One way to assess impacts of nutrition supplements to health is to measure physical activity. Physical activity can be measured with small devices called "accelerometers". Before they can be used, the devices need to be validated in the population in question. Objectives of this study are to test accelerometers in field conditions and validate their use in 16-18 months old Malawian toddlers. This study does not have a pre-set hypothesis.
Accelerometers have not been validated in children under 2 years of age. In this study 50 toddlers from Lungwena will be recruited. The participants will wear an ActiGraph GT3X+ -accelerometer fitted on their waist with an elastic belt for 7 days. During the measuring, they will have two videotaped one-hour activity observations while wearing and additional accelerometer device fitted on their ankle. The output from the two devices will be compared to observed activity classified with CPAF-method.
Study Type
OBSERVATIONAL
Enrollment
56
University of Malawi, College of Medicine
Mangochi, Malawi
Feasibility/acceptability
Proportion of participants (%) of wearing the accelerometer device for 4 days, 6 hours per day (defined from the accelerometer output data).
Time frame: 7-day accelerometer measurement
Cut-off point values for sedentary, light, moderate, and vigorous activity
Videotaped observation of physical activity is classified by CPAF-method. Vector magnitude of the ActiGraph device attached to hip is compared to this gold standard and cut-off point values are determined by ROC curve analysis. Values are presented in counts/15 seconds. Sensitivity and specificity of the determined cut-point values in predicting the right activity class are also calculated.
Time frame: First one-hour observation
Sensitivity and specificity of the determined cut-off point values
Activity count cut-off points derived from the first one-hour observation are cross-validated by determining their sensitivity and specificity in predicting the right activity class during the second one-hour observation.
Time frame: Second one-hour observation
Intra- and inter-subject variation in time spent in different activity classes
Intra-subject variation in time (%) spent in different activity classes between different days and inter-subject variation in time spent in different activity classes are determined.
Time frame: 7-day accelerometer measurement
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