BACKGROUND: The purpose of this study was to determine if primary care physicians could utilize data collection tools to accurately identify glycemic abnormalities in structured, episodic self-monitoring of blood glucose (SMBG) data from patients with non-insulin-treated type 2 diabetes and whether use of these SMBG data would influence their therapeutic decisions. METHODS: Twenty-three case studies demonstrating several glycemic states (normoglycemia, elevated fasting glucose, elevated postprandial glucose, all elevated glucose, and hypoglycemia) were presented to 61 primary care physicians who evaluated the cases based upon A1C data, alone and then in combination with SMBG data. SMBG data were presented in five formats. Participants were to identify the specific glucose pattern, determine the necessity for therapy change, and select specific therapeutic changes. Participant assessments were compared with assessments made by a panel of diabetes care specialists. RESULTS: Most (78%) participants identified the same primary blood glucose feature identified by the diabetes specialists; 93.8% agreed with the diabetes care specialists regarding the need for therapy modification. The majority (77%) of participants changed the way they would manage the case after evaluating case studies with SMBG data made available to them. Eighty-six percent of participants considered the SMBG data to be of equal value or more valuable than an A1C test result; 71% of participants strongly agreed that they are now more likely to recommend structured, episodic SMBG to their non-insulin-treated type 2 diabetes mellitus patients. CONCLUSIONS: Primary care physicians can correctly identify glycemic abnormalities in SMBG data obtained through structured, episodic SMBG. Additional studies are needed to determine the clinical impact of similar testing regimens in primary care practice settings.
BACKGROUND: The purpose of this study was to determine if primary care physicians could utilize data collection tools to accurately identify glycemic abnormalities in structured, episodic self-monitoring of blood glucose (SMBG) data from patients with non-insulin-treated type 2 diabetes and whether use of these SMBG data would influence their therapeutic decisions. METHODS: Twenty-three case studies demonstrating several glycemic states (normoglycemia, elevated fasting glucose, elevated postprandial glucose, all elevated glucose, and hypoglycemia) were presented to 61 primary care physicians who evaluated the cases based upon A1C data, alone and then in combination with SMBG data. SMBG data were presented in five formats. Participants were to identify the specific glucose pattern, determine the necessity for therapy change, and select specific therapeutic changes. Participant assessments were compared with assessments made by a panel of diabetes care specialists. RESULTS: Most (78%) participants identified the same primary blood glucose feature identified by the diabetes specialists; 93.8% agreed with the diabetes care specialists regarding the need for therapy modification. The majority (77%) of participants changed the way they would manage the case after evaluating case studies with SMBG data made available to them. Eighty-six percent of participants considered the SMBG data to be of equal value or more valuable than an A1C test result; 71% of participants strongly agreed that they are now more likely to recommend structured, episodic SMBG to their non-insulin-treated type 2 diabetes mellitus patients. CONCLUSIONS: Primary care physicians can correctly identify glycemic abnormalities in SMBG data obtained through structured, episodic SMBG. Additional studies are needed to determine the clinical impact of similar testing regimens in primary care practice settings.
Authors: William Polonsky; Lawrence Fisher; Charles Schikman; Deborah Hinnen; Christopher Parkin; Zhihong Jelsovsky; Linda Amstutz; Matthias Schweitzer; Robin Wagner Journal: BMC Fam Pract Date: 2010-05-18 Impact factor: 2.497
Authors: William H Polonsky; Lawrence Fisher; Charles H Schikman; Deborah A Hinnen; Christopher G Parkin; Zhihong Jelsovsky; Bettina Petersen; Matthias Schweitzer; Robin S Wagner Journal: Diabetes Care Date: 2011-02 Impact factor: 19.112
Authors: Helena W Rodbard; Oliver Schnell; Jeffrey Unger; Christen Rees; Linda Amstutz; Christopher G Parkin; Zhihong Jelsovsky; Nathan Wegmann; Matthias Axel-Schweitzer; Robin S Wagner Journal: Diabetes Care Date: 2012-02-16 Impact factor: 19.112
Authors: Deborah A Greenwood; Shelley A Blozis; Heather M Young; Thomas S Nesbitt; Charlene C Quinn Journal: J Med Internet Res Date: 2015-07-21 Impact factor: 5.428