Factors influencing the impact of pharmacogenomic prescribing on adherence to nicotine replacement therapy: A qualitative study of participants from a randomized controlled trial

Different doses, durations and modes of delivery of nicotine replacement therapy for smoking cessation

BACKGROUND

Nicotine replacement therapy (NRT) aims to replace nicotine from cigarettes. This helps to reduce cravings and withdrawal symptoms, and ease the transition from cigarette smoking to complete abstinence. Although there is high-certainty evidence that NRT is effective for achieving long-term smoking abstinence, it is unclear whether different forms, doses, durations of treatment or timing of use impacts its effects.

OBJECTIVES

To determine the effectiveness and safety of different forms, deliveries, doses, durations and schedules of NRT, for achieving long-term smoking cessation.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group trials register for papers mentioning NRT in the title, abstract or keywords, most recently in April 2022.

SELECTION CRITERIA

We included randomised trials in people motivated to quit, comparing one type of NRT use with another. We excluded studies that did not assess cessation as an outcome, with follow-up of fewer than six months, and with additional intervention components not matched between arms. Separate reviews cover studies comparing NRT to control, or to other pharmacotherapies.

DATA COLLECTION AND ANALYSIS

We followed standard Cochrane methods. We measured smoking abstinence after at least six months, using the most rigorous definition available. We extracted data on cardiac adverse events (AEs), serious adverse events (SAEs) and study withdrawals due to treatment.  MAIN RESULTS: We identified 68 completed studies with 43,327 participants, five of which are new to this update. Most completed studies recruited adults either from the community or from healthcare clinics. We judged 28 of the 68 studies to be at high risk of bias. Restricting the analysis only to those studies at low or unclear risk of bias did not significantly alter results for any comparisons apart from the preloading comparison, which tested the effect of using NRT prior to quit day whilst still smoking.  There is high-certainty evidence that combination NRT (fast-acting form plus patch) results in higher long-term quit rates than single form (risk ratio (RR) 1.27, 95% confidence interval (CI) 1.17 to 1.37; I2 = 12%; 16 studies, 12,169 participants). Moderate-certainty evidence, limited by imprecision, indicates that 42/44 mg patches are as effective as 21/22 mg (24-hour) patches (RR 1.09, 95% CI 0.93 to 1.29; I2 = 38%; 5 studies, 1655 participants), and that 21 mg patches are more effective than 14 mg (24-hour) patches (RR 1.48, 95% CI 1.06 to 2.08; 1 study, 537 participants). Moderate-certainty evidence, again limited by imprecision, also suggests a benefit of 25 mg over 15 mg (16-hour) patches, but the lower limit of the CI encompassed no difference (RR 1.19, 95% CI 1.00 to 1.41; I2 = 0%; 3 studies, 3446 participants). Nine studies tested the effect of using NRT prior to quit day (preloading) in comparison to using it from quit day onward. There was moderate-certainty evidence, limited by risk of bias, of a favourable effect of preloading on abstinence (RR 1.25, 95% CI 1.08 to 1.44; I2 = 0%; 9 studies, 4395 participants). High-certainty evidence from eight studies suggests that using either a form of fast-acting NRT or a nicotine patch results in similar long-term quit rates (RR 0.90, 95% CI 0.77 to 1.05; I2 = 0%; 8 studies, 3319 participants). We found no clear evidence of an effect of duration of nicotine patch use (low-certainty evidence); duration of combination NRT use (low- and very low-certainty evidence); or fast-acting NRT type (very low-certainty evidence). Cardiac AEs, SAEs and withdrawals due to treatment were all measured variably and infrequently across studies, resulting in low- or very low-certainty evidence for all comparisons. Most comparisons found no clear evidence of an effect on these outcomes, and rates were low overall. More withdrawals due to treatment were reported in people using nasal spray compared to patches in one study (RR 3.47, 95% CI 1.15 to 10.46; 1 study, 922 participants; very low-certainty evidence) and in people using 42/44 mg patches in comparison to 21/22 mg patches across two studies (RR 4.99, 95% CI 1.60 to 15.50; I2 = 0%; 2 studies, 544 participants; low-certainty evidence).

AUTHORS' CONCLUSIONS

There is high-certainty evidence that using combination NRT versus single-form NRT and 4 mg versus 2 mg nicotine gum can result in an increase in the chances of successfully stopping smoking. Due to imprecision, evidence was of moderate certainty for patch dose comparisons. There is some indication that the lower-dose nicotine patches and gum may be less effective than higher-dose products. Using a fast-acting form of NRT, such as gum or lozenge, resulted in similar quit rates to nicotine patches. There is moderate-certainty evidence that using NRT before quitting may improve quit rates versus using it from quit date only; however, further research is needed to ensure the robustness of this finding. Evidence for the comparative safety and tolerability of different types of NRT use is limited. New studies should ensure that AEs, SAEs and withdrawals due to treatment are reported.

Barriers and Facilitators of Adherence to Nicotine Replacement Therapy: A Systematic Review and Analysis Using the Capability, Opportunity, Motivation, and Behaviour (COM-B) Model

BACKGROUND

Poor adherence to nicotine replacement therapy (NRT) is associated with low rates of smoking cessation. Hence, this study aims to identify and map patient-related factors associated with adherence to NRT using the capability, opportunity, motivation, and behaviour (COM-B) model.

METHODS

A systematic review was conducted by searching five databases (MEDLINE, Scopus, EMBASE, CINAHL, and PsycINFO) and grey literature on 30 August 2020. Data were extracted, thematically analysed, and mapped to the COM-B model. The Joanna Briggs Institute (JBI) critical appraisal tool was utilised to assess the quality of studies.

RESULTS

A total of 2929 citations were screened, and 26 articles with a total of 13,429 participants included. Thirty-one factors were identified and mapped to COM-B model: psychological capability (forgetfulness, education), physical capability (level of nicotine dependence, withdrawal symptoms), reflective motivation (perception about NRT and quitting), automatic motivation (alcohol use, stress, depression), physical opportunity (cost), and social opportunity (social support). The most prominent element associated with adherence was reflective motivation followed by physical capability and automatic motivation.

CONCLUSIONS

Multiple personal, social, and environmental factors affect NRT adherence. Hence, it is recommended to implement a multifaceted behavioural intervention incorporating factors categorised under the COM-B model, which is the hub of the behaviour change wheel (BCW) to improve adherence and quitting.

Pharmacogenomic-Based Decision Support to Predict Adherence to Medications

Poor adherence is associated with worse disease outcomes. Pharmacogenomics provides a possible intervention to address adherence. We hypothesized that pharmacogenomic-informed care could increase adherence. Patients in a prospective case-control study underwent preemptive pharmacogenomic genotyping with results available for provider use at the point of care; controls (not genotyped) were treated by the same providers. Over 6,000 e-prescriptions for 39 medications with actionable pharmacogenomic information were analyzed. Composite adherence, measured by modified proportion of days covered (mPDC), was compared between cases/controls and genomically concordant vs. genomically higher-risk medications. Overall, 536 patients were included. No difference in mean mPDC was observed due to availability of pharmacogenomic guidance. However, case patients prescribed high-risk pharmacogenomic medications were more than twice as likely to have low mPDC for these medications compared with genomically concordant prescriptions (odds ratio = 2.4 (1.03-5.74), P < 0.05). This study is the first to show that composite pharmacogenomic information predicts adherence.