Wrist Accelerometer Cut Points for Classifying Sedentary Behavior in Children.

INTRODUCTION: This study aimed to examine the validity and accuracy of wrist accelerometers for classifying sedentary behavior (SB) in children. METHODS: Fifty-seven children (5-8 and 9-12 yr) completed an ~170-min protocol, including 15 semistructured activities and transitions. Nine ActiGraph (GT3X+) and two GENEActiv wrist cut points were evaluated. Direct observation was the criterion measure. The accuracy of wrist cut points was compared with that achieved by the ActiGraph hip cut point (≤25 counts per 15 s) and the thigh-mounted activPAL3. Analyses included equivalence testing, Bland-Altman procedures, and area under the receiver operating curve (ROC-AUC). RESULTS: The most accurate ActiGraph wrist cut points (Kim; vector magnitude, ≤3958 counts per 60 s; vertical axis, ≤1756 counts per 60 s) demonstrated good classification accuracy (ROC-AUC = 0.85-0.86) and accurately estimated SB time in 5-8 yr (equivalence P = 0.02; mean bias = 4.1%, limits of agreement = -20.1% to 28.4%) and 9-12 yr (equivalence P 0.05) and classification accuracy (ROC-AUC = 0.79-0.80) was lower than for ActiGraph hip and activPAL3. CONCLUSION: The most accurate SB ActiGraph (Kim) and GENEActiv (Schaefer) wrist cut points can be applied in children with similar confidence as the ActiGraph hip cut point (≤25 counts per 15 s), although activPAL3 was generally more accurate.This study was funded by the National Heart Foundation of Australia (G11S5975). DPC is supported by an Australian Research Council Discovery Early Career Researcher Award (DE140101588). ADO is supported by a National Heart Foundation of Australia Career Development Fellowship (CR11S 6099). TH is funded by a National Health and Medical Research Council Early Career Fellowship (APP1070571). The work of UE and SB is funded by the UK Medical Research Council (MC_UU_12015/3). ST is supported by the National Health and Medical Research Council Centre of Research Excellence on Sitting Time and Chronic Disease Prevention (APP1057608)

Validation of the SenseWear Mini activity monitor in 5-12-year-old children.

OBJECTIVES: This study aimed to validate SenseWear Mini software algorithm versions 2.2 (SW2.2) and 5.2 (SW5.2) for estimating energy expenditure (EE) in children. DESIGN: Laboratory-based validation study. METHODS: 57 children aged 5-12 y completed a protocol involving 15 semi-structured sedentary (SED), light-intensity (LPA), and moderate- to vigorous-intensity (MVPA) physical activities. EE was estimated using portable indirect calorimetry (IC). The accuracy of EE estimates (kcal·min-1) from SW2.2 and SW5.2 were examined at the group level and individual level using the mean absolute percentage error (MAPE), Bland-Altman plots and equivalence testing. RESULTS: MAPE values were lower for SW5.2 (30.1±10.7%) than for SW2.2 (44.0±6.2%). Although mean differences for SW5.2 were smaller than for SW2.2 during SED (-0.23±0.22 vs. -0.61±0.20kcal·min-1), LPA (-0.69±0.76 vs. -1.07±0.46kcal·min-1) and MVPA (-2.22±1.15 vs. -2.57±1.15kcal·min-1), limits of agreement did not decrease for the updated algorithms. For all activities, SW2.2 and SW5.2 were not equivalent to IC (p>0.05). Errors increased with increasing intensity. CONCLUSION: The current SenseWear Mini algorithms SW5.2 underestimated EE. The overall improved accuracy for SW5.2 was not accompanied with improved accuracy at the individual level and EE estimates were not equivalent to IC.This study was funded by the National Heart Foundation of Australia (G11S5975).This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.jsams.2016.04.01

Validation of thigh-based accelerometer estimates of postural allocation in 5-12 year-olds.

OBJECTIVES: To validate activPAL3™ (AP3) for classifying postural allocation, estimating time spent in postures and examining the number of breaks in sedentary behaviour (SB) in 5-12 year-olds. DESIGN: Laboratory-based validation study. METHODS: Fifty-seven children completed 15 sedentary, light- and moderate-to-vigorous intensity activities. Direct observation (DO) was used as the criterion measure. The accuracy of AP3 was examined using a confusion matrix, equivalence testing, Bland-Altman procedures and a paired t-test for 5-8y and 9-12y. RESULTS: Sensitivity of AP3 was 86.8%, 82.5% and 85.3% for sitting/lying, standing, and stepping, respectively, in 5-8y and 95.3%, 81.5% and 85.1%, respectively, in 9-12y. Time estimates of AP3 were equivalent to DO for sitting/lying in 9-12y and stepping in all ages, but not for sitting/lying in 5-12y and standing in all ages. Underestimation of sitting/lying time was smaller in 9-12y (1.4%, limits of agreement [LoA]: -13.8 to 11.1%) compared to 5-8y (12.6%, LoA: -39.8 to 14.7%). Underestimation for stepping time was small (5-8y: 6.5%, LoA: -18.3 to 5.3%; 9-12y: 7.6%, LoA: -16.8 to 1.6%). Considerable overestimation was found for standing (5-8y: 36.8%, LoA: -16.3 to 89.8%; 9-12y: 19.3%, LoA: -1.6 to 36.9%). SB breaks were significantly overestimated (5-8y: 53.2%, 9-12y: 28.3%, p<0.001). CONCLUSIONS: AP3 showed acceptable accuracy for classifying postures, however estimates of time spent standing were consistently overestimated and individual error was considerable. Estimates of sitting/lying were more accurate for 9-12y. Stepping time was accurately estimated for all ages. SB breaks were significantly overestimated, although the absolute difference was larger in 5-8y. Surveillance applications of AP3 would be acceptable, however, individual level applications might be less accurate.National Heart Foundation of Australia (Grant ID: G11S5975), Australian Research Council (Discovery Early Career Researcher Award (Grant ID: DE140101588)), National Heart Foundation of Australia (Career Development Fellowship (Grant ID: CR11S 6099)), National Health and Medical Research Council (Early Career Fellowship (APP1070571)), Medical Research Council (Grant ID: MCUU 12015/3