Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study

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Abstract

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.

Trial Registration

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

International Registered Report Identifier (IRRID)

DERR1-10.2196/24799

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Most cited references 20

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Clinically applicable deep learning for diagnosis and referral in retinal disease

Jeffrey De Fauw, Joseph Ledsam, Bernardino Romera-Paredes … (2018)

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Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study

Pu Wang, Tyler Berzin, Jeremy Glissen Brown … (2019)

Objective The effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs). We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR. Design In an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection. The primary outcome was ADR. Results Of 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis. The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p<0.001) and the mean number of adenomas per patient (0.53vs0.31, p<0.001). This was due to a higher number of diminutive adenomas found (185vs102; p<0.001), while there was no statistical difference in larger adenomas (77vs58, p=0.075). In addition, the number of hyperplastic polyps was also significantly increased (114vs52, p<0.001). Conclusions In a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps. The cost–benefit ratio of such effects has to be determined further. Trial registration number ChiCTR-DDD-17012221; Results.

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A validation study of Fitbit Charge 2™ compared with polysomnography in adults

Aimee Goldstone, Stephanie Claudatos, Ian M. Colrain … (2017)

We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2™), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults. In-lab PSG and Fitbit Charge 2™ data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19-61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS, > 15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2™ sleep data were compared using paired t-tests, Bland-Altman plots, and epoch-by-epoch (EBE) analysis. In the main group, Fitbit Charge 2™ showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep ("light sleep"), 0.49 accuracy in detecting N3 sleep ("deep sleep"), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2™ significantly (p < 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2™ outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland-Altman agreement limits for all sleep measures. Fitbit Charge 2™ correctly identified 82% of PSG-defined non-REM-REM sleep cycles across the night. Similar outcomes were found for the PLMS group. Fitbit Charge 2™ shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2™ accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).

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Author and article information

Contributors

Eiji Kawamoto:

ORCID: https://orcid.org/0000-0003-3920-9939

Departments of Molecular and Pathobiology and Cell Adhesion BiologyMie University Graduate School of Medicine2-174 EdobashiTsu City, Mie, 514-8507Japan81 59 232 503681 59 231 5209a_2.uk@mac.com

Journal

Journal ID (nlm-ta): JMIR Res Protoc

Journal ID (iso-abbrev): JMIR Res Protoc

Journal ID (publisher-id): ResProt

Title: JMIR Research Protocols

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 1929-0748

Publication date Collection: March 2021

Publication date (Electronic): 18 March 2021

Volume: 10

Issue: 3

Electronic Location Identifier: e24799

Affiliations

[1 ] Departments of Molecular and Pathobiology and Cell Adhesion Biology Mie University Graduate School of Medicine Tsu City, Mie Japan

[2 ] Departments of Emergency and Disaster Medicine Mie University Graduate School of Medicine Tsu City, Mie Japan

[3 ] Emergency and Critical Care Center Mie University Hospital Tsu City, Mie Japan

[4 ] Department of Medical Informatics Mie University Hospital Tsu City, Mie Japan

[5 ] Department of Electrical and Computer Engineering Rice University Houston, TX United States

[6 ] Department of Neuropsychiatry Mie University Graduate School of Medicine Tsu City, Mie Japan

[7 ] Center for Physical and Mental Health Mie University Tsu City, Mie Japan

[8 ] Mie Prefectural Mental Medical Center Tsu City, Mie Japan

Author notes

Corresponding Author: Eiji Kawamoto a_2.uk@ 123456mac.com

Author information

Asami Ito-Masui https://orcid.org/0000-0002-2086-4231

Eiji Kawamoto https://orcid.org/0000-0003-3920-9939

Ryota Sakamoto https://orcid.org/0000-0003-0411-856X

Han Yu https://orcid.org/0000-0003-1944-4173

Akane Sano https://orcid.org/0000-0003-4484-8946

Eishi Motomura https://orcid.org/0000-0003-3013-9284

Hisashi Tanii https://orcid.org/0000-0003-1879-4278

Shoko Sakano https://orcid.org/0000-0002-6530-6225

Ryo Esumi https://orcid.org/0000-0003-4954-9750

Hiroshi Imai https://orcid.org/0000-0003-0738-3633

Motomu Shimaoka https://orcid.org/0000-0003-1930-3397

Article

Publisher ID: v10i3e24799

DOI: 10.2196/24799

PMC ID: 8088862

PubMed ID: 33626497

SO-VID: 788da0b1-1a9d-4ded-9d39-47adb0dd0118

Copyright © ©Asami Ito-Masui, Eiji Kawamoto, Ryota Sakamoto, Han Yu, Akane Sano, Eishi Motomura, Hisashi Tanii, Shoko Sakano, Ryo Esumi, Hiroshi Imai, Motomu Shimaoka. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 18.03.2021.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.

History

Date received : 7 October 2020

Date revision requested : 17 November 2020

Date revision received : 10 January 2021

Date accepted : 24 February 2021

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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

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Abstract

Background

Objective

Methods

Results

Conclusions

Trial Registration

International Registered Report Identifier (IRRID)

Related collections

JMIR: Research Protocols

Most cited references 20

Clinically applicable deep learning for diagnosis and referral in retinal disease

Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study

A validation study of Fitbit Charge 2™ compared with polysomnography in adults

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