In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including “happy,” as a positive emotion and “anxiety,” “sad,” “frustrated,” as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information. To predict four different emotions, RF also showed a modest area under the receiver operating characteristic curve (AUC-ROC) of 0.70. CVRR, RMSSD, SDNN, high frequency (HF), and low frequency (LF) mostly contributed to the predictions. GBT displayed the second highest AUC (0.69). Comprehensive analyses revealed the benefits of the prediction accuracy of the RF and GBT methods and were beneficial to establish models to predict emotions based on autonomic nervous system indicators. The results implicated SDNN, RMSSD, CVRR, LF, and HF as important parameters for the predictions.

The Great East Japan Earthquake devasted the old community in coastal areas characterized by primary industry. The number of unemployed people increased from 150,000 to 190,000 after the earthquake. All of the adult residents of Shichigahama (18 years old or older), located in the coastal area of the Miyagi prefecture, whose houses were totally or majorly damaged, were recruited for a survey conducted in October 2011. All of the residents who responded with written informed consent were included in this study. Among 904 individuals who had a job before the Great East Japan Earthquake, 19% became unemployed. Concerning gender and age, 9% of young men, 34% of elderly men, 21% of young women, and 49% of elderly women became unemployed. Concerning the type of industry, 38%, 15%, and 16% of people who had belonged to the primary, secondary, and tertiary industries, respectively, before the disaster became unemployed. Those who became unemployed exhibited a significantly higher risk of insomnia compared to those who maintained jobs. The study pointed out the severe impact of the Great East Japan Earthquake on populations who had belonged to the primary industry, especially among elderly women, and its effect on sleep conditions.

No abstract

Post‐disaster mental health and psychosocial support have drawn attention in Japan after the 1995 Great Hanshin‐Awaji Earthquake, with mental health care centers for the affected communities being organized. After the catastrophe, a reconstruction budget was allocated to organize mental health care centers to provide psychosocial support for communities affected by the 2007 Chūetsu offshore earthquake, the 2011 Great East Japan Earthquake, and the 2016 Kumamoto Earthquake. There were several major improvements in post‐disaster mental health measures after the Great East Japan Earthquake. The Disaster Psychiatric Assistance Team system was organized after the earthquake to orchestrate disaster response related to the psychiatric health system and mental health of the affected communities. Special mental health care efforts were drawn to the communities affected by the nuclear power plant accident through Chemical, Biological, Radiological, Nuclear, and high yield Explosives, being succeeded by measures against the coronavirus pandemic. As another new movement after the Great East Japan Earthquake, the number of surveys involving communities affected by disasters has soared. More than 10 times the number of scientific publications were made in English during the decade following the Great East Japan Earthquake, compared with the previous decades. In this review, we examined the results and issues acquired in the 10 years since the Great East Japan Earthquake, proposing evidence‐based disaster psychiatry as the direction of future mental health measures related to emergency preparedness and response.

After disasters, people are often forced to reconstruct or move to new residences. This study aimed to reveal the association between the types of reconstructed residences and psychosocial or psychiatric conditions among the population. A total of 1071 adult residents in a coastal town, whose houses were destroyed by the tsunami caused by the Great East Japan Earthquake, enrolled in the study five years after the disaster. The type of reconstructed post-disaster residences (reconstructed on the same site/disaster-recovery public condominium/mass-translocation to higher ground/privately moving to remote areas) and the current psychosocial indicators were investigated. The results revealed that individuals living in public condominiums showed significantly worse scores on the Lubben Social Network Scale-6 (p < 0.0001) and the Center for Epidemiologic Studies Depression Scale (p < 0.0001), and slightly worse scores on the Kessler Psychological Distress Scale (p = 0.035) and the Impact of Event Scale-Revised (p = 0.028). Lower psychosocial indicator scores in the public condominium group were more remarkable in younger adults aged < 65 years. Insomnia evaluated using the Athens Insomnia Scale was not different among the four residential types. In summary, residents moving into disaster-recovery public condominiums are likely to have less social interaction, be more depressed, and may need additional interventions.