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The objective of this study is to recruit influential community members using Snowball Sampling Methods. Community members identified through social network analysis as influential and well-connected will be trained as community health workers (CHW) using the Diabetes Empowerment Education Program (DEEP). These CHW will be used in a future trial to educate other members of the community.
The proposed research employs the Integrated Behavior Model (IBM) to address fundamental drivers of behavior for older rural-dwelling adults with type-two diabetes mellitus (T2DM) in the context of their social network. Social network analysis (SNA) is characterized as the examination and interpretation of relational connections. This approach can be used to map social networks and understand relationships between individuals as it pertains to social support. Snowball recruitment methods will be used to recruit participants in this study. In Aim 1, individuals who are connected to several individuals within their community and have the capacity to influence norms that hinder T2DM self-care will be identified. Ten individuals who have received medical care from the Center of Excellence in Rural Health (CERH) within the past 12 months will be identified. Initial recruitment will be conducted by a community liaison in Leslie County. This core group of 10 "seeds" will be used to conduct the first wave of interviews. Since the aim of social network analysis is to understand a community by mapping the relationships that connect them as a network, each seed will be asked to identify 3 persons believed to be influential in their community. Those who are identified in wave 1 will also be asked to identify 3 people each from their community who are also respected as leaders and can be trusted to provide health-related information to community members. This process will continue for 4 waves of recruitment (N˜200). Participants will be asked to provide demographic information including age, sex, race/ethnicity, education, employment status, contact information (address, telephone number, email). An interview guide will be adapted from a previously published intervention assessing social networks and health behavior informed by preliminary data and the expert opinion of my team of mentors and collaborators. The interviews will begin with two broad general questions asking "who" is included in their social network and "how" members of the social network provide social support; using open-ended questions to elicit maximal information with minimal bias. Interview questions will explore the following domains: size and structure of social network; resources available through social network; beliefs of those in social network regarding general healthy behavior; knowledge of available healthy lifestyle choices; and perceived acceptable healthy behaviors (social norms). Data will be collected from CHWs pre- and post-training to assess the following: T2DM knowledge: Diabetes Knowledge Questionnaire (DKQ) a 24-item survey that has a reliability coefficient of 0.78 and showed sensitivity to a T2DM knowledge intervention. Health literacy: Newest Vital Sign (NVS) is a 6-item survey to assess health literacy comprehension and numeracy. Means and paired t-tests will be conducted to determine if health literacy and T2DM knowledge improved in each CHW during the DEEP training.
Age
18 - 99 years
Sex
ALL
Healthy Volunteers
Yes
UK Center for Excellence in Rural Health, Kentucky Homeplace
Hyden, Kentucky, United States
Stinnett Area Community Center
Stinnett, Kentucky, United States
Start Date
May 3, 2019
Primary Completion Date
August 10, 2023
Completion Date
August 10, 2023
Last Updated
December 3, 2024
91
ACTUAL participants
Diabetes Empowerment Education Program
OTHER
Lead Sponsor
Brittany L Smalls
Collaborators
NCT04564391
NCT03851627
Data Source & Attribution
This clinical trial information is sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.
Modifications: This data has been reformatted for display purposes. Eligibility criteria have been parsed into inclusion/exclusion sections. Location data has been geocoded to enable distance-based search. For the authoritative and most current information, please visit ClinicalTrials.gov.
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