TY - JOUR
T1 - Innovative Parkinson's Disease Patients' Motor Skills Assessment
T2 - The i-PROGNOSIS Paradigm
AU - Dias, Sofia Balula
AU - Grammatikopoulou, Athina
AU - Diniz, José Alves
AU - Dimitropoulos, Kosmas
AU - Grammalidis, Nikos
AU - Zilidou, Vicky
AU - Savvidis, Theodore
AU - Konstantinidis, Evdokimos
AU - Bamidis, Panagiotis D.
AU - Jaeger, Hagen
AU - Stadtschnitzer, Michael
AU - Silva, Hugo
AU - Telo, Gonçalo
AU - Ioakeimidis, Ioannis
AU - Ntakakis, George
AU - Karayiannis, Fotis
AU - Huchet, Estelle
AU - Hoermann, Vera
AU - Filis, Konstantinos
AU - Theodoropoulou, Elina
AU - Lyberopoulos, George
AU - Kyritsis, Konstantinos
AU - Papadopoulos, Alexandros
AU - Delopoulos, Anastasios
AU - Trivedi, Dhaval
AU - Chaudhuri, K. Ray
AU - Klingelhoefer, Lisa
AU - Reichmann, Heinz
AU - Bostantzopoulou, Sevasti
AU - Katsarou, Zoe
AU - Iakovakis, Dimitrios
AU - Hadjidimitriou, Stelios
AU - Charisis, Vasileios
AU - Apostolidis, George
AU - Hadjileontiadis, Leontios J.
N1 - Funding Information:
The research leading to these results has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 690494— i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions.
Funding Information:
The authors would like to thank all PD patients who volunteered to participate in this study for their time, commitment, and contribution. Funding. The research leading to these results has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 690494?i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions.
Publisher Copyright:
© Copyright © 2020 Dias, Grammatikopoulou, Diniz, Dimitropoulos, Grammalidis, Zilidou, Savvidis, Konstantinidis, Bamidis, Jaeger, Stadtschnitzer, Silva, Telo, Ioakeimidis, Ntakakis, Karayiannis, Huchet, Hoermann, Filis, Theodoropoulou, Lyberopoulos, Kyritsis, Papadopoulos, Delopoulos, Trivedi, Chaudhuri, Klingelhoefer, Reichmann, Bostantzopoulou, Katsarou, Iakovakis, Hadjidimitriou, Charisis, Apostolidis and Hadjileontiadis.
PY - 2020/7/21
Y1 - 2020/7/21
N2 - Being the second most common neurodegenerative disease, Parkinson's disease (PD) can be symptomatically treated, although, unfortunately, it cannot be cured yet. Moreover, diagnosing and assessing PD patients is a complex process, requiring continuous monitoring. In this vein, the design, development, and validation of innovative assessment tools may be helpful in the management of patients with PD, in particular. Based on intelligent ICT interventions, the i-PROGNOSIS project intends to mitigate PD's specific symptoms, such as neurological movement disorders of gait, balance, coordination, and posture, already characterized in the early phase of the disease. From this perspective, an innovative iPrognosis motor assessment tool is presented here, taking into consideration the Unified Parkinson Disease Rating Scale (UPDRS) Part III motor skills testing items, for evaluating the motor skills status. The efficiency of the proposed Assessment Tests to reflect the motor skills status, similarly to the UPDRS Part III items, was validated via 27 participants (18 males; mean age = 62 years, SD = 10.36 years; range, 43–79 years) with early (n = 10) and moderate (n = 17) PD who performed the Assessment Tests. Features from the latter were then correlated with the corresponding clinically assessed UPDRS Part III items, and statistically significant negative correlations (range, −0.364 to −0.802) were identified between the median values of the Assessment Tests and the UPDRS Part III items. In this vein, the iPrognosis Assessment Tests were integrated within the personalized interventions of the i-PROGNOSIS project, providing alternative means of assessing their effect on the PD patient's motor skills enhancement. The promising results presented here elaborate on the concept of using ICT-based assessment means to achieve comparable outcomes with the clinical standards in motor skills assessment.
AB - Being the second most common neurodegenerative disease, Parkinson's disease (PD) can be symptomatically treated, although, unfortunately, it cannot be cured yet. Moreover, diagnosing and assessing PD patients is a complex process, requiring continuous monitoring. In this vein, the design, development, and validation of innovative assessment tools may be helpful in the management of patients with PD, in particular. Based on intelligent ICT interventions, the i-PROGNOSIS project intends to mitigate PD's specific symptoms, such as neurological movement disorders of gait, balance, coordination, and posture, already characterized in the early phase of the disease. From this perspective, an innovative iPrognosis motor assessment tool is presented here, taking into consideration the Unified Parkinson Disease Rating Scale (UPDRS) Part III motor skills testing items, for evaluating the motor skills status. The efficiency of the proposed Assessment Tests to reflect the motor skills status, similarly to the UPDRS Part III items, was validated via 27 participants (18 males; mean age = 62 years, SD = 10.36 years; range, 43–79 years) with early (n = 10) and moderate (n = 17) PD who performed the Assessment Tests. Features from the latter were then correlated with the corresponding clinically assessed UPDRS Part III items, and statistically significant negative correlations (range, −0.364 to −0.802) were identified between the median values of the Assessment Tests and the UPDRS Part III items. In this vein, the iPrognosis Assessment Tests were integrated within the personalized interventions of the i-PROGNOSIS project, providing alternative means of assessing their effect on the PD patient's motor skills enhancement. The promising results presented here elaborate on the concept of using ICT-based assessment means to achieve comparable outcomes with the clinical standards in motor skills assessment.
KW - i-PROGNOSIS
KW - motor assessment tests
KW - motor skills decline
KW - parkinson's disease (PD)
KW - unified parkinson disease rating scale (UPDRS) part III
UR - http://www.scopus.com/inward/record.url?scp=85100075898&partnerID=8YFLogxK
U2 - 10.3389/fcomp.2020.00020
DO - 10.3389/fcomp.2020.00020
M3 - Article
AN - SCOPUS:85100075898
SN - 2624-9898
VL - 2
JO - Frontiers in Computer Science
JF - Frontiers in Computer Science
M1 - 20
ER -