Tracking Tool Research Articles

Tracking Wildfires With Weather Radars

AbstractThere is a need for nowcasting tools to provide timely and accurate updates on the location and rate of spread (ROS) of large wildfires, especially those impacting communities in the wildland urban interface. In this study, we demonstrate how fixed‐site weather radars can be used to fill this gap. Specifically, we develop and test a radar‐based fire‐perimeter tracking tool that leverages the tendency for local maxima in the radar reflectivity to be collocated with active fire perimeters. Reflectivity maxima are located using search radials from points inside a fire polygon, and perimeters are updated at intervals of ∼10 min. The algorithm is tested using publicly available Next Generation Weather Radar radar data for two large and destructive wildfires, the Camp and Bear Fires, both occurring in northern California, USA. The radar‐based fire perimeters are compared with available, albeit limited, satellite and airborne infrared observations, showing good agreement with conventional fire‐tracking tools. The radar data also provide insights into fire ROS, revealing the importance of long‐range spotting in generating ROS that exceeds conventional estimates. One limitation of this study is that high‐resolution fire perimeter validation data are sparsely available, precluding detailed error quantification for the radar estimates drawn from samples spanning a range of environmental conditions and radar configurations. Nevertheless, the radar tracking approach provides the basis for improved situational awareness during high‐impact fires.

The advanced practice nurse as a health care influencer

The skills that advanced practice nurses use with patients, health care professionals and within the health care system enable them to exercise real leadership. The use of conceptual models and theories in nursing, tools for tracking and valuing clinical judgment, and nursing diagnoses and other concepts are essential to guaranteeing their disciplinary identity and ensuring interprofessional collaboration.

Validity and Reliability of the Smart Food Diary Keenoa Against Recovery Biomarkers: A Study Protocol

ObjectivesDietary assessment provides essential data for nutrition research, but current methods have limitations impeding the accuracy of reported intakes. Keenoa is a new mobile food diary that integrates artificial intelligence food recognition. Participants take pictures of their meals, specify foods and beverages consumed and estimate portion sizes with the help of visual pictograms. Data are analyzed from the Canadian Nutrient File v2015 and other national databases. Objectives are 1) To assess Keenoa's validity and reliability for energy, protein, potassium and sodium intakes against recovery biomarkers; 2) to determine the optimal number of tracking days for maximal validity; 3) to assess the appreciation and usability from participants.MethodsAdult participants (18–70 y, n = 120) will be recruited for this 3-month study. They will track their food intake using a weighted written food diary (WFD) and Keenoa, in a randomized order, at month 1 (4 consecutive days), 2 (7 days) and 3 (4 days). At month 2, urinary nitrogen, sodium and potassium excretion will be measured from 24-h urine collections on day 3 of each tracking tool. A subsample of n = 30 will undergo measurement of total energy expenditure using the gold-standard doubly labeled water method. Intakes in other key nutrients will be compared to those reported by WFD, as a relative comparator considered the most accurate among conventional methods. Proportions of under/over-reporters will be analyzed by gender and BMI category. Perceived usability of the Keenoa application will be assessed using the validated System Usability Scale (SUS). The validity of Keenoa will be tested by comparing reported intakes of multiple days against measured recovery biomarkers with paired t-tests and Wilcoxon Signed rank test, as applicable, and Bland Altman's test for bias. Reliability will be tested by comparing repeated measures over time with intraclass correlations. P-values < 0.05 will be considered significant.ResultsN/AConclusionsIf proven accurate, reliable, and appreciated by users, this innovative tool could be used by researchers in a cost-effective manner. It could contribute to expanding nutrition knowledge and support research on the role of nutrition in health.Funding SourcesCanadian Foundation for Dietetics Research.

71-LB: Real-World Evidence of Arabic Digital Therapeutics for People Living with Type 2 Diabetes in Qatar

Background: Around 17% of people in Qatar live with diabetes. This study reports on the impact of an Arabic digital therapeutics product, named Droobi Health™ (DH) on glycemic control in patients with T2DM. Methods: DH provides participant with a 16-week digital behavior change program tailored for people with T2DM. The programs are based on DSME content and AADE7 guidelines for T2DM, with the content localized to be culturally relevant. The DH app gives participants access to chat with a real-human CDE, tracking tools for blood glucose, weight, food intake, and physical activity, and weekly lessons. Participants at primary healthcare centers with T2DM and a starting A1c equal to or greater than 8% were provided the DH intervention. A post-intervention A1C was measured after three months. This study presents results from 633 participants who completed follow-up A1c. Results: The mean pre and post-intervention A1c for 633 participants was 9.67% and 8.76% respectively, showing a mean reduction of 0.91% (95% CI 0.73%-1.09%, P≤0.001) . For women, the use of DH was associated with a statistically significant A1c reduction of 0.73% (95% CI 0.42%-1.04%, P≤0.001) . For men, the use of DH was associated with a statistically significant A1c reduction of 0.99% (95% CI 0.75%-1.21%, P≤0.001) . Conclusion: Based on the results, participants have improved glycemic control outcomes, particularly male participants, underscoring DH’s potential to deliver a clinically effective intervention for people with T2DM in Qatar and across the Middle East. Disclosure N. Jandali: Stock/Shareholder; Droobi LLC. R. Saad: Employee; Droobi LLC.

Conductor less Bus Ticketing System Using RFID Technology

Abstract: The main objective of this paper is to generate passenger tickets using RFID technology for the fare of a particular destination. The appropriate amount is deducted from the RFID card. In addition to that, in proposal system RFID has proven to be one of the most promising technologies in recent years and can be effectively employed in various applications since it is economical and widely used tool for tracking and locating purposes. Radio-frequency identification (RFID) is a technology that uses electromagnetic fields to automatically identify and track tags attached to objects .A small radio transponder, a radio receiver, and a radio transmitter make up an RFID system The RFID application, on the other hand, has become a popular tool for both tracking transit vehicles and the public ticketing system.

Performance Evaluation of Three DNA Sample Tracking Tools in a Whole Exome Sequencing Workflow.

Next-generation sequencing applications are becoming indispensable for clinical diagnostics. These experiments require numerous wet- and dry-laboratory steps, each one increasing the probability of a sample swap or contamination. Therefore, identity confirmation at the end of the process is recommended to ensure the right data are used for each patient. We tested three commercially available, single nucleotide polymorphism (SNP)-based sample tracking kits in a diagnostic workflow to evaluate their ease of use and performance. The coverage uniformity, on-target specificity, sample identification, and genotyping performance were determined to assess the reliability and cost effectiveness of each kit. Hands-on time and manual steps are almost identical for the kits from pxlence and Nimagen. The Swift kit has an extra purification step, making it the longest and most demanding protocol. Furthermore, the Swift kit failed to correctly genotype 26 of the 46 samples. The Nimagen kit identified all but one sample and the pxlence kit unambiguously identified all samples, making it the most reliable and robust kit of this evaluation. The Nimagen kit showed poor on-target mapping rates, resulting in deeper sequencing needs and higher sequencing costs compared with the other two kits. Our conclusion is that the Human Sample ID kit from pxlence is the most cost effective of the three tested tools for DNA sample tracking and identification.

Spatio-temporal and contextual cues to support reflection in physical activity tracking

Physical activity reconstruction is a process whereby self-trackers reflect on their physical activities and goals in an episodic fashion by recalling a series of past experiences and events. Tracking tools often include spatio-temporal cues (i.e. maps and timelines) to provide further context to these physical activity patterns, yet little is known about whether these features actually aid reflection. Here, we report a four-week user study (N=26) run with FitReconstruct, an app that fosters reconstruction of physical activity data using spatio-temporal and contextual cues to support recall of episodic information. We found that compared to a baseline version, presenting spatio-temporal and contextual cues increased the quality of reflection and user satisfaction. These findings inform design recommendations to support explicit reflective prompts in physical activity tracking apps.

An easy and efficient power generator with ultrahigh voltage for lighting, charging and self-powered systems

Can you imagine that when you slide on the polymer plate with your hands, the generated electromagnetic field can drive 28-watt fluorescent tube lamps and can even charge the mobile phone? The progress of science and technology subverts traditional cognition. Here, an easy and efficient power generator (EEPG) with ultrahigh voltage is invented, which can light up 28-watt fluorescent tube lamps. The average output voltage and current of planar EEPG can reach up to nearly 5000 V and 116 μA, respectively. Not only can it be used for lighting, but also for charging, and even for mobile phones. The rotary EEPG can be used to harvest wind energy and tidal energy, which overcomes the shortcomings of traditional electromagnetic wind power generation working only when the wind speed is greater than 5 m/s. In addition, the EEPG power generator can be utilized as a footswitch and trajectory tracking tool. This is likely to become an easy and effective method of power generation in the world. It can be expected that mobile phones can be charged by just rubbing their backs. Collecting the green wind energy more easily and efficiently will be truly realized.

Application of Diffusion Kurtosis Imaging in Evaluating Acute Xerostomia in Nasopharyngeal Carcinoma Treated With Induction Chemotherapy Plus Concurrent Chemoradiotherapy.

PurposeThe aim of this study was to identify the efficacy of diffusion kurtosis imaging (DKI) in tracking and monitoring the dynamic change of parotid glands (PGs), submandibular glands (SMGs), sublingual glands (SLGs), and acute xerostomia in nasopharyngeal carcinoma (NPC) patients treated with induction chemotherapy (IC) plus concurrent chemoradiotherapy (CCRT).MethodsThe prospective study recruited 42 participants treated with IC+CCRT. All patients underwent DKI scanning six times: before IC, before RT, in the middle of the RT course, immediately after RT, and 1 and 3 months post-RT. Mean diffusion coefficient (MD) and mean kurtosis (MK) of PG, SMG, SLG, saliva flow rate measured under resting (uSFR) and stimulated condition (sSFR), and xerostomia questionnaire (XQ) scores were recorded.ResultsAt each time point, sSFR was significantly higher than uSFR (p < 0.05 for all). MD of the salivary glands and XQ scores increased over time while MK, uSFR, and sSFR decreased. After IC, the significant differences were detected in MD and MK of bilateral SMG and MK of the left SLG (p < 0.05 for all), but not in MD and MK of PG, uSFR, sSFR, and XQ scores. After RT, sSFR at 1m-RT decreased significantly (p = 0.03) while no significant differences were detected in uSFR and XQ scores. Moderate-strong correlations were detected in ΔMD-PG-R%, ΔMK-PG-R%, ΔMD-PG-L%, ΔMK-PG-L%, ΔMD-SMG-R%, ΔMK-SMG-R%, ΔMD-SMG-L%, ΔMK-SMG-L%, and ΔMD-SLG-R%, with correlation coefficients (p < 0.05 for all) ranging from 0.401 to 0.714. ΔuSFR% was correlated with ΔMD-SMG% (p = 0.01, r = −0.39), ΔMD-SLG% (p < 0.001, r = −0.532), and ΔMK-SMG% (p < 0.001, r = −0.493). ΔsSFR% correlated with ΔMD-PG% (p = 0.001, r = −0.509), ΔMD-SMG% (p = 0.015, r = −0.221), and ΔMK-PG% (p < 0.001, r = 0.524). ΔXQ% was only correlated with ΔMK-PG% (p = 0.004, r = 0.433).ConclusionDKI is a promising tool for tracking and monitoring the acute damage of PG, SMG, and SLG induced by IC+CCRT in NPC patients.

Commitment Indicators for Tracking Sustainable Design Decisions in Construction Projects

The construction industry is considered one of the largest contributors to climate change through its consumption of natural resources and generation of greenhouse gases. Much of this can be attributed to inadequate decision making and follow-up within construction companies. To mitigate this problem, considerable research on Sustainable Development (SD) reports on decision support systems have been developed in order to make sound decisions with respect to the environment. Nonetheless, and despite the availability of such tools, these systems fail to track the commitment to SD decisions and goals during the different phases of construction projects in general and the design phase in particular. As such, this study identified three standard SD indicators: waste reduction, energy consumption, and carbon emissions as the main contributors, and developed the framework to track the project stakeholders’ commitment to the relevant SD indicators during the project design phase. The developed framework was validated via an expert panel and used to create a Sustainable Development Commitment Tracking Tool (SDCTT-D). The SDCTT-D tool was also applied in an infrastructure project case study. The results of this study gauged the usability of the developed tool and corroborated the research premise.

Quantifying the Reporting, Coverage and Consistency of Key Indicators in Mangrove Restoration Projects

Mangroves are often cleared for aquaculture, agriculture, and coastal development despite the range of benefits for people and nature that they provide. In response to these losses, there are multiple global, and regional efforts aimed at accelerating mangrove forest restoration, resulting in many restoration projects being implemented and managed by different groups with highly diverse objectives. The information reported from these restoration projects is extremely variable, limiting our ability to identify whether desired objectives have been met or key factors that determine effective and durable restoration have been applied. To address this problem, we developed a holistic monitoring framework that captures the key indicators of restoration, spanning project aims, intervention type, costs, and ecological and socioeconomic outcomes. Subsequently, using a systematic literature search, we examined 123 published case studies to identify the range and quality of reported information on restoration, relative to our framework. We found that there were many gaps in reporting, for multiple indicators. Sections related to site conditions prior to restoration (reported in only 32% of case studies) and socioeconomic outcomes (26%) were consistently missing from most project reporting. Conversely, information on the type of intervention was reported for all case studies, and the aims of the project (reported in 76% of case studies) and ecological monitoring (82%) were far more prevalent. Generally, the restoration literature did not follow any specific framework in terms of reporting which likely contributed to the gaps in the information recorded. These gaps hinder comparisons between case studies, inhibiting the ability to learn lessons from previous restoration attempts by identifying commonalities. The need for more structure and consistent reporting supports the development of a standard restoration tracking tool that can facilitate the comparison of restoration efforts, aiding the implementation of future projects.

Simulation study of the space charge limit in heavy-ion synchrotrons

The SIS100 synchrotron as a part of the new Facility for Antiproton and Ion Research (FAIR) accelerator facility at GSI should be operated at the ``space charge limit'' for light- and heavy-ion beams. Beam losses due to space-charge-induced resonance crossing should not exceed a few percent during a full cycle. The recent advances in the performance of particle tracking tools with self-consistent solvers for the 3D space charge forces now allow us to reliably identify low-loss areas in tune space, considering the full SIS100 accumulation plateau of one second (160 000 turns) duration. A realistic magnet error model, extracted from precise bench measurements of the SIS100 main dipole and quadrupole magnets, is included in the simulations. Previously, such beam dynamics simulations required non-self-consistent space charge models. By comparing to the self-consistent simulations results, we are now able to demonstrate that the predictions from such faster space charge models can be used to identify low-loss regions with sufficient accuracy. The findings are applied by identifying a low-loss working point region in SIS100 for the design FAIR beam parameters. The bunch intensity at the space charge limit is determined. Several countermeasures to space charge are proposed to enlarge the low-loss area and to further increase the space charge limit.

Investigation of Intelligent Substation Inspection Robot by Using Mobile Data

Substation equipment inspection is essential for the power industry. The expansion of the smart grid scale improves the transmission capacity and enhances the likelihood of power plant facilities failure. To ensure the safety of the electric power supply, it is essential to inspect substation equipment. Metal commercial equipment can be traversed by remote inspection robots equipped with magnetic wheels. It is possible to use robots like this to examine equipment and pipelines remotely. In many cases, these gadgets are able to scale vertical surfaces and even traverse obstacles with a variety of shapes. Finally, researchers in the field of robotics have indicated that challenges such as restricted onboard battery capacity, undependable line fault detection, electrical insulation, power mechanism, and advanced control techniques for outer wind disruption are highly promising research areas. To build an unmanned, intelligent, and succeeded substation, the substation progressively implements inspection robots instead of physical exertion. Hence, in this paper, the mobile-based Intelligent Tracking Framework (MITF) has been proposed using inspection robots. This inspection robot is autonomous and can be used for various tracking tools: visual, infrared, and partial charge–discharge camera. The robot is integrated with a camera and thermal infrared imager sensors that have been collectively designated as workload. These inspection sensors are used to detect environmental parameters such as reading meters, evaluation thermoelectric temperature. The accurate localization of working loads and the inspection robot electromagnetic interference within substations have been resolved. This mobile robot delivers innovative monitoring and precise detection for the unmanned substation and smart substation. The suggested approach’s effectiveness is verified through experiment results based on the electrical equipment of the substation. The experimental outcome of the proposed method boosts the Meter Reading Analysis (94.19%), Transmission Capacity Analysis (98.5%), Workload Analysis (98.9%), Temperature Analysis (97.6%), and Safety Analysis (95.41%).

Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining

The complexity of industrial processes imposes a lot of challenges in building accurate and representative causal models for abnormal events diagnosis, control and maintenance of equipment and process units. This paper presents an innovative data-driven causality modeling approach using interpretable machine learning and process mining techniques, in addition to human expertise, to efficiently and automatically capture the complex dynamics of industrial systems. The approach tackles a significant challenge in the causality analysis community, which is the discovery of high-level causal models from low-level continuous observations. It is based on the exploitation of event data logs by analyzing the dependency relationships between events to generate accurate multi-level models that can take the form of various state-event diagrams. Highly accurate and trustworthy patterns are extracted from the original data using interpretable machine learning integrated with a model enhancement technique to construct event data logs. Afterward, the causal model is generated from the event log using the inductive miner technique, which is one of the most powerful process mining techniques. The causal model generated is a Petri net model, which is used to infer causality between important events as well as a visualization tool for real-time tracking of the system’s dynamics. The proposed causality modeling approach has been successfully tested based on a real industrial dataset acquired from complex equipment in a Kraft pulp mill located in eastern Canada. The generated causality model was validated by ensuring high model fitness scores, in addition to the process expert’s validation of the results.

Developing a novel Bifidobacterium phage quantitative polymerase chain reaction-based assay for tracking untreated wastewater

Microbial source tracking (MST) tools provide insights on fecal pollution levels in aquatic environments using predominantly quantitative PCR (qPCR) assays that target host-associated molecular marker genes. Existing wastewater-associated marker genes have shown limited or significant cross-reactions with non-human fecal samples. In this study, we mined the current Gut Phage Database (GPD) and designed a novel untreated wastewater-specific Bifidobacterium phage qPCR assay (i.e., Bifi assay). The sensitivity and specificity of the Bifi marker genes were assessed by collectively analyzing untreated (n = 33) and treated (n = 15) wastewater and non-human fecal samples (i.e., Rabbit, mouse, cow, horse, pig, chicken, sheep, dog, deer, kangaroos; n = 113) in Shenzhen, Guangdong Province, China and Brisbane, Australia. Bifi assay revealed 100% host-specificity against non-human fecal samples collected from Shenzhen and Brisbane. Furthermore, this marker gene was also detected in all untreated and treated wastewater samples, whose concentrations ranged from 5.54 to 6.83 log10 GC/L. In Shenzhen, the concentrations of Bifi marker gene were approximately two orders of magnitude lower than Bacteroides (HF183/BacR287 assay) and CrAssphage (CPQ_56 assay). The concentration of Bifi marker gene in untreated wastewater from Brisbane was 1.35 log10 greater than those in Shenzhen. Our results suggest that Bifi marker gene has the potential to detect and quantify the levels of human fecal pollution in Shenzhen and Brisbane. If additional detection sensitivity is required for environmental studies, Bifi marker gene should be paired with either CrAssphage or HF183/BacR287 marker genes.

Bayesian Correlation Filter Learning With Gaussian Scale Mixture Model for Visual Tracking

Correlation filters (CF), a popular tool for visual tracking, suffer from unwanted boundary effects due to the periodic assumption needed for FFT implementation. To address this issue, spatially regularized discriminative correlation filters (SRDCF) have been proposed by introducing a weighting matrix to the regularization term. However, the existing design of spatial weighting matrix is often heuristic and non-adaptive. Inspired by recent advances in joint discrimination and reliability learning for correlation tracking, we propose a principled Bayesian correlation filter learning method using Gaussian scale mixture (GSM) model. The key idea is to decompose each CF coefficient into the product of a positive scalar multiplier and a Gaussian random variable. Treating positive multipliers as weighting coefficients, GSM-based modeling of CFs leads to a spatially adaptive regularization strategy with improved capability of handling various appearance-related uncertainty factors (e.g., scale variation, out-of-plane rotation, and motion blur). Moreover, by imposing a sparse prior over the multipliers, we can jointly learn multipliers and CFs under a unified Bayesian estimation framework. Structured GSM model allows us to better exploit the spatial correlations among CFs and further improve the tracking performance. Experimental results on OTB-2013, OTB-2015, Temple Color-128, VOT-2016, and VOT-2017 show that our tracking method performs favorably when compared with current state-of-the-art methods.

Cover Picture: In vivo CT imaging tracking of stem cells labeled with Au nanoparticles (View 3/2022)

Computed tomography (CT) provides a noninvasive, sensitive and real-time imaging and tracking tool for monitoring and in-depth understanding of delivery, migration, and final fate of transplanted stem cells in regenerative medicine and other biomedical applications. In article number 20200119, Jie Huang and coworkers present an overview of the recent progress in gold nanoparticle (AuNP)-based CT imaging tracking of stem cells, with focuses on new strategies to improve cellular uptake of AuNPs for better CT imaging contrast, and on multimodal imaging tracking of stem cells in vivo. Finally, they discuss major challenges and possible future directions of the research.

Age and sex trends among mental health-related emergency department visits in North Carolina

Whether patients present to the emergency department (ED) with physical ailments and comorbid psychiatric needs or primary psychiatric complaints, understanding differences in clinically relevant age and sex patterns over time is crucial to optimal psychiatric care in the ED setting. We used population-level surveillance data provided by the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) from January 1, 2008, through December 31, 2014. Mental health-related (MHR) ED visits were identified by International Classification of Diseases, Clinical Modification 9th revision (ICD-9-CM) codes analyzed in the Agency for Healthcare Research and Quality (AHRQ) clinical classification software groupings of related diagnostic categories. Trends were assessed based on total and average annual visit counts. We identified approximately 4 million MHR ED visits. The average number of visits per year was highest among 50-year-olds, while patients over the age of 90 had the highest proportion of their ED visits associated with an MHR code. Mood disorders were more prevalent among females, while substance use disorders were more prevalent among males. Within MHR categories, age-related peaks did not differ by sex except for suicide and self-inflicted injury. Whether it be a teenage boy presenting with suicidal ideation, a middle-aged man presenting with alcohol abuse, or an elderly female presenting with dementia, ED MHR visits’ needs vary across the lifespan. Understanding these trends is important to holistic patient care.

The General Registry of Autologous Fat Transfer: Concept, Design, and Analysis of Fat Grafting Complications.

The American Society of Plastic Surgeons and The Plastic Surgery Foundation launched GRAFT, the General Registry of Autologous Fat Transfer, in October of 2015. This web-accessible registry addresses the need for prospective and systematic data collection, to determine the rates of unfavorable outcomes (complications) of fat grafting. Understanding and avoiding the factors that lead to complications can help establish safe practices for fat grafting. Data collected between October of 2015 and November of 2019 were summarized for age, sex, indications, processing techniques, and fat graft volume. Rates of complications for fat grafting to various anatomical areas were calculated. The General Registry of Autologous Fat Transfer collected data on 7052 fat grafting procedures from 247 plastic surgery practices. The mean age of the patients in the registry was 51 years (range, 1 to 89 years), 94 percent were female, and 64 percent of the procedures were for aesthetic indications. Whereas the overall complication rate was low (5.01 percent), the complication rates for fat grafting to the breast and buttocks (7.29 percent and 4.19 percent, respectively) were higher than those for face and other areas (1.94 percent and 2.86 percent, respectively). Oil cysts (2.68 percent) and infections (1.64 percent) were the most common complications of breast fat grafting, whereas seroma (1.84 percent) and palpable mass (1.33 percent) were most common for fat grafting to buttocks. Palpable mass (0.54 percent) and infections (0.54 percent) were most common for fat grafting to face. The General Registry of Autologous Fat Transfer provides a valuable tool for prospective tracking of fat grafting techniques and complications. Data collected in the registry show low rates of complications for all recipient areas treated with fat grafting. GRAFT collects real world data on complications of autologous fat grafting procedures. The data collected over 4 years shows low rates of complications for fat grafting. The benchmarking tools available in GRAFT can help enhance techniques and safety of fat grafting. Risk, III.

Understanding implementation costs of a pediatric weight management intervention: an economic evaluation protocol

BackgroundUnderstanding the cost and/or cost-effectiveness of implementation strategies is crucial for organizations to make informed decisions about the resources needed to implement and sustain evidence-based interventions (EBIs). This economic evaluation protocol describes the methods and processes that will be used to assess costs and cost-effectiveness across implementation strategies used to improve the reach, adoption, implementation, and organizational maintenance of an evidence-based pediatric weight management intervention- Building Health Families (BHF).MethodsA within-trial cost and cost-effectiveness analysis (CEA) will be completed as part of a hybrid type III effectiveness-implementation trial (HEI) designed to examine the impact of an action Learning Collaborative (LC) strategy consisting of network weaving, consultee-centered training, goal-setting and feedback, and sustainability action planning to improve the adoption, implementation, organizational maintenance, and program reach of BHF in micropolitan and surrounding rural communities in the USA, over a 12-month period. We discuss key features of implementation strategy components and the associated cost collection and outcome measures and present brief examples on what will be included in the CEA for each discrete implementation strategy and how the results will be interpreted. The cost data will be collected by identifying implementation activities associated with each strategy and using a digital-based time tracking tool to capture the time associated with each activity. Costs will be assessed relative to the BHF program implementation and the multicomponent implementation strategy, included within and external to a LC designed to improve reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) of BHF. The CEA results will be reported by RE-AIM outcomes, using the average cost-effectiveness ratio or incremental cost-effectiveness ratio. All the CEAs will be performed from the community perspective.DiscussionThe proposed costing approach and economic evaluation framework for dissemination and implementation strategies and EBI implementation will contribute to the evolving but still scant literature on economic evaluation of implementation and strategies used and facilitate the comparative economic analysis.Trial registrationClinicalTrials.gov NCT04719442. Registered on January 22, 2021.