Browsing by Author "Yu, Han"

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    Effect of an Internet–Delivered Cognitive Behavioral Therapy–Based Sleep Improvement App for Shift Workers at High Risk of Sleep Disorder: Single-Arm, Nonrandomized Trial
    (JMIR Publications, 2023) Ito-Masui, Asami; Sakamoto, Ryota; Matsuo, Eri; Kawamoto, Eiji; Motomura, Eishi; Tanii, Hisashi; Yu, Han; Sano, Akane; Imai, Hiroshi; Shimaoka, Motomu
    Background: Shift workers are at high risk of developing sleep disorders such as shift worker sleep disorder or chronic insomnia. Cognitive behavioral therapy (CBT) is the first-line treatment for insomnia, and emerging evidence shows that internet-based CBT is highly effective with additional features such as continuous tracking and personalization. However, there are limited studies on internet-based CBT for shift workers with sleep disorders. Objective: This study aimed to evaluate the impact of a 4-week, physician-assisted, internet-delivered CBT program incorporating machine learning–based well-being prediction on the sleep duration of shift workers at high risk of sleep disorders. We evaluated these outcomes using an internet-delivered CBT app and fitness trackers in the intensive care unit. Methods: A convenience sample of 61 shift workers (mean age 32.9, SD 8.3 years) from the intensive care unit or emergency department participated in the study. Eligible participants were on a 3-shift schedule and had a Pittsburgh Sleep Quality Index score ≥5. The study comprised a 1-week baseline period, followed by a 4-week intervention period. Before the study, the participants completed questionnaires regarding the subjective evaluation of sleep, burnout syndrome, and mental health. Participants were asked to wear a commercial fitness tracker to track their daily activities, heart rate, and sleep for 5 weeks. The internet-delivered CBT program included well-being prediction, activity and sleep chart, and sleep advice. A job-based multitask and multilabel convolutional neural network–based model was used for well-being prediction. Participant-specific sleep advice was provided by sleep physicians based on daily surveys and fitness tracker data. The primary end point of this study was sleep duration. For continuous measurements (sleep duration, steps, etc), the mean baseline and week-4 intervention data were compared. The 2-tailed paired t test or Wilcoxon signed rank test was performed depending on the distribution of the data. Results: In the fourth week of intervention, the mean daily sleep duration for 7 days (6.06, SD 1.30 hours) showed a statistically significant increase compared with the baseline (5.54, SD 1.36 hours; P=.02). Subjective sleep quality, as measured by the Pittsburgh Sleep Quality Index, also showed statistically significant improvement from baseline (9.10) to after the intervention (7.84; P=.001). However, no significant improvement was found in the subjective well-being scores (all P>.05). Feature importance analysis for all 45 variables in the prediction model showed that sleep duration had the highest importance. Conclusions: The physician-assisted internet-delivered CBT program targeting shift workers with a high risk of sleep disorders showed a statistically significant increase in sleep duration as measured by wearable sensors along with subjective sleep quality. This study shows that sleep improvement programs using an app and wearable sensors are feasible and may play an important role in preventing shift work–related sleep disorders.
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    Further Improvements in Human Emotion and Wellbeing Prediction: Personalization, Modality Fusion, and Semi-Supervised Learning
    (2022-06-07) Yu, Han; Sano, Akane
    Human physiological and behavioral data have been continuously collected and studied, empowered by modern wearable devices. Among plenty of research areas, emotion and wellbeing estimation has become a rising topic. Researchers and developers found that there is potential to decode human emotion and wellbeing with physiological and behavioral data. Several outstanding studies have shown promising results and provide incentives for the future development of this area. Nevertheless, there are challenges that remain, such as modality missing, heterogeneity among subjects, and label sparseness. In this thesis, we addressed obstacles in modeling physiological and behavioral data. (1) We proposed a job-role based multi-task and multi-label learning to build models for different groups of populations with correlated labels; (2) We proposed a modality fusion network to adaptively fit parameters and infer emotion prediction even with missing data modalities; (3) We proposed a personalized attention model to learn the heterogeneity in sensor data and labels among individuals; (4) We designed a semi-supervised learning framework to learn representations from the massive unlabeled physiological sequences. We evaluated the proposed methods on data sets collected in the wild as well as using public data sets. The evaluations showed significant improvements in our approaches compared to the baseline models. Further, we conducted the model interpretability analysis to identify the critical part of the input signal that contributes to the proposed deep learning models.
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    Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study
    (JMIR, 2021) Ito-Masui, Asami; Kawamoto, Eiji; Sakamoto, Ryota; Yu, Han; Sano, Akane; Motomura, Eishi; Tanii, Hisashi; Sakano, Shoko; Esumi, Ryo; Imai, Hiroshi; Shimaoka, Motomu
    Background: Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect, and analyze the work-life balance of health care workers with irregular sleeping and working habits using wearable sensors that can continuously monitor biometric data under real-life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy. Objective: In this study, we aim to develop and evaluate the effect of a new internet-based cognitive behavioral therapy for SWSD (iCBTS). This system includes current methods such as medical sleep advice, as well as machine learning well-being prediction to improve the sleep durations of shift workers and prevent declines in their well-being. Methods: This study consists of two phases: (1) preliminary data collection and machine learning for well-being prediction; (2) intervention and evaluation of iCBTS for SWSD. Shift workers in the intensive care unit at Mie University Hospital will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their well-being. They will subsequently be provided with an iCBTS app for 4 weeks. Sleep and well-being measurements between baseline and the intervention period will be compared. Results: Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 has started in October 2020. Preliminary results are expected to be available by summer 2021. Conclusions: iCBTS empowered with well-being prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well-being. Findings of this study will reveal the potential of this system for improving sleep disorders among shift workers. Clinical Trial: UMIN Clinical Trials Registry UMIN000036122 (phase 1), UMIN000040547 (phase 2); https://tinyurl.com/dkfmmmje, https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046284