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Electric aversion and individualized imagery therapy in alcoholism: a controlled experiment
Alcohol (Fayetteville, N.Y.)
|March 1, 1984
Summary
This study found electric aversion therapy ineffective for alcoholism treatment. Patient personality traits, specifically calmness and self-reliance, predicted better outcomes regardless of therapy received.
Area of Science:
- Psychiatry
- Behavioral Therapy
- Clinical Psychology
Background:
- Alcoholism is a complex addiction requiring diverse treatment approaches.
- Aversion therapy, using unpleasant stimuli, has been explored for treating alcohol dependence.
Purpose of the Study:
- To evaluate the efficacy of electric aversion therapy as an adjunct to standard alcoholism treatment.
- To investigate the influence of personality traits on treatment outcomes in alcoholic patients.
Main Methods:
- Randomized controlled trial comparing electric aversion therapy with sham therapy.
- Participants received individualized imagery of drinking situations paired with shock or sham shock.
- General alcoholism treatment including relaxation training was provided to all patients.
- Long-term follow-up assessed treatment effectiveness.
- The 16 PF Personality Test was administered to assess baseline personality characteristics.
Main Results:
- No significant differences in treatment outcomes were observed between the electric aversion therapy and sham therapy groups.
- Patients with higher scores on the 16 PF Personality Test for calmness, self-reliance, placidity, and discipline showed better long-term outcomes.
- These positive personality traits were independent of the assigned treatment group.
Conclusions:
- The study found no evidence supporting the efficacy of electric aversion therapy for alcoholism under the tested conditions.
- Pre-existing personality traits, particularly those indicating emotional stability and self-control, appear to be significant predictors of successful alcoholism treatment outcomes.
- Further research is needed to explore effective therapeutic interventions and identify patient characteristics that predict treatment response.

