The prevalence of non-communicable diseases, such as diabetes and cardiovascular disease, is rising in low- and middle-income countries (LMICs). Health behavior change (HBC) interventions such as the widely used Diabetes Prevention Program (DPP) are effective at reducing chronic disease risk, but have not been adapted for LMICs. Leveraging mobile health (mHealth) technology such as text messaging (SMS) to enhance reach and participant engagement with these interventions has great promise, yet we lack evidence-informed approaches to guide the integration of SMS specifically to support HBC interventions in LMIC contexts. To address this gap, we integrated guidance from the mHealth literature with expertise and first-hand experience to establish specific development steps for building and implementing SMS systems to support HBC programming in LMICs. Specifically, we provide real-world examples of each development step by describing our experience in designing and delivering an SMS system to support a culturally-adapted DPP designed for delivery in South Africa. We outline eight key SMS development steps, including: 1) determining if SMS is appropriate; 2) developing system architecture and programming; 3) developing theory-based messages; 4) developing SMS technology; 5) addressing international SMS delivery; 6) testing; 7) system training and technical support; and 8) cost considerations. We discuss lessons learned and extractable principles that may be of use to other mHealth and HBC researchers working in similar LMIC contexts.Trial registration Clinicaltrials.gov, NCT03342274 . Registered 10 November 2017.

Specifications of what and how much health behavior change (BC) content within research interventions are needed to advance BC science, its implementation, and dissemination. We analyzed the types and dosages of the smallest potentially active BC ingredients and associated behavioral prescriptions intended to be delivered in an ongoing physical activity optimization trial for older adults (Ready Steady 3.0 [RS3]). We defined BC types as behavior change techniques (BCT) and behavioral prescriptions. Our protocol integrated the BCT Taxonomy coding procedures with BCT roles (primary or secondary) and, when relevant, linkages to behavioral prescriptions. Primary BCTs targeted theoretical mechanisms of action, whereas secondary BCTs supported primary BCT delivery. Behavioral prescriptions represented what participants were encouraged to do with each primary BCT in RS3 (ascertain, practice, implement). We assessed dosage parameters of duration, frequency, and amount in each BCT and prescription. Results provided a catalog of in-depth, multidimensional content specifications with 12 primary BCTs, each supported by 2-7 secondary BCTs, with dosages ranging from 2 to 8 weeks, 1 to 8 contacts, and 5 to 451 minutes. Minutes spent on behavioral prescriptions varied: ascertain (1 to 41), practice (5 to 315), and implement (0 to 38). Results can be organized and summarized in varied ways (e.g., by content component) to strengthen future assessments of RS3 fidelity and intervention refinement. Results highlight potential benefits of this early, integrated approach to analyzing BC content and frames questions about how such information might be incorporated and disseminated with reporting research outcomes.
The focus of this case study was to assess what and how much behavior change content was within an intervention still under development—by integrating existing frameworks for classifying behavior change techniques, dosages, and behavioral prescriptions. Findings provide the first set of procedures available for collecting, coding, and analyzing data representing the types of behavior change techniques in an intervention, their durations, frequencies, and amounts, and their linkages to what participants are asked to do. Applying these procedures to the protocol and materials in an ongoing study (Ready Steady 3.0) yielded a detailed, multidimensional catalog of the smallest potentially active behavior change ingredients in its intervention, including behavior change techniques, intended uses, and intended dosages. Findings also showcased how this information can be summarized and organized in various ways to strengthen fidelity evaluations and future intervention development. Researchers can use and adapt these new procedures for reporting behavior change content within individual intervention studies. Findings also highlight the potential benefits of this early, integrated approach to analyzing behavior change content and frame questions about how such information might be incorporated and disseminated with reporting research outcomes.

Addressing patient health and care behaviors that underlie much of chronic disease continues to challenge providers, medical practices, health systems, and insurers. Improving health and care as described by the Quadruple Aim requires innovation at the front lines of clinical care: the doctor-patient interaction and office practice. This article describes the use of Lean Six Sigma in a quality improvement (QI) effort to design an effective and scalable method for physicians to prescribe health coaching for healthy behaviors in a primary care medical home within a large integrated delivery and financing system. Building on the national Agency for Healthcare Research and Quality and Robert Wood Johnson Foundation-funded Prescription for Health multisite demonstration, this QI case study provides important lessons for transforming patient-physician-practice support systems to better address lifestyle and care management challenges critical to producing better outcomes.

Background: Smoking continues to be a public health problem worldwide. Smoking and tobacco use are associated with cardiovascular diseases that include coronary heart disease, atherosclerosis, cerebrovascular disease, and abdominal aortic aneurysm. Programs for quitting smoking have played a significant role in reduction of smoking in the United States. The smoking cessation interventions include counseling, nicotine replacement therapy, buproprion therapy, and varenicline therapy. The success rates with each of these approaches vary with clear need for improvement. Moreover, there is a need for a robust theory that can guide smoking cessation counseling interventions and increase the success rates. A fourth generation approach using multi-theory model (MTM) of health behavior change is introduced in this article for smoking cessation. An approach for developing and evaluating an intervention for smoking cessation is presented along with a measurement tool. Methods: A literature review reifying the MTM of health behavior change for smoking cessation has been presented. An instrument designed to measure constructs of MTM and associated smoking cessation behavior has been developed. Results: The instrument developed is available for validation, reliability and prediction study pertaining to smoking cessation. The intervention is available for testing in a randomized control trial involving smokers. Conclusion: MTM is a robust theory that holds promise for testing and application to smoking cessation.