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Record W390574 · doi:10.1002/cl2.135

PROTOCOL: Effect of early, brief computerized interventions on risky alcohol use and risky cannabis use among young people: protocol for a systematic review

2014· review· en· W390574 on OpenAlexaff
Sabine Wollscheid, Lin Fang, Wendy Nilsen, Geir Smedslund, Asbjørn Steiro, Karianne Thune Hammerstrøm, Lillebeth Larun

Bibliographic record

VenueCampbell Systematic Reviews · 2014
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychological interventionBinge drinkingPublic healthEnvironmental healthCannabisLaw enforcementMedicinePoison controlSuicide preventionPsychiatryPolitical scienceLawNursing

Abstract

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Risky use of alcohol or recreational drugs among young people remains a prominent public health issue (United Nations, 2003; United Nations Office on Drugs and Crime, 2010b). The United Nations Office on Drugs and Crime (UNODC) has argued for a public health approach to prevent alcohol and recreational drug abuse, using interventions that provide assistance and counselling. This approach provides services at an early stage to drug and alcohol users who are at risk but who remain socially included (United Nations, 2003). Many countries have made substantial efforts at multiple levels ranging from government policy initiatives to primary health care services in an attempt to minimise the long-term consequences of alcohol and cannabis use. For example, Roche and Freeman (2012) have illustrated the advantages of implementing screening, and of brief, early interventions for young people with alcohol and drug problems. However, risky use of alcohol and recreational drugs remains a prominent health problem. Alcohol misuse presents a substantial societal burden due to the costs related to health care, prevention, crime, law enforcement and welfare assistance, as well as the costs resulting from reduced productivity and increased mortality (Thavorncharoensap, Teerawattananon, Yothasamut, Lertpitakpong, & Chaikledkaew, 2009). The WHO Global Survey on Alcohol and Health (2008) found a trend towards increased drinking among young people aged 18-25 years over the last five years, with an 80 percent increase in risky alcohol consumption in the participating countries (WHO, 2011). Risky and harmful drinking patterns such as binge drinking and drinking to intoxication have also increased over time among young people (Editorial Lancet, 2008; The National Center on Addiction and Substance Abuse at Columbia University, 2007). Small to moderate levels of alcohol might not be harmful, but high consumption of alcohol is directly related to risky behaviours such as intoxicated driving (Cherpitel, Ye, Bond, & Borges, 2003) and interpersonal violence (Foran & O'Leary, 2012). In addition, binge drinking can have both short- and long-term negative impacts on an individual's health. Lopez-Caneda et al. (2013), for instance, found an association between binge drinking and anomalous neural activity related to working memory processes, and college students who binge drink have been shown to have a higher risk of developing alcohol dependence in later years (Jennison, 2004). Cannabis is the most widely used and trafficked illicit drug worldwide (United Nations Office on Drugs and Crime, 2012). Cannabis is a general term to describe the psychoactive preparations of the plant Cannabis sativa. While marijuana refers to the cannabis leaves or other crude plant material, the term hashish describes the drug produced by drying the resin exuded by the marijuana plant (Brecher, 1972). Cannabis is commonly smoked, with or without being mixed with tobacco – but can also be consumed orally. The United Nations Office on Drugs and Crime (UNODC) has estimated that between 2.9 to 4.3 percent of the world population aged 15-64 (between 129 and 191 million people) used cannabis at least once in 2008 (United Nations Office on Drugs and Crime, 2010a). Whereas the use of inhalants (defined as substances producing chemical vapours such as found in beauty products like hairspray) is more commonly used among young people and might decline with age, the use of marijuana and hashish increases with age (Mosher, Rotolo, Phillips, Krupski, & Stark, 2004). Cannabis use has increased in the U.S. since 2007 in the 13 to 18 year age group and daily marijuana use reached a 30-year peak among high school students aged 17-18 years in 2011 (Johnston, O'Malley, Bachman, & Schulenberg, 2012). Regular cannabis use among young people presents a social burden due to costs arising from increased health care use and a higher risk for school-drop out. Although there appears to be no standard measure for 'low' or 'high' frequencies of cannabis use, we define 'high users of cannabis' according to the literature as users with at least weekly consumption of the drug (Lev-Ran, Le Foll, McKenzie, George, & Rehm, 2013). It has been reported that regular cannabis use among young people may have a negative impact on their cognitive functioning (Ramaekers, Berghaus, van Laar, & Drummer, 2004; Ramaekers et al., 2006), and on the developing brain (Pope, Grubera, Hudsona, Cohanea, & Yurgelun-Toddb, 2003). The current literature suggests a positive association between frequency of cannabis use and the risk of developing a mental illness, especially psychotic disorders (Løberg et al., 2012). On the other hand, there is some evidence that cannabis use can have a positive impact on creativity (Schafer et al., 2012), and is helpful in the treatment of certain ailments such as cancer (Robson, 2001) and multiple sclerosis (Iskedjiana, Berezaa, Gordonc, Piwkoa, & Einarsona, 2007). Other risk factors including unprotected sexual behaviour and risky driving behaviour have been found to be associated with increased use of alcohol (Jennison, 2004; Karam, Kypri, & Salamoun, 2007; J. Miller, Naimi, Brewer, & Jones, 2007) and of cannabis (Anderson & Stein, 2011; Hall & Degenhardt, 2009). In addition, the development of substance abuse disorders in later life has been found associated with the use of alcohol (Odgers et al., 2008) and cannabis (Behrendt, Wittchen, Hofler, Lieb, & Beesdo, 2009) during youth. Thus, there is a need for early interventions to reduce or eliminate the use of alcohol and cannabis among young people in order to prevent them from falling into a downward spiral that may lead to substance abuse related behaviours and ailments in adulthood. Brief interventions have the singular focus of targeting problematic behavior in a systematic and specific manner (O'Leary & Monti, 2004). For the purpose of this review, brief interventions are defined as follows: any preventive or therapeutic activity (delivered by a health worker, psychologist, social worker, or volunteer worker) given within a maximum of four structured therapy sessions, each of short duration (W. Miller, Zweben, DiClemente, & Rychtarik, 1992) that lasts between five and ten minutes with a maximum total time of one hour (Babor, 1994). Previous reviews suggest that 'brief interventions', so defined, can be effective in reducing the burden of alcohol (Rehm et al., 2004), and cannabis (Bernstein et al., 2009) use. The National Institute for Health and Clinical Excellence (NICE) differentiates between two main types of brief interventions, namely Structured Brief Advice and Extended Brief Interventions. Structured Brief Advice can be used with time constraints (e.g., 5 minutes) as a first step for adults (aged 18 and over) who have been classified as high-risk drinkers. In contrast, most Extended Brief Interventions can be classified as short versions of Motivational Interviewing (NICE, 2010). Examples are the 'Motivational Enhancement Therapy' originally developed as a four-session intervention in 'Project MATCH' in the US (W. Miller et al., 1992), and 'Drinker's check-up' (Hester, Squires, & Delaney, 2005; W. Miller, Sovereign, & Krege, 1988; NICE, 2010) consisting of assessment, feedback, and decision-making modules. Computerized brief interventions include both online and offline interventions (e.g., CD-Rom, software, websites and downloadable applications) delivered via electronic devices such as personal computers, tablets and smart phones. The main advantage of a computerized brief intervention is that it can reach large audiences at a low cost and simultaneously simulate an 'interpersonal therapeutic component' by targeting recipients' feedback. Moreover, computerized, brief intervention appeals to younger people who have been growing up with digital media. Many studies targeting young people such as high school and university students use computerized interventions (Carey, Carey, Henson, Maisto, & DeMartini, 2011; Carey, Scott-Sheldon, Elliott, Bolles, & Carey, 2009; Carney, Bronwyn, & Louw, 2011; White et al., 2010). As young people are underrepresented among users of standard face-to-face alcohol and other drug specialist services, the computer might be an effective medium to reach this population (White et al., 2010). In one study, 53 percent of Internet users aged 18-29 had searched online for information on a specific disease and medical problem (Fox & Duggan, 2013), and 14 percent had searched specifically for information on alcohol and drug problems (Fox, 2006). Computerized interventions often consist of two feedback components: targeted feedback and tailored feedback (W. Miller, 2002). Whereas the term 'targeted feedback' refers to feedback according to the needs of a whole group, for example, to students with risky alcohol and cannabis use, the term 'tailored feedback' refers to feedback that is individualized and tailored to a single-person's needs (Kreuter & Skinner, 2000). 'Automated' computerized interventions may be combined with a brief session of counseling given by a real time 'counselor' such as a psychologist or social worker at the other end of the electronic link (Kristjansdottir et al., 2013). In the case of early, brief computerized interventions, software programs can be used instead of health care professionals or other staff to screen effectively for substance use. This type of screening process is more anonymous and may thus encourage participants to give more honest information. Interventions that are consistent and of high quality can be provided via computers, tablets, or smart phones (including using the Internet) and can also give information tailored to the individual participant (Moyer & Finney, 2004/2005). The assessment module aims to classify the user as either a low, medium, high or very high risk drinker and provides recommendation on whether he or she might benefit from a more formal treatment program. The feedback module gives information on the user's score after each assessment and responds to the client's general reaction to such feedback. Initially, the decision-making module allows users to specify their level of readiness to change. Those who declare themselves ready to change are provided with a menu of goal options. After deciding which goal option to follow, users are lead through exercises to develop a plan of change, and are also provided with references to additional Web links, self-help groups and materials and a list of therapists. Those who do not show a readiness to change are offered the option of receiving some basic information before ending the program (Moyer & Finney, 2004/2005). Gender and education might influence the effect of brief, computerized interventions. For example, males and young adults with higher education use digital media more than females and young adults with lower education (OECD, 2008). These two groups may therefore be more likely to benefit from computerized interventions because their past experience is likely to have led to more efficient use of digital media. Brief interventions have been suggested as working through two main mechanisms: (1) by making the clients think differently about their alcohol/cannabis use, and (2) by providing them with skills to change their behavior if they are motivated to change. It has been suggested that the assessment component of brief interventions alone might lead to behavioral change (Bien, Miller, & Tonigan, 1993) particularly in emergency department settings (Longabaugh et al., 1995). In addition, studies drawing on time-line follow-back assessments have shown some reductions in the use of alcohol and other substances over time (LaBrie, Lamb, Pedersen, & Quinlan, 2006; Suffoletto, Callaway, Kristan, Kraemer, & Clark, 2012). Previous reviews and meta-analyses using "Motivational Interviewing" (MI) (Smedslund et al., 2011), internet-based interventions (Tait & Christensen, 2010) and online alcohol interventions (White et al., 2010) have studied the effects of computerized brief interventions delivered both as stand-alone or in combination with face-to-face interventions. First, Smedslund et al. (2011) focused on the effect of 'motivational interviewing' in general on substance abuse among persons who abused or were dependent upon substances, and included all individuals who met this criterion without limitation to age. Second, in examining the effect of fully automated Internet-based interventions, Tait and Christensen (2010) limited their review to studies targeting young people not older than 25 years with problematic substance use, and they did not explicitly differentiate between specific substances, and focused solely on brief computerized interventions. Third, White et al. (2010) included studies on the effect of online-alcohol interventions more generally, without limitation to age and time range. In general, these reviews have focused either on the universal prevention of problematic substance use, or on the treatment and rehabilitation of individuals who have established substance dependency. Most focus solely on computerized interventions, are limited to college students, and exclude other groups of young people who are not attending college. The current review investigates whether stand-alone early, brief computerized interventions prevent the development of established alcohol and/or cannabis problems in young people aged 15-25 years showing risky behavior. This has not been systematically studied before. The objective of this review is to assess the effectiveness of early, brief computerized interventions on alcohol and cannabis use by young people aged 15 to 25 years who are high or risky consumers of either one or both of these substances by synthesizing data from rigorous high-quality studies. We will include studies where units (e.g., persons, therapists, institutions) are allocated randomly or quasi-randomly to an early, brief computerized intervention and at least one other comparator condition. Both efficacy studies (where the treatment is studied under ideal conditions) and effectiveness studies (where the treatment is studied under real-world conditions) will be included. We will include studies where early, brief computerized interventions are used as a stand-alone treatment. Eligible comparators include no intervention, waiting list control or alternative brief intervention, which may be computerized or delivered face-to-face. We will exclude studies using non-randomised procedures for allocation (such as self-selection). Examples of eligible studies include that conducted by Voogt, Poelen, Kleinjan, Lemmers, and Engels (2011) suggesting the effectiveness of a web-based brief alcohol intervention in reducing heavy drinking among young people aged 15 to 20 years with a low educational background, the study by Voogt, Poelen, Lemmers, and Engels (2012) illustrating the effectiveness of a web-based brief alcohol intervention in reducing heavy drinking among college students, and the study conducted by Bingham et al. (2010) on the efficacy of a web-based, tailored, alcohol prevention/intervention program for college students. The review will include studies in which the participants are young people between 15 and 25 years of age who are high or risky consumers of alcohol or cannabis, or both. We will include studies of university students and of senior high school students even if no further information on age is provided. We will exclude studies that state only that the participants were young. High or risky consumption of alcohol is defined as either (a) consuming at least five (for males) or four (for females) drinks during any one drinking session, or (b) consuming more than fourteen (for males) or more than seven (for females) drinks a week (National Institute on Alcohol Abuse and Alcoholism, 2013). In the US, a standard drink is defined as one which contains about 0.6 fluid ounces or 14 grams of 'pure' alcohol. High or risky consumption of cannabis is defined in different ways; whereas some scholars view risky consumption of cannabis as daily or near-daily use (Fischer et al., 2011), others use a broader definition to include those who consume cannabis at least once a week (Webb, Ashton, Kelly, & Kamali, 1996). In this review, we define risky cannabis use among young people as the frequent consumption of cannabis at least once a week. Existing studies on the effect of brief computerized interventions on risky drug use have usually defined young people in a range 15 to 25 years (Bingham et al., 2010; Voogt et al., 2011; Voogt et al., 2012), and we have limited our target group accordingly. If we find studies comprising interventions to reduce the use of other types of substances simultaneously (e.g. cocaine), we will exclude them unless they analyse results on risky alcohol or cannabis use separately. If the study includes young people over 25 years of age, the mean age should be 25 years or less. If the study includes young people under 15, the mean age should be not be less than 15 years. The definition of 'young people' might vary in different countries and cultures. In addition, the debut age for using alcohol and cannabis is assumed to vary between different countries. For the purposes of this review, we define 'early intervention' as being delivered at an early stage of substance use (an 'indicated prevention'). An 'indicated preventive strategy' targets individuals at high-risk who have been identified as having minimal but detectable signs foreshadowing alcohol and cannabis abuse (O'Connell, Boat, & Warner, 2009). Early, brief computerized interventions appear to be an important tool to stop the development of severe alcohol and cannabis use among young people at risk. This review will include all types of early, brief computerized interventions regardless of medium, provider or theoretical framework. These may be 'automatic only' or delivered with the involvement of trained real-time 'counselors' (e.g., social workers, psychologists, or other health care workers). Automatic interventions and interventions with trained real-time counselors will be analyzed separately. Studies with booster sessions will be included but analyzed separately. This review will only include 'brief' interventions defined as any preventive or therapeutic activity (such as is delivered by a health worker, psychologist, or volunteer worker) given within a maximum four structured therapy sessions, each of short duration (W. Miller et al., 1992) that typically lasts between five and ten minutes with a maximum total time in treatment of one hour (Babor, 1994). The comparator condition can be: an alternative early, brief intervention, no intervention or waiting list control. We will compare changes in use (e.g. frequency, quantity or peak consumption, occasions, drinking days) between intervention and comparators at baseline and at all follow-ups. Alcohol use and cannabis use will be analyzed separately. The exact duration of follow-up will be recorded for each study. We will search for on-going studies in ClinicalTrials.gov and will contact experts in the fields to identify unpublished reports, on-going studies and studies that were not retrieved in the We will a of the of of the of & Clinical for the years to and will search all from on Brief Interventions for Alcohol & Other we will the of included studies and systematic reviews in the If there are studies in which more than one eligible intervention group is with a control group, we will only include one intervention to using the control group more than once in the If two types of early, brief computerized interventions are with an control intervention, we will include the most If one type of early, brief computerized intervention is a early, brief intervention and an control intervention, we will the control If there are follow-up we will analyse the data from each separately. If multiple are used to a primary in the study, we will either use mean in order to a The screening of studies will in two two review will the and of each and score either to or if both review score will the be at this If at least one review or the will be to to will be in and the screening will be review will the and score or If there is a review will whether to include the study. We will not the of individual but we will a of references and the screening as a group in order to develop high between before we screen the of the studies. from each study will be by two review using a specifically developed data to information about intervention, control and We will the for with as under In a study, all between groups will by This to all both and In there may be important between These can be and can appear in as well as in the It is to control for but not for can be used after the intervention to control for but such may in the results et al., 2003). We will compare the of treatment and control groups at and at and long-term For we will and for we will mean We will use percent as of the the We will use the information and to assess whether the is for that there is a effect in a a of and of we that a total of is for a mean of For of and the are and In one has to be to The may as an If the population of the study of a total of risky alcohol in four with 25 in each and two are to the intervention and the other two to the the to use in the is not but The effective of a intervention group in a is by a quantity the effect is usually assumed intervention The effect is where is the and is the If we include any in this review, we will attempt to measure the The total in the can be into between groups and within groups The is as The of participants can be used in the if the is used as a the is reported in primary studies. For data both the of participants and the the can be by the effect & 2011). If the is not reported in primary studies we will a for not from the reports, we will contact by to any data or the for data to a to be made on whether these are at or at will be at if the for is as to of the In contrast, data will be at if the for is as related to the data are not at we will analyse only the data are at we will the of a and will the data with and as if they were The of will be after a will be from standard and these are not reported in we will contact the of the primary studies. If this is we will attempt to effect data from or to effect using or software from information such as If these are we will attempt to use the in of the & 2011). If but not are we will from other studies. We will assess for among primary studies using the and & and will any and We will use to provide information about if there are more than ten included studies. We are that are not by not in a If is likely will be we will search for and will compare the with where this is will be if the are to the comparators and if the studies do not have high risk of We will in each case whether is we reach If meta-analyses are we will results using because we that the studies will be interventions and If meta-analyses are not to be we will the results for each individual study by a brief intervention with an alternative brief intervention will be separately. We will by control group type for between different interventions, and will use to in mean effects control group We will also the effect of baseline frequency of We will analyse effects for studies including targeted feedback and for including targeted and tailored feedback. The studies will be for the different time If there are primary we will classify them according to these in attempt to identify of We will (e.g., on to to are related to We will for the of the by examining the effect of the studies to be included to those with low will be in but we will in if the of studies is The systematic review by the and the of of the and the review and in the and the of the and the in on of the Smedslund and the provided in on web-based and brief interventions. developed the of level one the of and of We will exclude the study if one of the to the is If the are or we will the for level the we will any where the recorded as during the first level In where the information is or where the is we will contact the of the study. to of of as low, of participants of data Other to of to the follow-up for eligible participants year of data year of data Gender of cannabis of alcohol Other of sessions of sessions between sessions Other brief intervention intervention list will be by using an of the & Alcohol use Cannabis use for change and reported where found in the

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.179
Threshold uncertainty score0.597

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.039
Meta-epidemiology (narrow)0.0070.007
Meta-epidemiology (broad)0.0240.016
Bibliometrics0.0070.008
Science and technology studies0.0040.004
Scholarly communication0.0090.009
Open science0.0040.004
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.1790.017

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.120
GPT teacher head0.444
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2014
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