Trial reportNicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco2020
Which Method of Assessing Depression and Anxiety Best Predicts Smoking Cessation: Screening Instruments or Self-Reported Conditions?
Trial report in Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Pharmacological interventions on smoking cessation: A systematic review and network meta-analysis.Frontiers in pharmacology · 2022Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
introductionAffective disorders and symptoms (ADS) are predictive of lower odds of quitting smoking. However, it is unknown which approach to assessing ADS best predicts cessation. This study compared a battery of ADS screening instruments with a single, self-report question on predicting cessation. Among those who self-reported ADS, we also examined if an additional question regarding whether participants believed the condition(s) might interfere with their ability to quit added predictive utility to the single-item question.
methodsParticipants (N = 2637) enrolled in a randomized controlled trial of web-based smoking treatments completed a battery of five ADS screening instruments and answered a single-item question about having ADS. Those with a positive self-report on the single-item question were also asked about their interference beliefs. The primary outcome was complete-case, self-reported 30-day point prevalence abstinence at 12 months.
resultsBoth assessment approaches significantly predicted cessation. Screening positive for ≥ one ADS in the battery was associated with 23% lower odds of quitting than not screening positive for any (p = .023); those with a positive self-report on the single-item had 39% lower odds of quitting than self-reporting no mental health conditions (p < .001). Area under the receiver operating characteristic curve values for the two assessment approaches were similar (p = .136). Adding the interference belief question to the single-item assessment significantly increased the area under the receiver operating characteristic curve value (p = .042).
conclusionsThe single-item question assessing ADS had as much predictive validity, and possibly more, than the battery of screening instruments for identifying participants at risk for failing to quit smoking. Adding a question about interference beliefs significantly increased the predictive utility of the single-item question. IMPLICATIONS: This is the first study to demonstrate that a single-item question assessing ADS has at least as much predictive validity, and possibly more, than a battery of validated screening instruments for identifying smokers at highest risk for cessation failure. This study also demonstrates adding a question about interference beliefs significantly adds to the predictive utility of a single, self-report question about mental health conditions. Findings from this study can be used to inform decisions regarding how to assess ADS in the context of tobacco treatment settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.