Simultaneous self-monitoring comparison of a supine algorithm-equipped wrist nocturnal home blood pressure monitoring device with an upper arm device.
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ABSTRACT:
SUBMITTER: Kario K
PROVIDER: S-EPMC8678689 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We investigated the optimal nighttime home blood pressure (BP) measurement schedule for wrist BP monitoring. Fifty hypertensive patients (mean age 68.9 ± 11.3 years) self-measured their nighttime BP hourly using a wrist-type nocturnal home BP monitoring device at home on two consecutive nights. Using the average 7.2 ± 1.5 measurements per night, we compared the clock-based index (average of three measurements at 2:00, 3:00, and 4:00 a.m.) and the bedtime-based index (average of three measurements at 2, 3, and 4 h after bedtime). The clock-based average was significantly higher than the bedtime-based average for both systolic BP (2.7 ± 8.2 mmHg, P = .002) and diastolic BP (1.9 ± 5.1 mmHg, P < .001). Compared to the average of all measurements throughout a night (the same definition of ambulatory BP monitoring, ie, from the time point of going to bed to awakening), the clock-based average was comparable (systolic/diastolic BP: -0.5 ± 5.5/-0.2 ± 3.7), whereas the bedtime-based average was significantly lower (-3.3 ± 5.0/-2.1 ± 3.6). Thus, the repeated measurement of wrist-measured nighttime BP at three clock-based time points per night provided reliable values. Further prospective studies of larger populations are required to confirm the optimal nighttime BP measurement schedule for wrist BP monitoring for the prediction of cardiovascular events.
| S-EPMC8678724 | biostudies-literature
Project description:The authors investigated the reproducibility of nighttime home blood pressure (BP) measured by a wrist-type BP monitoring device. Forty-six hypertensive patients (mean 69.0±11.6 years, 56.5% male) self-measured their nighttime BP hourly using simultaneously worn wrist-type and upper arm-type nocturnal home BP monitoring devices at home on two consecutive nights. Using the average 7.4±1.3 measurements on the first night and the average 7.0 ± 1.8 measurements on the second night, the authors assessed the reliability and the reproducibility of nighttime BP measured on the two nights. The difference between nights in systolic BP (SBP) measured by the wrist-device was not significant (1.6±7.0 mmHg, p = .124), while the difference in diastolic BP (DBP) was marginally significant (1.4±4.9 mmHg, p = .050). The intraclass correlation coefficients (ICCs) for agreement between nights were high both in SBP and DBP average (SBP: 0.835, DBP: 0.804). Averaging only three points of SBP resulted in lower ICC values, but still indicated good correlations (ICC > 0.6). On the other hand, the correlations of the standard deviation and average real variability of SBP between nights were low, with ICCs of 0.220 and 0.436, respectively. In conclusion, the average SBP values measured on the first night were reliable even when averaging only three readings. The reproducibility of nighttime BP variability seemed inferior to that of BP average; it might be better to measure nighttime BP over multiple nights to assess BP variability. However, this hypothesis needs verification in other study population. In addition, our study population had well-controlled BP, which limits the generalizability of this findings to all hypertensive patients.
| S-EPMC8678720 | biostudies-literature
Project description:BackgroundAmbulatory blood pressure monitoring (ABPM) is important in evaluating average 24-hour blood pressure (BP) levels, circadian rhythm, sleeping BP and BP variability but many patients are reluctant to use standard ABPM devices.MethodsWe compared two validated ABPM devices, the BPro tonometric wrist monitor and the A&D TM-2430 oscillometric upper arm monitor, for agreement of recordings and acceptability in 37 hypertensive patients (aged 55±9 years).ResultsSuccessful BP measurements were less frequent with the wrist-type than the arm-type device during the sleeping (66.3% vs. 92.9%, P <0.01) and awake periods (56.2% vs. 86.5%, P <0.01). Comparable paired readings showed no significant difference in systolic BP but diastolic BP (DBP) values were higher with the wrist compared to the arm monitor (24-hour 89±13 vs. 85±14 mmHg, P <0.01) with similar differences awake and sleeping. Bland-Altman analysis showed some large discrepancies between individual arm and wrist monitor measurements. More patients found the wrist monitor more comfortable to use than the arm monitor.ConclusionsDespite the difference in individual BP measurements and the systematic overestimation of DBP values with the BPro device, wrist monitors with good patient acceptability may be useful to facilitate ABPM in some patients to provide additional information about cardiovascular risk and response to antihypertensive therapies.
| S-EPMC8352047 | biostudies-literature
Project description:BackgroundInertial Measurement Unit (IMU)-based wearable sensors have found common use to track arm activity in daily life. However, classifying a high number of arm motions with single IMU-based systems still remains a challenging task. This paper explores the possibility to increase the classification accuracy of these systems by incorporating a thermal sensor. Increasing the number of arm motions that can be classified is relevant to increasing applicability of single-device wearable systems for a variety of applications, including activity monitoring for athletes, gesture control for video games, and motion classification for physical rehabilitation patients. This study explores whether a thermal sensor can increase the classification accuracy of a single-device motion classification system when evaluated with healthy participants. The motions performed are reproductions of exercises described in established rehabilitation protocols.MethodsA single wrist-mounted device was built with an inertial sensor and a thermal sensor. This device was worn on the wrist, was battery powered, and transmitted data over Bluetooth to computer during recording. A LabVIEW Graphical User Interface (GUI) instructed the user to complete 24 different arm motions in a pre-randomized order. The received data were pre-processed, and secondary features were calculated on these data. These features were processed with Principal Component Analysis (PCA) for dimensionality reduction and then several machine learning models were applied to select the optimal model based on speed and accuracy. To test the effectiveness of the scheme, 11 healthy subjects participated in the trials.ResultsAverage personalized classification model accuracies of 93.55% were obtained for 11 healthy participants. Generalized model accuracies of 82.5% indicated that the device can classify arm motions on a user without prior training. The addition of a thermal sensor significantly increased classification accuracy of a single wrist-mounted inertial device, from 75 to 93.55%, (F(1,20)?=?90.53, p?=?7.25e-09).ConclusionThis study found that the addition of the thermal sensor improved the classification accuracy of 24 arm motions from 75 to 93.55% for a single-device system. Our results provide evidence that a single device can be used to classify a relatively large number of arm motions from arm rehabilitation protocols. While this study provides a conceptual proof-of-concept with a healthy population, additional investigation is required to evaluate the performance of this system for specific applications, such as activity classification for physically affected stroke survivors undergoing home-based rehabilitation.
| S-EPMC6505300 | biostudies-literature
Project description:OBJECTIVES:Pancreatic microcirculation has an essential role in orchestrating pancreatic homeostasis. Inherent complexity and technological limitation lead to interobserver variability and 1-sided microcirculatory data. Here, we introduce a multimodal device and computer algorithm-based platform for monitoring and visualizing integrated pancreatic microcirculation profiles. METHODS:After anesthetizing and exposing pancreas tissue of BALB/c mice, probes of Oxygen to See, Microx TX3, and MoorVMS-LDF2 were positioned at pancreas in situ to capture the pancreatic microcirculatory oxygen (hemoglobin oxygen saturation, relative amount of hemoglobin, and partial oxygen pressure) and microhemodynamic data (microvascular blood perfusion and velocity). To assess and visualize pancreatic microcirculation profiles, raw data of pancreatic microcirculation profiles were processed and transformed using interquartile range and min-max normalization by Python and Apache ECharts. RESULTS:The multimodal device-based platform was established and 3-dimensional microcirculatory modules were constructed. Raw data sets of pancreatic microcirculatory oxygen and microhemodynamic were collected. The outlier of data set was adjusted to the boundary value and raw data set was preprocessed. Normalized pancreatic microcirculation profiles were integrated into the 3-dimensional histogram and scatter modules, respectively. The 3-dimensional modules of pancreatic microcirculation profiles were then generated. CONCLUSIONS:We established a multimodal device and computer algorithm-based monitoring platform for visualizing integrated pancreatic microcirculation profiles.
| S-EPMC7447165 | biostudies-literature
Project description:BackgroundAs the sensing capabilities of wearable devices improve, there is increasing interest in their application in medical settings. Capabilities such as heart rate monitoring may be useful in hospitalized patients as a means of enhancing routine monitoring or as part of an early warning system to detect clinical deterioration.ObjectiveTo evaluate the accuracy of heart rate monitoring by a personal fitness tracker (PFT) among hospital inpatients.MethodsWe conducted a prospective observational study of 50 stable patients in the intensive care unit who each completed 24 hours of heart rate monitoring using a wrist-worn PFT. Accuracy of heart rate recordings was compared with gold standard measurements derived from continuous electrocardiographic (cECG) monitoring. The accuracy of heart rates measured by pulse oximetry (Spo2.R) was also measured as a positive control.ResultsOn a per-patient basis, PFT-derived heart rate values were slightly lower than those derived from cECG monitoring (average bias of -1.14 beats per minute [bpm], with limits of agreement of 24 bpm). By comparison, Spo2.R recordings produced more accurate values (average bias of +0.15 bpm, limits of agreement of 13 bpm, P<.001 as compared with PFT). Personal fitness tracker device performance was significantly better in patients in sinus rhythm than in those who were not (average bias -0.99 bpm vs -5.02 bpm, P=.02).ConclusionsPersonal fitness tracker-derived heart rates were slightly lower than those derived from cECG monitoring in real-world testing and not as accurate as Spo2.R-derived heart rates. Performance was worse among patients who were not in sinus rhythm. Further clinical evaluation is indicated to see if PFTs can augment early warning systems in hospitals.Trial registrationClinicalTrials.gov NCT02527408; https://clinicaltrials.gov/ct2/show/NCT02527408 (Archived by WebCite at http://www.webcitation.org/6kOFez3on).
| S-EPMC5050383 | biostudies-literature
Project description:BackgroundRehabilitation robotics is progressing towards developing robots that can be used as advanced tools to augment the role of a therapist. These robots are capable of not only offering more frequent and more accessible therapies but also providing new insights into treatment effectiveness based on their ability to measure interaction parameters. A requirement for having more advanced therapies is to identify how robots can 'adapt' to each individual's needs at different stages of recovery. Hence, our research focused on developing an adaptive interface for the GENTLE/A rehabilitation system. The interface was based on a lead-lag performance model utilising the interaction between the human and the robot. The goal of the present study was to test the adaptability of the GENTLE/A system to the performance of the user.MethodsPoint-to-point movements were executed using the HapticMaster (HM) robotic arm, the main component of the GENTLE/A rehabilitation system. The points were displayed as balls on the screen and some of the points also had a real object, providing a test-bed for the human-robot interaction (HRI) experiment. The HM was operated in various modes to test the adaptability of the GENTLE/A system based on the leading/lagging performance of the user. Thirty-two healthy participants took part in the experiment comprising of a training phase followed by the actual-performance phase.ResultsThe leading or lagging role of the participant could be used successfully to adjust the duration required by that participant to execute point-to-point movements, in various modes of robot operation and under various conditions. The adaptability of the GENTLE/A system was clearly evident from the durations recorded. The regression results showed that the participants required lower execution times with the help from a real object when compared to just a virtual object. The 'reaching away' movements were longer to execute when compared to the 'returning towards' movements irrespective of the influence of the gravity on the direction of the movement.ConclusionsThe GENTLE/A system was able to adapt so that the duration required to execute point-to-point movement was according to the leading or lagging performance of the user with respect to the robot. This adaptability could be useful in the clinical settings when stroke subjects interact with the system and could also serve as an assessment parameter across various interaction sessions. As the system adapts to user input, and as the task becomes easier through practice, the robot would auto-tune for more demanding and challenging interactions. The improvement in performance of the participants in an embedded environment when compared to a virtual environment also shows promise for clinical applicability, to be tested in due time. Studying the physiology of upper arm to understand the muscle groups involved, and their influence on various movements executed during this study forms a key part of our future work.
| S-EPMC3849953 | biostudies-literature
Project description:Background: Uterine activity (UA) monitoring is an essential element of pregnancy management. The gold-standard intrauterine pressure catheter (IUPC) is invasive and requires ruptured membranes, while the standard-of-care, external tocodynamometry (TOCO)'s accuracy is hampered by obesity, maternal movements, and belt positioning. There is an urgent need to develop telehealth tools enabling patients to remotely access care. Here, we describe and demonstrate a novel algorithm enabling remote, non-invasive detection and monitoring of UA by analyzing the modulation of the maternal electrocardiographic and phonocardiographic signals. The algorithm was designed and implemented as part of a wireless, FDA-cleared device designed for remote pregnancy monitoring. Two separate prospective, comparative, open-label, multi-center studies were conducted to test this algorithm. Methods: In the intrapartum study, 41 laboring women were simultaneously monitored with IUPC and the remote pregnancy monitoring device. Ten patients were also monitored with TOCO. In the antepartum study, 147 pregnant women were simultaneously monitored with TOCO and the remote pregnancy monitoring device. Results: In the intrapartum study, the remote pregnancy monitoring device and TOCO had sensitivities of 89.8 and 38.5%, respectively, and false discovery rates (FDRs) of 8.6 and 1.9%, respectively. In the antepartum study, a direct comparison of the remote pregnancy monitoring device to TOCO yielded a sensitivity of 94% and FDR of 31.1%. This high FDR is likely related to the low sensitivity of TOCO. Conclusion: UA monitoring via the new algorithm embedded in the remote pregnancy monitoring device is accurate and reliable and more precise than TOCO standard of care. Together with the previously reported remote fetal heart rate monitoring capabilities, this novel method for UA detection expands the remote pregnancy monitoring device's capabilities to include surveillance, such as non-stress tests, greatly benefiting women and providers seeking telehealth solutions for pregnancy care.
| S-EPMC9343786 | biostudies-literature
Project description: Not available
| S-EPMC7859653 | biostudies-literature
Project description:BackgroundWearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist-worn devices for AF detection using photoplethysmography-based algorithms perform only periodic checks when the user is stationary and are US Food and Drug Administration cleared for prediagnostic uses without intended use for clinical decision-making. There is an unmet need for medical-grade diagnostic wrist-worn devices that provide long-term, continuous AF monitoring.Methods and resultsWe evaluated the performance of a wrist-worn device with lead-I ECG and continuous photoplethysmography (Verily Study Watch) and photoplethysmography-based convolutional neural network for AF detection and burden estimation in a prospective multicenter study that enrolled 117 patients with paroxysmal AF. A 14-day continuous ECG monitor (Zio XT) served as the reference device to evaluate algorithm sensitivity and specificity for detection of AF in 15-minute intervals. A total of 91 857 intervals were contributed by 111 subjects with evaluable reference and test data (18.3 h/d median watch wear time). The watch was 96.1% sensitive (95% CI, 92.7%-98.0%) and 98.1% specific (95% CI, 97.2%-99.1%) for interval-level AF detection. Photoplethysmography-derived AF burden estimation was highly correlated with the reference device burden (R2=0.986) with a mean difference of 0.8% (95% limits of agreement, -6.6% to 8.2%).ConclusionsContinuous monitoring using a photoplethysmography-based convolutional neural network incorporated in a wrist-worn device has clinical-grade performance for AF detection and burden estimation. These findings suggest that monitoring can be performed with wrist-worn wearables for diagnosis and clinical management of AF.Registration informationURL: https://www.clinicaltrials.gov; Unique identifier: NCT04546763.
| S-EPMC10727259 | biostudies-literature