Prediction of near-term increases in suicidal ideation in recently depressed patients with bipolar II disorder using intensive longitudinal data

Not missing at random: Missing data are associated with clinical status and trajectories in an electronic monitoring longitudinal study of bipolar disorder

There is limited information on the association between participants' clinical status or trajectories and missing data in electronic monitoring studies of bipolar disorder (BD). We collected self-ratings scales and sensor data in 145 adults with BD. Using a new metric, Missing Data Ratio (MDR), we assessed missing self-rating data and sensor data monitoring activity and sleep. Missing data were lowest for participants in the midst of a depressive episode, intermediate for participants with subsyndromal symptoms, and highest for participants who were euthymic. Over a mean ± SD follow-up of 246 ± 181 days, missing data remained unchanged for participants whose clinical status did not change throughout the study (i.e., those who entered the study in a depressive episode and did not improve, or those who entered the study euthymic and remained euthymic). Conversely, when participants' clinical status changed during the study (e.g., those who entered the study euthymic and experienced the occurrence of a depressive episode), missing data for self-rating scales increased, but not for sensor data. Overall missing data were associated with participants' clinical status and its changes, suggesting that these are not missing at random.

Major depressive disorder with suicidal ideation or behavior in Chinese population: A scoping review of current evidence on disease assessment, burden, treatment and risk factors

BACKGROUND

Identifying and managing major depressive disorder (MDD) patients with suicidal ideation or behavior (MDSI) is critical for reducing the disease burden. This scoping review aims to map the existing evidence related to MDSI in the Chinese population.

METHOD

A scoping review was conducted to summarize the published evidence regarding epidemiology or disease burden, evaluation, diagnosis, management, and prognosis of MDSI. The search strategy imposed restriction on English or Chinese publications between 1 January 2011 and 28 February 2022.

RESULTS

Of the 14,005 identified records, 133 met the eligibility criteria and were included for analysis. The included studies were characterized as high heterogeneity in evaluation of suicidal ideation or behavior. Compared with MDD patients without suicidal ideation or behavior, MDSI patients were more likely to suffer from psychological and somatic symptoms, social function impairment, and lower quality of life. Younger age, female gender, longer disease course, and comorbid psychological or physical symptoms were consistently found to be risk factors of suicidal ideation or behavior. Relevant research gaps remain regarding comprehensive evaluation of standard clinical diagnosis, disease burden, social-cultural risk factors, and effectiveness of interventions targeting MDSI. Studies with large sample size, representative population are warranted to provide high-quality evidence.

CONCLUSIONS

MDD patients with suicidal ideation or behavior should be prioritized in treatment and resource allocation. Heterogeneity exists in the definition and evaluation of MDSI in different studies. To better inform clinical practice, it is imperative to establish a unified standard for evaluation and diagnosis of suicidal ideation or behavior among MDD population.

Apps and gaps in bipolar disorder: A systematic review on electronic monitoring for episode prediction

BACKGROUND

Long-term clinical monitoring in bipolar disorder (BD) is an important therapeutic tool. The availability of smartphones and wearables has sparked the development of automated applications to remotely monitor patients. This systematic review focus on the current state of electronic (e-) monitoring for episode prediction in BD.

METHODS

We systematically reviewed the literature on e-monitoring for episode prediction in adult BD patients. The systematic review was done according to the guidelines for reporting of systematic reviews and meta-analyses (PRISMA) and was registered in PROSPERO on April 29, 2020 (CRD42020155795). We conducted a search of Web of Science, MEDLINE, EMBASE, and PsycINFO (all 2000-2020) databases. We identified and extracted data from 17 published reports on 15 relevant studies.

RESULTS

Studies were heterogeneous and most had substantial methodological and technical limitations. Models varied widely in their performance. Published metrics were too heterogeneous to lend themselves to a meta-analysis. Four studies reported sensitivity (range: 0.21 - 0.95); and two reported specificity for prediction of mood episodes (range: 0.36 - 0.99). Two studies reported accuracy (range: 0.64 - 0.88) and four reported area under the curve (AUC; range: 0.52-0.95). Overall, models were better in predicting manic or hypomanic episodes, but their performance depended on feature type.

LIMITATIONS

Our conclusions are tempered by the lack of appropriate information impeding our ability to synthesize the available evidence.

CONCLUSIONS

Given the clinical variability in BD, predicting mood episodes remains a challenging task. Emerging e-monitoring technology for episode prediction in BD requires more development before it can be adopted clinically.

Capturing Clinical Symptoms with Ecological Momentary Assessment: Convergence of Momentary Reports of Psychotic and Mood Symptoms with Diagnoses and Standard Clinical Assessments

Objective: The development and deployment of technology-based assessments of clinical symptoms are increasing. This study used ecological momentary assessment (EMA) to examine clinical symptoms and relates these sampling results to structured clinical ratings. Methods: Three times a day for 30 days, participants with bipolar disorder (n=71; BPI) or schizophrenia (n=102; SCZ) completed surveys assessing five psychosis-related and five mood symptoms, in addition to reporting their location and who they were with at the time of survey completion. Participants also completed Positive and Negative Syndrome Scale (PANSS) interviews with trained raters. Mixed-model repeated-measures (MMRM) analyses examined diagnostic effects and the convergence between clinical ratings and EMA sampling. Results: In total, 12,406 EMA samples were collected, with 80-percent adherence to prompts. EMA-reported psychotic symptoms manifested substantial convergence with equivalent endpoint PANSS items. Patients with SCZ had more severe PANSS and EMA psychotic symptoms. There were no changes in symptom severity scores as a function of the number of previous assessments. Conclusions: EMA surveyed clinical symptoms converged substantially with commonly used clinical rating scales in a large sample, with high adherence. This suggested that remote assessment of clinical symptoms is valid and practical and was not associated with alterations in symptoms as a function of reassessment, with additional benefits of "in the moment" sampling, such as eliminating recall bias and the need for informant reports.

Suicidal thoughts, suicidal behaviours and self-harm in daily life: A systematic review of ecological momentary assessment studies

Background

Ecological Momentary Assessments (EMA) offer an approach to understand the daily risk factors of suicide and self-harm of individuals through the use of self-monitoring techniques using mobile technologies.

Objectives

This systematic review aimed to examine the results of studies on suicidality risk factors and self-harm that used Ecological Momentary Assessments.

Methods

Pubmed and PsycINFO databases were searched up to April 2020. Bibliographies of eligible studies were hand-searched, and 744 abstracts were screened and double-coded for inclusion.

Results

The 49 studies using EMA included in the review found associations between daily affect, rumination and interpersonal interactions and daily non-suicidal self-injury (NSSI). Studies also found associations between daily negative affect and positive affect, social support, sleep, and emotions and a person's history of suicide and self-harm. Associations between daily suicide thoughts and self-harm, and psychopathology factors measured at baseline were also observed.

Conclusions

Research using EMA has the potential to offer clinicians the ability to understand the daily predictors, or risk factors, of suicide and self-harm. However, there are no clear reporting standards for EMA studies on risk factors for suicide. Further research should utilise longitudinal study designs, harmonise datasets and use machine learning techniques to identify patterns of proximal risk factors for suicide behaviours.

Electronic Ecological Momentary Assessment (EMA) in youth with bipolar disorder: Demographic and clinical predictors of electronic EMA adherence

BACKGROUND

Ecological momentary assessment (EMA) is increasingly used to characterize patients' daily lives, monitor mood, and test efficacy of treatment interventions. However, few studies have examined patient characteristics impacting adherence with EMA protocols, and to our knowledge, no such study has been conducted in youth with bipolar disorder (BD).

METHODS

As part of a larger observational study, 14- to 21-year-olds diagnosed with BD, and who were between episodes of illness (n = 39, 19.0 ± 2.05 Mean ± Standard Deviation years old, 74.4% female) and psychiatrically healthy controls (n = 47, 18.3 ± 2.40 years old, 66.0% female) completed baseline diagnostic and symptom severity interviews, and were instructed to complete diary assessments of mood, sleep, and behavior electronically three times per day for 21 consecutive days (i.e., in total 5418 (or 63 per person) diary entries). Multiple regression was used to examine effects of BD participants' demographic and clinical characteristics on diary completion rates.

RESULTS

53.8 ± 9.3 diary entries per person were actually completed. Adherence rates were high (87.5% of healthy controls and 80.4% of adolescents with BD), but were still significantly poorer in youth with BD. Adequate adherence (≥80%) rates were also significantly poorer in youth with BD relative to healthy controls (56.4% versus 83.0%). Among youth with BD, more lifetime suicide attempts and higher current mood elevation symptom severity predicted significantly poorer adherence.

LIMITATIONS

Limited sample size/generalizability.

CONCLUSIONS

Findings highlight the importance of considering the impact of patient characteristics on adherence with EMA protocols among youth with severe mental illness.

Directionality of change in obsessive compulsive disorder (OCD) and suicidal ideation over six years in a naturalistic clinical sample✰

BACKGROUND

Obsessive compulsive disorder (OCD) is associated with elevated suicide risk, but the directionality of the association between OCD severity and suicidal ideation has not been established, which was the goal of this study.

METHODS

Participants (n = 325) were adults with either a current or past diagnosis of Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) OCD who were assessed annually for suicidal ideation and OCD symptom severity for six years. Cross-lagged panel analyses statistically compared unidirectional and bidirectional models over time. Serious suicide-related adverse events were reported.

RESULTS

The best-fitting and most parsimonious model included paths predicting suicidal ideation from OCD symptom severity, but not vice versa. These results were confirmed by comparing a model with cross-lagged paths constrained equal to a freely estimated model. Higher OCD symptom severity in a given year was associated with a higher suicidal ideation severity in the subsequent year. Five suicide-related adverse events were reported throughout the duration of the study, including two suicide deaths and three suicide attempts.

LIMITATIONS

The study relied on a single-item, annual measure of suicidal ideation in adults, with substantial variability in severity of suicide risk, and missing data increased with later observations in the study.

DISCUSSION

OCD symptom severity predicted next year suicidal ideation severity. In contrast, suicidal ideation severity in a given year did not predict next-year OCD symptom severity in this OCD sample. Thus, rather than waiting for suicidal ideation to resolve, clinicians should consider providing empirically supported treatments for OCD.

Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications

Mental disorders are highly prevalent and often remain untreated. Many limitations of conventional face-to-face psychological interventions could potentially be overcome through Internet-based and mobile-based interventions (IMIs). This chapter introduces core features of IMIs, describes areas of application, presents evidence on the efficacy of IMIs as well as potential effect mechanisms, and delineates how Artificial Intelligence combined with IMIs may improve current practices in the prevention and treatment of mental disorders in adults. Meta-analyses of randomized controlled trials clearly show that therapist-guided IMIs can be highly effective for a broad range of mental health problems. Whether the effects of unguided IMIs are also clinically relevant, particularly under routine care conditions, is less clear. First studies on IMIs for the prevention of mental disorders have shown promising results. Despite limitations and challenges, IMIs are increasingly implemented into routine care worldwide. IMIs are also well suited for applications of Artificial Intelligence and Machine Learning, which provides ample opportunities to improve the identification and treatment of mental disorders. Together with methodological innovations, these approaches may also deepen our understanding of how psychological interventions work, and why. Ethical and professional restraints as well as potential contraindications of IMIs, however, should also be considered. In sum, IMIs have a high potential for improving the prevention and treatment of mental health disorders across various indications, settings, and populations. Therefore, implementing IMIs into routine care as both adjunct and alternative to face-to-face treatment is highly desirable. Technological advancements may further enhance the variability and flexibility of IMIs, and thus even further increase their impact in people's lives in the future.

Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps

PURPOSE OF REVIEW

As rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been proposed, are in development, or are already available today. This review seeks to assess their clinical evidence and help the reader understand the current state of the field.

RECENT FINDINGS

Advances in smartphone sensing, machine learning methods, and mobile apps directed towards reducing suicide offer promising evidence; however, most of these innovative approaches are still nascent. Further replication and validation of preliminary results is needed. Whereas numerous promising mobile and sensor technology based solutions for real time understanding, predicting, and caring for those at highest risk of suicide are being studied today, their clinical utility remains largely unproven. However, given both the rapid pace and vast scale of current research efforts, we expect clinicians will soon see useful and impactful digital tools for this space within the next 2 to 5 years.