BACKGROUND: Little is known about the reach of Internet self-management interventions. PURPOSE: The aim of this study was to evaluate different definitions of participation rate and compare characteristics among subcategories of participants and nonparticipants on demographic and clinical factors using de-identified electronic medical record data. METHODS: Data are presented on recruitment results and characteristics of 2,603 health maintenance organization members having type 2 diabetes invited to participate in an Internet self-management program. RESULTS: There was a 37% participation rate among all members attempted to contact and presumed eligible. There were several significant differences between participants and nonparticipants and among subgroups of participants (e.g., proactive volunteers vs. telephone respondents) on factors including age, income, ethnicity, smoking rate, education, blood pressure, and hemoglobin A1c. CONCLUSION: These results have important implications for the impact of different recruitment methods on health disparities and generalization of results. We provide recommendations for reporting of eligibility rate, participation rate, and representativeness analyses.
RCT Entities:
BACKGROUND: Little is known about the reach of Internet self-management interventions. PURPOSE: The aim of this study was to evaluate different definitions of participation rate and compare characteristics among subcategories of participants and nonparticipants on demographic and clinical factors using de-identified electronic medical record data. METHODS: Data are presented on recruitment results and characteristics of 2,603 health maintenance organization members having type 2 diabetes invited to participate in an Internet self-management program. RESULTS: There was a 37% participation rate among all members attempted to contact and presumed eligible. There were several significant differences between participants and nonparticipants and among subgroups of participants (e.g., proactive volunteers vs. telephone respondents) on factors including age, income, ethnicity, smoking rate, education, blood pressure, and hemoglobin A1c. CONCLUSION: These results have important implications for the impact of different recruitment methods on health disparities and generalization of results. We provide recommendations for reporting of eligibility rate, participation rate, and representativeness analyses.
Authors: Diane K King; Russell E Glasgow; Deborah J Toobert; Lisa A Strycker; Paul A Estabrooks; Diego Osuna; Andrew J Faber Journal: Diabetes Care Date: 2010-02-11 Impact factor: 17.152
Authors: Beverly B Green; Andy Bogart; Jessica Chubak; Sally W Vernon; Leo S Morales; Richard T Meenan; Sharon S Laing; Sharon Fuller; Cynthia Ko; Ching-Yun Wang Journal: Am J Prev Med Date: 2012-04 Impact factor: 5.043
Authors: Russell E Glasgow; V Paul Doria-Rose; Muin J Khoury; Mohammed Elzarrad; Martin L Brown; Kurt C Stange Journal: J Natl Cancer Inst Date: 2013-04-11 Impact factor: 13.506
Authors: Alexander P Cotter; Nefertiti Durant; April A Agne; Andrea L Cherrington Journal: J Diabetes Complications Date: 2013-12-12 Impact factor: 2.852
Authors: Russell E Glasgow; Deanna Kurz; Diane King; Jennifer M Dickman; Andrew J Faber; Eve Halterman; Tim Woolley; Deborah J Toobert; Lisa A Strycker; Paul A Estabrooks; Diego Osuna; Debra Ritzwoller Journal: Patient Educ Couns Date: 2011-09-15
Authors: Russell E Glasgow; Deanna Kurz; Jennifer M Dickman; Diego Osuna; Lisa Strycker; Diane K King Journal: Transl Behav Med Date: 2012-09 Impact factor: 3.046
Authors: Debra P Ritzwoller; Anna S Sukhanova; Russell E Glasgow; Lisa A Strycker; Diane K King; Bridget Gaglio; Deborah J Toobert Journal: Transl Behav Med Date: 2011-09-01 Impact factor: 3.046