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

Decho Surangsrirat; Panyawut Sri-Iesaranusorn; Attawit Chaiyaroj; Peerapon Vateekul; Roongroj Bhidayasiri

doi:10.1038/s41598-022-06572-2

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

Sci Rep. 2022 Feb 24;12(1):3142. doi: 10.1038/s41598-022-06572-2.

Authors

Decho Surangsrirat¹, Panyawut Sri-Iesaranusorn², Attawit Chaiyaroj³, Peerapon Vateekul⁴, Roongroj Bhidayasiri^{5

6}

Affiliations

¹ Assistive Technology and Medical Devices Research Center, National Science and Technology Development Agency, Pathum Thani, Thailand. decho.sur@nstda.or.th.
² Mathematical Informatics, Information Science, Nara Institute of Science and Technology, Nara, Japan.
³ Entertainment Technology, School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan.
⁴ Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
⁵ Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand.
⁶ The Academy of Science, The Royal Society of Thailand, Bangkok, Thailand.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Female
Humans
Male
Mobile Applications*
Parkinson Disease / physiopathology*
Patient Acuity
Smartphone*
Walking*