Parkinson’s disease severity clustering based on tapping activity on mobile device

Abstract

In this study, we investigated the relationship between finger tapping tasks on the smartphone and the MDS-UPDRS I–II and PDQ-8 using the mPower dataset. mPower is a mobile application-based study for monitoring key indicators of PD progression and diagnosis. Currently, it is one of the largest, open access, mobile Parkinson’s Disease studies. Data from seven modules with a total of 8,320 participants who provided the data of at least one task were released to the public researcher. The modules comprise demographics, MDS-UPDRS I–II, PDQ-8, memory, tapping, voice, and walking. Finger-tapping is one of the tasks that easy to perform and has been analyzed for the quantitative measurement of PD. Therefore, participants who performed both the tapping activity and MDS-UPDRS I–II rating scale were selected for our analysis. Note that the MDS-UPDRS mPower Survey only contains parts of the original scale and has not been clinimetrically tested for validity and reliability. We obtained a total of 1851 samples that contained the tapping activity and MDS-UPDRS I–II for the analysis. Nine features were selected to represent tapping activity. K-mean was applied as an unsupervised clustering algorithm in our study. For determining the number of clusters, the elbow method, Sihouette score, and Davies–Bouldin index, were employed as supporting evaluation metrics. Based on these metrics and expert opinion, we decide that three clusters were appropriate for our study. The statistical analysis found that the tapping features could separate participants into three severity groups. Each group has different characteristics and could represent different PD severity based on the MDS-UPDRS I–II and PDQ-8 scores. Currently, the severity assessment of a movement disorder is based on clinical observation. Therefore, it is highly dependant on the skills and experiences of the trained movement disorder specialist who performs the procedure. We believe that any additional methods that could potentially assist with quantitative assessment of disease severity, without the need for a clinical visit would be beneficial to both the healthcare professionals and patients.

An Artificial Intelligence Video-Based Assessment of Upper-limb Bradykinesia in Parkinson’s Disease

“…This is an interesting observation that sheds light on the different mechanisms involved in the finger closing and opening movements and how these are affected by PD (Yokoe et al, 2009). Moreover, the fact that opening or closing movements might represent important movement features in PD indicates that modern approaches to assess finger tapping performance based on touchscreens or keyboards are missing important information related to the speed of the different phases of the movement, further supporting the notion that video-based assessments might be preferred for the evaluation of parkinsonism (Akram et al, 2022;Surangsrirat et al, 2022).…”

Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis

“…62002198, No. 61902208 Goni et al (2021) 42 PD In-the-wild (NR) 970 (59.85, 9.05, 35%) 1630 (46.84, 10.05, 15.2%) Clinical assessment Smartphone application with 4 tasks: gait, balance, voice and tapping Subject level 700 features extracted, comprising statistical features of time and frequency locomotion NA Least absolute shrinkage and selection operator (LASSO), RF, SVM NR Surangsrirat et al (2022) 41 PD In-the-wild (NR) 1851 (44.27, 0.44, 31.5%) NA Self-reports Demographics, MDS-UPDRS I–II, PDQ-8, memory, tapping, voice, and walking Subject level High and low order statistics of keystroke dynamics NA K-means unsupervised clustering National Science and Technology Development Agency (NSTDA), Thailand Zulueta et al (2021) 62 Bipolar disorder In-the-wild (35 months) 227 (35, 11, 75%) 117 (41, 16, 60%) Self-reports Keystroke dynamics and typing metadata (autocorrect and backspace rate) Session level Low order statistics of keystroke dynamics, entropy (complexity) features NA RF Mood Challenge for Research kit 1R01MH120168 Ross et al (2021) 61 Bipolar disorder In-the-wild (2 months) 11 (47, 10.6, 72.7%) 8 (46.1, 10.6, 62.5%) Hybrid (clinical assessment and self-reports) Keystroke timing data Session level Low-order statistics Longitudinal mixed effects NA T...…”

“…Moreover, exploiting the passive nature of typing data acquisition, data from 970 PD patients, part of the mPower database 45 , facilitated the detection of early motor decline through Support Vector Machine and Random Forests 42 . Similarly, using the mPower database, unsupervised clustering of smartphone tapping data was used to discriminate the severity of motor symptoms in PD 41 .…”

Development and Implementation of The Frog-In-Maze Game to Study Upper limb Movement in People with Parkinson’s Disease

“…Tapping behaviors have been demonstrated to be affected early in disease 1 , relate to disease severity 2,21 , and have been successfully deployed on mobile phone based platforms for real world data collection [18][19][20] . We have also previously shown in a simple tapping task, as in the more advanced game described in this work, that nger tapping amplitude relates to gait stride-length 17 .…”

“…Multiple groups have developed mobile aps that include measurement of bradykinesia using nger tapping behaviors including the Roche PD Mobile Application 18 , the CloudUPDRS Parkinson's study 19 and the mPOWER study. 20 Tapping results from the mPOWER dataset have shown utility in splitting people with PD into groups based on disease severity, 21 and amongst the 5 features collected was the best classi er for the PD group. 20 One group has developed a game called the "goalkeeper game" to assess motor and cognitive performance in PD.…”