Analysis of Physical Activity Among Free–Living Nonagenarians From a Sardinian Longevous Population

2018 ◽  
Vol 26 (2) ◽  
pp. 254-258 ◽  
Author(s):  
Giovanni Mario Pes ◽  
Maria Pina Dore ◽  
Alessandra Errigo ◽  
Michel Poulain
Keyword(s):  
Physical Activity ◽  
Free Living

scholarly journals A Comparison of Two Motion Sensors for the Assessment of Free-Living Physical Activity of Adolescents

2010 ◽  
Vol 7 (4) ◽  
pp. 1558-1576 ◽  
Author(s):  
Roman Cuberek ◽  
Walid El Ansari ◽  
Karel Frömel ◽  
Krzysztof Skalik ◽  
Erik Sigmund
Keyword(s):  
Physical Activity ◽  
Motion Sensors ◽  
Free Living

scholarly journals Objective measurement of physical activity in Macaca fascicularis

1982 ◽  
Vol 16 (3) ◽  
pp. 240-243
Author(s):  
Wayne T. Corbett ◽  
Harry M. Schey ◽  
A. W. Green
Keyword(s):  
Physical Activity ◽  
Standard Deviation ◽  
Macaca Fascicularis ◽  
Living Environment ◽  
Activity Levels ◽  
Free Living ◽  
Activity Measurements ◽  
Active Intermediate ◽  
The Mean ◽  
The Relationship

The mean and standard deviation over 24 h for 3 groups of animals - active, intermediate and inactive - in physical activity units were 10948 ± 3360, 2611 ± 1973 and 484 ± 316 respectively. The differences were significant ( P = 0·004), demonstrating the ability of the method to distinguish between groups that can be visibly differentiated. The small within-animal physical activity standard deviation (18·85 PAU) obtained in another group, suggests that it also yields reliable physical activity measurements for non-human primates. The monitoring device used can discriminate between individual nonhuman primate physical activity levels in a free-living environment and does not alter daily behaviour. This makes possible the study of the relationship between physical activity and atherosclerosis in nonhuman primates.

scholarly journals A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living

2021 ◽  
pp. 1-8
Author(s):  
Kerstin Bach ◽  
Atle Kongsvold ◽  
Hilde Bårdstu ◽  
Ellen Marie Bardal ◽  
Håkon S. Kjærnli ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Ground Truth ◽  
Daily Physical Activity ◽  
Gradient Boosting ◽  
Free Living ◽  
Recording Time ◽  
Learning Classifier ◽  
Extreme Gradient Boosting ◽  
Activity Types

Introduction: Accelerometer-based measurements of physical activity types are commonly used to replace self-reports. To advance the field, it is desirable that such measurements allow accurate detection of key daily physical activity types. This study aimed to evaluate the performance of a machine learning classifier for detecting sitting, standing, lying, walking, running, and cycling based on a dual versus single accelerometer setups during free-living. Methods: Twenty-two adults (mean age [SD, range] 38.7 [14.4, 25–68] years) were wearing two Axivity AX3 accelerometers positioned on the low back and thigh along with a GoPro camera positioned on the chest to record lower body movements during free-living. The labeled videos were used as ground truth for training an eXtreme Gradient Boosting classifier using window lengths of 1, 3, and 5 s. Performance of the classifier was evaluated using leave-one-out cross-validation. Results: Total recording time was ∼38 hr. Based on 5-s windowing, the overall accuracy was 96% for the dual accelerometer setup and 93% and 84% for the single thigh and back accelerometer setups, respectively. The decreased accuracy for the single accelerometer setup was due to a poor precision in detecting lying based on the thigh accelerometer recording (77%) and standing based on the back accelerometer recording (64%). Conclusion: Key daily physical activity types can be accurately detected during free-living based on dual accelerometer recording, using an eXtreme Gradient Boosting classifier. The overall accuracy decreases marginally when predictions are based on single thigh accelerometer recording, but detection of lying is poor.

Analyzing Free-Living Physical Activity of Older Adults in Different Environments Using Body-Worn Activity Monitors

2010 ◽  
Vol 18 (2) ◽  
pp. 171-184 ◽  
Author(s):  
P. Margaret Grant ◽  
Malcolm H. Granat ◽  
Morag K. Thow ◽  
William M. Maclaren
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Sedentary Time ◽  
Likelihood Estimation ◽  
Day Hospital ◽  
Free Living ◽  
Activity Monitors ◽  
The Third ◽  
Patient Groups ◽  
Restricted Maximum Likelihood Estimation

This study measured objectively the postural physical activity of 4 groups of older adults (≥65 yr). The participants (N= 70) comprised 3 patient groups—2 from rehabilitation wards (cityn= 20, 81.8 ± 6.7 yr; ruraln= 10, 79.4 ± 4.7 yr) and the third from a city day hospital (n= 20, 74.7 ± 7.9 yr)—and a healthy group to provide context (n= 20, 73.7 ± 5.5 yr). The participants wore an activity monitor (activPAL) for a week. A restricted maximum-likelihood-estimation analysis of hourly upright time (standing and walking) revealed significant differences between day, hour, and location and the interaction between location and hour (p< .001). Differences in the manner in which groups accumulated upright and sedentary time (sitting and lying) were found, with the ward-based groups sedentary for prolonged periods and upright for short episodes. This information may be used by clinicians to design appropriate rehabilitation interventions and monitor patient progress.

Abstract 17026: Effects of Supervised Exercise Therapy on Free-Living Physical Activity Among Patients With Symptomatic Peripheral Artery Disease

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Erica Schorr ◽  
Mary Whipple ◽  
Diane Treat-Jacobson
Keyword(s):  
Physical Activity ◽  
Peripheral Artery Disease ◽  
Exercise Therapy ◽  
Sedentary Time ◽  
Peripheral Artery ◽  
Activity Tracking ◽  
Free Living ◽  
Supervised Exercise ◽  
Artery Disease ◽  
Tracking Devices

Introduction: Evidence supporting the effects of supervised exercise therapy (SET) on alleviating symptoms and improving walking ability for patients with symptomatic peripheral artery disease (PAD) is robust and well recognized. However, little is known about the impact of SET on free-living physical activity (PA). The aim of this study was to examine the relationship between participation in SET and changes in free-living PA among individuals in the the EX ercise Training to Reduce Claudication: Arm ER gometry versus T readmill Walking ( EXERT ) trial. Methods: In this randomized, controlled trial, 104 participants (mean age 68±9; 29% female) were allocated to receive treadmill (TM) exercise (n=41), upper body ergometry (UBE) exercise (n=42), or usual-care (UC) (n=21) for 12 weeks. Exercise participants attended SET three times per week; UC participants met with study staff weekly. PA was measured over 7 days via waist-worn ActiGraph accelerometers at baseline, 6, and 12 weeks. Steps per day was the primary outcome. Secondary outcomes were proportion of time in light and moderate to vigorous physical activity (MVPA), and sedentary time. PA was controlled for in TM participants by using SET logs. Results were analyzed using descriptive statistics, two-sample t-tests, and analysis of variance. Results: Regardless of randomization, average daily steps were low at baseline and 6 weeks (4,013 steps, p =.72; and 3,911 steps, p =.84, respectively), and slightly higher at 12 weeks (4,307 steps; p =.93). Although not statistically significant but perhaps clinically relevant, UBE participants exhibited greater increases in MVPA over 12 weeks (0.9% to 1.3%; F =.48, p =.62) compared to TM (1.2% to 1.3%; F =.35, p =.71) and UC (1.3% to 1.5%, F =.03, p =.97); similarly all participants exhibited reductions in sedentary time and increases in free-living PA between baseline and 12 weeks. Conclusions: These data suggest individuals with PAD attending SET replace sedentary time with light or moderate intensity PA regardless of exercise modality. Despite study participants meeting the recommended daily steps for adults with chronic conditions (3,500-5,500 steps), it is suspected that they did not reach the daily goal of 30 minutes of enhanced PA to reduce health risks. Future research should incorporate activity tracking devices that can provide feedback on PA as an approach to meet daily PA goals. Activity tracking devices used in conjunction with SET may further improve walking distance, symptom management, and quality of life among patients with symptomatic PAD.

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