Analytical plan
We designed a two-step strategy to reduce the data. First, we sampled the data at every 5 s (s), 30 s, 60 s, 120 s and 240 s to determine if we could reduce the frequency of pressure data examination without losing important information. Second, we used correlation coefficients to evaluate the similarity between adjacent frames to identify position changes and determine how we could coalesce frames collected during periods of stillness. Previous work on digital image correlation used similar methods and showed that correlation provides an effective measure on the similarity of images and enables us to identify the position and use of a single image to represent long static periods without position change [We defined two variables to illustrate the difference between adjacent frames. The first was the total number of sensors only activated in one of the two frames. The second variable was the sum of absolute pressure difference between two frames.We divided a patient’s monitor time into three categories: time not-on-bed, active time on-bed with position changes within short time intervals, and on-bed stillness where there was no movement while on the bed. Not-on-bed time referred to the time period where the patient was not on the bed during the study period and defined as the sum of time periods when the number of activated sensors was less than 500, indicating that the patient was no longer lying or sitting on the bed. The use of less than 500 activated sensors to determine not on bed status was selected based on a visual inspection of all the individual images with less than 500 activated sensors. This excluded very few images while reducing the noise on the analysis. Identification of position changes depends on the selection of threshold of correlation coefficients. Active time was defined as the sum of all the time periods with continuous position changes. Two periods with position changes were combined if their time gap was less than 120 s to reduce the noise of calculation.We used different plots to evaluate the performance characteristics and usefulness of different sampling frequencies and correlation coefficient thresholds to compress the data. Heat maps and scatter plots were used as a similarity measure to illustrate the spatial difference of pressure values between frames. Boxplots of differences in activated sensors and pressure differences were used to illustrate overall similarity of frames at different correlation coefficients. Lastly, a time series plot was used to show the continuous change of correlation coefficients, mean pressure values, number of activated sensor and number of sensors with pressure value > 40 mm Hg within 24 h for selected patients at different sampling frequency. The threshold value of > 40 mm Hg was based on the distribution of data from a pilot study we conducted [