Forecasting Health and Wellbeing for Shift Workers Using Job-Role Based Deep Neural Network

Yu, Han; Itoh, Asami; Sakamoto, Ryota; Shimaoka, Motomu; Sano, Akane

doi:10.1007/978-3-030-70569-5_6

Cited by 7 publications

(4 citation statements)

References 48 publications

(56 reference statements)

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“…Well-being prediction was based on these 5 self-reported labels that were collected twice daily (8 AM and 8 PM) and reported on a scale of 0 to 100. To predict well-being scores, we developed a job-based multitask and multilabel convolutional neural network–based well-being prediction model using pilot data from 31 participants with 906 days of data collection [ 46 ]. A total of 23 daily features were extracted from the fitness tracker and daily surveys.…”

Section: Methodsmentioning

confidence: 99%

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

Ito-Masui¹,

Sakamoto²,

Matsuo³

et al. 2023

J Med Internet Res

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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. International Registered Report Identifier (IRRID) RR2-10.2196/24799.

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Section: Methodsmentioning

confidence: 99%

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

Ito-Masui¹,

Sakamoto²,

Matsuo³

et al. 2023

J Med Internet Res

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“…Wearable devices can further enable physiological recording in a real-world setting (i.e., "in-the-wild"). Data collected through activity trackers, such as Fitbit (54), have been used to predict health and wellbeing status (55), detect daily stressful events (56) or predict future mood (57)(58)(59). Schmid et al have used wearable sensors to track healthcare workers' heart rate variability (HRV) during mindfulness exercises (60).…”

Section: Prior Workmentioning

confidence: 99%

Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers

Paromita

Mundnich

Nadarajan

et al. 2023

Front. Digit. Health

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IntroductionIntelligent ambulatory tracking can assist in the automatic detection of psychological and emotional states relevant to the mental health changes of professionals with high-stakes job responsibilities, such as healthcare workers. However, well-known differences in the variability of ambulatory data across individuals challenge many existing automated approaches seeking to learn a generalizable means of well-being estimation. This paper proposes a novel metric learning technique that improves the accuracy and generalizability of automated well-being estimation by reducing inter-individual variability while preserving the variability pertaining to the behavioral construct.MethodsThe metric learning technique implemented in this paper entails learning a transformed multimodal feature space from pairwise similarity information between (dis)similar samples per participant via a Siamese neural network. Improved accuracy via personalization is further achieved by considering the trait characteristics of each individual as additional input to the metric learning models, as well as individual trait base cluster criteria to group participants followed by training a metric learning model for each group.ResultsThe outcomes of the proposed models demonstrate significant improvement over the other inter-individual variability reduction and deep neural baseline methods for stress, anxiety, positive affect, and negative affect.DiscussionThis study lays the foundation for accurate estimation of psychological and emotional states in realistic and ambulatory environments leading to early diagnosis of mental health changes and enabling just-in-time adaptive interventions.

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“…In addition to ELF-EMF exposure, job stress can be increased because of shift work. Irregular work shifts (7 a.m. to 6 p.m.) 14 engage almost 20% of the workforce in the world 15 . Reduced consciousness and performance, especially during night shifts and daily sleeping difficulties, such as narcolepsy, are caused by work shifts.…”

Section: Introductionmentioning

confidence: 99%

Job Stress among Workers Exposed to Extremely Low Frequency Electric and Magnetic Fields in a Combined Cycle Power Plant

Abarghoee¹,

Halvani²,

Kargar-Shouroki³

et al. 2022

JEHSD

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Introduction: Exposure to the low frequency electric and magnetic fields (ELF-EMF) is very common in workers occupied in the combined cycle power plant during work shifts The present study aimed to measure ELF-EMF flux density among shift and non-shift workers, determine job stress among workers and office workers, and identify major factors associated with job stress in the studied groups. Materials and Methods: In this cross-sectional study, the exposed group was divided into 75 shift workers and 75 non-shift workers. Seventy-five office workers were selected as the reference group. The participants’ exposure to ELF-EMF was measured by the EMF-828 device. In addition, Osipow Occupational Stress Questionnaire was used to assess the participants’ stress levels. Results: Maximum levels of ELF-EMF among shift workers, non-shift workers, and office workers were 28.67 μT, 23.43 μT, and 0.06 μT, respectively. Although the explosion rate to ELF-EMF was higher in the shift and non-shift workers than the office one's, this rate in both of them were lower than the recommended limit as suggested by the American Conference of Governmental Industrial Hygienists (ACGIH). The highest stress score was related to shift workers exposed to ELF-EMF, in which 42.7% of them experienced moderate to high levels of job stress. Multivariate linear regression analysis showed a significant association between shift work and exposure to ELF-EMF with increased stress. Conclusion: The findings suggested that exposure to ELF-EMF, even at low intensities, is associated with increased stress. Moreover, Shift work is another risk factor for stress.

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Forecasting Health and Wellbeing for Shift Workers Using Job-Role Based Deep Neural Network

Cited by 7 publications

References 48 publications

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

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

Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers

Job Stress among Workers Exposed to Extremely Low Frequency Electric and Magnetic Fields in a Combined Cycle Power Plant

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