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How integrating DFSS into agile software development can help address the human aspects of these processes A German university of applied sciences with about 5,500 students needed new software due to COVID-19-related laws and decrees that required universities to perform contact tracking in case of potential COVID-19 exposure. Agile is a software development philosophy based on using self-organized teams.2 The goal of agile is to develop a basic working version of the software quickly, and continuously improve the software and add more features in accordance with customer or stakeholder wishes.3 Unlike many other software development methods, agile does not have predefined stages or documents4 and is ideally suited to coping with evolving and changing stakeholder requirements.5 One advantage of agile is discovering software design flaws quicker than classic stage-based software development models.6 Finding design flaws quickly is advantageous because fixing the software design late in the project is costly and time consuming. The problem statement was a verbal "digitize contact tracking," and there were frequent attempts to expand the project to include additional objectives such as ensuring people maintained social distancing, registering students for study spaces and improving carbon dioxide monitoring. Leveraging DFSS While agile is well suited to software development, it is less suited to dealing with many of the organizational problems that were encountered. [...]future projects would benefit from integrating a DFSS framework into such projects.
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Social isolation has affected people globally during the COVID-19 pandemic and had a major impact on older adult's well-being. Chatbot interventions may be a way to provide support to address loneliness and social isolation in older adults. The aims of the current study were to (1) understand the distribution of a chatbot's net promoter scores, (2) conduct a thematic analysis on qualitative elaborations to the net promoter scores, (3) understand the distribution of net promoter scores per theme, and (4) conduct a single word analysis to understand the frequency of words present in the qualitative feedback. A total of 7,099 adults and older adults consented to participate in a chatbot intervention on reducing social isolation and loneliness. The average net promoter score (NPS) was 8.67 out of 10. Qualitative feedback was provided by 766 (10.79%) participants which amounted to 898 total responses. Most themes were rated as positive (517), followed by neutral (311) and a minor portion as negative (70). The following five themes were found across the qualitative responses: positive outcome (277, 30.8%), user did not address question (262, 29.2%), bonding with the chatbot (240, 26.7%), negative technical aspects (70, 7.8%), and ambiguous outcome (49, 5.5%). Themes with a positive valence were found to be associated with a higher NPS. The word "help" and it's variations were found to be the most frequently used words, which is consistent with the thematic analysis. These results show that a chatbot for social isolation and loneliness was perceived positively by most participants. More specifically, users were likely to personify the chatbot (e.g., "Cause I feel like I have a new friend!") and perceive positive personality features such as being non-judgmental, caring, and open to listen. A minor portion of the users reported dissatisfaction with chatting with a machine. Implications will be discussed.
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The Covid-19 pandemic situation and social restrictions as the impact have resulted in many problems, including mental and emotional health in society. The existence of mobile application technology through smartphones, which many people own, can be a means to assist, especially people with emotional problems. We have developed the EmoHealth Application for this purpose. The EmoHealth application aims to make people get easier to manage and regulate their daily emotions. The EmoHealth has been developed based on Artificial Intelligence. It has four main features, namely: emotional assessment, emotional journey, emo insight, and chatbot features, and is currently developed only in the Bahasa version. This research aims to test the EmoHealth application's usability to get users' feedback before it is widely published. Tests are carried out to ensure that the EmoHealth application is usable and suitable for user needs. The USE Questionnaire is used because it is one of the most frequently used methods in testing mobile-based applications. The USE questionnaire has four attributes: usefulness, ease of use, ease of learning, and satisfaction. Reliability testing aims to measure the consistency of the questionnaire test. The result usability score is 75.9%, which implied that the EmoHealth application can be used well and satisfactorily. © 2022 IEEE.
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Following the COVID-19 pandemic and the resultant remote teaching that lockdown enforced the requirements and suitability of physical learning spaces such as studios can be questioned. This paper seeks to understand the requirements of students in this decade of their physical studio spaces. Using focus groups, surveys and user feedback activities students on the Product Design programme were asked to evaluate the studio spaces within the university as they returned to on-campus learning and provide qualitative feedback on their experiences. It was found students still require physical studios that allow them to undertake their design work and utilise tools and the space in a way they are unable to in other learning and domestic environments. Most importantly students require a space that allows them to understand and conceptualise project work, engage in discipline-specific discourse and feel a sense of ownership to encourage creative thinking. © Proceedings of the 24th International Conference on Engineering and Product Design Education: Disrupt, Innovate, Regenerate and Transform, E and PDE 2022. All rights reserved.
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BACKGROUND: Chronic pain is a prolonged condition that deteriorates one's quality of life. Treating chronic pain requires a multicomponent approach, and in many cases, there are no "silver bullet" solutions. Mobile health (mHealth) is a rapidly expanding category of solutions in digital health with proven potential in chronic pain management. OBJECTIVE: This study aims to contrast the viewpoints of 2 groups of people with chronic pain concerning mHealth: people who have adopted the use of mHealth and those who have not. We highlight the benefits of mHealth solutions for people with chronic pain and the perceived obstacles to their increased adoption. We also provide recommendations to encourage people to try mHealth solutions as part of their self-care. METHODS: The Prolific crowdsourcing platform was used to collect crowdsourced data. A prescreening questionnaire was released to determine what type of pain potential participants have and whether they are currently using mHealth solutions for chronic pain. The participants were invited based on their experience using mHealth to manage their pain. Similar questions were presented to mHealth users and nonusers. Qualitative and quantitative analyses were performed to determine the outcomes of this study. RESULTS: In total, 31 responses were collected from people (aged 19-63 years, mean 31.4, SD 12.1) with chronic pain who use mHealth solutions. Two-thirds (n=20, 65%) of the users identified as female and 11 (35%) as male. We matched these mHealth users with an equal number of nonusers: 31 responses from the pool of 361 participants in the prescreening questionnaire. The nonusers' ages ranged from 18 to 58 years (mean 30.8, SD 11.09), with 15 (50%) identifying as female and 15 (50%) as male. Likert-scale questions were analyzed using the Mann-Whitney-Wilcoxon (MWW) test. Results showed that the 2 groups differed significantly on 10 (43%) of 23 questions and shared similar views in the remaining 13 (57%). The most significant differences were related to privacy and interactions with health professionals. Of the 31 mHealth users, 12 (39%) declared that using mHealth solutions has made interacting with health or social care professionals easier (vs n=22, 71%, of nonusers). The majority of the nonusers (n=26, 84%) compared with about half of the users (n=15, 48%) expressed concern about sharing their data with, for example, third parties. CONCLUSIONS: This study investigated how mHealth is currently used in the context of chronic pain and what expectations mHealth nonusers have for mHealth as a future chronic pain management tool. Analysis revealed contrasts between mHealth use expectations and actual usage experiences, highlighting privacy concerns toward mHealth solutions. Generally, the results showed that nonusers are more concerned about data privacy and expect mHealth to facilitate interacting with health professionals. The users, in contrast, feel that such connections do not exist.
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BACKGROUND: Following the outbreak of COVID-19, several studies have reported that young adults encountered a rise in anxiety symptoms, which could negatively affect their quality of life. Promising evidence suggests that mobile apps with biofeedback, serious games, breathing exercises, and positive messaging, among other features, are useful for anxiety self-management and treatment. OBJECTIVE: This study aimed to develop and evaluate the usability of a biofeedback-based app with serious games for young adults with anxiety in the United Arab Emirates (UAE). METHODS: This study consists of two phases: Phase I describes the design and development of the app, while Phase II presents the results of a usability evaluation by experts. To elicit the app's requirements during Phase I, we conducted (1) a survey to investigate preferences of young adults in the UAE for mobile games for stress relief; (2) an analysis of serious games for anxiety; and (3) interviews with mental health professionals and young adults in the UAE. In Phase II, five experts tested the usability of the developed app using a set of Nielsen's usability heuristics. RESULTS: A fully functional biofeedback-based app with serious games was co-designed with mental health professionals. The app included 4 games (ie, a biofeedback game, card game, arcade game, and memory game), 2 relaxation techniques (ie, a breathing exercise and yoga videos), and 2 additional features (ie, positive messaging and a mood tracking calendar). The results of Phase II showed that the developed app is efficient, simple, and easy to use. Overall, the app design scored an average of 4 out of 5. CONCLUSIONS: The elicitation techniques used in Phase I resulted in the development of an easy-to-use app for the self-management of anxiety. Further research is required to determine the app's usability and effectiveness in the target population.
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Data integration is a classical problem in databases, typically decomposed into schema matching, entity matching and record merging. To solve the latter, it is mostly assumed that ground truth can be determined, either as master data or from user feedback. However, in many cases, this is not the case because firstly the merging processes cannot be accurate enough, and also the data gathering processes in the different sources are simply imperfect and cannot provide high quality data. Instead of enforcing consistency, we propose to evaluate how concordant or discordant sources are as a measure of trustworthiness (the more discordant are the sources, the less we can trust their data). Thus, we define the discord measurement problem in which given a set of uncertain raw observations or aggregate results (such as case/hospitalization/death data relevant to COVID-19) and information on the alignment of different data (for example, cases and deaths), we wish to assess whether the different sources are concordant, or if not, measure how discordant they are. Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Blood or plasma transmission is one of the most effective treatments for critical diseases like Covid 19. Nowadays, voluntary blood donation has become the major source of blood supply. Several mobile applications are currently available to establish the initial communication between blood donors and receivers. Recommending the right potential donor during a blood search can save the life of a critical patient with an immediate response from the donor. However, the requirement of an advanced recommendation system has not been addressed by any of the existing mobile applications. In our research work, we have designed a real-time, intelligent, and rational recommendation system using sentiment analysis of the user's feedback, response rate of the donor, and the current geo-location information and finally develop a cross-platform application for blood collection and distribution system. To process and generate features from the user feedback, we have designed a Bi-directional LSTM-based deep learning model. The quality of the recommendation of the potential donors has significantly improved. Moreover, we have conducted rigorous requirement analysis from real users and evaluated the performance of our application through both indoor and outdoor testing. © 2021 IEEE.