Strengths of genetics as a risk factor for addiction:

• Evidence supporting genetic component of addiction: See studies above.

Weaknesses of genetics as a risk factor for addiction:

• Interactionism: Despite this genetic component, there is clearly more to addiction than just genetics. Firstly, a person may live in an environment where they never come across addictive substances and so their genetic vulnerability never materialises as actual addiction. Secondly, even identical twins don’t have 100% concordance rates for addiction. As such, the role of genetics in addiction should be understood from an interactionist perspective: Genetic vulnerability + environment = addiction.
• Variation between substances: Evidence suggests genetic vulnerability to addiction varies between different substances/behaviours. For example, a person may have genetics that make them particularly vulnerable to developing gambling addiction but not alcohol addiction.
• Usual methodological issues with twin studies: Twin studies typically assume that both identical (monozygotic) and non-identical (dizygotic) are raised in identical environments and so any differences in concordance rates are due to genetics. However, this assumption is not certain. For example, parents are likely to treat non-identical twins more differently than identical twins and so this environmental factor could also contribute to differences in concordance rates. This could mean genetic influences on addiction are exaggerated.

Strengths of stress as a risk factor for addiction:

• Face plausibility: It’s easy to understand how acute stress could lead to addiction. Stress is an unpleasant feeling and addictive substances (e.g. alcohol, cigarettes, drugs) alleviate this feeling.
• Evidence supporting the role of stress as a risk factor for addiction: Several studies have demonstrated a correlation between stress and addiction. For example, Tavolacci et al (2013) assessed stress levels of French students using a questionnaire and found that stressed students were more likely to smoke, abuse alcohol, or develop cyberaddiction.
• Practical applications: Understanding the link between stress and addiction can be used to reduce addiction and relapse rates. For example, doctors could measure stress levels and direct at-risk patients towards resources that help them cope with stress and reduce their risk of addiction.

Weaknesses of stress as a risk factor for addiction:

• Correlation vs. causation: Although stress and addiction are correlated, this does not automatically prove stress causes addiction. Being addicted is itself a stressful situation and so the direction of causation could go the other way: Addiction may cause stress (rather than stress causing addiction).
• Other factors: Although stress appears to be a risk factor for addiction, it is not the only one. The various risk factors – genetics, personality, family and peer influence – all combine to cause (or reduce the risk of) addiction.

Strengths of personality as a risk factor for addiction:

• Supporting evidence: Some studies support a connection between personality traits and addiction. For example, a meta-analysis by Howard et al (1997) found high novelty seeking was correlated with increased risk of alcohol abuse in teenagers and young adults – just as Cloninger’s tri-dimensional theory predicts.

Weaknesses of personality as a risk factor for addiction:

• Conflicting evidence for the role of personality in addiction: However, the same meta-analysis by Howard et al (1997) described above found evidence for a link between the other two traits (high reward dependence and low response to danger) and alcohol abuse was much less consistent.
• Correlation vs causation: Although there is evidence that personality traits are correlated with addiction, this does not automatically mean that having those personality traits causes one to be at greater risk of addiction. It could be that being addicted to drugs changes one’s personality (and so the direction of causation goes the other way).
• Other factors: Although personality appears to be a risk factor for addiction, it is not the only one. The various risk factors – genetics, stress, family and peer influence – all combine to cause (or reduce the risk of) addiction.

Strengths of family influence as a risk factor for addiction:

• Evidence supporting the role of family influence in addiction: Several studies support the role of family influence on addiction, such as Livingston et al (2010) described above. Another example is Akers and Lee (1996), who looked at smoking behaviours of 454 adolescents over a 5 year period. The researchers found that family influence (via social learning theory) made the children more likely to start and continue smoking cigarettes.

Weaknesses of family influence as a risk factor for addiction:

• Variations in family influence: Various factors, such as the age of the child and the extent to which they identify with the family member, influence the strength of family influence on addiction. For example, seeing a parent smoke at age 12 may be a greater risk factor for cigarette addiction than at age 4, when the child doesn’t understand what is going on. Similarly, if the child’s father smokes but the child identifies more with the mother, this reduces the strength of family influence.
• Other factors: Although family influence appears to be a risk factor for addiction, it is not the only one. The various risk factors – genetics, stress, personality, peer influence – all combine to cause (or reduce the risk of) addiction. Another factor that is relevant to social learning theory explanations is mediating processes, which are cognitive processes where a person decides whether to imitate a behaviour or not. For example, mediating processes may mean a child observes their parents smoking but decides not to imitate that behaviour.

Strengths of peer influence as a risk factor for addiction:

• Evidence supporting the role of peer influence in addiction: Several studies have found correlations between addiction and having friends who are addicts. The hypothesis that having peers who use drugs will increase the likelihood that a person becomes addicted to drugs also has face plausibility: It just seems common sense.

Weaknesses of peer influence as a risk factor for addiction:

• Possibility that peer influence on addiction is exaggerated: Despite the face validity of peer influence on addiction, its influence may be exaggerated. For example, a review by Bauman and Ennett (1996) found that many studies cite peer influence as a reason for substance abuse without actually testing this claim against evidence.
• Correlation vs. causation: Even if addiction and certain peer groups are correlated, it is difficult to prove which comes first. It may be the case that having peers who use drugs increases the likelihood that that person also uses and becomes addicted to drugs. But it may also be the case that people who use drugs seek out peers who also share an addiction to that drug. For example, a person with an alcohol addiction may seek out friends who like going to pubs and drinking a lot of alcohol as this enables them to indulge their addiction.
• Variations in peer influence: Like with family, different factors – e.g. age and the extent to which a person identifies with their peer – influence the strength of peer influence. For example, a 40 year old would probably be less influenced by friends who smoke cigarettes than they would as a teenager.
• Other factors: Although peer influence appears to be a risk factor for addiction, it is not the only one. It is difficult to determine the relative strength of the various risk factors – genetics, stress, personality, family influence – on addiction. There are also other factors not listed on the syllabus, such as influence from the media (e.g. films and music) and advertising.

Strengths of neurochemical explanations of gambling addiction:

• Evidence supporting the dopamine desensitisation hypothesis: Several studies have found nicotine causes dopamine release. For example, table 3 of Sharma and Brody (2009) summarises several brain scan studies that show increases in dopamine activity following nicotine administration.

Weaknesses of neurochemical explanations of gambling addiction:

• Methodological concerns: However, many studies linking nicotine and dopamine were conducted on animals rather than humans. As such, the findings may not be valid when applied to the human dopamine response to nicotine.
• Other factors: Although dopamine almost certainly plays a role in nicotine addiction, it’s likely that other neurochemical factors are at play. For example, nicotine also triggers the release of endogenous opioids, which also have pleasurable effects in the body. This suggests that the dopamine desensitisation hypothesis is an incomplete explanation of nicotine addiction.

Strengths of learning theory explanations of nicotine addiction:

• Evidence supporting learning theory explanations: Studies provide support for various learning theory explanations of nicotine addiction. For example:
  • Operant conditioning: Over a series of 24×45 minute sessions, Levin et al (2010) gave rats two waterspouts to drink from: One normal and one that administered nicotine. As the sessions progressed, the rats increasingly drank from the nicotine waterspout, which suggests this behaviour was conditioned via positively reinforcement.
  • Cue reactivity (classical conditioning): A meta-analysis by Carter and Tiffany (1999) found cigarette addicts reported higher cravings to smoke and demonstrated physiological responses associated with cravings (e.g. increased heart rate) when exposed to smoking-related cues.
  • Social learning theory: A report by the US Office of the Surgeon General (2012) reviewed multiple studies, many of which support the social learning explanation of nicotine addiction. The section concludes: “the evidence is suggestive of a […] causal role for peer group influences.”
• More complete explanation: Neurochemical explanations (and operant conditioning) explain why people continue to smoke, but not why they start smoking in the first place. However, social learning theory explains this first stage: People are motivated to try smoking from observing the rewards of other people who smoke. This suggests that a complete explanation of nicotine addiction must incorporate social learning theory.

Weaknesses of learning theory explanations of nicotine addiction:

• Incomplete: Learning theory is unable to explain why some people get addicted to nicotine but others don’t. For example, one person might smoke cigarettes occasionally and not become addicted, whereas another person might smoke the same amount and become addicted. This suggests other factors – e.g. genetics, personality – are needed to fully explain why people get addicted to nicotine.

Strengths of learning theory explanations of gambling addiction:

• Evidence supporting learning theory explanations: Several studies support learning theory explanations of gambling addiction. For example, Parke and Griffiths (2004) describe several ways gambling behaviour is reinforced, such as winning money, the thrill of gambling, and praise from peers. They also argue that some losses – near misses – positively reinforce gambling behaviour, as these raise hopes of future success.

Weaknesses of learning theory explanations of gambling addiction:

• Other factors: Although there is strong evidence supporting the role of operant conditioning in gambling addiction, it does not provide a complete explanation. For example, operant conditioning can’t explain why some people get addicted and others don’t: One person may become addicted to gambling after a few wins whereas another person may never gamble again after the exact same experience. This suggests other factors – e.g. genetics, personality – are needed to fully explain why people get addicted to gambling.
• Doesn’t explain why people start gambling: Operant conditioning only explains why people continue to gamble, not why they start gambling in the first place. As such, other learning theory explanations, such as social learning theory, may be needed to provide a complete account of gambling addiction.

Strengths of cognitive explanations of gambling addiction:

• Evidence supporting cognitive explanations: A key study supporting cognitive explanations of gambling addiction is Griffiths (1994). In this study, 30 regular gamblers and 30 controls were each given £3 to gamble on slot machines. Griffiths recorded what the participants said as they gambled and interviewed them after. He found that the regular gamblers were more than 5 times as likely to say irrational things (e.g. “the machine doesn’t like me”) as the controls, and that the regular gamblers were significantly more likely to believe winning was about skill rather than luck. This supports the hypothesis that gambling addicts are prone to cognitive bias.
• Practical applications: Understanding the thought processes and cognitive biases of gambling addicts could be used to treat gambling addiction. For example, some addicts may not be aware their thoughts are irrational (e.g. overestimating the role of skill and paying selective attention to wins while ignoring losses) and so cognitive behavioural therapy could help gambling addicts identify these thoughts, challenge them as irrational, and break their gambling addiction.

Weaknesses of cognitive explanations of gambling addiction:

• Methodological concerns: Much of the evidence for cognitive explanations of gambling addiction come from self-report methods, which may not be valid. For example, Dickerson and O’Connor (2006) argue that much of what people say when gambling does not reflect what they actually believe in day-to-day life.
• Correlation vs. causation: As always, you can raise questions about the direction of causation. Even if cognitive bias and gambling addiction are correlated, it doesn’t automatically prove that cognitive bias causes gambling addiction. It could be the case that, over time, gambling addiction causes one’s thinking to become more irrational and biased.

Strengths of drug therapy:

• Evidence supporting the efficacy of drug therapy: This obviously varies depending on the drug and which addiction it is used to treat, but several drug therapies are supported by evidence. For example, a systematic review of 11 studies by the Cochrane Drugs and Alcohol Group (2009) found methadone successfully reduces heroin use. For smoking, a meta-analysis by Hughes et al (2003) found over-the-counter nicotine replacement therapy (e.g. gum, patches) was effective at helping people quit smoking.
• Can be combined with other therapies: Drug therapy can be combined with other approaches (such as behavioural interventions and cognitive behavioural therapy) to provide a holistic and more effective approach to reducing addiction.

Weaknesses of drug therapy:

• Side effects: Many drugs used to treat addiction have serious side effects that have to be weighed against the benefits. For example, methadone (the agonist used to treat heroin and opiate addiction) is itself highly addictive, although typically less so than the drugs it replaces. Drug therapies may also cause dangerous or unpleasant side effects (e.g. headaches, nausea), which increase the likelihood of the addict discontinuing the therapy.

Strengths of aversion therapy:

• Evidence supporting aversion therapy: Although there are few experimental trials, the available evidence suggests emetic drugs are an effective way of reducing alcohol addiction. For example, based on data from private hospitals, Elkins (1991) reports that approximately 60% of alcoholics treated with emetic drugs remain alcohol-free one year later.

Weaknesses of aversion therapy:

• Conflicting evidence: Although there is evidential support for some forms of aversion therapy (e.g. emetic drugs for alcohol addiction), other forms of aversion therapy appear to be less effective. For example, Cannon and Baker (1981) assigned 20 alcoholic subjects to one of three groups: Emetic drugs, electric shock therapy, or no aversion therapy. The researchers found that only the emetic drugs successfully reduced alcohol addiction. Further, in the case of smoking, a review of 25 trials by Hajek and Stead (2001) found insufficient evidence to support the effectiveness of aversion therapy for treating nicotine addiction.
• Ethical concerns: Aversion therapy works by inflicting pain and suffering on addicts, which may be considered unethical. In part because of this, aversion therapy has been largely phased out in favour of covert sensitisation.

Strengths of covert sensitisation:

• Evidence supporting covert sensitisation: Studies suggest covert sensitisation may be an effective treatment for some forms of addiction. For example, in a study of alcohol addicts by Ashem and Donner (1968), 40% of patients treated with covert sensitisation were alcohol-free after 6 months compared to 0% of controls. In the case of gambling addiction, McConaghy et al (1983) randomly assigned 20 gambling addicts to receive either covert sensitisation or aversion therapy via electric shock. After one year, 90% of subjects who received covert sensitisation had reduced their gambling, compared to just 30% for aversion therapy.
• Lower risk of side effects: Covert sensitisation has a much lower risk of side effects than drug therapy and even other behavioural treatments such as aversion therapy. For example, treating alcoholics with disulfiram causes pain and nausea and in extreme cases can result in heart attack and death. In contrast, imagining feeling unwell is less likely to cause severe or lasting negative effects.

Weaknesses of covert sensitisation:

• Conflicting evidence: Although there are examples of success with covert sensitisation, many studies question whether it actually works.
• Differences between patients: Covert sensitisation is likely to be more effective for patients with vivid imaginations. If the patient is unable to picture the negative images in their mind, it won’t create a strong enough association to reduce their addiction.

Strengths of cognitive behavioural therapy:

• Evidence supporting CBT: Some studies have found CBT to be an effective treatment for addiction. For example, Petry et al (2006) randomly assigned 231 gambling addicts to receive one of three treatment plans: Gamblers Anonymous meetings, Gamblers anonymous meetings + cognitive behavioural workbook exercises, or Gamblers Anonymous meetings + 8 sessions of CBT. The groups that received cognitive behavioural treatments saw greater reductions in gambling activity than those who didn’t, suggesting CBT and related treatments are an effective treatment for gambling addiction.

Weaknesses of cognitive behavioural therapy:

• Questions about long-term efficacy: Although a review by Cowlishaw et al (2012) found CBT to be an effective treatment for gambling addiction in the short term (0-3 months post-treatment), the authors found little evidence to support its efficacy over longer time periods (9-12 months post-treatment). Further, the authors found methodological issues with many of the studies reviewed and argue that this could mean the benefits of CBT are overestimated.
• Lack of standardisation: The many different approaches and methods of CBT make it difficult to determine which aspects of CBT are actually effective (if any).
• Differences between patients: It requires a lot of insight and continuous effort to change one’s thought patterns and so people who are unable to do this are less likely to benefit from CBT. Further, CBT is likely to be more effective for addictions with a strong cognitive basis (e.g. gambling addiction) rather than a biological one (e.g. nicotine addiction).

Strengths of planned behaviour theory:

• Evidence supporting planned behaviour theory applied to addiction: Hagger et al (2011) found support for all three factors – personal attitudes, subjective norms, and perceived behavioural control – in predicting an individual’s intention to reduce their drinking. Further, this overall level of intention was also an accurate predictor of units of alcohol consumed, providing support for the validity of the model. In another study on gambling addiction, Oh and Hsu (2001) found correlations between level of intention (as described in the theory of planned behaviour) and actual gambling behaviours, providing further support for the model.
• Practical applications: If intention accurately predicts behavioural change then the theory of planned behaviour will have various practical applications such as more effective treatment strategies and more efficient use of resources. For example, if an alcoholic has no intention to quit (e.g. due to his personal attitudes), then covert sensitisation will likely be a waste of time and money as he will be unlikely to commit to the treatment. Instead, therapies aimed at changing his personal attitudes (and thus increasing his intention to quit) will be more effective.

Weaknesses of planned behaviour theory:

• Conflicting evidence: Although a meta-analysis of 47 studies by Webb and Sheeran (2006) found some correlation between level of intention and behavioural change, the effect was very small. This suggests that the role of intention (the key element of the theory of planned behaviour) may be overestimated in predicting behavioural change.
• Methodological concerns: Testing the theory of planned behaviour requires use of self-report techniques (e.g. questionnaires to gauge personal attitudes), which can be highly unreliable. For example, interpretation of attitudes and social norms is highly subjective and so different participants may report the same information differently. Further, subjects may lie to give socially desirable responses, for example by downplaying the amount of alcohol they drink.

Strengths of Prochaska’s six-stage model:

• Evidence supporting Prochaska’s six-stage model applied to addiction: A meta-analysis of five studies by Velicer et al (2007) found a consistent success rate of 22-26% for anti-smoking treatment programs tailored to each individual’s stage within the six-stage model. The authors also found that a smoker’s stage within the six-stage model was a more accurate predictor of quitting than demographic factors such as age, race, or gender.
• Practical applications: If Prochaska’s six-stage model is accurate, then it will result in more effective treatment strategies and more efficient use of resources. For example, when someone is in the preparation stage, focusing too much on the costs and benefits of quitting will be a waste of time as the person has already made the decision to quit. Instead, the most effective intervention at this stage will be to help the person make their plan for quitting.
• Positive attitude to relapse: Recovering addicts often relapse several times before quitting their addiction. The six-stage model is realistic in acknowledging this and sees relapse as part of recovery rather than failure.

Weaknesses of Prochaska’s six-stage model:

• Conflicting evidence: Aveyard et al (2009) randomly assigned 2,471 smokers to receive either treatment tailored to their stage on the six-stage model or general treatment that was not tailored to their stage of quitting. The researchers found no significant difference in effectiveness between the two treatments. This suggests that tailoring interventions according to the six-stage model is not effective, contradicting the Velicer et al (2007) study above.
• Arbitrary cut-offs: The six-stage model defines various stages according to time horizons. For example, people in the preparation stage are planning to quit their addiction within the next month, but if someone is planning to quit within the next month + 1 day they are defined as being in the contemplation stage. This cut-off seems arbitrary as there is basically no difference between the two examples in reality. This suggests the stages of the six-stage model are mostly arbitrary and made up rather than accurate descriptions of distinct stages of behavioural change.