Objective Quantification of In-Hospital Patient Mobilization after Cardiac Surgery Using Accelerometers: Selection, Use, and Analysis

Abstract

Cardiac surgery patients infrequently mobilize during their hospital stay. It is unclear for patients why mobilization is important, and exact progress of mobilization activities is not available. The aim of this study was to select and evaluate accelerometers for objective qualification of in-hospital mobilization after cardiac surgery. Six static and dynamic patient activities were defined to measure patient mobilization during the postoperative hospital stay. Device requirements were formulated, and the available devices reviewed. A triaxial accelerometer (AX3, Axivity) was selected for a clinical pilot in a heart surgery ward and placed on both the upper arm and upper leg. An artificial neural network algorithm was applied to classify lying in bed, sitting in a chair, standing, walking, cycling on an exercise bike, and walking the stairs. The primary endpoint was the daily amount of each activity performed between 7 a.m. and 11 p.m. The secondary endpoints were length of intensive care unit stay and surgical ward stay. A subgroup analysis for male and female patients was planned. In total, 29 patients were classified after cardiac surgery with an intensive care unit stay of 1 (1 to 2) night and surgical ward stay of 5 (3 to 6) nights. Patients spent 41 (20 to 62) min less time in bed for each consecutive hospital day, as determined by a mixed-model analysis (p < 0.001). Standing, walking, and walking the stairs increased during the hospital stay. No differences between men (n = 22) and women (n = 7) were observed for all endpoints in this study. The approach presented in this study is applicable for measuring all six activities and for monitoring postoperative recovery of cardiac surgery patients. A next step is to provide feedback to patients and healthcare professionals, to speed up recovery.

Identification and Quantification of Activities Common to Intensive Care Patients; Development and Validation of a Dual-Accelerometer-Based Algorithm

“…Just as in other studies using two accelerometers [ 12 , 13 , 16 , 26 ], we were able to correctly identify different activities, such as lying and sitting. This is essential, as IC patients spent most of their time engaged in these activities [ 11 ].…”

“…The various activities of the protocols were chosen based on the literature and on input from highly experienced ICU staff. The latter input in particular was important, and led to the inclusion of activities performed with and without instructions, and even more importantly, with the help of healthcare providers, which has not happened in previous similar studies [ 13 , 16 , 17 , 26 ]. It is possible that the validity of the new method is slightly lower when the activities have been performed by IC patients.…”

Factors Determining the Functional State of Cardiac Surgery Patients with Complicated Postoperative Period

“…It may also require additional staffing with adequate training in the rehabilitation of these patients [ 22 ]. In addition, postoperative in-hospital mobilization should be adjusted to the patient’s functional status at that specific moment, building up from sitting in a chair (which should be initiated as early as possible, including in the ICU) and increasing the functional status by walking, biking on an exercise bike or even walking on stairs [ 23 , 24 ]. Otherwise, early mobilization may not lead to a reduction in hospitalization times or a reduction in the cost of treatment [ 11 ].…”

A movement-artefact-free heart-rate prediction system

“…Participants' age, sex, length, and weight were collected through a questionnaire at the end of the experiment. We applied inertial measurement units (IMUs) attached to the chest and upper leg for physical activity information according to our previous study [5] and prior knowledge about human movement analysis. The IMUs were of the type Xsens MTw Awinda [6] and measure six signals: linear accelerations (in m/s 2 ) in the x-, y-and z-axis and rotational velocities (in degree/s) around the x-, y-and z-axis.…”

“…According to our previous study [5] and human movement prior knowledge, 12 features were extracted from each IMU to describe average body component position due to gravity and physical activity dynamics. The features were the average value and the standard deviation of each IMU's six signals.…”