Correlation of Quantitative Motor State Assessment Using a Kinetograph and Patient Diaries in Advanced PD: Data from an Observational Study

Introduction Effective management and development of new treatment strategies for response fluctuations in advanced Parkinson’s disease (PD) largely depends on clinical rating instruments such as the PD home diary. The Parkinson’s kinetigraph (PKG) measures movement accelerations and analyzes the spectral power of the low frequencies of the accelerometer data. New algorithms convert each hour of continuous PKG data into one of the three motor categories used in the PD home diary, namely motor Off state and On state with and without dyskinesia. Objective To compare quantitative motor state assessment in fluctuating PD patients using the PKG with motor state ratings from PD home diaries. Methods Observational cohort study on 24 in-patients with documented motor fluctuations who completed diaries by rating motor Off, On without dyskinesia, On with dyskinesia, and asleep for every hour for 5 consecutive days. Simultaneously collected PKG data (recorded between 6 am and 10 pm) were analyzed and calibrated to the patient’s individual thresholds for Off and dyskinetic state by novel algorithms classifying the continuous accelerometer data into these motor states for every hour between 6 am and 10 pm. Results From a total of 2,040 hours, 1,752 hours (87.4%) were available for analyses from calibrated PKG data (7.5% sleeping time and 5.1% unclassified motor state time were excluded from analyses). Distributions of total motor state hours per day measured by PKG showed moderate-to-strong correlation to those assessed by diaries for the different motor states (Pearson’s correlations coefficients: 0.404–0.658), but inter-rating method agreements on the single-hour-level were only low-to-moderate (Cohen’s κ: 0.215–0.324). Conclusion The PKG has been shown to capture motor fluctuations in patients with advanced PD. The limited correlation of hour-to-hour diary and PKG recordings should be addressed in further studies.

[1]  Bernard Ravina,et al.  The montreal cognitive assessment as a screening tool for cognitive impairment in Parkinson's disease , 2008, Movement disorders : official journal of the Movement Disorder Society.

[2]  Björn Eskofier,et al.  Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson's disease , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Paolo Bonato,et al.  Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors , 2009, IEEE Transactions on Information Technology in Biomedicine.

[4]  C. Gielen,et al.  Detection and assessment of the severity of Levodopa‐induced dyskinesia in patients with Parkinson's disease by neural networks , 2000, Movement disorders : official journal of the Movement Disorder Society.

[5]  A. Beck,et al.  An inventory for measuring depression. , 1961, Archives of general psychiatry.

[6]  J. Winkler,et al.  Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease , 2013, PloS one.

[7]  Alan Godfrey,et al.  Validation of an Accelerometer to Quantify a Comprehensive Battery of Gait Characteristics in Healthy Older Adults and Parkinson's Disease: Toward Clinical and at Home Use , 2016, IEEE Journal of Biomedical and Health Informatics.

[8]  J. Hughes,et al.  Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases. , 1992, Journal of neurology, neurosurgery, and psychiatry.

[9]  S. Shiffman,et al.  Patient non-compliance with paper diaries , 2002, BMJ : British Medical Journal.

[10]  H. Reichmann,et al.  Nonmotor fluctuations in Parkinson disease , 2013, Neurology.

[11]  William W. McDonald,et al.  Depression rating scales in Parkinson's disease: Critique and recommendations , 2007, Movement disorders : official journal of the Movement Disorder Society.

[12]  J. Growdon,et al.  Using wearable sensors to predict the severity of symptoms and motor complications in late stage Parkinson's Disease , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  P. May,et al.  Quantitative Assessment of Neuroleptic‐Induced Extrapyramidal Symptoms: Clinical and Nonclinical Approaches , 1983, Clinical neuropharmacology.

[14]  S. Fahn Unified Parkinson's Disease Rating Scale , 1987 .

[15]  M. Horne,et al.  An Objective Fluctuation Score for Parkinson's Disease , 2015, PloS one.

[16]  A. Bonnet,et al.  [The Unified Parkinson's Disease Rating Scale]. , 2000, Revue neurologique.

[17]  Diane C. Tsai Recent Developments in Parkinson's Disease , 1986, The Yale Journal of Biology and Medicine.

[18]  R. Hauser,et al.  Patient Evaluation of a Home Diary to Assess Duration and Severity of Dyskinesia in Parkinson Disease , 2006, Clinical neuropharmacology.

[19]  D. Heldman,et al.  Motion sensor dyskinesia assessment during activities of daily living. , 2014, Journal of Parkinson's disease.

[20]  S. Gielen,et al.  Automatic assessment of levodopa‐induced dyskinesias in daily life by neural networks , 2003, Movement disorders : official journal of the Movement Disorder Society.

[21]  L. Seeberger,et al.  A Home Diary to Assess Functional Status in Patients with Parkinson's Disease with Motor Fluctuations and Dyskinesia , 2000, Clinical neuropharmacology.

[22]  H. Reichmann,et al.  Quantitative assessment of non-motor fluctuations in Parkinson’s disease using the Non-Motor Symptoms Scale (NMSS) , 2015, Journal of Neural Transmission.

[23]  P. Kempster,et al.  Automated assessment of bradykinesia and dyskinesia in Parkinson's disease. , 2012, Journal of Parkinson's disease.

[24]  M. Hoehn,et al.  Parkinsonism , 1967, Neurology.

[25]  C. Clarke,et al.  Systematic review of levodopa dose equivalency reporting in Parkinson's disease , 2010, Movement disorders : official journal of the Movement Disorder Society.

[26]  Björn Eskofier,et al.  Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson's disease , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  Dimitrios I. Fotiadis,et al.  An automated methodology for levodopa-induced dyskinesia: Assessment based on gyroscope and accelerometer signals , 2012, Artif. Intell. Medicine.

[28]  Dimitrios I. Fotiadis,et al.  On automated assessment of Levodopa-induced dyskinesia in Parkinson's disease , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  S. Papapetropoulos Patient Diaries As a Clinical Endpoint in Parkinson's Disease Clinical Trials , 2012, CNS neuroscience & therapeutics.

[30]  B. Bloem,et al.  Quantitative wearable sensors for objective assessment of Parkinson's disease , 2013, Movement disorders : official journal of the Movement Disorder Society.