scholarly journals Monitoring Energy Expenditure Using a Multi-Sensor Device—Applications and Limitations of the SenseWear Armband in Athletic Populations

2017 ◽  
Vol 8 ◽  
Author(s):  
Karsten Koehler ◽  
Clemens Drenowatz
Keyword(s):  
Energy Expenditure ◽  
Sensewear Armband ◽  
Sensor Device ◽  
Device Applications

scholarly journals Energy Expenditure Evaluation in Humans and Non-Human Primates by SenseWear Armband. Validation of Energy Expenditure Evaluation by SenseWear Armband by Direct Comparison with Indirect Calorimetry

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e73651 ◽  
Author(s):  
Francesca Casiraghi ◽  
Raweewan Lertwattanarak ◽  
Livio Luzi ◽  
Alberto O. Chavez ◽  
Alberto M. Davalli ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Sensewear Armband

scholarly journals The Use of SenseWear Armband for Assessment of Daily Energy Expenditure and the Relation to Body Fat Distribution and Nutritional Intake in Lean Women with Polycystic Ovary Syndrome

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Gulcan Arusoglu
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Energy Expenditure ◽  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Nutritional Intake ◽  
Control Group ◽  
Ovary Syndrome ◽  
Sensewear Armband ◽  
Segmental Body Composition

Objective. To evaluate nutritional intake, energy expenditure, and segmental body composition in lean women with polycystic ovary syndrome (PCOS) and compare them with age- and body mass index- (BMI-) matched control women. Methods. 32 nonobese patients with PCOS and 31 age- and BMI-matched healthy women were included in the study. Energy expenditure and physical activity level were assessed by metabolic Holter equipment (SenseWear Armband, SWA) which was never previously used in lean PCOS population. Food intake is recorded with 24 hours of food record. Segmental body composition analysis was assessed by bioelectrical impedance analyses (BIA). Results. Mean BMI was 22.64 ± 3.64 and 21.55 ± 2.77 kg/m2 (p=0.185) in PCOS and control groups, respectively. Mean age was 22.03 ± 4.21 and 21.71 ± 2.67 year (p=0.720), respectively. No significant differences were found in total energy intake and percentage of carbohydrates, fats, and other micronutrients (p>0.05). Energy percentage of proteins (%12.73 ± 1.98, p=0.008) was statistically lower in subjects versus the control group. The measurements of physical activity duration (PAD) (1.40 ± 0.87/2.18 ± 0.99 hours, p=0.002), active energy expenditure (372.35 ± 198.32/494.10 ± 186.50 kcal, p=0.018), and step counting (9370.03 ± 3587.49/11730.90 ± 3564.31 steps, p=0.013) measurement of the PCOS group were lower than the control group, respectively. Conclusions. New diagnosed women with PCOS had similar distribution and quantity of body fat parameters and nutritional status when compared to healthy women. Control subjects were found more active in energy expenditure.

Validation Of Sensewear Armband To Assess Energy Expenditure Of Adolescents During Various Modes Of Activity

2005 ◽  
Vol 37 (Supplement) ◽  
pp. S437
Author(s):  
Kim Crawford ◽  
Robert J. Robertson ◽  
Ray Burdett ◽  
Fredric L. Goss ◽  
John M. Jakicic ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Sensewear Armband

scholarly journals Is the SenseWear Armband accurate enough to quantify and estimate energy expenditure in healthy adults?

2017 ◽  
Vol 5 (5) ◽  
pp. 97-97 ◽  
Author(s):  
Alejandro Santos-Lozano ◽  
Adrián Hernández-Vicente ◽  
Raúl Pérez-Isaac ◽  
Fernanda Santín-Medeiros ◽  
Carlos Cristi-Montero ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Healthy Adults ◽  
Sensewear Armband

scholarly journals Solid-State Battery Technologies for Advanced IoT Sensor Device Applications

Seikei-Kakou ◽  
2019 ◽  
Vol 31 (9) ◽  
pp. 334-337
Author(s):  
Junji Akimoto ◽  
Hiroshi Nagata ◽  
Kunimitsu Kataoka ◽  
Tadayoshi Akao ◽  
Jun Akedo
Keyword(s):  
Solid State ◽  
Sensor Device ◽  
Solid State Battery ◽  
Device Applications

Validity of the SenseWear Armband to Assess Energy Expenditure in Graded Walking

2015 ◽  
Vol 12 (2) ◽  
pp. 178-183
Author(s):  
Gianluca Vernillo ◽  
Aldo Savoldelli ◽  
Barbara Pellegrini ◽  
Federico Schena
Keyword(s):  
Energy Expenditure ◽  
Sensewear Armband

scholarly journals Nanomaterials for Sensor Device Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-1
Author(s):  
Liang-Wen Ji ◽  
Sheng-Joue Young ◽  
Chih-Hung Hsiao ◽  
Artde Donald Kin-Tak Lam
Keyword(s):  
Sensor Device ◽  
Device Applications

scholarly journals Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure

2016 ◽  
Vol 41 (7) ◽  
pp. 758-766 ◽  
Author(s):  
John B. Correa ◽  
John W. Apolzan ◽  
Desti N. Shepard ◽  
Daniel P. Heil ◽  
Jennifer C. Rood ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Weight Change ◽  
Medium Effect ◽  
Sensewear Armband ◽  
Activity Monitors ◽  
Daily Energy ◽  
Total Daily Energy Expenditure ◽  
Calorie Diet ◽  
Physical Activity Monitors ◽  
Monitoring Devices

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland–Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity.

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