Enhancing Self-Management of T2DM with In-Home Technology

Slide Presentation from the AHRQ 2008 Annual Conference

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On September 9, 2008, Edith Burns, made this presentation at the 2008 Annual Conference. Select to access the PowerPoint® presentation (1.8 MB).

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Slide 1

Enhancing Self-Management of T2DM with In-Home Technology

Edith Burns, MD
Medical College of Wisconsin
Milwaukee, WI.

Slide 2

• T2DM [Type 2 diabetes mellitus]:
  • 92% of all diabetes.
  • 10% of adults.
    • 20% of adults > 65 years of age.
  • High cost:
    • Treatment, management.
    • Complications.

Slide 3

• Optimum management requires patients to take volitional control of a process that is automatic in healthy individuals.
  • Self-regulation/Control processes take place in "real world" settings- day-to-day life at home, work.

Slide 4

• Common-sense Models of Illness
  • Life experience of acute illness teaches us to use symptoms as indicators of sickness-wellness.
  • In most chronic illnesses, symptoms are unreliable as indicators of disease status.
  • Better to utilize objective measures by performing self-monitoring (e.g., self-monitoring of blood glucose [SMBG], blood pressure [BP]).

Slide 5

• T2DM is a "chaotic" disease:
  • Multiple factors contribute to acute fluctuations in blood glucose levels.
  • Individual SMBG measures at any given point in time may provide ambiguous feedback.
• Can we teach patients to learn to use SMBG more effectively to become better self managers of a chaotic disease?

Slide 6

Study Design

• Test an automated reminder and feedback system (ASMM).
• Randomized, prospective, "usual care" control.
• System provides reminders AND feedback.
• Table:
  • Non-vets T2DM.
    • Usual Care: 50.
    • Intervention ASMM: 50.
  • VA T2DM
    • Usual Care: 50.
    • Intervention ASMM: 50.
• Total of 200 participants.
• Four in-home visits; intervention begins at visit 2 after 3 months.
• Exit interview at 15 months.

Slide 7

Qualities Desired in the Assisted-Self-Management Monitor (ASMM)

• Physical Properties:
  • Home-based:
    • Small footprint.
    • Limited components.
    • Installation.
  • Ease of use:
    • Simple docking system.
    • "Hidden" technology.
• Ability to individualize:
  • Reminders:
    • Primary Care Physician (PCP) & participant-determined schedule.
    • Patient "controls" the technology.

Slide 8

The slide shows a photograph of both a blood sugar and ketone monitoring device and a monitoring device designed for the Diabetes Self-Management Study.

Slide 9

Qualities Desired in the Assisted-Self-Management Monitor (ASMM), continued

• Feedback:
  • Timely—importance of what the results mean at the time.
    • Scheduled measures.
    • Unscheduled measures.
    • Symptoms?
    • Relationship to management behaviors (timing):
      • Diet.
      • Exercise.
  • Overall control:
    • Trend data.
      • Minimizes "catastrophizing" of single readings.

Slide 10

Monitor Blood Sugar Readings: Objective Measures

The slide shows a diagram which presents two cycles:

Cycle 1

• "NO symptoms; can do what need & want to do."
• Ask myself how do I feel?
• Answer: okay; no symptoms.
• Should I take meds, diet to control diabetes if I feel okay?
• NO.

Cycle 2

• Dr. says test shows HIGH blood sugar.
• I don't know my blood sugar level—I can't feel it.
• It's time to test my blood sugar.
• Act Plan: use the glucometer & computer.
• Monitor & appraise blood sugar readings.
• If high: take medication, exercise, etc.
• Act Plans: take meds, exercise!

• Note: Proper timing and consideration is necessary for this to work—DO THE NUMBERS MAKE SENSE?!!

Slide 11

The slide shows a diagram of the glucose downloading process from a meter.

• Shows the.
• Light-blue boxes = computer logic.
• Green boxes = patient input.
• Individualized in logic: 1) scheduled glucose reading times, 2) goals for scheduled time for trend summary.
• Trend summary begins after 10 readings.
• SD based on 25 readings.

• Note: ASMM Algorithm-Draft 2/8/2008 Version 9.

Slide 12

System Demonstration

Slide 13

Co-Investigators & Research Team

• Jeffrey Whittle, MD.
• Paul Knudson, MD.
• Sergei Tarima, PhD.
• Bambi Wessel, MS.
• Alexis Dye, MA.
• Stephen Flax, PhD.
• Joan Pleuss, CDE, RD.
• Colin Strub, BS.
• Kristin Wiescorek, BS.
• Howard Leventhal, PhD1.

• Note: 1 Center for Health & Behavior, Rutgers University and UMDNJ, New Brunswick, NJ.

Slide 14

Blank Slide

Slide 15

Summary

• Increasing frequency and consistency of SMBG led to improved glycemic control.
• Higher baseline depression scores had higher baseline HbA1c and showed greater improvement over time.
• Improvement in HbA1c was not correlated to baseline cognitive function.

Slide 16

• Expanded study to rigorously test this system:
  • Illness cognition, change over time.
  • Reminder function.
  • Expanded feedback:
    • Trends in control.
    • Unscheduled measures.
    • Relating measures to diet and activity.

Current as of January 2009

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Internet Citation:

Enhancing Self-Management of T2DM with In-Home Technology. Slide Presentation from the AHRQ 2008 Annual Conference (Text Version). January 2009. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/about/annualmtg08/090908slides/Burns.htm

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