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A pragmatic assessment of the influence of data processing on Bluetooth oximetry in pulmonary rehabilitation
Alec Bass1, Anne E Holland2, Janet Bondarenko3
1Institut Universitaire de Cardiologie et de Pneumologie de Québec (IUCPQ), Québec, Canada; Respiratory Reasearch@Alfred, School of Translational Medicine, Monash University, Melbourne, Australia; École de réadaptation, Université de Montréal, Montréal, Canada.
Background:
The diversification of modes of delivery and the increasing availability of smart devices, such as Bluetooth oximeters, may improve access to pulmonary rehabilitation. However, the performance of these devices is negatively affected by exercising. Given their high sampling rates, appropriate data processing methods may help reduce artifacts and improve data quality. Thus, this study explored how data processing influences the performance of a Bluetooth oximeter during pulmonary rehabilitation.
Methods:
Fourteen individuals were recruited from the ambulatory and home-based rehabilitation programs at the Alfred Hospital (Melbourne, Australia). Oximetry was collected during a single rehabilitation session and included resting (2 min) and one or two occasions of exercising (5 min of walking and/or cycling, or Modified Incremental Step Test). Two devices were used (one Bluetooth and one wired oximeter) to simultaneously collect data. The data from the Bluetooth oximeter (collected at 1 Hz) was processed using 3-s, 15-s, and 30-s running averages and agreement with the wired oximeter was assessed using Bland-Altman plots. Failure rates were also calculated and compared between data processing methods (Cochran Q tests).
Results:
Narrowing of limits of agreements for pulsed oxygen saturation and heart rate were seen with 15 and 30-s running averages for the resting condition only, with no apparent differences for exercising conditions. Overall failure rates were significantly reduced (adjusted P < 0.05) for 15 and 30-s running averages (failure rates of 36 and 35%, respectively), when compared to 3-s running averages (failure rate of 39%).
Conclusion:
Averaging remote pulse oximeter readings over longer time periods may increase data availability without affecting inter-device agreement during exercise.
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