Related Experiment Video
Updated: Aug 7, 2025

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
Published on: August 6, 2013
Psychometric properties of smokeless tobacco dependence measures: A COSMIN systematic review
Vaibhav P Thawal1, Flora Tzelepis2, Tanmay Bagade3
1School of Medicine and Public Health (SMPH), University of Newcastle, Newcastle, Australia; Hunter Medical Research Institute, New Lambton Heights, Newcastle, Australia; Priority Research Centre for Health Behaviour, University of Newcastle, Newcastle, Australia.
Background:
A comprehensive assessment of the quality of the psychometric properties of smokeless tobacco (SLT) dependence measures is necessary to help researchers and health professionals decide on the most appropriate measure to use when assessing dependence and planning cessation treatment. The aim of this systematic review was to identify and critically appraise measures for assessing dependence on SLT products.
Methods:
The study team searched MEDLINE, CINAHL, PsycINFO, EMBASE and SCOPUS databases. We included English-language studies describing the development or psychometric properties of an SLT dependence measure. Two reviewers independently extracted data and appraised risk of bias using the rigorous Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) guidelines.
Results:
Sixteen studies assessing 16 unique measures were eligible for assessment. Eleven studies were conducted in the United States; two in Taiwan and one each in Sweden, Bangladesh, and Guam. Of the sixteen measures, none of the measures was rated as "A" (can be recommended for use) as per COSMIN standards primarily due to limitations in structural validity and internal consistency. Nine measures (FTND-ST, FTQ-ST-9, FTQ-ST-10, OSSTD, BQDS, BQDI, HONC, AUTOS and STDS) were rated as "B": having potential for assessing dependence, although further assessment of psychometric properties is needed. Four measures, MFTND-ST, TDS, GN-STBQ and SSTDS having high quality evidence for an insufficient measurement property were rated as "C" and were not supported for use as per COSMIN standards. The remaining three brief measures HSTI, ST-QFI and STDI (consisting of <3 items) were rated inconclusive due to the inability of assessment of structural validity (minimum 3 items required for factor analysis), which is a prerequisite for assessment of internal consistency per the recommendations by the COSMIN framework.
Conclusion:
Further validation is required for the current tools that assess dependence on SLT products. Given the concerns related to the structural validity of these tools, a need may also exist to develop new measures for use by clinicians and researchers for assessing dependence on SLT products.
Prospero:
CRD42018105878.
More Related Videos
09:25Methods to Evaluate Cytotoxicity and Immunosuppression of Combustible Tobacco Product Preparations
Published on: January 10, 2015
06:39Electroencephalographic, Heart Rate, and Galvanic Skin Response Assessment for an Advertising Perception Study: Application to Antismoking Public Service Announcements
Published on: August 28, 2017
Related Concept Videos
Drug Dependence
Self-Report Tests of Personality
Stimulants
Cocaine can be administered via snorting, injection, or smoking. It primarily functions by blocking the reuptake of dopamine, resulting in a euphoric high characterized by an intense sensation of happiness and...
CNS Depressants: Alcohol and Nicotine
Drugs Acting on Autonomic Ganglia: Stimulants
Ganglionic stimulants activate NM nicotinic receptors in autonomic ganglia, falling into two categories: nicotine mimetics [e.g., lobeline, dimethylpiperazine, tetramethylammonium] and muscarinic receptor agonists [e.g., muscarine, methacholine]. The first category's action is rapid and blocked by nicotinic receptor antagonists, while the second category's action is delayed and blocked by atropine-like agents. Nicotine, an alkaloid, affects the heart rate by stimulating...
Statistical Methods for Analyzing Epidemiological Data