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Original Research

Statistical Process Control Improves The Feasibility Of Remote Physiological Monitoring In Patients With Chronic Obstructive Pulmonary Disease

, , ORCID Icon, , &
Pages 2485-2496 | Published online: 13 Nov 2019

Figures & data

Figure 1 Schema for daily remote patient monitoring.

Notes: Patients were asked to seat themselves at a table in front of the monitoring equipment. They then applied the finger pulse oximeter. They were then taken through a series of screens which presented questions about their symptoms (CLEAR-Sx), treatment adherence (CLEAR-Rx) and physical activity (CLEAR-Ex). Next, they performed an inspiratory capacity maneuver followed by a forced vital capacity maneuver. Finally, they removed the pulse oximeter. The target completion time was set at 10 mins.
Figure 1 Schema for daily remote patient monitoring.

Figure 2 Diagrammatic representation of the statistical process control monitoring algorithm.

Notes: The 7-day rolling average for each physiological variable was tracked along with the standard deviation and prediction interval. Whenever a daily recorded value exceeded the 2-sided 95% prediction interval, then it was considered technically unacceptable and repeated. The lower of the two values was then captured. Whenever a daily recorded value fell below the 1-sided 95% prediction interval, then it was repeated and if still below the 1-sided 95% prediction interval, then it was considered to be a clinical event.
Figure 2 Diagrammatic representation of the statistical process control monitoring algorithm.

Table 1 Baseline Assessments For Subjects Undergoing Remote Patient Monitoring

Table 2 Summary Of Adherence To Each Component Of Remote Monitoring

Table 3 Breakdown Of Adherence To Each Component Of Remote Monitoring

Table 4 Summary Of Subset Daily Adherence Scores

Table 5 Breakdown Of Subset Adherence To Each Component Of Remote Monitoring