A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data
Bradykinesia is a cardinal hallmark of Parkinson’s disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed...
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/11/5238 |
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| author | Jeroen G. V. Habets Rachel K. Spooner Varvara Mathiopoulou Lucia K. Feldmann Johannes L. Busch Jan Roediger Bahne H. Bahners Alfons Schnitzler Esther Florin Andrea A. Kühn |
| author_facet | Jeroen G. V. Habets Rachel K. Spooner Varvara Mathiopoulou Lucia K. Feldmann Johannes L. Busch Jan Roediger Bahne H. Bahners Alfons Schnitzler Esther Florin Andrea A. Kühn |
| author_sort | Jeroen G. V. Habets |
| collection | DOAJ |
| description | Bradykinesia is a cardinal hallmark of Parkinson’s disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed automated bradykinesia scoring tools are proprietary and are not suitable for capturing intraday symptom fluctuation. We assessed finger tapping (i.e., Unified Parkinson’s Disease Rating Scale (UPDRS) item 3.4) in 37 people with Parkinson’s disease (PwP) during routine treatment follow ups and analyzed their 350 sessions of 10-s tapping using index finger accelerometry. Herein, we developed and validated ReTap, an open-source tool for the automated prediction of finger tapping scores. ReTap successfully detected tapping blocks in over 94% of cases and extracted clinically relevant kinematic features per tap. Importantly, based on the kinematic features, ReTap predicted expert-rated UPDRS scores significantly better than chance in a hold out validation sample (n = 102). Moreover, ReTap-predicted UPDRS scores correlated positively with expert ratings in over 70% of the individual subjects in the holdout dataset. ReTap has the potential to provide accessible and reliable finger tapping scores, either in the clinic or at home, and may contribute to open-source and detailed analyses of bradykinesia. |
| first_indexed | 2024-03-11T02:57:03Z |
| format | Article |
| id | doaj.art-c4ed289c288241fb818af890b913d9db |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:57:03Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c4ed289c288241fb818af890b913d9db2023-11-18T08:34:29ZengMDPI AGSensors1424-82202023-05-012311523810.3390/s23115238A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-DataJeroen G. V. Habets0Rachel K. Spooner1Varvara Mathiopoulou2Lucia K. Feldmann3Johannes L. Busch4Jan Roediger5Bahne H. Bahners6Alfons Schnitzler7Esther Florin8Andrea A. Kühn9Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, GermanyInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, GermanyMovement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, GermanyMovement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, GermanyMovement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, GermanyMovement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, GermanyInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, GermanyInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, GermanyInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, GermanyMovement Disorder and Neuromodulation Unit, Department of Neurology, Charité Universitaetsmedizin Berlin, 10117 Berlin, GermanyBradykinesia is a cardinal hallmark of Parkinson’s disease (PD). Improvement in bradykinesia is an important signature of effective treatment. Finger tapping is commonly used to index bradykinesia, albeit these approaches largely rely on subjective clinical evaluations. Moreover, recently developed automated bradykinesia scoring tools are proprietary and are not suitable for capturing intraday symptom fluctuation. We assessed finger tapping (i.e., Unified Parkinson’s Disease Rating Scale (UPDRS) item 3.4) in 37 people with Parkinson’s disease (PwP) during routine treatment follow ups and analyzed their 350 sessions of 10-s tapping using index finger accelerometry. Herein, we developed and validated ReTap, an open-source tool for the automated prediction of finger tapping scores. ReTap successfully detected tapping blocks in over 94% of cases and extracted clinically relevant kinematic features per tap. Importantly, based on the kinematic features, ReTap predicted expert-rated UPDRS scores significantly better than chance in a hold out validation sample (n = 102). Moreover, ReTap-predicted UPDRS scores correlated positively with expert ratings in over 70% of the individual subjects in the holdout dataset. ReTap has the potential to provide accessible and reliable finger tapping scores, either in the clinic or at home, and may contribute to open-source and detailed analyses of bradykinesia.https://www.mdpi.com/1424-8220/23/11/5238Parkinson’s diseasebradykinesiafinger tappingaccelerometeropen-sourcemachine learning |
| spellingShingle | Jeroen G. V. Habets Rachel K. Spooner Varvara Mathiopoulou Lucia K. Feldmann Johannes L. Busch Jan Roediger Bahne H. Bahners Alfons Schnitzler Esther Florin Andrea A. Kühn A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data Sensors Parkinson’s disease bradykinesia finger tapping accelerometer open-source machine learning |
| title | A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data |
| title_full | A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data |
| title_fullStr | A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data |
| title_full_unstemmed | A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data |
| title_short | A First Methodological Development and Validation of ReTap: An Open-Source UPDRS Finger Tapping Assessment Tool Based on Accelerometer-Data |
| title_sort | first methodological development and validation of retap an open source updrs finger tapping assessment tool based on accelerometer data |
| topic | Parkinson’s disease bradykinesia finger tapping accelerometer open-source machine learning |
| url | https://www.mdpi.com/1424-8220/23/11/5238 |
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