Search

BACKGROUND Prolonged time of computer use increases the prevalence of ocular problems, including eye strain, tired eyes, irritation, redness, blurred vision, and double vision, which are collectively referred to as computer vision syndrome (CVS). Approximately 70% of computer users have vision-related problems. For these reasons, properly designed interventions for users with CVS are required. To design an effective screen intervention for preventing or improving CVS, we must understand the effective interfaces of computer-based interventions. OBJECTIVE In this study, we aimed to explore the interface elements of computer-based interventions for CVS to set design guidelines based on the pros and cons of each interface element. METHODS We conducted an iterative user study to achieve our research objective. First, we conducted a workshop to evaluate the overall interface elements that were included in previous systems for CVS (n=7). Through the workshop, participants evaluated existing interface elements. Based on the evaluation results, we eliminated the elements that negatively affect intervention outcomes. Second, we designed our prototype system LiquidEye that includes multiple interface options (n=11). Interface options included interface elements that were positively evaluated in the workshop study. Lastly, we deployed LiquidEye in the real world to see how the included elements affected the intervention outcomes. Participants used LiquidEye for 14 days, and during this period, we collected participants’ daily logs (n=680). Additionally, we conducted prestudy and poststudy surveys, and poststudy interviews to explore how each interface element affects participation in the system. RESULTS User data logs collected from the 14 days of deployment were analyzed with multiple regression analysis to explore the interface elements affecting user participation in the intervention (LiquidEye). Statistically significant elements were the instruction page of the eye resting strategy (P=.01), goal setting of the resting period (P=.009), compliment feedback after completing resting (PP=.02), and CVS symptom-like effects (P=.004). CONCLUSIONS Based on the study results, we suggested design implications to consider when designing computer-based interventions for CVS. The sophisticated design of the customization interface can make it possible for users to use the system more interactively, which can result in higher engagement in managing eye conditions. There are important technical challenges that still need to be addressed, but given the fact that this study was able to clarify the various factors related to computer-based interventions, the findings are expected to contribute greatly to the research of various computer-based intervention designs in the future.

Artificial intelligence (AI) algorithms are making remarkable achievements even in creative fields such as aesthetics. However, whether those outside the machine learning (ML) community can sufficiently interpret or agree with their results, especially in such highly subjective domains, is being questioned. In this paper, we try to understand how different user communities reason about AI algorithm results in subjective domains. We designed AI Mirror, a research probe that tells users the algorithmically predicted aesthetic scores of photographs. We conducted a user study of the system with 18 participants from three different groups: AI/ML experts, domain experts (photographers), and general public members. They performed tasks consisting of taking photos and reasoning about AI Mirror's prediction algorithm with think-aloud sessions, surveys, and interviews. The results showed the following: (1) Users understood the AI using their own group-specific expertise; (2) Users employed various strategies to close the gap between their judgments and AI predictions overtime; (3) The difference between users' thoughts and AI pre-dictions was negatively related with users' perceptions of the AI's interpretability and reasonability. We also discuss design considerations for AI-infused systems in subjective domains.

Automated journalism refers to the generation of news articles using computer programs. Although it is widely used in practice, its user experience and interface design remain largely unexplored. To understand the user perception of an automated news system, we designed NewsRobot, a research prototype that automatically generated news on major events of the PyeongChang 2018 Winter Olympic Games in real-time. It produces six types of news by combining two kinds of content (general/individualized) and three styles (text, text+image, text+image+sound). A total of 30 users participated in using NewsRobot, completing surveys and interviews on their experience. Our findings are as follows: (1) Users preferred individualized news yet considered it less credible, (2) more presentation elements were appreciated but only if their quality was assured, and (3) NewsRobot was considered factual and accurate yet shallow in depth. Based on our findings, we discuss implications for designing automated journalism user interfaces.

Background Emotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS). Objective This study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system. Methods The machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent–based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA. Results The following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P Conclusions This study introduces a data-driven approach for analyzing behavior patterns of social website users with EE behaviors. We discovered the possibility of the LDA topic model as an exploratory user study method for abnormal behaviors in medical research. We also investigated the possibilities of ML- and topic modeling–based classifiers to automatically categorize text-based behavioral data, which could be applied to personalized medicine in future research.

BACKGROUND Emotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS). OBJECTIVE This study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system. METHODS The machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent–based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA. RESULTS The following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P CONCLUSIONS This study introduces a data-driven approach for analyzing behavior patterns of social website users with EE behaviors. We discovered the possibility of the LDA topic model as an exploratory user study method for abnormal behaviors in medical research. We also investigated the possibilities of ML- and topic modeling–based classifiers to automatically categorize text-based behavioral data, which could be applied to personalized medicine in future research.

As the uses of conversational agents increase, the affective and social abilities of agents become important with their functional abilities. Agents that lack affective abilities could frustrate users during interaction. This study applied personality to implement the natural feedback of conversational agents referring to the concept of affective computing. Two types of feedback were used to express conversational agents&rsquo, personality: (1) visual feedback and (2) verbal cues. For visual feedback, participants (N = 45) watched visual feedback with different colors and motions. For verbal cues, participants (N = 60) heard different conditions of agents&rsquo, voices with different scripts. The results indicated that the motions of visual feedback were more significant than colors. Fast motions could express distinct and positive personalities. Different verbal cues were perceived as different personalities. The perceptions of personalities differed according to the vocal gender. This study provided design implications for personality expressions applicable to diverse interfaces.

Background Prolonged time of computer use increases the prevalence of ocular problems, including eye strain, tired eyes, irritation, redness, blurred vision, and double vision, which are collectively referred to as computer vision syndrome (CVS). Approximately 70% of computer users have vision-related problems. For these reasons, properly designed interventions for users with CVS are required. To design an effective screen intervention for preventing or improving CVS, we must understand the effective interfaces of computer-based interventions. Objective In this study, we aimed to explore the interface elements of computer-based interventions for CVS to set design guidelines based on the pros and cons of each interface element. Methods We conducted an iterative user study to achieve our research objective. First, we conducted a workshop to evaluate the overall interface elements that were included in previous systems for CVS (n=7). Through the workshop, participants evaluated existing interface elements. Based on the evaluation results, we eliminated the elements that negatively affect intervention outcomes. Second, we designed our prototype system LiquidEye that includes multiple interface options (n=11). Interface options included interface elements that were positively evaluated in the workshop study. Lastly, we deployed LiquidEye in the real world to see how the included elements affected the intervention outcomes. Participants used LiquidEye for 14 days, and during this period, we collected participants’ daily logs (n=680). Additionally, we conducted prestudy and poststudy surveys, and poststudy interviews to explore how each interface element affects participation in the system. Results User data logs collected from the 14 days of deployment were analyzed with multiple regression analysis to explore the interface elements affecting user participation in the intervention (LiquidEye). Statistically significant elements were the instruction page of the eye resting strategy (P=.01), goal setting of the resting period (P=.009), compliment feedback after completing resting (P Conclusions Based on the study results, we suggested design implications to consider when designing computer-based interventions for CVS. The sophisticated design of the customization interface can make it possible for users to use the system more interactively, which can result in higher engagement in managing eye conditions. There are important technical challenges that still need to be addressed, but given the fact that this study was able to clarify the various factors related to computer-based interventions, the findings are expected to contribute greatly to the research of various computer-based intervention designs in the future.

BACKGROUND In addition to addiction and substance abuse, motivational interviewing (MI) is increasingly being integrated in treating other clinical issues such as mental health problems. Most of the many technological adaptations of MI, however, have focused on delivering the action-oriented treatment, leaving its relational component unexplored or vaguely described. This study intended to design a conversational sequence that considers both technical and relational components of MI for a mental health concern. OBJECTIVE This case study aimed to design a conversational sequence for a brief motivational interview to be delivered by a Web-based text messaging application (chatbot) and to investigate its conversational experience with graduate students in their coping with stress. METHODS A brief conversational sequence was designed with varied combinations of MI skills to follow the 4 processes of MI. A Web-based text messaging application, Bonobot, was built as a research prototype to deliver the sequence in a conversation. A total of 30 full-time graduate students who self-reported stress with regard to their school life were recruited for a survey of demographic information and perceived stress and a semistructured interview. Interviews were transcribed verbatim and analyzed by Braun and Clarke’s thematic method. The themes that reflect the process of, impact of, and needs for the conversational experience are reported. RESULTS Participants had a high level of perceived stress (mean 22.5 [SD 5.0]). Our findings included the following themes: Evocative Questions and Clichéd Feedback; Self-Reflection and Potential Consolation; and Need for Information and Contextualized Feedback. Participants particularly favored the relay of evocative questions but were less satisfied with the agent-generated reflective and affirming feedback that filled in-between. Discussing the idea of change was a good means of reflecting on themselves, and some of Bonobot’s encouragements related to graduate school life were appreciated. Participants suggested the conversation provide informational support, as well as more contextualized feedback. CONCLUSIONS A conversational sequence for a brief motivational interview was presented in this case study. Participant feedback suggests sequencing questions and MI-adherent statements can facilitate a conversation for stress management, which may encourage a chance of self-reflection. More diversified sequences, along with more contextualized feedback, should follow to offer a better conversational experience and to confirm any empirical effect.

On Korean websites, people often use several Korean vowels or consonants solely, which is the way to express emotion. This way of expression has scarcely been examined seriously, though it is one of the most popular online slangs. This is an exploratory study that reveals how and how much these expressions are used on social media. Data crawling technique was used as a way to examine the usage of vowels and consonants in ordinary situations. 6 `Facebook pages` popular among Korean users are selected, and about 2.5 million comments are collected from posts in those pages. 10 expressions, including `ㅋ(K)`, `ㅎ(H)`, `ㅜ(U)`, are counted and analyzed from the collected texts of comments. As a result, several patterns of using vowels and consonants are found. Especially, `ㅋ(K)` is used much more frequently than other vowels or consonants, and used more continually. It is also found that the patterns of using vowels or consonants in writing comments changes as the mood of posts or the characteristics of Facebook pages.

Background: In addition to addiction and substance abuse, motivational interviewing (MI) is increasingly being integrated in treating other clinical issues such as mental health problems. Most of the many technological adaptations of MI, however, have focused on delivering the action-oriented treatment, leaving its relational component unexplored or vaguely described. This study intended to design a conversational sequence that considers both technical and relational components of MI for a mental health concern. Objective: This case study aimed to design a conversational sequence for a brief motivational interview to be delivered by a Web-based text messaging application (chatbot) and to investigate its conversational experience with graduate students in their coping with stress. Methods: A brief conversational sequence was designed with varied combinations of MI skills to follow the 4 processes of MI. A Web-based text messaging application, Bonobot, was built as a research prototype to deliver the sequence in a conversation. A total of 30 full-time graduate students who self-reported stress with regard to their school life were recruited for a survey of demographic information and perceived stress and a semistructured interview. Interviews were transcribed verbatim and analyzed by Braun and Clarke’s thematic method. The themes that reflect the process of, impact of, and needs for the conversational experience are reported. Results: Participants had a high level of perceived stress (mean 22.5 [SD 5.0]). Our findings included the following themes: Evocative Questions and Cliched Feedback; Self-Reflection and Potential Consolation; and Need for Information and Contextualized Feedback. Participants particularly favored the relay of evocative questions but were less satisfied with the agent-generated reflective and affirming feedback that filled in-between. Discussing the idea of change was a good means of reflecting on themselves, and some of Bonobot’s encouragements related to graduate school life were appreciated. Participants suggested the conversation provide informational support, as well as more contextualized feedback. Conclusions: A conversational sequence for a brief motivational interview was presented in this case study. Participant feedback suggests sequencing questions and MI-adherent statements can facilitate a conversation for stress management, which may encourage a chance of self-reflection. More diversified sequences, along with more contextualized feedback, should follow to offer a better conversational experience and to confirm any empirical effect.

As society becomes increasingly saturated with information, the design of that information becomes ever more important. However, rather than reinforcing the agency of the user, many information design products limit one’s possibilities for action. Take bus route maps (Figure 1), for instance. No problems emerge when looking at each bus route separately, but when users try to compare them to one another, it is difficult to understand their relationship. Only scattered information is made available, so that users are unable to form a clear, holistic understanding of how the bus service operates. Consequently, if an unexpected delay in a bus service were to occur, passengers might not be able to find and take an alternate route. Although a variety of approaches are available to resolve this problem, using system diagrams is a method that allows information designers to consider the holistic context. It is necessary not only to understand the system itself but also to study diagrams as a means of effectively describing the system, which is abstract in nature. The use of diagrams is a key component in communicating the holistic structure of an information system; however, a lack of rigorous discussion in the field means that designers often have difficulty examining systems as an integral part of their work. The purpose of this article is to provide a theoretical framework that broadens designers’ conception of system diagrams and enables them to design diagrams that can be effectively applied to various situations, needs, and design problems. In the first half of the article, we introduce four kinds of system diagrams and analyze different examples; the second half of the article focuses on how different modes of thinking are used to address varied needs and goals in the design process.

Bronchodilator response (BDR) is assessed to estimate the reversibility of airflow obstruction. Bronchial hyperresponsiveness (BHR) is a characteristic feature of asthma and is usually measured by means of bronchial challenges using direct or indirect stimuli. The aim of the present study was to compare BHR to methacholine (direct) and that to adenosine 5'-monophosphate (AMP) (indirect) with regard to their relationships to BDR in asthmatic children. Methacholine and AMP challenge tests were performed on 138 children with mild-to-moderate asthma, and the provocative concentration causing a 20% decline in forced expiratory volume in 1 s (FEV₁) (PC₂₀) was determined for each challenge. BDR was calculated as the change in FEV(1), expressed as a percentage of the initial value, after inhalation of 400 μg salbutamol. Methacholine PC₂₀ correlated significantly but weakly with BDR (r = -0.254; p = 0.003). However, there was a significant and strong correlation between AMP PC₂₀ and BDR (r = -0.489; p = 0.000). For AMP PC₂₀, the relationship was closer than for methacholine PC₂₀ (p = 0.024 for comparison between correlation coefficients). The same figures were observed when BDR was expressed as a percentage of the predicted value. A stronger correlation of BDR with AMP PC₂₀ than with methacholine PC₂₀ suggests that BDR may be better reflected by BHR as assessed by AMP challenge than by methacholine challenge.

Drivers need assistance when navigating an unfamiliar route. In-vehicle navigation systems have improved in recent years due to the technology advances, but are sometimes problematic because of information overload while driving. To address the attentional demands of reading a map while driving, we have developed the MOVE (Maps Optimized for Vehicular Environments) in-car navigation display, which provides situationally appropriate navigation information to the driver through optimization of map information. In this paper, we describe the iterative design and evaluation process that shaped the MOVE system. We describe early map reading and navigation studies that led to early designs for our system. We present a study on visual search tasks that refined the renditions used for the system. Finally, we present a study on the effectiveness of several variations of a contextually optimized route map visualization with a desktop steering system. The result of this study shows that MOVE’s contextually optimized navigation information can reduce the driver’s perceptual load significantly. Our laboratory experiment shows that the total map display fixation time was decreased six-fold, and the number of glances to interpret the map display were decreased about threefold, when comparing the contextually optimized display to a static display.

The main purpose of this paper is to estimate panel data models with endogenous regressors and nonadditive unobserved individual heterogeneity including, for example, linear and nonlinear models where all the parameters can vary across individuals. The quantities of interest are means, variances, and other moments of the individual parameters. Since estimates of these quantities based on individual by individual GMM estimation can be severely biased due to the incidental parameter problem, we develop bias corrections that give more accurate estimates in moderately long panels. These corrections, derived from large-T expansions of the finite-sample bias of fixed effects GMM estimators, reduce the order of the bias from O(T¡1) to O(T¡2) and center the asymptotic distributions at the true values in moderately long panels under asymptotic sequences where n = o(T3). An empirical example on cigarette demand based on Becker, Grossman and Murphy (1994) shows significant heterogeneity in the price effect across U.S. states.