Real-world Programs Research Articles

Patient Adherence to a Real-World Digital, Asynchronous Weight Loss Program in Australia That Combines Behavioural and GLP-1 RA Therapy: A Mixed Methods Study.

Increasingly large numbers of people are using digital weight loss services (DWLSs) to treat being overweight and obesity. Although it is widely agreed that digital modalities improve access to care in general, obesity stakeholders remain concerned that many DWLSs are not comprehensive or sustainable enough to deliver meaningful health outcomes. This study adopted a mixed methods approach to assess why and after how long patients tend to discontinue Australia's largest DWLS, a program that combines behavioural and pharmacological therapy under the guidance of a multidisciplinary care team. We found that in a cohort of patients who commenced the Eucalyptus DWLS between January and June 2022 (n = 5604), the mean program adherence was 171.2 (±158.2) days. Inadequate supplying of a patient's desired glucose-like peptide-1 receptor agonist medication was the most common reason for discontinuation (43.7%), followed by program cost (26.2%), result dissatisfaction (9.9%), and service dissatisfaction (7.2%). Statistical tests revealed that ethnicity and age both had a significant effect on patient adherence. These findings suggest that DWLSs have the potential to improve access to comprehensive, continuous obesity care, but care models need to improve upon the one observed in the Eucalyptus Australia DWLS to mitigate common real-world program attrition factors.

Utilizing comprehensive next-generation sequencing to determine the clinical significance of copy number variation in advanced/metastatic non-small cell lung cancer.

e15174 Background: Large population-based comprehensive tumor genomic profiling using next-generation sequencing (NGS) technology offers insight into the detection of copy number variation (CNV) in advanced/metastatic solid cancers. However, the significance and role of CNV in advanced-stage non-small cell lung cancer (NSCLC) is limited. In this analysis, we investigated the incidence and impact of CNV on patient outcomes. Methods: Patients were recruited as part of an ongoing real-world multicentre prospective NSCLC Precision Oncology Programme in Hong Kong. All patients underwent tissue and liquid NGS using Foundation One CDx and Foundation One Liquid CDx, respectively. Patients were classified into two groups based on CNV status: group 1 (CNV+) was defined as tumors with CNV reported; group 2 (CNV-) was defined as tumors with no CNV reported. The association of clinical factors with CNV was reported using Chi-square tests. The overall survival (OS) was calculated from the date of diagnosis of advanced-stage cancer to the date of death or the last follow-up date. Survival analysis was estimated using the Kaplan-Meier method with the log-rank test. Results: 353 patients were included in this analysis. NGS was performed in 216 (61%) tumor tissues, and 137 (39%) liquid specimens. 37% (n = 129) were female, and 50% (n = 175) were ever-smoker. 63% (n = 223) of our patient cohort harbored druggable mutations. The detection rate of CNV was 74% in tissue and 14% in liquid specimens. Patients with brain, liver, or bone metastasis (p = 0.018) and/or TMB-high (TMB-H) (p = 0.0014) were associated with CNV+. Next, we assessed the impact of CNV on treatment outcomes. Among patients who received immunotherapy ± chemotherapy, CNV+ was associated with a median OS of 20.4 months compared to 31.4 months in the CNV- group (HR:2.09, 95% CI: 1.05-4.16, p = 0.034). In contrast, there was no statistically significant difference in OS among the CNV+ and CNV- patient groups treated with chemotherapy alone. Although not significant, there was a trend towards poorer OS to immunotherapy ± chemotherapy in the TMB-H CNV+ cohort (23.6 months versus 31.4 months; p = 0.082). Conclusions: We demonstrate the significance of CNV as a prognostic tool for the prediction of survival outcomes among lung cancer patients. Our data suggests that CNV represents a distinct entity in advanced/metastatic NSCLC, associated with poorer survival outcomes to immunotherapy.

WolfFuzz: A Dynamic, Adaptive, and Directed Greybox Fuzzer

As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward energy distribution for the seeds. To address these issues, a dynamic distance-weighting-based distance estimation strategy is proposed first, which facilitates strategies for seed distribution that take energy into consideration. Second, to overcome the limitations of current seed energy distribution strategies, the gray wolf optimizer (GWO) is improved by integrating four strategies, leading to the development of the improved gray wolf optimizer (IGWO). Lastly, an adaptive search algorithm is proposed, and the WolfFuzz prototype tool is implemented. In vulnerability recurrence scenarios, WolfFuzz is 3.2× faster on average compared with the baseline and reproduces 76.4% of existing bugs faster. WolfFuzz also discovers nine different types of bugs in seven real-world programs.

CatchFuzz: Reliable active anti-fuzzing techniques against coverage-guided fuzzer

Fuzzing techniques that can automatically detect software vulnerabilities are used widely today. However, attackers also abuse these fuzzing techniques to find software vulnerabilities in target programs. Researchers have proposed a number of anti-fuzzing techniques in response to this issue, but most of them cause significant computational overhead regardless of whether programs operate under the fuzzing environment or not. To this point, we develop a new anti-fuzzer called CatchFuzz, in which an anti-fuzzing algorithm is loaded only after detecting the fuzzing environment. CatchFuzz then breaks down the fuzzing strategy by directly disordering information used by the fuzzing system. These features ensure that there is little performance degradation during normal usage and make a fuzzer interpret an interesting input value as uninteresting. Also, CatchFuzz surpasses existing anti-fuzzing techniques by significantly reducing the number of detected crashes, while also addressing their current limitations. We conduct multiple empirical tests with nine real-world programs to evaluate CatchFuzz and compare our method with existing anti-fuzzers. Our tests show that CatchFuzz identifies the fuzzing environment with an accuracy of 99.6% and a false positive rate of 0.5%. CatchFuzz exhibits highly improved anti-fuzzing performance, as demonstrated by the significant reduction in the number of detected unique crashes by 95.39%.

Automatic Repair of Quantum Programs via Unitary Operation

With the continuous advancement of quantum computing (QC), the demand for high-quality quantum programs (QPs) is growing. In order to avoid program failure, in software engineering, the technology of automatic program repair (APR) employs appropriate patches to remove potential bugs without the intervention of a human. However, the method tailored for repairing defective QPs is still absent. This paper proposes a new APR method named \(\texttt {UnitAR} \) that can repair QPs via unitary operation automatically. Based on the characteristics of superposition and entanglement in QC, the paper constructs an algebraic model and adopts a generate-and-validate approach for the repair procedure. Furthermore, the paper presents two schemes that can respectively promote the efficiency of generating patches and guarantee the effectiveness of applying patches. For the purpose of evaluating the proposed method, the paper selects 29 mutated versions as well as 5 real-world buggy programs as the objects, and introduces two traditional APR approaches \(\texttt {GenProg} \) and \(\texttt {TBar} \) as baselines. According to the experiments, \(\texttt {UnitAR} \) can fix 23 buggy programs, and this method demonstrates the highest efficiency and effectiveness among 3 APR approaches. Besides, the experimental results further manifest the crucial roles of two constituents involved in the framework of \(\texttt {UnitAR} \) .

Trastuzumab deruxtecan in previously treated HER2-positive metastatic or unresectable breast cancer: Real-life data from the temporary use authorization program in France.

Early access program (formerly cohort Temporary Authorization for Use) was granted for trastuzumab deruxtecan (T-DXd) in France based on DESTINY-Breast01 trial which demonstrated its efficacy and safety in HER2-positive metastatic/unresectable breast cancer after ≥2 anti-HER2-based regimens received at metastatic stage. This multicenter real-world early access program included HER2-positive metastatic/unresectable breast patients pretreated with at least two lines of anti-HER2 regimens who received T-DXd 5.4 mg/kg intravenously in monotherapy every 3 weeks. Four hundred and fifty-nine patients (median age, 58 years; hormone receptor-positive, 67%; brain metastases, 28.1%) received T-DXd. Before inclusion, 81.7% of patients had radiation therapy and 76.5% had undergone surgery. Median number of prior metastatic treatment lines was four (range, 2-22); 99.8% patients had received trastuzumab, 94.8% trastuzumab emtansine and 79.3% pertuzumab. Follow-up was performed from September 30, 2020 to March 30, 2021; when the early access program stopped, the median duration of T-DXd treatment was 3.4 (range, 0-7.8) months. In 160 patients with available tumor assessment, objective response rate was 56.7% and 12.1% had progression. In 57 patients with available brain tumor assessment, complete or partial intracranial response was reported for 35.7% patients and 5.4% had progression. A total of 17 (3.7%) patients with interstitial lung disease (ILD) was reported with no cases of ILD-related death. In this early access program in patients with heavily pretreated HER2-positive metastatic/unresectable breast cancer, T-DXd had antitumor activity with a similar response to that reported in previous clinical studies. T-DXd was well tolerated and no new safety signals were observed.

Understanding and Finding Java Decompiler Bugs

Java decompilers are programs that perform the reverse process of Java compilers, i.e., they translate Java bytecode to Java source code. They are essential for reverse engineering purposes and have become more sophisticated and reliable over the years. However, it remains challenging for modern Java decompilers to reliably perform correct decompilation on real-world programs. To shed light on the key challenges of Java decompilation, this paper provides the first systematic study on the characteristics and causes of bugs in mature, widely-used Java decompilers. We conduct the study by investigating 333 unique bugs from three popular Java decompilers. Our key findings and observations include: (1) Although most of the reported bugs were found when decompiling large, real-world code, 40.2% of them have small test cases for bug reproduction; (2) Over 80% of the bugs manifest as exceptions, syntactic errors, or semantic errors, and bugs with source code artifacts are very likely semantic errors; (3) 57.7%, 39.0%, and 41.1% of the bugs respectively are attributed to three stages of decompilers—loading structure entities from bytecode, optimizing these entities, and generating source code from these entities; (4) Bugs in decompilers’ type inference are the most complex to fix; and (5) Region restoration for structures like loop, sugaring for special structures like switch, and type inference of variables of generic types or indistinguishable types are the three most significant challenges in Java decompilation, which to some extent explains our findings in (3) and (4). Based on these findings, we present JD-Tester, a differential testing framework for Java decompilers, and our experience of using it in testing the three popular Java decompilers. JD-Testerutilizes different Java program generators to construct executable Java tests and finds exceptions, syntactic, and semantic inconsistencies (i.e. bugs) between a generated test and its compiled-decompiled version (through compilation and execution). In total, we have found 62 bugs in the three decompilers, demonstrating both the effectiveness of JD-Tester, and the importance of testing and validating Java decompilers.

HyperGo: Probability-based directed hybrid fuzzing

Directed grey-box fuzzing (DGF) is a target-guided fuzzing intended for testing specific targets (e.g., the potential buggy code). Despite numerous techniques proposed to enhance directedness, the existing DGF techniques still face challenges, such as taking into account the difficulty of reaching different basic blocks when designing the fitness metric, and promoting the effectiveness of symbolic execution (SE) when solving the complex constraints in the path to the target. In this paper, we propose a directed hybrid fuzzer called HyperGo. To address the challenges, we introduce the concept of path probability and combine the probability with distance to form an adaptive fitness metric called probability-based distance. By combining the two factors, probability-based distance can adaptively guide DGF toward paths that are closer to the target and have more easy-to-satisfy path constraints. Then, we put forward an Optimized Symbolic Execution Complementary (OSEC) scheme to combine DGF and SE in a complementary manner. The OSEC would prune the unreachable branches and unsolvable branches, and prioritize symbolic execution of the seeds whose paths are closer to the target and have more branches that are difficult to be covered by DGF. We evaluated HyperGo on 2 benchmarks consisting of 25 programs with a total of 120 target sites. The experimental results show that HyperGo achieves 37.75×, 29.11×, 23.34×, 95.61× and 143.22× speedup compared to AFLGo, AFLGoSy, BEACON, WindRanger, and ParmeSan, respectively in reaching target sites, and 3.44×, 3.63×, 4.10×, 3.26×, and 3.00× speedup in exposing known vulnerabilities. Moreover, HyperGo discovered 10 undisclosed vulnerabilities from 5 real-world programs.

An Innovative Patient-Centred Approach to Heart Failure Management: The Best Care Heart Failure Integrated Disease-Management Program

BackgroundThe management of heart failure (HF) is challenging because of the complexities in recommended therapies. Integrated disease management (IDM) is an effective model, promoting guideline-directed care, but the impact of IDM in the community setting requires further evaluation. MethodsA retrospective evaluation of community-based IDM. Patient characteristics were described, and outcomes using a pre- and post-intervention design were HF-related health-service use, quality of life, and concordance with guideline-directed medical therapy (GDMT). Results715 patients were treated in the program (2016 to 2023), 219 in a community specialist–care clinic, and 496 in 25 primary-care clinics. The overall cohort was predominantly male (60%), with a mean age of 73.5 years (± 10.7), and 60% with HF with reduced ejection fraction. In patients with ≥ 6 months of follow-up (n = 267), pre vs post annualized rates of HF-related acute health-service use decreased from 36.3 to 8.5 hospitalizations per 100 patients per year, P < 0.0001, from 31.8 to 13.1 emergency department visits per 100 patients per year, P < 0.0001, and from 152.8 to 110.0 urgent physician visits per 100 patients per year, P = 0.0001. The level of concordance with GDMT improved; the number of patients receiving triple therapy and quadruple therapy increased by 10.1% (95% confidence interval [CI], 2.4%,17.8%) and 19.6% (95% CI, 12.0%, 27.3%), respectively. Within these groups, optimal dosing was achieved in 42.5% (95% CI, 32.0%, 53.6%) and 35.0% (95% CI, 23.1%, 48.4%), respectively. In patients with at least one follow-up visit (n = 286), > 50% experienced a clinically relevant improvement in their quality of life. ConclusionsA community-based IDM program for HF, may reduce HF-related acute health-service use, improve quality of life and level of concordance with GDMT. These encouraging preliminary outcomes from a real-world program evaluation require confirmation in a randomized controlled trial.

Benralizumab in severe eosinophilic asthma by previous biologic use and key clinical subgroups: real-world XALOC-1 programme.

Pivotal phase 3 trials and real-world studies have demonstrated benralizumab's overall efficacy and safety in severe eosinophilic asthma (SEA). Additional large-cohort data are needed to confirm its real-world effectiveness in SEA according to previous biologic use and key baseline characteristics important for treatment selection. XALOC-1 is a large, multinational, retrospective, observational, real-world study programme of benralizumab in adults with SEA. This 48-week integrated analysis assessed annualised exacerbation rate (AER), maintenance oral corticosteroid (mOCS) use, asthma symptom control and lung function during a 12-month baseline period and up to 48 weeks after benralizumab initiation. Subgroup analyses were based on previous biologic use and key baseline clinical characteristics (mOCS use, blood eosinophil count, exacerbation history, age at asthma diagnosis, fractional exhaled nitric oxide level and presence of atopy and chronic rhinosinusitis with nasal polyps). Out of 1002 patients analysed, 380 were biologic-experienced. At week 48, 71.3% were exacerbation-free (versus 17.2% at baseline); relative reduction in AER was 82.7% overall and 72.9% in biologic-experienced patients; rates were maintained across all key clinical characteristic subgroups. Of patients using mOCS at baseline (n=274), 47.4% (130 out of 274) eliminated their use by week 48; the mean reduction from baseline in daily dose was 51.2% and, notably, 34.9% in biologic-experienced patients (n=115). Clinically significant improvements in asthma symptom control and lung function were observed. In this large, real-world programme, SEA patients treated with benralizumab had substantial improvements in clinical outcomes irrespective of previous biologic use and key clinical characteristics important to therapeutic decision-making in clinical practice.

Leveraging Routinely Collected Program Data to Inform Extrapolated Size Estimates for Key Populations in Namibia: Small Area Estimation Study.

Estimating the size of key populations, including female sex workers (FSW) and men who have sex with men (MSM), can inform planning and resource allocation for HIV programs at local and national levels. In geographic areas where direct population size estimates (PSEs) for key populations have not been collected, small area estimation (SAE) can help fill in gaps using supplemental data sources known as auxiliary data. However, routinely collected program data have not historically been used as auxiliary data to generate subnational estimates for key populations, including in Namibia. To systematically generate regional size estimates for FSW and MSM in Namibia, we used a consensus-informed estimation approach with local stakeholders that included the integration of routinely collected HIV program data provided by key populations' HIV service providers. We used quarterly program data reported by key population implementing partners, including counts of the number of individuals accessing HIV services over time, to weight existing PSEs collected through bio-behavioral surveys using a Bayesian triangulation approach. SAEs were generated through simple imputation, stratified imputation, and multivariable Poisson regression models. We selected final estimates using an iterative qualitative ranking process with local key population implementing partners. Extrapolated national estimates for FSW ranged from 4777 to 13,148 across Namibia, comprising 1.5% to 3.6% of female individuals aged between 15 and 49 years. For MSM, estimates ranged from 4611 to 10,171, comprising 0.7% to 1.5% of male individuals aged between 15 and 49 years. After the inclusion of program data as priors, the estimated proportion of FSW derived from simple imputation increased from 1.9% to 2.8%, and the proportion of MSM decreased from 1.5% to 0.75%. When stratified imputation was implemented using HIV prevalence to inform strata, the inclusion of program data increased the proportion of FSW from 2.6% to 4.0% in regions with high prevalence and decreased the proportion from 1.4% to 1.2% in regions with low prevalence. When population density was used to inform strata, the inclusion of program data also increased the proportion of FSW in high-density regions (from 1.1% to 3.4%) and decreased the proportion of MSM in all regions. Using SAE approaches, we combined epidemiologic and program data to generate subnational size estimates for key populations in Namibia. Overall, estimates were highly sensitive to the inclusion of program data. Program data represent a supplemental source of information that can be used to align PSEs with real-world HIV programs, particularly in regions where population-based data collection methods are challenging to implement. Future work is needed to determine how best to include and validate program data in target settings and in key population size estimation studies, ultimately bridging research with practice to support a more comprehensive HIV response.

Evaluation of a smoking cessation program for adults with severe mental illness in a public mental health service.

Smoking rates have decreased in the general population but remain high among people with severe mental illness (SMI). An individualized smoking cessation program was tested with 99 adults with SMI. The program showed it is possible to help people with SMI smoke fewer cigarettes and reduce nicotine addiction. Customized smoking cessation programs are essential for those with high nicotine dependence and mental health challenges. Mental health services should offer tailored tobacco cessation programs because these programs can improve the health of people with mental illness who smoke. It is important for mental health services to follow government guidelines and provide evidence-based support. INTRODUCTION: Despite significant reductions in smoking rates in the general population over recent decades, smoking rates remain relatively unchanged among people with SMI. To evaluate the feasibility and preliminary effectiveness of the Keep Quitting in Mind pilot program, an individualized smoking cessation program for people experiencing SMI. In total, 99 adult participants with SMI and engaged with a community mental health service, participated in the intervention. The intervention included motivational interviewing and goal setting, in addition to provision of pharmaceutical aids (including nicotine replacement therapy). Analysis determined that the Keep Quitting in Mind pilot program was feasible in a public adult mental health service and participation in the program was associated with reductions in the number of cigarettes smoked daily and level of nicotine addiction. This real-world pilot program demonstrated feasibility and potential effectiveness in reducing smoking among adults with mental illness. Aligning with government guidelines, tailored smoking cessation programs are crucial due to high nicotine dependence and mental health complexities. Given the high rates of cigarette smoking by people with mental illness and the cardiometabolic health risks associated with this, mental health services should consider adding evidence-based and bespoke tobacco cessation programs as part of core business.

Octopus : Scaling Value-Flow Analysis via Parallel Collection of Realizable Path Conditions

Value-flow analysis is a fundamental technique in program analysis, benefiting various clients, such as memory corruption detection and taint analysis. However, existing efforts suffer from the low potential speedup that leads to a deficiency in scalability. In this work, we present a parallel algorithm Octopus to collect path conditions for realizable paths efficiently. Octopus builds on the realizability decomposition to collect the intraprocedural path conditions of different functions simultaneously on-demand and obtain realizable path conditions by concatenation, which achieves a high potential speedup in parallelization. We implement Octopus as a tool and evaluate it over 15 real-world programs. The experiment shows that Octopus significantly outperforms the state-of-the-art algorithms. Particularly, it detects NULL-pointer-dereference bugs for the project llvm with 6.3 MLoC within 6.9 minutes under the 40-thread setting. We also state and prove several theorems to demonstrate the soundness, completeness, and high potential speedup of Octopus . Our empirical and theoretical results demonstrate the great potential of Octopus in supporting various program analysis clients. The implementation has officially deployed at Ant Group, scaling the nightly code scan for massive FinTech applications.

Interactive Abstract Interpretation with Demanded Summarization

We consider the problem of making expressive, interactive static analyzers compositional . Such a technique could help bring the power of server-based static analyses to integrated development environments (IDEs), updating their results live as the code is modified. Compositionality is key for this scenario, as it enables reuse of already-computed analysis results for unmodified code. Previous techniques for interactive static analysis either lack compositionality, cannot express arbitrary abstract domains, or are not from-scratch consistent. We present demanded summarization, the first algorithm for incremental compositional analysis in arbitrary abstract domains that guarantees from-scratch consistency. Our approach analyzes individual procedures using a recent technique for demanded analysis, computing summaries on demand for procedure calls. A dynamically updated summary dependency graph enables precise result invalidation after program edits, and the algorithm is carefully designed to guarantee from-scratch-consistent results after edits, even in the presence of recursion and in arbitrary abstract domains. We formalize our technique and prove soundness, termination, and from-scratch consistency. An experimental evaluation of a prototype implementation on synthetic and real-world program edits provides evidence for the feasibility of this theoretical framework, showing potential for major performance benefits over non-demanded compositional analyses.

Patient Adherence to a Digital Real-World GLP-1 RA-Supported Weight-Loss Program in the UK: A Retrospective Cohort Study

Patient Adherence to a Digital Real-World GLP-1 RA-Supported Weight-Loss Program in the UK: A Retrospective Cohort Study

DiPri : Distance-based Seed Prioritization for Greybox Fuzzing

Greybox fuzzing is a powerful testing technique. Given a set of initial seeds, greybox fuzzing continuously generates new test inputs to execute the program under test and drives executions with code coverage as feedback. Seed prioritization is an important step of greybox fuzzing that helps greybox fuzzing choose promising seeds for input generation in priority. However, mainstream greybox fuzzers like AFL++ and Zest tend to neglect the importance of seed prioritization. They may pick seeds plainly according to the sequential order of the seeds being queued or an order produced with a random-based approach, which may consequently degrade their performance in exploring code and exposing bugs. In the meantime, existing state-of-the-art techniques like Alphuzz and K-Scheduler adopt complex strategies to schedule seeds. Although powerful, such strategies also inevitably incur great overhead and will reduce the scalability of the proposed technique. In this paper, we propose a novel distance-based seed prioritization approach named DiPri to facilitate greybox fuzzing. Specifically, DiPri evaluates the queued seeds according to seed distances and chooses the outlier ones, which are the farthest from the others, in priority to improve the probabilities of discovering previously unexplored code regions. To make a profound evaluation of DiPri , we prototype DiPri on AFL++ and conduct large-scale experiments with four baselines and 24 C/C++ fuzz targets, where eight are from widely adopted real-world projects, eight are from the coverage-based benchmark FuzzBench, and eight are from the bug-based benchmark Magma. The results obtained through a fuzzing exceeding 50,000 CPU hours suggest that DiPri can (1) insignificantly influence the host fuzzer’s capability of code coverage by slightly improving the branch coverage on the eight targets from real-world projects and slightly reducing the branch coverage on the eight targets from FuzzBench, and (2) improve the host fuzzer’s capability of finding bugs by triggering five more Magma bugs. Besides the evaluation with the three C/C++ benchmarks, we integrate DiPri into the Java fuzzer Zest and conduct experiments on a Java benchmark composed of five real-world programs for more than 8,000 CPU hours to empirically study the scalability of DiPri . The results with the Java benchmark demonstrate that DiPri is pretty scalable and can help the host fuzzer find bugs more consistently.

Adopting the RE-AIM analytic framework for rural program evaluation: experiences from the Advance Care Planning via Group Visits (ACP-GV) national evaluation.

To support rigorous evaluation across a national portfolio of grants, the United States Department of Veterans Affairs (VA) Office of Rural Health (ORH) adopted an analytic framework to guide their grantees' evaluation of initiatives that reach rural veterans and to standardize the reporting of outcomes and impacts. Advance Care Planning via Group Visits (ACP-GV), one of ORH's Enterprise-Wide Initiatives, also followed the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. ACP-GV is a national patient-centered intervention delivered in a large, veterans integrated healthcare system. This manuscript describes how RE-AIM was used to evaluate this national program and lessons learned from ORH's annual reporting feedback to ACP-GV on their use of the framework to describe evaluation impacts. We used patient, provider, and site-level administrative health care data from the VA Corporate Data Warehouse and national program management databases for federal fiscal years (FY) spanning October 1, 2018-September 30, 2023. Measures included cumulative and past FY metrics developed to assess program impacts. RE-AIM constructs included the following cumulative and annual program evaluation results. ACP-GV reached 54,167 unique veterans, including 19,032 unique rural veterans between FY 2018 to FY 2023. During FY 2023, implementation adherence to the ACP-GV model was noted in 91.7% of program completers, with 55% of these completers reporting a knowledge increase and 14% reporting a substantial knowledge increase (effectiveness). As of FY 2023, 66 ACP-GV sites were active, and 1,556 VA staff were trained in the intervention (adoption). Of the 66 active sites in FY 2023, 27 were sites previously funded by ORH and continued to offer ACP-GV after the conclusion of three years of seed funding (maintenance). Lessons learned developing RE-AIM metrics collaboratively with program developers, implementers, and evaluators allowed for a balance of clinical and scientific input in decision-making, while the ORH annual reporting feedback provided specificity and emphasis for including both cumulative, annual, and rural specific metrics. ACP-GV's use of RE-AIM metrics is a key step towards improving rural veteran health outcomes and describing real world program impacts.

Factors Associated With Abstinence Among Young Adult Smokers Enrolled in a Real-world Social Media Smoking Cessation Program.

Social media platforms are promising to provide smoking cessation support. This study aimed to identify baseline factors associated with cigarette smoking abstinence among young adult smokers enrolled in a real-world social media-based smoking cessation program. We analyzed data from young adult smokers (aged 18-30 years) participating in a publicly available Facebook-based smoking cessation program serving the San Francisco Bay Area. The analytic sample consisted of 248 participants who completed both the baseline and follow-up surveys at 3 months. Multivariable logistic regression analysis determined baseline factors significantly associated with self-reported 7-day cigarette smoking abstinence at 3 months. Participants were race/ethnically diverse, well-educated, and 47.6% reported LGB + sexual identity. Those who reported dual use of cigarettes and e-cigarettes in the past 30 days (vs. cigarette use only), current alcohol users (vs. non-users), and those aged 25-30 years (vs. 18-24 years) were significantly less likely to report 7-day abstinence at 3 months. Non-daily smokers (vs. daily smokers) and those with high desire to quit smoking (vs. low to moderate desire) were more likely to report abstinence. Results also showed reduction in the percentage of e-cigarette and other tobacco product use among participants. Social media interventions may be more effective for young adult non-daily smokers and those with high desire to quit smoking. Smoking cessation programs may help reduce use of other tobacco products among treatment-seeking smokers. Smoking cessation interventions for young adults need to explicitly address dual use of cigarettes and e-cigarettes and use of alcohol. Findings of this study highlight the need for future interventions to address dual use of cigarettes and e-cigarettes and use of alcohol to improve cigarette smoking abstinence outcomes. The reduction in the use of other tobacco products among program participants indicates that social media smoking cessation programs may exert a broader positive influence on overall tobacco consumption. The large number of LGB+ smokers participating in the program suggests social media is a promising cessation channel for this hard-to-reach group, warranting further study.

Testing concolic execution through consistency checks

Symbolic execution is a well-known software testing technique that evaluates how a program runs when considering a symbolic input, i.e., an input that can initially assume any concrete value admissible for its data type. The dynamic twist of this technique is dubbed concolic execution and has been demonstrated to be a practical technique for testing even complex real-world programs. Unfortunately, developing concolic engines is hard. Indeed, an engine has to correctly instrument the program to build accurate symbolic expressions, which represent the program computation. Furthermore, to reason over such expressions, it has to interact with an SMT solver. Hence, several implementation bugs may emerge within the different layers of an engine.In this article, we consider the problem of testing concolic engines. In particular, we propose several testing strategies whose main intuition is to exploit the concrete state kept by the executor to identify inconsistencies within the symbolic state. We integrated our strategies into three state-of-the-art concolic executors (SymCC, SymQEMU, and Fuzzolic, respectively) and then performed several experiments to show that our ideas can find bugs in these frameworks. Overall, our approach was able to discover more than 12 bugs across these engines.

Abstract TMP106: Use of Net Promoter Score to Quantify the Participant Experience With a Telehealth Lifestyle Management and Cardiovascular Risk Reduction Coaching Program

Introduction: Improving the patient experience of care is 1 of the 3 dimensions of healthcare’s “Triple Aim.” The net promoter score (NPS) measures willingness to recommend a company’s services and is widely used by Fortune 500 companies as an indicator of customer loyalty/satisfaction. The NPS is increasingly being used to assess patients’ healthcare experience. We sought to quantify the experience of participants in the same telehealth lifestyle management and CVD risk reduction coaching program used in multiple NIH/PCORI-funded stroke-related multi-center RCTs (namely, SAMMPRIS, CREST-2, CAPTIVA and TEAMS-BP) by measuring the NPS in real-world (i.e., non-RCT) program participants. Methods: Participants in the coaching program typically have an opportunity to complete a voluntary satisfaction survey after 12 weeks and 1 year of participation and annually thereafter. The survey is conducted online or via telephone. For this analysis the NPS was calculated, from 17,929 consecutive surveys completed by non-RCT program participants, using responses on an 11-point scale (10 = "strongly agree"; 0 = "strongly disagree") to this item: “I would recommend the program to others.” Scores can range from -100 (worst) to +100 (best). In industry, a positive NPS is well regarded, and scores ≥50 are thought to highlight an excellent experience. Results: Three categories of responders were identified and classified as ‘promoters’ (rating = 9 or 10; i.e., those who would likely recommend the program = 78% of responses), ‘passives’ (rating = 7 or 8; i.e., those who are satisfied but would probably not actively promote the program = 16% of responses) and ‘detractors’ (rating = ≤6; i.e., those who might actively discourage participation = 6% of responses). The NPS (calculated from the % of promoters minus the % of detractors) was +72, a score comparable to that achieved by the most popular/successful commercial organizations in other industries. Conclusion: This is the first study to utilize the NPS to evaluate the participant experience with a telehealth coaching program that has been/is currently being successfully deployed in multiple stroke-related multi-center RCTs. The data show that real-world program participants have an NPS indicative of very high satisfaction.