CHEST: Clinician Smoking-Cessation Awareness May Be Low

And smokers may be more likely to quit if they participate in a pulmonary rehabilitation program
By Rick Ansorge
HealthDay Reporter

THURSDAY, Nov. 5 (HealthDay News) -- Hospital workers are unlikely to have formal training in smoking-cessation methods. In addition, patients may be more likely to quit if they participate in pulmonary rehabilitation programs or know their true lung age, according to research presented at the 75th annual international scientific assembly of the American College of Chest Physicians, held from Oct. 31 to Nov. 5 in San Diego.

Gao L. Chen, of the University of Arizona in Tucson, and colleagues surveyed 250 hospital employees. They found that less than 50 percent of physicians, and only 24 percent of nurses and 15 percent of medical technicians had any formal training in smoking-cessation methods, and that only 54 percent of physicians were aware of patient support resources.

Emilie Chan-Thim, of Concordia University in Montreal, and colleagues compared smoking cessation rates in 27 chronic obstructive pulmonary disease patients who participated in a pulmonary rehabilitation program and 386 patients who received standard care. They found that the pulmonary rehabilitation group had a higher short-term cessation rate and sustained quit rate.

A third study presented by researchers from the from Biomedical Research Institute at Harbor-UCLA Medical Center in Torrance, Calif., suggested that a new lung-age formula based on percent FEV1/FEV6 may more accurately calculate smokers' true lung age than current lung-age formulas based on height and FEV1 or FVC. Their study of 3,500 smokers showed that smoking is associated with mean increases of more than 25 years in lung age by age 50 years and older. "When current-smokers' airflow values are low but still within statistically normal limits, it is sensible and more persuasive to tell them their increased physiological lung age rather than that their spirometry is normal," the researchers conclude.

Abstract - Chen
Abstract - Chan-Thim
Abstract - UCLA
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