Enhancing Self-Management of T2DM with In-Home Technology (Text Version)

Slide Presentation from the AHRQ 2008 Annual Conference

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

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.