Evaluating the Reliability of Neuro-QWERTY Index, a Computer-Based Method for Early Diagnostic of Parkinson's Disease

Document Type : Original Article (s)

Authors

1 PhD in Biostatistics, Social Development and Health Promotion Research Center, Gonabad University of Medical Sciences, Gonabad, Iran

2 Associate Professor, Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran

3 Assistant Professor, Department of Physics, School of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Background: Early diagnose of Parkinson's disease has been possible by using neuro-QWERTY index, a computational algorithm for analyzing users’ interactions with a computer keyboard. This research was devoted to study the reliability of neuro-QWERTY index; as detecting the reliability is necessary to clear the usefulness of diagnostic and screening tests.Methods: Early Parkinson's disease database, which was used in aggregated format by Giancardo et al. in 2016 to introduce neuro-QWERTY index, and to assess its validity, was used in this study in original format to assess the test-retest reliability of neuro-QWERTY index. Intraclass correlation coefficient (ICC), Bland-Altman plot, and coefficient of repeatability (CR) were used for statistical analyses. Moreover, using DeLong’s statistical test, area under curves (AUCs) of receiver operating curves (ROCs) was compared for test and retest scores, and their average scores.Findings: Intraclass correlation coefficient was 0.94, with a 95% confidence interval (CI) of 0.89 to 0.97. Bland-Altman plot showed that differences between test and retest scores were small. Coefficient of repeatability was 0.04, with a 95% CI of 0.03 to 0.05. The area under curves were 0.89, 0.90, and 0.92 for receiver operating curves of test score, retest score, and their average score, respectively. Differences between the areas under curves of receiver operating curves were not significant (P ≥ 0.25).Conclusion: Neuro-QWERTY index have a good reliability. Therefore, it could act as a suitable candidate for screening of Parkinson's disease, considering simplicity and inexpensiveness of its measurements, no side effects, good validity in terms of sensitivity and specificity, and as shown in this study, its acceptable reliability.

Keywords

References

  1. Wong SL, Gilmour H, Ramage-Morin PL. Parkinson's disease: Prevalence, diagnosis and impact. Health Rep 2014; 25(11): 10-4.
  2. Elbaz A, Carcaillon L, Kab S, Moisan F. Epidemiology of Parkinson's disease. Rev Neurol (Paris) 2016; 172(1): 14-26.
  3. Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol 2017; 16(11): 877-97.
  4. Rossi A, Berger K, Chen H, Leslie D, Mailman RB, Huang X. Projection of the prevalence of Parkinson's disease in the coming decades: Revisited. Mov Disord 2018; 33(1): 156-9.
  5. Weerkamp NJ, Tissingh G, Poels PJ, Zuidema SU, Munneke M, Koopmans RT, et al. Parkinson disease in long term care facilities: a review of the literature. J Am Med Dir Assoc 2014; 15(2): 90-4.
  6. von ,Campenhausen S, Bornschein B, Wick R, Botzel K, Sampaio C, Poewe W, et al. Prevalence and incidence of Parkinson's disease in Europe. Eur Neuropsychopharmacol 2005; 15(4): 473-90.
  7. Pringsheim T, Jette N, Frolkis A, Steeves TD. The prevalence of Parkinson's disease: a systematic review and meta-analysis. Mov Disord 2014; 29(13): 1583-90.
  8. Gigante AF, Pellicciari R, Iliceto G, Liuzzi D, Mancino PV, Custodero GE, et al. Rest tremor in Parkinson's disease: Body distribution and time of appearance. J Neurol Sci 2017; 375: 215-9.
  9. Marek K, Jennings D, Seibyl J. Dopamine agonists and Parkinson's disease progression: What can we learn from neuroimaging studies. Ann Neurol 2003; 53)Suppl 3): S160-S166.
  10. Marek K, Jennings D. Can we image premotor Parkinson disease? Neurology 2009; 72(7 Suppl): S21-S26.
  11. Goldman SM. A diagnostic algorithm for Parkinson's disease: what next? Lancet Neurol 2015; 14(10): 971-3.
  12. Nalls MA, McLean CY, Rick J, Eberly S, Hutten SJ, Gwinn K, et al. Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study. Lancet Neurol 2015; 14(10): 1002-9.
  13. Oung QW, Muthusamy H, Lee HL, Basah SN, Yaacob S, Sarillee M, et al. Technologies for Assessment of Motor Disorders in Parkinson's Disease: A Review. Sensors (Basel ) 2015; 15(9): 21710-45.
  14. Lotankar S, Prabhavalkar KS, Bhatt LK. Biomarkers for Parkinson's Disease: Recent Advancement. Neurosci Bull 2017; 33(5): 585-97.
  15. Li S, Le W. Biomarker Discovery in Parkinson's Disease: Present challenges and future opportunities. Neurosci Bull 2017; 33(5): 481-2.
  16. Giancardo L, Sanchez-Ferro A, Arroyo-Gallego T, Butterworth I, Mendoza CS, Montero P, et al. Computer keyboard interaction as an indicator of early Parkinson's disease. Sci Rep 2016; 6: 34468.
  17. Maxim LD, Niebo R, Utell MJ. Screening tests: a review with examples. Inhal Toxicol 2014; 26(13): 811-28.
  18. Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000; 101(23): E215-E220.
  19. Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 2016; 15(2): 155-63.
  20. Gordis L. Epidemiology. 5th ed. Philadelphia, PA: Saunders; 2013. p. 105.
Journal of Isfahan Medical School
Volume 36, Issue 480 - Serial Number 480
March and April 2018
Pages 542-547

Files

History

  • Receive Date: 24 April 2018
  • Revise Date: 07 November 2024
  • Accept Date: 06 April 2022

Share

How to cite

Statistics