RESUMEN

Introduction: Treatment-resistant depression (TRD) presents a significant challenge, affecting approximately 30% of individuals diagnosed with major depressive disorder and leading to poor treatment responses. Innovations in digital mental health, especially online mindfulness-based cognitive therapy (eMBCT), offer promising avenues for enhancing access to effective mental health care for individuals with TRD in a clinical setting. Objective: The aim of this study was to examine the feasibility of eMBCT in an individual clinical context to decrease depressive symptoms for TRD. Methods: Conducted at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil, this parallel-arm, randomized controlled feasibility trial involved outpatients diagnosed with TRD, aged 18 and above. Of the 39 outpatients invited, 28 were randomized into two groups: an intervention group receiving the eMBCT program (n = 15) and a control group (n = 13). The intervention, consisting of an 8-week course, was delivered via live video sessions. Following the assessment period, participants in the control group were offered the eMBCT intervention. Assessments using standardized questionnaires were conducted at the start and end of the study. Results: Within the eMBCT group, improvements were observed in depression symptoms (Z = -3.423; p = 0.001; effect size r = 0.78), anxiety symptoms (Z = -3.361; p = 0.001; effect size r = 0.77), with no significant changes in the control group. Comparatively, the eMBCT group showed significant reductions in depression symptoms and improvements in clinical global impressions over the control group (BDI2: U = 30.5; p = 0.015; effect size r = 0.47, CGI1: U = 21.0; p = 0.004; effect size r = 0.56). Conclusion: eMBCT in an individual format combined with medication, appears to be a feasible treatment for TRD, decreasing symptoms of depression. In a future trial the control group may have a manualized intervention. Clinical trial registration: The Brazilian Clinical Trials Registry: (https://ensaiosclinicos.gov.br/rg/RBR-6zndpbv) and RBR-6zndpbv.

RESUMEN

BACKGROUND: Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications. OBJECTIVE: This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications. METHODS: The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising sociodemographic and health-related features. A Bayesian network was used for feature selection. Selected features were then used to train machine learning models to predict DS, operationalized as a score of ≥10 on the 9-item Patient Health Questionnaire. The study also analyzed the impact of varying sensitivity rates on the reduction of screening interviews compared to a random approach. RESULTS: The methodology allows the users to make an informed trade-off among sensitivity, specificity, and a reduction in the number of interviews. At the thresholds of 0.444, 0.412, and 0.472, determined by maximizing the Youden index, the models achieved sensitivities of 0.717, 0.741, and 0.718, and specificities of 0.644, 0.737, and 0.766 for PROACTIVE, PNS 2013, and PNS 2019, respectively. The area under the receiver operating characteristic curve was 0.736, 0.801, and 0.809 for these 3 data sets, respectively. For the PROACTIVE data set, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 data set, the features were the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 data set shared 3 of the most influential features with the PNS 2013 data set. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS data sets differ from those found in the PROACTIVE data set. An empirical analysis demonstrated that using the proposed model led to a potential reduction in screening interviews of up to 52% while maintaining a sensitivity of 0.80. CONCLUSIONS: This study developed a novel methodology for identifying individuals with DS, demonstrating the utility of using Bayesian networks to identify the most significant features. Moreover, this approach has the potential to substantially reduce the number of screening interviews while maintaining high sensitivity, thereby facilitating improved early identification and intervention strategies for individuals experiencing DS.

Asunto(s)

Algoritmos , Teorema de Bayes , Depresión , Humanos , Depresión/diagnóstico , Adulto , Femenino , Masculino , Brasil/epidemiología , Persona de Mediana Edad , Aprendizaje Automático , Tamizaje Masivo/métodos , Sensibilidad y Especificidad , Encuestas Epidemiológicas

RESUMEN

Background: Community-based psychosocial support (CB-PSS) interventions utilizing task sharing and varied (in-person, remote) modalities are essential strategies to meet mental health needs, including during the COVID-19 pandemic. However, knowledge gaps remain regarding feasibility and effectiveness. Methods: This study assesses feasibility, acceptability and preliminary effectiveness of a CB-PSS intervention for conflict-affected adults in Colombia through parallel randomized controlled trials, one delivered in-person (n = 165) and the other remotely (n = 103), implemented during the COVID-19 pandemic and national protests. Interventions were facilitated by nonspecialist community members and consisted of eight problem-solving and expressive group sessions. Findings: Attendance was moderate and fidelity was high in both modalities. Participants in both modalities reported high levels of satisfaction, with in-person participants reporting increased comfort expressing emotions and more positive experiences with research protocols. Symptoms of depression, anxiety and posttraumatic stress disorder improved among in-person participants, but there were no significant changes for remote participants in comparison to waitlist controls. Implications: This CB-PSS intervention appears feasible and acceptable in both in-person and remote modalities and associated with reduction in some forms of distress when conducted in-person but not when conducted remotely. Methodological limitations and potential explanations and areas for future research are discussed, drawing from related studies.

RESUMEN

BACKGROUND: Mental health status assessment is mostly limited to clinical or research settings, but recent technological advances provide new opportunities for measurement using more ecological approaches. Leveraging apps already in use by individuals on their smartphones, such as chatbots, could be a useful approach to capture subjective reports of mood in the moment. OBJECTIVE: This study aimed to describe the development and implementation of the Identifying Depression Early in Adolescence Chatbot (IDEABot), a WhatsApp-based tool designed for collecting intensive longitudinal data on adolescents' mood. METHODS: The IDEABot was developed to collect data from Brazilian adolescents via WhatsApp as part of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study. It supports the administration and collection of self-reported structured items or questionnaires and audio responses. The development explored WhatsApp's default features, such as emojis and recorded audio messages, and focused on scripting relevant and acceptable conversations. The IDEABot supports 5 types of interactions: textual and audio questions, administration of a version of the Short Mood and Feelings Questionnaire, unprompted interactions, and a snooze function. Six adolescents (n=4, 67% male participants and n=2, 33% female participants) aged 16 to 18 years tested the initial version of the IDEABot and were engaged to codevelop the final version of the app. The IDEABot was subsequently used for data collection in the second- and third-year follow-ups of the IDEA-RiSCo study. RESULTS: The adolescents assessed the initial version of the IDEABot as enjoyable and made suggestions for improvements that were subsequently implemented. The IDEABot's final version follows a structured script with the choice of answer based on exact text matches throughout 15 days. The implementation of the IDEABot in 2 waves of the IDEA-RiSCo sample (140 and 132 eligible adolescents in the second- and third-year follow-ups, respectively) evidenced adequate engagement indicators, with good acceptance for using the tool (113/140, 80.7% and 122/132, 92.4% for second- and third-year follow-up use, respectively), low attrition (only 1/113, 0.9% and 1/122, 0.8%, respectively, failed to engage in the protocol after initial interaction), and high compliance in terms of the proportion of responses in relation to the total number of elicited prompts (12.8, SD 3.5; 91% out of 14 possible interactions and 10.57, SD 3.4; 76% out of 14 possible interactions, respectively). CONCLUSIONS: The IDEABot is a frugal app that leverages an existing app already in daily use by our target population. It follows a simple rule-based approach that can be easily tested and implemented in diverse settings and possibly diminishes the burden of intensive data collection for participants by repurposing WhatsApp. In this context, the IDEABot appears as an acceptable and potentially scalable tool for gathering momentary information that can enhance our understanding of mood fluctuations and development.

RESUMEN

BACKGROUND: Transcranial electrical stimulation (tES) is considered effective and safe for depression, albeit modestly, and prone to logistical burdens when performed in external facilities. Investigation of portable tES (ptES), and potentiation of ptES with remote psychological interventions have shown positive, but preliminary, results. RESEARCH DESIGN: We report the rationale and design of an ongoing multi-arm, randomized, double-blind, sham-controlled clinical trial with digital features, using ptES and internet-based behavioral therapy (iBT) for major depressive disorder (MDD) (NCT04889976). METHODS: We will evaluate the efficacy, safety, tolerability and usability of (1) active ptES + active iBT ('double-active'), (2) active ptES + sham iBT ('ptES-only'), and (3) sham ptES + sham iBT ('double-sham'), in adults with MDD, with a Hamilton Depression Rating Scale - 17 item version (HDRS-17) score ≥ 17 at baseline, during 6 weeks. Antidepressants are allowed in stable doses during the trial. RESULTS: We primarily co-hypothesize changes in HDRS-17 will be greater in (1) 'double-active' compared to 'ptES-only,' (2) 'double-active' compared to 'double-sham,' and (3) 'ptES-only' compared to 'double-sham.' We aim to enroll 210 patients (70 per arm). CONCLUSIONS: Our results should offer new insights regarding the efficacy and scalability of combined ptES and iBT for MDD, in digital mental health.

Asunto(s)

Trastorno Depresivo Mayor , Estimulación Transcraneal de Corriente Directa , Adulto , Terapia Conductista , Depresión , Trastorno Depresivo Mayor/psicología , Trastorno Depresivo Mayor/terapia , Método Doble Ciego , Humanos , Internet , Ensayos Clínicos Controlados Aleatorios como Asunto , Estimulación Transcraneal de Corriente Directa/métodos , Estimulación Magnética Transcraneal/métodos , Resultado del Tratamiento

RESUMEN

Two randomized controlled trials (RCTs) in Brazil and Peru demonstrated the effectiveness of CONEMO, a digital intervention supported by trained nurses or nurse assistants (NAs), to reduce depressive symptoms in people with diabetes and/or hypertension. This paper extends the RCTs findings by reflecting on the conditions needed for its wider implementation in routine care services. A qualitative study using semi-structured interviews and content analysis was conducted with nurses/NAs, clinicians, healthcare administrators, and policymakers. Informants reported that CONEMO would be feasible to implement in their health services, but some conditions could be improved before its scale-up: reducing workloads of healthcare workers; raising mental health awareness among clinicians and administrators; being able to inform, deliver and accompany the intervention; assuring appropriate training and supervision of nurses/NAs; and supporting the use of technology in public health services and by patients, especially older ones. We discuss some suggestions on how to overcome these challenges.