Biomedical risk assessment is the process of giving people feedback on the effects of smoking on their body. The physical effects of smoking can be assessed using various measurements, and some people think this could be used as a tool to encourage people to quit smoking. We reviewed the evidence about whether giving adult smokers feedback on the effects of smoking on their body helps them quit smoking.
This review includes 20 studies using a variety of measurements. One study included two measurements, for a total of 21 measurements assessed. The main feedback measurements we assessed were the level of carbon monoxide in people's breath (a sign of current smoking), measures of lung function (a sign of lung damage from smoking), genetic tests to provide individual risk of cancer, and ultrasound of major arteries in the neck to measure the amount of plaque (a risk factor for stroke). We grouped studies into three categories according to the type of feedback people were given: feedback on exposure to smoking (five studies); feedback on a person's risk for smoking-related diseases (five studies); and feedback on the harms of smoking (11 studies). The studies included a total of 9262 people. All participants were adult smokers, and both men and women were included (although one study performed in a clinic for army veterans included only 4% women). Most studies were conducted in general practices or ambulatory clinics. All of the studies lasted at least six months. The reported evidence is current as of March 2018.
We did not find evidence that giving smokers feedback on their smoking exposure, their genetic risk of smoking-related disease, or the effects of smoking on their body helps them quit smoking. The most promising results were for giving people feedback on the harm smoking does to their bodies. The studies did not report on harms or side effects of providing feedback. However, given the nature of the measurements (lung or blood tests), we would expect the risk of harms to be low.
Certainty of evidence
Because of problems with the way some of the studies were conducted, we think that further research is likely to change our conclusions.
There is little evidence about the effects of biomedical risk assessment as an aid for smoking cessation. The most promising results relate to spirometry and carotid ultrasound, where moderate-certainty evidence, limited by imprecision and risk of bias, did not detect a statistically significant benefit, but confidence intervals very narrowly missed one, and the point estimate favoured the intervention. A sensitivity analysis removing those studies at high risk of bias did detect a benefit. Moderate-certainty evidence limited by risk of bias did not detect an effect of feedback on smoking exposure by CO monitoring. Low-certainty evidence, limited by risk of bias and imprecision, did not detect a benefit from feedback on smoking-related risk by genetic marker testing. There is insufficient evidence with which to evaluate the hypothesis that multiple types of assessment are more effective than single forms of assessment.
A possible strategy for increasing smoking cessation rates could be to provide smokers with feedback on the current or potential future biomedical effects of smoking using, for example, measurement of exhaled carbon monoxide (CO), lung function, or genetic susceptibility to lung cancer or other diseases.
The main objective was to determine the efficacy of providing smokers with feedback on their exhaled CO measurement, spirometry results, atherosclerotic plaque imaging, and genetic susceptibility to smoking-related diseases in helping them to quit smoking.
For the most recent update, we searched the Cochrane Tobacco Addiction Group Specialized Register in March 2018 and ClinicalTrials.gov and the WHO ICTRP in September 2018 for studies added since the last update in 2012.
Inclusion criteria for the review were: a randomised controlled trial design; participants being current smokers; interventions based on a biomedical test to increase smoking cessation rates; control groups receiving all other components of intervention; and an outcome of smoking cessation rate at least six months after the start of the intervention.
We used standard methodological procedures expected by Cochrane. We expressed results as a risk ratio (RR) for smoking cessation with 95% confidence intervals (CI). Where appropriate, we pooled studies using a Mantel-Haenszel random-effects method.
We included 20 trials using a variety of biomedical tests interventions; one trial included two interventions, for a total of 21 interventions. We included a total of 9262 participants, all of whom were adult smokers. All studies included both men and women adult smokers at different stages of change and motivation for smoking cessation. We judged all but three studies to be at high or unclear risk of bias in at least one domain. We pooled trials in three categories according to the type of biofeedback provided: feedback on risk exposure (five studies); feedback on smoking-related disease risk (five studies); and feedback on smoking-related harm (11 studies). There was no evidence of increased cessation rates from feedback on risk exposure, consisting mainly of feedback on CO measurement, in five pooled trials (RR 1.00, 95% CI 0.83 to 1.21; I2 = 0%; n = 2368). Feedback on smoking-related disease risk, including four studies testing feedback on genetic markers for cancer risk and one study with feedback on genetic markers for risk of Crohn's disease, did not show a benefit in smoking cessation (RR 0.80, 95% CI 0.63 to 1.01; I2 = 0%; n = 2064). Feedback on smoking-related harm, including nine studies testing spirometry with or without feedback on lung age and two studies on feedback on carotid ultrasound, also did not show a benefit (RR 1.26, 95% CI 0.99 to 1.61; I2 = 34%; n = 3314). Only one study directly compared multiple forms of measurement with a single form of measurement, and did not detect a significant difference in effect between measurement of CO plus genetic susceptibility to lung cancer and measurement of CO only (RR 0.82, 95% CI 0.43 to 1.56; n = 189).