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Pubmed for Handhelds

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  • Title: Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheelchair users with spinal cord injury.
    Author: García-Massó X, Serra-Añó P, García-Raffi LM, Sánchez-Pérez EA, López-Pascual J, Gonzalez LM.
    Journal: Spinal Cord; 2013 Dec; 51(12):898-903. PubMed ID: 23999111.
    Abstract:
    STUDY DESIGN: Cross-sectional validation study. OBJECTIVES: The goals of this study were to validate the use of accelerometers by means of multiple linear models (MLMs) to estimate the O2 consumption (VO2) in paraplegic persons and to determine the best placement for accelerometers on the human body. SETTING: Non-hospitalized paraplegics' community. METHODS: Twenty participants (age=40.03 years, weight=75.8 kg and height=1.76 m) completed sedentary, propulsion and housework activities for 10 min each. A portable gas analyzer was used to record VO2. Additionally, four accelerometers (placed on the non-dominant chest, non-dominant waist and both wrists) were used to collect second-by-second acceleration signals. Minute-by-minute VO2 (ml kg(-1) min(-1)) collected from minutes 4 to 7 was used as the dependent variable. Thirty-six features extracted from the acceleration signals were used as independent variables. These variables were, for each axis including the resultant vector, the percentiles 10th, 25th, 50th, 75th and 90th; the autocorrelation with lag of 1 s and three variables extracted from wavelet analysis. The independent variables that were determined to be statistically significant using the forward stepwise method were subsequently analyzed using MLMs. RESULTS: The model obtained for the non-dominant wrist was the most accurate (VO2=4.0558-0.0318Y25+0.0107Y90+0.0051YND2-0.0061ZND2+0.0357VR50) with an r-value of 0.86 and a root mean square error of 2.23 ml kg(-1) min(-1). CONCLUSIONS: The use of MLMs is appropriate to estimate VO2 by accelerometer data in paraplegic persons. The model obtained to the non-dominant wrist accelerometer (best placement) data improves the previous models for this population.
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