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Background: Several previous studies investigated physical activity of older adults using wearable devices, but more studies need to develop normative values for chronic disease conditions. This study aimed to investigate physical activity using a triaxial activity monitor in community-dwelling older Japanese adults with and without lifestyle-related diseases. Methods: Data from a total of 732 community-dwelling older Japanese men and women were collected and analyzed in a cross-sectional study. The participants' physical activity was assessed for seven consecutive days by a triaxial accelerometer. Physical activity was assessed by number of lifestyle-related diseases and six lifestyle-related diseases categories by gender. Physical activity was assessed separately for total, locomotive, and nonlocomotive physical activity. Results: Participants with multiple (two or more) diseases had significantly lower total light-intensity physical activity (LPA; 278.5 ± 8.4 min/day) and nonlocomotive LPA (226.4 ± 7.0 min/day) versus without diseases in men. Compared in each disease category, total LPA and nonlocomotive LPA was significantly lower in men with hypertension and diabetes. Total sedentary time was significantly higher in men with hypertension, diabetes, and heart disease. Locomotive LPA was significantly lower in men with diabetes. In women, locomotive moderate- to vigorous-intensity physical activity was significantly higher in women with diabetes, and nonlocomotive moderate- to vigorous-intensity physical activity was significantly lower in women with heart disease. Conclusion: This study demonstrated that older Japanese men with multiple lifestyle-related diseases had lower physical activity. In each disease category, hypertension, diabetes, and heart disease affected lower physical activity, especially in men. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

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%.