Sit versus stand : can sitting be accurately identified using MTI accelerometer data?
Marshall, Alison L., Rachele, Jerome N., Marshall, Lee-Anne J., Lai, Junior, & Jones, Lee V. (2010) Sit versus stand : can sitting be accurately identified using MTI accelerometer data? In American College of Sports Medicine Annual Meeting 2010, 2-5 June 2010, Baltimore, ML.
High levels of sitting have been linked with poor health outcomes. Previously a pragmatic MTI accelerometer data cut-point (100 count/min-1) has been used to estimate sitting. Data on the accuracy of this cut-point is unavailable. PURPOSE: To ascertain whether the 100 count/min-1 cut-point accurately isolates sitting from standing activities. METHODS: Participants fitted with an MTI accelerometer were observed performing a range of sitting, standing, light & moderate activities. 1-min epoch MTI data were matched to observed activities, then re-categorized as either sitting or not using the 100 count/min-1 cut-point. Self-report demographics and current physical activity were collected. Generalized estimating equation for repeated measures with a binary logistic model analyses (GEE), corrected for age, gender and BMI, were conducted to ascertain the odds of the MTI data being misclassified. RESULTS: Data were from 26 healthy subjects (8 men; 50% aged <25 years; mean BMI (SD) 22.7(3.8)m/kg2). MTI sitting and standing data mode was 0 count/min-1, with 46% of sitting activities and 21% of standing activities recording 0 count/min-1. The GEE was unable to accurately isolate sitting from standing activities using the 100 count/min-1 cut-point, since all sitting activities were incorrectly predicted as standing (p=0.05). To further explore the sensitivity of MTI data to delineate sitting from standing, the upper 95% confidence interval of the mean for the sitting activities (46 count/min-1) was used to re-categorise the data; this resulted in the GEE correctly classifying 49% of sitting, and 69% of standing activities. Using the 100 count/min-1 cut-point the data were re-categorised into a combined ‘sit/stand’ category and tested against other light activities: 88% of sit/stand and 87% of light activities were accurately predicted. Using Freedson’s moderate cut-point of 1952 count/min-1 the GEE accurately predicted 97% of light vs. 90% of moderate activities. CONCLUSION: The distributions of MTI recorded sitting and standing data overlap considerably, as such the 100 count/min -1 cut-point did not accurately isolate sitting from other static standing activities. The 100 count/min -1 cut-point more accurately predicted sit/stand vs. other movement orientated activities.
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|Item Type:||Conference Item (Poster)|
|Keywords:||Physical Activity, Accelerometer, Sedentary|
|Subjects:||Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700)|
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700) > Health Promotion (111712)
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health|
Current > Schools > School of Exercise & Nutrition Sciences
Current > Schools > School of Public Health & Social Work
|Deposited On:||06 Oct 2011 08:32|
|Last Modified:||06 Oct 2011 08:32|
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