End-user Support Research Articles

Informed Decision Making on the Uptake of Evidence-Based Smoking Cessation Assistance by Individuals Motivated to Quit Smoking

Evidence-based cessation assistance increases cessation rates. Activating preferences during decision making could improve effectiveness further. Decision aids (DAs) facilitate deciding by taking preferences into account. To develop effective DAs, potential end users' (i.e., individuals motivated to quit) needs and experts' viewpoints should be considered. Therefore, the aim of this needs assessment was: (1) To explore end users' needs and (2) to obtain consensus among smoking cessation counsellors and scientific experts to develop a self-administered DA to support end users in choosing cessation assistance. Data was gathered via two approaches: (1) twenty semi-structured interviews with potential end users and (2) two three-round Delphi studies with 61 counsellors and 44 scientific experts. Interview data and the first Delphi rounds were analysed qualitatively, the other Delphi rounds were analysed quantitatively. Potential end users acquired information in different ways, e.g., via own experiences. Important characteristics to decide between tools varied, however effectiveness and costs were commonly reported. Experts reached consensus on 38 and 40 statements, e.g., tools should be appropriate for users' addiction level. Although some trends emerged, due to the variation among stakeholders, a 'one size fits all'-approach is undesirable. This heterogeneity should be considered, e.g., by enabling users to customise the DA.

Metaheuristics approach to improve data analysis process for the healthcare sector

The metaheuristics approach is important for solving many engineering applications, including healthcare. In the healthcare sector, many problems related to patients’ treatments are not covered, and it is one of the issues for healthcare services across the globe. Healthcare support functions like clinical decision support, patient profiling, customized care, and health tracking could be the area where data analysis effectively provides end-user support. This paper focused on the metaheuristics algorithm concept with data mining technologies to provide a healthcare sector solution mechanism. This paper identifies the data analysis problem of the healthcare sector. It reviews various recent technologies used in healthcare and related sectors. It focused on the importance of metaheuristics algorithms, artificial intelligence, and implementation methodologies.

Socio-Technical Perspective for Electronic Tax Information System in Tanzania

Socio-technical systems theory has rarely been used by system architects in setting up computing systems. However, the role of socio-technical concepts in computing, which is becoming social in nature, has made the concepts more relevant and commercial. Tax information systems are examples of such systems because they are influenced by external variables such as the political environment, technological trends, and social environment, introducing complexity in their deployment and determining the type of e-services and their delivery to a diverse group of people. It was observed that in Tanzania there is resistance, reluctance and minimal use of electronic tax system because of insufficient end-user support and their involvement in constructing the system. Therefore, there is need to develop an electronic tax information system using socio-technical systems perspectives to ensure design of an efficient user-friendly tax administration system. The research used the qualitative approach, featuring case studies in Korea, Chile, Tanzania, and Denmark. The study used best practices from the Organization for Economic Cooperation and Development (OECD) to benchmark Tanzania Revenue Authority current practices. It was found that tax models implemented are techno-centric push models, which don’t attract its use by tax payers and requiring human intervention in its operation, hence not cost-effective. As the first and relevant phase in socio-technical system development, this paper presents the problem definition and analysis of e-Tax collection system in Tanzania.

The use a virtual interactive system to enhance gynecologic oncology multi-disciplinary care in Zambia

BackgroundGynecologic malignancies represent a significant proportion of the increasing cancer burden in Zambia. The care and outcomes of cancer patients improves in a multidisciplinary team but insufficient coordination in weak health systems is a barrier to this model of care. The World Health Organization (WHO) identifies digital health interventions as tools to bridge the gap between challenges and health systems’ goals in low resource environments. A low-cost innovative virtual interaction system (VIS) was integrated into the gynecologic oncology multidisciplinary tumor board to enhance patient navigation and management. MethodsPaper-based forms used by each specialist group participating in the tumor board were collected and placed on a digital platform. Tumor board members were interviewed to assess their acceptability of the new digital modules and ensure correct nomenclature was being used for data entry. This was followed by an orientation and launch of VIS. ResultsFor a 12-week period following the launch, 197 gynecologic oncology patients were registered at the Zambian national cancer center (Cancer Diseases Hospital), of which 130 were entered in the VIS, and of those less than half had management decisions recorded. The median time from first visit to simulation for radiotherapy was 52 days; from simulation to start of treatment 102.5 days. Eighteen (14 %) of the 130 patients entered into the VIS were recorded as lost to follow up. ConclusionThe introduction and implementation of a digital intervention for navigation and management of gynecologic patients in a low resource environment proved both feasible and acceptable. It provides an instant easily accessible platform for important information on time intervals and delays in the patient pathway. End user orientation and support is an integral part to its successful integration and consistent management of the data is required to maximize its impact on efficiency, effectiveness and patient care.

Finding the Value in Biobanks: Enhancing the CTRNet Locator

Biobanks are a critical piece of Research Infrastructure (RI). However, biobanks need to accept the reality of a life cycle for RIs. Until recently, strategies to sustain biobanks have been commonly focused on ways to maintain current operational models. However, sustaining biobanks as they exist today may be increasingly challenging in the face of the disruption in health and research priorities caused by the COVID-19 pandemic. In this opinion article, we review the current and emerging future drivers of biobank value for their researchers, institutions, and funders, highlighting utilization and impact of research performed using the biobank as key measures of future value. While biobanks can only indirectly influence the specific impact of the research performed, they can transform themselves to more actively redefine utilization to their advantage. Utilization means more than the balance of samples and data in versus out. Utilization means redirecting expertise to best support end users, and importantly, closing the operating gap between biobanks and their end users who seek to find the right biospecimens and data to pursue their research. We discuss the specific role of locators (those created by public investment) in closing this gap and the need for additional tools for researchers, before and subsequent to connecting with locators. For the former, we specifically propose that more support is needed to assist researchers in the decision as to how to best obtain biospecimens and navigate the options as to whether finding existing biospecimens and data held by a biobank is the optimal solution for a given project, or whether the optimal solution is either contracting with a biobank to collect samples or creating a new biobank. We believe this type of biospecimen navigator platform will help to maximize utilization of current biobank resources, and also promote the services and expertise in biobanks to better serve researchers' needs.

Abstract 25: End User Support for Telemedicine Oncology Care in Sub Saharan Africa

Abstract Purpose: Health Information Technology has hugely improved patient healthcare with fast and simple information access and communication. Telemedicine uses telecommunications technology as a tool to deliver health care to populations with limited access to care. International Cancer Institute (ICI) number one goal is to provide end-to-end solutions for optimization of care on patients' journey in sub-Saharan Africa. Supporting the end-user clearly shows there is a lot going on “under the skin” in healthcare Information technology (IT) to bring information access to life and patients closer to the oncology experts. Methods: In 2020, ICI IT team provided end-user support in bi-weekly multidisciplinary oncology –tumour boards with specialists from Africa and the world. It also provided support in Provider-to-provider tele-consults for patients in 10 partner counties in Kenya. The IT team also provided online support to healthcare providers to access online oncology preceptorships/training and webinars in different aspects of the cancer control continuum. Result: The IT team was able to support over 80 Multidisciplinary tumor boards, ensuring a team-based care approach to delivering care. Provider-to-provider tele-oncology ensured a platform to support interprofessional care and mentorship to non-specialists in remote sites. It has also provided one on one county patient consultations and hence ensured delivery of cancer services in 10 county government clinics & 8 private health facilities. Over 300 patient consults have been completed. Online platforms have ensured healthcare provider training despite COVID 19 restrictions; ICI has successfully trained over 1000 health care providers using eLearning and online platforms. Conclusion IT support has successfully provided assistance to physicians, healthcare providers, and patients with technical & non-technical support demonstrating the potential of delivering specialized oncology consultations and improved access to information in primary care settings with limited resources. Citation Format: Lamech Sigu, Emmah Achieng, Chite Asirwa, Gloria Kitur. End User Support for Telemedicine Oncology Care in Sub Saharan Africa [abstract]. In: Proceedings of the 9th Annual Symposium on Global Cancer Research; Global Cancer Research and Control: Looking Back and Charting a Path Forward; 2021 Mar 10-11. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2021;30(7 Suppl):Abstract nr 25.

Heart disease prediction using Advanced Machine Learning Algorithms

It might have happened so many times that you or someone yours need doctors help immediately, but they are not available due to some reason. The Heart Disease Prediction application is an end user support to the online. Here, we propose a web application that allows users to get instant guidance on their heart disease through an intelligent system online. The application is fed with various details and the heart disease associated with those details. The applications allows user to share their heart related issues. It then processes user specific details to check for various illnesses that could be associated with it. Here we use some intelligent data mining techniques to the most accurate that could be associated with patient‟s details. Based on result, system automatically shows the result specific doctors for further treatment and the system allows user to view doctor‟s details.

A Systematic Review of Commercial Smart Gloves: Current Status and Applications.

Smart gloves have been under development during the last 40 years to support human-computer interaction based on hand and finger movement. Despite the many devoted efforts and the multiple advances in related areas, these devices have not become mainstream yet. Nevertheless, during recent years, new devices with improved features have appeared, being used for research purposes too. This paper provides a review of current commercial smart gloves focusing on three main capabilities: (i) hand and finger pose estimation and motion tracking, (ii) kinesthetic feedback, and (iii) tactile feedback. For the first capability, a detailed reference model of the hand and finger basic movements (known as degrees of freedom) is proposed. Based on the PRISMA guidelines for systematic reviews for the period 2015–2021, 24 commercial smart gloves have been identified, while many others have been discarded because they did not meet the inclusion criteria: currently active commercial and fully portable smart gloves providing some of the three main capabilities for the whole hand. The paper reviews the technologies involved, main applications and it discusses about the current state of development. Reference models to support end users and researchers comparing and selecting the most appropriate devices are identified as a key need.

Intelligent Generation of Storylines for Immersive Point and Click Games

In this paper, we present an intelligent support End-User Developers (EUDevs) in creating plot lines for Point and Click games on the web. We introduce a story generator and the associated user interface, which help the EUDev in defining the game plot starting from the images providing the game setting. In particular, we detail a pipeline for creating such game plots starting from 360 degrees images. We identify salient objects in equirectangular images, and we compose the output with other two neural networks for the generation: one generating captions for 2D images and one generating the plot text. The provided suggestions can be further developed by the EUDev, modifying the generated text and saving the result. The interface supports the control of different parameters of the story generator using a user-friendly vocabulary. The results of a user study show good effectiveness and usability of the proposed interface.

Characterizing Visual Programming Approaches for End-User Developers: A Systematic Review

Recently many researches have explored the potential of visual programming in robotics, the Internet of Things (IoT), and education. However, there is a lack of studies that analyze the recent evidence-based visual programming approaches that are applied in several domains. This study presents a systematic review to understand, compare, and reflect on recent visual programming approaches using twelve dimensions: visual programming classification, interaction style, target users, domain, platform, empirical evaluation type, test participants' type, number of test participants, test participants' programming skills, evaluation methods, evaluation measures, and accessibility of visual programming tools. The results show that most of the selected articles discussed tools that target IoT and education, while other fields such as data science, robotics are emerging. Further, most tools use abstractions to hide implementation details and use similar interaction styles. The predominant platforms for the tools are web and mobile, while desktop-based tools are on the decline. Only a few tools were evaluated with a formal experiment, whilst the remaining ones were evaluated with evaluation studies or informal feedback. Most tools were evaluated with students with little to no programming skills. There is a lack of emphasis on usability principles in the design stage of the tools. Additionally, only one of the tools was evaluated for expressiveness. Other areas for exploration include supporting end users throughout the life cycle of applications created with the tools, studying the impact of tutorials on improving learnability, and exploring the potential of machine learning to improve debugging solutions developed with visual programming.

Identifying Persuasive Design Principles and Behavior Change Techniques Supporting End User Values and Needs in eHealth Interventions for Long-Term Weight Loss Maintenance: Qualitative Study.

BackgroundAn increasing number of eHealth interventions aim to support healthy behaviors that facilitate weight loss. However, there is limited evidence of the effectiveness of the interventions and little focus on weight loss maintenance. Knowledge about end user values and needs is essential to create meaningful and effective eHealth interventions, and to identify persuasive system design (PSD) principles and behavior change techniques (BCTs) that may contribute to the behavior change required for successful long-term weight loss maintenance.ObjectiveThis study aimed to provide insight into the design of eHealth interventions supporting behavior change for long-term weight maintenance. The study sought to identify the values and needs of people with obesity aiming to maintain weight after weight loss, and to identify PSD principles, BCTs, and design requirements that potentially enable an eHealth intervention to meet end user values and needs.MethodsThis study presents the concept of integrating PSD principles and BCTs into the design process of eHealth interventions to meet user values and needs. In this study, individual interviews and focus groups were conducted with people with obesity (n=23) and other key stakeholders (n=27) to explore end user values and needs related to weight loss maintenance. Design thinking methods were applied during the focus group sessions to identify design elements and to explore how eHealth solutions can support the needs to achieve sustainable weight loss maintenance. The PSD model and behavior change taxonomy by Michie were used to identify PSD principles and BCT clusters to meet end user values and needs.ResultsA total of 8 key end user values were identified, reflecting user needs for weight loss maintenance support: self-management, personalized care, autonomy, feel supported, positive self-image, motivation, happiness, and health. Goals and planning, feedback and monitoring, repetition and substitution, shaping knowledge, social support, identity, and self-belief were some of the BCT clusters identified to address these concepts, together with PSD principles such as personalization, tailoring, self-monitoring, praise, and suggestions.ConclusionsThe process of translating end user values and needs into design elements or features of eHealth technologies is an important part of the design process. To our knowledge, this is the first study to explore how PSD principles and BCTs can be integrated when designing eHealth self-management interventions for long-term weight loss maintenance. End users and other key stakeholders highlighted important factors to be considered in the design of eHealth interventions supporting sustained behavior change. The PSD principles and BCTs identified provide insights and suggestions about design elements and features to include for supporting weight loss maintenance. The findings indicate that a combination of BCTs and PSD principles may be needed in evidence-based eHealth interventions to stimulate motivation and adherence to support healthy behaviors and sustained weight loss maintenance.Trial RegistrationClinicalTrials.gov NCT04537988; https://clinicaltrials.gov/ct2/show/NCT04537988

Allocation of applications to Fog resources via semantic clustering techniques: with scenarios from intelligent transportation systems

The fast development in IoT and Cloud technologies has propelled the emergence of a variety of computing paradigms, among which Fog and Edge computing are salient computing technologies. Such new paradigms are opening up new opportunities to implement novel application scenarios, not possible before, by supporting features of mobility, edge intelligence and end-user support. This, however, comes with new computing challenges. One such challenge is the allocation of applications to Fog and Edge nodes. Indeed, for some application scenarios larger computing capacity might be needed. Therefore, due to co-existence of computing devices of different computing granularity, techniques for grouping up and clustering resources into virtual nodes of larger computing capacity are required. In this paper we present some clustering techniques for creating virtual computing nodes from Fog/Edge nodes by combining semantic description of resources with semantic clustering techniques. Then, we use such clusters for optimal allocation (via heuristics and Liner Programming) of applications to virtual computing nodes. Simulation results are reported to support the feasibility of the model and efficacy of the proposed approach. First Fit Heuristic Algorithm (FFHA) outperformed ILP method for medium and large size instances. Likewise, FFHA performed more consistently than ILP on various experimental setting. Finally, the results showed that the proposed clustering techniques deliver relatively fast response times, while enabling the service of a larger number of applications, with more demanding requirements.

A framework to diagnose the causes of river ecosystem deterioration using biological symptoms

Abstract River assessments are predominantly based on biological metrics and indices selected or designed to integrate the impact of multiple causes of deterioration (stressors) operating at various spatial scales. Yet, the integrative nature of many bioassessment systems does not allow for tracing back individual stressors and their influence on the overall assessment result. Thus, river managers often fail to link bioassessment with programmes of management measures, to improve ecological quality. Here, we present a novel diagnostic approach that allows to estimate the probability of individual stressors being causal for biological degradation at the scale of individual riverine ecosystems. Similar to medical diagnosis, we use various symptoms (macroinvertebrate metrics) and probabilistically link them to various potential causes of ecological status degradation (stressors). Symptoms and causes are informed by a training dataset of 157 samples (stressors, taxa lists) from central European lowland rivers and are linked through a Bayesian network (BN). Three separate BNs addressing three different spatial scales (catchment, reach and site) are presented. Water quality‐related causes are most influential at the catchment scale, while hydromorphological causes prevail at finer scales. Causes indicating riparian degradation are most influential at the reach scale. Many symptoms show strong linkages to causes and reveal ecologically meaningful relationships, thus pointing at the potential diagnostic utility of the symptoms selected. BNs are validated using an independent dataset of 47 samples. Overall, model accuracies range 53%–58% for the three BNs, while for individual nodes (causes and symptoms) up to 100% concordance of predicted and actual node states in the validation data is achieved. The BNs are implemented as interactive online diagnostic tools to allow end users an easy application. Synthesis and applications. Bayesian inference can greatly assist the diagnosis of potential causes of ecosystem deterioration based on a selection of diagnostic biological metrics. If integrated into a Bayesian network, symptoms and potential causes can be linked and inform management decisions on appropriate measures, to improve biological and ecological status. Diagnostic Bayesian networks thus support end users bridge the gap between biological monitoring and appropriate programmes of management measures.

More research less waste

More research less waste

Developing augmented reality capabilities for industry 4.0 small enterprises: Lessons learnt from a content authoring case study

Augmented reality (AR) has been proposed as a disruptive and enabling technology within the Industry 4.0 manufacturing paradigm. The complexity of the AR content creation process results in an inability for Small Enterprise (SE) to create bespoke, flexible AR training support “in-house” and is a potential barrier to industrial adoption of AR. Presently, AR content creation requires a range of specialist knowledge (e.g. 3D modelling, interface design, programming and spatial tracking) and may involve infrastructure changes (e.g. fiducial markers, cameras) and disruption to workflow. The research reported in this paper concerns the development and deployment of an Augmented Repair Training Application (ARTA); a template-based interface to support end user (shop floor) AR content creation. The proposed methodology and implementation are discussed and evaluated in a real-world industrial case study in collaboration with a Small Enterprise (SE) in the Used and Waste Electronic and Electrical Equipment sector (UEEE/WEEE). The need for end user friendly templates is presented in the conclusion alongside further related work.

A social network for supporting end users in the composition of services: definition and proof of concept

Nowadays, end users are surrounded by plenty of services that are somehow supporting their daily routines and activities. Involving end users into the process of service creation can allow end users to benefit from a cheaper, faster, and better service provisioning. Even though we can already find tools that face this challenge, they consider end users as isolate individuals. In this paper, we investigate how social networks can be used to improve the composition of services by end users. To do so, we propose a graph-based definition of a social structure, and analyse how social connections can be exploited to both facilitate end users to discover services through browsing these connections, and recommend services to end users during the composition activity. As proof of concept, we implement and evaluate the proposed social network in the context of EUCalipTool, a mobile end-user environment for composing services.

Assessing Users’ Privacy and Security Concerns of Smart Home Technologies

Abstract Smart Home technologies have the potential to increase the quality of life, home security and facilitate elderly care. Therefore, they require access to a plethora of data about the users’ homes and private lives. Resulting security and privacy concerns form a relevant barrier to adopting this promising technology. Aiming to support end users’ informed decision-making through addressing the concerns we first conducted semi-structured interviews with 42 potential and little-experienced Smart Home users. Their diverse concerns were clustered into four themes that center around attacks on Smart Home data and devices, the perceived loss of control, the trade-off between functionality and security, and user-centric concerns as compared to concerns on a societal level. Second, we discuss measures to address the four themes from an interdisciplinary perspective. The paper concludes with recommendations for addressing user concerns and for supporting developers in designing user-centered Smart Home technologies.

A General Approach to Select Location and Ratings of Energy Storage Systems in Local Area Energy Networks

The European directive “Clean Energy for All Europeans” depicts for 2030 a new scenario for low-voltage distribution grids, where each end user will have the right to trade energy and offer demand response in the electrical market either individually or in aggregation with others. This creates a new perspective for renewable energy sources (RESs) and energy storage systems (ESSs), which become key elements to allow end users playing a substantial role in the electrical market. While many distributed RESs are already in place in low-voltage grids, the ESSs are still in infancy and will become a crucial asset of future microgrids. The optimal selection of ESS ratings and location within an energy network is still an open problem, and its solution can impact considerably the grid performance and return of investment for energy sources and distribution infrastructure. This paper presents a general methodology to select the best location and ratings of energy storage units complying with the needs of local area energy networks. The aim is to support end users and aggregators to plan their investments and operate the grid so as to attain the role and functionality envisioned by the European directive.

Supporting End-User Understanding of Classification Errors: Visualization and Usability Issues

Classifiers are applied in many domains where classification errors have significant implications. However, end-users may not always understand the errors and their impact, as error visualizations are typically designed for experts and for improving classifiers. We discuss the specific needs of classifiers' end-users, and a simplified visualization designed to address them. We evaluate this design with users from three levels of expertise, and compare it with ROC curves and confusion matrices. We identify key difficulties with understanding the classification errors, and how visualizations addressed or aggravated them. The main issues concerned confusions of the actual and predicted classes (e.g., confusion of False Positives and False Negatives). The machine learning terminology, complexity of ROC curves, and symmetry of confusion matrices aggravated the confusions. The end-user-oriented visualization reduced the difficulties by using several visual features to clarify the actual and predicted classes, and more tangible metrics and representation. Our results contribute to supporting end-users' understanding of classification errors, and informed decisions when choosing or tuning classifiers.

An Evaluation of System End-User Support during Implementation of an Electronic Health Record Using the Model for Improvement Framework.

Electronic health record (EHR) system implementation is complex. Strong support for clinicians and other end-users during the initial phase of implementation requires dedicated resources and commitment to ensure a smooth transition, reduce frustration, and assist in adoption. Evaluate end-user support processes and personnel employed during the initial phase of EHR implementation at an academic medical center and identify facilitators of success, challenges, and lessons learned. Using the model for improvement framework, this case report describes the activities that follow the concepts of planning, doing, studying, and acting (PDSA) when providing on-site support to system end users during an EHR implementation in a complex health care system. Strengths included engaged and supportive leadership, use of internal support personnel, use of zone leaders, daily huddles for effective dissemination of information, and an evaluation survey tool to provide data supporting rapid changes in support personnel allocation. Challenges primarily surrounded the management of over 1,000 external support personnel which included limited EHR system knowledge, scheduling, and transportation logistics. Implementation of EHR systems continues. Supporting end users is one aspect of these complex projects that require dedicated resources to manage effectively. Organizations taking a more proactive approach, based on learning from experiences, as well as from other health care organizations, can improve their ability to take on this challenge armed with best practices and lessons learned.