Discussion
MINOR
We utilized a two-step strategy to reduce raw spatial–temporal pressure data while preserving information. In the first step, a decrease of sampling frequency significantly reduced data size without compromising resolution. Based on our testing of the data, an optimal sampling frequency of every 60 s was found for the pressure data. The use of sampling frequency of every 60 s meant that we only needed to retain 1.7% (1/60) of raw data during pre-processing.In the second step, we focused on the evaluation of the similarity between adjacent frames to identify lying position changes and coalesce the data during periods of stillness. Selection of an appropriate correlation coefficient threshold for position changes should strike a balance between major and minor position changes depending on the specific purpose of the analysis. The maximum number of active periods for 24 h is 80 if the threshold of correlation coefficient is set at 0.90. Therefore, we estimated that we would probably need ~ 160 frames within 24 h to represent the pressure states of a study participant. In total, this meant we needed ~ 480 frames (160*3), over 72 h, of collected data for our analyses without any relevant loss of information. These 480 frames represented ~ 0.185% of the raw data. The two-step process significantly reduced data size without the loss of information.Data reduction is an important component of big data analytics. There are six core properties of big data—volume, variety, value, velocity, veracity, and variability[To the best of our knowledge, our paper is unique in reporting on a data reduction approach for pre-processing of pressure data from a large clinical trial. There are other studies that have looked at interface pressure in a small group of participants over short or long periods of time. Peterson et al. looked at changes in interface pressures of routine repositioning in a convenience sample of 23 hospitalized bedridden patients[