Abstract
Study Objectives
Sleep irregularity is associated with elevated blood pressure (BP). Whether sleep regularity decreases BP is not known. We hypothesized that an intervention involving 2 weeks of sleep regularization would decrease BP in people with hypertension.
Methods
Eleven people with hypertension (four males/seven females, age: mean 53 [range 45–62 years]), body mass index (32 ± 6 kg/m2), but no other chronic disease were studied. We measured ambulatory BP and assessed sleep using actigraphy over a free-living baseline week. For the next 2 weeks, participants were asked to go to bed at the same time every night before reassessing their ambulatory BP. The standard deviations of bedtimes and sleep onset were used to measure variability. The minimal detectable change (MDC) in BP was calculated from 48 h of ambulatory BP monitoring to analyze individual responses.
Results
Bedtime variability (32.4 ± SD 17 vs. 7 ± 10 min, p = .001) and sleep onset variability (30 ± 17 vs. 7 ± 8 min, p = .011) decreased following the intervention; no other sleep parameters changed. Bedtime regularization significantly reduced 24-h systolic BP (−4 ± 4 mmHg) and diastolic BP (−3 ± 3 mmHg), mainly due to reduced nighttime systolic BP (−5 ± 7 mmHg) and diastolic BP (−4 ± 5 mmHg), all p < .05. ≥50 per cent of participants decreased their BP by more than the MDC95 for 24-h BP.
Conclusions
In this proof-of-concept study, 2 weeks of bedtime regularization decreased 24-h and nighttime BP in people with hypertension, suggesting that this may be a simple, yet low-risk, adjunctive strategy to control BP in many people with hypertension. This ought to be tested in a larger randomized controlled trial.
Keywords: bedtime regularization, blood pressure, hypertension therapy, sleep regularization, circadian timing, sleep schedule
Statement of Significance Hypertension is a public health epidemic. More than 75 per cent of people with hypertension do not have adequate control of their blood pressure (BP), suggesting that innovative adjunct strategies are needed. We found that 2 weeks of bedtime regularization significantly reduced 24-h BP in individuals with hypertension, primarily due to a decrease in nighttime BP. This intervention improved BP even in people who were already on anti-hypertensive medications. Our findings, which need to be confirmed in a randomized controlled trial, suggest that bedtime regularization may be a simple, low-risk adjuvant therapy in people with hypertension.
Introduction
Hypertension (HTN) is a leading risk factor for cardiovascular diseases and affects ~50% adults in the United States [1]. Although more than half of those with HTN take medications, only 20% can control their BP to <130/80 mmHg, suggesting that current treatment paradigms for HTN are insufficient, and there is a need for adjunctive strategies [1]. Blood pressure (BP) is influenced by several daily behaviors, including sleep patterns. Indeed, there is copious literature linking short sleep duration to the risk of developing HTN [2, 3]. Notably, the American Heart Association added 7–9 h of daily sleep duration to its “Life’s Essential 8” for cardiovascular health, along with a sleep health score based on sleep duration [4]. Nevertheless, sleep is multifactorial; numerous sleep-related indices, in addition to sleep duration, are also associated with cardiovascular disease [5,6]. For instance, a large global study has shown that inconsistent sleep onset time is a crucial factor in the development of HTN in midlife. Specifically, Scott and colleagues determined that merely a half-hour increase in day-to-day sleep onset increases the risk for HTN by >30% [7]. Coleman and colleagues also published similar results using health record data from the All of Us Research Program [8]. To begin to understand whether regularizing bedtime can improve BP, we conducted a short proof-of-concept intervention study in people with HTN to determine whether 2 weeks of bedtime regularization would decrease BP. Since sleep regularity is associated with blunted nighttime dipping, we also explored whether this pilot intervention affected nighttime dipping of BP [9].
Materials and Methods
This study was approved by the human subject protection board at Oregon Health & Science University (IRB# 23776). We have followed STROBE reporting guidelines as applicable to this proof-of-concept study (Supplementary Material S1).
Eleven midlife adults with HTN (four males/seven females, age: mean 53 ± SD 6 years, body mass index 32 ± 6 kg/m2) were studied (see Table 1 for participant characteristics). Based on the 2017 guidelines for prevention, detection, evaluation, and management of high BP in adults, and to ensure safe participation, HTN was defined as resting BP between 130/80 and 160/100 mmHg, or taking anti-hypertensive medications [10]. We studied people with resting BP between 130/80 and 160/100 mmHg measured in the morning in triplicate on two separate days, or taking medications for high BP. Using extensive screening, including questionnaires, 48 h ambulatory monitoring, at-home sleep apnea screening (WatchPAT, ZOLL itamar, Caesarea, Israel), and a physical exam, we excluded participants with any other chronic diseases, medication use (except for anti-hypertensives), pregnancy, smoking, illicit drug use, or shift work. This proof-of-concept study is an exploratory component of a larger study, in which participants completed a circadian constant routine protocol and two overnight visits before participating in this bedtime regularization trial, following at least a one-month washout period (NCT05184933).
Table 1.
Participant characteristics
| Variable | Mean ± SD |
|---|---|
| Age | 52.7 ± 5.4 years |
| Body mass index | 32 ± 6 kg/m2 |
| Systolic blood pressure | 128.6 ± 11.9 mmHg |
| Diastolic blood pressure | 82.4 ± 10.3 mmHg |
| Heart rate | 69 ± 9.9 beats per minute |
Participants included seven females and four males. Four participants were medicated and were prescribed the following medications: S01 (male): Losartan and Triamterene hydrochlorothiazide; S02 (female): Telmisartan; S03 (male): Olmesartan; S04 (female): Amlodipine.
Baseline week
Participants were asked to maintain their habitual sleep schedules and daily routines, except for refraining from using any substances, including marijuana, which was specified as an exclusion criterion. Anti-HTN medications as prescribed by their physician were allowed. During the baseline week, sleep and activity behavior from the participants were collected using a detailed sleep diary, an activity monitor (ActiGraph, LLC, Pensacola, FL, USA), and call-ins to a time-stamped voicemail for when participants slept or woke up. We measured 24-h BP on the last day of the baseline week. BP was measured every 20 min during the day and every 30 min during the night [11].
Bedtime regularization weeks
We asked participants to simply self-select a bedtime and endeavor to go to bed at the same time for 2 weeks, instructing them to avoid napping during the daytime. No other instructions were provided, except to continue refraining from any exclusionary substances as during the baseline week. We repeated the at-home measurements during the baseline week. We measured 24-h BP on the last day of the 2 weeks of bedtime regularization. BP was measured every 20 min during the day and every 30 min during the night [11].
To assess the efficacy of the intervention, bedtime regularity was evaluated as the standard deviation of bedtimes reported by participants in their sleep diaries, and sleep onset time regularity was assessed as the standard deviation of sleep onset times measured using actigraphy.
Actigraphy analysis
Participants wore an activity monitor on their non-dominant wrist throughout the study period, except during showering. Sleep duration, sleep efficiency, sleep onset time, and sleep latency were derived using the Cole-Kripke algorithm in ActiLife 6 and adjusted based on information from the sleep diary, confirmed against call-in times [12].
Statistical analysis
The participants’ characteristics are shown in Table 1. Four participants were taking anti-HTN medications. The standard deviations of bedtimes and sleep durations were used to indicate their variabilities. We then performed paired sample two-tailed tests for all dependent variables. Significance was set as p < .05. Finally, to analyze data at the individual level, we calculated the minimal detectable change at the 95% confidence interval (MDC95), as follows: MDC95 = 1.96 × √2 × standard error measurement [13]. We calculated the MDC95 (in mmHg for BP and beats per minute for heart rate [HR]) using the 24-h average systolic and diastolic BP, and HR collected during 24-h ambulatory BP monitoring 2 days during the eligibility screening (Day 1 and Day 2). We used the classical test theory approach to calculate SEM using Intraclass correlation (ICC) due to our data characteristics, scale, and clinical variables [14].
Results
After the intervention, participants had a more regular bedtime as reported in their sleep diary with a decrease in bedtime variability (32.4 ± SD 17 vs. 7 ± 10 min [95% of the change: 12.6 to 38 min], t = 4.4, p = .001), and more regular sleep time represented by a decrease in sleep onset time variability (30 ± 17 vs. 7 ± 8 min [95% CI of the change: 6.6 to 37.8 min], t = 3.2, p = .011) measured with actigraphy.
One participant did not have nighttime BP measurements and was not included in the 24 h or nighttime analysis, including MDC95 of 24-h. This participant was included in the daytime analysis. The bedtime regularization intervention resulted in a significantly reduced 24-h systolic BP (−4 ± SD 4 mmHg [95% CI: 0.6 to 6.9 mmHg], t = 2.7, p = .025, Figure 1A) and 24-h diastolic BP (−3 ± 3 mmHg [95% CI: 1.2 to 4.9 mmHg], t = 3.8, p = .004, Figure 1C). Twenty four-h HR did not change (−1 ± 5 bpm [95% CI: −2.6 to 4.5 bpm], t = 0.62, p = .55, Figure 1E). Similarly, nighttime systolic BP (−5 ± 7 mmHg [95% CI: 0.3 to 9.9 mmHg], t = 2.39, p = .040, Figure 2B) and diastolic BP (−4 ± 5 mmHg [95% CI: 0.3 to 6.9 mmHg], t = 2.48, p = .035, Figure 2D) significantly reduced as a result of the intervention, and nighttime HR did not change (−1 ± 7 bpm [95% CI: −3.2 to 6.2 bpm], t = 0.69, p = .503, Figure 2F). There was no significant difference in daytime systolic BP (−3 ± 8 mmHg [95% CI: −2.0 to 8.5 mmHg], t = 1.4, p = .200, Figure 2A), while daytime diastolic BP (−3 ± 5 mmHg [95% CI: 0.36 to 6.4 mmHg], t = 2.253, p = .048, Figure 2C) was significantly reduced. Daytime HR did not change (0 ± 7 bpm [95% CI: −5.3 to 4.4 bpm], t = −2.1, p = .835, Figure 2E). There was no difference in nocturnal BP dipping across the intervention (10 ± 6% vs. 12 ± 7% [95% CI of the change: −4% to 8%], t = 0.73, p = .48).
Figure 1.
Effect of bedtime regularization on 24-h blood pressure and individual responses. Two weeks of bedtime regularization significantly reduced 24-h systolic BP (panel A) and diastolic BP (panel C), but did not change heart rate (panel E). Fifty per cent participants decreased their 24-h systolic BP over the minimum detectable change of 5 mmHg (panel B), and 60% decreased their 24-h diastolic BP over the minimum detectable change of 2.5 mmHg (panel D). Only 20% participants crossed the threshold for heart rate (panel F).
Figure 2.
Effects of bedtime regularization on daytime and nighttime blood pressures. Two weeks of bedtime regularization did not significantly affect daytime systolic BP (panel A) or heart rate (panel E); however, it significantly reduced daytime diastolic BP (panel C). The intervention significantly reduced nighttime systolic (panel B) and diastolic (panel D) BP, but did not change heart rate (panel F).
The ICCs and their 95% CIs for 24-h measurements were as follows: 24-h systolic BP ICC 0.914 (95% CI: 0.675 to 0.978); 24-h diastolic BP ICC 0.937 (95% CI: 0.761 to 0.984); and 24-h HR ICC 0.951 (95% CI: 0.814 to 0.988). Analysis of individuals’ responses revealed that 50 per cent of participants decreased 24-h systolic BP by more than the MDC95 of 5.0 mmHg (Figure 1B) and 60 per cent decreased 24-h diastolic BP by more than the MDC95 of 2.5 mmHg (Figure 1D). Only 20 per cent of participants decreased HR by more than MDC95 of 4.7 bpm (Figure 1F). Of the four participants taking anti-HTN medications, three participants reduced their average 24 h SBP (−5, −7, and −4 mmHg), whereas one participant had an increase of 1 mmHg following the intervention. Thus, 50% of the medicated participants crossed the MDC95 threshold for 24-h systolic BP.
Bedtime regularization did not affect sleep parameters such as duration (8.3 ± 0.6 vs. 8.3 ± 0.5 h, p = .910), latency (6 ± 3 vs. 6 ± 5 min, p = .671), or efficiency (89.3 ± 5.0 vs. 88.4 ± 5.4%, p = .139).
Discussion
Sleep onset time irregularity is associated with an increased risk for HTN [7]. We conducted a proof-of-concept intervention study to test whether simply instructing people to regularize their bedtime without changing any other behaviors can decrease BP in people with HTN. We discovered that, as expected, 2 weeks of this intervention improved bedtime regularization and significantly decreased 24-h and nighttime systolic and diastolic BP.
We measured 24-h ambulatory BP because it is a significantly better predictor of cardiovascular risk compared to office BP [15], and HTN societies worldwide unanimously recommend ambulatory BP monitoring as the gold-standard test to confirm and help treat HTN [16–18]. Furthermore, in people with HTN, compared to daytime BP, overnight BP is a better predictor of cardiovascular risk [19, 20]. A large study of 17 000 people confirmed that nighttime BP provides significant cardiovascular prognostic information separate from 24-h BP [21]. Thus, the results of our study are clinically relevant from a methodological standpoint. The decline in systolic BP of 4–5 mmHg over 24-h and during the night with simply regularizing bedtime for 2 weeks is comparable to the overall benefits of >4 weeks of regular exercise training [22] or salt reduction [23], and can thus be considered as an adjunct therapy for BP control. Furthermore, a 5 mmHg reduction in nighttime BP (which we observed in the current study) has been shown to reduce adverse cardiovascular event risk by >10 per cent in patients with HTN with other comorbidities [24, 25].
Our intervention did not improve sleep parameters other than bedtime and sleep onset variability, and the decrease in BP is unlikely to be associated with actigraphically derived sleep parameters. The ceiling and floor effects have been identified as influencing factors in therapies using either hypnotics or behavioral interventions targeting sleep parameters [26]. This is especially true in good sleepers who usually do not show improvement in their sleep parameters because there is no room for improvement [26]. In this sense, our participants were screened for the absence of any sleep disorders by a certified sleep doctor during a physical exam. Thus, the absence of improvement in sleep parameters is not a surprise in the current study. Although the mechanisms of BP change were beyond the scope of this pilot interventional study, a regularization of participants’ bedtime could affect the circadian timing system (e.g. phase angle of entrainment) and potentially reduce BP [27]. For instance, it is known that young people with irregular sleep have a later dim-light melatonin onset and a low amplitude day/night light rhythm [28]. It is possible that regularizing bedtime, which resulted in a regularized sleep onset time, could have reduced nighttime light exposure, leading to a better alignment of the circadian system to potentiate BP control during the night. However, future work is needed to test this hypothesis [29].
Strengths and limitations
Our study has several strengths. For instance, we rigorously screened our participants to ensure that HTN was not associated with other chronic diseases in this sample. Furthermore, there was no participant with sleep disorders. We measured bedtime using sleep diary information that was confirmed by call-ins. Additionally, the actigraphy analysis revealed that bedtime regularization improved sleep onset time regularity. We used 24-h ambulatory BP measurements, which are more sensitive to identify the risk of adverse cardiovascular events than office BP measurements [30]. We acknowledge that our sample size was small and the absence of a control group in parallel that did not regularize their bedtime. However, the analysis at the individual level revealed a positive response of 50–60 per cent of the participants on reducing their 24-h BP, which supports our findings as a hypothesis-generating study. We also did not control for covariates, including age, gender, body mass index, and medication use, due to the lack of sample size, which is a limitation. Future studies can incorporate a gold-standard randomized controlled trial design to test whether our results can be replicated. More mechanistic insights into the biological underpinnings of this result, and whether these gains can be sustained over a longer period, are also logical next steps of this research.
In conclusion, in this proof-of-concept study, 2 weeks of bedtime regularization significantly reduces 24-h primarily due to a decrease in nighttime BP in people with HTN. This intervention appears to help even people already taking anti-hypertensive medications. These improvements encourage randomized controlled trials to investigate this question further and isolate underlying mechanisms. If our results are confirmed and the underlying mechanisms are isolated in larger mechanistic randomized controlled trials, bedtime regularity interventions could be low-cost and highly scalable interventions to reduce cardiovascular risk [6].
Supplementary Material
Acknowledgments
We thank our participants for their time and effort.
Contributor Information
Saurabh S Thosar, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States; School of Nursing, Oregon Health & Science University, Portland, OR, United States; Knight Cardiovascular Institute, School of Medicine, Oregon Health & Science University, Portland, OR, United States; OHSU-PSU School of Public Health, Oregon Health & Science University, Portland, OR, United States.
Alakananda M Sreeramadas, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States.
Megan Jones, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States.
Nicole Chaudhary, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States.
Cassidy Floyd-Driscoll, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States.
Andrew W McHill, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States; School of Nursing, Oregon Health & Science University, Portland, OR, United States.
Christopher T Minson, Department of Human Physiology, University of Oregon, Eugene, OR, United States.
Robert Rope, Division of Nephrology and Hypertension, School of Medicine, Oregon Health & Science University, Portland, OR, United States.
Jonathan S Emens, Portland VA Medical Center, Portland, OR, United States.
Steven A Shea, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States.
Leandro C Brito, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States.
Author contributions
Saurabh S. Thosar (Conceptualization [lead], Data curation [lead], Formal analysis [lead], Funding acquisition [equal], Investigation [lead], Methodology [lead], Project administration [lead], Supervision [lead], Writing—original draft [lead], Writing—review & editing [lead]), Alakananda M. Sreeramadas (Data curation [lead], Formal analysis [supporting], Methodology [equal], Writing—review & editing [equal]), Megan Jones (Data curation [supporting], Formal analysis [supporting], Investigation [supporting], Methodology [lead], Writing—review & editing [equal]), Nicole Chaudhary (Data curation [supporting], Investigation [supporting], Project administration [lead], Writing—review & editing [equal]), Cassidy Floyd-Driscoll (Data curation [supporting], Formal analysis [supporting], Investigation [supporting], Methodology [supporting], Writing—review & editing [equal]), Andrew W. McHill (Conceptualization [supporting], Funding acquisition [supporting], Methodology [supporting], Writing—review & editing [equal]), Christopher T. Minson (Conceptualization [supporting], Funding acquisition [supporting], Methodology [supporting], Writing—review & editing [equal]), Robert Rope (Investigation [equal], Methodology [supporting], Writing—review & editing [equal]), Jonathan Emens (Conceptualization [supporting], Investigation [equal], Methodology [supporting], Writing—review & editing [supporting]), Steven A. Shea (Conceptualization [supporting], Investigation [supporting], Methodology [equal], Writing—review & editing [equal]), Leandro Campos Brito (Data curation [equal], Formal analysis [equal], Investigation [equal], Methodology [equal], Project administration [supporting], Supervision [equal], Writing—original draft [supporting], Writing—review & editing [equal])
Funding
This work was supported by National Institutes of Health grants R01HL163232 (S.S.T.) and R35HL155681 (S.A.S.), American Heart Association grant 24CDA1267757 (L.C.B.), and the Oregon Institute of Occupational Health Sciences at Oregon Health & Science University via funds from the Division of Consumer and Business Services of the State of Oregon (ORS-656.630).
Disclosure statement
Financial disclosure: None to report.
Non-financial disclosure: None to report.
Data availability
The data supporting this study’s findings will be available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data supporting this study’s findings will be available from the corresponding author upon reasonable request.