Vickybot, a Chatbot for Anxiety-Depressive Symptoms and Work-Related Burnout in Primary Care and Health Care Professionals: Development, Feasibility, and Potential Effectiveness Studies

Background: Many people attending primary care (PC) have anxiety-depressive symptoms and work-related burnout compounded by a lack of resources to meet their needs. The COVID-19 pandemic has exacerbated this problem, and digital tools have been proposed as a solution. Objective: We aimed to present the development, feasibility, and potential effectiveness of Vickybot, a chatbot aimed at screening, monitoring, and reducing anxiety-depressive symptoms and work-related burnout, and detecting suicide risk in patients from PC and health care workers. Methods: Healthy controls (HCs) tested Vickybot for reliability. For the simulation study, HCs used Vickybot for 2 weeks to simulate different clinical situations. For feasibility and effectiveness study, people consulting PC or health care workers with mental health problems used Vickybot for 1 month. Self-assessments for anxiety (Generalized Anxiety Disorder 7-item) and depression (Patient Health Questionnaire-9) symptoms and work-related burnout (based on the Maslach Burnout Inventory) were administered at baseline and every 2 weeks. Feasibility was determined from both subjective and objective user-engagement indicators (UEIs). Potential effectiveness was measured using paired 2-tailed t tests or Wilcoxon signed-rank test for changes in self-assessment scores. Results: Overall, 40 HCs tested Vickybot simultaneously, and the data were reliably transmitted and registered. For simulation, 17 HCs (n=13, 76{\%} female; mean age 36.5, SD 9.7 years) received 98.8{\%} of the expected modules. Suicidal alerts were received correctly. For the feasibility and potential effectiveness study, 34 patients (15 from PC and 19 health care workers; 76{\%} [26/34] female; mean age 35.3, SD 10.1 years) completed the first self-assessments, with 100{\%} (34/34) presenting anxiety symptoms, 94{\%} (32/34) depressive symptoms, and 65{\%} (22/34) work-related burnout. In addition, 27{\%} (9/34) of patients completed the second self-assessment after 2 weeks of use. No significant differences were found between the first and second self-assessments for anxiety (t8=1.000; P=.34) or depressive (t8=0.40; P=.70) symptoms. However, work-related burnout scores were moderately reduced (z=−2.07, P=.04, r=0.32). There was a nonsignificant trend toward a greater reduction in anxiety-depressive symptoms and work-related burnout with greater use of the chatbot. Furthermore, 9{\%} (3/34) of patients activated the suicide alert, and the research team promptly intervened with successful outcomes. Vickybot showed high subjective UEI (acceptability, usability, and satisfaction), but low objective UEI (completion, adherence, compliance, and engagement). Vickybot was moderately feasible. Conclusions: The chatbot was useful in screening for the presence and severity of anxiety and depressive symptoms, and for detecting suicidal risk. Potential effectiveness was shown to reduce work-related burnout but not anxiety or depressive symptoms. Subjective perceptions of use contrasted with low objective-use metrics. Our results are promising but suggest the need to adapt and enhance the smartphone-based solution to improve engagement. A consensus on how to report UEIs and validate digital solutions, particularly for chatbots, is required.We are grateful to all participants. GA is supported by a Rio Hortega 2021 grant (CM21/00017) from the Spanish Ministry of Health financed by the Instituto de Salud Carlos III (ISCIII) and cofinanced by Fondo Social Europe Plus. MS was supported by a grant from the Baszucki Brain Research Fund. AM is supported by the Agència de Gestió d’Ajudes Universitàries I de Investigació—PANDÈMIES 2020 grant (PI047003) from the Generalitat de Catalunya. IG thanks the support of the Spanish Ministry of Science and Innovation (PI19/00954) integrated into the Plan Nacional de I+D+I and cofinanced by the ISCIII-Subdirección General de Evaluación y el Fondos Europeos de la Unión Europea (FEDER, Fondo Social Europe, Next Generation European Union or Plan de Recuperación Transformación y Resiliencia_PRTR); the Instituto de Salud Carlos III; the CIBER of Mental Health (CIBERSAM); and the Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement (2017 SGR 1365), CERCA Programme or Generalitat de Catalunya as well as the Fundació Clínic per la Recerca Biomèdica (Pons Bartran 2022-FRCB_PB1_2022). AHY’s independent research was funded by the National Institute for Health Research Biomedical Research Centre in South London and Maudsley National Health Service Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the National Health Service, National Institute for Health and Care Research, or Department of Health. JR is supported by a Miguel Servet II contract (CPII19/00009), funded by ISCIII and cofunded by the European Social Fund “Investing in your future.” CT has been supported through a “Miguel Servet” postdoctoral contract (CPI14/00175) and a Miguel Servet II contract (CPII19/00018) and thanks the support of the Spanish Ministry of Innovation and Science (PI17/01066 and PI20/00344), funded by the Instituto de Salud Carlos III and cofinanced by the European Union (FEDER) “Una manera de hacer Europa.” AMA thanks the support of the Spanish Ministry of Science and Innovation (PI18/00789, PI21/00787) integrated into the Plan Nacional de I+D+I and cofinanced by ISCIII-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER); the ISCIII; the CIBER of Mental Health (CIBERSAM); the Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement (2017 SGR 1365); the CERCA Programme; and the Departament de Salut de la Generalitat de Catalunya for the Pla estratègic de recerca I innovació en salut (PERIS) grant SLT006/17/00177. AM thanks the support of the Spanish Ministry of Science and Innovation (PI19/00672) integrated into the Plan Nacional de I+D+I and cofinanced by the ISCIII-Subdirección General de Evaluación and the FEDER. GF is supported by a fellowship from “La Caixa” Foundation (ID 100010434)—fellowship code—LCF/BQ/DR21/11880019. SA has been supported by a Sara Borrell contract (CD20/00177), funded by ISCIII and founded by the European Social Fund “Investing in your future.” EV thanks the support of the Spanish Ministry of Science, Innovation and Universities (PI15/00283, PI18/00805, PI19/00394, PI21/00787, and CPII19/00009) integrated into the Plan Nacional de I+D+I and cofinanced by the ISCIII-Subdirección General de Evaluación and the FEDER; the ISCIII; the CIBER of Mental Health (CIBERSAM); the Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement (2017 SGR 1365), and the CERCA Programme or Generalitat de Catalunya. We would like to thank the Departament de Salut de la Generalitat de Catalunya for the PERIS grant SLT006/17/00357. DHM´s research was supported by Juan Rodés JR18/00021 granted by the ISCIII. The PRESTO project has been funded by Fundació Clínic per a la Recerca Biomèdica through the Pons Bartran 2020 grant (PI046549). The development of a version of the digital solution adapted to health workers is funded by the Spanish Foundation for Psychiatry and Mental Health, Spanish Psychiatric Society, and Spanish Society of Biological Psychiatry (PI046813). The enhancement of the digital solution with Natural Language Processing techniques in a chatbot user-interface in collaboration with the text mining technologies in the health domain of the Barcelona Supercomputing Center is funded by the Agència de Gestió d’Ajudes Universitàries I de Investigació—PANDÈMIES 2020 grant (PI047003), from La Generalitat de Catalunya.Peer Reviewed"Article signat per 50 autors/es: Gerard Anmella; Miriam Sanabra; Mireia Primé-Tous; Xavier Segú; Myriam Cavero; Ivette Morilla; Iria Grande; Victoria Ruiz; Ariadna Mas; Inés Martín-Villalba; Alejandro Caballo; Julia-Parisad Esteva; Arturo Rodríguez-Rey; Flavia Piazza; Francisco José Valdesoiro; Claudia Rodriguez-Torrella; Marta Espinosa; Giulia Virgili; Carlota Sorroche; Alicia Ruiz; Aleix Solanes; Joaquim Radua; María Antonieta Also; Elisenda Sant; Sandra Murgui; Mireia Sans-Corrales; Allan H Young; Victor Vicens; Jordi Blanch; Elsa Caballeria; Hugo López-Pelayo; Clara López; Victoria Olivé; Laura Pujol ; Sebastiana Quesada; Brisa Solé; Carla Torrent; Anabel Martínez-Aran; Joana Guarch; Ricard Navinés; Andrea Murru; Giovanna Fico; Michele de Prisco; Vicenzo Oliva; Silvia Amoretti ; Casimiro Pio-Carrino; María Fernández-Canseco; Marta Villegas; Eduard Vieta; Diego Hidalgo-Mazzei"Postprint (published version

Unravelling potential severe psychiatric repercussions on healthcare professionals during the COVID-19 crisis

The coronavirus disease 2019 (COVID-19) outbreak is putting healthcare professionals, especially those in the frontline, under extreme pressures, with a high risk of experiencing physical exhaustion, psychological disturbances, stigmatization, insomnia, depression and anxiety. We report the case of a general practitioner, without relevant somatic or psychiatric history that experienced a 'brief reactive psychosis (298.8)' under stressful circumstances derived from COVID-19. She presented with delusional ideas of catastrophe regarding the current pandemic situation, delusions of self-reference, surveillance and persecution, with high affective and behavioural involvement. Physical examination and all further additional investigations did not reveal any secondary causes. She was administered olanzapine 10 mg with significant psychopathological improvement being later discharged with indications to maintain the treatment. To our knowledge this is the first reported case of severe mental illness in a healthcare professional without previous psychiatric history due to COVID-19 outbreak. Around 85% of patients presenting a brief psychotic disorder will develop a potentially disabling serious psychotic illness in the long-term. This case represents the potentially serious mental health consequences on healthcare professionals throughout the COVID-19 crisis and emphasizes the need to implement urgent measures to maintain staff mental health during the current pandemic

Real-world Implementation of a Smartphone-Based Psychoeducation Program for Bipolar Disorder: Observational Ecological Study

Background: SIMPLe is an internet-delivered self-management mobile app for bipolar disorder (BD) designed to combine technology with evidence-based interventions and facilitate access to psychoeducational content. The SIMPLe app was launched to the real world to make it available worldwide within the context of BD treatment. Objective: The main aims of this study are as follows: to describe app use, engagement, and retention rates based on server data; to identify patterns of user retention over the first 6-month follow-up of use; and to explore potential factors contributing to discontinuation of app use. Methods: This was an observational ecological study in which we pooled available data from a real-world implementation of the SIMPLe app. Participation was open on the project website, and the data-collection sources were a web-based questionnaire on clinical data and treatment history administered at inclusion and at 6 months, subjective data gathered through continuous app use, and the use patterns captured by the app server. Characteristics and engagement of regular users, occasional users, and no users were compared using 2-tailed t tests or analysis of variance or their nonparametric equivalent. Survival analysis and risk functions were applied to regular users' data to examine and compare use and user retention. In addition, a user evaluation analysis was performed based on satisfaction, perceived usefulness, and reasons to discontinue app use. Results: We included 503 participants with data collected between 2016 and 2018, of whom 77.5% (n=390) used the app. Among the app users, 44.4% (173/390) completed the follow-up assessment, and data from these participants were used in our analyses. Engagement declined gradually over the first 6 months of use. The probability of retention of the regular users after 1 month of app use was 67.4% (263/390; 95% CI 62.7%-72.4%). Age (P=.002), time passed since illness onset (P<.001), and years since diagnosis of BD (P=.048) correlate with retention duration. In addition, participants who had been diagnosed with BD for longer used the app on more days (mean 97.73, SD 69.15 days; P=.002) than those who had had a more recent onset (mean 66.49, SD 66.18 days; P=.002) or those who had been diagnosed more recently (mean 73.45, SD 66 days; P=.01). Conclusions: The user retention rate of the app decreased rapidly after each month until reaching only one-third of the users at 6 months. There exists a strong association between age and app engagement of individuals with BD. Other variables such as years lived with BD, diagnosis of an anxiety disorder, and taking antipsychotics seem relevant as well. Understanding these associations can help in the definition of the most suitable user profiles for predicting trends of engagement, optimization of app prescription, and management

A 12-month prospective study on the time to hospitalization and clinical management of a cohort of bipolar type I and schizoaffective bipolar patients

Background: Schizoaffective disorder, bipolar type (SAD) and bipolar disorder I (BD) present a large clinical overlap. In a 1-year follow-up, we aimed to evaluate days to hospitalization (DTH) and predictors of relapse in a SAD-BD cohort of patients. Methods: A 1-year, prospective, naturalistic cohort study considering DTH as primary outcome and incidence of direct and indirect measures of psychopathological compensation as secondary outcomes. Kaplan-Meyer survival analysis with Log-rank Mantel-Cox test compared BD/SAD subgroups as to DTH. After bivariate analyses, Cox regression was performed to assess covariates possibly associated with DTH in diagnostic subgroups. Results: Of 836 screened patients, 437 were finally included (SAD = 105; BD = 332). Relapse rates in the SAD sample was n = 26 (24.8%) vs. n = 41 (12.3%) in the BD sample (p = 0.002). Mean ± SD DTH were 312.16 ± 10.6 (SAD) vs. 337.62 ± 4.4 (BD) days (p = 0.002). Patients with relapses showed more frequent suicide acts, violent behaviors, and changes in pharmacological treatments (all p 0.0005). Conclusions: SAD patients relapse earlier with higher hospitalization rates and violent behavior during psychotic episodes whereas bipolar patients have more suicide attempts. Psychiatric/psychological follow-up visits may delay hospitalizations by closely monitoring symptoms of self- and hetero-aggression

Duration of untreated illness and bipolar disorder: time for a new definition? Results from a cross-sectional study

Background: We primarily aimed to explore the associations between duration of untreated illness (DUI), treatment response, and functioning in a cohort of patients with bipolar disorder (BD). Methods: 261 participants with BD were recruited. DUI was defined as months from the first affective episode to the start of a mood-stabilizer. The functioning assessment short test (FAST) scores and treatment response scores for lithium, valproate, or lamotrigine according to the Alda Scale Total Score (TS) were compared between patients with short (<24 months) or long DUI. Differences in FAST scores among good (GR; TS≥7), poor (PR; TS=2-6), or non-responders (NR; TS<2) to each mood-stabilizer were analyzed. Linear regression was computed using the FAST global score as the dependent variable. Results: DUI and FAST scores showed no statistically significant correlation. Patients with a longer DUI showed poorer response to lithium (Z=-3.196; p<0.001), but not to valproate or lamotrigine. Response to lithium (β=-1.814; p<0.001), number of hospitalizations (β=0.237; p<0.001), and illness duration (β=0.160; p=0.028) were associated with FAST total scores. GR to lithium was associated with better global functioning compared to PR or NR [H=27.631; p<0.001]. Limitations: The retrospective design could expose our data to a recall bias. Also, only few patients were on valproate or lamotrigine treatment. Conclusions: Poor functioning in BD could be the result of multiple affective relapses, rather than a direct effect of DUI. A timely diagnosis with subsequent effective prophylactic treatment, such as lithium, may prevent poor functional outcomes in real-world patients with BD

Automated mood disorder symptoms monitoring from multivariate time-series sensory data:getting the full picture beyond a single number

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.</p

Affective temperaments mediate aggressive dimensions in bipolar disorders: A cluster analysis from a large, cross-sectional, international study

Background: Affective temperaments show potential for aggressive behavior (AB) preventive strategies in bipolar disorder (BD). We aim to define intra-diagnostic subgroups of patients with BD based on homogeneous behaviors related to AB. Subsequently, to assess whether affective temperament dimensions may contribute to the presence and severity of AB. Methods: Patients with BD were recruited. AB was evaluated through the modified overt aggression scale (MOAS); affective temperaments were assessed with the TEMPS-A. A cluster analysis was conducted based on TEMPS-A and MOAS scores. Stepwise backward logistic regression models were used to identify the predictive factors of cluster membership. Results: 799 patients with BD were enrolled. Three clusters were determined: non-aggressive (55.5 %), self-aggressive (18 %), and hetero-aggressive (26.5 %). Depressive, irritable, and anxious temperament scores significantly increased from the non-aggressive (lower) to the self-aggressive (intermediate) and the hetero-aggressive group (highest). A positive history of a suicide attempt (B = 5.131; OR = 169.2, 95 % CI 75.9; 377) and rapid cycling (B = -0.97; OR = 0.40, 95 % CI 0.17; 0.95) predicted self-aggressive cluster membership. Atypical antipsychotics (B = 1.19; OR = 3.28, 95 % CI 2.13; 5.06) or SNRI treatment (B = 1.09; OR = 3, 95 % CI 1.57; 5.71), psychotic symptoms (B = 0.73; OR = 2.09, 95 % CI 1.34; 3.26), and history of a suicide attempt (B = -1.56; OR = 0.20, 95 % CI 0.11; 0.38) predicted hetero-aggressive cluster membership. Limitations: Recall bias might have affected the recollection of AB. Conclusions: Clinical factors orientate the prevention of different ABs in BD. Affective temperaments might play a role in preventing AB since patients with more pronounced affective temperaments might have an increased risk of showing AB, in particular hetero-AB

Deconstructing major depressive episodes across unipolar and bipolar depression by severity and duration: a cross-diagnostic cluster analysis on a large, international, observational study

A cross-diagnostic, post-hoc analysis of the BRIDGE-II-MIX study was performed to investigate how unipolar and bipolar patients suffering from an acute major depressive episode (MDE) cluster according to severity and duration. Duration of index episode, Clinical Global Impression-Bipolar Version-Depression (CGI-BP-D) and Global Assessment of Functioning (GAF) were used as clustering variables. MANOVA and post-hoc ANOVAs examined between-group differences in clustering variables. A stepwise backward regression model explored the relationship with the 56 clinical-demographic variables available. Agglomerative hierarchical clustering with two clusters was shown as the best fit and separated the study population (n = 2314) into 65.73% (Cluster 1 (C1)) and 34.26% (Cluster 2 (C2)). MANOVA showed a significant main effect for cluster group (p < 0.001) but ANOVA revealed that significant between-group differences were restricted to CGI-BP-D (p < 0.001) and GAF (p < 0.001), showing greater severity in C2. Psychotic features and a minimum of three DSM-5 criteria for mixed features (DSM-5-3C) had the strongest association with C2, that with greater disease burden, while non-mixed depression in bipolar disorder (BD) type II had negative association. Mixed affect defined as DSM-5-3C associates with greater acute severity and overall impairment, independently of the diagnosis of bipolar or unipolar depression. In this study a pure, non-mixed depression in BD type II significantly associates with lesser burden of clinical and functional severity. The lack of association for less restrictive, researched-based definitions of mixed features underlines DSM-5-3C specificity. If confirmed in further prospective studies, these findings would warrant major revisions of treatment algorithms for both unipolar and bipolar depression

Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning:Prospective, Exploratory, Observational Study

BACKGROUND: Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection.OBJECTIVE: In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables' data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task.METHODS: We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks-from emotion recognition in Super Mario players to stress detection in undergraduates-and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre-deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients.RESULTS: SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability.CONCLUSIONS: We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.</p

Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder

Substance use disorder (SUD) is a common comorbidity in individuals with bipolar disorder (BD), and it is associated with a severe course of illness, making early identification of the risk factors for SUD in BD warranted. We aimed to identify, through machine-learning models, the factors associated with different types of SUD in BD. We recruited 508 individuals with BD from a specialized unit. Lifetime SUDs were defined according to the DSM criteria. Random forest (RF) models were trained to identify the presence of (i) any (SUD) in the total sample, (ii) alcohol use disorder (AUD) in the total sample, (iii) AUD co-occurrence with at least another SUD in the total sample (AUD+SUD), and (iv) any other SUD among BD patients with AUD. Relevant variables selected by the RFs were considered as independent variables in multiple logistic regressions to predict SUDs, adjusting for relevant covariates. AUD+SUD could be predicted in BD at an individual level with a sensitivity of 75% and a specificity of 75%. The presence of AUD+SUD was positively associated with having hypomania as the first affective episode (OR = 4.34 95% CI = 1.42-13.31), and the presence of hetero-aggressive behavior (OR = 3.15 95% CI = 1.48-6.74). Machine-learning models might be useful instruments to predict the risk of SUD in BD, but their efficacy is limited when considering socio-demographic or clinical factors alone