Device-assessed sleep health among older patients with heart failure: An actigraphy-based study

Device-assessed sleep health among older patients with heart failure: An actigraphy-based study | 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 Research Article Device-assessed sleep health among older patients with heart failure: An actigraphy-based study Sunanthiny Krishnan, Shirley Sze, Shelley Taylor, Charlotte L Edwardson, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7409629/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 Background Poor sleep quality is common among older adults and in those with heart failure (HF). Sleep quality is typically assessed using self-reported questionnaires which are subject to recall bias. Actigraphy-based assessment permits more objective evaluation of sleep. We examined sleep among older patients with HF using actigraphy and examined associations between sleep outcomes and cardiac biomarkers, functional performance and quality of life (QoL). Methods We recruited 150 patients aged ≥ 65 with diagnosis of HF. They were given a wrist-accelerometer to wear for 7 days. Patients completed 4-meter walk test (4MWT), handgrip strength test (HGST), Timed Up and Go test (TUGT), Barthel Index (BI), Kansas City Cardiomyopathy Questionnaire (KCCQ-12) and frailty assessment (Clinical Frailty Scale, CFS). Sleep outcomes calculated include sleep period time window, sleep duration, sleep onset and wake up time, wake after sleep onset (WASO), sleep interruptions, sleep regularity index (SRI) and sleep efficiency (SE). Poor sleep quality was defined as SE < 80%. Regression analysis was used to examine associations between sleep outcomes and other variables adjusting for age, gender and comorbidities. Results Accelerometry data from 145 participants were analysed. Sixty-one (42%) patients had poor sleep quality. These patients had significantly higher plasma NT-proBNP ( p = 0.044). No statistically significant difference was noted in 4MWT, HGST, TUGT, BI, KCCQ-12 and CFS between patients with SE < 80% and those with SE ≥ 80%. Lower SE was associated with worse frailty status and lower BI scores; lower SRI was associated with worse NYHA class, frailty, BI scores and QoL measures; longer WASO was associated with slower gait speed. Conclusions Forty-two percent of older patients with HF had poor sleep quality; they had significantly higher NT-proBNP levels. Poor sleep quality was associated with higher functional dependence and frailty. Sleep irregularity affected HF symptom load, frailty, functional performance and QoL, while sleep fragmentation was associated with impaired gait speed. These findings highlight the need to consider sleep assessment in the comprehensive management of older adults with HF. Accelerometer Heart failure Older people Sleep quality Figures Figure 1 Figure 2 KEY SUMMARY POINTS Aim To undertake objective assessment of sleep quality among older patients living with heart failure (HF). Findings Older patients with HF have poor sleep outcomes that are associated with frailty, functional performance, HF symptom burden and quality of life. Message Findings from this study highlight the importance of considering sleep assessment in the comprehensive management of older adults living with HF. INTRODUCTION Poor sleep health is associated with cardiovascular disease (CVD) [ 1 ]. Both short and long duration of sleep are linked to increased risk of cardiovascular outcomes and all-cause mortality [ 2 , 3 , 4 ]. A meta-analysis of 17 cohort studies with over 311 000 participants reports that insomnia, defined as the subjective feeling of non-restorative sleep including difficulty initiating or maintaining sleep, is associated with 33% higher risk of CVD mortality [ 5 ]. Among patients living with heart failure (HF), poor sleep quality is high with a reported prevalence between 50%-80% [ 6 – 9 ]. Restless sleep, difficulty initiating and maintaining sleep, poor sleep continuity and early morning awakening are commonly reported sleep disturbances in this clinical cohort [ 6 , 10 , 11 ]. The wearing effect of sleep deprivation on quality of life (QoL) of patients with HF is well documented. Decline in sleep quality has also been shown to impair functional performance, including activities of daily living, social independence and cognitive functioning [ 11 ]. The aetiology of sleep disruption in patients with HF is rarely singular but often a composite of multiple factors. Symptoms of congestion such as paroxysmal nocturnal dyspnoea and orthopnoea are among the most frequently cited causes, alongside nocturia [ 12 ]. Sleep disordered breathing, specifically obstructive sleep apnoea, and central sleep apnoea with Cheyne-Stokes breathing, are also implicated in HF populations irrespective of phenotype, with prevalence up to 60% [ 13 ]. Interestingly, daytime somnolence, typically a result of poor night time sleep, is independently associated with increased risk of cardiovascular mortality [ 14 , 15 ]. In recent years, there has been a blooming interest in assessment of sleep quality amongst patients with HF. However, few studies have adopted device-based assessment of sleep with even fewer specifically focussing on older people with HF. In most cases, these studies rely on retrospective, self-reported sleep using questionnaires such as the Pittsburg Sleep Quality Index (PSQI) [ 16 ], Athens Insomnia Scale (AIS) [ 17 ] and Insomnia Severity Index (ISI) [ 18 ]. These subjective measures require a long recall period, ranging from 2 to 4 weeks, which arguably challenges the reliability of the results [ 19 ], particularly in older people. Whilst polysomnography remains the gold standard for assessment of sleep, the procedure is costly and intrusive to patients’ normal sleep routine [ 20 ]. Actigraphy-based sleep assessment is a feasible, objective and non-invasive alternative allowing monitoring of patients’ sleep pattern in their natural environment. Additionally, actigraphy allows collection of sleep data over an extended period of time (days or weeks) which can provide more reliable estimates of sleep parameters compared to polysomnography, which is typically performed for only one or two nights [ 21 ]. Importantly, the validity of actigraphy-based sleep evaluation has been established against polysomnography, with an estimated agreement ranging from 91–93% [ 21 , 22 ]. In this study, we sought to summarise sleep health amongst older patients with HF using actigraphy and examine associations between sleep outcomes and cardiac biomarkers, functional performance and health-related QoL. METHODS Study Population We recruited 150 participants aged ≥ 65 years with a diagnosis of HF for at least one year. Diagnosis of HF was established based on the presence of HF symptoms (e.g., breathlessness, pedal oedema and reduced effort tolerance), echocardiographic evidence of cardiac dysfunction and/or raised natriuretic peptide levels [ 23 ]. Patients with documented cognitive impairment, history of hospitalisation within the past 2 weeks and recent wrist injury of the non-dominant arm (where the accelerometer is worn) were excluded. Recruitment took place at an outpatient HF clinic within a tertiary cardiology service in a National Health Service (NHS) hospital in the UK between March – October 2023. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Research Ethics Committee of Health Research Authority and Health Care Research Wales (REC Ref: 22/EM/0172). Written informed consent was obtained from all participants prior to the study. Data Collection i. Baseline Assessments All patients were reviewed by a cardiologist on the day of enrolment. Heart failure symptom burden was ascertained using the New York Heart Association (NYHA) Functional Classification. All patients had an echocardiogram to assess left ventricular ejection fraction (LVEF) and blood tests including N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels, full blood count and renal function. Patients with LVEF < 40% were categorised as having HF with reduced ejection fraction (HFrEF), 41–49% as HF with mildly reduced ejection fraction (HFmrEF) and 50% or greater as HF with preserved ejection fraction (HFpEF) [ 23 ]. Patients underwent several additional assessments including evaluation of functional performance (i.e., usual gait speed, handgrip strength, dynamic balance, activities of daily living), frailty status and health-related quality of life. Usual Gait Speed Participants’ gait speed was assessed using the standardised 4-meter walk test [ 24 ]. Patients were instructed to walk from a standing start at their usual pace on a flat 4-meter course and timed using a stopwatch. Participants were allowed to use their usual assistive devices during the test and the average reading of two attempts was used for analysis. Handgrip Strength Handgrip strength, a surrogate for muscle strength [ 25 ] was assessed using an analogue Smedley spring dynamometer. Participants’ maximum-effort isometric contraction was measured three times using their dominant hand, with a brief period to rest between each trial. The maximum reading of the three trials was taken for analysis. Dynamic Balance The timed up and go test (TUGT) is a validated instrument that assesses mobility and balance in older adults [ 26 ]. Participants were instructed to stand up from a chair of a standard height, walk a distance of 3 meters at their usual pace, turn around, return to the chair and sit down. They were allowed to use their usual walking aids but no other physical assistance was given during the test. Participants began the task seated on a chair with their backs and arms resting. Timing commenced when participants attempted to stand up from the chair and stopped when they returned to the seated position. A stopwatch was used to measure the time taken to complete the test. Activities of Daily Living Functional capacity in performing activities of daily living (ADL) was measured using the Barthel Index (BI) [ 27 ], which comprises of ten domains of ADL, each scored on an ordinal scale based on the patient’s physical independence. Patients and/or their accompanying caregivers were interviewed on the former’s ability to perform each of the ADL and scored accordingly. With a total score ranging from 0 to 100, higher scores reflect better ability to performed ADLs independently. Frailty Baseline frailty status was determined using the Clinical Frailty Scale (CFS) [ 28 ], which categorises degree of frailty on a nine-point scale based on patient’s global function and cognition, with 1 being very fit and 9, terminally ill. Patients with a CFS score ≥ 5 were deemed frail. Health Related Quality of Life (HRQoL) Kansas City Cardiomyopathy Questionnaire (KCCQ-12), a HF-specific HRQoL measure, was used to assess the impact of HF on patients’ health status [ 29 ]. A shorter version of the original 23-item questionnaire, KCCQ-12 retains the psychometric properties of the full instrument and measures health on four domains: physical limitation (3 items), symptom frequency (4 items), quality of life (2 items) and social limitations (3 items). An overall summary score was also calculated (average of physical limitation, symptom frequency, quality of life and social limitation). ii. Actigraphy Accelerometer Patients were fitted with a GENEActiv triaxial accelerometer (ActivInsights, Cambridgeshire, UK), on their non-dominant wrist for 7 days continuously (24 hours/day). Each device was initialised to record at a frequency of 25 Hz. During the device wear-period, patients were instructed to complete a sleep diary, recording their light-off time at night and morning wake time. The sleep diary was used to guide the accelerometry algorithm to identify the main sleep period [ 30 ]. Accelerometry Data Processing Data from the accelerometers were downloaded as “bin” files using the GENEActiv PC software (version 3.2) and analysed using the R-package GGIR version 3.0–9 [ 31 , 32 ]. Participants were excluded from analysis if they did not have 3 valid days of recordings (defined as ≥ 16 hours wear-time within 24 hours) including 3 full nights [ 33 ]. In GGIR, sleep is detected based on a period of Sustained Inactivity Bout (SIB), defined as the absence of wrist rotation (z-axis) by more than 5 degrees for longer than 5 minutes [ 32 ]. As SIB is non-specific and encompasses all episodes of rests during a 24-hour period including daytime sleep and sedentary behaviour, the sleep diary was used to differentiate the main nightly sleep period time (SPT) from other SIBs. Only SIB that occurred during the SPT were classed as sleep. Patients’ self-reported lights-off and wake times were entered manually into the system to guide the algorithm to accurately detect the SPT-window. Sleep Parameters Sleep measures studied include SPT-window, sleep duration, sleep onset time, wake up time, duration of wake after sleep onset (WASO), sleep efficiency, number of sleep interruptions and sleep regularity index (SRI). Definitions of each sleep parameter are provided in Supplementary Table S1 . Figure 1 illustrates these metrics in the context of an overnight sleep. SPT , Sleep Period Time; WASO , Wake after sleep onset We selected sleep efficiency (SE) as the primary proxy for sleep quality as actigraphy-derived SE has been shown to positively mirror self-perceived quality of sleep [ 34 ]. Furthermore, SE is strongly associated with risk of mortality in older people, with the risk increasing substantially when SE is below 80% [ 35 ]. Therefore, in this study, we defined poor sleep quality as SE < 80%. Patients with poor and good sleep quality are referred to as poor and good sleepers , respectively. Statistical Analysis Shapiro-Wilk test indicated that the distribution of sleep data departed from normality. Therefore, non-parametric statistics were used in all analyses. The study population was stratified based on their SE, i.e., < 80% (poor sleep quality) vs ≥ 80% (good sleep quality). Comparison of continuous variables between the two categories were analysed using Mann-Whitney U Test and the Chi-squared test was employed for comparison between categorical variables. Linear regression models were used to assess associations between sleep outcomes and markers of HF severity, functional status and HRQoL. Results were adjusted for potential confounders of poor sleep i.e., age, gender and number of comorbidities. All analyses were performed using SPSS version 28 (SPSS Inc., Chicago, IL). A two-tailed P- value of < 0.05 was considered statistically significant. RESULTS Study Participants A total of 145 participants were included in the final analysis; five participants were excluded due to poor quality data. Median age of the study population was 80 (range 65-94 years), 64% were male and 84% were Caucasian. Median NT-proBNP level was 2040 ng/L (IQR = 1117 - 4915), with a normal range of 20 – 200 ng/L; one fifth had NYHA class III/IV symptoms; 47% had HF with preserved ejection fraction (HFpEF) (Table 1). The median SPT-window and sleep duration were 8.1 hours/day and 6.3 hours/day respectively. The median SE of the study population was 81.8%. The number of sleep interruptions experienced throughout the night ranged from 3 to 25 per night (median, 13). (Table 1). Good Sleepers vs Poor Sleepers Of 145 participants, 42% (n = 61) were poor sleepers (i.e, SE < 80%); they had significantly higher levels of NT-proBNP compared to the good sleepers (3141 ng/L vs 1592 ng/L, p = 0.044) and were more likely to be South Asian. Poor sleepers tended to have higher HF symptom burden (NYHA Class III/IV), although this did not reach statistical significance. There was no discernible difference in HF phenotype, frailty status, types or number of comorbidities between the two sleep groups. Quality of life measures were similar across both groups (Table 2). Sub-analysis of specific sleep parameters, indicated that the SPT window did not differ between good sleepers and poor sleepers. However, the poor sleeper group had shorter sleep duration within the SPT window (5.7 hours vs 6.8 hours, p < 0.001), higher number of sleep interruptions per night (14 vs 12, p = 0.024) and thus spent more time awake during their sleep window (WASO 2.3 hours vs 1.1 hour, p < 0.001). Their sleep pattern was also significantly less regular between nights in comparison with that of good sleepers’ (SRI 40.0 vs 47.8, p = 0.009) (Supplementary Table S2). Box plots comparing the various sleep metrics between good and poor sleepers are presented in Figure 2. Association Between Sleep Parameters and HF Severity, Functional Status and QoL i. Sleep Parameters and HF severity Lower SRI (poorer sleep regularity) was associated with higher NYHA class (worse HF symptoms). The association remained significant after accounting for age, gender and number of comorbidities (β = -0.009, p = 0.007). There were no significant associations between other sleep parameters and NT-proBNP or LVEF (Table 3). ii. Sleep Parameters and Functional Performance Lower SE was associated with higher CFS (worsening frailty status) (adjusted β = -0.017, p = 0.014) and lower BI score (higher dependence on others for ADLs) (adjusted β = 0.271, p = 0.016) (Supplementary Table S3). Longer WASO was associated with slower gait speed (adjusted β = -0.039, p = 0.040), lower BI score (adjusted β = -4.477, p < 0.001) and poorer frailty status (adjusted β = 0.251, p < 0.001). Lower SRI was also associated with lower BI score (adjusted β = 0.310, p < 0.001) and worse frailty (adjusted β = -0.017, p < 0.001). No associations were observed between sleep parameters and handgrip strength or dynamic balance. iii. Sleep Parameters and HRQoL Amongst sleep parameters, lower SRI was associated with lower scores across all domains of KCCQ-12, except KCCQ-QoL (Supplementary Table S4). The predictive strength of SRI is strongest for social limitation (adjusted β = 0.500, p < 0.001), followed by physical limitation (adjusted β = 0.415, p < 0.001) and frequency of HF symptoms (adjusted β = 0.241, p = 0.038). Longer night time awakening (WASO) was associated with poorer HF-related physical function (adjusted β = -4.170, p = 0.036) and social limitation (adjusted β = -4.620, p = 0.032). DISCUSSION In this study, we assessed sleep quality in older patients living with HF using wrist-actigraphy over a 7-day period. Of the 145 patients studied, we found 42% had poor sleep quality evidenced by actigraphic SE. Despite a similar sleep window duration to the good sleepers, the poor sleepers experienced remarkably fragmented sleep as recorded by the high number of sleep interruptions during the night and wakefulness after sleep onset (WASO). Consequently, these patients had significantly shorter duration of uninterrupted sleep, with a median of 5.7 hours, to be compared to the 7–8 hours of sleep recommended for older adults [ 36 ]. Notably, median serum NT-proBNP levels were elevated twofold in poor sleepers compared to those with better SE. This observation may reflect greater prevalence of orthopnoea and paroxysmal nocturnal dyspnoea in the context of more advanced or poorly controlled HF. Alternatively, or additionally, poor sleep might induce activation of the sympathetic nervous system, thus inflicting haemodynamic burden to the heart with the observed effect on NT-proBNP levels. Studies support that sleep restriction [ 37 ] and fragmentation [ 38 , 39 ] increase heart rate, blood pressure and sympathetic cardiac modulation. In the current study, sleep regularity index (SRI), a relatively new metric, emerged as an important determinant of HF severity. We found that patients with a more irregular sleep/wake pattern experienced more advanced HF symptoms (i.e., NYHA class), independent of age, gender and concomitant diseases. These patients had higher ADL dependency and tended to be more frail, with poorer HRQoL outcomes. Prior studies have linked actigraphy-based sleep irregularity with higher risk of incident CVD [ 40 ] and all- cause mortality [ 41 ], although HF per se was not included in the analyses. Furthermore, poor sleep regularity was found to be a stronger predictor of mortality than sleep duration [ 41 ], a premise that lends weight to our study findings whereby sleep duration was associated with neither HF severity nor functional performance. This further cements the need for deeper investigation into SRI as an indicator of cardiovascular health, particularly in older populations with HF. We did not observe an association between LVEF and sleep quality in our population, in keeping with previous studies [ 7 , 10 ]. Our study also elucidates an association between WASO, a surrogate for sleep fragmentation, and functional capacity in patients with HF. We found that patients with greater night time awakening had slower gait speed and were more ADL-dependent with worse frailty status. Expectedly, they expressed lower satisfaction with their overall QoL. Our findings concur with works of Dam and colleagues who reported similar outcomes between WASO and gait speed, albeit exclusively in older men [ 42 ]. Whilst the mechanism underlying the interplay between WASO and gait speed in HF population is not well understood, it could be mediated by factors such as excessive daytime sleepiness [ 43 ] and sleep disordered breathing [ 44 ], which were not explored in the current work. Larger studies are needed to investigate the causal relationship between wakefulness, physical functions and the effect on disease trajectory in this population. Finally, we note with interest the higher prevalence of poor sleep quality among South Asian patients compared to Caucasian or Black participants. We might postulate that this may reflect more advanced or poorly controlled HF in South Asian patients; however, neither NYHA class nor NT-proBNP differed between Asian and Caucasian patients. The small sample size further limits a robust statistical analysis and assessment of these findings. Beyond the clinical lens, factors such as acculturation [ 45 ] and socioeconomic background [ 46 ] could also have played a role in the sleep disparity observed within the ethnic minority in our study. This study has several limitations. Firstly, given the cross-sectional design of the study, we were unable to establish a cause-effect relationship between sleep metrics and clinical parameters, physical function and QoL. Second, participants’ sleep hygiene was not studied and their possible confounding effect on sleep pattern cannot be ruled out. However, commonly proscribed sleep hygiene behaviours such as before-bed caffeine consumption, exercise shortly before bedtime and daytime napping, do not appear to increase the odds of poor sleep efficiency [ 35 ]. Third, our sample size is limited and based on a single population, and therefore the observations may not be generalisable to other populations. Further research is required to validate our findings in other cohorts of patients with HF. CONCLUSIONS Poor sleep quality is common among older patients with HF. Forty-two percent of our study participants had sleep efficiency < 80%. Lower sleep efficiency was associated with worse frailty and lower functional independence. Patients with lower sleep regularity experienced more advanced HF symptoms and poorer QoL, in addition to worse frailty status and functional independence. Furthermore, those with greater sleep fragmentation (i.e., WASO) demonstrated slower gait speed. Future studies are warranted to investigate mechanisms contributing to poor sleep quality in HF populations, with the aim of developing personalised interventions to improve QoL and clinical outcomes in older people with HF. Declarations COMPETING INTERESTS SS is supported by NIHR clinical lectureship; received research funding from British Heart Foundation. All other authors declare no competing interest. 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Demographic Characteristics of Study Participants Demographics Study Participants N = 145 Age, year - median (range) 80 (65-94) Gender (Male) 93 (64%) Ethnicity Caucasian South Asian Black 122 (84%) 21 (15%) 2 (1%) NYHA Class (III/IV) 32 (22%) NT-proBNP, ng/L 2040 (1117 - 4915) HF Phenotype HFrEF HFmrEF HFpEF 55 (38%) 22 (15%) 68 (47%) Frailty (CFS ≥ 5) 97 (67%) Sleep parameters Sleep efficiency, % SPT-window, hour Sleep duration, hour Wake after sleep onset, hour No. of sleep interruptions, n Sleep regularity index 81.8 (73.6 – 86.7) 8.1 (7.0 – 8.9) 6.3 (5.4 – 7.2) 1.5 (1.1 – 2.1) 13 (10 – 17) 45.4 (31.1 – 56.0) *continuous variables reported as median (IQR), categorical variables reported as N (%). NYHA, New York Heart Association; NT-proBNP , N-terminal pro-B type natriuretic peptide; HFrEF , heart failure with reduced ejection fraction; HFmrEF , heart failure with mildly reduced ejection fraction; HFpEF , heart failure with preserved ejection fraction; CFS , Clinical Frailty Scale; SPT , Sleep Period Time Table 2 . Characteristics of Good vs Poor Sleepers Demographics Good sleepers* (Sleep efficiency ≥ 80%) N = 84 Poor sleepers* (Sleep efficiency < 80%) N = 61 p -value Age, year (median, range) 81 (65-94) 78 (66-92) 0.126 Gender (Male) 49 (53%) 44 (47%) 0.087 BMI, kg/m 2 28 (24-32) 28 (25-32) 0.925 Ethnicity Caucasian South Asian Black 77 (63%) 6 (29%) 1 (50%) 45 (37%) 15 (71%) 1 (50%) 0.012 NT-proBNP, ng/L 1592 (819-4751) 3141 (1330-5646) 0.044 HF Phenotype HFrEF HFmrEF HFpEF 30 (55%) 14 (64%) 40 (59%) 25 (45%) 8 (36%) 28 (41%) 0.750 NYHA Class (III/IV) 15 (47%) 17 (53%) 0.151 Cardiac Devices CRT-P CRT-D 17 (59%) 6 (40%) 12 (41%) 9 (60%) 0.933 0.137 Frailty (CFS ≥ 5) 55 (57%) 42 (43%) 0.670 Comorbidities Hypertension OSA AF IHD COPD T2DM CKD Mental illness Cancer Cerebrovascular disease No. of comorbidities, ≥ 5 37 (55%) 8 (67%) 54 (61%) 18 (51%) 8 (53%) 20 (48%) 26 (50%) 4 (100%) 23 (79%) 8 (80%) 14 (48%) 30 (45%) 4 (33%) 34 (39%) 17 (49%) 7 (47%) 22 (52%) 26 (50%) 0 (0%) 6 (21%) 2 (20%) 15 (52%) 0.541 0.522 0.298 0.371 0.703 0.108 0.148 0.139 0.009 0.192 0.239 KCCQ-12 Overall KCCQ-PL KCCQ-SF KCCQ-QoL KCCQ-SL 55.5 (35.7-68.8) 50.0 (33.3-72.9) 60.4 (51.0-75.0) 62.5 (25.0-75.0) 58.3 (33.3-75.0) 51.6 (34.9-70.3) 41.7 (20.8-58.3) 62.5 (35.4-79.2) 50.0 (37.5-75.0) 50.0 (33.3-75.0) 0.439 0.092 0.578 0.872 0.338 Barthel Index, % 95 (85-100) 90 (75-100) 0.129 Handgrip strength, kg 20 (20-30) 22 (17-28) 0.524 Walk speed, m/sec 0.54 (0.36-0.67) 0.48 (0.35-0.65) 0.373 TUGT, sec 16.1 (11.1-23.9) 19.5 (13.0-24.0) 0.120 *continuous variables reported as median (IQR), categorical variables reported as N (%). BMI, body mass index; HFrEF , heart failure with reduced ejection fraction; HFmrEF , heart failure with mildly reduced ejection fraction; HFpEF , heart failure with preserved ejection fraction; CRT-P, cardiac resynchronisation therapy-pacemaker; CRT-D , cardiac resynchronisation therapy- defibrillator; OSA , obstructive sleep apnoea; AF , atrial fibrillation; IHD , ischaemic heart disease; COPD , chronic obstructive pulmonary disease; T2DM , type 2 diabetes mellitus; CKD , chronic kidney disease; KCCQ-PL , Kansas City Cardiomyopathy Questionnaire-Physical Limitation; KCCQ-SF , Kansas City Cardiomyopathy Questionnaire-Symptom Frequency; KCCQ-QoL , Kansas City Cardiomyopathy Questionnaire-Quality of Life; KCCQ-SL , Kansas City Cardiomyopathy Questionnaire-Social Limitation; TUGT , Timed Up and Go Test Table 3. Linear regression models for sleep metrics with NT-proBNP, LVEF and NYHA class Variables Sleep efficiency SPT-Window WASO Sleep Onset Wake Up Time SRI Sleep Duration No. of sleep interruptions β (SE) p -value β (SE) p -value β (SE) p -value β (SE) p -value β (SE) p -value β (SE) p -value β (SE) p -value β (SE) p -value Model 1 NT-proBNP -32.495 (32.451) 0.318 128.238 (220.283) 0.561 232.270 (337.529) 0.492 -321.849 (251.062) 0.202 -194.851 (278.091) 0.485 -0.093 (21.473) 0.997 35.451 (241.901) 0.884 60.563 (86.301) 0.484 LVEF 0.075 (0.088) 0.397 0.230 (0.598) 0.701 -0.480 (0.917) 0.601 -0.326 (0.685) 0.635 -0.079 (0.756) 0.917 0.029 (0.058) 0.625 0.523 (0.655) 0.426 -0.066 (0.235) 0.780 NYHA Class -0.004 (0.005) 0.407 -0.009 (0.034) 0.805 0.077 (0.052) 0.142 0.050 (0.039) 0.202 0.046 (0.043) 0.285 -0.009 (0.003) 0.007 -0.050 (0.037) 0.186 -0.002 (0.013) 0.906 Model 2 NT-proBNP -48.632 (31.560) 0.126 213.526 (212.633) 0.317 425.610 (331.067) 0.201 -427.221 (242.886) 0.081 -178.621 (268.900) 0.508 4.547 (20.760) 0.827 46.264 (233.007) 0.843 124.214 (83.702) 0.140 LVEF 0.048 (0.089) 0.592 0.436 (0.597) 0.466 -0.145 (0.933) 0.877 -0.508 (0.687) 0.460 0.037 (0.755) 0.961 0.030 (0.058) 0.609 0.591 (0.651) 0.366 -0.004 (0.236) 0.986 NYHA Class -0.006 (0.005) 0.237 0.004 (0.034) 0.918 0.101 (0.053) 0.057 0.043 (0.039) 0.278 0.056 (0.043) 0.194 -0.009 (0.003) 0.007 -0.045 (0.037) 0.224 0.002 (0.014) 0.871 SPT , Sleep Period Time; WASO , Wake After Sleep Onset; SRI , Sleep Regularity Index; NT-proBNP , N-terminal pro-B type natriuretic peptide; LVEF , Left Ventricular Ejection Fraction; NYHA, New York Heart Association Model 1 Unadjusted β coefficient (standard error) Model 2 Adjusted for age, gender and number of comorbidities Supplementary Files SupplementaryTablesEGM.pdf 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-7409629","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506247855,"identity":"ecdac05f-c8c9-4855-870d-a6d56cb7c637","order_by":0,"name":"Sunanthiny Krishnan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie2RMQrCQBAAVwJnc9F2g5B8wRAQBR+zaVLZ2aQQDAhnI9haiG+wsr4joM09IDYi5AMBWxGVNGKRs7S4qZaFgVkWwGL5Txyo0iGwz1UrMyitjcZakb8qjiuwHn9SgvWp7Ls79DvtPL9V6QW6S8m8TYPSLyYReQeMGE8SlHoKqIl5+yYFuSPDA8YC+QCUIIACmHc1hMl4i3OB3VulHgSBSQFJEakMiSEHVBm9UsEQ9rolzI4YCp4MUB+JhzpejJrOf4f17rNxECzzskpn5PunXJ1XTWHfcPMjLRaLxWLkCWldSU+61N0+AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4895-5354","institution":"University of Leicester Department of Cardiovascular Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sunanthiny","middleName":"","lastName":"Krishnan","suffix":""},{"id":506247856,"identity":"53e454c6-9333-4cee-9771-f49608160ac1","order_by":1,"name":"Shirley Sze","email":"","orcid":"","institution":"University of Leicester Department of Cardiovascular Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shirley","middleName":"","lastName":"Sze","suffix":""},{"id":506247857,"identity":"45679f07-e21c-4723-83b3-4ec8cff83a4c","order_by":2,"name":"Shelley Taylor","email":"","orcid":"","institution":"University of Leicester Diabetes Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Shelley","middleName":"","lastName":"Taylor","suffix":""},{"id":506247858,"identity":"0892a202-9c79-4023-bb67-7e5efa3c55a5","order_by":3,"name":"Charlotte L Edwardson","email":"","orcid":"","institution":"University of Leicester Diabetes Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Charlotte","middleName":"L","lastName":"Edwardson","suffix":""},{"id":506247859,"identity":"be302a6d-5eb3-4154-8396-f69d1cbb02d9","order_by":4,"name":"Alex V. Rowlands","email":"","orcid":"","institution":"University of Leicester Diabetes Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"V.","lastName":"Rowlands","suffix":""},{"id":506247860,"identity":"cba20953-7409-46fc-a5f4-368f111f159a","order_by":5,"name":"Iain B Squire","email":"","orcid":"","institution":"University of Leicester Department of Cardiovascular Sciences","correspondingAuthor":false,"prefix":"","firstName":"Iain","middleName":"B","lastName":"Squire","suffix":""}],"badges":[],"createdAt":"2025-08-19 14:33:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7409629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7409629/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90543391,"identity":"bb0d8398-700f-4450-a17a-09b4b42b4d99","added_by":"auto","created_at":"2025-09-04 00:11:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51192,"visible":true,"origin":"","legend":"\u003cp\u003eA schematic representation of sleep parameters derived from accelerometry data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSPT\u003c/strong\u003e, Sleep Period Time; \u003cstrong\u003eWASO\u003c/strong\u003e, Wake after sleep onset\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7409629/v1/52ea92bd95c079c46056c523.png"},{"id":90545077,"identity":"215fb226-333e-42ec-9d91-86cf019a958e","added_by":"auto","created_at":"2025-09-04 00:27:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78765,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of accelerometry-derived sleep parameters between \u003cem\u003egood\u003c/em\u003e and \u003cem\u003epoor sleepers\u003c/em\u003e of older people with HF. \u003cstrong\u003e(A) \u003c/strong\u003eSleep duration\u003cstrong\u003e (B) \u003c/strong\u003eWake after sleep onset\u003cstrong\u003e (C) \u003c/strong\u003eNumber of sleep interruptions\u003cstrong\u003e (D) \u003c/strong\u003eSleep regularity index\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7409629/v1/866c2710088a507e9adac285.png"},{"id":91636114,"identity":"68b2b0de-6054-445a-81ff-d87de3f9dc03","added_by":"auto","created_at":"2025-09-18 13:53:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1263638,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7409629/v1/5777187e-8151-4c6d-b998-946ec1bab4e9.pdf"},{"id":90543394,"identity":"1080626d-be6f-4b15-bdd2-26a0003cea57","added_by":"auto","created_at":"2025-09-04 00:11:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":256239,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesEGM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7409629/v1/f7e4f5ad8079a3c185ff1317.pdf"}],"financialInterests":"","formattedTitle":"Device-assessed sleep health among older patients with heart failure: An actigraphy-based study","fulltext":[{"header":"KEY SUMMARY POINTS","content":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo undertake objective assessment of sleep quality among older patients living with heart failure (HF).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOlder patients with HF have poor sleep outcomes that are associated with frailty, functional performance, HF symptom burden and quality of life.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMessage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFindings from this study highlight the importance of considering sleep assessment in the comprehensive management of older adults living with HF.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003ePoor sleep health is associated with cardiovascular disease (CVD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Both short and long duration of sleep are linked to increased risk of cardiovascular outcomes and all-cause mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A meta-analysis of 17 cohort studies with over 311 000 participants reports that insomnia, defined as the subjective feeling of non-restorative sleep including difficulty initiating or maintaining sleep, is associated with 33% higher risk of CVD mortality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among patients living with heart failure (HF), poor sleep quality is high with a reported prevalence between 50%-80% [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Restless sleep, difficulty initiating and maintaining sleep, poor sleep continuity and early morning awakening are commonly reported sleep disturbances in this clinical cohort [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe wearing effect of sleep deprivation on quality of life (QoL) of patients with HF is well documented. Decline in sleep quality has also been shown to impair functional performance, including activities of daily living, social independence and cognitive functioning [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The aetiology of sleep disruption in patients with HF is rarely singular but often a composite of multiple factors. Symptoms of congestion such as paroxysmal nocturnal dyspnoea and orthopnoea are among the most frequently cited causes, alongside nocturia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Sleep disordered breathing, specifically obstructive sleep apnoea, and central sleep apnoea with Cheyne-Stokes breathing, are also implicated in HF populations irrespective of phenotype, with prevalence up to 60% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Interestingly, daytime somnolence, typically a result of poor night time sleep, is independently associated with increased risk of cardiovascular mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, there has been a blooming interest in assessment of sleep quality amongst patients with HF. However, few studies have adopted device-based assessment of sleep with even fewer specifically focussing on older people with HF. In most cases, these studies rely on retrospective, self-reported sleep using questionnaires such as the Pittsburg Sleep Quality Index (PSQI) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Athens Insomnia Scale (AIS) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Insomnia Severity Index (ISI) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These subjective measures require a long recall period, ranging from 2 to 4 weeks, which arguably challenges the reliability of the results [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], particularly in older people.\u003c/p\u003e\u003cp\u003eWhilst polysomnography remains the gold standard for assessment of sleep, the procedure is costly and intrusive to patients\u0026rsquo; normal sleep routine [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Actigraphy-based sleep assessment is a feasible, objective and non-invasive alternative allowing monitoring of patients\u0026rsquo; sleep pattern in their natural environment. Additionally, actigraphy allows collection of sleep data over an extended period of time (days or weeks) which can provide more reliable estimates of sleep parameters compared to polysomnography, which is typically performed for only one or two nights [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Importantly, the validity of actigraphy-based sleep evaluation has been established against polysomnography, with an estimated agreement ranging from 91\u0026ndash;93% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we sought to summarise sleep health amongst older patients with HF using actigraphy and examine associations between sleep outcomes and cardiac biomarkers, functional performance and health-related QoL.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003e We recruited 150 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years with a diagnosis of HF for at least one year. Diagnosis of HF was established based on the presence of HF symptoms (e.g., breathlessness, pedal oedema and reduced effort tolerance), echocardiographic evidence of cardiac dysfunction and/or raised natriuretic peptide levels [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Patients with documented cognitive impairment, history of hospitalisation within the past 2 weeks and recent wrist injury of the non-dominant arm (where the accelerometer is worn) were excluded. Recruitment took place at an outpatient HF clinic within a tertiary cardiology service in a National Health Service (NHS) hospital in the UK between March \u0026ndash; October 2023. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Research Ethics Committee of Health Research Authority and Health Care Research Wales (REC Ref: 22/EM/0172). Written informed consent was obtained from all participants prior to the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ei. Baseline Assessments\u003c/h3\u003e\n\u003cp\u003eAll patients were reviewed by a cardiologist on the day of enrolment. Heart failure symptom burden was ascertained using the New York Heart Association (NYHA) Functional Classification. All patients had an echocardiogram to assess left ventricular ejection fraction (LVEF) and blood tests including N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels, full blood count and renal function. Patients with LVEF\u0026thinsp;\u0026lt;\u0026thinsp;40% were categorised as having HF with reduced ejection fraction (HFrEF), 41\u0026ndash;49% as HF with mildly reduced ejection fraction (HFmrEF) and 50% or greater as HF with preserved ejection fraction (HFpEF) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePatients underwent several additional assessments including evaluation of functional performance (i.e., usual gait speed, handgrip strength, dynamic balance, activities of daily living), frailty status and health-related quality of life.\u003c/p\u003e\n\u003ch3\u003eUsual Gait Speed\u003c/h3\u003e\n\u003cp\u003eParticipants\u0026rsquo; gait speed was assessed using the standardised 4-meter walk test [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Patients were instructed to walk from a standing start at their usual pace on a flat 4-meter course and timed using a stopwatch. Participants were allowed to use their usual assistive devices during the test and the average reading of two attempts was used for analysis.\u003c/p\u003e\n\u003ch3\u003eHandgrip Strength\u003c/h3\u003e\n\u003cp\u003eHandgrip strength, a surrogate for muscle strength [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] was assessed using an analogue Smedley spring dynamometer. Participants\u0026rsquo; maximum-effort isometric contraction was measured three times using their dominant hand, with a brief period to rest between each trial. The maximum reading of the three trials was taken for analysis.\u003c/p\u003e\n\u003ch3\u003eDynamic Balance\u003c/h3\u003e\n\u003cp\u003eThe timed up and go test (TUGT) is a validated instrument that assesses mobility and balance in older adults [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Participants were instructed to stand up from a chair of a standard height, walk a distance of 3 meters at their usual pace, turn around, return to the chair and sit down. They were allowed to use their usual walking aids but no other physical assistance was given during the test. Participants began the task seated on a chair with their backs and arms resting. Timing commenced when participants attempted to stand up from the chair and stopped when they returned to the seated position. A stopwatch was used to measure the time taken to complete the test.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eActivities of Daily Living\u003c/h2\u003e\u003cp\u003eFunctional capacity in performing activities of daily living (ADL) was measured using the Barthel Index (BI) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which comprises of ten domains of ADL, each scored on an ordinal scale based on the patient\u0026rsquo;s physical independence. Patients and/or their accompanying caregivers were interviewed on the former\u0026rsquo;s ability to perform each of the ADL and scored accordingly. With a total score ranging from 0 to 100, higher scores reflect better ability to performed ADLs independently.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFrailty\u003c/h3\u003e\n\u003cp\u003eBaseline frailty status was determined using the Clinical Frailty Scale (CFS) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which categorises degree of frailty on a nine-point scale based on patient\u0026rsquo;s global function and cognition, with 1 being very fit and 9, terminally ill. Patients with a CFS score\u0026thinsp;\u0026ge;\u0026thinsp;5 were deemed frail.\u003c/p\u003e\n\u003ch3\u003eHealth Related Quality of Life (HRQoL)\u003c/h3\u003e\n\u003cp\u003eKansas City Cardiomyopathy Questionnaire (KCCQ-12), a HF-specific HRQoL measure, was used to assess the impact of HF on patients\u0026rsquo; health status [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A shorter version of the original 23-item questionnaire, KCCQ-12 retains the psychometric properties of the full instrument and measures health on four domains: physical limitation (3 items), symptom frequency (4 items), quality of life (2 items) and social limitations (3 items). An overall summary score was also calculated (average of physical limitation, symptom frequency, quality of life and social limitation).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eii. Actigraphy\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eAccelerometer\u003c/h2\u003e\u003cp\u003ePatients were fitted with a GENEActiv triaxial accelerometer (ActivInsights, Cambridgeshire, UK), on their non-dominant wrist for 7 days continuously (24 hours/day). Each device was initialised to record at a frequency of 25 Hz. During the device wear-period, patients were instructed to complete a sleep diary, recording their light-off time at night and morning wake time. The sleep diary was used to guide the accelerometry algorithm to identify the main sleep period [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAccelerometry Data Processing\u003c/h2\u003e\u003cp\u003eData from the accelerometers were downloaded as \u0026ldquo;bin\u0026rdquo; files using the GENEActiv PC software (version 3.2) and analysed using the R-package GGIR version 3.0\u0026ndash;9 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Participants were excluded from analysis if they did not have 3 valid days of recordings (defined as \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;16 hours wear-time within 24 hours) including 3 full nights [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn GGIR, sleep is detected based on a period of Sustained Inactivity Bout (SIB), defined as the absence of wrist rotation (z-axis) by more than 5 degrees for longer than 5 minutes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As SIB is non-specific and encompasses all episodes of rests during a 24-hour period including daytime sleep and sedentary behaviour, the sleep diary was used to differentiate the main nightly sleep period time (SPT) from other SIBs. Only SIB that occurred during the SPT were classed as sleep. Patients\u0026rsquo; self-reported lights-off and wake times were entered manually into the system to guide the algorithm to accurately detect the SPT-window.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSleep Parameters\u003c/h2\u003e\u003cp\u003eSleep measures studied include SPT-window, sleep duration, sleep onset time, wake up time, duration of wake after sleep onset (WASO), sleep efficiency, number of sleep interruptions and sleep regularity index (SRI). Definitions of each sleep parameter are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates these metrics in the context of an overnight sleep.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSPT\u003c/b\u003e, Sleep Period Time; \u003cb\u003eWASO\u003c/b\u003e, Wake after sleep onset\u003c/p\u003e\u003cp\u003eWe selected sleep efficiency (SE) as the primary proxy for sleep quality as actigraphy-derived SE has been shown to positively mirror self-perceived quality of sleep [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, SE is strongly associated with risk of mortality in older people, with the risk increasing substantially when SE is below 80% [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, in this study, we defined poor sleep quality as SE\u0026thinsp;\u0026lt;\u0026thinsp;80%. Patients with poor and good sleep quality are referred to as \u003cem\u003epoor\u003c/em\u003e and \u003cem\u003egood sleepers\u003c/em\u003e, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eShapiro-Wilk test indicated that the distribution of sleep data departed from normality. Therefore, non-parametric statistics were used in all analyses. The study population was stratified based on their SE, i.e., \u0026lt; 80% (poor sleep quality) \u003cem\u003evs\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;80% (good sleep quality). Comparison of continuous variables between the two categories were analysed using Mann-Whitney U Test and the Chi-squared test was employed for comparison between categorical variables. Linear regression models were used to assess associations between sleep outcomes and markers of HF severity, functional status and HRQoL. Results were adjusted for potential confounders of poor sleep i.e., age, gender and number of comorbidities. All analyses were performed using SPSS version 28 (SPSS Inc., Chicago, IL). A two-tailed \u003cem\u003eP-\u003c/em\u003evalue of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eStudy Participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 145 participants were included in the final analysis; five participants were excluded due to poor quality data. Median age of the study population was 80 (range 65-94 years), 64% were male and 84% were Caucasian. Median NT-proBNP level was 2040 ng/L (IQR = 1117 - 4915), with a normal range of 20 \u0026ndash; 200 ng/L; one fifth had NYHA class III/IV symptoms; 47% had HF with preserved ejection fraction (HFpEF) (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe median SPT-window and sleep duration were 8.1 hours/day and 6.3 hours/day respectively. The median SE of the study population was 81.8%. The number of sleep interruptions experienced throughout the night ranged from 3 to 25 per night (median, 13). (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood Sleepers \u003cem\u003evs\u003c/em\u003e Poor Sleepers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf 145 participants, 42% (n = 61) were poor sleepers (i.e, SE \u0026lt; 80%); they had significantly higher levels of NT-proBNP compared to the good sleepers (3141 ng/L vs 1592 ng/L, \u003cem\u003ep\u003c/em\u003e = 0.044) and were more likely to be South Asian. Poor sleepers tended to have higher HF symptom burden (NYHA Class III/IV), although this did not reach statistical significance. There was no discernible difference in HF phenotype, frailty status, types or number of comorbidities between the two sleep groups. Quality of life measures were similar across both groups (Table 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSub-analysis of specific sleep parameters, indicated that the SPT window did not differ between good sleepers and poor sleepers. However, the poor sleeper group had shorter sleep duration within the SPT window (5.7 hours \u003cem\u003evs\u003c/em\u003e 6.8 hours, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), higher number of sleep interruptions per night (14 \u003cem\u003evs\u003c/em\u003e 12, \u003cem\u003ep\u003c/em\u003e = 0.024) and thus spent more time awake during their sleep window (WASO 2.3 hours \u003cem\u003evs\u003c/em\u003e 1.1 hour, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Their sleep pattern was also significantly less regular between nights in comparison with that of good sleepers\u0026rsquo; (SRI 40.0 \u003cem\u003evs\u003c/em\u003e 47.8, \u003cem\u003ep\u003c/em\u003e = 0.009) (Supplementary Table S2). Box plots comparing the various sleep metrics between good and poor sleepers are presented in Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between Sleep Parameters and HF Severity, Functional Status and QoL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei. Sleep Parameters and HF severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLower SRI (poorer sleep regularity) was associated with higher NYHA class (worse HF symptoms). The association remained significant after accounting for age, gender and number of comorbidities (\u0026beta; = -0.009, \u003cem\u003ep\u003c/em\u003e = 0.007). There were no significant associations between other sleep parameters and NT-proBNP or LVEF (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eii. Sleep Parameters and Functional Performance\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLower SE was associated with higher CFS (worsening frailty status) (adjusted \u0026beta; = -0.017, \u003cem\u003ep\u003c/em\u003e = 0.014) and lower BI score (higher dependence on others for ADLs) (adjusted \u0026beta; = 0.271, \u003cem\u003ep\u003c/em\u003e = 0.016) (Supplementary Table S3). Longer WASO was associated with slower gait speed (adjusted \u0026beta; = -0.039, \u003cem\u003ep\u003c/em\u003e = 0.040), lower BI score (adjusted \u0026beta; = -4.477, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and poorer frailty status (adjusted \u0026beta; = 0.251, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Lower SRI was also associated with lower BI score (adjusted \u0026beta; = 0.310, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and worse frailty (adjusted \u0026beta; = -0.017, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo associations were observed between sleep parameters and handgrip strength or dynamic balance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eiii. Sleep Parameters and HRQoL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmongst sleep parameters, lower SRI was associated with lower scores across all domains of KCCQ-12, except KCCQ-QoL (Supplementary Table S4). The predictive strength of SRI is strongest for social limitation (adjusted \u0026beta; = 0.500, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), followed by physical limitation (adjusted \u0026beta; = 0.415, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and frequency of HF symptoms (adjusted \u0026beta; = 0.241, \u003cem\u003ep\u003c/em\u003e = 0.038). Longer night time awakening (WASO) was associated with poorer HF-related physical function (adjusted \u0026beta; = -4.170, \u003cem\u003ep\u003c/em\u003e = 0.036) and social limitation (adjusted \u0026beta; = -4.620, \u003cem\u003ep\u003c/em\u003e = 0.032).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we assessed sleep quality in older patients living with HF using wrist-actigraphy over a 7-day period. Of the 145 patients studied, we found 42% had poor sleep quality evidenced by actigraphic SE. Despite a similar sleep window duration to the good sleepers, the poor sleepers experienced remarkably fragmented sleep as recorded by the high number of sleep interruptions during the night and wakefulness after sleep onset (WASO). Consequently, these patients had significantly shorter duration of uninterrupted sleep, with a median of 5.7 hours, to be compared to the 7\u0026ndash;8 hours of sleep recommended for older adults [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, median serum NT-proBNP levels were elevated twofold in poor sleepers compared to those with better SE. This observation may reflect greater prevalence of orthopnoea and paroxysmal nocturnal dyspnoea in the context of more advanced or poorly controlled HF. Alternatively, or additionally, poor sleep might induce activation of the sympathetic nervous system, thus inflicting haemodynamic burden to the heart with the observed effect on NT-proBNP levels. Studies support that sleep restriction [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and fragmentation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] increase heart rate, blood pressure and sympathetic cardiac modulation.\u003c/p\u003e\u003cp\u003eIn the current study, sleep regularity index (SRI), a relatively new metric, emerged as an important determinant of HF severity. We found that patients with a more irregular sleep/wake pattern experienced more advanced HF symptoms (i.e., NYHA class), independent of age, gender and concomitant diseases. These patients had higher ADL dependency and tended to be more frail, with poorer HRQoL outcomes. Prior studies have linked actigraphy-based sleep irregularity with higher risk of incident CVD [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and all- cause mortality [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], although HF per se was not included in the analyses. Furthermore, poor sleep regularity was found to be a stronger predictor of mortality than sleep duration [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], a premise that lends weight to our study findings whereby sleep duration was associated with neither HF severity nor functional performance. This further cements the need for deeper investigation into SRI as an indicator of cardiovascular health, particularly in older populations with HF. We did not observe an association between LVEF and sleep quality in our population, in keeping with previous studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study also elucidates an association between WASO, a surrogate for sleep fragmentation, and functional capacity in patients with HF. We found that patients with greater night time awakening had slower gait speed and were more ADL-dependent with worse frailty status. Expectedly, they expressed lower satisfaction with their overall QoL. Our findings concur with works of Dam and colleagues who reported similar outcomes between WASO and gait speed, albeit exclusively in older men [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Whilst the mechanism underlying the interplay between WASO and gait speed in HF population is not well understood, it could be mediated by factors such as excessive daytime sleepiness [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and sleep disordered breathing [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which were not explored in the current work. Larger studies are needed to investigate the causal relationship between wakefulness, physical functions and the effect on disease trajectory in this population.\u003c/p\u003e\u003cp\u003eFinally, we note with interest the higher prevalence of poor sleep quality among South Asian patients compared to Caucasian or Black participants. We might postulate that this may reflect more advanced or poorly controlled HF in South Asian patients; however, neither NYHA class nor NT-proBNP differed between Asian and Caucasian patients. The small sample size further limits a robust statistical analysis and assessment of these findings. Beyond the clinical lens, factors such as acculturation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and socioeconomic background [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] could also have played a role in the sleep disparity observed within the ethnic minority in our study.\u003c/p\u003e\u003cp\u003eThis study has several limitations. Firstly, given the cross-sectional design of the study, we were unable to establish a cause-effect relationship between sleep metrics and clinical parameters, physical function and QoL. Second, participants\u0026rsquo; sleep hygiene was not studied and their possible confounding effect on sleep pattern cannot be ruled out. However, commonly proscribed sleep hygiene behaviours such as before-bed caffeine consumption, exercise shortly before bedtime and daytime napping, do not appear to increase the odds of poor sleep efficiency [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Third, our sample size is limited and based on a single population, and therefore the observations may not be generalisable to other populations. Further research is required to validate our findings in other cohorts of patients with HF.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003ePoor sleep quality is common among older patients with HF. Forty-two percent of our study participants had sleep efficiency\u0026thinsp;\u0026lt;\u0026thinsp;80%. Lower sleep efficiency was associated with worse frailty and lower functional independence. Patients with lower sleep regularity experienced more advanced HF symptoms and poorer QoL, in addition to worse frailty status and functional independence. Furthermore, those with greater sleep fragmentation (i.e., WASO) demonstrated slower gait speed. Future studies are warranted to investigate mechanisms contributing to poor sleep quality in HF populations, with the aim of developing personalised interventions to improve QoL and clinical outcomes in older people with HF.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e\u003cp\u003eSS is supported by NIHR clinical lectureship; received research funding from British Heart Foundation. All other authors declare no competing interest.\u003c/p\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLloyd-Jones, D. M., Allen, N. B., Anderson, C. A. M., Black, T., Brewer, L. C., Foraker, R. E., Grandner, M. A., Lavretsky, H., Perak, A. 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Socioeconomic Background and Self-Reported Sleep Quality in Older Adults during the COVID-19 Pandemic: An Analysis of the English Longitudinal Study of Ageing (ELSA). \u003cem\u003eInternational journal of environmental research and public health\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(5), 4534. https://doi.org/10.3390/ijerph20054534\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Demographic Characteristics of Study Participants\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"491\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 274px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Participants\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 145\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eAge, year - median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e80 (65-94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eGender (Male)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e93 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Caucasian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;South Asian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e122 (84%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21 (15%)\u003c/p\u003e\n \u003cp\u003e2 (1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eNYHA Class (III/IV)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e32 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eNT-proBNP, ng/L\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e2040 (1117 - 4915)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHF Phenotype\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;HFrEF\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;HFmrEF\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;HFpEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e55 (38%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22 (15%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68 (47%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003eFrailty (CFS \u0026ge; 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e97 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 274px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep parameters\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Sleep efficiency, %\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;SPT-window, hour\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Sleep duration, hour\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Wake after sleep onset, hour\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;No. of sleep interruptions, n\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Sleep regularity index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e81.8 (73.6 \u0026ndash; 86.7)\u003c/p\u003e\n \u003cp\u003e8.1 (7.0 \u0026ndash; 8.9)\u003c/p\u003e\n \u003cp\u003e6.3 (5.4 \u0026ndash; 7.2)\u003c/p\u003e\n \u003cp\u003e1.5 (1.1 \u0026ndash; 2.1)\u003c/p\u003e\n \u003cp\u003e13 (10 \u0026ndash; 17)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45.4 (31.1 \u0026ndash; 56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*continuous variables reported as median (IQR), categorical variables reported as N (%).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNYHA,\u003c/strong\u003e New York Heart Association; \u003cstrong\u003eNT-proBNP\u003c/strong\u003e, N-terminal pro-B type natriuretic peptide; \u003cstrong\u003eHFrEF\u003c/strong\u003e, heart failure with reduced ejection fraction; \u003cstrong\u003eHFmrEF\u003c/strong\u003e, heart failure with mildly reduced ejection fraction; \u003cstrong\u003eHFpEF\u003c/strong\u003e, heart failure with preserved ejection fraction; \u003cstrong\u003eCFS\u003c/strong\u003e, Clinical Frailty Scale; \u003cstrong\u003eSPT\u003c/strong\u003e, Sleep Period Time\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Characteristics of Good \u003cem\u003evs\u003c/em\u003e Poor Sleepers\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood sleepers*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Sleep efficiency \u0026ge; 80%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoor sleepers*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Sleep efficiency \u0026lt; 80%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 61\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;p\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eAge, year (median, range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e81 (65-94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e78 (66-92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eGender (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e49 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e44 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e28 (24-32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp;28 (25-32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Caucasian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; South Asian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77 (63%)\u003c/p\u003e\n \u003cp\u003e6 (29%)\u003c/p\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (37%)\u003c/p\u003e\n \u003cp\u003e15 (71%)\u003c/p\u003e\n \u003cp\u003e1 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eNT-proBNP, ng/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e1592 (819-4751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e3141 (1330-5646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHF Phenotype\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eHFrEF\u003c/p\u003e\n \u003cp\u003eHFmrEF\u003c/p\u003e\n \u003cp\u003eHFpEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e30 (55%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (64%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40 (59%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e25 (45%)\u003c/p\u003e\n \u003cp\u003e8 (36%)\u003c/p\u003e\n \u003cp\u003e28 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eNYHA Class (III/IV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e15 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e17 (53%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiac Devices\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; CRT-P\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; CRT-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17 (59%)\u003c/p\u003e\n \u003cp\u003e6 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12 (41%)\u003c/p\u003e\n \u003cp\u003e9 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eFrailty (CFS \u0026ge; 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e55 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e42 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Hypertension\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; OSA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; AF\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; IHD\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; COPD\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; T2DM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; CKD\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Mental illness\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Cancer\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Cerebrovascular disease\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo. of comorbidities, \u0026ge; 5\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37 (55%)\u003c/p\u003e\n \u003cp\u003e8 (67%)\u003c/p\u003e\n \u003cp\u003e54 (61%)\u003c/p\u003e\n \u003cp\u003e18 (51%)\u003c/p\u003e\n \u003cp\u003e8 (53%)\u003c/p\u003e\n \u003cp\u003e20 (48%)\u003c/p\u003e\n \u003cp\u003e26 (50%)\u003c/p\u003e\n \u003cp\u003e4 (100%)\u003c/p\u003e\n \u003cp\u003e23 (79%)\u003c/p\u003e\n \u003cp\u003e8 (80%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30 (45%)\u003c/p\u003e\n \u003cp\u003e4 (33%)\u003c/p\u003e\n \u003cp\u003e34 (39%)\u003c/p\u003e\n \u003cp\u003e17 (49%)\u003c/p\u003e\n \u003cp\u003e7 (47%)\u003c/p\u003e\n \u003cp\u003e22 (52%)\u003c/p\u003e\n \u003cp\u003e26 (50%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003cp\u003e6 (21%)\u003c/p\u003e\n \u003cp\u003e2 (20%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKCCQ-12\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003eKCCQ-PL\u003c/p\u003e\n \u003cp\u003eKCCQ-SF\u003c/p\u003e\n \u003cp\u003eKCCQ-QoL\u003c/p\u003e\n \u003cp\u003eKCCQ-SL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e55.5 (35.7-68.8)\u003c/p\u003e\n \u003cp\u003e50.0 (33.3-72.9)\u003c/p\u003e\n \u003cp\u003e60.4 (51.0-75.0)\u003c/p\u003e\n \u003cp\u003e62.5 (25.0-75.0)\u003c/p\u003e\n \u003cp\u003e58.3 (33.3-75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51.6 (34.9-70.3)\u003c/p\u003e\n \u003cp\u003e41.7 (20.8-58.3)\u003c/p\u003e\n \u003cp\u003e62.5 (35.4-79.2)\u003c/p\u003e\n \u003cp\u003e50.0 (37.5-75.0)\u003c/p\u003e\n \u003cp\u003e50.0 (33.3-75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eBarthel Index, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e95 (85-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e90 (75-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eHandgrip strength, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e20 (20-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e22 (17-28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eWalk speed, m/sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e0.54 (0.36-0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e0.48 (0.35-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 184px;\"\u003e\n \u003cp\u003eTUGT, sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 173px;\"\u003e\n \u003cp\u003e16.1 (11.1-23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 172px;\"\u003e\n \u003cp\u003e19.5 (13.0-24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*continuous variables reported as median (IQR), categorical variables reported as N (%).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI,\u003c/strong\u003e body mass index; \u003cstrong\u003eHFrEF\u003c/strong\u003e, heart failure with reduced ejection fraction; \u003cstrong\u003eHFmrEF\u003c/strong\u003e, heart failure with mildly reduced ejection fraction; \u003cstrong\u003eHFpEF\u003c/strong\u003e, heart failure with preserved ejection fraction; \u003cstrong\u003eCRT-P,\u003c/strong\u003e cardiac resynchronisation therapy-pacemaker; \u003cstrong\u003eCRT-D\u003c/strong\u003e, cardiac resynchronisation therapy- defibrillator; \u003cstrong\u003eOSA\u003c/strong\u003e, obstructive sleep apnoea; \u003cstrong\u003eAF\u003c/strong\u003e, atrial fibrillation; \u003cstrong\u003eIHD\u003c/strong\u003e, ischaemic heart disease; \u003cstrong\u003eCOPD\u003c/strong\u003e, chronic obstructive pulmonary disease; \u003cstrong\u003eT2DM\u003c/strong\u003e, type 2 diabetes mellitus; \u003cstrong\u003eCKD\u003c/strong\u003e, chronic kidney disease; \u003cstrong\u003eKCCQ-PL\u003c/strong\u003e, Kansas City Cardiomyopathy Questionnaire-Physical Limitation; \u003cstrong\u003eKCCQ-SF\u003c/strong\u003e, Kansas City Cardiomyopathy Questionnaire-Symptom Frequency; \u003cstrong\u003eKCCQ-QoL\u003c/strong\u003e, Kansas City Cardiomyopathy Questionnaire-Quality of Life; \u003cstrong\u003eKCCQ-SL\u003c/strong\u003e, Kansas City Cardiomyopathy Questionnaire-Social Limitation; \u003cstrong\u003eTUGT\u003c/strong\u003e, Timed Up and Go Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eLinear regression models for sleep metrics with NT-proBNP, LVEF and NYHA class\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"1077\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep efficiency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPT-Window\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWASO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Onset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWake Up Time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of sleep interruptions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" style=\"width: 1077px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT-proBNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-32.495 (32.451)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e128.238 (220.283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e232.270 (337.529)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-321.849 (251.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-194.851 (278.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.093 (21.473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n 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style=\"width: 47px;\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNYHA Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.004 (0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.009 (0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.077 (0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.050 (0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.046 (0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.009 (0.003)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.050 (0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.002 (0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"17\" style=\"width: 1077px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT-proBNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-48.632 (31.560)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e213.526 (212.633)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e425.610 (331.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-427.221 (242.886)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-178.621 (268.900)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4.547 (20.760)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e46.264 (233.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e124.214 (83.702)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.048 (0.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.436 (0.597)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.145 (0.933)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.508 (0.687)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.037 (0.755)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.030 (0.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.591 (0.651)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.004 (0.236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNYHA Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.006 (0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.004 (0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.101 (0.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.043 (0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.056 (0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.009 (0.003)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.045 (0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSPT\u003c/strong\u003e, Sleep Period Time; \u003cstrong\u003eWASO\u003c/strong\u003e, Wake After Sleep Onset; \u003cstrong\u003eSRI\u003c/strong\u003e, Sleep Regularity Index;\u003cstrong\u003e\u0026nbsp;NT-proBNP\u003c/strong\u003e, N-terminal pro-B type natriuretic peptide; \u003cstrong\u003eLVEF\u003c/strong\u003e, Left Ventricular Ejection Fraction;\u003cstrong\u003e\u0026nbsp;NYHA,\u003c/strong\u003e New York Heart Association\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e Unadjusted \u0026beta; coefficient (standard error)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e Adjusted for age, gender and number of comorbidities\u003c/p\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":"Accelerometer, Heart failure, Older people, Sleep quality","lastPublishedDoi":"10.21203/rs.3.rs-7409629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7409629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePoor sleep quality is common among older adults and in those with heart failure (HF). Sleep quality is typically assessed using self-reported questionnaires which are subject to recall bias. Actigraphy-based assessment permits more objective evaluation of sleep. We examined sleep among older patients with HF using actigraphy and examined associations between sleep outcomes and cardiac biomarkers, functional performance and quality of life (QoL).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe recruited 150 patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 with diagnosis of HF. They were given a wrist-accelerometer to wear for 7 days. Patients completed 4-meter walk test (4MWT), handgrip strength test (HGST), Timed Up and Go test (TUGT), Barthel Index (BI), Kansas City Cardiomyopathy Questionnaire (KCCQ-12) and frailty assessment (Clinical Frailty Scale, CFS). Sleep outcomes calculated include sleep period time window, sleep duration, sleep onset and wake up time, wake after sleep onset (WASO), sleep interruptions, sleep regularity index (SRI) and sleep efficiency (SE). Poor sleep quality was defined as SE\u0026thinsp;\u0026lt;\u0026thinsp;80%. Regression analysis was used to examine associations between sleep outcomes and other variables adjusting for age, gender and comorbidities.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAccelerometry data from 145 participants were analysed. Sixty-one (42%) patients had poor sleep quality. These patients had significantly higher plasma NT-proBNP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044). No statistically significant difference was noted in 4MWT, HGST, TUGT, BI, KCCQ-12 and CFS between patients with SE\u0026thinsp;\u0026lt;\u0026thinsp;80% and those with SE\u0026thinsp;\u0026ge;\u0026thinsp;80%. Lower SE was associated with worse frailty status and lower BI scores; lower SRI was associated with worse NYHA class, frailty, BI scores and QoL measures; longer WASO was associated with slower gait speed.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eForty-two percent of older patients with HF had poor sleep quality; they had significantly higher NT-proBNP levels. Poor sleep quality was associated with higher functional dependence and frailty. Sleep irregularity affected HF symptom load, frailty, functional performance and QoL, while sleep fragmentation was associated with impaired gait speed. These findings highlight the need to consider sleep assessment in the comprehensive management of older adults with HF.\u003c/p\u003e","manuscriptTitle":"Device-assessed sleep health among older patients with heart failure: An actigraphy-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 00:03:06","doi":"10.21203/rs.3.rs-7409629/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":"e11c4510-7f5a-4439-9ae7-83efe7094460","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T13:45:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 00:03:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7409629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7409629","identity":"rs-7409629","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}