Evaluation of Actical equations and thresholds to predict physical activity intensity in young children

Abstract

This version is available at https://strathprints.strath.ac.uk/52800/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.1 Evaluation of Actical equations and thresholds to predict physical activity intensity in young childrenXanne Janssen AbstractThis study examined the validity of current Actical activity energy expenditure equations and intensity cut-points in preschoolers using activity energy expenditure and direct observation as criterion measures. Forty 4-6 year-olds (5.3±1.0 years) completed a 150-min room calorimeter protocol involving age-appropriate sedentary behaviours, light intensity physical activities and moderate-to-vigorous intensity physical activities. Activity energy expenditure and/or physical activity intensity were calculated using Actical equations and cut-points by Adolph, Evenson, Pfeiffer and Puyau. Predictive validity was examined using paired sample t-tests. Classification accuracy was evaluated using weighted kappas, sensitivity, specificity, and area under the receiver operating and ≥ 287 counts15s -1 when classifying sedentary behaviour, light intensity physical activity and moderate-to-vigorous intensity physical activity, respectively, is recommended.

Longitudinal development of cancer‐related fatigue and physical activity in childhood cancer patients

“…Accelerometer measurements had previously been validated in healthy children aged 7 to 18 years . Data were collected with a 15‐second epoch interval . Valid measurements required a wear time of at least 500 min/day.…”

“…22 Data were collected with a 15-second epoch interval. 23 Valid measurements required a wear time of at least 500 min/day. When data showed 60 minutes of consecutive zeros on the readout, these data were coded as non-wear time and excluded from the analyses.…”

Issues Related to Measuring and Interpreting Objectively Measured Sedentary Behavior Data

“…Sedentary behavior was defined using a cut point of ≤ 25 counts per 15 seconds for the ActiGraph (Janssen et al, 2013b). For the Actical, sedentary behavior was classified using a cut point of ≤ 6 counts per 15 seconds (Janssen et al, 2015). Standing still was calculated by summing up all epochs classified by the activPAL as 15 seconds of standing (excluding stepping).…”

“…In toddlers, preschoolaged children, and school-aged children the ActiGraph ≤ 100 counts per minute cut point appeared most accurate when estimating time spent in sedentary behaviors (Janssen et al, 2012;Trost, Fees, Haar, Murray, & Crowe, 2012;Trost, Loprinzi, Moore, & Pfeiffer, 2011). For the Actical, only one study examined the accuracy of several sedentary behavior cut points in preschool-aged children and found that a cut point of ≤ 6 counts per 15 seconds provided the most accurate estimates (Janssen et al, 2015).…”

Objectively measured sedentary behaviour and health and development in children and adolescents: systematic review and meta‐analysis

“…by stratifying for quartiles of MVPA). To determine the range of sedentary behaviour cut-points considered valid for each activity monitor, we consulted relevant studies and reviews for the most common devices such as the ActiGraph (30)(31)(32)(33) or Actical (11,31,(34)(35)(36) or examined supporting evidence cited in each study. Items were coded as 'present' (1) or 'absent/unclear' (0), and low ROB was classified as the presence of ≥3/4 items.…”