Predicting emotional states using behavioral markers derived from passively sensed data: Data-driven machine learning approach

Background: Mental health disorders affect multiple aspects of patients’ lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient’s mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective: This study aims to present a machine learning–based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days’ worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals’ overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days’ data. Conclusions: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients’ mood states.This project has received funding from the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie grant agreement number 813533. This work was partly supported by the Spanish government (Ministerio de Ciencia e Innovación) under grants TEC2017-92552-EXP and RTI2018-099655-B-100; the Comunidad de Madrid under grants IND2017/TIC-7618, IND2018/TIC-9649, IND2020/TIC-17372, and Y2018/TCS-4705; the BBVA Foundation under the Domain Alignment and Data Wrangling with Deep Generative Models (Deep-DARWiN) project; and the European Union (European Regional Development Fund and the European Research Council) through the European Union's Horizon 2020 Research and Innovation Program under grant 714161. The authors thank Enrique Baca-Garcia for providing demographic and clinical data and assisting in interpreting and summarizing the data

From eHealth to iHealth: Transition to participatory and personalized medicine in mental health

Clinical assessment in psychiatry is commonly based on findings from brief, regularly scheduled in-person appointments. Although critically important, this approach reduces assessment to cross-sectional observations that miss essential information about disease course. The mental health provider makes all medical decisions based on this limited information. Thanks to recent technological advances such as mobile phones and other personal devices, electronic health (eHealth) data collection strategies now can provide access to real-Time patient self-report data during the interval between visits. Since mobile phones are generally kept on at all times and carried everywhere, they are an ideal platform for the broad implementation of ecological momentary assessment technology. Integration of these tools into medical practice has heralded the eHealth era. Intelligent health (iHealth) further builds on and expands eHealth by adding novel built-in data analysis approaches based on (1) incorporation of new technologies into clinical practice to enhance real-Time self-monitoring, (2) extension of assessment to the patient's environment including caregivers, and (3) data processing using data mining to support medical decision making and personalized medicine. This will shift mental health care from a reactive to a proactive and personalized discipline.This research was partially support by Instituto de Salud Carlos III (PI16/01852 Grant), Plan Nacional de Drogas (20151073 Project), and American Foundation for Suicide Prevention (LSRG-1-005-16). SB’s work was supported by Fondation de l’Avenir, the French Embassy in Madrid; MMPR's work was supported by a National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award (YIA) grant and a KL2 Faculty Scholar (KL2TR001435) grant (PI: Perez-Rodriguez

Can the Holmes-Rahe Social Readjustment Rating Scale (SRRS) Be Used as a Suicide Risk Scale? An Exploratory Study

The objective of this research was to examine whether the Holmes-Rahe Social Readjustment Rating Scale, a life event scale, can be used to identify suicide attempters. The Holmes-Rahe Social Readjustment Rating Scale\u27s ability to identify suicide attempters was tested in 1183 subjects (478 suicide attempters, 197 psychiatric inpatients, and 508 healthy controls) using the Fisher Linear Discriminant Analysis and traditional psychometric methods. The Fisher Linear Discriminant Analysis outperformed traditional psychometric approaches (area under the curve: 0.85 vs. 0.78; p \u3c 0.05) and indicated that this scale may be used to identify suicide attempters. The life events that better characterized suicide attempters were change in frequency of arguments, marital separation, and personal injury. The Holmes-Rahe Social Readjustment Rating Scale may help identify suicide attempters

Stability of childhood anxiety disorder diagnoses: a follow-up naturalistic study in psychiatric care

Few studies have examined the stability of major psychiatric disorders in pediatric psychiatric clinical populations. The objective of this study was to examine the long-term stability of anxiety diagnoses starting with pre-school age children through adolescence evaluated at multiple time points. Prospective cohort study was conducted of all children and adolescents receiving psychiatric care at all pediatric psychiatric clinics belonging to two catchment areas in Madrid, Spain, between 1 January, 1992 and 30 April, 2006. Patients were selected from among 24,163 children and adolescents who received psychiatric care. Patients had to have a diagnosis of an ICD-10 anxiety disorder during at least one of the consultations and had to have received psychiatric care for the anxiety disorder. We grouped anxiety disorder diagnoses according to the following categories: phobic disorders, social anxiety disorders, obsessive–compulsive disorder (OCD), stress-related disorders, and "other" anxiety disorders which, among others, included generalized anxiety disorder, and panic disorder. Complementary indices of diagnostic stability were calculated. As much as 1,869 subjects were included and had 27,945 psychiatric/ psychological consultations. The stability of all ICD-10 anxiety disorder categories studied was high regardless of the measure of diagnostic stability used. Phobic and social anxiety disorders showed the highest diagnostic stability, whereas OCD and "other" anxiety disorders showed the lowest diagnostic stability. No significant sex differences were observed on the diagnostic stability of the anxiety disorder categories studied. Diagnostic stability measures for phobic, social anxiety, and "other" anxiety disorder diagnoses varied depending on the age at first evaluation. In this clinical pediatric outpatient sample it appears that phobic, social anxiety, and stress-related disorder diagnoses in children and adolescents treated in community outpatient services may have high diagnostic stability

Worldwide impact of economic cycles on suicide trends over 3 decades: Differences according to level of development. A mixed effect model study

Objectives: To investigate the trends and correlations of gross domestic product (GDP) adjusted for purchasing power parity (PPP) per capita on suicide rates in 10 WHO regions during the past 30 years. Design: Analyses of databases of PPP-adjusted GDP per capita and suicide rates. Countries were grouped according to the Global Burden of Disease regional classification system. Data sources: World Bank’s official website and WHO’s mortality database. Statistical analyses: After graphically displaying PPP-adjusted GDP per capita and suicide rates, mixed effect models were used for representing and analysing clustered data. Results: Three different groups of countries, based on the correlation between the PPP-adjusted GDP per capita and suicide rates, are reported: (1) positive correlation: developing (lower middle and upper middle income) Latin-American and Caribbean countries, developing countries in the South East Asian Region including India, some countries in the Western Pacific Region (such as China and South Korea) and high-income Asian countries, including Japan; (2) negative correlation: high-income and developing European countries, Canada, Australia and New Zealand and (3) no correlation was found in an African country. Conclusions: PPP-adjusted GDP per capita may offer a simple measure for designing the type of preventive interventions aimed at lowering suicide rates that can be used across countries. Public health interventions might be more suitable for developing countries. In high-income countries, however, preventive measures based on the medical model might prove more usefulAll authors have completed the Unified Competing Interest form. Dr. Blasco-Fontecilla acknowledges the Spanish Ministry of Health (Rio Hortega CMO8/00170; SAF2010-21849), Alicia Koplowitz Foundation and Conchita Rabago Foundation for funding his post-doctoral stage at CHRU, Montpellier, France

Shift in social media app usage during covid-19 lockdown and clinical anxiety symptoms: Machine learning-based ecological momentary assessment study

Background: Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. Objective: We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. Methods: The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. Results: Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. Conclusions: Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae.JR was supported by the American Psychiatric Association 2021 Junior Psychiatrist Research Colloquium (NIDA R-13 grant). ES received funding from the European Union Horizon 2020 research and innovation program (Marie Sklodowska-Curie grant 813533). AA is supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (RTI2018-099655-B-I00), the Comunidad de Madrid (Y2018/TCS-4705 PRACTICO-CM), and the BBVA Foundation (Deep-DARWiN grant)

Genetic heterogeneity in the toxicity to systemic adenoviral gene transfer of interleukin-12

Despite the efficacy of IL-12 in cancer experimental models, clinical trials with systemic recombinant IL-12 showed unacceptable toxicity related to endogenous IFNgamma production. We report that systemic administration of a recombinant adenovirus encoding IL-12 (AdCMVmIL-12) has a dramatically different survival outcome in a number of mouse pure strains over a wide range of doses. For instance at 2.5 x 10(9) p.f.u., systemic AdCMVmIL-12 killed all C57BL/6 mice but spared all BALB/c mice. Much higher IFNgamma concentrations in serum samples of C57BL/6 than in those from identically treated BALB/c were found. Causes for heterogeneous toxicity can be traced to differences among murine strains in the levels of gene transduction achieved in the liver, as assessed with adenovirus coding for reporter genes. In accordance, IL-12 serum concentrations are higher in susceptible mice. In addition, sera from C57BL/6 mice treated with AdCMVmIL-12 showed higher levels of IL-18, a well-known IFNgamma inducer. Interestingly, lethal toxicity in C57BL/6 mice was abolished by administration of blocking anti-IFNgamma mAbs and also by simultaneous depletion of T cells, NK cells, and macrophages. These observations together with the great dispersion of IFNgamma produced by human PBMCs upon in vitro stimulation with IL-12, or infection with recombinant adenovirus encoding IL-12, suggest that patients might also show heterogeneous degrees of toxicity in response to IL-12 gene transfer

Acquired potential N-glycosylation sites within the tumor-specific immunoglobulin heavy chains of B-cell malignancies

Background and Objectives. Among B-cell malignancies, follicular lymphomas (FL) more frequently show acquired, potential N-glycosylation sites (AGS) within tumor-specific immunoglobulin. The aim of this study was to extend this observation and to evaluate the pattern of presentation of AGS within five different forms of B-cell lymphoma. Design and Methods. We sequenced the tumor-specific immunoglobulin heavy chain variable region fragment, including complementarity-determining regions 2 and 3, of forty-seven consecutive patients with a B-cell malignancy enrolled in idiotype vaccine clinical trials. This sequencing approach is known to allow the identification of most AGS. We then statistically analyzed differences in presentation pattern, in terms of tumor histology, immunoglobulin isotype, AGS location and amino acid composition. Results. All twenty-four FL cases presented with at least one AGS, whereas the vast majority of four B-cell lymphoma types other than FL did not. The non- FL group of tumors included four cases of Burkitt’s lymphoma, six of diffuse large cell lymphoma, seven mantle cell lymphomas and six small lymphocytic lymphomas. Most IgM-bearing follicular lymphoma cases featured their AGS within complementarity-determining region 2, as opposed to those bearing an IgG, which mostly displayed the AGS within complementarity- determining region 3. The vast majority of AGS located within either complementarity- determining region ended with a serine residue, whereas those located within framework regions mostly featured threonine as the last amino acid residue. Interpretation and Conclusions. In our series, all cases of FL had AGS within their tumor-specific immunoglobulin heavy chain variable regions. In contrast, most B-cell malignancies other than FL did not. Further studies are warranted in order to establish the possible meaning of these findings in terms of disease pathogenesis, their diagnostic value in doubtful cases and their potential implications for immunotherapy

Personality Disorders and Health Problems Distinguish Suicide Attempters from Completers in a Direct Comparison

Background Whether suicide attempters and completers represent the same population evaluated at different points along a progression towards suicide death, overlapping populations, or completely different populations is a problem still unresolved. Methods 446 Adult suicide attempters and knowledgeable collateral informants for 190 adult suicide probands were interviewed. Sociodemographic and clinical data was collected for both groups using semi-structured interviews and structured assessments. Univariate analyses and logistic regression models were conducted to explore the similarities and differences between suicide attempters and completers. Results Univariate analyses yielded significant differences in sociodemographics, recent life events, impulsivity, suicide intent, and distribution of Axis I and II disorders. A logistic regression model aimed at distinguishing suicide completers from attempters properly classified 90% of subjects. The most significant variables that distinguished suicide from attempted suicide were the presence of narcissistic personality disorder (OR=21.4; 95% CI=6.8–67.7), health problems (OR=20.6; 95% CI=5.6–75.9), male sex (OR=9.6; 95% CI=4.42–20.9), and alcohol abuse (OR=5.5; 95% CI=2.3–14.2). Limitations Our study shares the limitations of studies comparing suicide attempters and completers, namely that information from attempters can be obtained from the subject himself, whereas the assessment of completers depends on information from close family or friends. Furthermore, different semi-structured instruments assessed Axis I and Axis II disorders in suicide attempters and completers. Finally, we have no data on inter-rater reliability data. Conclusions Suicide completers are more likely to be male and suffer from alcohol abuse, health problems (e.g. somatic illness), and narcissistic personality disorder. The findings emphasize the importance of implementing suicide prevention programs tailored to suicide attempters and completers