Processing Speed Test (PST): A Self-Administered iPad®-Based Tool for Assessing MS-Related Cognitive Dysfunction (S33.001)

OBJECTIVE: To examine the relationship between performance on the Processing Speed Test (PST), an iPad®-based app for measuring cognition in MS, and self-reported fatigue, employment, and cognitive status. BACKGROUND: Cognitive dysfunction occurs in >50% of MS patients but is infrequently assessed in the clinic setting. The PST app is a self-administered test of information processing speed that simulates the technician-administered Symbol Digit Modalities Test (SDMT). The purpose of this study was to examine the validity of the PST in comparison to the SDMT. DESIGN/METHODS: Self-administered PST and technician-administered SDMT scores were derived from 51 MS patients representing a range of MS severity, and 40 age and gender-matched healthy controls (HC). Test-retest reliability, concurrent validity between PST and SDMT, sensitivity in distinguishing MS from HC participants, and relationship between PST and SDMT scores and self-reports of fatigue, work status, and cognitive performance were determined. RESULTS: We previously reported high levels of test-retest reliability and sensitivity for both the PST and SDMT; concurrent validity is also high, indicating the two measures are tapping comparable cognitive abilities. Scores on the PST and SDMT were significantly lower in unemployed MS patients compared to those working full- or part-time. On the NeuroQOL, a self-report measure of fatigue correlated significantly with PST (r=-0.34) and SDMT (r=-0.32), but neither PST nor SDMT correlated with two self-report cognitive measures. CONCLUSIONS: PST has favorable psychometric properties in comparison to SDMT and correlates equally well with SDMT in relationship to work status and fatigue. PST has several important advantages over SDMT: 1) cognitive performance data can be obtained without technician intervention, 2) unlike the SDMT, PST has multiple forms to minimize practice effects, and 3) additional measures (e.g., inter-response times and learning curves) can be computed that are not possible with the SDMT. Study Supported by: Novartis Pharmaceuticals and the National Multiple Sclerosis Society Disclosure: Dr. Rao has received personal compensation for activities with Novartis, Biogen Idec, and Genzyme Corp. Dr. Rao has received personal compensation in an editorial capacity for the American Psychological Association. Dr. Rao has received research support from Biogen Idec and Novartis. Dr. Alberts has received personal compensation for activities with Boston Scientific Corporation, Juniper Health Systems, and I1 Biometrics. Dr. Alberts has received research support from the National Institutes of Health, and the National Football League Players Association. Dr. Miller has received personal compensation in an editorial capacity for Quality of Life Research. Dr. Miller has received research support from Novartis. Dr. Bethoux has received personal compensation for activities with Acorda Therapeutics, Biogen Idec, Merz Pharma, Medtronic Inc., and Allergan, Inc. Dr. Bethoux has received research support from Acorda Therapeutics, Innovative Neurotronics, and Medtronic Inc. Dr. Lee has nothing to disclose. Dr. Stough has nothing to disclose. Dr. Reece has nothing to disclose. Dr. Mourany has nothing to disclose. Dr. Schindler has nothing to disclose. Dr. Hirsch has nothing to disclose. Dr. Rudick has received personal compensation for activities with Novartis and Genzyme Corporation. Dr. Rudick has received license fee payments from a patent. Dr. Rudick has received research support from Genzyme Corporation and Novartis.