scholarly journals Facilitating Web-Based Collaboration in Evidence Synthesis (TaskExchange): Development and Analysis (Preprint)

2017 ◽  
Author(s):  
Tari Turner ◽  
Emily Steele ◽  
Chris Mavergames ◽  
Julian Elliott ◽  
Keyword(s):  
Health Research ◽  
Research Evidence ◽  
New Technologies ◽  
Guideline Development ◽  
Evidence Synthesis ◽  
Agile Development ◽  
User Engagement ◽  
Web Based ◽  
Search Tasks ◽  
Wide Range

BACKGROUND The conduct and publication of scientific research are increasingly open and collaborative. There is growing interest in Web-based platforms that can effectively enable global, multidisciplinary scientific teams and foster networks of scientists in areas of shared research interest. Designed to facilitate Web-based collaboration in research evidence synthesis, TaskExchange highlights the potential of these kinds of platforms. OBJECTIVE This paper describes the development, growth, and future of TaskExchange, a Web-based platform facilitating collaboration in research evidence synthesis. METHODS The original purpose of TaskExchange was to create a platform that connected people who needed help with their Cochrane systematic reviews (rigorous syntheses of health research) with people who had the time and expertise to help. The scope of TaskExchange has now been expanded to include other evidence synthesis tasks, including guideline development. The development of TaskExchange was initially undertaken in 5 agile development phases with substantial user engagement. In each phase, software was iteratively deployed as it was developed and tested, enabling close cycles of development and refinement. RESULTS TaskExchange enables users to browse and search tasks and members by keyword or nested filters, post and respond to tasks, sign up to notification emails, and acknowledge the work of TaskExchange members. The pilot platform has been open access since August 2016, has over 2300 members, and has hosted more than 630 tasks, covering a wide range of research synthesis-related tasks. Response rates are consistently over 75%, and user feedback has been positive. CONCLUSIONS TaskExchange demonstrates the potential for new technologies to support Web-based collaboration in health research. Development of a relatively simple platform for peer-to-peer exchange has provided opportunities for systematic reviewers to get their reviews completed more quickly and provides an effective pathway for people to join the global health evidence community.

scholarly journals The views of health guideline developers on the use of automation in health evidence synthesis

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Anneliese Arno ◽  
Julian Elliott ◽  
Byron Wallace ◽  
Tari Turner ◽  
James Thomas
Keyword(s):  
Diffusion Of Innovation ◽  
Systematic Reviews ◽  
Diffusion Of Innovations ◽  
New Technologies ◽  
Guideline Development ◽  
Evidence Synthesis ◽  
Analytical Framework ◽  
Relative Advantage ◽  
Full Potential ◽  
Novel Technologies

Abstract Background The increasingly rapid rate of evidence publication has made it difficult for evidence synthesis—systematic reviews and health guidelines—to be continually kept up to date. One proposed solution for this is the use of automation in health evidence synthesis. Guideline developers are key gatekeepers in the acceptance and use of evidence, and therefore, their opinions on the potential use of automation are crucial. Methods The objective of this study was to analyze the attitudes of guideline developers towards the use of automation in health evidence synthesis. The Diffusion of Innovations framework was chosen as an initial analytical framework because it encapsulates some of the core issues which are thought to affect the adoption of new innovations in practice. This well-established theory posits five dimensions which affect the adoption of novel technologies: Relative Advantage, Compatibility, Complexity, Trialability, and Observability. Eighteen interviews were conducted with individuals who were currently working, or had previously worked, in guideline development. After transcription, a multiphase mixed deductive and grounded approach was used to analyze the data. First, transcripts were coded with a deductive approach using Rogers’ Diffusion of Innovation as the top-level themes. Second, sub-themes within the framework were identified using a grounded approach. Results Participants were consistently most concerned with the extent to which an innovation is in line with current values and practices (i.e., Compatibility in the Diffusion of Innovations framework). Participants were also concerned with Relative Advantage and Observability, which were discussed in approximately equal amounts. For the latter, participants expressed a desire for transparency in the methodology of automation software. Participants were noticeably less interested in Complexity and Trialability, which were discussed infrequently. These results were reasonably consistent across all participants. Conclusions If machine learning and other automation technologies are to be used more widely and to their full potential in systematic reviews and guideline development, it is crucial to ensure new technologies are in line with current values and practice. It will also be important to maximize the transparency of the methods of these technologies to address the concerns of guideline developers.

scholarly journals The views of health guideline developers on the use of automation in health evidence synthesis

2020 ◽  
Author(s):  
Anneliese Downey Arno ◽  
Julian Elliott ◽  
Byron Wallace ◽  
Tari Turner ◽  
James Thomas
Keyword(s):  
Diffusion Of Innovation ◽  
Systematic Reviews ◽  
Diffusion Of Innovations ◽  
New Technologies ◽  
Guideline Development ◽  
Evidence Synthesis ◽  
Analytical Framework ◽  
Relative Advantage ◽  
Full Potential ◽  
Novel Technologies

Abstract Background: The increasingly rapid rate of evidence publication has made it difficult for evidence synthesis – systematic reviews and health guidelines -- to be continually kept up to date. One proposed solution for this is the use of automation in health evidence synthesis. Guideline developers are key gatekeepers in the acceptance and use of evidence, and therefore their opinions on the potential use of automation are crucial. Methods: The objective of this study was to analyze the attitudes of guideline developers towards the use of automation in health evidence synthesis. The Diffusion of Innovations framework was chosen as an initial analytical framework because it encapsulates some of the core issues which are thought to affect the adoption of new innovations in practice. This well-established theory posits five dimensions which affect the adoption of novel technologies: Relative Advantage, Compatibility, Complexity, Trialability, and Observability.Eighteen interviews were conducted with individuals who were currently working, or had previously worked, in guideline development. After transcription, a multiphase mixed deductive and grounded approach was used to analyze the data. First, transcripts were coded with a deductive approach using Rogers’ Diffusion of Innovation as the top-level themes. Second, sub-themes within the framework were identified using a grounded approach.Results: Participants were consistently most concerned with the extent to which an innovation is in line with current values and practices (i.e. Compatibility in the Diffusion of Innovations framework). Participants were also concerned with Relative Advantage and Observability, which were discussed in approximately equal amounts. For the latter, participants expressed a desire for transparency in methodology of automation software. Participants were noticeably less interested in Complexity and Trialability, which were discussed infrequently. These results were reasonably consistent across all participants. Conclusions: If machine learning and other automation technologies are to be used more widely and to their full potential in systematic reviews and guideline development, it is crucial to ensure new technologies are in line with current values and practice. It will also be important to maximize the transparency of the methods of these technologies to address the concerns of guideline developers.

scholarly journals Examining Predictors of Real-World User Engagement with Self-Guided eHealth Interventions: Analysis of Mobile Apps and Websites Using a Novel Dataset (Preprint)

2018 ◽  
Author(s):  
Amit Baumel ◽  
John M Kane
Keyword(s):  
Therapeutic Alliance ◽  
Product Design ◽  
Real World ◽  
Research Evidence ◽  
Mobile Apps ◽  
Mobile App ◽  
Visual Design ◽  
User Engagement ◽  
Web Based ◽  
Google Play

BACKGROUND The literature suggests that the product design of self-guided electronic health (eHealth) interventions impacts user engagement. Traditional trial settings, however, do not enable the examination of these relationships in real-world use. OBJECTIVE This study aimed to examine whether the qualities of product design, research evidence, and publicly available data predict real-world user engagement with mobile and Web-based self-guided eHealth interventions. METHODS This analysis included self-guided mobile and Web-based eHealth interventions available to the public—with their qualities assessed using the Enlight suite of scales. Scales included Usability, Visual Design, User Engagement, Content, Therapeutic Persuasiveness, Therapeutic Alliance, Credibility, and Research Evidence. Behavioral data on real-world usage were obtained from a panel that provides aggregated nonpersonal information on user engagement with websites and mobile apps, based on a time window of 18 months that was set between November 1, 2016 and April 30, 2018. Real-world user engagement variables included average usage time (for both mobile apps and websites) and mobile app user retention 30 days after download. RESULTS The analysis included 52 mobile apps (downloads median 38,600; interquartile range [IQR] 116,000) and 32 websites (monthly unique visitors median 5689; IQR 30,038). Results point to moderate correlations between Therapeutic Persuasiveness, Therapeutic Alliance, and the 3 user engagement variables (.31≤rs≤.51; Ps≤.03). Visual Design, User Engagement, and Content demonstrated similar degrees of correlation with mobile app engagement variables (.25≤rs≤.49; Ps≤.04) but not with average usage time of Web-based interventions. Positive correlations were also found between the number of reviews on Google Play and average app usage time (r=.58; P<.001) and user retention after 30 days (r=.23; P=.049). Although several product quality ratings were positively correlated with research evidence, the latter was not significantly correlated with real-world user engagement. Hierarchical stepwise regression analysis revealed that either Therapeutic Persuasiveness or Therapeutic Alliance explained 15% to 26% of user engagement variance. Data on Google Play (number of reviews) explained 15% of the variance of mobile app usage time above Enlight ratings; however, publicly available data did not significantly contribute to explaining the variance of the other 2 user-engagement variables. CONCLUSIONS Results indicate that the qualities of product design predict real-world user engagement with eHealth interventions. The use of real-world behavioral datasets is a novel way to learn about user behaviors, creating new avenues for eHealth intervention research.

scholarly journals The views of health guideline developers on the use of automation in health evidence synthesis

2020 ◽  
Author(s):  
Anneliese Downey Arno ◽  
Julian Elliott ◽  
Byron Wallace ◽  
Tari Turner ◽  
James Thomas
Keyword(s):  
Machine Learning ◽  
Systematic Reviews ◽  
Diffusion Of Innovations ◽  
New Technologies ◽  
Guideline Development ◽  
Evidence Synthesis ◽  
Relative Advantage ◽  
Full Potential ◽  
Crowd Sourcing ◽  
Novel Technologies

Abstract Background The increasingly rapid rate of evidence publication has made it difficult for evidence synthesis – systematic reviews and health guidelines -- to be continually kept up-to-date maintain the most up-to-date data. One proposed solution for this is the use of automation in health evidence synthesis. Guideline developers are key gatekeepers in the acceptance and use of evidence, and therefore their opinions on the potential use of automation are crucial. Methods The objective of this study was to analyse the attitudes of guideline developers towards the use of machine learning and crowd-sourcing in evidence. The Diffusion of Innovations framework was chosen as an initial analytical framework because it encapsulates some of the core issues which are thought to affect the adoption of new innovations in practice. This well-established theory posits five dimensions which affect the adoption of novel technologies: Relative Advantage , Compatibility , Complexity , Trialability , and Observability . Eighteen interviews were conducted with individuals who were currently working, or had previously worked, in guideline development. After transcription, a multiphase mixed deductive and grounded approach was used to analyse the data. First, transcripts were coded with a deductive approach using Rogers’ Diffusion of Innovation as the top-level themes. Second, sub-themes within the framework were identified using a grounded approach. Results Participants were consistently most concerned with the extent to which an innovation is in line with current values and practices (ie. Compatibility in the Diffusion of Innovations framework. Participants were also concerned with Relative Advantage and Observability , which were discussed in approximately equal amounts. For the latter, participants expressed a desire for transparency in methodology of automation software. Participants were noticeably less interested in Complexity and Trialability , which were discussed infrequently. These results were reasonably consistent across all participants. Conclusions If machine learning and other automation technologies are to be used more widely and to their full potential in systematic reviews and guideline development, it is crucial to ensure new technologies are in line with current values and practice. It will also be important to maximize the transparency of the methods of these technologies to address the concerns of guideline developers.

Current State of Practice in Transit Ridership Prediction: Results from a Survey of Canadian Transit Agencies

2019 ◽  
Vol 2673 (8) ◽  
pp. 179-191
Author(s):  
Ehab Diab ◽  
Dena Kasraian ◽  
Eric J. Miller ◽  
Amer Shalaby
Keyword(s):  
New Technologies ◽  
Prediction Models ◽  
Web Based ◽  
Current State ◽  
Transit Ridership ◽  
Level Of Satisfaction ◽  
Wide Range ◽  
Comprehensive Examination ◽  
Transit Agencies ◽  
In Transit

With the emergence of new technologies, new data sources, and software, it is important to understand the current approaches used by transit agencies in ridership prediction. This study reports the results of a recent web-based survey conducted in 2018 among 36 Canadian transit agencies to understand their current state of ridership prediction practice. The study presents a wide range of results, starting from agencies’ used prediction methods to the challenges faced by transit agencies as a result of the observed changes in ridership estimates after the introduction of new automated data collection systems. The study also discusses the transit agencies’ level of satisfaction with the currently used methods and data inputs and factors that are incorporated in their methods. In addition, it develops a better understanding of the requirements of robust ridership prediction models from the transit agencies’ perspective. This paper provides planners and researchers with a comprehensive examination of the different aspects and issues that are related to the current state of transit agencies’ ridership prediction practices.

scholarly journals The Use of Web-Based Technologies in Health Research Participation: Qualitative Study of Consumer and Researcher Experiences (Preprint)

2018 ◽  
Author(s):  
Patrick Cheong-Iao Pang ◽  
Shanton Chang ◽  
Karin Verspoor ◽  
Ornella Clavisi
Keyword(s):  
New Media ◽  
Social Networking Sites ◽  
Health Research ◽  
Design Research ◽  
New Technologies ◽  
Academic Research ◽  
Research Participation ◽  
Research Projects ◽  
Web Based ◽  
Analysis Methodology

BACKGROUND Health consumers are often targeted for their involvement in health research including randomized controlled trials, focus groups, interviews, and surveys. However, as reported by many studies, recruitment and engagement of consumers in academic research remains challenging. In addition, there is scarce literature describing what consumers look for and want to achieve by participating in research. OBJECTIVE Understanding and responding to the needs of consumers is crucial to the success of health research projects. In this study, we aim to understand consumers’ needs and investigate the opportunities for addressing these needs with Web-based technologies, particularly in the use of Web-based research registers and social networking sites (SNSs). METHODS We undertook a qualitative approach, interviewing both consumer and medical researchers in this study. With the help from an Australian-based organization supporting people with musculoskeletal conditions, we successfully interviewed 23 consumers and 10 researchers. All interviews were transcribed and analyzed with thematic analysis methodology. Data collection was stopped after the data themes reached saturation. RESULTS We found that consumers perceive research as a learning opportunity and, therefore, expect high research transparency and regular updates. They also consider the sources of the information about research projects, the trust between consumers and researchers, and the mobility of consumers before participating in any research. Researchers need to be aware of such needs when designing a campaign for recruitment for their studies. On the other hand, researchers have attempted to establish a rapport with consumer participants, design research for consumers’ needs, and use technologies to reach out to consumers. A systematic approach to integrating a variety of technologies is needed. CONCLUSIONS On the basis of the feedback from both consumers and researchers, we propose 3 future directions to use Web-based technologies for addressing consumers’ needs and engaging with consumers in health research: (1) researchers can make use of consumer registers and Web-based research portals, (2) SNSs and new media should be frequently used as an aid, and (3) new technologies should be adopted to remotely collect data and reduce administrative work for obtaining consumers’ consent.

scholarly journals Developing a reporting guideline to improve meta-ethnography in health research: the eMERGe mixed-methods study

2019 ◽  
Vol 7 (4) ◽  
pp. 1-116 ◽  
Author(s):  
Maggie Cunningham ◽  
Emma F France ◽  
Nicola Ring ◽  
Isabelle Uny ◽  
Edward AS Duncan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Mixed Methods ◽  
Health Research ◽  
Guideline Development ◽  
Evidence Synthesis ◽  
Good Practice ◽  
Future Research ◽  
Qualitative Evidence Synthesis ◽  
Qualitative Evidence ◽  
The Impact

BackgroundMeta-ethnography is a commonly used methodology for qualitative evidence synthesis. Research has identified that the quality of reporting of published meta-ethnographies is often poor and this has limited the utility of meta-ethnography findings to influence policy and practice.ObjectiveTo develop guidance to improve the completeness and clarity of meta-ethnography reporting.Methods/designThe meta-ethnography reporting guidance (eMERGe) study followed the recommended approach for developing health research reporting guidelines and used a systematic mixed-methods approach. It comprised (1) a methodological systematic review of guidance in the conduct and reporting of meta-ethnography; (2) a review and audit of published meta-ethnographies, along with interviews with meta-ethnography end-users, to identify good practice principles; (3) a consensus workshop and two eDelphi (Version 1, Duncan E, Swinger K, University of Stirling, Stirling, UK) studies to agree guidance content; and (4) the development of the guidance table and explanatory notes.ResultsResults from the methodological systematic review and the audit of published meta-ethnographies revealed that more guidance was required around the reporting of all phases of meta-ethnography conduct and, in particular, the synthesis phases 4–6 (relating studies, translating studies into one another and synthesising translations). Following the guidance development process, the eMERGe reporting guidance was produced, comprising 19 items grouped into the seven phases of meta-ethnography.LimitationsThe finalised guidance has not yet been evaluated in practice; therefore, it is not possible at this stage to comment on its utility. However, we look forward to evaluating its uptake and usability in the future.ConclusionsThe eMERGe reporting guidance has been developed following a rigorous process in line with guideline development recommendations. The guidance is intended to improve the clarity and completeness of reporting of meta-ethnographies, and to facilitate use of the findings within the guidance to inform the design and delivery of services and interventions in health, social care and other fields. The eMERGe project developed a range of training materials to support use of the guidance, which is freely available atwww.emergeproject.org(accessed 26 March 2018). Meta-ethnography is an evolving qualitative evidence synthesis methodology and future research should refine the guidance to accommodate future methodological developments. We will also investigate the impact of the eMERGe reporting guidance with a view to updating the guidance.Study registrationThis study is registered as PROSPERO CRD42015024709 for the stage 1 systematic review.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

“Click for Support” – Guidelines for Effective Web-Based Interventions for Young People in Selective Drug Prevention

2015 ◽  
Vol 61 (6) ◽  
pp. 367-376 ◽  
Author(s):  
Doris Sarrazin ◽  
Rebekka Steffens
Keyword(s):  
Young People ◽  
Delphi Study ◽  
New Technologies ◽  
Guideline Development ◽  
Drug Prevention ◽  
Target Group ◽  
New Psychoactive Substances ◽  
Special Focus ◽  
Second Phase ◽  
Web Based

Abstract. Objective: New technologies and modern media play an important role in the daily life and communication of young people. The objective of the EU-funded project “Click for Support” is to develop a guideline for effective web-based interventions for young people in the field of selective drug prevention, with a special focus on illicit drugs and new psychoactive substances. The target group is young drug consumers between 14 and 21 years. A second objective is to promote the application of new technologies like social networks by prevention professionals. Method: The project is divided in two main parts: The first phase includes research on web-based interventions and assessment workshops with the target group, and the second phase consists of actual guideline development, based on the previously gained results and knowledge. A Delphi study serves to determine recommendations and statements that were discussed ambiguously. Conclusion: Following completion of the first project phase we now have a large amount of information and knowledge concerning the current supply of web-based interventions in many European countries as well as important elements and aspects the offers should include – and most importantly, the preferences and needs of the target group. Based on this knowledge we plan to develop the guideline during the next project phase. We will use a Delphi study to discuss uncertain aspects together with a panel of experts in two rounds. The final guideline will be available in July 2015 and will be translated into ten different languages.

Site specific agricultural soil management with the use of new technologies

2013 ◽  
Vol 16 (1) ◽  
pp. 59-67
Keyword(s):  
New Technologies ◽  
Digital Interface ◽  
Appropriate Treatment ◽  
Wide Range ◽  
Soil Variation ◽  
Soil Information ◽  
Remote Sensing Techniques ◽  
Agricultural Areas

<p>The Soil Science Institute of Thessaloniki produces new digitized Soil Maps that provide a useful electronic database for the spatial representation of the soil variation within a region, based on in situ soil sampling, laboratory analyses, GIS techniques and plant nutrition mathematical models, coupled with the local land cadastre. The novelty of these studies is that local agronomists have immediate access to a wide range of soil information by clicking on a field parcel shown in this digital interface and, therefore, can suggest an appropriate treatment (e.g. liming, manure incorporation, desalination, application of proper type and quantity of fertilizer) depending on the field conditions and cultivated crops. A specific case study is presented in the current work with regards to the construction of the digitized Soil Map of the regional unit of Kastoria. The potential of this map can easily be realized by the fact that the mapping of the physicochemical properties of the soils in this region provided delineation zones for differential fertilization management. An experiment was also conducted using remote sensing techniques for the enhancement of the fertilization advisory software database, which is a component of the digitized map, and the optimization of nitrogen management in agricultural areas.</p>

Investigating the Efficacy and Cost-Effectiveness of a Web-based Self-help Program for People With Adjustment Problems After an Accident (SelFIT): Protocol for a Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Julia Hegy ◽  
Noemi Anja Brog ◽  
Thomas Berger ◽  
Hansjoerg Znoj
Keyword(s):  
Health Care ◽  
Psychological Distress ◽  
Cost Effectiveness ◽  
Well Being ◽  
User Friendliness ◽  
Adjustment Problems ◽  
Health Care Consumption ◽  
Web Based ◽  
Self Help ◽  
Wide Range

BACKGROUND Accidents and the resulting injuries are one of the world’s biggest health care issues often causing long-term effects on psychological and physical health. With regard to psychological consequences, accidents can cause a wide range of burdens including adjustment problems. Although adjustment problems are among the most frequent mental health problems, there are few specific interventions available. The newly developed program SelFIT aims to remedy this situation by offering a low-threshold web-based self-help intervention for psychological distress after an accident. OBJECTIVE The overall aim is to evaluate the efficacy and cost-effectiveness of the SelFIT program plus care as usual (CAU) compared to only care as usual. Furthermore, the program’s user friendliness, acceptance and adherence are assessed. We expect that the use of SelFIT is associated with a greater reduction in psychological distress, greater improvement in mental and physical well-being, and greater cost-effectiveness compared to CAU. METHODS Adults (n=240) showing adjustment problems due to an accident they experienced between 2 weeks and 2 years before entering the study will be randomized. Participants in the intervention group receive direct access to SelFIT. The control group receives access to the program after 12 weeks. There are 6 measurement points for both groups (baseline as well as after 4, 8, 12, 24 and 36 weeks). The main outcome is a reduction in anxiety, depression and stress symptoms that indicate adjustment problems. Secondary outcomes include well-being, optimism, embitterment, self-esteem, self-efficacy, emotion regulation, pain, costs of health care consumption and productivity loss as well as the program’s adherence, acceptance and user-friendliness. RESULTS Recruitment started in December 2019 and is ongoing. CONCLUSIONS To the best of our knowledge, this is the first study examining a web-based self-help program designed to treat adjustment problems resulting from an accident. If effective, the program could complement the still limited offer of secondary and tertiary psychological prevention after an accident. CLINICALTRIAL ClinicalTrials.gov NCT03785912; https://clinicaltrials.gov/ct2/show/NCT03785912?cond=NCT03785912&draw=2&rank=1

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