Characteristics Of Users Research Articles

The use and subjective experience of sleep apps and their relationship with personality characteristics among young adults

IntroductionSleep app use has become widespread in recent years. Still, understanding of characteristics of users and the impact of sleep app usage remains limited.MethodsA sample of 940 young adults (18–30 years) living in the UK were recruited from Prolific (online research platform offering researchers access to survey panels) and invited to participate in a survey on the use and experience with sleep apps. Both sleep app users and non-users were invited. The survey included questions about demographics, morningness (Horne–Östberg Morningness–Eveningness Questionnaire-reduced scale), insomnia symptoms (Bergen Insomnia Scale), personality (HEXACO: Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience), in addition to sleep app usage and how such use is experienced. A logistic regression analysis was conducted to identify characteristics of sleep app users. Factor analysis was used to elucidate dimensions of sleep app use experiences. Scores on the items making up each factor were then regressed on demographic variables, morningness, insomnia symptoms and personality.ResultsIn all, 36.8% of the participants reported using or having used sleep apps. Use was positively associated with age, insomnia symptoms, and conscientiousness. The factor analysis revealed three factors coined: trust/objectification (trust in sleep app results), negative (e.g., becoming stressed) and positive (e.g., prioritize sleep more) perceived effects. Trust/objectification was positively associated with age and agreeableness, and inversely related to high education and openness to experience. Insomnia symptoms and emotionality were associated with perceived negative effects. Morningness and male gender were associated with perceived positive effects whereas high education had an inverse relationship with perceived positive effects.DiscussionThe use of sleep apps was widespread. Different responses to sleep app usage were observed and linked to various individual variables. The findings suggest that perceived effects of sleep apps usage should be considered in light of individual differences. Research on sleep apps is still in its early stages, and several suggestions (recommendations) for future studies are outlined.

Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation

Adding explanations to recommender systems is said to have multiple benefits, such as increasing user trust or system transparency. Previous work from other application areas suggests that specific user characteristics impact the users’ perception of the explanation. However, we rarely find this type of evaluation for recommender systems explanations. This paper addresses this gap by surveying 124 papers in which recommender systems explanations were evaluated in user studies. We analyzed their participant descriptions and study results where the impact of user characteristics on the explanation effects was measured. Our findings suggest that the results from the surveyed studies predominantly cover specific users who do not necessarily represent the users of recommender systems in the evaluation domain. This may seriously hamper the generalizability of any insights we may gain from current studies on explanations in recommender systems. We further find inconsistencies in the data reporting, which impacts the reproducibility of the reported results. Hence, we recommend actions to move toward a more inclusive and reproducible evaluation.

Survey among adult users of semaglutide for weight loss in Denmark: User characteristics, treatment expectations and experienced effects.

Since the launch of semaglutide for weight loss (Wegovy®, SEMA-WL) in late 2022, there has been a huge increase in new users of SEMA-WL. We explored characteristics, treatment expectations and experienced effectiveness and side effects among Danish adults using SEMA-WL. A questionnaire-based survey was conducted from September to December 2023 across 29 Danish community pharmacies. During a 10-day period, pharmacies invited consecutive individuals aged ≥18 years redeeming a prescription on SEMA-WL to complete an electronic questionnaire. A total of 1013 individuals were invited to study participation of which 55% (n = 559) accepted. Users of SEMA-WL were primarily female (78%, n = 434) and middle-aged adults (36-65 years; 76%, n = 423), with a median body mass index of 36 (interquartile range [IQR] 32-40) at treatment initiation. Two in three users had personally asked their general practitioner to initiate treatment (70%, n = 393). Four in ten users had not discussed expected treatment duration with their general practitioner (42%, n = 237), almost half (46%, n = 258) expected treatment for a limited time period, while only 11% (n = 64) expected lifelong treatment. Self-reported median weight loss was 5.3% (IQR 3.2-8.5) for users treated for <3 months, 10% (IQR 8.2-14) for 3-5 months of treatment and 17% (IQR 13-21) for those treated for >5 months. More than half of users (59%, n = 330) had experienced side effects, most commonly nausea (35%, n = 193) and constipation (29%, n = 160). This study provides valuable insights into the characteristics, treatment expectations and effects experienced by Danish adult users of SEMA-WL.

Exploring the ‘Dark Side’ of AI‐Powered Digital Assistants: A Moderated Mediation Model of Antecedents and Outcomes of Perceived Creepiness

ABSTRACTArtificial intelligence‐powered digital assistants (DAs) offer benefits, but their downsides can hinder user adoption and limit market potential. Although research exists on users' experiences with DAs, less is known about how user characteristics influence perceptions of a ‘dark side’—perceived creepiness. This study explores how perceived creepiness, user characteristics (uncertainty avoidance), and anxieties triggered by DAs (privacy concerns and technology anxiety) relate to distrust and disengagement from DAs. Based on the stressor‐strain‐outcome and person‐environment fit models, a research model was proposed and tested using survey data from 509 DA users analysed with Amos version 29 software. The results reveal that privacy concerns, uncertainty avoidance, and technology anxiety positively influence perceived creepiness and distrust of DAs, leading to disengagement. Moreover, uncertainty avoidance moderates these relationships, with users higher in uncertainty avoidance experiencing a stronger link between privacy concerns/technology anxiety and perceived creepiness. Importantly, the study shows indirect effects mediated by perceived creepiness, with uncertainty avoidance further moderating these indirect relationships. These findings contribute to literature by highlighting the interplay between user characteristics, DA‐induced anxieties, perceived creepiness, and user disengagement with DAs.

Features of Medan's Millennials and Z-Gens as Social Media Users

Objectives: The study aimed at examining two generations behavior (Generation Y and Generation Z), especially on social media. The respondents are Gen Y and Gen Z in Medan City, North Sumatra Province, Indonesia. The study focuses on similarities and differences in digital communication behavior between these two generations on social media. Stakeholders such as the government that formulates public policies, industry, politicians, and so on, need to map and understand what the characteristics of social media users are their citizens. Methods: The researchers used quantitative methods with descriptive statistical analysis techniques with a total of 400 respondents. Data was collected using questionnaires and analyzed using the statistical utility SPSS. Results: Research has shown that Generation Y prefers Facebook, while Generation Z prefers Instagram and YouTube. Both generations spend 30–60 minutes on social media, especially at night. While the level of caution on social media tends to appear in Gen Z. A significant difference between the two generations can be seen in the topics they post on social media, Gen Y posts topics about family more often. Meanwhile, Gen Z posts more about achievements. But in terms of how to communicate with each other, these two generations are no different. The majority of these two generations interact online every day with their friends, be it through social media, chat applications, or gaming. Conclusions: Social media interaction has replaced interaction in the real world. Generation Y and Generation Z, as social media users, develop different communication behaviors compared to the other older generations.

Characteristics of green loan users and the green policy mix

Characteristics of green loan users and the green policy mix

508. Cabotegravir Long-Acting for Pre-Exposure Prophylaxis (PrEP): Real World Data on On-Time Dosing, HIV Testing and HIV Acquisition from the OPERA Cohort

Abstract Background Cabotegravir long-acting (CAB LA) pre-exposure prophylaxis (PrEP) consists of 2 initiation injections given 1 month apart, followed by a continuation injection every 2 months, with HIV testing before each injection. We compared characteristics of CAB LA and oral PrEP users and described patterns of CAB LA dosing and HIV testing. Methods All individuals ≥12 years old with a new oral PrEP or ≥1 CAB LA injection from 21Dec2021-30Jun2023 in the OPERA cohort were included. CAB LA initiation was completed if the first 2 initiation injections were received ≤60 days apart. Patterns of injections were described in complete initiators as on-time (1 month after the first or 2 months after subsequent injections, ±7 days), delayed ( &amp;gt;7 days after the target), requiring re-initiation per label (≥91 days without injection), or ≥2 injection targets missed (≥128 days without injection). HIV testing ≤1 week before/at each injection and cases of HIV acquisition were described. Results The 770 CAB LA users were more likely to be women, on Medicaid, and have a longer history of care at the clinic than the 18603 new oral PrEP users (Table 1). Of 119 (16%) incomplete initiators, 56% received ≥1 additional injection (Table 2). Most complete initiators received all injections on-time (68%). Of the 206 complete initiators with any delayed injections, 50% received their injection ≤1 week late. A third went ≥91 days and 20% went ≥128 days without an injection (Table 3). Only 66% received an HIV test before the first injection, and 47% before each subsequent injection. Two cases of HIV acquisition were observed among CAB LA PrEP users (Fig 1). Conclusion In this large real-world US cohort of 770 individuals receiving 3568 CAB LA injections, while injection delays were observed, most were short. The presence of additional injections after long delays requiring re-initiation suggests ongoing interest in long-acting prevention. No information was available on risk perception or changes in need for PrEP, which could influence patterns of PrEP use. While oral bridging or on-demand PrEP may have been used, it could not be assessed. HIV testing was not conducted as frequently as recommended by the FDA, and the two HIV cases identified could not be linked to CAB LA PrEP use due to its prior discontinuation or to the lack of testing. Disclosures Anthony M. Mills, MD, Abbott: Grant/Research Support|Emit Bio: Grant/Research Support|Gilead: Advisor/Consultant|Gilead: Grant/Research Support|Merck: Advisor/Consultant|Merck: Grant/Research Support|ViiV Healthcare: Advisor/Consultant|ViiV Healthcare: Grant/Research Support Laurence Brunet, PhD, EMD Serono: Research support to my employer|Gilead Sciences: Research support to my employer|Merck &amp; Co.: Research support to my employer|TheraTechnologies: Research support to my employer|ViiV Healthcare: Research support to my employer Gerald Pierone, Jr., MD, GSK: Grant/Research Support|VIIV: Grant/Research Support Michael G. Sension, MD, Gilead: Grant/Research Support|Gilead: Honoraria|Viiv: Honoraria Karam Mounzer, MD, Epividian: Board Member|Epividian: Honoraria|GS: Advisor/Consultant|GS: Grant/Research Support|GS: Honoraria|Merck: Advisor/Consultant|THERA Technologies: Grant/Research Support|ViiV healthcare: Advisor/Consultant|ViiV healthcare: Grant/Research Support|ViiV healthcare: Honoraria Jennifer S. Fusco, BS, EMD Serono: Research support to my employer|Gilead Sciences: Research support to my employer|Merck &amp; Co.: Research support to my employer|TheraTechnologies: Research support to my employer|ViiV Healthcare: Research support to my employer Carolyn A. Brown, MSPH, PhD, ViiV Healthcare/GSK: Stocks/Bonds (Public Company) Vani Vannappagari, MBBS, MPH, PhD, GSK: Stocks/Bonds (Public Company)|ViiV Healthcare: Full time Employee|ViiV Healthcare: Stocks/Bonds (Public Company) Piotr Budnik, MBBCh FCP(SA), GSK: Stocks/Bonds (Public Company)|ViiV Healthcare: Employee Gregory P. Fusco, MD, MPH, EMD Serono: Research support to my employer|Gilead Sciences: Research support to my employer|Merck &amp; Co.: Research support to my employer|TheraTechnologies: Research support to my employer|ViiV Healthcare: Research support to my employer

Flexible Pressure Sensors Based on Polyvinylidene Fluoride: A Critical Review

With the advent of the intelligent era, flexible piezoelectric tactile sensors, as key components for sensing information and transmitting signals, have received worldwide attention. However, piezoelectric pressure sensors are still currently limited, which severely restricts their practical applications. Furthermore, the demonstrations conducted in labs are not accurate to real-world scenarios. Thus, there is an urgent need to further optimize the intrinsic piezoelectric performance and usage characteristics to meet application requirements. As a representative piezoelectric, polyvinylidene fluoride (PVDF) exhibits significant advantages in terms of excellent flexibility, chemical stability, high electromechanical conversion, low cost, and appropriate acoustic impedance, which allow it to serve as the core matrix in flexible pressure sensors. This paper aims to summarize very recent progress in flexible piezoelectric sensors based on PVDF, including their composition modulation, structure optimization, and applications. Based on a comprehensive summary of recent representative studies, we propose rational perspectives and strategies regarding PVDF-based piezoelectric sensors and provide some new insights for the research and industrial communities.

P-972. Getting Real Clean: Implementation of Virtual Reality Training for Cleaning and Low-Level Disinfection of Portable Medical Equipment

Abstract Background Failure to clean and disinfect portable medical equipment (PME) is common, with reported contamination rates between 25% and 100% across a wide range of PME. Virtual reality (VR) is a promising tool for training healthcare personnel (HCP). Despite emerging evidence of its effectiveness as a pedagogical tool, implementation of VR training for infection prevention and control (IPC) and specifically cleaning and low-level disinfection (LLD) of non-critical PME is scarce.Table 1:End User Characteristics (N=29). Methods A VR training module was developed by subject matter experts in IPC and competency-based education to provide instruction on cleaning and LLD of PME. Convenience sampling design was used to recruit HCP end users. A semi-structured interview guide was developed by qualitative assessment experts to evaluate end user experience. Double coding of verbatim transcripts was independently performed by project staff, with a senior analyst reviewing the analysis.Figure 1:Barriers and Facilitators to Using VR for IPC Training Organized by Theme. Results Between January 2024-April 2024, 29 end users from 3 healthcare facilities completed the VR training and semi-structured interviews. More than half (n=16, 55%) did not have prior VR experience (Table 1). Three main themes emerged, including both barriers and facilitators to adoption (Figure 1). End users reported several barriers to their VR Headset Experience, including blurry vision (n=16, 55%); negative physical sensations (n=9, 31%); and difficulty navigating the controllers (n=12, 41%) (Table 2). Despite noted barriers facilitators were identified: end users described the VR IPC Module Experience as “immersive” (n=12, 41%), “fun” (n=8, 28%), and “helpful” (n = 18, 62%). End users' Attitude Towards VR for IPC was overall positive, while noting barriers regarding implementation. End users also identified specific high-value teaching points (Figure 2).Table 2:Barriers and Facilitators by Frequency Reported. Conclusion While VR training is increasingly used in medical education, efforts to assess its implementation for training in IPC are lacking. This study identified the perceived value of VR training, while highlighting technical and implementation concerns. Future efforts should focus on strategies to mitigate technical and physical sensation concerns, and the evaluation of VR as a teaching modality.Figure 2:VR Scenarios and Teaching Points End Users Reported as Helpful. In frame 2A, user draws a disinfectant wipe from the canister and is prompted that the wipe is not wet and asked what they want to do. User then must discard the wipe and get a fresh, wet one, to advance. Teaching point: Wet time/Contact time requires that the product be wet when applied. In frame 2B, if user tries to enter or exit the patient room without performing hand hygiene (either soap and water wash or alcohol-based hand rub), they will be alerted of the missed step. Teaching point: when to perform hand hygiene in the context of the course. In frame 2C, user is alerted that the equipment did not remain wet for full wet time/contact time and is prompted to take action. Teaching point: importance of equipment remaining wet for the defined contact time to achieve disinfection. Disclosures All Authors: No reported disclosures

The Relationship Between Gamification User Types, Demographic Factors, and Gaming Habits

Understanding users and consequent personalization opportunities have become a major area of interest in gamification and UX research. Currently, personalization is mainly based on player typologies, which might give a partial picture of the plethora of user attributes. Addressing this challenge, in this study, we investigate the connections of the Hexad gamification user types, demographic factors, and gaming habits to understand how different user factors are related. Our results indicated significant but weak associations between user types and demographic factors and no significant association with gaming frequency-related factors. These results suggest that researchers and designers might need to consider more than the dominant factors to create personalized environments. We also provide exploratory suggestions on possible strategies to personalize gamification based on Hexad and other user factors. Our study contributes to the fields of user modeling and gamification, providing new insights into how different user characteristics are related while opening space for the conduction of new studies in the field.

Self-sampling and self-testing for HIV at a commercial and community-based test provider in the Netherlands: user preferences and usability

HIV self-sampling and -testing (HIVSS/ST) reduces testing barriers and potentially reaches populations who may not test otherwise. In the Netherlands, at-home HIV tests became commercially available around 2016, but data on user experiences are limited. This study aimed to explore characteristics of users and their experiences with HIVSS/ST. In 2022 and 2023, a survey was distributed among users of one online commercial provider and one community-based provider; either users ordered a commercial online HIVSS/ST or they sought a free-of-charge HIVST via the community-based provider. Questions included usability, preferences, and barriers of HIVSS/ST-testing. We compared characteristics and experiences of users between providers, risk groups and first-time and repeat testers. In total, 133 users completed the survey; 89 (67%) via the commercial provider and 44 (33%) via the community-based provider. Response rates per provider were 2% and 26%, respectively. Users who bought their test at the commercial provider were more often Gay and Bisexual Men (GBM) (42%), 35 + years (43%), and born in the Netherlands (89%), compared to those recruited through the community-provider (respectively 27%, 25%, 14%). GBM were more often repeat and recent HIVSS/ST-testers, and using pre-exposure prophylaxis (PrEP). Women and heterosexual men were more likely to buy an HIVSS/ST as part of a combination Sexually Transmitted Infections (STI) test package. Overall, main reasons for choosing HIVSS/ST were saving time (42%), anonymity (36%) and not having to talk to a GP (35%). Twenty-two percent of the study participants experienced some problems performing the HIVSS/ST, the most reported problem was obtaining sufficient blood through the finger prick (71%). Recommendations to improve accessibility of HIVSS/ST included more awareness (advertising by trustful providers), more access locations (pharmacies/supermarkets/schools) and lower costs. Our findings indicate that HIVSS/ST is a valuable additional HIV testing method for users, but more insight into the contribution of HIVSS/ST to HIV prevention policies is needed.

Multi-objective classification prediction method for coal gangue based on image feature extraction

ABSTRACT Coal gangue is a solid waste discharged during the coal production process, consisting of rocks and minerals, and is a nonmetal mineral resource with attributes suitable for comprehensive utilization. This study explores the pre-classification issue of coal gangue comprehensive utilization through image recognition methods. Initially, coal gangue was categorized into four types – Residual coal, Gray gangue, Red gangue, and White gangue-based on its appearance, chemical composition, phase composition, and usage characteristics. Subsequently, the color features, texture features, and shape features of different types of coal gangue were analyzed using box plots, correlation coefficients, and cluster analysis. Finally, by comprehensively considering color, texture, and shape features, and by simplifying highly correlated variables, a support vector machine was employed for classification prediction. The overall recognition accuracy rate for multi-target classification reached 89%.

Attitudes and barriers to mobile mental health interventions among first-year college students: a mixed-methods study

Objective This mixed-methods study examined attitudes, barriers, and preferences for mobile mental health interventions among first-year college students. Participants 351 students (64% women; 51% non-Hispanic White; 66% Heterosexual) from two campuses completed self-report assessments and 10 completed individual semi-structured interviews. Methods Paired t-tests compared attitudes for various mHealth applications and logistic regressions examined sociodemographic and clinical characteristics of mental health app users. Themes, topics, and quotes from interviews were derived through rapid qualitative analysis. Results Mental health applications were less used and perceived to be less helpful than other mHealth applications. Past mental health app use was best predicted by past use of formal mental health care. Conclusions Mobile health interventions have significant potential to diversify mental health services for students. Despite limited engagement with these resources, openness to digital interventions among students is quite high. Improving intervention features and increasing problem-recognition to facilitate help-seeking may result in greater uptake.

Demographic analysis of trainee teachers: Insights into e-content usage patterns at teacher's training institutes in Malaysia

This study examines the usage characteristics of electronic content (e-content) by trainee teachers in Teacher Training Institutes (IPG) in Malaysia, particularly how these usage patterns paralleled the age, sex, and level of education of the trainee's demographic variables. 330 respondents used quantitative measures to get the level of e-content utilization among the trainees. Younger trainees and people with high information literacy skills tend to utilize e-content to learn more effectively. The intra-group study also determined the concerns of trainees from rural setups regarding technology and internet coverage. Using a combination of descriptive and inferential statistics, this study presents key aspects that the IPG management should focus on to develop better ways of promoting e-content usage to enhance teaching and learning processes. The implications of the results further on the policies regarding the implementation of efficient changes to the access to information as well as infrastructural development in the Malaysian educational setting have also stated why all troops underwent a satisfactory education program and became full members of the society.

An Integrated Stacking Ensemble Model for Natural Gas Purchase Prediction Incorporating Multiple Features

Accurate prediction of natural gas purchase volumes is crucial for both the economy and the environment. It not only facilitates the rational allocation of resources for companies but also helps to reduce operational costs. Although existing prediction methods have achieved some success in addressing the nonlinear relationships in natural gas purchases, there remains potential for further improvement. To address this issue, a stacking ensemble learning model was developed to enhance the ability to handle complex nonlinear problems. This model integrates diverse algorithms and incorporates weather factors, while regionalizing characteristics of natural gas usage, thereby achieving accurate forecasts of natural gas purchase volumes. We selected three distinctly different base models—Informer, multiple linear regression (MLR), and support vector regression (SVR)—for our research. By conducting four different feature combination experiments for each base model, including weather, time, regional, and usage features, we constructed 12 foundational models. Subsequently, we integrated these base models using a meta-learner to form the final stacking ensemble model. The experimental results indicate that the stacking ensemble model outperforms individual models across key metrics, including R2, MRE, and RMSE. Notably, the R2 values improved by 4–15% compared to the 12 base models. The model was subsequently applied to predict natural gas purchase volumes in Pi County, Chengdu, China. In November 2024, a side-by-side comparison of the predicted and actual data revealed a maximum error of just 5.39%. This exceptional accuracy effectively meets forecasting requirements, underscoring the model’s predictive strength in the energy sector.

Pulmonary rehabilitation healthcare professionals understanding and experiences of the protected characteristics of service users: A qualitative analysis.

Health inequalities can affect access and uptake to pulmonary rehabilitation (PR). An individual's protected characteristics (age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion or belief, sex and sexual orientation) may contribute to health inequalities. Healthcare professionals (HCPs) experiences of the inclusivity and representativeness of PR services and knowledge of protected characteristics are unknown, however are vital for the identification and resolution of health inequalities. This qualitative study explored HCPs understanding of protected characteristics and their perception of the inclusivity, representativeness and equitable benefit of their PR services. Semi-structured qualitative interviews were conducted in person or via videoconferencing with HCPs involved in PR from two healthcare providers. Interviews were analysed using reflexive thematic analysis. 12 interviews were conducted with physiotherapists (n = 6), occupational therapists (n = 2), nurses (n = 2) and exercise physiologists (n = 2). Participants had a median (IRQ) age of 43 (13) and 75% (n = 9) were female. Four themes were generated. 1: 'I don't really know as much as I should' [about protected characteristics]; 2: It's uncomfortable collecting protected characteristics…; 3: 'I don't think [service users] are as representative as they could be'; 4: A conventional rehabilitation programme does not meet the needs of all. This study highlighted several challenges in HCPs understanding of protected characteristics and the representativeness of PR that must be addressed to ensure equity. Strategies, to understand barriers in accessing PR that limit representativeness should be explored.

Twitter User Geolocation Based on Location Feature Enhancement

User location discovery from social media is crucial for location-based services like emergency awareness and event monitoring. Existing approaches generally integrate user-generated text features and social relationships, but insufficiently explore location-specific features and geographically proximate relationships, leading to suboptimal accuracy. In this paper, we propose a Twitter user geolocation method based on location feature enhancement, to better capture the location characteristics in users’ tweets and social relationships. Specifically, a user tweet representation algorithm based on location feature separation (TwLS) is designed. By leveraging words’ location-aware weight matrix and pre-trained embeddings, TwLS calculates a tweet representation for each user in every location, explicitly indicating the relevance between users and various locations. Additionally, we develop the local celebrity discovery method (LocCel) to construct social networks by identifying and preserving geographically concentrated high-degree nodes while filtering noise. Thereby LocCel enhances local relationships and strengthens location-proximate connections within the user social network. Experiments on two real-world datasets show that our method outperforms seven baselines, improving user geolocation accuracy by 3.1% ∼ 8.1% and 1.8% ∼ 8.8%, while reducing median error by 22.2% ∼ 52.8% and 19.4% ∼ 50.7%, respectively.

Research on the construction of intelligent-aided design platform for green buildings

While socio-economic development and technological advancement bring great convenience to our life, energy consumption and environmental pollution have also become the core issues affecting human survival and development. This study focuses on the development of an intelligent design platform for green buildings, aimed at promoting sustainability through low energy consumption and reduced environmental impact. The study of existing national standards and requirements for the intelligent design of green buildings, combined with the unique characteristics of green buildings and user needs, has led to the development of an optimized auxiliary design platform system. By incorporating algorithm optimization and modeling techniques, this platform enhances the design process. Experimental evaluations of the system reveal that the intelligent auxiliary design offers more accurate predictions, thereby improving user service and experience. Additionally, the system contributes to energy efficiency and environmental sustainability by reducing the building’s ecological footprint. This research highlights the potential of intelligent systems to facilitate more efficient and environmentally friendly green building designs.

Proactivity of Chatbots, Task Types and User’s Characteristics When Interacting with Artificial Intelligence (AI) Chatbots

The advancement of artificial intelligence technology has enabled chatbots to mimic human conversations to the point of being nearly indistinguishable from humans. This study investigates the impact of chatbot proactivity on user experience, considering task types and user characteristics. Experiments were conducted using proactive and non-proactive chatbots for business-related and non-business-related tasks, evaluating user characteristics (age, gender, AI experience level, education level, personality traits) and user experience (perceived usefulness, perceived ease of use, perceived satisfaction, trust). The results showed that proactive chatbots were positively evaluated in terms of perceived usefulness, perceived satisfaction, and trust, but no significant difference was found in perceived ease of use compared to non-proactive chatbots. Additionally, task types and user personality traits (extraversion, agreeableness) significantly influenced user experience. This study highlights the critical role of chatbot proactivity in providing satisfying interaction experiences and emphasizes the importance of designing chatbots considering task and user characteristics.

Development of Personas and Journey Maps for Artificial Intelligence Agents Supporting the Use of Health Big Data: Human-Centered Design Approach.

The rapid proliferation of artificial intelligence (AI) requires new approaches for human-AI interfaces that are different from classic human-computer interfaces. In developing a system that is conducive to the analysis and use of health big data (HBD), reflecting the empirical characteristics of users who have performed HBD analysis is the most crucial aspect to consider. Recently, human-centered design methodology, a field of user-centered design, has been expanded and is used not only to develop types of products but also technologies and services. This study was conducted to integrate and analyze users' experiences along the HBD analysis journey using the human-centered design methodology and reflect them in the development of AI agents that support future HBD analysis. This research aims to help accelerate the development of novel human-AI interfaces for AI agents that support the analysis and use of HBD, which will be urgently needed in the near future. Using human-centered design methodology, we collected data through shadowing and in-depth interviews with 16 people with experience in analyzing and using HBD. We identified users' empirical characteristics, emotions, pain points, and needs related to HBD analysis and use and created personas and journey maps. The general characteristics of participants (n=16) were as follows: the majority were in their 40s (n=6, 38%) and held a PhD degree (n=10, 63%). Professors (n=7, 44%) and health care personnel (n=10, 63%) represented the largest professional groups. Participants' experiences with big data analysis varied, with 25% (n=4) being beginners and 38% (n=6) having extensive experience. Common analysis methods included statistical analysis (n=7, 44%) and data mining (n=6, 38%). Qualitative findings from shadowing and in-depth interviews revealed key challenges: lack of knowledge on using analytical solutions, crisis management difficulties during errors, and inadequate understanding of health care data and clinical decision-making, especially among non-health care professionals. Three types of personas and journey maps-health care professionals as big data analysis beginners, health care professionals who have experience in big data analytics, and non-health care professionals who are experts in big data analytics-were derived. They showed a need for personalized platforms tailored to the user level, appropriate direction through a navigation function, a crisis management support system, communication and sharing among users, and expert linkage service. The knowledge obtained from this study can be leveraged in designing an AI agent to support future HBD analysis and use. This is expected to further increase the usability of HBD by helping users perform effective use of HBD more easily.