Predicting Adult Pulmonary Ventilation Volume and Wearing Compliance by On-Board Accelerometry During Personal Level Exposure Assessments

Abstract

Background: Metabolic functions typically increase with human activity, but optimal methods to characterize activity levels for real-time predictions of ventilation volume (l/min) during exposure assessments have not been available. Could tiny, triaxial accelerometers be incorporated into personal level monitors to define periods of acceptable wearing compliance, and allow the exposures (μg/m3) to be extended to potential doses in μg/min/kg of body weight?

Objectives: In a pilot effort, we tested: 1) whether appropriately-processed accelerometer data could be utilized to predict compliance and in linear regressions to predict ventilation volumes in real time as an on-board component of personal level exposure sensor systems, and 2) whether locating the exposure monitors on the chest in the breathing zone, provided comparable accelerometric data to other locations more typically utilized (waist, thigh, wrist, etc.).

Methods: Prototype exposure monitors from RTI International and Columbia University were worn on the chest by a pilot cohort of adults while conducting an array of scripted activities (all <10 METS), spanning common recumbent, sedentary, and ambulatory activity categories. Referee Wocket accelerometers that were placed at various body locations allowed comparison with the chest-located exposure sensor accelerometers. An Oxycon Mobile mask was used to measure oral-nasal ventilation volumes in-situ. For the subset of participants with complete data (n= 22), linear regressions were constructed (processed accelerometric variable versus ventilation rate) for each participant and exposure monitor type, and Pearson correlations computed to compare across scenarios.

Results: Triaxial accelerometer data were demonstrated to be adequately sensitive indicators for predicting exposure monitor wearing compliance. Strong linear correlations (R values from 0.77 to 0.99) were observed for all participants for both exposure sensor accelerometer variables against ventilation volume for recumbent, sedentary, and ambulatory activities with MET values ~<6. The RTI monitors mean R value of 0.91 was slightly higher than the Columbia monitors mean of 0.86 due to utilizing a 20 Hz data rate instead of a slower 1 Hz rate. A nominal mean regression slope was computed for the RTI system across participants and showed a modest RSD of +/-36.6%. Comparison of the correlation values of the exposure monitors with the Wocket accelerometers at various body locations showed statistically identical regressions for all sensors at alternate hip, ankle, upper arm, thigh, and pocket locations, but not for the Wocket accelerometer located at the dominant-side wrist location (R=0.57; p=0.016).

Conclusions: Even with a modest number of adult volunteers, the consistency and linearity of regression slopes for all subjects were very good with excellent within-person Pearson correlations for the accelerometer versus ventilation volume data. Computing accelerometric standard deviations allowed good sensitivity for compliance assessments even for sedentary activities. These pilot findings supported the hypothesis that a common linear regression is likely to be usable for a wider range of adults to predict ventilation volumes from accelerometry data over a range of low to moderate energy level activities. The predicted volumes would then allow real-time estimates of potential dose, enabling more robust panel studies. The poorer correlation in predicting ventilation rate for an accelerometer located on the wrist suggested that this location should not be considered for predictions of ventilation volume.

Keywords: Ventilation volume; adults; personal exposure; potential dose; triaxial accelerometry; wearing compliance.

Figures

Figure 1

Prototype RTI MicroPEM™ (v2.7) and Columbia Black Carbon monitors worn in shirt pocket locations

Figure 2

Placement of exposure and accelerometric sensors on an adult during scripted testing, with the Oxycon Mobile face mask for characterizing ventilation volume (V). Not shown is the backpack containing data logging modules.

Figure 3

RTI Composite Regression Slopes (ACCEL versus V by Oxycon); activities 1 through 16, showing the median value of 1.43. Highlighted age and BMI values reflect participants over 70 years of age, and outside a BMI range of 20 to 30, respectively.

Figure 4

Example regression plots for Participant #30 comparing including only low-energy activities 1 to 16 ( ), with cycling ( ) indoor (and outdoor) regressions to illustrate the dramatic slope change. R2 data are provided (but not plotted) for a regression merging low and high METS activities (1 to 22), showing the much poorer correlation, but similar slope.

Figure 5

Example of Measured V for Participant #30 across selected scripted activities, illustrating the consistency of the ACCEL variable in predicting ventilation volume, and the potential biases if cycling was (black △) or was not (blue ; see color in online version only) identified a prior to define the appropriate regression to characterize the actual, measured V ( ).