Effect of Functional Imagery Training on Cannabis Approach, Craving and Use in Heavy Cannabis Users
Daniël Klerks University of Amsterdam
Abstract
Functional Imagery Training (FIT) is a newly developed imagery based intervention. This study investigated the influence of FIT on cannabis approach, craving and use in fifteen heavy cannabis users. Participants chose a non-cannabis related activity that they would like to do more often for the following week and were instructed to do so, especially during craving episodes. An adapted Stimulus Response Compatibility task (SRC) measured the approach biases towards cannabis and goal related stimuli, the Craving Experience Questionnaire (CEQ) measured craving, and with the Timeline Follow-back method (TLFB) cannabis use was measured. Participants were randomly assigned to participate in a single FIT session between or after measurements. Against expectations, FIT did not influence cannabis approach, craving and use.
Recently there has been an increasing amount of research focused on the recognition of and treatment for cannabis dependence (Dennis, Babor, Rocbuck, & Donaldson, 2002). Cannabis dependence is defined as a problematic pattern of cannabis use despite clinically significant
impairment or distress (American Psychiatric Association, 2013). In the general population, 1 to 7% is diagnosed with cannabis dependence (Chen, O'Brien, & Anthony, 2005). Both individual and professional attempts to quit cannabis are accompanied by marked difficulty in remaining abstinent (Budney, Roffman, Stephens, & Walker, 2007) and at the same time the demand for interventions concerning cannabis addiction has increased over the last decades (Degenhardt et al., 2008). One of the few longitudinal studies concerning the course of cannabis addiction depicted cannabis to be a more persistent addiction than heroin, cocaine, and alcohol, with 50 to 90 % still using cannabis 5 years after treatment (Hubbard, Craddock, & Anderson, 2003). Two of the major reasons why addiction interventions lack efficiency seem to be the absence of motivation to decrease cannabis use (Jonge, 2005) and the inability to reduce craving (Tiffany and Wray, 2012). Functional Imagery Training (FIT; Andrade, May, & Kavanagh, 2012; Kavanagh, Andrade, May, & Connor, 2014; May, Andrade, Kavanagh, & Hetherington, 2012) is a newly developed imagery based intervention that
enhances the motivation to change maladaptive habitual behavior and promotes coping with
craving. Imagining the benefits of decreasing drug behavior (i.e. act towards drug use) enhances the motivation to change and makes the behavioral change more likely to occur (Andrade, Khalil, Dickson, May, & Kavanagh, 2016). So far the effectiveness of FIT has only been recorded
concerning snacking (Andrade et al., 2016). The present study aims to examine the influence of FIT on cannabis use.
The repeated exposure to cannabis is positively associated with social, physical and cognitive consequences, such as impairments in educational performance, career development (Brook, Balka & Whiteman, 1999), respiratory function, immune system (Tashkin,
Baldwin,Sarafian, Dubinett, Roth, 2002), reaction time (Hall, Degenhardt, & Lynskey, 2001), concentration (Ashton, 2001), short-term memory (Ashton, 2001), intelligence (Brook et al., 1999), and delinquent and risky sexual behavior (Brook et al., 1999). Moreover, regular cannabis users seem to have cognitive biases (i.e. systematic pattern of deviation in judgment) towards drug related cues concerning attention, evaluation, and approach (Field, Eastwood, Bradley, & Mogg, 2006).
Addictive behavior can be characterized by the continuation of drug use despite the awareness of these negative consequences. The incentive-sensitization theory of Robinson and Berridge (1993) explains this seemingly contradictory phenomenon. They theorize that
hypersensitization of the mesolimbic dopamine system due to drug exposure, leads to incentive sensitization. This enhanced drug related cue-reactivity generates an increased relatively
subconscious motivation to use drugs, termed 'wanting' (i.e. craving). From that moment on, drug related stimuli elicit approach actions and consummatory behaviors, which take over the
motivational properties of the drug itself (Berridge, 2001). At the same time, the appraisal of the drug (liking) decreases due to the negative influences of long term drug use. So 'wanting' and liking dissociate in addicts during the continuation of drug use (Berridge, Robinson, & Aldridge, 2009). This theory suggests that cue-reactivity can be quite a powerful perpetuating mechanism
A considerable amount of theories concerning the mechanisms behind addiction use a dual process model (Wiers & Stacy, 2006), considering both controlling and automatic processes to be important (Wiers et al., (2007). Wiers and colleagues (2007) argue that drug behavior is governed by two neuroanatomically distinct systems that interact with each other: a fast impulsive system and a slower regulatory system (see also Strack & Deutch, 2004). The fast impulsive system becomes sensitized following drug exposure as described by the theory of Robinson and Berridge (1993), leading to more craving (Fillmore & Vogel-Sprott, 2006). The regulatory system is defected by the short term influences of drugs (Filmmore & Vogel-sprott, 2006) and the long term repeated
exposure to drugs (Wiers et al., 2007). Furthermore, automatic processes predict problematic behavior more effectively when controlling processes are relatively weak (Wiers,Gladwin, Hofmann, Salemink, & Ridderinkhof, 2013). In this way automatic processes take over the controlling processes, and much of the drug behavior becomes habitual (Tiffany, 1990).
Research regarding automatic processes is mainly focused on cognitive biases in addicts. So far most research concerning these kind of cognitive biases focused on either alcohol or tobacco, while research regarding cannabis was mostly focused on attentional biases only (Field et al., 2006). Still, the available research on cannabis related cue-reactivity is in line with above-mentioned theories. Field and colleagues (2006) found that regular cannabis users show deviations regarding the attention, evaluation, and approach of cannabis related pictorial cues in comparison to non-users. So concerning cannabis users, the attentional bias is the tendency to faster draw and longer hold attention to cannabis related cues than non-users (Jones, Jones, Blundell & Bruce, 2002; Field et al., 2006). The evaluative bias is the tendency to rate cannabis related cues less negative than non-users, due to more positive associations with cannabis (Field, Mogg, & Bradley, 2004) and less influence of negative associations with cannabis (Wiers, Houben, Smulders, Conrod, & Jones, 2006). The approach bias is the tendency to rather approach than avoid a cannabis related cue and is determined by respectively positive and negative associations with cannabis (Wiers, 2009).
Moreover, these cognitive biases are not only a product, but also a predictor of repeated drug use (Ames, Grenard, Thush, Sussman, Wiers & Stacy, 2007; Cousijn, Goudriaan, & Wiers, 2011;
Franken, 2003) and influence each other continuously (Strack & Deutch, 2004). This in turn leads to more impulsive behaviors when controlling processes are weak (e.g. low executive functioning; Wiers et al., 2013). Therefore it is not surprising that cannabis users make faster approach than avoid responses to cannabis related stimuli compared to non-users (Field et al., 2006) and that this approach bias is a better predictor for the escalation of cannabis use than craving and weekly cannabis use. The strength of these cognitive biases and their destructive consequences can be diminished by either changing the impulsive processes directly, or strengthening the controlling processes (Wiers, 2009).
Recently there has been a new stream of research interested in the possibility to change the impulsive processes directly (Wiers, 2009). This resulted in new task-based interventions that manipulate cognitive biases by training participants to change their response towards drug related cues. Cognitive Bias Modification (CBM) is a collection of interventions focused on changing the attentional bias (i.e. Attentional Retraining, where participants focus on neutral cues and ignore drug related cues), the evaluative bias (i.e. Evaluative Conditioning and Counterconditioning; where participants associate respectively unpleasant cues and strong negative outcomes with drug related cues) and the approach bias (Approach Bias Retraining; i.e. participants approach neutral cues and avoid drug related cues). Altogether CBM interventions show promising results: Attentional
Retraining decreases the attentional bias in alcohol addicts (for an overview see Wiers et al., 2013), Evaluative Conditioning and Counterconditioning decrease the evaluative bias in alcohol and chocolate addicts (respectively Houben, Havermans, & Wiers, 2010; Houben, Schoenmakers, & Wiers, 2010 and Gucht, Baeyens, Vansteenwegen, Hermans, & Beckers, 2010) and Approach Bias Retraining changes the approach bias into an avoidance bias in alcohol addicts (Wiers et al., 2011). However, it is not yet investigated if these effects are present considering cannabis and if there are any long-term effects (Hertel, & Mathews, 2011).
One way to strengthen controlling processes is by enhancing the motivation to quit drugs through motivational interviewing (MI; Miller & Rollnick, 2012), a goal-oriented and client-centered method to explore ambivalence and enhance self-insight. During MI, therapists are
encouraged to be empathic, empower hope and optimism, understand the clients motivation and own problem solutions, and resist telling clients what to do (i.e. core features MI; Matulich, 2013). These MI techniques are suggested to evoke change talk (Rosengren, 2009), which is verbalization of desire, ability, or reason to change behavior. Considering multiple meta-analysis, MI
interventions were found to be effective in reducing alcohol and drug dependence (respectively project MATCH research group, 1997 and Hettema, Steele, and Miller, 2005; Jensen et al., 2011).
Another way to strengthen controlling processes is by enhancing the ability to change drug behavior by using Implementation Intentions (II; Gollwitzer, 1993). II is a strategy to promote achieving a desired outcome by specifying when, where and how one needs to act to achieve the outcome. It uses an if-then format: if a certain situation occurs, then a certain goal behavior (i.e. act towards goal achievement) has to be initiated. II interventions were found to be effective on goal achievement in non-pathological participants, alcohol and opiate addicts (respectively Gollwitzer & Sheeran, 2006, Brandstätter, Lengfelder, & Gollwitzer, 2001 and Hagger et al., 2012).
The interventions named above all seem to be somewhat effective to change drug behavior but focus only on the regulatory or the impulsive system. Interventions that both enhance the controlling processes and decrease the impulsive processes are theorized to be more effective to change drug behavior. In this view interventions that decrease the cognitive biases directly and at the same time promote motivation and ability to change drug behavior are theorized to be distinctly effective.
Functional Imagery Training (FIT; Andrade et al., 2012; Kavanagh, Andrade, May, & Connor, 2014; May, Andrade, Kavanagh, & Hetherington, 2012) is a newly developed client-centered imagery based intervention that might be doing just that. FIT is based on the Elaborated Intrusion (EI) theory (Kavanagh, Andrade & May, 2005) which focuses on decision making by following competing goals. This theory proposes that decisions are influenced by desires, which in turn are activated by verbal, emotional, physiological and environmental cues associated with the desired outcome (May, Andrade, Panabokke & Kavanagh, 2004). These desires are at first assumed to be unconsciously represented in the mind based on sensory, affective and motor output
information (Barsalou, 2008), and are then 'activated' when the impact of the trigger breaks over the awareness threshold. This activation leads to intrusive thoughts about the desire (i.e. initiation of craving), which can trigger the conscious search for information associated with the decision (i.e. elaboration, enhanced craving) and therefore influence goal-directed behavior.
Following EI theory, CBM only decreases the probability that an intrusive thought about drug behavior will occur, but does not change the elaboration process (and craving strength) when it does occur (May et al., 2012). MI and II both work on the elaboration process. Similar to II, FIT elicits thoughts about the when, where and how one needs to act to achieve the outcome, but does this by using imagery instead of only talking about it (Andrade et al., 2012). Since II in combination with Mental Imagery produces an even bigger effect on goal achievement then II alone (Knäuper, Roseman, Johnson, & Krantz, 2009), FIT is theorized to be highly effective. FIT uses the core features of MI to reveal the benefits of- and enhance the motivation- to change (Andrade et al., 2016; Miller & Rollnick, 2012). FIT uses imagery as well as motivational interviewing, which suggests it to be at least as effective in changing behavior as MI. Since addiction interventions that use an individual format are more promising than others (for an overview see: Carney & Myers, 2012) FIT, just like MI and II, is delivered in a personally relevant way.
A major advantage over the above-mentioned interventions is that during FIT clients can actually experience what it is like to change their behavior without doing any form of hard effort. This is an advantage over MI and II since imagery illicit stronger emotional responses than merely thinking about something (Holmes, & Mathews, 2005; Holmes, Mathews, Mackintsh, & Dalgleish, 2008). Moreover, imagery is considered the most effective form of elaboration, as one mentally simulates the sight, sound, smell, taste and feel of a pleasurable experience (Andrade, May, & Kavanagh, 2012). Benefits of changing behavior are theorized to be emotionally overwhelming and therefore more easily to recall in decisive situations. This is exactly what Lennox and colleagues (2015) found in gym members that wanted to exercise more often. This study found that imagery frequency was positively related to goal behavior frequency and motivational thoughts concerning change. Since clients can explore barriers and solutions during imagery, and therefore quite vividly
experience overcoming these barriers on their own strength, FIT is theorized to promote goal planning. Imagery about strategies to achieve goal behavior indeed predicted effective goal planning and goal achievement (Gollwitzer & Sheeran, 2008).
EI theory suggests that episodes of craving are dominated by imagery (i.e. craving imagery). Research found that the vividness and frequency of sensory imagery of the desire is associated with strength of craving (respectively May et al., 2008; Kavanagh et al., 2009 and Jauregui-Lobera, Bolanos-Rios, Valero & Prieto, 2012) and drug use (Kavanagh et al., 2009; May et al., 2014). Because suppression of intrusive thoughts about the desire counterproductively lead to stronger desire (salkovskis & Reynolds, 1994), interventions need to focus on accepting intrusive thoughts, and enhance unrelated imagery to avoid elaboration (Himlton, Fawson, May, Andrade & Kavanagh, 2013). FIT uses imagery of functional behavior in situations that elicit craving imagery, trying to outweigh the immediate temptation in favor of a more longer-term healthy goal (May et al., 2012, 2014). This way, clients will be trained to resist their cravings in even the most tempting situations. The capacity of the working memory is limited, therefore when craving imagery occupies the working memory and new imagery is added, the older craving imagery will be reduced or replaced (May et al,. 2012). Research found that goal related imagery reduces cravings concerning food, coffee and cigarette addictions (respectively Kemps & Tiggeman, 2007, 2009 and Versland & Rosenberg, 2007) and found that it does this more effectively than other cognitive tasks (Knäuper, Pillay, Lacaille, McCollam, & Kelso, 2011).
So the decision to either use cannabis or choose a more long-term healthy goal is influenced by craving imagery, which in turn can be reduced by goal related imagery. The present study investigated the effectiveness of a single FIT session on cannabis approach, craving and use in heavy cannabis users. It examined a one hour FIT intervention, since addiction interventions do not need to be long-lasting to be effective (Carney & Meyers, 2012). This format has been successfully used before to prove the effectiveness of FIT on snacking reduction (Andrade et al., 2016). Above mentioned studies taken altogether suggest that FIT is an effective way to motivate behavioral change regarding heavy cannabis users. This study compared a group of heavy cannabis users who
received a single FIT session to those who did not receive treatment. The cannabis related approach bias and goal related approach bias were expected to respectively decrease and increase regarding those who received FIT, whereas no differences were expected regarding those who did not receive treatment. Similarly, decreases in cannabis use and craving were expected concerning the former, whereas no differences were expected concerning the latter.
Method Participants
Seventeen participants were recruited at cannabis festivals, outlets (coffee shops) and through advertisements in universities, cannabis forums and social media. This study only included adult participants (18 years and older) with an average cannabis use of at least four days a week (based on Os, Bak, Hanssen, Bijl, Graaf & Verdoux, 2002) and adequate Dutch language skills. Since cannabis users have a higher risk for psychotic pathology (Gage, Hickman, & Zammit, 2016) and participants need to focus on imagined sensations during the experiment, participants with any current or past experience with psychotic symptoms were excluded (measured with the
Gewaarwordingenlijst, Lange, Schrieken, Blankers,Van de Ven, & Slot, 2000). This, together with the fact that the imagery tasks were voluntary and positively focused (i.e. focus on benefits and positive affect) suggested no risk for psychotic symptoms induced by imagery during this experiment. Data was not used if participants (a) did not seriously participate during tasks and questionnaires (e.g. giving no or irrelevant answers), (b) were under influence of alcohol and/or drugs during testing phases (judged by means of self report and observation), and/or (c) scored deviantly on one of the main tasks or the exit-interview. Two participants met one or more of above criteria, therefore data of a total of fifteen participants was used for analysis.
Design
This study will conduct a mixed factorial design, using ‘intervention’ as the independent variable, with FIT in the FIT-condition and no treatment in the Control-condition. The dependent
variables are the cannabis and goal related approach biases, measured with the adapted Stimulus Response Compatibility task (SRC), craving measured with the Craving Experience Questionnaire (CEQ), and cannabis use measured with the Timeline Follow-back method (TLFB). Each dependent variable was measured twice, before and after manipulation.
Materials
Approach biases. The approach biases will be measured with a personalized version of the Stimulus Response Compatibility task (SRC; De Houwer, Crombez, Baeyens, & Hermans, 2001). The SRC has been successfully used before to assess the approach bias towards alcohol, cigarette and cannabis related stimuli (respectively Bradley, Field, Mogg & de Houwer, 2004; Field, Caren, & Fernie, 2011; Field, Kiernan, Eastwoord, & Child, 2008; Field, Mogg, & Bradley, 2005; Mogg, Field, & Bradley, 2005 and Cousijn et al., 2012; Field et al., 2006). This study uses the SRC instead of the Approach Avoidance Task (i.e. only other evidenced based task to measure the approach bias), because the SRC is suggested to be more reliable (Field et al., 2011; Mogg et al., 2005; Wiers et al., 2013). During the original SRC participants typically need to move a manikin (human-like matchstick figure) either towards (approach) or away (avoid) from a drug related stimulus or neutral stimulus by pressing the corresponding keys on the keyboard (Mogg et al., 2005). Not only
approach and avoid instructions can vary, the starting position of the manikin (either under or above the stimulus) can vary too. Participants are then required to move the manikin as soon as it appears by pushing the B key (down) or the Y key (up). Reaction time (RT) was recorded in milliseconds to measure the approach biases (i.e. faster approach RT opposed to avoid RT). This adapted version contained three adjustments regarding the original. Firstly, this study used cannabis, neutral and goal related stimuli. Secondly, this SRC was constructed in four blocks: one block to approach cannabis related stimuli and avoid neutral stimuli, one block to approach goal related stimuli and avoid neutral stimuli and two blocks the other way around (i.e. approach neutral, avoid respectively cannabis or goal related stimuli). Lastly, this SRC used words opposed to pictures as stimuli. This SRC was labeled personalized because it only used highly relevant stimuli: words that were most
associated with the participant's cannabis use and goal behavior (obtained by self-report, see procedure). This was then matched with neutral stimuli according to the amount of characters (e.g. 'Coffeeshop' and 'Headphones' or 'Jogging' and 'Mailroom'). In each block ten different words were presented, five words associated with the participants cannabis use or goal behavior and five matched words. Each word was repeated five times in each block (50 words per block, 200 words total), and block sequence was randomized. Prior to these four main blocks, two trial blocks were presented so participants could practice the task (approach and avoid color related words, e.g. 'blue', 'red', 'green' etc., eight words per block, 16 words total). This SRC took approximately 12 minutes from start to finish. This SRC task was programmed in Presentation software and presented on a 15,6 inch HP laptop with an AMD A8 processor. The reliability of the SRC in this study was low (Cronbach's alpha = .45).
Craving. Craving was measured with a translated version of the Craving Experience Questionnaire (CEQ-S11; May et al., 2014, see appendix A and B for respectively the original and the translated version), a psychometrically sound questionnaire based on the EI Theory. The CEQ-S11 can measure all types of drug related cravings. The CEQ-CEQ-S11 is designed in a similar way as the Alcohol Craving Experience Questionnaire (ACE; Statham et al., 2011) and contains 22 questions about craving strength and frequency. Each question has to be answered on a scale of 0 ('not at all') to 10 ('extremely'/'constantly') and is similarly scored (e.g. 10 is worth 10 points). A general craving index was derived by summing the scores on all questions and dividing it by 22 (total amount of items). The reliability of the CEQ in this study was moderate (Cronbach's alpha = .62).
Cannabis Use. Cannabis use was measured with a Dutch version of the Timeline Follow-Back method (TLFB; Sobell & Sobell, 1992, see appendix C). The TLFB is a quantitative self-report method that measures estimates of alcohol, cannabis, cigarette and other drug use by means of a visual calendar to enhance recall. Participants wrote down their cannabis use for each day, while using important events in their actual calendar as a mnemonic to remind them of how their week went. Furthermore, they could choose to calculate the number of joints or the amount of
grams per day. This study used only the cannabis section of the TLFB and measured cannabis use twice, once for the month prior to the first session and once for the week prior to the second session. This study did not use a longer retrospective period for the TLFB in the first session, since a month has been argued as the appropriate time to measure change in cannabis use due to an intervention considering addiction research (Hjorthoj et al., 2011; Robinson, Sobell, Sobell, & Leo, 2014). The TLFB is proven to be a highly reliable method to measure cannabis use (Robinson, Sobell, Sobell, & Leo, 2014). A meta-analysis found TLFB to be a valid instrument to assess substance use compared to biological samples (Hjorthoj, Hjorthoj, & Nordentoft, 2011). Moreover, scores on the TLFB and the amount of THC found in blood plasma of cannabis users correlate strongly with each other, indicating the TLFB to be a valid tool to measure cannabis use. The reliability of the TLFB in this study was moderate (Cronbach's alpha = .61).
Psychotic symptoms. The Gewaarwordingenlijst (Lange et al., 2000; see appendix D) is a Dutch psychotic screening questionnaire used for research participation, which measures any past or current experiences with psychotic symptoms. Its consists of eight self-report items and uses a five point Likert-system. The Gewaarwordingenlijst is a reliable instrument and has a moderate internal consistency (Lange et al., 2000) .
Functional Imagery Training. Functional Imagery Training can be seen as a semi-structured motivational intervention with behavioral exercises concerning imagery. This study uses an adapted version of the original FIT script (Andrade and colleagues, 2016; See appendix E, F and G for respectively the original version and the adapted version in both English and Dutch). This FIT started with an acknowledgment of the participant’s wish to increase its goal behavior. The
negatives of not-changing and positives of changing were extensively discussed, followed by a brief summary that highlights change-talk (MI part 1). Then the purpose of FIT was explained and
illustrated with a commonly used imagery exercise concerning the cutting of a lemon (Holmes & Mathews, 2005). Participants were then asked to imagine a specific situation in which they achieved their goal to increase their non-cannabis related activity, focusing on the sensory and emotional benefits of the behavioral change. During this task, participants had to address when, where and
how they would need to act to achieve their goal (II part 1). After this, participants got a brief psycho-education about craving imagery and how to reduce craving effectively. Participants then started to practice this by firstly imagining using cannabis to illicit craving imagery, and then
imagining achieving their goal behavior to reduce craving. Participants’ experiences were asked and change-talk was highlighted (MI part 2). Participants were asked about their confidence to increase their goal behavior for the following week, and were asked to image a past event where they
effectively increased their goal behavior. If they could not think of one, they were asked to image an event where they effectively used self-control strategies. After this, participants were asked to imagine getting started with their goal and solving the challenges they would encounter and again they had to address the when, where and how they needed act to achieve their goal (second II part). Furthermore, participants were explained that they could use imagery during a daily habit (e.g. hand washing, walking the stairs, eating breakfast etc.), and time effectiveness of using imagery was highlighted. They were encouraged to practice this with the experimenter if possible. Participants also received a five minute audio file containing an instructed imagery training (See appendix H and I for respectively the transcript in English and Dutch). Lastly, participants were encouraged to imagine doing their goal behavior and feeling good about it during possible episodes of craving in the following week.
Procedure
During recruitment, potential participants were asked to fill in a screening form containing questions to determine if inclusion and exclusion criteria were met (see appendix J). Appointments for both sessions were made next. Participants could choose to do the experiment at their own home or in the research lab. To increase convenience and generalization outside the FIT session,
participants were encouraged to do the FIT session in their own environment. Participants were asked to be completely sober during both sessions. When permission was given, sessions were recorded to control for between session consistencies. FIT was given by a clinical psychology graduate with prior MI experience, who received a private FIT course by Jackie Andrade, one of the
inventors of FIT (Andrade, May, & Kavanagh, 2012). In both conditions, the participant firstly filled in the informed consent form and then received information on the content of the two sessions. In the first session, the participant chose a non-cannabis related activity that they would like to do more often than normally during the following week (e.g. jogging, studying, reading etc.). Participant and experimenter then started a brainstorm session to find the five words most
associated with their cannabis use, followed by five words most associated with their goal behavior (hence personalized SRC). The experimenter implemented these words together with the matched neutral words in the SRC. The SRC was conducted next, followed by the CEQ and TLFB. After this, the FIT-condition received FIT. The Control-condition did not receive FIT. At the end of the first session, both conditions were encouraged to implement their goal behavior during the following week, especially during moments of cannabis craving. The experimenter motivated the participant to do their absolute best in favor of the study. At the start of the second session, the participant was asked about their week regarding their goal behavior and cannabis use. This was followed by second measurements concerning respectively the SRC, the CEQ and TLFB. Then the Control-condition had FIT. The second session ended with three demographic questions (i.e. age, sex and highest completed education) and some questions concerning experiences and effort during the experiment (exit-interview; see appendix K).
Analyses
Prior to the analysis, participants with deviant scores regarding the SRC, exclusion criteria and exit-interview were excluded (i.e. respectively scoring 2SD above or under the mean, meeting one or more exclusion criteria and/or scoring '1' on either of the four questions in the exit-interview; see participants section) and reliability analyses of the SRC, CEQ and TLFB were executed.
Assumptions regarding the main analysis were tested and general demographic statistics were calculated. To measure potential demographic differences between conditions, T-tests and chi-square tests were executed. Similar to prior SRC studies (Bradley et al., 2004; Field et al., 2006; Field et al., 2008; Mogg et al., 2005; Styrkowiec1 & Szczepanowski, 2013) mean RT's were
calculated for each block separately ('approach cannabis', 'avoid cannabis', 'approach goal', 'avoid goal') and only RT's for correct responses were included. Approach RT's were subtracted from the avoid RT's to calculate absolute approach bias scores (i.e. a higher positive score equals stronger approach bias towards neutral, cannabis or goal related stimuli). The relative approach bias scores were calculated next by subtracting the approach bias scores concerning neutral stimuli from those concerning cannabis and goal related stimuli (i.e. positive score indicate an approach bias to either cannabis or goal stimuli, relative to neutral stimuli; see Table 1). Two-way mixed design ANOVA's were conducted to compare the relative approach bias scores between-subjects ('group',
FIT-condition vs. Control-FIT-condition) and within-subjects ('time', pre- vs. post-treatment), with one ANOVA considering cannabis related stimuli and one considering goal related stimuli. Difference scores were calculated by subtracting the relative approach bias scores during pre-treatment with those of during post-treatment (see table 1). The difference scores were then used in a correlation analysis to measure if changes in individual cannabis related approach bias scores were associated with those in goal related approach bias scores. Craving data was analyzed using a two-way mixed design ANOVA, comparing CEQ scores between-subjects ('group', FIT-condition vs. Control-condition) and within-subjects ('time', pre- vs. post-treatment). Using 0.32 grams as the mean amount of grams per joint (Ridgeway & Kilmer, 2016), calculations were made to make an overall TLFB score for participants who used the amount of grams to fill in the TLFB, and those who used the amount of joints. Cannabis use data was analyzed using a two-way mixed design ANOVA, comparing mean TLFB scores between-subjects ('group', FIT-condition vs. Control-condition) and within-subjects ('time', pre- vs. post-treatment).
Results
The relative approach bias scores considering cannabis and goal related stimuli were expected to respectively decrease and increase over time in the FIT-condition compared to the Control-condition. These changes (i.e. difference scores) were expected to be negatively associated
with each other. The mean CEQ and TLFB scores were expected to decrease more over time in the FIT-condition then in the Control-condition.
The assumptions for the ANOVA analysis were satisfied. The assumption of independent observations was not violated, the participants were randomly assigned to either the FIT-condition or the Control-condition. The assumption of normality was tested with the Shapiro-Wilk test and was not violated in the FIT-condition and Control-condition for the relative approach bias scores considering cannabis related stimuli (respectively p = .35, p = .56) and goal related stimuli (respectively p = .71, p = .12). The assumption of homogeneity of variances was tested with the Box's M test and was not violated for the relative approach bias scores considering cannabis related stimuli (p = .09) and goal related stimuli (p = .32). With respect to gender, education and age, there were no significant differences between the FIT-condition and the Control-condition (see table 2 and 3).
Table 1
Relative Approach Bias Scores and Difference Scores
Session 1 Session 2
i
Group Cannabis Goal Cannabis Goal
FIT 335.62 255.88 399 357.75
Control 264.43 429.29 551.14 671.71
Total 301.93 336.80 470.01 504.27
Difference Scores
Group Cannabis Goal
Control 287.71 55.71
Total 168.07 67.73
Note. Relative approach bias scores (cannabis/goal approach bias minus neutral approach bias) and difference cores
(approach bias differences between sessions) in ms.
Tabel 2
Demographic Data Considering Mean and Standard Deviation
Variable Category Valuescore M SD
Gender Woman Man 0 1 1.07 .26 Education VMBO/MBO 0 HAVO/HBO 1 VWO/WO 2 2.33 .82 Age 24.87 3.48 Table 3
Demographic Differences Between the FIT- and Control-conditions
FIT (n=8) Control (n=7) Total (n=15)
Variable Category N % N % N % X²/t p Gender Man 7 87.5 7 100 14 93.3 0.94 .53 Education VMBO/MBO 1 12.5 2 28,6 3 20 0.77 .68 HAVO/HBO 2 25 2 28,6 4 26.66 VWO/WO 5 62,5 3 42,8 8 53.33 Age Mean 25.25 24.43 24,87 0.44 .24
Note. Independent samples T-test is used to measure gender differences and Chi-Square tests are used to measure differences in Education
and Age.
Table 4
Main- and Interaction - Effects of Group and Time on the Relative Approach Bias Towards Cannabis and Goal related Stimuli
Cannabis Goal CEQ TLFB i Group F p F p F p F p Group .07 .79 2.06 .18 .38 .55 .57 .46 Time .89 .36 1.04 .33 3.01 .11 2.03 .18 Group*Time .37 .55 .17 .68 .07 .79 .09 .78
Note. Group: FIT-condition and Control-condition and Time: Session 1 and session 2
Two ANOVA analysis were conducted to examine the main and interaction effects of group and time on the relative approach bias scores considering cannabis and goal related stimuli (see table 4). There was no significant interaction between the effects of group and time on the relative approach bias scores considering cannabis related stimuli, F (1,13) = .37, p = .55, and goal related stimuli F (1,13) = .17, p = .69. Simple main effects analysis showed that the relative approach bias score considering cannabis related stimuli did not significantly differ between the FIT- and the Control-condition and between pre- and post-treatment, respectively F (1,13) = .07 and .89, p = .79 and .36. Similarly, no significant differences were found concerning the relative approach bias scores considering goal related stimuli, respectively F (1,13) = 2.06 and 1.04, p = .18 and .33. Moreover, the correlation analysis yielded no significant relation between the cannabis and goal related difference scores, r = .41, p = .14.
Two ANOVA analysis were conducted to examine the main and interaction effects of group and time on the CEQ and TLFB scores (see table 4). There was no significant interaction between the effects of group and time on the CEQ scores, F (1,13) = .07, p = .79, and on the TLFB scores, F (1,13) = .09 p = .78. Simple main effects analysis showed that the CEQ scores did not significantly differ between the FIT- and the Control-condition and between pre- and post-treatment, respectively F (1,13) = .38 and 3.01, p = .55 and .11. Similarly, no significant differences were found concerning the TLFB scores, respectively F (1,13) = .57 and 2.03, p = .46 and .18.
Discussion
This study investigated the effectiveness of FIT on cannabis approach, craving and use in heavy cannabis users. Heavy cannabis users who received a single FIT session between
measurements were compared to a control group of heavy cannabis users who received no treatment between measurements. As opposed to what was expected, FIT did not minimize the cannabis approach bias, nor enhance the approach bias towards goal related stimuli. The strength of both approach biases were the same in the FIT- and control-group and during pre- and post-treatment. Changes in the individual cannabis approach biases were not associated with those in the approach biases towards goal related stimuli. Findings considering cannabis craving and use were against prior expectations as well. FIT had no significant effect on cannabis craving and use. The amount of cannabis craving and use was the same in the FIT- and control-group and during pre- and post-treatment.
Since FIT uses the core features of MI and II interventions, and both interventions are considered effective in promoting behavioral change (respectively Miller & Rollnick, 2012; Rosengren, 2009 and Gollwitzer, 1993; Gollwitzer & Sheeran, 2006) in addiction (respectively Hettema, Steele, & Miller, 2005; Jensen at all., 2011; project MATCH research group, 1997 and Brandstätter, Lengfelder, & Gollwitzer, 2001; Hagger et al., 2012), it is surprising that this study found no significant effects of FIT on cannabis approach, craving and use. This becomes even more surprising since FIT has major advantages over MI and II considering barrier exploration, recall during decision situations and promoting an alternative for cannabis (see introduction; respectively Andrade, May, & Kavanagh, 2012; Holmes, & Mathews, 2005; Lennox et al., 2015, Gollwitzer & Sheeran, 2008 and May et al., 2012, 2014).
One possible explanation of this seemingly contradictory finding is that a single FIT session might not be enough to cure a deep-rooted disease like addiction. Addiction can be seen as chronic disease and it is hard to achieve full recovery (Budney, Roffman, Stephens, & Walker, 2007), especially in cannabis users (Hubbard, Craddock, & Anderson, 2003). The American National Institute on Drug Abuse states that a minimum of three months treatment is the threshold for
significant improvements in drug behavior (National Institutes of Health, 1999). Moreover,
treatment duration in general has a negative effect on drug use (Hser, Grella, Chou, & Anglin, 1998; Zhang, Friedmann & Gerstein, 2003) and the alcohol approach bias (Eberl, Wiers, Pawelczack, Rinck, Bekcer, & Lindenmeyer, 2013). So although FIT uses motivational methods, treatment duration might have been insufficient for participants to engage with treatment techniques and promote motivation to change behavior (Garnick, Lee, Chalk, Gastfriend, Horgan, McCorry, et al., 2002).
It is indeed questionable whether participants were motivated enough to practice imagery and reduce their cannabis use. Since this population was quite hard to recruit on an unpaid basis, the experimenter chose not to use an inclusion criteria considering the readiness to change drug
behavior. This presumably means that most participants did not want to reduce their cannabis use, since cannabis abusers generally do not feel they want to change their drug use behavior (McBride, Curry, Stephens, Wells, Roffman, & Hawkins, 1994; Melnick, De Leon, Hawke, Jainchill, &
Kressel, 1997). Moreover, none of the participants managed to do their imagery practice at least one time a day during the week between measurements. This can be seen as a supporting argument considering an absence of motivation, especially since imagery frequency is positively related to motivation and goal behavior frequency (Lennox et al., 2015).
One limitation of this study is that the sample size was small, indicating a low power. Following the a priori power-analysis and given a moderate effect size and high power (G*Power version 3.1; Faul, Erdfelder, Buchner & Lang, 2009), at least thirty-four heavy cannabis users were needed to conduct a proper analysis. In this study only fifteen participants were analyzed and a Partial Eta Squared effect size of .07 was found over the non-significant results. The post-hoc power analysis resulted in a power of only .19, indicating a reduced likelihood that these results reflect an absence of clinically important effects in reality.
Another limitation of this study is that the SRC task, CEQ questionnaire and FIT script used in this study might not have been psychometrically sound, leading to possible biases regarding the findings of this study. Both the SRC and CEQ were adapted versions (respectively a personalized
and translated version) of the corresponding original psychometrically sound methods. However, in both cases only test-retest reliability was measured (respectively low and moderate), so the validity of these adapted instruments is unknown. Likewise, the FIT script was translated and this
translation was not psychometrically tested. Taken altogether chances are reasonable that measurement biases could have distorted the findings of this study.
Studies on imagery treatment for addiction is limited and researchers have only just started exploring this field. Future research should continue investigating the effectiveness of FIT on cannabis approach, craving and use. Treatment duration should be sufficient in order to achieve engagement with treatment techniques and motivation for behavioral change. The adapted SRC task, CEQ questionnaire, and FIT script should be examined in order to get a profound
understanding of their psychometric properties. Kavanagh, Andrade and May, the inventors of FIT (2012) are presently examining the effect of FIT in a four year randomized control project, where FIT is compared to existing treatments for alcohol addiction (May, 2015). Still, a focus on cannabis as well as other addictions is needed, and FIT should be compared to the common evidenced based addiction treatments like CBT, Motivational approaches and the Twelve-step Program.
The present study examined the influence of the newly developed imagery based
intervention FIT, in heavy cannabis users. On its basis, it has been provisionally concluded that a single FIT session does not influence cannabis approach, craving, and use. Although single FIT interventions have been previously used effectively to promote behavioral change (Andrade et al., 2016), this treatment format might be insufficient to change more maladaptive behavior as seen in drug addicts. Jonge (2005) highlights that addiction interventions in general lack efficiency to motivate for behavioral change. Researchers should therefore continue investigating new ways to motivate addicts to change their drug behavior and explore the role of FIT and imagery in doing so. This could in turn promote more effective treatment to change drug behavior and therefore diminish the burden of care and disease concerning addiction.
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Appendix C: Dutch Version of the TLFB-Cannabis (p. 32-33) In de tabellen geef je aan hoeveel cannabis je de afgelopen maand hebt gebruikt.
Begin bij afgelopen dag (gisteren) en ga dan steeds een dag verder terug in de week. Weet je het niet precies meer, bedenk dan eerst wat je die dag precies hebt gedaan (bijvoorbeeld aan de hand van je agenda) en maak dan een zo nauwkeurig mogelijke schatting. Iedere dag dat je niets hebt gebruikt vul je 0 in.
Let op: vermeld je cannabis gebruik in aantal jointjes en in aantal gram.
Datum vandaag: Timeline Follow-Back (TLFB) Cannabis Gebruik
Zondag Joints Gram Joints Gram Joints Gram Maandag Joints Gram Joints Gram Joints Gram Dinsdag Joints Gram Joints Gram Joints Gram Woensdag Joints Gram Joints Gram Joints Gram Donderdag Joints Gram Joints Gram Joints Gram Vrijdag Joints Gram Joints Gram Joints Gram Zaterdag Joints Gram Joints Gram Joints Gram Zondag Joints Gram Joints Gram
Maandag Joints Gram Joints Gram Dinsdag Joints Gram Joints Gram Woensdag Joints Gram Joints Gram Donderdag Joints Gram Joints Gram Vrijdag Joints Gram Joints Gram Zaterdag Joints Gram Joints Gram
In de tabel geef je aan hoeveel cannabis je de afgelopen week hebt gebruikt.
Begin bij afgelopen dag (gisteren) en ga dan steeds een dag verder terug in de week. Weet je het niet precies meer, bedenk dan eerst wat je die dag precies hebt gedaan (bijvoorbeeld aan de hand van je agenda) en maak dan een zo nauwkeurig mogelijke schatting. Iedere dag dat je niets hebt gebruikt vul je 0 in.
Let op: vermeld je cannabis gebruik in aantal jointjes en in aantal gram.
Datum vandaag: Timeline Follow-Back (TLFB)
Cannabis Gebruik
Zondag Joints Gram Joints Gram Maandag Joints Gram Joints Gram Dinsdag Joints Gram Joints Gram Woensdag Joints Gram Joints Gram Donderdag Joints Gram Joints Gram Vrijdag Joints Gram Joints Gram Zaterdag Joints Gram Joints Gram
Appendix D: Gewaarwordingenlijst Gewaarwordingenlijst:
In deze lijst staan 8 uitspraken over zintuiglijke ervaringen en gedachtes. We willen graag weten welke uitspraken je op jezelf van toepassing vindt.
Omcirkel bij elke uitspraak één van de volgende mogelijkheden: 1 sterk mee oneens
2 mee oneens
3 niet mee eens, maar ook niet mee oneens 4 mee eens
5 helemaal mee eens
mee eens
In de afgelopen vijf jaar is het voorgekomen …’ niet wel
1. dat ik boodschappen doorkrijg van stemmen in of vlakbij mijn hoofd 1 2 3 4 5 2. dat ik me vanwege mijn capaciteiten verheven voel boven andere mensen 1 2 3 4 5 3. dat mijn gedachten een dag of langer zo snel door mijn hoofd gaan dat ze
mijn concentratie bemoeilijken 1 2 3 4 5
4. dat ik stemmen hoor die andere mensen niet kunnen horen 1 2 3 4 5 5. dat ik voor kortere of langere tijd niet weet waar ik mij bevind, met wie
ik praat en/of welke dag het is 1 2 3 4 5
6. dat ik meen dat anderen mijn gedachten kunnen lezen 1 2 3 4 5 7. dat ik dingen zie die mensen om mij heen niet kunnen zien 1 2 3 4 5
Appendix E: Original FIT Script (p. 34-39) Introduction
“Thank you for coming in today and for volunteering to take part in this study. If you have any questions or concerns during this session, please feel free to ask at any time.”
“Are you happy for me to audio record our session today? It is just a record for myself to make sure everyone gets the same treatment. The recordings will not be used for any other purpose or listened to by anyone, but myself or my supervisors and you can ask to have your recording destroyed at any time of the trial.”
“I would also like to let you know that you are free to stop this session at any time and withdraw any other data that you have provided.”
“And if there is anything that makes you feel uncomfortable in today’s session, please let me know and we can just skip that part.”
“I’m looking forward to hearing about your experiences but before we get into that would it be ok if I let you know first which of the treatment groups you have been allocated to and what that means?” “You have been allocated to the Functional Imagery Training group. What this means is that we will have two sessions where can talk about your weight and what, if anything, you want to do about it, and some ideas for how you can make a change if you want to. One of those sessions will be here today, and the next one will happen by phone in a week’s time. We’ll then have….booster calls. Functional Imagery Training is based on new research showing how mental imagery can strengthen motivation and help people achieve their goals. Do you have any questions about any of that? ‘What led you to sign up for this study?’
“Do you already have an idea of what you want to do?”
“Where does physical activity fit with your goal?” (If not already stated)
“Is it OK if we pick this up again a bit later? I have a couple of questionnaires for you that will show us how your diet and activity levels are at the moment; if that is okay with you?” (IPAQ,
FFQ)
“As you were filling in the questionnaires was there anything that surprised you about your diet?” “And about how physically active you have been over the past week?”
“Is it OK if we just focus on the physical activity aspects of this today?” MI part
“From what I understood earlier, you’ve been thinking about increasing your physical activity." “What do you think will get better/change if you increase your activity?”(Followed by reflection or exploration…)
“… Is there anything else?”
“What are the most important things that will get better?”
“Why is that really important?” Draw out importance or need, by reflections—e.g. it sounds like that is quite important to you.
“Okay, I understand. And what may happen, if you try to imagine, in the future if you don’t change anything?”
“Does that worry or concern you? ...Why?”
“You said earlier that there were a few things you might try to get started with being more physically active. Which one of those would you like to focus on today?”
Or if nothing specific was said: “Are there any specific changes you would like to make concerning how active you are, any ideas you may already have and like to pursue?”
“When you start working on your goal of xxx, would you notice any changes even in the first week?”
