A New Questionnaire to Assess Respiratory Symptoms (The Respiratory Symptom Experience Scale): Quantitative Psychometric Assessment and Validation Study

Background Smokers often experience respiratory symptoms (eg, morning cough), and those who stop smoking, including those who do so by switching completely to electronic nicotine delivery systems (ENDS), may experience reductions in symptoms. Existing respiratory symptom questionnaires may not be suitable for studying these changes, as they are intended for patient populations, such as those with chronic obstructive pulmonary disease (COPD). Objective This study aimed to develop a respiratory symptom questionnaire appropriate for current smokers and for assessing changes when smokers stop smoking. Methods The Respiratory Symptom Experience Scale (RSES) was derived from existing instruments and subject matter expert input and refined through cognitive debriefing interviews (n=49). Next, for purposes of the quantitative psychometric evaluation, the RSES was administered to smokers (n=202), former smokers (no tobacco use in >6 months; n=200), and switchers (n=208, smokers who switched to ENDS for >6 months), all of whom had smoked for at least 10 years (mean age 33 years). Participants, who averaged 62 (SD 12) years of age, included 28% (173/610) with respiratory allergy symptoms and 17% (104/610) with COPD. Test-retest reliability was assessed by repeat assessment after 1 week in 128 participants. Results A generalized partial credit model confirmed that the response options were ordered, and a parallel analysis using principal components confirmed that the scale was unidimensional. With allowance for 2 sets of correlated errors between pairs of items, a 1-factor graded response model fit the data. Discrimination parameters were approximately 1 or greater for all items. Scale reliability was 0.80 or higher across a broad range of severity (standardized scores −0.40 to 3.00). Test-retest reliability (absolute intraclass correlation) was good, at 0.89. RSES convergent validity was supported by substantial differences (Cohen d=0.74) between those with and without a diagnosis of respiratory disease (averaging 0.57 points, indicating that differences of this size or smaller represent meaningful differences). RSES scores also strongly differentiated those with and without COPD (d=1.52). Smokers’ RSES scores were significantly higher than former smokers’ scores (P<.001). Switchers’ RSES scores were significantly lower than smokers’ scores (P<.001) and no different from former smokers’ scores (P=.34). Conclusions The RSES fills an important gap in the existing toolkit of respiratory symptom questionnaires; it is a reliable and valid tool to assess respiratory symptoms in adult current and former smokers, including those who have switched to noncombusted nicotine products. This suggests that the scale is sensitive to respiratory symptoms that develop in smokers and to their remission when smokers quit or switch to noncombusted nicotine products intended to reduce the harm of smoking. The findings also suggest that switching from cigarettes to ENDS may improve respiratory health.

[1]  L. Brose,et al.  Heated tobacco products for smoking cessation and reducing smoking prevalence , 2020, The Cochrane database of systematic reviews.

[2]  Arielle S. Selya,et al.  Dual Use of Cigarettes and JUUL: Trajectory and Cigarette Consumption. , 2021, American journal of health behavior.

[3]  M. Goniewicz,et al.  How effective are electronic cigarettes for reducing respiratory and cardiovascular risk in smokers? A systematic review , 2020, Harm Reduction Journal.

[4]  A. Vahratian,et al.  Electronic Cigarette Use Among U.S. Adults, 2018. , 2020, NCHS data brief.

[5]  S. Jordt,et al.  What are the respiratory effects of e-cigarettes? , 2019, BMJ.

[6]  M. Caruso,et al.  The effect of e-cigarette aerosol emissions on respiratory health: a narrative review , 2019, Expert review of respiratory medicine.

[7]  Jennifer L. Pearson,et al.  Transitions in electronic cigarette use among adults in the Population Assessment of Tobacco and Health (PATH) Study, Waves 1 and 2 (2013–2015) , 2018, Tobacco Control.

[8]  J. O'Loughlin,et al.  Reasons for quitting smoking in young adult cigarette smokers. , 2018, Addictive behaviors.

[9]  S. Stanos National Academies of Sciences, Engineering, and Medicine (NASEM). , 2017, Pain medicine.

[10]  A. Pickard,et al.  Respiratory and Bronchitic Symptoms Predict Intention to Quit Smoking among Current Smokers with, and at Risk for, Chronic Obstructive Pulmonary Disease. , 2016, Annals of the American Thoracic Society.

[11]  B. Lushniak,et al.  The Health consequences of smoking—50 years of progress : a report of the Surgeon General , 2014 .

[12]  J. Templin,et al.  Linking outcomes from peabody picture vocabulary test forms using item response models. , 2012, Journal of speech, language, and hearing research : JSLHR.

[13]  Office on Smoking How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General , 2010 .

[14]  P. Jones,et al.  Development and first validation of the COPD Assessment Test , 2009, European Respiratory Journal.

[15]  Kenneth S. Yew,et al.  Diagnosis of chronic obstructive pulmonary disease. , 2008, American family physician.

[16]  Dirkje S Postma,et al.  Health and Quality of Life Outcomes , 2003 .

[17]  Sally J. Singh,et al.  Development of a symptom specific health status measure for patients with chronic cough: Leicester Cough Questionnaire (LCQ) , 2003, Thorax.

[18]  J. Stoker,et al.  The Department of Health and Human Services. , 1999, Home healthcare nurse.

[19]  E. Muraki A Generalized Partial Credit Model , 1997 .

[20]  K. Cummings,et al.  Predictors of smoking cessation in a cohort of adult smokers followed for five years. , 1997, Tobacco control.

[21]  E. Muraki Information Functions of the Generalized Partial Credit Model , 1993 .

[22]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[23]  Ware J.E.Jr.,et al.  THE MOS 36- ITEM SHORT FORM HEALTH SURVEY (SF- 36) CONCEPTUAL FRAMEWORK AND ITEM SELECTION , 1992 .

[24]  S. Cummings,et al.  Quitting smoking: reasons for quitting and predictors of cessation among medical patients. , 1992, Journal of general internal medicine.

[25]  P. Jones,et al.  The St George's Respiratory Questionnaire. , 1991, Respiratory medicine.

[26]  S. Green How Many Subjects Does It Take To Do A Regression Analysis. , 1991, Multivariate behavioral research.

[27]  Frank L. Schmidt,et al.  The Relative Efficiency of Regression and Simple Unit Predictor Weights in Applied Differential Psychology , 1971 .

[28]  F. Samejima Estimation of latent ability using a response pattern of graded scores , 1968 .