Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth
about
Variation in population levels of physical activity in European children and adolescents according to cross-European studies: a systematic literature review within DEDIPACNational youth sedentary behavior and physical activity daily patterns using latent class analysis applied to accelerometryRationale, design and methods for the RIGHT Track Health Study: pathways from childhood self-regulation to cardiovascular risk in adolescence.An Internet-supported Physical Activity Intervention Delivered in Secondary Schools Located in Low Socio-economic Status Communities: Study Protocol for the Activity and Motivation in Physical Education (AMPED) Cluster Randomized Controlled Trial.Smartphone apps to improve fitness and increase physical activity among young people: protocol of the Apps for IMproving FITness (AIMFIT) randomized controlled trialA spherical-plot solution to linking acceleration metrics with animal performance, state, behaviour and lifestyle.Validity and reliability of a modified english version of the physical activity questionnaire for adolescents.Are participant characteristics from ISCOLE study sites comparable to the rest of their country?Home environment relationships with children's physical activity, sedentary time, and screen time by socioeconomic statusClassification of occupational activity categories using accelerometry: NHANES 2003-2004School-travel by public transit: Rethinking active transportationChange in objectively measured physical activity during the transition to adolescenceAssociation between birth weight and objectively measured sedentary time is mediated by central adiposity: data in 10,793 youth from the International Children's Accelerometry DatabaseHow is active transport associated with children's and adolescents' physical activity over time?The influence of friends and psychosocial factors on physical activity and screen time behavior in adolescents: a mixed-methods analysis.Validation of Accelerometer Thresholds and Inclinometry for Measurement of Sedentary Behavior in Young Adult University Students.Objectively measured sedentary behaviour and moderate and vigorous physical activity in different school subjects: a cross-sectional study.Pregnancy distress gets under fetal skin: Maternal ambulatory assessment & sex differences in prenatal developmentUse of a two-regression model for estimating energy expenditure in childrenThe Active for Life Year 5 (AFLY5) school-based cluster randomised controlled trial protocol: detailed statistical analysis planSchoolyard physical activity of 6-11 year old children assessed by GPS and accelerometry.Effectiveness and feasibility of lowering playground density during recess to promote physical activity and decrease sedentary time at primary school.Effects of the 3-year Sigue la Huella intervention on sedentary time in secondary school students.Association between neighborhood walkability and GPS-measured walking, bicycling and vehicle time in adolescentsMotivational Outcomes and Predictors of Moderate-to-Vigorous Physical Activity and Sedentary Time for Adolescents in the Sigue La Huella Intervention.Prediction of energy expenditure and physical activity in preschoolersChildren's sedentary behaviour: descriptive epidemiology and associations with objectively-measured sedentary time.Study protocol: effects of school gardens on children's physical activityDevelopmental Trajectories of Physical Activity, Sports, and Television Viewing During Childhood to Young Adulthood: Iowa Bone Development StudyAccelerometer data reduction in adolescents: effects on sample retention and biasInnovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk FactorsAre context-specific measures of parental-reported physical activity and sedentary behaviour associated with accelerometer data in 2-9-year-old European children?Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents' physical activity irrespective of accelerometer brand.Adolescent physical activity levels: discrepancies with accelerometer data analysis.Comparison of ActiGraph GT3X+ and Actical accelerometer data in 9-11-year-old Canadian children.Examination of different accelerometer cut-points for assessing sedentary behaviors in children.Separating bedtime rest from activity using waist or wrist-worn accelerometers in youth.Apps for IMproving FITness and Increasing Physical Activity Among Young People: The AIMFIT Pragmatic Randomized Controlled TrialBuilt environment and physical activity in New Zealand adolescents: a protocol for a cross-sectional studyOverweight, obesity, steps, and moderate to vigorous physical activity in children
P2860
Q26746949-FB797125-80CA-43F9-94BE-7E1571AD4692Q27303684-C8E11F8D-19AE-461A-8D0C-4A95A6BF9BEBQ27310173-4F1C4D95-B99B-400A-B182-11E2B7F16F90Q27310665-3C5C4B5E-896D-4A4A-807A-3361F2ADA1DAQ27310775-B3D6F667-A5DF-4A36-9642-7E0AD8F6B4DFQ27317254-28647447-D988-4471-87A1-8A077EF5F55AQ27319815-29B333B7-C8F2-4A5F-90F2-6D40BBE5CD40Q27322822-F71EAABD-B811-47D4-BF18-EB0477925C8AQ28271857-93309CDD-DA8C-4216-A172-30B9718E16D7Q28383114-22F7CC36-A05E-4F70-BF95-987A8C8152F6Q28602427-4D8D951B-B0B5-4409-86CA-1607ADFC26A8Q28646070-4217639E-E7BF-4E8C-B773-764B1EF76370Q28648450-56889C57-492B-4A7B-BA95-E75FE154B814Q28742158-B8307FA5-1571-4A16-ADBC-46BB84400D66Q30277015-4BC6477A-35FD-44B4-BCA5-E6CDF026420EQ30278559-1712167F-3E3F-428D-B3B0-8DAFDC25F2CDQ30397839-A9F2AFEA-A88C-42E1-9E2F-AEE718B1F3ADQ30404533-2A6C056A-3035-470C-B318-38BE54096552Q30513132-26B00A59-2026-4CA7-9EC7-BA5D963FA100Q30542195-08491513-E137-4344-9B82-795000AB970AQ30543572-D58AE308-AEA7-4903-A3B7-1239E0B0226DQ30561403-458402BA-3801-42EC-B26B-90347EBAC4B1Q30564379-6D967515-54B2-4C68-ACAD-BC1F1FA853A9Q30564803-A525BD56-0F72-400D-B948-55C6B1D91A9AQ30570365-F917397C-6151-4922-9604-CF0593E13FF4Q30577539-D47753E5-B0DB-4751-B17B-160FD7440526Q30597034-DB6E04FD-C762-4CCF-A5EC-F15B89F1BCD8Q30620040-A213860A-A850-4DE5-854C-1BBAB38E16EFQ30667124-A35BD193-CD94-4AB2-BB59-5E38077A7F8EQ30721257-2E3D5955-D93B-412C-B7EC-41B892A12E58Q30788093-E7C24394-CA46-48BB-990E-1EFC3A0E1587Q30828284-6B520627-4A5B-4246-AFE5-3E99C042BEB9Q30985739-BCE096D3-3B95-4710-8819-89B7E518E010Q31047640-F73EE614-92DB-4B65-8281-670707BF1AC0Q31082871-3DA49116-2714-4A84-B352-A132304F9AA4Q31155579-4FF20534-AF63-434F-97DA-1A9C37CA4862Q31158532-1BFB1704-7969-407D-BB62-247A858D005BQ33444909-64E6E951-A50C-4C74-ADD4-78083C81CAE8Q33569558-9AFF0316-7E7D-491C-BAC9-FCD1D96E0FA0Q33578898-8D035643-11AD-4E85-AD02-D17A186D2570
P2860
Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth
description
scientific article published on 01 July 2011
@en
wetenschappelijk artikel
@nl
наукова стаття, опублікована в липні 2011
@uk
name
Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth
@en
Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth
@nl
type
label
Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth
@en
Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth
@nl
prefLabel
Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth
@en
Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth
@nl
P1476
Comparison of accelerometer cut points for predicting activity intensity in youth
@en
P2093
Karin A Pfeiffer
Rebecca Moore
P304
P356
10.1249/MSS.0B013E318206476E
P577
2011-07-01T00:00:00Z