Details of a Researcher - Kinoshita, Shotaro

BioScience Trends (International Research and Cooperation Association for Bio & Socio-Sciences Advancement (IRCA-BSSA)) [Epub ahead of print] ( 5 ) 492 - 494 2024.10

Joint Work, Accepted, ISSN 1881-7815

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Assessment of Stress and Well-Being of Japanese Employees Using Wearable Devices for Sleep Monitoring Combined With Ecological Momentary Assessment: Pilot Observational Study

Shotaro Kinoshita, Sayaka Hanashiro, Shiori Tsutsumi, Kiko Shiga, Momoko Kitazawa, Yasuyo Wada, Jun Inaishi, Kazuhiro Kashiwagi, Toshikazu Fukami, Yasumasa Mashimo, Kazumichi Minato, Taishiro Kishimoto

JMIR Formative Research (JMIR Publications Inc.) [in press] e49396 2024.05

Joint Work, Accepted

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Ageing population in Japan: Immediate shake-up in healthcare required

Shotaro Kinoshita, Taishiro Kishimoto

QJM: An International Journal of Medicine (Oxford University Press (OUP)) [Epub ahead of print] hcae097 2024.05

Joint Work, Accepted, ISSN 1460-2725

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Anti-obesity drugs, eating disorders, and thinness among Japanese young women

Shotaro Kinoshita, Taishiro Kishimoto

The Lancet Diabetes & Endocrinology (Elsevier BV) 12 ( 2 ) 90 - 92 2024.02

Joint Work, Accepted, ISSN 2213-8587

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Updating the Japanese healthcare system to meet the needs of an aging society

Shotaro Kinoshita, Taishiro Kishimoto

JMA Journal [In press] 2024

Joint Work, Accepted

Associations between glycemic variability, sleep quality, and daily steps in subjects without diabetes using wearable devices

Jun Inaishi, Kazuhiro Kashiwagi, Shotaro Kinoshita, Yasuyo Wada, Sayaka Hanashiro, Kiko Shiga, Momoko Kitazawa, Shiori Tsutsumi, Hiroyuki Yamakawa, Taishiro Kishimoto

Metabolism Open 20 100263 - 100263 2023.12

Joint Work, Accepted, ISSN 2589-9368

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Live two-way video versus face-to-face treatment for depression, anxiety, and obsessive-compulsive disorder: A 24-week randomized controlled trial

Taishiro Kishimoto, Shotaro Kinoshita, Momoko Kitazawa, Akitoyo Hishimoto, Takeshi Asami, Akira Suda, Shogyoku Bun, Toshiaki Kikuchi, Mitsuhiro Sado, Akihiro Takamiya, Masaru Mimura, Yasunori Sato, Ryo Takemura, Kengo Nagashima, Takashi Nakamae, Yoshinari Abe, Tetsufumi Kanazawa, Yasuo Kawabata, Hiroaki Tomita, Koichi Abe, Seiji Hongo, Hiroshi Kimura, Aiko Sato, Hisashi Kida, Kei Sakuma, Michitaka Funayama, Naoya Sugiyama, Kousuke Hino, Toru Amagai, Maki Takamiya, Hideyuki Kodama, Kenichi Goto, Shuichiro Fujiwara, Hisanobu Kaiya, Kiichiro Nagao

Psychiatry and Clinical Neurosciences [Epub ahead of print] ( 4 ) 220 - 228 2023.12

Joint Work, Lead author, Accepted

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Psychiatrists’ Perspectives on Advantages, Disadvantages and Challenging for Promotion Related to Telemedicine: Japan’s Clinical Experience During COVID-19 Pandemic

Shotaro Kinoshita, Momoko Kitazawa, Yoshinari Abe, Akira Suda, Takashi Nakamae, Tetsufumi Kanazawa, Hiroaki Tomita, Akitoyo Hishimoto, Taishiro Kishimoto

Journal of Technology in Behavioral Science (Springer Science and Business Media LLC) [Epub ahead of Print] 532 - 541 2023.12

Joint Work, Accepted

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Assessment of glycemic variability and lifestyle behaviors in healthy nondiabetic individuals according to the categories of body mass index

Kazuhiro Kashiwagi, Jun Inaishi, Shotaro Kinoshita, Yasuyo Wada, Sayaka Hanashiro, Kiko Shiga, Momoko Kitazawa, Shiori Tsutsumi, Hiroyuki Yamakawa, Junichiro Irie, Taishiro Kishimoto

PLOS ONE (Public Library of Science (PLoS)) 18 ( 10 ) e0291923 - e0291923 2023.10

Joint Work, Accepted

Background

There are limited data about the association between body mass index (BMI), glycemic variability (GV), and life-related factors in healthy nondiabetic adults.

Methods

This cross-sectional study was carried out within our ethics committee-approved study called “Exploring the impact of nutrition advice on blood sugar and psychological status using continuous glucose monitoring (CGM) and wearable devices”. Prediabetes was defined by the HbA1c level of 5.7–6.4% and /or fasting glucose level of 100–125 mg/dL. Glucose levels and daily steps were measured for 40 participants using Free Style Libre and Fitbit Inspire 2 under normal conditions for 14 days. Dietary intakes and eating behaviors were assessed using a brief-type self-administered dietary history questionnaire and a modified questionnaire from the Obesity Guidelines.

Results

All indices of GV were higher in the prediabetes group than in the healthy group, but a significant difference was observed only in mean amplitude of glycemic excursions (MAGE). In the multivariate analysis, only the presence of prediabetes showed a significant association with the risk of higher than median MAGE (Odds, 6.786; 95% CI, 1.596–28.858; P = 0.010). Additionally, the underweight (BMI < 18.5) group had significantly higher value in standard deviation (23.7 ± 3.5 vs 19.8 ± 3.7 mg/dL, P = 0.038) and coefficient variability (22.6 ± 4.6 vs 18.4 ± 3.2%, P = 0.015), compared to the normal group. This GV can be partially attributed to irregularity of eating habits. On the contrary, the overweight (BMI ≥ 25) group had the longest time above the 140 or 180 mg/dL range, which may be due to eating style and taking fewer steps (6394 ± 2337 vs 9749 ± 2408 steps, P = 0.013).

Conclusions

Concurrent CGM with diet and activity monitoring could reduce postprandial hyperglycemia through assessment of diet and daily activity, especially in non- normal weight individuals.

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Conspiracy Theories, Anti-Science, and Anti-Medicine in the COVID-19 Pandemic

Shotaro Kinoshita

CBEL Report (東京大学生命倫理連携研究機構) 6 ( 1 ) 80 - 81 2023.07

Single Work, Accepted

Telepsychiatry vs. Face-to-Face Treatment: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Hagi Katsuhiko, Kurokawa Shunya, Takamiya Akihiro, Fujikawa Mayu, Kinoshita Shotaro, Iizuka Mari, Furukawa Shota, Eguchi Yoko, Kishimoto Taishiro

The British Journal of Psychiatry (in press) ( 3 ) 407 - 414 2023.05

Joint Work, Accepted

BACKGROUND: The COVID-19 pandemic has transformed healthcare significantly and telepsychiatry is now the primary means of treatment in some countries. AIMS: To compare the efficacy of telepsychiatry and face-to-face treatment. METHOD: A comprehensive meta-analysis comparing telepsychiatry with face-to-face treatment for psychiatric disorders. The primary outcome was the mean change in the standard symptom scale scores used for each psychiatric disorder. Secondary outcomes included all meta-analysable outcomes, such as all-cause discontinuation and safety/tolerability. RESULTS: We identified 32 studies (n = 3592 participants) across 11 mental illnesses. Disease-specific analyses showed that telepsychiatry was superior to face-to-face treatment regarding symptom improvement for depressive disorders (k = 6 studies, n = 561; standardised mean difference s.m.d. = -0.325, 95% CI -0.640 to -0.011, P = 0.043), whereas face-to-face treatment was superior to telepsychiatry for eating disorder (k = 1, n = 128; s.m.d. = 0.368, 95% CI 0.018-0.717, P = 0.039). No significant difference was seen between telepsychiatry and face-to-face treatment when all the studies/diagnoses were combined (k = 26, n = 2290; P = 0.248). Telepsychiatry had significantly fewer all-cause discontinuations than face-to-face treatment for mild cognitive impairment (k = 1, n = 61; risk ratio RR = 0.552, 95% CI 0.312-0.975, P = 0.040), whereas the opposite was seen for substance misuse (k = 1, n = 85; RR = 37.41, 95% CI 2.356-594.1, P = 0.010). No significant difference regarding all-cause discontinuation was seen between telepsychiatry and face-to-face treatment when all the studies/diagnoses were combined (k = 27, n = 3341; P = 0.564). CONCLUSIONS: Telepsychiatry achieved a symptom improvement effect for various psychiatric disorders similar to that of face-to-face treatment. However, some superiorities/inferiorities were seen across a few specific psychiatric disorders, suggesting that its efficacy may vary according to disease type.

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Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol

Taishiro Kishimoto, Shotaro Kinoshita, Toshiaki Kikuchi, Shogyoku Bun, Momoko Kitazawa, Toshiro Horigome, Yuki Tazawa, Akihiro Takamiya, Jinichi Hirano, Masaru Mimura, Kuo-ching Liang, Norihiro Koga, Yasushi Ochiai, Hiromi Ito, Yumiko Miyamae, Yuiko Tsujimoto, Kei Sakuma, Hisashi Kida, Gentaro Miura, Yuko Kawade, Akiko Goto, Fumihiro Yoshino

Frontiers in Psychiatry (Frontiers Media SA) 13 1025517 - 1025517 2022.12

Joint Work, Accepted

INTRODUCTION: Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices. METHODS AND ANALYSIS: Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set. DISCUSSION: Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device. CLINICAL TRIAL REGISTRATION: [https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].