User profiles of an electronic mental health tool for ecological momentary assessment: MEmind

Temporal prediction of suicidal ideation in an ecological momentary assessment study with recurrent neural networks

INTRODUCTION

Ecological Momentary Assessment (EMA) holds promise for providing insights into daily life experiences when studying mental health phenomena. However, commonly used mixed-effects linear statistical models do not fully utilize the richness of the ultidimensional time-varying data that EMA yields. Recurrent Neural Networks (RNNs) provide an alternative data analytic method to leverage more information and potentially improve prediction, particularly for non-normally distributed outcomes.

METHODS

As part of a broader research study of suicidal thoughts and behavior in people with borderline personality disorder (BPD), eighty-four participants engaged in EMA data collection over one week, answering questions multiple times each day about suicidal ideation (SI), stressful events, coping strategy use, and affect. RNNs and mixed-effects linear regression models (MEMs) were trained and used to predict SI. Root mean squared error (RMSE), mean absolute percent error (MAPE), and a pseudo-R2 accuracy metric were used to compare SI prediction accuracy between the two modeling methods.

RESULTS

RNNs had superior accuracy metrics (full model: RMSE = 3.41, MAPE = 42 %, pseudo-R2 = 26 %) compared with MEMs (full model: RMSE = 3.84, MAPE = 56 %, pseudo-R2 = 16 %). Importantly, RNNs showed significantly more accurate prediction at higher values of SI. Additionally, RNNs predicted, with significantly higher accuracy, the SI scores of participants with depression diagnoses and of participants with higher depression scores at baseline.

CONCLUSION

In this EMA study with a moderately sized sample, RNNs were better able to learn and predict daily SI compared with mixed-effects models. RNNs should be considered as an option for EMA analysis.

Perceived burdensomeness, thwarted belongingness, and hopelessness as predictors of future suicidal ideation in Spanish university students

The Interpersonal Theory of Suicide (IPTS) has received support for its role in understanding suicidal thoughts and behaviors. However, few longitudinal studies have focused on testing this theory in university students. The present study aimed to confirm the theoretical model of the IPTS in a sample of 225 Spanish university students, using path analysis in a longitudinal study. We assessed thwarted belongingness and perceived burdensomeness at T1 and hopelessness and suicidal ideation at T2, 12-14 weeks later. Moreover, we assessed suicidal ideation weekly for 14 weeks. Path analyses confirmed the Interpersonal Theory of Suicide model, with thwarted belongingness and perceived burdensomeness as direct and indirect predictors of suicidal ideation through hopelessness. Providers of guidance and clinical services in university settings should be trained to identify perceived burdensomeness, social belongingness, hopelessness, and suicidal ideation when screening for suicide prevention.

Meaning in life mediates the association between depressive symptoms and future frequency of suicidal ideation in Spanish university students: A longitudinal study

INTRODUCTION

There is a need for longitudinal studies that focus on protective factors against suicide in Spain. We analyzed the estimated prevalence of suicidal ideation in a sample of Spanish university students. Second, we explored the relationship between future suicidal ideation, depressive symptoms, suicidal ideation at T1, and meaning in life and its dimensions of meaning and purpose. Third, we analyzed the mediation role of meaning in life between depressive symptoms and suicidal ideation evaluated with Ecological Momentary Assessment (EMA).

METHOD

In this longitudinal study, a total of 718 Spanish university students were assessed at T1, of whom 279 completed questionnaires along with EMA (T2).

RESULTS

The estimated prevalence of suicidal ideation was 8.4%. Levels of depressive symptoms were positively correlated with suicidal ideation and negatively with meaning in life and its dimensions of meaning and purpose. Meaning in life and its dimensions mediated the relationship between depressive symptoms and subsequent suicidal ideation.

DISCUSSION

There is a high prevalence of suicidal ideation among Spanish university students, and it is associated with depressive symptoms and meaning in life, with the latter acting as a protective factor. Thus, psychotherapeutic prevention programs from a logotherapeutic perspective could help to reduce students' suicide risk.

Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study

BACKGROUND

Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients' functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily.

OBJECTIVE

This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers.

METHODS

One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison.

RESULTS

Our machine learning-based models for predicting patients' WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time.

CONCLUSIONS

Our findings show the feasibility of using machine learning-based methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models' decisions-an important aspect in clinical practice.

Temporal Generative Models for Learning Heterogeneous Group Dynamics of Ecological Momentary Data

One of the goals of precision psychiatry is to characterize mental disorders in an individualized manner, taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection of Ecological Momentary Assessments (EMAs) that capture multiple responses in real-time at high frequency. However, EMA data is often multi-dimensional, correlated, and hierarchical. Mixed-effects models are commonly used but may require restrictive assumptions about the fixed and random effects and the correlation structure. The Recurrent Temporal Restricted Boltzmann Machine (RTRBM) is a generative neural network that can be used to model temporal data, but most existing RTRBM approaches do not account for the potential heterogeneity of group dynamics within a population based on available covariates. In this paper, we propose a new temporal generative model, the Heterogeneous-Dynamics Restricted Boltzmann Machine (HDRBM), to learn the heterogeneous group dynamics and demonstrate the effectiveness of this approach on simulated and real-world EMA data sets. We show that by incorporating covariates, HDRBM can improve accuracy and interpretability, explore the underlying drivers of the group dynamics of participants, and serve as a generative model for EMA studies.

Automatic patient functionality assessment from multimodal data using deep learning techniques - Development and feasibility evaluation

Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations. This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients. Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.

Text mining methods for the characterisation of suicidal thoughts and behaviour

Traditional research methods have shown low predictive value for suicidal risk assessments and limitations to be applied in clinical practice. The authors sought to evaluate natural language processing as a new tool for assessing self-injurious thoughts and behaviors and emotions related. We used MEmind project to assess 2838 psychiatric outpatients. Anonymous unstructured responses to the open-ended question "how are you feeling today?" were collected according to their emotional state. Natural language processing was used to process the patients' writings. The texts were automatically represented (corpus) and analyzed to determine their emotional content and degree of suicidal risk. Authors compared the patients' texts with a question used to assess lack of desire to live, as a suicidal risk assessment tool. Corpus consists of 5,489 short free-text documents containing 12,256 tokenized or unique words. The natural language processing showed an ROC-AUC score of 0.9638 when compared with the responses to lack of a desire to live question. Natural language processing shows encouraging results for classifying subjects according to their desire not to live as a measure of suicidal risk using patients' free texts. It is also easily applicable to clinical practice and facilitates real-time communication with patients, allowing better intervention strategies to be designed.

User profiles of electronic ecological momentary assessment in outpatient child and adolescent mental health services

INTRODUCTION

Electronic ecological momentary assessment (EMA) can provide precise information regarding day-to-day functioning of patients overcoming some of the limitations of usual clinical evaluation; however adherence to this methodology might be a major threat. Research and application of EMA concerning clinical settings remains scant. Our goal was to study the user profiles of EMA in a clinical sample of adolescents.

MATERIAL AND METHODS

209 adolescents following an outpatient mental health treatment accepted to use EMA. They were evaluated in different sociodemographic and clinical variables as well as the use that they made of EMA.

RESULTS

39.7% of patients were considered users and 60.3% non-active users. Certain self-harm behaviours were more common in the group of active users, while hyperkinetic disorders were more common in the group of non-active users. A regression analysis revealed that non-suicidal self-injury (OR=2.99) and hyperkinetic disorders (OR=0.51) were related to the use of EMA.

CONCLUSION

This preliminary study adds novel and promising information about EMA use in clinical practice. Adolescents with self-harm behaviours EMA seem more prone to use this tool. Our study provides support for actively monitoring self-harm behaviours with EMA. Future studies might consider a comprehensive analysis of adherence and EMA data collection.

What seems to explain suicidality in Yucatan Mexican young adults? Findings from an app-based mental health screening test using the SMART-SCREEN protocol

The relationship between suicidality, depression, anxiety, and well-being was explored in young adults (median age 20.7 years) from the State of Yucatan (Mexico), which has a suicide rate double that of other Mexican states. A cross-sectional study was carried out in 20 universities in Yucatan and 9,366 students were surveyed using validated questionnaires built into a smartphone app, applying partial least squares structural equation models. High suicide risk was assessed in 10.8% of the sample. Clinically relevant depression and anxiety levels were found in 6.6% and 10.5% of the sample, respectively, and 67.8% reported high well-being. Comparably higher levels of suicide risk, depression and anxiety, and lower well-being were found in women, who were also somewhat older than men in our study. Furthermore, path analysis in the structural equation model revealed that depression was the main predictor of suicidal behaviour as well as of higher anxiety levels and lower self-perceived well-being in the total sample and in both genders. Our findings draw attention to the association between suicidality, depression, anxiety, and well-being in Yucatan young adults and gender differences with this regard. Mental health screening via smartphone might be a useful tool to reach large populations and contribute to mental health policies, including regional suicide prevention efforts.

Comparative study of the pencil-and-paper and digital formats of the Spanish DARS scale

The Dimensional Anhedonia Rating Scale (DARS) is a novel questionnaire to assess anhedonia of recent validation. In this work, we aim to study the equivalence between the traditional paper-and-pencil and the digital format of DARS. Sixty-nine patients filled the DARS in a paper-based and digital versions. We assessed differences between formats (Wilcoxon test), validity of the scales [Kappa and intraclass correlation coefficients (ICCs)], and reliability (Cronbach's alpha and Guttman's coefficient). We calculated the comparative fit index and the root mean squared error (RMSE) associated with the proposed one-factor structure. Total scores were higher for paper-based format. Significant differences between both formats were found for three items. The weighted Kappa coefficient was approximately 0.40 for most of the items. Internal consistency was greater than 0.94, and the ICC for the digital version was 0.95 and 0.94 for the paper-and-pencil version (F = 16.7, p < 0.001). Comparative Adjustment Index was 0.97 for the digital DARS and 0.97 for the paper-and-pencil DARS, and RMSE was 0.11 for the digital DARS and 0.10 for the paper-and-pencil DARS. We concluded that the digital DARS is consistent in many respects with the paper-and-pencil questionnaire, but equivalence with this format cannot be assumed without caution.

Low Level of Agreement Between Self-Report and Clinical Assessment of Passive Suicidal Ideation

OBJECTIVE

Discrepancies between patient reports during clinical evaluations and self-reported suicide ideation are of vital importance. We study the agreement in passive suicidal ideation between reports made by clinicians and patients' self-reports.

METHOD

Wish of death in 648 outpatients was assessed by attending clinicians. Within 24 h after clinical evaluation, patients completed a self-report questionnaire in which they were asked whether they had no desire to live. We used cluster analysis to determine the clinical profile of a population of patients according to the concordance between reports made by clinicians and self-reported information.

RESULTS

A low level of agreement (kappa = 0.072) was found between clinicians and patients, as 56.4% (n = 366) of clinician reports classified as containing no death-related ideas although on self-report the patient did state that they had no desire to live. In this group containing discrepancies between the two reports, two clusters were found to have shared characteristics: female sex, middle age, cohabitation, active employment, no history of suicidal behavior, and diagnosis of neurotic, stress-related, and somatoform disorders. In a third, more severe cluster, patients self-reported sleep disturbances, less appetite, poor treatment adherence, and aggressiveness.

CONCLUSIONS

We found low agreement between self-reports and clinician assessments regarding the death wish. Self-reporting may be useful in assessing suicide risk. HIGHLIGHTSLow agreement was found between self-reports and clinician assessments regarding passive suicidal ideation.Most patients in whom the clinician underestimated the risk of suicide were women.Our results suggest that clinicians require adequate documentation of suicidal risk assessment to identify the high-risk population.

Smartphone-based Ecological Momentary Intervention for secondary prevention of suicidal thoughts and behaviour: protocol for the SmartCrisis V.2.0 randomised clinical trial

INTRODUCTION

Suicide is one of the leading public health issues worldwide. Mobile health can help us to combat suicide through monitoring and treatment. The SmartCrisis V.2.0 randomised clinical trial aims to evaluate the effectiveness of a smartphone-based Ecological Momentary Intervention to prevent suicidal thoughts and behaviour.

METHODS AND ANALYSIS

The SmartCrisis V.2.0 study is a randomised clinical trial with two parallel groups, conducted among patients with a history of suicidal behaviour treated at five sites in France and Spain. The intervention group will be monitored using Ecological Momentary Assessment (EMA) and will receive an Ecological Momentary Intervention called 'SmartSafe' in addition to their treatment as usual (TAU). TAU will consist of mental health follow-up of the patient (scheduled appointments with a psychiatrist) in an outpatient Suicide Prevention programme, with predetermined clinical appointments according to the Brief Intervention Contact recommendations (1, 2, 4, 7 and 11 weeks and 4, 6, 9 and 12 months). The control group would receive TAU and be monitored using EMA.

ETHICS AND DISSEMINATION

This study has been approved by the Ethics Committee of the University Hospital Fundación Jiménez Díaz. It is expected that, in the near future, our mobile health intervention and monitoring system can be implemented in routine clinical practice. Results will be disseminated through peer-reviewed journals and psychiatric congresses. Reference number EC005-21_FJD. Participants gave informed consent to participate in the study before taking part.

TRIAL REGISTRATION NUMBER

NCT04775160.

Comparison of disability between common mental disorders and severe mental disorders using WHODAS 2.0

INTRODUCTION

Mental disorders are among the leading causes of disability worldwide. Despite the fact that severe mental disorders (SMD) are associated with high disability, the impact of common mental disorders (CMD) is not negligible. In this work, we compare the disability measured with the WHODAS 2.0 scale of both diagnostic groups at the Mental Health Nurse facility.

MATERIAL AND METHODS

Sociodemographic data, clinical diagnosis and disability scores were collected, using the WHODAS 2.0 scale, of the patients attended by the Mental Health specialist nurse at the Infanta Elena de Valdemoro Hospital (Madrid) and disability was compared in patients with SMD and CMD, using the Student t test.

RESULTS

Our study sample consisted of 133 patients. Patients with CMD showed greater disability compared to patients with SMD. It was observed that the disability associated with CMD is higher, compared to SMD, this difference being significant for the domain of work (p < 0.001) and participation in society (p = 0.041).

CONCLUSIONS

In this study we showed that the level of disability associated with CMD was higher in certain areas compared to SMD, this difference was of special relevance for the «Work» and «Participation» domains. This may serve to adapt the interventions aimed at these people and improve their quality of life.

Real-world feasibility and acceptability of real-time suicide risk monitoring via smartphones: A 6-month follow-up cohort

Active and passive Ecological Momentary Assessment of suicide risk is crucial for suicide prevention. We aimed to assess the feasibility and acceptability of active and passive smartphone-based EMA in real-world conditions in patients at high risk for suicide. We followed 393 patients at high risk for suicide for six months using two mobile health applications: the MEmind (active) and the eB2 (passive). Retention with active EMA was 79.3% after 1 month and 22.6% after 6 months. Retention with passive EMA was 87.8% after 1 month and 46.6% after 6 months. Satisfaction with the MEmind app, uninstalling the eB2 app and diagnosis of eating disorders were independently associated with stopping active EMA. Satisfaction with the eB2 app and uninstalling the MEmind app were independently associated with stopping passive EMA. Smartphone-based active and passive EMA are feasible and may increase accessibility to mental healthcare.

Trajectory Analysis of Suicidal Ideation in Spanish College Students Using Ecological Momentary Assessment

Introduction

Suicide is a preventable death in young people. It is well known that suicide behavior is a multicausal phenomenon. However, suicidal ideation (SI) commonly underlies suicide, and Ecological Momentary Assessment (EMA) can help us to better characterize it and its risk and protective factors in the short term. We aimed, first, to investigate the estimated prevalence and trajectories of SI in a community sample of Spanish college students using an EMA methodology and, second, explore the associations between risk and protective factors and SI categorized as moderate or low.

Materials and Methods

A total of 737 participants followed the EMA during a period of 6 months. We estimated the prevalence and trajectories of SI and the associations between depressive symptoms, positive and negative affect, thwarted belongingness, perceived burdensomeness, cognitive reappraisal, emotional suppression, and purpose in life with the MEmind smartphone App. SI was assessed 14 times during this period.

Results

Twenty-eight participants referred to SI at least once in longitudinal assessments. We found a lack of curvature and, thus, a relatively stable trajectory of SI. Two groups of latent dimensions were observed related to risk and protective factors of SI. One latent dimension of the risk factors (higher levels of thwarted belongingness, perceived burdensomeness, depressive symptoms, negative affect, and emotional suppression) best represented the group with moderate levels of SI, and a second latent dimension of protective variables (positive affect, cognitive reappraisal, and purpose in life) best represented the group with lower levels of SI.

Discussion

These findings may indicate that students with a sense of having a life worth living, in addition to having the ability to reevaluate their negative beliefs, are less likely to experience high levels of SI. Therefore, purpose in life would be a protective factor against the presence of SI.

A cross-diagnostic study of Adherence to Ecological Momentary Assessment: Comparisons across study length and daily survey frequency find that early adherence is a potent predictor of study-long adherence

Background

Ecological momentary assessment (EMA) offers a highly valid strategy to assess everyday functioning in people with severe mental illness. Adherence is generally good, but several questions regarding the impact of study length, daily density of sampling, and symptom severity on adherence remain.

Methods

EMA adherence in two separate studies was examined. One sampled participants with schizophrenia (n=106) and healthy controls (n=76) 7 times per day for 7 days and the other sampled participants with schizophrenia (n=104) and participants with bipolar illness (n=76) 3 times per day for 30 days. Participants were asked where they were, who they were with, what they were doing and how they were feeling in both studies. The impact of rates of very early adherence on eventual adherence was investigated across the samples, and adherence rates were examined for associations with mood state and most common location when answering surveys.

Results

Median levels of adherence were over 80% across the samples, and the 10^th percentile for adherence was approximately 45% of surveys answered. Early adherence predicted study-long adherence quite substantially in every sample. Mood states did not correlate with adherence in the patient samples and being home correlated with adherence in only the bipolar sample.

Implications

Adherence was quite high and was not correlated with the length of the study or the density of sampling per study day. There was a tendency for bipolar participants who were more commonly away from home to answer fewer surveys but overall adherence for the bipolar patients was quite high. These data suggest that early nonadherence is a potential predictor of eventual nonadherence and study noncompletion.

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 (F_1,72=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.

Comparison of disability between common mental disorders and severe mental disorders using WHODAS 2.0

INTRODUCTION

Mental disorders are among the leading causes of disability worldwide. Despite the fact that severe mental disorders (SMD) are associated with high disability, the impact of common mental disorders (CMD) is not negligible. In this work, we compare the disability measured with the WHODAS 2.0 scale of both diagnostic groups at the Mental Health Nurse facility.

MATERIAL AND METHODS

Sociodemographic data, clinical diagnosis and disability scores were collected, using the WHODAS 2.0 scale, of the patients attended by the Mental Health specialist nurse at the Infanta Elena de Valdemoro Hospital (Madrid) and disability was compared in patients with SMD and CMD, using the Student t test.

RESULTS

Our study sample consisted of 133 patients. Patients with CMD showed greater disability compared to patients with SMD. It was observed that the disability associated with CMD is higher, compared to SMD, this difference being significant for the domain of work (p<0.001) and participation in society (p=0.041).

CONCLUSIONS

In this study we showed that the level of disability associated with CMD was higher in certain areas compared to SMD, this difference was of special relevance for the «Work» and «Participation» domains. This may serve to adapt the interventions aimed at these people and improve their quality of life.

Use of Ecological Momentary Assessment Through a Passive Smartphone-Based App (eB2) by Patients With Schizophrenia: Acceptability Study

Background

Ecological momentary assessment (EMA) tools appear to be useful interventions for collecting real-time data on patients’ behavior and functioning. However, concerns have been voiced regarding the acceptability of EMA among patients with schizophrenia and the factors influencing EMA acceptability.

Objective

The aim of this study was to investigate the acceptability of a passive smartphone-based EMA app, evidence-based behavior (eB2), among patients with schizophrenia spectrum disorders and the putative variables underlying their acceptance.

Methods

The participants in this study were from an ongoing randomized controlled trial (RCT) of metacognitive training, consisting of outpatients with schizophrenia spectrum disorders (F20-29 of 10th revision of the International Statistical Classification of Diseases and Related Health Problems), aged 18-64 years, none of whom received any financial compensation. Those who consented to installation of the eB2 app (users) were compared with those who did not (nonusers) in sociodemographic, clinical, premorbid adjustment, neurocognitive, psychopathological, insight, and metacognitive variables. A multivariable binary logistic regression tested the influence of the above (independent) variables on “being user versus nonuser” (acceptability), which was the main outcome measure.

Results

Out of the 77 RCT participants, 24 (31%) consented to installing eB2, which remained installed till the end of the study (median follow-up 14.50 weeks) in 14 participants (70%). Users were younger and had a higher education level, better premorbid adjustment, better executive function (according to the Trail Making Test), and higher cognitive insight levels (measured with the Beck Cognitive Insight Scale) than nonusers (univariate analyses) although only age (OR 0.93, 95% CI 0.86-0.99; P=.048) and early adolescence premorbid adjustment (OR 0.75, 95% CI 0.61-0.93; P=.01) survived the multivariable regression model, thus predicting eB2 acceptability.

Conclusions

Acceptability of a passive smartphone-based EMA app among participants with schizophrenia spectrum disorders in this RCT where no participant received financial compensation was, as expected, relatively low, and linked with being young and good premorbid adjustment. Further research should examine how to increase EMA acceptability in patients with schizophrenia spectrum disorders, in particular, older participants and those with poor premorbid adjustment.

Trial Registration

ClinicalTrials.gov NCT04104347; https://clinicaltrials.gov/ct2/show/NCT04104347

Disturbed sleep as a clinical marker of wish to die: A smartphone monitoring study over three months of observation

BACKGROUND

Smartphone monitoring could contribute to the elucidation of the correlates of suicidal thoughts and behaviors (STB). In this study, we employ smartphone monitoring and machine learning techniques to explore the association of wish to die (passive suicidal ideation) with disturbed sleep, altered appetite and negative feelings.

METHODS

This is a prospective cohort study carried out among adult psychiatric outpatients with a history of STB. A daily questionnaire was administered through the MEmind smartphone application. Participants were followed-up for a median of 89.8 days, resulting in 9,878 person-days. Data analysis employed a machine learning technique called Indian Buffet Process.

RESULTS

165 patients were recruited, 139 had the MEmind mobile application installed on their smartphone, and 110 answered questions regularly enough to be included in the final analysis. We found that the combination of wish to die and sleep problems was one of the most relevant latent features found across the sample, showing that these variables tend to be present during the same time frame (96 hours).

CONCLUSIONS

Disturbed sleep emerges as a potential clinical marker for passive suicidal ideation. Our findings stress the importance of evaluating sleep as part of the screening for suicidal behavior. Compared to previous smartphone monitoring studies on suicidal behavior, this study includes a long follow-up period and a large sample.

Patients at high risk of suicide before and during a COVID-19 lockdown: ecological momentary assessment study

The coronavirus disease 2019 (COVID-19) outbreak may have affected the mental health of patients at high risk of suicide. In this study we explored the wish to die and other suicide risk factors using smartphone-based ecological momentary assessment (EMA) in patients with a history of suicidal thoughts and behaviour. Contrary to our expectations we found a decrease in the wish to die during lockdown. This is consistent with previous studies showing that suicide rates decrease during periods of social emergency. Smartphone-based EMA can allow us to remotely assess patients and overcome the physical barriers imposed by lockdown.

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.

Early administration of aripiprazole long-acting injectable in acute inpatients with schizophrenia: a clinical report

Fifty-one patients suffering an acute episode of schizophrenia and treated with aripiprazole long-acting injectable (ALAI) were chosen to elaborate an observational study in two in-patient units in Spain, in order to examine the effects of early administration during a hospital admission. When treatment with ALAI is administered in the first week of admission (in 31 patients, 60.78%), hospitalization time is significantly reduced, 12.1 days on average. It can be concluded that ALAI is an effective treatment for these patients. Analysis in economic terms and comparison with other LAI antipsychotics are interesting lines for further research.

Psychiatric Profiles of eHealth Users Evaluated Using Data Mining Techniques: Cohort Study

BACKGROUND

New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps.

OBJECTIVE

This study aimed to reveal the profiles of users of a mental health app through machine learning techniques.

METHODS

We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login.

RESULTS

The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features.

CONCLUSIONS

User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.

Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients

There is growing concern that the social and physical distancing measures implemented in response to the Covid-19 pandemic may negatively impact health in other areas, via both decreased physical activity and increased social isolation. Here, we investigated whether increased engagement with digital social tools may help mitigate effects of enforced isolation on physical activity and mood, in a naturalistic study of at-risk individuals. Passively sensed smartphone app use and actigraphy data were collected from a group of psychiatric outpatients before and during imposition of strict Covid-19 lockdown measures. Data were analysed using Gaussian graphical models: a form of network analysis which gives insight into the predictive relationships between measures across timepoints. Within-individuals, we found evidence of a positive predictive path between digital social engagement, general smartphone use, and physical activity-selectively under lockdown conditions (N = 127 individual users, M = 6201 daily observations). Further, we observed a positive relationship between social media use and total daily steps across individuals during (but not prior to) lockdown. Although there are important limitations on the validity of drawing causal conclusions from observational data, a plausible explanation for our findings is that, during lockdown, individuals use their smartphones to access social support, which may help guard against negative effects of in-person social deprivation and other pandemic-related stress. Importantly, passive monitoring of smartphone app usage is low burden and non-intrusive. Given appropriate consent, this could help identify people who are failing to engage in usual patterns of digital social interaction, providing a route to early intervention.

Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge

Depressed patients present with motor activity abnormalities, which can be easily recorded using actigraphy. The extent to which actigraphically recorded motor activity may predict inpatient clinical course and hospital discharge remains unknown. Participants were recruited from the acute psychiatric inpatient ward at Hospital Rey Juan Carlos (Madrid, Spain). They wore miniature wrist wireless inertial sensors (actigraphs) throughout the admission. We modeled activity levels against the normalized length of admission-'Progress Towards Discharge' (PTD)-using a Hierarchical Generalized Linear Regression Model. The estimated date of hospital discharge based on early measures of motor activity and the actual hospital discharge date were compared by a Hierarchical Gaussian Process model. Twenty-three depressed patients (14 females, age: 50.17 ± 12.72 years) were recruited. Activity levels increased during the admission (mean slope of the linear function: 0.12 ± 0.13). For n = 18 inpatients (78.26%) hospitalised for at least 7 days, the mean error of Prediction of Hospital Discharge Date at day 7 was 0.231 ± 22.98 days (95% CI 14.222-14.684). These n = 18 patients were predicted to need, on average, 7 more days in hospital (for a total length of stay of 14 days) (PTD = 0.53). Motor activity increased during the admission in this sample of depressed patients and early patterns of actigraphically recorded activity allowed for accurate prediction of hospital discharge date.

Comparative study of paper-and-pencil and electronic formats of the Snaith-Hamilton Pleasure Scale

The gold standard for measuring anhedonia is the Snaith-Hamilton Pleasure Scale (SHAPS). To date, there are no validated electronic versions of this questionnaire. We aim to study the equivalence between the traditional paper-and-pencil format and a digital version of the SHAPS. A group of 67 patients completed both SHAPS formats, and differences between formats were assessed. McNemar's test showed no significant differences between the two systems. The Kappa coefficient was over 40% for most items, and reliability was above 0.8, showing good to excellent levels of internal consistency. Thus, we have demonstrated a close equivalence between paper-and-pencil and electronic SHAPS.

Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study

BACKGROUND

Smartphone-based ecological momentary assessment (EMA) is a promising methodology for mental health research. The objective of this study is to determine the feasibility of smartphone-based active and passive EMA in psychiatric outpatients and student controls.

METHODS

Two smartphone applications -MEmind and eB²- were developed for behavioral active and passive monitoring. The applications were tested in psychiatric patients with a history of suicidal thoughts and/or behaviors (STB), psychiatric patients without a history of STB, and student controls. Main outcome was feasibility, measured as response to recruitment, retention, and EMA compliance. Secondary outcomes were patterns of smartphone usage.

RESULTS

Response rate was 87.3% in patients with a history of STB, 85.1% in patients without a history of STB, and 75.0% in student controls. 457 participants installed the MEmind app (120 patients with a history of STB and 337 controls) and 1,708 installed the eB² app (139 patients with a history of STB, 1,224 patients with no history of STB and 346 controls). For the MEmind app, participants were followed-up for a median of 49.5, resulting in 22,622 person-days. For the eB² application, participants were followed-up for a median of 48.9 days, resulting in 83,521 person-days. EMA compliance rate was 65.00% in suicidal patients and 75.21% in student controls. At the end of the follow-up, over 60% of participants remained in the study.

LIMITATIONS

Cases and controls were not matched by age and sex. Cases were patients who were receiving adequate psychopharmacological treatment and attending their appointments, which may result in an overstatement of clinical compliance.

CONCLUSIONS

Smartphone-based active and passive monitoring are feasible methods in psychiatric patients in real-world settings. The development of applications with friendly interfaces and directly useful features can help increase engagement without using incentives. The MEmind and eB² applications are promising clinical tools that could contribute to the management of mental disorders. In the near future, these applications could serve as risk monitoring devices in the clinical practice.

Detecting risk of suicide attempts among Chinese medical college students using a machine learning algorithm

BACKGROUND

Suicide has become one of the most prominent concerns for public health and wellness; however, detecting suicide risk factors among individuals remains a big challenge. The aim of this study was to develop a machine learning algorithm that could effectively and accurately identify the probability of suicide attempts in medical college students.

METHODS

A total of 4,882 medical students were enrolled in this cross-sectional study. Self-report data on socio-demographic and clinical characteristics were collected online via website or through the widely used social media app, WeChat. 5-fold cross validation was used to build a random forest model with 37 suicide attempt predictors. Model performance was measured for sensitivity, specificity, area under the curve (AUC), and accuracy. All analyses were conducted in MATLAB.

RESULTS

The random forest model achieved good performance [area under the curve (AUC) = 0.9255] in predicting suicide attempts with an accuracy of 90.1% (SD = 0.67%), sensitivity of 73.51% (SD = 2.33%) and specificity of 91.68% (SD = 0.82%).

LIMITATION

The participants are primarily females and medical students.

CONCLUSIONS

This study demonstrates that the random forest model has the potential to predict suicide attempts among medical college students with high accuracy. Our findings suggest that application of the machine learning model may assist in improving the efficiency of suicide prevention.

Universal mental health screening with a focus on suicidal behaviour using smartphones in a Mexican rural community: protocol for the SMART-SCREEN population-based survey

INTRODUCTION

Mental disorders represent the second cause of years lived with disability worldwide. Suicide mortality has been targeted as a key public health concern by the WHO. Smartphone technology provides a huge potential to develop massive and fast surveys. Given the vast cultural diversity of Mexico and its abrupt orography, smartphone-based resources are invaluable in order to adequately manage resources, services and preventive measures in the population. The objective of this study is to conduct a universal suicide risk screening in a rural area of Mexico, measuring also other mental health outcomes such as depression, anxiety and alcohol and substance use disorders.

METHODS AND ANALYSIS

A population-based cross-sectional study with a temporary sampling space of 9 months will be performed between September 2019 and June 2020. We expect to recruit a large percentage of the target population (at least 70%) in a short-term survey of Milpa Alta Delegation, which accounts for 137 927 inhabitants in a territorial extension of 288 km².They will be recruited via an institutional call and a massive public campaign to fill in an online questionnaire through mobile-assisted or computer-assisted web app. This questionnaire will include data on general health, validated questionnaires including Well-being Index 5, Patient Health Questionnaire-9, Generalized Anxiety Disorder Scale 2, Alcohol Use Disorders Identification Test, selected questions of the Drug Abuse Screening Test and Columbia-Suicide Severity Rating Scales and Diagnostic and statistical manual of mental disorders (DSM-5) questions about self-harm.We will take into account information regarding time to mobile app response and geo-spatial location, and aggregated data on social, demographical and environmental variables. Traditional regression modelling, multilevel mixed methods and data-driven machine learning approaches will be used to test hypotheses regarding suicide risk factors at the individual and the population level.

ETHICS AND DISSEMINATION

Ethical approval (002/2019) was granted by the Ethics Review Board of the Hospital Psiquiátrico Yucatán, Yucatán (Mexico). This protocol has been registered in ClinicalTrials.gov. The starting date of the study is 3 September 2019. Results will serve for the planning and healthcare of groups with greater mental health needs and will be disseminated via publications in peer-reviewed journal and presented at relevant mental health conferences.

TRIAL REGISTRATION NUMBER

NCT04067063.

Characterizing the phenomenology of passive suicidal ideation: a systematic review and meta-analysis of its prevalence, psychiatric comorbidity, correlates, and comparisons with active suicidal ideation

BACKGROUND

Compared to active ideation, passive ideation remains relatively understudied and its clinical importance poorly defined. The weight that should be accorded passive ideation in clinical risk assessment is therefore unclear.

METHODS

We conducted a systematic review and meta-analysis of the prevalence of passive ideation, its psychiatric comorbidity, associated sociodemographic characteristics, as well as psychological and environmental correlates. For reference, pooled effects were also calculated for direct comparisons of passive and active ideation with respect to potential correlates. Relevant articles published since inception to 9 September 2019 were identified through a systematic search of MEDLINE and PsycINFO.

RESULTS

A total of 86 studies were included in this review. The prevalence of passive ideation was high across sample types, ranging from 5.8% for 1-year prevalence to 10.6% for lifetime prevalence in the general population. Passive ideation was strongly associated with sexual minority status, psychiatric comorbidity, psychological characteristics implicated in risk, and suicide attempts. Preliminary evidence exists for a large association with suicide deaths. The effect sizes for individual correlates of passive and active ideation were largely equivalent and mostly non-significant in head-to-head comparisons.

CONCLUSIONS

Passive ideation is a prevalent clinical phenomenon associated with significant psychiatric comorbidity. Current evidence also suggests notable similarities exist between passive and active ideation in terms of psychiatric comorbidity and psychological and other characteristics traditionally associated with risk.

Study protocol of a randomised clinical trial testing whether metacognitive training can improve insight and clinical outcomes in schizophrenia

BACKGROUND

Although insight in schizophrenia spectrum disorders (SSD) has been associated with positive outcomes, the effect size of previous treatments on insight has been relatively small to date. The metacognitive basis of insight suggests that metacognitive training (MCT) may improve insight and clinical outcomes in SSD, although this remains to be established.

METHODS

This single-center, assessor-blind, parallel-group, randomised clinical trial (RCT) aims to investigate the efficacy of MCT for improving insight (primary outcome), including clinical and cognitive insight, which will be measured by the Schedule for Assessment of Insight (Expanded version) (SAI-E) and the Beck Cognitive Scale (BCIS), respectively, in (at least) n = 126 outpatients with SSD at three points in time: i) at baseline (T0); ii) after treatment (T1) and iii) at 1-year follow-up (T2). SSD patients receiving MCT and controls attending a non-intervention support group will be compared on insight level changes and several clinical and cognitive secondary outcomes at T1 and T2, whilst adjusting for baseline data. Ecological momentary assessment (EMA) will be piloted to assess functioning in a subsample of participants.

DISCUSSION

To the best of our knowledge, this will be the first RCT testing the effect of group MCT on multiple insight dimensions (as primary outcome) in a sample of unselected patients with SSD, including several secondary outcomes of clinical relevance, namely symptom severity, functioning, which will also be evaluated with EMA, hospitalizations and suicidal behaviour.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT04104347. Date of registration: 26/09/2019 (Retrospectively registered).

Precision Medicine and Suicide: an Opportunity for Digital Health

PURPOSE OF REVIEW

A better understanding of suicide phenomena is needed, and precision medicine is a promising approach toward this aim. In this manuscript, we review recent advances in the field, with particular focus on the role of digital health.

RECENT FINDINGS

Technological advances such as smartphone-based ecological momentary assessment and passive collection of information from sensors provide a detailed description of suicidal behavior and thoughts. Further, we review more traditional approaches in the field of genetics. We first highlight the need for precision medicine in suicidology. Then, in light of recent and promising research, we examine the role of smartphone-based information collection using explicit (active) and implicit (passive) means to construct a digital phenotype, which should be integrated with genetic and epigenetic data to develop tailored therapeutic and preventive approaches for suicide.

Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol

BACKGROUND

The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information's for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone's native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk.

METHOD/DESIGN

The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations.

DISCUSSION

Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients' data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants' daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patient.

TRIAL REGISTRATION NUMBER

NCT03720730. Retrospectively registered.

Deep Sequential Models for Suicidal Ideation From Multiple Source Data

This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly sampled data sequences. In our method, we model each of them with a recurrent neural network, and both sequences are aligned by concatenating the hidden state of each of them using temporal marks. Furthermore, we incorporate attention schemes to improve performance in long sequences and time-independent pre-trained schemes to cope with very short sequences. Using a database of 1023 patients, our experimental results show that the addition of EMA records boosts the system recall to predict the suicidal ideation diagnosis from 48.13% obtained exclusively from EHR-based state-of-the-art methods to 67.78%. Additionally, our method provides interpretability through the t-distributed stochastic neighbor embedding (t-SNE) representation of the latent space. Furthermore, the most relevant input features are identified and interpreted medically.

Clinicians' Concerns About Mobile Ecological Momentary Assessment Tools Designed for Emerging Psychiatric Problems: Prospective Acceptability Assessment of the MEmind App

BACKGROUND

Many mental disorders are preceded by a prodromal phase consisting of various attenuated and unspecific symptoms and functional impairment. Electronic health records are generally used to capture these symptoms during medical consultation. Internet and mobile technologies provide the opportunity to monitor symptoms emerging in patients' environments using ecological momentary assessment techniques to support preventive therapeutic decision making.

OBJECTIVE

The objective of this study was to assess the acceptability of a Web-based app designed to collect medical data during appointments and provide ecological momentary assessment features.

METHODS

We recruited clinicians at 4 community psychiatry departments in France to participate. They used the app to assess patients and to collect data after viewing a video of a young patient's emerging psychiatric consultation. We then asked them to answer a short anonymous self-administered questionnaire that evaluated their experience, the acceptability of the app, and their habit of using new technologies.

RESULTS

Of 24 practitioners invited, 21 (88%) agreed to participate. Most of them were between 25 and 45 years old, and greater age was not associated with poorer acceptability. Most of the practitioners regularly used new technologies, and 95% (20/21) connected daily to the internet, with 70% (15/21) connecting 3 times a day or more. However, only 57% (12/21) reported feeling comfortable with computers. Of the clinicians, 86% (18/21) would recommend the tool to their colleagues and 67% (14/21) stated that they would be interested in daily use of the app. Most of the clinicians (16/21, 76%) found the interface easy to use and useful. However, several clinicians noted the lack of readability (8/21, 38%) and the need to improve ergonometric features (4/21, 19%), in particular to facilitate browsing through various subsections. Some participants (5/21, 24%) were concerned about the storage of medical data and most of them (11/21, 52%) seemed to be uncomfortable with this.

CONCLUSIONS

We describe the first step of the development of a Web app combining an electronic health record and ecological momentary assessment features. This online tool offers the possibility to assess patients and to integrate medical data easily into face-to-face conditions. The acceptability of this app supports the feasibility of its broader implementation. This app could help to standardize assessment and to build up a strong database. Used in conjunction with robust data mining analytic techniques, such a database would allow exploration of risk factors, patterns of symptom evolution, and identification of distinct risk subgroups.

Mining social networks to improve suicide prevention: A scoping review

Attention about the risks of online social networks (SNs) has been called upon reports describing their use to express emotional distress and suicidal ideation or plans. On the Internet, cyberbullying, suicide pacts, Internet addiction, and "extreme" communities seem to increase suicidal behavior (SB). In this study, the scientific literature about SBs and SNs was narratively reviewed. Some authors focus on detecting at-risk populations through data mining, identification of risks factors, and web activity patterns. Others describe prevention practices on the Internet, such as websites, screening, and applications. Targeted interventions through SNs are also contemplated when suicidal ideation is present. Multiple predictive models should be defined, implemented, tested, and combined in order to deal with the risk of SB through an effective decision support system. This endeavor might require a reorganization of care for SNs users presenting suicidal ideation.

Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study

BACKGROUND

The emergence of smartphones, wearable sensor technologies, and smart homes allows the nonintrusive collection of activity data. Thus, health-related events, such as activities of daily living (ADLs; eg, mobility patterns, feeding, sleeping, ...) can be captured without patients' active participation. We designed a system to detect changes in the mobility patterns based on the smartphone's native sensors and advanced machine learning and signal processing techniques.

OBJECTIVE

The principal objective of this work is to assess the feasibility of detecting mobility pattern changes in a sample of outpatients with depression using the smartphone's sensors. The proposed method processed the data acquired by the smartphone using an unsupervised detection technique.

METHODS

In this study, 38 outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) participated. The Evidence-Based Behavior (eB²) app was downloaded by patients on the day of recruitment and configured with the assistance of a physician. The app captured the following data: inertial sensors, physical activity, phone calls and message logs, app usage, nearby Bluetooth and Wi-Fi connections, and location. We applied a change-point detection technique to location data on a sample of 9 outpatients recruited between April 6, 2017 and December 14, 2017. The change-point detection was based only on location information, but the eB² platform allowed for an easy integration of additional data. The app remained running in the background on patients' smartphone during the study participation.

RESULTS

The principal outcome measure was the identification of mobility pattern changes based on an unsupervised detection technique applied to the smartphone's native sensors data. Here, results from 5 patients' records are presented as a case series. The eB² system detected specific mobility pattern changes according to the patients' activity, which may be used as indicators of behavioral and clinical state changes.

CONCLUSIONS

The proposed technique could automatically detect changes in the mobility patterns of outpatients who took part in this study. Assuming these mobility pattern changes correlated with behavioral changes, we have developed a technique that may identify possible relapses or clinical changes. Nevertheless, it is important to point out that the detected changes are not always related to relapses and that some clinical changes cannot be detected by the proposed method.

Prévention du suicide et santé connectée

L’évaluation ponctuelle du risque suicidaire habituellement conduite aux urgences, après un geste suicidaire, ne rend pas compte de son évolution après la sortie des soins, alors même que le risque de récidive reste important plusieurs mois après. Dans ces conditions, les possibilités d’identification, et donc de prise en charge, des patients à risque suicidaire sont limitées. Le développement de la santé connectée (eHealth) donne désormais accès en temps réel à des informations sur l’état de santé d’un patient entre deux séjours en centre de soins. Cette extension de l’évaluation clinique à l’environnement du patient permet de développer des outils d’aide à la décision face à la gestion du risque suicidaire.

Ecological Momentary Assessment and Mood Disorders in Children and Adolescents: a Systematic Review

PURPOSE OF REVIEW

The aim of the present review is to systematically examine published data regarding ecological momentary assessment (EMA) in children and adolescents with mood disorders.

RECENT FINDINGS

EMA is increasingly used to collect participant's information in their real environment and in real time. There are multiple studies focused on the evaluation of mood disorders in children and adolescents, but only a few of them used EMA protocols. Results found in this review showed a wide variability of works with different fields of study, methodological approaches, and EMA protocols. More than 60% of EMA studies in children and adolescents with mood disorders were conducted via phone call, showing high completion rates with data missing in 5 to 11.5% of the calls. Length of studies varied from a 4-day EMA protocol to a maximum of 8 weeks. Positive and negative affect, daily activities, and social context were the main EMA measures. Despite the limited number of studies using EMA in children and adolescents with mood disorders, EMA was useful in assessing mood symptoms in the moment and in patients' real-life environment. Studies also showed high completion and satisfaction rates. Although web pages and apps use have been increasing over the past years, the evidence base is still scarce. Future studies can facilitate understanding of EMA methodology among youth with mood disorders.

WHODAS 2.0 as a Measure of Severity of Illness: Results of a FLDA Analysis

WHODAS 2.0 is the standard measure of disability promoted by World Health Organization whereas Clinical Global Impression (CGI) is a widely used scale for determining severity of mental illness. Although a close relationship between these two scales would be expected, there are no relevant studies on the topic. In this study, we explore if WHODAS 2.0 can be used for identifying severity of illness measured by CGI using the Fisher Linear Discriminant Analysis (FLDA) and for identifying which individual items of WHODAS 2.0 best predict CGI scores given by clinicians. One hundred and twenty-two patients were assessed with WHODAS 2.0 and CGI during three months in outpatient mental health facilities of four hospitals of Madrid, Spain. Compared with the traditional correction of WHODAS 2.0, FLDA improves accuracy in near 15%, and so, with FLDA WHODAS 2.0 classifying correctly 59.0% of the patients. Furthermore, FLDA identifies item 6.6 (illness effect on personal finances) and item 4.5 (damaged sexual life) as the most important items for clinicians to score the severity of illness.

Review of Use and Integration of Mobile Apps Into Psychiatric Treatments

PURPOSE OF REVIEW

Mental health practitioners should understand the features of current, publicly available apps; the features of novel, research apps; and issues behind the integration of mobile apps and digital health services into clinical workflows.

RECENT FINDINGS

The review is based on a research literature and the authors' clinical and healthcare administration experiences. Articles searched-on telepsychiatry, telemental health, mobile mental health, informatics, cellular phone, ambulatory monitoring, telemetry, and algorithms-were restricted to 2016 and 2017. Technologies are used in a variety of clinical settings, including patients with varying mental illness severity, social supports, and technological literacy. Good practices for evaluating apps, understanding user needs, and training and educating users can increase success rates. Ethics and risk management should be considered. Mobile apps are versatile. Integrating apps into psychiatric treatment requires addressing both patient and clinical workflows, design and usability principles, accessibility, social concerns, and digital health literacy.

User profiles of an electronic mental health tool for ecological momentary assessment: MEmind

Ecological momentary assessment (EMA) is gaining importance in psychiatry. This article assesses the characteristics of patients who used a new electronic EMA tool: the MEmind Wellness Tracker. Over one year, 13811 adult outpatients in our Psychiatry Department were asked to use MEmind. We collected information about socio-demographic data, psychiatric diagnoses, illness severity, stressful life events and suicidal thoughts/behavior. We compared active users (N = 2838) and non-active users (N = 10,973) of MEmind and performed a Random Forest analysis to assess which variables could predict its use. Univariate analyses revealed that MEmind-users were younger (42.2 ± 13.5 years versus 48.5 ± 16.3 years; χ²  = 18.85; P < 0.001) and more frequently diagnosed with anxiety related disorders (57.9% versus 46.7%; χ²  = 105.92; P = 0.000) than non-active users. They were more likely to report thoughts about death and suicide (up to 24% of active users expressed wish for death) and had experienced more stressful life events than non-active users (57% versus 48.5%; χ²  = 64.65; P < 0.001). In the Random Forest analysis, 31 variables showed mean decrease accuracy values higher than zero with a 95% confidence interval (CI), including sex, age, suicidal thoughts, life threatening events and several diagnoses. In the light of these results, strategies to improve EMA and e-Mental Health adherence are discussed.