Artificial Intelligence in Digital Mental Health

The prevention of diseases considered a scourge of our society, as for example mental illness, particularly anxiety disorders and depressive states, is a primary and urgent goal today and a priority axis of the EU. Mental illness includes many clinical conditions associated with several changes that include limitations related with social interaction or several tasks such as sleeping through the night, doing homework, making friends, thinking capacity and reality understanding, deficits in communication skills, and difficulties in developing appropriate emotional and behavioural response. Artificial intelligence has gained a prominent role in the management and delivery of healthcare. There is a growth in mobile devices applied to health with high mobility, connectivity, and processing capacity. This chapter provides an analysis of the actual trends regarding the main problems that can be dealt with using AI in mental healthcare and the corresponding main techniques used to deal with these problems. Additionally, some case studies for using AI for mental health care are described.

Performance Evaluation of a Voice-Based Depression Assessment System Considering the Number and Type of Input Utterances

It is empirically known that mood changes affect facial expressions and voices. In this study, the authors have focused on the voice to develop a method for estimating depression in individuals from their voices. A short input voice is ideal for applying the proposed method to a wide range of applications. Therefore, we evaluated this method using multiple input utterances while assuming a unit utterance input. The experimental results revealed that depressive states could be estimated with sufficient accuracy using the smallest number of utterances when positive utterances were included in three to four input utterances.

A Privacy-Oriented Approach for Depression Signs Detection Based on Speech Analysis

Currently, AI-based assistive technologies, particularly those involving sensitive data, such as systems for detecting mental illness and emotional disorders, are full of confidentiality, integrity, and security compromises. In the aforesaid context, this work proposes an algorithm for detecting depressive states based on only three never utilized speech markers. This reduced number of markers offers a valuable protection of personal (sensitive) data by not allowing for the retrieval of the speaker’s identity. The proposed speech markers are derived from the analysis of pitch variations measured in speech data obtained through a tale reading task performed by typical and depressed subjects. A sample of 22 subjects (11 depressed and 11 healthy, according to both psychiatric diagnosis and BDI classification) were involved. The reading wave files were listened to and split into a sequence of intervals, each lasting two seconds. For each subject’s reading and each reading interval, the average pitch, the pitch variation (T), the average pitch variation (A), and the inversion percentage (also called the oscillation percentage O) were automatically computed. The values of the triplet (Ti, Ai, Oi) for the i-th subject provide, all together, a 100% correct discrimination between the speech produced by typical and depressed individuals, while requiring a very low computational cost and offering a valuable protection of personal data.

Within- and across-day patterns of interplay between depressive symptoms and related psychopathological processes: a dynamic network approach during the COVID-19 pandemic

Background In order to understand the intricate patterns of interplay connected to the formation and maintenance of depressive symptomatology, repeated measures investigations focusing on within-person relationships between psychopathological mechanisms and depressive components are required. Methods This large-scale preregistered intensive longitudinal study conducted 68,240 observations of 1706 individuals in the general adult population across a 40-day period during the COVID-19 pandemic to identify the detrimental processes involved in depressive states. Daily responses were modeled using multi-level dynamic network analysis to investigate the temporal associations across days, in addition to contemporaneous relationships between depressive components within a daily window. Results Among the investigated psychopathological mechanisms, helplessness predicted the strongest across-day influence on depressive symptoms, while emotion regulation difficulties displayed more proximal interactions with symptomatology. Helplessness was further involved in the amplification of other theorized psychopathological mechanisms including rumination, the latter of which to a greater extent was susceptible toward being influenced rather than temporally influencing other components of depressive states. Distinctive symptoms of depression behaved differently, with depressed mood and anhedonia most prone to being impacted, while lethargy and worthlessness were more strongly associated with outgoing activity in the network. Conclusions The main mechanism predicting the amplifications of detrimental symptomatology was helplessness. Lethargy and worthlessness revealed greater within-person carry-over effects across days, providing preliminary indications that these symptoms may be more strongly associated with pushing individuals toward prolonged depressive state experiences. The psychopathological processes of rumination, helplessness, and emotion regulation only exhibited interactions with the depressed mood and worthlessness component of depression, being unrelated to lethargy and anhedonia. The findings have implications for the impediment of depressive symptomatology during and beyond the pandemic period. They further outline the gaps in the literature concerning the identification of psychopathological processes intertwined with lethargy and anhedonia on the within-person level.

Relationships Between Care Recipients’ Psychological Symptoms and Family Caregivers’ Depressive States

This cross-sectional study aims to clarify the relationship between the number of behavioral and psychological symptoms in long-term care recipients and family caregivers’ depressive states. Participants were 38 family caregivers who had provided care for their relatives for 6 years or more; they were recruited from in-home care settings in Aichi, Gifu, and Shiga Prefectures, Japan. Participants answered a self-administered questionnaire assessing their depressive state using the Japanese version of the Center for Epidemiologic Studies for Depression Scale (CES-D). They also answered questions inquiring about behavioral and psychological symptoms of care recipients, including resistance to care, irritability, and feelings of persecution. Using CES-D scores, 11 participants were categorized as depressed and 27 as non-depressed. Depressive symptoms were significantly greater in those with more behavioral and psychological symptoms, following adjustment for confounding factors. The odds ratio of being in a depressive state was 9.43 higher for those with more than 4 behavioral and psychological symptoms compared to those with none, showing a distinct threshold for the influence of behavioral and psychological symptoms on depressive state. Knowing the number of behavioral and psychological symptoms of care recipients may help quickly identify depressed caregivers and alleviate depressive symptoms.

ANXIETY-DEPRESSIVE STATES IN SOMATIC DISEASES AND THEIR CORRECTION BY MEANS OF SPIRITUAL CULTURE THERAPY METHODS

Тревожно-депрессивные расстройства широко распространены у пациентов с соматической патологией. Повышенный уровень тревоги, стресс, депрессия являются факторами риска развития и прогрессирования соматических заболеваний. Показана высокая сопряженность тревожно-депрессивных расстройств с заболеваниями сердечно-сосудистой и дыхательной систем, пищеварительного тракта, с нарушениями обмена веществ. У большого количества пациентов соматические (вегетативные) проявления являются вторичными, возникая на фоне тревожных расстройств. Большинство пациентов с соматическими заболеваниями, несмотря на выраженный дезадаптирующий характер тревоги, не получают специализированной помощи.

The Prevalence of Adolescent Depression in Modern Russian Society

One of the pressing problems of modern society is the problem of teenage depression. The article presents the results of an empirical study, which involved 600 adolescents aged 15-16 years. The aim of the study was to analyze the prevalence of depressive conditions in modern adolescents. As a result of a comparative analysis of the empirical data obtained with the data of other studies, it was concluded that at present in adolescence, manifestations of depressive states are quite frequent, while objective changes in the social environment lead to an increase in the frequency and intensity of depressiveness indicators.

Psychoses after typhus. Vinokurova, Prof. Gilyarovsky (Journal of Psychol., Neurol. and Psychiatry, 1922, I)

Typhus brings a number of severe changes to the mental sphere of patients, which are characterized as asthenic-depressive states of emotional weakness, and clinically can be united into one group under the name of post-infectious asthenia.

Within and across-day patterns of interplay between depressive symptoms and related psychopathological processes: A dynamic network approach during the COVID-19 pandemic

In order to understand the intricate patterns of interplay connected to the formation and maintenance of depressive symptomatology, repeated measures investigations focusing on within-person relationships between psychopathological mechanisms and depressive components are required. This large-scale preregistered intensive longitudinal study conducted 68 240 observations of 1706 individuals in the general adult population across a 40-day period during the COVID-19 pandemic to identify the detrimental processes involved in depressive states. Daily responses were modeled using multi-level dynamic network analysis to investigate temporal associations across days, in addition to contemporaneous relationships between depressive components within a daily window. Among the investigated psychopathological mechanisms, helplessness predicted the strongest across-day influence on depressive symptoms, while emotion regulation difficulties displayed more proximal interactions with symptomatology. Helplessness was further involved in the amplification of other theorized psychopathological mechanisms including rumination, the latter of which to a greater extent was susceptible toward being influenced rather than temporally influencing other components involved in depressive states. Distinctive symptoms of depression behaved differently, with depressed mood and anhedonia most prone to being impacted, while lethargy and worthlessness were more strongly associated with outgoing activity in the network. These results have implications for the impediment of detrimental depressive symptomatology during the present pandemic and beyond the pandemic period.