Actigraphy-Based Sleep Disruption and Diurnal Biomarkers of Autonomic Function in Paroxysmal Atrial Fibrillation

Actigraphy-Based Sleep Disruption and Diurnal Biomarkers of Autonomic Function in Paroxysmal Atrial Fibrillation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Actigraphy-Based Sleep Disruption and Diurnal Biomarkers of Autonomic Function in Paroxysmal Atrial Fibrillation Sepideh Khazaie, Lu Wang, Farhad Kaffashi, Mina K. Chung, Catherine M. Heinzinger, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4547962/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 Introduction: Sleep architectural disruption is associated with atrial fibrillation (AF); however, associated autonomic influences remain unclear and it is unknown if this detriment persists during wakefulness. We hypothesize sleep disruption and autonomic dysfunction have diurnal patterning in patients with paroxysmal AF. Methods We analyzed data from the Sleep Apnea and Atrial Fibrillation Biomarkers and Electrophysiologic Atrial Triggers (SAFEBEAT) study designed to examine paroxysmal AF and sleep apnea, including simultaneous collection of continuous electrocardiogram monitoring (Heartrak Telemetry®) and actigraphy (Actiwatch GTX) for 7–21 days. Heart rate variability (HRV) measures in time-domain (standard deviation of normal-to-normal (NN) intervals (SDNN), coefficient of variation (CV)) and frequency-domain (low frequency power (LFP), high frequency power (HFP)) were used as surrogates of autonomic function and averaged per sleep/wake per day. A linear mixed-effects model assuming compound symmetry correlation structure was used to assess the relationship of HRV with actigraphy-derived sleep data. Results The analytic sample (age 60.1 ± 12.0 years, body mass index 32.6 ± 6.7 kg/m2, 36% female, 75% White) included 100 participants with paroxysmal AF. Longer sleep latency was associated with lower HFP during wakefulness (coefficient − 0.0501, p = 0.031). Higher sleep efficiency was associated with increased SDNN (coefficient 0.0007, p = 0.014) and CV (coefficient 0.0167, p = 0.047). Higher arousal index was associated with increased CV (coefficient 0.0166, p = 0.007) and LFP (coefficient 0.0232, p = 0.003). During sleep, longer average awakenings duration was associated with increased LFP/HFP ratio (coefficient 0.1977, p < 0.001) and reduced HFP (coefficient − 0.1338, p < 0.001). Significant sleep-wake interactions were observed for sleep latency with HFP (p = 0.024), sleep efficiency with SDNN and CV (both p < 0.01), WASO with SDNN, CV, and LFP (all p < 0.05), and frequency of awakenings with CV and LFP (both p < 0.05). Conclusions Actigraphy-based measures of sleep disruption were associated with autonomic function alterations exhibiting diurnal variability in paroxysmal AF. Greater overall HRV and parasympathetic modulation were related to better sleep quality. Increased sympathetic activation was associated with sleep fragmentation. Results provide insights into differential autonomic dysfunction related to sleep disruption that may contribute to atrial arrhythmogenesis. Cardiac Arrhythmias Sleep Apnea Continuous Positive Airway Pressure Heart Rate Variability Figures Figure 1 Figure 2 Introduction Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting over 37 million individuals globally ( 1 ). AF is associated with a 5-fold increased risk of stroke and 2-fold increased risk of mortality ( 2 ). Sleep-disordered breathing (SDB), which includes sleep apnea, is characterized by repetitive apnea and hypopnea events and is highly prevalent in AF, with studies reporting a wide range of 21–80% prevalence ( 3 ). Both SDB and AF demonstrate alterations in cardiac autonomic tone, which may promote arrhythmogenesis ( 4 ). Heart rate variability (HRV), which reflects oscillations in autonomic inputs to the sinoatrial node, provides a non-invasive assessment of cardiac autonomic function ( 5 ). Reduced HRV signifies increased sympathetic and/or decreased parasympathetic modulation and has been associated with negative cardiovascular outcomes ( 6 ). In AF patients, decreased HRV predicts AF progression, while restoration of sinus rhythm has been associated with improved HRV ( 7 ), ( 8 ). AF and SDB are independently associated with autonomic dysfunction based on HRV analyses ( 7 ), ( 9 ). However, the interrelationships between SDB, HRV, and AF remain incompletely understood. Prior studies have been limited by heterogeneous AF populations, lack of concurrent HRV and SDB assessments, and non-standardized HRV methodologies ( 10 ). Actigraphy using accelerometer data provides an objective evaluation of sleep-wake patterns through continuous activity monitoring ( 11 ). Simultaneous actigraphy and electrocardiogram (ECG) data collection enables the investigation of dynamic autonomic alterations related to sleep disruption, with heart rate variability (HRV) measures being derived from the collected ECG data. While associations between subjective poor sleep and altered cardiac autonomic control have been reported in conditions like chronic fatigue ( 12 ), objective actigraphy-based sleep assessments better quantify sleep disruption. Actigraphy monitoring for 24 hours or longer captures real-world sleep habits, rather than relying on patient questionnaires in a limited snapshot ( 13 ). Prior studies have validated actigraphy against polysomnography for estimating sleep efficiency, wake after sleep onset, and other metrics ( 14 ), ( 15 ). Thus, actigraphy allows detailed characterization of sleep-wake patterns and their relationships to autonomic function. Elucidating the nature of diurnal variations in autonomic function related to SDB may provide insights into circadian patterns of arrhythmogenesis in AF. We aimed to characterize the differential relationships of sleep disruption measured via actigraphy with HRV during sleep versus wakefulness in patients with paroxysmal AF and SDB. We hypothesize that greater sleep disruption is associated with a decrease in heart rate variability (HRV), indicating autonomic dysfunction. Furthermore, we propose that these associations persist into wakefulness, rather than being limited to the sleep period. Methods Study Population This analysis included participants enrolled in the Sleep Apnea and Atrial Fibrillation Biomarkers and Electrophysiologic Atrial Triggers (SAFEBEAT) study (NCT02576587). SAFEBEAT was a prospective cohort study conducted at two academic medical centers from 2012 to 2017. Participants were recruited from cardiology and electrophysiology clinics. Inclusion criteria were age ≥ 55 years, paroxysmal AF defined as self-terminating episodes lasting < 7 days, and an Apnea-Hypopnea Index (AHI) ≥ 15. Exclusion criteria included permanent AF, valvular disease, coronary artery disease, heart failure, hyperthyroidism, prior cardiac surgery or ablation, and other medical comorbidities ( 16 ). The Cleveland Clinic IRB and University Hospitals Case Medical Center IRB each approved this study, and informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations. Actigraphy Monitoring Participants underwent continuous wrist actigraphy monitoring (Actiwatch Spectrum, Philips Respironics) for 7–21 days. The actigraph device contains a piezoelectric accelerometer that records limb movements in 3-axes. Participants were instructed to wear the actigraph at all times except when showering or submerged in water. Actigraphy data were analyzed using the Actiware software v6.0.2 in 60-second epochs. Sleep intervals were marked using event markers, sleep diaries, and rest intervals. The following sleep parameters were derived and averaged across all sleep periods ( 17 ): sleep latency, total sleep time, sleep efficiency, wakefulness after sleep onset (WASO), number of awakenings, arousal index, and average awakenings duration. Sleep latency is defined as the time from going to bed to sleep onset; total sleep time is the total time asleep after sleep onset; sleep efficiency is calculated as the total sleep time divided by time in bed multiplied by 100%; WASO represents the total time awake after sleep onset until final awakening; the number of awakenings is the count of episodes where the subject transitions from sleep to wakefulness, each lasting a minimum of 60 seconds; arousal index is the number of arousals per hour of sleep, indicating sleep fragmentation; and average awakenings duration refers to the average length of these awakenings. Electrocardiogram Monitoring Continuous electrocardiogram (ECG) recordings were obtained over the same period as actigraphy using a single-channel telemetry system (Heartrak ECAT, Philips Respironics) with a sampling rate of 250 Hz. Participants were instructed to wear the ECG sensors at all times except during bathing, when actigraphy and ECG monitoring were paused concurrently. HRV Analysis HRV measures were derived from normal-to-normal (NN) beat intervals during sinus rhythm on the ECG recordings after excluding segments with noise, ectopy or arrhythmias. Only 5-minute ECG segments meeting stability criteria were analyzed to ensure stationarity ( 18 ). The following HRV measures were examined ( 19 ): In the time-domain, the measures examined included Mean NN (the average of all normal-to-normal intervals), SDNN (standard deviation of normal-to-normal intervals), RMSSD (root mean square of successive differences between normal heartbeats), CV (coefficient of variation of normal-to-normal intervals), and short-term and long-term variability from the Poincaré plot (SD1 and SD2, respectively). In the frequency-domain, we examined low frequency power (LFP), high frequency power (HFP), and the low-to-high frequency power ratio (LHR). Additionally, non-linear measures of variability and complexity, such as the detrended fluctuation analysis (DFA) parameters α1 and α2, were also explored. HRV indices were computed using custom software developed and implemented in Matlab, and these values were averaged over the total monitoring duration. This analysis was separately conducted for sleep and wake periods as determined by actigraphy. Statistical Analysis Participant characteristics were summarized using descriptive statistics. A linear mixed effects model with a compound symmetry covariance structure was used to assess the associations of HRV measures (dependent variables) with actigraphy sleep parameters (independent variables) during sleep and wake periods. Age, sex, race, body mass index (BMI), and relevant medications including anti-hypertensives (ACE inhibitors, ARBs, beta-blockers, calcium channel blockers, diuretics), anti-arrhythmics, anti-depressants, cholesterol-lowering drugs, hypoglycemics, sedatives/sleeping aids were included as covariates. Sleep-wake interactions were examined for each actigraphy index. A p-value < 0.05 was considered statistically significant. All statistical analyses were performed in SAS v9.4 (SAS Institute, Cary NC). Results Study Population The analytic sample was comprised of100 participants with paroxysmal AF and moderate-severe SDB. Participants underwent actigraphy monitoring for an average of 7.31 ± 2.46 days with an average monitoring duration of 7.16 ± 1.11 hours per day. Table 1 summarizes the baseline demographic and clinical characteristics. The mean age was 60.1 ± 12.0 years, 64% were male, 85% were Caucasian, and the mean BMI was 32.6 ± 6.7 kg/m 2 . Hypertension was present in 57% and diabetes in 13%. The majority (83%) were taking antihypertensive medications and 59% were on antithrombotic therapy. Table 1 Characteristics of Participants with Moderate to Severe Sleep Disordered Breathing Total (N = 100) Factor Statistics Age (years), mean (SD) 60.1 ± 12.0 Body Mass Index (kg/m 2 ), mean (SD) 32.6 ± 6.7 Gender N (%) Female 36 (36.0) Male 64 (64.0) Ethnicity Hispanic or Latino/a 1 (1.00) NOT Hispanic or Latino/a 99 (99.0) Race Caucasian 85 (85.0) African American 15 (15.0) High Blood Pressure or Hypertension 57 (57.0) Diabetes 13 (13.0) High Blood Cholesterol 62 (62.0) Heart Attack 3 (3.0) Stroke 4 (4.0) Depression 15 (15.0) Combination of medications: Ace Inhibitors,Antihypertensive,Alpha-2 Blocker,Beta-Blocker,Calcium blocker,Diuretic,Nitrates 83 (83.0) Ace Inhibitors (Capoten, Vasotec, Zestril, Captopril, Altace, Lisinopril) 27 (27.0) Angiotensin receptor blocker (Hyzaar, Cozaar, Valsartan) 6 (6.0) Antiarrhythmic 19 (19.0) Antiarrhythmic: Class 1a (Na Channel Block, Intermediate) Quinidine, Procainamide, Disoprymide 0 (0.00) Antiarrhythmic: Class 1b (Na Channel Block, Fast) Lidocaine, Phenytoin, Mexiletine, Tocainide 1 (1.00) Antiarrhythmic: Class 1c (Na Channel Block, Slow) Flecainide, Propafenone, Moricizine 20 (20.0) Antiarrhythmic: Class II Beta Blocker 1 (1.00) Antiarrhythmic: Class III (K + Channel Blocker) Amiodarone, Sotalol, Ibutilide, Dofetilide, Dronedarone 6 (6.0) Antiarrhythmic: Class IV Slow Channel Blockers (Ca Channel Block) Verapamil, Diltiazem 11 (11.0) Antiarrhythmic: Class V (Unknown Mechanism) Adenosine, Digoxin, Magnesuim Sulfate 1 (1.00) Antiarrhythmic: Other 2 (2.0) Antidepressants (SSRI) 3 (3.0) Antidepressants, Tricyclic (Elavil, Tofranil, Pamelor) 0 (0.00) Antidepressants (SNRI) Effexor, Cymbalta, Pristiq 1 (1.00) Antidepressants, Other (Wellbutrin) 1 (1.00) Antihypertensive (Hydralazine, Clonidine) 2 (2.0) Beta-Blocker (Inderal, Lopressor, Tenormin, Corgard, Atenolol, Propranolol) 57 (57.0) Calcium-channel blocker (Calan, Procardia, Cardizem) 16 (16.0) Cholesterol-lowering drugs (Mevacor, Pravachol, Zocor, Lipitor) 43 (43.0) Cholesterol-lowering drugs, Other (Gemfibrozil, Zetia) 9 (9.0) Diuretic, Loop (Lasix, Furosemide) 3 (3.0) Diuretic, Thiazide (Hydrochlorothiazide) 17 (17.0) Diuretic, Other (Aldosterone Antagonist, Spironolactone) 3 (3.0) Hypoglycemic, Oral (Glyburide, Glucophage) 10 (10.0) Insulin (Diabetes) 2 (2.0) Sedative hypnotics (Valium, Xanax, Ativan, Librium) 8 (8.0) Sleeping Medicine (Ambien, Trazodone) 4 (4.0) Statistics presented as Mean ± SD, N (column %). Associations between Sleep Indices and HRV Table 2 displays the associations between actigraphy-derived sleep measures and HRV parameters during sleep versus wake periods. Table 2 Associations of Heart Rate Variability with Actigraphy Sleep Measures by Sleep and Wakefulness Sleep Wakefulness Variable Estimate (95%CI) p-value Estimate (95%CI) p-value P of interaction Association with Actigraphy-Based Sleep Latency Time Domain Indices MNN 0.0032 (-0.0071,0.0134) 0.55 -0.0061 (-0.0143,0.0021) 0.15 0.15 SDNN 0.0004 (-0.0007,0.0014) 0.49 0.0001 (-0.0007,0.0009) 0.84 0.66 RMSSD 0.0006 (-0.0005,0.0017) 0.26 0.0001 (-0.0008,0.0009) 0.88 0.41 DFA_Alpha1 -0.0044 (-0.0193,0.0105) 0.56 0.0092 (-0.0027,0.0211) 0.13 0.14 DFA_Alpha2 -0.0047 (-0.0244,0.0149) 0.64 0.0025 (-0.0132,0.0181) 0.76 0.56 CV* 0.0073 (-0.0230,0.0387) 0.64 0.0044 (-0.0199,0.0293) 0.72 0.88 SD1* 0.0221 (-0.0217,0.0679) 0.33 -0.0180 (-0.0517,0.0170) 0.31 0.14 SD2* 0.0130 (-0.0193,0.0464) 0.43 -0.0011 (-0.0266,0.0251) 0.94 0.49 SDRatio* 0.0075 (-0.0304,0.0469) 0.70 -0.0169 (-0.0466,0.0136) 0.27 0.30 Frequency Domain Indices LFP* 0.0135 (-0.0248,0.0534) 0.49 -0.0069 (-0.0370,0.0242) 0.66 0.40 HFP* 0.0314 (-0.0272,0.0934) 0.30 -0.0501 (-0.0934,-0.0048) 0.031 0.024 LHR* -0.0030 (-0.0761,0.0758) 0.94 0.0295 (-0.0312,0.0940) 0.35 0.50 Association with Actigraphy-Based Sleep Efficiency Time Domain Indices MNN 0.0022 (-0.0031,0.0075) 0.42 0.0062 (0.0006,0.0117) 0.029 0.24 SDNN -0.0003 (-0.0008,0.0003) 0.31 0.0007 (0.0001,0.0013) 0.014 0.004 RMSSD -0.0001 (-0.0007,0.0005) 0.74 0.0003 (-0.0003,0.0009) 0.37 0.31 DFA_Alpha1 -0.0013 (-0.0090,0.0064) 0.73 0.0030 (-0.0050,0.0110) 0.46 0.38 DFA_Alpha2 0.0022 (-0.0077,0.0121) 0.67 0.0055 (-0.0047,0.0158) 0.29 0.61 CV* -0.0151 (-0.0306,0.0005) 0.058 0.0167 (0.0002,0.0335) 0.047 0.002 SD1* -0.0108 (-0.0330,0.0119) 0.35 0.0132 (-0.0104,0.0374) 0.27 0.097 SD2* -0.0121 (-0.0285,0.0045) 0.15 0.0268 (0.0091,0.0447) 0.003 < 0.001 SDRatio* -0.0005 (-0.0201,0.0195) 0.96 -0.0142 (-0.0343,0.0063) 0.17 0.28 Frequency Domain Indices LFP* -0.0025 (-0.0222,0.0175) 0.80 -0.0168 (-0.0370,0.0037) 0.11 0.26 HFP* 0.0068 (-0.0232,0.0377) 0.66 -0.0163 (-0.0467,0.0151) 0.30 0.23 LHR* -0.0171 (-0.0550,0.0223) 0.39 -0.0007 (-0.0407,0.0410) 0.97 0.51 Association with Actigraphy-Based Total Minutes in Bed Time Domain Indices MNN -0.0001 (-0.0003,0.0001) 0.19 -0.0000 (-0.0002,0.0001) 0.61 0.54 SDNN 0.0000 (-0.0000,0.0000) 0.20 -0.0000 (-0.0000,0.0000) 0.98 0.32 RMSSD -0.0000 (-0.0000,0.0000) 0.47 0.0000 (-0.0000,0.0000) 0.60 0.35 DFA_Alpha1 0.0002 (-0.0001,0.0005) 0.13 -0.0000 (-0.0003,0.0002) 0.78 0.17 DFA_Alpha2 -0.0001 (-0.0005,0.0002) 0.49 -0.0002 (-0.0006,0.0002) 0.30 0.79 CV* 0.0006 (0.0000,0.0012) 0.036 -0.0002 (-0.0007,0.0004) 0.60 0.047 SD1* -0.0002 (-0.0011,0.0006) 0.57 -0.0001 (-0.0010,0.0007) 0.74 0.86 SD2* 0.0006 (-0.0000,0.0012) 0.055 -0.0001 (-0.0007,0.0005) 0.66 0.073 SDRatio* -0.0006 (-0.0014,0.0001) 0.074 0.0000 (-0.0007,0.0007) 0.95 0.16 Frequency Domain Indices LFP* 0.0000 (-0.0007,0.0007) 0.99 0.0010 (0.0003,0.0018) 0.005 0.031 HFP* -0.0009 (-0.0020,0.0002) 0.11 -0.0000 (-0.0011,0.0011) 0.94 0.25 LHR* 0.0016 (0.0002,0.0031) 0.022 0.0008 (-0.0007,0.0022) 0.29 0.36 Association with Actigraphy-Based Total Sleep Time Time Domain Indices MNN -0.0001 (-0.0003,0.0001) 0.23 -0.0000 (-0.0002,0.0002) 0.85 0.45 SDNN 0.0000 (-0.0000,0.0000) 0.30 0.0000 (-0.0000,0.0000) 0.78 0.57 RMSSD -0.0000 (-0.0000,0.0000) 0.37 0.0000 (-0.0000,0.0000) 0.52 0.24 DFA_Alpha1 0.0002 (-0.0001,0.0006) 0.12 -0.0000 (-0.0003,0.0003) 0.88 0.20 DFA_Alpha2 -0.0001 (-0.0005,0.0003) 0.62 -0.0002 (-0.0006,0.0002) 0.38 0.77 CV* 0.0006 (-0.0001,0.0012) 0.088 -0.0001 (-0.0007,0.0006) 0.81 0.14 SD1* -0.0004 (-0.0013,0.0005) 0.40 -0.0001 (-0.0010,0.0009) 0.88 0.60 SD2* 0.0006 (-0.0001,0.0012) 0.10 -0.0000 (-0.0007,0.0007) 0.98 0.21 SDRatio* -0.0008 (-0.0016,0.0000) 0.058 -0.0000 (-0.0009,0.0008) 0.91 0.18 Frequency Domain Indices LFP* 0.0000 (-0.0008,0.0008) 0.97 0.0011 (0.0002,0.0019) 0.011 0.056 HFP* -0.0010 (-0.0022,0.0002) 0.11 -0.0001 (-0.0014,0.0011) 0.83 0.30 LHR* 0.0018 (0.0002,0.0034) 0.026 0.0008 (-0.0008,0.0024) 0.31 0.36 Association with Actigraphy-Based Wake After Sleep Onset Time Domain Indices MNN -0.0007 (-0.0017,0.0003) 0.16 -0.0008 (-0.0018,0.0002) 0.10 0.86 SDNN 0.0001 (-0.0000,0.0002) 0.088 -0.0001 (-0.0002,0.0000) 0.21 0.019 RMSSD 0.0000 (-0.0001,0.0001) 0.74 -0.0000 (-0.0001,0.0001) 0.83 0.66 DFA_Alpha1 0.0005 (-0.0009,0.0020) 0.47 -0.0006 (-0.0020,0.0008) 0.39 0.21 DFA_Alpha2 -0.0012 (-0.0031,0.0006) 0.20 -0.0012 (-0.0031,0.0006) 0.19 0.99 CV* 0.0042 (0.0012,0.0071) 0.005 -0.0021 (-0.0050,0.0009) 0.17 < 0.001 SD1* 0.0017 (-0.0025,0.0059) 0.43 -0.0018 (-0.0059,0.0024) 0.41 0.20 SD2* 0.0034 (0.0003,0.0065) 0.031 -0.0031 (-0.0061,0.0000) 0.052 0.001 SDRatio* -0.0009 (-0.0045,0.0028) 0.64 0.0016 (-0.0020,0.0053) 0.39 0.29 Frequency Domain Indices LFP* 0.0001 (-0.0036,0.0037) 0.97 0.0055 (0.0019,0.0092) 0.003 0.021 HFP* -0.0027 (-0.0083,0.0029) 0.34 0.0022 (-0.0034,0.0078) 0.45 0.17 LHR* 0.0059 (-0.0014,0.0133) 0.11 0.0029 (-0.0044,0.0102) 0.44 0.52 Association with Actigraphy-Based Number of Awakenings Time Domain Indices MNN -0.0027 (-0.0068,0.0013) 0.19 -0.0027 (-0.0067,0.0013) 0.19 0.99 SDNN 0.0003 (-0.0001,0.0007) 0.20 0.0001 (-0.0003,0.0005) 0.77 0.42 RMSSD 0.0002 (-0.0002,0.0007) 0.30 0.0001 (-0.0003,0.0005) 0.63 0.65 DFA_Alpha1 0.0010 (-0.0049,0.0068) 0.75 -0.0021 (-0.0079,0.0037) 0.48 0.41 DFA_Alpha2 -0.0001 (-0.0077,0.0074) 0.97 -0.0009 (-0.0085,0.0066) 0.81 0.87 CV* 0.0166 (0.0044,0.0289) 0.007 0.0003 (-0.0116,0.0124) 0.96 0.034 SD1* 0.0143 (-0.0031,0.0319) 0.11 -0.0001 (-0.0171,0.0173) 0.99 0.19 SD2* 0.0136 (0.0008,0.0266) 0.037 -0.0028 (-0.0154,0.0099) 0.66 0.041 SDRatio* 0.0015 (-0.0135,0.0167) 0.84 0.0027 (-0.0122,0.0179) 0.72 0.90 Frequency Domain Indices LFP* -0.0050 (-0.0199,0.0101) 0.51 0.0232 (0.0080,0.0387) 0.003 0.003 HFP* 0.0067 (-0.0162,0.0301) 0.57 -0.0012 (-0.0239,0.0219) 0.91 0.59 LHR* -0.0006 (-0.0300,0.0298) 0.97 0.0196 (-0.0103,0.0505) 0.20 0.29 Association with Actigraphy-Based Average Awakening Length Time Domain Indices MNN 0.0002 (-0.0136,0.0141) 0.97 -0.0060 (-0.0196,0.0076) 0.39 0.48 SDNN 0.0008 (-0.0006,0.0021) 0.29 -0.0021 (-0.0034,-0.0007) 0.003 0.002 RMSSD -0.0008 (-0.0023,0.0006) 0.27 -0.0008 (-0.0023,0.0006) 0.25 0.99 DFA_Alpha1 0.0064 (-0.0136,0.0263) 0.53 -0.0068 (-0.0265,0.0128) 0.49 0.30 DFA_Alpha2 -0.0257 (-0.0517,0.0003) 0.053 -0.0105 (-0.0360,0.0150) 0.42 0.37 CV* 0.0193 (-0.0216,0.0619) 0.36 -0.0465 (-0.0842,-0.0073) 0.021 0.010 SD1* -0.0507 (-0.1048,0.0068) 0.083 -0.0456 (-0.0992,0.0112) 0.11 0.89 SD2* 0.0256 (-0.0179,0.0711) 0.25 -0.0534 (-0.0929,-0.0121) 0.012 0.004 SDRatio* -0.0638 (-0.1106,0.0146) 0.012 0.0196 (-0.0305,0.0723) 0.45 0.009 Frequency Domain Indices LFP* 0.0418 (-0.0106,0.0970) 0.12 0.0308 (-0.0202,0.0845) 0.24 0.75 HFP* -0.1338 (-0.1988,0.0636) < 0.001 0.0443 (-0.0328,0.1276) 0.27 < 0.001 LHR* 0.1977 (0.0821,0.3257) < 0.001 -0.0157 (-0.1092,0.0877) 0.76 0.002 Abbreviations used in the table: · MNN: Mean NN Interval · SDNN: Standard Deviation of NN Intervals · RMSSD: Root Mean Square of Successive Differences · CV: Coefficient of Variation · SD1: Standard Deviation of Short-Term Heart Rate Variability · SD2: Standard Deviation of Long-Term Heart Rate Variability · SDRatio: Ratio of SD1 to SD2 · LFP: Power in the Low Frequency Range (0.04–0.15 Hz) · HFP: Power in the High Frequency Range (0.15–0.4 Hz) · LHR: Ratio of Low Frequency Power to High Frequency Power Associations between Sleep Latency and HRV Longer sleep latency, reflecting greater difficulty falling asleep, was associated with reduced HFP during wakefulness (coefficient − 0.0501, 95% CI [-0.0934,-0.0048], p = 0.031). However, no relationship was observed between sleep latency and HFP during sleep. A significant sleep-wake interaction was present for the association between sleep latency and HFP (p = 0.024), indicating this relationship differed between sleep versus wake periods. Associations between Sleep Efficiency and HRV Higher sleep efficiency was associated with increased SDNN, CV and Poincaré plot SD2 during wakefulness (SDNN coefficient 0.0007, 95% CI [0.0001,0.0013], p = 0.014; CV coefficient 0.0167, 95% CI [0.0002,0.0335], p = 0.047; SD2 coefficient 0.0268, 95% CI [0.0091,0.0447], p = 0.003). No relationships were observed between sleep efficiency and HRV metrics during sleep. Significant sleep-wake interactions were present for SDNN and CV (both p < 0.01), denoting that these associations differed between sleep and wake periods. Associations between Total Sleep Time and HRV In contrast to other sleep parameters, total sleep time did not demonstrate significant associations with any HRV measures during either sleep or wakefulness. No sleep-wake interactions were statistically significant for the relationships between total sleep time and all HRV indices. Associations between WASO and HRV Higher WASO, was associated with increased coefficient of variation (CV) and Poincaré plot SD2 during sleep (CV coefficient 0.0042, 95% CI [0.0012,0.0071], p = 0.005; SD2 coefficient 0.0034, 95% CI [0.0003,0.0065], p = 0.031). WASO was also associated with increased low frequency power (LFP) during wakefulness (coefficient 0.0055, 95% CI [0.0019,0.0092], p = 0.003). Significant sleep-wake interactions were present for CV, SD2 and LFP (all p < 0.05), signifying that these relationships differed between sleep and wake periods. Associations between Number of Awakenings and HRV More frequent nocturnal awakenings were associated with increased CV during sleep (coefficient 0.0166, 95% CI [0.0044,0.0289], p = 0.007). The number of awakenings was also associated with increased LFP during wakefulness (coefficient 0.0232, 95% CI [0.0080,0.0387], p = 0.003). Significant sleep-wake interactions were observed for both CV and LFP (both p < 0.05), denoting differential relationships by sleep-wake state. Average Awakening Duration Longer average awakening duration was associated with increased LHR and decreased HFP during sleep (LHR: coefficient 0.1977, 95% CI [0.0821,0.3257], p < 0.001; HFP: coefficient − 0.1338, 95% CI [-0.1988, -0.0636], p < 0.001). In contrast, longer awakening length was associated with reduced SDNN, CV and SD2 in wakefulness. Significant sleep-wake interactions were present for each of these HRV measures (all p < 0.01). Figure 1 highlights the differential associations between specific sleep indices and HRV parameters during sleep versus wakefulness. Figure 2 illustrates the sleep-wake interactions for HRV relationships with sleep latency, sleep efficiency, WASO and number of awakenings. Objective measures of sleep disruption demonstrated significant associations with alterations in HRV indicative of autonomic dysfunction. Notably, these relationships varied between sleep and wake periods, with more consistent HRV changes observed during wakefulness. Discussion In this study of individuals with moderate to severe OSA and paroxysmal AF, we observed significant associations between actigraphy-derived measures of sleep disruption and alterations in HRV indicative of autonomic dysfunction in patients with paroxysmal AF and sleep disordered breathing. Importantly, these relationships exhibited diurnal variation, with greater HRV changes observed during wakefulness compared to sleep. Our findings of greater degree of sleep disruption associated with HRV alterations more pronounced during wakefulness align with known circadian patterns of autonomic activity, which could provide insights into daily cycles of AF onset in the setting of at least a moderate degree of sleep disordered breathing. The associations of poorer sleep quality with reduced HRV suggest that sleep disturbances may promote autonomic imbalance in AF patients. Possible mechanisms include activation of inflammatory pathways and oxidative stress by sleep deprivation that modulate cardiac autonomic function ( 20 ), ( 21 ). Sleep fragmentation with frequent arousals can also directly provoke surges in sympathetic activity and vagal withdrawal ( 22 ). Shortened and fragmented sleep can induce inflammatory cytokines like C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-alpha) ( 23 ), ( 24 ), which have been associated with reduced HRV through effects on the sinoatrial node ( 25 ). Additionally, CRP levels are elevated during daytime compared to nocturnal AF episodes ( 26 ), ( 27 ), further linking inflammatory pathways to circadian patterns of arrhythmogenesis. Our findings thus align with evidence that inflammatory processes induced by sleep disruption may alter cardiac autonomic function, reflected in the HRV changes observed. This also indicates that the effects of sleep impairment extending beyond the sleep period into wakefulness may be mediated by sustained sympathetic excitation and impaired vagal recovery after sleep disruption ( 26 ). Our observations of sleep disruption and altered HRV during wake periods corroborate established circadian rhythms in autonomic functions. These insights potentially elucidate the daily fluctuations in AF occurrence. Sympathetic nervous system tone typically peaks during daytime waking hours, while parasympathetic activity is more prominent at night during sleep. This variation may offer additional insights into the sympathetic and parasympathetic balance in different sleep-wake schedules. ( 28 ), ( 29 ). This circadian variation in autonomic balance has been associated with increased propensity for ventricular arrhythmias in the morning period of time ( 30 ). Similarly, in AF patients, the persistent HRV changes we observed during daytime wakefulness following sleep disruption suggest autonomic dysfunction is not limited solely to the sleep period. The observation of more prominent HRV changes during wakefulness has implications for understanding circadian patterns of arrhythmogenesis in AF, though further study relating these HRV metrics to timing of arrhythmia onset is needed. Overall, our findings highlight the interplay between sleep disturbances and autonomic dysregulation showing diurnal variability in this population. Our results provide novel insights into diurnal autonomic dysfunction related to sleep impairment in AF patients with sleep apnea. However, potential limitations should be acknowledged. This microlongitudinal study design with repeated measures cannot establish causality. Confounding from unmeasured factors cannot be excluded. Actigraphy has reduced sensitivity to detect wakefulness compared to polysomnography. Nonetheless, the strengths of the study include the use of continuous objective sleep monitoring and continuous ECG recording over multiple days. This approach allows for a detailed and accurate assessment of sleep patterns and their impact on cardiovascular health within individuals. Moreover, our robust HRV analytics provide a sophisticated analysis of autonomic function, offering insights into the intricate interplay between sleep and cardiac health. Our findings are internally consistent across multiple HRV measures and align with experimental evidence indicating causal effects of sleep impairment on autonomic function (31; 32; 33) Interventional studies of total and partial sleep deprivation have demonstrated reductions in HRV metrics including a range of spectral and frequency-based measures ( 34 ). This supports sleep disruption as a potential contributor rather than merely a biomarker of autonomic imbalance and underlying cardiovascular dysfunction. Our results suggest utility for actigraphy-based sleep assessment to identify AF patients who may be at risk for autonomic dysfunction and associated outcomes, e.g. progression of arrhythmia burden. Targeting sleep quality improvement as a modifiable risk factor could potentially optimize autonomic function in this population. This is supported by studies showing alterations in HRV after treatment of sleep apnea with positive airway pressure ( 35 ). Further research is warranted to determine whether optimizing sleep quality can improve cardiac autonomic control and reduce arrhythmia susceptibility in AF patients with sleep disorders. In conclusion, indices of worsened sleep disruption demonstrated significant associations with HRV alterations indicating autonomic dysfunction in patients with paroxysmal AF and sleep apnea. Importantly, these associations were more consistently observed during wakefulness compared to sleep. Our findings suggest that sleep disruption may contribute to diurnal variability in cardiac autonomic function, which could potentially influence circadian patterns of AF. Additional studies exploring the arrhythmogenic mechanisms associated with sleep-autonomic interactions in AF are necessary. Declarations Acknowledgments. We would like to thank the participants of the SAFEBEAT cohort for contributing their valuable time to provide the data used for this work that will allow us to advance insights in sleep apnea and atrial fibrillation. We also acknowledge the work of research coordinator, Joan Aylor, and research polysomnologist, Rawan Nawabit. Conflict of interest statement. The authors have no conflicts to disclose. HKW serves on the advisory board of Resmed. Data availability. The data generated during this study is available from the corresponding author upon reasonable request. References Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. Giuseppe Lippi, Fabian Sanchis-Gomar , Gianfranco Cervellin. s.l. : Int J Stroke, 2021 Feb, Vols. 16(2):217-221. 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Valentina Magagnin, Tito Bassani, Vlasta Bari, Maurizio Turiel, Roberto Maestri, Gian Domenico Pinna, Alberto Porta. s.l. : Physiol Meas, 2011 Nov, Vols. 32(11):1775-86. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Electrophysiology., Task Force of the European Society of Cardiology and the North American Society of Pacing and. s.l. : Circulation, 1996 Mar. Behaviorally Assessed Sleep and Susceptibility to the Common Cold. Aric A Prather, Denise Janicki-Deverts , Martica H Hall , Sheldon Cohen. s.l. : Sleep, 2015 Sep, Vols. 1;38(9):1353-9. Sleep deprivation, oxidative stress and inflammation. Fatin Atrooz, Samina Salim. s.l. : Adv Protein Chem Struct Biol, 2020, Vols. 119:309-336. Sympathetic-nerve activity during sleep in normal subjects. V K Somers, M E Dyken, A L Mark, F M Abboud. s.l. : N Engl J Med, 1993 Feb, Vols. 4;328(5):303-7. Sleep deprivation and activation of morning levels of cellular and genomic markers of inflammation. Michael R Irwin, Minge Wang, Capella O Campomayor, Alicia Collado-Hidalgo, Steve Cole. s.l. : Arch Intern Med, 2006 Sep, Vols. 18;166(16):1756-62. Effect of sleep loss on C-reactive protein, an inflammatory marker of cardiovascular risk. Hans K Meier-Ewert, Paul M Ridker, Nader Rifai, Meredith M Regan, Nick J Price, David F Dinges, Janet M Mullington. s.l. : J Am Coll Cardiol, 2004 Feb . The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. Julian F Thayer, Shelby S Yamamoto, Jos F Brosschot. s.l. : Int J Cardio, 2010 May, Vols. 28;141(2):122-31. C-reactive protein elevation in patients with atrial arrhythmias: inflammatory mechanisms and persistence of atrial fibrillation. M K Chung, D O Martin, D Sprecher, O Wazni, A Kanderian, C A Carnes, J A Bauer, P J Tchou, M J Niebauer, A Natale, D R Van Wagoner. s.l. : Circulation, 2001 Dec, Vols. 11;104(24):2886-91. Inflammation in atrial fibrillation. Yutao Guo, Gregory Y H Lip, Stavros Apostolakis. s.l. : J Am Coll Cardiol, 2012 Dec, Vols. 4;60(22):2263-70. An Overview of Heart Rate Variability Metrics and Norms. Fred Shaffer, J. P. Ginsberg. s.l. : Front Public Health., 2017 Sep, Vol. 5: 258. The central autonomic network: functional organization, dysfunction, and perspective. Benarroch, E E. s.l. : Mayo Clin Proc, 1993, Vols. 68(10):988-1001. Circadian variation in the frequency of sudden cardiac death. J E Muller, P L Ludmer, S N Willich, G H Tofler, G Aylmer, I Klangos, P H Stone. s.l. : Circulation, 1987 Jan, Vols. 75(1):131-8. Vierra J, Boonla O, Prasertsri P. Effects of sleep deprivation and 4-7-8 breathing control on heart rate variability, blood pressure, blood glucose, and endothelial function in healthy young adults. Physiol Rep. 2022 Jul and 10.14814/phy2., 10(13):e15389. doi:. Bourdillon N, Jeanneret F, Nilchian M, Albertoni P, Ha P, Millet GP. Sleep Deprivation Deteriorates Heart Rate Variability and Photoplethysmography. Front Neurosci. 2021 Apr 8, 33897355, 15:642548. doi: 10.3389/fnins.2021.642548. PMID: and PMC8060636., PMCID:. Schlagintweit J, Laharnar N, Glos M, Zemann M, Demin AV, Lederer K, Penzel T, Fietze I. Effects of sleep fragmentation and partial sleep restriction on heart rate variability during night. Sci Rep. 2023 Apr 17 and 10.1038/s41598-023-33013-5., 13(1):6202. doi:. Cardiovascular effects of partial sleep deprivation in healthy volunteers. Josilene L Dettoni, Fernanda Marciano Consolim-Colombo, Luciano F Drager, Marcelo C Rubira, Silvia Beatriz P Cavasin de Souza, Maria Claudia Irigoyen, Cristiano Mostarda, Suellen Borile, Eduardo M Krieger, Heitor Moreno Jr, Geraldo Lorenzi-Filho. s.l. : J Appl Physiol, 2012 Jul, Vols. 113(2):232-6. Continuous positive airway pressure increases heart rate variability in heart failure patients with obstructive sleep apnoea. Matthew P Gilman, John S Floras, Kengo Usui, Yasuyuki Kaneko, Richard S T Leung, T Douglas Bradley. s.l. : Clin Sci (Lond), 2008 Feb, Vols. 114(3):243-9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4547962","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":318101458,"identity":"24eb9061-2cc7-475b-b3c5-c66a1b274853","order_by":0,"name":"Sepideh Khazaie","email":"","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":false,"prefix":"","firstName":"Sepideh","middleName":"","lastName":"Khazaie","suffix":""},{"id":318101461,"identity":"088fb696-32ea-4767-a730-6e2b569bc247","order_by":1,"name":"Lu Wang","email":"","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Wang","suffix":""},{"id":318101463,"identity":"3e0a30b6-0925-45b8-b815-4039c6a2d00f","order_by":2,"name":"Farhad Kaffashi","email":"","orcid":"","institution":"Case Western Reserve University","correspondingAuthor":false,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Kaffashi","suffix":""},{"id":318101466,"identity":"8be43e8d-dffe-4086-b41e-5ba63c835997","order_by":3,"name":"Mina K. 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The regression coefficients and p-values are displayed for each sleep parameter.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4547962/v1/be87fd078540bd63c7d8cdf0.png"},{"id":59435647,"identity":"6fed03d9-f452-4f59-b5cd-5f90c6bfa6b0","added_by":"auto","created_at":"2024-07-01 19:11:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":262330,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction plots displaying the sleep-wake interactions for the relationships between select actigraphy sleep measures and heart rate variability parameters. The regression lines illustrate the differing slopes between sleep (red) and wakefulness (blue). Significant sleep-wake interactions (p\u0026lt;0.05) were present for sleep latency with high frequency power, sleep efficiency with SDNN and CV, wake after sleep onset with SDNN, CV, and low frequency power, and number of awakenings with CV and low frequency power.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4547962/v1/5f7aa6b43134ad64076bc3b7.png"},{"id":65810336,"identity":"2cff435d-c1da-46e3-915b-66a5847834c6","added_by":"auto","created_at":"2024-10-03 04:09:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1670849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4547962/v1/c739063a-de41-41f9-97db-b0f63a76bbe6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Actigraphy-Based Sleep Disruption and Diurnal Biomarkers of Autonomic Function in Paroxysmal Atrial Fibrillation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting over 37\u0026nbsp;million individuals globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). AF is associated with a 5-fold increased risk of stroke and 2-fold increased risk of mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Sleep-disordered breathing (SDB), which includes sleep apnea, is characterized by repetitive apnea and hypopnea events and is highly prevalent in AF, with studies reporting a wide range of 21\u0026ndash;80% prevalence (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Both SDB and AF demonstrate alterations in cardiac autonomic tone, which may promote arrhythmogenesis (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHeart rate variability (HRV), which reflects oscillations in autonomic inputs to the sinoatrial node, provides a non-invasive assessment of cardiac autonomic function (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Reduced HRV signifies increased sympathetic and/or decreased parasympathetic modulation and has been associated with negative cardiovascular outcomes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In AF patients, decreased HRV predicts AF progression, while restoration of sinus rhythm has been associated with improved HRV (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAF and SDB are independently associated with autonomic dysfunction based on HRV analyses (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, the interrelationships between SDB, HRV, and AF remain incompletely understood. Prior studies have been limited by heterogeneous AF populations, lack of concurrent HRV and SDB assessments, and non-standardized HRV methodologies (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eActigraphy using accelerometer data provides an objective evaluation of sleep-wake patterns through continuous activity monitoring (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Simultaneous actigraphy and electrocardiogram (ECG) data collection enables the investigation of dynamic autonomic alterations related to sleep disruption, with heart rate variability (HRV) measures being derived from the collected ECG data. While associations between subjective poor sleep and altered cardiac autonomic control have been reported in conditions like chronic fatigue (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), objective actigraphy-based sleep assessments better quantify sleep disruption. Actigraphy monitoring for 24 hours or longer captures real-world sleep habits, rather than relying on patient questionnaires in a limited snapshot (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Prior studies have validated actigraphy against polysomnography for estimating sleep efficiency, wake after sleep onset, and other metrics (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Thus, actigraphy allows detailed characterization of sleep-wake patterns and their relationships to autonomic function.\u003c/p\u003e \u003cp\u003eElucidating the nature of diurnal variations in autonomic function related to SDB may provide insights into circadian patterns of arrhythmogenesis in AF. We aimed to characterize the differential relationships of sleep disruption measured via actigraphy with HRV during sleep versus wakefulness in patients with paroxysmal AF and SDB. We hypothesize that greater sleep disruption is associated with a decrease in heart rate variability (HRV), indicating autonomic dysfunction. Furthermore, we propose that these associations persist into wakefulness, rather than being limited to the sleep period.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThis analysis included participants enrolled in the Sleep Apnea and Atrial Fibrillation Biomarkers and Electrophysiologic Atrial Triggers (SAFEBEAT) study (NCT02576587). SAFEBEAT was a prospective cohort study conducted at two academic medical centers from 2012 to 2017. Participants were recruited from cardiology and electrophysiology clinics. Inclusion criteria were age\u0026thinsp;\u0026ge;\u0026thinsp;55 years, paroxysmal AF defined as self-terminating episodes lasting\u0026thinsp;\u0026lt;\u0026thinsp;7 days, and an Apnea-Hypopnea Index (AHI)\u0026thinsp;\u0026ge;\u0026thinsp;15. Exclusion criteria included permanent AF, valvular disease, coronary artery disease, heart failure, hyperthyroidism, prior cardiac surgery or ablation, and other medical comorbidities (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The Cleveland Clinic IRB and University Hospitals Case Medical Center IRB each approved this study, and informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eActigraphy Monitoring\u003c/h2\u003e \u003cp\u003eParticipants underwent continuous wrist actigraphy monitoring (Actiwatch Spectrum, Philips Respironics) for 7\u0026ndash;21 days. The actigraph device contains a piezoelectric accelerometer that records limb movements in 3-axes. Participants were instructed to wear the actigraph at all times except when showering or submerged in water. Actigraphy data were analyzed using the Actiware software v6.0.2 in 60-second epochs. Sleep intervals were marked using event markers, sleep diaries, and rest intervals. The following sleep parameters were derived and averaged across all sleep periods (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e): sleep latency, total sleep time, sleep efficiency, wakefulness after sleep onset (WASO), number of awakenings, arousal index, and average awakenings duration. Sleep latency is defined as the time from going to bed to sleep onset; total sleep time is the total time asleep after sleep onset; sleep efficiency is calculated as the total sleep time divided by time in bed multiplied by 100%; WASO represents the total time awake after sleep onset until final awakening; the number of awakenings is the count of episodes where the subject transitions from sleep to wakefulness, each lasting a minimum of 60 seconds; arousal index is the number of arousals per hour of sleep, indicating sleep fragmentation; and average awakenings duration refers to the average length of these awakenings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eElectrocardiogram Monitoring\u003c/h2\u003e \u003cp\u003eContinuous electrocardiogram (ECG) recordings were obtained over the same period as actigraphy using a single-channel telemetry system (Heartrak ECAT, Philips Respironics) with a sampling rate of 250 Hz. Participants were instructed to wear the ECG sensors at all times except during bathing, when actigraphy and ECG monitoring were paused concurrently.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHRV Analysis\u003c/h2\u003e \u003cp\u003eHRV measures were derived from normal-to-normal (NN) beat intervals during sinus rhythm on the ECG recordings after excluding segments with noise, ectopy or arrhythmias. Only 5-minute ECG segments meeting stability criteria were analyzed to ensure stationarity (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The following HRV measures were examined (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e): In the time-domain, the measures examined included Mean NN (the average of all normal-to-normal intervals), SDNN (standard deviation of normal-to-normal intervals), RMSSD (root mean square of successive differences between normal heartbeats), CV (coefficient of variation of normal-to-normal intervals), and short-term and long-term variability from the Poincar\u0026eacute; plot (SD1 and SD2, respectively). In the frequency-domain, we examined low frequency power (LFP), high frequency power (HFP), and the low-to-high frequency power ratio (LHR). Additionally, non-linear measures of variability and complexity, such as the detrended fluctuation analysis (DFA) parameters α1 and α2, were also explored.\u003c/p\u003e \u003cp\u003eHRV indices were computed using custom software developed and implemented in Matlab, and these values were averaged over the total monitoring duration. This analysis was separately conducted for sleep and wake periods as determined by actigraphy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eParticipant characteristics were summarized using descriptive statistics. A linear mixed effects model with a compound symmetry covariance structure was used to assess the associations of HRV measures (dependent variables) with actigraphy sleep parameters (independent variables) during sleep and wake periods. Age, sex, race, body mass index (BMI), and relevant medications including anti-hypertensives (ACE inhibitors, ARBs, beta-blockers, calcium channel blockers, diuretics), anti-arrhythmics, anti-depressants, cholesterol-lowering drugs, hypoglycemics, sedatives/sleeping aids were included as covariates. Sleep-wake interactions were examined for each actigraphy index. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed in SAS v9.4 (SAS Institute, Cary NC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe analytic sample was comprised of100 participants with paroxysmal AF and moderate-severe SDB. Participants underwent actigraphy monitoring for an average of 7.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46 days with an average monitoring duration of 7.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11 hours per day. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline demographic and clinical characteristics. The mean age was 60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0 years, 64% were male, 85% were Caucasian, and the mean BMI was 32.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7 kg/m\u003csup\u003e2\u003c/sup\u003e. Hypertension was present in 57% and diabetes in 13%. The majority (83%) were taking antihypertensive medications and 59% were on antithrombotic therapy.\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\u003eCharacteristics of Participants with Moderate to Severe Sleep Disordered Breathing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index (kg/m\u003csup\u003e2\u003c/sup\u003e), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (36.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (64.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic or Latino/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOT Hispanic or Latino/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (99.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (85.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Blood Pressure or Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (57.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Blood Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (62.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Attack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombination of medications: Ace Inhibitors,Antihypertensive,Alpha-2 Blocker,Beta-Blocker,Calcium blocker,Diuretic,Nitrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (83.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAce Inhibitors (Capoten, Vasotec, Zestril, Captopril, Altace, Lisinopril)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (27.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngiotensin receptor blocker (Hyzaar, Cozaar, Valsartan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiarrhythmic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (19.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiarrhythmic: Class 1a (Na Channel Block, Intermediate) Quinidine, Procainamide, Disoprymide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiarrhythmic: Class 1b (Na Channel Block, Fast) Lidocaine, Phenytoin, Mexiletine, Tocainide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiarrhythmic: Class 1c (Na Channel Block, Slow) Flecainide, Propafenone, Moricizine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (20.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiarrhythmic: Class II Beta Blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiarrhythmic: Class III (K\u0026thinsp;+\u0026thinsp;Channel Blocker) Amiodarone, Sotalol, Ibutilide, Dofetilide, Dronedarone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiarrhythmic: Class IV Slow Channel Blockers (Ca Channel Block) Verapamil, Diltiazem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (11.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiarrhythmic: Class V (Unknown Mechanism) Adenosine, Digoxin, Magnesuim Sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiarrhythmic: Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidepressants (SSRI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidepressants, Tricyclic (Elavil, Tofranil, Pamelor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidepressants (SNRI) Effexor, Cymbalta, Pristiq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntidepressants, Other (Wellbutrin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive (Hydralazine, Clonidine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-Blocker (Inderal, Lopressor, Tenormin, Corgard, Atenolol, Propranolol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (57.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium-channel blocker (Calan, Procardia, Cardizem)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol-lowering drugs (Mevacor, Pravachol, Zocor, Lipitor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (43.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol-lowering drugs, Other (Gemfibrozil, Zetia)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (9.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic, Loop (Lasix, Furosemide)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic, Thiazide (Hydrochlorothiazide)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (17.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic, Other (Aldosterone Antagonist, Spironolactone)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoglycemic, Oral (Glyburide, Glucophage)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (10.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin (Diabetes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedative hypnotics (Valium, Xanax, Ativan, Librium)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (8.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleeping Medicine (Ambien, Trazodone)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStatistics\u0026nbsp;presented\u0026nbsp;as\u0026nbsp;Mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD,\u0026nbsp;N\u0026nbsp;(column\u0026nbsp;%).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between Sleep Indices and HRV\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the associations between actigraphy-derived sleep measures and HRV parameters during sleep versus wake periods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of Heart Rate Variability with Actigraphy Sleep Measures by Sleep and Wakefulness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eWakefulness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEstimate (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP of interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAssociation with Actigraphy-Based Sleep Latency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0032 (-0.0071,0.0134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0061 (-0.0143,0.0021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0004 (-0.0007,0.0014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001 (-0.0007,0.0009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0006 (-0.0005,0.0017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001 (-0.0008,0.0009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0044 (-0.0193,0.0105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0092 (-0.0027,0.0211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0047 (-0.0244,0.0149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0025 (-0.0132,0.0181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0073 (-0.0230,0.0387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0044 (-0.0199,0.0293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0221 (-0.0217,0.0679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0180 (-0.0517,0.0170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0130 (-0.0193,0.0464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0011 (-0.0266,0.0251)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDRatio*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0075 (-0.0304,0.0469)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0169 (-0.0466,0.0136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequency Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0135 (-0.0248,0.0534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0069 (-0.0370,0.0242)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0314 (-0.0272,0.0934)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0501 (-0.0934,-0.0048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0030 (-0.0761,0.0758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0295 (-0.0312,0.0940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociation with Actigraphy-Based Sleep Efficiency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0022 (-0.0031,0.0075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0062 (0.0006,0.0117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0003 (-0.0008,0.0003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0007 (0.0001,0.0013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0001 (-0.0007,0.0005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0003 (-0.0003,0.0009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0013 (-0.0090,0.0064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0030 (-0.0050,0.0110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0022 (-0.0077,0.0121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0055 (-0.0047,0.0158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0151 (-0.0306,0.0005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0167 (0.0002,0.0335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0108 (-0.0330,0.0119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0132 (-0.0104,0.0374)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0121 (-0.0285,0.0045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0268 (0.0091,0.0447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDRatio*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0005 (-0.0201,0.0195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0142 (-0.0343,0.0063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequency Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0025 (-0.0222,0.0175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0168 (-0.0370,0.0037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0068 (-0.0232,0.0377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0163 (-0.0467,0.0151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0171 (-0.0550,0.0223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0007 (-0.0407,0.0410)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociation with Actigraphy-Based Total Minutes in Bed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0001 (-0.0003,0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0000 (-0.0002,0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0000 (-0.0000,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0000 (-0.0000,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0000 (-0.0000,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000 (-0.0000,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0002 (-0.0001,0.0005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0000 (-0.0003,0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0001 (-0.0005,0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0002 (-0.0006,0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0006 (0.0000,0.0012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0002 (-0.0007,0.0004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0002 (-0.0011,0.0006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0001 (-0.0010,0.0007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0006 (-0.0000,0.0012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0001 (-0.0007,0.0005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDRatio*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0006 (-0.0014,0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000 (-0.0007,0.0007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequency Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0000 (-0.0007,0.0007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0010 (0.0003,0.0018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0009 (-0.0020,0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0000 (-0.0011,0.0011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0016 (0.0002,0.0031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0008 (-0.0007,0.0022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociation with Actigraphy-Based Total Sleep Time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0001 (-0.0003,0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0000 (-0.0002,0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0000 (-0.0000,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000 (-0.0000,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0000 (-0.0000,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000 (-0.0000,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0002 (-0.0001,0.0006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0000 (-0.0003,0.0003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0001 (-0.0005,0.0003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0002 (-0.0006,0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0006 (-0.0001,0.0012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0001 (-0.0007,0.0006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0004 (-0.0013,0.0005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0001 (-0.0010,0.0009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0006 (-0.0001,0.0012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0000 (-0.0007,0.0007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDRatio*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0008 (-0.0016,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0000 (-0.0009,0.0008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequency Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0000 (-0.0008,0.0008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0011 (0.0002,0.0019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0010 (-0.0022,0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0001 (-0.0014,0.0011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0018 (0.0002,0.0034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0008 (-0.0008,0.0024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociation with Actigraphy-Based Wake After Sleep Onset\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0007 (-0.0017,0.0003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0008 (-0.0018,0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0001 (-0.0000,0.0002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0001 (-0.0002,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0000 (-0.0001,0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0000 (-0.0001,0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0005 (-0.0009,0.0020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0006 (-0.0020,0.0008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0012 (-0.0031,0.0006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0012 (-0.0031,0.0006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0042 (0.0012,0.0071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0021 (-0.0050,0.0009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0017 (-0.0025,0.0059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0018 (-0.0059,0.0024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0034 (0.0003,0.0065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0031 (-0.0061,0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDRatio*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0009 (-0.0045,0.0028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0016 (-0.0020,0.0053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequency Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0001 (-0.0036,0.0037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0055 (0.0019,0.0092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0027 (-0.0083,0.0029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0022 (-0.0034,0.0078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0059 (-0.0014,0.0133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0029 (-0.0044,0.0102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociation with Actigraphy-Based Number of Awakenings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0027 (-0.0068,0.0013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0027 (-0.0067,0.0013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0003 (-0.0001,0.0007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001 (-0.0003,0.0005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0002 (-0.0002,0.0007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001 (-0.0003,0.0005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0010 (-0.0049,0.0068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0021 (-0.0079,0.0037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0001 (-0.0077,0.0074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0009 (-0.0085,0.0066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0166 (0.0044,0.0289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0003 (-0.0116,0.0124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0143 (-0.0031,0.0319)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0001 (-0.0171,0.0173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0136 (0.0008,0.0266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0028 (-0.0154,0.0099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDRatio*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0015 (-0.0135,0.0167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0027 (-0.0122,0.0179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequency Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0050 (-0.0199,0.0101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0232 (0.0080,0.0387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0067 (-0.0162,0.0301)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0012 (-0.0239,0.0219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0006 (-0.0300,0.0298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0196 (-0.0103,0.0505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociation with Actigraphy-Based Average Awakening Length\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTime Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0002 (-0.0136,0.0141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0060 (-0.0196,0.0076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0008 (-0.0006,0.0021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0021 (-0.0034,-0.0007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0008 (-0.0023,0.0006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0008 (-0.0023,0.0006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0064 (-0.0136,0.0263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0068 (-0.0265,0.0128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFA_Alpha2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0257 (-0.0517,0.0003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0105 (-0.0360,0.0150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0193 (-0.0216,0.0619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0465 (-0.0842,-0.0073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0507 (-0.1048,0.0068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0456 (-0.0992,0.0112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0256 (-0.0179,0.0711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0534 (-0.0929,-0.0121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDRatio*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.0638 (-0.1106,0.0146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0196 (-0.0305,0.0723)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFrequency Domain Indices\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.0418 (-0.0106,0.0970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0308 (-0.0202,0.0845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFP*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.1338 (-0.1988,0.0636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0443 (-0.0328,0.1276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.1977 (0.0821,0.3257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0157 (-0.1092,0.0877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations used in the table:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026middot; MNN: Mean NN Interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026middot; SDNN: Standard Deviation of NN Intervals\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026middot; RMSSD: Root Mean Square of Successive Differences\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026middot; CV: Coefficient of Variation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026middot; SD1: Standard Deviation of Short-Term Heart Rate Variability\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026middot; SD2: Standard Deviation of Long-Term Heart Rate Variability\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026middot; SDRatio: Ratio of SD1 to SD2\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026middot; LFP: Power in the Low Frequency Range (0.04\u0026ndash;0.15 Hz)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026middot; HFP: Power in the High Frequency Range (0.15\u0026ndash;0.4 Hz)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026middot; LHR: Ratio of Low Frequency Power to High Frequency Power\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between Sleep Latency and HRV\u003c/h2\u003e \u003cp\u003eLonger sleep latency, reflecting greater difficulty falling asleep, was associated with reduced HFP during wakefulness (coefficient \u0026minus;\u0026thinsp;0.0501, 95% CI [-0.0934,-0.0048], p\u0026thinsp;=\u0026thinsp;0.031). However, no relationship was observed between sleep latency and HFP during sleep. A significant sleep-wake interaction was present for the association between sleep latency and HFP (p\u0026thinsp;=\u0026thinsp;0.024), indicating this relationship differed between sleep versus wake periods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between Sleep Efficiency and HRV\u003c/h2\u003e \u003cp\u003eHigher sleep efficiency was associated with increased SDNN, CV and Poincar\u0026eacute; plot SD2 during wakefulness (SDNN coefficient 0.0007, 95% CI [0.0001,0.0013], p\u0026thinsp;=\u0026thinsp;0.014; CV coefficient 0.0167, 95% CI [0.0002,0.0335], p\u0026thinsp;=\u0026thinsp;0.047; SD2 coefficient 0.0268, 95% CI [0.0091,0.0447], p\u0026thinsp;=\u0026thinsp;0.003). No relationships were observed between sleep efficiency and HRV metrics during sleep. Significant sleep-wake interactions were present for SDNN and CV (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), denoting that these associations differed between sleep and wake periods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between Total Sleep Time and HRV\u003c/h2\u003e \u003cp\u003eIn contrast to other sleep parameters, total sleep time did not demonstrate significant associations with any HRV measures during either sleep or wakefulness. No sleep-wake interactions were statistically significant for the relationships between total sleep time and all HRV indices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between WASO and HRV\u003c/h2\u003e \u003cp\u003eHigher WASO, was associated with increased coefficient of variation (CV) and Poincar\u0026eacute; plot SD2 during sleep (CV coefficient 0.0042, 95% CI [0.0012,0.0071], p\u0026thinsp;=\u0026thinsp;0.005; SD2 coefficient 0.0034, 95% CI [0.0003,0.0065], p\u0026thinsp;=\u0026thinsp;0.031). WASO was also associated with increased low frequency power (LFP) during wakefulness (coefficient 0.0055, 95% CI [0.0019,0.0092], p\u0026thinsp;=\u0026thinsp;0.003). Significant sleep-wake interactions were present for CV, SD2 and LFP (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), signifying that these relationships differed between sleep and wake periods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between Number of Awakenings and HRV\u003c/h2\u003e \u003cp\u003eMore frequent nocturnal awakenings were associated with increased CV during sleep (coefficient 0.0166, 95% CI [0.0044,0.0289], p\u0026thinsp;=\u0026thinsp;0.007). The number of awakenings was also associated with increased LFP during wakefulness (coefficient 0.0232, 95% CI [0.0080,0.0387], p\u0026thinsp;=\u0026thinsp;0.003). Significant sleep-wake interactions were observed for both CV and LFP (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), denoting differential relationships by sleep-wake state.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAverage Awakening Duration\u003c/h2\u003e \u003cp\u003eLonger average awakening duration was associated with increased LHR and decreased HFP during sleep (LHR: coefficient 0.1977, 95% CI [0.0821,0.3257], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; HFP: coefficient \u0026minus;\u0026thinsp;0.1338, 95% CI [-0.1988, -0.0636], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, longer awakening length was associated with reduced SDNN, CV and SD2 in wakefulness. Significant sleep-wake interactions were present for each of these HRV measures (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e highlights the differential associations between specific sleep indices and HRV parameters during sleep versus wakefulness. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the sleep-wake interactions for HRV relationships with sleep latency, sleep efficiency, WASO and number of awakenings. Objective measures of sleep disruption demonstrated significant associations with alterations in HRV indicative of autonomic dysfunction. Notably, these relationships varied between sleep and wake periods, with more consistent HRV changes observed during wakefulness.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study of individuals with moderate to severe OSA and paroxysmal AF, we observed significant associations between actigraphy-derived measures of sleep disruption and alterations in HRV indicative of autonomic dysfunction in patients with paroxysmal AF and sleep disordered breathing. Importantly, these relationships exhibited diurnal variation, with greater HRV changes observed during wakefulness compared to sleep. Our findings of greater degree of sleep disruption associated with HRV alterations more pronounced during wakefulness align with known circadian patterns of autonomic activity, which could provide insights into daily cycles of AF onset in the setting of at least a moderate degree of sleep disordered breathing.\u003c/p\u003e \u003cp\u003eThe associations of poorer sleep quality with reduced HRV suggest that sleep disturbances may promote autonomic imbalance in AF patients. Possible mechanisms include activation of inflammatory pathways and oxidative stress by sleep deprivation that modulate cardiac autonomic function (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Sleep fragmentation with frequent arousals can also directly provoke surges in sympathetic activity and vagal withdrawal (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Shortened and fragmented sleep can induce inflammatory cytokines like C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-alpha) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), which have been associated with reduced HRV through effects on the sinoatrial node (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Additionally, CRP levels are elevated during daytime compared to nocturnal AF episodes (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), further linking inflammatory pathways to circadian patterns of arrhythmogenesis. Our findings thus align with evidence that inflammatory processes induced by sleep disruption may alter cardiac autonomic function, reflected in the HRV changes observed. This also indicates that the effects of sleep impairment extending beyond the sleep period into wakefulness may be mediated by sustained sympathetic excitation and impaired vagal recovery after sleep disruption (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur observations of sleep disruption and altered HRV during wake periods corroborate established circadian rhythms in autonomic functions. These insights potentially elucidate the daily fluctuations in AF occurrence. Sympathetic nervous system tone typically peaks during daytime waking hours, while parasympathetic activity is more prominent at night during sleep. This variation may offer additional insights into the sympathetic and parasympathetic balance in different sleep-wake schedules. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This circadian variation in autonomic balance has been associated with increased propensity for ventricular arrhythmias in the morning period of time (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Similarly, in AF patients, the persistent HRV changes we observed during daytime wakefulness following sleep disruption suggest autonomic dysfunction is not limited solely to the sleep period. The observation of more prominent HRV changes during wakefulness has implications for understanding circadian patterns of arrhythmogenesis in AF, though further study relating these HRV metrics to timing of arrhythmia onset is needed. Overall, our findings highlight the interplay between sleep disturbances and autonomic dysregulation showing diurnal variability in this population.\u003c/p\u003e \u003cp\u003eOur results provide novel insights into diurnal autonomic dysfunction related to sleep impairment in AF patients with sleep apnea. However, potential limitations should be acknowledged. This microlongitudinal study design with repeated measures cannot establish causality. Confounding from unmeasured factors cannot be excluded. Actigraphy has reduced sensitivity to detect wakefulness compared to polysomnography. Nonetheless, the strengths of the study include the use of continuous objective sleep monitoring and continuous ECG recording over multiple days. This approach allows for a detailed and accurate assessment of sleep patterns and their impact on cardiovascular health within individuals. Moreover, our robust HRV analytics provide a sophisticated analysis of autonomic function, offering insights into the intricate interplay between sleep and cardiac health.\u003c/p\u003e \u003cp\u003eOur findings are internally consistent across multiple HRV measures and align with experimental evidence indicating causal effects of sleep impairment on autonomic function (31; 32; 33) Interventional studies of total and partial sleep deprivation have demonstrated reductions in HRV metrics including a range of spectral and frequency-based measures (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This supports sleep disruption as a potential contributor rather than merely a biomarker of autonomic imbalance and underlying cardiovascular dysfunction.\u003c/p\u003e \u003cp\u003eOur results suggest utility for actigraphy-based sleep assessment to identify AF patients who may be at risk for autonomic dysfunction and associated outcomes, e.g. progression of arrhythmia burden. Targeting sleep quality improvement as a modifiable risk factor could potentially optimize autonomic function in this population. This is supported by studies showing alterations in HRV after treatment of sleep apnea with positive airway pressure (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Further research is warranted to determine whether optimizing sleep quality can improve cardiac autonomic control and reduce arrhythmia susceptibility in AF patients with sleep disorders.\u003c/p\u003e \u003cp\u003eIn conclusion, indices of worsened sleep disruption demonstrated significant associations with HRV alterations indicating autonomic dysfunction in patients with paroxysmal AF and sleep apnea. Importantly, these associations were more consistently observed during wakefulness compared to sleep. Our findings suggest that sleep disruption may contribute to diurnal variability in cardiac autonomic function, which could potentially influence circadian patterns of AF. Additional studies exploring the arrhythmogenic mechanisms associated with sleep-autonomic interactions in AF are necessary.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments.\u0026nbsp;\u003c/strong\u003eWe would like to thank the participants of the SAFEBEAT cohort for contributing their valuable time to provide the data used for this work that will allow us to advance insights in sleep apnea and atrial fibrillation. We also acknowledge the work of research coordinator, Joan Aylor, and research polysomnologist, Rawan Nawabit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement.\u0026nbsp;\u003c/strong\u003eThe authors have no conflicts to disclose. HKW serves on the advisory board of Resmed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability.\u003c/strong\u003e The data generated during this study is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cem\u003eGlobal epidemiology of atrial fibrillation: An increasing epidemic and public health challenge.\u0026nbsp;\u003c/em\u003eGiuseppe Lippi, Fabian Sanchis-Gomar , Gianfranco Cervellin. s.l. : Int J Stroke, 2021 Feb, Vols. 16(2):217-221.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eAssociation of Atrial Fibrillation Without Cardiovascular Comorbidities and Stroke Risk: From the REGARDS Study.\u0026nbsp;\u003c/em\u003eMatthew J Singleton, Muhammad Imtiaz-Ahmad , Hooman Kamel , Wesley T O\u0026apos;Neal , Suzanne E Judd , Virginia J Howard , George Howard , Elsayed Z Soliman , Prashant D Bhave. 2020 Jun, J Am Heart Assoc, p. 16;9(12):e016380.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eAtrial fibrillation and sleep-disordered breathing.\u0026nbsp;\u003c/em\u003eFlorent Lavergne, Laurent Morin, Jeff Armitstead, Adam Benjafield, Glenn Richards, Holger Woehrle. s.l. : J Thorac Dis, 2015 Dec, Vols. 7(12): E575\u0026ndash;E584.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eSleep-Disordered Breathing and Cardiac Arrhythmias in Adults: Mechanistic Insights and Clinical Implications: A Scientific Statement From the American Heart Association.\u0026nbsp;\u003c/em\u003eReena Mehra, Mina K. 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Matthew P Gilman, John S Floras, Kengo Usui, Yasuyuki Kaneko, Richard S T Leung, T Douglas Bradley. s.l. : Clin Sci (Lond), 2008 Feb, Vols. 114(3):243-9.\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Cardiac Arrhythmias, Sleep Apnea, Continuous Positive Airway Pressure, Heart Rate Variability","lastPublishedDoi":"10.21203/rs.3.rs-4547962/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4547962/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eSleep architectural disruption is associated with atrial fibrillation (AF); however, associated autonomic influences remain unclear and it is unknown if this detriment persists during wakefulness. We hypothesize sleep disruption and autonomic dysfunction have diurnal patterning in patients with paroxysmal AF.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from the Sleep Apnea and Atrial Fibrillation Biomarkers and Electrophysiologic Atrial Triggers (SAFEBEAT) study designed to examine paroxysmal AF and sleep apnea, including simultaneous collection of continuous electrocardiogram monitoring (Heartrak Telemetry\u0026reg;) and actigraphy (Actiwatch GTX) for 7\u0026ndash;21 days. Heart rate variability (HRV) measures in time-domain (standard deviation of normal-to-normal (NN) intervals (SDNN), coefficient of variation (CV)) and frequency-domain (low frequency power (LFP), high frequency power (HFP)) were used as surrogates of autonomic function and averaged per sleep/wake per day. A linear mixed-effects model assuming compound symmetry correlation structure was used to assess the relationship of HRV with actigraphy-derived sleep data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe analytic sample (age 60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0 years, body mass index 32.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7 kg/m2, 36% female, 75% White) included 100 participants with paroxysmal AF. Longer sleep latency was associated with lower HFP during wakefulness (coefficient \u0026minus;\u0026thinsp;0.0501, p\u0026thinsp;=\u0026thinsp;0.031). Higher sleep efficiency was associated with increased SDNN (coefficient 0.0007, p\u0026thinsp;=\u0026thinsp;0.014) and CV (coefficient 0.0167, p\u0026thinsp;=\u0026thinsp;0.047). Higher arousal index was associated with increased CV (coefficient 0.0166, p\u0026thinsp;=\u0026thinsp;0.007) and LFP (coefficient 0.0232, p\u0026thinsp;=\u0026thinsp;0.003). During sleep, longer average awakenings duration was associated with increased LFP/HFP ratio (coefficient 0.1977, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and reduced HFP (coefficient \u0026minus;\u0026thinsp;0.1338, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant sleep-wake interactions were observed for sleep latency with HFP (p\u0026thinsp;=\u0026thinsp;0.024), sleep efficiency with SDNN and CV (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), WASO with SDNN, CV, and LFP (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and frequency of awakenings with CV and LFP (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eActigraphy-based measures of sleep disruption were associated with autonomic function alterations exhibiting diurnal variability in paroxysmal AF. Greater overall HRV and parasympathetic modulation were related to better sleep quality. Increased sympathetic activation was associated with sleep fragmentation. Results provide insights into differential autonomic dysfunction related to sleep disruption that may contribute to atrial arrhythmogenesis.\u003c/p\u003e","manuscriptTitle":"Actigraphy-Based Sleep Disruption and Diurnal Biomarkers of Autonomic Function in Paroxysmal Atrial Fibrillation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-01 19:11:24","doi":"10.21203/rs.3.rs-4547962/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":"b4e6d5b1-2cc5-4103-9c50-9a73ddefc426","owner":[],"postedDate":"July 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-03T04:09:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-01 19:11:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4547962","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4547962","identity":"rs-4547962","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}