iADAPT: Comprehensive Informatics Framework for Comparative Effectiveness Research (CER) Dissemination
Study Details
Study Description
Brief Summary
In this project the investigators seek to utilize our experience for developing a comprehensive informatics framework for rapid adaptation and dissemination of Comparative Effectiveness Research (CER) products tailored to different categories of health consumers including difficult-to-reach patients. Based on our previous successful experience in computer-assisted education, the investigators will refine the current CO-ED platform to implement and test a novel system for individualized continuous patient education (iCOPE). The iCOPE platform will be specifically designed to support rapid adaptation, customization, and dissemination of the CER products to the difficult-to-reach populations. The iCOPE platform will implement universal means for customized delivery of CER information in the format of interactive self-paced educational modules, quick "question & answer" guides, and interactive decision aids. In addition, the iCOPE platform will support the innovative concept of continuous patient health education by providing patients with easy access to the interactive CER updates via web, MP3 players and phone-based interactive voice response (IVR) technology. Though iCOPE will be designed to support the whole spectrum of CER products, in this project the investigators will focus on the Comparative Effectiveness Research Summary Guide (CERSG) entitled "Pills for Type 2 Diabetes."
The following primary hypothesis will be tested in the RCT: Use of the iCOPE platform will be associated with improvement in CERSG knowledge in elderly at 6 months after the intervention.
The investigators will also examine the impact of iCOPE on medication adherence self-efficacy, diabetes medication satisfaction, HbA1c, and CERSG acceptance.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
Detailed Description
Interactive, computer-based education has the potential to greatly increase interest, because the learner actively participates in the learning process (Fox, 2009). In addition, the involvement of auditory, visual, and interactive learning strategies can increase recall of information. Computer-assisted educational programs may incorporate features that promote ease of use, be written in multiple languages, be scripted at a level that addresses the needs of low literacy learners, and be viewed as often as needed by a patient (Fox, 2009).
Several studies reported results of using computer technology in educating elderly patients about health conditions. Stromberg et al (2006) used a single-session, interactive computer-based educational program about chronic heart failure in elderly patients. They demonstrated that interactive computer-mediated education may be effectively used to increase patients' knowledge, about heart failure. Another study, conducted by Lin et al (2009), evaluated the usability of a touch-screen-enabled personal education program (PEP). The results showed that the system was evaluated as usable and useful, and older adults were satisfied with their experience. Similar findings were reported by Neafsey et al (2008) who also evaluated a patient-centered computer-mediated program using touch screen computers. Authors reported high satisfaction of the older adult users, increased knowledge and self-efficacy for avoiding adverse self-medication behaviors.
Elderly patients are less likely than younger patients to seek incidental information on their condition via internet. (Tian & Robinson, 2008) Elderly patients therefore are less likely to navigate internet looking for information about evidence or treatment guidelines for their conditions. More targeted efforts may be needed to reach such populations. Disseminating information via combination of high technology media along with traditional media that are easily usable is helpful (Longo, 2005). A recent review of barriers and drivers of health IT use by elderly, very ill, and underserved, revealed that such technology can play a role in offering effective interventions. The consumer perceptions on benefits from use of system, convenience of use, and familiar technology were all important factors for intervention success (Jimison et al., 2008).
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| No Intervention: Control Group The study participants randomized to the Group 2 will serve as a control. At the beginning of the study, they will receive a CERSG brochure printed from the AHRQ site. They will be asked to study it to the best of their ability throughout the day. |
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| Experimental: iCOPE Intervention group iCOPE intervention group will receive a CERSG brochure via the iCOPE system. |
Other: iCOPE Intervention group
The study participants in the intervention group will use iCOPE platform providing adapted CERSG content in an interactive and tailored way. The intervention provided by the iCOPE platform will be implemented with the understanding that it should be user-friendly and not time consuming. Initially, all participants will be asked to complete the computer-mediated interactive CERSG module via touch screen tablet PC provided at hospital, in a primary care office, and in senior centers. The total time to complete the curriculum is about 30 min, however the patients will be able to distribute this time throughout a day at their convenience. Once the educational curriculum is completed, the intervention subjects will receive access to other components of the iCOPE platform.
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Outcome Measures
Primary Outcome Measures
- Change in CERSG Knowledge Score from baseline to 24-hr and to 6-month follow up [The Knowledge Test will be administered at the baseline, 24-hr after the introduction of the CERSG, and at the 6-month follow-up.]
CERSG Knowledge Score will be used to assess the overall patient knowledge of the information provided in the CERSG entitled "Pills for Type 2 Diabetes." The score will represent a percentage of correct answers to 30 true/false items designed in accordance with the content of the CERSG.
Secondary Outcome Measures
- Medication adherence self-efficacy [The medication adherence self-efficacy measure will be administered at the baseline, 24-hr after the introduction of the CERSG, and at the 6-month follow-up.]
Medication adherence self-efficacy will measure self-efficacy of taking medications.
- Diabetes Medication Satisfaction [Diabetes medication satisfaction measure will be administered at the baseline and at the 6-month follow-up.]
Diabetes Medication Satisfaction measure is a diabetes-specific measure, assessing medication treatment satisfaction in three domains: Efficacy, Burden, Symptoms (Side Effects).
- Glucosylated hemoglobin (HbA1c) [Glucosylated hemoglobin (HbA1c) values will be obtained closest to the date of the baseline evaluation and 6-month follow-up.]
Glucosylated hemoglobin (HbA1c) will be obtained from Electronic Medical Record (Centricity) at Johns Hopkins Community Physicians (JHCP).
- CERSG Acceptance [The CERSG acceptance measure will be administered at the baseline, 24-hr after the introduction of the CERSG, and at the 6-month follow-up.]
CERSG acceptance will measure patient acceptance and comprehension of CERSG
Eligibility Criteria
Criteria
Inclusion Criteria:
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have clinical diagnosis of Type 2 diabetes;
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take oral diabetes medications;
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be 65 years of age or older at the time of randomization;
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plan to remain in the Maryland/DC area until the completion of the study follow-up;
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understand English at the 5th grade level.
Exclusion Criteria:
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plan to leave Maryland/DC area before the completion of the study follow- up;
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presence of significant cognitive impairment based on a Mini-Mental State Examination (MMSE) score ≤23
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do not speak English.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Johns Hopkins University | Baltimore | Maryland | United States | 21205 |
| 2 | Johns Hopkins Bayview Medical Center | Baltimore | Maryland | United States | 21224 |
| 3 | Johns Hopkins University | Baltimore | Maryland | United States | 21224 |
Sponsors and Collaborators
- Johns Hopkins University
Investigators
- Principal Investigator: Joseph Finkelstein, MD, PhD, Johns Hopkins University
Study Documents (Full-Text)
None provided.More Information
Publications
- Brod M, Christensen T, Kongsø JH, Bushnell DM. Examining and interpreting responsiveness of the Diabetes Medication Satisfaction measure. J Med Econ. 2009;12(4):309-16. doi: 10.3111/13696990903337017.
- Finkelstein J, Lapshin O, Cha E. Feasibility of promoting smoking cessation among methadone users using multimedia computer-assisted education. J Med Internet Res. 2008 Nov 3;10(5):e33. doi: 10.2196/jmir.1089.
- Finkelstein J, Lapshin O, Wasserman E. Randomized study of different anti-stigma media. Patient Educ Couns. 2008 May;71(2):204-14. doi: 10.1016/j.pec.2008.01.002. Epub 2008 Mar 4.
- Finkelstein J, Lapshin O. Reducing depression stigma using a web-based program. Int J Med Inform. 2007 Oct;76(10):726-34. Epub 2006 Sep 20.
- Fitzgerald JT, Funnell MM, Hess GE, Barr PA, Anderson RM, Hiss RG, Davis WK. The reliability and validity of a brief diabetes knowledge test. Diabetes Care. 1998 May;21(5):706-10.
- Huang JP, Chen HH, Yeh ML. A comparison of diabetes learning with and without interactive multimedia to improve knowledge, control, and self-care among people with diabetes in Taiwan. Public Health Nurs. 2009 Jul-Aug;26(4):317-28. doi: 10.1111/j.1525-1446.2009.00786.x.
- Jimison H, Gorman P, Woods S, Nygren P, Walker M, Norris S, Hersh W. Barriers and drivers of health information technology use for the elderly, chronically ill, and underserved. Evid Rep Technol Assess (Full Rep). 2008 Nov;(175):1-1422. Review.
- Lapshin OV, Sharma K, Finkelstein J. Depression education for primary care patients using a web-based program. AMIA Annu Symp Proc. 2005:1017.
- Longo DR. Understanding health information, communication, and information seeking of patients and consumers: a comprehensive and integrated model. Health Expect. 2005 Sep;8(3):189-94.
- Morisky DE, Ang A, Krousel-Wood M, Ward HJ. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens (Greenwich). 2008 May;10(5):348-54.
- Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986 Jan;24(1):67-74.
- Neafsey PJ, Anderson E, Peabody S, Lin CA, Strickler Z, Vaughn K. Beta testing of a network-based health literacy program tailored for older adults with hypertension. Comput Inform Nurs. 2008 Nov-Dec;26(6):311-9. doi: 10.1097/01.NCN.0000336466.17811.e7.
- Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005 Aug 4;353(5):487-97. Review.
- Tian Y, Robinson JD. Incidental health information use and media complementarity: a comparison of senior and non-senior cancer patients. Patient Educ Couns. 2008 Jun;71(3):340-4. doi: 10.1016/j.pec.2008.02.006. Epub 2008 Mar 26.
- Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics. 1988 Dec;44(4):1049-60. Erratum in: Biometrics 1989 Mar;45(1):347.
- NA_00040442