Comparison of a General and Conditional Measure of E-Cigarette Harm Perceptions

Abstract

Measures of tobacco product harm perceptions are important in research, given their association with tobacco use. Despite recommendations to use more specific harm and risk perception measures, limited research exists comparing different wordings. We present exploratory survey data comparing young adults’ (ages 18–29) responses to a general e-cigarette harm perception measure (“How harmful, if at all, do you think vaping/using an e-cigarette is to a user’s health?”) with a more specific conditional measure, which personalized the behavior/harm (“imagine you vaped,” “your health”) and presented a specific use condition (exclusive daily vaping) and timeframe (10 years). Data were collected in January 2019 (n = 1006). Measures were highly correlated (r = 0.76, Cronbach’s α = 0.86), and most (65%) provided consistent responses, although more participants rated e-cigarettes as very or extremely harmful using the conditional (51.6%) versus the general (43.9%) harm measure. However, significant differences in harm ratings were not observed among young adults who currently vaped. Correlations between each harm perception measure and measures of e-cigarette use intentions were similar. More specifically worded harm perception measures may result in somewhat higher e-cigarette harm ratings than general measures for some young adults. Additional research on best practices for measuring e-cigarette and other tobacco harm perceptions is warranted.

Associations between absolute and relative electronic cigarette harm perceptions and information‐seeking behaviours among US adult current, former and never smokers

“…The harm perception items also referenced general, as opposed to specific, harms to health. Further, the harm perception questions did not condition on behaviour or length of time of product use [10,35,36]. A recent study found that an absolute harm perception measure that personalised the risk (harm to 'your health') and specified frequency and time of use (exclusive daily vaping for the next 10 years) was associated with higher harm ratings among exclusive cigarette smokers relative to a general measure, similar to ours [35].…”

Tackling Smoker Misperceptions about E-cigarettes using Expert Videos

“…The effectiveness of correction is therefore determined in part by the receptivity of the target audience to change . Although there is now considerable evidence on the presence of e-cigarette misperceptions among smokers 3,16,25 , relatively little research has examined how to correct these misperceptions. Our research not only finds that misperceptions can be corrected, but that this can be achieved among non-vapers (79% reported never vaping), a group for whom e-cigarette misinformation is greatest 3 suggests that counter-attitudinal information is aversive, so individuals may avoid videos such as these that challenge their beliefs and behaviours 29,30 .…”

“…Here we use multiple items, as has been recommended, to increase the validity of this measure 16 . This also allows us to 1) identify the specific misbeliefs that underpin negative perceptions towards e-cigarettes in smokers, so that these can be individually targeted in future public information campaigns, and 2) explore the impact of the informational campaigns on specific harm perception beliefs, giving us a more detailed picture of the efficacy of pre-existing informational campaigns.…”

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Strategies and Lessons Learned During Cleaning of Data From Research Panel Participants: Cross-sectional Web-Based Health Behavior Survey Study

“…Often, these research studies are available to potential participants via web-based platforms and may offer incentives for participation [5,6]. Researchers have used web-based platforms and research panels to study a wide variety of topics, including smoking cessation [7][8][9], social and behavioral determinants of health [10], eating habits [11], treatment seeking behaviors [12], social media use and experiences [13], participation in clinical trials research [14], virtual harassment and cyberbullying [15], addiction research [16,17], and infectious disease prevention behaviors [18,19], among others.…”