Thomas D Parsons1,2, Timothy McMahan2, Robert Kane3. 1. a Department of Psychology , University of North Texas , Denton , TX , USA. 2. b Computational Neuropsychology and Simulation Laboratory , University of North Texas , Denton , TX , USA. 3. c Cognitive Consults and Technology LLC , Washington , DC , USA.
Abstract
OBJECTIVE: Clinical neuropsychologists have long underutilized computer technologies for neuropsychological assessment. Given the rapid advances in technology (e.g. virtual reality; tablets; iPhones) and the increased accessibility in the past decade, there is an on-going need to identify optimal specifications for advanced technologies while minimizing potential sources of error. Herein, we discuss concerns raised by a joint American Academy of Clinical Neuropsychology/National Academy of Neuropsychology position paper. Moreover, we proffer parameters for the development and use of advanced technologies in neuropsychological assessments. METHOD: We aim to first describe software and hardware configurations that can impact a computerized neuropsychological assessment. This is followed by a description of best practices for developers and practicing neuropsychologists to minimize error in neuropsychological assessments using advanced technologies. We also discuss the relevance of weighing potential computer error in light of possible errors associated with traditional testing. Throughout there is an emphasis on the need for developers to provide bench test results for their software's performance on various devices and minimum specifications (documented in manuals) for the hardware (e.g. computer, monitor, input devices) in the neuropsychologist's practice. CONCLUSION: Advances in computerized assessment platforms offer both opportunities and challenges. The challenges can appear daunting but are a manageable and require informed consumers who can appreciate the issues and ask pertinent questions in evaluating their options.
OBJECTIVE: Clinical neuropsychologists have long underutilized computer technologies for neuropsychological assessment. Given the rapid advances in technology (e.g. virtual reality; tablets; iPhones) and the increased accessibility in the past decade, there is an on-going need to identify optimal specifications for advanced technologies while minimizing potential sources of error. Herein, we discuss concerns raised by a joint American Academy of Clinical Neuropsychology/National Academy of Neuropsychology position paper. Moreover, we proffer parameters for the development and use of advanced technologies in neuropsychological assessments. METHOD: We aim to first describe software and hardware configurations that can impact a computerized neuropsychological assessment. This is followed by a description of best practices for developers and practicing neuropsychologists to minimize error in neuropsychological assessments using advanced technologies. We also discuss the relevance of weighing potential computer error in light of possible errors associated with traditional testing. Throughout there is an emphasis on the need for developers to provide bench test results for their software's performance on various devices and minimum specifications (documented in manuals) for the hardware (e.g. computer, monitor, input devices) in the neuropsychologist's practice. CONCLUSION: Advances in computerized assessment platforms offer both opportunities and challenges. The challenges can appear daunting but are a manageable and require informed consumers who can appreciate the issues and ask pertinent questions in evaluating their options.
Keywords:
Computerized testing; neurocognition; neuropsychological assessment; neuropsychological test validity
Authors: Nikki H Stricker; Emily S Lundt; Kelly K Edwards; Mary M Machulda; Walter K Kremers; Rosebud O Roberts; David S Knopman; Ronald C Petersen; Michelle M Mielke Journal: Clin Neuropsychol Date: 2018-11-10 Impact factor: 3.535
Authors: Robert Toups; Theresa J Chirles; Johnathon P Ehsani; Jeffrey P Michael; John P K Bernstein; Matthew Calamia; Thomas D Parsons; David B Carr; Jeffrey N Keller Journal: Innov Aging Date: 2022-01-07
Authors: Carrie Esopenko; Jessica Meyer; Elisabeth A Wilde; Amy D Marshall; David F Tate; Alexander P Lin; Inga K Koerte; Kimberly B Werner; Emily L Dennis; Ashley L Ware; Nicola L de Souza; Deleene S Menefee; Kristen Dams-O'Connor; Dan J Stein; Erin D Bigler; Martha E Shenton; Kathy S Chiou; Judy L Postmus; Kathleen Monahan; Brenda Eagan-Johnson; Paul van Donkelaar; Tricia L Merkley; Carmen Velez; Cooper B Hodges; Hannah M Lindsey; Paula Johnson; Andrei Irimia; Matthew Spruiell; Esther R Bennett; Ashley Bridwell; Glynnis Zieman; Frank G Hillary Journal: Brain Imaging Behav Date: 2021-01-06 Impact factor: 3.978
Authors: Cynthia A Berg; Deborah J Wiebe; Yana Suchy; Sara L Turner; Jonathan Butner; Ascher Munion; Amy Hughes Lansing; Perrin C White; Mary Murray Journal: Diabetes Care Date: 2018-08-21 Impact factor: 19.112
Authors: Jennifer Ferrar; Gareth J Griffith; Caroline Skirrow; Nathan Cashdollar; Nick Taptiklis; James Dobson; Fiona Cree; Francesca K Cormack; Jennifer H Barnett; Marcus R Munafò Journal: J Med Internet Res Date: 2021-06-18 Impact factor: 5.428
Authors: Elena Tsoy; Alissa Bernstein Sideman; Stefanie D Piña Escudero; Maritza Pintado-Caipa; Suchanan Kanjanapong; Tala Al-Rousan; Lingani Mbakile-Mahlanza; Maira Okada de Oliveira; Myriam De la Cruz Puebla; Stelios Zygouris; Aya Ashour Mohamed; Hany Ibrahim; Collette A Goode; Bruce L Miller; Victor Valcour; Katherine L Possin Journal: J Alzheimers Dis Date: 2021 Impact factor: 4.160