Developing mHealth Interventions with Implementation in Mind: Application of the Multiphase Optimization Strategy (MOST) Preparation Phase to Diabetes Prevention Programming (Preprint)

2022 ◽  
Author(s):  
Megan MacPherson ◽  
Kohle Merry ◽  
Sean Locke ◽  
Mary Jung
Keyword(s):  
Phase I ◽  
Conceptual Model ◽  
Diabetes Prevention ◽  
Healthcare Providers ◽  
Intervention Development ◽  
Diabetes Prevention Program ◽  
Optimization Strategy ◽  
Mhealth Intervention ◽  
Multiphase Optimization Strategy ◽  
The Impact

UNSTRUCTURED With thousands of mHealth solutions on the market, patients and healthcare providers struggle to identify which solution to use/prescribe. The lack of evidence-based mHealth solutions may be due to limited research on intervention development and continued use of traditional research methods for mHealth evaluation. The Multiphase Optimization Strategy (MOST) is a framework which aids in developing interventions which are economical, affordable, scalable, and effective (EASE). MOST Phase I highlights the importance of formative intervention development, a stage often overlooked and rarely published. The aim of MOST Phase I is to identify candidate intervention components, create a conceptual model, and define the optimization objective. While MOST sets these three targets, the framework itself does not provide robust guidance on how to conduct quality research within Phase I, and what steps can be taken to identify potential intervention components, develop the conceptual model, and achieve intervention EASE with the implementation context in mind. To advance the applicability of MOST within the field of implementation science, this paper provides an account of the methods used to develop an mHealth intervention. Specifically, we provide a comprehensive example of how to achieve the goals of MOST Phase I by outlining the formative development of an mHealth prompting intervention within a diabetes prevention program. Additionally, recommendations are proposed for future researchers to conduct formative research on mHealth interventions with implementation in mind. Given its considerable reach, mHealth has the potential to positively impact public health by decreasing implementation costs and improving accessibility. MOST is well-suited for the efficient development and optimization of mHealth interventions. By using an implementation-focused lens and outlining the steps in developing an mHealth intervention using MOST Phase I, this work can may guide future intervention developers towards maximizing the impact of mHealth outside of the research laboratory.

scholarly journals The Impact of a Yearlong Diabetes Prevention Program-Based Lifestyle Intervention on Cardiovascular Health Metrics

2021 ◽  
Vol 12 ◽  
pp. 215013272110298
Author(s):  
Susan M. Devaraj ◽  
Bonny Rockette-Wagner ◽  
Rachel G. Miller ◽  
Vincent C. Arena ◽  
Jenna M. Napoleone ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Lifestyle Intervention ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Cardiovascular Health ◽  
Diabetes Prevention Program ◽  
Behavioral Risk ◽  
Community Based ◽  
The Impact

Introduction The American Heart Association created “Life’s Simple Seven” metrics to estimate progress toward improving US cardiovascular health in a standardized manner. Given the widespread use of federally funded Diabetes Prevention Program (DPP)-based lifestyle interventions such as the Group Lifestyle Balance (DPP-GLB), evaluation of change in health metrics within such a program is of national interest. This study examined change in cardiovascular health metric scores during the course of a yearlong DPP-GLB intervention. Methods Data were combined from 2 similar randomized trials offering a community based DPP-GLB lifestyle intervention to overweight/obese individuals with prediabetes and/or metabolic syndrome. Pre/post lifestyle intervention participation changes in 5 of the 7 cardiovascular health metrics were examined at 6 and 12 months (BMI, blood pressure, total cholesterol, fasting plasma glucose, physical activity). Smoking was rare and diet was not measured. Results Among 305 participants with complete data (81.8% of 373 eligible adults), significant improvements were demonstrated in all 5 risk factors measured continuously at 6 and 12 months. There were significant positive shifts in the “ideal” and “total” metric scores at both time points. Also noted were beneficial shifts in the proportion of participants across categories for BMI, activity, and blood pressure. Conclusion AHA-metrics could have clinical utility in estimating an individual’s cardiovascular health status and in capturing improvement in cardiometabolic/behavioral risk factors resulting from participation in a community-based translation of the DPP lifestyle intervention.

A conceptual model and key variables for guiding supportive interventions for family caregivers of people receiving palliative care

2003 ◽  
Vol 1 (4) ◽  
pp. 353-365 ◽  
Author(s):  
PETER HUDSON
Keyword(s):  
Palliative Care ◽  
Conceptual Model ◽  
Family Caregivers ◽  
Family Caregiver ◽  
Intervention Development ◽  
World Health ◽  
The Family ◽  
Key Variables ◽  
Supportive Interventions ◽  
The Impact

According to the World Health Organization, the patient and family should be viewed as the “unit of care” when palliative care is required. Therefore family caregivers should receive optimal supportive care from health professionals. However, the impact of supporting a dying relative is frequently described as having negative physical and psychosocial sequalae. Furthermore, family caregivers consistently report unmet needs and there has been a dearth of rigorous supportive interventions published. In addition, comprehensive conceptual frameworks to navigate the family caregiver experience and guide intervention development are lacking. This article draws on Lazarus and Folkman's seminal work on the transactional stress and coping framework to present a conceptual model specific to family caregivers of patients receiving palliative care. A comprehensive account of key variables to aid understanding of the family caregiver experience and intervention design is provided.

scholarly journals The preparation phase in the multiphase optimization strategy (MOST): a systematic review and introduction of a reporting checklist

2021 ◽  
Author(s):  
Ryan R Landoll ◽  
Sara E Vargas ◽  
Kristen B Samardzic ◽  
Madison F Clark ◽  
Kate Guastaferro
Keyword(s):  
Systematic Review ◽  
Behavioral Interventions ◽  
Intervention Development ◽  
Future Research ◽  
Optimization Strategy ◽  
Design Elements ◽  
Alternative Intervention ◽  
Multiphase Optimization Strategy ◽  
Preparation Phase ◽  
Almost All

Abstract Multicomponent behavioral interventions developed using the multiphase optimization strategy (MOST) framework offer important advantages over alternative intervention development models by focusing on outcomes within constraints relevant for effective dissemination. MOST consists of three phases: preparation, optimization, and evaluation. The preparation phase is critical to establishing the foundation for the optimization and evaluation phases; thus, detailed reporting is critical to enhancing rigor and reproducibility. A systematic review of published research using the MOST framework was conducted. A structured framework was used to describe and summarize the use of MOST terminology (i.e., preparation phase and optimization objective) and the presentation of preparation work, the conceptual model, and the optimization. Fifty-eight articles were reviewed and the majority focused on either describing the methodology or presenting results of an optimization trial (n = 38, 66%). Although almost all articles identified intervention components (96%), there was considerable variability in the degree to which authors fully described other elements of MOST. In particular, there was less consistency in use of MOST terminology. Reporting on the MOST preparation phase is varied, and there is a need for increased focus on explicit articulation of key design elements and rationale of the preparation phase. The proposed checklist for reporting MOST studies would significantly advance the use of this emerging methodology and improve implementation and dissemination of MOST. Accurate reporting is essential to reproducibility and rigor of scientific trials as it ensures future research fully understands not only the methodology, but the rationale for intervention and optimization decisions.

scholarly journals Simulation and minimization: Technical advances for factorial experiments designed to optimize clinical interventions

2019 ◽  
Author(s):  
Jocelyn Lara Kuhn ◽  
Radley Christopher Sheldrick ◽  
Sarabeth Broder-Fingert ◽  
Andrea Chu ◽  
Lisa Fortuna ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Random Element ◽  
Optimization Strategy ◽  
Randomized Experiments ◽  
Factorial Experiments ◽  
Sample Sizes ◽  
Multiphase Optimization Strategy ◽  
Technical Advances ◽  
Healthcare Interventions ◽  
The Impact

Abstract Background: The Multiphase Optimization Strategy (MOST) is designed to maximize the impact of clinical healthcare interventions, which are typically multicomponent and increasingly complex. MOST often relies on factorial experiments to identify which components of an intervention are most effective, efficient, and scalable. When assigning participants to conditions in factorial experiments, researchers must be careful to select the assignment procedure that will result in balanced sample sizes and equivalence of covariates across conditions while maintaining unpredictability. Methods: In the context of a MOST optimization trial with a 2x2x2x2 factorial design, we used computer simulation to empirically test five subject allocation procedures: simple randomization, stratified randomization with permuted blocks, maximum tolerated imbalance (MTI), minimal sufficient balance (MSB), and minimization. We compared these methods across the 16 study cells with respect to sample size balance, equivalence on key covariates, and unpredictability. Leveraging an existing dataset to compare these procedures, we conducted 250 computerized simulations using bootstrap samples of 304 participants. Results: Simple randomization, the most unpredictable procedure, generated poor sample balance and equivalence of covariates across the 16 study cells. Stratified randomization with permuted blocks performed well on stratified variables but resulted in poor equivalence on other covariates and poor balance. MTI, MSB, and minimization had higher complexity and cost. MTI resulted in balance close to pre-specified thresholds and a higher degree of unpredictability, but poor equivalence of covariates. MSB had 19.7% deterministic allocations, poor sample balance and improved equivalence on only a few covariates. Minimization was most successful in achieving balanced sample sizes and equivalence across a large number of covariates, but resulted in 34% deterministic allocations. Small differences in proportion of correct guesses were found across the procedures. Conclusions: Computer simulation was highly useful for evaluating tradeoffs among randomization procedures. Based on the computer simulation results and priorities within the study context, minimization with a random element was selected for the planned research study. Minimization with a random element, as well as computer simulation to make an informed randomization procedure choice, are utilized infrequently in randomized experiments but represent important technical advances that researchers implementing multi-arm and factorial studies should consider.

scholarly journals Developing a Psychological–Behavioral Intervention in Cardiac Patients Using the Multiphase Optimization Strategy: Lessons Learned From the Field

2019 ◽  
Vol 54 (3) ◽  
pp. 151-163
Author(s):  
Jeff C Huffman ◽  
Rachel A Millstein ◽  
Christopher M Celano ◽  
Brian C Healy ◽  
Elyse R Park ◽  
...  
Keyword(s):  
Physical Activity ◽  
Decision Making ◽  
Behavioral Intervention ◽  
Development Program ◽  
Intervention Development ◽  
Development Project ◽  
Lessons Learned ◽  
Optimization Strategy ◽  
Coronary Syndrome ◽  
Multiphase Optimization Strategy

Abstract Background The Multiphase Optimization Strategy (MOST) is an approach to systematically and efficiently developing a behavioral intervention using a sequence of experiments to prepare and optimize the intervention. Purpose Using a 6 year MOST-based behavioral intervention development project as an example, we outline the results—and resulting decision-making process—related to experiments at each step to display the practical challenges present at each stage. Methods To develop a positive psychology (PP) based intervention to promote physical activity after an acute coronary syndrome (N = 255 across four phases), we utilized qualitative, proof-of-concept, factorial design, and randomized pilot experiments, with iterative modification of intervention content and delivery. Results Through this multiphase approach, we ultimately developed a 12 week, phone-delivered, combined PP-motivational interviewing intervention to promote physical activity. Across stages, we learned several important lessons: (a) participant and interventionist feedback is important, even in later optimization stages; (b) a thoughtful and systematic approach using all information sources is required when conflicting results in experiments make next steps unclear; and (3) new approaches in the field over a multiyear project should be integrated into the development process. Conclusions A MOST-based behavioral intervention development program can be efficient and effective in developing optimized new interventions, and it may require complex and nuanced decision-making at each phase.

Abstract P438: The Impact of a Yearlong Diabetes Prevention Program-based Lifestyle Intervention on Cardiovascular Health Metrics

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Susan Devaraj ◽  
Bonny Rockette-Wagner ◽  
Vincent Arena ◽  
Rachel G Miller ◽  
Jenna Napoleone ◽  
...  
Keyword(s):  
Physical Activity ◽  
Blood Pressure ◽  
Lifestyle Intervention ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Fasting Blood Glucose ◽  
Diabetes Prevention Program ◽  
Rank Test ◽  
Cvd Risk ◽  
The Impact

Introduction: The AHA created “Life’s Simple Seven” metrics to measure progress toward the goal of improving the cardiovascular (CV) health of all Americans, classifying each metric as “ideal”, “intermediate,” or “poor”. Few studies have examined the impact of behavioral lifestyle interventions on CV health metrics. We evaluated changes in CV health metrics during the course of a CDC recognized Diabetes Prevention Program-based lifestyle intervention known as Group Lifestyle Balance (DPP-GLB). Hypothesis: DPP-GLB will be associated with improvements in CV health metrics after 6 months of intervention and maintenance of these improvements at 12 months post-baseline. Methods: We used combined data from two similar intervention trials (occurring 6 years apart) offering a 12 month DPP-GLB program in the community setting to overweight/obese individuals with prediabetes and/or metabolic syndrome. Changes in individual CV health metrics (BMI, blood pressure, total cholesterol, fasting blood glucose, physical activity; measures of smoking and diet were not available) and total metric score (sum of metric profile where ideal=2, intermediate=1 and poor=0 for each metric, possible “total “range of 0-10) were considered after 6 and 12 months of intervention. Results: Among 222 participants (76%) with complete data for all 5 metrics at intervention baseline, 6 and 12 month follow up, there was a significant beneficial shift from baseline to 6 and 12 months in the proportion of participants within CV health metric categories for BMI, physical activity and blood pressure (Figure 1). Total metric score also improved significantly (p<0.01, signed-rank test) at 6 [median (IQR) change: +1.0 (0-1.0)] and 12 months [median (IQR) change: 0.0 (0-1.0)]. Significant improvement was also seen in the median number of ideal metrics at 6 and 12 months (p<0.01 for both). Conclusions: The DPP-GLB intervention was successful in improving CV health metrics at both 6 and 12 months, demonstrating the potential of this program to decrease CVD risk.

Empowering Native Adolescents: Responsibility for Their Health Behaviors

2021 ◽  
Vol 45 (1) ◽  
pp. 3-16
Author(s):  
Rachel A. Chambers ◽  
Dane Hautala ◽  
Anne Kenney ◽  
Summer Rosenstock ◽  
Marissa Begay ◽  
...  
Keyword(s):  
Native American ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Latent Class ◽  
Diabetes Prevention Program ◽  
Transition Analysis ◽  
Home Based ◽  
The Impact ◽  
Diabetes And Obesity

Objectives: In this study, we assess the impact of a home-based diabetes prevention program, Together on Diabetes (TOD), on adolescent responsibility-taking for tasks related to diabetes risk. Methods: Participants were Native American youth ages 10-19 with or at risk of type 2 diabetes who participated in a 12-session, 6-month diabetes prevention program with an adult caretaker. Assessments completed at baseline, 6-month, and 12-month follow-up include demographics and the Diabetes and Obesity Task Sharing (DOTS) Questionnaire. We used latent class analysis (LCA) at baseline to examine heterogeneity in DOTS responses. We identified 3 classes (adolescent, shared, caretaker). We used latent transition analysis to examine stability and change in latent status at baseline, 6- and 12-month follow-up. Results: At baseline, the mean age of participants was 13.6 years and 55.9% were boys. From baseline to 6-month follow-up, the adolescent class was most stable, whereas the shared and caretaker classes were less stable. For participants who transition from the adolescent class, most transition to shared class compared to caretaker class. Conclusions: TOD helps to empower Native American adolescents to take responsibility for their health and engage with their caregivers in these decisions.

Evaluation of a mathematical model of diabetes progression against observations in the Diabetes Prevention Program

2012 ◽  
Vol 303 (2) ◽  
pp. E200-E212 ◽  
Author(s):  
Thomas Hardy ◽  
Eyas Abu-Raddad ◽  
Niels Porksen ◽  
Andrea De Gaetano
Keyword(s):  
Mathematical Model ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Diabetes Incidence ◽  
Cell Protection ◽  
Data Set ◽  
Diabetes Progression ◽  
The Impact

The seminal publication of the Diabetes Prevention Program (DPP) results in 2002 has provided insight into the impact of major therapies on the development of diabetes over a time span of a few years. In the present work, the publicly available DPP data set is used to calibrate and evaluate a recently developed mechanistic mathematical model for the long-term development of diabetes to assess the model's ability to predict the natural history of disease progression and the effectiveness of preventive interventions. A general population is generated from which virtual subject samples corresponding to the DPP enrollment criteria are selected. The model is able to reproduce with good fidelity the observed time courses of both diabetes incidence and average glycemia, under realistic hypotheses on evolution of disease and efficacy of the studied therapies, for all treatment arms. Model-based simulations of the long-term evolution of the disease are consistent with the transient benefits observed with conventional therapies and with promising effects of radical improvement of insulin sensitivity (as by metabolic surgery) or of β-cell protection. The mechanistic diabetes progression model provides a credible tool by which long-term implications of antidiabetic interventions can be evaluated.

scholarly journals Using the Multiphase Optimization Strategy (MOST) Framework to Test Intervention Delivery Strategies: A Study Protocol

2019 ◽  
Author(s):  
Sarabeth Broder-Fingert ◽  
Jocelyn Lara Kuhn ◽  
Radley Christopher Sheldrick ◽  
Andrea Chu ◽  
Lisa Fortuna ◽  
...  
Keyword(s):  
Health Services ◽  
Factorial Design ◽  
Behavioral Health ◽  
Study Protocol ◽  
Optimization Strategy ◽  
Behavioral Health Services ◽  
Intervention Delivery ◽  
Multiphase Optimization Strategy ◽  
Delivery Strategies ◽  
The Impact

Abstract Background Delivery of behavioral interventions is complex, as the majority of interventions consist of multiple components used either simultaneously, sequentially, or both. The importance of clearly delineating delivery strategies within these complex interventions - and furthermore understanding the impact of each strategy on effectiveness - has recently emerged as an important facet of intervention research. Yet, few methodologies exist to prospectively test the effectiveness of delivery strategies and how they impact implementation. In the current paper, we describe a study protocol for a large randomized controlled trial in which we will use the Multiphase Optimization Strategy (MOST) – a novel framework developed to optimize interventions - to test the effectiveness of intervention delivery strategies using a factorial design. We apply this framework to delivery of Family Navigation (FN), an evidence-based care management strategy designed to reduce disparities and improve access to behavioral health services, and test four components related to its implementation. Methods/Design The MOST framework contains three distinct phases: Preparation, Optimization, and Evaluation. The preparation phase for this study occurred previously. The current study consists of the optimization and evaluation phases. Children ages three-to-twelve years-old who are detected as “at-risk” for behavioral health disorders (n=304) at a large, urban federally qualified community health center will be referred to a Family Partner – a bi-cultural, bi-lingual member of the community with training in behavioral health and systems navigation – who will perform FN. Families will then be randomized to one of 16 possible combinations of FN delivery strategies (2x2x2x2 factorial design). The primary outcome measure will be achieving a family-centered goal related to behavioral health services within 90 days of randomization. Implementation data on fidelity, acceptability, feasibility, and cost of each strategy will also be collected. Results from the primary and secondary outcomes will be reviewed by our team of stakeholders to optimize FN delivery for implementation and dissemination based on effectiveness, efficiency, and cost. Discussion In this protocol paper, we describe how the MOST Framework can be used to improve intervention delivery. These methods will be useful for future studies testing intervention delivery strategies and their impact on implementation.

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