scholarly journals Blazar OJ 287 after First VHE Activity: Tracking the Reemergence of the HBL-like Component in 2020

2021 ◽  
Vol 921 (1) ◽  
pp. 18
Author(s):  
Pankaj Kushwaha ◽  
Main Pal ◽  
Nibedita Kalita ◽  
Neeraj Kumari ◽  
Sachindra Naik ◽  
...  
Keyword(s):  
Activity Tracking ◽  
Blazar Oj 287

Activity Tracking After Surgery: Does It Correlate With Postoperative Complications?

2021 ◽  
pp. 000313482098881
Author(s):  
Yehonatan Nevo ◽  
Tali Shaltiel ◽  
Naama Constantini ◽  
Danny Rosin ◽  
Mordechai Gutman ◽  
...  
Keyword(s):  
Postoperative Complications ◽  
Enhanced Recovery ◽  
Length Of Hospital Stay ◽  
Inverse Correlation ◽  
Complication Rates ◽  
Activity Tracking ◽  
Primary Endpoints ◽  
Specific Complication ◽  
Secondary Endpoints ◽  
Enhanced Recovery Programs

Background Postoperative ambulation is an important tenet in enhanced recovery programs. We quantitatively assessed the correlation of decreased postoperative ambulation with postoperative complications and delays in gastrointestinal function. Methods Patients undergoing major abdominal surgery were fitted with digital ankle pedometers yielding continuous measurements of their ambulation. Primary endpoints were the overall and system-specific complication rates, with secondary endpoints being the time to first passage of flatus and stool, the length of hospital stay, and the rate of readmission. Results 100 patients were enrolled. We found a significant, independent inverse correlation between the number of steps on the first and second postoperative days (POD1/2) and the incidence of complications as well as the recovery of GI function and the likelihood of readmission ( P < .05). POD2 step count was an independent risk factor for severe complications ( P = .026). Discussion Digitally quantified ambulation data may be a prognostic biomarker for the likelihood of severe postoperative complications.

scholarly journals Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3757 ◽  
Author(s):  
Alejandro José Laguna Sanz ◽  
José Luis Díez ◽  
Marga Giménez ◽  
Jorge Bondia
Keyword(s):  
Physical Activity ◽  
Estimation Error ◽  
Glucose Monitoring ◽  
Linear Regression Models ◽  
Activity Tracking ◽  
Metabolic Equivalent ◽  
Optimal Inputs ◽  
Input Model ◽  
Continuous Glucose Monitors ◽  
Monitoring Devices

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets” (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices.

scholarly journals Development and Evaluation of an Accelerometer-Based Protocol for Measuring Physical Activity Levels in Cancer Survivors: Development and Usability Study

10.2196/18491 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18491
Author(s):  
Tracy E Crane ◽  
Meghan B Skiba ◽  
Austin Miller ◽  
David O Garcia ◽  
Cynthia A Thomson
Keyword(s):  
Physical Activity ◽  
Ovarian Cancer ◽  
Data Collection ◽  
Cancer Survivors ◽  
Accelerometer Data ◽  
Activity Tracking ◽  
Activity Data ◽  
Convenience Sample ◽  
Time Points ◽  
Activity Measurements

Background The collection of self-reported physical activity using validated questionnaires has known bias and measurement error. Objective Accelerometry, an objective measure of daily activity, increases the rigor and accuracy of physical activity measurements. Here, we describe the methodology and related protocols for accelerometry data collection and quality assurance using the Actigraph GT9X accelerometer data collection in a convenience sample of ovarian cancer survivors enrolled in GOG/NRG 0225, a 24-month randomized controlled trial of diet and physical activity intervention versus attention control. Methods From July 2015 to December 2019, accelerometers were mailed on 1337 separate occasions to 580 study participants to wear at 4 time points (baseline, 6, 12, and 24 months) for 7 consecutive days. Study staff contacted participants via telephone to confirm their availability to wear the accelerometers and reviewed instructions and procedures regarding the return of the accelerometers and assisted with any technology concerns. Results We evaluated factors associated with wear compliance, including activity tracking, use of a mobile app, and demographic characteristics with chi-square tests and logistic regression. Compliant data, defined as ≥4 consecutive days with ≥10 hours daily wear time, exceeded 90% at all study time points. Activity tracking, but no other characteristics, was significantly associated with compliant data at all time points (P<.001). This implementation of data collection through accelerometry provided highly compliant and usable activity data in women who recently completed treatment for ovarian cancer. Conclusions The high compliance and data quality associated with this protocol suggest that it could be disseminated to support researchers who seek to collect robust objective activity data in cancer survivors residing in a wide geographic area.

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