Data Collection In Context Research Articles

Optimizing federated learning approaches with hybrid Convolutional Neural Networks ‐ Bidirectional Encoder Representations from Transformers for precise estimation of average localization errors in wireless sensor networks

SummaryWireless sensor networks (WSNs) require precise node location in order to function properly, and they are essential in many different applications. In this research, we propose a unique method to maximize the accurate estimate of average localization errors (ALEs) in WSNs by utilizing federated learning (FL) approaches. The suggested approach combines the extraction of spatial features with collection of contextual data through integration of hybrid convolutional neural network–bidirectional encoder representations from transformers (CNN‐BERT) model. Effectively applying min–max normalization to input features minimizes data from flowing into test, validation, and training sets. The technique is centered around a collaborative learning architecture, wherein the weights of the model are iteratively assessed and modified on centralized server. The suggested methodology's effectiveness in an array of settings is illustrated by mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) values for node localization in WSN. Network B demonstrated accuracy with an MAE of 0.05, MSE of 0.03, and RMSE of 0.10; Network C demonstrated strong accuracy with an MAE of 0.09, MSE of 0.08, and RMSE of 0.12; and Network A generated an MAE of 0.04, MSE of 0.06, and RMSE of 0.08. Furthermore, the centralized server, which is crucial for collaborative learning, obtained exceptional MAE of 0.01, MSE of 0.02, and RMSE of 0.99 demonstrating the superiority of the FL‐optimized method in improving localization accuracy. The outcomes demonstrate the FL‐optimized hybrid model's superiority over conventional methods in providing accurate node localization in a variety of WSN settings.

Determining university’s readiness to implement AI technologies for personalizing educational paths

The key issue of the article is to determine readiness of HEIs to implement artificial intelligence and machine learning technologies for personalization of students’ individual educational trajectories (hereinafter – IET). In case of compliance of HEIs with the proposed groups of factors, including developed information architecture, there is an opportunity to use multidimensional structured and unstructured data more technologically to improve various types of university activities. High-tech marketing analytical tools facilitate the collection of contextual data and insights into various processes within university, as well as in-depth analysis of learners’ digital footprints. Methods of machine learning, predictive analytics, and modern generative neural networks allow to create recommendation services, with the help of which individual educational trajectories are formed by machine intelligence, simultaneously considering hundreds of parameters. Beyond the tasks of IET formation, machine intelligence can successfully solve tasks for other stakeholders of the university such as professors, researchers, and administration.

InternetofThings for beyond-the-laboratory prosthetics research.

Research on upper-limb prostheses is typically laboratory-based. Evidence indicates that research has not yet led to prostheses that meet user needs. Inefficient communication loops between users, clinicians and manufacturers limit the amount of quantitative and qualitative data that researchers can use in refining their innovations. This paper offers a first demonstration of an alternative paradigm by which remote, beyond-the-laboratory prosthesis research according to user needs is feasible. Specifically, the proposed Internet of Things setting allows remote data collection, real-time visualization and prosthesis reprogramming through Wi-Fi and a commercial cloud portal. Via a dashboard, the user can adjust the configuration of the device and append contextual information to the prosthetic data. We evaluated this demonstrator in real-time experiments with three able-bodied participants. Results promise the potential of contextual data collection and system update through the internet, which may provide real-life data for algorithm training and reduce the complexity of send-home trials.This article is part of the theme issue ‘Advanced neurotechnologies: translating innovation for health and well-being’.

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review.

BackgroundMental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients’ interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies.ObjectiveThis article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective.MethodsWe performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends.ResultsA total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal.ConclusionsThis systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

A vision for contextualized evaluation of remote collaboration supported by AR

Remote collaboration using Augmented Reality (AR) has potential to support physically distributed team-members that need to achieve a common goal by increasing knowledge retention, improving understanding and awareness of the problem and its context. In this vein, the path to achieve usable, realistic and impactful solutions must entail an explicit understanding regarding how remote collaboration occurs through AR and how it may help contribute to a more effective work effort. Thus, characterization and evaluation of the collaborative process is paramount, but a particularly challenging endeavor, due to the multitude of aspects that define the collaboration effort. In this context, the work presented here contributes with a critical analysis, discussing current evaluation efforts, identifying limitations and opportunities. Then, we outline a conceptual framework to support researchers in conducting evaluations in a more structured manner. To instrument this vision, an evaluation toolkit is proposed to support contextual data collection and analysis in such scenarios and obtain an additional perspective on selected dimensions of collaboration. We illustrate the usefulness and versatility of the toolkit through a case study on remote maintenance, comparing two distinct methods: sharing of video and AR-based annotations. Last, we discuss the results obtained, showing the proposed vision allows to have an additional level of insights to better understand what happened, eliciting a more complete characterization of the work effort.

Sustainability Assessment and Agricultural Supply Chains Evidence-Based Multidimensional Analyses as Tools for Strategic Decision-Making—The Case of the Pineapple Supply Chain in Benin

Contributing to Sustainable Development Goals and Agenda 2030 is a shared objective of all institutions and people. The challenges differ according to the characteristics of every context. In developing countries, strongly dependent on the agricultural sector, agricultural supply chains are recognized as crucial for economic growth and enablers for livelihood improvement. Moreover, sustainable development issues are correlated and can meet in agricultural supply chains. For several decades, parallel to decision-makers, the research community has elaborated sustainability assessment tools. Such tools evolved to fit with actuality, but it is challenging to find decision-making support tools for sustainable development adequate in agricultural supply chains and developing countries contexts. There is a necessity to define evidence-based tools and exhaustive analytical frameworks according to sustainability multidimensionality and strategical tradeoffs necessity. The VCA4D method aims to go beyond the limits of previous methods. It proposes a combination of multidisciplinary analytical tools applied empirically to analyze agricultural supply chains in their context. It provides evidence-based analytical results allowing to identify enablers for strategic sustainable and inclusive interventions. However, to even better meet contextual exhaustiveness’s expectations and indicators’ robustness to lead to relevant interventions, we should insist on a stricter framing of contextual data collection processes.

Impact of Pre-visit Contextual Data Collection on Patient-Physician Communication and Patient Activation: a Randomized Trial.

Patient contextual data (PCD) are often missing from electronic health records, limiting the opportunity to incorporate preferences and life circumstances into care. Engaging patients through tools that collect and summarize such data may improve communication and patient activation. However, differential tool adoption by race might widen health care disparities. Determine if a digital tool designed to collect and present PCD improves communication and patient activation; secondarily, evaluate if use impacts outcomes by race. A pragmatic, two-armed, non-blinded, randomized controlled trial conducted during 2019 in a primary care setting. The PCD tool (PatientWisdom) invited patients to identify preferences, values, goals, and barriers to care. Patients were randomized to a standard pre-visit email or facilitated enrollment with dedicated outreach to encourage use of the tool. Outcomes of interest were post-visit patient communication and patient activation measured by the Communication Assessment Tool (CAT) and Patient Activation Measure (PAM), respectively. Outcomes were evaluated using treatment-on-the-treated (TOT) and intention-to-treat (ITT) principles. A total of 301 patients were enrolled. Facilitated enrollment resulted in a five-fold increase in uptake of the PCD tool. TOT analysis indicated that the PCD tool was associated with notable increases in specific CAT items rated as excellent: "treated me with respect" (+ 13 percentage points; p = 0.04), "showed interest in my ideas" (+ 14 percentage points; p = 0.03), "showed care and concern" (+ 16 percentage points; p = 0.02), and "spent about the right amount of time with me" (+ 11 percentage points; p = 0.05). There were no significant pre/post-visit differences in PAM scores between arms (- 4.41 percentage points; p = 0.58). ITT results were similar. We saw no evidence of the treatment effect varying by race in ITT or TOT analyses. The inclusion of PCD enhanced essential aspects of patient-provider communication but did not affect patient activation. Outcomes did not differ by race. Clincaltrials.gov identifier: NCT03766841.

The Impact of Previsit Contextual Data Collection on Patient-Provider Communication and Patient Activation: Study Protocol for a Randomized Controlled Trial.

BackgroundPatient-centered care is respectful of and responsive to individual patient preferences, needs, and values. To provide patient-centered care, clinicians need to know and incorporate patients’ context into their communication and care with patients. Patient contextual data (PCD) encompass social determinants of health and patients’ needs, values, goals, and preferences relevant to their care. PCD can be challenging to collect as a routine component of the time-limited primary care visit.ObjectiveThis study aims to determine if patient-provider communication and patient activation are different for patient users and patient nonusers of an electronic health record (EHR)–integrated PCD tool and assess if the impact of using PCD on patient-provider communication and patient activation differs for Black and White patients.MethodsWe describe a randomized controlled trial of a prospective cohort of non-Hispanic White and Black patients who receive primary care services at a midwestern academic health care system in the United States. We will evaluate whether providing PCD through a consumer informatics tool enhances patient-provider communication, as measured by the Communication Assessment Tool, and we will evaluate patient activation, as measured by the Patient Activation Measure for PCD tool users and nonusers. Furthermore, owing to racial disparities in care and communication, we seek to determine if the adoption and use of the tool might narrow the differences between patient groups.ResultsThe trial was funded in November 2017 and received local ethics review approval in February 2019. The study began recruitment in April 2019 and enrollment concluded in October 2019 with 301 participants. The analysis was completed in May 2020, and trial results are expected to be published in winter 2020.ConclusionsRecently, there has been increased attention to the role of health information technology tools to enable patients to collaborate with providers through the sharing of PCD. The adoption of such tools may overcome the barriers of current EHRs by directly engaging patients to submit their contextual data. Effectively, these tools would support the EHR in providing a more holistic understanding of the patient. Research further supports that individuals who have robust digital engagement using consumer informatics tools have higher participation in treatment follow-up and self-care across populations. Therefore, it is critical to investigate interventions that elicit and share patients’ social risks and care preferences with the health care team as a mechanism to improve individualized care and reduce the gap in health outcomes.Trial RegistrationClinicalTrials.gov NCT03766841; https://clinicaltrials.gov/ct2/show/NCT03766841International Registered Report Identifier (IRRID)RR1-10.2196/20309

Paco—Applying Computational Methods to Scale Qualitative Methods

For several years we have been building and using an open mobile research platform, called Paco, that enables the scaling of qualitative research through quantitative, computational techniques. The platform provides a mechanism to design and deliver remote research instruments to mobile devices in the field and it provides mechanisms to abstract and develop new research tools.The most immediate way the platform scales qualitative research is by enabling researchers to visually design, deploy, and manage research instruments comprised of surveys, triggers and sensor logging without needing to program or build a new mobile app. The combination of sensors, surveys and triggers supports idiographic, phenomenological, qualitative inquiry as well as contextual data collection in participants’ natural setting.Stepping back, the platform is an experiment in scaling the generation of new instruments and the generation of new knowledge in the science itself. Under the covers, Paco is implemented as an open construction kit of research components for all to use and modify as they like. Its design enables computational thinking [Wing] about both qualitative and quantitative behavioral research. It makes it possible to generate an infinite combination of research instruments from basic building blocks. Specifically, it borrows concepts and practices from programming language design, software architecture, and the software community.By writing down the elements and methods for research instruments in a precisely specified, machine‐executable language, they become more clear. This makes them better understood. This scales the generation of knowledge in behavioral science.Automated tools cannot replace the researcher. Ethnography has a very deep rich practice of immersive field work and analysis. The researcher is the ultimate instrument for understanding what is significant both individually and culturally within any study. Paco merely offers tools to support and advance the practice by scaling methods, automating parts that are amenable, and, by facilitating precise characterization of the data and data collection protocols.There are many challenges to how well computational methods can model and support behavioral research, particularly the qualitative methods used in ethnography. We finish with a discussion of some of the theoretical and practical challenges and how our method meets, and doesn't meet, those challenges.

Online Research with Informal Caregivers: Opportunities and Challenges

In this article we discuss the opportunities and challenges of using the Internet as part of an emergent methodology in qualitative research. Utilising a deliberative process of democratic enquiry, an online forum was used to identify important capabilities for the health of informal caregivers for older people in New Zealand. Recognising the difficulties associated with recruiting and interviewing informal caregivers, the study was advertised and conducted via the Internet. Over four months, 60 informal caregivers, from varied backgrounds, contributed to an online forum created specifically for the research. The asynchronous, anonymous format facilitated participants’ personal accounts of caregiving, which highlighted the tensions and complexity of caregivers’ lives. This resulted in a key finding that emotions, particularly attachment, play a significant role in the day-to-day experience of informal eldercare provision. Overall, we found that Internet-mediated research offers an innovative option for qualitative data collection, which has potential for including hard-to-reach populations and enabling insightful, contextual data collection.

Comparison of methods to identify crop productivity constraints in developing countries. A review

Selecting a method for identifying actual crop productivity constraints is an important step for triggering innovation processes. Applied methods can be diverse and although such methods have consequences for the design of intervention strategies, documented comparisons between various methods are scarce. Different variables can be used to characterize these methods. To typify them, we used two of these variables in a heuristic model: control over the research process and represented opinion. Here, we review 16 published papers that present outcomes of different methods to identify productivity constraints. The major findings are the following: (1) Variation in methods is wide. (2) Applying the heuristic model results in three main clusters of methods: farmer-control/farmer-opinion, scientist-control/scientist-opinion, and scientist-control/farmer-opinion. (3) These clusters are scale level dependent. As a follow up, we compared in a case study the three different methods, representative for the three main clusters of the heuristic model, in order to assess their congruency. These methods (focus group discussion, individual surveys, and contextual data collection) were applied in four localities in Tigray, Northern Ethiopia. We found that congruency between the methods, as indicated by Spearman-ρ correlations, was not significant. In addition, we found that outcomes of individual surveys and contextual data collection among the different locations were correlated (R > 0.70). No such correlation was found using focus group discussion. Both findings indicate that for a specific location different methods yielded different constraints and that variability between the locations is not reflected by using individual surveys and contextual data collection. Combined the review and case study demonstrate that process control and represented opinion have a manifest impact on generated outcomes. Because outcomes of productivity constraints assessments are methodology dependent, researchers are recommended to justify a priori their choice of method using the presented heuristic model.

Network localization and navigation via cooperation

Network localization and navigation give rise to a new paradigm for communications and contextual data collection, enabling a variety of new applications that rely on position information of mobile nodes (agents). The performance of such networks can be significantly improved via the use of cooperation. Therefore, a deep understanding of information exchange and cooperation in the network is crucial for the design of location-aware networks. This article presents an exploration of cooperative network localization and navigation from a theoretical foundation to applications, covering technologies and spatiotemporal cooperative algorithms.

Quality information on the internet: Norwegian library students on the search

PurposeIn the autumn of 2006, two groups of students from the Faculty of Journalism, Library and Information Science at Oslo University College participated in a cooperative project with the Norwegian online reference service, Biblioteksvar.no (Ask The Library (ATL)). The students were in their third term of studies and received instruction in retrieval systems and tools. The main purpose of this paper is to evaluate the performance of the students in actual reference work. It seeks to examine a sample of questions and answers. In a wider perspective, the study highlights the value of this kind of educational strategy.Design/methodology/approachThe evaluation criteria in this study are based on a qualitative analysis of actual questions and answers from the ATL, student summaries of their own work on the project and interviews with professional librarians working with the ATL service. A substantial amount of data has been collected during the project period.FindingsAnalysis shows that the students generally performed very well. This corroborates the findings from a similar project last year. The students' answers had the same level of quality as the responses supplied by professional, experienced librarians working on the ATL service. Possible reasons for this result are discussed. The study confirms the importance of the reference interview for increasing the quality of the responses.Practical implicationsThe results of the study indicate that the ATL service should not only provide links to information sources in response to user questions, but should also include a short explanation of how the links are found. Further, patron satisfaction surveys are useful. The collection of contextual data about the question and the patron may improve the quality of the response from the service. It is recommended that the SMS‐based service be included in the student project next year so that the students will have the opportunity to participate in all three of the currently available ATL services from Biblioteksvar.no.Originality/valueThe paper may inspire other faculties and schools of information studies to cooperate more closely with the library profession and to prepare students for their future careers as reference librarians through participation in actual reference work from a relatively early stage in the program of study. The project described in this paper illustrates the change from traditional lecture‐based teaching to problem‐based learning in projects as outlined in The Quality Reform in Norwegian Higher Education.

Older Carers in Ageing Societies: An Evaluation of a Respite Care Program for Older Carers in Western Australia

ABSTRACT The Australian Red Cross Older Carers Program was developed in 2003 to support the unique needs of “older carers” aged 65 and older (50 if Indigenous) who care for a person (a care recipient, usually a family member) aged 18 or older who have a permanent disability. The aim of the program was to provide intensive case planning, management, and volunteer support that would assist older carers to more readily access respite and continue their caring role in the home. To help achieve this end, ongoing individualised and holistic assistance involving older carers in decision making was an integral component of the Older Carers Program, as was the use of regular home visits by program staff. The Older Carers Program evaluation was both a process and outcome evaluation. Much of the evaluation research focused on the extent to which it had met its stated aims and objectives. However, because the program was quite new and innovative there was a significant further focus upon the processes and activities within the program. At the time the evaluation research was conducted (May 2005), the program had been in operation for 22 months. The population group was identified as those persons (older carers) who had been part of the program between July 2003 and March 2005. Of the 96 older carers who had accessed the program in the specified period, 62 agreed to participate in our research. The methodology consisted of an audit or the program database, in-depth interviews with older carers, and contextual data collection involving program staff and other stakeholders.

A REVIEW OF SEPARATE AND JOINT STRATEGIES FOR THE USE OF DATA ON REVEALED AND STATED CHOICES

This paper examines recent developments in household travel surveys that collect data for use in transportation planning and modeling efforts. The paper then introduces the notion of a total survey design and elaborates on what is meant by this concept. This is done first in the context of standard surveys of revealed choices. The paper discusses aspects of content and respondent burden and illustrates the potential to reduce respondent burden through careful consideration of content, question design, and question ordering. The paper also explores some issues of survey "friendliness" particularly with respect to activity surveys versus time-use surveys, with some observations about the potential of time-use surveys to eliminate some of the burden and content problems of previous diary designs. The remainder of the paper concentrates on the issue of collecting stated-response data and examines two alternative methods for collection: simultaneous collection of the contextual information and "on-the-fly" development of the alternatives for the stated-response questions, or sequential collection of contextual data and development of the stated-response questions. The paper also addresses issues of respondent burden that arise in the administration of stated-response surveys. The paper concludes with exploration of some of the reasons for collecting stated-response data, with particular emphasis on the US situation. In conclusion, the paper stresses again the need for a total design concept for collection of stated-response data, as well as for the simpler collection of more standard revealed choice data.