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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

Sample weekly paired glucose testing data analysis, by software designed for the study, and sample message text for feedback to participants through asynchronous secure messaging via the electronic health record.
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figure3: Sample weekly paired glucose testing data analysis, by software designed for the study, and sample message text for feedback to participants through asynchronous secure messaging via the electronic health record.

Mentions: The CDEs reviewed health session and SMBG data in the Health Suite Web portal, stratified by a stoplight system with red indicating missing data or data above or below predetermined thresholds, yellow indicating pending data, and green indicating all data within range. CDEs telephoned participants, at predetermined times, when SMBG data indicated an urgent situation, such as severe hypoglycemia (1 value <50 mg/dL) or hyperglycemia (1 value >450 mg/dL). CDEs also telephoned participants if they reported a change in their feet or a new problem with medication by answering “yes” to health session questions. The Web portal data were reviewed by CDEs during normal business hours Monday through Friday. Data entered during nonbusiness hours were reviewed the following business day. Glucose data were analyzed weekly via software specifically designed for the intervention and evaluated against ADA goals of 80-130 mg/dL before meals, ≤180 mg/dL 2 hours postmeal, and a 30-50 point change between premeal to postmeal. After SMBG analysis, CDEs generated a virtual visit via asynchronous secure messaging through the EHR using the secure message feature. CDEs created a virtual encounter in the EHR, then “copy and pasted” a summary of SMBG pattern analysis data along with personalized feedback and individualized care coordination to reinforce action plans to create the virtual visit for both participants and providers to read (Figure 3). CDEs telephoned participants at weeks 4, 8, and 12 for a 30-minute discussion of SMBG trends, patterns, and goal achievement using motivational interviewing to identify opportunities to improve glucose values. If SMBG data did not improve after 4 weeks, CDEs discussed medication options with patients and/or primary care providers using shared decision making [29]. CDEs incorporated virtual visit data, both preprandial and postprandial glucose, to suggest medication changes, including insulin therapy. Medication changes were ordered by primary care providers via the EHR. Participants were instructed to use paired glucose testing or a monthly, 3-day 7-point glucose profile until the 6-month A1c. CDEs documented patient contact in the study database.


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)

Sample weekly paired glucose testing data analysis, by software designed for the study, and sample message text for feedback to participants through asynchronous secure messaging via the electronic health record.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

figure3: Sample weekly paired glucose testing data analysis, by software designed for the study, and sample message text for feedback to participants through asynchronous secure messaging via the electronic health record.
Mentions: The CDEs reviewed health session and SMBG data in the Health Suite Web portal, stratified by a stoplight system with red indicating missing data or data above or below predetermined thresholds, yellow indicating pending data, and green indicating all data within range. CDEs telephoned participants, at predetermined times, when SMBG data indicated an urgent situation, such as severe hypoglycemia (1 value <50 mg/dL) or hyperglycemia (1 value >450 mg/dL). CDEs also telephoned participants if they reported a change in their feet or a new problem with medication by answering “yes” to health session questions. The Web portal data were reviewed by CDEs during normal business hours Monday through Friday. Data entered during nonbusiness hours were reviewed the following business day. Glucose data were analyzed weekly via software specifically designed for the intervention and evaluated against ADA goals of 80-130 mg/dL before meals, ≤180 mg/dL 2 hours postmeal, and a 30-50 point change between premeal to postmeal. After SMBG analysis, CDEs generated a virtual visit via asynchronous secure messaging through the EHR using the secure message feature. CDEs created a virtual encounter in the EHR, then “copy and pasted” a summary of SMBG pattern analysis data along with personalized feedback and individualized care coordination to reinforce action plans to create the virtual visit for both participants and providers to read (Figure 3). CDEs telephoned participants at weeks 4, 8, and 12 for a 30-minute discussion of SMBG trends, patterns, and goal achievement using motivational interviewing to identify opportunities to improve glucose values. If SMBG data did not improve after 4 weeks, CDEs discussed medication options with patients and/or primary care providers using shared decision making [29]. CDEs incorporated virtual visit data, both preprandial and postprandial glucose, to suggest medication changes, including insulin therapy. Medication changes were ordered by primary care providers via the EHR. Participants were instructed to use paired glucose testing or a monthly, 3-day 7-point glucose profile until the 6-month A1c. CDEs documented patient contact in the study database.

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