ABSTRACT
OBJECTIVES: There is limited literature describing the effect exercise may have on depression and an individual's health-related quality of life (HRQoL) in Parkinson's Disease (PD). We aim to quantify this effect. METHODS: A cross-sectional questionnaire was administered to 60 PD subjects. The Parkinson's Disease Questionnaire-39 (PDQ-39) summary index and the Geriatric Depression Scale-30 were used to quantify HRQoL and depression, respectively. Data were obtained on exercise habits. ANOVA and multivariate linear regression analysis were used to calculate mean differences in HRQoL. RESULTS: Depression was consistently related to HRQoL (p < 0.05). Of those who exercised as an adult before PD diagnosis, 49.02% (n = 25) reported depression as compared to 88.89% (n = 8) of those who did not report adult exercise (p = 0.03). Those who exercised frequently as an adult prior to PD diagnosis had a better PDQ-39 Cognitive Index (p = 0.03). Those who were not depressed and were currently exercising had a significantly higher HRQoL than those who were depressed and did not exercise (p < 0.01). DISCUSSION: Exercising and depression may interact to affect HRQoL. Thus, coordination of mental health evaluation and exercise regimens in persons with PD may improve HRQoL.
Subject(s)
Depression/psychology , Exercise , Parkinson Disease/psychology , Quality of Life , Aged , Aged, 80 and over , Case-Control Studies , Cross-Sectional Studies , Depression/complications , Female , Humans , Male , Parkinson Disease/complications , Severity of Illness Index , Surveys and QuestionnairesABSTRACT
Objectives Parkinson's disease is the second most common neurodegenerative movement disorder in the United States. Patients' opinions of technology-based tools for education and communication as related to Parkinson's disease are unclear with little documented research addressing the issue. The goal of this research was to investigate patient opinions about technology-based tools with a focus on differences between patients of different age groups. Methods A cross-sectional survey was used to assess views on using multiple different electronic methods for receiving instructions and communicating with healthcare providers in 109 Parkinson's disease patients. Results Approximately 28% (n = 28) of the subjects reported having unmet needs related to Parkinson's disease. Those 65 and over were less likely to believe that using technology to communicate with the healthcare center would result in themselves having a better understanding of their care (odds ratio = 0.36, 95% confidence interval: 0.14, 0.95). Those over 75 had a lower odds of being willing to use electronic methods (odds ratio = 0.33, 95% confidence interval: 0.14, 0.79), a lower odds of believing that technology would result in better self-understanding of medical needs (odds ratio = 0.27, 95% confidence interval: 0.12, 0.63) and a lower odds of believing that technology would result in their healthcare providers better understanding their needs (odds ratio = 0.32, 95% confidence interval: 0.14, 0.73). Discussion The results of this study indicate that older Parkinson's disease patients report a less favorable view regarding the role of technology in communicating with healthcare providers and for understanding their care.
Subject(s)
Biomedical Technology , Communication , Disease Management , Parkinson Disease/rehabilitation , Patient Acceptance of Health Care/psychology , Adult , Age Factors , Aged , Aged, 80 and over , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , United StatesABSTRACT
Two tri-axial accelerometers were placed on the wrists (one on each hand) of the patients with Parkinson's disease (PD) and a non-PD control group. Subjects were asked to perform three of the upper extremity motor function tasks from the Unified Parkinson's Disease Rating Scale (UPDRS) test. The tasks were: 1) finger tapping, 2) opening and closing of palms, and 3) pronation-supination movements of the forearms. The inertia signals were wirelessly received and stored on a computer for further off-line analysis. Various features such as range, standard deviation, entropy, time to accomplish the task, and maximum frequency present in the signal were extracted and compared. The results showed that among the studied population, "standard deviation", "range", "entropy", "time" and "max frequency" are the best to worst features, respectively, to distinguish between the non-PD and PD subjects. Furthermore, using the mentioned features, it is more probable to distinguish between the non-PD and PD subjects from tasks 2 and 3 as opposed to task 1.
