BACKGROUND: Using the data from 56,365 individuals, from 185 countries, and a Nokia Health Wireless blood pressure (BP) monitor, we investigated real-world characteristics of BP variability (BPV). METHODS: All included individuals self-measured and uploaded their BP using Bluetooth at least 20 times over a period of ≥1 month at a frequency and duration of their choosing. In total, 16,904,844 BP measurements were analyzed, with a median of 146 measurements per person (interquartile range [IQR] 73-321) over a median of 14 months (IQR 7-31). SD, coefficient of variation, maximum BP, and maximum minus minimum BP difference were all calculated as measures of BPV. RESULTS: BPV showed a distinct pattern, influenced by season of year, day of week, and time of day. BPV index was higher in females compared with males (P < 0.001) and increased with age (P < 0.001). Compared to the weekend, the weekday BPV index was significantly higher, and this finding was more prominent in females (P = 0.001). In multivariate analysis, BPV index were significantly associated with age, gender, geographic location, and mean BP values. CONCLUSION: Using the largest BP data set we are aware of, with the benefits and limitations of real-world measurement, we could show the pattern of BPV and provide reference values that may be helpful in understanding the nature of BPV as self-measurement at home becomes more common, and help guide individualized management.
BACKGROUND: Using the data from 56,365 individuals, from 185 countries, and a Nokia Health Wireless blood pressure (BP) monitor, we investigated real-world characteristics of BP variability (BPV). METHODS: All included individuals self-measured and uploaded their BP using Bluetooth at least 20 times over a period of ≥1 month at a frequency and duration of their choosing. In total, 16,904,844 BP measurements were analyzed, with a median of 146 measurements per person (interquartile range [IQR] 73-321) over a median of 14 months (IQR 7-31). SD, coefficient of variation, maximum BP, and maximum minus minimum BP difference were all calculated as measures of BPV. RESULTS: BPV showed a distinct pattern, influenced by season of year, day of week, and time of day. BPV index was higher in females compared with males (P < 0.001) and increased with age (P < 0.001). Compared to the weekend, the weekday BPV index was significantly higher, and this finding was more prominent in females (P = 0.001). In multivariate analysis, BPV index were significantly associated with age, gender, geographic location, and mean BP values. CONCLUSION: Using the largest BP data set we are aware of, with the benefits and limitations of real-world measurement, we could show the pattern of BPV and provide reference values that may be helpful in understanding the nature of BPV as self-measurement at home becomes more common, and help guide individualized management.
Authors: Jeff Whittle; Amy I Lynch; Rikki M Tanner; Lara M Simpson; Barry R Davis; Mahboob Rahman; Paul K Whelton; Suzanne Oparil; Paul Muntner Journal: Clin J Am Soc Nephrol Date: 2016-02-18 Impact factor: 8.237
Authors: G Parati; G S Stergiou; R Asmar; G Bilo; P de Leeuw; Y Imai; K Kario; E Lurbe; A Manolis; T Mengden; E O'Brien; T Ohkubo; P Padfield; P Palatini; T G Pickering; J Redon; M Revera; L M Ruilope; A Shennan; J A Staessen; A Tisler; B Waeber; A Zanchetti; G Mancia Journal: J Hum Hypertens Date: 2010-06-03 Impact factor: 3.012
Authors: Paul Muntner; Jeff Whittle; Amy I Lynch; Lisandro D Colantonio; Lara M Simpson; Paula T Einhorn; Emily B Levitan; Paul K Whelton; William C Cushman; Gail T Louis; Barry R Davis; Suzanne Oparil Journal: Ann Intern Med Date: 2015-09-01 Impact factor: 25.391
Authors: Sarah L Stevens; Sally Wood; Constantinos Koshiaris; Kathryn Law; Paul Glasziou; Richard J Stevens; Richard J McManus Journal: BMJ Date: 2016-08-09