Psychometric characteristics of daily diaries for the Patient-Reported Outcomes Measurement Information System (PROMIS®): a preliminary investigation
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The Patient-Reported Outcomes (PRO) Measurement Information System (PROMIS®) has developed assessment tools for numerous PROs, most using a 7-day recall format. We examined whether modifying the recall period for use in daily diary research would affect the psychometric characteristics of several PROMIS measures.
Daily versions of short-forms for three PROMIS domains (pain interference, fatigue, depression) were administered to a general population sample (n = 100) for 28 days. Analyses used multilevel item response theory (IRT) models. We examined differential item functioning (DIF) across recall periods by comparing the IRT parameters from the daily data with the PROMIS 7-day recall IRT parameters. Additionally, we examined whether the IRT parameters for day-to-day within-person changes are invariant to those for between-person (cross-sectional) differences in PROs.
Dimensionality analyses of the daily data suggested a single dimension for each PRO domain, consistent with PROMIS instruments. One-third of the daily items showed uniform DIF when compared with PROMIS 7-day recall, but the impact of DIF on the scale level was minor. IRT parameters for within-person changes differed from between-person parameters for 3 depression items, which were more sensitive for measuring change than between-person differences, but not for pain interference and fatigue items. Notably, mean scores from daily diaries were significantly lower than the PROMIS 7-day recall norms.
The results provide initial evidence supporting the adaptation of PROMIS measures for daily diary research. However, scores from daily diaries cannot be directly interpreted on PROMIS norms established for 7-day recall.
KeywordsPROMIS® Patient-reported outcomes Electronic diaries Item response theory
This research was supported by a grant from the National Institutes of Health (1 U01AR057948-01). We would like to thank Gim Yen Toh and Laura Wolff for their assistance with data collection.
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