Reshmi Pal1, John Mendelson2, Odile Clavier3, Mathew J Baggott4, Jeremy Coyle5, Gantt P Galloway6. 1. a Assistant Research Scientist, Friends Research Institute , Addiction & Pharmacology Research Laboratory , San Francisco , CA , USA. 2. b Senior Research Scientist, Friends Research Institute , Addiction & Pharmacology Research Laboratory , San Francisco , CA , USA. 3. c Engineer, Creare Inc. , Hanover , NH , USA. 4. d Data Scientist , Genentech , South San Francisco , CA , USA. 5. e Ph.D. Candidate, University of California-Berkeley , Biostatistics , Berkeley , CA , USA. 6. f Senior Research Scientist, Friends Research Institute , Addiction & Pharmacology Research Laboratory , San Francisco , CA , USA.
Abstract
INTRODUCTION: In methamphetamine (MA) users, drug-induced neurocognitive deficits may help to determine treatment, monitor adherence, and predict relapse. To measure these relationships, we developed an iPhone app (Neurophone) to compare lab and field performance of N-Back, Stop Signal, and Stroop tasks that are sensitive to MA-induced deficits. METHODS: Twenty healthy controls and 16 MA-dependent participants performed the tasks in-lab using a validated computerized platform and the Neurophone before taking the latter home and performing the tasks twice daily for two weeks. RESULTS: N-Back task: there were no clear differences in performance between computer-based vs. phone-based in-lab tests and phone-based in-lab vs. phone-based in-field tests. Stop-Signal task: difference in parameters prevented comparison of computer-based and phone-based versions. There was significant difference in phone performance between field and lab. Stroop task: response time measured by the speech recognition engine lacked precision to yield quantifiable results. There was no learning effect over time. On an average, each participant completed 84.3% of the in-field NBack tasks and 90.4% of the in-field Stop Signal tasks (MA-dependent participants: 74.8% and 84.3%; healthy controls: 91.4% and 95.0%, respectively). Participants rated Neurophone easy to use. CONCLUSION: Cognitive tasks performed in-field using Neurophone have the potential to yield results comparable to those obtained in a laboratory setting. Tasks need to be modified for use as the app's voice recognition system is not yet adequate for timed tests.
INTRODUCTION: In methamphetamine (MA) users, drug-induced neurocognitive deficits may help to determine treatment, monitor adherence, and predict relapse. To measure these relationships, we developed an iPhone app (Neurophone) to compare lab and field performance of N-Back, Stop Signal, and Stroop tasks that are sensitive to MA-induced deficits. METHODS: Twenty healthy controls and 16 MA-dependent participants performed the tasks in-lab using a validated computerized platform and the Neurophone before taking the latter home and performing the tasks twice daily for two weeks. RESULTS: N-Back task: there were no clear differences in performance between computer-based vs. phone-based in-lab tests and phone-based in-lab vs. phone-based in-field tests. Stop-Signal task: difference in parameters prevented comparison of computer-based and phone-based versions. There was significant difference in phone performance between field and lab. Stroop task: response time measured by the speech recognition engine lacked precision to yield quantifiable results. There was no learning effect over time. On an average, each participant completed 84.3% of the in-field NBack tasks and 90.4% of the in-field Stop Signal tasks (MA-dependent participants: 74.8% and 84.3%; healthy controls: 91.4% and 95.0%, respectively). Participants rated Neurophone easy to use. CONCLUSION: Cognitive tasks performed in-field using Neurophone have the potential to yield results comparable to those obtained in a laboratory setting. Tasks need to be modified for use as the app's voice recognition system is not yet adequate for timed tests.