Management and governance
The research advisory group comprised a respiratory physician (RD), an academic general practitioner (TG), and the principal investigator (GP). A core management group, comprising principal investigator, practice manager, two practice nurses, healthcare assistant, and a patient representative, was responsible for the day to day running of the project.
Sampling and recruitment
A power calculation indicated the need for about 300 participants to have 80% power to detect a 10% difference in smoking cessation rate (for example, 5% in one group v 15% in the other). Assuming an attrition rate of up to 50%, we aimed to recruit 600 participants. We searched computerised patient records from five general practices in Hertfordshire to identify people aged 35 and over who had been recorded as a smoker in the previous 12 months. We excluded those receiving oxygen and those with a history of lung cancer, tuberculosis, asbestosis, silicosis, bronchiectasis, or pneumonectomy. We sent a letter of invitation to participate in the study and a research information sheet. Two weeks later, we telephoned all those who had not already responded, offering an invitation to participate and to answer any queries. Those who could not be contacted by telephone were sent a second letter. Recruitment started in February 2004 and follow-up was completed in March 2007.
All potential participants were asked to confirm that they were current smokers, had understood the information provided, and would be available for re-assessment in 12 months. Baseline data included age, smoking history in pack years (average daily number of cigarettes smoked divided by 20 and multiplied by the number of years of smoking), medical history for exclusion criteria (see above), medication (especially use of steroids or antibiotics for chest infections in the preceding 12 months), and comorbidity including chronic bronchitis or emphysema, asthma, other lung disease, diabetes, treatment for blood pressure, stroke, coronary heart disease (angina or heart attack), or other heart disease. These comorbidities were not used as exclusion criteria but to confirm baseline comparability of groups.
All participants underwent standard measurements of lung function (FEV1
, FVC (forced vital capacity), FEV1
/FVC) with a Micromedical spirometer. Reversibility of airways obstruction was measured according to standard British Thoracic Society guidelines (over 15% and at least 400 ml improvement in FEV1
after 400 µg salbutamol via a spacer).8
Both groups were told that their lung function would be measured again after 12 months to see whether it had deteriorated. They were not randomised until after spirometry had been completed. All participants were strongly encouraged to give up smoking and advised how to access local NHS smoking cessation clinics.
We used two instruments to confirm baseline comparability of groups: the St George’s respiratory questionnaire and Prochaska’s stages of change questions in relation to smoking. The St George’s respiratory questionnaire is a validated questionnaire designed to be self administered under supervision and to measure the impact of respiratory diseases (in particular asthma and chronic obstructive pulmonary disease) on an individual’s life.9
Like other quality of life instruments, it has the potential to identify a threshold response to therapy or compare the response to different therapies, or both.10
Scores of 7 or below indicate normal lung function. We adapted stage of change questions (with permission) from Prochaska and DiClemente’s model in which smokers are asked three questions and classified on the basis of their response as in the “pre-contemplative,” “contemplative,” “preparation,” or “action”’ phase (table 1).
Table 1 Stages of change questions (adapted from Prochaska14)
A clerk (who then took no further part in the study) prepared 600 sequentially numbered opaque sealed envelopes, each containing a card with allocation group determined by computer generated random number (odd = intervention). If the participant met the inclusion criteria and gave consent, he or she was entered into the study and underwent baseline spirometry. The next numbered envelope in the series was then opened to determine allocation group.
Instruments and tests
All data collectors were trained in the use of MicroLab 3500 spirometers (Micro Medical, Chatham, Kent), which were newly purchased at the start of the study. Spirometry readings were checked for internal reliability on three criteria: at least two FEV1 readings within 5% of each other; good quality time volume curve; and the internal spirometer computer display had to register “good blow.” Smoking cessation at follow-up was initially assessed by measuring carbon monoxide concentrations with a Smoke Check SC01 monitor (Micro Medical, Chatham, Kent). This model has a carbon monoxide range of 0-500 ppm and a sensitivity of 1 ppm.
One of two independent nurses, who were blinded to allocation group, collected saliva samples for cotinine testing and recorded those who continued to take nicotine replacement therapy. Specimens were processed by ABS Laboratories, Medical Toxicology Unit, London. The optimum cut-off point to distinguish smokers from non-smokers is 14.2 ng/ml, which correctly classifies 99% of non-smokers and 96% of smokers. As the half life of cotinine is about 20 hours, the test would detect most people who had smoked a cigarette within the past 24-48 hours.
Estimation of lung age
Figure 1, adapted from the work of Fletcher and Peto, illustrates how smoking effectively “ages” the lungs.11
The examples illustrated show how the lungs can deteriorate more rapidly with smoking, as if they are ageing faster. Smoking cessation will not allow the lungs to return to normal but reduction in function or “ageing” will then occur at a normal rate. Originally, calculation of lung age was based on estimates developed by Morris and Temple with reference linear regression equations to establish the best method.5
They showed that FEV1
was the best test for calculating lung age mathematically (box). In practice, the lung age is automatically generated by adjustment of the settings of the spirometer.
Fig 1 Graph of lung function against age showing how smoking accelerates age related decline in lung function (adapted from Fletcher and Peto11)
Lung age calculation formula developed by Morris and Temple5
Lung age=2.87×height (in inches)−(31.25×observed FEV1 (litres)−39.375
Lung age=3.56×height (in inches)−(40 ×observed FEV1 (litres)−77.28
Information given to participants
Participants in the intervention group were given their results verbally, immediately after randomisation, in the form of “lung age” with a graphic display (figs 1 and 2) . The graphs were used as a visual aid to explain how the lung function normally reduces gradually with age and that smoking can damage lungs as if they are ageing more rapidly than normal. As an example a line can be draw vertically up from the horizontal axis (fig 2) from “age 52” to reach the bold blue curve illustrating the lung function of the “susceptible smoker” and then horizontally to the curve representing those who have “never smoked” and lung function at age 75. Furthermore they were told that smoking cessation would slow down the rate of deterioration of the lung function back to normal but would not repair the damage already done.
Fig 2 Explaining lung age to participants (adapted from Fletcher and Peto11)
In the intervention group, if the lung age was equal to or less than the individual’s chronological age, he or she was informed that test result was normal. If lung age was greater than chronological age, we gave them the “lung age” in years.
We did not tell those in the control group their results but informed them that they would be invited for a second test after 12 months to “see if there had been any change in lung function.” If the examiner was pressed for more information, he or she could tell participants that they would receive a letter with more information from the research doctor within four weeks.
The principal research doctor (GP) reviewed all the results, checked the quality of the spirometry tracing, and considered the result in the light of clinical data. When there was doubt, he sent the results to a chest physician (RD) for interpretation and advice. Within four weeks of data collection the research doctor sent all participants an individualised letter. Written results were given to the control group as simple FEV1 (litres per second) with no further explanation. Written results were given to the intervention group as “lung age.”
The letter to both groups included the phrase “This type of lung function test does not tell us anything about the risk of other serious diseases related to smoking such as lung cancer or heart disease or stroke. Smoking cessation is therefore still important for all people regardless of their age or the results of these lung tests.” All participants were given written contact details of the local NHS smoking cessation services.
In both groups, when reversibility testing indicated asthma (over 15% and at least 400 ml improvement in FEV1 after 400 µg salbutamol via a spacer) we advised participants to attend their general practitioner for further management, and informed the general practitioner separately. When spirometry findings suggested restrictive lung disease, we sent the participant and his or her general practitioner a letter to alert them to the advisability of further investigation and guidelines on referral to secondary care.
The primary outcome measure was verified cessation of smoking 12 months after the initial recruitment interview and examination. Secondary outcomes were changes in daily consumption of cigarettes and the identification of new diagnoses.
Follow-up and confirmation of cessation
Participants underwent follow-up examination with repeat spirometry after 12 months. Self reported quitters had carbon monoxide breath testing immediately for confirmation of smoking cessation, and they were informed that they would be contacted by an independent nurse for a saliva test for cotinine measurement.
We analysed data on an intention to treat basis and performed statistical analysis with SPSS version 11.0. We used unpaired t tests for continuous data and χ2 tests for categorical data, except when expected cells were found to be less than 5, in which case we used Fisher’s exact test.
To test the hypothesis that severity of lung damage predicts quit success, we used the t test to compare the mean “lung age deficit” (difference of lung age minus chronological age) between quitters and non-quitters within the intervention group.
Assessment of costs
Though we did not carry out a full economic evaluation, we had accurate data on the time taken to carry out the spirometry tests and for results to be communicated to patients by letter. We calculated costs in terms of the time spent per patient processed and also per successful quitter.