Fig 1.
Simplified decision tree (DT) for the classification of accelerometer recordings (counts/epoch) as bedrest or wake.
The DT uses different algorithm parameters values (block length, threshold, bedrest-end trigger, and bedrest-start trigger) for waist-worn and wrist-worn accelerometers and has a four-step process to cycle through the data.
Table 1.
Characteristics of study participants.
Table 2.
Characteristics of bedrest and wake periods.
Table 3.
Medians for accuracy, sensitivity, and specificity for selected combinations of algorithm parameters.
The development group medians are reported for Receiver Operating Characteristic (ROC) procedures for waist-worn and wrist-worn accelerometry data. Optimal combinations are shown in bold.
Table 4.
Comparison of medians of bedrest classification from waist- or wrist-worn accelerometer in the development and validation groups with classification obtained using room calorimeter.
Fig 2.
Plots of showing the tradeoff between sensitivity (y-axis) and 1-specificity (x-axis).
(A) Data from waist-worn accelerometers (B) Data from wrist-worn accelerometers. Each open circle [○] represents a respective combination of threshold (counts/min) for bedrest end (counts/min), bedrest start (counts/min), and block length (min). The solid circle [●] represents the selected optimal combination. The corresponding values are in Table 2 (bolded). The solid triangle [▲] represents the validation set. The solid square [■] in B represents Cole-Kripke algorithm.
Table 5.
Comparison of medians of bedrest classification from accelerometer placed on wrist calculated using Cole-Kripke automated algorithm and the decision tree (DT) with classification obtained using room calorimeter.