User-Centered Framework for Implementation of Technology (UFIT): Development of an Integrated Framework for Designing Clinical Decision Support Tools Packaged With Tailored Implementation Strategies.

BACKGROUND: Electronic health record-based clinical decision support (CDS) tools can facilitate the adoption of evidence into practice. Yet, the impact of CDS beyond single-site implementation is often limited by dissemination and implementation barriers related to site- and user-specific variation in workflows and behaviors. The translation of evidence-based CDS from initial development to implementation in heterogeneous environments requires a framework that assures careful balancing of fidelity to core functional elements with adaptations to ensure compatibility with new contexts. OBJECTIVE: This study aims to develop and apply a framework to guide tailoring and implementing CDS across diverse clinical settings. METHODS: In preparation for a multisite trial implementing CDS for pediatric overweight or obesity in primary care, we developed the User-Centered Framework for Implementation of Technology (UFIT), a framework that integrates principles from user-centered design (UCD), human factors/ergonomics theories, and implementation science to guide both CDS adaptation and tailoring of related implementation strategies. Our transdisciplinary study team conducted semistructured interviews with pediatric primary care clinicians and a diverse group of stakeholders from 3 health systems in the northeastern, midwestern, and southeastern United States to inform and apply the framework for our formative evaluation. RESULTS: We conducted 41 qualitative interviews with primary care clinicians (n=21) and other stakeholders (n=20). Our workflow analysis found 3 primary ways in which clinicians interact with the electronic health record during primary care well-child visits identifying opportunities for decision support. Additionally, we identified differences in practice patterns across contexts necessitating a multiprong design approach to support a variety of workflows, user needs, preferences, and implementation strategies. CONCLUSIONS: UFIT integrates theories and guidance from UCD, human factors/ergonomics, and implementation science to promote fit with local contexts for optimal outcomes. The components of UFIT were used to guide the development of Improving Pediatric Obesity Practice Using Prompts, an integrated package comprising CDS for obesity or overweight treatment with tailored implementation strategies. TRIAL REGISTRATION: ClinicalTrials.gov NCT05627011; https://clinicaltrials.gov/study/NCT05627011.

Engaging Operational Partners Is Critical for Successful Implementation of Research Products: a Coincidence Analysis of Access-Related Projects in the Veterans Affairs Healthcare System.

BACKGROUND/OBJECTIVE: The Veterans Health Administration (VHA) has prioritized timely access to care and has invested substantially in research aimed at optimizing veteran access. However, implementing research into practice remains challenging. Here, we assessed the implementation status of recent VHA access-related research projects and explored factors associated with successful implementation. DESIGN: We conducted a portfolio review of recent VHA-funded or supported projects (1/2015-7/2020) focused on healthcare access ("Access Portfolio"). We then identified projects with implementable research deliverables by excluding those that (1) were non-research/operational projects; (2) were only recently completed (i.e., completed on or after 1/1/2020, meaning that they were unlikely to have had time to be implemented); and (3) did not propose an implementable deliverable. An electronic survey assessed each project's implementation status and elicited barriers/facilitators to implementing deliverables. Results were analyzed using novel Coincidence Analysis (CNA) methods. PARTICIPANTS/KEY RESULTS: Among 286 Access Portfolio projects, 36 projects led by 32 investigators across 20 VHA facilities were included. Twenty-nine respondents completed the survey for 32 projects (response rate = 88.9%). Twenty-eight percent of projects reported fully implementing project deliverables, 34% reported partially implementing deliverables, and 37% reported not implementing any deliverables (i.e., resulting tool/intervention not implemented into practice). Of 14 possible barriers/facilitators assessed in the survey, two were identified through CNA as "difference-makers" to partial or full implementation of project deliverables: (1) engagement with national VHA operational leadership; (2) support and commitment from local site operational leadership. CONCLUSIONS: These findings empirically highlight the importance of operational leadership engagement for successful implementation of research deliverables. Efforts to strengthen communication and engagement between the research community and VHA local/national operational leaders should be expanded to ensure VHA's investment in research leads to meaningful improvements in veterans' care. The Veterans Health Administration (VHA) has prioritized timely access to care and has invested substantially in research aimed at optimizing veteran access. However, implementing research findings into clinical practice remains challenging, both within and outside VHA. Here, we assessed the implementation status of recent VHA access-related research projects and explored factors associated with successful implementation. Only two factors were identified as "difference-makers" to adoption of project findings into practice: (1) engagement with national VHA leadership or (2) support and commitment from local site leadership. These findings highlight the importance of leadership engagement for successful implementation of research findings. Efforts to strengthen communication and engagement between the research community and VHA local/national leaders should be expanded to ensure VHA's investment in research leads to meaningful improvements in veterans' care.

Envisioning clinical trials as complex interventions.

Clinical trials are critical components of modern health care and infrastructure. Trials benefit society through scientific advancement and individual patients through trial participation. In fact, billions of dollars are spent annually in support of these benefits. Despite the massive investments, clinical trials often fail to accomplish their primary aims and trial enrollment rates remain low. Prior efforts to improve trial conduct and enrollment have had limited success, perhaps due to oversimplification of the complex, multilevel nature of trials. For these reasons, the authors propose applying implementation science to the clinical trials context. In this commentary, the authors posit clinical trials as complex, multilevel evidence-based interventions with significant societal and individual benefits yet with persistent gaps in implementation. An application of implementation science concepts to the clinical trials context as means to build common vocabulary and establish a platform for applying implementation science and practice to improve clinical trial conduct is introduced. Applying implementation science to the clinical trials context can augment improvement efforts and build capacity for better and more efficient evidence-based care for all patients and trial stakeholders throughout the clinical trials enterprise.

Applying User-Centered Design to Develop Practical Strategies that Address Overuse in Primary Care.

INTRODUCTION: Engaging patients and frontline clinicians in re-designing clinical care is essential for improving care delivery in a complex clinical environment. This study sought to assess an innovative user-centered design approach to improving clinical care quality, focusing on the use cases of de-intensifying non-beneficial care within the following areas: (1) de-intensifying diabetes treatment in high-risk patients; (2) stopping screening for carotid artery stenosis in asymptomatic patients; and (3) stopping colorectal cancer screening in average-risk, older adults. METHODS: The user-centered design approach, consisting of patient and patient-clinician charrettes (defined as intensive workshops where key stakeholders collaborate to develop creative solutions to a specific problem) and participant surveys, has been described previously. Following the charrettes, we used inductive coding to identify and categorize themes emerging from the de-intensification ideas prioritized by participants as well as facilitator notes and audio recordings from the charrettes. RESULTS: Thirty-five patients participated in the patient design charrettes, generating 134 unique de-intensification ideas and prioritizing 32, which were then distilled into six patient-generated principles of de-intensification by the study team. These principles provided a starting point for a subsequent patient-clinician charrette. In this follow-up charrette, 9 patients who had participated in an earlier patient design charrette collaborated with 7 clinicians to generate 63 potential de-intensification solutions. Six of these potential solutions were developed into multi-faceted, fully operationalized de-intensification strategies. DISCUSSION: The de-intensification strategies that patients and clinicians prioritized and operationalized during the co-design charrette process were detailed and multi-faceted. Each component of a strategy had a rationale based on feasibility, practical considerations, and ways of overcoming barriers. The charrette-based process may be a useful way to engage clinicians and patients in developing the complex and multi-faceted strategies needed to improve care delivery.

Planning and Reporting Effective Web-Based RAND/UCLA Appropriateness Method Panels: Literature Review and Preliminary Recommendations.

BACKGROUND: The RAND/UCLA Appropriateness Method (RAM), a variant of the Delphi Method, was developed to synthesize existing evidence and elicit the clinical judgement of medical experts on the appropriate treatment of specific clinical presentations. Technological advances now allow researchers to conduct expert panels on the internet, offering a cost-effective and convenient alternative to the traditional RAM. For example, the Department of Veterans Affairs recently used a web-based RAM to validate clinical recommendations for de-intensifying routine primary care services. A substantial literature describes and tests various aspects of the traditional RAM in health research; yet we know comparatively less about how researchers implement web-based expert panels. OBJECTIVE: The objectives of this study are twofold: (1) to understand how the web-based RAM process is currently used and reported in health research and (2) to provide preliminary reporting guidance for researchers to improve the transparency and reproducibility of reporting practices. METHODS: The PubMed database was searched to identify studies published between 2009 and 2019 that used a web-based RAM to measure the appropriateness of medical care. Methodological data from each article were abstracted. The following categories were assessed: composition and characteristics of the web-based expert panels, characteristics of panel procedures, results, and panel satisfaction and engagement. RESULTS: Of the 12 studies meeting the eligibility criteria and reviewed, only 42% (5/12) implemented the full RAM process with the remaining studies opting for a partial approach. Among those studies reporting, the median number of participants at first rating was 42. While 92% (11/12) of studies involved clinicians, 50% (6/12) involved multiple stakeholder types. Our review revealed that the studies failed to report on critical aspects of the RAM process. For example, no studies reported response rates with the denominator of previous rounds, 42% (5/12) did not provide panelists with feedback between rating periods, 50% (6/12) either did not have or did not report on the panel discussion period, and 25% (3/12) did not report on quality measures to assess aspects of the panel process (eg, satisfaction with the process). CONCLUSIONS: Conducting web-based RAM panels will continue to be an appealing option for researchers seeking a safe, efficient, and democratic process of expert agreement. Our literature review uncovered inconsistent reporting frameworks and insufficient detail to evaluate study outcomes. We provide preliminary recommendations for reporting that are both timely and important for producing replicable, high-quality findings. The need for reporting standards is especially critical given that more people may prefer to participate in web-based rather than in-person panels due to the ongoing COVID-19 pandemic.

Clinician documentation of patient centered care in the electronic health record.

BACKGROUND: In this study we sought to explore the possibility of using patient centered care (PCC) documentation as a measure of the delivery of PCC in a health system. METHODS: We first selected 6 VA medical centers based on their scores for a measure of support for self-management subscale from a national patient satisfaction survey (the Survey for Healthcare Experience-Patients). We accessed clinical notes related to either smoking cessation or weight management consults. We then annotated this dataset of notes for documentation of PCC concepts including: patient goals, provider support for goal progress, social context, shared decision making, mention of caregivers, and use of the patient's voice. We examined the association of documentation of PCC with patients' perception of support for self-management with regression analyses. RESULTS: Two health centers had < 50 notes related to either tobacco cessation or weight management consults and were removed from further analysis. The resulting dataset includes 477 notes related to 311 patients total from 4 medical centers. For a majority of patients (201 out of 311; 64.8%) at least one PCC concept was present in their clinical notes. The most common PCC concepts documented were patient goals (patients n = 126; 63% clinical notes n = 302; 63%), patient voice (patients n = 165, 82%; clinical notes n = 323, 68%), social context (patients n = 105, 52%; clinical notes n = 181, 38%), and provider support for goal progress (patients n = 124, 62%; clinical notes n = 191, 40%). Documentation of goals for weight loss notes was greater at health centers with higher satisfaction scores compared to low. No such relationship was found for notes related to tobacco cessation. CONCLUSION: Providers document PCC concepts in their clinical notes. In this pilot study we explored the feasibility of using this data as a means to measure the degree to which care in a health center is patient centered. PRACTICE IMPLICATIONS: clinical EHR notes are a rich source of information about PCC that could potentially be used to assess PCC over time and across systems with scalable technologies such as natural language processing.

The LEAP Program: Quality Improvement Training to Address Team Readiness Gaps Identified by Implementation Science Findings.

BACKGROUND: Integrating evidence-based innovations (EBIs) into sustained use is challenging; most implementations in health systems fail. Increasing frontline teams' quality improvement (QI) capability may increase the implementation readiness and success of EBI implementation. OBJECTIVES: Develop a QI training program ("Learn. Engage. Act. Process." (LEAP)) and evaluate its impact on frontline obesity treatment teams to improve treatment delivered within the Veterans Health Administration (VHA). DESIGN: This was a pre-post evaluation of the LEAP program. MOVE! coordinators (N = 68) were invited to participate in LEAP; 24 were randomly assigned to four starting times. MOVE! coordinators formed teams to work on improvement aims. Pre-post surveys assessed team organizational readiness for implementing change and self-rated QI skills. Program satisfaction, assignment completion, and aim achievement were also evaluated. PARTICIPANTS: VHA facility-based MOVE! teams. INTERVENTIONS: LEAP is a 21-week QI training program. Core components include audit and feedback reports, structured curriculum, coaching and learning community, and online platform. MAIN MEASURES: Organizational readiness for implementing change (ORIC); self-rated QI skills before and after LEAP; assignment completion and aim achievement; program satisfaction. KEY RESULTS: Seventeen of 24 randomized teams participated in LEAP. Participants' self-ratings across six categories of QI skills increased after completing LEAP (p< 0.0001). The ORIC measure showed no statistically significant change overall; the change efficacy subscale marginally improved (p < 0.08), and the change commitment subscale remained the same (p = 0.66). Depending on the assignment, 35 to 100% of teams completed the assignment. Nine teams achieved their aim. Most team members were satisfied or very satisfied (81-89%) with the LEAP components, 74% intended to continue using QI methods, and 81% planned to continue improvement work. CONCLUSIONS: LEAP is scalable and does not require travel or time away from clinical responsibilities. While QI skills improved among participating teams and most completed the work, they struggled to do so amid competing clinical priorities.

Improving Smoking and Blood Pressure Outcomes: The Interplay Between Operational Changes and Local Context.

PURPOSE: We undertook a study to identify conditions and operational changes linked to improvements in smoking and blood pressure (BP) outcomes in primary care. METHODS: We purposively sampled and interviewed practice staff (eg, office managers, clinicians) from a subset of 104 practices participating in EvidenceNOW-a multisite cardiovascular disease prevention initiative. We calculated Clinical Quality Measure improvements, with targets of 10-point or greater absolute improvements in the proportion of patients with smoking screening and, if relevant, counseling and in the proportion of hypertensive patients with adequately controlled BP. We analyzed interview data to identify operational changes, transforming these into numeric data. We used Configurational Comparative Methods to assess the joint effects of multiple factors on outcomes. RESULTS: In clinician-owned practices, implementing a workflow to routinely screen, counsel, and connect patients to smoking cessation resources, or implementing a documentation change or a referral to a resource alone led to an improvement of at least 10 points in the smoking outcome with a moderate level of facilitation support. These patterns did not manifest in health- or hospital system-owned practices or in Federally Qualified Health Centers, however. The BP outcome improved by at least 10 points among solo practices after medical assistants were trained to take an accurate BP. Among larger, clinician-owned practices, BP outcomes improved when practices implemented a second BP measurement when the first was elevated, and when staff learned where to document this information in the electronic health record. With 50 hours or more of facilitation, BP outcomes improved among larger and health- and hospital system-owned practices that implemented these operational changes. CONCLUSIONS: There was no magic bullet for improving smoking or BP outcomes. Multiple combinations of operational changes led to improvements, but only in specific contexts of practice size and ownership, or dose of external facilitation.

Understanding providers' attitudes and key concerns toward incorporating CVD risk prediction into clinical practice: a qualitative study.

BACKGROUND: Although risk prediction has become an integral part of clinical practice guidelines for cardiovascular disease (CVD) prevention, multiple studies have shown that patients' risk still plays almost no role in clinical decision-making. Because little is known about why this is so, we sought to understand providers' views on the opportunities, barriers, and facilitators of incorporating risk prediction to guide their use of cardiovascular preventive medicines. METHODS: We conducted semi-structured interviews with primary care providers (n = 33) at VA facilities in the Midwest. Facilities were chosen using a maximum variation approach according to their geography, size, proportion of MD to non-MD providers, and percentage of full-time providers. Providers included MD/DO physicians, physician assistants, nurse practitioners, and clinical pharmacists. Providers were asked about their reaction to a hypothetical situation in which the VA would introduce a risk prediction-based approach to CVD treatment. We conducted matrix and content analysis to identify providers' reactions to risk prediction, reasons for their reaction, and exemplar quotes. RESULTS: Most providers were classified as Enthusiastic (n = 14) or Cautious Adopters (n = 15), with only a few Non-Adopters (n = 4). Providers described four key concerns toward adopting risk prediction. Their primary concern was that risk prediction is not always compatible with a "whole patient" approach to patient care. Other concerns included questions about the validity of the proposed risk prediction model, potential workflow burdens, and whether risk prediction adds value to existing clinical practice. Enthusiastic, Cautious, and Non-Adopters all expressed both doubts about and support for risk prediction categorizable in the above four key areas of concern. CONCLUSIONS: Providers were generally supportive of adopting risk prediction into CVD prevention, but many had misgivings, which included concerns about impact on workflow, validity of predictive models, the value of making this change, and possible negative effects on providers' ability to address the whole patient. These concerns have likely contributed to the slow introduction of risk prediction into clinical practice. These concerns will need to be addressed for risk prediction, and other approaches relying on "big data" including machine learning and artificial intelligence, to have a meaningful role in clinical practice.

Facility-level conditions leading to higher reach: a configurational analysis of national VA weight management programming.

BACKGROUND: While the Veterans Health Administration (VHA) MOVE! weight management program is effective in helping patients lose weight and is available at every VHA medical center across the United States, reaching patients to engage them in treatment remains a challenge. Facility-based MOVE! programs vary in structures, processes of programming, and levels of reach, with no single factor explaining variation in reach. Configurational analysis, based on Boolean algebra and set theory, represents a mathematical approach to data analysis well-suited for discerning how conditions interact and identifying multiple pathways leading to the same outcome. We applied configurational analysis to identify facility-level obesity treatment program arrangements that directly linked to higher reach. METHODS: A national survey was fielded in March 2017 to elicit information about more than 75 different components of obesity treatment programming in all VHA medical centers. This survey data was linked to reach scores available through administrative data. Reach scores were calculated by dividing the total number of Veterans who are candidates for obesity treatment by the number of "new" MOVE! visits in 2017 for each program and then multiplied by 1000. Programs with the top 40 % highest reach scores (n = 51) were compared to those in the lowest 40 % (n = 51). Configurational analysis was applied to identify specific combinations of conditions linked to reach rates. RESULTS: One hundred twenty-seven MOVE! program representatives responded to the survey and had complete reach data. The final solution consisted of 5 distinct pathways comprising combinations of program components related to pharmacotherapy, bariatric surgery, and comprehensive lifestyle intervention; 3 of the 5 pathways depended on the size/complexity of medical center. The 5 pathways explained 78 % (40/51) of the facilities in the higher-reach group with 85 % consistency (40/47). CONCLUSIONS: Specific combinations of facility-level conditions identified through configurational analysis uniquely distinguished facilities with higher reach from those with lower reach. Solutions demonstrated the importance of how local context plus specific program components linked together to account for a key implementation outcome. These findings will guide system recommendations about optimal program structures to maximize reach to patients who would benefit from obesity treatment such as the MOVE!

Correction: Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network's Common Measures Working Group.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Short-Term VA Health Care Expenditures Following a Health Risk Assessment and Coaching Trial.

BACKGROUND: Short-term health care costs following completion of health risk assessments and coaching programs in the VA have not been assessed. OBJECTIVE: To compare VA health care expenditures among veterans who participated in a behavioral intervention trial that randomized patients to complete a HRA followed by health coaching (HRA + coaching) or to complete the HRA without coaching (HRA-alone). DESIGN: Four-hundred seventeen veterans at three Veterans Affairs (VA) Medical Centers or Clinics were randomized to HRA + coaching or HRA-alone. Veterans randomized to HRA-alone (n = 209) were encouraged to discuss HRA results with their primary care team, while veterans randomized to HRA + coaching (n = 208) received two brief telephone-delivered health coaching calls. PARTICIPANTS: We included 411 veterans with available cost data. MAIN MEASURES: Total VA health expenditures 6 months following trial enrollment were estimated using a generalized linear model with a gamma distribution and log link function. In exploratory analysis, model-based recursive partitioning was used to determine whether the intervention effect on short-term costs differed among any patient subgroups. KEY RESULTS: Most participants were male (85%); mean age was 56, and mean body mass index was 34. From the generalized linear model, 6-month estimated mean total VA expenditures were similar ($8665 for HRA + coaching vs $9900 for HRA-alone, p = 0.25). In exploratory subgroup analysis, among unemployed veterans with good sleep and fair or poor perceived health, mean observed expenditures in the HRA + coaching group were higher than in the HRA-alone group ($12,814 vs $7971). Among unemployed veterans with good sleep and good general health, mean observed expenditures in the HRA + coaching group were lower than in the HRA-alone group ($5082 vs $11,612). CONCLUSIONS: Compared to completing and receiving HRA results, working with health coaches to set actionable health behavior change goals following HRA completion did not reduce short-term health expenditures. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT01828567.

Implementation of Evidence-Based Practice for Benign Paroxysmal Positional Vertigo in the Emergency Department: A Stepped-Wedge Randomized Trial.

STUDY OBJECTIVE: We evaluated a strategy to increase use of the test (Dix-Hallpike's test [DHT]) and treatment (canalith repositioning maneuver [CRM]) for benign paroxysmal positional vertigo in emergency department (ED) dizziness visits. METHODS: We conducted a stepped-wedge randomized trial in 6 EDs. The population was visits with dizziness as a principal reason for the visit. The intervention included educational sessions and decision aid materials. Outcomes were DHT or CRM documentation (primary), head computed tomography (CT) use, length of stay, admission, and 90-day stroke events. The analysis was multilevel logistic regression with intervention, month, and hospital as fixed effects and provider as a random effect. We assessed fidelity with monitoring intervention use and semistructured interviews. RESULTS: We identified 7,635 dizziness visits during 18 months. The DHT or CRM was documented in 1.5% of control visits (45/3,077; 95% confidence interval 1% to 1.9%) and 3.5% of intervention visits (159/4,558; 95% confidence interval 3% to 4%; difference 2%, 95% confidence interval 1.3% to 2.7%). Head CT use was lower in intervention visits compared with control visits (44.0% [1,352/3,077] versus 36.9% [1,682/4,558]). No differences were observed in admission or 90-day subsequent stroke risk. In fidelity evaluations, providers who used the materials typically reported positive clinical experiences but provider engagement was low at facilities without an emergency medicine residency program. CONCLUSION: These findings provide evidence that an implementation strategy of a benign paroxysmal positional vertigo-focused approach to ED dizziness visits can be successful and safe in promoting evidence-based care. Absolute rates of DHT and CRM use, however, were still low, which relates in part to our broad inclusion criteria for dizziness visits.

Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial.

BACKGROUND: Though maintaining physical conditioning and a healthy weight are requirements of active military duty, many US veterans lose conditioning and rapidly gain weight after discharge from active duty service. Mobile health (mHealth) interventions using wearable devices are appealing to users and can be effective especially with personalized coaching support. We developed Stay Strong, a mobile app tailored to US veterans, to promote physical activity using a wrist-worn physical activity tracker, a Bluetooth-enabled scale, and an app-based dashboard. We tested whether adding personalized coaching components (Stay Strong+Coaching) would improve physical activity compared to Stay Strong alone. OBJECTIVE: The goal of this study is to compare 12-month outcomes from Stay Strong alone versus Stay Strong+Coaching. METHODS: Participants (n=357) were recruited from a national random sample of US veterans of recent wars and randomly assigned to the Stay Strong app alone (n=179) or Stay Strong+Coaching (n=178); both programs lasted 12 months. Personalized coaching components for Stay Strong+Coaching comprised of automated in-app motivational messages (3 per week), telephone-based human health coaching (up to 3 calls), and personalized weekly goal setting. All aspects of the enrollment process and program delivery were accomplished virtually for both groups, except for the telephone-based coaching. The primary outcome was change in physical activity at 12 months postbaseline, measured by average weekly Active Minutes, captured by the Fitbit Charge 2 device. Secondary outcomes included changes in step counts, weight, and patient activation. RESULTS: The average age of participants was 39.8 (SD 8.7) years, and 25.2% (90/357) were female. Active Minutes decreased from baseline to 12 months for both groups (P<.001) with no between-group differences at 6 months (P=.82) or 12 months (P=.98). However, at 12 months, many participants in both groups did not record Active Minutes, leading to missing data in 67.0% (120/179) for Stay Strong and 61.8% (110/178) for Stay Strong+Coaching. Average baseline weight for participants in Stay Strong and Stay Strong+Coaching was 214 lbs and 198 lbs, respectively, with no difference at baseline (P=.54) or at 6 months (P=.28) or 12 months (P=.18) postbaseline based on administrative weights, which had lower rates of missing data. Changes in the number of steps recorded and patient activation also did not differ by arm. CONCLUSIONS: Adding personalized health coaching comprised of in-app automated messages, up to 3 coaching calls, plus automated weekly personalized goals, did not improve levels of physical activity compared to using a smartphone app alone. Physical activity in both groups decreased over time. Sustaining long-term adherence and engagement in this mHealth intervention proved difficult; approximately two-thirds of the trial's 357 participants failed to sync their Fitbit device at 12 months and, thus, were lost to follow-up. TRIAL REGISTRATION: ClinicalTrials.gov NCT02360293; https://clinicaltrials.gov/ct2/show/NCT02360293. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/12526.

Health Coaching Has Differential Effects on Veterans with Limited Health Literacy and Numeracy: a Secondary Analysis of ACTIVATE.

BACKGROUND: Health coaching is an effective behavior change strategy. Understanding if there is a differential impact of health coaching on patients with low health literacy has not been well investigated. OBJECTIVE: To determine whether a telephone coaching intervention would result in similar improvements in enrollment in prevention programs and patient activation among Veterans with low versus high health literacy (specifically, reading literacy and numeracy). DESIGN: Secondary analysis of a randomized controlled trial. PARTICIPANTS: Four hundred seventeen Veterans with at least one modifiable risk factor: current smoker, BMI ≥ 30, or < 150 min of moderate physical activity weekly. METHODS: A single-item assessment of health literacy and a subjective numeracy scale were assessed at baseline. A logistic regression and general linear longitudinal models were used to examine the differential impact of the intervention compared to control on enrollment in prevention programs and changes in patient activation measures (PAM) scores among patients with low versus high health literacy. RESULTS: The coaching intervention resulted in higher enrollment in prevention programs and improvements in PAM scores compared to usual care regardless of baseline health literacy. The coaching intervention had a greater effect on the probability of enrollment in prevention programs for patients with low numeracy (intervention vs control difference of 0.31, 95% CI 0.18, 0.45) as compared to those with high numeracy (0.13, 95% CI - 0.01, 0.27); the low compared to high differential effect was clinically, but not statistically significant (0.18, 95% CI - 0.01, 0.38; p = 0.07). Among patients with high numeracy, the intervention group had greater increases in PAM as compared to the control group at 6 months (mean difference in improvement 4.8; 95% CI 1.7, 7.9; p = 0.003). This led to a clinically and statistically significant differential intervention effect for low vs high numeracy (- 4.6; 95% CI - 9.1, - 0.15; p = 0.04). CONCLUSIONS: We suggest that health coaching may be particularly beneficial in behavior change strategies in populations with low numeracy when interpretation of health risk information is part of the intervention. CLINICALTRIALS. GOV IDENTIFIER: NCT01828567.

The Diabetes Prevention Program for Underserved Populations: A Brief Review of Strategies in the Real World.

IN BRIEF This review highlights examples of the translation of the Diabetes Prevention Program (DPP) to underserved populations. Here, underserved populations are defined as groups whose members are at greater risk for health conditions such as diabetes but often face barriers accessing treatment. Strategies to develop and evaluate future DPP translations are discussed.

Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network's Common Measures Working Group.

PurposeImplementation research provides a structure for evaluating the clinical integration of genomic medicine interventions. This paper describes the Implementing Genomics in Practice (IGNITE) Network's efforts to promote (i) a broader understanding of genomic medicine implementation research and (ii) the sharing of knowledge generated in the network.MethodsTo facilitate this goal, the IGNITE Network Common Measures Working Group (CMG) members adopted the Consolidated Framework for Implementation Research (CFIR) to guide its approach to identifying constructs and measures relevant to evaluating genomic medicine as a whole, standardizing data collection across projects, and combining data in a centralized resource for cross-network analyses.ResultsCMG identified 10 high-priority CFIR constructs as important for genomic medicine. Of those, eight did not have standardized measurement instruments. Therefore, we developed four survey tools to address this gap. In addition, we identified seven high-priority constructs related to patients, families, and communities that did not map to CFIR constructs. Both sets of constructs were combined to create a draft genomic medicine implementation model.ConclusionWe developed processes to identify constructs deemed valuable for genomic medicine implementation and codified them in a model. These resources are freely available to facilitate knowledge generation and sharing across the field.

A Coaching by Telephone Intervention on Engaging Patients to Address Modifiable Cardiovascular Risk Factors: a Randomized Controlled Trial.

BACKGROUND: A large proportion of deaths and chronic illnesses can be attributed to three modifiable risk factors: tobacco use, overweight/obesity, and physical inactivity. OBJECTIVE: To test whether telephone-based health coaching after completion of a comprehensive health risk assessment (HRA) increases patient activation and enrollment in a prevention program compared to HRA completion alone. DESIGN: Two-arm randomized trial at three sites. SETTING: Primary care clinics at Veterans Affairs facilities. PARTICIPANTS: Four hundred seventeen veterans with at least one modifiable risk factor (BMI ≥ 30, < 150 min of at least moderate physically activity per week, or current smoker). INTERVENTION: Participants completed an online HRA. Intervention participants received two telephone-delivered health coaching calls at 1 and 4 weeks to collaboratively set goals to enroll in, and attend structured prevention programs designed to reduce modifiable risk factors. MEASUREMENTS: Primary outcome was enrollment in a structured prevention program by 6 months. Secondary outcomes were Patient Activation Measure (PAM) and Framingham Risk Score (FRS). RESULTS: Most participants were male (85%), white (50%), with a mean age of 56. Participants were eligible, because their BMI was ≥ 30 (80%), they were physically inactive (50%), and/or they were current smokers (39%). When compared to HLA only at 6 months, health coaching intervention participants reported higher rates of enrollment in a prevention program, 51 vs 29% (OR = 2.5; 95% CI: 1.7, 3.9; p < 0.0001), higher rates of program participation, 40 vs 23% (OR = 2.3; 95% CI: 1.5, 3.6; p = 0.0004), and greater improvement in PAM scores, mean difference 2.5 (95% CI: 0.2, 4.7; p = 0.03), but no change in FRS scores, mean difference 0.7 (95% CI - 0.7, 2.2; p = 0.33). CONCLUSIONS: Brief telephone health coaching after completing an online HRA increased patient activation and increased enrollment in structured prevention programs to improve health behaviors. CLINICALTRIALS. GOV IDENTIFIER: NCT01828567.

A System-Level Approach to Overweight and Obesity in the Veterans Health Administration.

Healthcare systems are challenged by steady increases in the number of patients who are overweight and obese. Large-scale, evidence-based behavioral approaches for addressing overweight and obesity have been successfully implemented in systems such as the Veterans Health Administration (VHA). These population-based interventions target reduction in risk for obesity-associated conditions through lifestyle change and weight loss, and are associated with modest weight loss. Despite the fact that VHA has increased the overall reach of these behavioral interventions, the number of high-risk overweight and obese patients continues to rise. Recommendations for weight loss medications and bariatric surgery are included in clinical practice guidelines for the management of overweight and obesity, but these interventions are underutilized. During a recent state of the art conference on weight management held by VHA, subject matter experts identified challenges and gaps, as well as potential solutions and overarching policy recommendations, for implementing an integrated system-wide approach for improving population-based weight management.

Behavioral Treatment for Veterans with Obesity: 24-Month Weight Outcomes from the ASPIRE-VA Small Changes Randomized Trial.

BACKGROUND: Small Changes (SC) is a weight management approach that demonstrated superior 12-month outcomes compared to the existing MOVE!® Weight Management Program at two Veterans Affairs (VA) sites. However, approaches are needed to help graduates of treatment continue to lose or maintain their weight over the longer term. OBJECTIVE: The purpose of the present study was to examine the effectiveness of a second year of low-intensity SC support compared to support offered by the usual care MOVE! programs. DESIGN: Following participation in the year-long Aspiring to Lifelong Health in VA (ASPIRE-VA) randomized controlled trial, participants were invited to extend their participation in their assigned program for another year. Three programs were extended to include six SC sessions delivered via telephone (ASPIRE-Phone) or an in-person group (ASPIRE-Group), or 12 sessions offered by the MOVE! programs. PARTICIPANTS: Three hundred thirty-two overweight/obese veterans who consented to extend their participation in the ASPIRE-VA trial by an additional year. MAIN MEASURES: Twenty-four-month weight change (kg). KEY RESULTS: Twenty-four months after baseline, participants in all three groups had modest weight loss (-1.40 kg [-2.61 to -0.18] in the ASPIRE-Group, -2.13 kg [-3.43 to -0.83] in ASPIRE-Phone, and -1.78 kg [-3.07 to -0.49] in MOVE!), with no significant differences among the three groups. Exploratory post hoc analyses revealed that participants diagnosed with diabetes initially benefited from the ASPIRE-Group program (-2.6 kg [-4.37 to 0.83]), but experienced significant weight regain during the second year (+2.8 kg [0.92-4.69]) compared to those without diabetes. CONCLUSIONS: Participants in all three programs lost weight and maintained a statistically significant, though clinically modest, amount of weight loss over a 24-month period. Although participants in the ASPIRE-Group initially had greater weight loss, treatment was not sufficient to sustain weight loss through the second year, particularly in veterans with diabetes. Consistent, continuous-care treatment is needed to address obesity in the VA.