Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.

Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.

Document Type

Open Access Thesis

Embargo Period

9-1-2016

Degree Program

Kinesiology

Degree Type

Master of Science (M.S.)

Year Degree Awarded

2016

Month Degree Awarded

September

Abstract

PURPOSE: The primary aim of this study was to examine the accuracy of a hip (Evenson algorithm) and wrist-worn (Crouter algorithm) accelerometer in assessing time spent in different intensity categories in pre-adolescent girls during semi-structured dance classes using direct observation (D.O.) as the criterion measure. The secondary aim of this study was to examine the validity of a wrist-worn accelerometer for dichotomizing pre-adolescent girls as meeting or not meeting different preselected doses of moderate-to-vigorous PA compared to the hip-worn accelerometer. METHODS: Data were collected and analyzed on a total of 6 participants (age = 10.22 ± 2.38) for the primary aim. Additionally, data was collected and analyzed on a total of 20 participants (age = 8.6 ± 1.6) for the secondary aim. RESULTS: Compared to D.O., the wrist-worn accelerometer was inaccurate in measuring time spent in light PA, vigorous PA and MVPA. Additionally, the hip-worn accelerometer was inaccurate in measuring time spent in sedentary time, light PA, vigorous PA and total PA. Further, for the secondary aim, there was a significant difference between device location and meeting PA dosage for three days and five days. CONCLUSION: Traditional accelerometer algorithms rely on the activity count cut-point method which provides mixed to poor results of activity intensity classification regardless of wear location. Future research should move away from the activity count cut-point method and aim to develop algorithms that use more of the rich data available from the accelerometers’ acceleration signal.

First Advisor

Sofiya Alhassan

Share

COinS