Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Introduction: Quantifying e-cigarette use is challenging because of the wide variety of products and the lack of a clear, objective demarcation of a use event. This study aimed to characterize the difference between retrospective and real-time measures of the quantity of e-cigarette use and identify the covariates that may account for discrepancies between the two types of measures.
Methods: This study analyzed data from 401 college student e-cigarette users in Indiana and Texas who responded to a web survey (retrospective) and 7-day ecological momentary assessments (EMA) (real-time) on their e-cigarette use behavior, dependence symptomatology, e-cigarette product characteristics, and use contexts from Fall 2019 to Fall 2021. Generalized linear mixed models were used to model the real-time measures of quantity offset by the retrospective average quantity.
Results: Although the number of times using e-cigarettes per day seems to be applicable to both retrospective and real-time measures, the number reported via EMA was 8.5 times the retrospective report. E-cigarette users with higher e-cigarette primary dependence motives tended to report more daily nicotine consumption via EMA than their retrospective reports (ie, perceived average consumption). Other covariates that were associated with discrepancies between real-time and retrospective reports included gender, nicotine concentration, using a menthol- or fruit-flavored product, co-use with alcohol, and being with others when vaping.
Conclusions: The study found extreme under-reporting of e-cigarette consumption on retrospective surveys. Important covariates identified to be associated with higher than average consumption may be considered as potential targets for future vaping interventions.
Implications: This is the first study that characterizes the direction and magnitude of the difference between retrospective and real-time measures of the quantity of e-cigarette use among young adults-the population most likely to use e-cigarettes. An average retrospective account of vaping events per day may significantly underestimate e-cigarette use frequency among young adults. The lack of insight into the degree of consumption among users with heavy primary dependence motives illustrates the importance of incorporating self-monitoring into cessation interventions.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445250 | PMC |
http://dx.doi.org/10.1093/ntr/ntad094 | DOI Listing |
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