Key User Research Articles

Ecosystem Services in the Context of Agroforestry—Results of a Survey among Agricultural Land Users in the Czech Republic

Agroforestry, the use of trees at the interface between agricultural and forestry systems, is a key component of the multifunctional European landscape, where it provides a whole range of ecosystem services. Its main potential lies in the provision of services including increasing economic yield in agriculture and providing anti-erosion measures. The main objective of this research is to assess the perception of the providing ecosystem services of woody plants growing in agroforestry by farmers as key land users in the Czech Republic. Different aspects influencing farmers’ subjective perceptions were identified, especially depending on conventional versus organic farming methods. Respondents’ views were mapped using a structured questionnaire, which allowed for independently assessing the importance of windbreaks. Respondents considered regulatory ecosystem services the most important ones, in particular reducing soil erosion and flood risk, or increasing the resilience of the landscape to drought. Respondents perceived the physical obstruction of trees during mechanical tillage and the tenancy relationship with the land as the most significant problem with the introduction of agroforestry systems. The aim of this study is to promote innovative approaches in the national agricultural strategy in agroforestry as one of the tools for climate change adaptation towards sustainable agricultural production.

Features of Disclosure of Information about Assets of Public Administration Sectors in the Balance Sheet

The article presents the development of the accounting of the public sector during the budget reform implemented in Russia in the late XXth — early XXIst centuries. The study showed a close relationship between the information request of users and the content of public sector accounting statements. It is found that the modern content of the balance sheet — one of the most important reporting forms is a consequence of changing approaches to the management of state (municipal) finances and objects of state (municipal) ownership, as well as the information needs of key users of reporting information. The article reveals the features of the first system — forming element of the balance sheet — an asset, and the criteria for assigning objects of public sector entities to assets. The analysis of federal accounting standards of public finance and other regulatory legal acts allowed us to systematize the requirements for the valuation of assets in the balance sheet. It is concluded that the expansion of valuation methods increases the qualitative characteristics that ensure the usefulness of reporting information for users. At the same time, the use of new methods of valuation of assets is problematic due to their insufficient development. The obtained results can be used for the further development of the valuation of objects, as well as by practitioners for the preparation of the balance sheet and the implementation of state financial control.

Recreational angler reporting as a tool for tracking the distribution of invasive Prussian carp (Carassius gibelio)

AbstractThe recent invasion of Carassius gibelio (commonly known as Prussian carp or Gibel carp) in freshwater environments in central Canada threatens native North American aquatic species and ecosystems. Accurate distribution information is essential for targeting control efforts but is challenging given the resources necessary to continually sample the species' potential distribution. We investigated the extent to which reports by recreational anglers—key resource users—could be used in a citizen science program to generate species distribution information, and factors affecting the accuracy of reporting for C. gibelio. Comparing the location of angler reports to the known distribution of C. gibelio generated by professional biological sampling across the region revealed that anglers can be a powerful resource for tracking an invasive species' distribution; 88% of the C. gibelio angler reports aligned with invaded watersheds (HUC‐8 [hydrological unit code 8], the second finest watershed unit) identified by professional biological sampling. For every report of C. gibelio received in a HUC‐8 area, the probability that area was invaded increased by more than 10 times (odds ratio = 10.26, ±95% CI: 4.4–29.7). Anglers' fish identification abilities were positively related to likelihood of reporting Carassius spp. (odds ratio = 2.52, ±95% CI:1.51–4.45). Anglers that fished more frequently were also more likely to have reported C. gibelio accurately (odds ratio = 1.00, ±95% CI: 0.99–1.01), although the mechanism behind this relationship is unclear. Our results suggest programs that engage recreational anglers in reporting could provide a cost‐effective alternative or complimentary tool for traditional Aquatic Invasive Species (AIS) population tracking.

The Potential of Smart Factories and Innovative Industry 4.0 Technologies—A Case Study of Different-Sized Companies in the Furniture Industry in Central Europe

New innovative technologies of Industry 4.0 are the key to the future development of the furniture industry, which is outdated because of its atypical production and small-series production. For applying the novel trends of Industry 4.0 to the furniture sector, the methodical support of managers, the key users of these technologies, is essential. As there is a lack of knowledge regarding implementation of Industry 4.0, this study focuses on the evaluation of the current status of furniture companies in terms of production structure and Industry 4.0 benefits/threats with the aim of proposing methodological solutions for the implementation of this trend across different-sized enterprises. Data are collected using conduct-structured interviews with project managers who describe their own experience with Industry 4.0 implementation in central Europe. All interviews are analyzed using qualitative content analysis. According to the stakeholders, innovative production and non-production technologies are essential for their enterprises. Application of such technologies increases the efficiency of the whole operation by 30%–50% over the five years since the first innovations were introduced, especially in enterprises with atypical production and large enterprises. This study should serve as the tool for adapting the environmental changes and promoting the innovation approaches of the Industry 4.0 strategies on the central European level.

Biometric-Based Key Generation and User Authentication Using Acoustic Characteristics of the Outer Ear and a Network of Correlation Neurons.

Trustworthy AI applications such as biometric authentication must be implemented in a secure manner so that a malefactor is not able to take advantage of the knowledge and use it to make decisions. The goal of the present work is to increase the reliability of biometric-based key generation, which is used for remote authentication with the protection of biometric templates. Ear canal echograms were used as biometric images. Multilayer convolutional neural networks that belong to the autoencoder type were used to extract features from the echograms. A new class of neurons (correlation neurons) that analyzes correlations between features instead of feature values is proposed. A neuro-extractor model was developed to associate a feature vector with a cryptographic key or user password. An open data set of ear canal echograms to test the performance of the proposed model was used. The following indicators were achieved: EER = 0.0238 (FRR = 0.093, FAR < 0.001), with a key length of 8192 bits. The proposed model is superior to known analogues in terms of key length and probability of erroneous decisions. The ear canal parameters are hidden from direct observation and photography. This fact creates additional difficulties for the synthesis of adversarial examples.

An update on the development of techniques for the determination and modelling of the impact behaviour of adhesives and bonded structures used in the automotive sector

The automotive sector is one of the key users of adhesive bonding, adopting this technology for the manufacture of high performance multi material structures, seeking to reduce vehicle weight and increase overall stiffness. In these industrial applications, the design of bonded structures is supported by numerical models, which require the determination of the mechanical properties of the adhesives and the adherends, taking into account factors such as temperature and strain rate dependence. However, while the design of bonded joints under quasi-static loadings is currently well understood, the design of impact resistant structures is much more complex, since it requires the establishment of accurate models for the strain rate dependent behaviour of the materials used in vehicle construction. This document provides an updated outlook on the research work being performed by the authors in this specific field. Most of the work is devoted to the analysis of the effects of strain rate on the behaviour of adhesives and adhesive joints. This includes high strain rate characterisation of the mechanical behaviour of modern crash resistant adhesives, using both standardised and custom testing procedures. Special experimental equipment has also been developed for impact testing procedures, including high strain rates and mixed mode conditions and full-size specimens.

Opinion Leaders and Structural Hole Spanners Influencing Echo Chambers in Discussions About COVID-19 Vaccines on Social Media in China: Network Analysis.

Social media provide an ideal medium for breeding and reinforcing vaccine hesitancy, especially during public health emergencies. Algorithmic recommendation-based technology along with users' selective exposure and group pressure lead to online echo chambers, causing inefficiency in vaccination promotion. Avoiding or breaking echo chambers largely relies on key users' behavior. With the ultimate goal of eliminating the impact of echo chambers related to vaccine hesitancy on social media during public health emergencies, the aim of this study was to develop a framework to quantify the echo chamber effect in users' topic selection and attitude contagion about COVID-19 vaccines or vaccinations; detect online opinion leaders and structural hole spanners based on network attributes; and explore the relationships of their behavior patterns and network locations, as well as the relationships of network locations and impact on topic-based and attitude-based echo chambers. We called the Sina Weibo application programming interface to crawl tweets related to the COVID-19 vaccine or vaccination and user information on Weibo, a Chinese social media platform. Adopting social network analysis, we examined the low echo chamber effect based on topics in representational networks of information, according to attitude in communication flow networks of users under different interactive mechanisms (retweeting, commenting). Statistical and visual analyses were used to characterize behavior patterns of key users (opinion leaders, structural hole spanners), and to explore their function in avoiding or breaking topic-based and attitude-based echo chambers. Users showed a low echo chamber effect in vaccine-related topic selection and attitude interaction. For the former, the homophily was more obvious in retweeting than in commenting, whereas the opposite trend was found for the latter. Speakers, replicators, and monologists tended to be opinion leaders, whereas common users, retweeters, and networkers tended to be structural hole spanners. Both leaders and spanners tended to be "bridgers" to disseminate diverse topics and communicate with users holding cross-cutting attitudes toward COVID-19 vaccines. Moreover, users who tended to echo a single topic could bridge multiple attitudes, while users who focused on diverse topics also tended to serve as bridgers for different attitudes. This study not only revealed a low echo chamber effect in vaccine hesitancy, but further elucidated the underlying reasons from the perspective of users, offering insights for research about the form, degree, and formation of echo chambers, along with depolarization, social capital, stakeholder theory, user portraits, dissemination pattern of topic, and sentiment. Therefore, this work can help to provide strategies for public health and public opinion managers to cooperate toward avoiding or correcting echo chamber chaos and effectively promoting online vaccine campaigns.

Blockchain-based distributed operation and incentive solution for P-RAN

Operators are coming up with new solutions that use grid topology as an innovative response to future mobility requirements and the increasing management costs for wireless access networks. Proximity Radio Access Network (P-RAN), which was proposed by the 3rd Generation Partnership Project (3GPP), expands the cellular structure from base station to intelligent terminal equipment through device to device (D2D) technology, which provides a cost-effective deployment solution for the 5G and 6G. The extensive deployment of P-RAN solution should be based on the active participation of a large number of distributed intelligent equipment users. Fair, reasonable and credible incentive solution is the key to realize the sustainable operation of this distributed system. This paper introduces a technical solution that applying blockchain technology to design P-RAN operating and incentive mechanism. Through blockchain technology, the current centralized P-RAN operation and incentive can be achieved in point-to-point way. By utilizing blockchain-based identity authentication and asymmetric key pairs, user and relay can confirm and sign the network usage information in sequence to reduce disputes. The abstract of all transactions related to P-RAN services can be recorded on blockchain immutably as the calculation basis of relay rewards. Through smart contracts and P-RAN incentive policy, relays that provide more and higher quality P-RAN service will be rewarded more, which can help to create positive feedback in this decentralized system. The tokens awarded to relays will be able to exchange through the blockchain network for the increase of the whole ecosystem utility. Blockchain-based P-RAN operation and incentive solution pioneer a way to the future heterogeneous network resource sharing and collaboration for supporting Web3.0 and Metaverse.

Supportive Care Strategies in Myelodysplastic Syndromes and Acute Myeloid Leukemia in Older Adults: A National Survey of Canadian Hematologists

BACKGROUND: Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) represent a spectrum of severe myeloid disorders characterized by marrow failure, cytopenias, and complications related to bleeding and infection. Due to patient and disease factors, many patients are treated with less-intensive therapy, such as transfusion support, erythropoietin stimulating agents, luspatercept, lenalidomide and hypomethylating agents. However, optimal supportive care strategies to minimize bleeding and infectious complications among older adults receiving less intensive therapy have not been systematically evaluated. OBJECTIVES: The objectives of the survey were to: a) evaluate self-reported use of tranexamic acid (TXA) and antimicrobial prophylaxis; b) identify perceived barriers and facilitators to the use of TXA and antimicrobial prophylaxis; c) characterize the degree of uncertainty regarding the benefits and harms of TXA and prophylactic antimicrobials in this patient population; and d) assess physician interest in future clinical trials of TXA and/or prophylactic antimicrobials. METHODS: We conducted a national cross-sectional survey of Canadian hematologists to explore current practice variation regarding the use of TXA and prophylactic antimicrobials in patients with MDS and AML treated with less intensive therapy, and evaluate equipoise for future trials. Survey items were generated iteratively through a combination of literature review and discussion with content experts until thematic saturation was achieved. The questionnaire was pilot-tested by collaborators prior to dissemination to assess the required completion time and provide feedback on flow, clarity, redundancy and comprehensiveness. The electronic platform REDCap was used to create and disseminate the survey tool. The survey was distributed to all hematologists affiliated with a Canadian university, representing both key knowledge users and potential participants in an investigative network for future clinical trials. RESULTS: The survey response rate was 52% (159/304 invited participants). Of the 159 hematologists, 105 indicated that they treated MDS/AML patients with less intensive therapy and responded to the further questions about this patient population. Most (97/105; 92%) hematologists worked in an academic/tertiary care setting; 8% (n=8/105) worked in a community setting. Prophylactic TXA was used by 57% (n=60/105) of hematologists; the most frequent reason for not using TXA was uncertainty about the benefits or harms. Nearly all (95%; 100/105) hematologists agreed that a trial of TXA is needed (n=78), or potentially needed (n=22). Use of prophylactic antimicrobials varied by the chemotherapy treatment regimen. The most frequently prescribed antibacterials were fluroquinolones (36% of respondents; n=38), trimethoprim/sulfamethoxazole (9%; n=9), and amoxicillin-clavulanate (5%; n=5). The most commonly used antifungals were fluconazole (35% of respondents; n=37) and voriconazole (19%; n=20). Valacyclovir (29% of respondents; n=30) and acyclovir (15%; n=16) were the antivirals prescribed. The most common reason for not using prophylactic antimicrobials was an insufficient level of evidence (70%; n=73). The majority (78%, n=82) of respondents agreed that a trial is needed to evaluate optimal antimicrobial prophylaxis in this at-risk patient population. CONCLUSIONS: Among survey respondents, there was variation in the use of supportive case strategies to address bleeding and infection risk in older adults with MDS/AML. Most hematologists who treat MDS/AML with less intensive therapy felt that clinical trials are needed to define the use of TXA and prophylactic antimicrobials in this patient population.

EXPERIENTIAL LEARNING BASED ERP SIMULATION TRAINING: CURRICULUM INNOVATION FOR ACCOUNTING EDUCATION

İşletme fonksiyonları ve süreçlerinin ağırlıklı teorik anlatıldığı işletmecilik okullarında, öğrenciler için muhasebe dersleri sıkıcı ve anlaşılmaz gelebilmektedir. Z kuşağı öğrencilerinin teknolojiye olan eğilimleri yüksektir. Bu nedenle ERP simülasyon derslerinin müfredatlara alınması öğrencilerin muhasebe derslerine ilgisini arttırabilir ve dijital teknoloji kullanım yetkinliğine sahip sektörel farkındalığı artmış öğrenciler yetiştirilebilir. ERP programları işletmelerin fiziki iş ortamına ait belgelerin veri girişinin yapıldığı ve binlerce raporun alınabildiği temel dijital bir platform ve işletmelerin fiziki ortamlarının simülasyonudur. ERP uygulama eğitimlerinde, ticaret, imalat ve üretim işletmelerine ait sanal işletme simülasyonları oluşturularak, muhasebe ve raporlama süreçleri deneyimsel öğrenme ile öğretilebilir. ERP simülasyon eğitimi, "Anahtar kullanıcı" ve "Son kullanıcı" seviyelerinde iki aşamalı olarak önerilmektedir. Böylece öğrenciler ERP programında işletmenin iş süreç tanımlamalarını, sistem tasarımını ve süreçler arası entegrasyonun nasıl kurulduğunu anlayabilir hem de modül kullanımını öğrenebilirler. Çalışmanın amacı, muhasebe ve raporlama eğitiminde, deneyimsel öğrenme temelli ve iki aşamalı ERP simülasyon eğitiminin; ticaret, üretim ve hizmet işletmelerindeki uygulamalarını içeren üç farklı ders (ERP Sim I, ERP Sim II ve ERP Sim III) dizaynı ile müfredatlara alınmasının önemini tartışmaya açmak ve uygulama öncesi ile uygulamada izlenecek yolları, öğretim yöntemi ile ilgili geliştirilen özel bilgiler ve içerik detaylarını (üretim işletmesi çerçevesinde) açıklamaktır.

Proposed Marketing Strategy of New Product: Case of Anti-Inflammatory Herbal Medicine for Ganeshfit Company

Herbal medicines are growing in the global market as global health awareness is increasing. The global market potential of this herbal medicine also has an impact on the development of the product market in Indonesia. New herbal medicinal product innovations are emerging. Ganeshfit Company Research and Development Team develops anti-inflammatory herbal medicinal products that have a safer long-term effect. The development of new products requires the right marketing strategy to enter the market. To find out the right strategy, the company can conduct an internal analysis of the company itself and conduct an external analysis for the industry as a whole. Therefore, the purpose of this research is to find out business solutions for Ganeshfit Companies, especially in terms of marketing strategies. To formulate recommendations and conclusions, this research will use mixed methods to gain insight and achieve goals. The research will be conducted in three phases. The first phase of the analysis is an analysis of the literature on competitors, internal, and external to the company. The second phase is the collection of qualitative data (through interviews with 5 key users) to determine consumer considerations. The last is the collection of quantitative data (a questionnaire with 177 respondents) to verify the results of the data obtained from the collection of qualitative data. The framework used for external analysis is PESTEL analysis, Porter's Five Forces analysis and Porter's Generic Strategy. For internal analysis, the frameworks used are SWOT analysis and VRIO analysis. To formulate a potential marketing strategy, this study also uses a simple marketing model from Kotler which includes market segmentation, targeting, positioning, and marketing mix.

A Structural Solution for Task Management in a Resident-Directed, Team-Based Radiotherapy Clinic

<h3>Purpose/Objective(s)</h3> The radiation oncology service at our urban safety net hospital is high volume and organized into a series of resident-directed Teams covering various disease sites. As configured in our OIS, task management was difficult, and OIS-related miscommunication led to rushed/excess work and risked patient impact. An initiative was started to reduce the incidence of such miscommunication and associated sequelae. <h3>Materials/Methods</h3> A root cause analysis was performed. Clinical and OIS processes were mapped and failure modes assessed. While the clinic was organized into Teams, the OIS was not, and manual assignment of users to tasks was required. This was time consuming and error prone, with errors most common after residents rotated, during cross coverage, and with long intervals between consultation and simulation. Scheduling therapists often scheduled the incorrect resident to at least one task. Multiple attendings had patients on any given Team, but each was unable to see the entire Team's tasks. Tasks associated to specific residents had to be accounted for after they switched rotations. OIS functionality was such that only attendings could be automatically assigned to tasks and that individual tasks could not be assigned to multiple user groups. An alternative solution was developed involving a specific naming schema for tasks, creation of virtual resources for task assignment representing Teams and key user groups, and procurement of Team-specific VOIP phones. <h3>Results</h3> The Team concept was integrated into the OIS, and missed tasks and other OIS-related miscommunication are no longer a common reason for process failure. A minimum set of actions necessary to meet needs was codified into a set of Team-based clinical pathways. Task naming used the convention "<responsible group> - <task>." Tasks assigned to Team resources allowed rapid Team-specific, user group-specific identification of rate-limiting actions when sorted alphabetically. This also allowed transparency into the status of key activities performed by all user groups. Attendings maintained an accurate personal list. The updated system was reliable, required fewer actions, and reduced cognitive demand for all users. For schedulers, the minimum number of clicks required went from 25 to 5 and the minimum number of choices went from 8 to 1. Work to determine which resident to schedule was eliminated as therapists could rely on the less variable Team concept. A forcing function detected likely failure modes of wrong attending and wrong Team. Team-specific VOIP phones allowed the correct resident to be contacted no matter their physical location while minimizing interruptions to co-residents. <h3>Conclusion</h3> A novel implementation of virtual task lists improved quality by eliminating systematic sources of error and excess work.

Influenza Vaccination in Perioperative Settings: A Teachable Moment.

Influenza Vaccination in Perioperative Settings: A Teachable Moment.

An Ontological Approach to Information Integration in Data Intensive Domains

The development and support of knowledge-based systems for experts in the field of social network analysis (SNA) is complicated because of the problems of viability maintenance that inevitably emerge in data intensive domains. Largely this is the case due to the properties of semi-structured objects and processes that are analyzed by data specialists using data mining techniques and others automated analytical tools. Firstly, new sources (e. g. online social networks, published databases) constantly become available for gathering, analyzing, and interpreting data. Thus, new sources should be modelled and embedded in existing data structures maintaining logical consistency. Secondly, new techniques and underlying algorithms are also constantly being developed. Therefore, analysis results should be integrated with source data, and metamodels that describe the integration should be adaptable and extensible. Thirdly, the dynamism of semi-structured objects entails constant changes in knowledge models produced by domain experts and knowledge engineers. Considering that the same data could be used by different domain experts it is crucial not only to support traceability of changes in models but also to ensure independence of expert interpretation of these models. The analysis of existing approaches to information integration shows lack of solutions implementing traceability of changes. This paper introduces a novel approach to information integration based on ontological and production knowledge models to fill this gap. A conceptual description of the approach and an underlying set-theoretical model are given. The main difference of the given approach from the existing ones is the uniformity to the integration of different kinds of ontologies as well as different versions of ontologies using rule-based model of ontological mappings, which is demonstrated by the example of solving the special case of the problem of identifying key users (so-called bridges) in social networks.

A large scale analysis of mHealth app user reviews

The global mHealth app market is rapidly expanding, especially since the COVID-19 pandemic. However, many of these mHealth apps have serious issues, as reported in their user reviews. Better understanding their key user concerns would help app developers improve their apps’ quality and uptake. While app reviews have been used to study user feedback in many prior studies, many are limited in scope, size and/or analysis. In this paper, we introduce a very large-scale study and analysis of mHealth app reviews. We extracted and translated over 5 million user reviews for 278 mHealth apps. These reviews were then classified into 14 different aspects/categories of issues reported. Several mHealth app subcategories were examined to reveal differences in significant areas of user concerns, and to investigate the impact of different aspects of mhealth apps on their ratings. Based on our findings, women’s health apps had the highest satisfaction ratings. Fitness activity tracking apps received the lowest and most unfavourable ratings from users. Over half of users who reported troubles leading them to uninstall mHealth apps gave a 1-star rating. Half of users gave the account and logging aspect only one star due to faults and issues encountered while registering or logging in. Over a third of users who expressed privacy concerns gave the app a 1-star rating. However, only 6% of users gave apps a one-star rating due to UI/UX concerns. 20% of users reported issues with handling of user requests and internationalisation concerns. We validated our findings by manually analysing a sample of 1,000 user reviews from each investigated aspect/category. We developed a list of recommendations for mHealth apps developers based on our user review analysis.

Barriers and Opportunities Regarding Implementation of a Machine Learning-Based Acute Heart Failure Risk Stratification Tool in the Emergency Department.

Hospital admissions for patients with acute heart failure (AHF) remain high. There is an opportunity to improve alignment between patient risk and admission decision. We recently developed a machine learning (ML)-based model that stratifies emergency department (ED) patients with AHF based on predicted risk of a 30-day severe adverse event. Prior to deploying the algorithm and paired clinical decision support, we sought to understand barriers and opportunities regarding successful implementation. We conducted semi-structured interviews with eight front-line ED providers and surveyed 67 ED providers. Audio-recorded interviews were transcribed and analyzed using thematic analysis, and we had a 65% response rate to the survey. Providers wanted decision support to be streamlined into workflows with minimal disruptions. Most providers wanted assistance primarily with ED disposition decisions, and secondarily with medical management and post-discharge follow-up care. Receiving feedback on patient outcomes after risk tool use was seen as an opportunity to increase acceptance, and few providers (<10%) had significant hesitations with using an ML-based tool after education on its use. Engagement with key front-line users on optimal design of the algorithm and decision support may contribute to broader uptake, acceptance, and adoption of recommendations for clinical decisions.

MicroFluID

RFID has been widely used for activity and gesture recognition in emerging interaction paradigms given its low cost, lightweight, and pervasiveness. However, current learning-based approaches on RFID sensing require significant efforts in data collection, feature extraction, and model training. To save data processing effort, we present MicroFluID, a novel RFID artifact based on a multiple-chip structure and microfluidic switches, which informs the input state by directly reading variable ID information instead of retrieving primitive signals. Fabricated on flexible substrates, four types of microfluidic switch circuits are designed to respond to external physical events, including pressure, bend, temperature, and gravity. By default, chips are disconnected into the circuit owing to the reserved gaps in transmission line. While external input or status change occurs, conductive liquid floating in the microfluidics channels will fill the gap(s), creating a connection to certain chip(s). In prototyping the device, we conducted a series of simulations and experiments to explore the feasibility of the multi-chip tag design, key fabrication parameters, interaction performance, and users' perceptions.

Exploring the Patient Experience with Patient-Reported Outcomes: A Qualitative, Multistakeholder Study.

Although the literature provides guidance regarding patient-reported outcome (PRO) implementation barriers, patients' perspectives are underreported. This study aimed to improve the understanding of patient experiences with PRO tools through examining perceptions of and attitudes toward PROs and expectations of data use after collection. Ethnographic human-centered design approaches were used to conduct free-form interviews. Two case studies of existing PRO use in clinics also were examined. Unstructured thematic analyses were performed using notes taken during these interviews. Patients generally reported a good understanding of the need for PRO collection, both for research and clinical use. Many expected that results would be acted upon by the clinicians promptly. Thematic analyses identified the following patient perception topics: transparency, individualization to patient needs, timely response, different "identities" while accessing care locally compared with at a destination center, and preference for brief PROs. Design and implementation of PRO assessments into patient care should include the patients as key end users. Transparency of the purpose for data collection is critical for broader patient adoption. Ensuring that only necessary and sufficient data are collected for clinical action, and associated research may help minimize burden and maximize patient participation.

P06-14 Development of a standardised evaluation framework (SEF) for physical activity interventions: Ensuring usability and usefulness

IntroductionWith a range of interventions available for promoting physical activity (PA) and only limited resources, it has become imperative to identify those that are both effective and feasible for real world application. A number of evaluation frameworks have been developed but often fail to be widely implemented due to the level of information required and time needed to complete. The purpose of this study is to develop a standardised evaluation framework (SEF) for PA interventions that is usable in practice but also collects information that enables evidence based decision making among key knowledge users.MethodsDevelopment of the framework was guided by Nutbeam and Bauman's evaluation cycle, encompassing elements of formative, process, impact and outcome evaluation. The SEF was developed through four stages involving a 1) review of the literature, 2) feedback from key stakeholders, 3) national consultation, and 4) focus groups with a practitioner advisory group. Elements of the Technology Acceptance Model were used to assess the perceived usefulness and usability of the SEF by key stakeholders.††ResultsTwelve relevant evaluation frameworks specific to PA interventions were identified through the literature review. Members of the project team (N = 3) identified commonalities across these frameworks, including general characteristics (N = 12), formative evaluation aspects (N = 5), process evaluation aspects (N = 15), impact evaluation aspects (N = 7) and outcome evaluation aspects (N = 3). Feedback across four stages, including consultations, two focus groups and an online survey provided feedback for creating a more usable and useful SEF. The current framework includes a minimum set of questions (i.e. monitoring template) for coordinators (N = 27) and participants (N = 9) with additional measures available for an in-depth evaluation where necessary.ConclusionI-PARC has seen the creation of a SEF that is moving towards a more usable approach for intervention evaluation in practice. The I-PARC SEF has the potential to be a usable resource to assess current PA interventions and provide knowledge regarding the potential scale up, adaptions or cessation of current practices. Feedback has also suggested a need for an online platform to collect the relevant information, capacity building resources and a support network to help with the use of the I-PARC SEF.

Influence prediction model for marketing campaigns on e-commerce platforms

E-commerce platforms are committed to planning various marketing campaigns to make profits, and the impact of marketing campaigns is determined by the number of consumers, as well as the good or bad planning scheme. Therefore, accurate prediction of user behavior and comprehensive analysis of marketing campaign trends can help e-commerce platforms to identify quality marketing campaigns, which are crucial for developing e-commerce platforms. In this paper, we propose a model for predicting the influence trend of marketing campaigns based on the key information between group users and marketing campaigns on e-commerce platforms. To begin, text representation learning is used to unify user representations to address the multi-label nature of platform users, considering that text representation learning can extract deeper correlations between different label information and learn key information in the marketing campaign-user network by mining the correlations between user labels. Furthermore, considering the leading power of key users in the e-commerce platform, the influential spread network is constructed for the characteristics of the group user network under the marketing campaign using the locality-sensitive hashing (LSH) algorithm and combined with the mining of key information in the marketing campaign-user network. Finally, to address the time-series nature of influence development, the influence spread network is fused, and the contextual relationships of the influence spread network are learned using a recurrent neural network (RNN) algorithm to propose a model for predicting the influence trend of marketing campaigns based on key information. Experiments have shown that the model can predict user engagement behavior in the context of a marketing campaign, as well as dynamically depict changes in trends in the influence of the campaign.