Sleep problems predict next-day suicidal thinking among adolescents: A multimodal real-time monitoring study following discharge from acute psychiatric care

2021 ◽  
Vol 33 (5) ◽  
pp. 1701-1721
Author(s):  
Catherine R. Glenn ◽  
Evan M. Kleiman ◽  
Jaclyn C. Kearns ◽  
Anne E. Boatman ◽  
Yeates Conwell ◽  
...  
Keyword(s):  
Real Time ◽  
Psychiatric Care ◽  
Sleep Problems ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Real Time Monitoring ◽  
Short Term ◽  
Suicidal Thinking ◽  
Suicidal Thoughts And Behaviors ◽  
Acute Psychiatric Care

AbstractSuicidal thoughts and behaviors (STBs) are major public health concerns among adolescents, and research is needed to identify how risk is conferred over the short term (hours and days). Sleep problems may be associated with elevated risk for STBs, but less is known about this link in youth over short time periods. The current study utilized a multimodal real-time monitoring approach to examine the association between sleep problems (via daily sleep diary and actigraphy) and next-day suicidal thinking in 48 adolescents with a history of STBs during the month following discharge from acute psychiatric care. Results indicated that specific indices of sleep problems assessed via sleep diary (i.e., greater sleep onset latency, nightmares, ruminative thoughts before sleep) predicted next-day suicidal thinking. These effects were significant even when daily sadness and baseline depression were included in the models. Moreover, several associations between daily-level sleep problems and next-day suicidal thinking were moderated by person-level measures of the construct. In contrast, sleep indices assessed objectively (via actigraphy) were either not related to suicidal thinking or were related in the opposite direction from hypothesized. Together, these findings provide some support for sleep problems as a short-term risk factor for suicidal thinking in high-risk adolescents.

scholarly journals Frequent Assessment of Suicidal Thinking does not Increase Suicidal Thinking: Evidence from a High-Resolution Real-Time Monitoring Study

2020 ◽  
Author(s):  
Daniel D.L. Coppersmith ◽  
Rebecca Fortgang ◽  
Evan Kleiman ◽  
Alexander Millner ◽  
April Yeager ◽  
...  
Keyword(s):  
Real Time ◽  
Time Increase ◽  
Monitoring Methods ◽  
Real Time Monitoring ◽  
Suicidal Thoughts ◽  
Suicidal Thinking ◽  
Monitoring Study ◽  
Suicidal Thoughts And Behaviors ◽  
Frequent Assessment ◽  
Repeated Assessment

Researchers, clinicians, and patients are increasingly using real-time monitoring methods to understand and predict suicidal thoughts and behaviors. These methods involve frequently assessing suicidal thoughts, but it is unknown if asking about suicide repeatedly is iatrogenic. We tested two questions about this approach: (1) does repeatedly assessing suicidal thinking over short periods of time increase suicidal thinking? (2) is more frequent assessment of suicidal thinking associated with more severe suicidal thinking? In a real-time monitoring study (N = 81, number of surveys = 9,819), we found no evidence to support the notion that repeated assessment of suicidal thoughts is iatrogenic.

Real‐time monitoring of nutrients in the C hangjiang E stuary reveals short‐term nutrient‐algal bloom dynamics

2017 ◽  
Vol 122 (7) ◽  
pp. 5390-5403 ◽  
Author(s):  
Kui Wang ◽  
Jianfang Chen ◽  
Xiaobo Ni ◽  
Dingyong Zeng ◽  
Dewang Li ◽  
...  
Keyword(s):  
Real Time ◽  
Algal Bloom ◽  
Real Time Monitoring ◽  
Short Term

Real-time monitoring of short-term voltage stability using PMU data

2014 ◽  
Author(s):  
Sambarta Dasgupta ◽  
Magesh Paramasivam ◽  
Umesh Vaidya ◽  
Venkataramana Ajjarapu
Keyword(s):  
Real Time ◽  
Voltage Stability ◽  
Real Time Monitoring ◽  
Short Term

Effect of frequent assessment of suicidal thinking on its incidence and severity: high-resolution real-time monitoring study

2021 ◽  
pp. 1-3
Author(s):  
Daniel D. L. Coppersmith ◽  
Rebecca G. Fortgang ◽  
Evan M. Kleiman ◽  
Alexander J. Millner ◽  
April L. Yeager ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Time Increase ◽  
Monitoring Methods ◽  
Real Time Monitoring ◽  
Suicidal Thoughts ◽  
Suicidal Thinking ◽  
Monitoring Study ◽  
Frequent Assessment ◽  
Repeated Assessment

Summary Researchers, clinicians and patients are increasingly using real-time monitoring methods to understand and predict suicidal thoughts and behaviours. These methods involve frequently assessing suicidal thoughts, but it is not known whether asking about suicide repeatedly is iatrogenic. We tested two questions about this approach: (a) does repeatedly assessing suicidal thinking over short periods of time increase suicidal thinking, and (b) is more frequent assessment of suicidal thinking associated with more severe suicidal thinking? In a real-time monitoring study (n = 101 participants, n = 12 793 surveys), we found no evidence to support the notion that repeated assessment of suicidal thoughts is iatrogenic.

Are suicidal thoughts reinforcing? A preliminary real-time monitoring study on the potential affect regulation function of suicidal thinking

2018 ◽  
Vol 232 ◽  
pp. 122-126 ◽  
Author(s):  
Evan M. Kleiman ◽  
Daniel D.L. Coppersmith ◽  
Alexander J. Millner ◽  
Peter J. Franz ◽  
Kathryn R. Fox ◽  
...  
Keyword(s):  
Real Time ◽  
Affect Regulation ◽  
Real Time Monitoring ◽  
Suicidal Thoughts ◽  
Suicidal Thinking ◽  
Monitoring Study ◽  
Regulation Function

scholarly journals Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7099
Author(s):  
Kyutae Kim ◽  
Jongpil Jeong
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Real Time Monitoring ◽  
Short Term ◽  
Term Memory ◽  
Anomalous Data ◽  
Long Short Term Memory

By monitoring a hydraulic system using artificial intelligence, we can detect anomalous data in a manufacturing workshop. In addition, by analyzing the anomalous data, we can diagnose faults and prevent failures. However, artificial intelligence, especially deep learning, needs to learn much data, and it is often difficult to get enough data at the real manufacturing site. In this paper, we apply augmentation to increase the amount of data. In addition, we propose real-time monitoring based on a deep-learning model that uses convergence of a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism. CNN extracts features from input data, and BiLSTM learns feature information. The learned information is then fed to the sigmoid classifier to find out if it is normal or abnormal. Experimental results show that the proposed model works better than other deep-learning models, such as CNN or long short-term memory (LSTM).

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