Real World Home Blood Pressure Variability in Over 56,000 Individuals With Nearly 17 Million Measurements

Kim, Kwang Il; Nikzad, Nima; Quer, Giorgio; Vegreville, Matthieu; Normand, Alexis; Schmidt, Nicolás; Topol, Eric J.; Steinhubl, Steven R.

doi:10.1093/ajh/hpx221

Cited by 24 publications

(20 citation statements)

References 25 publications

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“… 1 Rather than representing random phenomena, those changes are the result of interactions between environmental and behavioral factors and the cardiovascular regulation system. 2 These give rise to long-term (clinic or visit-to-visit) variability, 3 mid-term (day-to-day) variability, usually assessed by home monitoring, 3 or short-term variability, known as circadian BP variation and assessed by 24-hour ambulatory monitoring. 4 These fluctuations have prognostic importance.…”

Section: Introductionmentioning

confidence: 99%

Circadian Blood Pressure Variations Computed From 1.7 Million Measurements in an Acute Hospital Setting

Mahdi

Watkinson

McManus

et al. 2019

American Journal of Hypertension

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BACKGROUND Knowledge of the circadian blood pressure (BP) variations in the acute hospital setting is very limited. METHODS This is a retrospective analysis of BP data for in-hospital patients stratified by age and sex. We used data collected with the help of a standardized electronic health record system between March 2014 and April 2018 on the adult general wards in 4 acute hospitals in Oxford, UK. RESULTS A total of 41,455 unique patient admissions with 1.7 million sets of vital-sign measurements have been included in the study. The typical 24-hour systolic BP profile (dipping pattern during sleep followed by a gradual increase during the day) was only seen in the younger age groups (up to 40–49 for men and 30–39 for women). For older age groups, there was a late nocturnal rise in systolic BP, the amplitude of which increased with age. The late nocturnal BP rise above the age of 50 was seen whether or not the patient was treated for or previously identified with hypertension. CONCLUSION Hospitalized patients’ circadian patterns of BP largely mirror those found in the community. High-quality hospital data may allow for the identification of patients at significant cardiovascular risk through either opportunistic screening or systematic screening.

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Section: Introductionmentioning

confidence: 99%

Circadian Blood Pressure Variations Computed From 1.7 Million Measurements in an Acute Hospital Setting

Mahdi

Watkinson

McManus

et al. 2019

American Journal of Hypertension

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“…Variability in blood pressure is predictive of future cardiovascular disease and morbidity, but has been challenging to characterize with traditional studies, particularly in real-world settings (as opposed to clinical settings which can influence vital signs). Kim et al 15 analyzed blood pressure readings from over 50,000 individuals (with 17 million measurements) using a wireless blood pressure monitor. They characterized blood pressure variability and how it changes with season of the year, day of the week, and time of day, for example, showing that variability is higher during weekdays, particularly for females.…”

Section: Highlights From the Literature: Insights And Challengesmentioning

confidence: 99%

“…Analyzing data from consumer users of apps and wearables has allowed researchers to characterize health indicators, such as blood pressure variability, 15 and points to promising new avenues of research, such as focusing physical activity interventions on the activity poor segment of a population. 11 We also see that external factors, such as the walkability of a city 11 and social pressures preventing sleep, 13 can have a strong influence on health behaviors.…”

Section: Highlights From the Literature: Insights And Challengesmentioning

confidence: 99%

“…Measurement error can arise from the inaccuracy of sensors in estimating quantities of interest (e.g., steps or sleep) and such errors can be systematic (e.g., wearables for tracking physical activity can systematically underestimate steps for slow walking 30 ). Kim and colleagues 15 use a device that has been validated according to established protocols. Some of the studies analyzing sleep, steps, and physical activity, conduct their own experiments, 26 cite validation studies in the scientific literature, 11 and/or compare values and trends to previous literature and datasets.…”

Section: Highlights From the Literature: Insights And Challengesmentioning

confidence: 99%

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Best practices for analyzing large-scale health data from wearables and smartphone apps

et al. 2019

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Smartphone apps and wearable devices for tracking physical activity and other health behaviors have become popular in recent years and provide a largely untapped source of data about health behaviors in the free-living environment. The data are large in scale, collected at low cost in the “wild”, and often recorded in an automatic fashion, providing a powerful complement to traditional surveillance studies and controlled trials. These data are helping to reveal, for example, new insights about environmental and social influences on physical activity. The observational nature of the datasets and collection via commercial devices and apps pose challenges, however, including the potential for measurement, population, and/or selection bias, as well as missing data. In this article, we review insights gleaned from these datasets and propose best practices for addressing the limitations of large-scale data from apps and wearables. Our goal is to enable researchers to effectively harness the data from smartphone apps and wearable devices to better understand what drives physical activity and other health behaviors.

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“…These risk factors are most commonly assessed via measurement at a single time point, with the result guiding clinical action (e.g., prescription for a cholesterol-lowering statin drug) [189]. However, many risk factors vary substantially and rhythmically over the course of hours and days [101112], suggesting that a single point measure may be more reliably interpreted when taken in temporal context. Indeed, accounting for short-timescale variability has improved the ability to anticipate cardiovascular events.…”

Section: Introductionmentioning

confidence: 99%

Free-Living Humans Cross Cardiovascular Disease Risk Categories Due to Daily Rhythms in Cholesterol and Triglycerides

Grant

Wolf

2019

Journal of Circadian Rhythms

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Cardiovascular disease risk assessment relies on single time-point measurement of risk factors. Although significant daily rhythmicity of some risk factors (e.g., blood pressure and blood glucose) suggests that carefully timed samples or biomarker timeseries could improve risk assessment, such rhythmicity in lipid risk factors is not well understood in free-living humans. As recent advances in at-home blood testing permit lipid data to be frequently and reliably self-collected during daily life, we hypothesized that total cholesterol, HDL-cholesterol or triglycerides would show significant time-of-day variability under everyday conditions. To address this hypothesis, we worked with data collected by 20 self-trackers during personal projects. The dataset consisted of 1,319 samples of total cholesterol, HDL-cholesterol and triglycerides, and comprised timeseries illustrating intra and inter-day variability. All individuals crossed at least one risk category in at least one output within a single day. 90% of fasted individuals (n = 12) crossed at least one risk category in one output during the morning hours alone (06:00–08:00) across days. Both individuals and the aggregated group show significant, rhythmic change by time of day in total cholesterol and triglycerides, but not HDL-cholesterol. Two individuals collected additional data sufficient to illustrate ultradian (hourly) fluctuation in triglycerides, and total cholesterol fluctuation across the menstrual cycle. Short-term variability of sufficient amplitude to affect diagnosis appears common. We conclude that cardiovascular risk assessment may be augmented via further research into the temporal dynamics of lipids. Some variability can be accounted for by a daily rhythm, but ultradian and menstrual rhythms likely contribute additional variance.

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Real World Home Blood Pressure Variability in Over 56,000 Individuals With Nearly 17 Million Measurements

Cited by 24 publications

References 25 publications

Circadian Blood Pressure Variations Computed From 1.7 Million Measurements in an Acute Hospital Setting

Circadian Blood Pressure Variations Computed From 1.7 Million Measurements in an Acute Hospital Setting

Best practices for analyzing large-scale health data from wearables and smartphone apps

Free-Living Humans Cross Cardiovascular Disease Risk Categories Due to Daily Rhythms in Cholesterol and Triglycerides

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