scholarly journals Associations of electronic device use before and after sleep with psychological distress in Chinese adults in Hong Kong: a cross-sectional study (Preprint)

10.2196/15403 ◽  
2019 ◽  
Author(s):  
Jung Jae Lee ◽  
Man Ping Wang ◽  
Tzu Tsun Luk ◽  
Ningyuan Guo ◽  
Sophia Siu-Chee Chan ◽  
...  
Keyword(s):  
Hong Kong ◽  
Psychological Distress ◽  
Electronic Device ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Chinese Adults ◽  
Cross Sectional ◽  
Before And After ◽  
Device Use

scholarly journals Associations of Electronic Device Use Before and After Sleep With Psychological Distress Among Chinese Adults in Hong Kong: Cross-Sectional Study (Preprint)

2019 ◽  
Author(s):  
Jung Jae Lee ◽  
Man Ping Wang ◽  
Tzu Tsun Luk ◽  
Ningyuan Guo ◽  
Sophia Siu-Chee Chan ◽  
...  
Keyword(s):  
Hong Kong ◽  
Psychological Distress ◽  
Depressive Symptoms ◽  
Electronic Device ◽  
Psychological Symptoms ◽  
High Rate ◽  
Chinese Adults ◽  
Cross Sectional ◽  
Before And After ◽  
Device Use

BACKGROUND Hong Kong has a high rate of electronic device (e-device; computer, smartphone, and tablet) use. However, little is known about the associations of the duration of e-device use before and after sleep with psychological symptoms. OBJECTIVE This study aimed to investigate the associations of the duration of e-device use before and after sleep with psychological distress. METHODS A probability-based telephone survey was conducted on 3162 Hong Kong adults (54.6% female; mean age 47.4 years, SD 18.3 years) in 2016. Multivariate linear and Poisson regressions were used to calculate adjusted regression coefficients (aBs) and prevalence ratios (aPRs) of anxiety and depressive symptoms (measured by Patient Health Questionnaire-4) for the duration from waking to the first e-device use (≥61, 31-60, 6-30, and ≤5 minutes) and the duration of e-device use before sleeping (≤5, 6-30, 31-60, and ≥61 minutes). RESULTS The first e-device use in ≤5 (vs ≥61) minutes after waking was associated with anxiety (aB 0.35, 95% CI 0.24-0.46; aPR 1.74, 95% CI 1.34-2.25) and depressive symptoms (aB 0.27, 95% CI 0.18-0.37; aPR 1.84, 95% CI 1.33-2.54). Using e-devices for ≥61 (vs ≤5) minutes before sleeping was also associated with anxiety (aB 0.17, 95% CI 0.04-0.31; aPR 1.32, 95% CI 1.01-1.73) and depressive symptoms (aB 0.17, 95% CI 0.05-0.28; aPR 1.47, 95% CI 1.07-2.02). E-device use both ≤5 minutes after waking and for ≥61 minutes before sleeping was strongly associated with anxiety (aB 0.68, 95% CI 0.47-0.90; aPR 2.64, 95% CI 1.90-3.67) and depressive symptoms (aB 0.55, 95% CI 0.36-0.74; aPR 2.56, 95% CI 1.69-3.88). CONCLUSIONS E-device use immediately (≤5 minutes) after waking and use for a long duration (≥61 minutes) before sleeping were associated with anxiety and depressive symptoms among Chinese adults in Hong Kong.

scholarly journals 732. Evaulation of Micafungin Treatment at Pediatric Patients: A cross-sectional Study from Tertiary Pediatric Centre

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S416-S417
Author(s):  
Kamile Arıkan ◽  
Nuri Bayram ◽  
İlker devrim ◽  
Ayküke Akaslan-Kara
Keyword(s):  
Amphotericin B ◽  
Liver Function Tests ◽  
Cross Sectional Study ◽  
Resistance Rate ◽  
Sectional Study ◽  
Candida Spp ◽  
Cross Sectional ◽  
Median Serum ◽  
Before And After ◽  
Resistance Patterns

Abstract Background Micafungin is one of three currently available echinocandin for treatment of candidiasis and candidemia. Methods Children who were treated for micafungin for possible or proven invasive Candidia infection between May 2017 and October 2019 were included. Results In this cross-sectional study, totally 78 children with a median age of 3 months (8 days -17 years), 50 (64.1%, F/M: 0.56) male were included. Thirty four (43.6%) patients were neonate, 26 (76 %) of them were premature. Thirty seven patients (47.4%) received micafungin for candidemia and 41 (52.6%) patients received micafungin empirically for IC. Twelve (32.4%) Candida spp cultured were C. albicans, the rest twenty five (67.6%) Candida spp were non-albicans Candida spp. The most commonly cultured Candida spp was Candida parapsilosis (C. parapsilosis) (n=13) followed by C. albicans (n=12), C. glabrata (n=3), C. tropicalis (n=3), C. guilliermondii (n=3), C. krusei (n=2) respectively. Resistance rate of C. parapsilosis (n=13) isolates to fluconazole, voriconazole, amphotericin B, caspofungin, micafungin were as follows respectively; 66.7%, 100%, 69.2%, 90.9%, 37.5% respectively. Resistance rate of C. albicans (n=11) isolates to fluconazole, voriconazole, amphotericin B, caspofungin, micafungin were as follows respectively; 50%, 50%, 12.5%, 42.9%, 0% respectively. None of the C. tropicalis, C. guilliermondii and C. krusei isolates were resistant to micafungin. Culture negativity could not be achieved at the end of 14th day of micafungin treatment in the 15 (16.9%) candidemia episodes. The most commonly isolated Candida spp in patients with treatment failure was C. parapsilosis (n=7), the other species were; C. albicans (n=5), C. guilliermondii (n=1), C. tropicalis (n=1) and C. tropicalis and C. guilliermondii coinfection (n=1) respectively. Median serum AST, ALT and creatinin levels didn’t increase during and at the end of micafungin therapy. None of these patients had experienced an anormal kidney or liver function tests due to micafungin usage. Characteristics of patients who received micafungin.and cultured Candida spp Antifungal resistance patterns of Candida spp. Laboratory change before and after micafungin treatment Conclusion Increase in fluconazole resistant Candida spp makes micafungin a reasonable and effective choice for suspected or proven invasive candidiasis Disclosures All Authors: No reported disclosures

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

BMJ Open ◽  
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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