Factors influencing the continuation of home blood pressure measurement in community-dwelling older adults: the NOSE study.

OBJECTIVE: This study aimed to identify the factors influencing home blood pressure measurement (HBPM) continuation in community-dwelling older adults. METHODS: A longitudinal analysis used the NOSE study intervention group datasets. The participants were encouraged HBPM with self-monitoring devices provided to them twice in the morning and twice in the evening. Every 7-day interval from the HBPM start date was defined as 1 week, and the number of HBPMs per week was counted. The first week in which the number of HBPMs was zero was defined as the week in which HBPM was discontinued. Participants who did not experienced discontinuation until the end of the observation period were considered complete survivors in the survival time analysis. RESULTS: Data from 437 participants were included in the analysis. Of these, 120 (27.5%) discontinued HBPM. In univariate analysis, factors significantly associated with HBPM discontinuation included exercise habits [hazard ratio per one unit 0.47; 95% confidence interval (CI) 0.31-0.69], social participation (hazard ratio 0.65; 95% CI 0.42-0.99), MoCA-J score (hazard ratio 0.94; 95% CI 0.90-0.98), and frailty (hazard ratio 5.20; 95% CI 2.87-9.43). In multivariate analysis, factors significantly associated with HBPM discontinuation included sex (hazard ratio 0.55; 95% CI 0.32-0.95; ref. = female individuals), smoking history (hazard ratio 1.69; 95% CI 1.02-2.80), exercise habits (hazard ratio 0.51; 95% CI 0.30-0.85), MoCA-J score (hazard ratio 0.93; 95% CI 0.88-0.98), and frailty (hazard ratio 3.31; 95% CI 1.50-7.29). CONCLUSION: Among community-dwelling older adults, female sex, smoking history, lack of exercise, cognitive decline, and frailty were identified as factors influencing HBPM discontinuation.

A prospective pilot trial of the health data monitoring system without educational content for patients with hypertension.

In Japan, a hypertension treatment aid application (CureApp Co., Ltd.) with educational content on hypertension was the first in the world to show significant blood pressure (BP) reduction in hypertensive patients in 2021. Omron Healthcare Corporation and I developed the Health data Monitoring System (HMS) without educational content, which allows patients to check their home BP values periodically and allows physicians to check their home BP data before the hospital visit. As a pilot study, nineteen patients with hypertension used HMS for six months. The percentage of patients achieving their antihypertensive goal increased from 16% to 37%. Mean home systolic BP decreased from 138.1 ± 11.8 mmHg to 130.2 ± 7.8 mmHg. The increase in number of home BP measurements was significantly associated with the amount of homed systolic BP reduction in men. In conclusion, HMS without educational content may be an adjunct to hypertension treatment.

Consideration of the reference value and number of measurements of the urinary sodium-to-potassium ratio based on the prevalence of untreated home hypertension: TMM Cohort Study.

The sodium-to-potassium (Na/K) ratio is known to be associated with blood pressure (BP). However, no reference value has been established since the urinary Na/K (uNa/K) ratio is known to have diurnal and day-to-day variations. Therefore, we investigated the number of days required to yield a better association between the morning uNa/K ratio and home BP (HBP) and determined a morning uNa/K ratio value that can be used as a reference value in participants who are not taking antihypertensive medication. This was a cross-sectional study using data from the Tohoku Medical Megabank Project Cohort Study. A total of 3122 participants borrowed HBP and uNa/K ratio monitors for 10 consecutive days. We assessed the relationship between the morning uNa/K ratio from 1 day to 10 days and home hypertension (HBP ≥ 135/85 mmHg) using multiple logistic regression models. Although a 1-day measurement of the morning uNa/K ratio was positively associated with home hypertension, multiple measurements of the morning uNa/K ratio were strongly related to home hypertension. The average morning uNa/K ratio was relatively stable after 3 days (adjusted odds ratio of home hypertension per unit increase in the uNa/K ratio for more than 3 days: 1.19-1.23). In conclusion, there was no threshold for the uNa/K ratio, and the morning uNa/K ratio was linearly associated with home hypertension. The Na/K ratio 2.0 calculated from the Dietary Reference Intakes for Japanese might be a good indication. Regarding the stability of the association between the morning uNa/K ratio and BP, more than 3 days of measurements is desirable.

Normative Data of Objectively Measured Physical Activity and Sedentary Time in Community-Dwelling Older Japanese.

BACKGROUND: The amounts of moderate-to-vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), and sedentary time (ST) by sex, age, and body mass index (BMI) in older Japanese adults have not been known. We conducted this study to determine the actual physical activity (PA) and ST in this population. SUBJECTS AND METHODS: A total of 3998 community-dwelling Japanese adults aged ≥65 years were investigated. Their levels of PA and ST and number of steps taken daily were assessed for seven consecutive days by a tri-axial accelerometer. Normative values of daily PA and ST were analyzed by age and BMI groups in the men and the women and are presented as mean, median, or decile. RESULTS: The subjects generally adhered to the PA guideline, i.e., ≥10 metabolic equivalents (METs)·hour MVPA per week. Older age was associated with lower adherence to the PA guideline. CONCLUSIONS: Normative values (mean, median, or decile) were yielded for MVPA, LPA, and ST based on accelerometer readings in a large sample of older community-dwelling Japanese adults. One-half of the subjects' waking time was spent being sedentary, and >70% of the subjects met the current PA guideline by engaging in MVPA.

Validity of estimating physical activity intensity using a triaxial accelerometer in healthy adults and older adults.

BACKGROUND: A triaxial accelerometer with an algorithm that could discriminate locomotive and non-locomotive activities in adults has been developed. However, in the elderly, this accelerometer has not yet been validated. The aim were to examine the validity of this accelerometer in the healthy elderly, and to compare the results with those derived in a healthy younger sample. METHODS: Twenty-nine healthy elderly subjects aged 60-80 years (Elderly), and 42 adults aged 20-59 years (Younger) participated. All subjects performed 11 activities, including locomotive and non-locomotive activities with a Douglas bag while wearing the accelerometer (Active style Pro HJA-750C). Physical activity intensities were expressed as metabolic equivalents (METs). The relationship between the METs measured using the Douglas bag and METs predicted using the accelerometer was evaluated. RESULTS: A significant correlation between actual and predicted METs was observed in both Elderly (r=0.85, p<0.001) and Younger (r=0.88, p<0.001). Predicted METs significantly underestimated compared with actual METs in both groups (p<0.001). The mean of the errors was -0.6±0.6 METs in Elderly and -0.1±0.5 METs in Younger. The degree of underestimation increased with increasing METs in Elderly (p<0.001). A stepwise multiple regression analysis revealed that predicted METs, age, and weight were related to actual METs in both groups. CONCLUSION: The degree of correlation between predicted and actual METs was comparable in elderly and younger participants, but the prediction errors were greater in elderly participants, particular at higher-intensity activities, which suggests that different predicting equations may be needed for the elderly.

Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals.

BACKGROUND: Heart rate (HR) during physical activity is strongly affected by the level of physical fitness. Therefore, to assess the effects of fitness, we developed predictive equations to estimate the metabolic equivalent (MET) of daily activities, which includes low intensity activities, by % HR reserve (%HRR), resting HR, and multiple physical characteristics. METHODS: Forty volunteers between the ages of 21 and 55 performed 20 types of daily activities while recording HR and sampling expired gas to evaluate METs values. Multiple regression analysis was performed to develop prediction models of METs with seven potential predictors, such as %HRR, resting HR, and sex. The contributing parameters were selected based on the brute force method. Additionally, leave-one-out method was performed to validate the prediction models. RESULTS: %HRR, resting HR, sex, and height were selected as the independent variables. %HRR showed the highest contribution in the model, while the other variables exhibited small variances. METs were estimated within a 17.3% difference for each activity, with large differences in document arrangement while sitting (+17%), ascending stairs (-8%), and descending stairs (+8%). CONCLUSIONS: The results showed that %HRR is a strong predictor for estimating the METs of daily activities. Resting HR and other variables were mild contributors. (201 words).

Estimating metabolic equivalents for activities in daily life using acceleration and heart rate in wearable devices.

BACKGROUND: Herein, an algorithm that can be used in wearable health monitoring devices to estimate metabolic equivalents (METs) based on physical activity intensity data, particularly for certain activities in daily life that make MET estimation difficult. RESULTS: Energy expenditure data were obtained from 42 volunteers using indirect calorimetry, triaxial accelerations and heart rates. The proposed algorithm used the percentage of heart rate reserve (%HRR) and the acceleration signal from the wearable device to divide the data into a middle-intensity group and a high-intensity group (HIG). The two groups were defined in terms of estimated METs. Evaluation results revealed that the classification accuracy for both groups was higher than 91%. To further facilitate MET estimation, five multiple-regression models using different features were evaluated via leave-one-out cross-validation. Using this approach, all models showed significant improvements in mean absolute percentage error (MAPE) of METs in the HIG, which included stair ascent, and the maximum reduction in MAPE for HIG was 24% compared to the previous model (HJA-750), which demonstrated a 70.7% improvement ratio. The most suitable model for our purpose that utilized heart rate and filtered synthetic acceleration was selected and its estimation error trend was confirmed. CONCLUSION: For HIG, the MAPE recalculated by the most suitable model was 10.5%. The improvement ratio was 71.6% as compared to the previous model (HJA-750C). This result was almost identical to that obtained from leave-one-out cross-validation. This proposed algorithm revealed an improvement in estimation accuracy for activities in daily life; in particular, the results included estimated values associated with stair ascent, which has been a difficult activity to evaluate so far.

Physical activity group classification algorithm using triaxial acceleration and heart rate.

As described in this paper, a physical activity classification algorithm is proposed for energy expenditure estimation. The proposed algorithm can improve the classification accuracy using both the triaxial acceleration and heart rate. The optimal classification also contributes to improvement of the accuracy of the energy expenditures estimation. The proposed algorithm employs three indices: the heart rate reserve (%HRreserve), the filtered triaxial acceleration, and the ratio of filtered and unfiltered acceleration. The percentage HRreserve is calculated using the heart rate at rest condition and the maximum heart rate, which is calculated using Karvonen Formula. Using these three indices, a decision tree is constructed to classify physical activities into five classes: sedentary, household, moderate (excluding locomotive), locomotive, and vigorous. Evaluation results show that the average classification accuracy for 21 activities is 91%.