Disgust but not Health Anxiety Graphic Warning Labels Reduce Motivated Attention in Smokers: A Study of P300 and Late Positive Potential Responses

Disgust but not Health Anxiety Graphic Warning Labels Reduce Motivated Attention in Smokers: A... Abstract Background Graphic health warning labels (GHWLs) on tobacco products attempt to leverage avoidance-promoting emotions, such as anxiety and disgust, to encourage cessation. Prior studies have relied on self-report or attentional metrics that may not accurately illuminate GHWLs’ ability to motivate change. This report evaluates the impact of disgust- and anxiety-based GHWLs on electroencephalograph (EEG) measures of motivated attention among two groups of smokers—those that report higher versus lower cigarette dependence. We hypothesized that both anxiety and disgust GHWLs would reduce appetitive attention, as indexed by lowered P300 (P3) and late positive potential (LPP) activations. Methods Sixty-one smokers provided demographic and smoking history before completing an oddball paradigm consisting of three counterbalanced stimuli blocks. Each block (100 trials) contained a neutral, GHWL-anxiety, or GHWL-disgust frequent image and a smoking cue as the oddball image (20%). Oddball trials for each block were averaged, P3 and LPP were identified at midline electrode positions (Fz, Cz, and Pz), and mean amplitude was analyzed. Results Separate mixed-model ANOVAs of P3 and LPP reactivity revealed disgust-focused GHWLs reduced motivated attentional processing. Conversely, the anxiety-focused GHWL appeared to increase the salience of the smoking cue (Fz only). Less-dependent smokers showed lower P3 reactivity than those with higher dependence at Fz, but greater P3 reactivity at Cz and Pz. Conclusion These results extend prior work in demonstrating that disgust, but not anxiety-based GHWLs, may reduce EEG-assessed motivated attention to smoking cues. Disgust may thus represent a more fruitful target for public health cessation efforts. Implications Most GHWL evaluations have focused on fear (or anxiety) elicitation rather than disgust, an emotion that may have a unique link to smoking, having evolved specifically to facilitate the avoidance of contaminants via oral incorporation. Analyses of P300 and LPP responses to GHWLs suggest that disgust-focused images interfere with the EEG-indexed attentional processing of smoking cues and do so better than health anxiety-focused messages. However, interaction effects at different electrode sites indicated that GHWLs have complex effects in more versus less-dependent smokers and that an understanding of how smoking cues and anti-smoking imagery become associated over time is needed to identify relevant targets for public health efforts. Introduction Graphic health warning labels (GHWLs) are used in smoking cessation campaigns in more than 70 countries.1 These labels involve the use of images to elicit avoidance-promoting emotions, such as health-related fear (or anxiety), disgust and, less commonly, guilt or embarrassment.2 In theory, exposure to emotion-inducing images should ultimately reduce consumption and/or uptake of tobacco use. However, while public health research suggests GHWLs underpin reductions in tobacco consumption, the deployment of images typically occurs concurrently with other legislative changes and thus findings are difficult to interpret.1,3 Additionally, while experimental data suggest GHWLs increase attentional processing and reduce the self-reported desire to smoke,1,4 studies do not directly or objectively assess the impact GHWLs have on the cognitive processes responsible for these changes. Evidence shows smokers exhibit cognitive biases in the processing of smoking-related stimuli. These biases, which encompass core processes such as attention and memory, facilitate the detection of smoking cues and probably help perpetuate addiction. Among smokers, attention is preferentially allocated to smoking cues, which are seen as particularly salient and reinforcing.5,6 Studies have thus begun to examine whether GHWLs may change cue processing in ways that should reduce smoking and/or encourage cessation.7–9 However, current knowledge is limited in several ways. First, most experimental work investigating the effect of GHWLs has relied on self-reported measures.10 These self-reported responses to GHWLs have suggested greater quit intentions and increased recognition of smoking’s health threat.4 However, “intentions” do not always lead to quitting, and thus a robust experimental design is needed to ensure that there are no other influencing factors.10 Other data suggest that GHWLs hold attention longer than traditional warning messages,11,12 and emotional stimuli lead to greater processing (as indexed by heart rate change) in nonsmokers.9 However, evidence among smokers indicates that, while high threat images may capture attention, they may also result in more efficient disengagement from images, compared with nonsmokers.13,14 Likewise, it remains unclear whether more attention or reduction in the desire to smoke promote quitting and tobacco aversion. Further insight can be gained by moving from pure attentional metrics to measures incorporating “motivational” elements of attention, and examining how responses to smoking cues can be attenuated via presentation of GHWLs. A second contribution of this report lies in separating and examining two avoidance-promoting emotions thought to underpin any effects associated with GHWLs.9 Historically, anti-smoking campaigns have concentrated on fear appeals, which have demonstrated stronger “threat” messages are more persuasive.15 However, GHWLs elicit a range of emotions,16 and the specific emotions best suited to reducing appetitive motivations are unclear. Labels mix the emotions they elicit and, because both fear and disgust evolved to promote avoidance,17,18 it is difficult to identify which might best be leveraged to interfere with the motivated attentional processes characterizing addiction to nicotine. While most GHWL evaluations have focused on fear (or anxiety),19 disgust may have a unique link to smoking having evolved specifically to promote the avoidance of potential contaminants, particularly via oral incorporation.17,20 Functionally, disgust is characterized by a rejection and avoidance dynamic that manifests in action tendencies, experiential and cognitive states, and expressive and physiological changes—all of which operate to promote withdrawal and avoidance of health threats.21 Thus, GHWLs eliciting disgust should be more effective than health anxiety at disrupting motivated attention toward smoking cues. This report contrasts the ability of GHWLs evoking these two emotions to reduce cue-related motivated attention. Finally, research suggests the effect of tobacco control strategies may vary as a function of nicotine dependence.22,23 Evidence from GHWLs and elsewhere suggests only certain groups of smokers, such as those who are motivated to quit, find graphic anti-smoking messages helpful.24,25 Other groups of smokers report high defiance; “boomerang” effects to anti-smoking messages are increasingly documented.10,25,26 In particular, heavily dependent smokers are known to deny health threats,10,27 suggesting that GHWLs may be less effective in this group. This report examines whether the degree of nicotine dependence moderated the impact of health anxiety and disgust GHWLs on the processing of smoking cues. In addressing these questions, this report presents an electroencephalograph (EEG) study of activation during GHWL presentation. We concentrate on differences in the P300 (P3) and the late positive potential (LPP), two key ERP components associated with the deployment of attentional resources to motivationally relevant stimuli.28,29 Like other substance abuse populations, smokers exhibit larger P3 and LPP amplitudes in response to smoking-related stimuli.6 Research also indicates P3 and LPP responses can be manipulated,30,31 including among smokers,32 meaning documenting reductions in these components may identify ways of reducing motivated attention to cues. For example, while most studies assess GHWL efficacy via self-report, one study found P3 activation was only reduced among highly emotive GHWLs.7 In moving measurement from pure attentional metrics to measures incorporating the “motivational” element of attention, the current report tests whether P3/LPP activity in response to smoking cues can be attenuated via presentation of disgust and health anxiety–GHWLs. Specifically, this study examined whether disgust and/or health-anxiety-inducing GHWLs reduce objectively assessed cue processing metrics among more and less-dependent smokers. It was hypothesized that both anxiety and disgust GHWLs would reduce attention-related cognitive processing of smoking cues, as indexed by reduced P3 and LPP activations, but disgust’s effects would be larger. Furthermore, we hypothesized that anxiety–GHWLs would be less effective among more dependent smokers. Methods Participants Sixty-three persons (a) reporting having smoked 100+ cigarettes in their lifetime and (b) currently smoking every day or most days33 were recruited through advertisements placed around university and central city locations. Ex-smokers and those who reported hypertension, cardiovascular disease, current somatic and psychiatric illness, or an insufficient understanding of the English language were excluded. Two participants were excluded after data collection; one due to excessive movement artifacts that were not correctable and one who withdrew. Data from 61 participants are included. Procedure All participants completed electronic consent procedures and an online questionnaire assessing demographic, smoking, and trait characteristics before being invited to attend a clinic session. Participants were asked to refrain from smoking for 1 hour before the appointment in an attempt to standardize time since their last cigarette without increasing craving due to nicotine deprivation rather than cue-specific potentiation.34 Upon arrival, participants’ written informed consent was obtained followed by a CO monitor test. All experimental sessions were scheduled between 8 am and 12 pm and lasted approximately 90 min (including 40 min of EEG setup). EEG was recorded using 64 active Ag/AgCl electrodes pre-set by the Easy-Cap electrode system (Falk Minow Services, Germany) according to the International 10/20 system. Six additional Ag/AgCl electrodes were connected as per standard protocols for vertical and horizontal electro-oculogram and heart rate measurement. Electrode impedance was kept below 10 kΩ. EEG data were collected continuously from the start of the baseline task to the end of the session. The BrainAmp MR Plus amplifier recorded EEG data at a sampling rate of 1,000 Hz (Brain Products GmbH, 2012). After EEG setup participants completed the experimental tasks. MATLAB (MATLAB 2015b, The MathWorks Inc., Natick, MA, United States) and PsychToolbox (Psychphysics Toolbox, version 3.0.11) computer software were used to control the experimental session. Baseline A 5-min baseline period was included to allow acclimation to the laboratory. Participants were given standardized instructions to sit quietly, focus on a fixation cross, and blink normally during this task. Resting EEG data and HR data were collected during this period (subject of another report). Craving and mood were collected at the end of this task. Visual Oddball Task The P3 and LPP are commonly studied using oddball paradigms, in which participants are instructed to identify the less-frequent (oddball) stimuli (20% of trials at random) presented among more frequent nontarget stimuli. The oddball paradigm is widely used to assess cue processing and requires more resource allocation to process the oddball stimuli and update working memory, leading to enhanced P3 responses35 that are further enhanced when stimuli are motivationally relevant.36 The current task consisted of three counterbalanced blocks (100 trials each) of images presented on a computer screen approximately 600 mm in front of the participant. Each image was presented for a maximum of 1,000 ms, at a rate of one every 2,000 ms. After each block (approximately 3 min in duration), participants rated mood and craving. After all three blocks were complete, a 3-min rest period was followed by further craving and mood ratings. Visual Stimuli Stimuli consisted of an oddball image, used in all three blocks of trials, and three frequent images, one for each block of trials which effectively formed three emotion conditions within which the oddball stimuli were viewed. The oddball stimulus was a color smoking-related image taken from the International Smoking Image Series.37 This image was chosen because of evidence it elicits high valence and arousal scores among smokers.37 The frequent stimuli were an image of a hand holding a pen (a nonsmoking neutral image from the ISIS series) and two GHWL images, one for disgust and one for anxiety. The anxiety and disgust GHWL images were taken from a series of images provided to the senior author by the Health Promotion Agency, the government agency responsible for developing the next round of GHWLs for New Zealand. Images included those currently being used on packaging in New Zealand or another country. To ensure suitability, 24 images from this Agency had text removed and were independently piloted among 89 New Zealand smokers and nonsmokers (15 daily smokers, 24 less than daily smokers, 22 past smokers, and 28 never smokers) to assess their valence, arousal, and affect induction. Average scores of the current smokers (N = 39) were calculated and the top-rated disgust and anxiety images identified. T-tests of the means confirmed the disgust image rated high in “disgust” and low in “health-related anxiety,” t(38) = 3.19, p < .01, d = .53. The same process was also employed to choose the health anxiety image, t(38) = 2.87, p < .01, d = .46. The selected disgust GHWL depicted lips covered in sores and puss, and scored the highest among smokers for inducing disgust. The selected health anxiety GHWL depicted a solemn looking man sitting in a hospital bed alone with a tracheotomy wound and scored the highest among smokers for inducing health-related anxiety. Measures Demographics and nicotine dependence were assessed before the experimental session. The six-item Fagerstrom Test for Nicotine Dependence (FTND; α = 0.61) was used to assess nicotine dependence.38 Scores on this scale range from 0 to 10 and items were aggregated, with a higher score indicating greater dependence. Craving during the experiment was assessed using a 100-point visual analogue scale (VAS) with three anchor points (no craving [left end], moderate craving [middle], extremely high craving [right end]). EEG Signal Processing Offline pre-processing was performed with the Brain Vision Analyzer 2.0 software (version 2.1.0.327, Brain Products GmbH, Germany). Sampling rate was reduced to 200 Hz. All channels were re-referenced to the common average reference and a digital pass-band filter set from 0.1 to 50 Hz (24 dβ/octave roll off). Vertical and horizontal eye movements and blinks were removed from continuous data using semiautomatic ICA ocular correction39 and the data were epoched into 1,200 ms periods spanning 200 ms prestimulus to 1,000 ms post oddball stimulus onset. Epochs were baseline corrected (using 100 ms prestimulus activity) and any epoch segments with EEG voltages exceeding ±150 µV or with a maximum voltage step (gradient) of 75 µV were excluded from further analysis by means of semiautomatic artifact rejection and visual inspection. The remaining data were averaged for each electrode, individual, and condition (neutral, disgust, and anxiety). Although P300 is typically maximal over central and parietal electrode positions35 some studies assessing attentional bias in smokers have only found frontally or fronto-centrally distributed differences.40,41 Additionally, research indicates LPP activity shifts from parietal to more fronto-cental sites over time.42,43 Therefore, ERP waveforms of the oddball trials for each condition at the midline electrode positions (Fz, Cz, and Pz) was of interest for this report.6 Waveforms for each electrode by condition can be found in Figure 1. Grand averages were used to identify the P3 component between 300 and 600 ms after stimuli onset6 and the LPP between 600 and 1000 ms.28 The mean amplitude (µV) across these time windows was exported for statistical analysis. Figure 1. View largeDownload slide Oddball grand average waveforms for each condition at each electrode position. The oddball waveform for each condition at each of the midline electrode positions. Black = neutral condition, red = disgust condition and blue = health anxiety condition; *Significant differences in P3 (300–600 ms) across conditions; ‡Significant difference in LPP (600–1,000 ms) across conditions. Figure 1. View largeDownload slide Oddball grand average waveforms for each condition at each electrode position. The oddball waveform for each condition at each of the midline electrode positions. Black = neutral condition, red = disgust condition and blue = health anxiety condition; *Significant differences in P3 (300–600 ms) across conditions; ‡Significant difference in LPP (600–1,000 ms) across conditions. Data Analysis Data were analyzed with IBM SPSS Statistics 22. A median split of the total FTND scores was used to categorize more dependent and less-dependent groups (FTND ≤3 vs. > 3). To test whether mean amplitude differed across electrode site in each of the oddball paradigm conditions as a function of nicotine dependence, a 3 (electrode position) × 3 (emotion GHWL condition) × 2 (dichotomized FTND score) mixed-model ANOVA was performed for P3 and LPP activity, separately. Paired-sample t-tests and simple contrasts were used to deconstruct main effects and interactions, and significance was adjusted accordingly. Results Demographic and Smoking Characteristics Table 1 shows demographic and smoking characteristics of the samples (N = 61). Dependence scores of participants reflected moderate (M = 3.36, SD = 2.59), similar to that of other samples.38 Table 1. Summary of Demographic and Sample Characteristics (N = 61)   Low nicotine dependence (n = 33)  High nicotine dependence (n = 28)  Total (N = 61)  Demographic   Age (years), M (SD)  27.52 (7.81)  30.86 (8.10)  29.05 (8.05)   Sex    Female  15  16  31    Male  18  12  30   BMI, M (SD)  27.50 (8.46)  28.93 (8.15)  28.15 (8.28)   Ethnicity (%)    NZ European  16 (48.4%)  12 (42.9%)  28 (45.9%)    NZ Maori  6 (18.1%)  7 (25.0%)  13 (21.3%)    Other  11 (33.3%)  9 (32.1%)  20 (32.8%)   Education (%)    High school qualification  16 (48.5%)  9 (32.1%)  25 (41.0%)    Tertiary qualification/degree or higher  16 (48.5%)  12 (42.9%)  28 (45.9%)    No school qualification  1 (3.0%)  5 (17.9%)  6 (9.8%)    Would rather not say  0  2 (7.1%)  2 (3.3%)   Income in NZD (%)    $19,999 or less  2 (6.0%)  7 (25.0%)  9 (14.7%)    $20,000 – $59,999  6 (18.2%)  9 (32.1%)  15 (24.6%)    $60,000 – $99,999  9 (27.3%)  3 (10.7%)  12 (19.7%)    $100,000 - $139,000  5 (15.2%)  3 (10.7%)  8 (13.1%)    More than $140,000  4 (12.1%)  1 (3.6%)  5 (8.2%)    Don’t know/would rather not say  7 (21.2%)  5 (17.9%)  12 (19.7%)   Handedness (%)    Right handed  26 (78.8%)  23 (82.1%)  49 (80.3%)    Left handed  5 (15.2%)  5 (17.9%)  10 (16.4%)    Ambidextrous  2 (6.1%)  0  2 (3.3%)  Smoking characteristics, M (SD)   Cigarettes/day***  5.88 (3.49)  16.57 (8.42)  10.79 (8.02)   Years smoked**  9.00 (7.69)  15.75 (7.82)  12.10 (8.40)  Baseline, M (SD)   CO level (ppm)***  13.70 (10.48)  29.54 (14.31)  21.00 (14.63)   Cravinga  24.30 (19.57)  36.96 (30.40)  30.11 (25.70)   Happinessb  2.97 (1.05)  3.11 (0.99)  3.03 (1.02)   Sadb  1.61 (0.86)  1.46 (0.84)  1.54 (0.85)   Fearb  1.52 (0.76)  1.57 (0.79)  1.54 (0.77)   Angerb  1.33 (0.74)  1.61 (1.13)  1.46 (0.94)   Disgustb  1.00 (0.0)  1.04 (0.19)  1.02 (0.13)   Interestb  3.55 (1.03)  3.71 (1.08)  3.62 (1.11)    Low nicotine dependence (n = 33)  High nicotine dependence (n = 28)  Total (N = 61)  Demographic   Age (years), M (SD)  27.52 (7.81)  30.86 (8.10)  29.05 (8.05)   Sex    Female  15  16  31    Male  18  12  30   BMI, M (SD)  27.50 (8.46)  28.93 (8.15)  28.15 (8.28)   Ethnicity (%)    NZ European  16 (48.4%)  12 (42.9%)  28 (45.9%)    NZ Maori  6 (18.1%)  7 (25.0%)  13 (21.3%)    Other  11 (33.3%)  9 (32.1%)  20 (32.8%)   Education (%)    High school qualification  16 (48.5%)  9 (32.1%)  25 (41.0%)    Tertiary qualification/degree or higher  16 (48.5%)  12 (42.9%)  28 (45.9%)    No school qualification  1 (3.0%)  5 (17.9%)  6 (9.8%)    Would rather not say  0  2 (7.1%)  2 (3.3%)   Income in NZD (%)    $19,999 or less  2 (6.0%)  7 (25.0%)  9 (14.7%)    $20,000 – $59,999  6 (18.2%)  9 (32.1%)  15 (24.6%)    $60,000 – $99,999  9 (27.3%)  3 (10.7%)  12 (19.7%)    $100,000 - $139,000  5 (15.2%)  3 (10.7%)  8 (13.1%)    More than $140,000  4 (12.1%)  1 (3.6%)  5 (8.2%)    Don’t know/would rather not say  7 (21.2%)  5 (17.9%)  12 (19.7%)   Handedness (%)    Right handed  26 (78.8%)  23 (82.1%)  49 (80.3%)    Left handed  5 (15.2%)  5 (17.9%)  10 (16.4%)    Ambidextrous  2 (6.1%)  0  2 (3.3%)  Smoking characteristics, M (SD)   Cigarettes/day***  5.88 (3.49)  16.57 (8.42)  10.79 (8.02)   Years smoked**  9.00 (7.69)  15.75 (7.82)  12.10 (8.40)  Baseline, M (SD)   CO level (ppm)***  13.70 (10.48)  29.54 (14.31)  21.00 (14.63)   Cravinga  24.30 (19.57)  36.96 (30.40)  30.11 (25.70)   Happinessb  2.97 (1.05)  3.11 (0.99)  3.03 (1.02)   Sadb  1.61 (0.86)  1.46 (0.84)  1.54 (0.85)   Fearb  1.52 (0.76)  1.57 (0.79)  1.54 (0.77)   Angerb  1.33 (0.74)  1.61 (1.13)  1.46 (0.94)   Disgustb  1.00 (0.0)  1.04 (0.19)  1.02 (0.13)   Interestb  3.55 (1.03)  3.71 (1.08)  3.62 (1.11)  M = mean; SD = standard deviation; BMI = body mass index; NZ = New Zealand; ppm = parts per million; **p < 0.01; ***p < 0.000. aCraving was assessed using a 100-point VAS scale. bMood was assessed using a 5-point Likert scale. View Large Table 1. Summary of Demographic and Sample Characteristics (N = 61)   Low nicotine dependence (n = 33)  High nicotine dependence (n = 28)  Total (N = 61)  Demographic   Age (years), M (SD)  27.52 (7.81)  30.86 (8.10)  29.05 (8.05)   Sex    Female  15  16  31    Male  18  12  30   BMI, M (SD)  27.50 (8.46)  28.93 (8.15)  28.15 (8.28)   Ethnicity (%)    NZ European  16 (48.4%)  12 (42.9%)  28 (45.9%)    NZ Maori  6 (18.1%)  7 (25.0%)  13 (21.3%)    Other  11 (33.3%)  9 (32.1%)  20 (32.8%)   Education (%)    High school qualification  16 (48.5%)  9 (32.1%)  25 (41.0%)    Tertiary qualification/degree or higher  16 (48.5%)  12 (42.9%)  28 (45.9%)    No school qualification  1 (3.0%)  5 (17.9%)  6 (9.8%)    Would rather not say  0  2 (7.1%)  2 (3.3%)   Income in NZD (%)    $19,999 or less  2 (6.0%)  7 (25.0%)  9 (14.7%)    $20,000 – $59,999  6 (18.2%)  9 (32.1%)  15 (24.6%)    $60,000 – $99,999  9 (27.3%)  3 (10.7%)  12 (19.7%)    $100,000 - $139,000  5 (15.2%)  3 (10.7%)  8 (13.1%)    More than $140,000  4 (12.1%)  1 (3.6%)  5 (8.2%)    Don’t know/would rather not say  7 (21.2%)  5 (17.9%)  12 (19.7%)   Handedness (%)    Right handed  26 (78.8%)  23 (82.1%)  49 (80.3%)    Left handed  5 (15.2%)  5 (17.9%)  10 (16.4%)    Ambidextrous  2 (6.1%)  0  2 (3.3%)  Smoking characteristics, M (SD)   Cigarettes/day***  5.88 (3.49)  16.57 (8.42)  10.79 (8.02)   Years smoked**  9.00 (7.69)  15.75 (7.82)  12.10 (8.40)  Baseline, M (SD)   CO level (ppm)***  13.70 (10.48)  29.54 (14.31)  21.00 (14.63)   Cravinga  24.30 (19.57)  36.96 (30.40)  30.11 (25.70)   Happinessb  2.97 (1.05)  3.11 (0.99)  3.03 (1.02)   Sadb  1.61 (0.86)  1.46 (0.84)  1.54 (0.85)   Fearb  1.52 (0.76)  1.57 (0.79)  1.54 (0.77)   Angerb  1.33 (0.74)  1.61 (1.13)  1.46 (0.94)   Disgustb  1.00 (0.0)  1.04 (0.19)  1.02 (0.13)   Interestb  3.55 (1.03)  3.71 (1.08)  3.62 (1.11)    Low nicotine dependence (n = 33)  High nicotine dependence (n = 28)  Total (N = 61)  Demographic   Age (years), M (SD)  27.52 (7.81)  30.86 (8.10)  29.05 (8.05)   Sex    Female  15  16  31    Male  18  12  30   BMI, M (SD)  27.50 (8.46)  28.93 (8.15)  28.15 (8.28)   Ethnicity (%)    NZ European  16 (48.4%)  12 (42.9%)  28 (45.9%)    NZ Maori  6 (18.1%)  7 (25.0%)  13 (21.3%)    Other  11 (33.3%)  9 (32.1%)  20 (32.8%)   Education (%)    High school qualification  16 (48.5%)  9 (32.1%)  25 (41.0%)    Tertiary qualification/degree or higher  16 (48.5%)  12 (42.9%)  28 (45.9%)    No school qualification  1 (3.0%)  5 (17.9%)  6 (9.8%)    Would rather not say  0  2 (7.1%)  2 (3.3%)   Income in NZD (%)    $19,999 or less  2 (6.0%)  7 (25.0%)  9 (14.7%)    $20,000 – $59,999  6 (18.2%)  9 (32.1%)  15 (24.6%)    $60,000 – $99,999  9 (27.3%)  3 (10.7%)  12 (19.7%)    $100,000 - $139,000  5 (15.2%)  3 (10.7%)  8 (13.1%)    More than $140,000  4 (12.1%)  1 (3.6%)  5 (8.2%)    Don’t know/would rather not say  7 (21.2%)  5 (17.9%)  12 (19.7%)   Handedness (%)    Right handed  26 (78.8%)  23 (82.1%)  49 (80.3%)    Left handed  5 (15.2%)  5 (17.9%)  10 (16.4%)    Ambidextrous  2 (6.1%)  0  2 (3.3%)  Smoking characteristics, M (SD)   Cigarettes/day***  5.88 (3.49)  16.57 (8.42)  10.79 (8.02)   Years smoked**  9.00 (7.69)  15.75 (7.82)  12.10 (8.40)  Baseline, M (SD)   CO level (ppm)***  13.70 (10.48)  29.54 (14.31)  21.00 (14.63)   Cravinga  24.30 (19.57)  36.96 (30.40)  30.11 (25.70)   Happinessb  2.97 (1.05)  3.11 (0.99)  3.03 (1.02)   Sadb  1.61 (0.86)  1.46 (0.84)  1.54 (0.85)   Fearb  1.52 (0.76)  1.57 (0.79)  1.54 (0.77)   Angerb  1.33 (0.74)  1.61 (1.13)  1.46 (0.94)   Disgustb  1.00 (0.0)  1.04 (0.19)  1.02 (0.13)   Interestb  3.55 (1.03)  3.71 (1.08)  3.62 (1.11)  M = mean; SD = standard deviation; BMI = body mass index; NZ = New Zealand; ppm = parts per million; **p < 0.01; ***p < 0.000. aCraving was assessed using a 100-point VAS scale. bMood was assessed using a 5-point Likert scale. View Large P3 Elicitation of High and Low Dependency by Visual Stimuli Condition The mixed model electrode (3) by emotion (3) by dependency (2) ANOVA showed P3 reactivity varied across electrodes (F(2,58) = 103.27, p < .001, ηP2 = 0.78). Pairwise t-tests revealed that each electrode differed from the others (all p’s < .001), with greatest reactivity found at Pz (M = 4.09, SE = 0.28), followed by Cz (M = 0.85, SE = 0.30), and finally Fz (M = −3.43, SE = 0.34). P3 reactivity also varied across the emotional conditions (F(2,58) = 6.26, p < .01, ηP2 = 0.18; Figure 1) with pairwise t-tests showing the disgust condition (M = 0.20, SE = 0.19) led to a smaller P3 response than the anxiety condition (M = 0.76, SE = 0.20; p = .01). While the difference between disgust and neutral (M = 0.56, SE = 0.22) conditions approached significance (p = .058), there were no differences between the neutral and anxiety conditions (p = .35). There was an interaction (Figure 2) between electrode site and condition (F(4,56) = 4.17, p < .01, ηP2 = 0.23). Follow-up tests revealed responses differed across conditions at Fz and Cz but not Pz (all p’s < .33). At Fz, the anxiety condition differed from the disgust (t(60) = 2.26, p < .05, d = .29) and differences between anxiety and neutral approached significance (p = .051). At Cz, the P3 response was lower in the disgust condition compared with the neutral (t(60) = −2.81, p < .001, d = .36) and anxiety conditions (t(60) = −3.49, p = .001, d = .45). Figure 2. View largeDownload slide P3 mean amplitude for each condition at each electrode site. P3 reactivity significantly differed by condition at Fz and Cz but not at Pz. At Fz, the anxiety condition (gray) led to a larger P3 response than the disgust (black) (*p < .05) and was also marginally less than the neutral condition (p = .051). At Cz, the P3 response was lower in the disgust condition compared to both the neutral (white) and anxiety conditions (**p’s ≤ .001). Figure 2. View largeDownload slide P3 mean amplitude for each condition at each electrode site. P3 reactivity significantly differed by condition at Fz and Cz but not at Pz. At Fz, the anxiety condition (gray) led to a larger P3 response than the disgust (black) (*p < .05) and was also marginally less than the neutral condition (p = .051). At Cz, the P3 response was lower in the disgust condition compared to both the neutral (white) and anxiety conditions (**p’s ≤ .001). Finally, there was also an interaction between the electrode site and dependency (F(2,58) = 3.92, p = .03, ηP2 = 0.12). Inspection of the simple contrasts showed an interaction when comparing lower dependence and higher dependence smokers’ P3 responses at Fz compared with those at Cz (F(1,59) = 7.91, p < .01, ηP2 = 0.12), and when comparing Fz with Pz (F(1,59) = 4.99, p < .05, ηP2 = 0.08) but not when comparing Cz and Pz (p = .34). Inspection of the interaction plot (Figure 3) suggests P3 responses were smaller for less-dependent smokers at Fz, but that this relationship was reversed at Cz and Pz sites, with more dependent smokers showing a smaller P3 response. Figure 3. View largeDownload slide P3 mean amplitude for low (dark gray) and high (light gray) dependence smokers at each electrode site. P3 reactivity differed by dependence group across the electrode sites. At Fz, P3 responses were smaller for less-dependent smokers but at Cz (p < .01) and Pz (p < .05), this relationship was reversed with high-dependent smokers showing a smaller P3 response. Figure 3. View largeDownload slide P3 mean amplitude for low (dark gray) and high (light gray) dependence smokers at each electrode site. P3 reactivity differed by dependence group across the electrode sites. At Fz, P3 responses were smaller for less-dependent smokers but at Cz (p < .01) and Pz (p < .05), this relationship was reversed with high-dependent smokers showing a smaller P3 response. LPP Elicitation of High and Low Dependence by Visual Stimuli Condition A mixed 3 (electrode) by 3 (emotion condition) by 2 (dependency) model ANOVA tested for differences in the LPP activation. LPP activity varied across electrodes (F(2,58) = 50.92, p < .001, ηP2 = 0.64), such that activation was larger at Cz (M = 2.44, SE = 0.24) than that at Fz (M = 0.93, SE = 0.34; p < .001), and Pz (M = 0.10, SE = 0.26; p < .001), but there was no difference between Fz and Pz (p = .11). LPP reactivity also differed between the emotion conditions (F(2,58) = 8.09, p = .001, ηP2 = 0.22), with pairwise t-tests showing disgust (M = 0.77, SE = 0.19) led to a smaller LPP response than the anxiety (M = 1.37, SE = 0.20; p = 0.001) and neutral (M = 1.33, SE = 0.23; p = .01). There was no difference between the neutral and anxiety conditions (p = .86). Finally, there was also an interaction between electrode and condition (F(4,56) = 5.63, p = .001, ηP2 = 0.29). Visual inspection of the interaction suggested differences were between the disgust and neutral, and disgust and anxiety conditions at Fz and Cz (Figure 4); therefore, t-tests were conducted at these sites. The differences between disgust and neutral (t(60) = −4.34, p < .001, d = .56) and between disgust and anxiety (t(60) = −4.05, p < .001, d = .52) at Cz were both significant. However, differences between disgust and neutral (p = .075) and disgust and anxiety (p = .083) at Fz only approached significance. Figure 4. View largeDownload slide LPP mean amplitude for each condition at each electrode site. LPP reactivity significantly differed by condition at Cz but not Fz or Pz. At Cz, the LPP response was lower in the disgust condition (black) compared with both the neutral (white) and anxiety conditions (grey) (**p’s ≤ .001). Figure 4. View largeDownload slide LPP mean amplitude for each condition at each electrode site. LPP reactivity significantly differed by condition at Cz but not Fz or Pz. At Cz, the LPP response was lower in the disgust condition (black) compared with both the neutral (white) and anxiety conditions (grey) (**p’s ≤ .001). Discussion The present study investigated possible modulation of EEG-indexed motivated attentional processing of smoking cues using neutral, health anxiety- and disgust-focused GHWLs. In partial support of hypotheses, findings revealed disgust-focused GHWLs disrupted attention-related cognitive processing. Surprisingly, the health anxiety-focused GHWL appeared to increase the salience of the smoking cue to some extent. Additionally, while less-dependent smokers showed a lower P3 response to all trials at Fz, this relationship was reversed at Cz and Pz with less-dependent smokers showing higher P3 reactivity. These results and the manner in which they enhance current understanding of how and for whom emotionally evocative GHWLs may be effective are discussed below. The first contribution lies in providing an examination of the impact GHWLs have on smoking cue-related motivated attention. Analyses show attentional processing was lower when smokers viewed cues to smoke in the context of disgust GHWLs, perhaps suggesting disgust interferes with the motivated attentional processing of cues. Prior experimental work suggests GHWLs grab attention and are better recalled than verbal warnings,8,11,12 increase quit intentions,1,2 and predict reports of lower craving.4 The difficulty with prior attentional work, however, is that there is no readily available way to determine how or whether “more” attention might translate into a change in behavior; greater attention to GHWLs might as easily indicate smoking-related stimuli are appealing or health threats increase attentional processing. More broadly, there is little evidence supporting the notion that attention or recall translate into quitting or cessation behavior. Conversely, greater attentional bias to visual and verbal smoking cues, as indexed via larger P3 and LPP responses, predicts a shorter time to relapse among abstinent smokers.44,45 These data thus suggest that because disgust GHWLs interfere with attentional bias to smoking cues, they may help characterize what constitutes an effective GHWL and thus help identify pathways toward reducing craving, encouraging cessation, and maintaining abstinence. The report’s second contribution lies in systematically contrasting the effects different types of affective content in GHWLs have on cue processing. Findings were broadly in line with a single prior EEG study showing lower P3 amplitudes in response to smoking cues when those cues were preceded by an emotive GHWL.7 However, in separating disgust from anxiety in GHWLs, this report implies that disgust is more likely to reduce attentional bias among smokers. Specifically, the P3, which represents increased attention allocation toward motivationally relevant cues,29,35 and LPP responses, representing more controlled processing of motivational stimuli,28 at Cz electrode site suggest lower motivated attention in the disgust condition while P3 findings at Fz suggest anxiety images actually led to greater attention given to the smoking cue. Although disgust and health anxiety are inconsistently separated in most prior research, support for the notion that disgust GHWLs, specifically, are better able to decrease motivated attention toward smoking cues is not surprising. A previous experimental study examining disgust and fear (or anxiety) inducing anti-smoking television ads found that those containing one emotional element were better encoded and more accurately recalled than ads that included both elements.9 More broadly, theory suggests that the disgust system originally evolved to promote health by reducing the risk of ingesting contaminants via the mouth, such as eating rotten/poisonous food.20 In this view then, disgust may specifically motivate the avoidance of smoking (an oral behavior) because it reduces the tendency to place things in the mouth. The disgust system has subsequently become broader in its function such that it now motivates the avoidance of a wider range of possible contamination threats. Experimental studies show that manipulated disgust increases avoidance of potentially disgusting sexual health examinations or tests46 and of disgusting stimuli in cancer decision-making studies47,48 while prospective studies show disgust predicts greater cognitive, emotional, and social avoidance among cancer patient groups.21 The current study implies such avoidance may also occur in smoking contexts. A recent study investigating the impact of emotional responses to GHWLs on adolescents’ perceptions of smoking found that more graphic warning labels evoked similar levels of disgust (but not fear or guilt) among smokers and nonsmokers suggesting evoked disgust may be able to uniquely overcome defensive processing mechanisms in a way that fear and guilt cannot.16 In line with these findings, the current report suggests disgust-focused GHWLs are not only able to disrupt attentional bias toward smoking cues (in comparison to neutral cues) but do so better than anxiety-focused GHWLs. Conversely, the current report provides some suggestion anxiety-inducing GHWLs may have paradoxical effects in the processing of smoking cues. Although anxiety also motivates avoidance,18 findings at Fz revealed P3 activity was greatest during the anxiety condition, indicating greater attentional bias toward the smoking cue. This pattern is consistent with data demonstrating, under some conditions, the induction of fear, anger, and stress can increase both craving and smoking;49,50 although the absence of findings linking negative affect and attentional bias to smoking cues make this more difficult to interpret.51 One possibility is the activation of anxiety in the context of smoking images creates a motivated search for information about the source of anxiety,18 a dynamic that, in this case, increases processing of the smoking images themselves. Further work in which discrete emotions are either manipulated or differentially present in GHWL stimuli is clearly needed. A final contribution of the current report lies in the investigation of whether nicotine dependence might moderate the effects of disgust and anxiety-promoting GHWLs on motivated attention. One ongoing challenge in tobacco control efforts regards how to facilitate cessation efforts among more dependent smokers. More dependent smokers have a harder time quitting and are more likely to relapse,52 as are those with a greater attentional bias toward smoking cues.45 Our findings in this regard were complex. On the one hand, analyses showed that P3 responses among those with greater nicotine dependence were generally greater than those with lower nicotine dependence at the Fz site; less-dependent smokers likely showed less attentional bias toward smoking cues. The absence of attenuated cue responses in the more dependent smokers might reflect attentional and appetitive responses to smoking cues becoming progressively greater and more automatized. On the other hand, however, more dependent smokers displayed a smaller P3 response at Cz and Pz compared with less-dependent smokers. P3 responses at more centro-parietal sites are thought to reflect context updating and subsequent memory storage,35 perhaps suggesting less-dependent smokers experienced the smoking cue oddball stimuli as more “different” from the nontarget stimuli (and thus requiring a greater update to working memory). Although interpretations must be considered preliminary, it is worth noting that GHWLs have been present on cigarette packaging in New Zealand since 2008. More dependent smokers will typically have had greater exposure to pairings of GHWLs and smoking cues. Thus, it is possible that while attentional bias to smoking cues in general is greater (the effect at Fz), it may be that less memory updating (the effects at Cz and Pz) is required among more dependent smokers because they have experienced large numbers of instances in which GHWLs and smoking cues were paired—the oddball is potentially less distinctive for this group. Further research investigating how the learned associations between GHWLs and cues to smoke may affect attentional responding in different subgroups of smokers is needed. Although this report represents a useful contribution to our understanding of how disgust- and anxiety-focused GHWLs may be better leveraged to aid cessation attempts by targeting attentional bias, these data have limitations. First, requiring smokers to view GHWLs for several minutes in a laboratory is different from daily life where tobacco products can be removed from packaging or covered. Additionally, smokers are often confronted with multiple cues simultaneously in their daily lives. Future research should move toward testing other influences on smoking and the cues to smoke, and examine whether GHWLs also interfere with processing in the context of these factors. For example, it may be that visual and internal cues (e.g., stress or negative affect) interact to affect cue-related craving and GHWLs have a role to play here.53 Hence, future research would benefit from testing how GHWLs affect motivated attention toward smoking cues when smokers are also stressed or exposed to other cues (such as alcohol) that more naturalistically mimic the natural elicitors of craving. Second, the current study only tested one anxiety- and one disgust-focused GHWL. While an empirical process was used to select the images, it may be that idiosyncratic elements of the particular images used are relevant to the findings. Our results should be replicated with other images to confirm that it is the disgust in general (rather than a specific picture), that reduces motivated attention toward smoking cues. Such limitations noted; this report has important implications regarding the design and use of GHWLs. It suggests that GHWLs depicting disgust-focused messages are capable of interfering with attentional processing of smoking cues and do so better than health anxiety-focused messages; health anxiety-focused messages appear to have a complex relationship with attentional bias in smoking addiction. In addition, these data indicate that GHWLs have a complex relationship with more versus less-dependent smokers and that an understanding of how smoking cues and anti-smoking imagery become associated over time may help identify relevant targets for public health efforts. Funding The project was funded by the Health Promotion Agency, a New Zealand government entity established under statute and overseen by the Ministry of Health. Funding is drawn from alcohol taxes and the problem gambling levy. Declaration of Interests None declared. References 1. Jung M. Implications of graphic cigarette warning labels on smoking behavior: an international perspective. J Cancer Prev . 2016; 21( 1): 21– 25. Google Scholar CrossRef Search ADS PubMed  2. Hammond D. Health warning messages on tobacco products: a review. Tob Control . 2011; 20( 5): 327– 337. Google Scholar CrossRef Search ADS PubMed  3. Noar SM, Francis DB, Bridges C, Sontag JM, Ribisl KM, Brewer NT. The impact of strengthening cigarette pack warnings: systematic review of longitudinal observational studies. Soc Sci Med . 2016; 164: 118– 129. Google Scholar CrossRef Search ADS PubMed  4. Noar SM, Hall MG, Francis DB, Ribisl KM, Pepper JK, Brewer NT. 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The effects of prolonged abstinence on the processing of smoking cues: an ERP study among smokers, ex-smokers and never-smokers. J Psychopharmacol . 2007; 21( 8): 873– 882. Google Scholar CrossRef Search ADS PubMed  42. Foti D, Hajcak G, Dien J. Differentiating neural responses to emotional pictures: evidence from temporal-spatial PCA. Psychophysiology . 2009; 46( 3): 521– 530. Google Scholar CrossRef Search ADS PubMed  43. Foti D, Hajcak G. Deconstructing reappraisal: descriptions preceding arousing pictures modulate the subsequent neural response. J Cogn Neurosci . 2008; 20( 6): 977– 988. Google Scholar CrossRef Search ADS PubMed  44. Attwood AS, O’Sullivan H, Leonards U, Mackintosh B, Munafò MR. Attentional bias training and cue reactivity in cigarette smokers. Addiction . 2008; 103( 11): 1875– 1882. Google Scholar CrossRef Search ADS PubMed  45. Waters AJ, Shiffman S, Sayette MA, Paty JA, Gwaltney CJ, Balabanis MH. Attentional bias predicts outcome in smoking cessation. Health Psychol . 2003; 22( 4): 378– 387. Google Scholar CrossRef Search ADS PubMed  46. McCambridge SA, Consedine NS. For whom the bell tolls: experimentally-manipulated disgust and embarrassment may cause anticipated sexual healthcare avoidance among some people. Emotion . 2014; 14( 2): 407– 415. Google Scholar CrossRef Search ADS PubMed  47. Reynolds LM, McCambridge SA, Bissett IP, Consedine NS. Trait and state disgust: an experimental investigation of disgust and avoidance in colorectal cancer decision scenarios. Health Psychol . 2014; 33( 12): 1495– 1506. Google Scholar CrossRef Search ADS PubMed  48. Reynolds LM, Lin YS, Zhou E, Consedine NS. Does a brief state mindfulness induction moderate disgust-driven social avoidance and decision-making? An experimental investigation. J Behav Med . 2015; 38( 1): 98– 109. Google Scholar CrossRef Search ADS PubMed  49. Heckman BW, Kovacs MA, Marquinez NS, et al.   Influence of affective manipulations on cigarette craving: a meta-analysis. 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Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nicotine and Tobacco Research Oxford University Press

Disgust but not Health Anxiety Graphic Warning Labels Reduce Motivated Attention in Smokers: A Study of P300 and Late Positive Potential Responses

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Oxford University Press
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© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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1462-2203
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1469-994X
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10.1093/ntr/ntx158
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Abstract

Abstract Background Graphic health warning labels (GHWLs) on tobacco products attempt to leverage avoidance-promoting emotions, such as anxiety and disgust, to encourage cessation. Prior studies have relied on self-report or attentional metrics that may not accurately illuminate GHWLs’ ability to motivate change. This report evaluates the impact of disgust- and anxiety-based GHWLs on electroencephalograph (EEG) measures of motivated attention among two groups of smokers—those that report higher versus lower cigarette dependence. We hypothesized that both anxiety and disgust GHWLs would reduce appetitive attention, as indexed by lowered P300 (P3) and late positive potential (LPP) activations. Methods Sixty-one smokers provided demographic and smoking history before completing an oddball paradigm consisting of three counterbalanced stimuli blocks. Each block (100 trials) contained a neutral, GHWL-anxiety, or GHWL-disgust frequent image and a smoking cue as the oddball image (20%). Oddball trials for each block were averaged, P3 and LPP were identified at midline electrode positions (Fz, Cz, and Pz), and mean amplitude was analyzed. Results Separate mixed-model ANOVAs of P3 and LPP reactivity revealed disgust-focused GHWLs reduced motivated attentional processing. Conversely, the anxiety-focused GHWL appeared to increase the salience of the smoking cue (Fz only). Less-dependent smokers showed lower P3 reactivity than those with higher dependence at Fz, but greater P3 reactivity at Cz and Pz. Conclusion These results extend prior work in demonstrating that disgust, but not anxiety-based GHWLs, may reduce EEG-assessed motivated attention to smoking cues. Disgust may thus represent a more fruitful target for public health cessation efforts. Implications Most GHWL evaluations have focused on fear (or anxiety) elicitation rather than disgust, an emotion that may have a unique link to smoking, having evolved specifically to facilitate the avoidance of contaminants via oral incorporation. Analyses of P300 and LPP responses to GHWLs suggest that disgust-focused images interfere with the EEG-indexed attentional processing of smoking cues and do so better than health anxiety-focused messages. However, interaction effects at different electrode sites indicated that GHWLs have complex effects in more versus less-dependent smokers and that an understanding of how smoking cues and anti-smoking imagery become associated over time is needed to identify relevant targets for public health efforts. Introduction Graphic health warning labels (GHWLs) are used in smoking cessation campaigns in more than 70 countries.1 These labels involve the use of images to elicit avoidance-promoting emotions, such as health-related fear (or anxiety), disgust and, less commonly, guilt or embarrassment.2 In theory, exposure to emotion-inducing images should ultimately reduce consumption and/or uptake of tobacco use. However, while public health research suggests GHWLs underpin reductions in tobacco consumption, the deployment of images typically occurs concurrently with other legislative changes and thus findings are difficult to interpret.1,3 Additionally, while experimental data suggest GHWLs increase attentional processing and reduce the self-reported desire to smoke,1,4 studies do not directly or objectively assess the impact GHWLs have on the cognitive processes responsible for these changes. Evidence shows smokers exhibit cognitive biases in the processing of smoking-related stimuli. These biases, which encompass core processes such as attention and memory, facilitate the detection of smoking cues and probably help perpetuate addiction. Among smokers, attention is preferentially allocated to smoking cues, which are seen as particularly salient and reinforcing.5,6 Studies have thus begun to examine whether GHWLs may change cue processing in ways that should reduce smoking and/or encourage cessation.7–9 However, current knowledge is limited in several ways. First, most experimental work investigating the effect of GHWLs has relied on self-reported measures.10 These self-reported responses to GHWLs have suggested greater quit intentions and increased recognition of smoking’s health threat.4 However, “intentions” do not always lead to quitting, and thus a robust experimental design is needed to ensure that there are no other influencing factors.10 Other data suggest that GHWLs hold attention longer than traditional warning messages,11,12 and emotional stimuli lead to greater processing (as indexed by heart rate change) in nonsmokers.9 However, evidence among smokers indicates that, while high threat images may capture attention, they may also result in more efficient disengagement from images, compared with nonsmokers.13,14 Likewise, it remains unclear whether more attention or reduction in the desire to smoke promote quitting and tobacco aversion. Further insight can be gained by moving from pure attentional metrics to measures incorporating “motivational” elements of attention, and examining how responses to smoking cues can be attenuated via presentation of GHWLs. A second contribution of this report lies in separating and examining two avoidance-promoting emotions thought to underpin any effects associated with GHWLs.9 Historically, anti-smoking campaigns have concentrated on fear appeals, which have demonstrated stronger “threat” messages are more persuasive.15 However, GHWLs elicit a range of emotions,16 and the specific emotions best suited to reducing appetitive motivations are unclear. Labels mix the emotions they elicit and, because both fear and disgust evolved to promote avoidance,17,18 it is difficult to identify which might best be leveraged to interfere with the motivated attentional processes characterizing addiction to nicotine. While most GHWL evaluations have focused on fear (or anxiety),19 disgust may have a unique link to smoking having evolved specifically to promote the avoidance of potential contaminants, particularly via oral incorporation.17,20 Functionally, disgust is characterized by a rejection and avoidance dynamic that manifests in action tendencies, experiential and cognitive states, and expressive and physiological changes—all of which operate to promote withdrawal and avoidance of health threats.21 Thus, GHWLs eliciting disgust should be more effective than health anxiety at disrupting motivated attention toward smoking cues. This report contrasts the ability of GHWLs evoking these two emotions to reduce cue-related motivated attention. Finally, research suggests the effect of tobacco control strategies may vary as a function of nicotine dependence.22,23 Evidence from GHWLs and elsewhere suggests only certain groups of smokers, such as those who are motivated to quit, find graphic anti-smoking messages helpful.24,25 Other groups of smokers report high defiance; “boomerang” effects to anti-smoking messages are increasingly documented.10,25,26 In particular, heavily dependent smokers are known to deny health threats,10,27 suggesting that GHWLs may be less effective in this group. This report examines whether the degree of nicotine dependence moderated the impact of health anxiety and disgust GHWLs on the processing of smoking cues. In addressing these questions, this report presents an electroencephalograph (EEG) study of activation during GHWL presentation. We concentrate on differences in the P300 (P3) and the late positive potential (LPP), two key ERP components associated with the deployment of attentional resources to motivationally relevant stimuli.28,29 Like other substance abuse populations, smokers exhibit larger P3 and LPP amplitudes in response to smoking-related stimuli.6 Research also indicates P3 and LPP responses can be manipulated,30,31 including among smokers,32 meaning documenting reductions in these components may identify ways of reducing motivated attention to cues. For example, while most studies assess GHWL efficacy via self-report, one study found P3 activation was only reduced among highly emotive GHWLs.7 In moving measurement from pure attentional metrics to measures incorporating the “motivational” element of attention, the current report tests whether P3/LPP activity in response to smoking cues can be attenuated via presentation of disgust and health anxiety–GHWLs. Specifically, this study examined whether disgust and/or health-anxiety-inducing GHWLs reduce objectively assessed cue processing metrics among more and less-dependent smokers. It was hypothesized that both anxiety and disgust GHWLs would reduce attention-related cognitive processing of smoking cues, as indexed by reduced P3 and LPP activations, but disgust’s effects would be larger. Furthermore, we hypothesized that anxiety–GHWLs would be less effective among more dependent smokers. Methods Participants Sixty-three persons (a) reporting having smoked 100+ cigarettes in their lifetime and (b) currently smoking every day or most days33 were recruited through advertisements placed around university and central city locations. Ex-smokers and those who reported hypertension, cardiovascular disease, current somatic and psychiatric illness, or an insufficient understanding of the English language were excluded. Two participants were excluded after data collection; one due to excessive movement artifacts that were not correctable and one who withdrew. Data from 61 participants are included. Procedure All participants completed electronic consent procedures and an online questionnaire assessing demographic, smoking, and trait characteristics before being invited to attend a clinic session. Participants were asked to refrain from smoking for 1 hour before the appointment in an attempt to standardize time since their last cigarette without increasing craving due to nicotine deprivation rather than cue-specific potentiation.34 Upon arrival, participants’ written informed consent was obtained followed by a CO monitor test. All experimental sessions were scheduled between 8 am and 12 pm and lasted approximately 90 min (including 40 min of EEG setup). EEG was recorded using 64 active Ag/AgCl electrodes pre-set by the Easy-Cap electrode system (Falk Minow Services, Germany) according to the International 10/20 system. Six additional Ag/AgCl electrodes were connected as per standard protocols for vertical and horizontal electro-oculogram and heart rate measurement. Electrode impedance was kept below 10 kΩ. EEG data were collected continuously from the start of the baseline task to the end of the session. The BrainAmp MR Plus amplifier recorded EEG data at a sampling rate of 1,000 Hz (Brain Products GmbH, 2012). After EEG setup participants completed the experimental tasks. MATLAB (MATLAB 2015b, The MathWorks Inc., Natick, MA, United States) and PsychToolbox (Psychphysics Toolbox, version 3.0.11) computer software were used to control the experimental session. Baseline A 5-min baseline period was included to allow acclimation to the laboratory. Participants were given standardized instructions to sit quietly, focus on a fixation cross, and blink normally during this task. Resting EEG data and HR data were collected during this period (subject of another report). Craving and mood were collected at the end of this task. Visual Oddball Task The P3 and LPP are commonly studied using oddball paradigms, in which participants are instructed to identify the less-frequent (oddball) stimuli (20% of trials at random) presented among more frequent nontarget stimuli. The oddball paradigm is widely used to assess cue processing and requires more resource allocation to process the oddball stimuli and update working memory, leading to enhanced P3 responses35 that are further enhanced when stimuli are motivationally relevant.36 The current task consisted of three counterbalanced blocks (100 trials each) of images presented on a computer screen approximately 600 mm in front of the participant. Each image was presented for a maximum of 1,000 ms, at a rate of one every 2,000 ms. After each block (approximately 3 min in duration), participants rated mood and craving. After all three blocks were complete, a 3-min rest period was followed by further craving and mood ratings. Visual Stimuli Stimuli consisted of an oddball image, used in all three blocks of trials, and three frequent images, one for each block of trials which effectively formed three emotion conditions within which the oddball stimuli were viewed. The oddball stimulus was a color smoking-related image taken from the International Smoking Image Series.37 This image was chosen because of evidence it elicits high valence and arousal scores among smokers.37 The frequent stimuli were an image of a hand holding a pen (a nonsmoking neutral image from the ISIS series) and two GHWL images, one for disgust and one for anxiety. The anxiety and disgust GHWL images were taken from a series of images provided to the senior author by the Health Promotion Agency, the government agency responsible for developing the next round of GHWLs for New Zealand. Images included those currently being used on packaging in New Zealand or another country. To ensure suitability, 24 images from this Agency had text removed and were independently piloted among 89 New Zealand smokers and nonsmokers (15 daily smokers, 24 less than daily smokers, 22 past smokers, and 28 never smokers) to assess their valence, arousal, and affect induction. Average scores of the current smokers (N = 39) were calculated and the top-rated disgust and anxiety images identified. T-tests of the means confirmed the disgust image rated high in “disgust” and low in “health-related anxiety,” t(38) = 3.19, p < .01, d = .53. The same process was also employed to choose the health anxiety image, t(38) = 2.87, p < .01, d = .46. The selected disgust GHWL depicted lips covered in sores and puss, and scored the highest among smokers for inducing disgust. The selected health anxiety GHWL depicted a solemn looking man sitting in a hospital bed alone with a tracheotomy wound and scored the highest among smokers for inducing health-related anxiety. Measures Demographics and nicotine dependence were assessed before the experimental session. The six-item Fagerstrom Test for Nicotine Dependence (FTND; α = 0.61) was used to assess nicotine dependence.38 Scores on this scale range from 0 to 10 and items were aggregated, with a higher score indicating greater dependence. Craving during the experiment was assessed using a 100-point visual analogue scale (VAS) with three anchor points (no craving [left end], moderate craving [middle], extremely high craving [right end]). EEG Signal Processing Offline pre-processing was performed with the Brain Vision Analyzer 2.0 software (version 2.1.0.327, Brain Products GmbH, Germany). Sampling rate was reduced to 200 Hz. All channels were re-referenced to the common average reference and a digital pass-band filter set from 0.1 to 50 Hz (24 dβ/octave roll off). Vertical and horizontal eye movements and blinks were removed from continuous data using semiautomatic ICA ocular correction39 and the data were epoched into 1,200 ms periods spanning 200 ms prestimulus to 1,000 ms post oddball stimulus onset. Epochs were baseline corrected (using 100 ms prestimulus activity) and any epoch segments with EEG voltages exceeding ±150 µV or with a maximum voltage step (gradient) of 75 µV were excluded from further analysis by means of semiautomatic artifact rejection and visual inspection. The remaining data were averaged for each electrode, individual, and condition (neutral, disgust, and anxiety). Although P300 is typically maximal over central and parietal electrode positions35 some studies assessing attentional bias in smokers have only found frontally or fronto-centrally distributed differences.40,41 Additionally, research indicates LPP activity shifts from parietal to more fronto-cental sites over time.42,43 Therefore, ERP waveforms of the oddball trials for each condition at the midline electrode positions (Fz, Cz, and Pz) was of interest for this report.6 Waveforms for each electrode by condition can be found in Figure 1. Grand averages were used to identify the P3 component between 300 and 600 ms after stimuli onset6 and the LPP between 600 and 1000 ms.28 The mean amplitude (µV) across these time windows was exported for statistical analysis. Figure 1. View largeDownload slide Oddball grand average waveforms for each condition at each electrode position. The oddball waveform for each condition at each of the midline electrode positions. Black = neutral condition, red = disgust condition and blue = health anxiety condition; *Significant differences in P3 (300–600 ms) across conditions; ‡Significant difference in LPP (600–1,000 ms) across conditions. Figure 1. View largeDownload slide Oddball grand average waveforms for each condition at each electrode position. The oddball waveform for each condition at each of the midline electrode positions. Black = neutral condition, red = disgust condition and blue = health anxiety condition; *Significant differences in P3 (300–600 ms) across conditions; ‡Significant difference in LPP (600–1,000 ms) across conditions. Data Analysis Data were analyzed with IBM SPSS Statistics 22. A median split of the total FTND scores was used to categorize more dependent and less-dependent groups (FTND ≤3 vs. > 3). To test whether mean amplitude differed across electrode site in each of the oddball paradigm conditions as a function of nicotine dependence, a 3 (electrode position) × 3 (emotion GHWL condition) × 2 (dichotomized FTND score) mixed-model ANOVA was performed for P3 and LPP activity, separately. Paired-sample t-tests and simple contrasts were used to deconstruct main effects and interactions, and significance was adjusted accordingly. Results Demographic and Smoking Characteristics Table 1 shows demographic and smoking characteristics of the samples (N = 61). Dependence scores of participants reflected moderate (M = 3.36, SD = 2.59), similar to that of other samples.38 Table 1. Summary of Demographic and Sample Characteristics (N = 61)   Low nicotine dependence (n = 33)  High nicotine dependence (n = 28)  Total (N = 61)  Demographic   Age (years), M (SD)  27.52 (7.81)  30.86 (8.10)  29.05 (8.05)   Sex    Female  15  16  31    Male  18  12  30   BMI, M (SD)  27.50 (8.46)  28.93 (8.15)  28.15 (8.28)   Ethnicity (%)    NZ European  16 (48.4%)  12 (42.9%)  28 (45.9%)    NZ Maori  6 (18.1%)  7 (25.0%)  13 (21.3%)    Other  11 (33.3%)  9 (32.1%)  20 (32.8%)   Education (%)    High school qualification  16 (48.5%)  9 (32.1%)  25 (41.0%)    Tertiary qualification/degree or higher  16 (48.5%)  12 (42.9%)  28 (45.9%)    No school qualification  1 (3.0%)  5 (17.9%)  6 (9.8%)    Would rather not say  0  2 (7.1%)  2 (3.3%)   Income in NZD (%)    $19,999 or less  2 (6.0%)  7 (25.0%)  9 (14.7%)    $20,000 – $59,999  6 (18.2%)  9 (32.1%)  15 (24.6%)    $60,000 – $99,999  9 (27.3%)  3 (10.7%)  12 (19.7%)    $100,000 - $139,000  5 (15.2%)  3 (10.7%)  8 (13.1%)    More than $140,000  4 (12.1%)  1 (3.6%)  5 (8.2%)    Don’t know/would rather not say  7 (21.2%)  5 (17.9%)  12 (19.7%)   Handedness (%)    Right handed  26 (78.8%)  23 (82.1%)  49 (80.3%)    Left handed  5 (15.2%)  5 (17.9%)  10 (16.4%)    Ambidextrous  2 (6.1%)  0  2 (3.3%)  Smoking characteristics, M (SD)   Cigarettes/day***  5.88 (3.49)  16.57 (8.42)  10.79 (8.02)   Years smoked**  9.00 (7.69)  15.75 (7.82)  12.10 (8.40)  Baseline, M (SD)   CO level (ppm)***  13.70 (10.48)  29.54 (14.31)  21.00 (14.63)   Cravinga  24.30 (19.57)  36.96 (30.40)  30.11 (25.70)   Happinessb  2.97 (1.05)  3.11 (0.99)  3.03 (1.02)   Sadb  1.61 (0.86)  1.46 (0.84)  1.54 (0.85)   Fearb  1.52 (0.76)  1.57 (0.79)  1.54 (0.77)   Angerb  1.33 (0.74)  1.61 (1.13)  1.46 (0.94)   Disgustb  1.00 (0.0)  1.04 (0.19)  1.02 (0.13)   Interestb  3.55 (1.03)  3.71 (1.08)  3.62 (1.11)    Low nicotine dependence (n = 33)  High nicotine dependence (n = 28)  Total (N = 61)  Demographic   Age (years), M (SD)  27.52 (7.81)  30.86 (8.10)  29.05 (8.05)   Sex    Female  15  16  31    Male  18  12  30   BMI, M (SD)  27.50 (8.46)  28.93 (8.15)  28.15 (8.28)   Ethnicity (%)    NZ European  16 (48.4%)  12 (42.9%)  28 (45.9%)    NZ Maori  6 (18.1%)  7 (25.0%)  13 (21.3%)    Other  11 (33.3%)  9 (32.1%)  20 (32.8%)   Education (%)    High school qualification  16 (48.5%)  9 (32.1%)  25 (41.0%)    Tertiary qualification/degree or higher  16 (48.5%)  12 (42.9%)  28 (45.9%)    No school qualification  1 (3.0%)  5 (17.9%)  6 (9.8%)    Would rather not say  0  2 (7.1%)  2 (3.3%)   Income in NZD (%)    $19,999 or less  2 (6.0%)  7 (25.0%)  9 (14.7%)    $20,000 – $59,999  6 (18.2%)  9 (32.1%)  15 (24.6%)    $60,000 – $99,999  9 (27.3%)  3 (10.7%)  12 (19.7%)    $100,000 - $139,000  5 (15.2%)  3 (10.7%)  8 (13.1%)    More than $140,000  4 (12.1%)  1 (3.6%)  5 (8.2%)    Don’t know/would rather not say  7 (21.2%)  5 (17.9%)  12 (19.7%)   Handedness (%)    Right handed  26 (78.8%)  23 (82.1%)  49 (80.3%)    Left handed  5 (15.2%)  5 (17.9%)  10 (16.4%)    Ambidextrous  2 (6.1%)  0  2 (3.3%)  Smoking characteristics, M (SD)   Cigarettes/day***  5.88 (3.49)  16.57 (8.42)  10.79 (8.02)   Years smoked**  9.00 (7.69)  15.75 (7.82)  12.10 (8.40)  Baseline, M (SD)   CO level (ppm)***  13.70 (10.48)  29.54 (14.31)  21.00 (14.63)   Cravinga  24.30 (19.57)  36.96 (30.40)  30.11 (25.70)   Happinessb  2.97 (1.05)  3.11 (0.99)  3.03 (1.02)   Sadb  1.61 (0.86)  1.46 (0.84)  1.54 (0.85)   Fearb  1.52 (0.76)  1.57 (0.79)  1.54 (0.77)   Angerb  1.33 (0.74)  1.61 (1.13)  1.46 (0.94)   Disgustb  1.00 (0.0)  1.04 (0.19)  1.02 (0.13)   Interestb  3.55 (1.03)  3.71 (1.08)  3.62 (1.11)  M = mean; SD = standard deviation; BMI = body mass index; NZ = New Zealand; ppm = parts per million; **p < 0.01; ***p < 0.000. aCraving was assessed using a 100-point VAS scale. bMood was assessed using a 5-point Likert scale. View Large Table 1. Summary of Demographic and Sample Characteristics (N = 61)   Low nicotine dependence (n = 33)  High nicotine dependence (n = 28)  Total (N = 61)  Demographic   Age (years), M (SD)  27.52 (7.81)  30.86 (8.10)  29.05 (8.05)   Sex    Female  15  16  31    Male  18  12  30   BMI, M (SD)  27.50 (8.46)  28.93 (8.15)  28.15 (8.28)   Ethnicity (%)    NZ European  16 (48.4%)  12 (42.9%)  28 (45.9%)    NZ Maori  6 (18.1%)  7 (25.0%)  13 (21.3%)    Other  11 (33.3%)  9 (32.1%)  20 (32.8%)   Education (%)    High school qualification  16 (48.5%)  9 (32.1%)  25 (41.0%)    Tertiary qualification/degree or higher  16 (48.5%)  12 (42.9%)  28 (45.9%)    No school qualification  1 (3.0%)  5 (17.9%)  6 (9.8%)    Would rather not say  0  2 (7.1%)  2 (3.3%)   Income in NZD (%)    $19,999 or less  2 (6.0%)  7 (25.0%)  9 (14.7%)    $20,000 – $59,999  6 (18.2%)  9 (32.1%)  15 (24.6%)    $60,000 – $99,999  9 (27.3%)  3 (10.7%)  12 (19.7%)    $100,000 - $139,000  5 (15.2%)  3 (10.7%)  8 (13.1%)    More than $140,000  4 (12.1%)  1 (3.6%)  5 (8.2%)    Don’t know/would rather not say  7 (21.2%)  5 (17.9%)  12 (19.7%)   Handedness (%)    Right handed  26 (78.8%)  23 (82.1%)  49 (80.3%)    Left handed  5 (15.2%)  5 (17.9%)  10 (16.4%)    Ambidextrous  2 (6.1%)  0  2 (3.3%)  Smoking characteristics, M (SD)   Cigarettes/day***  5.88 (3.49)  16.57 (8.42)  10.79 (8.02)   Years smoked**  9.00 (7.69)  15.75 (7.82)  12.10 (8.40)  Baseline, M (SD)   CO level (ppm)***  13.70 (10.48)  29.54 (14.31)  21.00 (14.63)   Cravinga  24.30 (19.57)  36.96 (30.40)  30.11 (25.70)   Happinessb  2.97 (1.05)  3.11 (0.99)  3.03 (1.02)   Sadb  1.61 (0.86)  1.46 (0.84)  1.54 (0.85)   Fearb  1.52 (0.76)  1.57 (0.79)  1.54 (0.77)   Angerb  1.33 (0.74)  1.61 (1.13)  1.46 (0.94)   Disgustb  1.00 (0.0)  1.04 (0.19)  1.02 (0.13)   Interestb  3.55 (1.03)  3.71 (1.08)  3.62 (1.11)    Low nicotine dependence (n = 33)  High nicotine dependence (n = 28)  Total (N = 61)  Demographic   Age (years), M (SD)  27.52 (7.81)  30.86 (8.10)  29.05 (8.05)   Sex    Female  15  16  31    Male  18  12  30   BMI, M (SD)  27.50 (8.46)  28.93 (8.15)  28.15 (8.28)   Ethnicity (%)    NZ European  16 (48.4%)  12 (42.9%)  28 (45.9%)    NZ Maori  6 (18.1%)  7 (25.0%)  13 (21.3%)    Other  11 (33.3%)  9 (32.1%)  20 (32.8%)   Education (%)    High school qualification  16 (48.5%)  9 (32.1%)  25 (41.0%)    Tertiary qualification/degree or higher  16 (48.5%)  12 (42.9%)  28 (45.9%)    No school qualification  1 (3.0%)  5 (17.9%)  6 (9.8%)    Would rather not say  0  2 (7.1%)  2 (3.3%)   Income in NZD (%)    $19,999 or less  2 (6.0%)  7 (25.0%)  9 (14.7%)    $20,000 – $59,999  6 (18.2%)  9 (32.1%)  15 (24.6%)    $60,000 – $99,999  9 (27.3%)  3 (10.7%)  12 (19.7%)    $100,000 - $139,000  5 (15.2%)  3 (10.7%)  8 (13.1%)    More than $140,000  4 (12.1%)  1 (3.6%)  5 (8.2%)    Don’t know/would rather not say  7 (21.2%)  5 (17.9%)  12 (19.7%)   Handedness (%)    Right handed  26 (78.8%)  23 (82.1%)  49 (80.3%)    Left handed  5 (15.2%)  5 (17.9%)  10 (16.4%)    Ambidextrous  2 (6.1%)  0  2 (3.3%)  Smoking characteristics, M (SD)   Cigarettes/day***  5.88 (3.49)  16.57 (8.42)  10.79 (8.02)   Years smoked**  9.00 (7.69)  15.75 (7.82)  12.10 (8.40)  Baseline, M (SD)   CO level (ppm)***  13.70 (10.48)  29.54 (14.31)  21.00 (14.63)   Cravinga  24.30 (19.57)  36.96 (30.40)  30.11 (25.70)   Happinessb  2.97 (1.05)  3.11 (0.99)  3.03 (1.02)   Sadb  1.61 (0.86)  1.46 (0.84)  1.54 (0.85)   Fearb  1.52 (0.76)  1.57 (0.79)  1.54 (0.77)   Angerb  1.33 (0.74)  1.61 (1.13)  1.46 (0.94)   Disgustb  1.00 (0.0)  1.04 (0.19)  1.02 (0.13)   Interestb  3.55 (1.03)  3.71 (1.08)  3.62 (1.11)  M = mean; SD = standard deviation; BMI = body mass index; NZ = New Zealand; ppm = parts per million; **p < 0.01; ***p < 0.000. aCraving was assessed using a 100-point VAS scale. bMood was assessed using a 5-point Likert scale. View Large P3 Elicitation of High and Low Dependency by Visual Stimuli Condition The mixed model electrode (3) by emotion (3) by dependency (2) ANOVA showed P3 reactivity varied across electrodes (F(2,58) = 103.27, p < .001, ηP2 = 0.78). Pairwise t-tests revealed that each electrode differed from the others (all p’s < .001), with greatest reactivity found at Pz (M = 4.09, SE = 0.28), followed by Cz (M = 0.85, SE = 0.30), and finally Fz (M = −3.43, SE = 0.34). P3 reactivity also varied across the emotional conditions (F(2,58) = 6.26, p < .01, ηP2 = 0.18; Figure 1) with pairwise t-tests showing the disgust condition (M = 0.20, SE = 0.19) led to a smaller P3 response than the anxiety condition (M = 0.76, SE = 0.20; p = .01). While the difference between disgust and neutral (M = 0.56, SE = 0.22) conditions approached significance (p = .058), there were no differences between the neutral and anxiety conditions (p = .35). There was an interaction (Figure 2) between electrode site and condition (F(4,56) = 4.17, p < .01, ηP2 = 0.23). Follow-up tests revealed responses differed across conditions at Fz and Cz but not Pz (all p’s < .33). At Fz, the anxiety condition differed from the disgust (t(60) = 2.26, p < .05, d = .29) and differences between anxiety and neutral approached significance (p = .051). At Cz, the P3 response was lower in the disgust condition compared with the neutral (t(60) = −2.81, p < .001, d = .36) and anxiety conditions (t(60) = −3.49, p = .001, d = .45). Figure 2. View largeDownload slide P3 mean amplitude for each condition at each electrode site. P3 reactivity significantly differed by condition at Fz and Cz but not at Pz. At Fz, the anxiety condition (gray) led to a larger P3 response than the disgust (black) (*p < .05) and was also marginally less than the neutral condition (p = .051). At Cz, the P3 response was lower in the disgust condition compared to both the neutral (white) and anxiety conditions (**p’s ≤ .001). Figure 2. View largeDownload slide P3 mean amplitude for each condition at each electrode site. P3 reactivity significantly differed by condition at Fz and Cz but not at Pz. At Fz, the anxiety condition (gray) led to a larger P3 response than the disgust (black) (*p < .05) and was also marginally less than the neutral condition (p = .051). At Cz, the P3 response was lower in the disgust condition compared to both the neutral (white) and anxiety conditions (**p’s ≤ .001). Finally, there was also an interaction between the electrode site and dependency (F(2,58) = 3.92, p = .03, ηP2 = 0.12). Inspection of the simple contrasts showed an interaction when comparing lower dependence and higher dependence smokers’ P3 responses at Fz compared with those at Cz (F(1,59) = 7.91, p < .01, ηP2 = 0.12), and when comparing Fz with Pz (F(1,59) = 4.99, p < .05, ηP2 = 0.08) but not when comparing Cz and Pz (p = .34). Inspection of the interaction plot (Figure 3) suggests P3 responses were smaller for less-dependent smokers at Fz, but that this relationship was reversed at Cz and Pz sites, with more dependent smokers showing a smaller P3 response. Figure 3. View largeDownload slide P3 mean amplitude for low (dark gray) and high (light gray) dependence smokers at each electrode site. P3 reactivity differed by dependence group across the electrode sites. At Fz, P3 responses were smaller for less-dependent smokers but at Cz (p < .01) and Pz (p < .05), this relationship was reversed with high-dependent smokers showing a smaller P3 response. Figure 3. View largeDownload slide P3 mean amplitude for low (dark gray) and high (light gray) dependence smokers at each electrode site. P3 reactivity differed by dependence group across the electrode sites. At Fz, P3 responses were smaller for less-dependent smokers but at Cz (p < .01) and Pz (p < .05), this relationship was reversed with high-dependent smokers showing a smaller P3 response. LPP Elicitation of High and Low Dependence by Visual Stimuli Condition A mixed 3 (electrode) by 3 (emotion condition) by 2 (dependency) model ANOVA tested for differences in the LPP activation. LPP activity varied across electrodes (F(2,58) = 50.92, p < .001, ηP2 = 0.64), such that activation was larger at Cz (M = 2.44, SE = 0.24) than that at Fz (M = 0.93, SE = 0.34; p < .001), and Pz (M = 0.10, SE = 0.26; p < .001), but there was no difference between Fz and Pz (p = .11). LPP reactivity also differed between the emotion conditions (F(2,58) = 8.09, p = .001, ηP2 = 0.22), with pairwise t-tests showing disgust (M = 0.77, SE = 0.19) led to a smaller LPP response than the anxiety (M = 1.37, SE = 0.20; p = 0.001) and neutral (M = 1.33, SE = 0.23; p = .01). There was no difference between the neutral and anxiety conditions (p = .86). Finally, there was also an interaction between electrode and condition (F(4,56) = 5.63, p = .001, ηP2 = 0.29). Visual inspection of the interaction suggested differences were between the disgust and neutral, and disgust and anxiety conditions at Fz and Cz (Figure 4); therefore, t-tests were conducted at these sites. The differences between disgust and neutral (t(60) = −4.34, p < .001, d = .56) and between disgust and anxiety (t(60) = −4.05, p < .001, d = .52) at Cz were both significant. However, differences between disgust and neutral (p = .075) and disgust and anxiety (p = .083) at Fz only approached significance. Figure 4. View largeDownload slide LPP mean amplitude for each condition at each electrode site. LPP reactivity significantly differed by condition at Cz but not Fz or Pz. At Cz, the LPP response was lower in the disgust condition (black) compared with both the neutral (white) and anxiety conditions (grey) (**p’s ≤ .001). Figure 4. View largeDownload slide LPP mean amplitude for each condition at each electrode site. LPP reactivity significantly differed by condition at Cz but not Fz or Pz. At Cz, the LPP response was lower in the disgust condition (black) compared with both the neutral (white) and anxiety conditions (grey) (**p’s ≤ .001). Discussion The present study investigated possible modulation of EEG-indexed motivated attentional processing of smoking cues using neutral, health anxiety- and disgust-focused GHWLs. In partial support of hypotheses, findings revealed disgust-focused GHWLs disrupted attention-related cognitive processing. Surprisingly, the health anxiety-focused GHWL appeared to increase the salience of the smoking cue to some extent. Additionally, while less-dependent smokers showed a lower P3 response to all trials at Fz, this relationship was reversed at Cz and Pz with less-dependent smokers showing higher P3 reactivity. These results and the manner in which they enhance current understanding of how and for whom emotionally evocative GHWLs may be effective are discussed below. The first contribution lies in providing an examination of the impact GHWLs have on smoking cue-related motivated attention. Analyses show attentional processing was lower when smokers viewed cues to smoke in the context of disgust GHWLs, perhaps suggesting disgust interferes with the motivated attentional processing of cues. Prior experimental work suggests GHWLs grab attention and are better recalled than verbal warnings,8,11,12 increase quit intentions,1,2 and predict reports of lower craving.4 The difficulty with prior attentional work, however, is that there is no readily available way to determine how or whether “more” attention might translate into a change in behavior; greater attention to GHWLs might as easily indicate smoking-related stimuli are appealing or health threats increase attentional processing. More broadly, there is little evidence supporting the notion that attention or recall translate into quitting or cessation behavior. Conversely, greater attentional bias to visual and verbal smoking cues, as indexed via larger P3 and LPP responses, predicts a shorter time to relapse among abstinent smokers.44,45 These data thus suggest that because disgust GHWLs interfere with attentional bias to smoking cues, they may help characterize what constitutes an effective GHWL and thus help identify pathways toward reducing craving, encouraging cessation, and maintaining abstinence. The report’s second contribution lies in systematically contrasting the effects different types of affective content in GHWLs have on cue processing. Findings were broadly in line with a single prior EEG study showing lower P3 amplitudes in response to smoking cues when those cues were preceded by an emotive GHWL.7 However, in separating disgust from anxiety in GHWLs, this report implies that disgust is more likely to reduce attentional bias among smokers. Specifically, the P3, which represents increased attention allocation toward motivationally relevant cues,29,35 and LPP responses, representing more controlled processing of motivational stimuli,28 at Cz electrode site suggest lower motivated attention in the disgust condition while P3 findings at Fz suggest anxiety images actually led to greater attention given to the smoking cue. Although disgust and health anxiety are inconsistently separated in most prior research, support for the notion that disgust GHWLs, specifically, are better able to decrease motivated attention toward smoking cues is not surprising. A previous experimental study examining disgust and fear (or anxiety) inducing anti-smoking television ads found that those containing one emotional element were better encoded and more accurately recalled than ads that included both elements.9 More broadly, theory suggests that the disgust system originally evolved to promote health by reducing the risk of ingesting contaminants via the mouth, such as eating rotten/poisonous food.20 In this view then, disgust may specifically motivate the avoidance of smoking (an oral behavior) because it reduces the tendency to place things in the mouth. The disgust system has subsequently become broader in its function such that it now motivates the avoidance of a wider range of possible contamination threats. Experimental studies show that manipulated disgust increases avoidance of potentially disgusting sexual health examinations or tests46 and of disgusting stimuli in cancer decision-making studies47,48 while prospective studies show disgust predicts greater cognitive, emotional, and social avoidance among cancer patient groups.21 The current study implies such avoidance may also occur in smoking contexts. A recent study investigating the impact of emotional responses to GHWLs on adolescents’ perceptions of smoking found that more graphic warning labels evoked similar levels of disgust (but not fear or guilt) among smokers and nonsmokers suggesting evoked disgust may be able to uniquely overcome defensive processing mechanisms in a way that fear and guilt cannot.16 In line with these findings, the current report suggests disgust-focused GHWLs are not only able to disrupt attentional bias toward smoking cues (in comparison to neutral cues) but do so better than anxiety-focused GHWLs. Conversely, the current report provides some suggestion anxiety-inducing GHWLs may have paradoxical effects in the processing of smoking cues. Although anxiety also motivates avoidance,18 findings at Fz revealed P3 activity was greatest during the anxiety condition, indicating greater attentional bias toward the smoking cue. This pattern is consistent with data demonstrating, under some conditions, the induction of fear, anger, and stress can increase both craving and smoking;49,50 although the absence of findings linking negative affect and attentional bias to smoking cues make this more difficult to interpret.51 One possibility is the activation of anxiety in the context of smoking images creates a motivated search for information about the source of anxiety,18 a dynamic that, in this case, increases processing of the smoking images themselves. Further work in which discrete emotions are either manipulated or differentially present in GHWL stimuli is clearly needed. A final contribution of the current report lies in the investigation of whether nicotine dependence might moderate the effects of disgust and anxiety-promoting GHWLs on motivated attention. One ongoing challenge in tobacco control efforts regards how to facilitate cessation efforts among more dependent smokers. More dependent smokers have a harder time quitting and are more likely to relapse,52 as are those with a greater attentional bias toward smoking cues.45 Our findings in this regard were complex. On the one hand, analyses showed that P3 responses among those with greater nicotine dependence were generally greater than those with lower nicotine dependence at the Fz site; less-dependent smokers likely showed less attentional bias toward smoking cues. The absence of attenuated cue responses in the more dependent smokers might reflect attentional and appetitive responses to smoking cues becoming progressively greater and more automatized. On the other hand, however, more dependent smokers displayed a smaller P3 response at Cz and Pz compared with less-dependent smokers. P3 responses at more centro-parietal sites are thought to reflect context updating and subsequent memory storage,35 perhaps suggesting less-dependent smokers experienced the smoking cue oddball stimuli as more “different” from the nontarget stimuli (and thus requiring a greater update to working memory). Although interpretations must be considered preliminary, it is worth noting that GHWLs have been present on cigarette packaging in New Zealand since 2008. More dependent smokers will typically have had greater exposure to pairings of GHWLs and smoking cues. Thus, it is possible that while attentional bias to smoking cues in general is greater (the effect at Fz), it may be that less memory updating (the effects at Cz and Pz) is required among more dependent smokers because they have experienced large numbers of instances in which GHWLs and smoking cues were paired—the oddball is potentially less distinctive for this group. Further research investigating how the learned associations between GHWLs and cues to smoke may affect attentional responding in different subgroups of smokers is needed. Although this report represents a useful contribution to our understanding of how disgust- and anxiety-focused GHWLs may be better leveraged to aid cessation attempts by targeting attentional bias, these data have limitations. First, requiring smokers to view GHWLs for several minutes in a laboratory is different from daily life where tobacco products can be removed from packaging or covered. Additionally, smokers are often confronted with multiple cues simultaneously in their daily lives. Future research should move toward testing other influences on smoking and the cues to smoke, and examine whether GHWLs also interfere with processing in the context of these factors. For example, it may be that visual and internal cues (e.g., stress or negative affect) interact to affect cue-related craving and GHWLs have a role to play here.53 Hence, future research would benefit from testing how GHWLs affect motivated attention toward smoking cues when smokers are also stressed or exposed to other cues (such as alcohol) that more naturalistically mimic the natural elicitors of craving. Second, the current study only tested one anxiety- and one disgust-focused GHWL. While an empirical process was used to select the images, it may be that idiosyncratic elements of the particular images used are relevant to the findings. Our results should be replicated with other images to confirm that it is the disgust in general (rather than a specific picture), that reduces motivated attention toward smoking cues. Such limitations noted; this report has important implications regarding the design and use of GHWLs. It suggests that GHWLs depicting disgust-focused messages are capable of interfering with attentional processing of smoking cues and do so better than health anxiety-focused messages; health anxiety-focused messages appear to have a complex relationship with attentional bias in smoking addiction. In addition, these data indicate that GHWLs have a complex relationship with more versus less-dependent smokers and that an understanding of how smoking cues and anti-smoking imagery become associated over time may help identify relevant targets for public health efforts. Funding The project was funded by the Health Promotion Agency, a New Zealand government entity established under statute and overseen by the Ministry of Health. Funding is drawn from alcohol taxes and the problem gambling levy. Declaration of Interests None declared. References 1. Jung M. Implications of graphic cigarette warning labels on smoking behavior: an international perspective. J Cancer Prev . 2016; 21( 1): 21– 25. Google Scholar CrossRef Search ADS PubMed  2. Hammond D. Health warning messages on tobacco products: a review. Tob Control . 2011; 20( 5): 327– 337. Google Scholar CrossRef Search ADS PubMed  3. Noar SM, Francis DB, Bridges C, Sontag JM, Ribisl KM, Brewer NT. The impact of strengthening cigarette pack warnings: systematic review of longitudinal observational studies. Soc Sci Med . 2016; 164: 118– 129. Google Scholar CrossRef Search ADS PubMed  4. Noar SM, Hall MG, Francis DB, Ribisl KM, Pepper JK, Brewer NT. 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Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Nicotine and Tobacco ResearchOxford University Press

Published: Jul 7, 2017

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