Feb. 13, 2003 (Phoenix) -- A team of stroke researchers has devised a one-minute test that can be used by ordinary people to diagnose stroke -- and the test is so simple that even a child can use it. Such an easy, quick test could potentially save thousands of stroke sufferers from the disabling effects by allowing faster treatment.

"It is just three simple steps" says Jane Brice, MD, assistant professor of emergency medicine at the University of North Carolina-Chapel Hill School of Medicine. Brice tells WebMD that the test is based on a scale developed by researchers at the University of Cincinnati. The three-part test, called the Cincinnati Pre-Hospital Stroke Scale (CPSS), can be used to diagnose most strokes, says Brice.

Brice and her colleagues measured the accuracy of the test by first teaching it to 100 healthy bystanders. The bystanders then performed the test on stroke survivors. To diagnose a stroke, the bystanders performed the following three steps:

1. Bystander told the patients, "Show me your teeth." The "smile test" is used to check for one-sided facial weakness -- a classic sign of stroke.

2. Then the patients were told to close their eyes and raise their arms. Stroke patients usually cannot raise both arms to the same height, a sign of arm weakness.

3. Finally, the patients were asked to repeat a simple sentence to check for slurring of speech, which is another classic sign of stroke. "In Cincinnati, the researchers asked patients to say, 'The sky is blue in Cincinnati,'" says Brice. But in the study, the researchers varied four simple phrases such as "Don't cry over spilled milk."

Overall, 97% of the bystanders were able to accurately follow the directions for giving the test, says Amy S. Hurwitz, a medical student at UNC who helped design the study.

The bystanders were 96% accurate at detecting speech problems and 97% accurate at spotting one-sided arm weakness. They were less accurate at detecting facial weakness -- with only a 72% accuracy rate for this test. But Hurwitz says, "It is difficult to detect differences in the smile of a stranger. We are hoping that in most cases the 'bystander' will actually be someone who knows the patient and so an unusual smile will be apparent."