Calibration of accelerometer output for children.
TL;DR: The calibration of four different accelerometers used most frequently to assess physical activity and sedentary behavior in children are reviewed and alternative data processing using the raw acceleration signal is recommended as a possible alternative approach where the actual acceleration pattern is used to characterize activity behavior.
Abstract: Understanding the determinants of physical activity behavior in children and youths is essential to the design and implementation of intervention studies to increase physical activity. Objective methods to assess physical activity behavior using various types of motion detectors have been recommended as an alternative to self-report for this population because they are not subject to many of the sources of error associated with children's recall required for self-report measures. This paper reviews the calibration of four different accelerometers used most frequently to assess physical activity and sedentary behavior in children. These accelerometers are the ActiGraph, Actical, Actiwatch, and the RT3 Triaxial Research Tracker. Studies are reviewed that describe the regression modeling approaches used to calibrate these devices using directly measured energy expenditure as the criterion. Point estimates of energy expenditure or count ranges corresponding to different activity intensities from several studies are presented. For a given accelerometer, the count cut points defining the boundaries for 3 and 6 METs vary substantially among the studies reviewed even though most studies include walking, running and free-living activities in the testing protocol. Alternative data processing using the raw acceleration signal is recommended as a possible alternative approach where the actual acceleration pattern is used to characterize activity behavior. Important considerations for defining best practices for accelerometer calibration in children and youths are presented.
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TL;DR: Measure physical activity decreased significantly between ages 9 and 15 years, and boys were more active than girls, spending 18 and 14 more minutes per day on the weekdays and weekends, respectively.
Abstract: Context Decreased physical activity plays a critical role in the increase in childhood obesity. Although at least 60 minutes per day of moderate-to-vigorous physical activity (MVPA) is recommended, few longitudinal studies have determined the recent patterns of physical activity of youth. Objective To determine the patterns and determinants of MVPA of youth followed from ages 9 to 15 years. Design, Setting, and Participants Longitudinal descriptive analyses of the 1032 participants in the 1991-2007 National Institute of Child Health and Human Development Study of Early Child Care and Youth Development birth cohort from 10 study sites who had accelerometer-determined minutes of MVPA at ages 9 (year 2000), 11 (2002), 12 (2003), and 15 (2006) years. Participants included boys (517 [50.1%]) and girls (515 [49.9%]); 76.6% white (n = 791); and 24.5% (n = 231) lived in low-income families. Main Outcome Measure Mean MVPA minutes per day, determined by 4 to 7 days of monitored activity. Results At age 9 years, children engaged in MVPA approximately 3 hours per day on both weekends and weekdays. Weekday MVPA decreased by 38 minutes per year, while weekend MVPA decreased by 41 minutes per year. By age 15 years, adolescents were only engaging in MVPA for 49 minutes per weekday and 35 minutes per weekend day. Boys were more active than girls, spending 18 and 13 more minutes per day in MVPA on the weekdays and weekends, respectively. The rate of decrease in MVPA was the same for boys and girls. The estimated age at which girls crossed below the recommended 60 minutes of MVPA per day was approximately 13.1 years for weekday activity compared with boys at 14.7 years, and for weekend activity, girls crossed below the recommended 60 minutes of MVPA at 12.6 years compared with boys at 13.4 years. Conclusion In this study cohort, measured physical activity decreased significantly between ages 9 and 15 years.
1,208 citations
TL;DR: In this article, the authors evaluated the classification accuracy of five sets of independently developed ActiGraph cut points using energy expenditure, measured by indirect calorimetry, as a criterion reference standard.
Abstract: TROST, S. G., P. D. LOPRINZI, R. MOORE, and K. A. PFEIFFER. Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth. Med. Sci. Sports Exerc., Vol. 43, No. 7, pp. 1360–1368, 2011. The absence of comparative validity studies has prevented researchers from reaching consensus regarding the application of intensity-related accelerometer cut points for children and adolescents. Purpose: This study aimed to evaluate the classification accuracy of five sets of independently developed ActiGraph cut points using energy expenditure, measured by indirect calorimetry, as a criterion reference standard. Methods: A total of 206 participants between the ages of 5 and 15 yr completed 12 standardized activity trials. Trials consisted of sedentary activities (lying down, writing, computer game), lifestyle activities (sweeping, laundry, throw and catch, aerobics, basketball), and ambulatory activities
1,149 citations
TL;DR: The development of portable accelerometers has made objective assessments of physical activity possible and nonlinear approaches to predict energy expenditure using accelerometer outputs from multiple sites and orientation can enhance accuracy.
Abstract: Purpose:This paper reviews accelerometry-based activity monitors, including single-site first-generation devices, emerging technologies, and analytical approaches to predict energy expenditure, with suggestions for further research and development.Methods:The physics and measurement principl
1,018 citations
TL;DR: This systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors.
Abstract: Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus. The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified. Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015. The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific. This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data. CRD42016039991.
1,009 citations
Cites background from "Calibration of accelerometer output..."
...PAEE [20, 22, 27, 82] [12, 20, 21, 68, 74, 75, 95, 106] NF NF [22] NF...
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...150 cpm VA [74] 0 (0) 2 (2) 5 (5) 1 (2)...
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...[74] Right hip NR 60 s VA B149 150–499 500–3999 4000–7599 C7600...
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...[73], and those internally developed [72] than with the rest of the cut-points tested [68, 74, 75] (all these cut-points were developed with former models of ActiGraph)....
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TL;DR: This paper summarizes the best practices and future research needs from five areas of accelerometers use: monitor selection, quality, and dependability; monitor use protocols; monitor calibration; analysis of accelerometer data; and integration with other data sources.
Abstract: Researchers are increasingly interested in the potential of accelerometers to improve our ability to measure and understand the health impacts of physical activity. Although accelerometers have been available commercially for more than 25 yr, broad consensus about how to use these tools has not been established. At a scientific conference in December 2004, a number of scientists were invited to present papers, serve as reactors or moderators to papers, present posters of original research, or serve as members of an audience knowledgeable about the use of accelerometers. During 2 1/2 d, information about best practices of accelerometer use was presented and suggestions for future research were made. From the collective experience of papers presented and discussions held, five areas of accelerometer use were described. This paper summarizes the best practices and future research needs from those five areas: monitor selection, quality, and dependability; monitor use protocols; monitor calibration; analysis of accelerometer data; and integration with other data sources. Suggestions for reporting standards for journal articles also are presented.
789 citations
References
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01 Jan 1993
TL;DR: This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.
Abstract: 1. Fundamentals of Speech Recognition. 2. The Speech Signal: Production, Perception, and Acoustic-Phonetic Characterization. 3. Signal Processing and Analysis Methods for Speech Recognition. 4. Pattern Comparison Techniques. 5. Speech Recognition System Design and Implementation Issues. 6. Theory and Implementation of Hidden Markov Models. 7. Speech Recognition Based on Connected Word Models. 8. Large Vocabulary Continuous Speech Recognition. 9. Task-Oriented Applications of Automatic Speech Recognition.
8,442 citations
TL;DR: The validation of the CSA and MM monitors against AEE and their calibration for sedentary, light, moderate, and vigorous thresholds certify these monitors as valid, useful devices for the assessment of physical activity in children.
Abstract: Objective: This study was designed to validate accelerometer-based activity monitors against energy expenditure (EE) in children; to compare monitor placement sites; to field-test the monitors; and to establish sedentary, light, moderate, and vigorous threshold counts.
Research Methods and Procedures: Computer Science and Applications Actigraph (CSA) and Mini-Mitter Actiwatch (MM) monitors, on the hip or lower leg, were validated and calibrated against 6-hour EE measurements by room respiration calorimetry, activity by microwave detector, and heart rate by telemetry in 26 children, 6 to 16 years old. During the 6 hours, the children performed structured activities, including resting metabolic rate (RMR), Nintendo, arts and crafts, aerobic warm-up, Tae Bo, treadmill walking and running, and games. Activity energy expenditure (AEE) computed as EE − RMR was regressed against counts to derive threshold counts.
Results: The mean correlations between EE or AEE and counts were slightly higher for MM-hip (r = 0.78 ± 0.06) and MM-leg (r = 0.80 ± 0.05) than CSA-hip (r = 0.66 ± 0.08) and CSA-leg (r = 0.73 ± 0.07). CSA and MM performed similarly on the hip (inter-instrument r = 0.88) and on the lower leg (inter-instrument r = 0.89). Threshold counts for the CSA-hip were <800, <3200, <8200, and ≥8200 for sedentary, light, moderate, and vigorous categories, respectively. For the MM-hip, the threshold counts were <100, <900, <2200, and ≥2200, respectively.
Discussion: The validation of the CSA and MM monitors against AEE and their calibration for sedentary, light, moderate, and vigorous thresholds certify these monitors as valid, useful devices for the assessment of physical activity in children.
1,066 citations
"Calibration of accelerometer output..." refers background or methods in this paper
...(20) for 26 children between the ages of 6 and 16....
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...Equations were also generated for total energy expenditure where total energy expenditure where basal metabolic rate (BMR) was included (20)....
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...(20) derived prediction equations to estimate METs or activity energy expenditure from counts....
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...(20) 26 6–16 Actigraph W, R, FL Energy expenditure 3200 8200 Eston et al....
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...(20) were presented in energy expenditure units, oxygen consumption was estimated using a 5 kcal·L 1 V̇O2 constant and their mean body mass of 40 kg....
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TL;DR: In this paper, the validity of the CSA activity monitor as a measure of children's physical activity using energy expenditure (EE) as a criterion measure was evaluated using three 5-min treadmill bouts at 3, 4, and 6 mph.
Abstract: Purpose:The purpose of this study was to evaluate the validity of the CSA activity monitor as a measure of children's physical activity using energy expenditure(EE) as a criterion measure.Methods:Thirty subjects aged 10 to 14 performed three 5-min treadmill bouts at 3, 4, and 6 mph, respecti
821 citations
TL;DR: It is concluded that a triaxial accelerometer provides the best assessment of activity and offers potential for large population studies.
Abstract: Eston, Roger G., Ann V. Rowlands, and David K. Ingledew.Validity of heart rate, pedometry, and accelerometry for predicting the energy cost of children’s activities.J. Appl. Physiol. 84(1): 362–371...
689 citations
TL;DR: The developed equation and these activity thresholds can be used for prediction of MET score from accelerometer counts and participation in various intensities of physical activity in adolescent girls.
Abstract: Purpose
To derive a regression equation that estimates metabolic equivalent (MET) from accelerometer counts, and to define thresholds of accelerometer counts that can be used to delineate sedentary, light, moderate, and vigorous activity in adolescent girls.
614 citations