Background: Migraine and other severe headaches can cause suffering and reduce functioning and productivity. Patients are the best source of information about such impact. Objective: To develop a new short form (HIT-6) for assessing the impact of headaches that has broad content coverage but is brief as well as reliable and valid enough to use in screening and monitoring patients in clinical research and practice. Methods: HIT-6 items were selected from an existing item pool of 54 items and from 35 items suggested by clinicians. Items were selected and modified based on content validity, item response theory (IRT) information functions, item internal consistency, distributions of scores, clinical validity, and linguistic analyses. The HIT-6 was evaluated in an Internet-based survey of headache sufferers (n = 1103) who were members of America Online (AOL). After 14 days, 540 participated in a follow-up survey. Results: HIT-6 covers six content categories represented in widely used surveys of headache impact. Internal consistency, alternate forms, and test–retest reliability estimates of HIT-6 were 0.89, 0.90, and 0.80, respectively. Individual patient score confidence intervals (95%) of app. ±5 were observed for 88% of all respondents. In tests of validity in discriminating across diagnostic and headache severity groups, relative validity (RV) coefficients of 0.82 and 1.00 were observed for HIT-6, in comparison with the Total Score. Patient-level classifications based in HIT-6 were accurate 88.7% of the time at the recommended cut-off score for a probability of migraine diagnosis. HIT-6 was responsive to self-reported changes in headache impact. Conclusions: The IRT model estimated for a 'pool' of items from widely used measures of headache impact was useful in constructing an efficient, reliable, and valid 'static' short form (HIT-6) for use in screening and monitoring patient outcomes.
Headache impact Headache impact test (HIT™) Health-related quality of life (HRQOL) HIT-6 HIT Item response theory (IRT) Migraine Reliability Validation