693 Combined effects of overtime work hours and exercise habits on psychological distress: a cross-sectional study among japanese white-collar workers

ObjectiveTo investigate the associations among procrastination (time inconsistency), work environment and obesity-related factors in Japanese male workers.DesignCross-sectional study.SettingData were collected at two work sites of Japanese electronics manufacturing company in 2015.Participants795 full-time male workers in a Japanese electric company, aged 35–64 years, who underwent health checkups in 2015.Main outcome measuresBody mass index (BMI), adult weight change, obesity (BMI ≥25 kg/m2), adult weight gain over 10 kg (AWG10) and metabolic syndrome (MetS). Multivariable linear and logistic regression analyses were performed to assess the associations of procrastination assessed by using a one-item questionnaire and white-collar and blue-collar work with obesity-related factors.ResultsWhite-collar workers with high procrastination levels showed positive associations with BMI (B: 0.75, 95% CI 0.06 to 1.44) and adult weight change (B: 1.77, 95% CI 0.26 to 3.29), and had increased odds of AWG10 (OR: 1.85, 95% CI 1.04 to 3.29) and MetS (OR: 2.29 95% CI 1.18 to 4.44) after adjustment for age, education, work-related factors and lifestyle factors. However, such positive associations were not observed among blue-collar workers.ConclusionsProcrastination and white-collar work might have a joint effect on weight gain during adulthood and consequential obesity.

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Association of psychological distress and religious coping tendencies in parents of children recently diagnosed with cancer: A cross‐sectional study

Pediatric Blood & Cancer ◽

10.1002/pbc.28991 ◽

2021 ◽

Author(s):

J. Gregory Dolan ◽

Douglas L. Hill ◽

Jennifer A. Faerber ◽

Laura E. Palmer ◽

Lamia P. Barakat ◽

...

Keyword(s):

Psychological Distress ◽

Religious Coping ◽

Cross Sectional Study ◽

Sectional Study ◽

Cross Sectional

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Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan

BMJ Open ◽

10.1136/bmjopen-2020-046265 ◽

2021 ◽

Vol 11 (6) ◽

pp. e046265

Author(s):

Shotaro Doki ◽

Shinichiro Sasahara ◽

Daisuke Hori ◽

Yuichi Oi ◽

Tsukasa Takahashi ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Psychological Distress ◽

Cross Sectional Study ◽

Sectional Study ◽

Cross Sectional ◽

Explanatory Variables ◽

The Neural Network ◽

Significant Difference ◽

Total Data

ObjectivesPsychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists.DesignCross-sectional study.SettingWe conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists.ParticipantsAn AI model of the neural network and six psychiatrists.Primary outcomeThe accuracies of the AI model and psychiatrists for predicting psychological distress.MethodsIn total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model.ResultsThe accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy.ConclusionsA machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.

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