Skip to main content
Medline Book to support NIHPA logoLink to Medline Book to support NIHPA
. 2024 Jul;28(30):1–187. doi: 10.3310/YDSL3294

Devices for remote continuous monitoring of people with Parkinson's disease: a systematic review and cost-effectiveness analysis.

Edward Cox, Ros Wade, Robert Hodgson, Helen Fulbright, Thai Han Phung, Nicholas Meader, Simon Walker, Claire Rothery, Mark Simmonds
PMCID: PMC11331379  PMID: 39021200

Abstract

BACKGROUND

Parkinson's disease is a brain condition causing a progressive loss of co ordination and movement problems. Around 145,500 people have Parkinson's disease in the United Kingdom. Levodopa is the most prescribed treatment for managing motor symptoms in the early stages. Patients should be monitored by a specialist every 6-12 months for disease progression and treatment of adverse effects. Wearable devices may provide a novel approach to management by directly monitoring patients for bradykinesia, dyskinesia, tremor and other symptoms. They are intended to be used alongside clinical judgement.

OBJECTIVES

To determine the clinical and cost-effectiveness of five devices for monitoring Parkinson's disease: Personal KinetiGraph, Kinesia 360, KinesiaU, PDMonitor and STAT-ON.

METHODS

We performed systematic reviews of all evidence on the five devices, outcomes included: diagnostic accuracy, impact on decision-making, clinical outcomes, patient and clinician opinions and economic outcomes. We searched MEDLINE and 12 other databases/trial registries to February 2022. Risk of bias was assessed. Narrative synthesis was used to summarise all identified evidence, as the evidence was insufficient for meta-analysis. One included trial provided individual-level data, which was re-analysed. A de novo decision-analytic model was developed to estimate the cost-effectiveness of Personal KinetiGraph and Kinesia 360 compared to standard of care in the UK NHS over a 5-year time horizon. The base-case analysis considered two alternative monitoring strategies: one-time use and routine use of the device.

RESULTS

Fifty-seven studies of Personal KinetiGraph, 15 of STAT-ON, 3 of Kinesia 360, 1 of KinesiaU and 1 of PDMonitor were included. There was some evidence to suggest that Personal KinetiGraph can accurately measure bradykinesia and dyskinesia, leading to treatment modification in some patients, and a possible improvement in clinical outcomes when measured using the Unified Parkinson's Disease Rating Scale. The evidence for STAT-ON suggested it may be of value for diagnosing symptoms, but there is currently no evidence on its clinical impact. The evidence for Kinesia 360, KinesiaU and PDMonitor is insufficient to draw any conclusions on their value in clinical practice. The base-case results for Personal KinetiGraph compared to standard of care for one-time and routine use resulted in incremental cost-effectiveness ratios of £67,856 and £57,877 per quality-adjusted life-year gained, respectively, with a beneficial impact of the Personal KinetiGraph on Unified Parkinson's Disease Rating Scale domains III and IV. The incremental cost-effectiveness ratio results for Kinesia 360 compared to standard of care for one-time and routine use were £38,828 and £67,203 per quality-adjusted life-year gained, respectively.

LIMITATIONS

The evidence was limited in extent and often low quality. For all devices, except Personal KinetiGraph, there was little to no evidence on the clinical impact of the technology.

CONCLUSIONS

Personal KinetiGraph could reasonably be used in practice to monitor patient symptoms and modify treatment where required. There is too little evidence on STAT-ON, Kinesia 360, KinesiaU or PDMonitor to be confident that they are clinically useful. The cost-effectiveness of remote monitoring appears to be largely unfavourable with incremental cost-effectiveness ratios in excess of £30,000 per quality-adjusted life-year across a range of alternative assumptions. The main driver of cost-effectiveness was the durability of improvements in patient symptoms.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42022308597.

FUNDING

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135437) and is published in full in Health Technology Assessment; Vol. 28, No. 30. See the NIHR Funding and Awards website for further award information.

Plain language summary

Parkinson’s disease is a brain condition causing loss of co-ordination and movement problems. Levodopa is the most prescribed treatment for early disease. Patients should be seen by a specialist every 6–12 months to assess their treatment needs. Wearable devices (like smart watches) may aid management by directly monitoring patients for disease symptoms including tremor and slowness of movement (bradykinesia), or side effects of treatment like involuntary movement (dyskinesia). This assessment considered the clinical and economic value of five wearable devices: Personal KinetiGraph, STAT-ON, Kinesia 360, KinesiaU and PDMonitor. We searched medical databases to find all studies of the five devices. We assessed the quality of these studies and reviewed their results. We found 77 studies of the devices. There was some evidence to suggest that Personal KinetiGraph can accurately measure bradykinesia and dyskinesia, leading to treatment modification in some patients, and a possible improvement in symptoms. The evidence for STAT-ON suggested it may be of value for diagnosing symptoms, but there is currently no evidence on its clinical value. There was insufficient evidence for Kinesia 360, KinesiaU and PDMonitor to draw any conclusions. An economic analysis was conducted to investigate whether using any of these technologies is economically viable. The economic analysis found that the quality-of-life benefits generated by remote monitoring devices were small relative to the additional costs of implementing them in the NHS. As such, none of the remote monitoring devices were good value for money when compared with the current standard of care.


Full text of this article can be found in Bookshelf.

References

  1. NHS England. Overview: Parkinson’s Disease. 2019. URL: www.nhs.uk/conditions/parkinsons-disease/ (accessed 30 January 2023).
  2. National Institute for Health and Care Excellence. Parkinson’s Disease in Adults. NICE Guideline [NG71]. London: NICE; 2017.
  3. Parkinson’s UK. Advanced Parkinson’s. 2021. URL: www.parkinsons.org.uk/information-and-support/advanced-parkinsons (accessed 30 January 2023).
  4. Parkinson’s UK. The Incidence and Prevalence of Parkinson’s in the UK: Results from the Clinical Practice Research Datalink Summary Report. London: Parkinson’s UK; 2018.
  5. NHS England. Clinical Commissioning Policy: Levodopa–Carbidopa Intestinal Gel (LCIG). London: NHS England; 2015.
  6. Ferreira JJ, Katzenschlager R, Bloem BR, Bonuccelli U, Burn D, Deuschl G, et al. Summary of the recommendations of the EFNS/MDS-ES review on therapeutic management of Parkinson’s disease. Eur J Neurol 2013;20:5–15. doi: 10.1111/j.1468-1331.2012.03866.x. [DOI] [PubMed]
  7. [Additional Information from Global Kinetics Corporation from Email Threads]. Data on File – Company Submissions; n.d.
  8. National Institute for Health and Care Excellence. [Request for information: PDMonitor]. Diagnostics Assessment Programme: Devices for Remote Continuous Monitoring of People with Parkinson’s Disease (Provisional Title). Data on File – Company Submissions; n.d.
  9. National Institute for Health and Care Excellence. [Request for Information: Kinesia 360]. Diagnostics Assessment Programme: Devices for Remote Continuous Monitoring of People with Parkinson’s Disease (Provisional Title). Data on File – Company Submissions; n.d.
  10. National Institute for Health and Care Excellence. [Request for Information: STAT-ON]. Devices for Remote Continuous Monitoring of People with Parkinson’s Disease (Provisional Title). Data on File – Company Submissions; n.d.
  11. National Institute for Health and Care Excellence. [Request for Information: KinesiaU]. Diagnostics Assessment Programme: Devices for Remote Continuous Monitoring of People with Parkinson’s Disease (Provisional Title). Data on File – Company Submissions; n.d.
  12. National Institute for Health and Care Excellence. Personal KinetiGraph for Remote Clinical Management of Parkinson’s Disease – Medtech Innovation Briefing. 2021. URL: www.nice.org.uk/advice/mib258/resources/personal-kinetigraph-for-remote-clinical-management-of-parkinsons-disease-pdf-2285965700131525 (accessed 30 January 2023).
  13. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLOS Med 2009;6:e1000097. doi: 10.1371/journal.pmed.1000097. [DOI] [PMC free article] [PubMed]
  14. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al., QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011;155:529–36. doi: 10.7326/0003-4819-155-8-201110180-00009. [DOI] [PubMed]
  15. Sterne JAC, Savovic J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ 2019;366:l4898. doi: 10.1136/bmj.l4898. [DOI] [PubMed]
  16. Centre for Reviews and Dissemination. Systematic Reviews: CRD’s Guidance for Undertaking Reviews in Health Care. York: CRD, University of York; 2009.
  17. Woodrow H, Horne MK, Fernando CV, Kotschet KE, Treat to Target Study Group. A blinded, controlled trial of objective measurement in Parkinson’s disease. NPJ Parkinsons Dis 2020;6:35. doi: 10.1038/s41531-020-00136-9. [DOI] [PMC free article] [PubMed]
  18. Bayés A, Samá A, Prats A, Pérez-López C, Crespo-Maraver M, Moreno JM, 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. [DOI] [PubMed]
  19. Bougea A, Palkopoulou M, Pantinaki S, Antonoglou A, Efthymiopoulou E. Validation of a real- time monitoring system to detect motor symptoms in patients with Parkinson’s disease treated with Levodopa Carbidopa Intestinal Gel. Mov Disord 2021;36(S 1):S204.
  20. Caballol N, Prats A, Quispe P, Ranchal MA, Alcaine S, Fondevilla F, et al. Early detection of Parkinson’s disease motor fluctuations with a wearable inertial sensor. Mov Disord 2020;35.
  21. Pérez-López C, Samà A, Rodríguez-Martín D, Català A, Cabestany J, Moreno-Arostegui JM, et al. Assessing motor fluctuations in Parkinson’s disease patients based on a single inertial sensor. Sensors 2016;16:2132. doi: 10.3390/s16122132. [DOI] [PMC free article] [PubMed]
  22. Pérez-López C, Samà A, Rodríguez-Martín D, Moreno-Aróstegui JM, Cabestany J, Bayes A, 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. [DOI] [PubMed]
  23. Perrote F, Zeppa G, Coca H, Figueroa S, de Battista JC. Evaluación de un sistema de sensores inerciales externos tipo holter en pacientes con enfermedad de Parkinson en Argentina. Neurologia 2021;13:153–8.
  24. Rodríguez-Martín D, Samà A, Pérez-López C, Català A, Moreno Arostegui JM, Cabestany J, et al. Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer. PLOS ONE 2017;12:e0171764. doi: 10.1371/journal.pone.0171764. [DOI] [PMC free article] [PubMed]
  25. Rodriguez-Martin DRM, Perez-Lopez CPL, Pie MP, Calvet JC, Catala ACM, Rodriguez-Molinero ARM, et al. Satisfaction survey on a Parkinson’s holter, a medical device for the monitoring of motor symptoms. Mov Disord 2021;36(S 1):S569–70.
  26. Rodríguez-Molinero A, Samà A, Pérez-Martínez DA, Pérez López C, Romagosa J, Bayés A, et al. Validation of a portable device for mapping motor and gait disturbances in Parkinson’s disease. JMIR Mhealth Uhealth 2015;3:e9. doi: 10.2196/mhealth.3321. [DOI] [PMC free article] [PubMed]
  27. Rodríguez-Molinero A, Samà A, Pérez-López C, Rodríguez-Martín D, Quinlan LR, Alcaine S, 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. [DOI] [PMC free article] [PubMed]
  28. Rodríguez-Molinero A, Pérez-López C, Samà A, de Mingo E, Rodríguez-Martín D, Hernández-Vara J, 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:e8. doi: 10.2196/rehab.8335. [DOI] [PMC free article] [PubMed]
  29. Rodríguez-Molinero A, Pérez-López C, Samà A, Rodríguez-Martín D, Alcaine S, Mestre B, et al. Estimating dyskinesia severity in Parkinson’s disease by using a waist-worn sensor: concurrent validity study. Sci Rep 2019;9:13434. doi: 10.1038/s41598-019-49798-3. [DOI] [PMC free article] [PubMed]
  30. Samà A, Pérez-López C, Rodríguez-Martín D, Català A, Moreno-Aróstegui JM, Cabestany J, et al. Estimating bradykinesia severity in Parkinson’s disease by analysing gait through a waist-worn sensor. Comput Biol Med 2017;84:114–23. doi: 10.1016/j.compbiomed.2017.03.020. [DOI] [PubMed]
  31. Samà A, Rodríguez-Martín D, Pérez-López C, Català A, Alcaine S, Mestre B, et al. Determining the optimal features in freezing of gait detection through a single waist accelerometer in home environments. Pattern Recognit Lett 2018;105:135–43.
  32. Santos Garcia D, Lopez Ariztegui N, Cubo E, Vinagre Aragon A, Garcia-Ramos R, Borrue C, et al. Clinical utility of a personalized and long-term monitoring device for Parkinson’s disease in a real clinical practice setting: an expert opinion survey on stat-on TM. Neurologia (Engl Ed) 2020;24:24. doi: 10.1016/j.nrl.2020.10.013. [DOI] [PubMed]
  33. Isaacson SH, Boroojerdi B, Waln O, McGraw M, Kreitzman DL, Klos K, 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;64:132–7. doi: 10.1016/j.parkreldis.2019.01.025. [DOI] [PubMed]
  34. Peacock D, Yoneda J, Thomson V, Wile D. Tailoring the use of wearable systems and telehealth for Parkinson’s disease. Parkinsonism Relat Disord 2021;89:111–2. doi: 10.1016/j.parkreldis.2021.07.004. [DOI] [PubMed]
  35. Pulliam CL, Heldman DA, Brokaw EB, Mera TO, Mari ZK, Burack MA. Continuous assessment of levodopa response in Parkinson’s disease using wearable motion sensors. IEEE Trans Biomed Eng 2018;65:159–64. doi: 10.1109/TBME.2017.2697764. [DOI] [PMC free article] [PubMed]
  36. Hadley AJ, Riley DE, Heldman DA. Real-world evidence for a smartwatch-based Parkinson’s motor assessment app for patients undergoing therapy changes. Digit Biomark 2021;5:206–15. doi: 10.1159/000518571. [DOI] [PMC free article] [PubMed]
  37. Kostikis N, Rigas G, Konitsiotis S, Fotiadis D. PDMonitor: a novel system for objective monitoring of Parkinson’s disease symptoms-efficacy and usability study. Mov Disord Clin Pract 2020;7(S 2):S21–2.
  38. National Institute for Health and Care Excellence. [Request for information: Parkinsons KinetiGraph Movement Recording System]. Parkinson’s KinetiGraph Movement Recording System for Remote Clinical Management of Parkinson’s Disease (Provisional Title). Data on File – Company Submissions; n.d.
  39. Braybrook M, O’Connor S, Churchward P, Perera T, Farzanehfar P, Horne M. An ambulatory tremor score for Parkinson’s disease. J Parkinsons Dis 2016;6:723–31. doi: 10.3233/JPD-160898. [DOI] [PubMed]
  40. Horne M, Kotschet K, McGregor S. The clinical validation of objective measurement of movement in Parkinson’s disease. Oruen 2016;2:16–23.
  41. 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. [DOI] [PMC free article] [PubMed]
  42. Khodakarami H, Farzanehfar P, Horne M. The use of data from the Parkinson’s KinetiGraph to identify potential candidates for device assisted therapies. Sensors 2019;19:2241. doi: 10.3390/s19102241. [DOI] [PMC free article] [PubMed]
  43. Khodakarami H, Ricciardi L, Contarino MF, Pahwa R, Lyons KE, Geraedts VJ, et al. Prediction of the levodopa challenge test in Parkinson’s disease using data from a wrist-worn sensor. Sensors 2019;19:5153. doi: 10.3390/s19235153. [DOI] [PMC free article] [PubMed]
  44. McGregor S, Churchward P, Soja K, O’Driscoll D, Braybrook M, Khodakarami H, et al. The use of accelerometry as a tool to measure disturbed nocturnal sleep in Parkinson’s disease. NPJ Parkinsons Dis 2018;4:1. doi: 10.1038/s41531-017-0038-9. [DOI] [PMC free article] [PubMed]
  45. Watts J, Khojandi A, Vasudevan R, Nahab FB, Ramdhani RA. Improving medication regimen recommendation for Parkinson’s disease using sensor technology. Sensors 2021;21:3553. doi: 10.3390/s21103553. [DOI] [PMC free article] [PubMed]
  46. Horne M, Volkmann J, Sannelli C, Luyet PP, Moro E. An evaluation of the Parkinson’s KinetiGraph (PKG) as a tool to support deep brain stimulation eligibility assessment in patients with Parkinson’s disease. Mov Disord 2017;32(S 2):458–9.
  47. Chen L, Cai G, Weng H, Yu J, Yang Y, Huang X, 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. [DOI] [PMC free article] [PubMed]
  48. Evans AH, Kettlewell J, Osborn S, Kotschet K, Griffiths RI, Horne M. A conditioned response as a biomarker of impulsive–compulsive behaviours in Parkinson’s disease. Mov Disord 2014;1:S314. doi: 10.1371/journal.pone.0089319. [DOI] [PMC free article] [PubMed]
  49. Griffiths RI, Kotschet K, Arfon S, Xu ZM, Johnson W, Drago J, et al. Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease. J Parkinsons Dis 2012;2:47–55. doi: 10.3233/JPD-2012-11071. [DOI] [PubMed]
  50. Guan I, Trabilsy M, Barkan S, Malhotra A, Hou Y, Wang F, et al. Comparison of the Parkinson’s KinetiGraph to off/on levodopa response testing: single center experience. Clin Neurol Neurosurg 2021;209:106890. doi: 10.1016/j.clineuro.2021.106890. [DOI] [PubMed]
  51. Hoglund A, Hagell P, Broman JE, Palhagen S, Sorjonen K, Fredrikson S, Svenningsson P. Associations between fluctuations in daytime sleepiness and motor and non-motor symptoms in Parkinson’s disease. Mov Disord Clin Pract 2021;8:44–50. doi: 10.1002/mdc3.13102. [DOI] [PMC free article] [PubMed]
  52. 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;18:116. doi: 10.1186/s12984-021-00905-4. [DOI] [PMC free article] [PubMed]
  53. Klingelhoefer L, Rizos A, Sauerbier A, McGregor S, Martinez-Martin P, Reichmann H, et al. Night-time sleep in Parkinson’s disease – the potential use of Parkinson’s KinetiGraph: a prospective comparative study. Eur J Neurol 2016;23:1275–88. doi: 10.1111/ene.13015. [DOI] [PubMed]
  54. Knudson M, Thomsen TH, Kjaer TW. Comparing objective and subjective measures of Parkinson’s disease using the Parkinson’s KinetiGraph. Front Neurol 2020;11:570833. doi: 10.3389/fneur.2020.570833. [DOI] [PMC free article] [PubMed]
  55. Kotschet K, Johnson W, McGregor S, Kettlewell J, Kyoong A, O’Driscoll DM, et al. Daytime sleep in Parkinson’s disease measured by episodes of immobility. Parkinsonism Relat Disord 2014;20:578–83. doi: 10.1016/j.parkreldis.2014.02.011. [DOI] [PubMed]
  56. Ossig C, Gandor F, Fauser M, Bosredon C, Churilov L, Reichmann H, 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:e0161559. doi: 10.1371/journal.pone.0161559. [DOI] [PMC free article] [PubMed]
  57. Tan EE, Hogg EJ, Tagliati M. The role of personal KinetiGraph TM fluctuator score in quantifying the progression of motor fluctuations in Parkinson’s disease. Funct Neurol 2019;34:21–8. [PubMed]
  58. Bergquist F, Ax A, Sjostrom A, Wallerstedt S. West Sweden Parkinson objective measurement registry study (westports). Mov Disord 2018;33(S 2):S356–7.
  59. Bergquist F, Gudmundsdottir T. Objective characterisation of Parkinson’s disease motor fluctuations with the Parkinson KinetiGraph – three years experience at a movement disorder clinic. J Parkinsons Dis 2016;6(S 1):220.
  60. Bogdanova-Mihaylova P, Kavanagh N, Walsh RA. Automated assessment of advanced motor Parkinson’s disease; a pilot study of the Parkinson’s KinetiGraph as an objective tool for measurement of motor fluctuations. Mov Disord 2016;31(S 2):S185.
  61. Dahlen M, Eriksson B, Bergquist F. Poor correlation between patients’ assessments of medication state and clinician’s interpretation of Parkinson’s KinetiGraph (PKG) objective recordings. Mov Disord 2014;1:S184.
  62. Dominey T, Carroll C. Using remotely collected data to identify Parkinson’s disease (PD) subtypes. Mov Disord 2018;33(S 2):S461–2.
  63. Fowler A, Lyons K, Sharma V, Pahwa R. Evaluating the clinical efficacy of the personal KinetiGraph movement recording system. Mov Disord 2017;32(S 2):449–50.
  64. Horne M, McGregor S, Hamilton G, O’Driscoll D, Blaze R, Churchward P. Measurement of night time sleep using an accelerometry based system. Mov Disord 2016;31(S 2):S120–1.
  65. Horne M, O’Connor S, Churchward P, Perera T, Braybrook M. Ambulatory assessment of tremor. Mov Disord 2016;31(S 2):S498.
  66. Lina C, Guoen C, Huidan W, Yingqing W, Ying C, Xiaochun C, et al. Application of PKG system for objective measurement and early identification in Parkinson’s disease. Mov Disord 2020;35(S 1):S405.
  67. Margolesky J, Luca C. Personal KinetiGraph devices assessing efficacy of continuous enteral carbidopa/levodopa infusion therapy. Mov Disord 2017;32(S 2):458.
  68. Dominey T, Kehagia AA, Gorst T, Pearson E, Murphy F, King E, Carroll C. Introducing the Parkinson’s KinetiGraph into routine Parkinson’s disease care: a 3-year single centre experience. J Parkinsons Dis 2020;10:1827–32. doi: 10.3233/JPD-202101. [DOI] [PMC free article] [PubMed]
  69. Evans L, Mohamed B, Thomas EC. Using telemedicine and wearable technology to establish a virtual clinic for people with Parkinson’s disease. BMJ Open Qual 2020;9:e001000. doi: 10.1136/bmjoq-2020-001000. [DOI] [PMC free article] [PubMed]
  70. Farzanehfar P, Woodrow H, Braybrook M, McGregor S, Evans A, Nicklason F, Horne M. Objective measurement in routine care of people with Parkinson’s disease improves outcomes. NPJ Parkinsons Dis 2018;4:10. doi: 10.1038/s41531-018-0046-4. [DOI] [PMC free article] [PubMed]
  71. Joshi R, Bronstein JM, Keener A, Alcazar J, Yang DD, Joshi M, Hermanowicz N. PKG movement recording system use shows promise in routine clinical care of patients with Parkinson’s disease. Front Neurol 2019;10:1027. doi: 10.3389/fneur.2019.01027. [DOI] [PMC free article] [PubMed]
  72. Krause E, Randhawa J, Mehanna R. Comparing subjective and objective response to medications in Parkinson’s disease patients using the personal KinetiGraph TM. Parkinsonism Relat Disord 2021;87:105–10. doi: 10.1016/j.parkreldis.2021.05.008. [DOI] [PubMed]
  73. Nahab F, Abu-Hussain H, Moreno L. Evaluation of clinical utility of the personal KinetiGraph® in the management of Parkinson disease. Adv Parkinson’s Dis 2019;8:42–61.
  74. Santiago A, Langston JW, Gandhy R, Dhall R, Brillman S, Rees L, Barlow C. Qualitative evaluation of the Personal KinetiGraphTM movement recording system in a Parkinson’s clinic. J Parkinsons Dis 2019;9:207–19. doi: 10.3233/JPD-181373. [DOI] [PMC free article] [PubMed]
  75. Sundgren M, Andreasson M, Svenningsson P, Noori RM, Johansson A. Does information from the Parkinson KinetiGraph TM (PKG) influence the neurologist’s treatment decisions? – an observational study in routine clinical care of people with Parkinson’s disease. J Pers Med 2021;11:05. doi: 10.3390/jpm11060519. [DOI] [PMC free article] [PubMed]
  76. Andriola M. Personal kineti graph (PKG) use in the identification of unknown, under-medicated Parkinson’s disease patients. Mov Disord 2017;32:e2.
  77. Bergquist F, Cvejtkovic A, Sjostrom AC, Wallerstedt S. Parkinson KinetiGraph – does it change the management of PD patients? Mov Disord 2019;34(S 2):S28–9.
  78. Chhabria N, Isaacson S. Clinical effect of the PKG watch in the management of Parkinson’s patients. Mov Disord 2018;33(S 1):S34–5.
  79. Duja S. PKG for the management of advance Parkinson’s disease. Mov Disord 2021;36(S 1):S558.
  80. Duja S, Mujeeb Q. Role of Parkinson’s KinetiGraph in routine management of Parkinson’s disease. Eur J Neurol 2021;28(S 1):315.
  81. Evans LA, Mohamed B, Thomas EC. Feasibility of a Wearable Technology Based Virtual Clinic for People with Parkinson’s. Age Ageing. Conference: British Geriatrics Society Communications to the Spring Meeting. Cardiff United Kingdom. Vol. 48, 2019.
  82. Farzanehfar P, Braybrook M, Kotschet K, Horne M. Objective measurement in clinical care of patients with Parkinson’s disease: an RCT using the PKG. Mov Disord 2017;32(S 2):445–6.
  83. Horne M, Kotschet K, Braybrook M. Objective measurement in clinical care of patients with Parkinson’s disease. Mov Disord 2016;31(S 2):S645.
  84. Horne M, Farzanehfar P, Woodrow H, Braybrook M, McGregor S, Evans A, et al. Objective measurement in routine care of people with Parkinson’s disease improves outcomes. Mov Disord 2018;33(S 2):S470–1. doi: 10.1038/s41531-018-0046-4. [DOI] [PMC free article] [PubMed]
  85. Jones S, Grose C, Mahon S, Williams T, Thomas C, Mohamed B. Does the Parkinson’s KinetiGraph change clinical practice? Mov Disord 2018;33(S 2):S507–8.
  86. Klingelhoefer L, Rizos A, Sauerbier A, Trivedi D, Inniss R, Perkins L, et al. Usefulness of Parkinson’s KinetiGraph in a Parkinson’s disease clinic – survey of 82 patients. Parkinsonism Relat Disord 2016;2:e37.
  87. Parkinson Study Group, Huntington Study Group, Dystonia Study Group,Tourette Syndrome Study Group, Cooperative Ataxia Group, and Tremor Research Group. 31st annual symposium on etiology, pathogenesis, and treatment of Parkinson disease and other movement disorders, Fort Myers, FL, USA, Vol. 32, 2017:e1–19. https://doi.org/10.1002/mds.27134 doi: 10.1002/mds.27134. [DOI] [PubMed]
  88. Lynch P, Jackson D, Tilden D, Horne M. Costs and outcomes for Parkinson’s disease patients who have their management adjusted by personal KinetiGraph (PKG). Mov Disord 2018;33(S 2):S511.
  89. Rao S, Ebenezer L, Raha S. Parkinson’s KinetiGraph (PKG) in clinical management of Parkinson’s disease. J Parkinsons Dis 2019;9:166–7.
  90. Thakur N, Ramatowski L. Personal kinetigraphtm (PKGTM) use in the routine clinical care of patients with Parkinson’s disease. Mov Disord 2017;32:e15.
  91. Thomas C, Mohamed B, Silverdale M, Kobylecki C, Osborne L, Saha R, et al. Impact of quantitative assessment of Parkinsonian symptoms using wearable technology on treatment decisions. Mov Disord 2019;34(S 2):S455–6. doi: 10.3233/JPD-191623. [DOI] [PMC free article] [PubMed]
  92. Wilson N, Mappilakkandy R, Smith M, Nithi K. Does Parkinsons KinetiGraph (PKG) recording help in clinical decision making? A local experience. Mov Disord 2017;32(S 2):446.
  93. Rasul A, Farooqi A, Martinez J, Margolesky J, Luca C. Objective Assessment of Bradykinesia and Dyskinesia in Advanced Parkinson’s Disease. Mov Disord. Conference: 1st Pan American Parkinson’s Disease and Mov Disord Congress, Miami, FL, USA, Vol. 32, 2017.
  94. Spengler D, Velez-Aldahondo V, Singer C, Luca C. Initial Deep Brain Stimulation Programming Optimization Using the Personal KinetiGraph (PKG) Movement Recording System. Neurology. Conference: 68th American Academy of Neurology Annual Meeting, AAN, Vol. 86, 2016.
  95. Tsamis KI, Rigas G, Nikolaos K, Fotiadis DI, Konitsiotis S. Accurate monitoring of Parkinson’s disease symptoms with a wearable device during COVID-19 pandemic. In Vivo 2021;35:2327–30. doi: 10.21873/invivo.12507. [DOI] [PMC free article] [PubMed]
  96. Chaudhuri KR, Hand A, Obam F, Belsey J. Cost-effectiveness analysis of the Parkinson’s KinetiGraph and clinical assessment in the management of Parkinson’s disease. J Med Econ 2022;25:774–82. doi: 10.1080/13696998.2022.2080437. [DOI] [PubMed]
  97. McCrone P, Allcock LM, Burn DJ. Predicting the cost of Parkinson’s disease. Mov Disord 2007;22:804–12. doi: 10.1002/mds.21360. [DOI] [PubMed]
  98. National Institute for Health and Care Excellence. Parkinson’s Disease. Appendix F: Full Health Economics Report. London: NICE Internal Clinical Guidelines; 2016. URL: www.nice.org.uk/guidance/ng71/evidence/appendix-f-he-report-pdf-4538466259 (accessed 30 January 2023).
  99. Xu J, Gong DD, Man CF, Fan Y. Parkinson’s disease and risk of mortality: meta-analysis and systematic review. Acta Neurol Scand 2014;129:71–9. doi: 10.1111/ane.12201. [DOI] [PubMed]
  100. National Institute for Health and Care Excellence. NICE Health Technology Evaluations: The Manual (PMG36). London: NICE; 2022. URL: www.nice.org.uk/process/pmg36/resources/nice-health-technology-evaluations-the-manual-pdf-72286779244741 (accessed 30 January 2023).
  101. Antonini A, Odin P, Pahwa R, Aldred J, Alobaidi A, Jalundhwala YJ, et al. The long-term impact of levodopa/carbidopa intestinal gel on ‘off’-time in patients with advanced Parkinson’s disease: a systematic review. Adv Ther 2021;38:2854–90. doi: 10.1007/s12325-021-01747-1. [DOI] [PMC free article] [PubMed]
  102. Holden SK, Finseth T, Sillau SH, Berman BD. 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;5:47–53. doi: 10.1002/mdc3.12553. [DOI] [PMC free article] [PubMed]
  103. Schrag A, Dodel R, Spottke A, Bornschein B, Siebert U, Quinn NP. Rate of clinical progression in Parkinson’s disease: a prospective study. Mov Disord 2007;22:938–45. doi: 10.1002/mds.21429. [DOI] [PubMed]
  104. Dams J, Klotsche J, Bornschein B, Reese JP, Balzer-Geldsetzer M, Winter Y, et al. Mapping the EQ-5D index by UPDRS and PDQ-8 in patients with Parkinson’s disease. Health Qual Life Outcomes 2013;11:35. doi: 10.1186/1477-7525-11-35. [DOI] [PMC free article] [PubMed]
  105. Davey P, Rajan N, Lees M, Aristides M. Cost-effectiveness of pergolide compared to bromocriptine in the treatment of Parkinson’s disease: a decision-analytic model. Value Health 2001;4:308–15. doi: 10.1046/j.1524-4733.2001.44039.x. [DOI] [PubMed]
  106. Fann JC, Chang KC, Yen AM, Chen SL, Chiu SY, Chen HH, Liou H-H. Cost-effectiveness analysis of deep brain stimulation for Parkinson disease in Taiwan. World Neurosurg 2020;138:e459–68. doi: 10.1016/j.wneu.2020.02.150. [DOI] [PubMed]
  107. Hjelmgren J, Ghatnekar O, Reimer J, Grabowski M, Lindvall O, Persson U, Hagell P. Estimating the value of novel interventions for Parkinson’s disease: an early decision-making model with application to dopamine cell replacement. Parkinsonism Relat Disord 2006;12:443–52. doi: 10.1016/j.parkreldis.2006.04.006. [DOI] [PubMed]
  108. Johnson SJ, Diener MD, Kaltenboeck A, Birnbaum HG, Siderowf AD. An economic model of Parkinson’s disease: implications for slowing progression in the United States. Mov Disord 2013;28:319–26. doi: 10.1002/mds.25328. [DOI] [PubMed]
  109. Lindgren P, Jönsson B, Duchane J. The cost-effectiveness of early cabergoline treatment compared to levodopa in Sweden. Eur J Health Econ 2003;4:37–42. doi: 10.1007/s10198-002-0144-3. [DOI] [PubMed]
  110. Nuijten MJ, Kosa J, Engelfriet P. Modeling the cost-effectiveness and budgetary impact for subpopulations. Eur J Health Econ 2003;4:70–8. doi: 10.1007/s10198-002-0156-z. [DOI] [PubMed]
  111. Postma MJ, Boersma C. Flexibility of Markov modeling for clinical pharmacoeconomics: illustration for cost-effectiveness in early Parkinson’s disease. Expert Rev Clin Pharmacol 2012;5:1–4. doi: 10.1586/ecp.11.69. [DOI] [PubMed]
  112. Shimbo T, Hira K, Takemura M, Fukui T. Cost-effectiveness analysis of dopamine agonists in the treatment of Parkinson’s disease in Japan. PharmacoEcon 2001;19:875–86. doi: 10.2165/00019053-200119080-00009. [DOI] [PubMed]
  113. Smala AM, Spottke EA, Machat O, Siebert U, Meyer D, Kohne-Volland R, et al. Cabergoline versus levodopa monotherapy: a decision analysis. Mov Disord 2003;18:898–905. doi: 10.1002/mds.10465. [DOI] [PubMed]
  114. Findley LJ, Lees A, Apajasalo M, Pitkanen A, Turunen H. Cost-effectiveness of levodopa/carbidopa/entacapone (Stalevo) compared to standard care in UK Parkinson’s disease patients with wearing-off. Curr Med Res Opin 2005;21:1005–14. doi: 10.1185/030079905X49653. [DOI] [PubMed]
  115. Linna M, Taimela E, Apajasalo M, Marttila RJ. Probabilistic sensitivity analysis for evaluating cost–utility of entacapone for Parkinson’s disease. Expert Rev Pharmacoecon Outcomes Res 2002;2:91–7. doi: 10.1586/14737167.2.2.91. [DOI] [PubMed]
  116. Nuijten MJ, van Iperen P, Palmer C, van Hilten BJ, Snyder E. Cost-effectiveness analysis of entacapone in Parkinson’s disease: a Markov process analysis. Value Health 2001;4:316–28. doi: 10.1046/j.1524-4733.2001.44037.x. [DOI] [PubMed]
  117. Palmer CS, Nuijten MJ, Schmier JK, Subedi P, Snyder EH. Cost effectiveness of treatment of Parkinson’s disease with entacapone in the United States. PharmacoEcon 2002;20:617–28. doi: 10.2165/00019053-200220090-00005. [DOI] [PubMed]
  118. Arnold RJG, Layton A, Rustay NR, Chen S. Cost-effectiveness of extended-release carbidopa-levodopa for advanced Parkinson’s disease. Am J Pharm Benefits 2017;9:23–9.
  119. Groenendaal H, Tarrants ML, Armand C. Treatment of advanced Parkinson’s disease in the United States: a cost–utility model. Clin Drug Investig 2010;30:789–98. doi: 10.2165/11538520-000000000-00000. [DOI] [PubMed]
  120. Hansen RN, Suh K, Serbin M, Yonan C, Sullivan SD. Cost-effectiveness of opicapone and entacapone in reducing OFF-time in Parkinson’s disease patients treated with levodopa/carbidopa. J Med Econ 2021;24:563–9. doi: 10.1080/13696998.2021.1916750. [DOI] [PubMed]
  121. Hudry J, Rinne JO, Keranen T, Eckert L, Cochran JM. Cost–utility model of rasagiline in the treatment of advanced Parkinson’s disease in Finland. Ann Pharmacother 2006;40:651–7. doi: 10.1345/aph.1G454. [DOI] [PubMed]
  122. Rudakova AV, Levin OS. Pharmacoeconomic aspects of combined treatment of advanced stage of Parkinson’s disease. Zh Nevrol Psikhiatr Im S S Korsakova 2017;117:96–100. doi: 10.17116/jnevro20171176296-100. [DOI] [PubMed]
  123. Haycox A, Armand C, Murteira S, Cochran J, Francois C. Cost effectiveness of rasagiline and pramipexole as treatment strategies in early Parkinson’s disease in the UK setting: an economic Markov model evaluation. Drugs Aging 2009;26:791–801. doi: 10.2165/11316770-000000000-00000. [DOI] [PubMed]
  124. Farkouh RA, Wilson MR, Tarrants ML, Castelli-Haley J, Armand C. Cost-effectiveness of rasagiline compared with first-line early Parkinson disease therapies. Am J Pharm Benefits 2012;4:99–107.
  125. Nuijten MJ. Incorporation of statistical uncertainty in health economic modelling studies using second-order Monte Carlo simulations. PharmacoEcon 2004;22:759–69. doi: 10.2165/00019053-200422120-00001. [DOI] [PubMed]
  126. Nuijten MJ, Rutten F. Combining a budgetary-impact analysis and a cost-effectiveness analysis using decision-analytic modelling techniques. PharmacoEcon 2002;20:855–67. doi: 10.2165/00019053-200220120-00006. [DOI] [PubMed]
  127. Pietzsch JB, Garner AM, Marks WJ Jr. Cost-effectiveness of deep brain stimulation for advanced Parkinson’s disease in the United States. Neuromodulation 2016;19:689–97. doi: 10.1111/ner.12474. [DOI] [PubMed]
  128. Eggington S, Valldeoriola F, Chaudhuri KR, Ashkan K, Annoni E, Deuschl G. The cost-effectiveness of deep brain stimulation in combination with best medical therapy, versus best medical therapy alone, in advanced Parkinson’s disease. J Neurol 2014;261:106–16. doi: 10.1007/s00415-013-7148-z. [DOI] [PMC free article] [PubMed]
  129. Lowin J, Bergman A, Chaudhuri KR, Findley LJ, Roeder C, Schifflers M, et al. A cost-effectiveness analysis of levodopa/carbidopa intestinal gel compared to standard care in late stage Parkinson’s disease in the UK. J Med Econ 2011;14:584–93. doi: 10.3111/13696998.2011.598201. [DOI] [PubMed]
  130. Kalabina S, Belsey J, Pivonka D, Mohamed B, Thomas C, Paterson B. Cost–utility analysis of levodopa carbidopa intestinal gel (Duodopa) in the treatment of advanced Parkinson’s disease in patients in Scotland and Wales. J Med Econ 2019;22:215–25. doi: 10.1080/13696998.2018.1553179. [DOI] [PubMed]
  131. Walter E, Odin P. Cost-effectiveness of continuous subcutaneous apomorphine in the treatment of Parkinson’s disease in the UK and Germany. J Med Econ 2015;18:155–65. doi: 10.3111/13696998.2014.979937. [DOI] [PubMed]
  132. Dams J, Balzer-Geldsetzer M, Siebert U, Deuschl G, Schuepbach WM, Krack P, et al., EARLYSTIM-Investigators. Cost-effectiveness of neurostimulation in Parkinson’s disease with early motor complications. Mov Disord 2016;31:1183–91. doi: 10.1002/mds.26740. [DOI] [PubMed]
  133. Lowin J, Sail K, Baj R, Jalundhwala YJ, Marshall TS, Konwea H, Chaudhuri KR. The cost-effectiveness of levodopa/carbidopa intestinal gel compared to standard care in advanced Parkinson’s disease. J Med Econ 2017;20:1207–15. doi: 10.1080/13696998.2017.1379411. [DOI] [PubMed]
  134. van Boven JF, Novak A, Driessen MT, Boersma C, Boomsma MM, Postma MJ. Economic evaluation of ropinirole prolonged release for treatment of Parkinson’s disease in the Netherlands. Drugs Aging 2014;31:193–201. doi: 10.1007/s40266-013-0150-4. [DOI] [PubMed]
  135. All Wales Medicines Strategy Group. Co-careldopa Intestinal Gel (Duodopa®). Penarth: AWMSG; 2007. URL: https://awttc.nhs.wales/files/appraisals-asar-far/appraisal-report-levodopa-carbidopa-intestinal-gel-duodopa-3397/ (accessed 30 January 2023).
  136. Espay AJ, Vaughan JE, Marras C, Fowler R, Eckman MH. Early versus delayed bilateral subthalamic deep brain stimulation for Parkinson’s disease: a decision analysis. Mov Disord 2010;25:1456–63. doi: 10.1002/mds.23111. [DOI] [PubMed]
  137. Fundament T, Eldridge PR, Green AL, Whone AL, Taylor RS, Williams AC, Schuepbach WMM. Deep brain stimulation for Parkinson’s disease with early motor complications: a UK cost-effectiveness analysis. PLOS ONE 2016;11:e0159340. doi: 10.1371/journal.pone.0159340. [DOI] [PMC free article] [PubMed]
  138. Meng Y, Pople CB, Kalia SK, Kalia LV, Davidson B, Bigioni L, et al. Cost-effectiveness analysis of MR-guided focused ultrasound thalamotomy for tremor-dominant Parkinson’s disease. J Neurosurg 2021;135:273–8. doi: 10.3171/2020.5.JNS20692. [DOI] [PubMed]
  139. Thach A, Kirson N, Zichlin ML, Dieye I, Pappert E, Williams GR. Cost-effectiveness of apomorphine sublingual film as an ‘on-demand’ treatment for ‘off’ episodes in patients with Parkinson’s disease. J Health Econ Outcomes Res 2021;8:82–92. doi: 10.36469/jheor.2021.29488. [DOI] [PMC free article] [PubMed]
  140. Chandler C, Folse H, Gal P, Chavan A, Proskorovsky I, Franco-Villalobos C, et al. Modeling long-term health and economic implications of new treatment strategies for Parkinson’s disease: an individual patient simulation study. J Mark Access Health Policy 2021;9:1922163. doi: 10.1080/20016689.2021.1922163. [DOI] [PMC free article] [PubMed]
  141. Ashburn A, Stack E, Pickering RM, Ward CD. A community-dwelling sample of people with Parkinson’s disease: characteristics of fallers and non-fallers. Age Ageing 2001;30:47–52. doi: 10.1093/ageing/30.1.47. [DOI] [PubMed]
  142. Goetz CG, Stebbins GT, Tilley BC. Calibration of unified Parkinson’s disease rating scale scores to movement disorder society-unified Parkinson’s disease rating scale scores. Mov Disord 2012;27:1239–42. doi: 10.1002/mds.25122. [DOI] [PubMed]
  143. Kalilani L, Friesen D, Boudiaf N, Asgharnejad M. The characteristics and treatment patterns of patients with Parkinson’s disease in the United States and United Kingdom: a retrospective cohort study. PLOS ONE 2019;14:e0225723. doi: 10.1371/journal.pone.0225723. [DOI] [PMC free article] [PubMed]
  144. Fasano A, Fung VSC, Lopiano L, Elibol B, Smolentseva IG, Seppi K, et al. Characterizing advanced Parkinson’s disease: OBSERVE-PD observational study results of 2615 patients. BMC Neurol 2019;19:50. doi: 10.1186/s12883-019-1276-8. [DOI] [PMC free article] [PubMed]
  145. Findley LJ, Wood E, Lowin J, Roeder C, Bergman A, Schifflers M. The economic burden of advanced Parkinson’s disease: an analysis of a UK patient dataset. J Med Econ 2011;14:130–9. doi: 10.3111/13696998.2010.551164. [DOI] [PubMed]
  146. Claxton K, Sculpher MJ, Briggs AH. Decision Modelling for Health Economic Evaluation. Oxford: Oxford University Press; 2006.
  147. National Institute for Health and Care Excellence. Diagnostics Assessment Programme. Devices for Remote Continuous Monitoring of People with Parkinson’s Disease: Final Scope. London: NICE; 2022. URL: www.nice.org.uk/guidance/gid-dg10047/documents/final-scope (accessed 30 January 2023).
  148. Okunoye O, Horsfall L, Marston L, Walters K, Schrag A. Mortality of people with Parkinson’s disease in a large UK-based cohort study: time trends and relationship to disease duration. Mov Disord 2021;36:2811–20. doi: 10.1002/mds.28727. [DOI] [PMC free article] [PubMed]
  149. England N. 2019/20 National Cost Collection Data Version 2. London: NHS England; n.d. URL: www.england.nhs.uk/costing-in-the-nhs/national-cost-collection/#nccdata2 (accessed 30 January 2023).
  150. Office for National Statistics. National Life Tables – Life Expectancy in the UK: 2018 to 2020. London: ONS; 2021. URL: www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/lifeexpectancies/bulletins/nationallifetablesunitedkingdom/2018to2020 (accessed 30 January 2023).
  151. Bäckström D, Granåsen G, Domellöf ME, Linder J, Jakobson Mo S, Riklund K, et al. Early predictors of mortality in parkinsonism and Parkinson disease: a population-based study. Neurology 2018;91:e2045–56. doi: 10.1212/WNL.0000000000006576. [DOI] [PMC free article] [PubMed]
  152. Young MK, Ng S-K, Mellick G, Scuffham PA. Mapping of the PDQ-39 to EQ-5D scores in patients with Parkinson’s disease. Qual Life Res 2012;22:1065–72. doi: 10.1007/s11136-012-0231-6. [DOI] [PubMed]
  153. Greenwell K, Gray WK, van Wersch A, van Schaik P, Walker R. Predictors of the psychosocial impact of being a carer of people living with Parkinson’s disease: a systematic review. Parkinsonism Relat Disord 2015;21:1–11. doi: 10.1016/j.parkreldis.2014.10.013. [DOI] [PubMed]
  154. Gumber A, Ramaswamy B, Thongchundee O. Effects of Parkinson’s on employment, cost of care, and quality of life of people with condition and family caregivers in the UK: a systematic literature review. Patient Relat Outcome Meas 2019;10:321–33. doi: 10.2147/PROM.S160843. [DOI] [PMC free article] [PubMed]
  155. Curtis LB. A Unit Costs of Health and Social Care 2020. Kent, Canterbury: Personal Social Services Research Unit, University of Kent; 2020. URL: https://kar.kent.ac.uk/84818/ (accessed 30 January 2023).
  156. Ng JH, See AAQ, Xu Z, King NKK. Longitudinal medication profile and cost savings in Parkinson’s disease patients after bilateral subthalamic nucleus deep brain stimulation. J Neurol 2020;267:2443–54. doi: 10.1007/s00415-020-09741-3. [DOI] [PubMed]
  157. David Charles P, Padaliya BB, Newman WJ, Gill CE, Covington CD, Fang JY, et al. Deep brain stimulation of the subthalamic nucleus reduces antiparkinsonian medication costs. Parkinsonism Relat Disord 2004;10:475–9. doi: 10.1016/j.parkreldis.2004.05.006. [DOI] [PubMed]
  158. Hacker ML, Currie AD, Molinari AL, Turchan M, Millan SM, Heusinkveld LE, et al. Subthalamic nucleus deep brain stimulation may reduce medication costs in early stage Parkinson’s disease. J Parkinsons Dis 2016;6:125–31. doi: 10.3233/JPD-150712. [DOI] [PMC free article] [PubMed]
  159. Zhao YJ, Wee HL, Chan Y-H, Seah SH, Au WL, Lau PN, et al. Progression of Parkinson’s disease as evaluated by Hoehn and Yahr stage transition times. Mov Disord 2010;25:710–6. doi: 10.1002/mds.22875. [DOI] [PubMed]
  160. Gumber A, Ramaswamy B, Ibbotson R, Ismail M, Thongchundee O, Harrop D, et al. Economic, Social and Financial Cost of Parkinson’s on Individuals, Carers and Their Families in the UK. Project Report. Sheffield: Centre for Health and Social Care Research, Sheffield Hallam University; 2017. URL: http://shura.shu.ac.uk/15930/12/Gumber%20Economic%20Social%20and%20Financial%20Cost%20of%20Parkinsons%20.pdf (accessed 30 January 2023).
  161. Abou L, Peters J, Wong E, Akers R, Dossou MS, Sosnoff JJ, Rice LA. Gait and balance assessments using smartphone applications in Parkinson’s disease: a systematic review. J Med Syst 2021;45:87. doi: 10.1007/s10916-021-01760-5. [DOI] [PMC free article] [PubMed]
  162. Adams JL, Lizarraga KJ, Waddell EM, Myers TL, Jensen-Roberts S, Modica JS, Schneider RB. Digital technology in movement disorders: updates, applications, and challenges. Curr Neurol Neurosci Rep 2021;21. doi: 10.1007/s11910-021-01101-6. [DOI] [PMC free article] [PubMed]
  163. AlMahadin G, Lotfi A, Zysk E, Siena FL, Carthy MM, Breedon P. Parkinson’s disease: current assessment methods and wearable devices for evaluation of movement disorder motor symptoms – a patient and healthcare professional perspective. BMJ Neurol Open 2020;20:419. doi: 10.1186/s12883-020-01996-7. [DOI] [PMC free article] [PubMed]
  164. Ancona S, Faraci FD, Khatab E, Fiorillo L, Gnarra O, Nef T, et al. Wearables in the home-based assessment of abnormal movements in Parkinson’s disease: a systematic review of the literature. J Neurol 2022;269:100–10. doi: 10.1007/s00415-020-10350-3. [DOI] [PubMed]
  165. Barrachina-Fernandez M, Maitin AM, Sanchez-Avila C, Romero JP. Wearable technology to detect motor fluctuations in Parkinson’s disease patients: current state and challenges. Sensors 2021;21:4188. doi: 10.3390/s21124188. [DOI] [PMC free article] [PubMed]
  166. Battista L, Romaniello A. A wearable tool for selective and continuous monitoring of tremor and dyskinesia in Parkinsonian patients. Parkinsonism Relat Disord 2020;77:43–7. doi: 10.1016/j.parkreldis.2020.06.020. [DOI] [PubMed]
  167. Battista L, Romaniello A. A wearable tool for continuous monitoring of movement disorders: clinical assessment and comparison with tremor scores. Neurol Sci 2021;42:4241–8. doi: 10.1007/s10072-021-05120-6. [DOI] [PubMed]
  168. Bendig J, Prieto-Jarabo M, Koppitz A, Falkenburger B, Reichmann H, Lowenbruck K. Usability assessments in patients with Parkinson’s disease: the foundation for a holistic telemedical solution (telepark). Mov Disord 2020;35(S 1):S643.
  169. Blaze RL, Tan J, Evans AH. Quantitative assessment of advanced therapies in Parkinson’s disease using the Parkinson KinetiGraph (PKG). Mov Disord 2016;31(S 2):S189.
  170. Brillman S, Isaacson SH. Assessment of dose failure and delayed-on with the time-to-on questionaire (TOQ) and Parkinson’s KinetiGraph in PD patients with motor fluctuations. Mov Disord 2015;1:S462.
  171. Canento T, Tan J, Cruse B, Wools C, Ghaly M, Beckley M, et al. Daytime sleepiness in Parkinson’s disease: an indicator of impaired activities of daily living. Mov Disord 2019;34(S 2):S244–5.
  172. Carroll C, Kobylecki C, Silverdale M, Thomas C, group PKG. Impact of quantitative assessment of Parkinson’s disease-associated symptoms using wearable technology on treatment decisions. J Parkinsons Dis 2019;9:601. doi: 10.3233/JPD-191623. [DOI] [PMC free article] [PubMed]
  173. Channa A, Popescu N, Ciobanu V. Wearable solutions for patients with Parkinson’s disease and neurocognitive disorder: a systematic review. Sensors 2020;20:2713. doi: 10.3390/s20092713. [DOI] [PMC free article] [PubMed]
  174. David Prakash B, Seah ISH, Tan LCS, Au WL. Differential effects of age and gender on hand motion tasks in an Asian population. Mov Disord 2013;1:S120.
  175. Del Prete E, Schmitt E, Meoni S, Fraix V, Castrioto A, Pelissier P, et al. Do non-motor fluctuations temporarily match on/off motor condition? Mov Disord 2019;34(S 2):S337.
  176. Del Prete E, Schmitt E, Meoni S, Fraix V, Castrioto A, Pelissier P, et al. Do neuropsychiatric fluctuations temporally match motor fluctuations in Parkinson’s disease? Neurol Sci 2022;19:19. doi: 10.1007/s10072-021-05833-8. [DOI] [PubMed]
  177. Dominey T, Hutchinson L, Pearson E, Murphy F, Bell L, Carroll C. Evaluating the clinical utility of the Parkinson’s KinetiGraph (PKGTM) in the remote management of Parkinson’s disease. Mov Disord 2018;33(S 2):S527–8.
  178. Edwards E, Partridge R, Ankeny U, Langley J, Whipps S, Whipps J, et al. Home based care: a care pathway innovation for Parkinson’s disease. Mov Disord 2020;35(S 1):S148–9.
  179. Edwards E, Partridge R, Ankeny U, Langley J, Whipps S, Whipps J, et al. Development of a home based care pathway for people with Parkinson’s. Mov Disord Clin Pract 2020;7(S 2):S32.
  180. Evans L, Mohamed B, Thomas EC. ‘So much easier’; patient’s perceptions of virtual clinics for Parkinson’s disease. Mov Disord 2019;34(S 2):S248–50.
  181. Evans L, Mohamed B, Thomas C. Using telemedicine and wearable technology to establish a virtual clinic for people with Parkinson’s. Mov Disord 2020;35(S 1):S648–9. doi: 10.1136/bmjoq-2020-001000. [DOI] [PMC free article] [PubMed]
  182. Evans L, Mohamed B, Thomas C. Is Parkinson’s Kinetigraph useful in frail patients with Parkinson’s disease? Prog Neurol Psychiatry 2021;25:24–6.
  183. Farley B, Bullock A, Kaul I, Nguyen D, Belfort G, Kanes S, et al. Validation of a wearable device for continuous tremor measurement in Parkinson’s disease and essential tremor. Mov Disord 2018;33(S 1):S66–7.
  184. Farzanehfar P, Churchward P, Horne M. Using an objective measure of movement to quantify night time sleep. Mov Disord 2017;32(S 2):936.
  185. Farzanehfar P, Horne M. Evaluation of the Parkinson’s KinetiGraph in monitoring and managing Parkinson’s disease. Expert Rev Med Devices 2017;14:583–91. doi: 10.1080/17434440.2017.1349608. [DOI] [PubMed]
  186. Flisar D, Piks B, Berlot R, Dreo J, Kramberger Gregoric M, Trost M, et al. Accelerometric assessment of movements in Parkinson’s disease fluctuators. Eur J Neurol 2016;2:394.
  187. Flisar D, Piks B, Meglic B, Pirtosek Z, Kramberger G. Objective movement recording in PD patient before and after stndbs. Mov Disord 2016;31(S 2):S182–3.
  188. Flisar D, Trost M, Zupancic Kriznar N, Kramberger Gregoric M, Meglic B, Georgiev D, et al. Accelerometric evaluation of motor performance in PD patients before and after stn-dbs treatment. Eur J Neurol 2018;25(S 2):421.
  189. Gao C, Smith S, Lones M, Jamieson S, Alty J, Cosgrove J, et al. Objective assessment of bradykinesia in Parkinson’s disease using evolutionary algorithms: clinical validation. Transl Neurodegener 2018;7:18. doi: 10.1186/s40035-018-0124-x. [DOI] [PMC free article] [PubMed]
  190. Gernon S, Fowler A, Lyons K, Pahwa R. Clinical Experience with Personal KinetiGraph Before and After Deep Brain Stimulation for Parkinson’s Disease. Neurology. Conference: 70th Annual Meeting of the American Academy of Neurology, AAN, Vol. 90, 2018.
  191. Ghoraani B, Galvin JE, Jimenez-Shahed J. Point of view: wearable systems for at-home monitoring of motor complications in Parkinson’s disease should deliver clinically actionable information. Parkinsonism Relat Disord 2021;84:35–9. doi: 10.1016/j.parkreldis.2021.01.022. [DOI] [PMC free article] [PubMed]
  192. Giuffrida JP, Riley DE, Maddux BN, Heldman DA. Clinically deployable Kinesia technology for automated tremor assessment. Mov Disord 2009;24:723–30. doi: 10.1002/mds.22445. [DOI] [PubMed]
  193. Giuffrida JP, Riley DE, Maddux BN, Heldmann DA. Clinically deployable kinesiatm technology for automated tremor assessment. Mov Disord 2009;24:723–30. doi: 10.1002/mds.22445. [DOI] [PubMed]
  194. Griffiths R, Kotschet K, Johnson W, Drago J, Evans A, Kempster P, et al. Automated ambulatory measurement of dyskinesia and bradykinesia. Mov Disord 2012;1:S98.
  195. Heldman DA, Espay AJ, LeWitt PA, Giuffrida JP. Clinician versus machine: reliability and responsiveness of motor endpoints in Parkinson’s disease. Parkinsonism Relat Disord 2014;20:590–5. doi: 10.1016/j.parkreldis.2014.02.022. [DOI] [PMC free article] [PubMed]
  196. Heldman DA, Giuffrida JP, Cubo E. Wearable sensors for advanced therapy referral in Parkinson’s disease. J Parkinsons Dis 2016;6:631–8. doi: 10.3233/JPD-160830. [DOI] [PubMed]
  197. Horne M, Osborn S, Evans A, Kotschet K. The effect on fluctuations of PD of advanced therapies measured by an objective long term monitoring system. Mov Disord 2014;1:S185–6.
  198. Isaacson S, Boroojerdi B, Klos K, Carson S, Markowitz M, Heldman D, et al. The impact of a wearable device on Parkinson’s disease motor symptom management in patients starting rotigotine transdermal patch. Mov Disord 2018;33(S 2):S520–1.
  199. Jansa J, Piks B, Flisar D, Gregoric Kramberger M, Pirtosek Z. Self-perception of daily performance in relation to motor performance in advanced Parkinson’s disease-case studies. Mov Disord 2016;31(S 2):S503.
  200. Johansson A, Lundgren M, Othman M, Nyholm D. Evaluation of device-assisted treatment using a wearable accelerometry wrist sensor. Mov Disord 2019;34(S 2):S911.
  201. Joshi R, Bronstein JM, Alcazar J, Yang D, Joshi M, Hermanowicz N. An observational study of PKG movement recording system use in routine clinical care of patients with Parkinson’s disease. Mov Disord 2019;34(S 1):S16–7. doi: 10.3389/fneur.2019.01027. [DOI] [PMC free article] [PubMed]
  202. Karl JA, Ouyang B, Goetz S, Metman LV. A novel dbs paradigm for axial features in Parkinson’s disease: a randomized crossover study. Mov Disord 2020;35:1369–78. doi: 10.1002/mds.28048. [DOI] [PubMed]
  203. Keogh A, Argent R, Anderson A, Caulfield B, Johnston W. Assessing the usability of wearable devices to measure gait and physical activity in chronic conditions: a systematic review. J Neuroeng Rehabil 2021;18:138. doi: 10.1186/s12984-021-00931-2. [DOI] [PMC free article] [PubMed]
  204. Kilincalp G, Sjostrom AC, Eriksson B, Holmberg B, Constantinescu R, Bergquist F. Predictive value of ambulatory objective movement measurement for outcomes of levodopa/carbidopa intestinal gel infusion. J Pers Med 2022;12:02. doi: 10.3390/jpm12010027. [DOI] [PMC free article] [PubMed]
  205. King E, Abraham J, Edwards E, Gorst T, Holley M, Inches J, et al. Evaluating a novel home-based care pathway for people with Parkinson’s disease. Mov Disord 2021;36(S 1):S216.
  206. Klingelhoefer L, Horne M, Rizos A, Sauerbier A, McGregor S, Trivedi D, et al. Sleep assessment in Parkinson’s disease-the use of Parkinson’s Kineti Graph. Mov Disord 2015;1:S162.
  207. Klingelhofer L, Rizos A, Horne M, Sauerbier A, McGregor S, De Micco R, et al. First comparison of continuous movement data (hauser-diary versus Parkinson’s KinetiGraph) in patients with Parkinson’s disease. Eur J Neurol 2016;2:530. doi: 10.1111/ene.13015. [DOI] [PubMed]
  208. Koivu M, Scheperjans F, Pekkonen E. Ambulatory movement measurement in evaluating deep brain stimulation effect in patients with advanced Parkinson’s disease. Mov Disord 2017;32(S 2):906–7.
  209. Kostikis N, Rigas G, Konitsiotis S, Fotiadis DI. Configurable offline sensor placement identification for a medical device monitoring Parkinson’s disease. Sensors 2021;21:7801. doi: 10.3390/s21237801. [DOI] [PMC free article] [PubMed]
  210. Kotschet K, Johnson W, Griffiths R, Horne M. Quantifying daytime sleepiness in Parkinson’s disease. Mov Disord 2012;1:S218–9.
  211. Krause E, Randhawa J, Mehanna R. Is the personal KinetiGraph useful in the management of Parkinson’s disease patients? A retrospective study from a tertiary movement disorder center. Mov Disord 2019;34(S 2):S379–80.
  212. Leake A, De Angelis A, Horne M, Paviour D, Coebergh J, Edwards M, et al. Exploring the relationship between motor and non-motor fluctuations in Parkinson’s disease: patient’s perspective, clinician’ s assessment and objective measures from a wearable device. Mov Disord 2019;34(S 2):S647–8.
  213. Lynch P, Zoellner Y, McGregor S, Home M. Objective data in Parkinson’s disease therapy management-a retrospective analysis of the Parkinson’s KinetiGraph (PKG) database. Mov Disord 2016;31(S 2):S183.
  214. Lynch P, Pahwa R, Bergquist F, Horne M. Objective data in Parkinson’s disease: a description of over 20,000 Parkinson’s symptom scores across the world using the personal KinetiGraph (PKG). Mov Disord 2018;33(S 2):S518.
  215. Lynch P, Pahwa R, Bergquist F, Horne M. Objective Data in Parkinson’s Disease: A Description of Over 10,000 Parkinson’s Symptom Scores Across the World Using the Personal KinetiGraph (PKG). Neurology. Conference: 70th Annual Meeting of the American Academy of Neurology, AAN, Vol. 90, 2018.
  216. Lynch P, Pahwa R, Bergquist F, Horne M. Continuous Objective Monitoring in Parkinson’s Disease: A Description of Over 25,000 Parkinson’s Symptom Scores Across the World Using the Personal KinetiGraph (PKG) Wearable Monitoring Device. Neurology. Conference: 71st Annual Meeting of the American Academy of Neurology, AAN, Vol. 92, 2019.
  217. Malhotra A, Barkan S, Lee A, Hellmers N, Sarva H, Henchcliffe C. Is the Parkinson’s KinetiGraph reflective of clinical off/on motor testing: single center experience. Mov Disord Clin Pract 2020;7(S 1):S69.
  218. Metta V, Batzu L, Leta V, Trivedi D, Powdleska A, Mridula KR, et al. Parkinson’s disease: personalized pathway of care for device-aided therapies (DAT) and the role of continuous objective monitoring (COM) using wearable sensors. J Pers Med 2021;11:19. doi: 10.3390/jpm11070680. [DOI] [PMC free article] [PubMed]
  219. Mirelman A, Hillel I, Rochester L, Del Din S, Bloem BR, Avanzino L, et al. Tossing and turning in bed: nocturnal movements in Parkinson’s disease. Mov Disord 2020;35:959–68. doi: 10.1002/mds.28006. [DOI] [PubMed]
  220. Mohamed B, Evans L, Shukir M, Abdullah A, Thomas C. Evaluating the use of Parkinson’s kinetigraphs in patients with Parkinson’s disease and frailty. Mov Disord 2020;35(S 1):S658.
  221. Morgan C, Rolinski M, McNaney R, Jones B, Rochester L, Maetzler W, et al. Systematic review looking at the use of technology to measure free-living symptom and activity outcomes in Parkinson’s disease in the home or a home-like environment. J Parkinsons Dis 2020;10:429–54. doi: 10.3233/JPD-191781. [DOI] [PMC free article] [PubMed]
  222. Morgante F, De Angelis A, Siri C, Horne M, Leake A, Paviour D, et al. Shedding light on the relationship between dyskinesia assessed by a wearable device and impulsive compulsive behaviour in Parkinson’s disease. Mov Disord 2019;34(S 2):S153.
  223. Mostile G, Giuffrida JP, Adam OR, Davidson A, Jankovic J. Correlation between kinesia system assessments and clinical tremor scores in patients with essential tremor. Mov Disord 2010;25:1938–43. doi: 10.1002/mds.23201. [DOI] [PubMed]
  224. Nahab F, Abuhussain H, Moreno L. Personal KinetiGraphTM movement recording system: an assessment of utility in a movement disorder clinic. Mov Disord 2018;33(S 2):S523.
  225. Pahwa R, Isaacson SH, Torres-Russotto D, Nahab FB, Lynch PM, Kotschet KE. Role of the personal KinetiGraph in the routine clinical assessment of Parkinson’s disease: recommendations from an expert panel. Expert Rev Neurother 2018;18:669–80. doi: 10.1080/14737175.2018.1503948. [DOI] [PubMed]
  226. Pahwa R, Dorsey R, Isaacson S, Kandukuri P, Jalundhwala Y, Kukreja P, et al. Evaluating the real-world impact of levodopa/carbidopa intestinal gel (LCIG) on motor symptoms using wearable sensors: evidence from provide study. Mov Disord 2019;34(S 2):S70–2.
  227. Pahwa R, Bergquist F, Horne M, Minshall ME. Objective measurement in Parkinson’s disease: a descriptive analysis of Parkinson’s symptom scores from a large population of patients across the world using the personal KinetiGraph R. J Clin Mov Disord 2020;7:5. doi: 10.1186/s40734-020-00087-6. [DOI] [PMC free article] [PubMed]
  228. Pai H. Exploring the use of wearable sensors in Parkinson’s disease and the detection of early morning periods. Eur J Neurol 2020;27(S 1):1097.
  229. Papapetropoulos S, Mitsi G. A 12-month, 2-arm, 2-period, randomized, controlled trial of a digital solution for the management of Parkinson’s disease (PD): rationale and study design. Mov Disord 2016;31(S 2):S185.
  230. Phillips RS, Wilson KA, Walter BL, Ridgel AL. Bradykinesia and timed up and go are improved after dynamic cycling in Parkinson’s disease. J Parkinsons Dis 2013;1:154.
  231. Podlewska A, Van Wamelen D, Sauerbier A, Leta V, Trivedi D, Parry M, et al. Wearable sensor use and monitoring effect of dopamine replacement therapy on motor parameters in a real life clinical setting. Mov Disord 2019;34(S 2):S295–6.
  232. Potter A, Newsome G, Kern G, Parsons A, Page D, Dalati Y, et al. Personal KinetiGraph and deep brain stimulation with Parkinson’s disease. Mov Disord 2020;35(S 1):S517.
  233. Powell A, Graham D, Portley R, Snowdon J, Hayes MW. Wearable technology to assess bradykinesia and immobility in patients with severe depression undergoing electroconvulsive therapy: a pilot study. J Psychiatr Res 2020;130:75–81. doi: 10.1016/j.jpsychires.2020.07.017. [DOI] [PubMed]
  234. Pulliam CL, Heldman DA, Burack MA, Mera TO. Continuous motion sensor assessment of Parkinson’s disease during activities of daily living. Mov Disord 2015;1:S420. doi: 10.3233/JPD-140348. [DOI] [PMC free article] [PubMed]
  235. Robertson EE, Hall DA, Pal G, Ouyang B, Liu Y, Joyce JM, et al. Tremorography in fragile x-associated tremor/ataxia syndrome, Parkinson’s disease and essential tremor. Clin Park Relat Disord 2020;3:100040. doi: 10.1016/j.prdoa.2020.100040. [DOI] [PMC free article] [PubMed]
  236. Rodriguez-Martin D, Perez-Lopez C, Sama A, Pie M, Catala A, Cabestany J, et al. Stat-on: a wearable inertial system to objectively evaluate motor symptoms in Parkinson’s disease. Mov Disord 2019;34(S 2):S296–7.
  237. Rodriguez-Martin DRM, Perez-Lopez CPL, Pie MP, Calvet JC, Catala ACM, Cabestany JCM, et al. Feasibility to detect Parkinson’s motor symptoms with a waist-worn Parkinson’s holter. Mov Disord 2021;36(S 1):S570.
  238. Rovini E, Maremmani C, Cavallo F. Automated systems based on wearable sensors for the management of Parkinson’s disease at home: a systematic review. Telemed J E Health 2019;25:167–83. doi: 10.1089/tmj.2018.0035. [DOI] [PubMed]
  239. Sachdev B, Buttery P, Morris R. Assessing the impact on bradykinesia and dyskinesia in patients with Parkinson’s disease undergoing deep brain stimulation using a novel and objective automated assessment tool. Stereotact Funct Neurosurg 2017;95(S 1):97.
  240. Santos García D, López Ariztegui N, Cubo E, Vinagre Aragón A, García Ramos R, Borrué C, et al. Use in clinical practice of a personalized and long-term monitoring device for Parkinson’s disease: STAT-ON. Mov Disord 2020;35(S 1):S663–4.
  241. Sasaki F, Oyama G, Sekimoto S, Nakamura R, Jo T, Iwamuro H, et al. Closed loop programming evaluation using external responses for deep brain stimulation (CLOVER-DBS). Mov Disord 2018;33(S 2):S139. doi: 10.1016/j.parkreldis.2021.01.023. [DOI] [PubMed]
  242. Sica M, Tedesco S, Crowe C, Kenny L, Moore K, Timmons S, et al. Continuous home monitoring of Parkinson’s disease using inertial sensors: a systematic review. PLOS ONE 2021;16:e0246528. doi: 10.1371/journal.pone.0246528. [DOI] [PMC free article] [PubMed]
  243. Sringean J, Taechalertpaisarn P, Jitkritsadakul O, Bhidayasiri R. Bilateral recording of Parkinson’s signs with the Parkinson’s KinetiGraph: assessing its utility to evaluate asymmetric features. Mov Disord 2014;2:S36–7.
  244. Stuijt C, Laar TV. Effect of pharmacist-led interventions on motor symptoms and quality of life in Parkinson’s patients: a pilot study. Mov Disord 2016;31(S 2):S639. doi: 10.1097/WNF.0000000000000260. [DOI] [PubMed]
  245. Stuijt C, Karapinar F, Van Den Bemt B, Van Laar T. Effect of pharmacist-led interventions on motor symptoms and quality of life in Parkinson’s patients: a pilot study. Int J Clin Pharm 2017;39:969. doi: 10.1097/WNF.0000000000000260. [DOI] [PubMed]
  246. Stuijt C, Karapinar-Carkit F, van den Bemt B, van Laar T. Effect of pharmacist-led interventions on (non)motor symptoms, medication-related problems, and quality of life in Parkinson disease patients: a pilot study. Clin Neuropharmacol 2018;41:14–9. doi: 10.1097/WNF.0000000000000260. [DOI] [PubMed]
  247. Sundgren M, Rousu P, Johansson A. Does information from the personal kinetigraphtm (PKG) influence neurologists’ treatment decisions? Mov Disord 2019;34(S 2):S299–300.
  248. Sung C, Danoudis M, Iansek R. Liquid sinemet in the management of complex motor fluctuations in advanced Parkinson’s disease. Mov Disord 2018;33(S 2):S128–9.
  249. Suttrup I, Zentsch V, Schroeder J, Warnecke T. Effects of safinamide in patients with Parkinson’s disease measured by Parkinson’s KinetiGraphTM. Mov Disord 2016;31(S 2):S623.
  250. Taddei RN, Leta V, Sauerbier A, Parry M, Podlewska A, Hall L, et al. Combined catechol-o-methyl-transferase inhibition and intrajejunal levodopa infusion: a real-life single-centre experience. Mov Disord 2018;33(S 2):S122.
  251. Tan E, Tagliati M, Hogg E, Horne M. The personal KinetiGraph fluctuator score identifies motor fluctuations in Parkinson’s disease. Mov Disord 2017;32(S 2):440.
  252. Thomas C, Mohammed B, Abdelgadir E, Silverdale MA, Kobylecki C, Osborne L, et al. Can implementation of technology transform the management of Parkinson’s? Lessons learnt from the Parkinson’s KinetiGraph (TM) (PKG (TM)) service evaluation project. Mov Disord 2017;32:e10.
  253. Thomas I, Bergquist F, Johansson D, Nyholm D, Memedi M, Westin J. Automated dosing schemes for administration of microtablets of levodopa for Parkinson’s disease, using wearable sensors. Mov Disord 2017;32(S 2):914–5.
  254. Thomas I, Alam M, Bergquist F, Johansson D, Memedi M, Nyholm D, Westin J. Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson’s disease: a first experience. J Neurol 2019;266:651–8. doi: 10.1007/s00415-019-09183-6. [DOI] [PMC free article] [PubMed]
  255. Thomsen T, Kjaer T, Jorgensen L, Haahr A, Winge K. A characterization of the ADL-level in daily life with Parkinson’s disease based on objective measurements and subjective experiences. Mov Disord 2019;34(S 2):S456.
  256. Titova N, Trivedi D, Bezdolny Y, Katunina E, Ray Chaudhuri K. Wearable sensor monitoring in denovo Parkinson’s disease using the Parkinson’s KinetiGraph: pilot data and correlations from the MoNS-PD cohort. Mov Disord 2020;35(S 1):S309.
  257. van den Bergh R, Bloem BR, Meinders MJ, Evers LJW. The state of telemedicine for persons with Parkinson’s disease. Curr Opin Neurobiol 2021;34:589–97. doi: 10.1097/WCO.0000000000000953. [DOI] [PMC free article] [PubMed]
  258. van Uem JMT, Maier KS, Santos AT, Fagerbakke O, Larsen F, Ferreira JJ, et al. Twelve-week sensor assessment in Parkinson’s disease: impact on quality of life. Mov Disord 2016;31(S 2):S173. doi: 10.1002/mds.26676. [DOI] [PubMed]
  259. van Uem JMT, Cerff B, Kampmeyer M, Prinzen J, Zuidema M, Hobert MA, et al. The association between objectively measured physical activity, depression, cognition, and health-related quality of life in Parkinson’s disease. Parkinsonism Relat Disord 2018;48:74–81. doi: 10.1016/j.parkreldis.2017.12.023. [DOI] [PubMed]
  260. van Wamelen D, Leta V, Podlewska A, Trivedi D, Min Wan Y, Metta V, et al. Wearable Sensor (Parkinson’s KinetiGraph) and Dopamine Transporter Imaging as Potential Biosignature for Constipation in Parkinson’s. Neurology. Conference: 71st Annual Meeting of the American Academy of Neurology, AAN, Vol. 92, 2019.
  261. van Wamelen D, Bendriss-Otiko T, Podlewska A, Leta V, Lazcano Ocampo C, Trivedi D, et al. Dyskinesia and bradykinesia severity patterns in Parkinson’s disease using a wearable sensor. Mov Disord 2020;35(S 1):S159–61.
  262. van Wamelen D, Batzu L, Podlewska A, Trivedi D, Ray Chaudhuri K. Wearable sensor motor outcomes across patients with Parkinson’s disease from Asian, Black, and Caucasian Ethnicity in the United Kingdom. Mov Disord 2021;36(S 1):S240.
  263. van Wamelen DJ, Sringean J, Trivedi D, Carroll CB, Schrag AE, Odin P, et al., International Parkinson and Movement Disorder Society Non Motor Parkinson’s Disease Study Group. Digital health technology for non-motor symptoms in people with Parkinson’s disease: futile or future? Parkinsonism Relat Disord 2021;89:186–94. doi: 10.1016/j.parkreldis.2021.07.032. [DOI] [PubMed]
  264. Watts J, Khojandi A, Niethammer M, Ramdhani R. Predicting MDS-UPDRS ratings for deep brain stimulation patients using wearable sensor data. Mov Disord 2021;36(S 1):S552–3.
  265. Williamson JR, Telfer B, Mullany R, Friedl KE. Detecting Parkinson’s disease from wrist-worn accelerometry in the U.K. Biobank. Sensors 2021;21:2047. doi: 10.3390/s21062047. [DOI] [PMC free article] [PubMed]
  266. Zampogna A, Manoni A, Asci F, Liguori C, Irrera F, Suppa A. Shedding light on nocturnal movements in Parkinson’s disease: evidence from wearable technologies. Sensors 2020;20:5171. doi: 10.3390/s20185171. [DOI] [PMC free article] [PubMed]
  267. Zhang H, Li C, Liu W, Wang J, Zhou J, Wang S. A multi-sensor wearable system for the quantitative assessment of Parkinson’s disease. Sensors 2020;20:6146. doi: 10.3390/s20216146. [DOI] [PMC free article] [PubMed]
  268. Zhang H, Song C, Rathore AS, Huang M-C, Zhang Y, Xu W. mHealth technologies towards Parkinson’s disease detection and monitoring in daily life: a comprehensive review. IEEE Rev Biomed Eng 2021;14:71–81. doi: 10.1109/RBME.2020.2991813. [DOI] [PubMed]
  269. Price J, Martin H, Ebenezer L, Cotton P, Shuri J, Martin A, et al. A service evaluation by Parkinson’s disease nurse specialists, of Parkinson’s KinetiGraph (PKG) movement recording system use in routine clinical care of patients with Parkinson’s disease. J Parkinsons Dis 2016;6(S 1):253.
  270. Price J, Martin H, Ebenezer L. A service evaluation of the clinical use of Parkinson’s KinetiGraph (PKG) movement recording system in the assessment of Parkinson’s disease patients with tremor. Mov Disord 2017;32(S 2):506.
  271. Rodríguez-Molinero A, Hernández-Vara J, Parez D, Catala A, Bayes A, Martínez JC. Randomized multicenter single-blind parallel-group trial to compare the efficacy of a holter for Parkinson symptoms against other clinical follow-up methods. J Parkinsons Dis 2019;9:97.
  272. UK PKG Registry: Multi-centre Real-world Registry of Personal KinetiGraph in 441 Patients with Parkinson’s Disease; n.d. URL: www.hra.nhs.uk/planning-and-improving-research/application-summaries/research-summaries/pkgreg/ (accessed 30 January 2023).
  273. ClinicalTrials.gov. Monitoring of Mobility of Parkinson’s Patients for Therapeutic Purposes – Clinical Trial. Bethesda, MD: National Library of Medicine (US); 2019. URL: https://clinicaltrials.gov/show/ NCT04176302 (accessed 30 January 2023).
  274. ClinicalTrials.gov. Evaluation of the Personal KinetiGraph™ (PKG™) to Improve Insight into Parkinson’s Disease Status. Bethesda, MD: National Library of Medicine (US); 2018. URL: https://clinicaltrials.gov/show/ NCT03741920 (accessed 30 January 2023).

RESOURCES