Search Results

You are looking at 1 - 2 of 2 items for :

  • Author: Zoe R. Knowles x
  • Sport and Exercise Science/Kinesiology x
  • Refine by Access: All Content x
Clear All Modify Search
Restricted access

Physical Activity Patterns in Youth With Intellectual Disabilities

Samantha J. Downs, Stuart J. Fairclough, Zoe R. Knowles, and Lynne M. Boddy

The aim of this study was to assess the physical activity (PA) patterns of youth with intellectual disabilities (ID). PA was monitored for 7 days in 70 participants, 5–15 years old, using accelerometers. There were 32 participants included in the final analysis. Habitual PA and the number of continuous bouts accrued for a range of bout lengths (5–600 s) for light (LPA), moderate (MPA), and vigorous (VPA) PA were calculated. Multivariate analysis of covariance was used to assess differences in the number of continuous bouts by sex, age, and ID group and between week and weekend days. Participants exhibited short sporadic bursts of activity. The number of continuous bouts decreased as the intensity and duration increased. Few differences in PA patterns were reported by sex, ID group, and age group and between week and weekend days, possibly due to the generally low PA levels in this population.

Restricted access

A Novel Mixed Methods Approach to Assess Children’s Sedentary Behaviors

Liezel Hurter, Anna M. Cooper-Ryan, Zoe R. Knowles, Lorna A. Porcellato, Stuart J. Fairclough, and Lynne M. Boddy

Purpose: Accurately measuring sedentary behavior (SB) in children is challenging by virtue of its complex nature. While self-report questionnaires are susceptible to recall errors, accelerometer data lacks contextual information. This study aimed to explore the efficacy of using accelerometry combined with the Digitising Children’s Data Collection (DCDC) for Health application (app), to capture SB comprehensively. Methods: 74 children (9–10 years old) wore ActiGraph GT9X accelerometers for 7 days. Each received a SAMSUNG Galaxy Tab4 (SM-T230) tablet, with the DCDC app installed and a specially designed sedentary behavior study downloaded. The app uses four data collection tools: 1) Questionnaire, 2) Take a photograph, 3) Draw a picture, and 4) Record my voice. Children self-reported their SB daily. Accelerometer data were analyzed using R-package GGIR. App data were downloaded and individual participant profiles created. SBs reported were grouped into categories and reported as frequencies. Results: Participants spent, on average, 629 min (i.e., 73% of their waking time) sedentary. App data revealed most of their out-of-school SB consisted of screen time (112 photos, 114 drawings, and screen time mentioned 135 times during voice recordings). Playing with toys, reading, arts and crafts, and homework were also reported across all four data capturing tools on the app. On an individual level, data from the app often explained irregular patterns in physical activity and SB observed in accelerometer data. Conclusion: This mixed methods approach to assessing SB adds context to accelerometer data, providing researchers with information needed for intervention design.