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Rehabilitation Dataset Directory: Dataset Profile
Dataset: Emergence and Evolution of Social Self-management of Parkinson's Disease ()
Basic Information | |
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Dataset Full Name | Emergence and Evolution of Social Self-management of Parkinson's Disease |
Dataset Acronym | |
Summary | The Emergence and Evolution of Social Self-Management of Parkinson's Disease study (SocM-PD) is a mixed-method (quantitative-qualitative) prospective cohort study of how people with Parkinson's disease and their primary caregiver (as available) naturalistically manage chronic disease, wellness and social life in their home and community. This longitudinal study is focused on social self-management. Social self-management is defined as the practices and experiences that ensure personal social comfort while supporting mental and physical well-being. Parkinson's Disease offers a model for studying the effect of physical disease on the social self-management of daily life when physical symptoms affect fundamental social capacities. The study's objective is to understand the emergence and evolution of the trajectories of the self-management of the social lives of people living with Parkinson's disease. The central hypothesis is that expressive capacity predicts systematic change in the pattern of social self-management and quality of life outcomes. The specific aims of the study are to: 1) characterize social self-management trajectories over the 3 year period; 2) estimate the degree to which expressive nonverbal capacity predicts the trajectory; and 3) determine the moderating effect of gender on the association between expressive capacity and change in social self-management. |
Key Terms | Mixed methods, Cohort, Chronic disease, Chronic disease management, Social life, Social contact, Social interactions, Social self-management, Social networks, Quality of life, Expressive capacity, Caregivers, Health behavior, Health care |
Study Design | Longitudinal |
Data Type(s) |
Clinical Survey |
Sponsoring Agency/Entity | Department of Health and Human Services (HHS), National Institutes of Health (NIH) |
Health Conditions/Disability Measures |
Health Condition(s) | Parkinson's disease (PD), Depression (Geriatric Depression Scale), |
Disability Measures | Functional limitations (ADLs and/or IADLs), Communication impairment, Cognitive disability- Montreal Cognitive Assessment (MoCA), Caregiver assistance need Study measures included the following components of the International Classification of Functioning, Disability and Health: Activities and participation, Body function, and Environment. |
Measures/Outcomes of Interest |
Topics | Employment, Income, Education, Employment, Social support, Social interaction, Social isolation, Social participation, Management of social activities; Social networks, Social comfort, Health, Well-being, Caregiving, Expressive nonverbal capacity, Medication |
Sample |
Sample Population | Persons with Parkinson's disease and their care partners in the Greater Boston Metropolitan Area and New England, United States. |
Sample Size/Notes | 139 participants total (Includes 86 Persons with Parkinson's disease and 53 of their participating caregivers) |
Unit of Observation | Individual/Patient and caregivers |
Continent(s) | North America |
Countries | United States |
Geographic Coverage | New England states: Vermont, Massachusetts, Maine, Connecticut New Hampshire |
Geographic Specificity | NA |
Data Collection |
Data Collection Mode | Clinical observations and surveys |
Years Collected | 2013-19 |
Data Collection Frequency | Baseline with seven in-person assessments occurring approximately every 6 months. An additional telephone call was made between the assessments. |
Strengths and Limitations |
Strengths | Well constructed study containing a wide variety of data collected consistently including detailed assessments, health events, and life events history. Low participant attrition over time. Researchers can analyze public use data online: https://www.icpsr.umich.edu/web/ADDEP/studies/37631/datadocumentation# Includes the following scales:
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Limitations | Qualitative data are not available as part of data collection at this time Not all caregivers participated. |
Data Details |
Primary Website | |
Data Access |
Both public use and a restricted use datasets are available. |
Data Access Requirements |
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Summary Tables/Reports | Variables interface provides basic frequencies: https://www.icpsr.umich.edu/web/ADDEP/studies/37631/variables Online analysis available based on public use data: https://www.icpsr.umich.edu/web/ADDEP/studies/37631/datadocumentation# |
Data Components | 15 datasets altogether, each available in two versions: a public use dataset and a restricted dataset
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Selected Papers |
Other Papers | Emergence and evolution of social self-management of Parkinson’s disease: study protocol for a 3-year prospective cohort study https://bmcneurol.biomedcentral.com/articles/10.1186/1471-2377-14-95 Profile of social self-management practices in daily life with Parkinson's disease is associated with symptom severity and health quality of life. Disabil Rehabil. 2020 Apr 1:1-13. doi: 10.1080/09638288.2020.1741035 |
Technical | Codebooks and questionnaires available here: https://www.icpsr.umich.edu/web/ADDEP/studies/37631/datadocumentation# |
Related Repositories |
Repositories |
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The Rehabilitation Research Cross-dataset Variable Catalog has been developed through the Center for Large Data Research & Data Sharing in Rehabilitation (CLDR). The Center for Large Data Research and Data Sharing in Rehabilitation involves a consortium of investigators from the University of Texas Medical Branch, Cornell University's Yang Tan Institute (YTI), and the University of Michigan. The CLDR is funded by NIH - National Institute of Child Health and Human Development, through the National Center for Medical Rehabilitation Research, the National Institute for Neurological Disorders and Stroke, and the National Institute of Biomedical Imaging and Bioengineering. (P2CHD065702).
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Acknowledgements: This tool was developed through the efforts of William Erickson and Arun Karpur, and web designers Jason Criss and Jeff Trondsen at Cornell University. Many thanks to graduate students Kyoung Jo Oh and Yeong Joon Yoon who developed much of the content used in this tool.
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