- Email: [email protected]
Patient adoption of an internet based diabetes medication tool to improve adherence: A pilot study
Accepted Manuscript Title: Patient adoption of an Internet based diabetes medication tool to improve adherence: a pilot study Authors: Scott A. Davis MA Delesha Carpenter PhD, MSPH Doyle M. Cummings PharmD Charles Lee MD Susan J. Blalock PhD Jennifer Elissa Scott BA Rodebaugh Lisa BSN, RN Stefanie P. Ferreri PharmD Betsy Sleath PhD PII: DOI: Reference:
S0738-3991(16)30319-6 http://dx.doi.org/doi:10.1016/j.pec.2016.07.024 PEC 5405
To appear in:
Patient Education and Counseling
Received date: Revised date: Accepted date:
7-12-2015 12-7-2016 14-7-2016
Please cite this article as: Davis Scott A, Carpenter Delesha, Cummings Doyle M, Lee Charles, Blalock Susan J, Scott Jennifer Elissa, Lisa Rodebaugh, Ferreri Stefanie P, Sleath Betsy.Patient adoption of an Internet based diabetes medication tool to improve adherence: a pilot study.Patient Education and Counseling http://dx.doi.org/10.1016/j.pec.2016.07.024 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1
Patient adoption of an Internet based diabetes medication tool to improve adherence: a pilot study
Scott A. Davis, MAa Delesha Carpenter, PhD, MSPHa Doyle M. Cummings, PharmDb Charles Lee, MDc Susan J. Blalock, PhDa Jennifer Elissa Scott, BAd Lisa Rodebaugh, BSN, RNb Stefanie P. Ferreri, PharmDe Betsy Sleath, PhDa,f a
Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, Chapel Hill, USA b
Department of Family Medicine and Public Health, Brody School of Medicine, East Carolina University, Greenville, USA c
Polyglot Systems, Inc., Morrisville, USA
d
Consortium for Implementation Science, Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA e
Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, Chapel Hill, USA f
Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill
Corresponding author at: Scott A. Davis, MA, University of North Carolina at Chapel Hill; 022 Beard Hall, Chapel Hill, NC 27599 USA; Office: 011-1-336/712-5740, Fax: 011-1-919-966-1634. E-Mail: [email protected]
2
Highlights
A video intervention improved patients’ self-efficacy in managing type 2 diabetes.
Seventy-three percent watched at least one module after the initial visit.
Videos were especially helpful for low literate patients.
Video interventions may help address common barriers to diabetes self-management.
Abstract Objective: To investigate the effect of a video intervention, MANAGING YOUR DIABETES MEDICINES, on patient self-efficacy, problems with using medication, and medication adherence in a rural, mostly African American population. Methods: Patients selected their problem areas in medication use and watched one of nine 2minute videos with a research assistant at a clinic or pharmacy and were given an access code to watch all the videos at their convenience. Outcomes were measured at baseline and 3-month follow-up. Results: Fifty-one patients were enrolled; 84% were African American and 80% were female (mean age: 54 years). Seventy-three percent watched at least one module after the initial visit. Improved self-efficacy was associated with a decrease in concerns about medications (r=-0.64). Low literate patients experienced greater improvement in self-efficacy than more literate patients (t=2.54, p=0.02). Patients’ mean number of problems declined from 6.14 to 5.03. The number of patients with high or medium adherence rose from 33% at baseline to 43% at 3-month follow-up. Conclusions: A practical, customized video intervention may help improve patient self-efficacy, reduce problems with medication use, and improve medication adherence in diabetes patients. Practice Implications: Providers should consider implementing technology-based interventions in the clinic to address common problems that patients have with self-management.
Keywords: educational video; self-efficacy; African American; health literacy; self-management; medication problems; Information-Motivation-Behavioral Skills Model Word Count: 1,723; References: 26; Tables: 3; Figures: 1
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1. Introduction Diabetes affects 25.8 million Americans,[1] and disproportionately affects African Americans, who are less likely to adhere to their medications, and thus control their diabetes, than White patients [2-5]. Because poor medication understanding and low adherence are common [6,7], medication management is a critical self-management skill for patients with diabetes [8]. Providers often lack adequate time to educate and motivate patients to take their diabetes medications [9]. Moreover, restrictions on insurance coverage for diabetes self-management education [10] and lack of access to health care necessitate that self-management interventions expand to non-clinical settings. Therefore, we developed a video-based intervention called MANAGING YOUR DIABETES MEDICINES (MYDM) using the “Information-MotivationBehavioral Skills Model” (IMB; Figure 1) that posits that a better informed, more motivated patient with requisite behavior skills is more likely to initiate and maintain health-promoting behaviors, like taking medications [11], as studies of the IMB model show [12,13]. Videos can be delivered online using smartphones. Among smartphone users, minorities, those with no college experience, and lower-income users report that their phone is their main source of Internet access [14], with African Americans using mobile phones for a wider range of activities than Whites [15,16]. The present intervention was designed to be culturally-informed to maximize usability among limited literacy African American and White patients who are at-risk for poorer health outcomes than more literate patients [17,18]. The purpose of this pilot study was to: (1) gather input from White and African American patients with diabetes who are having difficulty adhering to their medicines about how to modify and improve our intervention, and (2) evaluate whether patient exposure to the intervention is associated with an increase in patient medication self-efficacy, a decrease in the number of
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reported problems in using diabetes medicines, and an increase in self-reported adherence to diabetes medications at 3 months. 2. Methods 2.1 Procedure Patients were recruited at a family medicine clinic and a pharmacy in eastern North Carolina. Patients were eligible for the study if they were: (a) age 18 or older, (b) diagnosed with type 2 diabetes, (c) taking at least one oral and/or injectable medication(s) for diabetes, (d) Englishspeaking, (e) non-adherent to their diabetes medicines on a Visual Analog Scale (VAS) [19], and (f) African American or White. Approval was obtained from the University of North Carolina and the East Carolina University Institutional Review Boards. First, patients were recruited to pilot test a newly developed diabetes self-efficacy scale[20] and the video intervention modules. Each of the 9 modules (Table 1) was about two minutes long and discussed strategies for dealing with a specific diabetes-related issue. Next, a new group of patients read through a list of nine problems in using diabetes medications and selected the problems that were most important to them. The list of problems was based on empirical studies as well as medication-related skills that could be enhanced to improve patients’ diabetes medication self-efficacy and adherence, according to the IMB framework. Patients only had to rank the problems they had experienced, so they did not have to rank all nine problems. The computer then listed all problem areas the patient had identified, and patients were then asked to prioritize in what order they wanted to watch the videos. The patients watched the first video module prioritized on their list during the visit. Participants were then given access codes to a secure website so that they could watch the rest of the internet-based video modules from home or work via an Internet-capable device.
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The research assistant called the patient at 1 week to assess their progress and at 4 and 8 weeks to ask what changes they made after watching the videos. The research assistant conducted a follow-up interview 3 months after the baseline visit. The team also used the access codes to electronically track how often patients accessed the online modules. 2.2 Measurement Patients were asked to evaluate the degree to which 14 potential problems or concerns in using diabetes medications affected them (“none”, “a little”, or “a lot”). We recoded each of these into dichotomous variables (none versus a little or a lot). Patients completed a 19-item diabetes medication self-efficacy scale.[20] Responses included: not at all confident (1), somewhat confident (2), and very confident (3). Scores ranged from 19 (lower self-efficacy) to 57 (higher self-efficacy) (α=0.87). Other measures included Morisky’s 8-item measure of self-reported medication adherence [21], the 4-item concerns and 5-item necessities subscales from the Beliefs about Medications Questionnaire [22], Rapid Estimate of Adult Literacy in Medicine (REALM) for health literacy [23], the PHQ-2 for depression [24], age, gender, race (African American vs. any other; patients who were part African American were coded as African American), the type and number of diabetes medications, self-reported health status of the patient, health insurance status, changes the patient made after the intervention, and what information from the intervention was shared with others and with whom. These were all closed-ended questions where patients chose from a list of response options. Health literacy, demographics, type and number of diabetes medications, health status, and health insurance status were collected at baseline only, and changes made after the
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intervention, information shared, and with whom, were collected at 3-month follow-up. All other measures were collected at both baseline and 3-month follow-up. 2.3. Analysis Descriptive statistics were calculated and a correlation matrix was computed. Changes from baseline to 3-month follow-up were assessed using paired t-tests for continuous variables or McNemar’s tests for categorical variables using SAS 9.4. 3. Results Baseline data included problems that patients experienced, self-efficacy, adherence, beliefs about medications, and depressive symptoms (Tables 2-3). Table 2 presents the demographics of the 51 study participants (of 74 screened, for a 69% response rate). Seventeen patients refused because they did not have interest in participating, 3 patients because they did not have time, 1 because he/she found it too confusing to manage, 1 because he/she worked nights, and 1 did not give a reason. Thirty-five patients completed the 3-month follow-up; there were no significant differences between those patients who completed the study vs. those who did not. Seventy-three percent of patients watched at least one module after the baseline visit (Table 1). Patients most commonly watched the modules at home (45%). Seventy-three percent of patients reported no technical issues with watching the modules, while the rest reported problems with computers (12%), browser or internet (12%), or access codes (4%). Diet was the most common behavior that patients reported changing (31%), followed by medication adherence (28%) and exercise (18%).
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Patient confidence in their ability to take medication “when you are with family members”, “when you feel you do not need them”, and “when your vision is blurry” all improved (0.29, 0.25, 0.24 points, respectively). The mean self-efficacy score for the 34 patients who completed the questionnaire at both time points was 46.6 at baseline and 48.0 at 3-month follow-up (95% CI: 1.3, 4.1; p=0.29). Greater improvement in self-efficacy was correlated (r=-0.64) with fewer medicationrelated concerns (p<0.0001). Also, patients with a REALM score of eighth grade or lower experienced a significantly greater improvement in their diabetes medication self-efficacy scores than patients who read at ninth grade or higher (t =2.54, p=0.02). Table 3 presents patient-reported problems or barriers in using diabetes medications. The mean (SD) number of problems that patients reported was 6.14 (3.09) at baseline and 5.03 (2.67) at 3-month follow-up, which was not statistically significant (t =-1.21; p=0.24). There was no statistically significant difference in the number of patients who were adherent according to the Morisky scale between baseline and 3-month follow-up (McNemar’s S=0.33; p=0.56). The number of patients who had high or medium adherence rose from 33% at baseline to 43% at 3-month follow-up. Patients most commonly shared the information they learned from the intervention with family (28%) and friends (14%) but rarely with healthcare providers. The most common information shared was regarding diet (18%). Patients also sometimes shared information about blood sugar facts, monitoring, testing, keeping a glucose log, or HbA1C (14%); medication adherence and regimen (12%); cost and resources (6%); and health problems (2%).
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4. Discussion and Conclusion 4.1. Discussion In this pilot study, MYDM showed potential to improve self-efficacy and reduce selfreported problems with taking diabetes medications in a sample of rural, mainly African American patients. Concerns about medications declined in tandem with improved self-efficacy. Although not statistically significant, adherence, self-efficacy, and number of problems showed trends in the expected direction based on the IMB model. Since misconceptions about diabetes are very common [25], effective interventions developed with input from the target populations are crucial in reducing health disparities. The high rate of watching the modules after the initial visit suggests that MYDM was well-received by the study population and is appropriately adapted to their medication concerns. It was very encouraging that after the intervention, patients reported fewer problems with paying for medications, paying for glucose monitoring supplies, and getting their medications from the pharmacy. These findings may indicate that a few minutes of additional attention to issues of cost and access during physician visits can alleviate these barriers to successful treatment. The high level of concern about side effects was consistent with previous studies showing that African Americans were more concerned about side effects from diabetes medications than Whites [4]. In addition, the number of patients who said they did not think they needed their medication remained constant at more than one-third of the study population. Motivating patients who do not believe they need medication may require more time-intensive interventions with considerable face-to-face contact initially, then periodic online follow-up with a nurse or other trusted provider [3,26].
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There were several limitations to the study. People who agreed to participate may not have been completely representative of the population; we could not track characteristics of people who refused to participate because of HIPAA. All measures were self-reported. Most patients were female, so some findings may not be generalizable to male patients. About 30% of the patients were lost to follow-up. The sample size was relatively small. Future work should be done with larger samples. 4.2. Conclusion Diabetes patients may benefit from a practical, customized intervention to improve adherence to their medications. The MYDM intervention should be tested further to see if it can reduce concerns about diabetes medications, improve medication self-efficacy, and reduce problems with obtaining and taking medications. 4.3. Practice Implications Providers should consider implementing technology-based interventions in the clinic to address common problems that patients have with self-management, thus improving patient selfefficacy and behavioral skills to successfully manage diabetes.
Informed consent and patient details The authors confirm all patient/personal identifiers have been removed or disguised so the patient/person(s) described are not identifiable and cannot be identified through the details of the story.
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Conflicts of interest The authors have no conflict of interest. All authors have contributed to, read, and approved the final article.
Acknowledgments This study was funded by the UNC Center for Diabetes Translation Research to Reduce Health Disparities, supported by Grant Number P30DK093002 from the National Institute of Diabetes and Digestive and Kidney Diseases. The project described was supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number 1UL1TR001111. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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5. C.Y. Osborn, K. Cavanaugh, K.A. Wallston, S. Kripalani, T.A. Elasy, R.L. Rothman, R.O. White. Health Literacy Explains Racial Disparities in Diabetes Medication Adherence. J. Health Commun. 16 (2011) 26878.
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12 10. D.M. Carpenter, E.B. Fisher, S.B. Greene, Shortcomings in Public and Private Insurance Coverage of Diabetes Self-Management Education and Support, Popul. Health Manag. 15 (2012):144-8.
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13. L.S. Mayberry, L.E. Egede, J.A. Wagner, C.Y. Osborn, Stress, depression and medication nonadherence in diabetes: test of the exacerbating and buffering effects of family support, J. Behav. Med. 38 (2015) 363-71.
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18. H.K. Seligman, F.F. Wang, J.L. Palacios, C.C. Wilson, C. Daher, J.D. Piette, D. Schillinger, Physician Notification of Their Diabetes Patients’ Limited Health Literacy: A Randomized, Controlled Trial, J. Gen. Intern. Med. 20 (2005) 1001-7.
13 19. D.P. Nau, D.T. Steinke, L.K. Williams, R. Austin, J.E. Lafata, G. Divine, M. Pladevall, Adherence analysis using visual analog scale versus claims-based estimation, Ann. Pharmacother. 41 (2007) 1792–7.
20. B. Sleath, D. Carpenter, S.J. Blalock, S.A. Davis, R.P. Hickson, C. Lee, S.P. Ferreri, J.E. Scott, L. Rodebaugh, D.M. Cummings, Development of a new diabetes medication self-efficacy scale and its association with both reported problems in using diabetes medications and self-reported adherence, Patient Prefer. Adherence 10 (2016) 1003-10.
21. D.E. Morisky, A. Ang, M. Krousel-Wood, H.J. Ward, Predictive Validity of a Medication Adherence Measure in an Outpatient Setting, J. Clin. Hypertens. 10 (2008) 348-54. 22. R. Horne, J. Weinman, Patients’ Beliefs about Prescribed Medicines and Their Role in Adherence to Treatment in Chronic Physical Illness, J. Psychosomatic Res. 47 (1999) 555-67.
23. T.C. Davis, S.W. Long, R.H. Jackson, E.J. Mayeaux, R.B. George, P.W. Murphy, M.A. Crouch, Rapid estimate of adult literacy in medicine: a shortened screening instrument, Fam. Med. 25 (1993) 391-5.
24. K. Kroenke, R.L. Spitzer, J.B. Williams, The Patient Health Questionnaire-2: validity of a two-item depression screener, Med. Care 41 (2003) 1284-92.
25. D.M. Mann, D. Ponieman, H. Leventhal, E.A. Halm, Misconceptions about Diabetes and its Management Among Low-Income Minorities with Diabetes, Diabetes Care, 32 (2009) 591-3.
26. D.K. King, D.J. Toobert, J.D. Portz, L.A. Strycker, A. Doty, C. Martin, J.M. Boggs, A.J. Faber, C.R. Geno, R.E. Glasgow, What patients want: relevant health information technology for diabetes self-management, Health Technol. 2 (2012) 147-57.
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Table 1. Number of times that patients watched each module (N=51). Module
Percent (n) who Mean (Range) number of watched module times watched “It’s hard for me to pay for my diabetes 51% (26) 1.3 (1 – 3) medicine.” “It’s hard for me to pay for my glucose 45% (23) 1.4 (1 – 7) monitoring supplies.” 1.5 (1 – 5) “I’m worried about my blood sugar going 49% (25) too low.” “I’m worried about side effects from my 53% (27) diabetes medicine.”
1.6 (1 – 4)
“I’m worried about health problems from diabetes. Will medicine really help me?” “It’s hard to manage my diabetes.” “It’s hard for me to fit taking my diabetes medicine into daily routine.” “It’s hard (or I’m afraid) to give myself a shot.” “It’s hard for me to get my medicines from the pharmacy.” Watched any module(s) after first meeting Total number of modules watched
75% (38)
1.9 (1 – 5)
29% (15) 28% (14)
1.1 (1 – 2) 1.4 (1 – 3)
29% (15)
1.1 (1 – 2)
24% (12)
1.1 (1 – 2)
73% (37) Not applicable
Not applicable 3.7 (1 – 8)
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Table 2. Demographics of study patients and scores at baseline (N=51). Race Black or African American Other Gender Female Male Medicines used Oral Injectable Health status Excellent Very good Good Fair Poor High blood pressure High cholesterol Have health insurance REALM score eighth grade or lower PHQ-2 score of “depressed”
Percent (n) 84% (43) 16% (8) 80% (41) 20% (10) 80% (41) 51% (26)
4% (2) 12% (6) 24% (12) 51% (26) 10% (5) 75% (38) 55% (28) 80% (41) 43% (22) 25% (13) Mean (SD) Age 54.0 (10.4) Number of diabetes medicines 2.1 (0.9) Visual analog scale of adherence 7.5 (2.5) Diabetes adherence self-efficacy score 45.4 (7.5) Beliefs about medications questionnaire (Concerns 12.7 (4.0) section) Beliefs about medications questionnaire (Necessities 18.7 (4.3) section)
Range 25 – 75 1–4 0.4 – 10.0 31 – 56 4 – 20 10 – 25
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Table 3. Reported problems or barriers to using diabetes medications. Statement
Percent (n) of patients Percent (n) of agreeing at baseline patients agreeing at (N=51) 3-month follow-up (N=35) It is hard for me to pay for my injectable 41% (21) 26% (9) diabetes medicines. It is hard for me to pay for my diabetes pills. 55% (28) 40% (14) It is hard for me to pay for my glucose 65% (33) 34% (12)* monitoring supplies. I’m worried about my blood sugar going too 51% (26) 51% (18) low. I’m worried about side effects from my 71% (36) 74% (26) diabetes medicine. I’m worried about health problems from 75% (38) 77% (27) diabetes. I don’t think that I need my medicine. 35% (18) 37% (13) There are times when I forget to take my 55% (28) 46% (16) diabetes medicines. It’s hard for me to manage so many 41% (21) 34% (12) medicines. I don’t understand what my medicine label is 6% (3) 9% (3) telling me to do. It’s hard for me to fit taking my diabetes 45% (23) 23% (8) medicines into my daily routine. It’s hard to give myself a shot. 18% (9) 14% (5) I’m afraid to give myself a shot. 24% (12) 20% (7) It’s hard for me to get my diabetes medicines 33% (17) 17% (6) from the pharmacy. *p<0.01. All other differences were nonsignificant (p>0.05).
S0738-3991(16)30319-6 http://dx.doi.org/doi:10.1016/j.pec.2016.07.024 PEC 5405
To appear in:
Patient Education and Counseling
Received date: Revised date: Accepted date:
7-12-2015 12-7-2016 14-7-2016
Please cite this article as: Davis Scott A, Carpenter Delesha, Cummings Doyle M, Lee Charles, Blalock Susan J, Scott Jennifer Elissa, Lisa Rodebaugh, Ferreri Stefanie P, Sleath Betsy.Patient adoption of an Internet based diabetes medication tool to improve adherence: a pilot study.Patient Education and Counseling http://dx.doi.org/10.1016/j.pec.2016.07.024 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1
Patient adoption of an Internet based diabetes medication tool to improve adherence: a pilot study
Scott A. Davis, MAa Delesha Carpenter, PhD, MSPHa Doyle M. Cummings, PharmDb Charles Lee, MDc Susan J. Blalock, PhDa Jennifer Elissa Scott, BAd Lisa Rodebaugh, BSN, RNb Stefanie P. Ferreri, PharmDe Betsy Sleath, PhDa,f a
Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, Chapel Hill, USA b
Department of Family Medicine and Public Health, Brody School of Medicine, East Carolina University, Greenville, USA c
Polyglot Systems, Inc., Morrisville, USA
d
Consortium for Implementation Science, Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA e
Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, Chapel Hill, USA f
Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill
Corresponding author at: Scott A. Davis, MA, University of North Carolina at Chapel Hill; 022 Beard Hall, Chapel Hill, NC 27599 USA; Office: 011-1-336/712-5740, Fax: 011-1-919-966-1634. E-Mail: [email protected]
2
Highlights
A video intervention improved patients’ self-efficacy in managing type 2 diabetes.
Seventy-three percent watched at least one module after the initial visit.
Videos were especially helpful for low literate patients.
Video interventions may help address common barriers to diabetes self-management.
Abstract Objective: To investigate the effect of a video intervention, MANAGING YOUR DIABETES MEDICINES, on patient self-efficacy, problems with using medication, and medication adherence in a rural, mostly African American population. Methods: Patients selected their problem areas in medication use and watched one of nine 2minute videos with a research assistant at a clinic or pharmacy and were given an access code to watch all the videos at their convenience. Outcomes were measured at baseline and 3-month follow-up. Results: Fifty-one patients were enrolled; 84% were African American and 80% were female (mean age: 54 years). Seventy-three percent watched at least one module after the initial visit. Improved self-efficacy was associated with a decrease in concerns about medications (r=-0.64). Low literate patients experienced greater improvement in self-efficacy than more literate patients (t=2.54, p=0.02). Patients’ mean number of problems declined from 6.14 to 5.03. The number of patients with high or medium adherence rose from 33% at baseline to 43% at 3-month follow-up. Conclusions: A practical, customized video intervention may help improve patient self-efficacy, reduce problems with medication use, and improve medication adherence in diabetes patients. Practice Implications: Providers should consider implementing technology-based interventions in the clinic to address common problems that patients have with self-management.
Keywords: educational video; self-efficacy; African American; health literacy; self-management; medication problems; Information-Motivation-Behavioral Skills Model Word Count: 1,723; References: 26; Tables: 3; Figures: 1
3
1. Introduction Diabetes affects 25.8 million Americans,[1] and disproportionately affects African Americans, who are less likely to adhere to their medications, and thus control their diabetes, than White patients [2-5]. Because poor medication understanding and low adherence are common [6,7], medication management is a critical self-management skill for patients with diabetes [8]. Providers often lack adequate time to educate and motivate patients to take their diabetes medications [9]. Moreover, restrictions on insurance coverage for diabetes self-management education [10] and lack of access to health care necessitate that self-management interventions expand to non-clinical settings. Therefore, we developed a video-based intervention called MANAGING YOUR DIABETES MEDICINES (MYDM) using the “Information-MotivationBehavioral Skills Model” (IMB; Figure 1) that posits that a better informed, more motivated patient with requisite behavior skills is more likely to initiate and maintain health-promoting behaviors, like taking medications [11], as studies of the IMB model show [12,13]. Videos can be delivered online using smartphones. Among smartphone users, minorities, those with no college experience, and lower-income users report that their phone is their main source of Internet access [14], with African Americans using mobile phones for a wider range of activities than Whites [15,16]. The present intervention was designed to be culturally-informed to maximize usability among limited literacy African American and White patients who are at-risk for poorer health outcomes than more literate patients [17,18]. The purpose of this pilot study was to: (1) gather input from White and African American patients with diabetes who are having difficulty adhering to their medicines about how to modify and improve our intervention, and (2) evaluate whether patient exposure to the intervention is associated with an increase in patient medication self-efficacy, a decrease in the number of
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reported problems in using diabetes medicines, and an increase in self-reported adherence to diabetes medications at 3 months. 2. Methods 2.1 Procedure Patients were recruited at a family medicine clinic and a pharmacy in eastern North Carolina. Patients were eligible for the study if they were: (a) age 18 or older, (b) diagnosed with type 2 diabetes, (c) taking at least one oral and/or injectable medication(s) for diabetes, (d) Englishspeaking, (e) non-adherent to their diabetes medicines on a Visual Analog Scale (VAS) [19], and (f) African American or White. Approval was obtained from the University of North Carolina and the East Carolina University Institutional Review Boards. First, patients were recruited to pilot test a newly developed diabetes self-efficacy scale[20] and the video intervention modules. Each of the 9 modules (Table 1) was about two minutes long and discussed strategies for dealing with a specific diabetes-related issue. Next, a new group of patients read through a list of nine problems in using diabetes medications and selected the problems that were most important to them. The list of problems was based on empirical studies as well as medication-related skills that could be enhanced to improve patients’ diabetes medication self-efficacy and adherence, according to the IMB framework. Patients only had to rank the problems they had experienced, so they did not have to rank all nine problems. The computer then listed all problem areas the patient had identified, and patients were then asked to prioritize in what order they wanted to watch the videos. The patients watched the first video module prioritized on their list during the visit. Participants were then given access codes to a secure website so that they could watch the rest of the internet-based video modules from home or work via an Internet-capable device.
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The research assistant called the patient at 1 week to assess their progress and at 4 and 8 weeks to ask what changes they made after watching the videos. The research assistant conducted a follow-up interview 3 months after the baseline visit. The team also used the access codes to electronically track how often patients accessed the online modules. 2.2 Measurement Patients were asked to evaluate the degree to which 14 potential problems or concerns in using diabetes medications affected them (“none”, “a little”, or “a lot”). We recoded each of these into dichotomous variables (none versus a little or a lot). Patients completed a 19-item diabetes medication self-efficacy scale.[20] Responses included: not at all confident (1), somewhat confident (2), and very confident (3). Scores ranged from 19 (lower self-efficacy) to 57 (higher self-efficacy) (α=0.87). Other measures included Morisky’s 8-item measure of self-reported medication adherence [21], the 4-item concerns and 5-item necessities subscales from the Beliefs about Medications Questionnaire [22], Rapid Estimate of Adult Literacy in Medicine (REALM) for health literacy [23], the PHQ-2 for depression [24], age, gender, race (African American vs. any other; patients who were part African American were coded as African American), the type and number of diabetes medications, self-reported health status of the patient, health insurance status, changes the patient made after the intervention, and what information from the intervention was shared with others and with whom. These were all closed-ended questions where patients chose from a list of response options. Health literacy, demographics, type and number of diabetes medications, health status, and health insurance status were collected at baseline only, and changes made after the
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intervention, information shared, and with whom, were collected at 3-month follow-up. All other measures were collected at both baseline and 3-month follow-up. 2.3. Analysis Descriptive statistics were calculated and a correlation matrix was computed. Changes from baseline to 3-month follow-up were assessed using paired t-tests for continuous variables or McNemar’s tests for categorical variables using SAS 9.4. 3. Results Baseline data included problems that patients experienced, self-efficacy, adherence, beliefs about medications, and depressive symptoms (Tables 2-3). Table 2 presents the demographics of the 51 study participants (of 74 screened, for a 69% response rate). Seventeen patients refused because they did not have interest in participating, 3 patients because they did not have time, 1 because he/she found it too confusing to manage, 1 because he/she worked nights, and 1 did not give a reason. Thirty-five patients completed the 3-month follow-up; there were no significant differences between those patients who completed the study vs. those who did not. Seventy-three percent of patients watched at least one module after the baseline visit (Table 1). Patients most commonly watched the modules at home (45%). Seventy-three percent of patients reported no technical issues with watching the modules, while the rest reported problems with computers (12%), browser or internet (12%), or access codes (4%). Diet was the most common behavior that patients reported changing (31%), followed by medication adherence (28%) and exercise (18%).
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Patient confidence in their ability to take medication “when you are with family members”, “when you feel you do not need them”, and “when your vision is blurry” all improved (0.29, 0.25, 0.24 points, respectively). The mean self-efficacy score for the 34 patients who completed the questionnaire at both time points was 46.6 at baseline and 48.0 at 3-month follow-up (95% CI: 1.3, 4.1; p=0.29). Greater improvement in self-efficacy was correlated (r=-0.64) with fewer medicationrelated concerns (p<0.0001). Also, patients with a REALM score of eighth grade or lower experienced a significantly greater improvement in their diabetes medication self-efficacy scores than patients who read at ninth grade or higher (t =2.54, p=0.02). Table 3 presents patient-reported problems or barriers in using diabetes medications. The mean (SD) number of problems that patients reported was 6.14 (3.09) at baseline and 5.03 (2.67) at 3-month follow-up, which was not statistically significant (t =-1.21; p=0.24). There was no statistically significant difference in the number of patients who were adherent according to the Morisky scale between baseline and 3-month follow-up (McNemar’s S=0.33; p=0.56). The number of patients who had high or medium adherence rose from 33% at baseline to 43% at 3-month follow-up. Patients most commonly shared the information they learned from the intervention with family (28%) and friends (14%) but rarely with healthcare providers. The most common information shared was regarding diet (18%). Patients also sometimes shared information about blood sugar facts, monitoring, testing, keeping a glucose log, or HbA1C (14%); medication adherence and regimen (12%); cost and resources (6%); and health problems (2%).
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4. Discussion and Conclusion 4.1. Discussion In this pilot study, MYDM showed potential to improve self-efficacy and reduce selfreported problems with taking diabetes medications in a sample of rural, mainly African American patients. Concerns about medications declined in tandem with improved self-efficacy. Although not statistically significant, adherence, self-efficacy, and number of problems showed trends in the expected direction based on the IMB model. Since misconceptions about diabetes are very common [25], effective interventions developed with input from the target populations are crucial in reducing health disparities. The high rate of watching the modules after the initial visit suggests that MYDM was well-received by the study population and is appropriately adapted to their medication concerns. It was very encouraging that after the intervention, patients reported fewer problems with paying for medications, paying for glucose monitoring supplies, and getting their medications from the pharmacy. These findings may indicate that a few minutes of additional attention to issues of cost and access during physician visits can alleviate these barriers to successful treatment. The high level of concern about side effects was consistent with previous studies showing that African Americans were more concerned about side effects from diabetes medications than Whites [4]. In addition, the number of patients who said they did not think they needed their medication remained constant at more than one-third of the study population. Motivating patients who do not believe they need medication may require more time-intensive interventions with considerable face-to-face contact initially, then periodic online follow-up with a nurse or other trusted provider [3,26].
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There were several limitations to the study. People who agreed to participate may not have been completely representative of the population; we could not track characteristics of people who refused to participate because of HIPAA. All measures were self-reported. Most patients were female, so some findings may not be generalizable to male patients. About 30% of the patients were lost to follow-up. The sample size was relatively small. Future work should be done with larger samples. 4.2. Conclusion Diabetes patients may benefit from a practical, customized intervention to improve adherence to their medications. The MYDM intervention should be tested further to see if it can reduce concerns about diabetes medications, improve medication self-efficacy, and reduce problems with obtaining and taking medications. 4.3. Practice Implications Providers should consider implementing technology-based interventions in the clinic to address common problems that patients have with self-management, thus improving patient selfefficacy and behavioral skills to successfully manage diabetes.
Informed consent and patient details The authors confirm all patient/personal identifiers have been removed or disguised so the patient/person(s) described are not identifiable and cannot be identified through the details of the story.
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Conflicts of interest The authors have no conflict of interest. All authors have contributed to, read, and approved the final article.
Acknowledgments This study was funded by the UNC Center for Diabetes Translation Research to Reduce Health Disparities, supported by Grant Number P30DK093002 from the National Institute of Diabetes and Digestive and Kidney Diseases. The project described was supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number 1UL1TR001111. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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Table 1. Number of times that patients watched each module (N=51). Module
Percent (n) who Mean (Range) number of watched module times watched “It’s hard for me to pay for my diabetes 51% (26) 1.3 (1 – 3) medicine.” “It’s hard for me to pay for my glucose 45% (23) 1.4 (1 – 7) monitoring supplies.” 1.5 (1 – 5) “I’m worried about my blood sugar going 49% (25) too low.” “I’m worried about side effects from my 53% (27) diabetes medicine.”
1.6 (1 – 4)
“I’m worried about health problems from diabetes. Will medicine really help me?” “It’s hard to manage my diabetes.” “It’s hard for me to fit taking my diabetes medicine into daily routine.” “It’s hard (or I’m afraid) to give myself a shot.” “It’s hard for me to get my medicines from the pharmacy.” Watched any module(s) after first meeting Total number of modules watched
75% (38)
1.9 (1 – 5)
29% (15) 28% (14)
1.1 (1 – 2) 1.4 (1 – 3)
29% (15)
1.1 (1 – 2)
24% (12)
1.1 (1 – 2)
73% (37) Not applicable
Not applicable 3.7 (1 – 8)
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Table 2. Demographics of study patients and scores at baseline (N=51). Race Black or African American Other Gender Female Male Medicines used Oral Injectable Health status Excellent Very good Good Fair Poor High blood pressure High cholesterol Have health insurance REALM score eighth grade or lower PHQ-2 score of “depressed”
Percent (n) 84% (43) 16% (8) 80% (41) 20% (10) 80% (41) 51% (26)
4% (2) 12% (6) 24% (12) 51% (26) 10% (5) 75% (38) 55% (28) 80% (41) 43% (22) 25% (13) Mean (SD) Age 54.0 (10.4) Number of diabetes medicines 2.1 (0.9) Visual analog scale of adherence 7.5 (2.5) Diabetes adherence self-efficacy score 45.4 (7.5) Beliefs about medications questionnaire (Concerns 12.7 (4.0) section) Beliefs about medications questionnaire (Necessities 18.7 (4.3) section)
Range 25 – 75 1–4 0.4 – 10.0 31 – 56 4 – 20 10 – 25
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Table 3. Reported problems or barriers to using diabetes medications. Statement
Percent (n) of patients Percent (n) of agreeing at baseline patients agreeing at (N=51) 3-month follow-up (N=35) It is hard for me to pay for my injectable 41% (21) 26% (9) diabetes medicines. It is hard for me to pay for my diabetes pills. 55% (28) 40% (14) It is hard for me to pay for my glucose 65% (33) 34% (12)* monitoring supplies. I’m worried about my blood sugar going too 51% (26) 51% (18) low. I’m worried about side effects from my 71% (36) 74% (26) diabetes medicine. I’m worried about health problems from 75% (38) 77% (27) diabetes. I don’t think that I need my medicine. 35% (18) 37% (13) There are times when I forget to take my 55% (28) 46% (16) diabetes medicines. It’s hard for me to manage so many 41% (21) 34% (12) medicines. I don’t understand what my medicine label is 6% (3) 9% (3) telling me to do. It’s hard for me to fit taking my diabetes 45% (23) 23% (8) medicines into my daily routine. It’s hard to give myself a shot. 18% (9) 14% (5) I’m afraid to give myself a shot. 24% (12) 20% (7) It’s hard for me to get my diabetes medicines 33% (17) 17% (6) from the pharmacy. *p<0.01. All other differences were nonsignificant (p>0.05).