Mobile Phone-Connected Wearable Motion Sensors to Assess Postoperative Mobilization

Geoff Appelboom, Blake E Taylor, Eliza Bruce, Clare C Bassile, Corinna Malakidis, Annie Yang, Brett Youngerman, Randy D'Amico, Sam Bruce, Olivier Bruyère, Jean-Yves Reginster, Emmanuel Pl Dumont, E Sander Connolly Jr, Geoff Appelboom, Blake E Taylor, Eliza Bruce, Clare C Bassile, Corinna Malakidis, Annie Yang, Brett Youngerman, Randy D'Amico, Sam Bruce, Olivier Bruyère, Jean-Yves Reginster, Emmanuel Pl Dumont, E Sander Connolly Jr

Abstract

Background: Early mobilization after surgery reduces the incidence of a wide range of complications. Wearable motion sensors measure movements over time and transmit this data wirelessly, which has the potential to monitor patient recovery and encourages patients to engage in their own rehabilitation.

Objective: We sought to determine the ability of off-the-shelf activity sensors to remotely monitor patient postoperative mobility.

Methods: Consecutive subjects were recruited under the Department of Neurosurgery at Columbia University. Patients were enrolled during physical therapy sessions. The total number of steps counted by the two blinded researchers was compared to the steps recorded on four activity sensors positioned at different body locations.

Results: A total of 148 motion data points were generated. The start time, end time, and duration of each walking session were accurately recorded by the devices and were remotely available for the researchers to analyze. The sensor accuracy was significantly greater when placed over the ankles than over the hips (P<.001). Our multivariate analysis showed that step length was an independent predictor of sensor accuracy. On linear regression, there was a modest positive correlation between increasing step length and increased ankle sensor accuracy (r=.640, r(2)=.397) that reached statistical significance on the multivariate model (P=.03). Increased gait speed also correlated with increased ankle sensor accuracy, although less strongly (r=.444, r(2)=.197). We did not note an effect of unilateral weakness on the accuracy of left- versus right-sided sensors. Accuracy was also affected by several specific measures of a patient's level of physical assistance, for which we generated a model to mathematically adjust for systematic underestimation as well as disease severity.

Conclusions: We provide one of the first assessments of the accuracy and utility of widely available and wirelessly connected activity sensors in a postoperative patient population. Our results show that activity sensors are able to provide invaluable information about a patient's mobility status and can transmit this data wirelessly, although there is a systematic underestimation bias in more debilitated patients.

Keywords: activity tracking; functional recovery; gait; mobilization; neurorehabilitation; physical therapy; physiotherapy; postoperative.

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Placement of the activity sensor.
Figure 2
Figure 2
Mean differences in ankle and hip tracker recording in subjects versus controls (left); mean differences in ankle and hip tracker recordings in subjects with and without a rolling walker (right).
Figure 3
Figure 3
Functional Ambulation Category (FAC) in relation to ankle sensor mean differences from the gold standard.
Figure 4
Figure 4
Scatterplot of ankle sensor differences in relation to average step length.

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