Constance Johnson & Randy Brown - Supporting Chronic Disease Management in a Virtual Environment: The Lessons Learned from a Diabetes Program at Duke University
Randy Brown, VP, Virtual Heroes Division Manager, ARA
Constance Johnson, Associate Professor and Senior Research Faculty in the Center for Nursing Research, Duke University School of Nursing
This presentation was given at the 2016 Serious Play Conference, hosted by the UNC Kenan-Flagler Business School.
Since little is known about the efficacy of health interventions in a VE, this study, conducted by Duke and Virtual Heroes, constitutes an innovative step in exploring how this type of environment can be suused to facilitate self-management behaviors in those with chronic diseases, in this case, diabetes. This program has good potential to improve care in an easily disseminated model that promotes cost-effective resource utilization.
DENTAL MANAGEMENT OF THE MEDICALLY COMPROMISED PATIENT DENTAL MANAGEMENT OF...
Similar to Constance Johnson & Randy Brown - Supporting Chronic Disease Management in a Virtual Environment: The Lessons Learned from a Diabetes Program at Duke University
The data and Information Literacy of runners: quantifying diet and activityPamela McKinney
Similar to Constance Johnson & Randy Brown - Supporting Chronic Disease Management in a Virtual Environment: The Lessons Learned from a Diabetes Program at Duke University (20)
Top Rated Call Girls Kerala ☎ 8250092165👄 Delivery in 20 Mins Near Me
Constance Johnson & Randy Brown - Supporting Chronic Disease Management in a Virtual Environment: The Lessons Learned from a Diabetes Program at Duke University
1. Supporting Chronic Disease
Management in a Virtual
Environment: The Lessons
Learned from a Diabetes
Program at Duke University
Constance M. Johnson, PhD, MS, FAAN
Associate Professor
Randy Brown
VP, Director Virtual Heroes Division
4. Diabetes
• Metabolic control reduces morbidity
& mortality
(DCCT, Diabetes Control and Complications Trial: The effect of
intensive treatment of diabetes on the development and progression of
long-term complications in insulin-dependent diabetes mellitus.
New England Journal of Medicine, 1993. 329: p. 977-986)
• Individuals with T2DM provide 99% of their own
care (Funnell, M.M. and R.M. Anderson, Patient empowerment: a look back, a look ahead.
Diabetes Educ, 2003. 29(3): p. 454-8.)
– Self-management (diet, exercise, glucose testing,
etc.)
5. Diabetes Self-Management Internet
Interventions
• Potential for cost-effective Internet interventions
– Not produced large effects on behavioral and
metabolic outcomes (Jackson, C.L., et al., A systematic review of interactive
computer-assisted technology in diabetes care. Interactive information technology in
diabetes care. J Gen Intern Med, 2006. 21(2): p. 105-10.; Yu, et al., Systematic review and
evaluation of web-accessible tools for management of diabetes and related cardiovascular
risk factors by patients and healthcare providers. J Am Med Inform Assoc. 2012 Jul-
Aug;19(4):514-22. doi: 10.1136/amiajnl-2011-000307.)
– Most Internet interventions are “flat” with
asynchronous communication
– Lack of usability, real-time feedback, and theoretical
foundation with comprehensive evaluation (El-Gayar, et al., A
systematic review of IT for diabetes self-management: are we there yet? Int J Med Inform.
2013 Aug;82(8):637-52. doi: 10.1016/j.ijmedinf.2013.05.006.)
6. Johnson, C., et al. (2014). International Journal of Virtual Communities and Social Networking ,5(3), 68-80, July-September2014.
Funded by the National Library of Medicine: 1 R21 LM010727-01
Second Life Impacts Diabetes Education and Self-Management
7. SLIDES Aims
• Primary aim: To assess its feasibility and
acceptability
• Secondary aim: To determine the preliminary
effects of participation in the SLIDES
intervention on:
– (1) metabolic control (HbA1c levels, blood pressure
and body mass index)
– (2) potential psychosocial mediating variables
8. Study Sample
• Participants with Type 2 Diabetes
• 21 - 75 years old
• Computer and Internet literate
• No severe diabetes related complications
or late stage chronic disease
9. Multidimensional Data
• Quantitative data
– Movement, interactions with objects & other
participants, proxemics
– Time spent in the site, frequency of log-ins
– Survey data – knowledge, self-management
behaviors, self-efficacy, perceived support
• Qualitative data
– Observational data, voice, text, email, forum, focus
groups
• Visual data
– Photos and videos
DCCT, NEJM, 1993, 329:977-986. – Diabetes Knowledge Scale
Barrera, M., Jr., et al., Am J Community Psychol, 2002. 30(5): p. 637-54 - Diabetes Support Scale
McCaul, K., R. Glasgow, and L. Schafer, Medical Care, 1987. 25(9): p. 868-881 – Outcome Expectancies Questionnaire
Toobert, D.J., S.E. Hampson, and R.E. Glasgow, Diabetes Care, 2000. 23(7): p. 943-50 – Summary of Diabetes Self-Care Activities
15. T-Tests for comparison of means
Social Support Healthy Diet Foot Care
0
1
2
3
4
5
6
7
Behavioral Outcomes
Baseline
3 Months
6 Months
ScoreorDaysPerWeek
*p=0.020
*p=0.036
*p=0.001
Barrera, M., Jr., et al., Am J Community Psychol, 2002. 30(5): p. 637-54 - Diabetes Support Scale
McCaul, K., R. Glasgow, and L. Schafer, Medical Care, 1987. 25(9): p. 868-881 – Outcome Expectancies Questionnaire
Toobert, D.J., S.E. Hampson, and R.E. Glasgow, Diabetes Care, 2000. 23(7): p. 943-50 – Summary of Diabetes Self-Care Activities
16. Metabolic Outcomes
Baseline
(mean + SD)
3 Months
(mean + SD)
6 Months
(mean + SD)
Weight (lbs) 217.5 + 45.1 215.6 + 45.7 208.5 + 43.8
BMI (kg/m2 ) 37.4 + 7.8 37.2 + 8.2 36.15 + 8.3
Systolic BP
(mmHg)
131 + 13.0 130 + 14.5 130 + 10.5
Diastolic BP
(mmHg)
75 + 10.8 75 + 11.2 78 + 9.4
HbA1c (%) 7.6 + 1.3 7.1 + 1.2 6.9 + 1.3
Johnson, C., et al. (2014). Feasibility and preliminary effects of a virtual environment for adults with type 2 diabetes: Pilot study.
JMIR Res Protoc 2014;3(2):e23)
17. Discussion
• Allows experiential learning
• Synchronous communication – people feel
like they are really there
• Shown to be a feasible and useful platform
for patients and educators/clinicians
• Scalability – multiple, geographically
widespread users assisted by relatively
few educators/professionals
• Social interaction is making the
difference
18. Diabetes Self-Management & Support
LIVE (Learning in Virtual Environments)
Funded by the NHLBI - 1 R01 HL118189-01
Applied Research Associates
Study data collected and managed using REDCap
19. Purpose of the Study
To determine whether participation in LIVE
which incorporates real-time diabetes self-
management training and support will be
associated with positive changes in health
behaviors and metabolic outcomes in adults
with T2D as compared to traditional
education and support in a website
Vorderstrasse, A, ….Johnson . (2105). Nursing Research November/December, 64(6):485-494
20. Design
• Multi-site RCT with longitudinal repeated
measures design
• 220 participants
– 110 randomized to LIVE
– 110 randomized to Control group - website
• Determine effects on diet, physical activity,
self-efficacy, diabetes knowledge, social
support, HbA1c, BP, BMI, lipid panels, waist
circumference at baseline, 3, 6, 12, and 18
months
21. Design
• First three months
– Diabetes education classes twice per week
– Participants to log-in twice per week & use Fitbit
– Surveys at baseline and three months
• Last nine months
– Diabetes education classes twice per week
– Participants to log-in at will & use Fitbit
– Surveys at six and twelve months
• 18 month follow-up surveys
39. Gamification
• Include rewards for achievement
– Potential to produce behavior change
• Skill Points
– Assigned to specific activities
– Assign points to using Fitbit
• Redeemable Points
– Clothing
– Play games
Zichermann, Cunningham, Gamification by Design
56. Control (n = 84) LIVE (n = 73)
White Some College $50 - 69K
0
10
20
30
40
50
60
70
80
90
100
58 56
29
46
25
41
55 52
30
34
20
25
LIVE
Control
Average Duration of Diabetes = 10.9 years
Average Age = 58.5 years old
57. Conclusion
• Usability – site is easy to use
– Learnability, memorability, satisfaction
• Engagement
– Dynamic content
– Gamification
– Social interaction
• Personalization
– Relate to their avatar
With more than 23 million people in the US affected by diabetes….
<number>
and an ever increasing number projected to have diabetes by the year 2025, we are facing not only the clinical care of record numbers of patients but also the challenge of providing education and support to them for self-management of their diabetes.
<number>
We know that metabolic control of diabetes reduces associated morbidity and mortality ; yet it remains the 7th leading cause of death in the US.
Patients face a large challenge as they self-manage about 99% of their own care on a daily basis.
<number>
Self-management interventions have demonstrated improvements in diabetes knowledge, self-management behaviors and metabolic control, primarily short-term effects.
We know that more frequent patient-provider interactions also result in positive short-term outcomes; yet they are costly and not sustainable.
Given the increasing access to the Internet, and its potential to reach many, this has begun to be explored as an avenue for diabetes education and support. However, internet diabetes interventions to date have not resulted in large effects on outcomes and many of the interventions are flat and utilize aysnchronous modes of communication.
<number>
Real time computer-generated 3D representations of a contrived or natural environment running over the Internet
Realism is perceived through sensory information
Users perceive “being there - presence
Users are represented as avatars that can mimic human behaviors
Communicate through voice or text chat
Navigate by walking, running, teleporting
<number>
<number>
Unreal
<number>
<number>
<number>
<number>
So I have given you a number of ideas on how to keep your users engaged and prevent attrition. But most importantly what you don’t is
<number>