Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson's disease

Florian Lipsmeier, Kirsten I Taylor, Ronald B Postuma, Ekaterina Volkova-Volkmar, Timothy Kilchenmann, Brit Mollenhauer, Atieh Bamdadian, Werner L Popp, Wei-Yi Cheng, Yan-Ping Zhang, Detlef Wolf, Jens Schjodt-Eriksen, Anne Boulay, Hanno Svoboda, Wagner Zago, Gennaro Pagano, Michael Lindemann, Florian Lipsmeier, Kirsten I Taylor, Ronald B Postuma, Ekaterina Volkova-Volkmar, Timothy Kilchenmann, Brit Mollenhauer, Atieh Bamdadian, Werner L Popp, Wei-Yi Cheng, Yan-Ping Zhang, Detlef Wolf, Jens Schjodt-Eriksen, Anne Boulay, Hanno Svoboda, Wagner Zago, Gennaro Pagano, Michael Lindemann

Abstract

Digital health technologies enable remote and therefore frequent measurement of motor signs, potentially providing reliable and valid estimates of motor sign severity and progression in Parkinson's disease (PD). The Roche PD Mobile Application v2 was developed to measure bradykinesia, bradyphrenia and speech, tremor, gait and balance. It comprises 10 smartphone active tests (with ½ tests administered daily), as well as daily passive monitoring via a smartphone and smartwatch. It was studied in 316 early-stage PD participants who performed daily active tests at home then carried a smartphone and wore a smartwatch throughout the day for passive monitoring (study NCT03100149). Here, we report baseline data. Adherence was excellent (96.29%). All pre-specified sensor features exhibited good-to-excellent test-retest reliability (median intraclass correlation coefficient = 0.9), and correlated with corresponding Movement Disorder Society-Unified Parkinson's Disease Rating Scale items (rho: 0.12-0.71). These findings demonstrate the preliminary reliability and validity of remote at-home quantification of motor sign severity with the Roche PD Mobile Application v2 in individuals with early PD.

Conflict of interest statement

The Authors declare no Competing Non-Financial Interests but the following Competing Financial Interests: FL, KIT, EVV, TK, AB, WLP, WC, YZ, DW, JSE, ABo, HS and GP reports personal fees from F. Hoffmann-La Roche Ltd. BM reports personal fees and other from F. Hoffmann-La Roche Ltd, during the conduct of the study; personal fees from Biogen, personal fees from Servier, non-financial support from Amprion, Grants and personal fees from Michael J. Fox Foundation for Parkinson's Research, Grants from DFG, Grants from EU (Horizon2020), Grants from Parkinson Fonds Deutschland, Grants from Hilde Ulrich Stiftung. WZ reports personal fees from F. Hoffmann-La Roche Ltd and Prothena Ltd. RBP reports Grants and personal fees from Fonds de la Recherche en Sante, Grants from Canadian Institute of Health Research, Grants from The Parkinson Society of Canada, Grants from Weston-Garfield Foundation, Grants from Michael J. Fox Foundation, Grants from Webster Foundation, Grants from National Institute of Health, Grants and personal fees from Roche, personal fees from Takeda, personal fees from Teva Neurosciences, personal fees from Biogen, personal fees from Boehringer Ingelheim, personal fees from Theranexus, personal fees from GE HealthCare, personal fees from Jazz Pharmaceuticals, personal fees from AbbVie, personal fees from Janssen, personal fees from Otsuko, personal fees from Phytopharmica, personal fees from Inception Sciences, other from Parkinson Canada, personal fees from Paladin. ML reports personal fees from F. Hoffmann-La Roche Ltd and has a patent US20190200915A1 pending to Hoffmann-La Roche Inc., a patent EP3701542A2 pending to F. Hoffmann-La Roche AG, a patent WO2019215230A1 pending to F. Hoffmann-La Roche AG, and a patent WO2021055443A1 pending to F. Hoffmann-La Roche AG.

© 2022. The Author(s).

Figures

Figure 1
Figure 1
Roche PD Mobile Application v2 active tests and passive monitoring and schedule of assessments. eSDMT, electronic Symbol Digit Modalities Test; PD, Parkinson’s disease.
Figure 2
Figure 2
Absolute Spearman’s correlations between baseline MDS-UPDRS Total and Subscores and Roche PD Mobile Application v2 active test and passive monitoring sensor features. eSDMT electronic Symbol Digit Modalities Test, MDS-UPDRS Movement Disorder Society—Unified Parkinson's Disease Rating Scale, PD Parkinson’s disease, PIGD Postural Instability/Gait Disorders.
Figure 3
Figure 3
Association of sensor features from upper limb bradykinesia tests and upper limb tremor tests with corresponding clinical MDS-UPDRS measures at baseline (ns = P > 0.05; * = P ≤ 0.05; ** = P ≤ 0.01; *** = P ≤ 0.001; **** = P ≤ 0.0001). L less affected side, M more affected side, MDS-UPDRS Movement Disorder Society—Unified Parkinson's Disease Rating Scale, UE Upper Extremity.
Figure 4
Figure 4
Association of sensor features from Phonation/Speech and eSDMT tests with corresponding clinical MDS-UPDRS measures at baseline (ns = P > 0.05; * = P ≤ 0.05; ** = P ≤ 0.01; *** = P ≤ 0.001; **** = P ≤ 0.0001). eSDMT electronic Symbol Digit Modalities test, MDS-UPDRS Movement Disorder Society—Unified Parkinson's Disease Rating Scale, MFCC2 Mel Frequency Cepstral Coefficient 2.
Figure 5
Figure 5
Association of sensor features from U-turn, Balance tests and Passive monitoring of gait with corresponding clinical MDS-UPDRS measures at baseline (ns = P > 0.05; * = P ≤ 0.05; ** = P ≤ 0.01; *** = P ≤ 0.001; **** = P ≤ 0.0001). MDS-UPDRS Movement Disorder Society—Unified Parkinson's Disease Rating Scale.
Figure 6
Figure 6
Correlations between active test sensor features from the more and less affected sides (M and L, respectively) with MDS-UPDRS Part III item scores evaluating M and L. L less affected side, M more affected side, MDS-UPDRS Movement Disorder Society—Unified Parkinson's Disease Rating Scale.

References

    1. Maetzler W, Liepelt I, Berg D. Progression of Parkinson's disease in the clinical phase: Potential markers. Lancet Neurol. 2009;8:1158–1171. doi: 10.1016/S1474-4422(09)70291-1.
    1. Sacks L, Kunkoski E. Digital health technology to measure drug efficacy in clinical trials for Parkinson's disease: A regulatory perspective. J. Parkinsons Dis. 2021;11:S111–S115. doi: 10.3233/JPD-202416.
    1. Espay AJ, et al. A roadmap for implementation of patient-centered digital outcome measures in Parkinson's disease obtained using mobile health technologies. Mov. Disord. Off. J. Mov. Disord. Soc. 2019;34:657–663. doi: 10.1002/mds.27671.
    1. Espay AJ, et al. Technology in Parkinson's disease: Challenges and opportunities. Mov. Disord. Off. J. Mov. Disord. Soc. 2016;31:1272–1282. doi: 10.1002/mds.26642.
    1. Lipsmeier F, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov. Disord. Off. J. Mov. Disord. Soc. 2018;33:1287–1297. doi: 10.1002/mds.27376.
    1. Dawson VL, Dawson TM. Promising disease-modifying therapies for Parkinson's disease. Sci. Transl. Med. 2019 doi: 10.1126/scitranslmed.aba1659.
    1. Parkinson Progression Marker Initiative The Parkinson progression marker initiative (PPMI) Prog. Neurobiol. 2011;95:629–635. doi: 10.1016/j.pneurobio.2011.09.005.
    1. Goetz C, et al. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results. Mov. Disord. Off. J. Mov. Disord. Soc. 2008;23:2129–2170. doi: 10.1002/mds.22340.
    1. Andrich D, Marais I. A Course in Rasch Measurement Theory: Measuring in the Educational, Social and Health Sciences. Springer; 2019.
    1. Regnault A, et al. Does the MDS-UPDRS provide the precision to assess progression in early Parkinson's disease? Learnings from the Parkinson's progression marker initiative cohort. J. Neurol. 2019;266:1927–1936. doi: 10.1007/s00415-019-09348-3.
    1. Postuma RB, et al. MDS clinical diagnostic criteria for Parkinson's disease. Mov. Disord. Off. J. Mov. Disord. Soc. 2015;30:1591–1601. doi: 10.1002/mds.26424.
    1. Simuni T, et al. Longitudinal change of clinical and biological measures in early Parkinson's disease: Parkinson's Progression Markers Initiative cohort. Mov. Disord. 2018;33:771–782. doi: 10.1002/mds.27361.
    1. Horvath K, et al. Minimal clinically important differences for the experiences of daily living parts of movement disorder society-sponsored unified Parkinson's disease rating scale. Mov. Disord. Off. J. Mov. Disord. Soc. 2017;32:789–793. doi: 10.1002/mds.26960.
    1. Sheng Y, Zhou X, Yang S, Ma P, Chen C. Modelling item scores of Unified Parkinson's Disease Rating Scale Part III for greater trial efficiency. Br. J. Clin. Pharmacol. 2021;87:3608–3618. doi: 10.1111/bcp.14777.
    1. Kyritsis K, et al. Assessment of real life eating difficulties in Parkinson's disease patients by measuring plate to mouth movement elongation with inertial sensors. Sci. Rep. 2021;11:1632. doi: 10.1038/s41598-020-80394-y.
    1. Jha A, et al. The CloudUPDRS smartphone software in Parkinson’s study: Cross-validation against blinded human raters. NPJ Parkinson's Dis. 2020;6:36. doi: 10.1038/s41531-020-00135-w.
    1. Stamate C, et al. The cloudUPDRS app: A medical device for the clinical assessment of Parkinson’s Disease. Pervasive Mob. Comput. 2018;43:146–166. doi: 10.1016/j.pmcj.2017.12.005.
    1. Arora S, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study. Parkinsonism Relat. Disord. 2015;21:650–653. doi: 10.1016/j.parkreldis.2015.02.026.
    1. Bot BM, et al. The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci. Data. 2016;3:160011. doi: 10.1038/sdata.2016.11.
    1. Johnson DK, Langford Z, Garner-Villarreal M, Morris JC, Galvin JE. Onset of mild cognitive impairment in Parkinson disease. Alzheimer Dis. Assoc. Disord. 2016;30:127–133. doi: 10.1097/WAD.0000000000000088.
    1. Hasan H, Athauda DS, Foltynie T, Noyce AJ. Technologies assessing limb bradykinesia in Parkinson's disease. J. Parkinsons Dis. 2017;7:65–77. doi: 10.3233/JPD-160878.
    1. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 2016;15:155–163. doi: 10.1016/j.jcm.2016.02.012.
    1. Rabelo AG, et al. Objective assessment of bradykinesia estimated from the wrist extension in older adults and patients with Parkinson's disease. Ann. Biomed. Eng. 2017;45:2614–2625. doi: 10.1007/s10439-017-1908-3.
    1. Movement Disorder Society Task Force on Rating Scales for Parkinson's Disease The Unified Parkinson's Disease Rating Scale (UPDRS): Status and recommendations. Mov. Disord. Off. J. Mov. Disord. Soc. 2003;18:738–750. doi: 10.1002/mds.10473.
    1. Kassavetis P, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov. Disord. Clin. Pract. 2015;3:59–64. doi: 10.1002/mdc3.12239.
    1. Lee CY, et al. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS ONE. 2016;11:e0158852. doi: 10.1371/journal.pone.0158852.
    1. Lalvay L, et al. Quantitative measurement of Akinesia in Parkinson's disease. Mov. Disord. Clin. Pract. 2016;4:316–322. doi: 10.1002/mdc3.12410.
    1. Orozco-Arroyave JR, et al. Apkinson: The smartphone application for telemonitoring Parkinson’s patients through speech, gait and hands movement. Neurodegener. Dis. Manag. 2020;10:137–157.
    1. Delrobaei M, Tran S, Gilmore G, McIsaac K, Jog M. Characterization of multi-joint upper limb movements in a single task to assess bradykinesia. J. Neurol. Sci. 2016;4:337–342. doi: 10.1016/j.jns.2016.07.056.
    1. Mentzel T, et al. Reliability and validity of an instrument for the assessment of bradykinesia. Psychiatry Res. 2016;238:189–195. doi: 10.1016/j.psychres.2016.02.011.
    1. di Biase L, et al. Quantitative analysis of bradykinesia and rigidity in Parkinson's disease. Front. Neurol. 2018;9:121. doi: 10.3389/fneur.2018.00121.
    1. Saunders-Pullman R, et al. Validity of spiral analysis in early Parkinson's disease. Mov. Disord. Off. J. Mov. Disord. Soc. 2007;23:531–537. doi: 10.1002/mds.21874.
    1. Zham P, Kumar D, Dabnichki P, Poosapadi Arjunan S, Raghav S. Distinguishing different stages of Parkinson’s disease using composite index of speed and pen-pressure of sketching a spiral. Front. Neurol. 2017;8:435. doi: 10.3389/fneur.2017.00435.
    1. Danna J, et al. Digitalized spiral drawing in Parkinson’s disease: A tool for evaluating beyond the written trace. Hum. Mov. Sci. 2019;65:80–88. doi: 10.1016/j.humov.2018.08.003.
    1. San Luciano M, et al. Digitized spiral drawing: A possible biomarker for early Parkinson’s disease. PLoS ONE. 2016;11:e0162799. doi: 10.1371/journal.pone.0162799.
    1. Memedi M, et al. Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease. Sensors (Basel) 2015;15:23727–23744. doi: 10.3390/s150923727.
    1. Creagh AP, et al. Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape Test. Physiol. Meas. 2020;41:054002. doi: 10.1088/1361-6579/ab8771.
    1. Umbricht D, Cheng WY, Lipsmeier F, Bamdadian A, Lindemann M. Deep learning-based human activity recognition for continuous activity and gesture monitoring for schizophrenia patients with negative symptoms. Front. Psychol. 2020;11:574375. doi: 10.3389/fpsyt.2020.574375.
    1. Rincon D, et al. Wristbands containing accelerometers for objective arm swing analysis in patients with Parkinson's disease. Sensors (Basel) 2020 doi: 10.3390/s20154339.
    1. Zampier VC, et al. Gait bradykinesia and hypometria decrease as arm swing frequency and amplitude increase. Neurosci. Lett. 2018;687:248–252. doi: 10.1016/j.neulet.2018.09.051.
    1. Huang X, et al. Both coordination and symmetry of arm swing are reduced in Parkinson's disease. Gait Posture. 2012;35:373–377. doi: 10.1016/j.gaitpost.2011.10.180.
    1. Thorp JE, Adamczyk PG, Ploeg HL, Pickett KA. Monitoring motor symptoms during activities of daily living in individuals with Parkinson's disease. Front. Neurol. 2018;9:1036. doi: 10.3389/fneur.2018.01036.
    1. Smith A. Symbol Digit Modalities Test Manual. Western Psychological Services; 1973.
    1. Lezak MD, Howieson DB, Bigler ED, Tranel D. Neuropsychological Assessment. 5. Oxford University Press; 2012.
    1. Pascoe M, Alamri Y, Dalrymple-Alford J, Anderson T, MacAskill M. The symbol-digit modalities test in mild cognitive impairment: Evidence from Parkinson's disease patients. Eur. Neurol. 2018;79:206–210. doi: 10.1159/000485669.
    1. Linortner P, et al. White matter hyperintensities related to Parkinson's disease executive function. Mov. Disord. Clin. Pract. 2020;7:629–638. doi: 10.1002/mdc3.12956.
    1. Fiorenzato E, et al. Brain amyloid contribution to cognitive dysfunction in early-stage Parkinson's Disease: The PPMI dataset. J. Alzheimer's Dis. JAD. 2018;66:229–237. doi: 10.3233/JAD-180390.
    1. Magee M, Copland D, Vogel AP. Motor speech and non-motor language endophenotypes of Parkinson's disease. Expert Rev. Neurother. 2019;19:1191–1200. doi: 10.1080/14737175.2019.1649142.
    1. Rusz J, Cmejla R, Ruzickova H, Ruzicka E. Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson's disease. J. Acoust. Soc. Am. 2011;129:350–367. doi: 10.1121/1.3514381.
    1. Scanlon BK, et al. An accelerometry-based study of lower and upper limb tremor in Parkinson's disease. J. Clin. Neurosci. 2013;20:827–830. doi: 10.1016/j.jocn.2012.06.015.
    1. Mellone S, Mancini M, King LA, Horak FB, Chiari L. The quality of turning in Parkinson's disease: A compensatory strategy to prevent postural instability? J. Neuroeng. Rehabil. 2016;13:39. doi: 10.1186/s12984-016-0147-4.
    1. Crenna P, et al. The association between impaired turning and normal straight walking in Parkinson's disease. Gait Posture. 2007;26:172–178. doi: 10.1016/j.gaitpost.2007.04.010.
    1. Salarian A, et al. iTUG, a sensitive and reliable measure of mobility. IEEE Trans. Neural Syst. Rehabil. Eng. 2010;18:303–310. doi: 10.1109/TNSRE.2010.2047606.
    1. Soke F, et al. Reliability and validity of the timed 360 degrees turn test in people with Parkinson's disease. Eur. Geriatr. Med. 2020;11:417–426. doi: 10.1007/s41999-019-00285-y.
    1. Mancini M, et al. Continuous monitoring of turning in Parkinson's disease: Rehabilitation potential. NeuroRehabilitation. 2015;37:3–10. doi: 10.3233/NRE-151236.
    1. Atrsaei A, et al. Gait speed in clinical and daily living assessments in Parkinson's disease patients: Performance versus capacity. NPJ Parkinsons Dis. 2021;7:24. doi: 10.1038/s41531-021-00171-0.
    1. Mancini M, et al. ISway: A sensitive, valid and reliable measure of postural control. J. Neuroeng. Rehabil. 2012;9:59. doi: 10.1186/1743-0003-9-59.
    1. Chen OY, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans. Biomed. Eng. 2020;67:3491–3500. doi: 10.1109/TBME.2020.2988942.
    1. Zhan A, et al. Using smartphones and machine learning to quantify Parkinson disease severity: The mobile Parkinson disease score. JAMA Neurol. 2018;75:876–880. doi: 10.1001/jamaneurol.2018.0809.
    1. Arora S, et al. Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD. Neurology. 2018;91:e1528–e1538. doi: 10.1212/WNL.0000000000006366.
    1. Lo C, et al. Predicting motor, cognitive & functional impairment in Parkinson's. Ann. Clin. Transl. Neurol. 2019;6:1498–1509. doi: 10.1002/acn3.50853.
    1. Taylor KI, Staunton H, Lipsmeier F, Nobbs D, Lindemann M. Outcome measures based on digital health technology sensor data: Data- and patient-centric approaches. NPJ Digit. Med. 2020;3:97. doi: 10.1038/s41746-020-0305-8.
    1. Dodge HH, et al. Use of high-frequency in-home monitoring data may reduce sample sizes needed in clinical trials. PLoS ONE. 2015;10:e0138095. doi: 10.1371/journal.pone.0138095.
    1. Pagano G, et al. A phase II study to evaluate the safety and efficacy of prasinezumab in early Parkinson's disease (PASADENA): Rationale, design, and baseline data. Front. Neurol. 2021;12:705407. doi: 10.3389/fneur.2021.705407.
    1. Mancini M, et al. Postural sway as a marker of progression in Parkinson's disease: A pilot longitudinal study. Gait Posture. 2012;36:471–476. doi: 10.1016/j.gaitpost.2012.04.010.
    1. Cheng, W. Y. et al. Smartphone-based continuous mobility monitoring of Parkinson's disease patients reveals impacts of ambulatory bout length on gait features. In IEEE Life Sciences Conference 166–169. 10.1109/LSC.2017.8268169 (2017).
    1. Cheng, W.-Y. et al. Large-scale continuous mobility monitoring of Parkinson’s disease patients using smartphones. In International Conference on Wireless Mobile Communication and Healthcare 12–19. 10.1007/978-3-319-98551-0_2 (2018).

Source: PubMed

Sponsor e collaboratori

Condizioni mediche

Interventi farmacologici

3
Sottoscrivi