scholarly journals Accelerometer-derived sleep onset timing and cardiovascular disease incidence: a UK Biobank cohort study

2021 ◽  
Author(s):  
Shahram Nikbakhtian ◽  
Angus B Reed ◽  
Bernard Dillon Obika ◽  
Davide Morelli ◽  
Adam C Cunningham ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Disease Incidence ◽  
Sleep Onset ◽  
Onset Time ◽  
Sensitivity Analyses ◽  
Accelerometer Data ◽  
Uk Biobank ◽  
Onset Timing ◽  
Sleep Parameters

Aims Growing evidence suggests that sleep quality is associated with cardiovascular risk. However, research in this area often relies upon recollection dependant questionnaires or diaries. Accelerometers provide an alternative tool for deriving sleep parameters measuring sleep patterns objectively. This study examines the associations between accelerometer derived sleep onset timing and cardiovascular disease (CVD). Methods and Results We derived sleep onset and waking up time from accelerometer data collected from 103,712 UK Biobank participants over a period of seven days. From this, we examined the association between sleep onset timing and CVD incidence using a series of Cox proportional hazards models. 3172 cases of CVD were reported during a mean follow-up period of 5.7 (±0.49) years. An age- and sex-controlled base analysis found that sleep onset time of 10:00pm-10:59pm was associated with the lowest CVD incidence. A fully adjusted model, additionally controlling for sleep duration, sleep irregularity, and established CVD risk factors, was unable to eliminate this association, producing hazard ratios of 1.24 (95% CI, 1.10-1.39; p<0.005), 1.12 (1.01-1.25; p=0.04), and 1.25 (1.02-1.52; p=0.03) for sleep onset <10:00pm, 11:00pm-11:59pm, and & ≥12:00am, respectively, compared to 10:00pm-10:59pm. Importantly, sensitivity analyses revealed this association was stronger in females, with only sleep onset <10:00pm significant for males. Conclusions Our findings suggest an independent relationship between sleep onset timing and risk of developing CVD, particularly for women. We also demonstrate the potential utility of collecting information about sleep parameters via accelerometry-capable wearable devices, which may serve as novel cardiovascular risk indicators.

scholarly journals Accelerometer-derived sleep onset timing and cardiovascular disease incidence: a UK Biobank cohort study

2021 ◽  
Author(s):  
Shahram Nikbakhtian ◽  
Angus B Reed ◽  
Bernard Dillon Obika ◽  
Davide Morelli ◽  
Adam C Cunningham ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Sleep Onset ◽  
Sensitivity Analyses ◽  
Poor Sleep ◽  
Accelerometer Data ◽  
Uk Biobank ◽  
Cvd Risk ◽  
Onset Timing ◽  
Sleep Parameters

Abstract Aims Growing evidence suggests that poor sleep health is associated with cardiovascular risk. However, research in this area often relies upon recollection dependent questionnaires or diaries. Accelerometers provide an alternative tool for measuring sleep parameters objectively. This study examines the association between wrist-worn accelerometer-derived sleep onset timing and cardiovascular disease (CVD). Methods and results We derived sleep onset and waking up time from accelerometer data collected from 103 712 UK Biobank participants over a period of 7 days. From this, we examined the association between sleep onset timing and CVD incidence using a series of Cox proportional hazards models. A total of 3172 cases of CVD were reported during a mean follow-up period of 5.7 (±0.49) years. An age- and sex-controlled base analysis found that sleep onset time of 10:00 p.m.–10:59 p.m. was associated with the lowest CVD incidence. An additional model, controlling for sleep duration, sleep irregularity, and established CVD risk factors, did not attenuate this association, producing hazard ratios of 1.24 (95% confidence interval, 1.10–1.39; P &lt; 0.005), 1.12 (1.01–1.25; P= 0.04), and 1.25 (1.02–1.52; P= 0.03) for sleep onset &lt;10:00 p.m., 11:00 p.m.–11:59 p.m., and ≥12:00 a.m., respectively, compared to 10:00 p.m.–10:59 p.m. Importantly, sensitivity analyses revealed this association with increased CVD risk was stronger in females, with only sleep onset &lt;10:00 p.m. significant for males. Conclusions Our findings suggest the possibility of a relationship between sleep onset timing and risk of developing CVD, particularly for women. We also demonstrate the potential utility of collecting information about sleep parameters via accelerometry-capable wearable devices, which may serve as novel cardiovascular risk indicators.

scholarly journals Accelerometer-Derived Sleep Onset Timing and Cardiovascular Disease Incidence: A UK Biobank Cohort Study

2021 ◽  
Author(s):  
Shahram Nikbakhtian ◽  
Angus Bruno Reed ◽  
Bernard Dillon Obika ◽  
Davide Morelli ◽  
Adam C. Cunningham ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Cohort Study ◽  
Disease Incidence ◽  
Sleep Onset ◽  
Uk Biobank ◽  
Onset Timing ◽  
Cardiovascular Disease Incidence

scholarly journals Gender Inequalities in Diagnostic Inertia around the Three Most Prevalent Cardiovascular Risk Studies: Protocol for a Population-Based Cohort Study

2021 ◽  
Vol 18 (8) ◽  
pp. 4054
Author(s):  
Concepción Carratala-Munuera ◽  
Adriana Lopez-Pineda ◽  
Domingo Orozco-Beltran ◽  
Jose A. Quesada ◽  
Jose L. Alfonso-Sanchez ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Health Care ◽  
Cohort Study ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Disease Incidence ◽  
Population Based ◽  
Gender Inequalities ◽  
Primary Health Care Centers

Evidence shows that objectives for detecting and controlling cardiovascular risk factors are not being effectively met, and moreover, outcomes differ between men and women. This study will assess the gender-related differences in diagnostic inertia around the three most prevalent cardiovascular risk factors: dyslipidemia, arterial hypertension, and diabetes mellitus, and to evaluate the consequences on cardiovascular disease incidence. This is an epidemiological and cohort study. Eligible patients will be adults who presented to public primary health care centers in a Spanish region from 2008 to 2011, with hypertension, dyslipidemia, or/and diabetes and without cardiovascular disease. Participants’ electronic health records will be used to collect the study variables in a window of six months from inclusion. Diagnostic inertia of hypertension, dyslipidemia, and/or diabetes is defined as the registry of abnormal diagnostic parameters—but no diagnosis—on the person’s health record. The cohort will be followed from the date of inclusion until the end of 2019. Outcomes will be cardiovascular events, defined as hospital admission due to ischemic cardiopathy, stroke, and death from any cause. The results of this study could inform actions to rectify the structure, organization and training of health care teams in order to correct the inequality.

scholarly journals Educational inequalities in statin treatment for preventing cardiovascular disease: cross-sectional analysis of UK Biobank

2020 ◽  
Author(s):  
Alice R Carter ◽  
Dipender Gill ◽  
Richard Morris ◽  
George Davey Smith ◽  
Amy E Taylor ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Primary Care ◽  
Cardiovascular Risk ◽  
Educational Attainment ◽  
Prescription Data ◽  
List Type ◽  
Uk Biobank ◽  
Cross Sectional ◽  
Statin Use ◽  
Years Of Schooling

AbstractImportanceThe most socioeconomically deprived individuals remain at the greatest risk of cardiovascular disease. Differences in risk adjusted use of statins between educational groups may contribute to these inequalities.ObjectiveTo identify whether people with lower levels of educational attainment are less likely to take statins for a given level of cardiovascular risk.DesignCross-sectional analysis of a population-based cohort study and linked longitudinal primary care records.SettingUK Biobank data from baseline assessment centres, linked primary care data and hospital episode statisticsParticipantsUK Biobank participants (N = 489 679, mean age = 56, 54% female) with complete data on educational attainment and self-reported medication use. Secondary analyses were carried out on a subsample of participants with linked primary care data (N = 217 675).Main outcome measuresStatin use self-reported to clinic nurses at baseline assessment centres, validated with linked prescription data in a subsample of participants in secondary analyses.ResultsGreater education was associated with lower statin use, whilst higher cardiovascular risk (assessed by QRISK3 score) was associated with higher statin use in both females and males. There was evidence of an interaction between QRISK3 and education, such that for the same QRISK3 score, people with more education were more likely to report taking statins. For example, in women with 7 years of schooling, equivalent to leaving school with no formal qualifications, a one unit increase in QRISK3 score was associated with a 6% higher odds of statin use (odds ratio (OR) 1.06, 95% CI 1.05, 1.06). In contrast, in women with 20 years of schooling, equivalent to obtaining a degree, a one unit increase in QRISK3 score was associated with an 11% higher odds of statin use (OR 1.11, 95% CI 1.10, 1.11). Comparable ORs in men were 1.04 (95% CI 1.04, 1.05) for men with 7 years of schooling and (95% CI 1.07, 1.07) for men with 20 years of schooling.ConclusionsFor the same level of cardiovascular risk, individuals with lower educational attainment are less likely to receive statins, likely contributing to health inequalities.SummaryWhat is already known on this topic?Despite reductions in the rates of cardiovascular disease in high income countries, individuals who are the most socioeconomically deprived remain at the highest risk.Although intermediate lifestyle and behavioural risk factors explain some of this, much of the effect remains unexplained.What does this study add?For the same increase in QRISK3 score, the likelihood of statin use increased more in individuals with high educational attainment compared with individuals with lower educational attainment.These results were similar when using UK Biobank to derive QRISK3 scores and when using QRISK scores recorded in primary care records, and when using self-reported statin prescription data or prescription data from linked primary care records.The mechanisms leading to these differences are unknown, but both health seeking behaviours and clinical factors may contribute.

scholarly journals P161 Regularity of sleep-wake patterns in the UK Biobank (N = 86 624) and an open-source tool to calculate the Sleep Regularity Index

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A73-A74
Author(s):  
D Windred ◽  
A Russell ◽  
A Burns ◽  
S Cain ◽  
A Phillips
Keyword(s):  
Mental Health ◽  
Open Source ◽  
Sleep Onset ◽  
R Package ◽  
Sleep Diary ◽  
Accelerometer Data ◽  
Uk Biobank ◽  
Physical And Mental Health ◽  
Regularity Index ◽  
Wake Patterns

Abstract Introduction Regular sleep-wake patterns aid in the maintenance of optimal physical and mental health, by helping to align environmental, behavioural, and physiological rhythms. The distribution of sleep regularity across the population has not been well documented. Furthermore, researchers currently lack tools to easily quantify sleep regularity. Method We have described sleep regularity in 86 624 UK Biobank participants (age (M±SD) = 62.45±7.84; 56.2% female) using data from wrist-worn accelerometers. Regularity was measured using the Sleep Regularity Index (SRI), which quantifies day-to-day similarity in sleep-wake patterns, and which is linked to cardio-metabolic and mental health outcomes. We developed an R package to calculate SRI from accelerometer data, which works in conjunction with GGIR (a validated accelerometer processing tool) to identify sleep-wake state, including naps and broken sleep. Results The SRI distribution had M±SD = 78.02±11.53, and median = 80.49. The least regular quintile (SRI&lt;70.2) had standard deviation of sleep onset = 2.23h, offset = 2.14h, and duration = 1.95h, compared with onset = 0.78h, offset = 0.85h, and duration = 0.95h in the most regular quintile (SRI&gt;87.3). Approximately 14% of participants exhibited large day-to-day shifts in sleep timing (&gt;3h) at least once per week. Discussion This is the largest description of sleep regularity to-date. The norms established here provide a reference for researchers and clinicians intending to quantify sleep regularity with the SRI. We have combined methods described here into an open-source R package to calculate SRI from accelerometer or sleep diary data, available for download via GitHub.

scholarly journals Associations of diarised sleep onset time, period and duration with total and central adiposity in a biethnic sample of young children: the Born in Bradford observational cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e044769
Author(s):  
Paul James Collings ◽  
Jane Elizabeth Blackwell ◽  
Elizabeth Pal ◽  
Helen L Ball ◽  
John Wright
Keyword(s):  
Waist Circumference ◽  
Young Children ◽  
South Asian ◽  
Sleep Onset ◽  
Onset Time ◽  
Central Adiposity ◽  
Z Score ◽  
White Children ◽  
Asian Children ◽  
Sleep Parameters

ObjectivesTo investigate associations of parent-reported sleep characteristics with adiposity levels in a biethnic sample of young children.DesignA cross-sectional observational study.SettingThe Born in Bradford 1000 study, UK.ParticipantsChildren aged approximately 18 months (n=209; 40.2% South Asian; 59.8% white) and 36 months (n=162; 40.7% South Asian; 59.3% white).Primary and secondary outcome measuresChildren’s body mass index (BMI) z-score, sum of two-skinfolds (triceps and subscapular) and waist circumference. Adjusted regression was used to quantify associations of sleep parameters with adiposity stratified by ethnicity and age group. The results are beta coefficients (95% CIs) and unless otherwise stated represent the difference in outcomes for every 1-hour difference in sleep parameters.ResultsThe average sleep onset time was markedly later in South Asian (21:26±68 min) than white children (19:41±48 min). Later sleep onset was associated with lower BMI z-score (−0.3 (−0.5 to −0.0)) and sum of two-skinfolds (−1.5 mm (−2.8 mm to −0.2 mm)) in white children aged 18 months and higher BMI z-score in South Asian children aged 36 months (0.3 (0.0–0.5)). Longer sleep duration on weekends than weekdays was associated with higher BMI z-score (0.4 (0.1–0.8)) and waist circumference (1.2 cm (0.3–2.2 cm)) in South Asian children aged 18 months, and later sleep onset on weekends than weekdays was associated with larger sum of two-skinfolds (1.7 mm (0.3–3.1 mm)) and waist circumference (1.8 cm (0.6–2.9 cm)). Going to sleep ≥20 min later on weekends than weekdays was associated with lower waist circumference in white children aged 18 months (−1.7 cm (−3.2 cm to −0.1 cm)).ConclusionsSleep timing is associated with total and central adiposity in young children but associations differ by age group and ethnicity. Sleep onset times and regular sleep schedules may be important for obesity prevention.

scholarly journals Lipoprotein (a) and 10-year Cardiovascular Disease Incidence in Apparently Healthy Individuals: A Sex-based Sensitivity Analysis from ATTICA Cohort Study

Angiology ◽  
2019 ◽  
Vol 70 (9) ◽  
pp. 819-829 ◽  
Author(s):  
Matina Kouvari ◽  
Demosthenes B. Panagiotakos ◽  
Christina Chrysohoou ◽  
Ekavi N. Georgousopoulou ◽  
Mary Yannakoulia ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Disease Incidence ◽  
Density Lipoprotein ◽  
Patient Characteristics ◽  
Apolipoprotein A1 ◽  
Sensitivity Analyses ◽  
Cvd Risk ◽  
Lipoprotein A ◽  
Apparently Healthy

The association between lipoprotein (a) (Lp(a)) and 10-year first fatal/nonfatal cardiovascular disease (CVD) risk in apparently healthy men and women was evaluated. The ATTICA prospective study was conducted during 2001-2012 and included n = 1514 men and n = 1528 women (age >18 years) from the greater Athens area, Greece. Follow-up CVD assessment (2011-2012) was achieved in n = 2020 participants (n = 317 cases); baseline Lp(a) was measured in n = 1890 participants. The recommended threshold of 50 mg/dL was used to define abnormal Lp(a) status. Ten-year CVD-event rate was 14% and 24% in participants with Lp(a) <50 and Lp(a) ≥50 mg/dL, respectively. Multivariate analysis revealed that participants with Lp(a) ≥50 mg/dL versus Lp(a) <50 mg/dL had about 2 times higher CVD risk (hazard ratio (HR) = 2.18, 95% confidence interval (CI) 1.11, 4.28). The sex-based analysis revealed that the independent Lp(a) effect was retained only in men (HR = 2.00, 95% CI 1.19, 2.56); in women, significance was lost after adjusting for lipid markers. Sensitivity analyses revealed that Lp(a) increased CVD risk only in case of abnormal high-density lipoprotein cholesterol, apolipoprotein A1, and triglycerides as well as low adherence to Mediterranean diet. Certain patient characteristics may be relevant when considering Lp(a) as a therapeutic or risk-prediction target.

scholarly journals 12 Being Non-Frail and Free From Cardiovascular Disease Reduces COVID-19 Risk in 269,164 Older UK Biobank Participants

2021 ◽  
Vol 50 (Supplement_1) ◽  
pp. i1-i6
Author(s):  
J Masoli ◽  
J Atkins ◽  
J Delgado ◽  
L Pilling ◽  
D Melzer
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Older Adults ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Frailty Index ◽  
Uk Biobank ◽  
Frail Older Adults ◽  
Increased Risk ◽  
Low Cardiovascular Risk

Abstract Background Older adults are at increased risk of COVID-19, resulting in public health shielding measures for all adults over 70 in the UK. Frailty has been proposed for risk stratification in COVID-19 with limited evidence. Cardiovascular risk factors hypertension, diabetes and raised BMI have been associated with increased COVID-19 risk. We sought to test if non-frail older adults with low cardiovascular risk had reduced COVID-19, to inform targeted shielding policies. Methods Fried and Rockwood frailty were ascertained at UK Biobank baseline (2006-2010) and electronic frailty index(eFI) in primary care data to 2017*. A cardiovascular disease risk score(CRS) consisting of smoking status, LDL-cholesterol, blood pressure, BMI, fasting glucose and physical activity was estimated at baseline. Frailty (baseline and eFI; eFI alone) and CRS were tested in logistic models against COVID-19 status and COVID-19 mortality to 14th June 2020 adjusted for demographics and technical covariates. Results N=269,164 UKB participants aged ≥65 at baseline (≥75years in 2020). 13.9% of COVID-19 positive were non-frail with low baseline CRS versus 41.8% frail with moderate/high CRS. Being non-frail and having low CRS were independently associated with reduced COVID-19. The composite of non-frail with low CRS compared to frail with moderate/high CRS had significantly reduced COVID-19 risk (composite non-frail with low CRS HR 0.61; 95% CI 0.45-0.84; p=0.0023; eFI non-frail with low CRS HR 0.16; 95%CI 0.07-0.36; p value=9.9x10-6) and COVID-19 mortality (composite non-frail HR 0.28; 95% CI 0.10-0.82; pvalue=0.02; eFI non-frail 0.07; 95% CI 0.02-0.28; pvalue=0.00014). Conclusion These results show that the COVID-19 risk in non-frail older adults with low cardiovascular risk was up to 84% lower than in those who were frail with cardiovascular risk factors. This could contribute to future work on stratification of shielding risk in older adults in future COVID-19 surges. *Planned data updates prior to the conference should enable updates to 2020.

scholarly journals Reallocating time from device-measured sleep, sedentary behaviour or light physical activity to moderate-to-vigorous physical activity is associated with lower cardiovascular disease risk

2020 ◽  
Author(s):  
Rosemary Walmsley ◽  
Shing Chan ◽  
Karl Smith-Byrne ◽  
Rema Ramakrishnan ◽  
Mark Woodward ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Cardiovascular Disease ◽  
Sedentary Behaviour ◽  
Lower Risk ◽  
Vigorous Physical Activity ◽  
Accelerometer Data ◽  
Uk Biobank ◽  
Light Physical Activity ◽  
Machine Learning Model

AbstractBackgroundModerate-to-vigorous physical activity (MVPA), light physical activity, sedentary behaviour and sleep have all been associated with cardiovascular disease (CVD). Due to challenges in measuring and analysing movement behaviours, there is uncertainty about how the association with incident CVD varies with the time spent in these different movement behaviours.MethodsWe developed a machine-learning model (Random Forest smoothed by a Hidden Markov model) to classify sleep, sedentary behaviour, light physical activity and MVPA from accelerometer data. The model was developed using data from a free-living study of 152 participants who wore an Axivity AX3 accelerometer on the wrist while also wearing a camera and completing a time use diary. Participants in UK Biobank, a prospective cohort study, were asked to wear an accelerometer (of the same type) for seven days, and we applied our machine-learning model to classify their movement behaviours. Using Compositional Data Analysis Cox regression, we investigated how reallocating time between movement behaviours was associated with CVD incidence.FindingsWe classified accelerometer data as sleep, sedentary behaviour, light physical activity or MVPA with a mean accuracy of 88% (95% CI: 87, 89) and Cohen’s kappa of 0·80 (95% CI: 0·79, 0·82). Among 87,509 UK Biobank participants, there were 3,424 incident CVD events. Reallocating time from any behaviour to MVPA, or reallocating time from sedentary behaviour to any behaviour, was associated with a lower risk of CVD. For example, for a hypothetical average individual, reallocating 20 minutes/day to MVPA from all other behaviours proportionally was associated with 9% (7%, 10%) lower risk of incident CVD, while reallocating 1 hour/day to sedentary behaviour was associated with 5% (3%, 7%) higher risk.InterpretationReallocating time from light physical activity, sedentary behaviour or sleep to MVPA, or reallocating time from sedentary behaviour to other behaviours, was associated with lower risk of incident CVD. Accurate classification of movement behaviours using machine-learning and statistical methods to address the compositional nature of movement behaviours enabled these insights. Public health interventions and guidelines should promote reallocating time to MVPA from other behaviours, as well as reallocating time from sedentary behaviour to light physical activity.FundingMedical Research Council.

scholarly journals Associations of diarised sleep onset time, period and duration with total and central adiposity in a biethnic sample of young children: the Born in Bradford study

2020 ◽  
Author(s):  
Paul J Collings ◽  
Jane E Blackwell ◽  
Elizabeth Pal ◽  
Helen Ball ◽  
John Wright
Keyword(s):  
South Asian ◽  
Sleep Onset ◽  
Onset Time ◽  
Abdominal Adiposity ◽  
Central Adiposity ◽  
Z Score ◽  
White Children ◽  
Time Period ◽  
Asian Children ◽  
Sleep Parameters

Objectives: To investigate associations of sleep timing, period and duration with total and abdominal adiposity in a biethnic sample of children aged 18 and 36 months (m) Design: Cross-sectional observational study Setting: The Born in Bradford 1000 study, UK Participants: Children aged approximately 18m (n=209; 40.2% South Asian; 59.8% White) and 36m (n=162; 40.7% South Asian; 59.3% White) Primary and secondary outcome measures: Parents completed a 3-day sleep diary from which children's average daily sleep onset time, period and duration were calculated. Weekday to weekend differences in sleep parameters were also derived. As outcomes, indices of total (BMI z-score and sum of 2-skinfolds) and abdominal adiposity (waist circumference) were measured. Adjusted regression was used to quantify associations of sleep parameters with adiposity by age group and ethnicity. Results: The average daily sleep onset time was markedly later in South Asian (9:26pm ± 68 mins) than White children (7:41pm ± 48 mins). Later sleep onset was associated with lower BMI z-score and sum of 2-skinfolds in White children aged 18m. In contrast, later sleep onset was associated with higher BMI z-score in South Asian children aged 36m. For weekday to weekend differences, longer sleep duration and later sleep onset on weekends than weekdays were both associated with higher total and abdominal adiposity in South Asian children aged 18m. On the contrary, compared to consistent sleep onset times, going to sleep ≥20 minutes later on weekends than weekdays was associated with lower waist circumference in White children aged 18m. Conclusions: Sleep timing is associated with total and central adiposity in young children but associations differ by age group and ethnicity. Sleep onset times and regular sleep schedules may be important for obesity prevention.

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