"How" Versus "Why" Messaging to Increase Uptake of Booster Vaccination Against COVID-19: Results of a Pragmatic Randomized Trial.

BACKGROUND: Messages aimed at increasing uptake of vaccines have been modestly successful, perhaps in part because they often focus on why individuals should receive a vaccine. Construal Level Theory posits that messages emphasizing "how" to get a vaccine may be more effective at encouraging vaccination than emphasizing "why." This message framing may be particularly important for COVID-19 booster acceptance. OBJECTIVE: To determine if pre-visit patient portal messages designed using Construal Level Theory increase rates of COVID-19 booster vaccination. DESIGN AND INTERVENTIONS: This 3-arm randomized trial was conducted across three large, diverse primary care clinics in Massachusetts between February and May 2022, testing the impact of "how" versus "why" framed pre-visit messages versus no messages ("usual care"). Messages were sent by patient portal two business days before a visit. PARTICIPANTS: Adults with upcoming primary care visits who had electronic health record evidence of receiving their initial COVID-19 vaccination series but not a booster dose. MAIN MEASURES: Receipt of a COVID-19 booster vaccination after the message was sent through the visit date (primary outcome) or 6 weeks (secondary outcome). KEY RESULTS: A total of 3665 patients were randomized (mean age: 53.5 years (SD: 17.3), 59% female, 65.2% White, 26.6% Hispanic), with 1249 to "how" 1199 to "why," and 1217 to usual care arms. Except for clinic and preferred language, characteristics were well balanced across arms. Rates of COVID-19 booster were 13.6% (usual care), 11.7% ("how") (odds ratio (OR) "how" vs usual care: 0.87, 95%CI: 0.67-1.14), and 13.7% ("why") ("why" vs usual care: OR: 1.01, 95%CI: 0.81-1.28). At 6 weeks, "why" outperformed "how" for vaccination (OR: 1.26, 95%CI: 1.06-1.49), with no difference versus usual care. CONCLUSIONS: We found no differences on visit booster receipt after single pre-visit portal messages designed using Construal Level Theory. Further studies to identify effective messaging interventions are needed, especially as additional doses are recommended. CLINICAL TRIAL REGISTRATION: This trial is registered at clinicaltrials.gov, ID: NCT04871776 . Initial release occurred 04/30/2021.

Reducing ethnic and racial disparities by improving undertreatment, control, and engagement in blood pressure management with health information technology (REDUCE-BP) hybrid effectiveness-implementation pragmatic trial: Rationale and design.

BACKGROUND: While racial/ethnic disparities in blood pressure control are documented, few interventions have successfully reduced these gaps. Under-prescribing, lack of treatment intensification, and suboptimal follow-up care are thought to be central contributors. Electronic health record (EHR) tools may help address these barriers and may be enhanced with behavioral science techniques. OBJECTIVE: To evaluate the impact of a multicomponent behaviorally-informed EHR-based intervention on blood pressure control. TRIAL DESIGN: Reducing Ethnic and racial Disparities by improving Undertreatment, Control, and Engagement in Blood Pressure management with health information technology (REDUCE-BP) (NCT05030467) is a two-arm cluster-randomized hybrid type 1 pragmatic trial in a large multi-ethnic health care system. Twenty-four clinics (>350 primary care providers [PCPs] and >10,000 eligible patients) are assigned to either multi-component EHR-based intervention or usual care. Intervention clinic PCPs will receive several EHR tools designed to reduce disparities delivered at different points, including a: (1) dashboard of all patients visible upon logging on to the EHR displaying blood pressure control by race/ethnicity compared to their PCP peers and (2) set of tools in an individual patient's chart containing decision support to encourage treatment intensification, ordering home blood pressure measurement, interventions to address health-related social needs, default text for note documentation, and enhanced patient education materials. The primary outcome is patient-level change in systolic blood pressure over 12 months between arms; secondary outcomes include changes in disparities and other clinical outcomes. CONCLUSION: REDUCE-BP will provide important insights into whether an EHR-based intervention designed using behavioral science can improve hypertension control and reduce disparities.

Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study.

BACKGROUND: Individuals with diabetes need regular support to help them manage their diabetes on their own, ideally delivered via mechanisms that they already use, such as their mobile phones. One reason for the modest effectiveness of prior technology-based interventions may be that the patient perspective has been insufficiently incorporated. OBJECTIVE: This study aims to understand patients' preferences for mobile health (mHealth) technology and how that technology can be integrated into patients' routines, especially with regard to medication use. METHODS: We conducted semistructured qualitative individual interviews with patients with type 2 diabetes from an urban health care system to elicit and explore their perspectives on diabetes medication-taking behaviors, daily patterns of using mobile technology, use of mHealth technology for diabetes care, acceptability of text messages to support medication adherence, and preferred framing of information within text messages to support diabetes care. The interviews were digitally recorded and transcribed. The data were analyzed using codes developed by the study team to generate themes, with representative quotations selected as illustrations. RESULTS: We conducted interviews with 20 participants, of whom 12 (60%) were female and 9 (45%) were White; in addition, the participants' mean glycated hemoglobin A1c control was 7.8 (SD 1.1). Overall, 5 key themes were identified: patients try to incorporate cues into their routines to help them with consistent medication taking; many patients leverage some form of technology as a cue to support adherence to medication taking and diabetes self-management behaviors; patients value simplicity and integration of technology solutions used for diabetes care, managing medications, and communicating with health care providers; some patients express reluctance to rely on mobile technology for these diabetes care behaviors; and patients believe they prefer positively framed communication, but communication preferences are highly individualized. CONCLUSIONS: The participants expressed some hesitation about using mobile technology in supporting diabetes self-management but have largely incorporated it or are open to incorporating it as a cue to make medication taking more automatic and less burdensome. When using technology to support diabetes self-management, participants exhibited individualized preferences, but overall, they preferred simple and positively framed communication. mHealth interventions may be improved by focusing on integrating them easily into daily routines and increasing the customization of content.

The impact of using reinforcement learning to personalize communication on medication adherence: findings from the REINFORCE trial.

Text messaging can promote healthy behaviors, like adherence to medication, yet its effectiveness remains modest, in part because message content is rarely personalized. Reinforcement learning has been used in consumer technology to personalize content but with limited application in healthcare. We tested a reinforcement learning program that identifies individual responsiveness ("adherence") to text message content and personalizes messaging accordingly. We randomized 60 individuals with diabetes and glycated hemoglobin A1c [HbA1c] ≥ 7.5% to reinforcement learning intervention or control (no messages). Both arms received electronic pill bottles to measure adherence. The intervention improved absolute adjusted adherence by 13.6% (95%CI: 1.7%-27.1%) versus control and was more effective in patients with HbA1c 7.5- < 9.0% (36.6%, 95%CI: 25.1%-48.2%, interaction p < 0.001). We also explored whether individual patient characteristics were associated with differential response to tested behavioral factors and unique clusters of responsiveness. Reinforcement learning may be a promising approach to improve adherence and personalize communication at scale.

Clinicians' and Patients' Perspectives on Hypertension Care in a Racially and Ethnically Diverse Population in Primary Care.

Importance: Hypertension control remains suboptimal, particularly for Black and Hispanic or Latino patients. A need exists to improve hypertension management and design effective strategies to efficiently improve the quality of care in primary care, especially for these at-risk populations. Few studies have specifically explored perspectives on blood pressure management by primary care providers (PCPs) and patients. Objective: To examine clinician and patient perspectives on barriers and facilitators to hypertension control within a racially and ethnically diverse health care system. Design, Setting, and Participants: This qualitative study was conducted in a large urban US health care system from October 1, 2020, to March 31, 2021, among patients with a diagnosis of hypertension from a racially and ethnically diverse population, for a range of hypertension medication use hypertension control, as well as practicing PCPs. Analysis was conducted between June 2021 and February 2022 using immersion-crystallization methods. Main Outcomes and Measures: Perspectives on managing blood pressure, including medication adherence and lifestyle, considerations for intensification, and experiences and gaps in using health information technology tools for hypertension, were explored using semistructured qualitative interviews. These cycles of review were continued until all data were examined and meaningful patterns were identified. Results: Interviews were conducted with 30 participants: 15 patients (mean [SD] age, 58.6 [16.2] years; 10 women [67%] and 9 Black patients [60%]) and 15 clinicians (14 PCPs and 1 medical assistant; 8 women [53%]). Eleven patients (73%) had suboptimally controlled blood pressure. Participants reported a wide range of experiences with hypertension care, even within the same clinics and health care system. Five themes relevant to managing hypertension for racially and ethnically diverse patient populations in primary care were identified: (1) difficulty with self-management activities, especially lifestyle modifications; (2) hesitancy intensifying medications by both clinicians and patients; (3) varying the timing and follow-up after changes in medication; (4) variation in blood pressure self-monitoring recommendations and uptake; and (5) limited specific functionality of current health information technology tools. Conclusions and Relevance: In this qualitative study of the views of PCPs and patients on hypertension control, the participants felt that more focus should be placed on lifestyle modifications than medications for hypertension, particularly for patients from racial and ethnic minority groups. Participants also expressed concerns about the existing functionality of health information technology tools to support increasingly asynchronous hypertension care. More intentional ways of supporting treatment intensification, self-care, and follow-up care are needed to improve hypertension management for racially and ethnically diverse populations in primary care.

REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial.

INTRODUCTION: Achieving optimal diabetes control requires several daily self-management behaviours, especially adherence to medication. Evidence supports the use of text messages to support adherence, but there remains much opportunity to improve their effectiveness. One key limitation is that message content has been generic. By contrast, reinforcement learning is a machine learning method that can be used to identify individuals' patterns of responsiveness by observing their response to cues and then optimising them accordingly. Despite its demonstrated benefits outside of healthcare, its application to tailoring communication for patients has received limited attention. The objective of this trial is to test the impact of a reinforcement learning-based text messaging programme on adherence to medication for patients with type 2 diabetes. METHODS AND ANALYSIS: In the REinforcement learning to Improve Non-adherence For diabetes treatments by Optimising Response and Customising Engagement (REINFORCE) trial, we are randomising 60 patients with suboptimal diabetes control treated with oral diabetes medications to receive a reinforcement learning intervention or control. Subjects in both arms will receive electronic pill bottles to use, and those in the intervention arm will receive up to daily text messages. The messages will be individually adapted using a reinforcement learning prediction algorithm based on daily adherence measurements from the pill bottles. The trial's primary outcome is average adherence to medication over the 6-month follow-up period. Secondary outcomes include diabetes control, measured by glycated haemoglobin A1c, and self-reported adherence. In sum, the REINFORCE trial will evaluate the effect of personalising the framing of text messages for patients to support medication adherence and provide insight into how this could be adapted at scale to improve other self-management interventions. ETHICS AND DISSEMINATION: This study was approved by the Mass General Brigham Institutional Review Board (IRB) (USA). Findings will be disseminated through peer-reviewed journals, clinicaltrials.gov reporting and conferences. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov (NCT04473326).

CDC MessageWorks: Designing and Validating a Social Marketing Tool to Craft and Defend Effective Messages.

The Centers for Disease Control and Prevention's Division of Cancer Prevention and Control, in partnership with Oak Ridge Associated Universities, designed an online social marketing strategy tool, MessageWorks, to help health communicators effectively formulate messages aimed at changing health behaviors and evaluate message tactics and audience characteristics. MessageWorks is based on the advisor for risk communication model that identifies 10 variables that can be used to predict target audience intentions to comply with health recommendations. This article discusses the value of the MessageWorks tool to health communicators and to the field of social marketing by (1) describing the scientific evidence supporting use of MessageWorks to improve health communication practice and (2) summarizing how to use MessageWorks and interpret the results it produces.