Can Chronic Visual Discomfort Measures Accurately Predict Acute Symptoms?

Purpose Several surveys exist to determine the severity and frequency of chronic symptoms related to visual discomfort. To our knowledge, there are no studies that investigate the potential of chronic visual discomfort ratings to predict acute discomfort symptoms that are experienced after tests of accommodation and vergence. We examined the ability of two measures of chronic visual discomfort symptoms to predict acute symptoms experienced. Methods The Conlon et al. and the convergence insufficiency symptom surveys were administered to 40 participants to assess chronic visual discomfort symptoms. Two measures were used to assess acute symptoms. The first consisted of four Likert-scaled questions relating to comfort level during last test, visual distortions or movement, discomfort caused by overhead lights, and presence of headache symptoms. These questions were asked before and after binocular examination, and the scores were used to generate a postexamination symptom score. The second measure of acute symptoms consisted of participants rating their general discomfort on a four-point Likert scale after each binocular test, and the ratings were summed to produce a General Symptom Score. Participants were then categorized into a high or low Post-exam symptom group and General symptom group. Data were analyzed with a binary logistic regression to determine whether the chronic surveys could predict acute symptom group classification. Results Approximately 75% of predictions were accurate for either chronic symptom survey. Headache, soreness, and eye-related questions were more reliable predictors of symptom acute outcome. Conclusions These data suggest that the Conlon and Convergence Insufficiency Symptom Survey (CISS) surveys are good predictors of acute symptoms induced during a binocular examination and validate the use of chronic symptom surveys as screening tools for symptomatic binocular dysfunction. Further investigation is required to determine predictability of accommodative or vergence performance.

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