Faculty Opinions recommendation of Attended Versus Unattended Automated Office Blood Pressure: A Systematic Review and Meta-analysis.

Author(s):  
Costas Tsioufis
2018 ◽  
Vol 21 (2) ◽  
pp. 148-155 ◽  
Author(s):  
Anastasios Kollias ◽  
Emelina Stambolliu ◽  
Konstantinos G. Kyriakoulis ◽  
Areti Gravvani ◽  
George S. Stergiou

2019 ◽  
Vol 26 (4) ◽  
pp. 293-303 ◽  
Author(s):  
Emmanuel A. Andreadis ◽  
Costas Thomopoulos ◽  
Charalampia V. Geladari ◽  
Vasilios Papademetriou

2019 ◽  
Vol 21 (4) ◽  
pp. 536-537
Author(s):  
Anastasios Kollias ◽  
Emelina Stambolliu ◽  
Konstantinos G. Kyriakoulis ◽  
Areti Gravvani ◽  
George S. Stergiou

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Yacong Bo ◽  
Kin-On Kwok ◽  
Kareen Ka-Yin Chu ◽  
Eppie Yu-Han Leung ◽  
Chun Pong Yu ◽  
...  

Abstract Purpose of Review Automated office blood pressure (AOBP) measurements may provide more accurate estimation of blood pressure (BP) than manual office blood pressure (MOBP) measurements. This systematic review investigated the diagnostic performance of AOBP and MOBP using ambulatory blood pressure measurement (ABPM) as reference. Several databases including MEDLINE, Embase, Scopus, and China Academic Journals were searched. Data were extracted, double-checked by two investigators, and were analysed using a random effects model. Recent Findings A total of 26 observational studies were included. The mean systolic/diastolic BP obtained by AOBP was not significantly different from that obtained by ABPM. The sensitivity and specificity of AOBP to detect elevated BP were approximately 70%. Fewer participants had white-coat hypertension on AOBP measurement than on MOBP measurement (7% versus 14%); however, about 13% had masked hypertension on AOBP measurement. The width of the limit of agreement comparing (i) AOBP and ABPM and (ii) MOBP and ABPM was comparable. Summary AOBP may reduce the rate of the observed white-coat effect but undermine masked hypertension. The current recommendation, however, is limited by the absence of high-quality studies and the high heterogeneity of our results. More high-quality studies using different AOBP machines and in different population are therefore needed.


2021 ◽  
Vol 39 (Supplement 1) ◽  
pp. e290
Author(s):  
Konstantinos Stavropoulos ◽  
Konstantinos Imprialos ◽  
Dimitrios Patoulias ◽  
Alexandra Katsimardou ◽  
Konstantinos Koutsampasopoulos ◽  
...  

2020 ◽  
Author(s):  
Dongjun Wu ◽  
Nicholas Buys ◽  
Guandong Xu ◽  
Jing Sun

UNSTRUCTURED Aims: This systematic review and meta-analysis aimed to evaluate the effects of wearable technologies on HbA1c, blood pressure, body mass index (BMI), and fastening blood glucose (FBG) in patients with diabetes. Methods: We searched PubMed, Scopus, Embase, the Cochrane database, and the Chinese CNKI database from last 15 years until August 2021. The quality of the 16 included studies was assessed using the PEDro scale, and random effect models were used to estimate outcomes, with I2 used for heterogeneity testing. Results: A significant reduction in HbA1c (-0.475% [95% CI -0.692 to -0.257, P<0.001]) was found following telemonitoring. However, the results of the meta-analysis did not show significant changes in blood pressure, BMI, and glucose, in the intervention group (P>0.05), although the effect size for systolic blood pressure (0.389) and diastolic blood pressure may indicate a significant effect. Subgroup analysis revealed statistically significant effects of wearable technologies on HbA1c when supported by dietetic interventions (P<0.001), medication monitoring (P<0.001), and relapse prevention (P<0.001). Online messages and telephone interventions significantly affected HbA1c levels (P<0.001). Trials with additional online face-to-face interventions showed greater reductions in HbA1c levels. Remote interventions including dietetic advice (P<0.001), medication (P<0.001), and relapse prevention (P<0.001) during telemonitoring showed a significant effect on HbA1c, particularly in patients attending ten or more intervention sessions (P<0.001). Conclusion: Wearable technologies can improve diabetes management by simplifying self-monitoring, allowing patients to upload their live measurement results frequently and thereby improving the quality of telemedicine. Wearable technologies also facilitate remote medication management, dietetic interventions, and relapse prevention.


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