scholarly journals Technology and health: Physical activity monitoring in the free living environment

2012 ◽  
Vol 34 ◽  
pp. 367-372 ◽  
Author(s):  
Daniel A. James ◽  
David E. Thiel ◽  
Keri J. Allen ◽  
Bridgit Abell ◽  
Sharon Kilbreath ◽  
...  
Keyword(s):  
Physical Activity ◽  
Activity Monitoring ◽  
Living Environment ◽  
Free Living ◽  
Physical Activity Monitoring

scholarly journals Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

2019 ◽  
Vol 8 (4) ◽  
pp. 45-54
Author(s):  
Matteo Vandoni ◽  
Vittoria Carnevale Pellino ◽  
Stefano Dell'Anna ◽  
Elena Ricagno ◽  
Giulia Liberali ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Wearable Devices ◽  
Activity Monitoring ◽  
Percentage Error ◽  
Free Living ◽  
Sensewear Armband ◽  
Absolute Percentage Error ◽  
Physical Activity Monitoring ◽  
Monitoring Devices

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p<.0001), 8,6% for FO (r=.69; P<.001), and 11.6% for AC (r=.81; p<.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

scholarly journals Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

2019 ◽  
Vol 8 (4) ◽  
pp. 45-54
Author(s):  
Matteo Vandoni ◽  
Vittoria Carnevale Pellino ◽  
Stefano Dell'Anna ◽  
Elena Ricagno ◽  
Giulia Liberali ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Wearable Devices ◽  
Activity Monitoring ◽  
Percentage Error ◽  
Free Living ◽  
Sensewear Armband ◽  
Absolute Percentage Error ◽  
Physical Activity Monitoring ◽  
Monitoring Devices

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p<.0001), 8,6% for FO (r=.69; P<.001), and 11.6% for AC (r=.81; p<.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

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.

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