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Measuring Physical Activity Change in Broad-Reach Intervention Trials

Marina M. Reeves, Alison L. Marshall, Neville Owen, Elisabeth A.H. Winkler, and Elizabeth G. Eakin


We compared the responsiveness to change (prepost intervention) of 3 commonly-used self-report measures of physical activity.


In a cluster-randomized trial of a telephone-delivered intervention with primary care patients, physical activity was assessed at baseline and 4 months (n = 381) using the 31-item CHAMPS questionnaire; the 6-item Active Australia Questionnaire (AAQ); and, 2 walking for exercise items from the US National Health Interview Survey (USNHIS). Responsiveness to change was calculated for frequency (sessions/week) and duration (MET·minutes/week) of walking and moderate-to-vigorous intensity physical activity.


The greatest responsiveness for walking frequency was found with the USNHIS (0.45, 95% CI: 0.19, 0.72) and AAQ (0.43, 95% CI: 0.19, 0.67), and for walking duration with the USNHIS (0.27, 95%CI 0.13, 0.41) and CHAMPS (0.24, 95% CI: 0.12, 0.36). For moderate-to-vigorous activity, responsiveness for frequency was slightly higher for the AAQ (0.50, 95% CI: 0.30, 0.69); for duration it was slightly higher for CHAMPS (0.32, 95% CI: 0.17, 0.47).


In broad-reach trials, brief self-report measures (USNHIS and AAQ) are useful for their comparability to population physical activity estimates and low respondent burden. These measures can be used without a loss in responsiveness to change relative to a more detailed self-report measure (CHAMPS).

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CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35+

John Bellettiere, Supun Nakandala, Fatima Tuz-Zahra, Elisabeth A.H. Winkler, Paul R. Hibbing, Genevieve N. Healy, David W. Dunstan, Neville Owen, Mikael Anne Greenwood-Hickman, Dori E. Rosenberg, Jingjing Zou, Jordan A. Carlson, Chongzhi Di, Lindsay W. Dillon, Marta M. Jankowska, Andrea Z. LaCroix, Nicola D. Ridgers, Rong Zablocki, Arun Kumar, and Loki Natarajan

Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35–99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL − CHAP-Adult) and 95% limits of agreement were: sedentary time −10.5 (−63.0, 42.0) min/day, breaks in sedentary time 1.9 (−9.2, 12.9) breaks/day, mean bout duration −0.6 (−4.0, 2.7) min, usual bout duration −1.4 (−8.3, 5.4) min, alpha .00 (−.04, .04), and time in ≥30-min bouts −15.1 (−84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: −2.0% (4.0%), −4.7% (12.2%), 4.1% (11.6%), −4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson’s correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.