Patients' Self-Report and Handwriting Performance Features as Indicators for Suspected Mild Cognitive Impairment in Parkinson's Disease.
Sara RosenblumSonya MeyerAriella RichardsonSharon Hassin-BaerPublished in: Sensors (Basel, Switzerland) (2022)
Early identification of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients can lessen emotional and physical complications. In this study, a cognitive functional (CF) feature using cognitive and daily living items of the Unified Parkinson's Disease Rating Scale served to define PD patients as suspected or not for MCI. The study aimed to compare objective handwriting performance measures with the perceived general functional abilities (PGF) of both groups, analyze correlations between handwriting performance measures and PGF for each group, and find out whether participants' general functional abilities, depression levels, and digitized handwriting measures predicted this CF feature. Seventy-eight participants diagnosed with PD by a neurologist (25 suspected for MCI based on the CF feature) completed the PGF as part of the Daily Living Questionnaire and wrote on a digitizer-affixed paper in the Computerized Penmanship Handwriting Evaluation Test. Results indicated significant group differences in PGF scores and handwriting stroke width, and significant medium correlations between PGF score, pen-stroke width, and the CF feature. Regression analyses indicated that PGF scores and mean stroke width accounted for 28% of the CF feature variance above age. Nuances of perceived daily functional abilities validated by objective measures may contribute to the early identification of suspected PD-MCI.
Keyphrases
- mild cognitive impairment
- end stage renal disease
- cognitive decline
- cystic fibrosis
- physical activity
- ejection fraction
- newly diagnosed
- machine learning
- chronic kidney disease
- deep learning
- atrial fibrillation
- pulmonary embolism
- prognostic factors
- peritoneal dialysis
- neural network
- risk factors
- patient reported
- brain injury
- bioinformatics analysis