Determinants of day-night difference in blood pressure, a comparison with determinants of daytime and night-time blood pressure

Graph-based association rule learning for context-based health monitoring to enable user-centered assistance

In response to the demographic change and the accompanying challenges for effective healthcare, approaches to enable using advancements of digitalization and IoT infrastructures as well as AI methods to deliver results in the field of personalized health assistance are necessary. In our research, we aim at enabling user-centered assistance with the help of networked sensors and Health Assistance Systems as well as learning methods based on connected graph data that model the shared system, user, and environmental context. In particular, this paper demonstrates a graph-based dynamic context model for a medication assistance system and presents an association rule learning method using Apriori algorithm to learn correlations between user vitals, activities as well as medication intake behavior. An application scenario for context-based heart rate monitoring is consequently presented as proof of concept, where associated contextual elements from the modeled context relating surges in monitored heart rate to environmental and user activity are shown.

Simple predictive score for nocturnal hypertension and masked nocturnal hypertension using home blood pressure monitoring in clinical practice

OBJECTIVE

The decision whether to measure night-time blood pressure (BP) is challenging as these values cannot be easily evaluated because of problems with measurement devices and related stress. Using the nationwide, practice-based Japan Morning Surge-Home BP Nocturnal BP study data, we developed a simple predictive score that physicians can use to diagnose nocturnal hypertension.

METHODS

We divided 2765 outpatients (mean age 63 years; hypertensive patients 92%) with cardiovascular risks who underwent morning, evening, and night-time home BP (HBP) measurements (0200, 0300, and 0400 h) into a calibration group ( n  = 2212) and validation group ( n  = 553). We used logistic-regression models in the calibration group to identify the predictive score for nocturnal hypertension (night-time HBP ≥120/70 mmHg) and then evaluated the score's predictive ability in the validation group.

RESULTS

In the logistic-regression model, male sex, increased BMI) (≥25 kg/m 2 ), diabetes, elevated urine-albumin creatinine ratio (UACR) (≥30 mg/g Cr), elevated office BP (≥140/90 mmHg) and home (average of morning and evening) BP (≥135/85 mmHg) had positive relationships with nocturnal hypertension. The predictive scores for nocturnal hypertension were 1 point (male, BMI, and UACR); 2 points (diabetes); 3 points (office BP ≥140/90 mmHg); 6 points (home BP ≥135/85 mmHg); total 14 points. Over 75% of the nocturnal hypertension cases in the validation group showed at least 10 points [AUC 0.691, 95% CI (0.647-0.735)]. We also developed a score for masked nocturnal hypertension, that is, nocturnal hypertension despite controlled daytime HBP.

CONCLUSION

We developed a simple predictive score for nocturnal hypertension that can be used in clinical settings and for diagnoses.

Seasonal Variation in Masked Nocturnal Hypertension: The J-HOP Nocturnal Blood Pressure Study

BACKGROUND

Little is known about seasonal variation in nighttime blood pressure (BP) measured by a home device. In this cross-sectional study, we sought to assess seasonal variation in nighttime home BP using data from the nationwide, practice-based Japan Morning Surge-Home BP (J-HOP) Nocturnal BP study.

METHODS

In this study, 2,544 outpatients (mean age 63 years; hypertensives 92%) with cardiovascular risks underwent morning, evening, and nighttime home BP measurements (measured at 2:00, 3:00, and 4:00 am) using validated, automatic, and oscillometric home BP devices.

RESULTS

Our analysis showed that nighttime home systolic BP (SBP) was higher in summer than in other seasons (summer, 123.3 ± 14.6 mmHg vs. spring, 120.7 ± 14.8 mmHg; autumn, 121.1 ± 14.8 mmHg; winter, 119.3 ± 14.0 mmHg; all P<0.05). Moreover, we assessed seasonal variation in the prevalence of elevated nighttime home SBP (≥120 mmHg) in patients with non-elevated daytime home SBP (average of morning and evening home SBP <135 mmHg; n = 1,565), i.e., masked nocturnal hypertension, which was highest in summer (summer, 45.6% vs. spring, 27.2%; autumn, 28.8%; winter, 24.9%; all P<0.05). Even in intensively controlled morning home SBP (<125 mmHg), the prevalence of masked nocturnal hypertension was higher in summer (summer, 27.4% vs. spring, 14.2%; autumn, 8.9%; winter, 9.0%; all P<0.05). The urine albumin-creatinine ratio in patients with masked nocturnal hypertension tended to be higher than that in patients with non-elevated both daytime and nighttime SBP throughout each season.

CONCLUSIONS

The prevalence of masked nocturnal hypertension was higher in summer than other seasons and the difference proved to be clinically meaningful.

Blood pressure phenotypes based on ambulatory monitoring in a general middle-aged population

BACKGROUND

Ambulatory blood pressure monitoring (ABPM) is increasingly recommended for clinical use, but more knowledge about the prevalence and variability in ABPM-derived phenotypes in the general population is needed. We describe these parameters in the community-based Swedish CArdioPulmonary bioImage Study (SCAPIS) cohort.

METHODS

We examined 5881 men and women aged 50-64 with 24-hour ABPM recordings using validated monitors. ABPM phenotypes were defined according to European guidelines. White coat hypertension was defined as elevated office BP (≥140/90 mmHg) with normal mean ambulatory BP (<135/85 mmHg in day-time, <120/70 mmHg in night-time, <130/80 mmHg over 24-h); and masked hypertension as normal office BP (<140/90 mmHg) with elevated mean ambulatory BP (≥135/85 mmHg in day-time, ≥120/70 mmHg in night-time, ≥130/80 mmHg over 24-h). Blood pressure variability was assessed using the coefficient of variation (CV), standard deviation (SD), and average real variability.

RESULTS

Based on the ABPM recordings, 36.9% of participants had 24-h hypertension, 40.7% had day-time hypertension, and 37.6% nocturnal hypertension. Among participants treated with anti-hypertensive drugs, one in three had elevated office blood pressures, and more than half had elevated 24-h, day-time or nocturnal blood pressures. Among participants without anti-hypertensive drugs, only one in six had elevated office blood pressures, but one in three had elevated 24-h, day-time or nocturnal blood pressures. Men had higher 24-h blood pressures, more masked hypertension, but less white-coat hypertension than women. The prevalence of white-coat hypertension increased with age, but not the prevalence of masked hypertension. A positive association between blood pressure level and variability was observed, and within-person and between-person SD and CV were of similar magnitude. The variance in ABPM on repeated measurements was substantial.

CONCLUSIONS

In the middle-aged general population, masked hypertension is an underappreciated problem on the population level.

Effectiveness of High-Intensity Interval Training Versus Moderate-Intensity Continuous Training in Hypertensive Patients: a Systematic Review and Meta-Analysis

PURPOSE OF REVIEW

The purpose of this meta-analysis is to compare the effects of moderate-intensity continuous training (MICT) and high-intensity interval training (HIIT) on blood pressure of hypertensive individuals.

RECENT FINDINGS

Continuous aerobic training programs are successful in health promotion and are effective in systolic blood pressure (SBP) and diastolic blood pressure (DBP) modulation. However, HIIT seems to be superior to MICT to improvement of cardiorespiratory fitness. PubMed, ScienceDirect, and Google Scholar were searched for randomized clinical trials that compared chronic effects of HIIT and MICT on BP in hypertensive subjects. Pre- and post-intervention changes in maximal oxygen uptake (VO₂max) between MICT and HIIT were analyzed. Both interventions presented significant differences in SBP (MICT: mean difference (MD), 3.7 mmHg [95% CI = 2.57, 4.82], p < 0.00001; and HIIT: MD, 5.64 mmHg [95% CI = 1.69, 9.52], p = 0.005) and in DBP (MICT: MD, 2.41 mmHg [95% CI = 1.09, 3.72], p = 0.0003; and HIIT: MD, 4.8 mmHg [95% CI = 2.9, 6.7], p < 0.00001) compared with the control group. No differences were found in the SBP values (MD, 1.13 mmHg [95% CI = - 0.01, 2.27], p = 0.05); however, differences were found between groups in DBP (MD, 1.63 mmHg [95% CI = 0.83, 2.44], p = 0.0001). In the secondary outcome, both interventions increased VO₂max in comparison with control groups (MICT: MD, 1.30 ml/kg/min [95% CI = 0.92, 1.68], p < 0.00001; and HIIT: MD, 4.90 ml/kg/min [95% CI = 3.77, 6.04], p < 0.00001), and HIIT promoted greater improvement than MICT (MD, 2.52 ml/kg/min [95% CI = 1.90, 3.13], p < 0.0001). In conclusion, HIIT and MICT promote reduction in SBP in adults with hypertension, and HIIT showed a greater magnitude in DBP reduction. For hypertensive patients, HIIT may be associated with a greater improvement in VO₂max than MICT might.