The ability to reliably measure real-world vaping behavior is critical to understand exposures to potential toxins. Commercially available mobile topography devices were originally designed to measure cigarette puffing behavior. Information regarding how applicable these devices are to the measurement of e-cigarette vaping topography is needed.
CReSS (Pocket) and SPA-M mobile topography devices were tested against the calibrated laboratory-based SPA-D device combined with an analytical smoking machine that generates programmable puffs with high precision. E-cigarettes’ puff topography was measured over a range of puff volumes (10-130 mL) at 2 and 5 seconds puff durations (using bell and square shaped puffs). “Real-world” topography data collected from 10 participants during one week of at-home vaping was also analyzed. Recording anomalies and limitations of the devices such as accuracy of detection of the puff end, flow rate drop-outs, unreported puffs, and abandoned vaping sessions for the CReSS; and multi-peak puffs for the SPA-M were defined.
The accuracy of puff volumes and durations was determined for both devices. The error for SPA-M was generally within ±10%, while that for the CReSS varied more widely. The CReSS consistently underestimated puff duration at higher flow rates.
CReSS and SPA-M mobile topography devices can be used for real-world e-cigarette topography measurements, but researchers have to be aware of the limitations. Both devices can provide accurate measurements only under certain puff parameter ranges. The SPA-M provided more accurate measurements under a wider range of puffing parameters than the CReSS. Summary data reported by both devices require thorough analysis of the raw data to avoid misleading data interpretation.
Results of this study provide researchers with valuable information about the capability of commercially available cigarette topography devices to measure real-world vaping behaviors. The differing measurement ranges of the two devices and puff recording limitations and anomalies should be taken into account during analysis and interpretation of real-world data.