Reliability and validity of a mobile tablet for assessing left/right judgements

Musculoskelet Sci Pract. 2019 Apr:40:45-52. doi: 10.1016/j.msksp.2019.01.010. Epub 2019 Jan 24.

Abstract

Background: Left/right judgement (LRJ) of body parts is commonly used to assess the ability to perform implicit motor imagery and the integrity of brain-grounded maps of the body. Clinically, LRJ are often undertaken using a mobile tablet, but the concurrent validity and reliability of this approach has not yet been established.

Objectives: To evaluate the concurrent validity and test-retest reliability of a mobile tablet for assessing LRJ.

Method: Participants completed LRJ for 50 hand images (Experiment 1), and 40 back, foot, or neck images (Experiment 2) using a mobile tablet and desktop computer in random order. Participants in Experiment 2 performed a repeat test the following day to assess test-retest reliability. Accuracy and response time (RT) were recorded.

Results: Twenty participants aged 55.3 (±6.7) years in Experiment 1, and 37 participants aged 38.2 (±12.3) years in Experiment 2, were recruited. Concurrent validity of the mobile tablet was good to excellent for hand judgements (ICC3,1 = 0.836 for RT; ICC = 0.909 for accuracy), and was good for back, foot, and neck judgements (ICC = 0.781 for accuracy; ICC = 0.880 for RT). Test-retest reliability of the mobile tablet was good to excellent (ICC = 0.824 for accuracy; ICC = 0.903 for RT).

Conclusions: The mobile tablet demonstrated good to excellent concurrent validity with the desktop computer in two separate samples. The mobile tablet also demonstrated good to excellent test-retest reliability. The mobile tablet for LRJ is a valid alternative to the original desktop version.

Keywords: Body schema; Concurrent validity; Motor imagery; left/right judgement.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Age Factors
  • Computers, Handheld*
  • Disability Evaluation
  • Female
  • Humans
  • Male
  • Middle Aged
  • Reaction Time / physiology*
  • Reproducibility of Results
  • Task Performance and Analysis