Hostname: page-component-54dcc4c588-smtgx Total loading time: 0 Render date: 2025-09-20T00:36:06.918Z Has data issue: false hasContentIssue false
  • English
  • Français

Smartphone-Based Neuropsychological Assessment in Parkinson’s Disease: Feasibility, Validity, and Contextually Driven Variability in Cognition

Published online by Cambridge University Press:  17 May 2021

Emma L. Weizenbaum Open the ORCID record for Emma L. Weizenbaum [Opens in a new window] ,
Daniel Fulford ,
John Torous ,
Emma Pinsky ,
Vijaya B. Kolachalama  and
Alice Cronin-Golomb
Emma L. Weizenbaum
Affiliation:
Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
Daniel Fulford
Affiliation:
Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA Department of Occupational Therapy and Rehabilitation Sciences, Boston University, Boston, MA, USA
John Torous
Affiliation:
Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
Emma Pinsky
Affiliation:
Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA Department of Psychology, Bryn Mawr College, Bryn Mawr, PA, USA
Vijaya B. Kolachalama
Affiliation:
Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA Department of Computer Science, and Faculty of Computing and Data Sciences, Boston University Alzheimer’s Disease Center; Boston University, Boston, MA, USA
Alice Cronin-Golomb*
Affiliation:
Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
*
*Correspondence and reprint requests to: Alice Cronin-Golomb, Ph.D., Department of Psychological and Brain Sciences, Boston University, 900 Commonwealth Ave., 2nd floor, Boston, MA 02215, USA. E-mail: alicecg@bu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives:

The prevalence of neurodegenerative disorders demands methods of accessible assessment that reliably captures cognition in daily life contexts. We investigated the feasibility of smartphone cognitive assessment in people with Parkinson’s disease (PD), who may have cognitive impairment in addition to motor-related problems that limit attending in-person clinics. We examined how daily-life factors predicted smartphone cognitive performance and examined the convergent validity of smartphone assessment with traditional neuropsychological tests.

Methods:

Twenty-seven nondemented individuals with mild–moderate PD attended one in-lab session and responded to smartphone notifications over 10 days. The smartphone app queried participants 5x/day about their location, mood, alertness, exercise, and medication state and administered mobile games of working memory and executive function.

Results:

Response rate to prompts was high, demonstrating feasibility of the approach. Between-subject reliability was high on both cognitive games. Within-subject variability was higher for working memory than executive function. Strong convergent validity was seen between traditional tests and smartphone working memory but not executive function, reflecting the latter’s ceiling effects. Participants performed better on mobile working memory tasks when at home and after recent exercise. Less self-reported daytime sleepiness and lower PD symptom burden predicted a stronger association between later time of day and higher smartphone test performance.

Conclusions:

These findings support feasibility and validity of repeat smartphone assessments of cognition and provide preliminary evidence of the effects of context on cognitive variability in PD. Further development of this accessible assessment method could increase sensitivity and specificity regarding daily cognitive dysfunction for PD and other clinical populations.

Keywords

Parkinson’s diseaseCognitionSmartphoneMobile phoneEcological sampling methodPrecision medicine

Information

Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 28 , Issue 4 , April 2022 , pp. 401 - 413
Copyright
Copyright © INS. Published by Cambridge University Press, 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Anderson, M. & Perrin, A. (2017). Technology Use among Seniors. Washington, DC: Pew Research Center for Internet & Technology.Google Scholar
Bauer, R.M., Iverson, G.L., Cernich, A.N., Binder, L.M., Ruff, R.M., & Naugle, R.I. (2012). Computerized neuropsychological assessment devices: joint position paper of the American Academy of Clinical Neuropsychology and the National Academy of Neuropsychology. The Clinical Neuropsychologist, 26(2), 177–196. https://doi.org/10.1080/13854046.2012.663001 CrossRefGoogle ScholarPubMed
Bell-McGinty, S., Podell, K., Franzen, M., Baird, A.D., & Williams, M.J. (2002). Standard measures of executive function in predicting instrumental activities of daily living in older adults. International Journal of Geriatric Psychiatry, 17(9), 828–834. https://doi.org/10.1002/gps.646 CrossRefGoogle ScholarPubMed
Bielak, A.A.M., Mogle, J., & Sliwinski, M.J. (2017). What did you do today? Variability in daily activities is related to variability in daily cognitive performance. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences. https://doi.org/10.1093/geronb/gbx145 CrossRefGoogle Scholar
Bot, B.M., Suver, C., Neto, E.C., Kellen, M., Klein, A., Bare, C., … Trister, A.D. (2016). The mPower study, Parkinson disease mobile data collected using ResearchKit. Scientific Data, 3(1), 160011. https://doi.org/10.1038/sdata.2016.11 CrossRefGoogle ScholarPubMed
Bradford, A., Kunik, M.E., Schulz, P., Williams, S., & Singh, H. (2009). Missed and delayed diagnosis of dementia in primary care prevalence and contributing factors. Alzheimer Disease & Associated Disorders, 23(4), 306–314.CrossRefGoogle ScholarPubMed
Breen, D., Evans, J., Farrell, K., Brayne, C., & Barker, R. (2013). Determinants of delayed diagnosis in Parkinson’s disease. Journal of Neurology, 260(8), 1978–1981. https://doi.org/10.1007/s00415-013-6905-3 CrossRefGoogle ScholarPubMed
Brose, A., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2012). Daily variability in working memory is coupled with negative affect: the role of attention and motivation. Emotion, 12(3), 605–617. https://doi.org/10.1037/a0024436 CrossRefGoogle ScholarPubMed
Cheon, S.-M., Park, M.J., Kim, W.-J., & Kim, J.W. (2009). Non-motor off symptoms in Parkinson’s disease. Journal of Korean Medical Science, 24(2), 311. https://doi.org/10.3346/jkms.2009.24.2.311 CrossRefGoogle ScholarPubMed
Christ, B.U., Combrinck, M.I., & Thomas, K.G.F. (2018). Both reaction time and accuracy measures of intraindividual variability predict cognitive performance in Alzheimer’s disease. Frontiers in Human Neuroscience, 12, 124. https://doi.org/10.3389/fnhum.2018.00124 CrossRefGoogle ScholarPubMed
Cronin-Golomb, A., Reynolds, G.O., Salazar, R.D., & Saint-Hilaire, M.-H. (2019). Parkinson’s disease and Parkinson-plus syndromes. In Alosco, M.L. & Stern, R.A. (Eds.), The Oxford handbook of adult cognitive disorders (pp. 599–630; By A. Cronin-Golomb, G. O. Reynolds, R. D. Salazar, & M.-H. Saint-Hilaire). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190664121.013.28 Google Scholar
Csikszentmihalyi, M. & Larson, R. (1987). Validity and reliability of the experience-sampling method. Journal of Nervous and Mental Disease, 175(9), 526–536.CrossRefGoogle ScholarPubMed
da Silva, F.C., da Iop, R.R., de Oliveira, L.C., Boll, A.M., de Alvarenga, J.G.S., Gutierres Filho, P.J.B., … da Silva, R. (2018). Effects of physical exercise programs on cognitive function in Parkinson’s disease patients: a systematic review of randomized controlled trials of the last 10 years. PLOS ONE, 13(2), e0193113. https://doi.org/10.1371/journal.pone.0193113 CrossRefGoogle ScholarPubMed
Dagum, P. (2018). Digital biomarkers of cognitive function. NPJ Digital Medicine, 1(1), 10. https://doi.org/10.1038/s41746-018-0018-4 CrossRefGoogle ScholarPubMed
de Frias, C.M., Dixon, R.A., Fisher, N., & Camicioli, R. (2007). Intraindividual variability in neurocognitive speed: a comparison of Parkinson’s disease and normal older adults. Neuropsychologia, 45(11), 2499–2507. https://doi.org/10.1016/j.neuropsychologia.2007.03.022 CrossRefGoogle ScholarPubMed
Dirnberger, G. & Jahanshahi, M. (2013). Executive dysfunction in Parkinson’s disease: a review. Journal of Neuropsychology, 7(2), 193–224. https://doi.org/10.1111/jnp.12028 CrossRefGoogle ScholarPubMed
Fahn, S., Elton, R., & UPDRS Program Members. (1987). Unified Parkinsons disease rating scale. In Fahn, S., Marsden, CD, Goldstein, M., & Calne, D. (Eds.), Recent developments in Parkinsons disease (Vol. 2, pp. 153–163). Florham Park, NJ: Macmillan Healthcare Information.Google Scholar
Friedman, J.H., Brown, R.G., Comella, C., Garber, C.E., Krupp, L.B., Lou, J.-S., … Taylor, C.B. (2007). Fatigue in Parkinson’s disease: a review. Movement Disorders, 22(3), 297–308. https://doi.org/10.1002/mds.21240 CrossRefGoogle ScholarPubMed
Haynes, B.I., Bauermeister, S., & Bunce, D. (2017). A systematic review of longitudinal associations between reaction time intraindividual variability and age-related cognitive decline or impairment, dementia, and mortality. Journal of the International Neuropsychological Society: JINS, 23(5), 431–445. https://doi.org/10.1017/S1355617717000236 CrossRefGoogle ScholarPubMed
Higginson, C.I., Lanni, K., Sigvardt, K.A., & Disbrow, E.A. (2013). The contribution of trail making to the prediction of performance-based instrumental activities of daily living in Parkinson’s disease without dementia. Journal of Clinical and Experimental Neuropsychology, 35(5), 530–539. https://doi.org/10.1080/13803395.2013.798397 CrossRefGoogle Scholar
Hirsch, M.A., van Wegen, E.E.H., Newman, M.A., & Heyn, P.C. (2018). Exercise-induced increase in brain-derived neurotrophic factor in human Parkinson’s disease: A systematic review and meta-analysis. Translational Neurodegeneration, 7(1), 7. https://doi.org/10.1186/s40035-018-0112-1 CrossRefGoogle ScholarPubMed
Hogan, D., Bailey, P., Black, S., Carswell, A., Chertkow, H., Clarke, B., … Thorpe, L. (2008). Diagnosis and treatment of dementia: 5. Nonpharmacologic and pharmacologic therapy for mild to moderate dementia. Canadian Medical Association Journal, 179(10), 1019–1026. https://doi.org/10.1503/cmaj.081103 CrossRefGoogle ScholarPubMed
Hughes, A.J., Daniel, S.E., Kilford, L., & Lees, A.J. (1992). Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. Journal of Neurology, Neurosurgery, and Psychiatry, 55(3), 181–184.CrossRefGoogle ScholarPubMed
Hurd, M.D., Martorell, P., Delavande, A., Mullen, K.J., & Langa, K.M. (2013). Monetary costs of dementia in the United States. The New England Journal of Medicine, 368(14), 1326–1334. https://doi.org/10.1056/NEJMsa1204629 CrossRefGoogle ScholarPubMed
Jefferies, L.N., Smilek, D., Eich, E., & Enns, J.T. (2008). Emotional valence and arousal interact in attentional control. Psychological Science, 19(3), 290–295. https://doi.org/10.1111/j.1467-9280.2008.02082.x CrossRefGoogle ScholarPubMed
Johns, M.W. (1991). A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep, 14(6), 540–545. https://doi.org/10.1093/sleep/14.6.540 CrossRefGoogle ScholarPubMed
Kudlicka, A., Hindle, J.V., Spencer, L.E., & Clare, L. (2018). Everyday functioning of people with Parkinson’s disease and impairments in executive function: a qualitative investigation. Disability and Rehabilitation, 40(20), 2351–2363. https://doi.org/10.1080/09638288.2017.1334240 CrossRefGoogle ScholarPubMed
Lange, E.B. (2005). Disruption of attention by irrelevant stimuli in serial recall. Journal of Memory and Language, 53(4), 513–531. https://doi.org/10.1016/j.jml.2005.07.002 CrossRefGoogle Scholar
Lipsmeier, F., Taylor, K.I., Kilchenmann, T., Wolf, D., Scotland, A., Schjodt-Eriksen, J., … Lindemann, M. (2018). Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson’s disease clinical trial: remote PD testing with smartphones. Movement Disorders, 33(8), 1287–1297. https://doi.org/10.1002/mds.27376 CrossRefGoogle Scholar
Llerena, K., Park, S.G., McCarthy, J.M., Couture, S.M., Bennett, M.E., & Blanchard, J.J. (2013). The Motivation and Pleasure Scale–Self-Report (MAP-SR): reliability and validity of a self-report measure of negative symptoms. Comprehensive Psychiatry, 54(5), 568–574. https://doi.org/10.1016/j.comppsych.2012.12.001 CrossRefGoogle ScholarPubMed
Lo, C., Arora, S., Baig, F., Lawton, M.A., El Mouden, C., Barber, T.R., … Hu, M.T. (2019). Predicting motor, cognitive & functional impairment in Parkinson’s. Annals of Clinical and Translational Neurology, acn3.50853. https://doi.org/10.1002/acn3.50853 CrossRefGoogle Scholar
Manly, T., Lewis, G.H., Robertson, I.H., Watson, P.C., & Datta, A.K. (2002). Coffee in the Cornflakes: Time-of-Day as a Modulator of Executive Response Control (Vol. 40). https://doi.org/10.1016/S0028-3932(01)00086-0 CrossRefGoogle Scholar
Miller, I.N., Neargarder, S., Risi, M.M., & Cronin-Golomb, A. (2013). Frontal and posterior subtypes of neuropsychological deficit in Parkinson’s disease. Behavioral Neuroscience, 127(2), 175–183. https://doi.org/10.1037/a0031357 CrossRefGoogle ScholarPubMed
Moore, R.C., Campbell, L.M., Delgadillo, J.D., Paolillo, E.W., Sundermann, E.E., Holden, J., … Swendsen, J. (2020). Smartphone-based measurement of executive function in older adults with and without HIV. Archives of Clinical Neuropsychology: The Official Journal of the National Academy of Neuropsychologists, 35(4), 347–357. https://doi.org/10.1093/arclin/acz084 CrossRefGoogle ScholarPubMed
Moore, R.C., Swendsen, J., & Depp, C.A. (2017). Applications for self-administered mobile cognitive assessments in clinical research: a systematic review. International Journal of Methods in Psychiatric Research, 26(4), e1562. https://doi.org/10.1002/mpr.1562 CrossRefGoogle ScholarPubMed
Morris, R., Martini, D.N., Smulders, K., Kelly, V.E., Zabetian, C.P., Poston, K., … Horak, F. (2019). Cognitive associations with comprehensive gait and static balance measures in Parkinson’s disease. Parkinsonism & Related Disorders, 69, 104–110. https://doi.org/10.1016/j.parkreldis.2019.06.014 CrossRefGoogle ScholarPubMed
Murray, D.K., Sacheli, M.A., Eng, J.J., & Stoessl, A.J. (2014). The effects of exercise on cognition in Parkinson’s disease: a systematic review. Translational Neurodegeneration, 3(1), 5. https://doi.org/10.1186/2047-9158-3-5 CrossRefGoogle ScholarPubMed
Overdorp, E.J., Kessels, R.P.C., Claassen, J.A., & Oosterman, J.M. (2016). The combined effect of neuropsychological and neuropathological deficits on instrumental activities of daily living in older adults: a systematic review. Neuropsychology Review, 26(1), 92–106. https://doi.org/10.1007/s11065-015-9312-y CrossRefGoogle ScholarPubMed
Pal, R., Mendelson, J., Clavier, O., Baggott, M.J., Coyle, J., & Galloway, G.P. (2016). Development and testing of a smartphone-based cognitive/neuropsychological evaluation system for substance abusers. Journal of Psychoactive Drugs, 48(4), 288–294. https://doi.org/10.1080/02791072.2016.1191093 CrossRefGoogle ScholarPubMed
Parsey, C.M. & Schmitter-Edgecombe, M. (2013). Applications of technology in neuropsychological assessment. The Clinical Neuropsychologist, 27(8), 1328–1361. https://doi.org/10.1080/13854046.2013.834971 CrossRefGoogle ScholarPubMed
Rentz, D.M., Dekhtyar, M., Sherman, J., Burnham, S., Blacker, D., Aghjayan, S.L., … Sperling, R.A. (2016). The feasibility of at-home iPad cognitive testing for use in clinical trials. The Journal of Prevention of Alzheimer’s Disease, 3(1), 8–12. https://doi.org/10.14283/jpad.2015.78 Google ScholarPubMed
Reynolds, G.O., Hanna, K.K., Neargarder, S., & Cronin-Golomb, A. (2017). The relation of anxiety and cognition in Parkinson’s disease. Neuropsychology, 31(6), 596–604. https://doi.org/10.1037/neu0000353 CrossRefGoogle ScholarPubMed
Roth, R., Gioia, G., & Isquith, P. (2005). BRIEF®-A - Behavior Rating Inventory of Executive Function®—Adult Version.Google Scholar
Salazar, R.D., Moon, K., Neargarder, S., & Cronin-Golomb, A. (2019). Spatial judgment in Parkinson’s disease: contributions of attentional and executive dysfunction. Behavioral Neuroscience, 133(4), 350–360.CrossRefGoogle ScholarPubMed
Schuster, R.M., Mermelstein, R.J., & Hedeker, D. (2016). Ecological momentary assessment of working memory under conditions of simultaneous marijuana and tobacco use. Addiction, 111(8), 1466–1476. https://doi.org/10.1111/add.13342 CrossRefGoogle ScholarPubMed
Sliwinski, M.J., Mogle, J.A., Hyun, J., Munoz, E., Smyth, J.M., & Lipton, R.B. (2018). Reliability and validity of ambulatory cognitive assessments. Assessment, 25(1), 14–30. https://doi.org/10.1177/1073191116643164 CrossRefGoogle ScholarPubMed
Sliwinski, M.J., Smyth, J.M., Hofer, S.M., & Stawski, R.S. (2006). Intraindividual coupling of daily stress and cognition. Psychology and Aging, 21(3), 545–557. (2006-11398-009). https://doi.org/10.1037/0882-7974.21.3.545 CrossRefGoogle ScholarPubMed
Timmers, C., Maeghs, A., Vestjens, M., Bonnemayer, C., Hamers, H., & Blokland, A. (2014). Ambulant cognitive assessment using a smartphone. Applied Neuropsychology: Adult, 21(2), 136–142. https://doi.org/10.1080/09084282.2013.778261 CrossRefGoogle ScholarPubMed
Tombaugh, T.N. (2004). Trail making test A and B: normative data stratified by age and education. Archives of Clinical Neuropsychology, 19(2), 203–214. https://doi.org/10.1016/S0887-6177(03)00039-8 CrossRefGoogle Scholar
Tomlinson, C.L., Stowe, R., Patel, S., Rick, C., Gray, R., & Clarke, C.E. (2010). Systematic review of levodopa dose equivalency reporting in Parkinson’s disease. Movement Disorders: Official Journal of the Movement Disorder Society, 25(15), 2649–2653. https://doi.org/10.1002/mds.23429 CrossRefGoogle ScholarPubMed
Torous, J., Wisniewski, H., Bird, B., Carpenter, E., David, G., Eduardo, E., … Keshavan, M. (2019). Creating a digital health smartphone app and digital phenotyping platform for mental health and diverse healthcare needs: An interdisciplinary and collaborative approach. Journal of Technology in Behavioral Science, 4, 73–85. https://doi.org/10.1007/s41347-019-00095-w CrossRefGoogle Scholar
van der Velden, R.M.J., Mulders, A.E.P., Drukker, M., Kuijf, M.L., & Leentjens, A.F.G. (2018). Network analysis of symptoms in a Parkinson patient using experience sampling data: An n = 1 study: symptom network analysis in Parkinson’s disease. Movement Disorders, 33(12), 1938–1944. https://doi.org/10.1002/mds.93 CrossRefGoogle Scholar
Wardenaar, K.J., van Veen, T., Giltay, E.J., de Beurs, E., Penninx, B.W.J.H., & Zitman, F.G. (2010). Development and validation of a 30-item short adaptation of the Mood and Anxiety Symptoms Questionnaire (MASQ). Psychiatry Research, 179(1), 101–106. https://doi.org/10.1016/j.psychres.2009.03.005 CrossRefGoogle Scholar
Watson, D., Clark, L.A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063.CrossRefGoogle ScholarPubMed
Wechsler, D. (1997). Wechsler Memory Scale–Third Edition. San Antonio, TX: The Psychological Corporation.Google Scholar
Weizenbaum, E., Torous, J., & Fuford, D. (2020). Cognition in context: understanding the everyday predictors of cognitive performance in a new era of measurement. JMIR Mhealth Uhealth, 8(7), e14328. https://doi.org/10.2196/14328 CrossRefGoogle Scholar
West, R., Murphy, K.J., Armilio, M.L., Craik, F.I.M., & Stuss, D.T. (2002). Effects of time of day on age differences in working memory. The Journals of Gerontology: Series B, 57(1), P10. https://doi.org/10.1093/geronb/57.1.P3 CrossRefGoogle ScholarPubMed
Wu, J.Q. & Cronin-Golomb, A. (2020). Temporal associations between sleep and daytime functioning in Parkinson’s disease: a smartphone-based ecological momentary assessment. Behavioral Sleep Medicine, 18(4), 560–569. https://doi.org/10.1080/15402002.2019.1629445 CrossRefGoogle ScholarPubMed
Zhan, A., Mohan, S., Tarolli, C., Schneider, R.B., Adams, J.L., Sharma, S., … Saria, S. (2018). Using smartphones and machine learning to quantify Parkinson disease severity: the mobile Parkinson disease score. JAMA Neurology, 75(7), 876. https://doi.org/10.1001/jamaneurol.2018.0809 CrossRefGoogle ScholarPubMed