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Overcoming Clinical Inertia: A Randomized Clinical Trial of a Telehealth Remote Monitoring Intervention Using Paired Glucose Testing in Adults With Type 2 Diabetes.

Greenwood DA, Blozis SA, Young HM, Nesbitt TS, Quinn CC - J. Med. Internet Res. (2015)

Bottom Line: Separate mixed-effects models were used to analyze data.Both groups lowered A(1c) with an estimated average decrease of 0.70 percentage points in usual care group and 1.11 percentage points in the treatment group with a significant difference of 0.41 percentage points at 6 months (SE 0.08, t159=-2.87, P=.005).Change in medication (SE 0.21, t157=-3.37, P=.009) was significantly associated with lower A(1c) level.

View Article: PubMed Central - HTML - PubMed

Affiliation: Clinical Performance Improvement Consultant, Office of Patient Experience, Quality and Clinical Effectiveness, Sutter Health, Sacramento, CA, United States. greenwd@sutterhealth.org.

ABSTRACT

Background: Type 2 diabetes mellitus is a worldwide challenge. Practice guidelines promote structured self-monitoring of blood glucose (SMBG) for informing health care providers about glycemic control and providing patient feedback to increase knowledge, self-efficacy, and behavior change. Paired glucose testing—pairs of glucose results obtained before and after a meal or physical activity—is a method of structured SMBG. However, frequent access to glucose data to interpret values and recommend actions is challenging. A complete feedback loop—data collection and interpretation combined with feedback to modify treatment—has been associated with improved outcomes, yet there remains limited integration of SMBG feedback in diabetes management. Incorporating telehealth remote monitoring and asynchronous electronic health record (EHR) feedback from certified diabetes educators (CDEs)—specialists in glucose pattern management—employ the complete feedback loop to improve outcomes.

Objective: The purpose of this study was to evaluate a telehealth remote monitoring intervention using paired glucose testing and asynchronous data analysis in adults with type 2 diabetes. The primary aim was change in glycated hemoglobin (A(1c))—a measure of overall glucose management—between groups after 6 months. The secondary aims were change in self-reported Summary of Diabetes Self-Care Activities (SDSCA), Diabetes Empowerment Scale, and Diabetes Knowledge Test.

Methods: A 2-group randomized clinical trial was conducted comparing usual care to telehealth remote monitoring with paired glucose testing and asynchronous virtual visits. Participants were aged 30-70 years, not using insulin with A1c levels between 7.5% and 10.9% (58-96 mmol/mol). The telehealth remote monitoring tablet computer transmitted glucose data and facilitated a complete feedback loop to educate participants, analyze actionable glucose data, and provide feedback. Data from paired glucose testing were analyzed asynchronously using computer-assisted pattern analysis and were shared with patients via the EHR weekly. CDEs called participants monthly to discuss paired glucose testing trends and treatment changes. Separate mixed-effects models were used to analyze data.

Results: Participants (N=90) were primarily white (64%, 56/87), mean age 58 (SD 11) years, mean body mass index 34.1 (SD 6.7) kg/m2, with diabetes for mean 8.2 (SD 5.4) years, and a mean A(1c) of 8.3% (SD 1.1; 67 mmol/mol). Both groups lowered A(1c) with an estimated average decrease of 0.70 percentage points in usual care group and 1.11 percentage points in the treatment group with a significant difference of 0.41 percentage points at 6 months (SE 0.08, t159=-2.87, P=.005). Change in medication (SE 0.21, t157=-3.37, P=.009) was significantly associated with lower A(1c) level. The treatment group significantly improved on the SDSCA subscales carbohydrate spacing (P=.04), monitoring glucose (P=.001), and foot care (P=.02).

Conclusions: An eHealth model incorporating a complete feedback loop with telehealth remote monitoring and paired glucose testing with asynchronous data analysis significantly improved A(1c) levels compared to usual care.

Trial registration: Clinicaltrials.gov NCT01715649; https://www.clinicaltrials.gov/ct2/show/NCT01715649 (Archived by WebCite at http://www.webcitation.org/6ZinLl8D0).

No MeSH data available.


Related in: MedlinePlus

Complete feedback loop for improved outcomes in diabetes management.
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figure1: Complete feedback loop for improved outcomes in diabetes management.

Mentions: In primary care, health care providers are often not prepared to interpret SMBG data, respond to patterns, and implement a complete feedback loop with tailored feedback for behavior change or treatment modifications [14,23]. Diabetes management programs with nurse care coordination [14,23] often include diabetes education provided by certified diabetes educators (CDEs), who are uniquely qualified to analyze SMBG data and problem solve with patients. Incorporating telehealth remote monitoring with CDE support employs the complete feedback loop to improve outcomes. Figure 1 shows the complete feedback loop elements [19]. The patient generates glucose data following targeted education on the elements of structured SMBG. Next, data are analyzed and synthesized by both the CDE and the patient using pattern management and evidence-based guidelines. In collaboration, the CDE and patient agree on modification of the existing treatment plan through active communication and tailored feedback from the CDE. Finally, a new action plan is developed using shared decision making and implemented by the patient and the cycle continues.


Overcoming Clinical Inertia: A Randomized Clinical Trial of a Telehealth Remote Monitoring Intervention Using Paired Glucose Testing in Adults With Type 2 Diabetes.

Greenwood DA, Blozis SA, Young HM, Nesbitt TS, Quinn CC - J. Med. Internet Res. (2015)

Complete feedback loop for improved outcomes in diabetes management.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4527012&req=5

figure1: Complete feedback loop for improved outcomes in diabetes management.
Mentions: In primary care, health care providers are often not prepared to interpret SMBG data, respond to patterns, and implement a complete feedback loop with tailored feedback for behavior change or treatment modifications [14,23]. Diabetes management programs with nurse care coordination [14,23] often include diabetes education provided by certified diabetes educators (CDEs), who are uniquely qualified to analyze SMBG data and problem solve with patients. Incorporating telehealth remote monitoring with CDE support employs the complete feedback loop to improve outcomes. Figure 1 shows the complete feedback loop elements [19]. The patient generates glucose data following targeted education on the elements of structured SMBG. Next, data are analyzed and synthesized by both the CDE and the patient using pattern management and evidence-based guidelines. In collaboration, the CDE and patient agree on modification of the existing treatment plan through active communication and tailored feedback from the CDE. Finally, a new action plan is developed using shared decision making and implemented by the patient and the cycle continues.

Bottom Line: Separate mixed-effects models were used to analyze data.Both groups lowered A(1c) with an estimated average decrease of 0.70 percentage points in usual care group and 1.11 percentage points in the treatment group with a significant difference of 0.41 percentage points at 6 months (SE 0.08, t159=-2.87, P=.005).Change in medication (SE 0.21, t157=-3.37, P=.009) was significantly associated with lower A(1c) level.

View Article: PubMed Central - HTML - PubMed

Affiliation: Clinical Performance Improvement Consultant, Office of Patient Experience, Quality and Clinical Effectiveness, Sutter Health, Sacramento, CA, United States. greenwd@sutterhealth.org.

ABSTRACT

Background: Type 2 diabetes mellitus is a worldwide challenge. Practice guidelines promote structured self-monitoring of blood glucose (SMBG) for informing health care providers about glycemic control and providing patient feedback to increase knowledge, self-efficacy, and behavior change. Paired glucose testing—pairs of glucose results obtained before and after a meal or physical activity—is a method of structured SMBG. However, frequent access to glucose data to interpret values and recommend actions is challenging. A complete feedback loop—data collection and interpretation combined with feedback to modify treatment—has been associated with improved outcomes, yet there remains limited integration of SMBG feedback in diabetes management. Incorporating telehealth remote monitoring and asynchronous electronic health record (EHR) feedback from certified diabetes educators (CDEs)—specialists in glucose pattern management—employ the complete feedback loop to improve outcomes.

Objective: The purpose of this study was to evaluate a telehealth remote monitoring intervention using paired glucose testing and asynchronous data analysis in adults with type 2 diabetes. The primary aim was change in glycated hemoglobin (A(1c))—a measure of overall glucose management—between groups after 6 months. The secondary aims were change in self-reported Summary of Diabetes Self-Care Activities (SDSCA), Diabetes Empowerment Scale, and Diabetes Knowledge Test.

Methods: A 2-group randomized clinical trial was conducted comparing usual care to telehealth remote monitoring with paired glucose testing and asynchronous virtual visits. Participants were aged 30-70 years, not using insulin with A1c levels between 7.5% and 10.9% (58-96 mmol/mol). The telehealth remote monitoring tablet computer transmitted glucose data and facilitated a complete feedback loop to educate participants, analyze actionable glucose data, and provide feedback. Data from paired glucose testing were analyzed asynchronously using computer-assisted pattern analysis and were shared with patients via the EHR weekly. CDEs called participants monthly to discuss paired glucose testing trends and treatment changes. Separate mixed-effects models were used to analyze data.

Results: Participants (N=90) were primarily white (64%, 56/87), mean age 58 (SD 11) years, mean body mass index 34.1 (SD 6.7) kg/m2, with diabetes for mean 8.2 (SD 5.4) years, and a mean A(1c) of 8.3% (SD 1.1; 67 mmol/mol). Both groups lowered A(1c) with an estimated average decrease of 0.70 percentage points in usual care group and 1.11 percentage points in the treatment group with a significant difference of 0.41 percentage points at 6 months (SE 0.08, t159=-2.87, P=.005). Change in medication (SE 0.21, t157=-3.37, P=.009) was significantly associated with lower A(1c) level. The treatment group significantly improved on the SDSCA subscales carbohydrate spacing (P=.04), monitoring glucose (P=.001), and foot care (P=.02).

Conclusions: An eHealth model incorporating a complete feedback loop with telehealth remote monitoring and paired glucose testing with asynchronous data analysis significantly improved A(1c) levels compared to usual care.

Trial registration: Clinicaltrials.gov NCT01715649; https://www.clinicaltrials.gov/ct2/show/NCT01715649 (Archived by WebCite at http://www.webcitation.org/6ZinLl8D0).

No MeSH data available.


Related in: MedlinePlus