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

Visit-to-visit variability in blood pressure and the risk of open-angle glaucoma in individuals without systemic hypertension: a nationwide population-based cohort study

Purpose

We aimed to evaluate the effect of visit-to-visit variability in blood pressure (BP) on the risk of open-angle glaucoma (OAG) in individuals without systemic hypertension using a population-based retrospective cohort study design.

Methods

The Korean National Health Insurance Service-National Health Screening Cohort database, which collected data of 209,226 individuals between 2002 and 2015, was used to analyze the data of 140,910 eligible participants. The mean follow-up duration was 8.3 years. Visit-to-visit BP variability was assessed using standard deviation (SD), coefficient of variation (CV), and variability independent of the mean (VIM). Participants were categorized into four groups according to BP variability quartiles. We verified the effect of BP variability by comparing participants of the first to third quartiles of BP variability groups with those belonging to the fourth quartile group. A Cox proportional hazards model was used to determine the hazard ratio (HR) of BP variability in cases of newly diagnosed OAG. Moreover, we conducted subgroup analyses using baseline characteristics.

Results

In the multivariable analyses, BP variability did not significantly increase the risk of OAG development. However, subgroup analyses revealed significant interactions between age and systolic BP variability in the development of OAG (CV: p = 0.008; SD: p = 0.007). For participants aged <60 years, the risk of OAG development significantly increased with high systolic BP variability (CV: HR, 1.18; 95% confidence interval [CI], 1.00-1.39; p = 0.049). We observed a similar trend using the SD and VIM as the parameters for systolic BP variability.

Conclusion

Higher visit-to-visit systolic BP variability was associated with an increased risk of OAG development in participants younger than 60 years of age without systemic hypertension. These results suggest that BP variability can be the considerable factor when assessing the risk of OAG, especially in relatively young people without systemic hypertension.

Prevalence and control of hypertension in a high HIV-prevalence setting, insights from a population based study in Botswana

In a population-based representative sample of adults residing in 22 communities in Botswana, a southern African country with high HIV prevalence, 1 in 4 individuals had high blood pressure. High blood pressure was less prevalent in adults with HIV than without HIV. Sixty percent of persons with high blood pressure had not previously been diagnosed. Among individuals with a prior diagnosis of high blood pressure who reported being prescribed anti-hypertension medications, almost half had elevated blood pressure, irrespective of HIV-status. One-third of adults in this setting (mainly men) declined free non-invasive blood pressure assessments in their households. In conclusion, our study highlights alarmingly high hypertension rates in the community, with low levels of awareness and control, emphasizing the urgent need for community level BP screening and active management to reach recommended targets.

Feasibility and measurement stability of smartwatch-based cuffless blood pressure monitoring: A real-world prospective observational study

Cuffless wearable devices are currently being developed for long-term monitoring of blood pressure (BP) in patients with hypertension and in apparently healthy people. This study evaluated the feasibility and measurement stability of smartwatch-based cuffless BP monitoring in real-world conditions. Users of the first smartwatch-based cuffless BP monitor approved in Korea (Samsung Galaxy Watch) were invited to upload their data from using the device for 4 weeks post calibration. A total of 760 participants (mean age 43.7 ± 11.9, 80.3% men) provided 35,797 BP readings (average monitoring 22 ± 4 days [SD]; average readings 47 ± 42 per participant [median 36]). Each participant obtained 1.5 ± 1.3 readings/day and 19.7% of the participants obtained measurements every day. BP showed considerable variability, mainly depending on the day and time of the measurement. There was a trend towards higher BP levels on Mondays than on other days of the week and on workdays than in weekends. BP readings taken between 00:00 and 04:00 tended to be the lowest, whereas those between 12:00 and 16:00 the highest. The average pre-post calibration error for systolic BP (difference in 7-day BP before and after calibration), was 6.8 ± 5.6 mmHg, and was increased with higher systolic BP levels before calibration. Smartwatch-based cuffless BP monitoring is feasible for out-of-office monitoring in the real-world setting. The stability of BP measurement post calibration and the standardization and optimal time interval for recalibration need further investigation.

Impact of national holidays and weekends on incidence of acute type A aortic dissection repair

Previous studies have demonstrated that environmental and temporal factors may affect the incidence of acute type A aortic dissection (ATAAD). Here, we aimed to investigate the hypothesis that national holidays and weekends influence the incidence of surgery for ATAAD. For the period 1st of January 2005 until 31st of December 2019, we investigated a hypothesised effect of (country-specific) national holidays and weekends on the frequency of 2995 surgical repairs for ATAAD at 10 Nordic cities included in the Nordic Consortium for Acute Type A Aortic Dissection (NORCAAD) collaboration. Compared to other days, the number of ATAAD repairs were 29% (RR 0.71; 95% CI 0.54-0.94) lower on national holidays and 26% (RR 0.74; 95% CI 0.68-0.82) lower on weekends. As day of week patterns of symptom duration were assessed and the primary analyses were adjusted for period of year, our findings suggest that the reduced surgical incidence on national holidays and weekends does not seem to correspond to seasonal effects or surgery being delayed and performed on regular working days.

An interactive fitness-for-use data completeness tool to assess activity tracker data

OBJECTIVE

To design and evaluate an interactive data quality (DQ) characterization tool focused on fitness-for-use completeness measures to support researchers' assessment of a dataset.

MATERIALS AND METHODS

Design requirements were identified through a conceptual framework on DQ, literature review, and interviews. The prototype of the tool was developed based on the requirements gathered and was further refined by domain experts. The Fitness-for-Use Tool was evaluated through a within-subjects controlled experiment comparing it with a baseline tool that provides information on missing data based on intrinsic DQ measures. The tools were evaluated on task performance and perceived usability.

RESULTS

The Fitness-for-Use Tool allows users to define data completeness by customizing the measures and its thresholds to fit their research task and provides a data summary based on the customized definition. Using the Fitness-for-Use Tool, study participants were able to accurately complete fitness-for-use assessment in less time than when using the Intrinsic DQ Tool. The study participants perceived that the Fitness-for-Use Tool was more useful in determining the fitness-for-use of a dataset than the Intrinsic DQ Tool.

DISCUSSION

Incorporating fitness-for-use measures in a DQ characterization tool could provide data summary that meets researchers needs. The design features identified in this study has potential to be applied to other biomedical data types.

CONCLUSION

A tool that summarizes a dataset in terms of fitness-for-use dimensions and measures specific to a research question supports dataset assessment better than a tool that only presents information on intrinsic DQ measures.

Home blood pressure monitoring: a position statement from the Korean Society of Hypertension Home Blood Pressure Forum

Home blood pressure measurement (HBPM) has the advantage of measuring blood pressure (BP) multiple times over a long period. HBPM effectively diagnoses stress-induced transient BP elevations (i.e., white coat hypertension), insufficient BP control throughout the day (i.e., masked hypertension), and even BP variability. In most cases, HBPM may increase self-awareness of BP, increasing the compliance of treatment. Cumulative evidence has reported better improved predictive values of HBPM in cardiovascular morbidity and mortality than office BP monitoring. In this position paper, the Korean Society of Hypertension Home Blood Pressure Forum provides comprehensive information and clinical importance on HBPM.

Novel Indices of Home Blood Pressure Variability and Hypertension-Mediated Organ Damage in Treated Hypertensive Patients

INTRODUCTION

Although multiple home blood pressure variability (HBPV) indices have been proposed, the superiority of one over another is not clear in treated hypertensives.

AIM

We evaluated the correlation between different indices of HBPV and hypertension-mediated organ damage (HMOD) in this population and determined predictors of greater HBPV.

METHODS

We included adult treated hypertensives who performed an HBP monitoring (duplicate sitting BP readings in the morning, afternoon, and evening for 4 days, Omron HEM-705CP-II), laboratory measurements, transthoracic echocardiogram and carotid-femoral pulse wave velocity. We selected HBPV indices from three different calculation approaches: coefficient of variation (CoV), difference between maximum and minimum BP (MMD), and morning BP increase (MI), and evaluated their correlation with left ventricular mass index, relative wall thickness (RWT), ejection fraction, arterial stiffness and estimated glomerular filtration rate through a correlation matrix. For those variability indices significantly associated with HMOD, we constructed multiple linear regression models to determine independent predictors of HBPV.

RESULTS

We included 204 patients, mean age 67.2 (± 13.8) years, 64% female. CoV and MMD for systolic BP showed the greatest correlation with HMOD. Factors independently associated both with CoV and MMD were: older age (b = 0.07; 95% CI 0.04-0.07; p < 0.001 and b = 0.4; 95% CI 0.2-0.5; p < 0.001, respectively), history of stroke (b = 3.6; 95% CI 0.9-6.4; p = 0.01 and b = 25.7; 95% CI 10.1-41.2; p = 0.001, respectively), and body mass index [b = - 0.1; 95% CI - 0.2 to (- 0.02); p = 0.01 and b = - 0.5; 95% CI - 0.9 to (- 0.1); p = 0.01, respectively].

CONCLUSION

CoV and MMD showed the greatest association with HMOD in treated hypertensives. Older age, history of stroke and lower body mass index were easy-to-detect predictors.

Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review

Background

There is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse.

Objective

This study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research.

Methods

The literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well.

Results

A total of 19 papers were included in this review. We identified three high-level factors that affect data quality—device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy.

Conclusions

Our study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices.

Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience

Biological age acceleration (BAA) models based on blood tests or DNA methylation emerge as a de facto standard for quantitative characterizations of the aging process. We demonstrate that deep neural networks trained to predict morbidity risk from wearable sensor data can provide a high-quality and cheap alternative for BAA determination. The GeroSense BAA model was trained and validated using steps per minute recordings from 103,830 one-week long and 2,599 of up to 2 years-long longitudinal samples and exhibited a superior association with life-expectancy over the average number of steps per day in, e.g., groups stratified by professional occupations. The association between the BAA and effects of lifestyles, the prevalence of future incidence of diseases was comparable to that of BAA from models based on blood test results. Wearable sensors let sampling of BAA fluctuations at time scales corresponding to days and weeks and revealed the divergence of organism state recovery time (resilience) as a function of chronological age. The number of individuals suffering from the lack of resilience increased exponentially with age at a rate compatible with Gompertz mortality law. We speculate that due to the stochastic character of BAA fluctuations, its mean and auto-correlation properties together comprise the minimum set of biomarkers of aging in humans.

Association between real-world home blood pressure measurement patterns and blood pressure variability among older individuals with hypertension: A community-based blood pressure variability study

Home blood pressure (BP) monitoring is a useful tool for hypertension management. BP variability (BPV) has been associated with an increased risk of cardiovascular events. However, little is known about the correlation between BPV and different measurement patterns of long-term home BP monitoring. This longitudinal cohort study aimed to assess the associations between dynamic BP measurement patterns and BPV. A total of 1128 participants (mean age, 77.4 ± 9.3 years; male, 51%) with 23 269 behavior measuring units were included. We used sliding window sampling to classify the home BP data with a regular 6-month interval into units in a sliding manner until the data are not continuous. Three measurement patterns (stable frequent [SF], stable infrequent [SI], and unstable [US]) were assessed based on the home BP data obtained within the first 3 months of the study, and the data in the subsequent 3 months were used to assess the BPV of that unit. We used linear mixed-effects model to assess the association between BP measurement patterns and BPV with adjustment for possible confounding factors including average BP. Average real variability and coefficient variability were used as measures of the BPV. No significant differences were observed in average BP between the SF, SI, and US patterns. However, BPV in the SF group was significantly lower than that in the US and SI groups (all p-values < .05). The BPV in SI and US groups was not significantly different. A stable and frequent BP measuring pattern was independently associated with a lower BPV.

Smartphone / smartwatch-based cuffless blood pressure measurement : a position paper from the Korean Society of Hypertension

Smartphone technology has spread rapidly around the globe. According to a report released by the Korea Information Society Development Institute, about 95% of Koreans aged more than 30 years old owned smartphones. Recently, blood pressure (BP) measurement using a photoplethysmography-based smartphone algorithm paired with the smartwatch is continuously evolving. In this document, the Korean Society of Hypertension intends to remark the current results of smartphone / smartwatch-based BP measurement and recommend optimal BP measurement methods using a smartphone device. We aim to increase the likelihood of success in implementing these new technologies into improved hypertension awareness, diagnosis, and control.

Progression of Beat-to-Beat Blood Pressure Variability Despite Best Medical Management

Supplemental Digital Content is available in the text.

Beat-to-beat variability in blood pressure (BP) is associated with recurrent stroke despite good control of hypertension. However, no study has identified rates of progression of beat-to-beat BP variability (BPV), its determinants, or which patient groups are particularly affected, limiting understanding of its potential as a treatment target. In consecutive patients one month after a transient ischaemic attack or nondisabling stroke (Oxford Vascular Study), continuous noninvasive BP was measured beat-to-beat over 5 minutes (Finometer). Arterial stiffness was measured by carotid-femoral pulse wave velocity (Sphygmocor). Repeat assessments were performed at the 5-year follow-up visit and agreement determined by intraclass correlation coefficient. Rates of progression of systolic BPV (SBPV) and diastolic BPV (DBPV) and their determinants were estimated by mixed-effect linear models, adjusted for age, sex, and cardiovascular risk factors. One hundred eighty-eight of 310 surviving, eligible patients had repeat assessments after a median of 5.8 years. Pulse wave velocity was highly reproducible but SBPV and DBPV were not (intraclass correlation coefficient: 0.71, 0.10, and 0.16, respectively), however, all 3 progressed significantly (pulse wave velocity, 2.39%, P<0.0001; SBPV, 8.36%, P<0.0001; DBPV, 9.7, P<0.0001). Rate of progression of pulse wave velocity, SBPV, and DBPV all increased significantly with age (P<0.0001), with an increasingly positive skew and were particularly associated with female sex (pulse wave velocity P=0.00035; SBPV P<0.0001; DBPV P<0.0001) and aortic mean SBP (SBPV P=0.037, DBPV P<0.0001). Beat-to-beat BP variability progresses significantly in high-risk patients, particularly in older individuals with elevated aortic systolic pressure. Beat-to-beat BPV and its progression represent potential new therapeutic targets to reduce cardiovascular risk.

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

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.

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

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.

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

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.