• Journal of Fashion Business
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• v.25 no.5
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• pp.88-98
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• 2021

In this study, the characteristics of digital anthropometric methods were determined with case studies. These methods were broadly classified into two categories: non-wearable and wearable. Then, these categories were further classified into four types: 3D Scanning, mobile app, smart clothing, and smart tool Among the non-wearable types, the "3D scanning" technique was based on the use of 3D hardware equipment. With this technique, the body shape was measured and the internal body information was obtained. Therefore, it is used in fields of healthcare and fitness. Among the wearable types, "Smart clothing" involves a special clothing that measures human body and a smartphone application. Both the components are linked to a fashion platform, which is based on the measured sizes that help shoppers. The "Smart tool" has the characteristic of measuring only with smart tools and smartphone applications; it does not involve the measurement of images. The common advantage of digital anthropometric methods are as follows: they reduce the time and cost of measurement by enabling self-measurement. Moreover, simple measurements are used to determine the size of anthropometry. Thereafter, it accumulates this data to track the continuous changes in size. From an industrial point of view, digital anthropometric technology should be used to increase sales. The on-demand market can be expanded as global consumers would throng the Korean fashion market. For the consumer, an avatar should be created to fit the user's size. This would provide a fun experience to the user.

• Journal of Distribution Science
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• v.21 no.7
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• pp.117-129
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• 2023

Purpose: With the number of COVID-19 cases declining and generational differences among how people use mobile apps, including health service apps, the goal of this research is to identify and analyze the factors that affect people's attitudes when using the Halodoc health service app during the third year of the pandemic. Research design, data, and methodology: This study proposes a quantitative analysis method based on PLS-SEM modeling. This study has used a questionnaire survey to collect randomized data from 268 Halodoc users from generations Y and Z in Jakarta. Results: Both the Y and Z generations believe there is a significant usefulness factor in the attitude toward using the application. The start of the pandemic period demonstrates that the urgency of using health service applications is no longer determined by performance expectations, effort, or social panic, but rather by these applications' usability. Conclusions: Even though a health service application is no longer considered an urgent service or a priority need, attitudes, and behaviors in using it emphasize the aspect of long-term benefits. These findings supplement other considerations and understandings in application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model in explaining attitudes and intention behaviors.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.378-387
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• 2018

With the advent of the fourth industrial revolution in recent years, the field of health care services is getting attention again. Accordingly, personalized medical systems through smart products are emerging in various forms. With the use of wearable tech and sensor system, health management and monitoring can be done anytime, anywhere without help of others. However, healthcare services for pregnant women are very scarce. Due to the low fertility rate the number of obstetrics and gynecology is decreasing and as a result, environment surrounding the uncomfortable pregnant women is getting worse. Pregnant women are unable to take a comfortable sleeping posture due to pregnancy. Various environmental factors such as noise, temperature and humidity decrease the quality of sleeping of pregnant women and hinder happy preaching. The purpose of this study is to develop a UI design that can manage sleeping by providing good sleeping posture information and improved sleeping environment for the health of pregnant women. We expect to apply the sensor technology of the 4th industrial age to maximize the sleep quality and quality of life of expectant mothers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.143-153
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• 2017

This was a descriptive survey to determine the intention of users to use smartphone healthcare applications (SHAs) and to clarify factors that may influence such intention. The data were collected during the month of April in 2015, using a structured self-report questionnaire that was distributed to 300 participants aged 20 to 70 years; 285 complete copies were used for the final analysis. The data were analyzed using descriptive statistics, independent t-test, one-way ANOVA, Pearson correlation coefficients, and hierarchical multiple regression. First, according to the results, the average intention to use SHAs was 3.28 out of 5, which varied according to age, final education, economy level, vacation, current disease, total period of smartphone use, and etc. Second, significant correlations were shown by exercise behavior, dietary management behavior, stress management, satisfaction with smartphone use, and satisfaction with using SHAs. Third, the explanatory power of the predictive model involving all general, health-related, smartphone use-related, and SHA use-related factors was 45.5%; and the economic level, interest, status, and awareness satisfaction of patients using SHA were identified to be the main influential factors. The results indicate that SHA developers need to put efforts into improving consumers' app recognition and to develop plans in provoking consumers' interests to increase the use of SHAs.

• Journal of Korean society of Dental Hygiene
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• v.19 no.4
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• pp.493-502
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• 2019

Objectives: This study aimed to investigate the current status of oral health applications developed for smartphones because they can be used as a new educational medium to manage and improve oral health. Methods: This study examined 60 basic oral health applications provided by Google Play Store and Apple App Store as of May 2019 and examined delivery contents, delivery methods, application types, and other information. Results: Apple included 65.4% of oral apps in the game category whereas Android included 64.3% in the education category (p>0.05). All Apple's apps and 71.4% of Android apps were developed overseas (p<0.01). The delivery contents were 61.5% for Brushing + tooth decay in Apple, and 78.6% for others (oral care products and gum diseases) in Android (p>0.05). For the delivery method, game + video was 65.4% in Apple, and game and other methods (text, image, augmented reality) was 42.9% in Android (p>0.05). In the case of application type, play type was the most common with 88.5% in Apple, and 46.4% play type and 39.3% other type (text, appreciation, problem-solving types) in Android (p<0.01). In addition, play type was high in both education (53.8%) and game (90.0%) categories (p>0.05). The average review score was 4.30 in the education category, 4.34 in the case of brushing and care (delivery contents), 4.37 in the case of using game + video (delivery methods), and 4.57 in the case of Play + other types (application type) (p>0.05). Conclusions: The use of healthcare apps is expected to increase owing to improved lifestyles, an increase in the elderly population, cost-effectiveness, and convenience that is not affected by time and place. Effective use of oral health apps will require the participation of dental professionals in the development process to identify the exact status, expand subjects, and provide appropriate information.

• Health Policy and Management
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• v.33 no.1
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• pp.29-39
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• 2023

Background: Breaking the chain of disease transmission from overseas is necessary to control new infectious diseases such as coronavirus disease 2019 effectively. In this study, we developed a mobile app called Self-Symptom Checker (SSC) to monitor the health of inbound travelers. Methods: SSC was developed for general users and administrators. The functions of SSC include non-repudiation using QR (quick response) codes, monitoring fever and respiratory symptoms, and requiring persons showing symptoms to undergo polymerase chain reaction tests at nearby screening stations following a review of reported symptoms by the Korea Disease Control and Prevention Agency, as well as making phone calls, via artificial intelligence or public health personnel, to individuals who have not entered symptoms to provide the necessary information. Results: From February 12 to March 27, 2020, 165,000 people who were subjected to the special entry procedure installed SSC. The expected number of public health officers and related resources needed per day would be 800 if only the phone was used to perform symptom monitoring during the above period. Conclusion: By applying SSC, more effective symptom monitoring was possible. The daily average number of health officers decreased to 100, or 13% of the initial estimate. SSC reduces the work burden on public healthcare personnel. SSC is an electronic solution conceived in response to health questionnaires completed by inbound travelers specified in the World Health Organization International Health Regulations as a requirement in the event of a pandemic.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.153-161
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• 2022

Physical activities are decreasing and sitting time is increasing due to the automation, smartization, and intelligence of necessary household items throughout daily life. Recent healthcare studies have reported that the likelihood of obesity, diabetes, cardiovascular disease, and early death increases in proportion to sitting time. In this paper, we develop a sitting posture correction cushion in real time using capacitive pressure sensor based on conductive textile. It develops a pressure sensor using conductive textile, a key component of the posture correction cushion, and develops a low power-based pressure measurement circuit. It provides a function to transmit sensor values measured in real time to smartphones using BLE short-range wireless communication on the posture correction cushion, and develops a mobile application to check the condition of the sitting posture through these sensor values. In the mobile app, you can visualize your sitting posture and check it in real time, and if you keep it in the wrong posture for a certain period of time, you can notify it through an alarm. In addition, it is possible to visualize the sitting time and posture accuracy in a graph. Through the correction cushion in this paper, we experiment with how effective it is to correct the user's posture by recognizing the user's sitting posture, and present differentiation and excellence compared to other product.

• Journal of agricultural medicine and community health
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• v.49 no.1
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• pp.37-49
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• 2024

Objectives: This study aimed to explore whether there are differences in smoking status between two regions of Wonju-City and Chuncheon-City, Gangwon State, and to determine whether the experience of smoking cessation programs in the region affects quit attempts. Methods: The study design was a cross-sectional study in which adults aged 19 and older living in two cities were surveyed using a pre-developed mobile app to investigate social capital for smoking cessation, and a total of 600 citizens were participated, including 310 in Wonju-City and 290 in Chuncheon-City. The statistical analysis was conducted using chi-square test and logistic regression analysis. Results: Wonju-City had a higher prevalence of current smoking than Chuncheon-City. Among smoking cessation programs operated by local public health centers, Wonju-City had a lower odds ratio for experience with smoking cessation education than Chuncheon-City (OR=0.52, 95% CI=0.33 to 0.81). When examining the effect of smoking cessation program experience on quit attempts, in Wonju-City, citizens who had completed smoking cessation education and used a smoking cessation clinic were more likely to attempt to quit than those who had not (OR=2.31 and OR=2.29, respectively). In Chuncheon-City, citizens who were aware of smoking cessation support services were 2.26 times more likely to attempt to quit smoking than those who were not, but statistical significance was not reached due to the small sample size. Conclusion: Therefore, healthcare organizations in both regions should develop more practical intervention strategies to increase smokers' quit attempts, reduce smoking rates in the community, and address regional disparities.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.