Community pharmacy interventions for health promotion: effects on professional practice and health outcomes.

BACKGROUND: Community pharmacies are an easily accessible and cost-effective platform for delivering health care worldwide, and the range of services provided has undergone rapid expansion in recent years. Thus, in addition to dispensing medication, pharmacy workers within community pharmacies now give advice on a range of health-promoting behaviours that aim to improve health and to optimise the management of long-term conditions. However, it remains uncertain whether these health-promotion interventions can change the professional practice of pharmacy workers, improve health behaviours and outcomes for pharmacy users and have the potential to address health inequalities. OBJECTIVES: To assess the effectiveness and safety of health-promotion interventions to change community pharmacy workers' professional practice and improve outcomes for users of community pharmacies. SEARCH METHODS: We searched MEDLINE, Embase, CENTRAL, six other databases and two trials registers to 6 February 2018. We also conducted reference checking, citation searches and contacted study authors to identify any additional studies. SELECTION CRITERIA: We included randomised trials of health-promotion interventions in community pharmacies targeted at, or delivered by, pharmacy workers that aimed to improve the health-related behaviour of people attending the pharmacy compared to no treatment, or usual treatment received in the community pharmacy. We excluded interventions where there was no interaction between pharmacy workers and pharmacy users, and those that focused on medication use only. DATA COLLECTION AND ANALYSIS: We used standard procedures recommended by Cochrane and the Effective Practice and Organisation of Care review group for both data collection and analysis. We compared intervention to no intervention or to usual treatment using standardised mean differences (SMD) and 95% confidence intervals (95% CI) (higher scores represent better outcomes for pharmacy user health-related behaviour and quality of life, and lower scores represent better outcomes for clinical outcomes, costs and adverse events). Interpretation of effect sizes (SMD) was in line with Cochrane recommendations. MAIN RESULTS: We included 57 randomised trials with 16,220 participants, described in 83 reports. Forty-nine studies were conducted in high-income countries, and eight in middle-income countries. We found no studies that had been conducted in low-income countries. Most interventions were educational, or incorporated skills training. Interventions were directed at pharmacy workers (n = 8), pharmacy users (n = 13), or both (n = 36). The clinical areas most frequently studied were diabetes, hypertension, asthma, and modification of cardiovascular risk. Duration of follow-up of interventions was often unclear. Only five studies gave details about the theoretical basis for the intervention, and studies did not provide sufficient data to comment on health inequalities. The most common sources of bias were lack of protection against contamination - mainly in individually randomised studies - and inadequate blinding of participants. The certainty of the evidence for all outcomes was moderate. We downgraded the certainty because of the heterogeneity across studies and evidence of potential publication bias. Professional practice outcomes We conducted a narrative analysis for pharmacy worker behaviour due to high heterogeneity in the results. Health-promotion interventions probably improve pharmacy workers' behaviour (2944 participants; 9 studies; moderate-certainty evidence) when compared to no intervention. These studies typically assessed behaviour using a simulated patient (mystery shopper) methodology. Pharmacy user outcomes Health-promotion interventions probably lead to a slight improvement in health-related behaviours of pharmacy users when compared to usual treatment (SMD 0.43, 95% CI 0.14 to 0.72; I2 = 89%; 10 trials; 2138 participants; moderate-certainty evidence). These interventions probably also lead to a slight improvement in intermediate clinical outcomes, such as levels of cholesterol or glycated haemoglobin, for pharmacy users (SMD -0.43, 95% CI -0.65 to -0.21; I2 = 90%; 20 trials; 3971 participants; moderate-certainty evidence). We identified no studies that evaluated the impact of health-promotion interventions on event-based clinical outcomes, such as stroke or myocardial infarction, or the psychological well-being of pharmacy users. Health-promotion interventions probably lead to a slight improvement in quality of life for pharmacy users (SMD 0.29, 95% CI 0.08 to 0.50; I2= 82%; 10 trials, 2687 participants; moderate-certainty evidence). Adverse events No studies reported adverse events for either pharmacy workers or pharmacy users. Costs We found that health-promotion interventions are likely to be cost-effective, based on moderate-certainty evidence from five of seven studies that reported an economic evaluation. AUTHORS' CONCLUSIONS: Health-promotion interventions in the community pharmacy context probably improve pharmacy workers' behaviour and probably have a slight beneficial effect on health-related behaviour, intermediate clinical outcomes, and quality of life for pharmacy users. Such interventions are likely to be cost-effective and the effects are seen across a range of clinical conditions and health-related behaviours. Nevertheless the magnitude of the effects varies between conditions, and more effective interventions might be developed if greater consideration were given to the theoretical basis of the intervention and mechanisms for effecting behaviour change.

Gamification for health promotion: systematic review of behaviour change techniques in smartphone apps.

OBJECTIVE: Smartphone games that aim to alter health behaviours are common, but there is uncertainty about how to achieve this. We systematically reviewed health apps containing gaming elements analysing their embedded behaviour change techniques. METHODS: Two trained researchers independently coded apps for behaviour change techniques using a standard taxonomy. We explored associations with user ratings and price. DATA SOURCES: We screened the National Health Service (NHS) Health Apps Library and all top-rated medical, health and wellness and health and fitness apps (defined by Apple and Google Play stores based on revenue and downloads). We included free and paid English language apps using 'gamification' (rewards, prizes, avatars, badges, leaderboards, competitions, levelling-up or health-related challenges). We excluded apps targeting health professionals. RESULTS: 64 of 1680 (4%) health apps included gamification and met inclusion criteria; only 3 of these were in the NHS Library. Behaviour change categories used were: feedback and monitoring (n=60, 94% of apps), reward and threat (n=52, 81%), and goals and planning (n=52, 81%). Individual techniques were: self-monitoring of behaviour (n=55, 86%), non-specific reward (n=49, 82%), social support unspecified (n=48, 75%), non-specific incentive (n=49, 82%) and focus on past success (n=47, 73%). Median number of techniques per app was 14 (range: 5-22). Common combinations were: goal setting, self-monitoring, non-specific reward and non-specific incentive (n=35, 55%); goal setting, self-monitoring and focus on past success (n=33, 52%). There was no correlation between number of techniques and user ratings (p=0.07; rs=0.23) or price (p=0.45; rs=0.10). CONCLUSIONS: Few health apps currently employ gamification and there is a wide variation in the use of behaviour change techniques, which may limit potential to improve health outcomes. We found no correlation between user rating (a possible proxy for health benefits) and game content or price. Further research is required to evaluate effective behaviour change techniques and to assess clinical outcomes. TRIAL REGISTRATION NUMBER: CRD42015029841.

Computerized advice on drug dosage to improve prescribing practice.

BACKGROUND: Maintaining therapeutic concentrations of drugs with a narrow therapeutic window is a complex task. Several computer systems have been designed to help doctors determine optimum drug dosage. Significant improvements in health care could be achieved if computer advice improved health outcomes and could be implemented in routine practice in a cost-effective fashion. This is an updated version of an earlier Cochrane systematic review, first published in 2001 and updated in 2008. OBJECTIVES: To assess whether computerized advice on drug dosage has beneficial effects on patient outcomes compared with routine care (empiric dosing without computer assistance). SEARCH METHODS: The following databases were searched from 1996 to January 2012: EPOC Group Specialized Register, Reference Manager; Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Ovid; EMBASE, Ovid; and CINAHL, EbscoHost. A "top up" search was conducted for the period January 2012 to January 2013; these results were screened by the authors and potentially relevant studies are listed in Studies Awaiting Classification. The review authors also searched reference lists of relevant studies and related reviews. SELECTION CRITERIA: We included randomized controlled trials, non-randomized controlled trials, controlled before-and-after studies and interrupted time series analyses of computerized advice on drug dosage. The participants were healthcare professionals responsible for patient care. The outcomes were any objectively measured change in the health of patients resulting from computerized advice (such as therapeutic drug control, clinical improvement, adverse reactions). DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data and assessed study quality. We grouped the results from the included studies by drug used and the effect aimed at for aminoglycoside antibiotics, amitriptyline, anaesthetics, insulin, anticoagulants, ovarian stimulation, anti-rejection drugs and theophylline. We combined the effect sizes to give an overall effect for each subgroup of studies, using a random-effects model. We further grouped studies by type of outcome when appropriate (i.e. no evidence of heterogeneity). MAIN RESULTS: Forty-six comparisons (from 42 trials) were included (as compared with 26 comparisons in the last update) including a wide range of drugs in inpatient and outpatient settings. All were randomized controlled trials except two studies. Interventions usually targeted doctors, although some studies attempted to influence prescriptions by pharmacists and nurses. Drugs evaluated were anticoagulants, insulin, aminoglycoside antibiotics, theophylline, anti-rejection drugs, anaesthetic agents, antidepressants and gonadotropins. Although all studies used reliable outcome measures, their quality was generally low.This update found similar results to the previous update and managed to identify specific therapeutic areas where the computerized advice on drug dosage was beneficial compared with routine care:1. it increased target peak serum concentrations (standardized mean difference (SMD) 0.79, 95% CI 0.46 to 1.13) and the proportion of people with plasma drug concentrations within the therapeutic range after two days (pooled risk ratio (RR) 4.44, 95% CI 1.94 to 10.13) for aminoglycoside antibiotics;2. it led to a physiological parameter more often within the desired range for oral anticoagulants (SMD for percentage of time spent in target international normalized ratio +0.19, 95% CI 0.06 to 0.33) and insulin (SMD for percentage of time in target glucose range: +1.27, 95% CI 0.56 to 1.98);3. it decreased the time to achieve stabilization for oral anticoagulants (SMD -0.56, 95% CI -1.07 to -0.04);4. it decreased the thromboembolism events (rate ratio 0.68, 95% CI 0.49 to 0.94) and tended to decrease bleeding events for anticoagulants although the difference was not significant (rate ratio 0.81, 95% CI 0.60 to 1.08). It tended to decrease unwanted effects for aminoglycoside antibiotics (nephrotoxicity: RR 0.67, 95% CI 0.42 to 1.06) and anti-rejection drugs (cytomegalovirus infections: RR 0.90, 95% CI 0.58 to 1.40);5. it tended to reduce the length of time spent in the hospital although the difference was not significant (SMD -0.15, 95% CI -0.33 to 0.02) and to achieve comparable or better cost-effectiveness ratios than usual care;6. there was no evidence of differences in mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants.For all outcomes, statistical heterogeneity quantified by I(2) statistics was moderate to high. AUTHORS' CONCLUSIONS: This review update suggests that computerized advice for drug dosage has some benefits: it increases the serum concentrations for aminoglycoside antibiotics and improves the proportion of people for which the plasma drug is within the therapeutic range for aminoglycoside antibiotics.It leads to a physiological parameter more often within the desired range for oral anticoagulants and insulin. It decreases the time to achieve stabilization for oral anticoagulants. It tends to decrease unwanted effects for aminoglycoside antibiotics and anti-rejection drugs, and it significantly decreases thromboembolism events for anticoagulants. It tends to reduce the length of hospital stay compared with routine care while comparable or better cost-effectiveness ratios were achieved.However, there was no evidence that decision support had an effect on mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants. In addition, there was no evidence to suggest that some decision support technical features (such as its integration into a computer physician order entry system) or aspects of organization of care (such as the setting) could optimize the effect of computerized advice.Taking into account the high risk of bias of, and high heterogeneity between, studies, these results must be interpreted with caution.

Genetic variation in the dopamine D4 receptor (DRD4) gene and smoking cessation: follow-up of a randomised clinical trial of transdermal nicotine patch.

Smokers of European ancestry (n=720) who participated in a double-blind, randomised, placebo-controlled trial of transdermal nicotine replacement therapy, were genotyped for two functional polymorphisms (variable number of tandem repeats (VNTR) and a C to T transition at position -521 (C-521T)) in the dopamine D4 receptor gene (DRD4) gene. Logistic regression models of abstinence at 12- and 26-week follow-ups were carried out separately for each polymorphism. For the DRD4 VNTR models, the main effect of treatment was significant at both 12-week (P=0.001) and 26-week (P=0.006) follow-ups, indicating an increased likelihood of successful cessation on active nicotine replacement therapy transdermal patch relative to placebo. The main effect of DRD4 VNTR genotype was associated with abstinence at 12-week follow-up (P=0.034), with possession of one or more copies of the long allele associated with reduced likelihood of cessation (17 vs 23%), but this effect was not observed at 26-week follow-up. For the DRD4 C-521T models, no main effect or interaction terms involving genotype were retained in the models at either 12- or 26-week follow-up. These data are consistent with observations from studies of the DRD2 gene that genetic variants related to relatively decreased dopaminergic tone in the mesocorticolimbic system are associated with increased risk for relapse to smoking following a cessation attempt.

Association of the mu-opioid receptor gene with smoking cessation.

We investigated the association of the OPRM1 genotype with long-term smoking cessation and change in body mass index (BMI) following a smoking cessation attempt among smokers who attempted to quit using the nicotine replacement therapy (NRT) patch or placebo in a randomized controlled trial, and were followed-up over an 8-year period following their initial cessation attempt. We also investigated possible sex differences in these relationships, given evidence for sex differences in smoking cessation and central opioid mechanisms, as well as some evidence for sex differences in response to NRT. Our results indicate that OPRM1 genotype may moderate the effect of transdermal nicotine patch compared to placebo during active treatment, with a benefit of active NRT treatment evident in the OPRM1 AA genotype group only and those carrying one or more copies of the G allele demonstrating no benefit of active NRT versus placebo patch. Our results also indicate a sex difference in change in BMI at 8-year follow-up following a smoking cessation attempt, with ex-smokers demonstrating an increase in BMI, and this increase being greater in female subjects than in male subjects. We did not observe any association of OPRM1 genotype with change in BMI, although there was a trend for genotype to influence the observed sex difference in change in BMI over time. Future studies should attempt to replicate these findings, and investigate the relationship between both short- and long-term weight gain and smoking cessation and investigate possible mechanisms that may underlie these processes. Future studies should also investigate the role of OPRM1 genotype and smoking cessation on other appetitive and reward behaviours such as alcohol consumption.

Association between the DRD2 gene Taq1A (C32806T) polymorphism and alcohol consumption in social drinkers.

An association between the DRD2 Taq1A (C32806T) polymorphism and social alcohol consumption in the opposite direction to that reported for alcoholism has recently been reported in a male Finnish sample. We attempted to replicate these findings in two independent samples, and extend on previous work by including female participants. The DRD2 A1 allele was significantly associated with reduced alcohol consumption in sample one (P=0.004) and sample two (P=0.015). In sample two there was a significant genotype x sex interaction (P=0.016), with the association of the A1 allele and reduced alcohol consumption significant in men only. This interaction was marginally significant (P=0.042) in a meta-analysis of combined data from both samples, and the main effect of genotype highly significant (P<0.001). Age at time of data collection and cigarette consumption were entered as covariates in all analyses. These results replicate recent previous findings and suggest a possibility that this association may exist in men only, or be stronger in men.

Computerised advice on drug dosage to improve prescribing practice.

BACKGROUND: Maintaining therapeutic concentrations of toxic drugs is a complex task. Several computer systems have been designed to help doctors determine optimum drug dosage. Significant improvements in health could be achieved if computer advice was shown to be beneficial. OBJECTIVES: To assess whether computer support for drug dosage benefits patients and hence whether it should be more widely available. SEARCH STRATEGY: We searched the Cochrane Effective Practice and Organisation of Care Group specialised register (June 1996), MEDLINE (1966 to June 1996), EMBASE (1980 to June 1996), hand searched the journal Therapeutic Drug Monitoring (1979 to June 1996), reference lists of articles and contacted experts in the field. SELECTION CRITERIA: Randomised trials, interrupted time series and controlled before and after studies of computerised advice on drug dosage. The participants were health professionals responsible for patient care. The outcomes were: any objectively measured change in the behaviour of the health care provider (such as changes in the dose of drug used); any change in the health of patients, resulting from computer support (such as adverse reactions to drugs). DATA COLLECTION AND ANALYSIS: Two reviewers independently extracted data and assessed study quality. MAIN RESULTS: Fifteen trials involving 1229 patients were included. The drugs studied were theophylline, warfarin, heparin, aminoglycosides, nitroprusside, lignocaine, oxytocin, fentanyl and midazolam. Interventions usually targeted doctors although some studies attempted to influence prescribing by pharmacists and nurses. All included studies took place on acute medical conditions in hospital settings. Although all studies used reliable outcome measures, sample size was often small and only two studies reported a sample size calculation. Computer support for drug dosage gave significant benefits reducing: 1. The time to achieve therapeutic control (standardised mean difference -0.44, 95% CI -0.70 to -0.17); 2. Toxic drug levels (risk difference -0.12, 95% CI -0.24 to -0.01); 3. Adverse reactions (risk difference -0.06, 95% CI -0.12 to 0.00); 4. Length of hospital stay (standardised mean difference -0.32, 95% CI -0.60 to -0.04). There was a tendency for computer support to result in higher doses of drugs, although this did not reach statistical significance. REVIEWER'S CONCLUSIONS: This systematic review provides evidence to support the use of computer assistance in determining drug dosage. Further clinical trials are necessary to determine whether the benefits seen in specialist applications can be realised in general use.

Association between polymorphisms in dopamine metabolic enzymes and tobacco consumption in smokers.

Central dopaminergic reward pathways give rise to dependence and are activated by nicotine. Allelic variants in genes involved in dopamine metabolism may therefore influence the amount of tobacco consumed by smokers. We developed assays for polymorphisms in dopamine beta-hydroxylase (DBH), monoamine oxidase (MAO) and catechol O-methyl transferase (COMT) using the polymerase chain reaction with sequence specific primers (PCR-SSP). We then typed 225 cigarette smokers to assess whether genotype was related to the number of cigarettes smoked a day. Smokers with DBH 1368 GG genotype smoked fewer cigarettes than those with GA/AA [mean difference -2.9 cigarettes, 95% confidence interval (CI) -5.5, -0.4; P = 0.022]. The effect reached statistical significance in women (-3.8, 95% CI -6.4, -1.0, P = 0.007) but not in men (-1.5, 95% CI -6.0, 3.0, P = 0.498). Overall, the effect was greater when analysis was confined to Caucasians (-3.8, 95% CI -6.6, -1.1, P = 0.007). Smokers with MAO-A 1460 TT/TO smoked more cigarettes than those with CC/CT/CO (2.9, 95% CI 0.6, 5.1, P = 0.013). Within each sex group, the trend was similar but not statistically significant (difference for men 2.9, 95% CI -1.0, 6.7; for women 2.0, 95% CI -0.7, 4.8). The effect of the allele was greater in smokers with a high body mass index (> 26) (difference 5.1, 95% CI 1.4, 8.8, P = 0.008). More heavy smokers (> 20 a day) had the DBH 1368A allele when compared to light smokers (< 10 a day). (Relative risk 2.3, 95% CI 1.1, 5.0, P = 0.024.) The trend for increasing prevalence of the DBH A allele in heavy smokers was greater when analysis was restricted to Caucasians (relative risk 3.2, 95% CI 1.3, 8.2, P = 0.004). Conversely, heavy smokers were less likely to have the MAO-A 1460C allele (relative risk 0.3, 95% CI 0.1, 0.7, P = 0.012). Variations in DBH and MAO predict whether a person is a heavy smoker and how many cigarettes they consume. Our results support the view that these enzymes help to determine a smoker's requirement for nicotine and may explain why some people are predisposed to tobacco addiction and why some find it very difficult to stop smoking. This finding has important implications for smoking prevention and offers potential for developing patient-specific therapy for smoking cessation.

Evaluation of computer support for prescribing (CAPSULE) using simulated cases.

OBJECTIVE: To evaluate the potential effect of computer support on general practitioners' prescribing, and to compare the effectiveness of three different support levels. DESIGN: Crossover experiment with balanced block design. SUBJECTS: Random sample of 50 general practitioners (42 agreed to participate) from 165 in a geographically defined area of Oxfordshire. INTERVENTIONS: Doctors prescribed for 36 simulated cases constructed from real consultations. Levels of computer support were control (alphabetical list of drugs), limited support (list of preferred drugs), and full support (the same list with explanations available for suggestions). MAIN OUTCOME MEASURES: Percentage of cases where doctors ignored a cheaper, equally effective drug; prescribing score (a measure of how closely prescriptions matched expert recommendations); interview to elicit doctors' views of support system. RESULTS: Computer support significantly improved the quality of prescribing. Doctors ignored a cheaper, equally effective drug in a median 50% (range 25%-75%) of control cases, compared with 36% (8%-67%) with limited support and 35% (0-67%) with full support (P < 0.001). The median prescribing score rose from 6.0 units (4.2-7.0) with control support to 6.8 (5.8 to 7.7) and 6.7 (5.6 to 7.8) with limited and full support (P < 0.001). Of 41 doctors, 36 (88%) found the system easy to use and 24 (59%) said they would be likely to use it in practice. CONCLUSIONS: Computer support improved compliance with prescribing guidelines, reducing the occasions when doctors ignored a cheaper, equally effective drug. The system was easy to operate, and most participating doctors would be likely to use it in practice.