Interventions to prevent obesity in children aged 12 to 18 years old.

BACKGROUND: Prevention of obesity in adolescents is an international public health priority. The prevalence of overweight and obesity is over 25% in North and South America, Australia, most of Europe, and the Gulf region. Interventions that aim to prevent obesity involve strategies that promote healthy diets or 'activity' levels (physical activity, sedentary behaviour and/or sleep) or both, and work by reducing energy intake and/or increasing energy expenditure, respectively. There is uncertainty over which approaches are more effective, and numerous new studies have been published over the last five years since the previous version of this Cochrane Review. OBJECTIVES: To assess the effects of interventions that aim to prevent obesity in adolescents by modifying dietary intake or 'activity' levels, or a combination of both, on changes in BMI, zBMI score and serious adverse events. SEARCH METHODS: We used standard, extensive Cochrane search methods. The latest search date was February 2023. SELECTION CRITERIA: Randomised controlled trials in adolescents (mean age 12 years and above but less than 19 years), comparing diet or 'activity' interventions (or both) to prevent obesity with no intervention, usual care, or with another eligible intervention, in any setting. Studies had to measure outcomes at a minimum of 12 weeks post baseline. We excluded interventions designed primarily to improve sporting performance. DATA COLLECTION AND ANALYSIS: We used standard Cochrane methods. Our outcomes were BMI, zBMI score and serious adverse events, assessed at short- (12 weeks to < 9 months from baseline), medium- (9 months to < 15 months) and long-term (≥ 15 months) follow-up. We used GRADE to assess the certainty of the evidence for each outcome. MAIN RESULTS: This review includes 74 studies (83,407 participants); 54 studies (46,358 participants) were included in meta-analyses. Sixty studies were based in high-income countries. The main setting for intervention delivery was schools (57 studies), followed by home (nine studies), the community (five studies) and a primary care setting (three studies). Fifty-one interventions were implemented for less than nine months; the shortest was conducted over one visit and the longest over 28 months. Sixty-two studies declared non-industry funding; five were funded in part by industry. Dietary interventions versus control The evidence is very uncertain about the effects of dietary interventions on body mass index (BMI) at short-term follow-up (mean difference (MD) -0.18, 95% confidence interval (CI) -0.41 to 0.06; 3 studies, 605 participants), medium-term follow-up (MD -0.65, 95% CI -1.18 to -0.11; 3 studies, 900 participants), and standardised BMI (zBMI) at long-term follow-up (MD -0.14, 95% CI -0.38 to 0.10; 2 studies, 1089 participants); all very low-certainty evidence. Compared with control, dietary interventions may have little to no effect on BMI at long-term follow-up (MD -0.30, 95% CI -1.67 to 1.07; 1 study, 44 participants); zBMI at short-term (MD -0.06, 95% CI -0.12 to 0.01; 5 studies, 3154 participants); and zBMI at medium-term (MD 0.02, 95% CI -0.17 to 0.21; 1 study, 112 participants) follow-up; all low-certainty evidence. Dietary interventions may have little to no effect on serious adverse events (two studies, 377 participants; low-certainty evidence). Activity interventions versus control Compared with control, activity interventions do not reduce BMI at short-term follow-up (MD -0.64, 95% CI -1.86 to 0.58; 6 studies, 1780 participants; low-certainty evidence) and probably do not reduce zBMI at medium- (MD 0, 95% CI -0.04 to 0.05; 6 studies, 5335 participants) or long-term (MD -0.05, 95% CI -0.12 to 0.02; 1 study, 985 participants) follow-up; both moderate-certainty evidence. Activity interventions do not reduce zBMI at short-term follow-up (MD 0.02, 95% CI -0.01 to 0.05; 7 studies, 4718 participants; high-certainty evidence), but may reduce BMI slightly at medium-term (MD -0.32, 95% CI -0.53 to -0.11; 3 studies, 2143 participants) and long-term (MD -0.28, 95% CI -0.51 to -0.05; 1 study, 985 participants) follow-up; both low-certainty evidence. Seven studies (5428 participants; low-certainty evidence) reported data on serious adverse events: two reported injuries relating to the exercise component of the intervention and five reported no effect of intervention on reported serious adverse events. Dietary and activity interventions versus control Dietary and activity interventions, compared with control, do not reduce BMI at short-term follow-up (MD 0.03, 95% CI -0.07 to 0.13; 11 studies, 3429 participants; high-certainty evidence), and probably do not reduce BMI at medium-term (MD 0.01, 95% CI -0.09 to 0.11; 8 studies, 5612 participants; moderate-certainty evidence) or long-term (MD 0.06, 95% CI -0.04 to 0.16; 6 studies, 8736 participants; moderate-certainty evidence) follow-up. They may have little to no effect on zBMI in the short term, but the evidence is very uncertain (MD -0.09, 95% CI -0.2 to 0.02; 3 studies, 515 participants; very low-certainty evidence), and they may not reduce zBMI at medium-term (MD -0.05, 95% CI -0.1 to 0.01; 6 studies, 3511 participants; low-certainty evidence) or long-term (MD -0.02, 95% CI -0.05 to 0.01; 7 studies, 8430 participants; low-certainty evidence) follow-up. Four studies (2394 participants) reported data on serious adverse events (very low-certainty evidence): one reported an increase in weight concern in a few adolescents and three reported no effect. AUTHORS' CONCLUSIONS: The evidence demonstrates that dietary interventions may have little to no effect on obesity in adolescents. There is low-certainty evidence that activity interventions may have a small beneficial effect on BMI at medium- and long-term follow-up. Diet plus activity interventions may result in little to no difference. Importantly, this updated review also suggests that interventions to prevent obesity in this age group may result in little to no difference in serious adverse effects. Limitations of the evidence include inconsistent results across studies, lack of methodological rigour in some studies and small sample sizes. Further research is justified to investigate the effects of diet and activity interventions to prevent childhood obesity in community settings, and in young people with disabilities, since very few ongoing studies are likely to address these. Further randomised trials to address the remaining uncertainty about the effects of diet, activity interventions, or both, to prevent childhood obesity in schools (ideally with zBMI as the measured outcome) would need to have larger samples.

Strategies for enhancing the implementation of school-based policies or practices targeting diet, physical activity, obesity, tobacco or alcohol use.

BACKGROUND: Several school-based interventions are effective in improving child diet and physical activity, and preventing excessive weight gain, and tobacco or harmful alcohol use. However, schools are frequently unsuccessful in implementing such evidence-based interventions. OBJECTIVES: 1. To evaluate the benefits and harms of strategies aiming to improve school implementation of interventions to address student diet, physical activity, tobacco or alcohol use, and obesity. 2. To evaluate the benefits and harms of strategies to improve intervention implementation on measures of student diet, physical activity, obesity, tobacco use or alcohol use; describe their cost or cost-effectiveness; and any harms of strategies on schools, school staff or students. SEARCH METHODS: We used standard, extensive Cochrane search methods. The latest search was between 1 September 2016 and 30 April 2021 to identify any relevant trials published since the last published review. SELECTION CRITERIA: We defined 'Implementation' as the use of strategies to adopt and integrate evidence-based health interventions and to change practice patterns within specific settings. We included any trial (randomised controlled trial (RCT) or non-randomised controlled trial (non-RCT)) conducted at any scale, with a parallel control group that compared a strategy to implement policies or practices to address diet, physical activity, overweight or obesity, tobacco or alcohol use by students to 'no intervention', 'usual' practice or a different implementation strategy. DATA COLLECTION AND ANALYSIS: We used standard Cochrane methods. Given the large number of outcomes reported, we selected and included the effects of a single outcome measure for each trial for the primary (implementation) and secondary (student health behaviour and obesity) outcomes using a decision hierarchy. Where possible, we calculated standardised mean differences (SMDs) to account for variable outcome measures with 95% confidence intervals (CI). For RCTs, we conducted meta-analyses of primary and secondary outcomes using a random-effects model, or in instances where there were between two and five studies, a fixed-effect model. The synthesis of the effects for non-randomised studies followed the 'Synthesis without meta-analysis' (SWiM) guidelines. MAIN RESULTS: We included an additional 11 trials in this update bringing the total number of included studies in the review to 38. Of these, 22 were conducted in the USA. Twenty-six studies used RCT designs. Seventeen trials tested strategies to implement healthy eating, 12 physical activity and six a combination of risk factors. Just one trial sought to increase the implementation of interventions to delay initiation or reduce the consumption of alcohol. All trials used multiple implementation strategies, the most common being educational materials, educational outreach and educational meetings. The overall certainty of evidence was low and ranged from very low to moderate for secondary review outcomes. Pooled analyses of RCTs found, relative to a control, the use of implementation strategies may result in a large increase in the implementation of interventions in schools (SMD 1.04, 95% CI 0.74 to 1.34; 22 RCTs, 1917 participants; low-certainty evidence). For secondary outcomes we found, relative to control, the use of implementation strategies to support intervention implementation may result in a slight improvement on measures of student diet (SMD 0.08, 95% CI 0.02 to 0.15; 11 RCTs, 16,649 participants; low-certainty evidence) and physical activity (SMD 0.09, 95% CI -0.02 to 0.19; 9 RCTs, 16,389 participants; low-certainty evidence). The effects on obesity probably suggest little to no difference (SMD -0.02, 95% CI -0.05 to 0.02; 8 RCTs, 18,618 participants; moderate-certainty evidence). The effects on tobacco use are very uncertain (SMD -0.03, 95% CIs -0.23 to 0.18; 3 RCTs, 3635 participants; very low-certainty evidence). One RCT assessed measures of student alcohol use and found strategies to support implementation may result in a slight increase in use (odds ratio 1.10, 95% CI 0.77 to 1.56; P = 0.60; 2105 participants). Few trials reported the economic evaluations of implementation strategies, the methods of which were heterogeneous and evidence graded as very uncertain. A lack of consistent terminology describing implementation strategies was an important limitation of the review. AUTHORS' CONCLUSIONS: The use of implementation strategies may result in large increases in implementation of interventions, and slight improvements in measures of student diet, and physical activity. Further research is required to assess the impact of implementation strategies on such behavioural- and obesity-related outcomes, including on measures of alcohol use, where the findings of one trial suggest it may slightly increase student risk. Given the low certainty of the available evidence for most measures further research is required to guide efforts to facilitate the translation of evidence into practice in this setting.

Real-time video counselling for smoking cessation.

BACKGROUND: Real-time video communication software such as Skype and FaceTime transmits live video and audio over the Internet, allowing counsellors to provide support to help people quit smoking. There are more than four billion Internet users worldwide, and Internet users can download free video communication software, rendering a video counselling approach both feasible and scalable for helping people to quit smoking. OBJECTIVES: To assess the effectiveness of real-time video counselling delivered individually or to a group in increasing smoking cessation, quit attempts, intervention adherence, satisfaction and therapeutic alliance, and to provide an economic evaluation regarding real-time video counselling. SEARCH METHODS: We searched the Cochrane Tobacco Addiction Group Specialised Register, CENTRAL, MEDLINE, PubMed, PsycINFO and Embase to identify eligible studies on 13 August 2019. We searched the World Health Organization International Clinical Trials Registry Platform and ClinicalTrials.gov to identify ongoing trials registered by 13 August 2019. We checked the reference lists of included articles and contacted smoking cessation researchers for any additional studies. SELECTION CRITERIA: We included randomised controlled trials (RCTs), randomised trials, cluster RCTs or cluster randomised trials of real-time video counselling for current tobacco smokers from any setting that measured smoking cessation at least six months following baseline. The real-time video counselling intervention could be compared with a no intervention control group or another smoking cessation intervention, or both. DATA COLLECTION AND ANALYSIS: Two authors independently extracted data from included trials, assessed the risk of bias and rated the certainty of the evidence using the GRADE approach. We performed a random-effects meta-analysis for the primary outcome of smoking cessation, using the most stringent measure of smoking cessation measured at the longest follow-up. Analysis was based on the intention-to-treat principle. We considered participants with missing data at follow-up for the primary outcome of smoking cessation to be smokers. MAIN RESULTS: We included two randomised trials with 615 participants. Both studies delivered real-time video counselling for smoking cessation individually, compared with telephone counselling. We judged one study at unclear risk of bias and one study at high risk of bias. There was no statistically significant treatment effect for smoking cessation (using the strictest definition and longest follow-up) across the two included studies when real-time video counselling was compared to telephone counselling (risk ratio (RR) 2.15, 95% confidence interval (CI) 0.38 to 12.04; 2 studies, 608 participants; I2 = 66%). We judged the overall certainty of the evidence for smoking cessation as very low due to methodological limitations, imprecision in the effect estimate reflected by the wide 95% CIs and inconsistency of cessation rates. There were no significant differences between real-time video counselling and telephone counselling reported for number of quit attempts among people who continued to smoke (mean difference (MD) 0.50, 95% CI -0.60 to 1.60; 1 study, 499 participants), mean number of counselling sessions completed (MD -0.20, 95% CI -0.45 to 0.05; 1 study, 566 participants), completion of all sessions (RR 1.13, 95% CI 0.71 to 1.79; 1 study, 43 participants) or therapeutic alliance (MD 1.13, 95% CI -0.24 to 2.50; 1 study, 398 participants). Participants in the video counselling arm were more likely than their telephone counselling counterparts to recommend the programme to a friend or family member (RR 1.06, 95% CI 1.01 to 1.11; 1 study, 398 participants); however, there were no between-group differences on satisfaction score (MD 0.70, 95% CI -1.16 to 2.56; 1 study, 29 participants). AUTHORS' CONCLUSIONS: There is very little evidence about the effectiveness of real-time video counselling for smoking cessation. The existing research does not suggest a difference between video counselling and telephone counselling for assisting people to quit smoking. However, given the very low GRADE rating due to methodological limitations in the design, imprecision of the effect estimate and inconsistency of cessation rates, the smoking cessation results should be interpreted cautiously. High-quality randomised trials comparing real-time video counselling to telephone counselling are needed to increase the confidence of the effect estimate. Furthermore, there is currently no evidence comparing real-time video counselling to a control group. Such research is needed to determine whether video counselling increases smoking cessation.

Economic evaluation of a healthy lifestyle intervention for chronic low back pain: A randomized controlled trial.

BACKGROUND: Economic evaluations which estimate cost-effectiveness of potential treatments can guide decisions about real-world healthcare services. We performed an economic evaluation of a healthy lifestyle intervention targeting weight loss, physical activity and diet for patients with chronic low back pain, who are overweight or obese. METHODS: Eligible patients with chronic low back pain (n = 160) were randomized to an intervention or usual care control group. The intervention included brief advice, a clinical consultation and referral to a 6-month telephone-based healthy lifestyle coaching service. The primary outcome was quality-adjusted life years (QALYs). Secondary outcomes were pain intensity, disability, weight and body mass index. Costs included intervention costs, healthcare utilization costs and work absenteeism costs. An economic analysis was performed from the societal perspective. RESULTS: Mean total costs were lower in the intervention group than the control group (-$614; 95%CI: -3133 to 255). The intervention group had significantly lower healthcare costs (-$292; 95%CI: -872 to -33), medication costs (-$30; 95%CI: -65 to -4) and absenteeism costs (-$1,000; 95%CI: -3573 to -210). For all outcomes, the intervention was on average less expensive and more effective than usual care, and the probability of the intervention being cost-effective compared to usual care was relatively high (i.e., 0.81) at a willingness-to-pay of $0/unit of effect. However, the probability of cost-effectiveness was not as favourable among sensitivity analyses. CONCLUSIONS: The healthy lifestyle intervention seems to be cost-effective from the societal perspective. However, variability in the sensitivity analyses indicates caution is needed when interpreting these findings. SIGNIFICANCE: This is an economic evaluation of a randomized controlled trial of a healthy lifestyle intervention for chronic low back pain. The findings suggest that a healthy lifestyle intervention may be cost-effective relative to usual care.

Economic evaluation of telephone-based weight loss support for patients with knee osteoarthritis: a randomised controlled trial.

BACKGROUND: The prevalence of knee osteoarthritis is increasing worldwide. Obesity is an important modifiable risk factor for both the incidence and progression of knee osteoarthritis. Consequently, international guidelines recommend all patients with knee osteoarthritis who are overweight receive support to lose weight. However, few overweight patients with this condition receive care to support weight loss. Telephone-based interventions are one potential solution to provide scalable care to the many patients with knee osteoarthritis. The objective of this study is to evaluate, from a societal perspective, the cost-utility and cost-effectiveness of a telephone-based weight management and healthy lifestyle service for patients with knee osteoarthritis, who are overweight or obese. METHODS: An economic evaluation was undertaken alongside a pragmatic randomised controlled trial. Between May 19 and June 30, 2015, 120 patients with knee osteoarthritis were randomly assigned to an intervention or usual care control group in a 1:1 ratio. Participants in the intervention group received a referral to an existing non-disease specific 6-month telephone-based weight management and healthy lifestyle service. Quality-adjusted life years (QALYs) was the utility measure and knee pain intensity, disability, weight, and body mass index (BMI) were the clinical measures of effect. Costs included intervention costs, healthcare utilisation costs (healthcare services and medication use) and absenteeism costs due to knee pain. Data was collected at baseline, 6 weeks and 26 weeks. The primary cost-effectiveness analysis was performed from the societal perspective. RESULTS: Mean cost differences between groups (intervention minus control) were $493 (95%CI: -3513 to 5363) for healthcare costs, $-32 (95%CI: -73 to 13) for medication costs, and $125 (95%CI: -151 to 486) for absenteeism costs. The total mean difference in societal costs was $1197 (95%CI: -2887 to 6106). For QALYs and all clinical measures of effect, the probability of the intervention being cost-effective compared with usual care was less than 0.36 at all willingness-to-pay values. CONCLUSIONS: From a societal perspective, telephone-based weight loss support, provided using an existing non-disease specific 6-month weight management and healthy lifestyle service was not cost-effective in comparison with usual care for overweight and obese patients with knee osteoarthritis. TRIAL REGISTRATION NUMBER: ACTRN12615000490572 , registered 18th May 2015.

Association between Australian adolescent alcohol use and alcohol use risk and protective factors in 2011 and 2014.

INTRODUCTION AND AIMS: Changes in risk and protective factors of adolescent alcohol use may be contributing to the recent decline in Australian adolescents alcohol use. The study aimed to determine the: (i) prevalence of alcohol use, risk and protective factors in 2011 and 2014; and (ii) association between alcohol use and risk and protective factors in 2011 and 2014. DESIGN AND METHODS: A repeat cross-sectional study was conducted. Grade 9-10 (aged 15-17 years) students from 32 Australian secondary schools were sampled in 2011 and 2014. A self-report survey collected data regarding alcohol use (ever, recent, 'binge drinking'), risk factors (e.g. alcohol use/permissive attitude to alcohol by friends/siblings/parents) and protective factors (e.g. self-efficacy; school/home/community support; peer caring relationships). Descriptive statistics were used to determine differences in alcohol use, risk and protective factors between 2011 and 2014. Adjusted multivariable logistic regression analyses examined associations between alcohol use, risk and protective factors separately in 2011 and 2014 (six models). RESULTS: Fewer adolescents reported alcohol use in 2014 compared with 2011 (ever: 56.6% vs. 67.9%, recent: 17.3% vs. 21.2%, 'binge drinking': 20.0% vs. 23.5%; 2011: n = 4366; 2014: n = 5199). Significant differences between 2014 and 2011 were found for some risk (five lower; one higher) and protective factors (four lower). Risk factors that were significantly lower in 2014 compared to 2011 were amongst variables with the strongest associations with alcohol use. DISCUSSION AND CONCLUSIONS: The strength of associations with alcohol use, and decrease in the prevalence of certain risk factors in 2014 compared to 2011, suggests such factors may be contributing to the decline in adolescent alcohol use.

Strategies for enhancing the implementation of school-based policies or practices targeting risk factors for chronic disease.

BACKGROUND: A number of school-based policies or practices have been found to be effective in improving child diet and physical activity, and preventing excessive weight gain, tobacco or harmful alcohol use. Schools, however, frequently fail to implement such evidence-based interventions. OBJECTIVES: The primary aims of the review are to examine the effectiveness of strategies aiming to improve the implementation of school-based policies, programs or practices to address child diet, physical activity, obesity, tobacco or alcohol use.Secondary objectives of the review are to: Examine the effectiveness of implementation strategies on health behaviour (e.g. fruit and vegetable consumption) and anthropometric outcomes (e.g. BMI, weight); describe the impact of such strategies on the knowledge, skills or attitudes of school staff involved in implementing health-promoting policies, programs or practices; describe the cost or cost-effectiveness of such strategies; and describe any unintended adverse effects of strategies on schools, school staff or children. SEARCH METHODS: All electronic databases were searched on 16 July 2017 for studies published up to 31 August 2016. We searched the following electronic databases: Cochrane Library including the Cochrane Central Register of Controlled Trials (CENTRAL); MEDLINE; MEDLINE In-Process & Other Non-Indexed Citations; Embase Classic and Embase; PsycINFO; Education Resource Information Center (ERIC); Cumulative Index to Nursing and Allied Health Literature (CINAHL); Dissertations and Theses; and SCOPUS. We screened reference lists of all included trials for citations of other potentially relevant trials. We handsearched all publications between 2011 and 2016 in two specialty journals (Implementation Science and Journal of Translational Behavioral Medicine) and conducted searches of the WHO International Clinical Trials Registry Platform (ICTRP) (http://apps.who.int/trialsearch/) as well as the US National Institutes of Health registry (https://clinicaltrials.gov). We consulted with experts in the field to identify other relevant research. SELECTION CRITERIA: 'Implementation' was defined as the use of strategies to adopt and integrate evidence-based health interventions and to change practice patterns within specific settings. We included any trial (randomised or non-randomised) conducted at any scale, with a parallel control group that compared a strategy to implement policies or practices to address diet, physical activity, overweight or obesity, tobacco or alcohol use by school staff to 'no intervention', 'usual' practice or a different implementation strategy. DATA COLLECTION AND ANALYSIS: Citation screening, data extraction and assessment of risk of bias was performed by review authors in pairs. Disagreements between review authors were resolved via consensus, or if required, by a third author. Considerable trial heterogeneity precluded meta-analysis. We narratively synthesised trial findings by describing the effect size of the primary outcome measure for policy or practice implementation (or the median of such measures where a single primary outcome was not stated). MAIN RESULTS: We included 27 trials, 18 of which were conducted in the USA. Nineteen studies employed randomised controlled trial (RCT) designs. Fifteen trials tested strategies to implement healthy eating policies, practice or programs; six trials tested strategies targeting physical activity policies or practices; and three trials targeted tobacco policies or practices. Three trials targeted a combination of risk factors. None of the included trials sought to increase the implementation of interventions to delay initiation or reduce the consumption of alcohol. All trials examined multi-strategic implementation strategies and no two trials examined the same combinations of implementation strategies. The most common implementation strategies included educational materials, educational outreach and educational meetings. For all outcomes, the overall quality of evidence was very low and the risk of bias was high for the majority of trials for detection and performance bias.Among 13 trials reporting dichotomous implementation outcomes-the proportion of schools or school staff (e.g. classes) implementing a targeted policy or practice-the median unadjusted (improvement) effect sizes ranged from 8.5% to 66.6%. Of seven trials reporting the percentage of a practice, program or policy that had been implemented, the median unadjusted effect (improvement), relative to the control ranged from -8% to 43%. The effect, relative to control, reported in two trials assessing the impact of implementation strategies on the time per week teachers spent delivering targeted policies or practices ranged from 26.6 to 54.9 minutes per week. Among trials reporting other continuous implementation outcomes, findings were mixed. Four trials were conducted of strategies that sought to achieve implementation 'at scale', that is, across samples of at least 50 schools, of which improvements in implementation were reported in three trials.The impact of interventions on student health behaviour or weight status were mixed. Three of the eight trials with physical activity outcomes reported no significant improvements. Two trials reported reductions in tobacco use among intervention relative to control. Seven of nine trials reported no between-group differences on student overweight, obesity or adiposity. Positive improvements in child dietary intake were generally reported among trials reporting these outcomes. Three trials assessed the impact of implementation strategies on the attitudes of school staff and found mixed effects. Two trials specified in the study methods an assessment of potential unintended adverse effects, of which, they reported none. One trial reported implementation support did not significantly increase school revenue or expenses and another, conducted a formal economic evaluation, reporting the intervention to be cost-effective. Trial heterogeneity, and the lack of consistent terminology describing implementation strategies, were important limitations of the review. AUTHORS' CONCLUSIONS: Given the very low quality of the available evidence, it is uncertain whether the strategies tested improve implementation of the targeted school-based policies or practices, student health behaviours, or the knowledge or attitudes of school staff. It is also uncertain if strategies to improve implementation are cost-effective or if they result in unintended adverse consequences. Further research is required to guide efforts to facilitate the translation of evidence into practice in this setting.

A school-based resilience intervention to decrease tobacco, alcohol and marijuana use in high school students.

BACKGROUND: Despite schools theoretically being an ideal setting for accessing adolescents and preventing initiation of substance use, there is limited evidence of effective interventions in this setting. Resilience theory provides one approach to achieving such an outcome through improving adolescent mental well-being and resilience. A study was undertaken to examine the potential effectiveness of such an intervention approach in improving adolescent resilience and protective factor scores; and reducing the prevalence of adolescent tobacco, alcohol and marijuana use in three high schools. METHODS: A non-controlled before and after study was undertaken. Data regarding student resilience and protective factors, and measures of tobacco, alcohol and marijuana use were collected from grade 7 to 10 students at baseline (n = 1449) and one year following a three year intervention (n = 1205). RESULTS: Significantly higher resilience and protective factors scores, and significantly lower prevalence of substance use were evident at follow up. CONCLUSIONS: The results suggest that the intervention has the potential to increase resilience and protective factors, and to decrease the use of tobacco, alcohol and marijuana by adolescents. Further more rigorous research is required to confirm this potential.