Computer mouse movement patterns: A potential marker of mild cognitive impairment

Adriana Seelye, Stuart Hagler, Nora Mattek, Diane B Howieson, Katherine Wild, Hiroko H Dodge, Jeffrey A Kaye, Adriana Seelye, Stuart Hagler, Nora Mattek, Diane B Howieson, Katherine Wild, Hiroko H Dodge, Jeffrey A Kaye

Abstract

Introduction: Subtle changes in cognitively demanding activities occur in MCI but are difficult to assess with conventional methods. In an exploratory study, we examined whether patterns of computer mouse movements obtained from routine home computer use discriminated between older adults with and without MCI.

Methods: Participants were 42 cognitively intact and 20 older adults with MCI enrolled in a longitudinal study of in-home monitoring technologies. Mouse pointer movement variables were computed during one week of routine home computer use using algorithms that identified and characterized mouse movements within each computer use session.

Results: MCI was associated with making significantly fewer total mouse moves (p<.01), and making mouse movements that were more variable, less efficient, and with longer pauses between movements (p<.05). Mouse movement measures were significantly associated with several cognitive domains (p's<.01-.05).

Discussion: Remotely monitored computer mouse movement patterns are a potential early marker of real-world cognitive changes in MCI.

Keywords: aging; cognitive assessment; computer use; early detection of cognitive decline; ecological validity; everyday functioning; functional assessment; instrumental activities of daily living (IADLs); mild cognitive impairment; remote monitoring; technology.

Figures

Fig. 1
Fig. 1
Graphical representation of a mouse movement.
Fig. 2
Fig. 2
Mouse movement variables by cognitive status. Mouse position data were recorded using the computer's internal representation of the mouse position in counts, thus mouse movements are characterized as changes in position with distances measured in counts. Abbreviations: IQR, interquartile range; MCI, mild cognitive impairment.
Fig. 3
Fig. 3
Scatterplot displaying median time taken to make a mouse movement in milliseconds (T) by global cognitive z-score among 62 older adults. Cognitive domain z-scores were calculated using group mean and standard deviations of the raw test scores from all cognitively intact subjects (CDR = 0) at study entry into the ORCATECH cohort (n = 180). Global cognition z-scores were tabulated from cognitive tests in the domains of working memory, attention and processing speed, memory, executive function, and visual perception/construction. Abbreviations: CDR, clinical dementia rating.

References

    1. Alzheimer's A. Alzheimer's disease facts and figures. Alzheimers Dement. 2015;11:332–384.
    1. Rockwood K. The measuring, meaning and importance of activities of daily living (ADLs) as an outcome. Int Psychogeriatr. 2007;19:467–482.
    1. Raghavan N., Samtani M.N., Farnum M., Yang E., Novak G, Grundman M. The ADAS-Cog revisited: Novel composite scales based on ADAS-Cog to improve efficiency in MCI and early AD trials. Alzheimers Dement. 2013;9(1 Suppl):S21–S31.
    1. Dorsey E.R., Venuto C., Venkataraman V., Harris D.A., Kieburtz K. Novel methods and technologies for 21st-century clinical trials: A review. JAMA Neurol. 2015;72:582–588.
    1. Liu E., Schmidt M.E., Margolin R., Sperling R., Koeppe R., Mason N.S. Amyloid-beta 11C-PiB-PET imaging results from 2 randomized bapineuzumab phase 3 AD trials. Neurology. 2015;85:692–700.
    1. Marquis S., Moore M.M., Howieson D.B., Sexton G., Payami H., Kaye J.A. Independent predictors of cognitive decline in healthy elderly persons. Arch Neurol. 2002;59:601–606.
    1. Snyder PJ, Jackson C.E., Petersen R.C., Khachaturian A.S., Kaye J., Albert M.S. Assessment of cognition in mild cognitive impairment: A comparative study. Alzheimers Dement. 2011;7:338–355.
    1. Bondi M.W., Edmonds E.C., Jak A.J., Clark L.R., Delano-Wood L., McDonald C.R. Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. J Alzheimers Dis. 2014;42:275–289.
    1. Bondi M.W., Smith G.E. Mild cognitive impairment: A concept and diagnostic entity in need of input from neuropsychology. J Int Neuropsychol Soc. 2014;20:129–134.
    1. Da X., Toledo J.B., Zee J., Wolk D.A., Xie S.X., Ou Y. Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. Neuroimage Clin. 2014;4:164–173.
    1. Chaytor N., Schmitter-Edgecombe M. The ecological validity of neuropsychological tests: A review of the literature on everyday cognitive skills. Neuropsychol Rev. 2003;13:181–197.
    1. Seelye A., Mattek N., Howieson D., Austin D., Wild K., Dodge H. Embedded online questionnaire measures are sensitive to identifying mild cognitive impairment. Alzheimer Dis Assoc Disord. 2015 In Press.
    1. Dodge H., Mattek N., Gregor M., Bowman M., Seelye A., Ybarra O. Social markers of mild cognitive impairment: Proportion of word counts in free conversational speech. Curr Alzheimer Res. 2015;12:513–519.
    1. Schmitter-Edgecombe M., Parsey C.M. Assessment of functional change and cognitive correlates in the progression from healthy cognitive aging to dementia. Neuropsychology. 2014;28:881–893.
    1. Jekel K., Damian M., Wattmo C., Hausner L., Bullock R., Connelly P.J. Mild cognitive impairment and deficits in instrumental activities of daily living: A systematic review. Alzheimers Res Ther. 2015;7:17.
    1. Kaye J., Mattek N., Dodge H.H., Campbell I., Hayes T., Austin D. Unobtrusive measurement of daily computer use to detect mild cognitive impairment. Alzheimers Dement. 2014;10:10–17.
    1. Hayes T.L., Larimer N., Adami A., Kaye J.A. Medication adherence in healthy elders: Small cognitive changes make a big difference. J Aging Health. 2009;21:567–580.
    1. Petersen J, Austin D, Yeagers J, Kaye J. Unobtrusive phone monitoring as a novel measure of cognitive function, in Alzheimer's Association International Conference. 2014: Amsterdam.
    1. Marshall S.C., Man-Son-Hing M., Charlton J., Molnar L.J., Koppel S., Eby D.W. The Candrive/Ozcandrive prospective older driver study: Methodology and early study findings. Accid Anal Prev. 2013;61:233–235.
    1. Dodge H., Kaye J., Zhu J., Mattek N., Kornfield J. Use of intra-individual distributions of daily acquired home-based measures increases RCT sensitivity. PLoS One. 2014;10 Supplement, Page P204.
    1. Lyons B.E., Austin D., Seelye A., Petersen J., Yeargers J., Riley T. Pervasive computing technologies to continuously assess Alzheimer's disease progression and intervention efficacy. Front Aging Neurosci. 2015;7:102.
    1. Malinowsky C., Almkvist O., Kottorp A., Nygard L. Ability to manage everyday technology: A comparison of persons with dementia or mild cognitive impairment and older adults without cognitive impairment. Disabil Rehabil Assist Technol. 2010;5:462–469.
    1. Kaye J.A., Maxwell S.A., Mattek N., Hayes T.L., Dodge H., Pavel M. Intelligent systems for assessing aging changes: Home-based, unobtrusive, and continuous assessment of aging. J Gerontol B Psychol Sci Soc Sci. 2011;66(Suppl 1):i180–i190.
    1. Folstein M.F., Folstein S.E., McHugh P.R. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–198.
    1. Morris J.C. The clinical dementia rating (CDR): Current version and scoring rules. Neurology. 1993;43:2412–2414.
    1. Yesavage J.A., Brink T.L., Rose T.L., Lum O., Huang V., Adey M. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res. 1982;17:37–49.
    1. Pfeffer R.I., Kurosaki T.T., Harrah C.H., Jr., Chance J.M., Filos S. Measurement of functional activities in older adults in the community. J Gerontol. 1982;37:323–329.
    1. Parmelee P.A., Thuras P.D., Katz I.R., Lawton M.P. Validation of the cumulative illness rating scale in a geriatric residential population. J Am Geriatr Soc. 1995;43:130–137.
    1. Jak A.J., Bondi M.W., Delano-Wood L., Wierenga C., Corey-Bloom J., Salmon D.P. Quantification of five neuropsychological approaches to defining mild cognitive impairment. Am J Geriatr Psychiatry. 2009;17:368–375.
    1. Albert M.S., DeKosky S.T., Dickson D., Dubois B., Feldman H.H., Fox N.C. The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7:270–279.
    1. Hagler S., Jimison H., Pavel M. Assessing executive function using a computer game: Computational modeling of cognitive processes. IEEE J Biomed Health Inform. 2013;18:1442–1452.
    1. Hagler S, Jimison H, Pavel M. Unobtrusive In-Home Assessment by Means of Everyday Computer Mouse Usage. IEEE Engineering in Medicine and Biology Society. 2011:1–4.
    1. Wechsler D. Wechsler Memory Scale-revised. New York: Psychological Corporation; 1987.
    1. Rosen W.G., Mohs R.C., Davis K.L. A new rating scale for Alzheimer's disease. Am J Psychiatry. 1984;141:1356–1364.
    1. Kaplan E., Goodglass H., Weintraub S. Lea & Febiger; Philadelphia, PA: 1983. Boston Naming Test.
    1. Chan A.S., Salmon D.P, Butters N., Johnson S.A. Semantic network abnormality predicts rate of cognitive decline in patients with probable Alzheimer's disease. Journal of the International Neuropsychological Society. 1995;1:297–303.
    1. Loonstra A.S, Tarlow A.R., Sellers A.H. COWAT metanorms across age, education, and gender. Appl Neuropsychol. 2001;8:161–166.
    1. Armitage S.G. An analysis of certain psychological tests used for the evaluation of brain injury. Psychology Monographs. 1946;60:1–47.
    1. Jensen A.R., Rohwer W.D., Jr. The Stroop color-word test: a review. Acta psychologica. 1966;25:36–93.
    1. Wecshler D. Psychological Corporation; New York: 1981. Manual for the Wechsler Adult Intelligence Scale-revised.
    1. Wechsler D. Pearson; San Antonio, Texas: 2008. Manual for the Wechsler Adult Intelligence Scale-Fourth Edition.
    1. Hagler S., Jimison H., Pavel M. Assessing executive function using a computer game: Computational modeling of cognitive processes. IEEE Journal of Biomedical and Health Informatics. 2013;18:1442–1452.
    1. Kaye J, Gregor M, Asgari M, Bowman M, Dodge H. Social biomarkers for early signs of dementia: Increased spoken word counts among older adults with mild cognitive impairment. Presented at the 2014 Alzheimer's Association International Meeting; The Netherlands.
    1. Silbert L, Lahna D, Dodge H, Promjunyakul N, Mattek N, Erten-Lyons D, et al. Less daily computer use is related to smaller hippocampal volumes in dementia-free elderly. Presented at the 2015 Alzheimer's Association International Conference; Washington, DC.
    1. Seligman SC, Giovannetti T. The Potential Utility of Eye Movements in the Detection and Characterization of Everyday Functional Difficulties in Mild Cognitive Impairment. Neuropsychol Rev. 2015;25:199–215.

Source: PubMed

Sponsors en medewerkers

Medische omstandigheden

Geneesmiddelinterventies

3
Abonneren