Completed Public Health & Healthcare Lungs & Breathing

Optimising pharmacist-based treatment for smoking cessation

In plain English

AI plain-English summary

Every year, 35,000 people in London try to quit smoking through a pharmacy, but roughly half of those using nicotine patches or lozenges get too low a dose to suppress their cravings effectively. This matters because smoking causes more than 100,000 deaths annually in the UK, and current nicotine replacement therapy is a blunt tool: it ignores the fact that people metabolise nicotine at very different rates. About 10% of smokers are actually overdosed on standard regimens, while many more are underdosed. The researchers aim to fix this by developing a personalised dosing system that uses a simple diagnostic test and a computer algorithm to tailor nicotine doses to each individual’s metabolic rate, delivered through community pharmacies. If the approach works, it could transform a routine pharmacy visit into a genuinely effective medical intervention. More smokers would successfully quit, fewer would suffer side effects from incorrect dosing, and the NHS would save money on wasted nicotine products. The project also builds pharmacists’ consultation skills, potentially increasing the number of smokers who even attempt to quit in the first place.

View original technical description
BACKGROUNDSmoking causes more than 100,000 deaths annually in the UK and is a major preventable cause of cardiovascular disease, chronic respiratory disease and cancer. [1] Thus smoking cessation is a major public health priority.[2] Nicotine replacement is an effective, well established treatment for tobacco dependence. Nicotine in the form of adhesive skin patches or lozenges reduces craving for tobacco and the urge to smoke. However, about half of smokers on standard replacement doses take in less nicotine than when smoking and are thus ‘underreplaced’. Systematic reviews suggest that increasing total nicotine dose in the form of co-therapy improves quit rates.[3] However there is considerable individual variation in nicotine metabolism so whilst many are under dosed, a significant proportion (about 10%) are overdosed. More appropriate dosing could lead to more effective treatment, fewer side effects and reduced drug costs, however this therapeutic approach has not yet been formally evaluated. Technology is available to determine individual nicotine metabolic rate easily. Pilot data shows personalised dose adjustment using a diagnostic test and an interpretive computer algorithm is feasible in pharmacies and suggests improved quit rates. We will develop a personalised smoking cessation intervention and an educational package to build pharmacists’ consultation skills and knowledge about nicotine replacement therapy, aiming to increase the numbers of smokers entering cessation programmes. 35,000 quit attempts take place in pharmacies in London each year [4] – increasing uptake and success rates could have a major impact on tobacco use. RESEARCH PLANSPHASE 1: Systematic review of studies on changing health behaviours in pharmacies to identify aspects of successful interventions that could be applied to smoking cessation, particularly underlying theories. We will update our review of computer advice on drug dosing to identify features of successful systems to incorporate into the intervention and perform a new review of decision support in pharmacies. PHASE 2: Observational studies on patients and pharmacists in successful and unsuccessful smoking cessation consultations. Detailed analysis of transcripts of informative consultations. Identification of strategies to promote recruitment to smoking cessation services and provide effective support during the quit attempt. PHASE 3: Systematic reviews and qualitative work will feed directly into development of educational intervention. Framework for the intervention is likely to be PACE methodology which we used successfully previously to promote effective use of medication for treatment of asthma. Elements of effective interventions identified in the reviews will be added eg (MI). PHASE 4: Two by two factorial RCT (1200 participants, 60 pharmacies) to evaluate effectiveness of (i) educational intervention (ii) personalised dosing v standard therapy v high dose nicotine replacement. Compare costs and side effects. RESEARCH TEAMExperts in health service research, clinical trials, genetics, behavioural psychology, sociology, pharmacology, community pharmacy, health economics, statistics and lay advisors. Links well-established in UK, France and USA and UK PCTs and Department of Health. Work builds on previous research in smoking cessation, developing pharmacist interventions and clinical trials. RESEARCH ENVIRONMENTPolicy makers at National and PCT level, trialists highly experienced in community based RCTs. Full support of Barts and The London, PCTU. State of the art Genome Centre. OUTPUTS OUTCOMES AND IMPACTSOutputs: effective methods of facilitating health behaviour change in pharmacies; computerised dose adjustment; effective pharmacist consultation techniques; effectiveness and cost analysis of personalised dose adjustment for nicotine replacement. Effectiveness of high dose nicotine replacement therapy. Outcomes: increased recruitment into smoking cessati

View the original record at the funder ↗

Related Research

Grants with similar aims, by meaning.

Smoking cessation: population and clinical approaches
Smoking: new approaches to cessation service delivery, prevention of passive smoke exposure in children, and healthcare cost estimation
A randomised trial to increase the uptake of smoking cessation services using personal targeted risk information and taster sessions
Helping people cope with temptations to smoke to reduce relapse: A factorial randomised controlled trial
How do smoking cessation medicines compare with respect to their neuropsychiatric safety: a systematic review, network meta-analysis and cost effectiveness analysis.

Original classification

Research

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.