Development of a mobile monitoring program for anxiety and depression in pregnancy and evaluation of 3-month results.

BACKGROUND: The aims of this study were to develop a mobile mental health application (app) to scan the symptoms of anxiety, depression, and related factors during pregnancy; examine the effect of the app on pregnant women; and determine the factors related to using such an app. METHODS: A software platform called Perinatal Anxiety Depression Monitoring Platform (PADIP) was developed. This study included 320 pregnant women: 160 in the PADIP group and 160 in the control group. The PADIP group was screened monthly for 3 months for depression, anxiety, and sleep quality, and instant feedback was provided on scale scores. RESULTS: During the follow-up period, there was a significant decrease in depression and anxiety scale scores in the PADIP group but no significant difference in scale scores in the control group. The interface used for the app was important for scale scores. It was preferred by pregnant women with a high education level, higher Perinatal Anxiety Screening Scale scores, and lower sleep quality scores. CONCLUSIONS: PADIP use was associated with a decrease in depression and anxiety scores of pregnant women. It was more useful for patients with higher education levels and a history of a psychiatric disorder, but further research is needed to develop a more comprehensive model.

The Impact of the COVID-19 Pandemic on the Interest in Antidepressants: An Analysis of Worldwide Internet Searches With Google Trends Data.

OBJECTIVE: It is known that in the digital age we live in, people try to get information on many medical issues through Internet searches. Especially as a result of the COVID-19 pandemic triggering mental problems and health professionals' stay-at-home warnings, it has become difficult for individuals to receive psychiatric help, and this has encouraged accessing information about mental problems and their treatments through Internet searches. In this context, infodemiologic research, especially with Google Trends (GT; Google LLC, Mountain View, California, United States), has become very popular in recent years. In our study, it was aimed to examine the interest in frequently used antidepressants and the effect of the COVID-19 pandemic on Internet searches. METHODS: Search densities for five antidepressant drugs (sertraline, fluoxetine, citalopram, venlafaxine, duloxetine) that are frequently used around the world were examined on GT on 24/07/2023, and these searches were compared. Searches made within the last five years (24/07/2018-24/07/2023) were included in this study. Images were obtained using GT and Microsoft Excel 2019 (Microsoft Corporation, Redmond, Washington), and appropriate statistical analyses were performed with the SPSS Statistics version 22 (IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.). RESULTS: Sertraline was the most sought-after antidepressant before, during, and after the COVID-19 pandemic in the world. The searches related to sertraline increased gradually during the pandemic period, and this increase continued in the post-pandemic period. Other antidepressants whose search for it increased with the pandemic are fluoxetine, duloxetine, and venlafaxine. Searches for citalopram decreased during the pandemic process compared to the pre-pandemic period. CONCLUSION: According to worldwide Internet searches, the prominence of some antidepressant group drugs during the pandemic period may be a reflection of the effects of the COVID-19 pandemic on mental health. Additionally, GT can provide psychiatrists with valuable insights into which depression medications are gaining popularity with the general public over time.

Detection of depression and anxiety in the perinatal period using Marine Predators Algorithm and kNN.

Undiagnosed prenatal anxiety and depression have the potential to worsen and have an adverse effect on both the mother and the infant. Although the diagnosis is made by specialist doctors, it is unclear which parameters are more effective. Especially in medicine, it is crucial to diagnose disease with high accuracy. For this reason, in this study, a questionnaire study was first conducted on pregnant women, and real original data were collected. Then, the Marine Predators Algorithm (MPA), one of the current metaheuristic algorithms inspired by nature, was combined with K-Nearest Neighbors (kNN) to determine high-priority features in the collected data. As a result, five of the 147 features selected by the proposed method were determined as high priority and approved by the doctors. In addition, the proposed method is compared with the Chi-square method, which is one of the filter-based feature selection methods. Thanks to the proposed feature selection method based on MPA and kNN, it has been observed that the classification gives more successful results in a shorter time with 98.11% success, and the model supports the diagnosis stage of the doctors.