ReviewDrug and alcohol dependence reports2024
Attentional bias in tobacco use disorder using eye tracking: A systematic review.
Review in Drug and alcohol dependence reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The influence of physical exercise on attentional bias: a systematic review and meta-analysis.Frontiers in psychiatry · 2026Pooled it
- Attentional bias toward emotional and cigarette cues: Insights from a computer mouse-tracking task in adults who smoke and adults who do not smoke.Drug and alcohol dependence · 2026Article
- Orienting bias towards electronic nicotine delivery system (ENDS) cues.Addictive behaviors reports · 2026Article
- Effects of aerobic exercise on approach-avoidance responses and prefrontal activity in individuals with tobacco dependence.Frontiers in sports and active living · 2026Article
- Psychological and Clinical Parameters as Predictors of Relapse in Alcohol-Dependent Patients During and After Extensive Inpatient Rehabilitation Treatment.Brain sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Attentional bias, defined as the disproportionate attentional allocation towards drug-related stimuli, is well-demonstrated in substance use disorders. However, studies investigating attentional bias in tobacco use disorder have revealed inconclusive findings. In recent years, eye-tracking technology has emerged as an innovative technique for exploring attentional bias. This systematic review aims to provide a comprehensive overview of eye-tracking studies examining attentional bias in tobacco use disorder. Methods: Using PRISMA guidelines, 18 papers that assessed attentional bias using eye-tracking technology among people who smoke cigarettes were extracted from the following databases: PsychINFO, MEDLINE, and EMBASE. Search terms included "attentional bias", "tobacco use disorder", and "eye tracking" and their respective subject headings and synonyms. Selected papers were assessed for methodological quality using a standardized procedure. Selected studies reviewed were categorized into studies making comparisons between 1) people who smoke and people who do not smoke and 2) between smoking-related cues and neutral cues among people who smoke. Results: Overall, most studies showed that people who smoke had significantly greater attentional bias to smoking-related cues, as indexed by greater dwell times and fixation counts. Although findings using measures of early orienting biases were mixed, people who smoke displayed a tendency to initially shift attention to smoking-related cues more frequently than neutral cues. Conclusions: While methodological inconsistencies across studies preclude any definitive conclusions, findings suggest that maintained attention may be a more precise reflection of the specific attentional processes influenced by incentive salience. Suggestions for future research include establishing methodological standards for future eye-tracking studies.
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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.