Digital Phenotyping in Clinical Neurology

Anoopum S. Gupta

doi:10.1055/s-0041-1741495

Seminars in Neurology, Table of Contents

Semin Neurol 2022; 42(01): 048-059
DOI: 10.1055/s-0041-1741495

Review Article

Digital Phenotyping in Clinical Neurology

Anoopum S. Gupta

¹Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts

› Author Affiliations

Abstract

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

Keywords

digital phenotyping - quantitative phenotyping - biomarkers - outcome measures

Full Text

References

References
1 Jain SH, Powers BW, Hawkins JB, Brownstein JS. The digital phenotype. Nat Biotechnol 2015; 33 (05) 462-463
2 Torous J, Kiang MV, Lorme J, Onnela JP. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR Ment Health 2016; 3 (02) e16
3 Insel TR. Digital phenotyping: technology for a new science of behavior. JAMA 2017; 318 (13) 1215-1216
4 Dorsey ER, Venuto C, Venkataraman V, Harris DA, Kieburtz K. Novel methods and technologies for 21st-century clinical trials: a review. JAMA Neurol 2015; 72 (05) 582-588
5 Leurent C, Ehlers MD. Digital technologies for cognitive assessment to accelerate drug development in Alzheimer's disease. Clin Pharmacol Ther 2015; 98 (05) 475-476
6 Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Sci Transl Med 2015; 7 (283) 283rv3
7 Bergethon P. Taking aim at Parkinson's. How the Internet of Things could revolutionize patient care. Health Manag Technol 2016; 37 (04) 12
8 Dorsey ER, Glidden AM, Holloway MR, Birbeck GL, Schwamm LH. Teleneurology and mobile technologies: the future of neurological care. Nat Rev Neurol 2018; 14 (05) 285-297
9 Doraiswamy PM, Narayan VA, Manji HK. Mobile and pervasive computing technologies and the future of Alzheimer's clinical trials. NPJ Digit Med 2018; 1: 1
10 Artusi CA, Mishra M, Latimer P. et al. Integration of technology-based outcome measures in clinical trials of Parkinson and other neurodegenerative diseases. Parkinsonism Relat Disord 2018; 46 (Suppl. 01) S53-S56
11 Perry B, Herrington W, Goldsack JC. et al. Use of mobile devices to measure outcomes in clinical research, 2010-2016: a systematic literature review. Digit Biomark 2018; 2 (01) 11-30
12 Stroud C, Onnela J-P, Manji H. Harnessing digital technology to predict, diagnose, monitor, and develop treatments for brain disorders. NPJ Digit Med 2019; 2: 44
13 Artusi CA, Imbalzano G, Sturchio A. et al. Implementation of mobile health technologies in clinical trials of movement disorders: underutilized potential. Neurotherapeutics 2020; 17 (04) 1736-1746
14 Ko P-RT, Kientz JA, Choe EK, Kay M, Landis CA, Watson NF. Consumer sleep technologies: a review of the landscape. J Clin Sleep Med 2015; 11 (12) 1455-1461
15 Kolla BP, Mansukhani S, Mansukhani MP. Consumer sleep tracking devices: a review of mechanisms, validity and utility. Expert Rev Med Devices 2016; 13 (05) 497-506
16 Khosla S, Deak MC, Gault D. et al; American Academy of Sleep Medicine Board of Directors. Consumer sleep technology: an American Academy of Sleep Medicine position statement. J Clin Sleep Med 2018; 14 (05) 877-880
17 Jory C, Shankar R, Coker D, McLean B, Hanna J, Newman C. Safe and sound? A systematic literature review of seizure detection methods for personal use. Seizure 2016; 36: 4-15
18 Ulate-Campos A, Coughlin F, Gaínza-Lein M, Fernández IS, Pearl PL, Loddenkemper T. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure 2016; 40: 88-101
19 Kurada AV, Srinivasan T, Hammond S, Ulate-Campos A, Bidwell J. Seizure detection devices for use in antiseizure medication clinical trials: a systematic review. Seizure 2019; 66: 61-69
20 Rao SM, Galioto R, Sokolowski M. et al. Multiple sclerosis performance test: validation of self-administered neuroperformance modules. Eur J Neurol 2020; 27 (05) 878-886
21 Zuber BL, Stark L, Cook G. Microsaccades and the velocity-amplitude relationship for saccadic eye movements. Science 1965; 150 (3702): 1459-1460
22 Sommer C. Exploring pain pathophysiology in patients. Science 2016; 354 (6312): 588-592
23 Rolke R, Baron R, Maier C. et al. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): standardized protocol and reference values. Pain 2006; 123 (03) 231-243
24 Bohannon RW. Test-retest reliability of hand-held dynamometry during a single session of strength assessment. Phys Ther 1986; 66 (02) 206-209
25 Robinson PN. Deep phenotyping for precision medicine. Hum Mutat 2012; 33 (05) 777-780
26 Delude CM. Deep phenotyping: the details of disease. Nature 2015; 527 (7576): S14-S15
27 Dorsey ER, Omberg L, Waddell E. et al. Deep phenotyping of Parkinson's Disease. J Parkinsons Dis 2020; 10 (03) 855-873
28 Bergethon PR. The neurophysical chemistry of autism: postulates from intelligence modeling. In: Blatt GJ. ed. The Neurochemical Basis of Autism: From Molecules to Minicolumns. Boston, MA: Springer US; 2010: 217-243
29 Aminoff MJ. The future of the neurologic examination. JAMA Neurol 2017; 74 (11) 1291-1292
30 Rao AK, Gordon AM, Marder KS. Coordination of fingertip forces during precision grip in premanifest Huntington's disease. Mov Disord 2011; 26 (05) 862-869
31 Tabrizi SJ, Scahill RI, Owen G. et al; TRACK-HD Investigators. Predictors of phenotypic progression and disease onset in premanifest and early-stage Huntington's disease in the TRACK-HD study: analysis of 36-month observational data. Lancet Neurol 2013; 12 (07) 637-649
32 Rao AK, Muratori L, Louis ED, Moskowitz CB, Marder KS. Spectrum of gait impairments in presymptomatic and symptomatic Huntington's disease. Mov Disord 2008; 23 (08) 1100-1107
33 Ilg W, Fleszar Z, Schatton C. et al. Individual changes in preclinical spinocerebellar ataxia identified via increased motor complexity. Mov Disord 2016; 31 (12) 1891-1900
34 Blekher T, Johnson SA, Marshall J. et al. Saccades in presymptomatic and early stages of Huntington disease. Neurology 2006; 67 (03) 394-399
35 Christova P, Anderson JH, Gomez CM. Impaired eye movements in presymptomatic spinocerebellar ataxia type 6. Arch Neurol 2008; 65 (04) 530-536
36 Velázquez-Pérez L, Seifried C, Abele M. et al. Saccade velocity is reduced in presymptomatic spinocerebellar ataxia type 2. Clin Neurophysiol 2009; 120 (03) 632-635
37 Vogel AP, Magee M, Torres-vega R. et al. Features of speech and swallowing dysfunction in pre-ataxic spinocerebellar ataxia type 2. Neurology 2020; 95 (02) e194-e205
38 Paulsen JS, Long JD, Johnson HJ. et al; PREDICT-HD Investigators and Coordinators of the Huntington Study Group. Clinical and biomarker changes in premanifest Huntington disease show trial feasibility: a decade of the PREDICT-HD study. Front Aging Neurosci 2014; 6: 78
39 Lin C-C, Ashizawa T, Kuo S-H. Collaborative efforts for spinocerebellar ataxia research in the United States: CRC-SCA and READISCA. Front Neurol 2020; 11: 902
40 Zhan A, Mohan S, Tarolli C. et al. Using smartphones and machine learning to quantify Parkinson disease severity: the mobile Parkinson disease score. JAMA Neurol 2018; 75 (07) 876-880
41 Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data 2013; 1 (02) 85-99
42 Long JD, Langbehn DR, Tabrizi SJ. et al. Validation of a prognostic index for Huntington's disease. Mov Disord 2017; 32 (02) 256-263
43 Tezenas du Montcel S, Durr A, Rakowicz M. et al. Prediction of the age at onset in spinocerebellar ataxia type 1, 2, 3 and 6. J Med Genet 2014; 51 (07) 479-486
44 Küffner R, Zach N, Norel R. et al. Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nat Biotechnol 2015; 33 (01) 51-57
45 Geifman N, Kennedy RE, Schneider LS, Buchan I, Brinton RD. Data-driven identification of endophenotypes of Alzheimer's disease progression: implications for clinical trials and therapeutic interventions. Alzheimers Res Ther 2018; 10 (01) 4
46 Venuto CS, Potter NB, Dorsey ER, Kieburtz K. A review of disease progression models of Parkinson's disease and applications in clinical trials. Mov Disord 2016; 31 (07) 947-956
47 Rentz DM, Parra Rodriguez MA, Amariglio R, Stern Y, Sperling R, Ferris S. Promising developments in neuropsychological approaches for the detection of preclinical Alzheimer's disease: a selective review. Alzheimers Res Ther 2013; 5 (06) 58
48 Kueper JK, Speechley M, Montero-Odasso M. The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): modifications and responsiveness in pre-dementia populations. A narrative review. J Alzheimers Dis 2018; 63 (02) 423-444
49 Dodge HH, Zhu J, Mattek NC, Austin D, Kornfeld J, Kaye JA. Use of high-frequency in-home monitoring data may reduce sample sizes needed in clinical trials. PLoS One 2015; 10 (09) e0138095
50 Bove R, White CC, Giovannoni G. et al. Evaluating more naturalistic outcome measures: a 1-year smartphone study in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm 2015; 2 (06) e162
51 Berry JD, Paganoni S, Carlson K. et al. Design and results of a smartphone-based digital phenotyping study to quantify ALS progression. Ann Clin Transl Neurol 2019; 6 (05) 873-881
52 Rutkove SB, Narayanaswami P, Berisha V. et al. Improved ALS clinical trials through frequent at-home self-assessment: a proof of concept study. Ann Clin Transl Neurol 2020; 7 (07) 1148-1157
53 Kardon R. Optical coherence tomography in papilledema: what am I missing?. J Neuroophthalmol 2014; 34 (Suppl): S10-S17
54 Davis KD, Aghaeepour N, Ahn AH. et al. Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities. Nat Rev Neurol 2020; 16 (07) 381-400
55 Wilkinson DJ. Stochastic modelling for quantitative description of heterogeneous biological systems. Nat Rev Genet 2009; 10 (02) 122-133
56 Ravits JM, La Spada AR. ALS motor phenotype heterogeneity, focality, and spread: deconstructing motor neuron degeneration. Neurology 2009; 73 (10) 805-811
57 Blei DM. Build, compute, critique, repeat: data analysis with latent variable models. Annu Rev Stat Appl 2014; 1: 203-232
58 Freimer N, Sabatti C. The human phenome project. Nat Genet 2003; 34 (01) 15-21
59 Robinson PN, Mundlos S. The human phenotype ontology. Clin Genet 2010; 77 (06) 525-534
60 Manchia M, Cullis J, Turecki G, Rouleau GA, Uher R, Alda M. The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases. PLoS One 2013; 8 (10) e76295
61 Weiner WJ. There is no Parkinson disease. Arch Neurol 2008; 65: 705-708
62 Espay AJ, Lang AE. Parkinson diseases in the 2020s and beyond: replacing clinico-pathologic convergence with systems biology divergence. J Parkinsons Dis 2018; 8 (s1) S59-S64
63 Höglinger GU, Respondek G, Stamelou M. et al; Movement Disorder Society-endorsed PSP Study Group. Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Mov Disord 2017; 32 (06) 853-864
64 Comeau D, Pfeifer N. Diagnosis of concussion on the sidelines. Semin Pediatr Neurol 2019; 30: 26-34
65 Molitor RJ, Ko PC, Ally BA. Eye movements in Alzheimer's disease. J Alzheimers Dis 2015; 44: 1-12
66 Crutcher MD, Calhoun-Haney R, Manzanares CM, Lah JJ, Levey AI, Zola SM. Eye tracking during a visual paired comparison task as a predictor of early dementia. Am J Alzheimers Dis Other Demen 2009; 24 (03) 258-266
67 Lagun D, Manzanares C, Zola SM, Buffalo EA, Agichtein E. Detecting cognitive impairment by eye movement analysis using automatic classification algorithms. J Neurosci Methods 2011; 201 (01) 196-203
68 Constantino JN, Kennon-McGill S, Weichselbaum C. et al. Infant viewing of social scenes is under genetic control and is atypical in autism. Nature 2017; 547 (7663): 340-344
69 Cazzato D, Leo M, Distante C, Voos H. When I look into your eyes: a survey on computer vision contributions for human gaze estimation and tracking. Sensors (Basel) 2020; 20 (13) E3739
70 Campbell K, Carpenter KL, Hashemi J. et al. Computer vision analysis captures atypical attention in toddlers with autism. Autism 2019; 23 (03) 619-628
71 Lai H-Y, Saavedra-Pena G, Sodini CG, Sze V, Heldt T. Measuring saccade latency using smartphone cameras. IEEE J Biomed Health Inform 2020; 24 (03) 885-897
72 Chang Z, Chen Z, Stephen CD. et al. Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning. Sci Rep 2020; 10 (01) 18641
73 Egger HL, Dawson G, Hashemi J. et al. Automatic emotion and attention analysis of young children at home: a ResearchKit autism feasibility study. NPJ Digit Med 2018; 1: 20
74 Sapiro G, Hashemi J, Dawson G. Computer vision and behavioral phenotyping: an autism case study. Curr Opin Biomed Eng 2019; 9: 14-20
75 Robin J, Harrison JE, Kaufman LD, Rudzicz F, Simpson W, Yancheva M. Evaluation of speech-based digital biomarkers: review and recommendations. Digit Biomark 2020; 4 (03) 99-108
76 Hannun A, Case C, Casper J. et al. Deep speech: scaling up end-to-end speech recognition. 2014 . Accessed December 16, 2021: http://arxiv.org/abs/1412.5567
77 McFee B, Raffel C, Liang D. et al. librosa: Audio and music signal analysis in python. In: Proceedings of the 14th Python in Science Conference; Austin, Texas, July 6–12, 2015: 18-25
78 Eyben F, Wöllmer M, Schuller B. Opensmile: the munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM International Conference on Multimedia. New York, NY: Association for Computing Machinery; 2010: 1459-1462
79 Boersma P, Weenink D. Praat: doing phonetics by computer [Computer program]. Version 6.0. 37. Retrieved February 2018;3: 2018
80 Stegmann GM. Early detection and tracking of bulbar changes in ALS via frequent and remote speech analysis. NPJ Digit Med 2020; 3 (01) 132
81 Lipsmeier F, Taylor KI, Kilchenmann T. et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord 2018; 33 (08) 1287-1297
82 Arora S, Baig F, Lo C. et al. Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD. Neurology 2018; 91 (16) e1528-e1538
83 Konig A, Satt A, Sorin A. et al. Use of speech analyses within a mobile application for the assessment of cognitive impairment in elderly people. Curr Alzheimer Res 2018; 15 (02) 120-129
84 Carrillo MC, Dishman E, Plowman T. Everyday technologies for Alzheimer's disease care: research findings, directions, and challenges. Alzheimers Dement 2009; 5 (06) 479-488
85 Gold M, Amatniek J, Carrillo MC. et al. Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer's disease clinical trials. Alzheimers Dement (N Y) 2018; 4: 234-242
86 Koo BM, Vizer LM. Mobile technology for cognitive assessment of older adults: a scoping review. Innov Aging 2019; 3 (01) igy038
87 Piau A, Wild K, Mattek N, Kaye J. Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: systematic review. J Med Internet Res 2019; 21 (08) e12785
88 Onoda K, Yamaguchi S. Revision of the cognitive assessment for dementia, iPad version (CADi2). PLoS One 2014; 9 (10) e109931
89 Mielke MM, Machulda MM, Hagen CE. et al. Performance of the CogState computerized battery in the Mayo Clinic Study on Aging. Alzheimers Dement 2015; 11 (11) 1367-1376
90 Rentz DM, Dekhtyar M, Sherman J. et al. The feasibility of at-home iPad cognitive testing for use in clinical trials. J Prev Alzheimers Dis 2016; 3 (01) 8-12
91 Scharre DW, Chang SI, Nagaraja HN, Vrettos NE, Bornstein RA. Digitally translated Self-Administered Gerocognitive Examination (eSAGE): relationship with its validated paper version, neuropsychological evaluations, and clinical assessments. Alzheimers Res Ther 2017; 9 (01) 44
92 Bissig D, Kaye J, Erten-Lyons D. Validation of SATURN, a free, electronic, self-administered cognitive screening test. Alzheimers Dement (N Y) 2020; 6 (01) e12116
93 Wu Y-H, Vidal J-S, de Rotrou J, Sikkes SAM, Rigaud AS, Plichart M. Can a tablet-based cancellation test identify cognitive impairment in older adults?. PLoS One 2017; 12 (07) e0181809
94 Rao SM, Losinski G, Mourany L. et al. Processing speed test: validation of a self-administered, iPad^®-based tool for screening cognitive dysfunction in a clinic setting. Mult Scler 2017; 23 (14) 1929-1937
95 Maillart E, Labauge P, Cohen M. et al. MSCopilot, a new multiple sclerosis self-assessment digital solution: results of a comparative study versus standard tests. Eur J Neurol 2020; 27 (03) 429-436
96 Middleton RM, Pearson OR, Ingram G. et al; UK MS Register Research Group. A rapid electronic cognitive assessment measure for multiple sclerosis: validation of cognitive reaction, an electronic version of the symbol digit modalities test. J Med Internet Res 2020; 22 (09) e18234
97 Germine L, Nakayama K, Duchaine BC, Chabris CF, Chatterjee G, Wilmer JB. Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments. Psychon Bull Rev 2012; 19 (05) 847-857
98 Reinecke K, Gajos KZ. LabintheWild: conducting large-scale online experiments with uncompensated samples. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. New York, NY: ACM; 2015: 1364-1378
99 Fellows RP, Dahmen J, Cook D, Schmitter-Edgecombe M. Multicomponent analysis of a digital trail making test. Clin Neuropsychol 2017; 31 (01) 154-167
100 Zygouris S, Ntovas K, Giakoumis D. et al. A preliminary study on the feasibility of using a virtual reality cognitive training application for remote detection of mild cognitive impairment. J Alzheimers Dis 2017; 56 (02) 619-627
101 Zygouris S, Iliadou P, Lazarou E. et al. Detection of mild cognitive impairment in an at-risk group of older adults: can a novel self-administered serious game-based screening test improve diagnostic accuracy?. J Alzheimers Dis 2020; 78 (01) 405-412
102 Coughlan G, Coutrot A, Khondoker M, Minihane AM, Spiers H, Hornberger M. Toward personalized cognitive diagnostics of at-genetic-risk Alzheimer's disease. Proc Natl Acad Sci U S A 2019; 116 (19) 9285-9292
103 Kaye J, Mattek N, Dodge HH. et al. Unobtrusive measurement of daily computer use to detect mild cognitive impairment. Alzheimers Dement 2014; 10 (01) 10-17
104 Seelye A, Hagler S, Mattek N. et al. Computer mouse movement patterns: a potential marker of mild cognitive impairment. Alzheimers Dement (Amst) 2015; 1 (04) 472-480
105 Tung JY, Rose RV, Gammada E. et al. Measuring life space in older adults with mild-to-moderate Alzheimer's disease using mobile phone GPS. Gerontology 2014; 60 (02) 154-162
106 Johansson D, Malmgren K, Alt Murphy M. Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review. J Neurol 2018; 265 (08) 1740-1752
107 FitzGerald JJ, Lu Z, Jareonsettasin P, Antoniades CA. Quantifying motor impairment in movement disorders. Front Neurosci 2018; 12: 202
108 Teshuva I, Hillel I, Gazit E, Giladi N, Mirelman A, Hausdorff JM. Using wearables to assess bradykinesia and rigidity in patients with Parkinson's disease: a focused, narrative review of the literature. J Neural Transm (Vienna) 2019; 126 (06) 699-710
109 Griffiths RI, Kotschet K, Arfon S. et al. Automated assessment of bradykinesia and dyskinesia in Parkinson's disease. J Parkinsons Dis 2012; 2 (01) 47-55
110 Horne MK, McGregor S, Bergquist F. An objective fluctuation score for Parkinson's disease. PLoS One 2015; 10 (04) e0124522
111 Gordon MF, Grachev ID, Mazeh I. et al. Quantification of motor function in Huntington disease patients using wearable sensor devices. Digit Biomark 2019; 3 (03) 103-115
112 Van Someren EJW, Pticek MD, Speelman JD, Schuurman PR, Esselink R, Swaab DF. New actigraph for long-term tremor recording. Mov Disord 2006; 21 (08) 1136-1143
113 Joundi RA, Brittain J-S, Jenkinson N, Green AL, Aziz T. Rapid tremor frequency assessment with the iPhone accelerometer. Parkinsonism Relat Disord 2011; 17 (04) 288-290
114 Woods AM, Nowostawski M, Franz EA. et al. Parkinson's disease and essential tremor classification on mobile device. Pervasive Mobile Comput 2014; 13: 1-12
115 Barrantes S, Sánchez Egea AJ, González Rojas HA. et al. Differential diagnosis between Parkinson's disease and essential tremor using the smartphone's accelerometer. PLoS One 2017; 12 (08) e0183843
116 Varghese J, Fujarski M, Hahn T, Dugas M, Warnecke T. The smart device system for movement disorders: preliminary evaluation of diagnostic accuracy in a prospective study. Stud Health Technol Inform 2020; 270: 889-893
117 Jha A, Menozzi E, Oyekan R. et al. The CloudUPDRS smartphone software in Parkinson's study: cross-validation against blinded human raters. NPJ Parkinsons Dis 2020; 6 (01) 36
118 Mahadevan N, Demanuele C, Zhang H. et al. Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device. NPJ Digit Med 2020; 3: 5
119 Gebruers N, Truijen S, Engelborghs S, Nagels G, Brouns R, De Deyn PP. Actigraphic measurement of motor deficits in acute ischemic stroke. Cerebrovasc Dis 2008; 26 (05) 533-540
120 Le Heron C, Fang K, Gubbi J. et al. Wireless accelerometry is feasible in acute monitoring of upper limb motor recovery after ischemic stroke. Cerebrovasc Dis 2014; 37 (05) 336-341
121 Thrane G, Emaus N, Askim T, Anke A. Arm use in patients with subacute stroke monitored by accelerometry: association with motor impairment and influence on self-dependence. J Rehabil Med 2011; 43 (04) 299-304
122 Lang CE, Wagner JM, Edwards DF, Dromerick AW. Upper extremity use in people with hemiparesis in the first few weeks after stroke. J Neurol Phys Ther 2007; 31 (02) 56-63
123 Uswatte G, Giuliani C, Winstein C, Zeringue A, Hobbs L, Wolf SL. Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: evidence from the extremity constraint-induced therapy evaluation trial. Arch Phys Med Rehabil 2006; 87 (10) 1340-1345
124 Gebruers N, Truijen S, Engelborghs S, De Deyn PP. Predictive value of upper-limb accelerometry in acute stroke with hemiparesis. J Rehabil Res Dev 2013; 50 (08) 1099-1106
125 de Niet M, Bussmann JB, Ribbers GM, Stam HJ. The stroke upper-limb activity monitor: its sensitivity to measure hemiplegic upper-limb activity during daily life. Arch Phys Med Rehabil 2007; 88 (09) 1121-1126
126 van der Pas SC, Verbunt JA, Breukelaar DE, van Woerden R, Seelen HA. Assessment of arm activity using triaxial accelerometry in patients with a stroke. Arch Phys Med Rehabil 2011; 92 (09) 1437-1442
127 Michielsen ME, Selles RW, Stam HJ, Ribbers GM, Bussmann JB. Quantifying nonuse in chronic stroke patients: a study into paretic, nonparetic, and bimanual upper-limb use in daily life. Arch Phys Med Rehabil 2012; 93 (11) 1975-1981
128 Urbin MA, Waddell KJ, Lang CE. Acceleration metrics are responsive to change in upper extremity function of stroke survivors. Arch Phys Med Rehabil 2015; 96 (05) 854-861
129 Arora S, Venkataraman V, Zhan A. et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord 2015; 21 (06) 650-653
130 Kassavetis P, Saifee TA, Roussos G. et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract (Hoboken) 2015; 3 (01) 59-64
131 Lee CY, Kang SJ, Hong S-K, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One 2016; 11 (07) e0158852
132 Pratap A, Grant D, Vegesna A. et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth 2020; 8 (10) e22108
133 Boukhvalova AK, Kowalczyk E, Harris T. et al. Identifying and quantifying neurological disability via smartphone. Front Neurol 2018; 9: 740
134 Arcuria G, Marcotulli C, Galasso C, Pierelli F, Casali C. 15-White Dots APP-Coo-Test: a reliable touch-screen application for assessing upper limb movement impairment in patients with cerebellar ataxias. J Neurol 2019; 266 (07) 1611-1622
135 Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A. et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng 2017; 64 (09) 1994-2002
136 Matarazzo M, Arroyo-Gallego T, Montero P. et al. Remote monitoring of treatment response in Parkinson's disease: the habit of typing on a computer. Mov Disord 2019; 34 (10) 1488-1495
137 White RW, Doraiswamy PM, Horvitz E. Detecting neurodegenerative disorders from web search signals. NPJ Digit Med 2018; 1: 18-21
138 Gajos KZ, Reinecke K, Donovan M. et al. Computer mouse use captures ataxia and parkinsonism, enabling accurate measurement and detection. Mov Disord 2020; 35 (02) 354-358
139 Zampogna A, Mileti I, Palermo E. et al. Fifteen years of wireless sensors for balance assessment in neurological disorders. Sensors (Basel) 2020; 20 (11) E3247
140 Shanahan CJ, Boonstra FMC, Cofré Lizama LE. et al. Technologies for advanced gait and balance assessments in people with multiple sclerosis. Front Neurol 2018; 8: 708
141 Ozinga SJ, Machado AG, Miller Koop M, Rosenfeldt AB, Alberts JL. Objective assessment of postural stability in Parkinson's disease using mobile technology. Mov Disord 2015; 30 (09) 1214-1221
142 Ozinga SJ, Linder SM, Alberts JL. Use of mobile device accelerometry to enhance evaluation of postural instability in Parkinson disease. Arch Phys Med Rehabil 2017; 98 (04) 649-658
143 Pérez-López C, Samà A, Rodríguez-Martín D. et al. Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer. Artif Intell Med 2016; 67: 47-56
144 Kegelmeyer DA, Kostyk SK, Fritz NE. et al. Quantitative biomechanical assessment of trunk control in Huntington's disease reveals more impairment in static than dynamic tasks. J Neurol Sci 2017; 376: 29-34
145 Hou Y-R, Chiu Y-L, Chiang S-L, Chen HY, Sung WH. Development of a smartphone-based balance assessment system for subjects with stroke. Sensors (Basel) 2019; 20 (01) E88
146 Block VAJ, Pitsch E, Tahir P, Cree BA, Allen DD, Gelfand JM. Remote physical activity monitoring in neurological disease: a systematic review. PLoS One 2016; 11 (04) e0154335
147 Snook EM, Motl RW, Gliottoni RC. The effect of walking mobility on the measurement of physical activity using accelerometry in multiple sclerosis. Clin Rehabil 2009; 23 (03) 248-258
148 Weikert M, Motl RW, Suh Y, McAuley E, Wynn D. Accelerometry in persons with multiple sclerosis: measurement of physical activity or walking mobility?. J Neurol Sci 2010; 290 (1–2): 6-11
149 Motl RW, Snook EM. Confirmation and extension of the validity of the Multiple Sclerosis Walking Scale-12 (MSWS-12). J Neurol Sci 2008; 268 (1–2): 69-73
150 Motl RW, Snook EM, Wynn DR, Vollmer T. Physical activity correlates with neurological impairment and disability in multiple sclerosis. J Nerv Ment Dis 2008; 196 (06) 492-495
151 Motl RW, Schwartz CE, Vollmer T. Continued validation of the Symptom Inventory in multiple sclerosis. J Neurol Sci 2009; 285 (1–2): 134-136
152 Motl RW, Dlugonski D, Suh Y, Weikert M, Fernhall B, Goldman M. Accelerometry and its association with objective markers of walking limitations in ambulatory adults with multiple sclerosis. Arch Phys Med Rehabil 2010; 91 (12) 1942-1947
153 Motl RW, McAuley E. Longitudinal analysis of physical activity and symptoms as predictors of change in functional limitations and disability in multiple sclerosis. Rehabil Psychol 2009; 54 (02) 204-210
154 Motl RW, McAuley E, Dlugonski D. Reactivity in baseline accelerometer data from a physical activity behavioral intervention. Health Psychol 2012; 31 (02) 172-175
155 Rand D, Eng JJ, Tang P-F, Jeng JS, Hung C. How active are people with stroke?: use of accelerometers to assess physical activity. Stroke 2009; 40 (01) 163-168
156 Rodríguez-Martín D, Samà A, Pérez-López C. et al. Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer. PLoS One 2017; 12 (02) e0171764
157 Din SD, Del Din S, Galna B. et al. Analysis of free-living gait in older adults with and without Parkinson's disease and with and without a history of falls: identifying generic and disease-specific characteristics. J Gerontol A Biol Sci Med Sci 2019; 74: 500-506
158 Block VJ, Lizée A, Crabtree-Hartman E. et al. Continuous daily assessment of multiple sclerosis disability using remote step count monitoring. J Neurol 2017; 264 (02) 316-326
159 Warmerdam E, Romijnders R, Welzel J, Hansen C, Schmidt G, Maetzler W. Quantification of arm swing during walking in healthy adults and Parkinson's disease patients: wearable sensor-based algorithm development and validation. Sensors (Basel) 2020; 20 (20) E5963
160 Rhea CK, Kuznetsov NA, Ross SE. et al. Development of a portable tool for screening neuromotor sequelae from repetitive low-level blast exposure. Mil Med 2017; 182 (S1): 147-154
161 Espay AJ, Bonato P, Nahab FB. et al; Movement Disorders Society Task Force on Technology. Technology in Parkinson's disease: challenges and opportunities. Mov Disord 2016; 31 (09) 1272-1282
162 Wilbanks J, Friend SH. First, design for data sharing. Nat Biotechnol 2016; 34 (04) 377-379
163 Coran P, Goldsack JC, Grandinetti CA. et al. Advancing the use of mobile technologies in clinical trials: recommendations from the clinical trials transformation initiative. Digit Biomark 2019; 3 (03) 145-154
164 Espay AJ, Hausdorff JM, Sánchez-Ferro Á. et al; Movement Disorder Society Task Force on Technology. A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies. Mov Disord 2019; 34 (05) 657-663
165 Warmerdam E, Hausdorff JM, Atrsaei A. et al. Long-term unsupervised mobility assessment in movement disorders. Lancet Neurol 2020; 19 (05) 462-470
166 Badawy R, Hameed F, Bataille L. et al. Metadata concepts for advancing the use of digital health technologies in clinical research. Digit Biomark 2019; 3 (03) 116-132
167 Bot BM, Suver C, Neto EC. et al. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci Data 2016; 3: 160011
168 Friendly M. A brief history of data visualization. In: Chen C-H, Härdle W, Unwin A. eds. Handbook of Data Visualization. Berlin, Heidelberg: Springer Berlin Heidelberg; 2008: 15-56