Using machine learning analyses of speech to classify levels of expressed emotion in parents of youth with mood disorders

Natural language processing for mental health interventions: a systematic review and research framework

Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP's potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients' clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers' characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.

Caregiver Expressed Emotion in Families of Youth With Spina Bifida: Demographic, Medical, and Family Correlates

OBJECTIVE

Caregiver expressed emotion (EE), an interview-based measure of emotional valence within an interpersonal relationship, is associated with psychosocial outcomes across multiple conditions. Guided by a model implicating a bidirectional role of "Chronic Family Stress" in the unfolding of EE in family environments, the current study examined demographic, medical, and family-level variables in association with EE in caregivers of children with spina bifida (SB).

METHODS

Data were combined from 2 distinct studies of families with a child with SB, resulting in a sample of 174 (ages 8-17). Linear regressions examined the family stressors and child variables in association with maternal and paternal warmth and criticism, as coded from EE interviews.

RESULTS

Higher levels of family stress were associated with paternal criticism (p = .03), while having non-Hispanic White children was associated with both maternal and paternal criticism (ps < .005). Having children younger in age (ps < .01) and without a shunt (ps < .01) was associated with higher warmth.

CONCLUSIONS

Family stressors, absence of the negative impacts of systemic racism, shunt status, and age appear to be associated with the expression of EE in caregivers of a child with SB. Findings highlight multiple assessment considerations, including assessing EE when children are younger to engage caregivers with children with SB when they are more likely to be expressing more warmth. Pinpointing factors associated with caregiver EE in SB will help to better identify families at risk for high levels of criticism and also aid in the development of targeted prevention and intervention programs.

Social and environmental variables as predictors of mania: a review of longitudinal research findings

Considerable evidence suggests that psychosocial variables can shape the course of bipolar disorder. Here, though, we focus on the more specific idea that the social environment can predict the course of mania. We systematically review evidence from longitudinal studies concerning how social support, family interactions, traumatic life events, and recent life events relate to the age of onset, the frequency of episode recurrence, and the severity of manic symptoms. Although we find some evidence that the course of mania can be worsened by social environmental factors, the links are specific. Among social variables, some studies indicate that conflict and hostility are predictive, but more general social relationship qualities have not been found to predict mania. Some research indicates that childhood trauma, and recent life events involving goal attainment or sleep disruption can predict mania. Taken together, the profile of variables involving recent exposure that are most predictive include those that are activating, reward-related, or sleep-disrupting, which fits with general psychological hypotheses of behavioral activation and sleep disruption as important for mania. We discuss gaps in the literature, and we note future directions for research, including the need for more integrative, longitudinal research on a fuller range of social and biological risk variables.

Digital phenotype of mood disorders: A conceptual and critical review

BACKGROUND

Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care.

OBJECTIVE

The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders.

METHODS

We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence.

RESULTS

Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV).

CONCLUSION

The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.