Abstract
Objective This study has been designed with the aim of using optimal scaling to perform the allocation of scores and to be able to construct an indicator of the Parkinson's Disease Gravity Index. Scores were assigned to interrelated dimensions that share information about the patient's situation, to have an objective, holistic tool which integrates scores so that doctors can have a comprehensive idea of the patient's situation. Patients and Methods. 120 consecutive patients with Parkinson's diagnosis were chosen according to the United Kingdom Parkinson's Disease Society Brain Bank criteria. Subsequently, all the chosen dimensions were transformed into interval variables for which the formula proposed by Sturges was used. Once the dimensions were transformed into interval variables, optimal scaling was carried out. Subsequently, the following attributes were analyzed: quality and acceptability of the data; reliability: internal consistency, reliability index, Cronbach's alpha, and standard error of measurement; finally, validity: convergent validity and validity for known groups. Results There were no missing data. An appropriate Cronbach's alpha value of 0.71 was gathered, and all items were found to be pertinent to the scale. The item homogeneity index was 0.36. Precision evaluated with the standard error of measurement was 7.8. The Parkinson's Disease Gravity Index discriminant validity (validity for known groups), assessed among the different stages of Hoehn and Yahr scale by the Kruskal–Wallis test, showed major significance (X2 = 32.7, p ≤ 0.001). Conclusions The Parkinson's Disease Gravity Index has shown adequate metric properties.
Highlights
IntroductionParkinson’s disease (PD) is a heterogeneous neurodegenerative process, which, by 2016, was estimated to affect some 6.1 million people [1]
Parkinson’s disease (PD) is a heterogeneous neurodegenerative process, which, by 2016, was estimated to affect some 6.1 million people [1].e onset of neurodegeneration of Parkinson’s disease is likely to occur several decades before the onset of motor symptoms
E following dimensions were included in the evaluation: (1) age dimension (AD) in years, (2) motor dimension (MD) evaluated with SPES-SCOPA [18], (3) depression dimension (DD) evaluated with HADS [19], (4) anxiety dimension (AxD) with the same, (5) cognitive dimension (CD) evaluated with PD-CRS [20], (6) apathy dimension (ApD) measured with AS [21], (7) fatigue dimension (FD) with D-FIS [22], (8) nonmotor dimension (NMD) with the Nonmotor Symptoms Scale (NMSS) [23], (9) psychosis dimension (PsD) measured with SCOPA-PC [24], and (10) sleep dimension (SD) evaluated with SCOPA-SLEEP [25]
Summary
Parkinson’s disease (PD) is a heterogeneous neurodegenerative process, which, by 2016, was estimated to affect some 6.1 million people [1]. E onset of neurodegeneration of Parkinson’s disease is likely to occur several decades before the onset of motor symptoms. Epigenetic, and environmental factors lead to structural alterations and protein deposits, especially alpha-synuclein, due to dysfunction in the ubiquitin-proteasome system, alteration of mitochondrial function, and oxidative stress [2]. Other PD subtypes have been proposed, such as autonomic dysfunction, cognitive deterioration, and REM sleep disorder. There is still a clear emphasis on the motor symptoms associated with PD [3]. Ere are different degrees of nonmotor symptoms of the disease (NMS) such as autonomic dysfunction, sensory disorders, integumentary system disorders, neurobehavioral disorders, sleep disorders, visual impairment, and other conditions [4] There is still a clear emphasis on the motor symptoms associated with PD [3]. ere are different degrees of nonmotor symptoms of the disease (NMS) such as autonomic dysfunction, sensory disorders, integumentary system disorders, neurobehavioral disorders, sleep disorders, visual impairment, and other conditions [4]
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