News Release

Test improves prediction of self-injurious behavior

Self-injuring individuals display implicit associations between cutting and the self

Peer-Reviewed Publication

Harvard University

Researchers have found a better way to predict self-injurious behavior by using a test that does not rely on the individual to articulate their thoughts, but instead assesses their implicit attitudes towards self-injury. This procedure addresses a major challenge in the identification of people who engage in self-injurious behavior, because such individuals are often intentionally uncommunicative in order to avoid unwanted treatment as well as unable to articulate their feelings.

Conducted by two Harvard professors, the study was led by Matthew Nock, an assistant professor of psychology, with Mahzarin Banaji, the Cabot professor of social ethics in the department of psychology and Pforzheimer professor at the Radcliffe Institute. The paper is published in the May issue of the American Journal of Psychiatry.

"This research represents a significant advance in risk assessment for self-injury. This test measures the associations people hold about self-injury using their own behavioral responses rather than their verbal report, and therefore it has the potential to significantly improve our ability to detect and predict self-injurious thoughts in a way not previously possible" says Nock.

The study included 89 adolescents between the ages of twelve and nineteen, of which 53 had a history of non-suicidal self injurious behavior, and 36 were non-injurers that comprised a comparison group. The group was given a version of the Implicit Association Test (IAT), adapted to measure mental associations to cutting of the body in particular because prior studies have shown that cutting is the most common form of self-injury.

The IAT was computer administered with participants using key board controls to quickly associate self with cutting or not. Based on the participant's reaction time when grouping self with cutting versus not, the test identified the strength of association between self and injury via cutting that the individual might not otherwise share with others or even be aware of herself.

In the identity test, participants grouped together words and phrases such as "Cutting" or "No Cutting" with words such as "Me" or "They." Those who engaged in self-injurious behavior were more likely to quickly and without error group the words that that are relevant to cutting with the words related to the self such as I, me, mine, myself.

In a second attitude test, participants were asked to group together words such as "Cutting" or "Not Cutting" with words that captured the categories "Good" and "Bad." While both groups showed an association between "Cutting" and "Bad," the association was significantly weaker for self-injurers.

In addition to the IAT, participants were also assessed using more conventional measures of self-injury, as well as obtaining measures of demographic factors, differences in IQ, and presence of psychiatric disorders. When considered in conjunction with the more traditional indicators of self-injurious behavior, the IAT significantly improved the prediction of who was a self-injurer above and beyond what was possible using the conventional self-report measures.

"Among the most important applications of the IAT is its ability to reveal the mind's state when it is psychologically compromised -- as it is in a full range of psychological disorders," says Banaji. "Clinical psychologists have been at the forefront of applying the IAT to improve the ability to predict and treat mental illness. Such applications show the importance of basic research in understanding mental illness."

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The IAT was developed in 1998 by Banaji, along with Anthony Greenwald of the University of Washington. Since then, more than 4.5 million tests have been completed, through the website at http://implicit.harvard.edu.

Nock and Banaji's work was funded by the National Institute of Mental Health, the Milton Fund and the Talley Fund of Harvard University.


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