This feasibility study assessed perceived acceptability, adherence rates, and reasons for nonadherence to smartphone-based ecological momentary assessment (EMA). At two sites, participants (n = 103) ages 65 years or older with diagnosed anxiety or depressive disorder and cognitive concerns responded three times daily to smartphone-based EMA questions assessing clinical outcomes for two 10-day periods. Quantitative and qualitative measures followed both 10-day EMA periods. Overall, 76% of participants completed surveys on ≥ 10 of the 20 assessment days, and 70% of participants completed at least 30% of the total surveys. Reasons for nonadherence included technical, logistical, physiological, and cognitive issues. Smartphone-based EMA is feasible in older adults with cognitive and emotional difficulties. EMA tools should be responsive to the needs and preferences of participants to ensure adequate acceptability and adherence in this population. Our findings can inform the design, development, and implementation of mobile technologies in older adults in research and clinical contexts.

The 17-year time span between discovery and application of evidence in practice has become a unifying challenge for implementation science and translational science more broadly. Further, global pandemics and social crises demand timely implementation of rapidly accruing evidence to reduce morbidity and mortality. Yet speed remains an understudied metric in implementation science. Prevailing evaluations of implementation lack a temporal aspect, and current approaches have not yielded rapid implementation. In this paper, we address speed as an important conceptual and methodological gap in implementation science. We aim to untangle the complexities of studying implementation speed, offer a framework to assess speed of translation (FAST), and provide guidance to measure speed in evaluating implementation. To facilitate specification and reporting on metrics of speed, we encourage consideration of stakeholder perspectives (e.g., comparison of varying priorities), referents (e.g., speed in attaining outcomes, transitioning between implementation phases), and observation windows (e.g., time from intervention development to first patient treated) in its measurement. The FAST framework identifies factors that may influence speed of implementation and potential effects of implementation speed. We propose a research agenda to advance understanding of the pace of implementation, including identifying accelerators and inhibitors to speed.

Background: Research supports the effectiveness of technology-based treatment approaches for substance use disorders. These approaches have the potential to broaden the reach of evidence-based care. Yet, there is limited understanding of factors associated with implementation of technology-based care approaches in different service settings. Objectives: In this study, we explored provider and staff perceptions of facilitators and barriers to implementation of a mobile phone substance use recovery support app with clients in 4 service settings. Methods: Interviews were conducted with leadership and provider stakeholders (N=12) from 4 agencies in the first year of an implementation trial of the mobile phone app. We used the Consolidated Framework for Implementation Research as the conceptual foundation for identifying facilitators and barriers to implementation. Results: Implementation process facilitators included careful planning of all aspects of implementation before launch, engaging a dedicated team to implement and foster motivation, working collaboratively with the app development team to address technical barriers and adapt the app to meet client and agency needs, and consistently reviewing app usage data to inform progress. Implementation support strategies included training all staff to promote organization awareness about the recovery support app and emphasize its priority as a clinical care tool, encouraging clients to try the technology before committing to use, scaling rollout to clients, setting clear expectations with clients about use of the app, and using peer coaches and consistent client-centered messaging to promote engagement. Perceived compatibility of the mobile phone app with agency and client needs and readiness to implement emerged as salient agency-level implementation facilitators. Facilitating characteristics of the recovery support app itself included evidence of its impact for recovery support, perceived relative advantage of the app over usual care, the ability to adapt the app to improve client use, and its ease of use. The mobile phone itself was a strong motivation for clients to opt in to use the app in settings that provided phones. App access was limited in settings that did not provide phones owing to lack of mobile phone ownership or incompatibility of the app with clients’ mobile phones. Individual differences in technology literacy and provider beliefs about substance use care either facilitated or challenged implementation. Awareness of patient needs and resources facilitated implementation, whereas external policies and regulations regarding technology use introduced barriers to implementation. Conclusions: The conceptually grounded facilitators and barriers identified in this study can guide systematic targeting of strategies to improve implementation of mobile phone interventions in community treatment settings. Results also inform the design of technology-based therapeutic tools. This study highlights directions for research with regard to implementation of technology-based behavioral health care approaches.

Accelerating innovation translation is a priority for improving healthcare and health. Although dissemination and implementation (D&I) research has made significant advances over the past decade, it has attended primarily to the implementation of long-standing, well-established practices and policies. We present a conceptual architecture for speeding translation of promising innovations as candidates for iterative testing in practice. Our framework to Design for Accelerated Translation (DART) aims to clarify whether, when, and how to act on evolving evidence to improve healthcare. We view translation of evidence to practice as a dynamic process and argue that much evidence can be acted upon even when uncertainty is moderately high, recognizing that this evidence is evolving and subject to frequent reevaluation. The DART framework proposes that additional factors – demand, risk, and cost, in addition to the evolving evidence base – should influence the pace of translation over time. Attention to these underemphasized factors may lead to more dynamic decision-making about whether or not to adopt an emerging innovation or de-implement a suboptimal intervention. Finally, the DART framework outlines key actions that will speed movement from evidence to practice, including forming meaningful stakeholder partnerships, designing innovations for D&I, and engaging in a learning health system.

PURPOSE: Quitting smoking improves patients' clinical outcomes, yet smoking is not commonly addressed as part of cancer care. The Cancer Center Cessation Initiative (C3I) supports National Cancer Institute–designated cancer centers to integrate tobacco treatment programs (TTPs) into routine cancer care. C3I centers vary in size, implementation strategies used, and treatment approaches. We examined associations of these contextual factors with treatment reach and smoking cessation effectiveness. METHODS: This cross-sectional study used survey data from 28 C3I centers that reported tobacco treatment data during the first 6 months of 2021. Primary outcomes of interest were treatment reach (reach)—the proportion of patients identified as currently smoking who received at least one evidence-based tobacco treatment component (eg, counseling and pharmacotherapy)—and smoking cessation effectiveness (effectiveness)—the proportion of patients reporting 7-day point prevalence abstinence at 6-month follow-up. Center-level differences in reach and effectiveness were examined by center characteristics, implementation strategies, and tobacco treatment components. RESULTS: Of the total 692,662 unique patients seen, 44,437 reported current smoking. Across centers, a median of 96% of patients were screened for tobacco use, median smoking prevalence was 7.4%, median reach was 15.4%, and median effectiveness was 18.4%. Center-level characteristics associated with higher reach included higher smoking prevalence, use of center-wide TTP, and lower patient-to-tobacco treatment specialist ratio. Higher effectiveness was observed at centers that served a larger overall population and population of patients who smoke, reported a higher smoking prevalence, and/or offered electronic health record referrals via a closed-loop system. CONCLUSION: Whole-center TTP implementation among inpatients and outpatients, and increasing staff-to-patient ratios may improve TTP reach. Designating personnel with tobacco treatment expertise and resources to increase tobacco treatment dose or intensity may improve smoking cessation effectiveness.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Primary care settings are uniquely positioned to reach individuals at-risk of alcohol use disorder through technology-delivered behavioral health interventions. Despite emerging effectiveness data, few efforts have been made to summarize the collective findings from these delivery approaches. Objective: The purpose of this study was to review recent literature on the use of technology to deliver, enhance, or support the implementation of alcohol-related interventions in primary care. We focused on addressing questions related to (1) categorization and target, (2) descriptive characteristics and context of delivery, (3) reported efficacy, and (4) factors influencing efficacy. Methods: We conducted a comprehensive search and systematic review of completed studies at the intersection of primary care, technology, and alcohol-related problems published from January 2000 to December 2018 within EBSCO databases, ProQuest Dissertations, and Cochrane Reviews. Of 2307 initial records, 42 were included and coded independently by two investigators. Results: Compared to the years of 2000-2009, published studies on technology-based alcohol interventions in primary care nearly tripled during the years of 2010-2018. Of the 42 studies, 28 (64%) were randomized controlled trials. Further, studies were rated on risk of bias and found to be predominantly low risk (n=18), followed by moderate risk (n=16), and high risk (n=8). The majority (71%) of studies reported reduced drinking or harm in all primary and secondary efficacy outcomes. Furthermore, 42% of studies reported that at least half of the primary and secondary efficacy outcomes of the technology-based interventions were superior to treatment as usual. High efficacy was associated with provider involvement and the reported use of an implementation strategy to deliver the technology-based intervention. Conclusions: Our systematic review highlights a pattern of growth in the number of studies evaluating technology-based alcohol interventions in primary care. While these interventions appear largely beneficial in primary care, outcomes may be enhanced by provider involvement and implementation strategy use. This review enables better understanding of the typologies and efficacy of these interventions and informs recommendations for those developing and implementing technology-based alcohol interventions in primary care settings.