a mobile-phone-based breath carbon monoxide

a mobile-phone-based breath carbon monoxide
NicotineNicotine
& Tobacco
Research Research Advance Access published January 27, 2014
& Tobacco
Original Investigation
A mobile-phone-based breath carbon monoxide meter to
detect cigarette smoking
Steven E. Meredith PhD1, Andrew Robinson BS2, Philip Erb MA3, Claire A. Spieler BS3, Noah Klugman2,
Prabal Dutta PhD2, Jesse Dallery PhD3
1Department
of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD;
of Michigan College of Engineering, Electrical Engineering and Computer Science, Ann Arbor, MI; 3Department of
Psychology, University of Florida, Gainesville, FL
2University
Corresponding Author: Steven E. Meredith, PhD, Department of Psychiatry and Behavioral Sciences, The Johns Hopkins
University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD 21224, USA. Telephone: 410-550-2693;
Fax: 410-550-0030; E-mail: [email protected]
Abstract
Introduction: Mobile phones hold considerable promise for delivering evidence-based smoking cessation interventions that
require frequent and objective assessment of smoking status via breath carbon monoxide (Breath CO) measurement. However,
there are currently no commercially available mobile-phone-based Breath CO meters. We developed a mobile-phone-based
Breath CO meter prototype that attaches to and communicates with a smartphone through an audio port. The purpose of the
current study was to evaluate the reliability and validity of Breath CO measures collected with the mobile meter prototype and
assess the usability and acceptability of the meter.
Methods: Participants included 20 regular smokers (≥10 cigarettes/day), 20 light smokers (<10 cigarettes/day), and 20 nonsmokers. Expired air samples were collected 4 times from each participant: twice with the mobile meter and twice with a commercially available Breath CO meter.
Results: Measures calculated by the mobile meter correlated strongly with measures calculated by the commercial meter
(r = .96, p < .001). In addition, the mobile meter accurately distinguished between smokers and nonsmokers. The area under the
receiver-operating characteristic curve for the mobile meter was 94.7%, and the meter had a combined sensitivity and specificity
of 1.86 at an abstinence threshold of ≤6 ppm. Responses on an acceptability survey indicated that smokers liked the meter and
would be interested in using it during a quit attempt.
Conclusions: The results of the current study suggest that a mobile-phone-based Breath CO meter is a reliable, valid, and
acceptable device for distinguishing between smokers and nonsmokers.
Introduction
Mobile phones have been adopted more rapidly than any other
consumer technology in human history (Rainie & Wellman,
2012). Worldwide, the number of mobile phone subscriptions
is approaching 7 billion (International Telecommunication
Union, 2013). In the United States, more than 90% of all adults
own cell phones (Rainie, 2013), and among adults with diverse
demographic profiles—including racial and ethnic minorities,
individuals living in rural communities, individuals from lower
income households, and individuals with no college education—cell phone ownership either approaches or exceeds 90%.
One factor contributing to the rapid dissemination of mobile
phone technology is the rise in popularity of smartphones.
The majority of U.S. adults, 56%, now own smartphones, and
this percentage is substantially higher among young adults
(18–29 years of age). Even among young adults with annual
household incomes less than $30,000, 77% own smartphones
(Smith, 2013). Because smartphones are widely used and
well integrated into the daily routines of millions of cigarette
smokers, they are promising tools for delivering evidencebased smoking cessation interventions via text messaging (see
Whittaker et al., 2012 for a review) and smartphone applications (i.e., “apps”; Backinger & Augustson, 2011).
Although many of the smoking cessation interventions that
are currently available on smartphones are not evidence based
(Abroms, Padmanabhan, Thaweethai, & Phillips, 2011), a
growing number of researchers are capitalizing on advances
in information and communication technology by delivering
evidence-based smoking cessation interventions (e.g., contingency management) via mobile phones (Hertzberg et al., 2013)
and personal computers (e.g., Meredith, Grabinski, & Dallery,
doi:10.1093/ntr/ntt275
© The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco.
All rights reserved. For permissions, please e-mail: [email protected]
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Received September 16, 2013; accepted December 11, 2013
A mobile-phone-based breath carbon monoxide meter
Methods
Participants
Participants (n = 60) were recruited from Gainesville, FL,
and surrounding communities through print media and word
of mouth. They were divided into three groups based on selfreported frequency of cigarette smoking during an initial phone
screening: regular smokers (≥10 cigarettes/day; n = 20), light
smokers (1–9 cigarettes/day; n = 20), and nonsmokers (0 cigarettes/day; n = 20). Qualified smokers reported smoking their
last cigarette within the previous 24 hr of phone screening. The
University of Florida Institutional Review Board approved all
study procedures.
Materials
Expired air samples were collected using a custom mobile
CO meter prototype and a piCO+ Smokerlyzer® (Bedfont
Scientific Ltd.). The mobile meter prototype consisted of two
components: a smartphone and an attachment containing a CO
sensor. The smartphone was an Apple iPhone 4 running iOS.
(Notably, any iOS device [e.g., iPad or iPod] will work with the
attachment.) The smartphone was loaded with a custom application that displayed instructions for use. It also displayed the
current and most recent maximum CO concentrations (in ppm
to the hundredths decimal place). The application facilitated
2-point calibration (i.e., 0 and 20 ppm CO), it allowed users
Page 2 of 8
to reset the maximum CO concentration to 0 prior to each use,
and it allowed users to transfer data over a cellular or WiFi
connection for remote data collection. The attachment connected to the smartphone through an existing audio headset
port. The port had two outputs (right and left audio channels)
and one input (a microphone channel). These channels were
used to power and configure the CO sensor and receive data
from the sensor. The attachment included an electrochemical
CO sensor cell (2CF CiTiceL®, City Technology, Ltd.), signal
conditioning electronics, embedded processing, and support
circuitry, all housed in a custom 3D-printed enclosure (1.25″
× 2″ × 2″). Cell output was conditioned using analog front end
(AFE) LMP91000 (Texas Instruments, Inc.). This integrated
circuit converted a small current that the sensor cell generated
into a digital value. A microcontroller (MSP430F1611, Texas
Instruments, Inc.) configured and read the AFE and communicated with the smartphone application. Data were digitally
encoded and processed by the microcontroller and smart phone
application. Power and data circuits and communication were
based on the HiJack system (Kuo et al., 2010). An image of the
mobile-phone-based Breath CO meter prototype can be found
in the Supplementary Material.
The Smokerlyzer® and mobile meter were calibrated at
least every 6 months using gas with a 20 ppm CO concentration (per manufacturers’ recommendations). In addition, the 20
ppm gas was used in quality control (QC) checks of the mobile
meter and Smokerlyzer® on the morning of every session day.
Meters were recalibrated when results of QC checks were outside a margin of error of ±2 ppm.
Procedure
At intake, participants provided informed consent and completed a psychosocial questionnaire that included questions
about demographics and smoking history, including the
Fagerström Test for Nicotine Dependence (FTND)—a six-item
questionnaire assessing nicotine dependence with a scale ranging from 0 to 10 (higher scores representing greater dependence; Heatherton, Kozlowski, Frecker, & Fagerström, 1991).
Each participant provided four breath samples: two samples
were collected and analyzed with the mobile CO meter prototype and two with the piCO+ Smokerlyzer®. The sequence
of measurements was standardized across all participants, such
that the first sample was collected with the mobile meter and,
thereafter, samples were analyzed by alternating between the
Smokerlyzer® and mobile meter (i.e., ABAB design).
Before providing a breath sample, participants were
instructed to take a deep breath, hold it for 15 s, and exhale
slowly into the meter. Research staff recorded the exhale duration and Breath CO level (in ppm) of each sample. A minimum
5 m inter-sample interval was required to elapse between each
breath sample. Smokers completed a usability and acceptability survey about the mobile CO meter prototype immediately
following the first Breath CO measurement. Participants rated
the usability and acceptability of the mobile meter on a Visual
Analog Scale (VAS; range 0–100, wherein 0 = strongly disagree and 100 = strongly agree) across several dimensions (e.g.,
ease of use, portability, and likelihood of using the device during a quit attempt), and they also answered several open-ended
questions (e.g., “What did you like least about this device?”).
At the end of each experimental session, participants received
a $40 retail gift card.
Downloaded from http://ntr.oxfordjournals.org/ at University of Michigan on January 29, 2014
2011; see Dallery & Raiff, 2011 for a review). An important feature of these interventions is objective assessment of smoking
status through frequent monitoring of breath carbon monoxide
(Breath CO; Stitzer & Bigelow, 1982), a biochemical marker
of cigarette smoking (Benowitz et al., 2002). Objective assessment of smoking is needed because smokers often misclassify themselves as nonsmokers during quit attempts (Noonan,
Jiang, & Duffy, 2013; Sillett, Wilson, Malcolm, & Ball, 1978).
In addition, some evidence suggests that Breath CO monitoring alone can help promote smoking reduction (Beard & West,
2012). However, the therapeutic benefits of this practice are
likely enhanced when combined with other behavioral treatment strategies (e.g., awarding financial incentives contingent
on abstinence; Meredith & Dallery, 2013).
Because mobile phones give researchers and practitioners
unprecedented access to smokers’ behavior, this communication technology has the potential to significantly enhance
behavioral interventions that require frequent and sustained
Breath CO monitoring. Yet, to our knowledge, there are no
commercially available mobile-phone-based Breath CO
meters. Thus, the purpose of the current study was to evaluate
the reliability, validity, and acceptability of a mobile-phonebased Breath CO meter prototype to assess smoking status.
We developed a compact and portable Breath CO detector
that attaches to and communicates with a smartphone through
an existing audio port using HiJack technology (Kuo, Verma,
Schmid, & Dutta, 2010). In addition, we developed an app
that can be used to calibrate the CO sensor, display Breath CO
measures, and send CO data to a remote server. The current
manuscript describes an investigation of the device’s usability
and acceptability, as well as an evaluation of the reliability and
validity of the device’s measurements.
Nicotine & Tobacco Research
Data Analysis
Results
During phone screenings, all participants who were classified
as regular smokers or light smokers reported smoking their
last cigarette within the previous 24 hr. However, self-report
data collected from participants during experimental sessions, which typically occurred several days following phone
screenings, showed that three light smokers had not smoked a
Smoking Characteristics
Regular smokers and light smokers differed in FTND scores
[t(34) = −4.83, p < .001], number of cigarettes smoked per
day [t(34) = −6.21, p < .001], and time since last cigarette
[t(35) = 2.25, p < .05]. Regular smokers scored higher than
light smokers on the FTND (M = 6.3 ± 1.28 vs. M = 3.2 ± 2.43),
smoked more cigarettes than light smokers (M = 21 ± 9.12 vs.
M = 6.7 ± 2.73), and reported significantly shorter average time
since last cigarette than light smokers (M = 22.5 ± 15.79 vs.
M = 121.6 ± 196.88).
Breath CO
A mixed factorial ANOVA revealed significant differences
in Breath CO across the four within-subject breath samples
[i.e., two measures from the mobile meter and two from the
Smokerlyzer®; F(3, 162) = 46.43, p < .001], and across the
three smoking groups [i.e., regular smokers, light smokers,
and nonsmokers; F(2, 54) = 46.43, p < .001], as well as a significant interaction between Breath CO and smoking group
[F(6, 165) = 32.09, p < .001]. With the mobile meter, regular
smokers provided significantly higher Breath CO measures
(M = 29.9 ppm ± 12.28) than light smokers (M = 13.0 ppm ±
12.30, p < .001), and both regular smokers and light smokers
provided significantly higher Breath CO measures than nonsmokers (M = 3.2 ppm ± .82, p < .001). Similarly, with the
Smokerlyzer®, regular smokers provided significantly higher
Breath CO measures (M = 43.5 ppm ± 18.15) than light smokers (M = 13.8 ppm ± 12.06, p < .001), and both regular smokers and light smokers provided significantly higher Breath CO
measures than nonsmokers (M = 4.2 ppm ± 1.81, p < .001). No
significant differences were observed in duration of exhalation
between the four breath samples or between the three smoking
groups.
Post-hoc analyses revealed that, across all smoking groups,
the first and second Breath CO measures from the mobile meter
prototype were similar and not significantly different from
each other (M = 15.4 and 15.6 ppm, respectively, p = .616).
However, the second measure from the Smokerlyzer® was
significantly higher than the first measure (M = 19.9 and 21.7
ppm, p < .001). In addition, Breath CO measures from the
Smokerlyzer® were significantly higher than those from the
mobile meter [t(113) = −7.13, p < .001].
The first and second Breath CO measures taken with the
mobile meter were strongly correlated with each other (r = .98,
p < .001), as were the first and second measures taken with
the Smokerlyzer® (r = .99, p < .001). As shown in Figure 1,
the Breath CO measures taken with the mobile meter were
also strongly correlated with the measures taken with the
Smokerlyzer® (r = .96, p < .001).
Differences in Breath CO measures and differences in durations of exhalation observed across the two measures taken with
the mobile meter were moderately correlated with one another
(r = .51, p < .001). Differences in Breath CO measures and
differences in durations of exhalation across the two measures
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Mixed factorial analyses of variance (ANOVAs) were conducted to assess differences in Breath CO levels and duration
of exhalation between the three smoking groups (i.e., regular smokers, light smokers, and nonsmokers) and between
the four breath samples (i.e., two from the mobile meter and
two from the Smokerlyzer®). Post-hoc comparisons were
conducted when main effects were observed. Independentsamples t tests were conducted to assess differences in FTND
scores, the number of cigarettes smoked per day, and time
since last cigarette. Results of the main effects were deemed
statistically significant at p < .05. Results of post-hoc analyses
were deemed statistically significant according to Bonferroni
error corrections.
Pearson product-moment correlation coefficients were
calculated to assess the relationship between Breath CO
measures obtained within and across the mobile meter and
the Smokerlyzer®. In addition, because previous research
suggests that duration of exhalation may influence Breath
CO in expired air (Raiff, Faix, Turturici, & Dallery, 2010),
correlations were calculated for the within-subject differences between Breath CO measures and differences
between corresponding durations of exhalation to determine
if changes in exhale duration were correlated with changes
in Breath CO.
The sensitivity and specificity of the mobile meter and
Smokerlyzer® were calculated at different Breath CO abstinence thresholds. In this context, sensitivity and specificity refer to the ability of the instrument to accurately detect
recent smoking or abstinence, respectively. Sensitivity was
calculated by determining the proportion of Breath CO samples provided by smokers that were positive (i.e., above the
CO abstinence threshold; true positives) across a range of CO
values. Specificity was calculated by determining the proportion of Breath CO samples provided by nonsmokers that were
negative (i.e., at or below the CO abstinence threshold; true
negatives) across a range of CO values. Self-reported smoking
status was used as the standard to classify Breath CO measures as true positives, false positives, true negatives, or false
negatives.
Receiver-operating characteristics (ROC) curves were
generated for both the mobile meter and the Smokerlyzer®
by plotting the percentage of true positives (i.e., sensitivity)
against the percentage of false positives (i.e., 100 − specificity).
Area-under-the-curve (AUC) and SE were calculated for each
plot and compared using a nonparametric method to assess
differences in two or more dependent ROC curves (DeLong,
DeLong, & Clarke-Pearson, 1988). The ROC analysis was
conducted with MedCalc Statistical Software V12.7.2. All
other statistical analyses were conducted with IBM® SPSS®
Statistics V21.0.
cigarette within the previous 24 hr. Due to the short half-life
of Breath CO (3–6 hr; Benowitz et al., 2002), breath analysis
would not be expected to detect CO from cigarette smoking
among these subjects (Javors, Hatch, & Lamb, 2005); thus,
their data were excluded from analyses.
A mobile-phone-based breath carbon monoxide meter
specificity (1.86) was ≤6 ppm. Thus, an abstinence threshold
of ≤6 ppm was the optimal cutoff for distinguishing between
smokers and nonsmokers. For the Smokerlyzer®, the abstinence threshold with the highest combined sensitivity and
specificity (1.83) was ≤9 ppm. Although only the most clinically relevant thresholds (≤1–10 ppm) are displayed in Table 1,
thresholds up to ≤15 ppm were tested. Conclusions were not
altered when these additional thresholds were included in the
analysis.
Figure 2 shows the ROC curves for the mobile meter and
Smokerlyzer®. The AUCs for the mobile meter (94.7%,
SE = 1.9%) and the Smokerlyzer® (91%, SE = 2.5%) were
significantly different (p < .05).
taken with the Smokerlyzer® were also moderately correlated
(r = .36, p < .01). Thus, a modest relationship between Breath
CO and duration of exhalation was observed, such that longer
durations of exhalation corresponded with higher Breath CO
measures among samples taken with the same meter. However,
this relationship was considerably weaker across the two
meters. The differences in Breath CO and the differences in
durations of exhalation across the first measures from each
meter were not significantly correlated (r = .21, p = .123), and
these differences across the second measures taken with each
meter were only weakly correlated (r = .27, p < .05).
Sensitivity and Specificity
Sensitivity and specificity among several potential Breath
CO abstinence thresholds for both the mobile meter and the
Smokerlyzer® are shown in Table 1. For the mobile meter, the
abstinence threshold with the highest combined sensitivity and
Usability and Acceptability
Figure 1. Correlation between breath carbon monoxide
(CO) measures taken with the mobile meter prototype and the
Bedfont piCO+ Smokerlyzer®.
Table 1. Sensitivity and Specificity of Mobile-Phone-Based Breath CO Meter Prototype and Bedfont piCO+
Smokerlyzer®
Abstinence thresholds for mobile meter prototype (ppm)
Sensitivity
Specificity
Sensitivity + specificity
1
2
3
4
5
6
7
8
9
10
1.00
0.00
1.00
1.00
0.03
1.03
0.98
0.43
1.41
0.93
0.85
1.78
0.86
0.95
1.81
0.86
1.00
1.86
0.85
1.00
1.85
0.83
1.00
1.83
0.79
1.00
1.79
0.74
1.00
1.74
Abstinence thresholds for Smokerlyzer® (ppm)
Sensitivity
Specificity
Sensitivity + specificity
1
2
3
4
5
6
7
8
9
10
1.00
0.00
1.00
1.00
0.10
1.10
0.98
0.45
1.43
0.91
0.70
1.61
0.90
0.85
1.75
0.88
0.85
1.73
0.85
0.93
1.78
0.83
0.95
1.78
0.83
1.00
1.83
0.79
1.00
1.79
Note. CO = carbon monoxide. Abstinence thresholds with highest combined sensitivity and specificity are in bold.
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Data from smokers (n = 37) who completed a usability and
acceptability survey are shown in Table 2. Notably, these participants had a mean age of 42 (SD = 13), 43% of them were
female, 14% were Hispanic, and 30% were White. In addition,
62% had no college education and 68% earned $200 or less
per month. These participants rated the mobile meter favorably on a VAS across all usability and acceptability dimensions
that were evaluated, including ease of use (M = 86 ± 19) and
portability (M = 84 ± 22). In addition, many smokers indicated
that they would be interested in using the device for selfmonitoring during a quit attempt (M = 74 ± 28) or to allow
clinicians to monitor their smoking status within the context
of a contingency management intervention (M = 78 ± 25). In
response to open-ended questions, participants indicated that
what they liked best about the device was that it was easy to
use (e.g., “It was easy”; n = 15), it was compact or portable
(e.g., “Portability”; n = 9), it was informative (e.g., “It let me
know what’s in my lungs”; n = 8), it was quick (e.g., “Quick
readings and results”; n = 7), it was accurate (e.g., “Looks
accurate”; n = 4), and it was quiet (“It was quiet”; n = 1).
Participants indicated that what they liked least about the
device was holding their breath or exhaling for several seconds
(e.g., “Having to hold breath”; n = 4), that the numbers on
the display were too small (e.g., “Numbers are small”; n = 2),
that the mouthpiece was attached directly to the phone (e.g.,
“Scared to blow it off the phone”; n = 2), that it was difficult
to use or understand (“Technical terms to lay person”; n = 1),
Nicotine & Tobacco Research
Figure 2. Receiver-operating characteristics curves for the mobile meter prototype and the Bedfont piCO+ Smokerlyzer®.
Survey item
M
SD
This device works well with few or no errors
This device is easy to use
This device is compact and portable
This device works quickly
The display on this device is easy to read
The display on this device is easy to understand
If I wanted to quit smoking, I would use this device on a daily basis to track my smoking
If I wanted to quit smoking, I would use this device on a daily basis so clinicians could track my smoking and
provide incentives when my readings indicate that I am smoke free
78
86
84
87
85
85
74
78
20
19
22
18
21
21
28
25
Note. CO = carbon monoxide. Rating scale ranged from 0 (strongly disagree) to 100 (strongly agree).
that it was too small (“Should be larger”; n = 1), and that it
was only compatible with one type of mobile phone (“So far,
it can be used with only one brand of cell phone”; n = 1).
Notably, 62% of smokers indicated that they disliked nothing
about the device (e.g., “Nothing,” “No dislikes at all”; n = 23).
Lastly, changes to the prototype that were suggested by participants included making the numbers on the display easier to
read (e.g., “Bigger numbers”; n = 7), making the device more
compact (e.g., “The device should be made sleek and small
for portable pocket travel”; n = 5), making it compatible with
other mobile phones (e.g., “Make it work with all carriers/
types of cell phones”; n = 2), making the results private (“A
privacy screen”; n = 1), making the app talk (“Have it talk”;
n = 1), and modifying the app to store and track CO measures
(“Record the daily smoking results to show progress”; n = 1).
Discussion
The results of the current study show that Breath CO measures collected with a mobile-phone-based Breath CO meter
prototype are reliable. The two measures that were collected
with the mobile meter were strongly correlated with each other
(r = .98). Further, the measures were strongly correlated with a
commercially available Breath CO meter, the Bedfont piCO+
Smokerlyzer® (r = .96; see Figure 1). Notably, the largest differences observed between Breath CO measures taken with the two
meters occurred among regular smokers. On average, regular smokers provided higher Breath CO measures with the Smokerlyzer®
(M = 43.5 ppm) than the mobile meter (M = 29.9 ppm). Importantly,
however, this difference was less pronounced among nonsmokers
and light smokers (i.e., smokers with Breath CO levels within the
clinically important range that includes abstinence thresholds).
Nevertheless, future studies should examine whether there is a
restriction of range problem with the mobile meter. That is, whether
the meter is less accurate at higher CO levels.
Overall, the results of the study suggest that measures taken
with the mobile meter were valid. Although Breath CO measures collected with the mobile meter were consistently lower
than measures collected with the Smokerlyzer® (Figure 1),
in the context of a smoking cessation intervention, the most
important function of a Breath CO meter is to detect recent
smoking or abstinence. The mobile meter performed this function well. In fact, the area under the ROC curve was even higher
for the mobile meter than it was for the Smokerlyzer® (see
Figure 2). Using an abstinence threshold of ≤6 ppm, the mobile
meter had a combined sensitivity and specificity of 1.86—a
sum that is comparable with commercially available Breath
CO meters. For example, the highest combined sensitivity and
specificity for the Vitalograph (Vitalograph Inc.) was 1.94 at ≤2
ppm (Cropsey, Eldridge, Weaver, Villalobos, & Stitzer, 2006),
1.7 at ≤4 ppm (Perkins, Karelitz, & Jao, 2013), and 1.56 at ≤2
ppm (Javors et al., 2005); the highest combined sensitivity and
specificity for the Bedfont EC-50 Smokerlyzer® was 1.73 at
≤6 ppm (Deveci, Deveci, Acik, & Ozan, 2004) and 1.77 at ≤5
ppm (Middleton & Morice, 2000); and the highest combined
sensitivity and specificity for the piCO+ Smokerlyzer® was
1.92 at ≤4 ppm (MacLaren et al., 2010), 1.89 at ≤4 ppm (Raiff
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Table 2. Usability and Acceptability of Mobile-Phone-Based Breath CO Meter Prototype
A mobile-phone-based breath carbon monoxide meter
Page 6 of 8
prototype, results of the current study also demonstrate that
smokers liked the mobile meter and that they would be interested in using the device during a quit attempt (see Table 2).
Participants’ comments and responses to open-ended questions
indicated that many of them were enthusiastic about the device.
For example, in response to the question, “What did you like
least about this device?” one participant wrote, “That I don’t
have one!” Another participant remarked that the mobile meter
is “amazing,” and one participant suggested that the meter
should be sold “in stores and at a low price because a lot of
low-income people are smokers and would want to use it.” The
favorable opinions of the mobile meter that were reported by
smokers as well as their self-reported willingness to use the
device during a quit attempt are important findings given that
reinforcing participant engagement in technology-based smoking cessation interventions is critical to treatment success
(Richardson et al., 2013).
The results of the current study should be interpreted within
the context of several limitations. First, the research staff relied
on self-report as the standard for distinguishing between smokers and nonsmokers. Future studies evaluating new Breath
CO meters should examine participants’ salivary or urinary
cotinine levels to verify smoking status. Second, many factors that have been shown to contribute to elevated Breath CO
were not measured or analyzed among participants in the current study. These factors include: recent smoking of cannabis
(Wu, Tashkin, Djahed, & Rose, 1988), recent exposure to passive tobacco smoke (Jarvis, Russell, & Feyerabend, 1983) or
other ambient sources of CO (e.g., air pollution; Crowley et al.,
1989), lung capacity (Terheggen-Lagro, Bink, Vreman, & van
der Ent, 2003), chronic lung disease (e.g., chronic obstructive
pulmonary disease; Sato et al., 2003), and other health conditions (e.g., lactose intolerance; McNeill, Owen, Belcher,
Sutherland, & Fleming, 1990). Future studies designed to
evaluate optimal Breath CO abstinence thresholds should control for these variables. Third, breath samples were collected
in a controlled laboratory setting. Future studies should evaluate the reliability and validity of the mobile meter in smokers’ natural environments. Future studies should also evaluate
how frequently the mobile meter needs to be recalibrated both
within the laboratory and in the natural environment.
Another potential limitation of the current study is the
sequence in which breath samples were collected and analyzed.
Samples were collected from each participant with the mobile
meter first. This procedure allowed researchers to administer
the usability and acceptability questionnaire before introducing the commercially available meter to participants. Although
some previous research suggests that there are no significant
sequence effects on Breath CO measures when repeated measurements are taken within close temporal proximity (Raiff
et al., 2010), results from the current study showed that the
second measure taken with the Smokerlyzer® (M = 21.7 ppm)
was significantly higher than the first measure (19.9 ppm).
Importantly, Jarvis, Belcher, Vesey, and Hutchison (1986) also
found that subsequent Breath CO measures were higher than
preceding measures. Thus, future studies designed to compare
the accuracy of multiple Breath CO meters should control for
potential sequence effects.
The results of the current study suggest that a mobilephone-based Breath CO meter is a reliable, valid, and
acceptable device for distinguishing between smokers and
nonsmokers. Moreover, because the majority of the population
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et al., 2010), 1.88 at ≤7 ppm (Erb, Raiff, Meredith, and Dallery,
in press), and 1.83 at ≤9 ppm (in the current study).
Notably, the variability in the abstinence threshold recommendations that have emerged from research and industry
suggests that a determination of which threshold to use in a
smoking cessation intervention should be based, in part, on
the make and model of the instrument being used to measure
Breath CO. However, as indicated above, even studies that
used the same model instrument have found optimal abstinence thresholds that are substantially different from one
another. In such cases, these discrepancies could be due to differences in the versions of the instrument used in each study
(i.e., even products with the same brand name are periodically
updated by manufacturers with new firmware, software, or
hardware). For example, the piCO+ Smokerlyzer® that was
used in MacLaren et al. (2010) and Raiff et al. (2010) was
discontinued in 2011 (i.e., prior to the release of the model
used in the current study; J. Aversano, coVita™, personal
communication, August 28, 2013). Advances in technology
adopted by manufacturers of the Smokerlyzer® (e.g., reducing cross-sensitivity to hydrogen; automating calibration)
may have influenced the sensitivity and/or specificity of this
instrument.
Variability among empirically derived Breath CO abstinence
thresholds could also be due to other variables (e.g., ambient CO in the local environment [Crowley, Andrews, Cheney,
Zerbe, & Petty, 1989]; duration of breath-holding [West,
1984]). Some environments, populations, and instruments may
require the use of higher or lower abstinence thresholds than
others. Thus, researchers and practitioners may benefit from
conducting their own investigations into the optimal abstinence
threshold for a smoking cessation intervention given their
unique environment, target population, breath sampling procedure, and Breath CO meter. A determination of which abstinence threshold to use may also depend on treatment goals.
Researchers or practitioners may find it more important to
capture all true negatives and less important to capture all true
positives (or vice versa). Notably, Table 1 shows that, for both
meters, a range of CO values (e.g., ≤4–10 ppm) could function as relatively accurate abstinence thresholds (i.e., combined
sensitivity and specificity > 1.6). Nevertheless, more systematic investigations of optimal Breath CO abstinence thresholds
are still needed; specifically, more prospective empirical studies designed to collect repeated measures of Breath CO among
larger samples of light smokers and nonsmokers. To date, many
investigations of optimal abstinence thresholds have relied on
secondary analyses of preexisting datasets that included only a
limited number of within-subject measurements collected from
light smokers.
Results from the current study show that the correlation
between Breath CO and exhale duration difference scores was
slightly stronger among measures taken with the mobile meter
(r = .51) relative to the Smokerlyzer® (r = .36). Although this
difference may reflect greater sensitivity of the mobile meter to
exhale duration, it may also reflect a difference in how Breath
CO measures were calculated with the mobile meter relative
to calculations made by the Smokerlyzer®. That is, measurements taken with the mobile meter were calculated to two
decimal places and, thus, had greater precision than the whole
number values that were calculated by the Smokerlyzer®.
In addition to demonstrating the reliability and validity of Breath CO measurements taken with the mobile meter
Nicotine & Tobacco Research
owns smartphones, this device holds exceptional promise as
a remote-monitoring tool to help researchers and practitioners
deliver evidence-based smoking cessation interventions that
require objective assessment of smoking status.
Supplementary Material
Supplementary Material can be found online at http://www.ntr.
oxfordjournals.org
Funding
Declaration of Interests
SEM has no real or potential conflict of interest to declare. AR
has consulted with CO2Meter, Inc. PE has no real or potential
conflict of interest to declare. CAS has no real or potential conflict of interest to declare. NK has no real or potential conflict
of interest to declare. PD has consulted with Seeed Technology
Inc. and IntelliQuit, LLC. JD has no real or potential conflict
of interest to declare.
Acknowledgments
The authors gratefully acknowledge B. Jarvis and R. Patel for
their help with participant recruitment and data collection.
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