One of the ultimate goals of studies on visualization literacy is to improve users’ visualization literacy through education and training. Even though users’ cognitive characteristics may significantly affect learning and responding processes in general, studies have addressed the relationships between users’ cognitive characteristics and visualization literacy. As a first step toward discovering the relationships, we conducted an empirical study to investigate the correlation between cognitive characteristics and visualization literacy. Our first study focuses on testing the correlation between visualization and three cognitive characteristics: numeracy, need for cognition, and visualizer-verbalizer style. In this study, we measured 178 participants’ visualization literacy and the level of the three cognitive characteristics using the Visualization Literacy Assessment Test (VLAT), the Decision Research Numeracy Test (DRNT), the Need for Cognition Scale (NCS), and the Verbalizer-Visualizer Questionnaire (VVQ) through a crowdsourcing experiment. Our test results confirmed that a correlation exists between visualization literacy and both numeracy and need for cognition. Based on our test results, we discuss the implications for education to enhance visualization literacy and future studies to investigate on related user characteristics.

Wearable activity trackers can motivate older adults to engage in the recommended daily amount of physical activity (PA). However, individuals may not maintain their use of the trackers over a longer period. To investigate the attitudes of activity tracker adoption and their effects on actual PA performance, we conducted a three-month study. We gave activity trackers to 16 older adults and assessed attitudes on activity tracker adoption through a survey during the study period. We extracted participants’ PA measures, step counts, and moderate and vigorous physical activity (MVPA) times. We observed significant differences in adoption attitudes during the three different periods (χ²(2, 48) = 6.27, p < 0.05), and PA measures followed similar decreasing patterns (F(83, 1357) = 12.56, 13.94, p < 0.00001). However, the Pearson correlation analysis (r = 0.268, p = 0.284) and a Bland–Altman plot indicated a bias between two PA measures. Positive attitudes at the initial stage did not persist through the study period, and both step counts and length of MVPA time showed waning patterns in the study period. The longitudinal results from both measures demonstrated the patterns of old adults’ long-term use and adoption. Considering the accuracy of the activity tracker and older adults’ athletic ability, MVPA times are more likely to be a reliable measure of older adults’ long-term use and successful adoption of activity trackers than step counts. The results support the development of better activity tracker design guidelines that would facilitate long-term adoption among older adults.

Interruption is a widespread phenomenon in human–computer interaction in modern working environments. To minimize the adverse impact or to maximize possible benefits of interruptions, a reliable approach to evaluate interruption cost needs to be established. In this paper, we suggest a new approach to evaluate the interruption cost by cognitive workload and task performance measures. The cognitive workload is assessed by pupil diameter changes and National Aeronautics and Space Administration (NASA) task load index. Task performance includes task completion time and task accuracy in a series of controlled laboratory experiments. This integrated approach was applied to three interruption coordination modes (i.e., the immediate, the negotiated, and the scheduled modes), which were designed based on McFarlane’s interruption coordination modes. Each mode consists of cognitive and skill tasks depending on the degree of mental demands providing four different task sets of interruptive task environments. Our results demonstrate that the negotiated mode shows a lower interruption cost than other modes, and primary task type and task similarity between primary and peripheral tasks are crucial in the evaluation of the cost. This study suggests a new approach evaluating interruption cost by cognitive workload and task performance measures. Applying this approach to various interruptive environments, disruptiveness of interruption was evaluated considering interruption coordination modes and task types, and the outcomes can support development of strategies to reduce the detrimental effects of unexpected and unnecessary interruptions.

Due to the development of sensing technology people can easily track their health in various ways, and the interest in personal healthcare data is increasing. Individuals are interested in controlling their wellness, which requires self-awareness and an understanding of various health conditions. Self-generated health data are easily accessed through mobile devices, and data visualization is commonly used in applications. A systematic literature review was conducted to better understand the role of visualizations and learn how to develop effective ones. Thirteen papers were analyzed for types of data, characteristics of visualizations, and effectiveness for healthcare management. The papers were selected because they represented research on personal health data and visualization in a non-clinical setting, and included health data tracked in everyday life. This paper suggests six levels for categorizing the efficacy of visualizations that take into account cognitive and physical changes in users. Recommendations for future work on conducting evaluations are also identified. This work provides a foundation for personal healthcare data as more applications are developed for mobile and wearable devices.

In recent years, music has been regarded as a promising non-pharmacological intervention for a number of physical and mental conditions. Five-elements music therapy—based on the five-element theory—is a unique non-pharmacological therapy of East Asian traditional medicine. It has the potential to effectively provide individualized music therapy to individuals with illness. However, one limitation of this music therapy is that the classification of the five elements and its application is mainly based on subjective judgment. The development of artificial intelligence (AI) has enabled the acoustic analysis of multi-factor sound sources. This can develop five-element music therapy. Here, we discussed the challenges proposed by the future combination of five-element music therapy and AI. Further, we hypothesized that AI may promote its use in the medical field.