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.

Abstract MP79: Physical Activity Patterns by Data-driven Model-based Clustering Improve Association with Cardiovascular Risk Factors in a Multiethnic Cohort: The Northern Manhattan Study

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Ken Cheung ◽  
Joshua Z Willey ◽  
Gary Yu ◽  
Palma Gervasi-Franklin ◽  
Melanie M Wall ◽  
...  
Keyword(s):  
Physical Activity ◽  
Activity Patterns ◽  
Data Driven ◽  
Clustering Method ◽  
Activity Data ◽  
Physical Activity Data ◽  
Physical Activity Patterns ◽  
Entire Cohort ◽  
Model Based ◽  
Metabolic Equivalents

Background: Physical activity is a complex modifiable risk factor (RF) for cardiovascular disease (CVD). Current methods to measure physical activity are limited by the use of summary scores such as total metabolic equivalents score (METS). Hypothesis: Physical activity patterns derived by a data-driven clustering method are associated with CVD RFs independently of METS. Methods: The Northern Manhattan Study is a prospective cohort of older, urban-dwelling, multiethnic, stroke-free individuals. Questionnaires were used to capture multi-dimensions of leisure-time physical activity, which was summarized with METS (total activity minutes х intensity in MET). Participants were grouped into previously defined METS categories (less than 1, greater than 14, and 1-14), and also into clusters by multivariate finite mixture modeling based on activity frequency, duration, energy expenditure, and number of activity types. Bayesian information criterion was used to decide number of clusters. Associations between model-based clusters and 4 RFs (diabetes, hypertension, obesity, high waist circumference) were assessed in the entire cohort and in each METS category; associations between METS and RFs were assessed in each cluster. Chi-squared test was used. Results: Physical activity data were available in 3293, with mean age 69 years, 63% women, and 52% Hispanic. Six clusters were identified and labeled I-VI (Table 1). Model-based clusters were associated with all four RFs (all p≤0.01), with clusters V and VI having lower RFs prevalence than the others: the association with obesity prevailed among those with 1≤METS≤14 (p<0.01); and with hypertension among those with METS>14 (p=0.03). METS categories were associated with all four RFs in the entire cohort (all p≤0.04); METS and RFs became no longer significantly associated within clusters. Conclusions: A data-driven clustering method for depicting physical activity data is a principled, generalizable approach to form subgroups associated with CVD RFs independently of METS.

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 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 Comparison of Preoperative Assessment of Patient’s Metabolic Equivalents (METs) Estimated from History versus Measured by Exercise Cardiac Stress Testing

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Adam S. Weinstein ◽  
Martin I. Sigurdsson ◽  
Angela M. Bader
Keyword(s):  
Cardiac Failure ◽  
Stress Testing ◽  
Stress Test ◽  
Preoperative Evaluation ◽  
Preoperative Assessment ◽  
Exercise Stress ◽  
Cardiac Stress ◽  
Metabolic Equivalents ◽  
Cardiac Stress Testing ◽  
Cardiac Stress Test

Background. Preoperative anesthetic evaluations of patients before surgery traditionally involves assessment of a patient’s functional capacity to estimate perioperative risk of cardiovascular complications and need for further workup. This is typically done by inquiring about the patient’s physical activity, with the goal of providing an estimate of the metabolic equivalents (METs) that the patient can perform without signs of myocardial ischemia or cardiac failure. We sought to compare estimates of patients’ METs between preoperative assessment by medical history with quantified assessment of METs via the exercise cardiac stress test. Methods. A single-center retrospective chart review from 12/1/2005 to 5/31/2015 was performed on 492 patients who had preoperative evaluations with a cardiac stress test ordered by a perioperative anesthesiologist. Of those, a total of 170 charts were identified as having a preoperative evaluation note and an exercise cardiac stress test. The METs of the patient estimated by history and the METs quantified by the exercise cardiac stress test were compared using a Bland–Altman plot and Cohen’s kappa. Results. Exercise cardiac stress test quantified METs were on average 3.3 METS higher than the METs estimated by the preoperative evaluation history. Only 9% of patients had lower METs quantified by the cardiac stress test than by history. Conclusions. The METs of a patient estimated by preoperative history often underestimates the METs measured by exercise stress testing. This demonstrates that the preoperative assessments of patients’ METs are often conservative which errs on the side of patient safety as it lowers the threshold for deciding to order further cardiac stress testing for screening for ischemia or cardiac failure.

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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