Migraine has been associated with sleep disturbances. Relationship between sleep quality and migraine frequency is yet to be determined. The present study aimed to investigate sleep disturbances among low-frequency, moderate-frequency, high-frequency, and chronic migraineurs, with and without auras, with well-controlled confounding variables.This cross-sectional controlled study included 357 subjects from an outpatient headache clinic in Taiwan. Standardized questionnaires were utilized to collect demographic, migraine, sleep, depression, anxiety, and restless leg syndrome characteristics in all participants. According to frequency of migraine attacks, patients were divided into 4 groups: with 1 to 4 migraine days per month, 5 to 8 migraine days in a month, 9 to 14 migraine days in a month, and >14 migraine days per month. The Pittsburgh Sleep Quality Index (PSQI) and subgroup items were used to evaluate sleep quality. The association between migraine frequency and sleep quality was investigated using multivariable linear regression and logistic regression.The PSQI total score was highest in patients with high frequent migraine (10.0 ± 3.4) and lowest in controls (7.0 ± 3.4) with a significant trend analysis (P for trend = 0.006). Migraine frequency had an independent effect on the items "Cannot get to sleep within 30 minutes" (P < 0.001), "Wake up in the middle of the night or early morning" (P < 0.001), "Bad dreams" (P = 0.001), "Pain" (P = 0.004), and "Quality of sleep" (P < 0.001). The result showed the effect of migraine frequency in both the aura-present (P for trend = 0.008) and the aura-absent subgroups (P for trend = 0.011).High migraine frequency correlates with poor sleep quality and a higher prevalence of poor sleepers. These associations occur in migraine with aura and without aura.

Background: ChatGPT may act as a research assistant to help organize the direction of thinking and summarize research findings. However, few studies have examined the quality, similarity (abstracts being similar to the original one), and accuracy of the abstracts generated by ChatGPT when researchers provide full-text basic research papers. Objective: We aimed to assess the applicability of an artificial intelligence (AI) model in generating abstracts for basic preclinical research. Methods: We selected 30 basic research papers from Nature, Genome Biology, and Biological Psychiatry. Excluding abstracts, we inputted the full text into ChatPDF, an application of a language model based on ChatGPT, and we prompted it to generate abstracts with the same style as used in the original papers. A total of 8 experts were invited to evaluate the quality of these abstracts (based on a Likert scale of 0-10) and identify which abstracts were generated by ChatPDF, using a blind approach. These abstracts were also evaluated for their similarity to the original abstracts and the accuracy of the AI content. Results: The quality of ChatGPT-generated abstracts was lower than that of the actual abstracts (10-point Likert scale: mean 4.72, SD 2.09 vs mean 8.09, SD 1.03; P<.001). The difference in quality was significant in the unstructured format (mean difference –4.33; 95% CI –4.79 to –3.86; P<.001) but minimal in the 4-subheading structured format (mean difference –2.33; 95% CI –2.79 to –1.86). Among the 30 ChatGPT-generated abstracts, 3 showed wrong conclusions, and 10 were identified as AI content. The mean percentage of similarity between the original and the generated abstracts was not high (2.10%-4.40%). The blinded reviewers achieved a 93% (224/240) accuracy rate in guessing which abstracts were written using ChatGPT. Conclusions: Using ChatGPT to generate a scientific abstract may not lead to issues of similarity when using real full texts written by humans. However, the quality of the ChatGPT-generated abstracts was suboptimal, and their accuracy was not 100%.