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Home blood pressure telemonitoring (HBPT) is witnessing rapid diffusion worldwide. Contemporary studies documented mainly short-term (6-12 months) effects of HBPT, and there are limited data about its uptake.
The aim of this study was to explore the 3-year use and determinants of HBPT, and the interactions with systolic and diastolic blood pressure (SBP/DBP) and overall blood pressure (BP) control rate.
HBPT records were obtained from a 3-year cohort of 5658 patients with hypertension in Jieshou, Anhui, China, and data from a structured household survey of a random sample (n=3005) of the cohort. The data analysis comprised (1) timeline trajectories of the rates of monthly active HBPT and mean SBP/DBP for overall and subgroups of patients with varied start-month SBP/DBP; and (2) multivariable linear, logistic, and percentile regression analyses using SBP/DBP, BP control rate, and yearly times of HBPT as the dependent variable, respectively.
HBPT was followed by mixed changes in mean monthly SBP/DBP for varied patient groups. The magnitude of changes ranged from –43 to +39 mmHg for SBP and from –27 to +15 mmHg for DBP. The monthly rates of active HBPT all exhibited a rapid and then gradually slower decline. When controlled for commonly reported confounders, times of HBPT in the last year were found to have decreasing correlation coefficients for SBP/DBP (from 0.16 to –0.35 and from 0.11 to –0.35, respectively) and for BP control rate (from 0.53 to –0.62).
HBPT had major and “target-converging” effects on SBP/DBP. The magnitude of changes was much greater than commonly reported. BP, variation in BP, and time were the most important determinants of HBPT uptake. Age, education, duration of hypertension, family history, and diagnosis of hypertension complications were also linked to the uptake but at weaker strength. There is a clear need for differentiated thinking over the application and assessment of HBPT, and for identifying and correcting/leveraging potential outdated/new opportunities or beliefs.
Home blood pressure telemonitoring (HBPT) is recommended in current hypertension management guidelines, and is witnessing rapid diffusion worldwide [
Given fluctuating BP readings; changing stages (eg, normal BP, high-normal BP, grades 1 and 2 hypertension) [
China has witnessed a rapid increase in the use of HBPT over the past decade. More and more residents are buying and using various types of HBPT devices. However, there is a general paucity of data about the effects and determinants of HBPT. Similar to studies in other countries, the limited publications on HBPT in China have focused primarily on comparing BP differences between the intervention and control groups, with little attention being paid to the determinants and differentiated effects of HBPT.
To fill this gap, the aim of this study was to use data from a relatively large-scale (5658 patients with hypertension) and long-term (up to 40 months) cohort in Jieshou, Anhui, China, for performing a relatively in-depth analysis of HBPT, with particular attention placed on comparing its effects and determinants across patient groups with varied levels of BP. As an inland county located in the middle and east of China, Jieshou is representative of the majority of counties in the nation.
The study was built upon two related and ongoing projects. The first was initiated by Jieshou Hospital, Anhui province, China, which aimed to improve hypertension management via HBPT. The project covered all patients diagnosed with hypertension (N=5658) in all villages (N=48) served by the Jieshou Hospital Consortium. The HBPT involved an electronic oscillometric upper-arm BP monitor installed with a voice speaker capable of automatically stating the resultant measurements and educational messages to the patient. The monitors were provided by IFLYTEK Co Ltd, and were confirmed to be easily useable by ordinary residents. The readings of the HBPT were synchronously sent to a remote central data center.
The second project is an RCT registered in ISRCTN (10999269). This project used a cluster randomized sample (n=3005) of the participants in the above HBPT project to test the efficacy of a novel personalized hypertension management package [
By the time this study was carried out, the HBPT project had gathered BP readings from the participants for over 40 months and the RCT had completed the baseline assessment, including a structured baseline household survey.
This study used the records from the HBPT project described above and part of the data from the corresponding baseline household survey. Each HBPT record consisted of four items: SBP, DBP, pulse per minute, and measurement date and time. The household survey took place from April to July 2021 via a structured questionnaire administered face to face. This study used 24 items from the questionnaire, soliciting information about: (1) sociodemographic characteristics, including age, sex, and education; (2) body height and weight; (3) age when hypertension was first diagnosed; and (4) hypertension-related symptoms and diagnoses (
Data analysis comprised three components: (1) descriptive statistics (numbers and percentages) of study subjects by sociodemographic categories, (2) calculation and presentation (in trajectory lines) of the rates of monthly active HBPT for overall subjects and for subgroups with varied mean SBP/DBP in the first month, (3) multivariable linear and percentile regression modeling of times of HBPT and SBP/DBP in the last year, and (4) multivariable logistic regression modeling of BP control rate.
The rate of monthly active HBPT was defined as the proportion of patients who had performed HBPT at least one time in the month under concern. The multivariable linear, logistic, and percentile regression models used similar independent, exposure, and confounder variables. The dependent variables included times of HBPT in the past year for overall participants and subgroups with varied mean SBP/DPB from HBPT in the last year and the BP control rate in the last year. The exposure variables consisted of mean SBP/DBP and variations in the coefficients of SBP/DBP in the last year. The confounder variables comprised sociodemographics and health conditions. The monthly mean SBP/DBP of any patient was defined as their hourly mean SBP/DBP, calculated as the sum of all SBP/DBP readings recorded within a given hour (eg, 8:00-8:59 AM), multiplied by the number of records within the same hour. The BP control rate was computed as the times of BP readings meeting SBP<140 mmHg and DBP<90 mmHg in the past year multiplied by the total BP readings during the same period.
The analysis regarding the monthly active HBPT used all participants enrolled in the HBPT project, whereas the regression modeling used all of the participants involved in the baseline survey. The logarithm of times using HBPT in the last year was used to transform the variable into a normal distribution. Detailed value assignment is shown in
This study has been approved by Anhui Medical University Biomedical Ethics Committee (number 20200936) and all the participants have signed (for those who are literate) or ticked (for those who are illiterate) the consent form.
Of the 3005 participants recruited in the baseline survey, 57% were women. The average age of the participants was 65.50 years. Their duration of hypertension was 9.50 years on average. Over half of the respondents had a family history of hypertension (
Sociodemographic and hypertension-related characteristics of participants (N=3005).
Variables | Sex | Total, n (%) | ||||||
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Male, n (%) | Female, n (%) |
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≤50 | 93 (7.20) | 102 (5.95) | 195 (6.49) | ||||
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51-60 | 345 (26.72) | 506 (29.52) | 851 (28.32) | ||||
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61-70 | 404 (31.29) | 500 (29.17) | 904 (30.08) | ||||
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>70 | 449 (34.78) | 606 (35.36) | 1055 (35.11) | ||||
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No school education | 234 (18.14) | 1037 (60.61) | 1271 (42.35) | ||||
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Primary school | 411 (31.86) | 518 (30.27) | 929 (31.96) | ||||
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Middle school or higher | 645 (50.00) | 156 (9.12) | 801 (26.69) | ||||
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<18.5 | 13 (1.06) | 16 (0.97) | 29 (1.00) | ||||
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1.8.5-23.9 | 307 (24.92) | 390 (23.58) | 697 (24.15) | ||||
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24-27.9 | 510 (41.40) | 698 (42.20) | 1208 (41.86) | ||||
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≥28 | 402 (32.63) | 550 (33.25) | 952 (32.99) | ||||
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≤4 | 401 (31.40) | 484 (28.69) | 885 (29.86) | ||||
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5-8 | 320 (25.06) | 433 (25.67) | 753 (25.40) | ||||
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9-12 | 252 (19.73) | 315 (18.67) | 567 (19.13) | ||||
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>12 | 304 (23.81) | 455 (26.97) | 759 (25.61) | ||||
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Yes | 642 (54.64) | 808 (51.50) | 1450 (52.84) | ||||
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No | 533 (45.36) | 761 (48.50) | 1294 (47.16) | ||||
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≤4 | 670 (51.90) | 589 (34.36) | 1259 (41.90) | ||||
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5-6 | 223 (17.27) | 327 (19.08) | 550 (18.30) | ||||
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7-8 | 151 (11.70) | 288 (16.80) | 439 (14.61) | ||||
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>8 | 247 (19.13) | 510 (29.75) | 757 (25.19) | ||||
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0 | 544 (42.14) | 638 (37.22) | 1182 (39.33) | ||||
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1 | 442 (34.24) | 614 (35.82) | 1056 (35.14) | ||||
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2 | 223 (17.27) | 312 (18.21) | 535 (17.81) | ||||
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>2 | 82 (6.35) | 150 (8.75) | 232 (7.72) | ||||
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Normal BPa, b | 200 (19.12) | 307 (23.15) | 507 (21.37) | ||||
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High-normal BPc | 458 (43.79) | 564(42.53) | 1022(43.09) | ||||
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Grade 1 hypertensiond | 346 (33.08) | 418 (31.52) | 764 (32.21) | ||||
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Grade 2 hypertensione | 42 (4.01) | 37 (2.79) | 79 (3.33) | ||||
Total | 1291 (43.00) | 1714 (57.00) | 3005 (100.00) |
aBP: blood pressure.
bNormal BP: systolic BP<130 and diastolic BP<85 mmHg.
cHigh-normal BP: 130≤systolic BP≤139 and/or 85≤diastolic BP≤89 mmHg.
dGrade 1 hypertension: 140≤systolic BP≤159 and/or 90≤diastolic BP≤99 mmHg.
eGrade 2 hypertension: systolic BP≥160 and/or diastolic BP≥100 mmHg.
Monthly mean SBP/DBP among cohorts with varied start-month mean SBP. DBP: diastolic blood pressure; M1 through to M40: month 1 through to month 40; SBP: systolic blood pressure.
Monthly rate of active HBPT by cohorts with varied start-month systolic blood pressure. HBPT: home blood pressure telemonitoring; M1 through to M40: month 1 through to month 40.
Multivariable linear and percentile regression modeling of mean systolic blood pressure (SBP) and diastolic blood pressure (DBP).
Variables | All patients | Percentiles of mean SBP/DBP (%) | ||||||||||||||||||||
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10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | ||||||||||||
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Correlation coefficient | —a | –1.14 | –0.75 | –0.47 | –0.26 | –0.04 | 0.20 | 0.46 | 0.73 | 1.21 | ||||||||||
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.60 | <.001 | <.001 | <.001 | <.001 | .15 | <.001 | <.001 | <.001 | <.001 | |||||||||||
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Correlation coefficient | 0.20 | 0.19 | 0.20 | 0.18 | 0.17 | 0.16 | 0.17 | 0.19 | 0.21 | 0.24 | ||||||||||
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<.001 | <.001 | <.001 | <.001 | <.001 | <.001 | <.001 | <.001 | <.001 | <.001 | |||||||||||
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Correlation coefficient | –0.05 | –0.05 | –0.02 | –0.03 | –0.06 | –0.06 | –0.05 | –0.04 | –0.07 | –0.08 | ||||||||||
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.07 | .21 | .61 | .27 | .04 | .06 | .08 | .25 | .06 | .14 | |||||||||||
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Correlation coefficient | –0.03 | 0.00 | –0.01 | –0.04 | –0.05 | –0.05 | –0.06 | –0.05 | –0.05 | 0.00 | ||||||||||
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.24 | .94 | .76 | .17 | .06 | .07 | .04 | .10 | .16 | .99 | |||||||||||
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Correlation coefficient | 0.08 | 0.14 | 0.10 | 0.11 | 0.10 | 0.08 | 0.09 | 0.04 | 0.05 | 0.05 | ||||||||||
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.001 | <.001 | .001 | <.001 | <.001 | .002 | <.001 | .11 | .12 | .33 | |||||||||||
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Correlation coefficient | 0.10 | 0.12 | 0.10 | 0.07 | 0.08 | 0.08 | 0.09 | 0.10 | 0.10 | 0.11 | ||||||||||
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<.001 | <.001 | .001 | .007 | .002 | .001 | .001 | <.001 | .003 | .03 | |||||||||||
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Correlation coefficient | 0.01 | 0.06 | –0.01 | –0.02 | –0.02 | –0.03 | –0.02 | 0.00 | 0.00 | 0.01 | ||||||||||
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.65 | .08 | .72 | .49 | .49 | .23 | .47 | .90 | .94 | .83 | |||||||||||
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Correlation coefficient | –0.01 | 0.01 | –0.06 | –0.06 | –0.03 | –0.01 | 0.01 | 0.00 | 0.00 | 0.03 | ||||||||||
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.60 | .78 | .06 | .02 | .24 | .80 | .74 | .94 | .99 | .59 | |||||||||||
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Correlation coefficient | –0.02 | 0.01 | 0.00 | 0.01 | 0.01 | 0.00 | –0.03 | –0.04 | –0.05 | –0.08 | ||||||||||
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.50 | .67 | .89 | .57 | .81 | .98 | .22 | .18 | .13 | .11 | |||||||||||
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Correlation coefficient | –0.09 | 0.16 | 0.10 | 0.04 | 0.01 | –0.04 | –0.11 | –0.18 | –0.27 | –0.35 | ||||||||||
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<.001 | <.001 | <.001 | .10 | .64 | .08 | <.001 | <.001 | <.001 | <.001 | |||||||||||
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Correlation coefficient | — | –1.16 | –0.74 | –0.48 | –0.22 | –0.03 | 0.21 | 0.42 | 0.74 | 1.13 | ||||||||||
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.89 | <.001 | <.001 | <.001 | <.001 | .18 | <.001 | <.001 | <.001 | <.001 | |||||||||||
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Correlation coefficient | –0.23 | –0.25 | –0.23 | –0.21 | –0.20 | –0.21 | –0.24 | –0.23 | –0.23 | –0.23 | ||||||||||
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<.001 | <.001 | <.001 | <.001 | <.001 | <.001 | <.001 | <.001 | <.001 | <.001 | |||||||||||
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Correlation coefficient | –0.11 | –0.10 | –0.10 | –0.06 | –0.09 | –0.07 | –0.09 | –0.13 | –0.18 | –0.16 | ||||||||||
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<.001 | .02 | .001 | .04 | .002 | .02 | .003 | <.001 | <.001 | .002 | |||||||||||
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Correlation coefficient | –0.02 | –0.03 | –0.01 | 0.01 | 0.02 | 0.01 | 0.00 | –0.02 | –0.06 | –0.05 | ||||||||||
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.54 | .48 | .66 | .79 | .51 | .69 | .95 | .57 | .08 | .33 | |||||||||||
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Correlation coefficient | 0.04 | 0.08 | 0.06 | 0.06 | 0.03 | 0.05 | 0.02 | 0.06 | 0.03 | 0.04 | ||||||||||
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.08 | .02 | .03 | .04 | .20 | .03 | .37 | .02 | .26 | .39 | |||||||||||
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Correlation coefficient | 0.03 | –0.04 | 0.04 | 0.04 | 0.03 | 0.05 | 0.04 | 0.03 | 0.05 | 0.02 | ||||||||||
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.17 | .32 | .14 | .12 | .20 | .04 | .12 | .19 | .13 | .63 | |||||||||||
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Correlation coefficient | 0.02 | 0.05 | 0.02 | 0.03 | 0.02 | 0.00 | 0.00 | –0.04 | –0.02 | 0.01 | ||||||||||
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.49 | .18 | .46 | .25 | .45 | .89 | .90 | .17 | .52 | .80 | |||||||||||
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Correlation coefficient | 0.00 | 0.05 | 0.01 | –0.03 | –0.01 | –0.03 | –0.01 | –0.01 | 0.01 | 0.01 | ||||||||||
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.92 | .18 | .71 | .31 | .70 | .30 | .63 | .81 | .84 | .82 | |||||||||||
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Correlation coefficient | –0.02 | 0.01 | 0.00 | –0.02 | –0.03 | –0.04 | –0.02 | –0.01 | –0.04 | –0.01 | ||||||||||
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.39 | .70 | .92 | .49 | .21 | .09 | .38 | .60 | .21 | .81 | |||||||||||
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Correlation coefficient | –0.11 | 0.11 | 0.01 | –0.04 | –0.04 | –0.05 | –0.13 | –0.17 | –0.24 | –0.35 | ||||||||||
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<.001 | .003 | .78 | .16 | .10 | .06 | <.001 | <.001 | <.001 | <.001 |
aNot applicable.
Multivariable logistic regression modeling of blood pressure control rate.
Variablesa | Model 1 (CVb=10%) | Model 2 (CV=20%) | Model 3 (CV=30%) | Model 4 (CV=40%) | Model 5 (CV=50%) | Model 6 (CV=60%) | Model 7 (CV=70%) | Model 8 (CV=80%) | Model 9 (CV=90%) | ||||||||||
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Bc | 1.25 | 0.61 | 0.27 | –0.05 | –0.35 | –0.76 | –1.24 | –1.63 | –2.49 | |||||||||
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ORd | 3.50 | 1.84 | 1.30 | 0.95 | 0.71 | 0.47 | 0.29 | 0.20 | 0.08 | |||||||||
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<.001 | <.001 | <.001 | .30 | <.001 | <.001 | <.001 | <.001 | <.001 | ||||||||||
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B | 0.17 | 0.19 | 0.19 | 0.12 | 0.08 | 0.03 | –0.04 | –0.05 | –0.06 | |||||||||
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OR | 1.19 | 1.20 | 1.21 | 1.13 | 1.09 | 1.03 | 0.96 | 0.95 | 0.94 | |||||||||
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.007 | .001 | <.001 | .02 | .13 | .55 | .50 | .49 | .49 | ||||||||||
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B | 0.12 | 0.09 | 0.09 | 0.14 | 0.11 | 0.16 | 0.19 | 0.18 | 0.20 | |||||||||
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OR | 1.13 | 1.09 | 1.10 | 1.15 | 1.12 | 1.17 | 1.21 | 1.19 | 1.22 | |||||||||
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.08 | .14 | .10 | .01 | .05 | .01 | .005 | .02 | .04 | ||||||||||
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B | 0.11 | 0.07 | 0.11 | 0.12 | 0.11 | 0.09 | 0.12 | 0.14 | 0.12 | |||||||||
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OR | 1.11 | 1.07 | 1.12 | 1.13 | 1.12 | 1.09 | 1.13 | 1.15 | 1.13 | |||||||||
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.12 | .25 | .05 | .03 | .05 | .14 | .08 | .06 | .22 | ||||||||||
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B | 0.04 | 0.00 | -0.01 | –0.04 | –0.06 | –0.09 | –0.14 | –0.14 | –0.17 | |||||||||
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OR | 1.04 | 1.00 | 0.99 | 0.96 | 0.94 | 0.92 | 0.87 | 0.87 | 0.84 | |||||||||
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.50 | .94 | .85 | .37 | .21 | .10 | .02 | .03 | .05 | ||||||||||
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B | –0.19 | –0.13 | –0.11 | –0.12 | –0.16 | –0.21 | –0.25 | –0.21 | –0.31 | |||||||||
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OR | 0.83 | 0.87 | 0.89 | 0.88 | 0.86 | 0.81 | 0.78 | 0.81 | 0.73 | |||||||||
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.001 | .007 | .02 | .01 | .002 | <.001 | <.001 | .004 | .003 | ||||||||||
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B | 0.02 | 0.02 | 0.01 | -0.02 | 0.05 | 0.08 | 0.03 | 0.00 | –0.04 | |||||||||
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OR | 1.02 | 1.02 | 1.01 | 0.98 | 1.05 | 1.09 | 1.03 | 1.00 | 0.96 | |||||||||
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.67 | .74 | .81 | .70 | .29 | .11 | .58 | .97 | .61 | ||||||||||
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B | 0.03 | –0.02 | –0.01 | –0.02 | –0.02 | –0.01 | –0.02 | 0.01 | –0.08 | |||||||||
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OR | 1.03 | 0.98 | 0.99 | 0.98 | 0.98 | 0.99 | 0.98 | 1.01 | 0.92 | |||||||||
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.61 | .68 | .86 | .65 | .69 | .89 | .69 | .92 | .38 | ||||||||||
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B | 0.15 | 0.10 | 0.09 | 0.10 | 0.02 | –0.03 | –0.01 | –0.06 | 0.04 | |||||||||
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OR | 1.16 | 1.11 | 1.09 | 1.10 | 1.02 | 0.97 | 0.99 | 0.94 | 1.04 | |||||||||
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.01 | .04 | .08 | .05 | .63 | .51 | .88 | .36 | .64 | ||||||||||
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B | 0.53 | 0.24 | 0.15 | 0.12 | 0.03 | 0.02 | –0.09 | –0.22 | –0.62 | |||||||||
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OR | 1.70 | 1.27 | 1.16 | 1.12 | 1.03 | 1.02 | 0.92 | 0.80 | 0.54 | |||||||||
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<.001 | <.001 | .002 | .02 | .58 | .75 | .12 | <.001 | <.001 |
aThe dependent variable in Models 1 to 9 was assigned 1 if the blood control rate of the patient under concern was greater than the CV or 0 otherwise.
bCV: cut-off value of blood pressure control rate.
cB: correlation coefficient.
dOR: odds ratio.
Multivariable percentile regression modeling of factors affecting times of home blood pressure telemonitoring (HBPT). The y-axis represents the regression coefficient. The x-axis represents quantiles of times of HBPT in the last year. DBP: diastolic blood pressure; SBP: systolic blood pressure.
Our study unveiled novel and meaningful BP trajectories after HBPT among hypertensive cohorts with varied mean SBP in the first month (
Implications of the “target-converging” effect remain to be carefully examined. It is well-established that “convergence” downward from above the “target” is beneficial to patients via various mechanisms, including a lower risk of cerebral hemorrhage [
The declining and varied rates of monthly active HBPT for different cohorts (
The decreasing trend over time following start of the HBPT project may be mainly attributed to increasing familiarity with the resultant SBP/DBP. In other words, when the patients’ ability to anticipate the results enhanced, their desire or interest in performing HBPT decreased. This is consistent with our findings (
Our multivariable percentile regression model also identified independent associations between HBPT and age, education, duration of hypertension, family history, and diagnosis of hypertension complications. Perceived risk may be the main reason underlying these relations. In other words, patients of older age, with better education, a longer duration of hypertension, more diagnoses, and family history may perceive themselves at an elevated risk for developing hypertension complications and thus become more active in HBPT [
Our study uncovered interesting variations in the relationships between HBPT and its influencing factors. Times of HBPT presented negative associations with mean DBP (
Similar variations in correlations were also observed in the models using SBP/DBP as the dependent variables. For example, age showed a sustained and positive association with SBP but a continuous negative link to DBP. These contradictory relations have been reported in various hypertensive populations, especially those dominated by relatively older patients with isolated systolic hypertension [
Our study has both strengths and limitations. This study used data from a relatively large-scale (5658 patients with hypertension) and long-term cohort. Relatively in-depth analysis of the determinants of HBPT was performed, with particular attention paid to subtle and differential interactions with the resultant BP outcome. This study thus produced useful trajectories of monthly mean SBP/DBP and monthly active rates of HBPT for up to 40 months. Multivariable linear, percentile, and logistic regression modeling of times of HBPT, mean SBP/DBP, and BP control rate as the dependent variables, respectively, enabled cross-checks and comparisons of the results.
This study also suffers from drawbacks. First, being performed at home by ordinary residents, the B
HBPT had major and “target-converging” effects on SBP/DBP. The “target” was the widely validated and accepted defining values of hypertension control (ie, SBP below 140 and/or DBP under 90 mmHg). HBPT was followed by SBP/DBP reductions or increases for cohorts with a mean BP higher or lower than the “target,” respectively. The magnitude of changes was a few times greater than commonly documented. These differentiated effects remained observable into the third year after initiation of HBPT. BP, variation in BP, and time were the most important determinants of HBPT uptake, whereas age, education, duration of hypertension, family history, and diagnosis of hypertension complications were also linked to the uptake but at apparently weaker strength. HBPT displayed stronger associations with the variation in SBP than in DBP.
There is a clear need for differentiated thinking over the application and assessment of HBPT. First, the traditional approach of simply comparing the effects in the intervention group as a whole with that in the control group is prone to underestimation of the actual influences of HBPT, since decreases in a portion of the patients were offset by increases in others. HBPT leads to BP decrease, stability, or increase depending on the complex and dynamic context of the patient under concern. These varied effects may not necessarily all be beneficial and merit careful scrutiny in the future. This study thus highlights the need for correcting outdated beliefs or practices and leveraging new opportunities with the application of HBPT. Second, the difference in the “white coat” effect suggests lower than traditional cut-off values of hypertension control when readings from HBPT were used. In other words, patients should be better educated about the “white coat” effect and that they need to exert further efforts to maintain their HBPT readings slightly below 140/90 mmHg. Third, the varied responses toward different levels of HBPT readings indicate selective telemonitoring, group-specific “targets,” or even personalized interventions. Fourth, relatively less attention paid to DBP than to SBP implies that additional efforts are needed to promote balanced awareness among patients. In particular, patients should be informed that DBP is as important as SBP and thus merits equal attention in self-monitoring.
Related questions used in the baseline household survey and value assignment.
Monthly mean SBP/DBP among cohorts with varied start-month mean DBP. DBP: diastolic blood pressure; SBP: systolic blood pressure; M1 through to M40: month 1 through to month 40.
Monthly rate of active HBPT by cohorts with varied start-month diastolic blood pressure. HBPT: home blood pressure telemonitoring; M1 through to M40: month 1 through to month 40.
Multivariable linear and percentile regression coefficients of times of home blood pressure telemonitoring (HBPT) .
blood pressure
cut-off value
diastolic blood pressure
home blood pressure telemonitoring
randomized controlled trial
systolic blood pressure
This study is supported by the National Natural Science Foundation of China (grant 72004002). The funding source has not played any role in the study design, analysis, or in the decision to submit the manuscript for publication.
QX and XZ contributed equally in conceiving this study and drafting this manuscript. RL, XG, and GL implemented the computational analysis. LZ and QW facilitated project implementation. DW accessed and verified all the data in the study. XS provided expertise for design of the study and revised and finalized the manuscript.
None declared.