scholarly journals Re‐examination of accelerometer data processing and calibration for the assessment of physical activity intensity

2019 ◽  
Vol 29 (10) ◽  
pp. 1442-1452 ◽  
Author(s):  
Daniel Arvidsson ◽  
Jonatan Fridolfsson ◽  
Mats Börjesson ◽  
Lars Bo Andersen ◽  
Örjan Ekblom ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Processing ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Activity Intensity

scholarly journals Reexamination of Accelerometer Calibration with Energy Expenditure as Criterion: VO2net Instead of MET for Age-Equivalent Physical Activity Intensity

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3377 ◽  
Author(s):  
Daniel Arvidsson ◽  
Jonatan Fridolfsson ◽  
Christoph Buck ◽  
Örjan Ekblom ◽  
Elin Ekblom-Bak ◽  
...  
Keyword(s):  
Physical Activity ◽  
Age Groups ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Free Living ◽  
Activity Intensity ◽  
Age Related ◽  
Younger Age ◽  
Metabolic Equivalent Of Task ◽  
Adolescents And Adults

Accelerometer calibration for physical activity (PA) intensity is commonly performed using Metabolic Equivalent of Task (MET) as criterion. However, MET is not an age-equivalent measure of PA intensity, which limits the use of MET-calibrated accelerometers for age-related PA investigations. We investigated calibration using VO2net (VO2gross − VO2stand; mL⋅min−1⋅kg−1) as criterion compared to MET (VO2gross/VO2rest) and the effect on assessment of free-living PA in children, adolescents and adults. Oxygen consumption and hip/thigh accelerometer data were collected during rest, stand and treadmill walk and run. Equivalent speed (Speedeq) was used as indicator of the absolute speed (Speedabs) performed with the same effort in individuals of different body size/age. The results showed that VO2net was higher in younger age-groups for Speedabs, but was similar in the three age-groups for Speedeq. MET was lower in younger age-groups for both Speedabs and Speedeq. The same VO2net-values respective MET-values were applied to all age-groups to develop accelerometer PA intensity cut-points. Free-living moderate-and-vigorous PA was 216, 115, 74 and 71 min/d in children, adolescents, younger and older adults with VO2net-calibration, but 140, 83, 74 and 41 min/d with MET-calibration, respectively. In conclusion, VO2net calibration of accelerometers may provide age-equivalent measures of PA intensity/effort for more accurate age-related investigations of PA in epidemiological research.

scholarly journals Ability of RT3 Accelerometer Cut Points to Detect Physical Activity Intensity in Ambulatory Children With Cerebral Palsy

2014 ◽  
Vol 31 (4) ◽  
pp. 310-324 ◽  
Author(s):  
Jennifer Ryan ◽  
Michael Walsh ◽  
John Gormley
Keyword(s):  
Physical Activity ◽  
Cerebral Palsy ◽  
Vigorous Physical Activity ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Cut Points ◽  
Cut Point ◽  
Activity Intensity ◽  
Metabolic Equivalents ◽  
Children With Cerebral Palsy

This study investigated the ability of published cut points for the RT3 accelerometer to differentiate between levels of physical activity intensity in children with cerebral palsy (CP). Oxygen consumption (metabolic equivalents; METs) and RT3 data (counts/min) were measured during rest and 5 walking trials. METs and corresponding counts/min were classified as sedentary, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) according to MET thresholds. Counts were also classified according to published cut points. A published cut point exhibited an excellent ability to classify sedentary activity (sensitivity = 89.5%, specificity = 100.0%). Classification accuracy decreased when published cut points were used to classify LPA (sensitivity = 88.9%, specificity = 79.6%) and MVPA (sensitivity = 70%, specificity = 95–97%). Derivation of a new cut point improved classification of both LPA and MVPA. Applying published cut points to RT3 accelerometer data collected in children with CP may result in misclassification of LPA and MVPA.

scholarly journals A Comparison of Preschoolers’ Physical Activity Indoors versus Outdoors at Child Care

2018 ◽  
Vol 15 (11) ◽  
pp. 2463 ◽  
Author(s):  
Pooja Tandon ◽  
Brian Saelens ◽  
Chuan Zhou ◽  
Dimitri Christakis
Keyword(s):  
Physical Activity ◽  
Child Care ◽  
Preschool Age ◽  
Accelerometer Data ◽  
Physical Activity Intensity ◽  
Activity Intensity ◽  
Preschool Age Children ◽  
Gps Devices ◽  
Outdoor Location ◽  
Indoor And Outdoor

The aims of this study were to quantify and examine differences in preschoolers’ indoor and outdoor sedentary time and physical activity intensity at child care using GPS devices and accelerometers. We conducted an observational study of 46 children (mean age 4.5 years, 30 boys, 16 girls) from five child care centers who wore accelerometers and GPS devices around their waists for five days during regular child care hours. GPS signal-to-noise ratios were used to determine indoor vs. outdoor location. Accelerometer data were categorized by activity intensity. Children spent, on average, 24% of child care time outdoors (range 12–37% by site), averaging 74 min daily outdoors (range 30–119 min), with 54% of children spending ≥60 min/day outdoors. Mean accelerometer activity counts were more than twice as high outdoors compared to indoors (345 (95) vs. 159 (38), (p < 0.001)), for girls and boys. Children were significantly less sedentary (51% of time vs. 75%) and engaging in more light (18% vs. 13%) and moderate-to-vigorous (MVPA) (31% vs. 12%) activity when outdoors compared to indoors (p < 0.001). To achieve a minute of MVPA, a preschooler needed to spend 9.1 min indoors vs. 3.8 min outdoors. Every additional 10 min outdoors each day was associated with a 2.9 min increase in MVPA (2.7 min for girls, 3.0 min for boys). Preschool-age children are twice as active and less sedentary when outdoors compared to indoors in child care settings. To help preschoolers achieve MVPA recommendations and likely attain other benefits, one strategy is to increase the amount of time they spend outdoors and further study how best to structure it.

scholarly journals Employing Machine Learning to Estimate Hallmark Measures of Physical Activities from Wrist-worn Devices Across Age Groups

2021 ◽  
Author(s):  
Mamoun T. Mardini ◽  
Chen Bai ◽  
Amal A. Wanigatunga ◽  
Santiago Saldana ◽  
Ramon Casanova ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Energy Expenditure ◽  
Age Groups ◽  
Physical Activities ◽  
Accelerometer Data ◽  
Activity Type ◽  
Physical Activity Intensity ◽  
Activity Intensity ◽  
Score Range

Wrist-worn fitness trackers and smartwatches are proliferating with an incessant attention towards health tracking. Given the growing popularity of wrist-worn devices across all age groups, a rigorous evaluation for recognizing hallmark measures of physical activities and estimating energy expenditure is needed to compare their accuracy across the lifespan. The goal of the study was to build machine learning models to recognize physical activity type (sedentary, locomotion, and lifestyle) and intensity (low, light, and moderate), identify individual physical activities, and estimate energy expenditure. The primary aim of this study was to build and compare models for different age groups: young [20-50 years], middle (50-70 years], and old (70-89 years]. Participants (n = 253, 62% women, aged 20-89 years old) performed a battery of 33 daily activities in a standardized laboratory setting while wearing a portable metabolic unit to measure energy expenditure that was used to gauge metabolic intensity. Tri-axial accelerometer collected data at 80-100 Hz from the right wrist that was processed for 49 features. Results from random forests algorithm were quite accurate in recognizing physical activity type, the F1-Score range across age groups was: sedentary [0.955 – 0.973], locomotion [0.942 – 0.964], and lifestyle [0.913 – 0.949]. Recognizing physical activity intensity resulted in lower performance, the F1-Score range across age groups was: sedentary [0.919 – 0.947], light [0.813 – 0.828], and moderate [0.846 – 0.875]. The root mean square error range was [0.835 – 1.009] for the estimation of energy expenditure. The F1-Score range for recognizing individual physical activities was [0.263 – 0.784]. Performances were relatively similar and the accelerometer data features were ranked similarly between age groups. In conclusion, data features derived from wrist worn accelerometers lead to high-moderate accuracy estimating physical activity type, intensity and energy expenditure and are robust to potential age-differences.

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 Natural Landscape, Infrastructure, and Health: The Physical Activity Implications of Urban Green Space Composition among the Elderly

2019 ◽  
Vol 16 (20) ◽  
pp. 3986 ◽  
Author(s):  
Carme Miralles-Guasch ◽  
Javier Dopico ◽  
Xavier Delclòs-Alió ◽  
Pablo Knobel ◽  
Oriol Marquet ◽  
...  
Keyword(s):  
Physical Activity ◽  
Well Being ◽  
Gps Tracking ◽  
Urban Residents ◽  
Accelerometer Data ◽  
Sedentary Behaviors ◽  
Physical Activity Intensity ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Activity Intensity

Urban green spaces (UGS) have been linked with a series of benefits for the environment, and for the physical health and well-being of urban residents. This is of great importance in the context of the aging of modern societies. However, UGS have different forms and characteristics that can determine their utilization. Common elements in UGS such as the type of vegetation and the type of surface are surprisingly understudied in regard to their relationship with the type of activity undertaken in UGS. This paper aims to explore the relationship between landscape diversity and the type of surface with the time spent and the physical activity intensity performed by seniors. To do so, this study uses GPS tracking data in combination with accelerometer data gathered from 63 seniors residing in Barcelona, Spain. Results showed that senior participants spent little time inside the analyzed UGS and sedentary behaviors (SBs) were more common than physical activities (PAs). The presence of pavement surfaces positively influenced the total time spent in UGS while gravel surfaces were negatively associated with time spent in active behaviors. The provision of well-defined and maintained paved areas and paths are some key infrastructures to be considered when designing UGS for overall urban residents and, especially, when aiming to potentiate the access for senior visitors.

Self-reported Physical Activity Intensity and Sleep Apnea Risk

2014 ◽  
Vol 46 ◽  
pp. 374
Author(s):  
Christopher E. Kline ◽  
Matthew P. Buman ◽  
Shawn D. Youngstedt ◽  
Barbara Phillips ◽  
Marco Tulio de Mello ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sleep Apnea ◽  
Physical Activity Intensity ◽  
Activity Intensity

The effect of real‐time monitoring of physical activity intensity in diabetic patients

2021 ◽  
Author(s):  
Rumi Tanaka ◽  
Kimie Fujita ◽  
Satoko Maeno ◽  
Kanako Yakushiji ◽  
Satomi Tanaka ◽  
...  
Keyword(s):  
Physical Activity ◽  
Real Time ◽  
Diabetic Patients ◽  
Physical Activity Intensity ◽  
Real Time Monitoring ◽  
Activity Intensity
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