continued

RESULTS

A total of 312 surveys were collected, resulting in 259 valid geographically dispersed profiles from addicted users. The sample included 130 males with a mean age of 31 and 129 females with a mean age of 33. Educational background was as follows: 30% had a high school degree or less, 38% had an associate's or bachelor's degree, 10% had a master's degree or doctorate, and 22% were still in school. Of the subjects, 15% had no vocational background (e.g., homemaker or retired), 31% were students₁ 6% were blue-collar workers (e.g., factor worker or auto mechanic), 22% were nontech white-collar workers (e.g., school teacher or bank teller), and 26% were high-tech white-collar workers (e.g., computer scientist or systems analyst).

Occupational type appears to be a determinant in the level of Internet usage in this study. These results suggest that nontech or high-tech white-collar workers are more likely to become addicted to the Internet than are blue-collar workers. White-collar employment may offer wider access to the Internet and greater salary potential, making the purchase of a home computer more affordable compared to those in blue-collar types of employment, which may explain these results.

Results from the BDI were a mean of 11.2 (SD 13.9), indicating mild to moderate levels of depression compared to normative data. Prior research showed that analysis of the ZDI provided a mean of 38.56 (SD = 10.24), also indicating mild to moderate levels of depression when compared to normal populations.~ Therefore, the BDI yielded similar results as the prior work suggesting that depression is a significant factor in the development of PIU.

DEPRESSION AND ADDICTION DISCUSSION

As noted with other addictive disorders, our findings suggest that increased levels of depression are associated with those who become addicted to the Internet. This suggests that clinical depression is significantly associated with increased levels of personal Internet use. These results should be interpreted with caution, however, as self-selected sample biases exist in this study coupled with the questionable accuracy of on-line responses.

This study suggests that accurate assessment of depression and PIU can improve early detection, especially when one is masked by primary symptoms of the other diagnosis. It is likely that low self-esteem, poor motivation, fear of rejection, and the need for approval associated with depressives contribute to increased Internet use, as prior research indicated that the interactive capabilities available on the Internet were found to be most addictive.² It is plausible that depressives are drawn to electronic communication because of the anonymous cover granted to them by talking with others through fictitious handles, which helps them overcome real-life interpersonal difficulties. Kiesler et al.¹⁴ found that computer-mediated communication weakens social influence by the absence of such nonverbal behavior as talking in the head set, speaking loudly, staring, touching, and gesturing. Therefore, the disappearance of facial expression, voice inflection, and eye contact makes electronic communication less threatening, thereby helping the depressive to overcome the initial awkwardness and intimidation in meeting and speaking with others. This anonymous two-way talk also helps depressives feel comfortable sharing ideas with others thanks to the personal control over the level of their communication, as they have time to plan, contemplate, and edit comments before sending an electronic message. Therefore, the treatment protocol should emphasize the primary psychiatric condition, if related to a subsequent impulse control problem, as addictive Internet use. Effective management of such psychiatric symptoms may indirectly correct PIU.

Based on the findings, it is concluded that evaluation of suspected cases of PIU should in-dude assessment for depression. These results, however, do not clearly indicate whether depression preceded the development of such Internet abuse or if it was a consequence. Young² showed that withdrawal from significant real-life relationships is a consequence of PIU. Therefore, the possibility exists that increased levels of social isolation subsequent to excessive time spent in front of a computer may result in increased depression rather than be a cause of such Internet overuse. Therefore, further experimentation with a more comprehensive level of analysis is necessary to examine cause and effect. Data collection should also include patients in treatment to eliminate the methodological limitations of an on-line survey and to improve the clinical utility of the information gathered. Finally, although it is unclear how PIU compares to other established addictions, future research should investigate if clinical depression is an etiologic factor in the development of any addictive syndrome, be it alcohol, gambling, or the Internet.

REFERENCES

1. Young, K.S. (1997, April 11). Leoels of depression and addiction underlying pathological Internet use. Poster presented at the annual meeting of the Eastern Psychological Association, Washington, DC.

2. Young, K.S. (1996, August 10). Internet addiction: The emergence of a new clinical disorder. Paper presented at the 104th annual meeting of the American Psycho-logical Association, Toronto.

3. Capuzzi, D., & Lecoq, L.L. (1983). Social and personal determination of adolescent use and abuse of alcohol and marijuana. Personnel and Guidance Journal, 62, 199-205.

4. Cox, W.M. (1985). Personality correlates of substance abuse. In M. Galizio & S.A. Maisto (Eds.), Determinants of substance abuse: Biological, psychological, and environmental factors (pp.209-246). New York: Plenum.

5. Lacey, H.J. (1993). Self-damaging and addictive behavior in bulimia nervosa: A catchment area study. British Journal of Psychiatry, 163, 190-194.

6. Lesieur, H.R., & Blume, S.B. ~993). Pathological gambling, eating disorders, and the psychoactive substance use disorders. Journal of Addictive Diseases, 12(3), 89-102.

7. Blaszczynski, A., McConaghy, N., & Frankova, A. (1991). Sensation seeking and pathological gambling. British Journal of Addiction, 81, 113-117.

8. Criffiths, M. (1990). The cognitive psychology of gambling. Journal of Gambling Studies, 6, 31~2.

9. Mobilia, P. (1993). Gambling as a rational addiction. Journal of Gambling Studies, 9(2), 121-151.

10. Zung, W.K. (1965). Self-rating depression scale. New York; Springer-Verlag.

11. Beck, A.T., Ward, C.M., Mendeleson, M., Mock, J.F., & Erbaugh, J.K. (1961). An inventory for measuring depression. Archives of General Psychiatry, 4, 5~-571.

12. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

13. Zuckerman, M. (1979). Sensation seeking behavior: Beyond the optimal level of arousal. Hillsdale, NJ: Erlbaum.

14. Kiesler, S., Siegal, I., & McGuire, T.W. (1984). Social psychological aspects of computer-mediated communication. American Psychologist, 39(10), 1123~134.

15. Cattell, R. (1975). Sixteen Personality Factor Inventory. The Institute of Personality and Ability, Inc., Champaign, IL

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