This symposium addressed the ongoing development of new technologies for the objective measurement of physical activity and diet and efforts to provide best practice guidelines for scientists developing, evaluating and using existing and new technologies for the objective measurement of physical activity. The research projects discussed and the workshop overview presented are components of the Genes, Environment, and Health Initiative (GEI) of the National Institutes of Health. The rationale, plans and progress of the GEI physical activity and diet initiative were presented. Detailed presentations described 2 projects focused on the use of mobile phone based systems designed to collect, process and store data; 1 uses multiple wireless accelerometers to detect body movement and the other uses a camera built into a mobile phone and advanced software to quantify dietary intake. Given the rapid development of new accelerometer-based physical activity measurement devices and analytical approaches, it is important that best practices be used by scientists and practitioners using theses devices. An overview of a “best practices” workshop held in July 2009 was presented. The presentations and discussions during this symposium made evident the progress, potential and challenges of implementing advanced technologies to enhance the measurement of physical activity and diet.
Alan K. Bourke, Espen A. F. Ihlen and Jorunn L. Helbostad
The measurement of physical activity patterns has the potential to reveal underlying causes of changes in modifiable risk-factors associated with health and well-being. Accurate classification of physical activity (PA) in free-living situations requires the use of a validated measurement system to
Stephen Herrmann and Brian G. Ragan
Edited by Michael G. Dolan
Colleen K. Kilanowski, Angela R. Consalvi and Leonard H. Epstein
Activity measurement using a uniaxial electronic pedometer was compared to a triaxial accelerometer and behavioral observation measurements for ten 7−12-year-old children studied during high intensity recreational and low intensity classroom periods. Correlations between all measures were significant for recreational and classroom periods combined, and recreational periods alone (r’s > .90, p < .001). Correlations between the pedometer and accelerometer were significantly lower during classroom versus recreational activities (0.98 vs. 0.50, p < .05). This may be due in part to the uniaxial pedometer being sensitive only to vertical and not back and forward or side to side movement.
Lynn B. Panton, Michael R. Kushnick, J. Derek Kingsley, Robert J. Moffatt, Emily M. Haymes and Tonya Toole
To evaluate physical activity with pedometers and health markers of chronic disease in obese, lower socioeconomic African American women.
Thirty-five women (48 ± 8 y) wore pedometers for 2 weeks. One-way analyses of variances were used to compare age, weight, body mass indices (BMI), and health markers of chronic disease (including blood pressure, cholesterol, triglycerides, glycosylated hemoglobin, fibrinogen, C-reactive protein) between women who were classified by steps per day as sedentary (SED < 5,000; 2,941 ± 1,161 steps/d) or active (ACT ≥ 5,000; 7,181 ± 2,398 steps/d).
ACT had significantly lower BMI (ACT: 37.2 ± 5.6; SED: 44.4 ± 7.2 kg/m2) and hip circumferences (ACT: 37.2 ± 5.6; SED: 44.4 ± 37.2 cm) and higher total cholesterol (ACT: 230 ± 53; SED: 191 ± 32 mg/dL) than SED. There were no differences in health markers of chronic disease between SED and ACT. Pearson product moment correlations showed significant negative correlations between steps/d and weight (r = –.42), BMI (r = –.46), and hip circumference (r = –.47).
Increased levels of physical activity were associated with reduced BMI and hip circumferences but were not associated with lower health markers for chronic disease in obese, lower socioeconomic African American women.
Jane F. Hislop, Cathy Bulley, Tom H. Mercer and John J. Reilly
The objectives of this study were to explore whether triaxial is more accurate than uniaxial accelerometry and whether shorter sampling periods (epochs) are more accurate than longer epochs. Physical activity data from uniaxial and triaxial (RT3) devices were collected in 1-s epochs from 31 preschool children (15 males, 16 females, 4.4 ± 0.8 yrs) who were videoed while they engaged in 1-hr of free-play. Video data were coded using the Children’s Activity Rating Scale (CARS). A significant difference (p < .001) in the number of minutes classified as moderate to vigorous physical activity (MVPA) was found between the RT3 and the CARS (p < .002) using the cut point of relaxed walk. No significant difference was found between the GT1M and the CARS or between the RT3 and the CARS using the cut point for light jog. Shorter epochs resulted in significantly greater overestimation of MVPA, with the bias increasing from 0.7 mins at 15-s to 3.2 mins at 60-s epochs for the GT1M and 0 mins to 1.7 mins for the RT3. Results suggest that there was no advantage of a triaxial accelerometer over a uniaxial model. Shorter epochs result in significantly higher number of minutes of MVPA with smaller bias relative to direct observation.
Pedro C. Hallal, Sandra Matsudo and José C. Farias Jr.
Barbara Sternfeld and Lisa Goldman-Rosas
Numerous instruments to measure self-reported physical activity (PA) exist, but there is little guidance for determining the most appropriate choice.
To provide a systematic framework for researchers and practitioners to select a self-reported PA instrument.
The framework consists of 2 components: a series of questions and a database of instruments. The questions encourage users to think critically about their specific needs and to appreciate the strengths and limitations of the available options. Instruments for the database were identified through existing literature and expert opinion.
Ten questions, ranging from study aim and study design to target population and logistical consideration, guide the researcher or practitioner in defining the criteria for an appropriate PA instruments for a given situation. No one question on its own determines the optimal choice, but taken together, they narrow the potential field. The database currently includes 38 different self-reported PA instruments, characterized by 18 different parameters.
The series of questions presented here, in conjunction with a searchable database of self-report PA instruments, provides a needed step toward the development of guiding principles and good practices for researchers and practitioners to follow in making an informed selection of a self-reported PA instrument.
Philippa M. Dall, Dawn A. Skelton, Manon L. Dontje, Elaine H. Coulter, Sally Stewart, Simon R. Cox, Richard J. Shaw, Iva Čukić, Claire F. Fitzsimons, Carolyn A. Greig, Malcolm H. Granat, Geoff Der, Ian J. Deary, Sebastien F.M. Chastin and On behalf of the Seniors USP Team
lack of participant engagement, however they are less able to compensate for monitors not returned because they are lost or damaged after removal by the participant or during transit in the postal service. In summary, there is growing research demonstrating that the objective measurement of physical