Smart Banking: User Characteristics and Their Effects on the Usage of Emerging Banking Applications

Respiratory rate (RR) is typically the first vital sign to change when a patient decompensates. Despite this, RR is often monitored infrequently and inaccurately. The Circadia Contactless Breathing Monitor™ (model C100) is a novel device that uses ultra-wideband radar to monitor RR continuously and un-obtrusively. Performance of the Circadia Monitor was assessed by direct comparison to manually scored reference data. Data were collected across a range of clinical and non-clinical settings, considering a broad range of user characteristics and use cases, in a total of 50 subjects. Bland–Altman analysis showed high agreement with the gold standard reference for all study data, and agreement fell within the predefined acceptance criteria of ±5 breaths per minute (BrPM). The 95% limits of agreement were −3.0 to 1.3 BrPM for a nonprobability sample of subjects while awake, −2.3 to 1.7 BrPM for a clinical sample of subjects while asleep, and −1.2 to 0.7 BrPM for a sample of healthy subjects while asleep. Accuracy rate, using an error margin of ±2 BrPM, was found to be 90% or higher. Results demonstrate that the Circadia Monitor can effectively and efficiently be used for accurate spot measurements and continuous bedside monitoring of RR in low acuity settings, such as the nursing home or hospital ward, or for remote patient monitoring.

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The effect of user characteristics in time series visualizations

Proceedings of the 25th International Conference on Intelligent User Interfaces ◽

10.1145/3377325.3377502 ◽

2020 ◽

Cited By ~ 1

Author(s):

Julia Sheidin ◽

Joel Lanir ◽

Cristina Conati ◽

Dereck Toker ◽

Tsvi Kuflik

Keyword(s):

Time Series ◽

User Characteristics

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What Kind of People Use Carsharing for Commuting? Case Study in Shanghai

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119841045 ◽

2019 ◽

Vol 2673 (5) ◽

pp. 770-778 ◽

Cited By ~ 4

Author(s):

Jianhong Ye ◽

Daoge Wang ◽

Hua Zhang ◽

Hong Yang

Keyword(s):

High Frequency ◽

Urban Areas ◽

Descriptive Analysis ◽

User Characteristics ◽

Logistic Regression Models ◽

Highly Educated ◽

Suburban Areas ◽

Commuting Mode ◽

Study Offer

Carsharing as a service has been growing rapidly worldwide. Its expansion has drawn wide attention in the research community with regard to the underlying driving factors and user characteristics. Despite these extensive investigations, there are still limited studies focusing on the examination of users using carsharing as a commuting mode. The answers to questions such as what kind of people would like to use carsharing for commuting and why they frequently use carsharing to commute are not clear. To enrich our understanding of these problems, this paper aims to investigate carsharing commuters in a mega city. Specifically, it intends to integrate the actual user order data with survey data from 1,920 participants to uncover the characteristics of carsharing commuters. Data from the Evcard carsharing systems in Shanghai were explicitly analyzed. Through descriptive analysis and logistic regression models, the characteristics and critical factors that affect the choice of carsharing as a commuting mode were captured. The results show that: 1. carsharing commuters mostly live or work in suburban areas in which public transport accessibility is limited; 2. carsharing commuters are more likely to be highly educated, in a higher income bracket, and older than other carsharing members; 3. high-frequency carsharing commuters own a reduced number of private cars; and 4. those high-frequency carsharing commuters with higher income are less sensitive to the carsharing costs caused by congestion. The findings in the study offer some insights into carsharing commuters and provide some supportive information for considering policies in developing carsharing systems in urban areas.

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Explaining Review-Based Recommendations: Effects of Profile Transparency, Presentation Style and User Characteristics

i-com ◽

10.1515/icom-2020-0021 ◽

2020 ◽

Vol 19 (3) ◽

pp. 181-200

Author(s):

Diana C. Hernandez-Bocanegra ◽

Jürgen Ziegler

Keyword(s):

Decision Making ◽

Individual Differences ◽

User Interface ◽

Recommender Systems ◽

Social Awareness ◽

User Characteristics ◽

Presentation Style ◽

User Reviews ◽

Decision Making Styles

Abstract Providing explanations based on user reviews in recommender systems (RS) may increase users’ perception of transparency or effectiveness. However, little is known about how these explanations should be presented to users, or which types of user interface components should be included in explanations, in order to increase both their comprehensibility and acceptance. To investigate such matters, we conducted two experiments and evaluated the differences in users’ perception when providing information about their own profiles, in addition to a summarized view on the opinions of other customers about the recommended hotel. Additionally, we also aimed to test the effect of different display styles (bar chart and table) on the perception of review-based explanations for recommended hotels, as well as how useful users find different explanatory interface components. Our results suggest that the perception of an RS and its explanations given profile transparency and different presentation styles, may vary depending on individual differences on user characteristics, such as decision-making styles, social awareness, or visualization familiarity.

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Presence Effects in Virtual Reality Based on User Characteristics: Attention, Enjoyment, and Memory

Electronics ◽

10.3390/electronics10091051 ◽

2021 ◽

Vol 10 (9) ◽

pp. 1051

Author(s):

Si Jung Kim ◽

Teemu H. Laine ◽

Hae Jung Suk

Keyword(s):

Virtual Reality ◽

Emotional State ◽

Eye Gaze ◽

Emotional Valence ◽

Media Content ◽

Eye Tracker ◽

User Characteristics ◽

Head Mounted Display ◽

Age Range ◽

Different Levels

Presence refers to the emotional state of users where their motivation for thinking and acting arises based on the perception of the entities in a virtual world. The immersion level of users can vary when they interact with different media content, which may result in different levels of presence especially in a virtual reality (VR) environment. This study investigates how user characteristics, such as gender, immersion level, and emotional valence on VR, are related to the three elements of presence effects (attention, enjoyment, and memory). A VR story was created and used as an immersive stimulus in an experiment, which was presented through a head-mounted display (HMD) equipped with an eye tracker that collected the participants’ eye gaze data during the experiment. A total of 53 university students (26 females, 27 males), with an age range from 20 to 29 years old (mean 23.8), participated in the experiment. A set of pre- and post-questionnaires were used as a subjective measure to support the evidence of relationships among the presence effects and user characteristics. The results showed that user characteristics, such as gender, immersion level, and emotional valence, affected their level of presence, however, there is no evidence that attention is associated with enjoyment or memory.

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How Much Observation Is Enough? Refining the Administration of SOPARC

Journal of Physical Activity and Health ◽

10.1123/jpah.8.8.1117 ◽

2011 ◽

Vol 8 (8) ◽

pp. 1117-1123 ◽

Cited By ~ 80

Author(s):

Deborah A. Cohen ◽

Claude Setodji ◽

Kelly R. Evenson ◽

Phillip Ward ◽

Sandra Lapham ◽

...

Keyword(s):

Physical Activity ◽

Los Angeles ◽

Interobserver Reliability ◽

Intraclass Correlation ◽

Activity Level ◽

Activity Levels ◽

User Characteristics ◽

Systematic Observation ◽

Robust Estimate ◽

Neighborhood Parks

Background:The Systematic Observation of Play and Recreation in Communities (SOPARC) was designed to estimate the number and characteristics of people using neighborhood parks by assessing them 4 times/day, 7 days/week. We tested whether this schedule was adequate and determined the minimum number of observations necessary to provide a robust estimate of park user characteristics and their physical activity levels.Methods:We conducted observations every hour for 14 hours per day during 1 summer and 1 autumn week in 10 urban neighborhood parks: 2 each in Los Angeles, CA; Albuquerque, NM; Columbus, OH; Durham, NC; and Philadelphia, PA. We counted park users by gender, age group, apparent race/ethnicity, and activity level. We used a standardized Cronbach’s alpha and intraclass correlation coefficients to test the reliability of using fewer observations.Results:We observed 76,632 individuals, an average of 547/park/day (range 155−786). Interobserver reliability ranged from 0.80 to 0.99. Obtaining a robust estimate of park user characteristics and their physical activity required a schedule of 4 days/week, 4 times/day.Conclusion:An abbreviated schedule of SOPARC was sufficient for estimating park use, park user characteristics, and physical activity. Applying these observation methods can augment physical activity surveillance.

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