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Cover of Comparing Three Methods to Help Patients Manage Type 2 Diabetes

Comparing Three Methods to Help Patients Manage Type 2 Diabetes

, MD, , MD, , MD, , PhD, , RN, , RD-CDE, , MS, , BS, , MD, , MD, , MD, , PhD, , BS, , CHW, and , CHW.

Author Information and Affiliations

Structured Abstract

Background:

Diabetes mellitus (DM) is a complex chronic disease with less than 63% of patients achieving a target HbA1c <7% and only 7% meeting combined glycemic, lipid, and blood pressure (BP) goals. Both community health workers (CHWs) and mobile health (mHealth) have the potential to improve patient-health care team communication and improve patient self-management.

Objectives:

We evaluated innovative strategies to improve care of DM for Medicaid patients using a cell phone (mHealth) and a CHW. We hypothesized that (1) mHealth plus a CHW is superior to the benefits of mHealth alone or a CHW alone; and (2) mHealth alone will improve self-management and DM outcomes compared with baseline. Our primary end point was increased achievement of wellness behaviors and clinical goals. Secondary end points included (1) HbA1c; (2) medical use (emergency department [ED] visits, hospitalizations, urgent office visits); (3) Low-density lipoprotein (LDL) cholesterol; (4) BP; (5) medication adherence; (6) diet, exercise, blood glucose monitoring, and BP testing; and (7) diabetes distress.

Methods:

A total of 166 Medicaid patients with type 2 DM, HbA1c >8.0%, with 3 or more out of 13 unmet wellness and clinical goals were randomized into 3 groups: Group 1 (n = 56) was assisted by the Voxiva Care4Life diabetes mHealth system (C4L) alone; group 2 (n = 56) was assisted by a CHW only; and group 3 (n = 54) had both C4L and a CHW. We recruited participants from the outpatient clinics of 3 Washington, DC, medical centers. We followed these participants for 12 months, comparing achievement of wellness behaviors and clinical outcome goals across the 3 health care strategies.

Results:

We saw the primary end point, increased number of met wellness and clinical goals, in all 3 groups (mean, 1.4 additional goals; P = .001). On average, groups improved goals met by 20% from baseline. Only 11 (6.6%) participants dropped out. At 12 months, HbA1c dropped 1.2% (P < .0001; nonsignificant difference across groups). Of the total participants, 51% achieved an HbA1c <9% and 30% achieved an HbA1c <8%. We observed improvements in medication adherence (P = .02), hospitalizations (P = .03), urgent care visits (P = .03), and diabetes distress (P < .0001), with no significant difference across groups. C4L use was sustained over 12-month follow-up, with participants receiving a mean 3.75 messages from C4L/day. Participants sent a median 3.9 messages into C4L /week. We saw a trend for higher participant-to-C4L messaging in the C4L + CHW group. Patient engagement with C4L weekly nonglucose measures (exercise, weight, medication adherence) was modest, with median response approximately 10 out of 52 weeks for all 3 groups. Participants were uniformly enthusiastic about participating in the program.

Summary:

The C4L + CHW strategy was not superior to C4L or CHW alone. All 3 approaches resulted in significant improvement of wellness and clinical goals, HbA1c, urgent health care use, and diabetes distress in an urban Medicaid population. The results of this study provide insight into the future use of CHWs and mHealth for the improvement of diabetes care.

Background

Diabetes mellitus (DM) is the seventh leading cause of death in the United States and the root cause of several disabling conditions.1 Within the past 6 years, the percentage of adults with diabetes in Washington, DC, ranged from 7.5% to 10.9%. Almost 15% of black and 3% of white DC residents are living with the disease.2 In addition to increased risk of DM, there are marked disparities in DM control for African Americans.3 Since 2005-2007, the age-adjusted death rate owing to DM for the general population declined from 31 out of 100 000 to 20 out of 100 000 in 2012-2014; however, death rates among blacks with DM are 6 times higher than among whites.1 Chronic care of DM remains a major health care challenge, with less than 63% of patients with DM achieving a target HbA1c <7% and only 7% meeting combined glycemic, lipid, and BP goals.4,5 There is a pressing need to identify effective, affordable interventions to decrease the morbidity and mortality from DM and its complications among this population.

Patient factors account for most (fully 98%) variability in HbA1c.6 This is heightened in an inner-city, low-income, high-risk population with limited access to health care providers, no stable medical home, reduced availability of medications and home medical tracking devices, low medical literacy, and poor self-management skills. Disease management is a system of coordinated health care interventions and communications in which patient self-care efforts are significant. The 2008 Robert Wood Johnson (RWJ) Foundation Diabetes Initiative (www.diabetesinitiative.org) summarized the benefits and challenges of patient self-management of DM, stating:

Diabetes is a “24/7” job for the rest of an individual's life. Resources and supports for self-management (eg, physicians and nurses, group classes, community health workers [CHWs], interactive e-health interventions, or community organizations) encourage healthy eating, physical activity, and healthy coping, which are critical to DM management.

Those to be served must be able to choose among a variety of appealing, easily available ways to learn the skills they need in order to carry out their DM self-management. When patients have questions about their DM they need convenient access to someone they can talk to. At the same time, they need to be contacted periodically to see how they are doing. Routine contacts by the health care team as well as “as-needed” options for patients are key to sustained self-management.

New approaches to enhance patient self-management behaviors and patient–health care provider communication are clearly needed. The Institute of Medicine has called for new technologies to shift our health care system from one that provides episodic and acute care toward one that nurtures healing relationships between patients and families and health care professionals.7,8 Patients report that they have inadequate information from health care providers for effective DM self-management, and a lack of practical advice on how to deal with their disease. Patients want assistance from their health care team on “how,” not just “what,” to deal with DM.9,10 We based our intervention and choice of end points on the key wellness behaviors and health process goals identified by patients to achieve successful self-management of their DM. The RWJ Foundation Diabetes Initiative reported the supports that individuals need to accomplish self-management. These include (1) individualized self-assessment; (2) collaborative goal setting; (3) skills for both disease management and healthy behaviors (eating, exercising, and healthy coping); (4) ongoing follow-up and support to help adjust plans as problems arise, stay motivated, and communicate with their providers; and (5) community resources. In this study, we compared innovative strategies to address these patient needs. Our approach aimed to enhance patients’ DM self-management by providing mobile health (mHealth) technologies with and without community health workers (CHWs) into a new care model.

In its 2002 report, Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, the Institute of Medicine found that “community health workers offer promise as a community-based resource to increase racial and ethnic minorities’ access to health care and to serve as a liaison between health care providers and the communities they serve.”9 CHW roles and activities complement the services of other health care team members. CHWs help patients navigate care systems, linking individuals with primary care physicians. They also conduct home visits as part of care coordination, provide interpretation, help identify and address barriers to care, and advocate to ensure clients receive appropriate and culturally competent services. A growing body of evidence documents the effectiveness of CHWs in DM care and education efforts.11-15 A 2006 review of 5 CHW programs serving adults with DM found that patients who worked with a CHW had more knowledge of their disease and better self-care skills (eg, in areas of diet, exercise, and glucose monitoring) than those patients who had no contact with a CHW.11 Patients with CHWs had fewer emergency department (ED) visits, improved provider monitoring of glycemic control, and improved rates of retinopathy screening. A recent meta-analysis of the impact of CHWs on lowering HbA1c in minority low-income type 2 DM patients demonstrated a mean lowering of 0.21%.14

We designed this project to move beyond the traditional role of the CHW by augmenting the management of chronic disease by utilizing new approaches with mobile health technology. The use of cell phones has expanded greatly in recent years for those in all income, ethnic, education, and age groups. The 2013 Pew Internet Project (www.pewinternet.org) reported 89% of adults own cell phones (89% African Americans, 89% Hispanics, and 88% whites). Cell phone ownership is 82% among those with incomes <$30 000 and 76% among those with no high school diploma. Of cell phone users with incomes <$30 000, 78% use SMS texting, 30% have smartphones, and 52% access the internet. Use of texting for health care is not only inexpensive but has immediate impact. Of text messages, 99% are read, and most are read within 3 minutes. Given the high (and rapidly growing) usage of cell phones among underserved patients, the combination of personalized interaction with CHWs and engagement with mHealth has the potential for high impact on improving health. Use of the cell phone as a component of a chronic disease management system offers a number of potential strengths and advantages: (1) Broad acceptance and use of the technology in the community across all socioeconomic groups; (2) cell phones are not exclusively “medical” and therefore do not require specific compliance; (3) the cell phone is inherently a user-centric (ie, patient) device, consistent with chronic disease models; (4) cell phone software provides real-time patient feedback; (5) the health care team has an enhanced view of the patient and their data, allowing individualized support with protocol-driven, computer-generated messages to patients; and (6) summary reports that are provided to the health care team and entered into the medical record highlight health status and gaps in achieving standards of care, promoting more timely and effective chronic disease management. A growing body of scientific literature has documented the effectiveness of cell phone–based interventions for improving health behaviors, including appointment attendance, medication adherence, and management of DM.16-22 In a 12-month randomized control trial, the WellDoc system combined with provider clinical decision support reduced HbA1c by 1.9% compared with 0.7% for usual care, a difference of 1.2% (P < .001).23 A recent comprehensive review of mHealth technologies for DM care found that mHealth interventions reduced HbA1c by an average of 0.83% (range, 0.2%-1.4%); however, mHealth was minimally successful at improving patient self-care, BP, and lipids.18 mHealth studies targeting exercise in patients with type 2 DM showed no effects.20 Additionally, studies have reported a high rate of discontinuation of use after 2 months and 6 months (32% and 66%, respectively).21,22,24 Many “links in the chain” in the chronic care model are designed to improve disease management, and the success of mHealth requires attention to all of these overlapping components.25

A combined CHW + mHealth strategy has the potential to address the complex challenges of DM, including both medical issues and the social determinants of chronic disease, and improve DM care. CHWs and an mHealth cell phone app support patient understanding of their disease and assist in each patient's self-care decisions. In addition, both the CHW and the mHealth system provide patients education about disease management, including lifestyle and medication information. The addition of CHWs to the health care team has the potential to significantly enhance patient-centered communication with health care providers and navigation using the mHealth system.

This study hypothesized that for Medicaid patients with type 2 DM, (1) the combination of CHWs and a patient-driven mHealth system will be superior to either a cell phone system alone or a CHW alone to activate and to improve a composite of wellness behaviors and clinical goals, and (2) the mHealth system alone for 12 months can improve a composite (number of goals met) of wellness behaviors and clinical goals by ≥25% compared with baseline.

Methods/Study Design

We designed and reviewed this study with the assistance of our stakeholder support group, which included 2 patient volunteers, a diabetes educator, the director of our regional Medicaid insurer, and the head of research for the American Diabetes Association. In addition, we based the concept and details of this project on patient feedback from our previous mHealth DM/hypertension (HTN) study.32

Study Population

This project targeted Medicaid patients with type 2 DM and uncontrolled HbA1c levels (≥8.0%) who were not effectively self-managing their DM on a daily basis and were not meeting ≥3 wellness behaviors and clinical goals. Patients were recruited from the Howard University Diabetes Treatment Center, the Medstar Washington Hospital Center Diabetes and Internal Medicine clinics, and the George Washington University Medical Faculty Associates Diabetes and internal medicine clinics in Washington, DC. The 3 clinics serve a large cohort of predominantly African American, high-risk, inner-city DM patients. Inclusion and exclusion criteria are listed below.

Inclusion Criteria

Patients met the following criteria to be recruited for the study:

  1. Aged 21 to 75 years
  2. Medicaid insurance coverage
  3. Fluent in English and able to read a text message
  4. Diagnosed with DM type 2 validated from electronic medical record (EMR)
  5. HbA1c ≥8%
  6. Three or more of the applicable wellness behaviors and clinical goals unmet. (The maximal number of wellness behaviors and clinical goals was 13, but for some patients maximum goals could be 11 or 12 if the patient did not have HTN and his or her medical team did not recommend daily aspirin.)

Exclusion Criteria

Patients were excluded if they met any of the following criteria:

  1. Terminal illness (expected survival of <1 year)
  2. Severe dementia or uncontrolled mental illness
  3. Gestational DM
  4. Use of an insulin pump
  5. Inability to perform self–BP measurement with a home monitor if hypertensive
  6. Inability to use a cell phone
  7. Inability to use software application on cell phone
  8. Pregnant or planning to get pregnant
  9. Stage 5 chronic kidney disease or end-stage renal disease on dialysis

This study focused on a composite of behavioral and clinical end points that reflected (1) self-care measures, (2) DM health care status, and (3) patient health care system preferences. Our research team selected 7 wellness behaviors and 6 health process/clinical goals that are key to improving patient-centered diabetes care and subsequent clinical outcome measures, based in part on the Peers for Progress Consensus Evaluation for Research on Self-management and Peer Support in Diabetes26 and with input from our provider and patient representatives.

Wellness Behaviors and Health Process Clinical Goals

  1. Self-monitor glucose ≥1/week (average over 1 year)
  2. Self-monitor BP ≥1/week (average over 1 year; optional depending on HTN history and medical team preference)
  3. Weight control: body mass index (BMI) of <28 or decrease <5% (lost 5% of weight in the past year) or enrolled in weight loss program during the past year
  4. Exercise >20 minutes 3 times/week in the 2 months before enrollment
  5. Not smoking or in cessation program
  6. Good medication adherence (Morisky score = 4)
  7. Taking aspirin (optional depending on health care provider preference)
  8. Screened for retinopathy in the past year
  9. Screened for renal function in the past year
  10. Screened for foot abnormalities in the past year
  11. Lipid testing in the past year
  12. HbA1c testing in the past 6 months
  13. At least 1 diabetes care visit in the past year

Cell Phone System

For this project, we selected the Voxiva Care4Life (C4L) mHealth diabetes management system, which has been specifically designed as a patient-driven program. C4L engages DM patients to track blood glucose and BP, take medications, and set and track exercise and weight goals using daily text reminders and personalized coaching messages. C4L provides real-time, automated clinical and behavioral patient coaching and decision support for the patient using self-care and adherence-enhancing strategies. Content is licensed from the American Diabetes Association (ADA) and aligned with ADA clinical guidelines. Educational and behavioral messaging is constructed incorporating cultural sensitivity and health literacy principles.

The key features of the C4L system can be accessed via secure text messaging and can be used on any cell phone handset. C4L features include the following: (1) reminders to check glucose and BP; (2) recording and transmission of physiologic parameters (glucose, BP) with alarms set by the health care team for specified critical values that are sent to the patient; (3) reminders to take medications; (4) tracking of lifestyle and behavioral goals using selected questionnaires; (5) summary reports for the patient and health care provider; and (6) educational and lifestyle modification tips to educate and support self-care skills.

C4L program questionnaires and monthly reports let patients tailor the system interaction based on their preference, their disease state and stage of change, and how their data can be leveraged by the health care team to further personalize and reinforce goal achievement. The patient and the provider team work together to determine individual DM goals, thereby selecting the C4L interaction frequency and action plans. C4L consists of modules that can be tailored by the patient as to which modules are active and how frequently and how many messages are sent for each module (Table 1). The participant can adjust or stop receiving messages either by logging on and changing the settings or by sending in “STOP.”

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Table 1

Care4Life System Options.

Patients received surveys throughout the intervention and, based on the current answers and comparative analysis, the types of messaging to and support of the patient could be adjusted. Additionally, survey results provide the care team with insight into which patients may need a step up or step down in the care level. C4L is created for literacy at the fifth-grade level. The C4L system is fully compliant with HIPAA regulations for privacy protection.

CHW Component

A CHW was assigned to the medical team at each of our 3 collaborating medical clinics. CHWs were recruited and trained by our community project partner the Capitol City Area Health Education Center (CC AHEC). Midway through the enrollment and follow-up phase of this study, the CC AHEC closed down and all 3 CHWs had to be replaced. We quickly switched our CHW partner to the Institute for Public Health Innovations (IPHi). Both the CC AHEC and IPHi trained individuals from the community who have the requisite skills to work closely with patients from underserved communities, coaching them on various strategies to address chronic health needs and identifying local support resources to optimize patient outcomes. The following basic tenets were integrated into the CHW training: (1) Bridge the gap between communities and health/social service systems, (2) help patients navigate health and human services systems, (3) advocate for individual and community needs, and (4) provide direct services. The GW/Howard/Medstar health care team staff and the Voxiva staff added competencies for the CHWs in DM and HTN care and the use of the C4L mHealth system. The CHWs were integrated into the medical team at each site and were responsible for providing overall support to participants providing personalized assistance to help them effectively participate in medical care, to practice disease self-management between medical appointments, and to effectively utilize the C4L mHealth technology. To standardize and structure CHW–patient interactions and achieve goals, we developed a CHW checklist as a tool at each visit to address (1) DM-specific issues, (2) HTN issues, (3) other medical issues (hospitalizations, ED and urgent care visits, serious medical mental health problems, smoking), (4) medications, (5) scheduling, (6) activity/exercise/diet, (7) social issues (Medical contacts, literacy, employment, dependent care, finances, housing, family, or other supports), and (8) cell phone issues (Table 2).

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Table 2

Community Health Worker Checklist.

At the initial visit, the study staff reviewed the project requirements and obtained written informed consent from the participants. IRBs at GW, Howard University, and the Medstar Washington Hospital Center reviewed and approved the project and patient consent form. All participants needed to have a working glucometer and test strips. Participants with HTN also needed to have a working home BP monitor. The study provided these devices to any participant in need of either monitor. The study team assessed participants’ ability to use these devices and provided training as needed to ensure the accuracy of measures obtained by the participant. The study team obtained baseline parameters, including demographics, questionnaires (Fisher Diabetes Distress,27 Morisky Medication Adherence28), and status of the wellness behaviors and health process clinical goals. We extracted study data from electronic medical records at each site and the assessors were blinded to allocation. Participants were randomized to 1 of the 3 study groups: group 1, C4L only; group 2, a CHW only; or group 3, C4L cell phone mHealth system plus a CHW. We performed randomization using sealed envelopes, using block randomization generated by the GW biostatistician. Block randomization ensured an equal sample size in all 3 groups at the study's conclusion. Participants randomized to use the cell phone system (groups 1 and 3) were set up and trained with cell phone texting capability and instructed by the study staff on how to use the C4L mobile health application. At the study onset, all participants met with the project staff to review their diabetes status, identify their personal wellness and clinical goals, and determine their preferred types and frequency of interaction with the C4L system and the CHWs. Each participant was followed for 12 months, during which time all participants were asked to perform self-monitoring of both blood glucose and BP and work toward achieving their chosen DM wellness/clinical goals. Participants in groups 2 and 3 were assigned a CHW. CHWs provided traditional patient support, including advice and assistance on managing their chronic disease (eg, phone calls, face-to-face meetings at time of clinic visits and home visits), navigation with their health care team/pharmacy/social services, access adequacy of medical supplies (BP cuff, glucometer, test strips). For group 3, in addition to the usual CHW role, the CHWs assisted participants with the use of the C4L mHealth system and provided participants and their health care team with a monthly C4L summary report.

For groups 1 and 3, C4L used text messaging. Patients were able to use their own cell phones and contracts (prepaid or long term). The study paid for the cost of adding text messaging. Patients without their own phones were provided a cell phone handset and basic phone contract that included unlimited text messaging capability for 12 months (29 out of 166 participants were provided phones and contracts). The research staff registered each participant into the C4L system on the participant's phone and provided training (and retraining as needed) on the phone features and how to access and use the C4L cell phone system until participants could use the phone independently. Study-site follow-up visits were scheduled at 6 months and 12 months to determine the status of wellness behaviors and clinical goals; questionnaires were repeated at each visit. HbA1c was obtained at baseline. Follow-up HbA1c, BP, and lipids were obtained from the electronic medical record, if available within 6 months of the final visit.

Group 1—C4L-Only Group

Each patient and the study team reviewed the patient's baseline and desired future wellness and clinical goals. Based on these goals, the patient selected setting levels in the C4L system (Table 1). Patients chose individual C4L settings for each item. Every month the study staff contacted the participant to provide his or her C4L monthly report, at which time participants could adjust C4L messages and the type of messages if desired. Participants had the option to increase or decrease the medication, exercise, diet, education, and motivation messages. Study staff gave both participants and providers a C4L summary report (glucose, BP trends, wellness and clinical goal status information) every month. The research coordinators provided active patients a monthly $15 incentive.

Group 2—CHW-Only Group

Each participant and the CHW reviewed the participant's baseline and desired future wellness and clinical DM goals. Based on these goals, the CHW worked with each participant to help organize his or her medical care and goal achievement. CHWs used their training to assist participants with not only what to do, but also how to do what was needed to meet their personal goals. Potential assistance could include working with home care providers, organizing and reconciling participants’ medications, coordinating medication refills, planning and navigating participants’ clinic visits and other communications with the health care team, and ensuring participants’ access to and knowledge of their glucometer and home BP monitor. Participants were routinely encouraged to express any medical concerns to their CHW, who was able to relay information to the health care team and aid in feedback to the participant (eg, understanding their disease, medications, possible drug side effects, symptoms, travel assistance, diet and other lifestyle advice). As “care coordinators,” CHWs facilitated care access across multiple providers as needed. During the project's first 2 months, the CHW conducted weekly calls with each participant and, when possible, face-to-face visits on the day of a clinic appointment. As patients became familiar with the role of and access to their CHW, each participant determined how often and for what circumstances he or she wished to be contacted by the CHW. At the same time, the CHW tracked participant progress as recommended by the participant and health care provider, to proactively provide information and engage each participant with his or her medical team. The research coordinators provided active patients a monthly $15 incentive.

Group 3—CHW + C4L Group

Each patient and the CHW reviewed the participant's baseline and desired future wellness and clinical DM goals. Based on these goals, the patient selected setting levels in the C4L system (Table 1). Similar to the group 2 (CHW-only) participants, the CHWs provided the same support to group 3 participants. During the project's first 2 months, the CHW conducted weekly calls or clinic meetings with each participant. As participants became familiar with the C4L cell phone system and the role of the CHW, each participant determined how often and for what circumstances he or she wished to be contacted by the C4L system and the CHW. Each month, the CHW provided the C4L summary report, at which time participants could adjust the number and type of C4L messages and CHW contacts. Participants could increase or decrease the C4L cell phone medication, exercise, diet, education, and motivation messages, and increase or decrease the frequency of CHW type (home or other) contacts. The CHWs acted as “digital navigators,” assisting each participant with his or her cell phone and the C4L app. They monitored C4L usage, glucose and BP measures, and the status of wellness and clinical goals by providing and reviewing with participants their monthly C4L reports. Based on blood glucose and BP entries, patients received support messages and queries either from CHWs or on their phone. If patients did not enter any glucose values in C4L over a 4-week period, the CHW contacted them to check on their status. Every month, or a day before scheduled office visits, the CHW printed out and scanned the patient C4L summary information (glucose, BP trends, wellness and clinical goal status information) into the EMR. The CHW conferred with the health care provider regularly to determine if any adjustments might be recommended and then provided any new advice to each participant. If the state of a participant's medical needs changed (eg, hospitalization, ED visit) or problems/concerns were identified by the participant or the CHW, the participant and the CHW were able to make contact by phone and/or face-to-face meeting for greater support.

We assessed readiness to change in all patients at baseline using the validated questionnaire by Peterson et al.31

Incentives

C4L participants who used their own phone and CHW-only participants were provided a $15/month incentive. Any C4L participants (with or without a CHW) who were provided a cell phone and contract received no additional incentive.

Primary study end points

At the baseline visit, the study staff determined by patient interview and chart review the status of the 11 to 13 applicable wellness behaviors and clinical goals for the year before study enrollment. Our primary end point was the number of wellness behaviors and clinical goals at 1 year compared with baseline.

Secondary end points

Secondary outcomes included improvement in both behavior and clinical end points. End points compared from baseline to year 1 and across the 3 treatment arms included the following:

  1. HbA1c
  2. BP
  3. LDL cholesterol (LDLc)
  4. Health care use: number of ED visits, hospitalizations, and unscheduled acute care clinic visits
  5. Brief Diabetes Distress Screening Instrument (Fisher et al, 8-item)27
  6. Medication adherence (Morisky 4-item scale)28
  7. Participant satisfaction with CHW and Voxiva C4L mHealth system:
    1. Subject Satisfaction Questionnaire (Table 3)
    2. Voxiva C4L 90-day and 180-day feedback surveys
    3. Participant poststudy focus groups
  8. Utilization of the C4L features: participant messages sent and received
  9. CHW use of the CHW checklist
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Table 3

Patient Satisfaction Questionnaire.

Statistical Analysis

We designed the study to detect a 25% improvement in composite wellness behaviors and clinical goals. From a survey of our 3 collaborating clinics, we observed that approximately 75% of our type 2 DM patients had 3 or more possible wellness and clinical end points that were not achieved in the past year and, on average, were meeting between 6 and 7 wellness goals. Assuming 60 participants per group at study end (n = 66 at start with 10% dropout), there would be adequate (>80%) power to detect an overall 25% (1.5-1.75) increase in the number of goals met from baseline. The study would also be adequately powered to detect a 25% difference in goals met between groups with the hypothesis that the C4L + CHW group would see a 50% difference from baseline as compared with a 25% difference for C4L alone. We calculated descriptive statistics as appropriate using means and SE or proportions for all characteristics and end points. We compared differences in continuous variables at baseline and year 1 across groups using analysis of variance with Tukey pairwise comparisons of Kruskal-Wallis with Dunn tests for pairwise comparisons as appropriate. We compared differences in categorical variables at baseline and year 1 across groups using Pearson chi-square with Tukey pairwise comparisons as appropriate. We conducted pairwise comparisons only if there were overall groups differences for a particular variable. We also examined differences by site by including a random effect for site in the analysis of variance models or stratifying the chi-square analysis by site.

The primary end point was the change in the number of wellness behaviors and clinical goals at 1 year compared with baseline. Because there were no apparent group differences at year 1, we compared overall differences between baseline and final values. For continuous variables, we used analysis of variance of difference scores, paired t tests, and Wilcoxon signed rank as appropriate to compare baseline to year 1 differences. For categorical variables, we compared differences in proportion from baseline to year 1 using a McNemar test.

The analysis was a complete case analysis; therefore, we did not include any participants with missing data from the final visit in the changes over time analysis. We used this approach because the percentage of patients with missing data was low (6%) and missing data occurred primarily because of dropouts after the initial study visit, equal across groups, and because missing patients were similar in baseline characteristics to patients with complete data. We performed no additional subgroup analyses owing to small sample size and relative homogeneity of the patient population's race, sex, socioeconomic status, and initial health status.

Results

Demographics

We enrolled 166 participants over 15 months. As shown in Table 4, a total of 155 participants completed the 12-month study participation. Only 11 participants did not have a 12-month visit (6.6% dropouts), which were evenly distributed across the 3 groups. There was 1 death, 1 participant moved out of the area, and 9 were lost to follow-up.

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Table 4

Patient Flow.

Group 1 (C4L only) had 56 participants; group 2 (CHW) had 56 participants; and group 3 (C4L + CHW) had 54 participants (Table 4). Participants averaged 52.9 years old and were mostly female (72.3%), African American (90.9%), unemployed (70.5%), and on public insurance (98.8%), and had type 2 DM for 13.8 years on average. A total of 78.3% had a high school education or greater, and most (88.5%) had a literacy level of seventh grade or higher (based on Rapid Estimate of Adult Literacy in Medicine). The overall group was obese (mean baseline BMI = 36.7), with baseline systolic BP of >140 mm Hg in 28.9% of patients. Assessment of readiness to change classified most participants at stage 2 or 3 (70.4%) on a scale of 1 to 5. Overall, there were no significant differences among the groups on demographics, baseline health status, and other baseline measurements (Table 5).

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Table 5

Baseline Characteristics.

At baseline, the composite of the 13 wellness and clinical goals met and not met were evenly balanced across the 3 groups (Table 6). Several participants did not have HTN and/or were not recommended to take aspirin; thus these goals were not required for those participants. These participants were still required to be missing 3 or more other goals at baseline for inclusion in this study. Overall, there were no significant differences among the groups on the number of wellness goals met at baseline. On average, participants met 7.7 wellness goals; the mean number of goals not met was 7.1. Of the individual goals, the most commonly met at baseline were (1) diabetes care visits, (2) glucose testing, (3) every 6 months HgA1c testing, and (4) annual lipid testing (Table 7). The goals least met at baseline were home BP testing (if applicable), high medication adherence (Morisky score = 4), exercise, and weight. Only blood glucose testing showed any difference in the proportion meeting the goal at baseline: 96.3% of participants in the C4L + CHW group met the goal compared with 85.7% in C4L and 75% in CHW (chi-square P = .01).

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Table 6

Composite of Wellness Goals and Behaviors: Baseline vs Year 1.

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Table 7

Individual Wellness Goals and Behavior Measures: Baseline vs Year 1.

Primary End Points

The primary hypothesis that C4L plus a CHW would improve the composite wellness and clinical goals over either intervention alone was not borne out in this study. Although an improvement occurred in the composite number of wellness goals and behaviors met overall, no differences occurred between the groups. At 1-year follow-up, 66.5% of the participants who completed the study increased the number of goals met from baseline. Overall, the average number of goals met increased by 1.4 (P = .001), an approximate 20% increase (Table 6).

Our other primary hypothesis was that participants using C4L alone would increase their composite wellness and clinical goals by 25% or greater compared with baseline (12-month historical control). At 1-year follow-up, 69% of C4L-only participants increased goals met. On average, the patients in the C4L group increased the number of goals met by 22% more than baseline (Table 6). However, as previously noted, this improvement was not significantly different from that seen in the CHW or C4L + CHW group.

The individual goal that improved most was home BP monitoring (Table 7). There were modest increases in annual urine testing (P = .03), annual foot evaluation (P = .004), and increased exercise (P = .004). Goals least improved were weight loss and smoking cessation. Although aspirin usage did not increase, it was not clear if this medication was recommended by each patient's medical team. Because the glucose monitoring goal had high adherence at baseline, there was not much room for improvement—only 9 participants went from not meeting to meeting the goal. Medication adherence as measured by the Morisky score increased an average of 0.2 points from baseline (P = .03).

Secondary End Points

Hemoglobin A1c

Of the 166 participants, 1-year follow-up HbA1c values were available for 145 participants (Table 8). The 21 missing values included the 11 dropouts and 10 participants who did not get a clinically indicated HbA1c blood test within the final 6 months (months 7-12) of follow-up. These missing measurements were equally distributed across the 3 study groups. HbA1c decreased significantly from a baseline mean of 10.6% compared with baseline for the overall study group by −1.2% (P < .0001). Year 1 HbA1c level was similar for all 3 study groups (P = .12). There was a significant increase in the number of patients who improved their HbA1c to <9% from baseline (25%) to year 1 (51%; P < .0001), but the differences between the groups at year 1 were not significant (P = .09). There was also a significant improvement overall in the number of patients with an HbA1c < 8% from baseline (0%) to year 1 (30%). Although we observed a significant group difference at year 1 in patients with an HbA1c <8%, this was likely attributable to baseline imbalance.

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Table 8

Baseline and Year 1 HbA1c.

LDLc Change

Specific LDLc values were available in 109 participants at baseline and 122 participants at 1-year follow-up (Table 9). The baseline values did not vary significantly by group. On average, the LDLc significantly decreased 11 mg/dL from baseline (P = .02 Wilcoxon signed rank) for all participants, but the decreases were not significantly different by group. Overall at year 1, 66% of patients had an LDLc <100 mg/dL, which was a significant increase from 55% at baseline (McNemar test P = .03). Interpretation of these data is limited because lipid testing was not required at baseline and 1 year and does not account for the impact of the patient dropouts.

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Table 9

Baseline and Year 1 LDLc.

Blood Pressure

Of the 142 participants for whom BP self-monitoring was deemed appropriate (diagnosis of HTN and/or medical team recommendation), only 27 (16.3%) were monitoring BP ≥1 time/week at baseline. At 1 year, 106 participants (68.3%) stated they measured home BP 1 or more times a week (P < .0001). This health behavior had a similar improvement in all 3 groups. (Table 7).

Fisher Brief Diabetes Distress Screening Instrument

Patient distress at baseline using the Fisher Brief Diabetes Distress Screening Instrument was moderate/high (score ≥12) overall and in all 3 groups (overall mean 16.4 [SE, 0.50]). At 1 year, the Fisher Diabetes Distress score improved to a moderate level (mean decrease, 3.9 [SE, 0.51] points; paired t test P < .0001). Improvement was similar in all 3 groups (analysis of variance, P = .52).

Health Care Use—ED Visits and Hospitalizations

At 1-year follow-up, there were significant decreases in the mean number of hospitalizations, from 1.8 (SE, 0.21) at baseline to 1.1 (SE, 0.01) at year 1 (Wilcoxon signed rank P = .02). There was also a significant decrease in acute care visits, with 0.5 (SE, 0.08) visits at baseline compared with 0.3 (SE, 0.07) visits at year 1 (Wilcoxon signed rank P = .03). There was a slight, but nonsignificant, decrease in mean ED visits, from 0.7 at baseline to 0.5 at year 1 (P = .21). These decreases were not significantly different across the groups.

Voxiva C4L Use Analysis

A total of 110 patients were assigned to the 2 C4L groups. Both the C4L-only (n = 56) and the C4L + CHW (n = 54) participants received a high volume of messages over the 12-month study participation period. Over 12 months, C4L and participants exchanged a total of 182 102 messages. C4L sent 151 822 messages to patients (average 3.8 messages/day; Table 10). Types of outgoing messages included education, reminders, feedback, support, and system messages (Tables 11 and 12). All patients elected to receive glucose testing reminders and most participants elected to receive weekly exercise surveys, weekly weight surveys, and medication reminders. Only a few participants elected to receive weekly BP reminders, and only 1 individual set up appointment reminders. Analysis by group (C4L alone vs C4L + CHW) showed no significant difference in the total number and types of messages exchanged in the C4L system.

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Table 10

Messages Sent From Care4Life to Participants.

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Table 11

Categories of Messages Sent From C4L to Participants.

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Table 12

Sample Messages Sent by Care4Life to Participants by Topic Over 1 Year.

Over 12 months, the 110 patients sent 30 280 messages to C4L (median, 3.9 messages/week; Table 13). There was a trend of greater patient interaction with C4L in the C4L + CHW group. The mean number of messages sent to C4L from the C4L-only group was 239 messages (3.1 messages/week) vs the 309 messages (4.1 messages/week) from the C4L + CHW group (P = nonsignificant).

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Table 13

Messages Sent From Participants to Care4Life.

We analyzed patient engagement and use of the C4L diabetes mHealth application regarding the amount of patient entries of glucose measures and the nonglucose features (BP, medication adherence, weight goals, and exercise goals). Glucose entries are displayed in Table 14. Over 52 weeks, the median glucose entries into C4L was 109.5 (2.1/week) for all 110 participants, 88 (2.7/week) for the C4L group, and 139 (1.7/week) for the C4L + CHW-only group. There was no significant difference across groups. Change in HbA1c was not associated with participants who sent below-the-median number of glucose entries (HbA1c change −1.13%, SE, 0.34) vs participants who sent above-the-median number of glucose entries (HbA1c change −1.52%, SE, 0.29). The mean and median number of glucose entries were categorized as “dangerously high” (>250 mg/dL), “dangerously low” (<70 mg/dL), “high” (before meal, 131-250 mg/dL, or after meal, >180 mg/dL), “low” (before meal, 70-80 mg/dL, or after meal, 80-89 mg/dL), or “ideal” (before meal, 80-130 mg/dL, or after meal, 90-180 mg/dL; Table 14). Among the 110 participants, 21% of glucose entries were dangerously high, 3% dangerously low, 29% high, 40% ideal, and 7% low. The distribution of reported glucose categories was not significantly different between the 2 C4L groups or the high and low responders (above or below the median glucose entries).

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Table 14

Mean and Median Number of Responses to C4L Blood Glucose Check Reminders Over 52 Weeks Categorized by Blood Glucose Levels.

Analysis of engagement with the C4L nonglucose features (weekly response for medication adherence, exercise goal, weight goal) for those participants who elected to use each feature is listed in Table 15. Patient entries for these 3 features (maximum 52/year) was low (median approximately 11 total responses/year for each of the 3 nonglucose categories). In addition, there was no significant relationship between patients who sent a high vs low number (above or below median) of message entries and improvement of the 3 corresponding wellness/clinical goals.

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Table 15

Mean and Median Number of Responses to Care4Life Weekly Non-glucose-related Reminders.

CHW Activity Analysis

We evaluated the use of our Diabetes Chronic Disease Checklist (Table 2) to determine the role of this tool on the standardization and structure of the role of the community health worker–patient interactions. The checklist addressed (1) medication issues, (2) diabetes-specific issues, (3) HTN-specific issues, (4) scheduling issues, (5) activity and exercise goals, (6) diet advice and goals, (7) other health issues, and (8) social needs. Analysis of data of a sample of 4 CHWs over a 4-month period included 105 patients, with an average of 5 visits per patient. The mean number of times a CHW addressed each checklist category per patient was 4.2 for medication issues, 4.1 for diabetes issues, 3.8 for HTN, 4.0 for scheduling, 3.9 for activity or exercise, 3.9 for diet, 3.8 for other health issues, and 3.8 for social needs.

Patient Study Feedback Surveys

We conducted patient feedback surveys at 12 months to assess patient program enhancement of self-management skills (Table 16a). There was uniform patient enthusiasm that the program improved their ability to track and control glucose and BP. The C4L + CHW group participants also felt that C4L and CHW strategies were complementary.

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Table 16a

Patient Survey Responses (N = 155).

The C4L system generated 90-day (Table 16b) and 180-day (Table 16c) questionnaires via the C4L app for both C4L groups. This survey compiled participants’ impressions of C4L's impact on diabetes knowledge and management, medication and appointment adherence, health goals, and overall rating of the C4L system. Similar to the main study feedback survey, participants’ responses to the C4L survey questions overall were highly positive in both groups at both time periods.

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Table 16b

Care4Life-Generated Feedback Survey: 90 Days.

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Table 16c

Care4Life-Generated Feedback Survey: 180 Days.

Discussion

Nowhere is the need for a shift more evident than in the care of patients with the chronic illnesses of DM and HTN. Chronic care of these high-cost conditions requires coordination of an extended multidisciplinary team of health care providers, making it an ideal starting point for developing new approaches to system change and improved outcomes.

The key challenge of a new system is to enhance relationships between the patient and the medical providers, with an emphasis on promoting health behavior change. Research has shown that CHWs are highly successful at engaging and helping sustain patients’ relationships with providers, and they help change behavior to improve health. The added advantage of an mHealth system is that it is literally in the hands of the patient, with instant feedback regarding the status of their disease. Both CHWs and mHealth have the potential to achieve this goal; however, until now, most CHW and mHealth programs have focused on monitoring blood glucose with nonselective health reminders rather than centering on patient-determined goals. Our project identified patient status of wellness behaviors and clinical goals and had the patient set future goals for their DM care. Using a CHW alone or the patient-adjustable C4L mHealth system with or without the assistance of a CHW, we tailored the information exchange (eg, physiologic measures, status of standard of care goals, medication updates, reminders, disease education, social issues) to target patient-specified wellness behaviors and clinical goals. Participation throughout this project's duration was high—only 11 participants dropped out, most early. Factors that promoted high adherence may have included the monthly $15 payment to each participant and a strong patient–CHW relationship. Although patient-initiated responses in the C4L system were only modest, the frequent C4L reminders may have improved patient behavior, which was not necessarily reflected in the number of times a patient made entries in C4L.

In this randomized controlled trial of 166 inner-city Medicaid patients with type 2 DM, we observed improved achievement of wellness and clinical goals, reduction of HbA1c, and reduced health care use using either or both C4L and CHWs for 1 year. This multicenter study involved patients followed for treatment in both general internal medical clinics and specialty diabetes clinics at 3 medical centers. Each site had a “champion” lead physician with a team approach to chronic care management. Patients and medical staff were encouraged to utilize C4L and communicate with a CHW embedded in the clinic team. Different from most other studies, a primary focus of our program was on patient behavior change targeting key wellness and clinical goals. After 12 months, 66% of participants increased achievement of a composite of 13 possible wellness and clinical goals by a mean of 1.4 goals. Goal improvement was similar in the 3 strategies of C4L alone, a CHW alone, and C4L + CHW. We observed that some goals were easier to improve, whereas other goals were more resistant to change. Home BP monitoring and improved medication adherence improved most; however, increased BP monitoring may have occurred more because we provided a free BP cuff to each patient, rather than the impact from the CHWs or C4L. Goals with moderate improvement were annual kidney, eye, and foot examinations and increased weekly exercise. Weight loss and smoking cessation improved least.

Improved achievement of exercise goals (greater than 20 minutes 3 times a week) to the weekly C4L query was by patient report and could not be verified, but the weekly C4L reminder and the support from the CHWs about goals may have led to increased exercise. Neither the CHWs nor C4L provided specific weight loss and smoking cessation programs, which may explain the limited improvement of these goals.

At 1-year follow-up, HbA1c decreased significantly (by −1.3%) for the total study population, with similar reductions in each group. This improved DM control matches, or exceeds, many prior medication and lifestyle interventions. Prior studies have shown mild improvement of HbA1c using CHWs (average −0.8%).18 In our project, the CHW-only group achieved HbA1c lowering at 1 year of −1.2%. This improvement is similar to outcomes reported in a review of community interventions that focused on African Americans with type 2 DM (HbA1c change range −0.55 to −1.23% [P < .05]).29 Our better-than-average reduction of HbA1c may have been assisted by our system integrating the CHWs into the medical team and standardizing CHW–patient interaction using a CHW-directed checklist.

Previous studies providing mHealth to patients with DM have shown variable improvement of HbA1c. The WellDoc randomized trial lowered HbA1c by 1.9% (net −1.2% compared with placebo), but this study excluded Medicare and Medicaid beneficiaries and participants were supported by WellDoc staff.23 Our project observed an improved HbA1c −1.3% in the C4L-only group and −1.2% in the C4L + CHW group in high-risk, inner-city Medicaid participants, with limited assistance from the study team for the C4L-only group. The study patients had a high baseline HbA1c (mean for all 3 groups was 10.6% with an HbA1c >9% in 74.7% of participants). After 1 year, a remarkable number of the total participants achieved an HbA1c <9% (51%), of which 30.3% had an HbA1c <8%.

A key goal for both mHealth and CHWs is promotion of medication adherence. In this study, based on the Morisky score, medication adherence improved significantly across all groups (Table 7). C4L has the important advantage of providing daily medication reminders. Our participants particularly appreciated the reminders in C4L, as noted in our final study questionnaires and our poststudy focus groups.

Both lipid values and systolic BPs improved significantly for the total study group, but no differences occurred across groups. Use of lipid-lowering drugs was high at baseline for all groups and we did not require follow-up lipid measurements during the study. BP data were limited to occasional office visits and did not represent BP response.

Chronic stress is common in patients living with diabetes and is a strongly associated with HbA1c, particularly among minority groups.30 It has been suggested that for some patients, high disease distress can influence self-management and medication adherence, with subsequent effects on glycemic control; for other patients, poor control can lead to distress, which can influence disease management. All 3 patient groups in our study reported moderate/high distress (Fisher diabetes distress scale [DDS] score ≥12).28 At baseline, the mean Fisher DDS score was 16.4 (SE, 0.5) for the total group. After 12 months, diabetes distress decreased significantly in all groups to a moderate level (Fisher DDS score, 12.6 ([SE, 0.47; P < .0001]). This favorable response suggests that participant establishment of a relationship between participants with both the C4L experience and interactions with CHWs may contribute to less functional impairment and improved self-management.

An important outcome from this study was the reduced need for urgent or emergency health care use. Both hospitalizations and acute care office visits fell significantly, with a trend toward fewer ED visits.

Our original hypothesis was that adding CHWs to mHealth would be a superior strategy to motivate patients to improve chronic diabetes outcomes. In this project, improvement of wellness/clinical goals, lower HbA1c, and reduced health care use in the C4L + CHW group was similar to the C4L and CHW groups alone. We remain enthusiastic that mHealth plus a CHW has the greatest potential for chronic care support. While mHealth can provide tips, reminders, and feedback daily, a CHW has a complementary role of addressing social needs, assisting patients with the mHealth system, and connecting patients with their medical team.

This study provided detailed insight into participant interactions with a specific mHealth application. The C4L mHealth system generated a steady stream of messages (educational tips, reminders, and queries) to participants throughout the 12-month study (mean, 3.75 messages/day; Tables 10-11). Although, given the option, few participants requested a change in the frequency of messages. Feedback from our poststudy focus group participants showed they particularly appreciated the reminders feature of C4L. Patient-generated entries into C4L varied widely across the patient cohort, with a median of 3.9/week. Most entries were glucose measures with a nonstatistical trend toward greater entries by the C4L + CHW group (median, 2.7/week) vs the C4L-only participants (median, 1.7/week; Table 12). Analysis of C4L data also provided insight into the types of glucose values recorded by participants, with 28% of glucose entries in the “dangerously high” (>250 mg/dL) or “dangerously low” (<70 mg/dL) range (Table 14). A C4L summary report of this value was sent to both participants and their health care team monthly, providing the opportunity to adjust lifestyle and medications. Adding a CHW to the medical team allowed more direct discussion of C4L data with participants and alerts to the medical team. Patient engagement with the weekly nonglucose messages was modest for exercise goals, weight goals, BPs, and medication adherence, with approximately 50% of participants responding to these queries less than once a month (Table 15). Although the number of patient entries into C4L for many participants, with and without a CHW, was low, these data do not necessarily reflect the full extent of engagement with the mHealth app. C4L provides a routine reminder system that activates patients to measure glucose, take medications, and improve their behaviors, even though patients may not take time to enter a response in the app. As recorded in the 90- and 180-day C4L-generated patient assessment questionnaire, nearly all patients indicated that C4L improved their diabetes care. This positive interaction of patients with C4L was further supported by the feedback obtained in our poststudy focus groups. A future system using Bluetooth connectivity with a glucometer and BP cuff is likely to result in increased documentation of patient self-management activity.

Other lessons learned from this study were how to maximize the roles of a CHW. Different CHW models have used face-to-face, group, peer focus, or peer-to-peer interactions.14 Our project contacts with participants included face-to-face meetings at home or in the clinic as well as phone calls. CHWs had added training in living with diabetes and HTN. In addition, CHW training in mHealth facilitated their role as digital navigators for participants’ use and understanding of C4L. Importantly, each CHW was integrated into the medical team. They were recognized by the medical staff and had access to medical records. This encouraged CHWs to provide C4L summary data, patient preferences, and both medical and social needs to the patients’ providers in a timely fashion. In addition, our CHW Diabetes Chronic Disease Checklist was an effective tool for structuring and standardizing CHW–patient interactions. This system ensured that the CHW routinely addressed both medical and social needs. We included CHWs in our monthly multisite team meetings.

Our study added support to prior research that demonstrated that the use of CHWs can result in positive outcomes in DM care for underserved populations. While mHealth has had some previous reports of success with DM management, our project is one of the few studies that has shown benefit in an urban, vulnerable Medicaid population. Our study participants were predominately inner-city Medicaid patients, of whom most were African American. Although this group is typical for inner-city patients with diabetes, all participants were receiving care at a university internal medicine or DM clinic. How the CHW and mHealth strategies would work in a community clinic setting, which may require supplemental infrastructure and staffing, is worthy of further study.

Limitations

Our data suggest that a CHW and/or the C4L system can improve chronic care of DM; however, our study design did not include a usual care comparison control group that had neither a CHW nor C4L. Although not a true usual care group, our CHW alone group, as noted previously, had a reduction of HbA1c comparable to that seen in prior CHW DM intervention studies, supporting the favorable outcomes seen in our 3 groups.

Providing participants with a monthly incentive may have introduced bias into this study. All participants received a $15 monthly stipend. Instead of the $15 monthly payment, a small subset of participants were incentivized by being provided free text messaging costs or, if needed, a cell phone handset and a cell phone contract that included text messaging service. These incentives may have kept participants engaged with the C4L and the CHW alone, as well as contributed to the low study dropout rate (only 6.6%).

There may be limited value to the lipid and BP end points because (1) the lipid data set was small, with less than half the participants having both baseline and 1-year follow-up measures; and (2) the significance of the baseline vs 1-year BP measurements is questionable because this result was based only on single measurements recorded in the patient's medical record. Also, providing a free home electronic BP cuff to all participants may have been the major reason for improved BP self-monitoring rather than C4L and the CHWs.

Benefits of the CHW-only and the C4L + CHW strategies may have been blunted by the need to replace all 3 of our CHWs midway through the project. CC AHEC, our original partner providing CHWs, suddenly closed all operations and all 3 CHWs were no longer available. We quickly replaced CC AHEC with the Institute for Public Health Innovations, a local organization that trains and deploys CHWs. During the 6-week gap in CHW coverage, our research coordinators filled in, maintaining phone and clinic support for both old and new participants in the CHW arms. Furthermore, upon review of CHW interactions with participants, we noted that 2 of the 3 original AHEC CHWs had much lower number of contacts with patients compared with the other CHWs. Although we provided the CHWs with guidance for patient interaction with our checklist, they were not strictly supervised over the course of the study to primarily focus on helping patients improve specific missing wellness/clinical goals. More intensive ongoing review of the CHWs’ activity on a routine basis might have enhanced their impact on both the primary and secondary outcomes. Realizing that more frequent oversight is recommended to maintain the quality of a CHW–patient experience, we added separate monthly group meetings for the new CHWs and the project leadership.

Based on our experience, new strategies should be developed that modify mHealth apps and the training and activities of CHWs, with a focus on addressing individual goals. These strategies should take advantage of the complementary and overlapping strengths of mHealth and CHWs. While C4L provides frequent reminders, tips, and feedback on status of goals, CHWs can act as personal coaches, digital navigators who support mHealth and can assist with social issues. Future versions of mHealth need to continue as a system with reminders, feedback, and education, but systems also need to address both medical and social needs (eg, organizing patients’ medications, transportation to clinic, how to contact their medical team, access to and understanding of medications, diet suggestions, and links with a diabetes educator and nutritionist). mHealth success may benefit from the addition of a CHW to address these needs and to serve as a digital navigator to assist with the cell phone app. Finally, integrating mHealth information into the electronic medical record with focused alerts should improve medical team awareness and responses to patient needs. Although all 3 strategies (C4L alone, CHW alone, and C4L + CHW) showed similar improvement of diabetes goals, based on the lessons learned using CHWs and C4L in this study, a modified combination diabetes support system may have a potential for superior outcomes.

Conclusions

Our project demonstrated that C4L and community health workers, alone and in combination, improve self-management skills and control of DM in an inner-city Medicaid population to a similar extent. Both C4L and CHWs integrated into a medical team activate and sustain patient engagement in DM care, promoting achievement of wellness and clinical goals, reducing HbA1c, and lowering health care use. CHWs support patients with DM by addressing both medical needs and the social determinants of chronic disease. In addition, CHWs may enhance patient engagement with mHealth by acting as digital navigators. In the future, a potential combination strategy may start by providing patients with a CHW and transitioning to mHealth support, with reintroduction of CHWs as needed to maintain patient healthy behaviors.

Medicaid patients are a key population that has been shown to benefit from assistance from CHWs. Although CHWs are most common in nonprofit and public health programs, CHWs are also becoming increasingly common in managed care settings. In addition, a growing number of community health centers that serve predominantly ethnic and racial minority populations use CHWs in their clinics, having them work closely with the health care team under the supervision of a health care provider. By integrating CHWs into the clinic staff, health centers can identify community members eligible for coverage and ensure that patients with health care coverage receive all necessary services for which they are eligible.

Our health care approach combining 2 complementary components—building a community health worker force and introducing the C4L mobile health technology—are easily available for replication and can be a template for sustainability and spread to other Medicaid clinic environments in the United States. There are many potential stakeholders in this new system, each of which needs to evaluate the evidence for adopting a role in promoting similar programs. We hope to promote the components of this project to multiple sites across the United States. Working with payors and patient advocacy groups, a sustainable business model needs to be created that will allow both CHWs and mHealth to become a permanent component of the US health care system.

References

1.
Statistics: examine the facts. American Diabetes Association. Accessed May 3, 2016. https://www.diabetes.org/resources/statistics
2.
Measuring what matters in DC. DC Health Matters. Accessed May 2, 2016. www​.dchealthmatters.org
3.
Spanakis EK, Golden SH. Race/ethnic difference in diabetes and diabetic complications. Curr Diab Rep. 2013;13(6):814-823. [PMC free article: PMC3830901] [PubMed: 24037313]
4.
National Center for Chronic Disease Prevention and Health Promotion. 2007 national diabetes fact sheet. Centers for Disease Control and Prevention; 2008.
5.
Sadaf SH, Fradkin J, Cowie CC. Poor control of risk factors for vascular disease among adults with previously diagnosed diabetes. JAMA. 2004;291(3):335-342. [PubMed: 14734596]
6.
Tuerk PW, Mueller M, Egede LE. Estimating physician effect on glycemic control in the treatment of diabetes. Diabetes Care. 2008;31(5):869-873. [PubMed: 18285552]
7.
Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. National Academies Press; 2001. [PubMed: 25057539]
8.
Institute of Medicine. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health care. National Academies Press; 2003. [PubMed: 25032386]
9.
Heister M, Spencer M, Forman J, et al. Participants’ assessments of the effects of a community health worker intervention on their diabetes self-management and interactions with healthcare providers. Am J Prev Med. 2009;37(6 Suppl 1):S270-S279. doi:10.1016/j.amepre.2009.08.016 [PMC free article: PMC3782259] [PubMed: 19896029] [CrossRef]
10.
Brownstein JN, Rosenthal EL. The challenge of evaluating CHA services. In: Rosenthal EL, Wiggins N, Brownstein JN, et al, eds. Report of the National Community Health Advisor Study. Mel and Enid Zuckerman Arizona College of Public Health; 1998:50-74.
11.
Norris SL, Chowdhury FM, Van Le K, et al. Effectiveness of community health workers in the care of persons with diabetes. Diabet Med. 2006;23(5):544-556. [PubMed: 16681564]
12.
Hargraves JL, Ferguson WJ, Lemay CA, Pernice J. Community health workers assisting patients with diabetes in self-management. J Ambul Care Manage. 2012;35(1):15-26. [PubMed: 22156952]
13.
Ruddock JS, Poindexter, Gary-Webb TL, Walker EA, Davis NJ. Innovative strategies to improve diabetes outcomes in disadvantaged populations. Diabet Med. 2016;33(6):723-733. [PubMed: 27194172]
14.
Palmas W, March D, Darakjy S, et al. Community health worker interventions to improve glycemic control in people with diabetes: a systematic review and meta-analysis. J Gen Intern Med. 2015;30(7):1004-1012. [PMC free article: PMC4471021] [PubMed: 25735938]
15.
Shah M, Kaselitz E, Heisler M. The role of community health workers in diabetes: update on current literature. Curr Diab Rep. 2013;13(2):163-171. [PMC free article: PMC3929361] [PubMed: 23345198]
16.
Skinner C, Finkelstein J. Using cell phones for chronic disease prevention and management. AMIA Annu Symp Proc. 2008;6:1137. [PubMed: 18999018]
17.
Car J, Gurol-Urganci I, de Jongh T, Vodopivec-Jamsek V, Atun R. Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database Syst Rev. 2012;(7):CD007458. doi:10.1002/14651858.CD007458.pub2 [PubMed: 22786507] [CrossRef]
18.
Garabedian LF, Ross-Degnan D, Wharam JF. Mobile phone and smartphone technologies for diabetes care and self-management. Curr Diab Rep. 2015;15(12):109. [PMC free article: PMC6525331] [PubMed: 26458380]
19.
Hartz J, Yingling L, Powell-Wiley TM.Use of mobile technology in the prevention and treatment of diabetes mellitus. Curr Cardiol Rep. 2016;18(12):130. [PubMed: 27826901]
20.
Connelly J, Kirk A, Masthoff J, MacRury S. The use of technology to promote physical activity in type 2 diabetes management: a systematic review. Diabet Med. 2013;30(12):1420-1432. [PubMed: 23870009]
21.
Bell AM, Fonda SJ, Walker MS, Schmidt V, Vygotsky RA. Mobile phone-based video messages for diabetes self-care support. J Diabetes Sci Technol. 2012;6(2):310-319. [PMC free article: PMC3380772] [PubMed: 22538140]
22.
Holmen H, Torbjormsen A, Wahl AK, Jenum AK, Smastuen MC. A mobile health intervention for self-management and lifestyle change for persons with type 2 diabetes, part 2: one-year results from the Norwegian randomized controlled trial renewing health. JMIR Mhealth UHealth. 2014;2(4):e57. doi:10.2196/mhealth.3882 [PMC free article: PMC4275495] [PubMed: 25499872] [CrossRef]
23.
Quinn CC, Shardell MD, Terrin, ML, Barr EA, Ballew SH, Gruber-Baldini AL. A cluster randomized trial of a mobile phone personalized behavioral intervention for blood glucose control. Diabetes Care. 2011;34(9):1934-1942. [PMC free article: PMC3161305] [PubMed: 21788632]
24.
Nobis S, Lehr D, Ebert DD, et al. Efficacy of a web-based intervention with mobile phone support in treating depressive symptoms in patients with type 1 and type 2 diabetes: a randomized controlled trial. Diabetes Care. 2015;38(5):776-783. [PubMed: 25710923]
25.
Katz R, Mesfin T, Barr K, Livingston M. Challenges implementing a community based diabetes self-management program: “It's not just about the cell phone.” J Health Commun. 2012;17(Suppl 1):67-72. [PubMed: 22548601]
26.
Peers for Progress Consensus Evaluation for Research on Self-Management and Peer Support in Diabetes: Evaluation measures, indicators, tools/instruments, and reference information.
27.
Fisher L, Glasgow RE, Mullan JT, Skaff MM, Polonsky WH. Development of a brief diabetes distress screening instrument. Ann Fam Med. 2008;6(3):246-252. [PMC free article: PMC2384991] [PubMed: 18474888]
28.
Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens. 2008:10(5):348-354. [PMC free article: PMC2562622] [PubMed: 18453793]
29.
Smalls BL, Walker RJ, Boniha HS, Campbell JA, Egede LE. Community interventions to improve glycemic control in African American with type 2 diabetes: a systemic review. Glob J Health Sci. 2015;7(5):171-182. [PMC free article: PMC4803865] [PubMed: 26156923]
30.
Hilliard ME, Yi-Frazier JP, Hessler D, Butler AM, Anderson BJ, Jaser S. Stress and A1c among people with diabetes across the lifespan. Curr Diab Rep. 2016;16(8):67. doi:10.1007/s11892-016-0761-3. [PMC free article: PMC4936828] [PubMed: 27287017] [CrossRef]
31.
Peterson KA, Hughes M. Readiness to change and clinical success in a diabetes educational program. J Am Board Fam Pract. 2002;15(4):266-271. [PubMed: 12150458]
32.
Katz R, Patel S, Cohen J, Young H. Enhancing diabetes and hypertension self-management: a randomized trial of a mHealth strategy in a community setting. Paper presented at: 75th Scientific Sessions of the American Diabetes Association; June 5-9, 2015; Boston, MA.

Acknowledgment

Research reported in this report was [partially] funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (#IH-1304-6797) Further information available at: https://www.pcori.org/research-results/2013/comparing-three-methods-help-patients-manage-type-2-diabetes

Original Project Title: Changing the Healthcare Delivery Model: A Community Health Worker/Mobile Chronic Care Team Strategy
PCORI ID: IH-1304-6797
ClinicalTrials.gov ID: NCT02093234

Suggested citation:

Katz RJ, Magee MF, Nunlee-Bland G, et al. (2019). Comparing Three Methods to Help Patients Manage Type 2 Diabetes. Patient-Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/4.2019.IH.13046797

Disclaimer

The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.

Copyright © 2019. George Washington University. All Rights Reserved.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License which permits noncommercial use and distribution provided the original author(s) and source are credited. (See https://creativecommons.org/licenses/by-nc-nd/4.0/

Bookshelf ID: NBK602704PMID: 38620346DOI: 10.25302/4.2019.IH.13046797

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