scholarly journals CardiacProfileR: An R package for extraction and visualisation of heart rate profiles from wearable fitness trackers

2018 ◽  
Author(s):  
Djordje Djordjevic ◽  
Beni K. Cawood ◽  
Sabrina K. Rispin ◽  
Anushi Shah ◽  
Leo H. H. Yim ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Stress Test ◽  
R Package ◽  
Exercise Stress ◽  
Stress Tests ◽  
Activity Data ◽  
Physical Activity Data ◽  
Rate Profile ◽  
Fitness Trackers

AbstractA person’s heart rate profile, which consists of resting heart rate, increase of heart rate upon exercise and recovery of heart rate after exercise, is traditionally measured by electrocardiography during a controlled exercise stress test. A heart rate profile is a useful clinical tool to identify individuals at risk of sudden death and other cardiovascular conditions. Nonetheless, conducting such exercise stress tests routinely is often inconvenient and logistically challenging for patients. The widespread availability of affordable wearable fitness trackers, such as Fitbit and Apple Watch, provides an exciting new means to collect longitudinal heart rate and physical activity data. We reason that by combining the heart rate and physical activity data from these devices, we can construct a person’s heart rate profile. Here we present an open source R package CardiacProfileR for extraction, analysis and visualisation of heart rate dynamics during physical activities from data generated from common wearable heart rate monitors. This package represents a step towards quantitative deep phenotyping in humans. CardiacProfileR is available via an MIT license at https://github.com/VCCRI/CardiacProfileR.

scholarly journals Smartphone-recorded physical activity for estimating cardiorespiratory fitness

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Micah T. Eades ◽  
Athanasios Tsanas ◽  
Stephen P. Juraschek ◽  
Daniel B. Kramer ◽  
Ernest Gervino ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cardiorespiratory Fitness ◽  
High Performance ◽  
Stress Test ◽  
Absolute Error ◽  
Activity Data ◽  
Physical Activity Data ◽  
Cardiac Stress ◽  
Metabolic Equivalents ◽  
Multivariable Regression

AbstractWhile cardiorespiratory fitness is strongly associated with mortality and diverse outcomes, routine measurement is limited. We used smartphone-derived physical activity data to estimate fitness among 50 older adults. We recruited iPhone owners undergoing cardiac stress testing and collected recent iPhone physical activity data. Cardiorespiratory fitness was measured as peak metabolic equivalents of task (METs) achieved on cardiac stress test. We then estimated peak METs using multivariable regression models incorporating iPhone physical activity data, and validated with bootstrapping. Individual smartphone variables most significantly correlated with peak METs (p-values both < 0.001) included daily peak gait speed averaged over the preceding 30 days (r = 0.63) and root mean square of the successive differences of daily distance averaged over 365 days (r = 0.57). The best-performing multivariable regression model included the latter variable, as well as age and body mass index. This model explained 68% of variability in observed METs (95% CI 46%, 81%), and estimated peak METs with a bootstrapped mean absolute error of 1.28 METs (95% CI 0.98, 1.60). Our model using smartphone physical activity estimated cardiorespiratory fitness with high performance. Our results suggest larger, independent samples might yield estimates accurate and precise for risk stratification and disease prognostication.

P4431Cardiopulmonary exerscise stress tests vs standard exercise stress tests in the rational prescription of physical activity in men

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
A Stuto ◽  
B Armaro ◽  
E Cosentino ◽  
G Canonico ◽  
A Ambu ◽  
...  
Keyword(s):  
Physical Activity ◽  
Oxygen Consumption ◽  
Heart Disease ◽  
Direct Measurement ◽  
Stress Test ◽  
Exercise Stress Test ◽  
Exercise Stress ◽  
Sustained Load ◽  
Stress Tests ◽  
Group B

Abstract Background The standard exercise stress test (ET) provides direct measurement of the load tolerated by the subject, but the cardiopulmonary exercise stress test (CPET) in addition to the measurement of the work load also provides direct measurement of the corresponding oxygen consumption. Objective The aim of the present study was to estimate the differences between the estimated oxygen consumption based on the load (ET) and the one directly measured with the CPET, and to highlight and quantify the inaccuracies of the indirect estimate of oxygen consumption and its consequences on the rational prescription of physical activity. Material and method The tests performed on 7544 males from January 2007 to October 2018 were analyzed. For each test the sustained load, the consumption of direct oxygen, and the estimated oxygen consumption based on the load sustained with the use of a formula provided by the American College of Sports Medicine and reported below: VO2max (ml/kg/min) = (10.51 x Watt) + (6.35 x weight in kg) − (10.49 x Età) + 519.3. The total population of the subjects examined was divided into two groups: Group A: 1358 subjects without signs of heart disease, and Group B: 6186 subjects with heart disease. Results In the total population the oxygen consumption (VO2) estimated on the basis of the load was overestimated in 22% of subjects, underestimated in 55% of subjects and overlapping in 23% of subjects. In Group B the calculated VO2 was overestimated in 38%, underestimated in 54% and overlapping in 8% of the subjects. In group A the calculated VO2 was overestimated in 33%, underestimated in 54% and overlapping in 9% of subjects. Conclusions In subjects with heart disease the VO2 calculated on the basis of the sustained load is overestimated or underestimated in 92% of subjects. The CPET through direct measurement of oxygen consumption provides a precise estimate of functional capacity, an essential prerequisite for a correct rational prescription of physical activity. Because of this peculiarity, the CPET is absolutely irreplaceable in cardiac patients in which a correct rational prescription of physical activity is crucial.

scholarly journals A roadmap: Social distancing physical activity data collection to protect those with intellectual disabilities

2021 ◽  
pp. 174462952110096
Author(s):  
Whitley J Stone ◽  
Kayla M Baker
Keyword(s):  
Physical Activity ◽  
Data Collection ◽  
Intellectual Disabilities ◽  
Research Methods ◽  
Virus Transmission ◽  
Future Research ◽  
Activity Data ◽  
Physical Activity Data ◽  
Social Distancing ◽  
Novel Coronavirus

The novel coronavirus may impact exercise habits of those with intellectual disabilities. Due to the mandated discontinuation of face-to-face research, investigators must adapt projects to protect all involved while collecting objective physical activity metrics. This brief report outlines a modification process of research methods to adhere to social distancing mandates present during COVID-19. Actions taken included electronic consent and assent forms, an electronic survey, and mailing an accelerometer with included instructions. The amended research methods were implemented without risk for virus transmission or undue burden on the research team, participant, or caregiver. Recruitment was likely impacted by the coronavirus-mediated quarantine, plausibly resulting in bias. Objective physical activity data collection can be sufficiently modified to protect those with intellectual disabilities and investigators. Future research designs may require greater participant incentives and the creation of in-home participation.

scholarly journals Concurrent Measurement of Global Positioning System and Event-Based Physical Activity Data: A Methodological Framework for Integration

2020 ◽  
pp. 1-14
Author(s):  
Anna M.J. Iveson ◽  
Malcolm H. Granat ◽  
Brian M. Ellis ◽  
Philippa M. Dall
Keyword(s):  
Physical Activity ◽  
Outcome Measures ◽  
Global Positioning System ◽  
Gps Data ◽  
Positioning System ◽  
Activity Data ◽  
Data Set ◽  
Physical Activity Data ◽  
Global Positioning ◽  
Event Based

Objective: Global positioning system (GPS) data can add context to physical activity data and have previously been integrated with epoch-based physical activity data. The current study aimed to develop a framework for integrating GPS data and event-based physical activity data (suitable for assessing patterns of behavior). Methods: A convenience data set of concurrent GPS (AMOD) and physical activity (activPAL) data were collected from 69 adults. The GPS data were (semi)regularly sampled every 5 s. The physical activity data output was presented as walking events, which are continuous periods of walking with a time-stamped start time and duration (to nearest 0.1 s). The GPS outcome measures and the potential correspondence of their timing with walking events were identified and a framework was developed describing data integration for each combination of GPS outcome and walking event correspondence. Results: The GPS outcome measures were categorized as those deriving from a single GPS point (e.g., location) or from the difference between successive GPS points (e.g., distance), and could be categorical, scale, or rate outcomes. Walking events were categorized as having zero (13% of walking events, 3% of walking duration), or one or more (52% of walking events, 75% of walking duration) GPS points occurring during the event. Additionally, some walking events did not have GPS points suitably close to allow calculation of outcome measures (31% of walking events, 22% of walking duration). The framework required different integration approaches for each GPS outcome type, and walking events containing zero or more than one GPS points.

scholarly journals Framework for Selecting and Benchmarking Mobile Devices in Psychophysiological Research

2019 ◽  
Author(s):  
Ian R Kleckner ◽  
Mallory Feldman ◽  
Matthew Goodwin ◽  
Karen S. Quigley
Keyword(s):  
Physical Activity ◽  
Consumer Electronics ◽  
Use Cases ◽  
Assessment Procedure ◽  
Laboratory System ◽  
Activity Data ◽  
Physiological Measurement ◽  
Physical Activity Data ◽  
Quantitative Analyses ◽  
Consumer Devices

Commercially available consumer electronics (smartwatches and wearable biosensors) are increasingly enabling acquisition of peripheral physiological and physical activity data inside and outside of laboratory settings. However, there is scant literature available for selecting and assessing the suitability of these novel devices for scientific use. To overcome this limitation, the current paper offers a framework to aid researchers in choosing and evaluating wearable technologies for use in empirical research. Our seven-step framework includes: (1) identifying signals of interest; (2) characterizing intended use cases; (3) identifying study-specific pragmatic needs; (4) selecting devices for evaluation; (5) establishing an assessment procedure; (6) performing qualitative and quantitative analyses on resulting data; and, if desired, (7) conducting power analyses to determine sample size needed to more rigorously compare performance across devices. We illustrate the application of the framework by comparing electrodermal, cardiovascular, and accelerometry data from a variety of commercial wireless sensors (Affectiva Q, Empatica E3, Empatica E4, Actiwave Cardio, Shimmer) relative to a well-validated, wired Mindware laboratory system. Our evaluations are performed in two studies (N=10, N=11) involving psychometrically sound, standardized tasks that include physical activity and affect induction. After applying our framework to this data, we conclude that only some commercially available consumer devices for physiological measurement are capable of wirelessly measuring peripheral physiological and physical activity data of sufficient quality for scientific use cases. Thus, the framework appears to be beneficial at suggesting steps for conducting more systematic, transparent, and rigorous evaluations of mobile physiological devices prior to deployment in studies.

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