scholarly journals Maternal diabetes status does not influence energy expenditure or physical activity in 5-year-old Pima Indian children

Diabetologia ◽  
1998 ◽  
Vol 41 (10) ◽  
pp. 1157-1162 ◽  
Author(s):  
A. D. Salbe ◽  
A. M. Fontvieille ◽  
D. J. Pettitt ◽  
E. Ravussin
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Maternal Diabetes ◽  
Indian Children ◽  
Diabetes Status

Energy expenditure and physical activity in patients with anorexia nervosa

2013 ◽  
Vol 121 (03) ◽  
Author(s):  
U Elbelt ◽  
V Haas ◽  
T Hofmann ◽  
S Jeran ◽  
H Pietz ◽  
...  
Keyword(s):  
Physical Activity ◽  
Anorexia Nervosa ◽  
Energy Expenditure

The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation (Preprint)

2020 ◽  
Author(s):  
Anis Davoudi ◽  
Mamoun T. Mardini ◽  
Dave Nelson ◽  
Fahd Albinali ◽  
Sanjay Ranka ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Energy Expenditure ◽  
Activity Recognition ◽  
Recognition Accuracy ◽  
Body Position ◽  
Computer Work ◽  
Accelerometer Data ◽  
Upper Arm ◽  
Lifestyle Activities

BACKGROUND Research shows the feasibility of human activity recognition using Wearable accelerometer devices. Different studies have used varying number and placement for data collection using the sensors. OBJECTIVE To compare accuracy performance between multiple and variable placement of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. METHODS Participants (n=93, 72.2±7.1 yrs) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary vs. non-sedentary, locomotion vs. non-locomotion, and lifestyle vs. non-lifestyle activities (e.g. leisure walk vs. computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on five different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used in developing Random Forest models to assess activity category recognition accuracy and MET estimation. RESULTS Model performance for both MET estimation and activity category recognition strengthened with additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03 to 0.09 MET increase in prediction error as compared to wearing all five devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for detection of locomotion (0-0.01 METs), sedentary (0.13-0.05 METs) and lifestyle activities (0.08-0.04 METs) compared to all five placements. The accuracy of recognizing activity categories increased with additional placements (0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. CONCLUSIONS Additional accelerometer devices only slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.

scholarly journals Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 861
Author(s):  
Kyeung Ho Kang ◽  
Mingu Kang ◽  
Siho Shin ◽  
Jaehyo Jung ◽  
Meina Li
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ensemble Model ◽  
Direct Calorimetry ◽  
Patch Type ◽  
Mortality And Morbidity ◽  
Squared Error ◽  
Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

scholarly journals PHYSICAL ACTIVITY LEVEL AND ENERGY EXPENDITURE ASSESSED BY ACCELEROMETRY IN 60Y+ BRAZILIAN SUBJECTS

2019 ◽  
Vol 25 (2) ◽  
pp. 116-120
Author(s):  
Luiz Antonio dos Anjos ◽  
Bruna de Andrade Messias da Silva ◽  
Vivian Wahrlich
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Physical Activity Level ◽  
Overweight And Obesity ◽  
Activity Level ◽  
Convenience Sample ◽  
Physical Activity Recommendations ◽  
Physical Activity Programs ◽  
Sedentary Activities ◽  
Activity Recommendations

ABSTRACT Objective: To assess the physical activity level (PAL) and the total daily energy expenditure (EE-TDEE) in a sample of ≥60y subjects from Niterói, Rio de Janeiro, Brazil. Methods: A convenience sample of 88 subjects recruited from recreational physical activity programs wore an accelerometer around the waist for seven consecutive days for at least 10h/day. Minute-by-minute EE was estimated from the counts per minute (CPM) data, and the daily sum yielded the TDEE. PAL (TDEE/BMR) with BMR calculated with the FAO/WHO predictive equation and a population-specific equation. Body composition was assessed by DXA. Results: Mean age (SD) was 69.2 (5.8) years, the prevalence of overweight and obesity was 36.4 and 25.0%, respectively, and excess body fat was 39.8%. The subjects spent 600min/day engaged in sedentary activities (CPM<100). Men engaged in 30min of moderate-to-vigorous physical activity (CPM≥1,952) daily, on average. The subjects were active on 34.5 and 18.0% of the weekdays and weekend days with a 1,400 steps/day difference between these days. TDEE was 1,731.5 (348.7) and 1,356.3 (223.7) kcal/day depending on the BMR prediction equation used. Mean PAL was lower than the maintenance level. Conclusions: The high prevalence of sedentary activities and the low percentage of subjects who met the physical activity recommendations indicate that physical activity programs must be adjusted so that the enrolled subjects can meet the physical activity recommendations, preferably with the activities objectively monitored. Population-specific equations improve the final estimation of TDEE and PAL. Level of Evidence I; Diagnostic studies - Investigating a diagnostic test.

Accelerometry calibration in people with class II-III obesity: Energy expenditure prediction and physical activity intensity identification

2020 ◽  
Vol 76 ◽  
pp. 104-109 ◽  
Author(s):  
Florêncio Diniz-Sousa ◽  
Lucas Veras ◽  
José Carlos Ribeiro ◽  
Giorjines Boppre ◽  
Vítor Devezas ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Class Ii ◽  
Physical Activity Intensity ◽  
Activity Intensity
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