Individual Information-Centered Approach for Handling Physical Activity Missing Data

The purpose of this study was to validate individual information (II)-centered methods for handling missing data, using data samples of 118 middle-aged adults and 9.1 older adults equipped with Yamax SW-200 pedometers and Acligraph accelerometers for 7 days. We used, a semisimulation approach to create six data sets: three physical activity outcome measurements (i.e., step counts, activity counts, and minutes of moderate to vigorous physical activity) for both groups (i.e., middle-aged adults and older adults). After analyzing each data set separately, we replaced missing-values with two II-centered and two group information (GI)-centered methods. Root mean square difference (RMSD), mean signed differences, paired t tests, and Pearson correlations were used to determine the effectiveness of the various recovery methods. Overall, the II-centered methods showed smaller RMSDs than the GI-centered methods for each data set in both groups. We found no significant mean differences between the known values and the replacement values in all conditions. The II-centered methods produced better results than GI-centered methods. We determined substituting Missing data points using the average of days remaining to be an accurate missing data recovery method for middle-aged adults' and older adults' pedometer and accelerometer data.