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6. Development of a Weight Loss Mobile App Linked With an Accelerometer for Use in the Clinic: Usability, Acceptability, and Early Testing of its Impact on the Patient-Doctor Relationship

Author

Choo, Seryung, Kim, Ju Young, Jung, Se Young, Kim, Sarah, Kim, Jeong Eun, Han, Jong Soo, Kim, Sohye, Kim, Jeong Hyun, Kim, Jeehye, Kim, Yongseok, Kim, Dongouk, and Steinhubl, Steve

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundAlthough complications of obesity are well acknowledged and managed by clinicians, management of obesity itself is often difficult, which leads to its underdiagnosis and undertreatment in hospital settings. However, tools that could improve the management of obesity, including self-monitoring, engagement with a social network, and open channels of communication between the patient and doctor, are limited in a clinic-based setting. ObjectiveThe objective of our study was to evaluate the usability and acceptability of a newly developed mobile app linked with an accelerometer and its early effects on patient-doctor relationships. MethodsFrom September 2013 to February 2014, we developed a mobile app linked with an accelerometer as a supportive tool for a clinic-based weight loss program. The app used information from electronic health records and delivered tailored educational material. Personal goal setting, as well as monitoring of weight changes and physical activity combined with feedback, are key features of the app. We also incorporated an interactive message board for patients and doctors. During the period of March 2014 to May 2014, we tested our mobile app for 1 month in participants in a hospital clinic setting. We assessed the app’s usability and acceptability, as well as the patient-doctor relationship, via questionnaires and analysis of app usage data. ResultsWe recruited 30 individuals (18 male and 12 female) for the study. The median number of log-ins per day was 1.21, with the most frequently requested item being setting goals, followed by track physical activities and view personal health status. Scales of the depth of the patient-doctor relationship decreased from 27.6 (SD 4.8) to 25.1 (SD 4.5) by a Wilcoxon signed rank test (P=.02). ConclusionsA mobile phone app linked with an accelerometer for a clinic-based weight loss program is useful and acceptable for weight management but exhibited less favorable early effects on patient-doctor relationships.

Published
2016
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10. PM exposure during pregnancy affects childhood asthma via placental epigenetic changes: Neuronal differentiation and proliferation and Notch signaling pathways

Author

Kim, Hyo-Bin, Lee, Si Hyeon, Yang, Dae Yeol, Lee, Seung-Hwa, Kim, Jeong-Hyun, Kim, Hwan-Cheol, Choi, Kil Yong, Lee, So-Yeon, Yang, Song-I, Suh, Dong In, Shin, Youn Ho, Kim, Kyung Won, Ahn, Kangmo, Choi, Suk-Joo, Kwon, Ja-Young, Kim, Soo Hyun, Jun, Jong Kwan, Lee, Mi-Young, Won, Hye-Sung, Kim, Kwoneel, and Hong, Soo-Jong

Published
2025
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29. When ChatGPT Gives Incorrect Answers: The Impact of Inaccurate Information by Generative AI on Tourism Decision-Making.

Author

Kim, Jeong Hyun, Kim, Jungkeun, Park, Jooyoung, Kim, Changju, Jhang, Jihoon, and King, Brian

Subjects
*GENERATIVE artificial intelligence, *CHATGPT, *CHATBOTS, *USER interfaces, *TRUST
Abstract

This study investigates how inaccurate information provided by ChatGPT impacts travelers' acceptance of recommendations. Six experiments were conducted based on the accessibility-diagnosticity framework. These examined the moderating role of the prominence and type of incorrect information and their effects on decision-making. The results show that participants perceived more accuracy and trustworthiness, leading to stronger intentions to visit when incorrect information was absent. However, there was a decline in their intentions to visit when incorrect information was present and more prominent or in the same domain. This effect diminished when multiple domains were involved or when participants were focused on the initial task. The research highlights that both the prominence and type of incorrect information are boundary conditions and provides insights into AI applications in tourism. Furthermore, it offers practical implications for online travel agencies in terms of user interface and user experience design planning. [ABSTRACT FROM AUTHOR]

Published
2025
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30. AI in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale.

Author

Kim, Jeong-Hyun, Hong, Hyeon, Lee, Kyuwon, Jeong, Yeji, Ryu, Hokyoung, Kim, Hyundo, Jang, Seong-Ho, Park, Hyeng-Kyu, Han, Jae-Young, Park, Hye Jung, Bae, Hasuk, Oh, Byung-Mo, Kim, Won-Seok, Lee, Sang Yoon, and Lee, Shi-Uk

Subjects
RESEARCH funding, RECEIVER operating characteristic curves, ARTIFICIAL intelligence, STATISTICAL sampling, SEVERITY of illness index, DIAGNOSIS, GAIT in humans, CONVOLUTIONAL neural networks, DESCRIPTIVE statistics, WORKFLOW, WALKING, DEEP learning, RESEARCH, ARTIFICIAL neural networks, STROKE, STROKE patients, COMPARATIVE studies, JUDGMENT (Psychology), VIDEO recording, SENSITIVITY & specificity (Statistics), CLASSIFICATION, DISEASE complications
Abstract

Background: The evaluation of gait function and severity classification of stroke patients are important to determine the rehabilitation goal and the level of exercise. Physicians often qualitatively evaluate patients' walking ability through visual gait analysis using naked eye, video images, or standardized assessment tools. Gait evaluation through observation relies on the doctor's empirical judgment, potentially introducing subjective opinions. Therefore, conducting research to establish a basis for more objective judgment is crucial. Objective: To verify a deep learning model that classifies gait image data of stroke patients according to Functional Ambulation Category (FAC) scale. Methods: Gait vision data from 203 stroke patients and 182 healthy individuals recruited from six medical institutions were collected to train a deep learning model for classifying gait severity in stroke patients. The recorded videos were processed using OpenPose. The dataset was randomly split into 80% for training and 20% for testing. Results: The deep learning model attained a training accuracy of 0.981 and test accuracy of 0.903. Area Under the Curve(AUC) values of 0.93, 0.95, and 0.96 for discriminating among the mild, moderate, and severe stroke groups, respectively. Conclusion: This confirms the potential of utilizing human posture estimation based on vision data not only to develop gait parameter models but also to develop models to classify severity according to the FAC criteria used by physicians. To develop an AI-based severity classification model, a large amount and variety of data is necessary and data collected in non-standardized real environments, not in laboratories, can also be used meaningfully. [ABSTRACT FROM AUTHOR]

Published
2025
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33. Analysis of treatment pattern of anti-dementia medications in newly diagnosed Alzheimer’s dementia using OMOP CDM

Author

Byun, JungHyun, Lee, Dong Yun, Jeong, Chang-Won, Kim, Yerim, Rhee, Hak Young, Moon, Ki Won, Heo, Jeongwon, Hong, Yoonki, Kim, Woo Jin, Nam, Seung-Joo, Choi, Hoon Sung, Park, Ji In, Chun, In Kook, Bak, So Hyeon, Lee, Kyoungyul, Byeon, Gi Hwan, Kim, Kyoung Lae, Kim, Jeong-Ah, Park, Young Joo, Kim, Jeong Hyun, Lee, Eun ju, Lee, Sang-Ah, Kwon, Sung Ok, Park, Sang-Won, Kasani, Payam Hosseinzadeh, Kim, Jung-Kyeom, Kim, Yeshin, Kim, Seongheon, and Jang, Jae-Won

Published
2022
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36. AI in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale

Author

Kim, Jeong-Hyun, primary, Hong, Hyeon, additional, Lee, Kyuwon, additional, Jeong, Yeji, additional, Ryu, Hokyoung, additional, Kim, Hyundo, additional, Jang, Seong-Ho, additional, Park, Hyeng-Kyu, additional, Han, Jae-Young, additional, Park, Hye Jung, additional, Bae, Hasuk, additional, Oh, Byung-Mo, additional, Kim, Won-Seok, additional, Lee, Sang Yoon, additional, and Lee, Shi-Uk, additional

Published
2024
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43. Progress of Metal Chalcogenides as Catalysts for Efficient Electrosynthesis of Hydrogen Peroxide.

Author

Kim, Jeong-Hyun, Lee, Jeong-Gyu, and Choi, Min-Jae

Subjects
*SUSTAINABILITY, *METAL catalysts, *HYDROGEN peroxide, *HYDROGEN content of metals, *ELECTROCATALYSTS, *OXYGEN reduction
Abstract

Hydrogen peroxide (H2O2) is a high-demand chemical, valued as a powerful and eco-friendly oxidant for various industrial applications. The traditional industrial method for producing H2O2, known as the anthraquinone process, is both costly and environmentally problematic. Electrochemical synthesis, which produces H2O2 using electricity, offers a sustainable alternative, particularly suited for small-scale, continuous on-site H2O2 generation due to the portability of electrocatalytic devices. For efficient H2O2 electrosynthesis, electrocatalysts must exhibit high selectivity, activity, and stability for the two-electron pathway-oxygen reduction reaction (2e− ORR). Transition-metal chalcogenide (TMC)-based materials have emerged as promising candidates for effective 2e− ORR due to their high activity in acidic environments and the abundance of their constituent elements. This review examines the potential of TMC-based catalysts in H2O2 electrosynthesis, categorizing them into noble-metal and non-noble-metal chalcogenides. It underscores the importance of achieving high selectivity, activity, and stability in 2e− ORR. By reviewing recent advancements and identifying key challenges, this review provides valuable insights into the development of TMC-based electrocatalysts for sustainable H2O2 production. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Prenatal PM2.5 exposure and vitamin D–associated early persistent atopic dermatitis via placental methylation

Author

Yang, Song-I, Lee, Seung-Hwa, Lee, So-Yeon, Kim, Hwan-Cheol, Kim, Hyo-Bin, Kim, Jeong-Hyun, Lim, Hyeyeun, Park, Min Jee, Cho, Hyun-Ju, Yoon, Jisun, Jung, Sungsu, Yang, Hyeon-Jong, Ahn, Kangmo, Kim, Kyung Won, Shin, Youn Ho, Suh, Dong In, Won, Hye-Sung, Lee, Mi-Young, Kim, Soo Hyun, Choi, Suk-Joo, Kwon, Ja-Young, Jun, Jong Kwan, and Hong, Soo-Jong

Published
2020
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