https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/462 Thu, 09 Apr 2026 01:07:17 GMT 2026-04-09T01:07:17Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/79512 Title: The Circadian Rhythm for Sleep digital therapeutic for insomnia: Conceptual background and single-arm feasibility study Authors: Seo, Minhee; Park, Soohyun; Jeong, Jaegwon; Nam, Yerim; Lee, Eunbi; Lee, Yujin; Yeom, Ji Won; Cho, Chul-Hyun; Kim, Leen; Lee, Jung-Been; Lee, Heon-Jeong Abstract: Objective: To assess the feasibility, acceptability, and preliminary effects of circadian rhythm for sleep (CRS), a mobile digital therapeutic that delivers closed-loop, wearable- and light-sensor-driven circadian coaching for insomnia. Methods: Six-week, single-arm feasibility study in adults with short-term or chronic insomnia. CRS provided daily recommendations emphasizing stable wake-up time, morning light exposure, and daytime activity. Feasibility and acceptability outcomes were assessed (completion, passive-sensor data capture/adherence, satisfaction), and the primary clinical outcome (exploratory) was change in Insomnia Severity Index (ISI) from baseline to Week 6; the key secondary clinical outcome (exploratory) was Pittsburgh Sleep Quality Index (PSQI). Objective sleep-wake metrics from wearable device were explored. Results: Twenty-three participants were enrolled; 20 completed the program (87.0%), and 16 comprised the prespecified analysis set based on data-fidelity criteria. Among these 16 participants, valid passive sensor data from the wearable and light sensors were captured on 88.6% of study days. ISI significantly improved from baseline to Week 6 (median 21.0 -> 14.0; p < .001 by within-subject analysis), and PSQI improved (mean 10.9 -> 7.7; p < .001; partial eta(2) approximate to 0.50). Objective wearable metrics (total sleep time, time-in-bed, sleep onset, wake time) did not change significantly over time in this short pilot. Satisfaction was favorable (mean 37.9/45). No adverse events occurred. Conclusions: CRS was feasible and acceptable, and was associated with within-subject improvements in subjective insomnia symptoms in this single-arm feasibility study; however, because there was no control group, these findings are preliminary and hypothesis-generating, supporting further evaluation in larger randomized controlled trials. Trial registry name: Clinical Research Information Service URL: https://cris.nih.go.kr Trial registration number: KCT0010801 Sun, 01 Mar 2026 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/79512 2026-03-01T00:00:00Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/77824 Title: Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety (SWARTS-DA) observational study in Korea Authors: Shin, Yu-Bin; Kim, Ah Young; Kim, Seonmin; Shin, Min-Sup; Choi, Jinhwa; Lee, Kyung Lyun; Lee, Jisu; Byun, Sangwon; Kim, Sujin; Lee, Heon-Jeong; Cho, Chul-Hyun Abstract: Introduction Depression and anxiety are highly prevalent mental health conditions that significantly affect quality of life and cause societal burdens. However, their detection and diagnosis rates remain low owing to the limitations of the current screening methods. With rapid technological advancements and the proliferation of consumer-grade wearable devices and smartphones, their integration into digital phenotyping research has enabled the unobtrusive screening for depression and anxiety in natural settings. The Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety study aims to develop prediction algorithms to identify individuals at risk for depressive and anxiety disorders, as well as those with mild-to-severe levels of either condition or both. By collecting comprehensive data using smartphones and smartwatches, this study aims to facilitate the translation of artificial intelligence-based early detection research into clinical impact, thereby potentially enhancing patient care through more accurate and timely interventions. Methods and analysis This cross-sectional observational study will enrol up to 2500 participants (at least 1000) aged 19-59 years from South Korea via social media outreach and clinical referrals. The eligible participants must use a compatible smartphone. Each participant will be followed up for 4 weeks. Data will be collected using a custom-developed smartphone application called PixelMood. Active data collection will include daily, weekly and monthly self-report questionnaires incorporating validated scales, such as the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7. Passive data from smartphones include information on physical activity, location, ambient light and smartphone usage patterns. Optionally, participants using the Apple Watch or Galaxy Watch devices can provide additional data on physiological responses and sleep health. The primary outcome will be the development of machine-learning algorithms to predict depression and anxiety based on these digital biomarkers. We will employ various machine-learning techniques, including random forest, support vector machine and deep-learning models. The secondary outcomes will include the association between digital biomarkers and clinical measures, and the feasibility and acceptability of data collection methods. Various features characterising mobile usage behaviours, physical/social activity, sleep patterns, resting physiological states and circadian rhythms will be exploited to serve as potential digital phenotyping markers. Advanced machine-learning and deep-learning techniques will be applied to multimodal data for model generation. Ethics and dissemination This study protocol was reviewed and approved by the Institutional Review Board of the Korea University Anam Hospital (approval number: 2023AN0506). The results of this study will be disseminated via multiple channels. The findings will be presented at local, national and international conferences in relevant fields, such as psychiatry, psychology and digital health. Manuscripts detailing the study results will be submitted to peer-reviewed journals for publication. Trial registration number The present study was registered with the Clinical Research Information Service (CRIS, https://cris.nih.go.kr; identifier: KCT0009183). Sun, 01 Jun 2025 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/77824 2025-06-01T00:00:00Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/77545 Title: Machine learning-based prediction of restless legs syndrome using digital phenotypes from wearables and smartphone data Authors: Jeong, Jingyeong; Jeon, Yoonseo; Kim, Hyungju; Yeom, Ji Won; Shin, Yu-Bin; Kim, Sujin; Pack, Seung Pil; Lee, Heon-Jeong; Cheong, Taesu; Cho, Chul-Hyun Abstract: Restless legs syndrome (RLS) is a relatively common neurosensory disorder that causes an irresistible urge for leg movement. RLS causes sleep disturbances and reduced quality of life, but accurate diagnosis remains challenging owing to the reliance on subjective reporting. This study aimed to propose a predictive machine learning model based on digital phenotypes for RLS diagnosis. Self-reported lifestyle data were integrated via a smartphone application with objective biometric data from wearable devices to obtain 85 features processed based on circadian rhythms. Prediction models used these features to distinguish between the non-RLS (International Restless Legs Study Group Severity Rating Scale [IRLS] score ≤ 10) and RLS symptom groups (10 < IRLS ≤ 20) and between the non-RLS and severe RLS symptom groups (IRLS > 20). The RF model showed the highest performance in predicting the RLS symptom group and XGB model in the severe RLS symptom group. For the RLS symptom group, when using only wearable device data, the AUC, accuracy, precision, recall, and F1 scores were 0.78, 0.70, 0.66, 0.84, and 0.74, respectively, while these scores combining wearable device and application data were 0.86, 0.76, 0.68, 1.00, and 0.81, respectively. For the severe RLS symptom group, when using only wearable device data, XGB achieved AUC, accuracy, precision, recall, and F1 scores of 0.66, 0.84, 0.89, 0.93, and 0.91, respectively, while these scores combining wearable device and application data were 0.70, 0.80, 0.88, 0.90, and 0.89, respectively. Diverse digital phenotypes clinically associated with RLS were processed based on circadian rhythms to demonstrate the potential of digital phenotyping for RLS prediction. Thus, our study establishes early detection and personalized management of RLS. Trial Registration: Clinical Research Information Service (CRIS) KCT0009175 (Registration data: Feb-15-2024) (https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&focus=reset_12&search_page=M&pageSize=10&page=undefined&seq=26133&status=5&seq_group=26133). © The Author(s) 2025. Thu, 01 May 2025 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/77545 2025-05-01T00:00:00Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/76551 Title: Exploring User Experience and the Therapeutic Relationship of Short-Term Avatar-Based Psychotherapy: Qualitative Pilot Study Authors: Jang, Byeul; Yuh, Chisung; Lee, Hyeri; Shin, Yu-Bin; Lee, Heon-Jeong; Kang, Eun Kyoung; Heo, Jeongyun; Cho, Chul-Hyun Abstract: The rapid advancement of telehealth has led to the emergence of avatar-based psychotherapy (ABP), which combines the benefits of anonymity with nonverbal communication. With the adoption of remote mental health services, understanding the efficacy and user experience of ABP has become increasingly important. This study aimed to explore the user experience and therapeutic relationship formation in short-term ABP environments, focusing on psychological effects, user satisfaction, and critical factors for implementation. This qualitative study involved 18 adult participants (8 women and 10 men). Participants engaged in two short-term ABP sessions (approximately 50 minutes per session) over 2 weeks, using an ABP metaverse system prototype. Semistructured in-depth interviews were conducted with both the participants and therapists before and after the ABP sessions. The interviews were conducted via an online platform, with each interview lasting approximately 30 minutes. The key topics included the sense of intimacy, communication effectiveness of avatar expressions, emotions toward one's avatar, concentration during sessions, and perceived important aspects of the ABP. Data were analyzed using thematic analysis. The analysis revealed 3 main themes with 8 subthemes: (1) reduction of psychological barriers through avatar use (subthemes: anonymity, ease of access, self-objectification, and potential for self-disclosure); (2) importance of the avatar-self-connection in therapeutic relationship formation (subthemes: avatar self-relevance and avatar-self-connection fostering intimacy and trust); and (3) importance of nonverbal communication (subthemes: significance of nonverbal expressions and formation of empathy and trust through nonverbal expressions). Participants reported enhanced comfort and self-disclosure owing to the anonymity provided by avatars, while emphasizing the importance of avatar customization and the role of nonverbal cues in facilitating communication and building rapport. This pilot study provides valuable insights into the short-term ABP user experience and therapeutic relationship formation. Our findings suggest that ABP has the potential to reduce barriers to therapy through anonymity, ease of access, and potential for self-disclosure, while allowing for meaningful nonverbal communication. The avatar-self-connection emerged as a crucial factor in the effectiveness of ABP, highlighting the importance of avatar customization in enhancing user engagement and therapeutic outcomes. Future research and development in ABP should focus on improving avatar customization options, enhancing the fidelity of nonverbal cues, and investigating the long-term effectiveness of ABP compared with traditional face-to-face therapy. © Byeul Jang, Chisung Yuh, Hyeri Lee, Yu-Bin Shin, Heon-Jeong Lee, Eun Kyoung Kang, Jeongyun Heo, Chul-Hyun Cho. Originally published in JMIR Human Factors (https://humanfactors.jmir.org). Sat, 01 Feb 2025 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/76551 2025-02-01T00:00:00Z