Use of Heart Rate Cutpoints to Assess Physical Activity Intensity in Sixth-Grade Girls

2000 ◽  
Vol 12 (3) ◽  
pp. 284-292 ◽  
Author(s):  
Karin M. Allor ◽  
James M. Pivarnik
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Adolescent Girls ◽  
Sixth Grade ◽  
Maximal Heart Rate ◽  
Treadmill Test ◽  
Physical Activity Intensity ◽  
Heart Rates ◽  
Activity Intensity ◽  
The Relationship

We calculated individual heart rate–oxygen consumption (HR–V̇O2) regression lines for 49 sixth-grade girls based on a treadmill test. From these data, we determined V̇O2 at HRs of 140 and 160 b · min−1 and 50%, 60%, and 75% of maximal heart rate reserve (MHRR), and the relationship between V̇O2 and %fat at given heart rates. Results indicated traditional 140 and 160 b · min−1 HR cutpoints were at the low end of exercise intensity (46 and 63% V̇O2max) and were negatively correlated with %fat. Heart rates at 50%, 60%, and 75% MHRR corresponded to 52%, 62%, and 76% of V̇O2max. Although the best method for analyzing HR data to describe physical activity intensity is unknown, use of 140 and 160 cutpoints may not describe vigorous or very hard exercise in adolescent girls as well as 75% MHRR. Researchers should also consider the effects of adiposity when using specific heart rate cutpoints.

Examining the Relationship Between Physical Activity Intensity and Adiposity in Young Women

2015 ◽  
Vol 12 (6) ◽  
pp. 764-769 ◽  
Author(s):  
Bruce W. Bailey ◽  
Pamela Borup ◽  
James D. LeCheminant ◽  
Larry A. Tucker ◽  
Jacob Bromley
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Body Fat ◽  
Young Women ◽  
Physical Activity Intensity ◽  
Fat Percentage ◽  
Bod Pod ◽  
Activity Intensity ◽  
Objectively Measured ◽  
The Relationship

Background:The purpose of this study was to assess the relationship between intensity of physical activity (PA) and body composition in 343 young women.Methods:Physical activity was objectively measured using accelerometers worn for 7 days in women 17 to 25 years. Body composition was assessed using the BOD POD.Results:Young women who spent less than 30 minutes a week performing vigorous PA had significantly higher body fat percentages than women who performed more than 30 minutes of vigorous PA per week (F = 4.54, P = .0113). Young women who spent less than 30 minutes per day in moderate to vigorous PA (MVPA) had significantly higher body fat percentages than those who obtained more than 30 minutes per day of MVPA (F = 7.47, P = .0066). Accumulating more than 90 minutes of MVPA per day was associated with the lowest percent body fat. For every 10 minutes spent in MVPA per day, the odds of having a body fat percentage above 32% decreased by 29% (P = .0002).Conclusion:Vigorous PA and MVPA are associated with lower adiposity. Young women should be encouraged to accumulate at least 30 minutes of MVPA per day, however getting more than 90 minutes a day is predictive of even lower levels of adiposity.

scholarly journals Application and Validation of Activity Monitors’ Epoch Lengths and Placement Sites for Physical Activity Assessment in Exergaming

2018 ◽  
Vol 7 (9) ◽  
pp. 268 ◽  
Author(s):  
Jungyun Hwang ◽  
Austin Fernandez ◽  
Amy Lu
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Intraclass Correlation ◽  
Activity Levels ◽  
Physical Activity Intensity ◽  
Activity Monitors ◽  
Epoch Length ◽  
Activity Intensity ◽  
Physical Activity Levels ◽  
Rate Sensor

We assessed the agreement of two ActiGraph activity monitors (wGT3X vs. GT9X) placed at the hip and the wrist and determined an appropriate epoch length for physical activity levels in an exergaming setting. Forty-seven young adults played a 30-min exergame while wearing wGT3X and GT9X on both hip and wrist placement sites and a heart rate sensor below the chest. Intraclass correlation coefficient indicated that intermonitor agreement in steps and activity counts was excellent on the hip and good on the wrist. Bland-Altman plots indicated good intermonitor agreement in the steps and activity counts on both placement sites but a significant intermonitor difference was detected in steps on the wrist. Time spent in sedentary and physical activity intensity levels varied across six epoch lengths and depended on the placement sites, whereas time spent from a 1-s epoch of the hip-worn monitors most accurately matched the relative exercise intensity by heart rate. Hip placement site was associated with better step-counting accuracy for both activity monitors and more valid estimation of physical activity levels. A 1-s epoch was the most appropriate epoch length to detect short bursts of intense physical activity and may be the best choice for data processing and analysis in exergaming studies examining intermittent physical activities.

HEART RATE AS AN INDICATOR OF PHYSICAL ACTIVITY INTENSITY IN ADOLESCENTS

2001 ◽  
Vol 33 (5) ◽  
pp. S250 ◽  
Author(s):  
U Ekelund ◽  
E Poortvliet ◽  
A Yngve ◽  
A Nilsson ◽  
A Hurtig-Wennl??f ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Physical Activity Intensity ◽  
Activity Intensity

scholarly journals Hubungan Perilaku Konsumsi dan Aktivitas Fisik dengan Diabetes Mellitus di Indonesia

2019 ◽  
Vol 47 (2) ◽  
pp. 97-106
Author(s):  
Made Agus Nurjana ◽  
Ni Nyoman Veridiana
Keyword(s):  
Physical Activity ◽  
Diabetes Mellitus ◽  
Physical Activity Intensity ◽  
Keywords Diabetes ◽  
Cross Sectional ◽  
Middle Income ◽  
Activity Intensity ◽  
Light Activity ◽  
Cross Sectional Design ◽  
The Relationship

Abstrak   Prevalensi Diabetes mellitus (DM) mengalami peningkatan secara global baik di negara berpenghasilan tinggi maupun negara berpenghasilan rendah dan menengah termasuk di Indonesia. Indonesia menduduki urutan ke empat dengan prevalensi diabetes tertinggi di dunia setelah India, China, dan Amerika Serikat. Tujuan dari tulisan ini adalah untuk mengkaji hubungan pola konsumsi dan aktivitas fisik dengan kejadian DM di Indonesia berdasarkan data Riskesdas tahun 2013.  Pengumpulan data dilakukan pada bulan Mei - Juni 2013 di 33 provinsi dan 497 kabupaten/kota di Indonesia. Desain penelitian ini adalah cross sectional. Jumlah sampel sebanyak 722.329 responden yang berusia 15 tahun ke atas. Hasil penelitian menunjukkan bahwa aktivitas fisik merupakan faktor risiko dominan terhadap kejadian DM di Indonesia. Masyarakat yang memiliki kebiasaan hanya melakukan aktifitas ringan mempunyai peluang untuk terkena DM 2,9 kali dibandingkan dengan masyarakat yang memiliki kebiasaan melakukan aktifitas berat, sedangkan masyarakat yang memiliki kebiasaan melakukan aktivitas sedang mempunyai peluang lebih rendah terkena DM yaitu 1,8 kali dibandingkan dengan aktivitas berat. Semakin berat aktivitas fisik yang dilakukan maka semakin sedikit kemungkinan terkena DM. Dalam mencegah semakin tingginya prevalensi DM di Indonesia maka diperlukan peningkatkan kesadaran masyarakat untuk meningkatkan intensitas aktivitas fisik terutama bagi masyarakat yang aktivitas fisiknya rendah.   Kata kunci : Diabetes mellitus, perilaku konsumsi, aktivitas fisik   Abstract   Prevalency Diabetes Mellitus (DM) experience increasing globally either in high income states or in the low and middle income states including Indonesia. Indonesia is the fourth prevalency Diabetes Mellitus in the world after India, China, and United States. The aim of this study is to analyze the relationship between consumsion pattern and physical activity on DM in Indonesia based on Riskesdas data in 2013. Data are gathered from may up to June 2013 in 33 provinces and 497 regencies/cities in Indonesia. The research is cross sectional design. The samples are 722.329 respondents aging among 15 years and over. The results show that the physical activity is the risk factor dominantly on the DM in Indonesia. Society having only light activity have a tendency to get DM 2.9 times compared to those who have the strongest activity, while those who are stronger activity have lower tendency to get DM that is 1.8 times compared to those who have the strongest activity. To prevent higher prevalency DM in Indonesia, it is expected to rise the societal care to increase physical activity intensity primarily for those who has the low physical activities.   Keywords : Diabetes mellitus, consumtive behavior, physical activity

Abstract P497: Association Between Accelerometer-estimated Physical Activity Intensity and Heart Rate Variability: Mediation by Glycemic Measures and Triglycerides (from The Cardia Study)

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Zachary C Pope ◽  
Kelley P Gabriel ◽  
Kara M Whitaker ◽  
Lin Y Chen ◽  
Pamela J Schreiner ◽  
...  
Keyword(s):  
Physical Activity ◽  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Fasting Glucose ◽  
Oral Glucose ◽  
Physical Activity Intensity ◽  
Cross Sectional ◽  
Rr Intervals ◽  
Activity Intensity

Introduction: We estimated cross-sectional associations between accelerometer-estimated light (LPA), moderate (MPA), and vigorous (VPA) intensity physical activity (PA) and heart rate variability (HRV), and tested mediation of these associations by glycemic control indices, blood lipids, and blood pressure. Hypothesis: PA is positively and independently associated with higher (improved) HRV. Glycemic measures are partial physiological mediators of these associations. Methods: Data were from 1,668 participants (X -age = 46 ± 4 yrs, 58% F, 40% black) in Year 20 (2005-06) of the Coronary Artery Risk Development in Young Adults (CARDIA) Fitness Study. The ActiGraph 7164 estimated participants’ mean min/d of LPA, MPA, and VPA over 7d. Three sequential 10-sec 12-lead ECG strips provided standard deviation of all normal RR intervals (SDNN) and root mean square of all successive RR intervals (rMSSD) HRV. Physiological mediators included fasting glucose and insulin as well as 2-hr oral glucose tolerance (OGTT), fasting triglycerides (TG), HDL-C, and systolic blood pressure (BP). Multiple linear regression, controlling for demographic and lifestyle confounders, assessed independent associations of PA with SDNN and rMSSD HRV per 1-SD. Mediation analyses computed the proportion of the PA-HRV associations attributable to physiological mediators. Results: Participants averaged 360.2 ± 83.8, 33.0 ± 22.0, and 2.7 ± 6.2 min/d of LPA, MPA, and VPA, respectively, with mean values for SDNN (32.6 ± 22.4 ms) and rMSSD (34.0 ± 24.8 ms) similar. VPA was associated with both HRV metrics (SDNN: std = .06 [.03, .10]; rMSSD: std = .08, [.05, .12]) and LPA with rMSSD only (std = .05, [.01, .08]). Fasting glucose and insulin mediated between 11.6%-20.7% of the association of VPA and LPA with HRV (Table). Triglycerides also mediated these associations (range: 9.6%-13.4%; Table). Conclusions: Accelerometer-estimated VPA and LPA were positively associated with higher HRV. These associations may be due most to glycemia and insulinemia.

scholarly journals The relationship between physical activity intensity and domains with cardiac autonomic modulation in adults

Medicine ◽  
2019 ◽  
Vol 98 (41) ◽  
pp. e17400 ◽  
Author(s):  
William R. Tebar ◽  
Raphael M. Ritti-Dias ◽  
Bruna T. C. Saraiva ◽  
Fernanda C. S. Gil ◽  
Leandro D. Delfino ◽  
...  
Keyword(s):  
Physical Activity ◽  
Autonomic Modulation ◽  
Physical Activity Intensity ◽  
Cardiac Autonomic Modulation ◽  
Activity Intensity ◽  
The Relationship

scholarly journals Stronger Association between High Intensity Physical Activity and Cardiometabolic Health with Improved Assessment of the Full Intensity Range Using Accelerometry

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1118
Author(s):  
Jonatan Fridolfsson ◽  
Mats Börjesson ◽  
Elin Ekblom-Bak ◽  
Örjan Ekblom ◽  
Daniel Arvidsson
Keyword(s):  
Physical Activity ◽  
High Intensity ◽  
Cardiometabolic Health ◽  
Accelerometer Data ◽  
Improved Method ◽  
Physical Activity Intensity ◽  
Activity Intensity ◽  
Intensity Physical Activity ◽  
Low Pass ◽  
The Relationship

An improved method of physical activity accelerometer data processing, involving a wider frequency filter than the most commonly used ActiGraph filter, has been shown to better capture variations in physical activity intensity in a lab setting. The aim of the study was to investigate how this improved measure of physical activity affected the relationship with markers of cardiometabolic health. Accelerometer data and markers of cardiometabolic health from 725 adults from two samples, LIV 2013 and SCAPIS pilot, were analyzed. The accelerometer data was processed using both the original ActiGraph method with a low-pass cut-off at 1.6 Hz and the improved method with a low-pass cut-off at 10 Hz. The relationship between the physical activity intensity spectrum and a cardiometabolic health composite score was investigated using partial least squares regression. The strongest association between physical activity and cardiometabolic health was shifted towards higher intensities with the 10 Hz output compared to the ActiGraph method. In addition, the total explained variance was higher with the improved method. The 10 Hz output enables correctly measuring and interpreting high intensity physical activity and shows that physical activity at this intensity is stronger related to cardiometabolic health compared to the most commonly used ActiGraph method.

scholarly journals Automatic Identification of Physical Activity Intensity and Modality from the Fusion of Accelerometry and Heart Rate Data

2016 ◽  
Vol 55 (06) ◽  
pp. 533-544 ◽  
Author(s):  
Pedro Benito ◽  
María Hernando ◽  
Fernando García-García
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Dimensionality Reduction ◽  
Automatic Classification ◽  
Automatic Identification ◽  
Physical Activity Intensity ◽  
Vigorous Exercise ◽  
Temporal Filtering ◽  
Linear Discriminant ◽  
Activity Intensity

SummaryBackground: Physical activity (PA) is essential to prevent and to treat a variety of chronic diseases. The automated detection and quantification of PA over time empowers lifestyle interventions, facilitating reliable exercise tracking and data-driven counseling.Methods: We propose and compare various combinations of machine learning (ML) schemes for the automatic classification of PA from multi-modal data, simultaneously captured by a biaxial accelerometer and a heart rate (HR) monitor. Intensity levels (low / moderate / vigorous) were recognized, as well as for vigorous exercise, its modality (sustained aerobic / resistance / mixed). In to -tal, 178.63 h of data about PA intensity (65.55 % low / 18.96 % moderate / 15.49 % vigorous) and 17.00 h about modality were collected in two experiments: one in free- living conditions, another in a fitness center under controlled protocols. The structure used for automatic classification comprised: a) definition of 42 time-domain signal features, b) dimensionality reduction, c) data clustering, and d) temporal filtering to exploit time redundancy by means of a Hidden Markov Model (HMM). Four dimensionality reduction techniques and four clustering algorithms were studied. In order to cope with class imbalance in the dataset, a custom performance metric was defined to aggregate recognition accuracy, precision and recall.Results: The best scheme, which comprised a projection through Linear Discriminant Ana -lysis (LDA) and k-means clustering, was evaluated in leave-one-subject-out cross-validation; notably outperforming the standard industry procedures for PA intensity classification: score 84.65 %, versus up to 63.60 %. Errors tended to be brief and to appear around transients.Conclusions: The application of ML techniques for pattern identification and temporal filtering allowed to merge accelerometry and HR data in a solid manner, and achieved markedly better recognition performances than the standard methods for PA intensity estimation.

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