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Mobile health (mHealth) and a patient activation program could serve as a model for improving health outcomes for patients in outpatient clinical settings by decreasing atherosclerotic cardiovascular disease risk score.
Objectives: Cardiovascular disease (CVD) deaths in patients with type 2 diabetes (T2D) are 2 to 4 times higher than among those without T2D. Our objective was to determine whether a patient activation program (Office-Guidelines Applied to Practice [Office-GAP]) plus a mobile health (mHealth) intervention compared with mHealth alone improved medication use and decreased 10-year atherosclerotic CVD (ASCVD) risk score in patients with T2D.
Study Design: Quasi-experimental design; Office-GAP plus mHealth vs mHealth only.
Methods: The Office-GAP intervention included (1) a patient activation group visit, (2) provider training, and (3) a decision support checklist used in real time during the encounter. The mHealth intervention included daily text messages for 15 weeks. Patients with T2D (hemoglobin A1c ≥ 8%) attending internal medicine residency clinics were randomly assigned to either the combined Office-GAP + mHealth group (Green) or mHealth-only group (White). After group visits, patients followed up with providers at 2 and 4 months. A generalized estimating equation regression model was used to compare change in medication use and ASCVD risk scores between the 2 arms at 0, 2, and 4 months.
Results: Fifty-one patients with diabetes (26 in Green team and 25 in White team) completed the study. The 10-year ASCVD risk score decreased in both groups (Green: –3.23; P = .06; White: –3.98; P = .01). Medication use increased from baseline to 4-month follow-up (statin: odds ratio [OR], 2.20; 95% CI, 1.32-3.67; aspirin: OR, 3.21, 95% CI, 1.44-7.17; angiotensin-converting enzyme inhibitor/angiotensin receptor blocker: OR, 2.67, 95% CI, 1.09-6.56). There was no significant difference in impact of the combined intervention (Office-GAP + mHealth) compared with mHealth alone.
Conclusions: Both Office-GAP + mHealth and mHealth alone increased the use of evidence-based medications and decreased 10-year ASCVD risk scores for patients with T2D in 4 months.
Am J Manag Care. 2022;28(11):e392-e398. https://doi.org/10.37765/ajmc.2022.89263
Mobile health (mHealth) and a patient activation program (Office-Guidelines Applied to Practice [Office-GAP]) could serve as a model for improving care and health outcomes for patients in outpatient clinical settings.
Despite nationwide improvements in cardiovascular disease (CVD) mortality and morbidity,1,2 CVD deaths in patients with type 2 diabetes (T2D) are 2 to 4 times higher than among those without T2D.3-5 Patients with diabetes have high risk for CVD, including stroke, peripheral arterial disease, cardiomyopathy, and congestive heart failure.6,7 Only 23% of adults with T2D are at goal for hemoglobin A1c (HbA1c), blood pressure (BP), and low-density lipoprotein cholesterol control.8 Medication nonadherence is likely a main factor in these low rates.9,10 Medication nonadherence results in approximately 125,000 preventable deaths a year.11 Approximately $290 billion is spent annually because of medication nonadherence, of which $100 billion is spent on hospitalization alone.12-14 Pharmacologic management through timely and continuous use of prescribed medications is key to control of CVD risk factors including hypertension and hyperlipidemia.15
Atherosclerotic CVD (ASCVD) risk has been used as a preventive measure and assessment in treating and preventing CVD. The ASCVD Risk Estimator, developed by the American College of Cardiology (ACC) and American Heart Association (AHA) in 2013 and grounded in the Framingham Heart Study, evaluates many variables for the estimation of 10-year CV risk: sex; age; ethnicity; total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol levels; systolic BP; diabetes; and smoking habits.16 Adults aged 40 to 75 years in primary prevention can be classified as borderline risk (10-year risk of ASCVD, 5% to < 7.5%), intermediate risk (7.5% to < 20%), and high risk (≥ 20%). Use of the cardiac risk score allows the quantification of the cumulative impact of changes in multiple risk factors and translates changes in physiologic parameters to risk estimates that are meaningful to patients and policy makers.17 Medications and lifestyle changes are indicated based on patients’ ASCVD score to decrease mortality and morbidity.
Treatment and prevention regimens are complex, requiring innovative approaches to extend support beyond face-to-face clinic visits. Mobile health (mHealth) interventions hold promise as a scalable support strategy. However, research increasingly suggests that mHealth alone may not change behavior enough to improve health outcomes.18,19
Our patient activation program, Office-Guidelines Applied to Practice (Office-GAP), trains patients and providers in shared decision-making (SDM) and the use of decision support tools (DSTs) to form strong patient-physician relationships and to increase patient engagement and adherence to care plans. Office-GAP has been shown to improve medication use, BP control, CVD knowledge, SDM, and patient and provider satisfaction with their communication at 6 months.20-22 However, improvements were not sustained. mHealth applications such as texting may have the potential to activate patients and reinforce adherence behavior.
The Office-GAP + mHealth intervention was designed to reduce BP and improve glycemic control and other CVD risk factors by engaging patients (and providers) through patient activation combined with mobile phone text messages to improve medication adherence and self-management for patients with diabetes.
The objective of this pilot study was to determine whether a patient activation program (Office-GAP) plus mHealth intervention compared with mHealth alone improved medication use and decreased 10-year ASCVD risk score in patients with T2D.
Study Design and Setting
We used a quasi-experimental, 2-arm design to improve collaboration between patients and providers and to improve outcomes for patients with diabetes seen in outpatient settings. The study was conducted in internal medicine resident clinics engaging 2 teams. Each team consisted of 16 resident doctors and 4 medical assistants supervised by 4 faculty attendings. The teams were allocated as an intervention team (Office-GAP + mHealth), called the Green team, and a control team (mHealth only), called the White team, by tossing a coin.23 The Green team providers received SDM training, including use of the GAP checklist, whereas the White team did not. The Michigan State University Institutional Review Board approved the study.
Inclusion criteria. Eligible patients were adults 18 years or older who had a diagnosis of T2D, with an HbA1c of at least 8%, with or without CVD, who could provide informed consent. Eligible patients were identified in the electronic health record based on the International Classification of Diseases, Tenth Revision (ICD-10) E08.00 diagnosis code for diabetes between December 2017 and March 2020. All 32 resident doctors in the internal medicine clinic participated in the pilot study.
Exclusion criteria. We excluded patients with cognitive impairment, dementia, or psychosis as determined by ICD-10 codes. Interpreters were provided for patients with limited English proficiency. Hypertension was defined as systolic BP of at least 140 mm Hg and diastolic BP of at least 90 mm Hg; goal systolic BP is less than 140 mm Hg and diastolic less than 90 mm Hg.24 All participants completed the informed consent process.
The Office-GAP intervention. The Office-GAP intervention has been well described in previous peer-reviewed publications.20-22 Briefly, after recruitment, patients on the Green and White teams attended (1) one scheduled group visit (90-120 minutes; 4-6 patients) conducted by trained research assistants (RAs) and (2) two follow-up visits with their primary care providers (PCPs) at 2 and 4 months after the group visit.
Provider training module delivered by study team. This module delivered to the Green team providers included (1) brief didactic presentation on effectiveness of medical therapy and behavior changes to manage diabetes, BP, and heart disease, based on the recommendations and guidelines of the ACC and the American Diabetes Association; (2) teaching care providers to elicit patients’ preferences and values; (3) introducing the DSTs and discussing best practice implementation; (4) review of the Smith evidence-based patient-centered method for establishing a provider-patient partnership,25,26 which enhances providers’ ability to integrate patient-centered methods with goal-setting, and the SDM skills described by Braddock et al27 and Elwyn et al,28 which include role-playing to model office visits; and (5) completion of pretraining and posttraining surveys to determine provider perceptions of the use of DSTs, SDM, and mHealth in their practice.
Patient education module. Patient intervention/training occurred during the group and follow-up visits attended by the Green and White teams. We obtained informed consent and Health Insurance Portability and Accountability Act authorization from patients and introduced them to the study purpose and protocol. The group visit for the Office-GAP + mHealth (Green) team is an SDM and goal-setting activation session wherein patients learn self-management behaviors, how to adhere to medication use, communication skills, and use of DSTs. They also learned how to use the Care4life texting service and confirm configuration of the service on their mobile phone. In the texting-only session (White team), participants received instruction on how to use the Care4life texting service and confirmed configuration on their mobile phone.29
Clinic visit (Office-GAP follow-up visit with providers). The Office-GAP Checklist (eAppendix [available at ajmc.com]) is a 1-page checklist that outlines all evidence-based medications for secondary prevention of CVD in patients with diabetes. The checklist is completed by providers in the Green team with direct patient involvement during office visits at 2 and 4 months. At the visit end, the patient and provider sign the checklist.
At these follow-up visits, providers who had received prior SDM training (Green team) completed the Office-GAP Checklist, which promoted evidence-based medication prescribing behavior and goal setting between patient and provider. The follow-up visits were patients’ regularly scheduled visits with their PCPs, not in addition to their usual care.
Mobile diabetes self-management texting intervention (Care4life texting program). This intervention was given to both teams. Care4life does not require patients to use a special app. Care4life engages patients in 2 ways: (1) patients receive daily Care4life messages appropriate to their diagnosis and medications (eg, “Have you checked your BP today?,” “What was your blood sugar this morning?,” “Did you take your medications today?”) along with appointment reminders for 15 weeks, and (2) patients respond to prompts and contact their provider’s office throughout the study via texting.
Medication adherence. We defined this by the adherence metric, which is the ratio of the number of days on which a patient takes their medication as prescribed to the total number of days they are expected to take them in that period.30,31 Self-reported adherence was assessed using the Adherence to Refills and Medications Scale (ARMS), a 14-item scale with 2 subscales for taking medications as prescribed and refilling medications on schedule,32,33 which had high internal consistency overall (Cronbach’s α = 0.81). The range for ARMS value ranges from 0 to 56, and a higher ARMS score indicates lower medication adherence.34,35
Medication use. Rates of using aspirin, angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs), and statins were obtained at baseline, 2 months, and 4 months. The medication use measure is fundamentally a measure of both providers’ prescribing and patients’ filling the prescription. Medication use was assessed by self-report at each visit and verified by patients bringing in all active medications. Only medications both prescribed by the provider and obtained by the patient were included in the analysis.
ASCVD 10-year risk scores.The ACC/AHA ASCVD Risk Estimator, released in 2013, was designed to assess the risk of an initial CV event.36 The pooled cohort equations predict the future risk of CVD and stroke. The ASCVD score includes components that we hypothesize will be improved by the intervention, including HbA1c, systolic BP, cholesterol levels, and smoking status. Using a cardiac risk score allows us to quantify the cumulative impact of changes in multiple risk factors.
Data were collected from survey forms completed by patients during the group visit and follow-up office visits. Chart abstractions were performed by trained RAs according to a predefined standardized data collection form. Patient race was self-reported. The study project manager continued to sample each reviewer’s charts to maintain quality control. Reliability of at least 98% was maintained throughout the study.
Continuous variables were summarized as mean (SD) at baseline and categorical variables were summarized as absolute frequency and percentage. T tests and χ2 tests were used to compare the differences between the Green and White groups. For small sample sizes in the categorical variables’ comparisons, the Fisher exact test was employed.
Because of the longitudinal nature of the study, correlated data analyses using a generalized estimating equations (GEE) model were conducted to describe the medication use over time (logistic link) and medication adherence/ASCVD change over time (identity link). The basic starting model imposed no linear structure of the time effects on the outcomes, but rather treated time as a categorical variable. The effects of being in the Green group compared with the White group were assessed using interactions between the intervention group indicator and time.
Because patients were randomly assigned to the Green group or White group, it is assumed that the study design has no selection bias. We tested the balances of demographic characteristics between the 2 groups at the baseline. There are still some significant differences in demographic characteristics (race and Charlson Comorbidity Index [CCI] score) between the 2 groups due to small sample size. To avoid possible confounding effects, GEE models control for gender, age, CCI, race, and body mass index (BMI). A P value of less than .05 is considered statistically significant in all analyses. All analyses were performed using SAS version 9.4 (SAS Institute).
Seventy-two patients met the inclusion criteria. Fifty-one patients participated and completed the study (Table 1). Reasons for nonparticipation or dropping out of the study included refusal to participate, not attending a scheduled visit, and inability to be contacted for scheduling. There was no statistical difference in the mean age of patients in the Green and White groups (54.3 vs 60.6 years, respectively; P = .08). There were no statistical differences between the Green and White groups in their mean BMI (34.8 vs 37.5; P = .23), gender (48% female vs 35% female; P = .32), and smoking history (24% vs 16%; P = .48). However, the Green group included more patients self-identifying their race as “other” (n = 5; 20%) compared with the White group (n = 1; 4%; P = .03). There were significant differences in the CCI scores, which estimate the burden of illness, with the White group having more moderately ill (46.2% vs 20%) and severely ill (15.4% vs 4%; P = .02) patients (Table 1).
Medication adherence was calculated with ARMS scores for both groups. Mean ARMS score decreased to 18.7 from 19.5 in the Green group and decreased to 19.8 from 20.2 in the White group at the 4-month interval (eAppendix Table 1), showing better adherence to evidence-based medications at 4 months than at baseline. Predicted ARMS change by GEE model at 4 months in the Green group is –0.83 (95% CI, –2.27 to 0.62; P = .26) and in the White group is –0.27 (95% CI, –1.66 to 1.12; P = .70) (Figure [A] and eAppendix Table 2).
Medication Use and Predictors of Medication Use
Use of a statin increased from 77.27% to 81.82% in the Green group and from 56.52% to 72.73% in the White group at 4 months (eAppendix Table 3). The GEE model shows there was statistically significant increased use of a statin in both groups at the 4-month interval (odds ratio [OR], 2.20; P = .002). There was no statistical difference between the groups (OR, 0.60; P = .198) in the use of a statin at 4-month follow-up (Table 2).
Aspirin use also increased from 30.00% to 50.00% in the Green group and from 26.67% to 56.25% in the White group (eAppendix Table 3). Use of aspirin increased at 4 months in both arms, and the increase was statistically significant (OR, 3.21; P = .004). There was again no statistical difference in the changes in aspirin use between the 2 arms at 4-month follow-up (OR, 0.73; P = .62) (Table 2).
Use of ACE inhibitors/ARBs also increased at 4 months in the Green group, from 86.36% to 90.91%, and in the White group, from 66.67% to 82.61% (eAppendix Table 3). The increase in use at 4 months was statistically significant in both groups (OR, 2.67; P = .032). Again, there was no statistically significant difference in changes between the 2 arms at 4-month follow-up (OR, 0.59; P = .492) (Table 2). GEE regression controlled for gender, age, CCI score, race, and BMI.
ASCVD Risk Score
Change in ASCVD score was calculated in only 31 of 51 patients, as some of the patients did not have a repeat lipid profile at 4 months, which was necessary to enable 10-year ASCVD risk score calculation. The mean (SD) 10-year ASCVD score was 22.3 (10.5) in the Green group at baseline, which decreased to 18.3 (9.97) in 4 months. The mean (SD) 10-year ASCVD score was 24.7 (18.8) in the White group at baseline and this decreased to 20.1 (16.3) at 4 months (eAppendix Table 4). GEE regression results (controlling for gender, age, CCI, race, and BMI) show that in the Green group the mean decrease in ASCVD score was 3.23 (95% CI, –6.53 to 0.07; P = .06) and in the White group the mean decrease was 3.98 (95% CI, –6.84 to –1.11; P = .02) at 4 months. There was no statistically significant difference in change in ASCVD scores between the 2 arms (Table 3 and Figure [B]).
Both the Green group and the White group increased the use of evidence-based medications and decreased ASCVD risk scores for patients with diabetes. There was no significant difference in the impact of the combined intervention compared with mHealth alone. Both groups showed better adherence to medication use and medication refills, with no statistical difference between the 2 groups. The estimation of the ASCVD score to quantify the cumulative impact of changes to multiple risk factors by the patient activation and mHealth interventions in high-risk diabetic populations revealed that the ASCVD scores decreased by 3 to 4 points in both groups. Only a few prior interventions have established a link to this important outcome.
Studies are beginning to show that mHealth interventions alone may not improve health outcomes. The MediSAFE-BP trial (NCT02727543) found no change in systolic BP levels among patients with poorly controlled BP who were randomly assigned to use a mobile app compared with those who did not use the application.19 App use correlated with a small increase in self-reported medication adherence, but this did not translate to a significant difference in BP control. A recent Agency for Healthcare Research and Quality review to evaluate the efficacy, usability, and features of commercially available mobile apps for diabetes self-management found that of the 11 apps studied, only 5 were associated with clinically significant improvement in HbA1c. None of the reviewed studies showed patient improvements in quality of life, BP, weight, or BMI. The review authors concluded that limited evidence suggests that apps when combined with additional support from a health care provider or study staff may improve diabetes outcomes and called for more rigorous and longer-term studies. Moreover, none of the included studies were considered high quality.18
Our preliminary work presented at a conference revealed that acombined Office-GAP + mHealth intervention significantly improved adherence to refills and to all prescribed medications and improved diabetic self-care compared with mHealth alone.24
Both interventions had efficacy in the short term and both proved to be feasible for implementation in the clinical setting, but we think the duration of 4 months in this study was too short to see differences emerge between the groups. Our ongoing randomized clinical trial will follow patients for 1 year.
Evidence-based guidelines recommend determination of the 10-year ASCVD risk score to guide decisions on initiation and intensification of medications that reduce CVD risk,35,36 but adoption by clinicians in general has been slow. Similar to our findings, a recent study by Cykert et al37 demonstrated a 4% absolute risk reduction in ASCVD risk score among all patients with a baseline score greater than 10% from baseline to 3 months post intervention. The study further revealed that a risk reduction achieved through their intervention is likely to prevent more than 5800 CV events over 10 years, resulting in improved CV health and possible cost savings.
Limitations and Strengths
Our study has certain limitations. This is a pilot study with a small sample size to confirm feasibility and potential effectiveness of the interventions. Office-GAP and the mHealth interventions were tested in residents’ clinics, not a randomized controlled trial, limiting the generalizability of our findings. The SDM training for providers was limited to 1 session and may degrade over time. However, use of the Office-GAP checklist during each encounter reinforced the SDM in follow-up interactions.
The encounters were not audio or video recorded and thus we have no definitive way to confirm how providers and patients engaged in SDM. The Office-GAP intervention is an integrated model, and we are unable to disentangle the effects of provider and patient education to explain our results. The increases in medication use and decreased 10-year ASCVD risk score may reflect more effective provider prescribing and communication practices, as well as more effective patient communication and activation. ASCVD risk score could not be calculated for all patients because of absence of some data in the electronic health record. Finally, we did not evaluate the cost of implementation of mHealth and Office-GAP programs.
Despite these limitations, the study has several strengths. First, Office-GAP connects 3 critical steps in chronic care—coordination, shared decisions, and self-management—in a feedback loop. mHealth acts as a reinforcement for self-management. This is a more narrow and appropriate use of the technology. Second, the Office-GAP checklist gets providers and patients on the same page. The checklist is very simple and easy to use at the point of care. Third, all providers in the Green group were involved in the training and implementation of the tools and assisted in identifying barriers to successful implementation, a strategy previously proven to be effective in influencing provider behavior.38
Our results demonstrate the effectiveness of both mHealth and Office-GAP in reducing 10-year ASCVD risk scores and increasing use of evidence-based medications. Addition of Office-GAP appeared to have no significant incremental impact after 4 months in this pilot study. Our results suggest that mHealth and Office-GAP could serve as models for improving care and health outcomes for patients in outpatient clinics. Further studies are needed to determine the effectiveness in a large sample, over a longer period, and to determine the cost-effectiveness of these approaches.
The authors give special thanks to the internal medicine residency clinic attendings, resident doctors, and staff for their time and contribution to this project. We thank Amanda Petrovsky for her clerical assistance. We would also like to thank our patient participants for their time and enthusiasm.
Author Affiliations: Division of Internal Medicine (AO, RT) and Division of Occupational and Environmental Medicine (LW), Department of Medicine (KK-B, MH-R), and Center for Bioethics and Social Justice (KK-B, MH-R), College of Human Medicine, Michigan State University, East Lansing, MI; College of Arts and Letters, Michigan State University (WH-D), East Lansing, MI; Oakland University William Beaumont School of Medicine (ZA), Rochester, MI; Michigan State University (AI), East Lansing, MI.
Source of Funding: Michigan State University institutional bridge funding.
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (AO, RT, KK-B, WH-D, ZA, AI, MH-R); acquisition of data (AO, RT, ZA, AI); analysis and interpretation of data (AO, RT, KK-B, WH-D, LW, ZA); drafting of the manuscript (AO, RT, KK-B, WH-D, LW, ZA, AI, MH-R); critical revision of the manuscript for important intellectual content (AO, RT, KK-B, WH-D, ZA, MH-R); statistical analysis (AO, LW); provision of patients or study materials (AO, ZA, AI); obtaining funding (AO, WH-D); administrative, technical, or logistic support (AO, ZA, AI); and supervision (AO, RT, ZA).
Address Correspondence to: Adesuwa Olomu, MD, MS, Division of Internal Medicine, Department of Medicine, College of Human Medicine, Michigan State University, 788 Service Rd, B323 Clinical Center, East Lansing, MI 48824. Email: email@example.com.
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