SIMON: A Digital Protocol to Monitor and Predict Suicidal Ideation

Digital phenotyping for mental health based on data analytics: A systematic literature review

Even though mental health is a human right, mental disorders still affect millions of people worldwide. Untreated and undertreated mental health conditions may lead to suicide, which generates more than 700,000 deaths annually around the world. The broad adoption of smartphones and wearable devices allowed the recording and analysis of human behaviors in digital devices, which might reveal mental health symptoms. This analysis constitutes digital phenotyping research, referring to frequent and constant measurement of human phenotypes in situ based on data from smartphones and other personal digital devices. Therefore, this article presents a systematic literature review providing a computer science view on data analytics for digital phenotyping in mental health. This study reviewed 5,422 articles from ten academic databases published up to September 2024, generating a final list of 74 studies. The investigated databases are ACM, IEEE Xplore, PsycArticles, PsycInfo, Pubmed, Science Direct, Scopus, Springer, Web of Science, and Wiley. We investigated ten research questions, considering explored data, employed devices, and techniques for data analysis. This review also organizes the application domains and mental health conditions, data analytics techniques, and current research challenges. This study found a growing research interest in digital phenotyping for mental health in recent years. Current approaches still present a high dependence on self-reported measures of mental health status, but there is evidence of the employment of smartphones for leveraging passive data collection. Traditional machine learning techniques are the main explored strategies for analyzing the large amount of collected data. In this regard, published approaches deeply focused on data analysis, generating opportunities concerning the implementation of resources for assisting individuals suffering from mental disorders.

Ecological Momentary Assessment in psychotherapy research: A systematic review

Ecological Momentary Assessment (EMA) stands as a valuable method to capture real-time data on individuals' daily experiences and behaviors. In recent years, the utilization of EMA as a measurement method has substantially increased with the majority of studies emphasizing its clinical utility. However, a comprehensive overview of its use in psychotherapy research is lacking. This study addresses that gap by systematically reviewing EMA's application in psychotherapy research. In total, 168 studies met the inclusion criteria and were classified according to clinical utilization. Six areas of clinical EMA application were identified: prediction of therapy outcome (n = 8), prediction of psychopathology (n = 40), prediction of biopsychosocial states (n = 44), evaluation of therapy outcome (n = 21), acquisition of further clinical insights into specific disorders (n = 68) and adaptation of treatment processes (n = 18). Despite studies consistently highlighting EMA's potential in tailoring psychotherapeutic treatments, its limited use in this area warrants further research. Drawing from our findings, we discuss future research directions for the direct application of EMA in psychotherapeutic settings.

Perceived responsiveness in suicidal ideation: An experience sampling study in psychiatric patients

INTRODUCTION

Perceived responsiveness, or the extent to which one feels understood, validated and cared for by close others, plays a crucial role in people's well-being. Can this interpersonal process also protect people at risk? We assessed whether fluctuations in suicidal ideation were associated with fluctuations in the degree of perceived responsiveness that psychiatric patients (admitted in the context of suicide or indicating suicidal ideation) experienced in daily interactions immediately after discharge.

METHODS

Fifty-seven patients reported on suicidal ideation (5 times a day) and perceived responsiveness (daily) for four consecutive weeks. The effects of established risk factors-thwarted belongingness, perceived burdensomeness, and hopelessness-were assessed as well.

RESULTS

The more patients felt that close others had been responsive to them, the less suicidal ideation they reported. At low levels of thwarted belongingness, perceived burdensomeness, or hopelessness, perceived responsiveness seemed to play a protective role, negatively co-occurring with suicidal ideation. When thwarted belongingness, perceived burdensomeness, and hopelessness were high, perceived responsiveness did not have an effect.

CONCLUSION

Perceived responsiveness could be a protective factor for suicidal ideation for people at risk only when they are experiencing low levels of negative perceptions. When experiencing highly negative perceptions, however, perceived responsiveness seems to matter less.

A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors

Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research.

What to do when the unwanted happens? Negative event management in studies on internet- and mobile-based interventions for youths and adults with two case reports

Background

Despite severely burdened individuals, often being excluded from research studies on internet- and mobile-based interventions (IMIs), negative events (NEs) including suicidal thoughts and behaviors (STBs) can still occur during a trial. NEs require monitoring and adequate safety measures. However, study protocols frequently lack comprehensive descriptions of procedures for managing NEs.

Aims

This study aimed to illustrate the assessment, monitoring, and procedures for addressing NEs in two studies on IMIs in adults and youth using case reports, to identify strengths and weaknesses of the NE management approaches, and to derive key learnings and recommendations.

Methods

Two case reports were drawn from two distinct IMI studies. The first study, PSYCHOnlineTHERAPY, evaluates the combination of an IMI with on-site psychotherapy for anxiety and depressive disorders in adults (adult blended study). The second study evaluates a standalone, therapist-guided IMI for post-traumatic stress disorder (PTSD) in youth (youth standalone study). Potential NEs were predefined depending on the study sample. The case studies thoroughly document the systematic recording and ongoing monitoring of NEs through self-report and observer-based assessments during the interventions. The cases illustrate a variety of NE management strategies, including automated and personalized approaches, adapted to the specific nature and severity of the NEs. The NE management approaches are visualized using decision trees.

Results

In the adult blended case study, online questionnaires detected STBs and triggered automated support information. As on-site therapy had already ended, a telephone consultation session allowed for the identification and discussion of the heightened intensity of suicidal thoughts, along with the development of specific additional help options. In the youth standalone case study, heightened tension in an adolescent with PTSD during trauma processing could be addressed in a telephone therapeutic session focusing on resource activation and emotion regulation. The referral to on-site treatment was supported. Overall, advantages of the NE management included automated procedures, multimodal assessment of a wide range of NEs, and standardized procedures tailored to different severity levels. Weaknesses included the use of single-item assessments for STBs and lack of procedures in case of deterioration or nonresponse to treatment.

Conclusion

This study provides practical insights and derives key learnings and recommendations regarding the management of NEs in different IMI contexts for both adults and youth.

Loneliness and suicide mitigation for students using GPT3-enabled chatbots

Mental health is a crisis for learners globally, and digital support is increasingly seen as a critical resource. Concurrently, Intelligent Social Agents receive exponentially more engagement than other conversational systems, but their use in digital therapy provision is nascent. A survey of 1006 student users of the Intelligent Social Agent, Replika, investigated participants' loneliness, perceived social support, use patterns, and beliefs about Replika. We found participants were more lonely than typical student populations but still perceived high social support. Many used Replika in multiple, overlapping ways-as a friend, a therapist, and an intellectual mirror. Many also held overlapping and often conflicting beliefs about Replika-calling it a machine, an intelligence, and a human. Critically, 3% reported that Replika halted their suicidal ideation. A comparative analysis of this group with the wider participant population is provided.

Inhibitory control and mood in relation to psychological resilience: an ecological momentary assessment study

Psychological resilience, the ability to adapt to adversity, is theorized to rely on intact inhibitory control (IC) mechanisms, which underlie one's ability to maintain goal-directed behavior by inhibiting prepotent responses. However, no study to date has explored daily fluctuations of IC performance in relation to resilience. Here, we examined the association between IC and mood measured daily in relation to psychological resilience in young adults in a stressful situation. Baseline resilience was obtained from 144 female and male soldiers during their basic combat training. Then, participants completed an ecological momentary assessment protocol, in which they reported their momentary mood and completed a short IC assessment twice/day for 2 weeks. A hierarchical linear modeling analysis revealed that psychological resilience moderated the relationship between momentary IC and momentary mood, such that better IC was associated with better mood only for those with higher, but not lower, self-reported psychological resilience at baseline. These results show that psychological resilience is manifested in the everyday association between IC and mood. Furthermore, they lend important support to cognitive models of resilience and may have significant contribution to our understanding of resilient behavior in real life.Trial Registration: MOH_2018-0-13_002451.

The use of advanced technology and statistical methods to predict and prevent suicide

In the past decade, two themes have emerged across suicide research. First, according to meta-analyses, the ability to predict and prevent suicidal thoughts and behaviours is weaker than would be expected for the size of the field. Second, review and commentary papers propose that technological and statistical methods (such as smartphones, wearables, digital phenotyping and machine learning) might become solutions to this problem. In this Review, we aim to strike a balance between the pessimistic picture presented by these meta-analyses and the optimistic picture presented by review and commentary papers about the promise of advanced technological and statistical methods to improve the ability to understand, predict and prevent suicide. We divide our discussion into two broad categories. First, we discuss the research aimed at assessment, with the goal of better understanding or more accurately predicting suicidal thoughts and behaviours. Second, we discuss the literature that focuses on prevention of suicidal thoughts and behaviours. Ecological momentary assessment, wearables and other technological and statistical advances hold great promise for predicting and preventing suicide, but there is much yet to do.

Utilizing digital predictive biomarkers to identify Veteran suicide risk

Veteran suicide is one of the most complex and pressing health issues in the United States. According to the 2020 National Veteran Suicide Prevention Annual Report, since 2018 an average of 17.2 Veterans died by suicide each day. Veteran suicide risk screening is currently limited to suicide hotlines, patient reporting, patient visits, and family or friend reporting. As a result of these limitations, innovative approaches in suicide screening are increasingly garnering attention. An essential feature of these innovative methods includes better incorporation of risk factors that might indicate higher risk for tracking suicidal ideation based on personal behavior. Digital technologies create a means through which measuring these risk factors more reliably, with higher fidelity, and more frequently throughout daily life is possible, with the capacity to identify potentially telling behavior patterns. In this review, digital predictive biomarkers are discussed as they pertain to suicide risk, such as sleep vital signs, sleep disturbance, sleep quality, and speech pattern recognition. Various digital predictive biomarkers are reviewed and evaluated as well as their potential utility in predicting and diagnosing Veteran suicidal ideation in real time. In the future, these digital biomarkers could be combined to generate further suicide screening for diagnosis and severity assessments, allowing healthcare providers and healthcare teams to intervene more optimally.

The Effectiveness of Mobile Apps for Monitoring and Management of Suicide Crisis: A Systematic Review of the Literature

Suicide risk is a multifaceted phenomenon, and many risk factors are involved in its complexity. In the last few decades, mental health apps have spread, providing economic and affordable strategies to prevent suicide. Therefore, the aim of this review is to identify original studies on mobile apps that target suicidal crises. The review follows PRISMA guidelines, searching through four major electronic databases (PubMed/MEDLINE, Scopus, PsycInfo and Web of Science) for relevant titles/abstracts published from January 2010 to May 2022. It includes original studies that explicitly analyze mobile apps for suicide prevention. A total of 32 studies met the inclusion criteria. Sixteen studies assessed the feasibility and acceptability of mobile apps, ten studies assessed the efficacy of mobile apps in preventing suicide, and six studies described randomized control trial protocols not yet implemented. Generally, the apps were judged by participants to be acceptable and helpful, and several improvements to enhance the functionality of apps were suggested. The efficacy of mobile apps, although limited and assessed with very heterogenous methods, was confirmed by most of the studies. Mobile apps could represent a helpful supplement to traditional prevention tactics, providing real-time monitoring of at-risk persons, personalized tools to cope with suicidal crises, and immediate access to specific support.

How Far Can Conversational Agents Contribute to IBD Patient Health Care—A Review of the Literature

Modern societies are facing health and healthcare challenges as never seen before. The digital world in which we are living today considers digital health interventions such as “internet-delivered” therapy (e-Therapy) or mobile apps as an integrated part of healthcare systems. Digital transformation in health care requires the active involvement of patients as the central part of healthcare interventions. In the case of chronic health conditions, such as inflammatory bowel disease (IBD), it is believed that the adoption of new digital tools helps to maintain and extend the health and care of patients, optimizing the course of the treatment of the disease. The study goal was to undertake a literature review associating the use of chatbot technology with IBD patients' health care. This study intends to support digital product developments, mainly chatbot for IBD or other chronic diseases. The work was carried out through two literature review phases. The first one was based on a systematic approach and the second was a scoping review focused only on Frontiers Journals. This review followed a planned protocol for search and selection strategy that was created by a research team discussion. Chatbot technology for chronic disease self-management can have high acceptance and usability levels. The more interaction with a chatbot, the more patients are able to increase their self-care practice, but there is a challenge. The chatbot ontology to personalize the communication still needed to have strong guidelines helping other researchers to define which Electronic Medical Records (EMRs) should be used in the chatbots to improve the user satisfaction, engagement, and dialog quality. The literature review showed us both evidence and success of these tools in other health disorders. Some of them revealed a huge potential for conversational agents as a part of digital health interventions.

Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation

People at risk of suicide tend to be isolated and cannot share their thoughts. For this reason, suicidal ideation monitoring becomes a hard task. Therefore, people at risk of suicide need to be monitored in a manner capable of identifying if and when they have a suicidal ideation, enabling professionals to perform timely interventions. This study aimed to develop the Boamente tool, a solution that collects textual data from users’ smartphones and identifies the existence of suicidal ideation. The solution has a virtual keyboard mobile application that passively collects user texts and sends them to a web platform to be processed. The platform classifies texts using natural language processing and a deep learning model to recognize suicidal ideation, and the results are presented to mental health professionals in dashboards. Text classification for sentiment analysis was implemented with different machine/deep learning algorithms. A validation study was conducted to identify the model with the best performance results. The BERTimbau Large model performed better, reaching a recall of 0.953 (accuracy: 0.955; precision: 0.961; F-score: 0.954; AUC: 0.954). The proposed tool demonstrated an ability to identify suicidal ideation from user texts, which enabled it to be experimented with in studies with professionals and their patients.