Objective measurement versus clinician-based assessment for Parkinson’s disease

References

• Ou Z, Pan J, Tang S, et al. Global trends in the incidence, prevalence, and years lived with disability of parkinson’s disease in 204 countries/territories from 1990 to 2019. Front Public Health. 2021;9:776847. doi: 10.3389/fpubh.2021.776847
   PubMed Web of Science ®Google Scholar
• Bloem BR, Okun MS, Klein C. Parkinson’s disease. The Lancet. 2021;397(10291):2284–2303. doi: 10.1016/S0140-6736(21)00218-X
   PubMed Web of Science ®Google Scholar
• Postuma RB, Berg D, Stern M, et al. MDS clinical diagnostic criteria for Parkinson’s disease: mDS-PD clinical diagnostic criteria. Mov Disord. 2015;30:1591–1601. doi: 10.1002/mds.26424
   PubMed Web of Science ®Google Scholar
• Armstrong MJ, Okun MS. Diagnosis and treatment of parkinson disease: a review. JAMA. 2020;323(6):548. doi: 10.1001/jama.2019.22360
   PubMed Web of Science ®Google Scholar
• Amboni M, Stocchi F, Abbruzzese G, et al. Prevalence and associated features of self-reported freezing of gait in Parkinson disease: the DEEP FOG study. Parkinsonism Relat Disord. 2015;21(6):644–649. doi: 10.1016/j.parkreldis.2015.03.028
   PubMed Web of Science ®Google Scholar
• Bastide MF, Meissner WG, Picconi B, et al. Pathophysiology of L-dopa-induced motor and non-motor complications in Parkinson’s disease. Prog Neurobiol. 2015;132:96–168. doi: 10.1016/j.pneurobio.2015.07.002
   PubMed Web of Science ®Google Scholar
• Fabbrini A, Guerra A. Pathophysiological mechanisms and experimental pharmacotherapy for L-Dopa-induced Dyskinesia. J Exp Pharmacol. 2021;13:469–485. doi: 10.2147/JEP.S265282
   PubMedGoogle Scholar
• Marras C, Lang AE. Outcome measures for clinical trials in Parkinson’s disease: achievements and shortcomings. Expert Rev Neurother. 2004;4(6):985–993. doi: 10.1586/14737175.4.6.985
   PubMedGoogle Scholar
• Rahman S, Griffin HJ, Quinn NP, et al. Quality of life in Parkinson’s disease: the relative importance of the symptoms. Mov Disord. 2008;23(10):1428–1434. doi: 10.1002/mds.21667
   PubMed Web of Science ®Google Scholar
• Stocchi F, Antonini A, Barone P, et al. Early DEtection of wEaring off in Parkinson disease: the DEEP study. Parkinsonism Relat Disord. 2014;20(2):204–211. doi: 10.1016/j.parkreldis.2013.10.027
   PubMed Web of Science ®Google Scholar
• Deuschl G, Antonini A, Costa J, et al. European academy of neurology/movement disorder society-European section guideline on the treatment of Parkinson’s disease: i. invasive therapies. Mov Disord. 2022;37(7):1360–1374. doi: 10.1002/mds.29066
   PubMed Web of Science ®Google Scholar
• Martinez-Martin P, Skorvanek M, Henriksen T, et al. Impact of advanced Parkinson’s disease on caregivers: an international real-world study. J Neurol. 2023;270(4):2162–2173. doi: 10.1007/s00415-022-11546-5
   PubMed Web of Science ®Google Scholar
• Antonini A, Pahwa R, Odin P, et al. Comparative effectiveness of device-aided therapies on quality of life and off-time in Advanced Parkinson’s disease: a systematic review and Bayesian network meta-analysis. CNS Drugs. 2022;36(12):1269–1283. doi: 10.1007/s40263-022-00963-9
   PubMed Web of Science ®Google Scholar
• Goetz CG, Nutt JG, Stebbins GT. The unified Dyskinesia rating scale: presentation and clinimetric profile. Mov Disord. 2008;23(16):2398–2403. doi: 10.1002/mds.22341
   PubMed Web of Science ®Google Scholar
• Horváth K, Aschermann Z, Ács P, et al. Minimal clinically important difference on the motor examination part of MDS-UPDRS. Parkinsonism Relat Disord. 2015;21(12):1421–1426. doi: 10.1016/j.parkreldis.2015.10.006
   PubMed Web of Science ®Google Scholar
• Martínez-Martín P, Rodríguez-Blázquez C, Alvarez M, et al. Parkinson’s disease severity levels and MDS-Unified Parkinson’s disease rating scale. Parkinsonism & Related Disorders. 2015;21(1):50–54. doi: 10.1016/j.parkreldis.2014.10.026
   PubMed Web of Science ®Google Scholar
• Antonini A, Martinez-Martin P, Chaudhuri RK, et al. Wearing-off scales in Parkinson’s disease: critique and recommendations. Mov Disord. 2011;26(12):2169–2175. doi: 10.1002/mds.23875
   PubMed Web of Science ®Google Scholar
• Bloem BR, Marinus J, Almeida Q, et al. Measurement instruments to assess posture, gait, and balance in ParkInson’s disease: critique and recommendations: posture, gait, and balance instruments in PD. Mov Disord. 2016;31:1342–1355. doi: 10.1002/mds.26572
   PubMed Web of Science ®Google Scholar
• Regnault A, Boroojerdi B, Meunier J, 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(8):1927–1936. doi: 10.1007/s00415-019-09348-3
   PubMed Web of Science ®Google Scholar
• Adams JL, Dinesh K, Snyder CW, et al. A real-world study of wearable sensors in Parkinson’s disease. NPJ Parkinsons Dis. 2021;7(1):106. doi: 10.1038/s41531-021-00248-w
   PubMed Web of Science ®Google Scholar
• Espay AJ, Bonato P, Nahab FB, et al. Technology in Parkinson’s disease: challenges and opportunities: technology in PD. Mov Disord. 2016;31:1272–1282. doi: 10.1002/mds.26642
   PubMed Web of Science ®Google Scholar
• Espay AJ, Hausdorff JM, Sánchez‐Ferro Á, et al. A roadmap for implementation of patient-centered digital outcome measures in Parkinson’s disease obtained using mobile health technologies. Mov Disord. 2019;34(5):657–663. doi: 10.1002/mds.27671
   PubMed Web of Science ®Google Scholar
• Landers M, Saria S, Espay AJ, et al. Will artificial intelligence replace the movement disorders specialist for diagnosing and managing Parkinson’s disease? J Park Dis. 2021;11(s1):S117–S122. doi: 10.3233/JPD-212545
   Google Scholar
• Monje MHG, Foffani G, Obeso J, et al. New sensor and wearable technologies to aid in the diagnosis and treatment monitoring of Parkinson’s disease. Annu Rev Biomed Eng. 2019;21(1):111–143. doi: 10.1146/annurev-bioeng-062117-121036
   PubMedGoogle Scholar
• Farzanehfar P, Woodrow H, Horne M. Sensor measurements can characterize fluctuations and wearing off in Parkinson’s disease and guide therapy to improve motor, non-motor and quality of life scores. Front Aging Neurosci. 2022;14:852992. doi: 10.3389/fnagi.2022.852992
   PubMed Web of Science ®Google Scholar
• Heldman DA, Giuffrida JP, Cubo E. Wearable sensors for advanced therapy referral in parkinson’s disease. J Park Dis. 2016;6(3):631–638. doi: 10.3233/JPD-160830
   Web of Science ®Google Scholar
• Heldman DA, Pulliam CL, Urrea Mendoza E, et al. Computer-guided deep brain stimulation programming for Parkinson’s disease. Neuromodulation: Technol Neural Interface. 2016;19(2):127–132. doi: 10.1111/ner.12372
   PubMed Web of Science ®Google Scholar
• Ossig C, Antonini A, Buhmann C, et al. Wearable sensor-based objective assessment of motor symptoms in Parkinson’s disease. J Neural Transm. 2016;123(1):57–64. doi: 10.1007/s00702-015-1439-8
   PubMed Web of Science ®Google Scholar
• Chandrabhatla AS, Pomeraniec IJ, Ksendzovsky A. Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson’s disease motor symptoms. NPJ Digit Med. 2022;5(1):32. doi: 10.1038/s41746-022-00568-y
   PubMed Web of Science ®Google Scholar
• Ferreira-Sánchez MDR, Moreno-Verdú M, Cano-de-la-Cuerda R. Quantitative measurement of rigidity in Parkinson’s disease: a systematic review. Sensors. 2020;20(3):880. doi: 10.3390/s20030880
   PubMed Web of Science ®Google Scholar
• Fujikawa J, Morigaki R, Yamamoto N, et al. Diagnosis and treatment of tremor in Parkinson’s disease using mechanical devices. Life. 2022;13(1):78. doi: 10.3390/life13010078
   PubMedGoogle Scholar
• Garcia-Agundez A, Eickhoff C. Towards objective quantification of hand tremors and BradykInesia using contactless sensors: a systematic review. Front Aging Neurosci. 2021;13:716102. doi: 10.3389/fnagi.2021.716102
   PubMed Web of Science ®Google Scholar
• Morgan C, Craddock I, Tonkin EL, et al. Protocol for PD SENSORS: parkinson’s disease symptom evaluation in a naturalistic setting producing outcome measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson’s disease. BMJ Open. 2020;10:e041303.
   PubMed Web of Science ®Google Scholar
• Reichmann H, Klingelhoefer L, Bendig J. The use of wearables for the diagnosis and treatment of Parkinson’s disease. J Neural Transm. 2023;130(6):783–791. doi: 10.1007/s00702-022-02575-5
   PubMed Web of Science ®Google Scholar
• Merola A, Sturchio A, Hacker S, et al. Technology-based assessment of motor and nonmotor phenomena in Parkinson disease. Expert Rev Neurother. 2018;18(11):825–845. doi: 10.1080/14737175.2018.1530593
   PubMed Web of Science ®Google Scholar
• Bologna M, Paparella G, Fasano A, et al. Evolving concepts on bradykinesia. Brain. 2020;143(3):727–750. doi: 10.1093/brain/awz344
   PubMedGoogle Scholar
• Bologna M, Suppa A, Di Stasio F, et al. Neurophysiological studies on atypical parkinsonian syndromes. Parkinsonism Relat Disord. 2017;42:12–21. doi: 10.1016/j.parkreldis.2017.06.017
   PubMed Web of Science ®Google Scholar
• Godwin A, Agnew M, Stevenson J. Accuracy of inertial motion sensors in static, quasistatic, and complex dynamic motion. J Biomech Eng. 2009;131(11):114501. doi: 10.1115/1.4000109
   PubMed Web of Science ®Google Scholar
• Topley M, Richards JG. A comparison of currently available optoelectronic motion capture systems. J Biomech. 2020;106:109820. doi: 10.1016/j.jbiomech.2020.109820
   PubMed Web of Science ®Google Scholar
• Alberto S, Cabral S, Proença J, et al. Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments. BMC neurol. 2021;21(1):331. doi: 10.1186/s12883-021-02354-x
   PubMed Web of Science ®Google Scholar
• Guo Y, Yang J, Liu Y, et al. Detection and assessment of Parkinson’s disease based on gait analysis: a survey. Front Aging Neurosci. 2022;14:916971. doi: 10.3389/fnagi.2022.916971
   PubMed Web of Science ®Google Scholar
• Williams S, Fang H, Relton SD, et al. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. 2021;8(1):69–75. doi: 10.1002/mdc3.13119
   PubMed Web of Science ®Google Scholar
• Scott B, Seyres M, Philp F, et al. Healthcare applications of single camera markerless motion capture: a scoping review. PeerJ. 2022;10:e13517. doi: 10.7717/peerj.13517
   PubMed Web of Science ®Google Scholar
• Artusi CA, Imbalzano G, Sturchio A, et al. Implementation of mobile health technologies in clinical trials of movement disorders: underutilized potential. Neurotherapeutics. 2020;17(4):1736–1746. doi: 10.1007/s13311-020-00901-x
   PubMed Web of Science ®Google Scholar
• FitzGerald JJ, Lu Z, Jareonsettasin P, et al. Quantifying motor impairment in movement disorders. Front Neurosci. 2018;12:202. doi: 10.3389/fnins.2018.00202
   PubMed Web of Science ®Google Scholar
• Sica M, Tedesco S, Crowe C, et al. Continuous home monitoring of Parkinson’s disease using inertial sensors: a systematic review. PLoS One. 2021;16(2):e0246528. doi: 10.1371/journal.pone.0246528
   PubMed Web of Science ®Google Scholar
• Algarni M, Fasano A. The overlap between essential tremor and Parkinson disease. Parkinsonism Relat Disord. 2018;46:S101–S104. doi: 10.1016/j.parkreldis.2017.07.006
   PubMed Web of Science ®Google Scholar
• Bologna M, Leodori G, Stirpe P, et al. Bradykinesia in early and advanced Parkinson’s disease. J Neurol Sci. 2016;369:286–291. doi: 10.1016/j.jns.2016.08.028
   PubMed Web of Science ®Google Scholar
• di Biase L, Summa S, Tosi J, et al. Quantitative analysis of bradykinesia and rigidity in Parkinson’s disease. Front Neurol. 2018;9:121. doi: 10.3389/fneur.2018.00121
   PubMed Web of Science ®Google Scholar
• Gao C, Smith S, Lones M, et al. Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation. Transl Neurodegener. 2018;7(1):18. doi: 10.1186/s40035-018-0124-x
   PubMedGoogle Scholar
• Bank PJM, Marinus J, Meskers CGM, et al. Optical hand tracking: a novel technique for the assessment of Bradykinesia in Parkinson’s disease. Mov Disord Clin Pract. 2017;4(6):875–883. doi: 10.1002/mdc3.12536
   PubMedGoogle Scholar
• Djuric-Jovicic MD, Bobic VN, Jecmenica-Lukic M, et al. Implementation of continuous wavelet transformation in repetitive finger tapping analysis for patients with PD. 2014 22nd Telecommun Forum Telfor TELFOR. 2014;541–544.
   Google Scholar
• Bologna M, Fabbrini G, Marsili L, et al. Facial bradykinesia. J Neurol Neurosurg Psychiatry. 2013;84(6):681–685. doi: 10.1136/jnnp-2012-303993
   PubMed Web of Science ®Google Scholar
• Bologna M, Berardelli I, Paparella G, et al. Altered kinematics of facial emotion expression and emotion recognition deficits are unrelated in Parkinson’s disease. Front Neurol. 2016;7. doi: 10.3389/fneur.2016.00230
   PubMed Web of Science ®Google Scholar
• Bandini A, Orlandi S, Escalante HJ, et al. Analysis of facial expressions in parkinson’s disease through video-based automatic methods. J Neurosci Methods. 2017;281:7–20. doi: 10.1016/j.jneumeth.2017.02.006
   PubMed Web of Science ®Google Scholar
• Pegolo E, Volpe D, Cucca A, et al. Quantitative evaluation of hypomimia in parkinson’s disease: a face tracking approach. Sensors. 2022;22(4):1358. doi: 10.3390/s22041358
   PubMed Web of Science ®Google Scholar
• Park BK, Kwon Y, Kim J-W, et al. AnalySis of viscoelastic properties of wrist joint for quantification of Parkinsonian rigidity. IEEE Trans Neural Syst Rehabil Eng. 2011;19(2):167–176. doi: 10.1109/TNSRE.2010.2091149
   PubMed Web of Science ®Google Scholar
• Caligiuri MP. Portable device for quantifying parkinsonian wrist rigidity. Mov Disord. 1994;9(1):57–63. doi: 10.1002/mds.870090109
   PubMed Web of Science ®Google Scholar
• Papadopoulos A, Iakovakis D, Klingelhoefer L, et al. Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques. Sci Rep. 2020;10(1):21370. doi: 10.1038/s41598-020-78418-8
   PubMed Web of Science ®Google Scholar
• Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, et al. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. 2015;19(6):1835–1842. doi: 10.1109/JBHI.2015.2471093
   PubMed Web of Science ®Google Scholar
• di Biase L, Di Santo A, Caminiti ML, et al. Gait analysis in Parkinson’s disease: an overview of the most accurate markers for diagnosis and symptoms monitoring. Sensors. 2020;20(12):3529. doi: 10.3390/s20123529
   PubMed Web of Science ®Google Scholar
• Pistacchi M. Gait analysis and clinical correlations in early Parkinson?s disease. Funct Neurol. 2017;32(1):28. doi: 10.11138/FNeur/2017.32.1.028
   PubMedGoogle Scholar
• Ťupa O, Procházka A, Vyšata O, et al. Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS kinect. Biomed Eng Online. 2015;14(1):97. doi: 10.1186/s12938-015-0092-7
   PubMedGoogle Scholar
• Zhao Y, Li J, Wang X, et al. A lightweight pose sensing scheme for contactless abnormal gait behavior measurement. Sensors. 2022;22(11):4070. doi: 10.3390/s22114070
   PubMed Web of Science ®Google Scholar
• Mirelman A, Ben or Frank M, Melamed M, et al. Detecting sensitive mobility features for Parkinson’s disease stages via machine learning. Mov Disord. 2021;36(9):2144–2155. doi: 10.1002/mds.28631
   PubMed Web of Science ®Google Scholar
• Dewey DC, Miocinovic S, Bernstein I, et al. Automated gait and balance parameters diagnose and correlate with severity in Parkinson disease. J Neurol Sci. 2014;345(1–2):131–138. doi: 10.1016/j.jns.2014.07.026
   PubMed Web of Science ®Google Scholar
• Caramia C, Torricelli D, Schmid M, et al. IMU-BaSed classification of parkinson’s disease from gait: a sensitivity analysis on sensor location and feature selection. IEEE J Biomed Health Inform. 2018;22(6):1765–1774. doi: 10.1109/JBHI.2018.2865218
   PubMed Web of Science ®Google Scholar
• Rodríguez-Martín D, Cabestany J, Pérez-López C, et al. A new paradigm in parkinson’s disease evaluation with wearable medical devices: a review of STAT-ONTM. Front Neurol. 2022;13:912343. doi: 10.3389/fneur.2022.912343
   PubMed Web of Science ®Google Scholar
• Tien I, Glaser SD, Aminoff MJ Characterization of gait abnormalities in Parkinson’s disease using a wireless inertial sensor system. 2010 Annu Int Conf IEEE Eng Med Biol; Buenos Aires, Argentina. 2010. p. 3353–3356.
   Google Scholar
• 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: remote PD testing with smartphones. Mov Disord. 2018;33:1287–1297.
   PubMed Web of Science ®Google Scholar
• Di Lazzaro G, Ricci M, Al-Wardat M, et al. Technology-based objective measures detect subclinical axial signs in untreated, de novo Parkinson’s disease. J Park Dis. 2020;10(1):113–122. doi: 10.3233/JPD-191758
   Web of Science ®Google Scholar
• Marcante A, Di Marco R, Gentile G, et al. Foot pressure wearable sensors for freezing of gait detection in Parkinson’s disease. Sensors. 2020;21(1):128. doi: 10.3390/s21010128
   PubMed Web of Science ®Google Scholar
• Iakovakis D, Mastoras RE, Hadjidimitriou S, et al. Smartwatch-based activity analysis during sleep for early Parkinson’s disease detection. 2020 42nd Annu Int Conf IEEE Eng Med Biol Soc EMBC; Montreal, QC, Canada. 2020. p. 4326–4329.
   Google Scholar
• Louter M, Maetzler W, Prinzen J, et al. Accelerometer-based quantitative analysis of axial nocturnal movements differentiates patients with Parkinson’s disease, but not high-risk individuals, from controls. J Neurol Neurosurg Psychiatry. 2015;86(1):32–37. doi: 10.1136/jnnp-2013-306851
   PubMed Web of Science ®Google Scholar
• McGregor S, Churchward P, Soja K, et al. The use of accelerometry as a tool to measure disturbed nocturnal sleep in Parkinson’s disease. Npj Park Dis. 2018;4(1):1. doi: 10.1038/s41531-017-0038-9
   PubMedGoogle Scholar
• Bologna M, Espay AJ, Fasano A, et al. Redefining Bradykinesia. Mov Disord. 2023;38(4):551–557. doi: 10.1002/mds.29362
   PubMed Web of Science ®Google Scholar
• Gaßner H, Raccagni C, Eskofier BM, et al. The diagnostic scope of sensor-based gait analysis in atypical Parkinsonism: further observations. Front Neurol. 2019;10:5. doi: 10.3389/fneur.2019.00005
   PubMed Web of Science ®Google Scholar
• Thenganatt MA, Louis ED. Distinguishing essential tremor from Parkinson’s disease: bedside tests and laboratory evaluations. Expert Rev Neurother. 2012;12(6):687–696. doi: 10.1586/ern.12.49
   PubMed Web of Science ®Google Scholar
• Isaias IU, Marotta G, Hirano S, et al. Imaging essential tremor. Mov Disord. 2010;25(6):679–686. doi: 10.1002/mds.22870
   PubMed Web of Science ®Google Scholar
• di Biase L, Brittain J-S, Shah SA, et al. Tremor stability index: a new tool for differential diagnosis in tremor syndromes. Brain. 2017;140:1977–1986. doi: 10.1093/brain/awx104
   PubMed Web of Science ®Google Scholar
• Surangsrirat D, Thanawattano C, Pongthornseri R, et al. Support vector machine classification of Parkinson’s disease and essential tremor subjects based on temporal fluctuation. 2016 38th. Annu Int Conf IEEE Eng Med Biol Soc EMBC; Orlando, FL, USA. 2016. p. 6389–6392.
   Google Scholar
• Horne MK, McGregor S, Bergquist F. An objective fluctuation score for Parkinson’s disease. PLoS One. 2015;10:e0124522. doi: 10.1371/journal.pone.0124522
   PubMedGoogle Scholar
• 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:e0183843. doi: 10.1371/journal.pone.0183843
   PubMed Web of Science ®Google Scholar
• Wile DJ, Ranawaya R, Kiss ZHT. Smart watch accelerometry for analysis and diagnosis of tremor. J Neurosci Methods. 2014;230:1–4. doi: 10.1016/j.jneumeth.2014.04.021
   PubMed Web of Science ®Google Scholar
• Locatelli P, Alimonti D, Traversi G, et al. Classification of essential tremor and Parkinson’s tremor based on a low-power wearable device. Electronics. 2020;9(10):1695. doi: 10.3390/electronics9101695
   Web of Science ®Google Scholar
• Xing X, Luo N, Li S, et al. Identification and classification of Parkinsonian and essential tremors for diagnosis using machine learning algorithms. Front Neurosci. 2022;16:701632. doi: 10.3389/fnins.2022.701632
   PubMed Web of Science ®Google Scholar
• Zhang B, Huang F, Liu J, et al. A novel posture for better differentiation between parkinson’s tremor and essential tremor. Front Neurosci. 2018;12:317. doi: 10.3389/fnins.2018.00317
   PubMed Web of Science ®Google Scholar
• Hossen A, Muthuraman M, Raethjen J, et al. Discrimination of Parkinsonian tremor from essential tremor by implementation of a wavelet-based soft-decision technique on EMG and accelerometer signals. Biomedical Signal Processing And Control. 2010;5(3):181–188. doi: 10.1016/j.bspc.2010.02.005
   Web of Science ®Google Scholar
• Rovini E, Maremmani C, Cavallo F. How wearable sensors can support Parkinson’s disease diagnosis and treatment: a systematic review. Front Neurosci. 2017;11:555. doi: 10.3389/fnins.2017.00555
   PubMed Web of Science ®Google Scholar
• Balachandar A, Algarni M, Oliveira L, et al. Are smartphones and machine learning enough to diagnose tremor? J Neurol. 2022;269(11):6104–6115. doi: 10.1007/s00415-022-11293-7
   PubMed Web of Science ®Google Scholar
• Fernandez KM, Roemmich RT, Stegemöller EL, et al. Gait initiation impairments in both essential tremor and Parkinson’s disease. Gait Posture. 2013;38(4):956–961. doi: 10.1016/j.gaitpost.2013.05.001
   PubMed Web of Science ®Google Scholar
• Lin S, Gao C, Li H, et al. Wearable sensor-based gait analysis to discriminate early Parkinson’s disease from essential tremor. J Neurol. 2023;270(4):2283–2301. doi: 10.1007/s00415-023-11577-6
   PubMed Web of Science ®Google Scholar
• Vescio B, Quattrone A, Nisticò R, et al. Wearable devices for assessment of tremor. Front Neurol. 2021;12:680011. doi: 10.3389/fneur.2021.680011
   PubMed Web of Science ®Google Scholar
• Chen L, Cai G, Weng H, et al. More sensitive identification for bradykinesia compared to tremors in Parkinson’s disease based on Parkinson’s KinetiGraph (PKG). Front Aging Neurosci. 2020;12:594701. doi: 10.3389/fnagi.2020.594701
   PubMed Web of Science ®Google Scholar
• Heldman DA, Giuffrida JP, Chen R, et al. The modified bradykinesia rating scale for Parkinson’s disease: reliability and comparison with kinematic measures. Mov Disord. 2011;26(10):1859–1863. doi: 10.1002/mds.23740
   PubMed Web of Science ®Google Scholar
• Espay AJ, Giuffrida JP, Chen R, et al. Differential response of speed, amplitude, and rhythm to dopaminergic medications in Parkinson’s disease. Mov Disord. 2011;26(14):2504–2508. doi: 10.1002/mds.23893
   PubMed Web of Science ®Google Scholar
• Pulliam CL, Heldman DA, Brokaw EB, et al. Continuous assessment of Levodopa response in Parkinson’s disease using wearable motion sensors. IEEE Trans Biomed Eng. 2018;65(1):159–164. doi: 10.1109/TBME.2017.2697764
   PubMed Web of Science ®Google Scholar
• Williams S, Relton SD, Fang H, et al. Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos. Artif Intell Med. 2020;110:101966. doi: 10.1016/j.artmed.2020.101966
   PubMed Web of Science ®Google Scholar
• Vignoud G, Desjardins C, Salardaine Q, et al. Video-based automated assessment of movement parameters consistent with MDS-UPDRS III in Parkinson’s disease. J Park Dis. 2022;12(7):2211–2222. doi: 10.3233/JPD-223445
   Web of Science ®Google Scholar
• Zampogna A, Manoni A, Asci F, et al. Shedding light on nocturnal movements in parkinson’s disease: evidence from wearable technologies. Sensors. 2020;20(18):5171. doi: 10.3390/s20185171
   PubMed Web of Science ®Google Scholar
• Bhidayasiri R, Trenkwalder C. Getting a good night sleep? The importance of recognizing and treating nocturnal hypokinesia in Parkinson’s disease. Parkinsonism Relat Disord. 2018;50:10–18. doi: 10.1016/j.parkreldis.2018.01.008
   PubMed Web of Science ®Google Scholar
• Bhidayasiri R, Sringean J, Anan C, et al. Quantitative demonstration of the efficacy of night-time apomorphine infusion to treat nocturnal hypokinesia in Parkinson’s disease using wearable sensors. Parkinsonism Relat Disord. 2016;33:S36–S41. doi: 10.1016/j.parkreldis.2016.11.016
   PubMed Web of Science ®Google Scholar
• Sringean J, Taechalertpaisarn P, Thanawattano C, et al. How well do Parkinson’s disease patients turn in bed? Quantitative analysis of nocturnal hypokinesia using multisite wearable inertial sensors. Parkinsonism Relat Disord. 2016;23:10–16. doi: 10.1016/j.parkreldis.2015.11.003
   PubMed Web of Science ®Google Scholar
• Sringean J, Anan C, Thanawattano C, et al. Time for a strategy in night-time dopaminergic therapy? An objective sensor-based analysis of nocturnal hypokinesia and sleeping positions in Parkinson’s disease. J Neurol Sci. 2017;373:244–248. doi: 10.1016/j.jns.2016.12.045
   PubMed Web of Science ®Google Scholar
• Mirelman A, Hillel I, Rochester L, et al. Tossing and turning in bed: nocturnal movements in Parkinson’s disease. Mov Disord. 2020;35(6):959–968. doi: 10.1002/mds.28006
   PubMed Web of Science ®Google Scholar
• Kwon Y, Park S-H, Kim J-W, et al. Quantitative evaluation of parkinsonian rigidity during intra-operative deep brain stimulation. Biomed Mater Eng. 2014;24(6):2273–2281. doi: 10.3233/BME-141040
   PubMed Web of Science ®Google Scholar
• Costa P, Rosas MJ, Vaz R, et al. Wrist rigidity assessment during deep brain stimulation surgery. 2015 37th. Annu Int Conf IEEE Eng Med Biol Soc EMBC; Milan, Italy. 2015. p. 3423–3426.
   Google Scholar
• Giuffrida JP, Riley DE, Maddux BN, et al. Clinically deployable KinesiaTM technology for automated tremor assessment. Mov Disord. 2009;24:723–730. doi: 10.1002/mds.22445
   PubMed Web of Science ®Google Scholar
• Battista L, Romaniello A. A wearable tool for selective and continuous monitoring of tremor and dyskinesia in Parkinsonian patients. Parkinsonism Relat Disord. 2020;77:43–47. doi: 10.1016/j.parkreldis.2020.06.020
   PubMed Web of Science ®Google Scholar
• Rigas G, Tzallas AT, Tsipouras MG, et al. Assessment of tremor activity in the Parkinson’s disease using a set of wearable sensors. IEEE Trans Inf Technol Biomed. 2012;16(3):478–487. doi: 10.1109/TITB.2011.2182616
   PubMedGoogle Scholar
• Hill EJ, Mangleburg CG, Alfradique-Dunham I, et al. Quantitative mobility measures complement the MDS-UPDRS for characterization of Parkinson’s disease heterogeneity. Parkinsonism Relat Disord. 2021;84:105–111. doi: 10.1016/j.parkreldis.2021.02.006
   PubMed Web of Science ®Google Scholar
• Mirelman A, Bonato P, Camicioli R, et al. Gait impairments in Parkinson’s disease. Lancet Neurol. 2019;18(7):697–708. doi: 10.1016/S1474-4422(19)30044-4
   PubMed Web of Science ®Google Scholar
• Schlachetzki JCM, Barth J, Marxreiter F, et al. Wearable sensors objectively measure gait parameters in Parkinson’s disease. PLoS One. 2017;12(10):e0183989. doi: 10.1371/journal.pone.0183989
   PubMed Web of Science ®Google Scholar
• Micó-Amigo ME, Kingma I, Heinzel S, et al. Potential markers of progression in idiopathic Parkinson’s disease derived from assessment of circular gait with a single body-fixed-sensor: a 5 year longitudinal study. Front Hum Neurosci. 2019;13:59. doi: 10.3389/fnhum.2019.00059
   PubMed Web of Science ®Google Scholar
• Moore ST, Yungher DA, Morris TR, et al. Autonomous identification of freezing of gait in Parkinson’s disease from lower-body segmental accelerometry. J Neuroeng Rehabil. 2013;10(1):19. doi: 10.1186/1743-0003-10-19
   PubMedGoogle Scholar
• Arami A, Poulakakis-Daktylidis A, Tai YF, et al. Prediction of gait freezing in parkinsonian patients: a binary classification augmented with time series Prediction. IEEE Trans Neural Syst Rehabil Eng. 2019;27(9):1909–1919. doi: 10.1109/TNSRE.2019.2933626
   PubMed Web of Science ®Google Scholar
• Borzì L, Mazzetta I, Zampogna A, et al. PredIction of freezing of gait in parkinson’s disease using wearables and machine learning. Sensors. 2021;21(2):614. doi: 10.3390/s21020614
   PubMed Web of Science ®Google Scholar
• Palmerini L, Rocchi L, Mazilu S, et al. Identification of characteristic motor patterns preceding freezing of gait in Parkinson’s disease using wearable sensors. Front Neurol. 2017;8:394. doi: 10.3389/fneur.2017.00394
   PubMed Web of Science ®Google Scholar
• Sigcha L, Borzì L, Pavón I, et al. Improvement of performance in freezing of gait detection in Parkinson’s disease using transformer networks and a single waist-worn triaxial accelerometer. Eng Appl Artif Intell. 2022;116:105482. doi: 10.1016/j.engappai.2022.105482
   Web of Science ®Google Scholar
• Pardoel S, Shalin G, Lemaire ED, et al. Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson’s disease. PLoS One. 2021;16(10):e0258544. doi: 10.1371/journal.pone.0258544
   PubMed Web of Science ®Google Scholar
• Samà A, Rodríguez-Martín D, Pérez-López C, et al. Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments. Pattern Recognit Lett. 2018;105:135–143. doi: 10.1016/j.patrec.2017.05.009
   Web of Science ®Google Scholar
• 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(2):e0171764. doi: 10.1371/journal.pone.0171764
   PubMed Web of Science ®Google Scholar
• Ahmadi A, Destelle F, Unzueta L, et al. 3D human gait reconstruction and monitoring using body-worn inertial sensors and kinematic modeling. IEEE Sensors J. 2016;16(24):8823–8831. doi: 10.1109/JSEN.2016.2593011
   Web of Science ®Google Scholar
• Soltaninejad S, Cheng I, Basu AK-F. Kin-FOG: Automatic Simulated Freezing of Gait (FOG) assessment system for Parkinson’s disease. Sensors. 2019;19(10):2416. doi: 10.3390/s19102416
   PubMed Web of Science ®Google Scholar
• Bjornestad A, Forsaa EB, Pedersen KF, et al. Risk and course of motor complications in a population-based incident Parkinson’s disease cohort. Parkinsonism Relat Disord. 2016;22:48–53. doi: 10.1016/j.parkreldis.2015.11.007
   PubMed Web of Science ®Google Scholar
• Rodríguez-Molinero A, Pérez-López C, Samà A, et al. A kinematic sensor and algorithm to detect motor fluctuations in Parkinson disease: validation study under real conditions of use. JMIR Rehabil Assist Technol. 2018;5(1):e8. doi: 10.2196/rehab.8335
   PubMedGoogle Scholar
• 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. doi: 10.1016/j.artmed.2016.01.001
   PubMed Web of Science ®Google Scholar
• Rodríguez-Molinero A, Samà A, Pérez-López C, et al. Analysis of correlation between an accelerometer-based algorithm for detecting Parkinsonian Gait and UPDRS subscales. Front Neurol. 2017;8:431. doi: 10.3389/fneur.2017.00431
   PubMed Web of Science ®Google Scholar
• Ghoraani B, Hssayeni MD, Bruack MM, et al. Multilevel features for sensor-based assessment of motor fluctuation in Parkinson’s disease subjects. IEEE J Biomed Health Inform. 2020;24(5):1284–1295. doi: 10.1109/JBHI.2019.2943866
   PubMed Web of Science ®Google Scholar
• Barrachina-Fernández M, Maitín AM, Sánchez-Ávila C, et al. Wearable technology to detect motor fluctuations in Parkinson’s disease patients: current state and challenges. Sensors. 2021;21(12):4188. doi: 10.3390/s21124188
   PubMed Web of Science ®Google Scholar
• Bayés À, Samá A, Prats A, et al. A “HOLTER” for Parkinson’s disease: validation of the ability to detect on-off states using the REMPARK system. Gait Posture. 2018;59:1–6. doi: 10.1016/j.gaitpost.2017.09.031
   PubMed Web of Science ®Google Scholar
• Ossig C, Gandor F, Fauser M, et al. Correlation of quantitative motor state assessment using a kinetograph and patient diaries in advanced PD: data from an observational study. PLoS One. 2016;11(8):e0161559. doi: 10.1371/journal.pone.0161559
   PubMed Web of Science ®Google Scholar
• Griffiths RI, Kotschet K, Arfon S, et al. Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease. J Park Dis. 2012;2(1):47–55. doi: 10.3233/JPD-2012-11071
   Web of Science ®Google Scholar
• Keijsers NLW, Horstink MWIM, Gielen SCAM. Automatic assessment of levodopa-induced dyskinesias in daily life by neural networks. Mov Disord. 2003;18(1):70–80. doi: 10.1002/mds.10310
   PubMed Web of Science ®Google Scholar
• Lopane G, Mellone S, Chiari L, et al. Dyskinesia detection and monitoring by a single sensor in patients with Parkinson’s disease: single sensor and dyskinesias in PD. Mov Disord. 2015;30:1267–1271. doi: 10.1002/mds.26313
   PubMed Web of Science ®Google Scholar
• Tsipouras MG, Tzallas AT, Rigas G, et al. An automated methodology for levodopa-induced dyskinesia: assessment based on gyroscope and accelerometer signals. Artif Intell Med. 2012;55(2):127–135. doi: 10.1016/j.artmed.2012.03.003
   PubMed Web of Science ®Google Scholar
• Pulliam CL, Burack MA, Heldman DA, et al. Motion sensor Dyskinesia assessment during activities of daily living. J Park Dis. 2014;4(4):609–615. doi: 10.3233/JPD-140348
   Web of Science ®Google Scholar
• Rodríguez-Molinero A, Pérez-López C, Samà A, et al. Estimating dyskinesia severity in Parkinson’s disease by using a waist-worn sensor: concurrent validity study. Sci Rep. 2019;9(1):13434. doi: 10.1038/s41598-019-49798-3
   PubMedGoogle Scholar
• Li MH, Mestre TA, Fox SH, et al. Automated assessment of levodopa-induced dyskinesia: evaluating the responsiveness of video-based features. Parkinsonism Relat Disord. 2018;53:42–45.
   PubMed Web of Science ®Google Scholar
• Antonini A, Stoessl AJ, Kleinman LS, et al. Developing consensus among movement disorder specialists on clinical indicators for identification and management of advanced Parkinson’s disease: a multi-country Delphi-panel approach. Curr Med Res Opin. 2018;34(12):2063–2073. doi: 10.1080/03007995.2018.1502165
   PubMed Web of Science ®Google Scholar
• Bogetofte H, Alamyar A, Blaabjerg M, et al. Levodopa therapy for Parkinson’s disease: history, current status and perspectives. CNS Neurol Disord Drug Targets. 2020;19(8):572–583. doi: 10.2174/1871527319666200722153156
   PubMed Web of Science ®Google Scholar
• Khodakarami H, Shokouhi N, Horne M. A method for measuring time spent in bradykinesia and dyskinesia in people with Parkinson’s disease using an ambulatory monitor. J Neuroeng Rehabil. 2021;18(1):116. doi: 10.1186/s12984-021-00905-4
   PubMed Web of Science ®Google Scholar
• Güney G, Jansen TS, Dill S, et al. Video-based hand movement analysis of ParkinsOn patients before and after medication using high-frame-rate videos and mediapipe. Sensors. 2022;22(20):7992. doi: 10.3390/s22207992
   PubMed Web of Science ®Google Scholar
• Guerra A, Colella D, Giangrosso M, et al. Driving motor cortex oscillations modulates bradykinesia in Parkinson’s disease. Brain. 2022;145(1):224–236. doi: 10.1093/brain/awab257
   PubMed Web of Science ®Google Scholar
• Bologna M, Guerra A, Paparella G, et al. Neurophysiological correlates of bradykinesia in Parkinson’s disease. Brain J Neurol. 2018;141(8):2432–2444. doi: 10.1093/brain/awy155
   Google Scholar
• van den Noort JC, Verhagen R, van Dijk KJ, et al. Quantification of hand motor symptoms in Parkinson’s disease: a proof-of-principle study using inertial and force sensors. Ann Biomed Eng. 2017;45(10):2423–2436. doi: 10.1007/s10439-017-1881-x
   PubMed Web of Science ®Google Scholar
• Suppa A, Kita A, Leodori G, et al. L-DOPA and freezing of gait in Parkinson’s disease: objective assessment through a wearable wireless system. Front Neurol. 2017;8:406. doi: 10.3389/fneur.2017.00406
   PubMed Web of Science ®Google Scholar
• Isaacson SH, Boroojerdi B, Waln O, et al. Effect of using a wearable device on clinical decision-making and motor symptoms in patients with Parkinson’s disease starting transdermal rotigotine patch: a pilot study. Parkinsonism Relat Disord. 2019;64:132–137. doi: 10.1016/j.parkreldis.2019.01.025
   PubMed Web of Science ®Google Scholar
• Bhidayasiri R, Sringean J, Chaiwong S, et al. Rotigotine for nocturnal hypokinesia in Parkinson’s disease: quantitative analysis of efficacy from a randomized, placebo-controlled trial using an axial inertial sensor. Parkinsonism Relat Disord. 2017;44:124–128. doi: 10.1016/j.parkreldis.2017.08.010
   PubMed Web of Science ®Google Scholar
• Farzanehfar P, Woodrow H, Braybrook M, et al. Objective measurement in routine care of people with Parkinson’s disease improves outcomes. Npj Park Dis. 2018;4(1):10. doi: 10.1038/s41531-018-0046-4
   PubMedGoogle Scholar
• Woodrow H, Horne MK, Fernando CV, et al. A blinded, controlled trial of objective measurement in Parkinson’s disease. NPJ Parkinsons Dis. 2020;6(1):35. doi: 10.1038/s41531-020-00136-9
   PubMed Web of Science ®Google Scholar
• Marxreiter F, Gaßner H, Borozdina O, et al. Sensor-based gait analysis of individualized improvement during apomorphine titration in Parkinson’s disease. J Neurol. 2018;265(11):2656–2665. doi: 10.1007/s00415-018-9012-7
   PubMed Web of Science ®Google Scholar
• Múrias Lopes E, Vilas-Boas MDC, Dias D, et al. iHandu: a novel quantitative wrist rigidity evaluation device for deep brain stimulation surgery. Sensors. 2020;20(2):331. doi: 10.3390/s20020331
   PubMed Web of Science ®Google Scholar
• Wu J, Yu N, Yu Y, et al. Intraoperative quantitative measurements for Bradykinesia evaluation during deep brain stimulation surgery using leap motion controller: a pilot study. Park Dis. 2021;2021:1–7. doi: 10.1155/2021/6639762
   Web of Science ®Google Scholar
• Yu N, Yu Y, Lin J, et al. A non-contact system for intraoperative quantitative assessment of bradykinesia in deep brain stimulation surgery. Comput Methods Programs Biomed. 2022;225:107005. doi: 10.1016/j.cmpb.2022.107005
   PubMed Web of Science ®Google Scholar
• Sasaki F, Oyama G, Sekimoto S, et al. Closed-loop programming using external responses for deep brain stimulation in Parkinson’s disease. Parkinsonism Relat Disord. 2021;84:47–51. doi: 10.1016/j.parkreldis.2021.01.023
   PubMed Web of Science ®Google Scholar
• Wenzel GR, Roediger J, Brücke C, et al. CLOVER-DBS: algorithm-guided deep brain stimulation-programming based on external sensor feedback evaluated in a prospective, randomized, crossover, double-blind, two-center study. J Park Dis. 2021;11(4):1887–1899. doi: 10.3233/JPD-202480
   Web of Science ®Google Scholar
• Heldman DA, Espay AJ, LeWitt PA, et al. Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson’s disease. Parkinsonism Relat Disord. 2014;20(6):590–595. doi: 10.1016/j.parkreldis.2014.02.022
   PubMed Web of Science ®Google Scholar
• Pulliam CL, Heldman DA, Orcutt TH, et al. Motion sensor strategies for automated optimization of deep brain stimulation in Parkinson’s disease. Parkinsonism Relat Disord. 2015;21(4):378–382. doi: 10.1016/j.parkreldis.2015.01.018
   PubMed Web of Science ®Google Scholar
• Lim I, van Wegen E, de Goede C, et al. Effects of external rhythmical cueing on gait in patients with Parkinson’s disease: a systematic review. Clin Rehabil. 2005;19(7):695–713. doi: 10.1191/0269215505cr906oa
   PubMed Web of Science ®Google Scholar
• Sweeney D, Quinlan L, Browne P, et al. A technological review of wearable cueing devices addressing freezing of gait in Parkinson’s disease. Sensors. 2019;19(6):1277. doi: 10.3390/s19061277
   PubMed Web of Science ®Google Scholar
• Dvorani A, Waldheim V, Jochner MCE, et al. Real-time detection of freezing motions in Parkinson’s patients for adaptive gait phase synchronous cueing. Front Neurol. 2021;12:720516. doi: 10.3389/fneur.2021.720516
   PubMed Web of Science ®Google Scholar
• Mazilu S, Calatroni A, Gazit E, et al. Prediction of freezing of gait in parkinson’s from physiological wearables: an exploratory study. IEEE J Biomed Health Inform. 2015;19(6):1843–1854. doi: 10.1109/JBHI.2015.2465134
   PubMed Web of Science ®Google Scholar
• Pablo MM, Carmen RB, Maria JF, et al. Guide to Assessment Scales in Parkinson’s Disease. Springer International Publishing AG; 2014. doi: 10.1007/978-1-907673-88-7
   Google Scholar
• Tarakad A. Clinical rating scales and quantitative assessments of movement disorders. Neurol Clin. 2020;38(2):231–254. doi: 10.1016/j.ncl.2019.12.001
   PubMed Web of Science ®Google Scholar
• Post B, Merkus MP, de Bie RMA, et al. Unified Parkinson’s disease rating scale motor examination: are ratings of nurses, residents in neurology, and movement disorders specialists interchangeable? Mov Disord. 2005;20(12):1577–1584. doi: 10.1002/mds.20640
   PubMed Web of Science ®Google Scholar
• Willis AW, Schootman M, Evanoff BA, et al. Neurologist care in Parkinson disease: a utilization, outcomes, and survival study. Neurology. 2011;77(9):851–857. doi: 10.1212/WNL.0b013e31822c9123
   PubMed Web of Science ®Google Scholar
• Evers LJW, Krijthe JH, Meinders MJ, et al. Measuring Parkinson’s disease over time: the real-world within-subject reliability of the MDS-UPDRS. Mov Disord. 2019;34(10):1480–1487. doi: 10.1002/mds.27790
   PubMed Web of Science ®Google Scholar
• Paradis E, Sutkin G. Beyond a good story: from Hawthorne effect to reactivity in health professions education research. Med Educ. 2017;51(1):31–39. doi: 10.1111/medu.13122
   PubMed Web of Science ®Google Scholar
• Warmerdam E, Hausdorff JM, Atrsaei A, et al. Long-term unsupervised mobility assessment in movement disorders. Lancet Neurol. 2020;19(5):462–470. doi: 10.1016/S1474-4422(19)30397-7
   PubMed Web of Science ®Google Scholar
• Holden SK, Finseth T, Sillau SH, et al. Progression of MDS-UPDRS scores over five years in De Novo Parkinson disease from the Parkinson’s progression markers initiative cohort. Mov Disord Clin Pract. 2018;5(1):47–53. doi: 10.1002/mdc3.12553
   PubMed Web of Science ®Google Scholar
• Richards M, Marder K, Cote L, et al. Reliability of symptom onset assessment in Parkinson’s disease. Mov Disord Off J Mov Disord Soc. 1994;9:340–342. doi: 10.1002/mds.870090313
   PubMed Web of Science ®Google Scholar
• Odin P, Chaudhuri KR, Volkmann J, et al. Viewpoint and practical recommendations from a movement disorder specialist panel on objective measurement in the clinical management of Parkinson’s disease. Npj Park Dis. 2018;4(1):14. doi: 10.1038/s41531-018-0051-7
   PubMedGoogle Scholar
• Hulzinga F, Nieuwboer A, Dijkstra BW, et al. The new freezing of gait questionnaire: unsuitable as an outcome in clinical trials? Mov Disord Clin Pract. 2020;7(2):199–205. doi: 10.1002/mdc3.12893
   PubMed Web of Science ®Google Scholar
• Shine JM, Matar E, Bolitho SJ, et al. Modeling freezing of gait in Parkinson’s disease with a virtual reality paradigm. Gait Posture. 2013;38(1):104–108. doi: 10.1016/j.gaitpost.2012.10.026
   PubMed Web of Science ®Google Scholar
• Biundo R, Weis L, Fiorenzato E, et al. CognItive rehabilitation in Parkinson’s disease: is it feasible? Arch Clin Neuropsychol. 2017;32(7):840–860. doi: 10.1093/arclin/acx092
   PubMed Web of Science ®Google Scholar
• Goetz CG, Stebbins GT, Blasucci LM, et al. Efficacy of a patient-training videotape on motor fluctuations for on-off diaries in parkinson’s disease. Mov Disord. 1997;12(6):1039–1041. doi: 10.1002/mds.870120631
   PubMed Web of Science ®Google Scholar
• Jenkinson PM, Edelstyn NMJ, Stephens R, et al. Why are some parkinson disease patients unaware of their Dyskinesias? Cogn Behav Neurol. 2009;22(2):117–121. doi: 10.1097/WNN.0b013e3181a722b0
   PubMed Web of Science ®Google Scholar
• Papapetropoulos S. Patient diaries as a clinical endpoint in Parkinson’s disease clinical trials. CNS Neurosci Ther. 2012;18(5):380–387. doi: 10.1111/j.1755-5949.2011.00253.x (Spyros).
   PubMed Web of Science ®Google Scholar
• Stone R. Coincidence or Connection? Science. 2002;296(5567):451–452. doi: 10.1126/science.296.5567.451
   PubMed Web of Science ®Google Scholar
• Högl B, Stefani A, Videnovic A. Idiopathic REM sleep behaviour disorder and neurodegeneration — an update. Nat Rev Neurol. 2018;14(1):40–55. doi: 10.1038/nrneurol.2017.157
   PubMed Web of Science ®Google Scholar
• Antonini A, Bravi D, Sandre M, et al. Immunization therapies for Parkinson’s disease: state of the art and considerations for future clinical trials. Expert Opin Investig Drugs. 2020;29(7):685–695. doi: 10.1080/13543784.2020.1771693
   PubMed Web of Science ®Google Scholar
• Jensen PH, Schlossmacher MG, Stefanis L. Who ever said it would be easy? Reflecting on two clinical trials targeting α‐synuclein. Mov Disord. 2023;38(3):378–384. doi: 10.1002/mds.29318
   PubMed Web of Science ®Google Scholar
• Lang AE, Siderowf AD, Macklin EA, et al. TriAl of cinpanemab in early Parkinson’s disease. N Engl J Med. 2022;387(5):408–420. doi: 10.1056/NEJMoa2203395
   PubMed Web of Science ®Google Scholar
• Pagano G, Taylor KI, Anzures-Cabrera J, et al. TriAl of prasinezumab in early-stage parkinson’s disease. N Engl J Med. 2022;387(5):421–432. doi: 10.1056/NEJMoa2202867
   PubMed Web of Science ®Google Scholar
• Ancona S, Faraci FD, Khatab E, et al. Wearables in the home-based assessment of abnormal movements in Parkinson’s disease: a systematic review of the literature. J Neurol. 2022;269(1):100–110. doi: 10.1007/s00415-020-10350-3
   PubMed Web of Science ®Google Scholar
• Antonini A, Reichmann H, Gentile G, et al. Toward objective monitoring of Parkinson’s disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor®. Front Neurol. 2023;14:1080752.
   PubMed Web of Science ®Google Scholar
• Antonini A, Odin P, Schmidt P, et al. Validation and clinical value of the MANAGE-PD tool: a clinician-reported tool to identify Parkinson’s disease patients inadequately controlled on oral medications. Parkinsonism Relat Disord. 2021;92:59–66. doi: 10.1016/j.parkreldis.2021.10.009
   PubMed Web of Science ®Google Scholar
• Luquin M-R, Kulisevsky J, Martinez-Martin P, et al. Consensus on the definition of advanced parkinson’s disease: a neurologists-based delphi study (CEPA Study). Park Dis. 2017;2017:1–8. doi: 10.1155/2017/4047392
   Web of Science ®Google Scholar
• Cosentino C, Baccini M, Putzolu M, et al. Effectiveness of physiotherapy on freezing of gait in parkinson’s disease: a systematic review and meta-analyses. Mov Disord. 2020;35(4):523–536. doi: 10.1002/mds.27936
   PubMed Web of Science ®Google Scholar
• Gao C, Liu J, Tan Y, et al. Freezing of gait in Parkinson’s disease: pathophysiology, risk factors and treatments. Transl Neurodegener. 2020;9(1):12. doi: 10.1186/s40035-020-00191-5
   PubMed Web of Science ®Google Scholar
• Kwok JYY, Smith R, Chan LML, et al. Managing freezing of gait in Parkinson’s disease: a systematic review and network meta-analysis. J Neurol. 2022;269(6):3310–3324. doi: 10.1007/s00415-022-11031-z
   PubMed Web of Science ®Google Scholar
• Antonini A, Nitu B. Apomorphine and levodopa infusion for motor fluctuations and dyskinesia in advanced Parkinson disease. J Neural Transm. 2018;125(8):1131–1135. doi: 10.1007/s00702-018-1906-0
   PubMed Web of Science ®Google Scholar
• Virbel-Fleischman C, Mousin F, Liu S, et al. Symptoms assessment and decision to treat patients with advanced Parkinson’s disease based on wearables data. NPJ Parkinsons Dis. 2023;9(1):45. doi: 10.1038/s41531-023-00489-x
   PubMed Web of Science ®Google Scholar
• 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:S53–S56.
   PubMed Web of Science ®Google Scholar
• Del Din S, Godfrey A, Mazzà C, et al. Free-living monitoring of parkinson’s disease: lessons from the field: wearable technology for parkinson’s disease. Mov Disord. 2016;31:1293–1313. doi: 10.1002/mds.26718
   PubMedGoogle Scholar
• Soh EML, Neo S, Saffari SE, et al. Longitudinal healthcare utilization and costs in Parkinson’s disease: pre-diagnosis to 9 years after. J Park Dis. 2022;12(3):957–966. doi: 10.3233/JPD-212982
   Web of Science ®Google Scholar
• Fasano A, Antonini A, Katzenschlager R, et al. Management of advanced therapies in parkinson’s disease patients in times of humanitarian crisis: the COVID-19 experience. Mov Disord Clin Pract. 2020;7(4):361–372. doi: 10.1002/mdc3.12965
   PubMed Web of Science ®Google Scholar