Applications for self-administered mobile cognitive assessments in clinical research: A systematic review

Int J Methods Psychiatr Res. 2017 Dec;26(4):e1562. doi: 10.1002/mpr.1562. Epub 2017 Mar 31.

Abstract

Frequent, brief and repeated self-administered mobile assessments of cognitive function, conducted in everyday life settings, are a promising complementary tool to traditional assessment approaches. Mobile cognitive assessments promote patient-centered care and might enhance capacity to inform individual-level outcomes over time (i.e. detecting subtle declines in cognitive function), as well as in assessing cognition and its correlates in the naturalistic environment. The goal of this systematic review was to assess the feasibility and psychometric properties of mobile cognitive assessments. Through a comprehensive search, we identified 12 articles using self-administered, mobile phone-based cognitive assessments. Studies sampled participants between 1 and 6 times per day for 1-14 days. Samples ranged in age from 14 to 83 years old and were generally healthy populations without cognitive impairment. Working memory was the most frequently-assessed cognitive domain (n = 7), followed by attention/reaction time (n = 4). Seven studies reported adherence, with mean adherence rates of 79.2%. In addition to positive evidence of feasibility, there was general support for high levels of between- and within-person reliability and construct validity. While research has only begun to explore the utility of mobile cognitive assessments, studies to-date indicate they may be a promising complementary tool to traditional assessment methods with potential to improve clinical care and research.

Keywords: cognition; ecological momentary assessment; patient-centered care; repeated sampling; technology.

Publication types

  • Review
  • Systematic Review

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Cognitive Dysfunction / diagnosis*
  • Diagnosis, Computer-Assisted / standards*
  • Diagnosis, Computer-Assisted / statistics & numerical data
  • Humans
  • Middle Aged
  • Mobile Applications*
  • Young Adult