populations\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. We expected that as T\u003csub\u003eC\u003c/sub\u003e decreased, we would see an increase in deep sleep; however, we did not see any significant relationship between T\u003csub\u003eC\u003c/sub\u003e and deep sleep in our cohorts. Furthermore, we found that in some cases sleep composition can remain relatively favorable even when T\u003csub\u003eC\u003c/sub\u003e is higher throughout the night and circadian rhythm does not mimic the expected U-shape (e.g. HRT women; Fig.\u0026nbsp;2). As very little is known about how body temperature and sleep composition are mechanistically related (e.g. the neuronal pathways), more research is needed to understand why this decoupling might occur from a cellular/molecular perspective with age.\u003c/p\u003e \u003cp\u003eThis apparent dissociation between body temperature and sleep with age begs the question: If T\u003csub\u003eC\u003c/sub\u003e and sleep composition are not tightly coupled in older adults, can an external thermal intervention still drive improvements through a lower T\u003csub\u003eC\u003c/sub\u003e and restoration of circadian rhythm? More specifically, when ATR restores the 24-hour T\u003csub\u003eC\u003c/sub\u003e rhythm, does this translate into meaningful improvements in sleep composition, CV recovery, or both? We found that ATR produced consistent improvements across cohorts in reinstating the U-shape of T\u003csub\u003eC\u003c/sub\u003e circadian rhythm during sleep. ATR was linked to a nightly decrease in T\u003csub\u003eC\u003c/sub\u003e across cohorts, along with lower T\u003csub\u003eC\u003c/sub\u003e mesor and higher T\u003csub\u003eC\u003c/sub\u003e amplitude across 24 hours, indicative of enhanced T\u003csub\u003eC\u003c/sub\u003e circadian waveform. These findings align with previous work showing that nocturnal cooling lowers T\u003csub\u003eC\u003c/sub\u003e\u003csup\u003e22\u003c/sup\u003e, though to our knowledge we are the first to show that ATR can restore the U-shaped circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e during sleep in older adults (Fig.\u0026nbsp;2a). Although the improvements in circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e did not lead to uniform improvements in sleep composition (as seen in data on younger adults), it may be that enhancing the U-shape of the T\u003csub\u003eC\u003c/sub\u003e curve during sleep resulted in metabolic, cognitive or emotional processing improvements that we did not measure. Previous research shows there are strong links between the T\u003csub\u003eC\u003c/sub\u003e rhythm during sleep and metabolic flexibility, cognitive flexibility and emotional processing\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e–\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe observed that ATR-related reductions in T\u003csub\u003eC\u003c/sub\u003e were positively correlated with reductions in sleeping HR across the night, indicating that a larger decrease in T\u003csub\u003eC\u003c/sub\u003e from ATR OFF to ON was associated with a greater reduction in sleeping HR (Fig.\u0026nbsp;1a). While decreases in HR have been observed with T\u003csub\u003eC\u003c/sub\u003e reductions before\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, to our knowledge we are the first to demonstrate a direct relationship between the two. We also observed that cooler ATR temperatures in the second half of the night were related to larger increases in HRV, suggesting that the magnitude of cooling may play an important role in parasympathetic activity during sleep\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Finally, ATR-related increases in T\u003csub\u003eC\u003c/sub\u003e amplitude were accompanied by increases in HR amplitude, largely driven by the nadir of T\u003csub\u003eC\u003c/sub\u003e and HR decreasing in tandem with ATR ON. Together, these findings support the conclusion that T\u003csub\u003eC\u003c/sub\u003e decreases via ATR can meaningfully improve CV recovery during sleep in older adults.\u003c/p\u003e \u003cp\u003eFor sleep composition, ATR's effects were selective rather than universal. ATR did not produce uniform sleep stage increases; rather, it rebalanced sleep composition by improving time spent in sleep stages where individuals were initially below- or above-average. This pattern is particularly relevant for older adults, given that sleep composition commonly shifts toward more light sleep and less deep/REM sleep with age\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Although we did not observe a direct link between changes in T\u003csub\u003eC\u003c/sub\u003e with sleep stages from ATR OFF to ON, we did observe clear differences in T\u003csub\u003eSK\u003c/sub\u003e based on sleep stage, similar to previous research\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. REM sleep was associated with the warmest T\u003csub\u003eSK\u003c/sub\u003e, whereas WASO was associated with the coldest T\u003csub\u003eSK\u003c/sub\u003e. Moreover, lower T\u003csub\u003eSK\u003c/sub\u003e values were associated with higher likelihood of wake relative to deep sleep. These findings partly support the notion that aging does not fundamentally alter the relationship between T\u003csub\u003eSK\u003c/sub\u003e and sleep-wake states, as lower T\u003csub\u003eSK\u003c/sub\u003e during WASO than during light sleep has also been reported in young men and women\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, Zhang et al.’s study in younger women\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e did not observe higher T\u003csub\u003eSK\u003c/sub\u003e during REM relative to light or deep sleep stages, and their absolute T\u003csub\u003eSK\u003c/sub\u003e values were approximately 0.2–0.6°C higher than those observed in our study. It is unclear whether the discrepancies reflect changes to skin temperature during sleep with aging or methodological differences in how T\u003csub\u003eSK\u003c/sub\u003e was computed (7-site estimate versus our 3-site estimate). Nevertheless, studies comparing younger and older populations’ skin temperatures during wake at rest similarly found T\u003csub\u003eSK\u003c/sub\u003e was lower by 0.3°C for the older group\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Although the lower T\u003csub\u003eSK\u003c/sub\u003e was not statistically significant, this supports the theory that skin temperatures may change with age during sleep. Further work using similar T\u003csub\u003eSK\u003c/sub\u003e estimates across age groups during sleep is needed to confirm these differences.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLimitations\u003c/em\u003e. Although our data suggest that the blunted circadian rhythm and the decline in sleep quality and cardiovascular recovery observed during the menopausal transition are largely attributable to natural aging, confirming that these changes are driven solely by aging would require inclusion of additional reference groups, such as premenopausal women and younger age-matched men. Additionally, HRT formulation or dose was not standardized across the HRT women in the study, which resulted in a wide range of progesterone values in the HRT group (SD = 8.0 µg/mL, see Supplementary Information, Table S2). As progesterone impacts temperature regulation and sleep\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, it may be that our HRT results would have been modified had we ensured everyone was on similar HRT dosing and formulations. However, doing so would have severely limited our sample sizes and applicability of results to all women taking HRT. Moreover, the generalizability of these results is somewhat limited by (1) the exclusion of individuals with chronic conditions, thus not adequately reflecting the disease burden present in the overall population at this age\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, and (2) only including participants that already owned an Eight Sleep Pod. Pod-owners can be presumed to be more invested in their sleep and general health, thus reflecting a selection bias in our data. However, participants’ average sleep metrics with ATR OFF were below-average, as expected with this age group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicating that sleep composition in these individuals matched that of the general older adult population. Lastly, because T\u003csub\u003eC\u003c/sub\u003e was scheduled for one night per ATR condition mid-week, we considered whether the T\u003csub\u003eC\u003c/sub\u003e differences between ATR ON vs. OFF could reflect short-term adaptation to ATR rather than an acute thermal effect. Several observations argue against this. First, physiological cold acclimation typically requires repeated exposures over ~ 1 week or longer\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, making it unlikely that one to two nights of mild surface cooling would produce meaningful acclimation. Second, the GI pill often remained in the system for more than 24 hours, allowing us to capture ≥ 2 nights of T\u003csub\u003eC\u003c/sub\u003e data from 70.4% of participants. When we evaluated the T\u003csub\u003eC\u003c/sub\u003e cosinor metrics from night 1 to night 2, there was no difference between the first night and subsequent nights, suggesting stable within-person rhythms across repeated measurements. Finally, with ATR ON, T\u003csub\u003eC\u003c/sub\u003e declined progressively across the sleep period and reached its nadir ~ 4–5 hours after sleep onset (Fig.\u0026nbsp;2a), consistent with an ongoing, real-time cooling influence rather than a fixed acclimation shift.\u003c/p\u003e \u003cp\u003eIn conclusion, we found that aging blunts (i.e. flattens) the circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e during sleep, independent of menopausal status. However, sleeping on a mattress cover with ATR reinstated the circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e and significantly improved CV recovery during sleep. Although the improvements in circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e did not lead to uniform improvements in sleep composition (as seen in data on younger adults), enhancing the U-shape of the T\u003csub\u003eC\u003c/sub\u003e curve during sleep has been shown to improve metabolic flexibility, cognitive flexibility, and emotional processing\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e–\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Overall, these findings support ATR as a promising non-pharmacological strategy to restore circadian rhythm of T\u003csub\u003eC\u003c/sub\u003e, improve CV recovery during sleep, and to selectively improve sleep composition in older adults with sub-optimal sleep quality, including postmenopausal women experiencing disturbed sleep. Future research should examine whether the ATR-induced improvements in T\u003csub\u003eC\u003c/sub\u003e and CV recovery lead to long-term reductions in CV, metabolic, and cognitive disease risk.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eParticipant Characteristics\u003c/h2\u003e\u003cp\u003e In total, 90 participants completed the experimental protocol, which comprised three cohorts of n = 30 each: 1) postmenopausal women using combination HRT (i.e. estrogen and progesterone), 2) postmenopausal nHRT, and 3) age-matched men not taking any supplement hormones (like testosterone). On average, participants weighed 161 ± 29 lb, were 5′7″ ± 3″ tall, and were 56 ± 6 years old. Additional participant characteristics are provided in Supplementary Information, Table S2. Urinary hormone levels of pregnanediol glucuronide, luteinizing hormone, follicle-stimulating hormone, and estrone-3-glucuronide were assessed the morning after Night 3 on each ATR condition (Mira Clarity Kit, Miracare, Pleasanton, California, US; see Supplementary Information, Table S2 for values).\u003c/p\u003e\u003ch3\u003eExperimental Design\u003c/h3\u003e\u003cp\u003eAll participants slept on their Eight Sleep Pod for 14 consecutive nights: one week with ATR disabled (ATR OFF, i.e. temperatures turned off) and one week with ATR enabled at their preferred temperature settings (ATR ON, i.e. temperatures turned on). The order of these conditions was randomized across participants, with half of participants starting with ATR ON and half of participants with ATR OFF. Temperature selections during the ATR ON week were recorded for each participant in Eight Sleep’s database (see Supplementary Information, Table S3, for mean Pod temperature values). Throughout the study, participants were instructed to maintain their normal routines regarding exercise, diet, alcohol consumption, and sleep habits to maximize ecological validity.\u003c/p\u003e\u003cp\u003eDuring the 14-day study and during the 2 nights before, participants continuously wore wearable rings (Generation 4, Oura Health, Oulu, Finland) to track their HR (both during sleep and across 24 hours), sleeping HRV, and sleep composition, including the duration of time spent in deep, light, and REM sleep stages, time awake, sleep onset latency, sleep efficiency, and total sleep time. Both weeks followed a similar 7-night structure, differing only in whether ATR was ON or OFF (Fig.\u0026nbsp;5). On Nights 2–4 of each week, participants recorded skin temperatures during sleep at three sites (forehead, left chest, and left foot) using iButton temperature loggers (Thermochron iButtons, Model DS1922L; Maxim Integrated, San Jose, USA). On Night 3 of each week, participants recorded their T\u003csub\u003ec\u003c/sub\u003e during sleep via gastrointestinal pills (e-Celsius Performance, BodyCAP, Hérouville Saint-Clair, France) (see the ‘Physiological data’ section in Methods for details).\u003c/p\u003e\u003ch2\u003eParticipant Exclusion Criteria\u003c/h2\u003e\u003cp\u003eIn total, 90 participants were enrolled and completed the study. All participants provided written informed consent in accordance with the Declaration of Helsinki. The study protocol was approved by the Sterling Institutional Review Board in June 2025 (IRB: 14003).\u003c/p\u003e\u003cp\u003eParticipants were excluded if they were under 45 years of age, transgender, did not own a Pod, unable to sleep on their Pod for 14 consecutive days, unwilling to have ATR OFF for 7 nights, could not comply with the testing protocol, or reported regularly sleeping less than 4 hours per night for more than half of the week. Participants were also excluded if they had CV disease, sleep disorders (e.g., insomnia, sleep apnea, narcolepsy), and/or one or more chronic diseases known to affect estrogen, progesterone, testosterone, glucose regulation, thermoregulation, and/or sleep quality and regularity. These exclusions minimized individual variability that could confound results.\u003c/p\u003e\u003cp\u003ePostmenopausal women confirmed through self-report that they had not had a menstrual period for at least 12 consecutive months\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Postmenopausal women were excluded if they had changed their HRT status (i.e. started or stopped taking HRT) in the 3 months prior to the study. Women in the HRT cohort were required to use combination estrogen and progesterone HRT, but there was no restriction on the dose of these hormones or in the application (e.g. oral pill, topical gel, etc). Women were also excluded if they had a current diagnosis of polycystic ovarian syndrome (PCOS) or endometriosis, and/or had an oophorectomy at any time in their life. These exclusion parameters were implemented to ensure a similar hormonal profile within each cohort.\u003c/p\u003e\u003cp\u003eTo minimize risks associated with taking the gastrointestinal pill, participants were excluded according the manufacturers' instructions: body weight below 88 lbs (40 kg) or a BMI exceeding 44.6, current or previous gastrointestinal disorders (e.g., gastroparesis, diverticulitis, Crohn’s disease, or recent gastrointestinal surgery), those unable to swallow a capsule-sized pill, those with implanted pacemakers or other electromagnetic medical devices, and/or a scheduled MRI during or within one week after the study period.\u003c/p\u003e\u003ch2\u003ePhysiological Data\u003c/h2\u003e\u003ch2\u003eThe Eight Sleep Pod’s Active Temperature Regulation\u003c/h2\u003e\u003cp\u003eThe Eight Sleep Pod is a temperature-regulated mattress cover that modulates bed surface temperature in real-time based on the biometric signals it captures throughout the night (e.g. HR, HRV, sleep stages). The system consists of a Hub, positioned beside the bed, which has a water reservoir that also heats and cools the water temperature flowing through the ATR cover. The temperature is controlled by the participant via the Eight Sleep app. Importantly, the Pod Cover allows independent temperature control for each side of the bed.\u003c/p\u003e\u003cp\u003eParticipants can set discrete temperatures in the Eight Sleep App (allowable range from 13–43°C) at three time points: (1) Bedtime phase, from bed entry until ~ 15 minutes after sleep onset; (2) Initial phase, covering the subsequent four hours; and (3) Final phase, from four hours post-sleep onset to wake. Within the Initial and Final phases, the Pod automatically adjusts temperatures based on the individual’s current sleep stage, their biological sex, and age. Generally, the Pod will cool slightly during deep and light sleep stages, and warm during REM sleep, to help maximize time in each sleep stage\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The mean ± SD temperature settings the cohorts chose are provided in Supplementary Information, Table S3. Ambient room temperature was slightly higher with ATR ON vs. OFF by 0.3 ± 0.1°C (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001); however, this small change in room temperature has not been shown to impact temperature regulation\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTwo functional modes were implemented: ATR ON and ATR OFF. During ATR ON, the Pod actively regulated bed surface temperature according to each participant’s programmed thermal profile with automatic adjustments based on the current sleep stage, while collecting continuous biometric data. During ATR OFF, temperature regulation was disabled, allowing only biometric data collection. Note that none of the CV recovery or sleep stage metrics reported in this paper are from the Pod – they are all from the wearable ring. Pod-derived sleep staging was only used to align T\u003csub\u003eSK\u003c/sub\u003e to sleep stages, as the Pod was able to detect when participants left the bed.\u003c/p\u003e\u003ch2\u003eSleep and Cardiovascular Recovery Metrics\u003c/h2\u003e\u003cp\u003eThe Oura Ring (Generation 4, Oura Health, Oulu, Finland) is a validated wearable device that continuously records physiological and behavioral parameters through photoplethysmography (PPG), skin temperature sensing, and a tri-axial accelerometer\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. For this study, Oura data were used to characterize both nightly sleep composition and associated CV recovery patterns. Four data summaries were downloaded from the Oura API: (1) night-level summary data, providing a single observation per night, (2) epoch-level (i.e. 30 s) sleep stage data, detailing the timing and duration of light, deep, and REM sleep, as well as awake periods throughout each sleep episode, (3) continuous HR and HRV estimates sampled every 5 minutes during sleep, and (4) continuous 24-hour HR data, sampled irregularly every 5–60 seconds depending on signal stability and participant motion\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The nightly summaries included mean or total estimates of the following: average HR (bpm), average HRV (ms), sleep efficiency (%), time spent awake (min), as well as stage-specific durations for light, deep, and REM sleep. Additional variables included total sleep time, sleep onset latency (min), bedtime start, and bedtime end. The sleep window was defined as the period from sleep onset (calculated as the time elapsed after bedtime start to sleep onset latency) until bedtime end, reflecting total sleep time. Oura’s HRV values are derived from interbeat interval (IBI) data collected during sleep, calculated as the root mean square of successive differences (RMSSD) between consecutive heartbeats.\u003c/p\u003e\u003ch2\u003eSkin Temperature Measurements\u003c/h2\u003e\u003cp\u003eSkin temperature was wirelessly recorded using iButtons (Thermochron iButtons, Model DS1922L; Maxim Integrated, San Jose, USA), a validated methodology for acquiring skin temperature\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, every 5 minutes at three sites during sleep on Nights 2–4. Participants wore iButtons on their forehead, left chest, and top of left foot. These locations allowed us to calculate the most accurate mean-weighted skin temperature that has been shown to reflect thermal sensation during sleep\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Devices were attached using 3M Transpore™ medical tape at least 30 minutes prior to sleep onset and were removed immediately upon waking. This protocol timing ensured thermal equilibration before sleep and minimized post-awakening artifacts. Skin temperature data were stored internally on the iButtons and then downloaded after participants shipped back the units.\u003c/p\u003e\u003ch2\u003eCore Body Temperature Monitoring\u003c/h2\u003e\u003cp\u003eT\u003csub\u003eC\u003c/sub\u003e was continuously monitored every minute during sleep via a gastrointestinal pill (e-Celsius Performance, BodyCAP, Hérouville Saint-Clair, France). This pill is a wireless, single-use device approximately the size of a standard vitamin capsule, designed to measure internal temperature from the intestines. The gastrointestinal pill has been validated against both esophageal and rectal temperatures and deemed reliable and comparable to these other T\u003csub\u003eC\u003c/sub\u003e locations\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. When taking the pill each week, participants were instructed to ingest the pill at least 4 hours before their bedtime on Night 3 to ensure that the pill traveled to the small intestines and temperature readings were normal before sleep onset. Once swallowed, the pill recorded T\u003csub\u003eC\u003c/sub\u003e and transmitted data in real time via bluetooth to a lightweight external monitor kept within a one-meter range of the participant. The monitor stored the temperature data locally and after participants shipped back the unit, temperatures were downloaded to a secure application for processing and analysis.\u003c/p\u003e\u003ch2\u003eData Preprocessing and Filtering\u003c/h2\u003e\u003cp\u003eData preprocessing and filtration were performed using Python (version 3.13.5) in Visual Studio Code (version 1.102.3). All data types were aligned to each participant’s local timezone.\u003c/p\u003e\u003cp\u003eAll data types were summarized not only as nightly means, but also separately for the first (Initial) and second (Final) portions of the night to assess time-of-night effects. For sleep, CV, and body temperature metrics, these phases were defined using each participant’s average sleep midpoint from the prior three months: Initial spanned sleep onset to the average midpoint, and Final spanned the average midpoint to sleep offset. Unless otherwise specified, “Initial/Final” refers to this midpoint-based segmentation. In contrast, ATR temperature phases followed the Pod’s built-in schedule: Initial corresponds to the first ~ 4 hours of sleep and Final corresponds to the period from ~ 4 hours post–sleep onset until waking.\u003c/p\u003e\u003cp\u003eBecause data were collected in free-living conditions across multiple sensors, filtration was required to remove artifacts, ensuring that analyses reflected typical sleep and thermophysiology. For all datatypes, nights/periods with extreme sleep duration (\u0026lt; 4 or \u0026gt; 14 h) were excluded. Sleep and CV outcomes were filtered by participant-relative outliers. This retained 1,408 nights from 90 participants for cohort comparisons of sleep composition and CV recovery (including 1,230 nights within the ATR ON/OFF experimental weeks). Skin temperature data were filtered to remove nights indicative of sensor detachment, poor-quality recordings, or partial detachment episodes identified by rapid non-physiological shifts (with minor trimming where appropriate), resulting in 552 nights from 89 participants. T\u003csub\u003eC\u003c/sub\u003e data were filtered to remove non-physiological periods (shipping and pre-ingestion), retain only stable physiological readings, remove beverage-related artifacts, and exclude nights with \u0026lt; 4 h of valid data. This yielded 408 nights from 87 participants. For circadian analyses, T\u003csub\u003eC\u003c/sub\u003e required continuous 24-h windows with exclusions for \u0026gt; 4 h missing data (with limited manual inclusions when gaps still allowed cosinor fitting), resulting in 279 24-h T\u003csub\u003eC\u003c/sub\u003e periods from 78 participants. Similarly, 24-h HR windows were excluded for gaps \u0026gt; 4 h, or insufficient data density for cosinor fitting, resulting in 669 24-h HR periods from 54 participants. Full filtration criteria and justification are provided in ‘Data Filtration’ in Supplementary Information. For a visual overview of the filtration pipeline, see Supplementary Information, Fig. S2.\u003c/p\u003e\u003ch2\u003eSleep Metrics and Cardiovascular Recovery\u003c/h2\u003e\u003cp\u003eThe proportions of deep, light, and REM sleep were derived by dividing the duration of each stage (in min) by the total sleep time (in min) and multiplying by 100 to obtain a percentage of time in each sleep stage. WASO percentage was computed as the time spent awake after falling asleep until waking in the morning, divided by total time in bed minus sleep onset latency, and expressed as a percentage. All other metrics (HR, HRV, sleep efficiency, sleep onset latency, total sleep time) were pulled directly from the Oura API.\u003c/p\u003e\u003cp\u003eTo calculate sleep composition in the Initial phase, the total time in light, deep and REM sleep from sleep onset to the night’s midpoint was summed both within and across each sleep stage to calculate the percent time in each sleep stage (time in each sleep stage divided by total sleep time in the Initial phase, multiplied by 100). Percent WASO in the Initial phase was calculated as the time awake divided by the time from sleep onset to the average midpoint multiplied by 100. HR and HRV mean estimates for the Initial phase were similarly determined by taking a mean of the continuously sampled (every 5 min) HR and HRV from sleep onset to the average midpoint. The same approach was used to obtain sleep composition, WASO, and CV mean estimates in the Final phase by using the same computations from the average sleep midpoint to sleep offset. For overview of the calculations for each sleep phase, see Fig.\u0026nbsp;6.\u003c/p\u003e\u003cp\u003eTo evaluate how ATR ON influenced sleep relative to their ATR OFF sleep composition, we calculated participant-level means for each sleep stage during ATR OFF and ON, and then computed the mean difference from ATR OFF to ON. Similarly, we computed ATR ON-induced changes in HRV and HR relative to ATR OFF. Percent change in HRV and HR was defined as (ON − OFF)/OFF×100, using each participant’s mean nightly values in each of the two ATR conditions.\u003c/p\u003e\u003ch2\u003eSkin Temperatures\u003c/h2\u003e\u003cp\u003eMean weighted skin temperature (T\u003csub\u003eSK\u003c/sub\u003e) was calculated at the data-point level (sampled every 5 minutes) using the following equation to reflect thermal sensation\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{T}_{SK\\:}\\:=\\:0.4\\:\\times\\:\\:{T}_{chest\\:}+0.4\\:\\times\\:\\:{T}_{forehead}+\\:0.2\\:\\times\\:\\:{T}_{foot}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eThe nightly means for T\u003csub\u003eSK\u003c/sub\u003e were calculated by taking the average T\u003csub\u003eSK\u003c/sub\u003e from the Pod-derived sleep onset to the Pod-derived offset (after filtration). To get a mean T\u003csub\u003eSK\u003c/sub\u003e for each sleep stage, across and within cohorts, each T\u003csub\u003eSK\u003c/sub\u003e datapoint was aligned with the sleep stage its timestamp aligned with according to the Pod’s sleep staging algorithm, then averaged within each sleep stage across the night. WASO episodes when participants left the bed during the night were excluded, as T\u003csub\u003eSK\u003c/sub\u003e values might have been altered due to movement and/or changing environments. Sleep-stage specific T\u003csub\u003eSK\u003c/sub\u003e was also calculated separately for the Initial and Final phases by taking the average T\u003csub\u003eSK\u003c/sub\u003e values within each sleep stage across the time-windows of the two phases.\u003c/p\u003e\u003ch2\u003eCore Temperature\u003c/h2\u003e\u003cp\u003eT\u003csub\u003eC\u003c/sub\u003e was sampled every minute and averaged nightly, using Pod-estimated sleep onset/offset timestamps after filtration. Body temperature (T\u003csub\u003eB\u003c/sub\u003e) was calculated as 0.9×T\u003csub\u003eC\u003c/sub\u003e + 0.1×T\u003csub\u003eSK\u003c/sub\u003e\u003csup\u003e54\u003c/sup\u003e using the mean nightly T\u003csub\u003eC\u003c/sub\u003e and T\u003csub\u003eSK\u003c/sub\u003e values for each participant during each condition.\u003c/p\u003e\u003cp\u003eT\u003csub\u003eC\u003c/sub\u003e changes from ATR OFF to ATR ON were calculated together with sleeping HR changes on a participant level. Mean nightly T\u003csub\u003eC\u003c/sub\u003e changes between conditions were calculated for nights with HR data available. Similarly, HR percent change was calculated as (ON − OFF)/OFF×100 using mean nightly HR values where T\u003csub\u003eC\u003c/sub\u003e data were available.\u003c/p\u003e\u003ch2\u003eCircadian Rhythm Data for Core Temperature and Heart Rate\u003c/h2\u003e\u003cp\u003eT\u003csub\u003eC\u003c/sub\u003e data for circadian rhythm analysis required continuous 24-hour periods for cosinor analysis (see ‘Data Analysis’). For each participant, the 24-hour windows started at the individual’s mean sleep onset time, calculated as the circular mean to account for the wrap-around from 24:00 to 00:00, across all valid nights (i.e. nights not removed during T\u003csub\u003eC\u003c/sub\u003e filtration) in the study. For visualization, a secondary 24-hour window starting one hour prior to sleep onset was created to better show the T\u003csub\u003eC\u003c/sub\u003e drop at sleep onset\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Because the sampling times varied slightly (by \u0026lt; 1 minute) across and within T\u003csub\u003eC\u003c/sub\u003e pills, we rounded each datapoint timestamp to the nearest minute to standardize timing across pills and enable minute-by-minute averaging for visualization.\u003c/p\u003e\u003cp\u003eThe 24-hour HR data also underwent cosinor analysis, with 24-hour periods starting at each participant’s mean sleep onset across the study. HR data were binned in 5-minute increments to prevent high frequency sampling during physical activity from biasing the cosinor fit. For visualization, 24-hour HR was plotted alongside T\u003csub\u003eC\u003c/sub\u003e, starting one hour before sleep onset, utilizing the same HR data as for the cosinor analysis.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAnalyses included only nights with complete data across all required variables. Therefore, statistical models comparing T\u003csub\u003esk\u003c/sub\u003e and sleep metrics included 527 nights from 89 participants. Models including T\u003csub\u003eC\u003c/sub\u003e and T\u003csub\u003esk\u003c/sub\u003e to predict T\u003csub\u003eB\u003c/sub\u003e included 273 nights from 85 participants, and the model predicting HR from T\u003csub\u003eB\u003c/sub\u003e included 258 nights from 85 participants. For ATR-related changes, differences from ATR OFF to ATR ON were calculated at the participant level. Comparisons of ATR-related changes in sleep composition based on ATR OFF estimates included 90 participants. Comparisons of ATR-related changes in T\u003csub\u003eC\u003c/sub\u003e and HR included 70 participants, while comparisons of ATR temperatures to HRV percent change included 90 participants.\u003c/p\u003e\u003cp\u003eAn a priori power analysis, based on parameter estimates from published literature\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, indicated that ~ 30 participants per cohort would provide 80% power to detect group-level differences in HRV and T\u003csub\u003eC\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eStatistical analyses were performed using R in R Studio (version 2025.05.1 + 513). Given that most dependent variables were measured on multiple nights within both ATR conditions (within-participant predictors), the data was primarily analyzed on a nightly basis utilizing linear mixed effects models (LMMs) with participant ID as a random intercept. The model uses the individual observations (nights) to make the overall estimates, with each participant having their own intercept for expected values in the dependent variable. LMMs were used for all comparisons except those involving participant-level mean estimates for ATR OFF and/or ATR-related changes. For cohort and ATR status comparisons, estimated marginal means contrasts were created for pairwise comparisons of the LMMs using the emmeans R-package. These contrasts had Tukey adjustments for multiple comparisons for between-cohort comparisons and Holm adjustments for within-cohort comparisons. As LMMs are robust to normality violations\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, heteroscedasticity and influential clusters were the primary focus of assumption testing. All LMMs and pairwise comparisons were run with CR2-adjusted estimates to account for potential heteroscedasticity and influential participants biasing the results.\u003c/p\u003e\u003cp\u003eFor participant-level ATR-related changes (e.g. comparing changes in sleep composition based on ATR OFF mean values), linear models with HC3-adjusted estimates were utilized to similarly prevent issues induced by heteroscedasticity and influential clusters. For correlation plots based on participant mean values, Pearson correlation coefficient (\u003cem\u003er\u003c/em\u003e) and associated p-values were reported.\u003c/p\u003e\u003cp\u003eCosinor analysis with a single harmonic was used to estimate 24-hour T\u003csub\u003eC\u003c/sub\u003e and HR dynamics. Cosinor fitting was conducted as a linear model using ordinary least squares (OLS) in base R to estimate \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{0}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{1}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{2}\\)\u003c/span\u003e\u003c/span\u003e following the equation below\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The mesor (24-hour average), amplitude (half-width of 24-hour max − min) and acrophase (hours since sleep onset where amplitude peaks) were extracted from the cosinor function\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e and analyzed for each day and subject using LMMs.\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:y\\left(t\\right)\\:=\\:{B}_{0}+{B}_{1}sin\\left(wt\\right)+{B}_{2}cos\\left(wt\\right),\\:where\\:w\\:=\\frac{2\\pi\\:}{24}\\:and\\:t\\:=\\:time$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:Mesor\\:={B}_{0}\\:,\\:Amplitude\\:=\\:\\sqrt{{B}_{1}^{2}+{B}_{2}^{2}},\\:Acrophase\\:=\\frac{atan2({B}_{1},{B}_{2})\\:mod\\:2\\pi\\:}{w}\\:$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eTo examine how mean T\u003csub\u003eSK\u003c/sub\u003e predicted sleep stage and WASO probability, a Bayesian multinomial logistic mixed-effects model was fitted using a categorical logit link with deep sleep as the reference category. The model was estimated across four Markov chains with 4,000 iterations per chain (first 2,000 for warm-up). Default brms priors were applied. The model outputs consisted of the log odds and credible intervals (CIs), which both converted to odds ratio (OR) and the CIs of OR for interpretability. These estimates represent the expected change in the odds of being in a given sleep stage (REM sleep, light sleep, or WASO) relative to deep sleep (model reference category), per 1°C increase in T\u003csub\u003eSK\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eWe wanted to identify the T\u003csub\u003esk\u003c/sub\u003e at which the model predicts people are more likely to be in one sleep stage versus another. To do this, posterior predicted probabilities were computed across a continuous temperature range to visualize how sleep stage likelihoods varied with T\u003csub\u003eSK\u003c/sub\u003e. From these predictions, we derived equal-likelihood temperatures (where two sleep stages were equally probable) and CI-overlap temperatures (where 95% CIs of adjacent sleep stages intersected). These crossing points reflect nonlinear transformations of the model estimates calculated from predicted probabilities rather than raw log-odds coefficients. Consequently, the reported crossing T\u003csub\u003esk\u003c/sub\u003e represents values on the posterior prediction surface, where population-level probabilities of two sleep stages converge, rather than direct translations of the underlying logit-scale effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eCompeting interests \u0026amp; funding\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was funded by Eight Sleep, Inc. Authors S.S.J., M.L.H., E.R.C., B.C.W., D.D.H., and N.E.M. are employees of Eight Sleep, Inc. and hold, or may be eligible to hold, equity in the company. They declare no other competing interests. Author T.M. declares support from the Canada Research Chairs Program (CRC-2022-00245); otherwise no other competing interests.\u003c/p\u003e \u003ch2\u003eMaterials \u0026amp; Correspondence\u003c/h2\u003e \u003cp\u003eAll correspondence and requests for materials should be addressed to corresponding author Nicole E. Moyen.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eConceptualization: S.S.J., M.L.H., D.D.H., N.E.M.; Data collection: M.L.H., E.R.C., S.S.J.; Data storage: E.R.C.; Data analysis and visualization: S.S.J., N.E.M; Data interpretation: S.S.J., N.E.M., M.L.H., T.M.; Writing (original draft): S.S.J., M.L.H., E.R.C., B.C.W., D.D.H., T.M., N.E.M.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe thank the study participants for their time and participation. We also thank the Eight Sleep team, particularly Natasha G. Ragland, for technical assistance and equipment preparation, and Jonathan Taso for firmware development that allowed us to turn ATR off.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated for this study are not publicly available due to proprietary restrictions.\u003c/p\u003e\u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eThe underlying code for this study is not publicly available for proprietary reasons but may be made available to qualified researchers on reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi J et al (2022) Sleep duration and health outcomes: an umbrella review. Sleep Breath 26:1479\u0026ndash;1501\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah AS et al (2025) Effects of Sleep Deprivation on Physical and Mental Health Outcomes: An Umbrella Review. 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Gut 29:302\u0026ndash;305\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Eight Sleep, Inc.","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"core body temperature, skin temperatures, sleep composition, heart rate, heart rate variability, women's health, active temperature regulation, hormone replacement therapy","lastPublishedDoi":"10.21203/rs.3.rs-8652742/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8652742/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSleep quality declines during menopause, yet the associated changes in body temperature and circadian rhythm during sleep remain poorly understood. In 90 postmenopausal women and age-matched men (1408 nights; mean ± SD: 56 ± 6 y), we examined how the menopausal transition alters the circadian rhythm of core temperature (T\u003csub\u003eC\u003c/sub\u003e), and whether these changes relate to sleep composition and cardiovascular recovery (i.e., heart rate and heart rate variability). Additionally, we evaluated whether sleeping on an active temperature-regulated mattress cover (ATR) could improve circadian rhythm and sleep. Men and women demonstrated a blunted core temperature rhythm during sleep, which was restored when sleeping with ATR as a result of increased amplitude and lowered mesor of the T\u003csub\u003eC\u003c/sub\u003e. These T\u003csub\u003eC \u003c/sub\u003echanges significantly related to improvements in cardiovascular recovery during sleep (3% lower heart rate and 11% higher heart rate variability, on average). Although improvements in T\u003csub\u003eC\u003c/sub\u003e and cardiovascular recovery did not uniformly translate to changes in sleep composition, restoring the U-shaped T\u003csub\u003eC\u003c/sub\u003e curve has been linked to benefits in metabolic flexibility and cognition. Together, these findings support ATR as a promising non-pharmacological strategy to restore T\u003csub\u003eC\u003c/sub\u003e rhythmicity and improve cardiovascular recovery during sleep in older adults, including postmenopausal women.\u003c/p\u003e","manuscriptTitle":"Overnight Temperature Regulation Improves Circadian Rhythm and Cardiovascular Recovery in Postmenopausal Women and Age-Matched Men","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 06:05:06","doi":"10.21203/rs.3.rs-8652742/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"948c3874-c462-4052-b10a-5c840c6a52ac","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61507990,"name":"Physiology"}],"tags":[],"updatedAt":"2026-01-27T20:18:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 06:05:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8652742","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8652742","identity":"rs-8652742","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}