Portable objective assessment of upper extremity motor function in Parkinson's disease
Abstract
Introduction Objective, portable measures of motor function for out-of-office assessments are needed in Parkinson's Disease (PD). This study had 3 objectives. First, to examine change in objective motor measurements in PD (as assessed with the Objective PD Measurement (OPDM) system). Second, to correlate objective measures with clinical features and putative PD cerebrospinal fluid (CSF) and dopaminergic imaging biomarkers. Third, to assess participant compliance with and perceptions of serial in-home motor assessments.Methods De novo PD subjects participating in this pilot study of the Parkinson Progression Markers Initiative (PPMI) completed OPDM assessments at home weekly for 3 months and in the clinic at baseline and 3-, 6-, and 12-months. Tasks included (i)digitography (ii)a repetitive hand tapping task and (iii)timed pegboard task. A global objective motor score (OMS) was derived from the latter three. MDS-UPDRS-III score was obtained at each time point, and CSF and dopamine transporter (DAT) SPECT at baseline.Results 27 participants, mean age 62.6 years, 19 male were included. A mean of 10.5 in-home assessments were completed. There was no significant change in in-home OMS over 12 weeks (p = 0.48). There was strong correlation between mean baseline OMS and MDS-UPDRS-III scores (spearman's rho = 0.60, p=<0.0001). Baseline OMS predicted 6-month MDS-UPDRS-III (β = 0.80, p = 0.0002) but not change in MDS-UPDRS-III score, DAT SPECT, or putative CSF biomarkers.Conclusions This study suggests that administration of in-home motor tasks as part of a large multi-center study is feasible and scores derived from these assessments may serve as surrogates of in-person clinician-assessed motor score.
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Arora and colleagues deployed an Android smartphone to 10 individuals with PD and 10 controls who performed four active tests four times daily for 1 month; adherence was acceptable at 69%, and random forest machine-learning successfully discriminated PD participants from controls.
“Arora and colleagues19 deployed an Android smartphone to 10 individuals with PD (mean motor MDS‐UPDRS score = 19.6 [standard deviation {SD} = 6.7]) and 10 controls, who performed four active tests (sustained phonation, gait, finger‐tapping, and reaction time) four times daily for 1 month. Adherence was acceptable at 69%, and random forest machine‐learning successfully discriminated PD participants”
From Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial (2018) · cites this paper as [27]
Cites
The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment
Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J. L., & Chertkow, H. (2005) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2005.53221.x · Cited by 25,553 · see citing works
Goetz, C. G., Tilley, B. C., Shaftman, S. R., Stebbins, G. T., Fahn, S., Martinez‐Martin, P., Poewe, W., Sampaio, C., Stern, M. B., Dodel, R., et al. (2008) · Movement Disorders · doi:10.1002/mds.22340 · Cited by 7,536 · see citing works
The Parkinson Progression Marker Initiative (PPMI)
MAREK, K. L., Jennings, D. L., Lasch, S., Siderowf, A. D., Tanner, C. M., Simuni, T., Coffey, C. S., Kieburtz, K. D., Flagg, E., Chowdhury, S., et al. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.09.005 · Cited by 1,888 · see citing works
Self-citationTechnology in Parkinson’s disease: challenges and opportunities
Espay, A. J., Bonato, P., Nahab, F. B., Maetzler, W., Dean, J. M., Klucken, J., Eskofier, B. M., Merola, A., Horak, F., Lang, A. E., et al. (2016) · Movement Disorders · doi:10.1002/mds.26642 · Cited by 654 · see citing works
Free‐living monitoring of Parkinson's disease: Lessons from the field
Din, S. D., Godfrey, A., Mazzà, C., Lord, S., & Rochester, L. (2016) · Movement Disorders · doi:10.1002/mds.26718 · Cited by 377 · see citing works
Szewczyk‐Królikowski, K., Tomlinson, P. R., Nithi, K. A., Wade‐Martins, R., Talbot, K., Ben‐Shlomo, Y., & Hu, M. T. M. (2013) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2013.09.025 · Cited by 293 · see citing works
Progression of motor impairment and disability in Parkinson disease
Alves, G. W., Wentzel‐Larsen, T., Aarsland, D., & Larsen, J. P. (2005) · Neurology · doi:10.1212/01.wnl.0000183359.50822.f2 · Cited by 270 · see citing works
Correlation of dopamine transporter imaging with parkinsonian motor handicap: How close is it?
Pirker, W. (2003) · Movement Disorders · doi:10.1002/mds.10579 · Cited by 236 · see citing works
Goetz, C. G., Stebbins, G. T., WOLFF, D. A., Deleeuw, W. W., Bronte-Stewart, H. M., Elble, R. J., Hallett, M., Nutt, J. G., Ramig, L. A., Sanger, T. D., et al. (2008) · Movement Disorders · doi:10.1002/mds.22379 · Cited by 152 · see citing works
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Lipsmeier, F., Taylor, K. I., Kilchenmann, T., Wolf, D., Scotland, A., Schjodt‐Eriksen, J., Cheng, W., Fernandez‐Garcia, I., Siebourg‐Polster, J., Jin, L., et al. (2018) · Movement Disorders · doi:10.1002/mds.27376 · Cited by 266 · see citing works
“Adherence was acceptable at 69%, and random forest machine-learning successfully discriminated PD participants from controls.(27) Using a similar approach on iPhones (Apple Inc.”
ResultThe Best Digital Biomarkers Papers of 2017
Snyder, C. W., Dorsey, E. R., & Atreja, A. (2018) · Digital Biomarkers · doi:10.1159/000489224 · Cited by 19 · see citing works
- Authors
- Chahine, L. M., Uribe, L., Hogarth, P., McNames, J., Siderowf, A., Marek, K., & Jennings, D.
- Affiliations
- University of Pennsylvania (US)
- University of Iowa (US)
- Oregon Health & Science University (US)
- Portland State University (US)
- Institute for Neurodegenerative Disorders (US)
- Eli Lilly (United States) (US)
- Corresponding author
- Chahine, L.M.
- Venue
- Parkinsonism & Related Disorders
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 43
- Pages
- 61-66
- ISSN
- 1353-8020 (ISSN-L)
- OpenAlex
- W2738670565
- Cited by
- 4
- References
- 10
- Citations by year
- 2020: 1 · 2018: 3
- FWCI
- 0.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Parkinsonism & Related Disorders (publishedVersion)
- PubMed (publishedVersion)
- PDXScholar (Portland State University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- Parkinson's disease, upper limb motor function
- MeSH terms
- Adult, Aged, Aged, 80 and over, Female, Hand, Humans, Male, Middle Aged, Movement, Parkinson Disease, Physical Examination, Psychomotor Performance, Severity of Illness Index, Disease Progression
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