Use of Ecological Momentary Assessment to Study Suicidal Thoughts and Behavior: a Systematic Review

Examining missingness at the momentary level in clinical research using ecological momentary assessment: Implications for suicide research

The use of intensive time sampling methods, such as ecological momentary assessment (EMA), has increased in clinical, and specifically suicide, research during the past decade. While EMA can capture dynamic intraindividual processes, repeated assessments increase participant burden, potentially resulting in low compliance. This study aimed to shed light on study-level and psychological variables, including suicidal ideation (SI), that may predict momentary prompt (i.e., prompt-to-prompt) completion. We combined data from three EMA studies examining mental health difficulties (N = 103; 10,656 prompts; 7144 completed), using multilevel models and machine learning to determine how well we can predict prompt-to-prompt completion and which variables are most important. The two most important variables in prompt-to-prompt completion were hours since the last prompt and time in study. Psychological variables added little predictive validity; similarly, trait-level SI demonstrated a small effect on prompt-to-prompt completion. Our study showed how study-level characteristics can be used to explain prompt-to-prompt compliance rates in EMA research, highlighting the potential for developing adaptive assessment schedules to improve compliance.

Empirical clustering to identify individuals for whom insomnia is more closely related to suicidal ideation

BACKGROUND

Although the effect sizes are modest, insomnia is consistently associated with suicidal thoughts and behaviors. Subgroup analyses can efficiently identify for whom insomnia is most relevant to suicidal ideation. To improve clinical case identification, the present study sought to identify subclusters of lifetime suicidal ideators for whom insomnia was most closely related to current suicidal ideation.

METHODS

Data on N = 4750 lifetime suicidal ideators were extracted from the Military Suicide Research Consortium's Common Data Elements. Data on sociodemographic characteristics, severity and history of suicidal thoughts and behaviors, and related clinical characteristics were clustered by unsupervised machine learning algorithms. Robust Poisson regression estimated cluster by insomnia associations with current suicidal ideation.

RESULTS

Three clusters were identified: a modest symptom severity cluster (N = 1757, 37.0 %), an elevated severity cluster (N = 1444 30.4 %), and a high severity cluster (N = 1549 32.6 %). In Cluster 1, insomnia was associated with current suicidal ideation (PRR 1.29 [1.13-1.46]) and remained significant after adjusting for sociodemographic and clinical covariates. In Cluster 2, insomnia was associated with current suicidal ideation (PRR 1.14 [1.01-1.30]), but not after adjusting for sociodemographic and clinical covariates. In Cluster 3, insomnia was associated with current suicidal ideation (PRR 1.12 [1.03-1.21]) and remained significant after adjusting for sociodemographic covariates, but not clinical covariates.

LIMITATIONS

Cross-sectional design, lack of diagnostic data, non-representative sample.

CONCLUSION

Insomnia appears more closely related to current suicidal ideation among modest severity individuals than other subgroups. Future work should use prospective designs and more comprehensive risk factor measures to confirm these findings.

Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care

There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual's level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care.

Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review

BACKGROUND

There has been an increased interest in understanding social anxiety (SA) and SA disorder (SAD) antecedents and consequences as they occur in real time, resulting in a proliferation of studies using ambulatory assessment (AA). Despite the exponential growth of research in this area, these studies have not been synthesized yet.

OBJECTIVE

This review aimed to identify and describe the latest advances in the understanding of SA and SAD through the use of AA.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic literature search was conducted in Scopus, PubMed, and Web of Science.

RESULTS

A total of 70 articles met the inclusion criteria. The qualitative synthesis of these studies showed that AA permitted the exploration of the emotional, cognitive, and behavioral dynamics associated with the experience of SA and SAD. In line with the available models of SA and SAD, emotion regulation, perseverative cognition, cognitive factors, substance use, and interactional patterns were the principal topics of the included studies. In addition, the incorporation of AA to study psychological interventions, multimodal assessment using sensors and biosensors, and transcultural differences were some of the identified emerging topics.

CONCLUSIONS

AA constitutes a very powerful methodology to grasp SA from a complementary perspective to laboratory experiments and usual self-report measures, shedding light on the cognitive, emotional, and behavioral antecedents and consequences of SA and the development and maintenance of SAD as a mental disorder.

Examining Passively Collected Smartphone-Based Data in the Days Prior to Psychiatric Hospitalization for a Suicidal Crisis: Comparative Case Analysis

BACKGROUND

Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots.

OBJECTIVE

This paper focuses on a comprehensive case review of 2 participants who reported past 1-month active suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research.

METHODS

To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots-text-captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one's phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots).

RESULTS

Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness.

CONCLUSIONS

Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual's psychological processes in the time leading up to a suicidal crisis.

Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment

OBJECTIVE

Extraction of digital markers from passive sensors placed in homes is a promising method for understanding real-world behaviors. In this study, machine learning (ML) and multilevel modeling (MLM) are used to examine types of digital markers and whether smart home sensors can predict cognitive functioning, lifestyle behaviors, and contextual factors measured through ecological momentary assessment (EMA).

METHOD

Smart home sensors were installed in the homes of 44 community-dwelling midlife and older adults for 3-4 months. Sensor data were categorized into eight digital markers. Participants responded to iPad-delivered EMA prompts 4×/day for 2 wk. Prompts included an n-back task and survey on recent (past 2 h) lifestyle and contextual factors.

RESULTS

ML marker rankings revealed that sensor counts (indicating increased activity) and time outside the home were among the most influential markers for all survey questions. Additionally, MLM revealed for every 1000 sensor counts, mental sharpness, social, physical, and cognitive EMA responses increased by 0.134-0.155 points on a 5-point scale. For every additional 30-minutes spent outside home, social, physical, and cognitive EMA responses increased by 0.596, 0.472, and 0.157 points. Advanced ML joint classification/regression significantly predicted EMA responses from smart home digital markers with error of 0.370 on a 5-point scale, and n-back performance with a normalized error of 0.040.

CONCLUSION

Results from ML and MLM were complimentary and comparable, suggesting that machine learning may be used to develop generalized models to predict everyday cognition and track lifestyle behaviors and contextual factors that impact health outcomes using smart home sensor data.

Chronic Pain, Hopelessness, and Suicide Risk Among Adult Medical Inpatients

BACKGROUND

Medically ill adults are at elevated risk for suicide. Chronic pain and hopelessness are associated with suicide; however, few studies have examined the interaction between chronic pain and hopelessness in predicting suicide risk among hospitalized adults.

OBJECTIVE

This study aimed to describe the association between chronic pain, hopelessness, and suicide risk, defined as recent suicidal ideation or lifetime suicidal behavior. In addition, we examined the interaction between chronic pain and hopelessness.

METHODS

This was a secondary analysis of a multisite study to validate the Ask Suicide-Screening Questions (ASQ) among adult medical inpatients. Participants reported if they experienced chronic pain that impacted daily life and if they felt hopeless about their medical condition and provided their current pain rating on a 1 to 10 scale, with 10 being the most severe pain. A t-test compared pain severity scores by ASQ outcome. A binary logistic regression model described the association between chronic pain, hopelessness, and suicide risk; parameter estimates are expressed as odds ratios (OR) for interpretation. The interaction between chronic pain and hopelessness was examined in both the transformed (logit) and natural (probability) scales of the generalized linear model.

RESULTS

The sample included 720 participants (53.2% male, 62.4% White, mean age: 50.1 [16.3] years, range = 18-93). On the ASQ, 15.7% (113/720) of patients screened positive. Half (360/720) of the sample self-reported chronic pain. Individuals who screened positive had higher pain rating scores than those who screened negative (t = -4.2, df = 147.6, P < 0.001). Among all patients, 27.2% (196/720) felt hopeless about their medical condition. In the logistic regression model, patients with chronic pain (adjusted OR: 2.29, 95% confidence interval [CI]: 1.21-4.43, P = 0.01) or hopelessness (adjusted OR: 5.69, 95% CI: 2.52-12.64, P < 0.001) had greater odds of screening positive on the ASQ. The interaction effect between pain and hopelessness was not significant in the transformed (B = -0.15, 95% CI: -1.11 to 0.82, P = 0.76) or natural (B = 0.08, 95% CI: -0.07 to 0.23, P = 0.28) scale.

CONCLUSIONS

There were significant independent associations between (1) chronic pain and suicide risk and between (2) hopelessness and suicide risk. Future research should examine the temporality and mechanisms underlying these relationships to inform prevention efforts for medically ill adults.

Anger as a Correlate of and Longitudinal Risk Factor for Suicidal Ideation in Adolescents

Suicide a leading cause of death among adolescents and is nearly always preceded by suicidal ideation (SI). Concerningly, SI during adolescence is not uncommon, as it is reported by as much as 20% of American youth. As such, SI in adolescence has been the subject of substantial research. Literature points to anger in adolescence as a relatively strong correlate of SI. However, work is limited, focusing on cross-sectional associations between anger in adolescence and SI and conceptualizing anger as a broad construct, failing to investigate the many narrow facets that comprise it. We address these gaps by investigating anger in adolescence as a (1) cross-sectional and (2) prospective correlate of SI and (3) investigating broad versus narrow conceptualizations of anger in adolescence as they relate to SI. Among two samples (Study 1, nationally representative community-based youth, n = 1,729; Study 2, high-risk juvenile justice involved youth, n = 1,406), anger in adolescence was cross-sectionally related to SI. However, when controlling for SI at baseline, anger in adolescence was not a prospective risk factor for SI at follow-up, nine years later. Finally, narrow facets of anger (e.g., argumentative, defiant, irritable, resentful, spiteful) were not more closely related than broad conceptualizations of anger to SI. These findings indicate that while anger in adolescence is cross-sectionally associated with SI, it should not necessarily be viewed as a valid risk factor for development of SI over the course of nearly a decade. Further, findings did not elucidate any narrow facets of anger that are particularly linked with SI.

Selecting an Ecological Momentary Assessment Platform: Tutorial for Researchers

BACKGROUND

Although ecological momentary assessment (EMA) has been applied in psychological research for decades, delivery methods have evolved with the proliferation of digital technology. Technological advances have engendered opportunities for enhanced accessibility, convenience, measurement precision, and integration with wearable sensors. Notwithstanding, researchers must navigate novel complexities in EMA research design and implementation.

OBJECTIVE

In this paper, we aimed to provide guidance on platform selection for clinical scientists launching EMA studies.

METHODS

Our team includes diverse specialties in child and adolescent behavioral and mental health with varying expertise on EMA platforms (eg, users and developers). We (2 research sites) evaluated EMA platforms with the goal of identifying the platform or platforms with the best fit for our research. We created a list of extant EMA platforms; conducted a web-based review; considered institutional security, privacy, and data management requirements; met with developers; and evaluated each of the candidate EMA platforms for 1 week.

RESULTS

We selected 2 different EMA platforms, rather than a single platform, for use at our 2 research sites. Our results underscore the importance of platform selection driven by individualized and prioritized laboratory needs; there is no single, ideal platform for EMA researchers. In addition, our project generated 11 considerations for researchers in selecting an EMA platform: (1) location; (2) developer involvement; (3) sample characteristics; (4) onboarding; (5) survey design features; (6) sampling scheme and scheduling; (7) viewing results; (8) dashboards; (9) security, privacy, and data management; (10) pricing and cost structure; and (11) future directions. Furthermore, our project yielded a suggested timeline for the EMA platform selection process.

CONCLUSIONS

This study will guide scientists initiating studies using EMA, an in vivo, real-time research tool with tremendous promise for facilitating advances in psychological assessment and intervention.

Assessment of Depression in Adults and Youth

This article selectively reviews the key issues and measures for the assessment of depressive disorders and symptoms in youth and adults. The first portion of the article addresses the nature and conceptualization of depression and some key issues that must be considered in its assessment. Next, the diagnostic interview and clinician- and self-administered rating scales that are most widely used to diagnose, screen for, and assess the severity of depression in adults and youth are selectively reviewed. In addition, the assessment of three transdiagnostic clinical features (anhedonia, irritability, and suicidality) that are frequently associated with both depression and other forms of psychopathology is discussed. The article concludes with some broad recommendations for assessing depression in research and clinical practice and suggestions for future research.

Association between cardiac event-induced PTSD symptoms and daily intrusive thoughts about cardiac risk: An ecological momentary assessment study

OBJECTIVE

Posttraumatic stress symptoms (PSS) due to acute cardiac events are common and may lead patients to avoid secondary prevention behaviors. However, patients' daily experience of cardiac event-induced PSS has not been studied after a potentially traumatic cardiac hospitalization.

METHOD

In an observational cohort study, 108 mostly male patients with coronary heart disease were recruited after evaluation for suspected acute coronary syndrome (ACS). One month later, PSS were assessed via telephone-administered PTSD Checklist for DSM-5 (PCL-5). The exposure of interest was elevated (PCL-5 ≥ 20) vs. non-elevated PSS (PCL-5 ≤ 5). The occurrence and severity of cardiac-related intrusive thoughts were assessed 5 times daily for 2 weeks via electronic surveys on a wrist-worn device.

RESULTS

Moderate-to-severe intrusive thoughts were experienced by 48.1% of patients but more commonly by elevated-PSS (n = 36; 66.7%) than non-elevated-PSS (n = 72; 38.9%) patients. After adjustment for demographic and clinical characteristics, elevated- vs. non-elevated-PSS patients had a 9-fold higher odds of experiencing a moderate-to-severe intrusive thought during each 2-h assessment interval (adjusted OR = 9.14, 95% CI [2.99, 27.92], p < .01). After adjustment, intrusive thoughts on a 0-to-6 point scale were over two times as intense for elevated-PSS vs. non-elevated-PSS patients.

CONCLUSIONS

Intrusive thoughts about cardiac risk were common in patients recently evaluated for ACS, but much more prevalent and intense in those with elevated vs non-elevated PSS.

Ecological Momentary Assessment of Social Approach and Avoidance Motivations in Serious Mental Illness: Connections to Suicidal Ideation and Symptoms

AIM

People with serious mental illness (SMI) are at an increased risk for suicide. Social approach and avoidance motivations are linked to social functioning, and social isolation is a risk factor for suicide. This study uses ecological momentary assessment (EMA) to understand social approach and avoidance motivations in relation to symptoms and suicidal ideation (SI).

METHODS

Participants (N = 128) diagnosed with schizophrenia, schizoaffective disorder, or a mood disorder with psychotic features completed assessments of SI and symptoms at baseline. They completed EMA surveys 3×/day for 10 days. EMA surveys included questions about approach and avoidance motivations and psychotic symptoms. Participants were split into four groups based on the median scores of approach and avoidance.

RESULTS

Participants with SI at baseline had higher mean social avoidance motivation, t(126) = 2.84, p = .003, and lower mean social approach motivation, t(126) = -2.44, p = .008, than participants without baseline SI. Greater baseline positive symptoms were related to greater mean avoidance, r = .231, p = .009, but not approach motivation. The low approach/high avoidance group had significantly higher current SI than those with high approach/low avoidance (p < .001). Overall, the low approach/high avoidance group reported more EMA-measured voices than the low approach/low avoidance group (p < .001) and the high approach/low avoidance group (p < .001). Similarly, the low approach/high avoidance group reported more EMA-measured suspiciousness than the low approach/low avoidance (p < .001) and the high approach/low avoidance groups (p < .001).

CONCLUSION

The results of this study point to the role of social approach and avoidance motivations in relation to SI and psychotic symptoms. Clinically, exposure therapies and cognitive behavioral therapies may help to address these social approach and avoidance processes linked to SI.

Changes in suicide-related tweets before and during the COVID-19 pandemic in France: The importance of social media monitoring in public health prediction

INTRODUCTION

The COVID-19 pandemic impacted mental health, as demonstrated by numerous studies. In recent years, especially during the pandemic, the use of social networks, including Twitter, increased. This suggests that this media could help with mental health monitoring, as attested by previous studies.

METHOD

We conducted a multidisciplinary study on French tweets that were posted between January 1, 2019, and December 31, 2021. We selected the tweets via the Twitter API (Application Programming Interface) using five keywords relating to suicide: want to die, suicidal ideation, commit suicide, suicidal, and suicide attempt. A word frequency analysis was performed, and the data were compared with the number of emergency visits for suicidal ideation before and during the COVID-19 pandemic as recorded by the French national suicide observatory.

RESULTS

We observed that 189,005 tweets were related to suicide in 2019, 261,993 in 2020 (+38.62% of that observed in 2019), and 301,177 in 2021 (+59.35% of that observed in 2019). We also observed an increase in the number of tweets containing control words in 2020 (+30.07% of that observed in 2019), but in 2021, the number almost fell back to the level of that in 2019 (+5.96% of that observed in 2019). Furthermore, the difference between both ratios (of suicide-related tweets and of tweets containing control words) was most significant during the third lockdown. The change in the number of suicide-related tweets followed a curve that overlapped with the change in the number of emergency visits following suicidal ideations, as reported by the French national suicide observatory. In conclusion, Twitter can be an adequate and reliable tool for screening for suicidal ideation in the general population.

Feasibility of Using Research Electronic Data Capture (REDCap) to Collect Daily Experiences of Parent-Child Dyads: Ecological Momentary Assessment Study

BACKGROUND

Intensive longitudinal data collection, including ecological momentary assessment (EMA), has the potential to reduce recall biases, collect more ecologically valid data, and increase our understanding of dynamic associations between variables. EMA is typically administered using an application that is downloaded on participants' devices, which presents cost and privacy concerns that may limit its use. Research Electronic Data Capture (REDCap), a web-based survey application freely available to nonprofit organizations, may allow researchers to overcome these barriers; however, at present, little guidance is available to researchers regarding the setup of EMA in REDCap, especially for those who are new to using REDCap or lack advanced programming expertise.

OBJECTIVE

We provide an example of a simplified EMA setup in REDCap. This study aims to demonstrate the feasibility of this approach. We provide information on survey completion and user behavior in a sample of parents and children recruited across Canada.

METHODS

We recruited 66 parents and their children (aged 9-13 years old) from an existing longitudinal cohort study to participate in a study on risk and protective factors for children's mental health. Parents received survey prompts (morning and evening) by email or SMS text message for 14 days, twice daily. Each survey prompt contained 2 sections, one for parents and one for children to complete.

RESULTS

The completion rates were good (mean 82%, SD 8%) and significantly higher on weekdays than weekends and in dyads with girls than dyads with boys. Children were available to respond to their own survey questions most of the time (in 1134/1498, 75.7% of surveys submitted). The number of assessments submitted was significantly higher, and response times were significantly faster among participants who selected SMS text message survey notifications compared to email survey notifications. The average response time was 47.0 minutes after the initial survey notification, and the use of reminder messages increased survey completion.

CONCLUSIONS

Our results support the feasibility of using REDCap for EMA studies with parents and children. REDCap also has features that can accommodate EMA studies by recruiting participants across multiple time zones and providing different survey delivery methods. Offering the option of SMS text message survey notifications and reminders may be an important way to increase completion rates and the timeliness of responses. REDCap is a potentially useful tool for researchers wishing to implement EMA in settings in which cost or privacy are current barriers. Researchers should weigh these benefits with the potential limitations of REDCap and this design, including staff time to set up, monitor, and clean the data outputs of the project.

One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study

BACKGROUND

Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach.

OBJECTIVE

We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation.

METHODS

We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested.

RESULTS

During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy.

CONCLUSIONS

We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.

Ecological Momentary Assessments and Passive Sensing in the Prediction of Short-Term Suicidal Ideation in Young Adults

Importance

Advancements in technology, including mobile-based ecological momentary assessments (EMAs) and passive sensing, have immense potential to identify short-term suicide risk. However, the extent to which EMA and passive data, particularly in combination, have utility in detecting short-term risk in everyday life remains poorly understood.

Objective

To examine whether and what combinations of self-reported EMA and sensor-based assessments identify next-day suicidal ideation.

Design, Setting, and Participants

In this intensive longitudinal prognostic study, participants completed EMAs 4 times daily and wore a sensor wristband (Fitbit Charge 3) for 8 weeks. Multilevel machine learning methods, including penalized generalized estimating equations and classification and regression trees (CARTs) with repeated 5-fold cross-validation, were used to optimize prediction of next-day suicidal ideation based on time-varying features from EMAs (affective, cognitive, behavioral risk factors) and sensor data (sleep, activity, heart rate). Young adult patients who visited an emergency department with recent suicidal ideation and/or suicide attempt were recruited. Identified via electronic health record screening, eligible individuals were contacted remotely to complete enrollment procedures. Participants (aged 18 to 25 years) completed 14 708 EMA observations (64.4% adherence) and wore a sensor wristband approximately half the time (55.6% adherence). Data were collected between June 2020 and July 2021. Statistical analysis was performed from January to March 2023.

Main Outcomes and Measures

The outcome was presence of next-day suicidal ideation.

Results

Among 102 enrolled participants, 83 (81.4%) were female; 6 (5.9%) were Asian, 5 (4.9%) were Black or African American, 9 (8.8%) were more than 1 race, and 76 (74.5%) were White; mean (SD) age was 20.9 (2.1) years. The best-performing model incorporated features from EMAs and showed good predictive accuracy (mean [SE] cross-validated area under the receiver operating characteristic curve [AUC], 0.84 [0.02]), whereas the model that incorporated features from sensor data alone showed poor prediction (mean [SE] cross-validated AUC, 0.56 [0.02]). Sensor-based features did not improve prediction when combined with EMAs. Suicidal ideation-related features were the strongest predictors of next-day ideation. When suicidal ideation features were excluded, an alternative EMA model had acceptable predictive accuracy (mean [SE] cross-validated AUC, 0.76 [0.02]). Both EMA models included features at different timescales reflecting within-day, end-of-day, and time-varying cumulative effects.

Conclusions and Relevance

In this prognostic study, self-reported risk factors showed utility in identifying near-term suicidal thoughts. Best-performing models required self-reported information, derived from EMAs, whereas sensor-based data had negligible predictive accuracy. These results may have implications for developing decision algorithms identifying near-term suicidal thoughts to guide risk monitoring and intervention delivery in everyday life.

Examining sleep disturbance components as near-term predictors of suicide ideation in daily life

Suicide ideation emerges and fluctuates over short timeframes (minutes, hours, days); however, near-term predictors of such fluctuations have not been well-elucidated. Sleep disturbance is a distal suicide risk factor, but less work has examined whether daily sleep disturbance predicts near-term changes in suicide ideation. We examined subjective sleep disturbance components as predictors of passive and active suicide ideation at the within-person (i.e., day-to-day changes within individuals relative to their own mean) and between-persons (individual differences relative to the sample mean) levels. A transdiagnostic sample of 102 at-risk young adults ages 18-35 completed a 21-day ecological momentary assessment protocol, during which they reported on sleep and passive and active suicide ideation. At the within-persons level, nightmares, sleep quality, and wake after sleep onset predicted passive suicide ideation, and sleep quality and wake after sleep onset predicted active suicide ideation. At the between-persons level, nightmares, sleep onset latency, and sleep quality were associated with passive suicide ideation, and sleep onset latency was associated with active suicide ideation. In contrast, suicide ideation did not predict subsequent sleep at the within-person level. Specific sleep disturbance components are near-term predictors of intraindividual increases in suicide ideation and may hold promise for suicide prevention and intervention.

Acceptability and feasibility of ecological momentary assessment with augmentation of passive sensor data in young adults at high risk for suicide

Ecological Momentary Assessment (EMA) and wearable sensor data have the potential to enhance prediction of suicide risk in real-world conditions. However, the feasibility of this methodology with high-risk populations, including over extended periods, warrants closer attention. This study examined the feasibility and acceptability of concurrent EMA and wearable sensor monitoring in young adults after emergency department (ED) care for suicide risk-related concerns. For 2 months after ED discharge, 106 participants (ages 18-25; 81.1% female) took part in EMA surveys (4x per day) and passive sensor (Fitbit) monitoring and completed an end-of-study phone interview. Overall adherence to EMA (62.1%) and wearable sensor (53.6%) was moderate and comparable to briefer protocols. Relative to EMAs (81%), fewer participants completed the full 8 weeks of Fitbit (63%). While lower initial hopelessness was linked to reduced EMA adherence, previous-day suicidal ideation predicted lower Fitbit adherence on the next day. Self-endorsed barriers to EMA and wearable sensor adherence were also examined. Participants tended to report positive experience with the protocol, with majority indicating EMAs were minimally burdensome, reporting that the Fitbit was generally comfortable, and expressing interest in participating in a similar study again. Findings provide support for the feasibility and acceptability of concurrent intensive self-report and wearable sensor data during a high-risk period. Implications and future directions are discussed.

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.

A test of invariance of the construct of suicidal ideation across three diverse samples

BACKGROUND

Suicide treatment research has placed major emphasis on preventing behavior, an observable phenomenon. Suicidal ideation (SI), however, is a distressing experience that annually impacts >15 million Americans; yet the construct validity of SI has not been well established, limiting empirical attention.

METHOD

We compared the coherence of three SI items (diminished wish to live, urge to die/controllability, suicide intent/expectations) and four related items across three adult samples (N = 314) that differed based on current SI severity and assessment time-scales (retrospective versus momentary).

RESULTS

A measurement model with scalar invariance had acceptable fit (CFI = 0.969, RMSEA = 0.076), indicating that the SI items consistently loaded onto one construct across samples. The structural model with metric invariance also had acceptable fit (CFI = 0.956, RMSEA = 0.063) and three of four related items (burdensome, hopelessness, and fatigue, but not agitation) were associated with SI across samples.

LIMITATIONS

Use of existing data limited the items considered.

CONCLUSIONS

Suicidal ideation is a coherent construct independent of the items used to assess it, the assessment's time-scale (retrospective versus momentary), or the severity of thoughts, and is worthy of greater empirical, clinical, and policy attention.

Suicidality and mood: the impact of trends, seasons, day of the week, and time of day on explicit and implicit cognitions among an online community sample

Decades of research have established seasonality effects on completed and attempted suicides, with rates increasing in spring. Little advancements have been made to explain this phenomenon, with most studies focusing almost exclusively on the number of suicide attempts and deaths. Using more than six years of data collected among a US, UK, and Canadian online community sample (N > 10,000), we used newly developed Prophet forecasting and autoregressive-integrated moving average time-series models to examine the temporal dynamics of explicit and implicit self-harm cognitions. We created three groups (past suicide attempters; suicide ideation and/or non-suicidal self-injury; no previous self-harm, suicidal thoughts, or behaviors). We found a general increase of negative self-harm cognitions across the six years and seasonality effects for mood and desire to die, particularly among those who previously made a suicide attempt. Negative explicit self-harm cognitions peaked in winter (December), with implicit self-harm showing a lagged peak of two months (February). Moreover, daily negative self-harm cognitions consistently peaked around 4-5 am, with implicit cognitions again showing a lagged effect (1-hour). Limitations include the volunteer sample not being representative and the cross-sectional nature of the data being unable to separate between-subject and within-subject structural trends in the time series. Our findings show that negative explicit and implicit cognitions precede the rise in suicidal behaviors in spring. We proposed a conceptual model of seasonal suicide risk that may offer fertile ground for theoretical advancements, including implications for clinical risk assessment and public policies regarding the availability of health services.

Multidimensional variability in ecological assessments predicts two clusters of suicidal patients

The variability of suicidal thoughts and other clinical factors during follow-up has emerged as a promising phenotype to identify vulnerable patients through Ecological Momentary Assessment (EMA). In this study, we aimed to (1) identify clusters of clinical variability, and (2) examine the features associated with high variability. We studied a set of 275 adult patients treated for a suicidal crisis in the outpatient and emergency psychiatric departments of five clinical centers across Spain and France. Data included a total of 48,489 answers to 32 EMA questions, as well as baseline and follow-up validated data from clinical assessments. A Gaussian Mixture Model (GMM) was used to cluster the patients according to EMA variability during follow-up along six clinical domains. We then used a random forest algorithm to identify the clinical features that can be used to predict the level of variability. The GMM confirmed that suicidal patients are best clustered in two groups with EMA data: low- and high-variability. The high-variability group showed more instability in all dimensions, particularly in social withdrawal, sleep measures, wish to live, and social support. Both clusters were separated by ten clinical features (AUC = 0.74), including depressive symptoms, cognitive instability, the intensity and frequency of passive suicidal ideation, and the occurrence of clinical events, such as suicide attempts or emergency visits during follow-up. Initiatives to follow up suicidal patients with ecological measures should take into account the existence of a high variability cluster, which could be identified before the follow-up begins.

A nurse-led mHealth intervention to alleviate depressive symptoms in older adults living alone in the community: A quasi-experimental study

BACKGROUND

The prevalence of geriatric depression has increased worldwide, becoming a major contributor to the burden of health care costs. Geriatric depression is difficult to detect in daily life because of its atypical presentation for each person. Therefore, there is an emerging need to develop personalised mHealth interventions for older adults with depression based on data from an ecological momentary assessment.

OBJECTIVE

To develop and evaluate the effect of a nurse-led mHealth intervention of geriatric depression in older adults living alone.

DESIGN

A quasi-experimental research design was used, and the study followed the transparent reporting of evaluations with a nonrandomised design statement.

SETTING

The nurse-led mHealth intervention was developed and evaluated in a community senior centre in Seoul, Korea.

PARTICIPANTS

Sixty-four older adults living alone with depressive symptoms were recruited between 1 October 2018 and 1 October 2019.

METHODS

Study participants were randomly assigned to the intervention or control groups by drawing lots. In the intervention group, nurses repeatedly assessed older adults' depressive symptoms using an ecological momentary assessment via a mobile tablet. The intervention consisted of weekly sessions, which included (1) standardised mHealth device training, (2) a nurse-led mHealth programme, and (3) art activities. The control group received care as usual. Intra- and inter-group differences were evaluated using paired t-tests and analysis of covariance was used to assess subjective depression symptoms. A linear mixed-model was used to analyse the relationship between groups and momentary scores over time.

RESULTS

The average age of the final sample was 76.2 years (SD = 6.06), 63.6 % (28/44) of whom were female. Compared with the control group (n = 23), the intervention group (n = 21) showed a decreased depression score (t = 4.041, p = .027). There was no statistical difference between the intervention and control groups based on traditional scales and the ecological momentary assessment. However, our data from the ecological momentary assessment captures clear fluctuating patterns across the days during the study, which traditional scales could not measure.

CONCLUSIONS

Most of the older adults successfully participated in a nurse-led mHealth intervention that included multiple components of a non-pharmacological approach to address depression. Mental health nurses should perform critical roles to personalise mHealth activities considering the older adult's autonomy and supportive decision-making, specifically when using high-technological intervention. Future research should maximise the methodological and clinical advantage of an ecological momentary assessment of geriatric depression.

REGISTRATION

Clinical Research Information Service number KCT0005073.

Symptom distress and suicidal ideation among Chinese ovarian cancer patients: A moderated mediation model of depression and suicide resilience

Objective

The aim of this study was to examine whether depression mediates the relationship between symptom distress and suicidal ideation in Chinese patients with ovarian cancer, and whether this mediating effect was moderated by suicide resilience.

Methods

From March to October 2022, this cross-sectional study was performed in a three Grade 3A hospital and an oncology specialty hospital in Wuhan, Hubei Province, China. Ultimately, 213 ovarian cancer patients completed anonymous self-report. Bootstrapping method was used for regression analysis to test the mediating and moderating effects.

Results

Among the 213 participants, 29.58% (n = 63) exhibited significant suicidal ideation. Symptom distress was positively associated with suicidal ideation, and depression partially mediated this relationship. Suicide resilience moderated the relationship between depression and suicidal ideation. In ovarian cancer patients with low suicide resilience, the effect of symptom distress on suicidal ideation through depression was greater, while in patients with high suicide resilience, this effect was attenuated.

Conclusion

Our study suggests that symptom distress could be more likely to lead to suicidal ideation as depression levels increase in ovarian cancer patients. Fortunately, suicide resilience could attenuate this negative effect.

Suicide risk assessments: Why are we still relying on these a decade after the evidence showed they perform poorly?

Suicide deaths have a profound impact on whānau and community and are a tragic loss. However, from a statistical point of view, suicide is a relatively rare event. Predicting rare events is difficult, and the implications for suicide prevention were highlighted in an important editorial in this journal more than a decade ago, yet little seems to have changed. Risk assessment that focuses on accurate prediction of suicide in real-world contexts is given a great deal of attention in mental health services, despite the fact that current scientific knowledge and best practice guidelines in this area highlight that it is unlikely to be a good basis on which to provide access to treatment. It is our view that we have a good enough understanding of the common conditions people who struggle with suicidal distress experience and energy is better directed at acting to reduce exposure to these conditions and providing treatment for those who seek it. Blueprints for successful suicide prevention exist. If we lessen the focus on prediction, we will have greater resources to focus on treatment and prevention.

Recent research advances in identification and prevention of youth suicide risk

PURPOSE OF THE REVIEW

The rate of youth suicidal behaviors has gradually increased over the last 15 years and continues to grow during the COVID-19 pandemic. This trend burdens mental health services and demands significant developments in risk detection and delivery of interventions to reduce the risk. In this article we outline significant advances and recent findings in youth suicide research that may facilitate strategies for identifying and preventing suicide risk among youth at risk in general and in specific risk groups.

RECENT FINDINGS

The rise in suicide and suicidal behaviors is most likely to affect young people of racial, ethnic, sexual, and gender identity minorities and those living in poverty or experiencing maltreatment. The suicide rate in children is rising and demands special attention. Proximal risk factors for suicidal behavior compared with suicidal ideation have been suggested to identify near-term suicidal risk. Effective and scalable prevention strategies were identified, and the role of new technologies in suicide prevention among youth is to be determined.

SUMMARY

To reach broader suicide prevention in youth and reduce the pressure on mental healthcare, public health approaches and improved service access for minority youth and those living in underserved areas of the world are needed.

The feasibility and acceptability of using smartphones to assess suicide risk among Spanish-speaking adult outpatients

OBJECTIVE

Hispanic/Latino adults, particularly Spanish speakers, are underserved in mental healthcare, and little is known about the day-to-day variation in their suicide risk. Smartphones have the potential to overcome geographical and linguistic barriers to mental health assessment and intervention. The purpose of the current study was to examine (a) the feasibility/acceptability of smartphone-based ecological momentary assessment (EMA) to assess suicide ideation and suicide risk factors among high-risk Spanish-speaking adults and (b) the degree of within-person variation of suicide ideation and suicide risk in this population.

METHOD

Sixteen primary Spanish-speaking psychiatry outpatients completed EMA measures of suicide ideation and suicide risk factors four times a day for 14 days.

RESULTS

A majority of participants consented to active and passive remote assessments and reported the acceptability of study procedures. Adherence to EMA was high and not associated with symptom severity. EMA instances completed were not associated with symptom severity at follow-up. Average point-to-point variability in suicide ideation and risk factors were moderate to high, respectively. EMA captured more dramatic changes than standard baseline and follow-up assessments.

CONCLUSIONS

Results provide preliminary support for the feasibility and acceptability of using smartphones to assess suicide risk in a real-time and real-world setting among high-risk Spanish-speaking adults.

The feasibility of using smartphone apps as treatment components for depressed suicidal outpatients

Mental health smartphone apps could increase the safety and self-management of patients at risk of suicide, but it is still unclear whether it is feasible to integrate such apps into routine mental healthcare. This study reports on the feasibility of using a safety planning app (BackUp) and a self-monitoring app (mEMA) as components of the routine treatment of depressed outpatients with suicidal ideation. Clinicians were trained in working with both of the apps, and they invited their eligible patients with suicidal ideation for study participation. Patients used the apps for 3 months and discussed these with their clinician during treatment. Patients completed assessments at baseline (T0), 4 weeks (T1) and post-test (T2, 12 weeks after baseline). Both patients and clinicians also participated in telephone interviews. Feasibility was assessed in terms of usability (score > 70 on System Usability Scale, SUS), acceptability (score > 20 on Client Satisfaction Questionnaire-8, CSQ-8), and uptake (sufficient rates of component completion and app usage in treatment). The sample included 17 adult outpatients (52.9% male, age range 20-50 years) diagnosed with a depressive disorder and suicidal ideation at baseline. BackUp was rated by patients at above the cut-off scores for usability (SUS mean score at T1 75.63 and at T2 77.71) and acceptability (CSQ-8 mean score at T1 23.42 and at T2 23.50). mEMA was similarly rated (SUS mean score at T1 75.83 and at T2 76.25; CSQ-8 mean score at T1 23.92 and at T2 22.75). Telephone interviews with patients and clinicians confirmed the usability and acceptability. The uptake criteria were not met. Our findings suggest that mobile safety planning and mobile self-monitoring can be considered acceptable and usable as treatment components for depressed suicidal outpatients, but the integration of apps into routine treatment needs to be further explored.

Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns

BACKGROUND

Intensive longitudinal methods (ILMs) for collecting self-report (e.g., daily diaries, ecological momentary assessment) and passive data from smartphones and wearable sensors provide promising avenues for improved prediction of depression and suicidal ideation (SI). However, few studies have utilized ILMs to predict outcomes for at-risk, non-clinical populations in real-world settings.

METHODS

Medical interns (N = 2881; 57 % female; 58 % White) were recruited from over 300 US residency programs. Interns completed a pre-internship assessment of depression, were given Fitbit wearable devices, and provided daily mood ratings (scale: 1-10) via mobile application during the study period. Three-step hierarchical logistic regressions were used to predict depression and SI at the end of the first quarter utilizing pre-internship predictors in step 1, Fitbit sleep/step features in step 2, and daily diary mood features in step 3.

RESULTS

Passively collected Fitbit features related to sleep and steps had negligible predictive validity for depression, and no incremental predictive validity for SI. However, mean-level and variability in mood scores derived from daily diaries were significant independent predictors of depression and SI, and significantly improved model accuracy.

LIMITATIONS

Work schedules for interns may result in sleep and activity patterns that differ from typical associations with depression or SI. The SI measure did not capture intent or severity.

CONCLUSIONS

Mobile self-reporting of daily mood improved the prediction of depression and SI during a meaningful at-risk period under naturalistic conditions. Additional research is needed to guide the development of adaptive interventions among vulnerable populations.

A meta-analysis on the affect regulation function of real-time self-injurious thoughts and behaviours

Prominent theories suggest that self-injurious thoughts and behaviours are negatively reinforced by decreased negative affect. The present meta-analysis quantifies effects from intensive longitudinal studies measuring negative affect and self-injurious thoughts and behaviours. We obtained data from 38 of the 79 studies (48%, 22 unique datasets) involving N = 1,644 participants (80% female, 75% white). Individual-participant data meta-analyses revealed changes in affect pre/post self-injurious thoughts and behaviours. In antecedent models, results supported increased negative affect before nonsuicidal self-injurious behaviour (k = 14, 95% CI 0.09 to 0.31) and suicidal thoughts (k = 14, 95% CI 0.03 to 0.19). For consequence models, negative affect was reduced following nonsuicidal self-injurious thoughts (k = 6, 95% CI -0.79 to -0.44), nonsuicidal self-injurious behaviours (k = 14, 95% CI -0.73 to -0.19) and suicidal thoughts (k = 13, 95% CI -0.79 to -0.23). Findings, which were not moderated by sampling strategies or sample composition, support the affect regulation function of self-injurious thoughts and behaviours.

The temporal relationships between defeat, entrapment and suicidal ideation: ecological momentary assessment study

BACKGROUND

Psychological models of suicidal experiences are largely based on cross-sectional or long-term prospective data with follow-up intervals typically greater than 1 year. Recent time-series analyses suggest that these models may not account for fluctuations in suicidal thinking that occur within a period of hours and/or days.

AIMS

We explored whether previously posited causal relationships between defeat, entrapment and suicidal ideation accounted for temporal associations between these experiences at small time intervals from 3 to 12 h.

METHOD

Participants (N = 51) completed an ecological momentary assessment (EMA) study, comprising repeated assessments at semi-random time points up to six times per day for 1 week, resulting in 1852 completed questionnaires. Multilevel vector autoregression was used to calculate temporal associations between variables at different time intervals (i.e. 3 to 12 h between measurements).

RESULTS

The results showed that entrapment severity was temporally associated with current and later suicidal ideation, consistently over these time intervals. Furthermore, entrapment had two-way temporal associations with defeat and suicidal ideation at time intervals of approximately 3 h. The residual and contemporaneous network revealed significant associations between all variables, of which the association between entrapment and defeat was the strongest.

CONCLUSIONS

Although entrapment is key in the pathways leading to suicidal ideation over time periods of months, our results suggest that entrapment may also account for the emergence of suicidal thoughts across time periods spanning a few hours.

Exploring the Risk of Suicide in Real Time on Spanish Twitter: Observational Study

Background

Social media is now a common context wherein people express their feelings in real time. These platforms are increasingly showing their potential to detect the mental health status of the population. Suicide prevention is a global health priority and efforts toward early detection are starting to develop, although there is a need for more robust research.

Objective

We aimed to explore the emotional content of Twitter posts in Spanish and their relationships with severity of the risk of suicide at the time of writing the tweet.

Methods

Tweets containing a specific lexicon relating to suicide were filtered through Twitter's public application programming interface. Expert psychologists were trained to independently evaluate these tweets. Each tweet was evaluated by 3 experts. Tweets were filtered by experts according to their relevance to the risk of suicide. In the tweets, the experts evaluated: (1) the severity of the general risk of suicide and the risk of suicide at the time of writing the tweet (2) the emotional valence and intensity of 5 basic emotions; (3) relevant personality traits; and (4) other relevant risk variables such as helplessness, desire to escape, perceived social support, and intensity of suicidal ideation. Correlation and multivariate analyses were performed.

Results

Of 2509 tweets, 8.61% (n=216) were considered to indicate suicidality by most experts. Severity of the risk of suicide at the time was correlated with sadness (ρ=0.266; P<.001), joy (ρ=–0.234; P=.001), general risk (ρ=0.908; P<.001), and intensity of suicidal ideation (ρ=0.766; P<.001). The severity of risk at the time of the tweet was significantly higher in people who expressed feelings of defeat and rejection (P=.003), a desire to escape (P<.001), a lack of social support (P=.03), helplessness (P=.001), and daily recurrent thoughts (P=.007). In the multivariate analysis, the intensity of suicide ideation was a predictor for the severity of suicidal risk at the time (β=0.311; P=.001), as well as being a predictor for fear (β=–0.009; P=.01) and emotional valence (β=0.007; P=.009). The model explained 75% of the variance.

Conclusions

These findings suggest that it is possible to identify emotional content and other risk factors in suicidal tweets with a Spanish sample. Emotional analysis and, in particular, the detection of emotional variations may be key for real-time suicide prevention through social media.

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 novel modality for real-time measurement of provider happiness

Objective

Physician burnout is at epidemic proportions, impacts clinical outcomes, and is very costly. Although there is emerging data about effective interventions, most physicians at risk of burnout do not seek help. Survey-based measures exist which can quantify burnout within populations, but these are usually only administered episodically. We hypothesized that a novel modality for real-time measurement of happiness and stressors would be acceptable, scalable, and could provide new actionable insights.

Materials

We developed a novel informatics system consisting of a networked smart button device, server, and analytics for measuring happiness, and stressors in real-time during clinical work. We performed an observational cohort study in 3 primary care clinics. Random and fixed effects modeling was used to analyze predictors of stress and happiness and we conducted a survey of usability and user acceptance of the novel system.

Results

We captured 455 recordings across 392 provider days from 14 primary care providers. In total, 85% of users found the device easy to use, and 87% would recommend the system to their colleagues. Happiness and stressors were observed in all working hours of the day, with a 22% reduction in feeling (the proportion of happiness to stressors) across a clinical day.

Discussion

We tested a novel system which providers found easy to use and enabled collection of detailed data. Limitations included being an observational study within a small number of clinics. A simple, unintrusive, scalable informatics system capable of measuring happiness, and stressors in real-time could be useful to healthcare organizations and teams.

Study Protocol for an Ecological Momentary Assessment Study: TempRes "Temporal Variability of Risk and Resilience Factors for Suicidal Ideation"

Suicide prevention is an important public mental health issue that can be significantly brought forward by recent advances in psychological research methods and assessment. The project "TempRes" aims to harness the power of Ecological Momentary Assessment (EMA) to investigate the transdiagnostic risk and resilience factors associated with suicidal ideation drawn from the most recent research in suicide prevention and personality assessment. Participants will comprise the general population (planned: N = 100) and a risk group (patients currently in psychosomatic or psychiatric treatment) (planned: N = 50). After a comprehensive baseline assessment, they will complete up to ten short assessments per day over the course of 10 days at roughly equidistant intervals. In detail, the project examines the interplay of biography (previous suicidal behavior, experiences of childhood maltreatment), individual differences (level of personality functioning), and time-varying factors (entrapment, loneliness, mood) with respect to the emergence and fluctuation of suicidal ideation. There are two main research foci: First, the project will provide an operationalization and empirical verification of a core assumption of the integrated motivational-volitional model of suicide (IMV model). It will test whether the interaction of the time-varying predictors entrapment with loneliness (as a motivational moderator) explains reports of suicidal ideation over time. Second, it will be the first to examine personality functioning (a transdiagnostic, psychodynamically grounded conceptualization of vulnerability to psychological crises over the life span) as a time-invariant predictor of suicidal ideation assessed within an intensive longitudinal study design. The main analyses will be built on linear mixed models. The overarching aim of the project is to gain a better understanding of the psychological dynamics underlying suicidal ideation in different populations by bringing together concepts from different theoretical traditions. This will inform prevention efforts geared toward the general public as well as intervention in clinical populations.

Implicit Cognition Tests for the Assessment of Suicide Risk: a Systematic Review

PURPOSE OF REVIEW

Suicide risk assessment is a challenge in clinical practice. Implicit measures may present with advantages with respect to explicit methods, and therefore may be useful for the assessment of suicide risk. We conducted a systematic review of 2 databases (PubMed and EMBASE) about implicit tests that measure suicide risk to explore their validity and reliability.

RECENT FINDINGS

Initial research revealed 321 articles. After the selection process, 31 articles were included in the review. The most death-related implicit cognition test used was the Death/Suicide Implicit association test (D/S IAT), followed by the Suicide Stroop Task. The Suicide Affect Misattribution Procedure (S-AMP) and the Death version of the Implicit Relational Assessment Procedure (D-IRAP) were also used. We found that the measures reviewed were generally valid for the assessment of past and future suicidal thoughts and behaviors, with statistically significant results regarding retrospective and prospective associations.

Feasibility and acceptability of experience sampling among LGBTQ+ young people with self-harmful thoughts and behaviours

This study was the first to determine whether it was feasible and acceptable to use experience sampling methods (ESM) among LGBTQ+ young people, who had current experiences of self-harm. Sixteen LGBTQ+ young people (16-25 years old) took part in the experience sampling study. This included a baseline assessment, a 7-day ESM assessment (participants were sampled six times a day using a phone app), and the option of an interview at the end of the 7-day ESM assessment. Feasibility data was descriptively analysed, with pilot ESM data presented. Qualitative data was thematically analysed to determine the acceptability (barriers and facilitators) of taking part in this study. Study feasibility was assessed by enrolment rate (55.2%), participant retention across assessment period (100%), ESM app feasibility (87.5%), and good adherence to total number of ESM surveys (67.6%). Individual study adherence ranged between 43 and 95.2%. Study acceptability was assessed by participant interviews. Thematic analysis indicated four superordinate themes; (i) Self-reflection and awareness; (ii) Practicalities of ESM surveys; (iii) Daily timeframes; and (iv) Suggestions for future studies. Pilot ESM data demonstrates that there was fluctuation of depressive and anxiety symptoms within- and between- participants over the course of the study, however, greater sample power is needed for full analysis. This study demonstrated that ESM designs are feasible and acceptable among LGBTQ+ young people with current experiences of self-harm. Pilot data indicated that specific experiences and moods are likely to be important to self-harm. These potentially have a temporal influence on self-harm behaviour or ideation, and therefore should be examined in a fully powered sample.

Practices for monitoring and responding to incoming data on self-injurious thoughts and behaviors in intensive longitudinal studies: A systematic review

Advancements in the understanding and prevention of self-injurious thoughts and behaviors (SITBs) are urgently needed. Intensive longitudinal data collection methods-such as ecological momentary assessment-capture fine-grained, "real-world" information about SITBs as they occur and thus have the potential to narrow this gap. However, collecting real-time data on SITBs presents complex ethical and practical considerations, including about whether and how to monitor and respond to incoming information about SITBs from suicidal or self-injuring individuals during the study. We conducted a systematic review of protocols for monitoring and responding to incoming data in previous and ongoing intensive longitudinal studies of SITBs. Across the 61 included unique studies/samples, there was no clear most common approach to managing these ethical and safety considerations. For example, studies were fairly evenly split between either using automated notifications triggered by specific survey responses (e.g., indicating current suicide risk) or monitoring and intervening upon (generally with a phone-based risk assessment) incoming responses (36%), using both automated notifications and monitoring/intervening (35%), or neither using automated notifications nor monitoring/intervening (29%). Certain study characteristics appeared to influence the safety practices used. Future research that systematically evaluates optimal, feasible strategies for managing risk in real-time monitoring research on SITBs is needed.

Assessing the Real-time Influence of Racism-Related Stress and Suicidality Among Black Men: Protocol for an Ecological Momentary Assessment Study

BACKGROUND

Suicide is the third leading cause of death among Black adults aged 18-35 years. Although men represent a majority of suicide deaths among Black adults, less is known regarding the extent to which unique cultural stressors, such as racism-related stress (eg, racial discrimination), are salient in exacerbating suicide risk among Black men. Moreover, few studies examine the daily influence of racism-related stressors on suicide outcomes using real-time smartphone-based approaches. Smartphone-based mobile health approaches using ecological momentary assessments (EMA) provide an opportunity to assess and characterize racism-related stressors as a culturally sensitive suicide risk factor among Black young adult men.

OBJECTIVE

The goal of this study is to describe a protocol development process that aims to capture real-time racism-related stressors and suicide outcomes using a smartphone-based EMA platform (MetricWire).

METHODS

Guided by the Interpersonal Theory of Suicide (ITS), we developed a brief EMA protocol using a multiphased approach. First, we conducted a literature review to identify brief measures previously used in EMA studies, with special emphasis on studies including Black participants. The identified measures were then shortened to items with the highest construct validity (eg, factor loadings) and revised to reflect momentary or daily frequency. Feasibility and acceptability of the study protocol will be assessed using self-report survey and qualitative responses. To protect participants from harm, a three-tier safety protocol was developed to identify participants with moderate, elevated, and acute risk based on EMA survey response to trigger outreach by the study coordinator.

RESULTS

The final EMA protocol, which will be completed over a 7-day period, is comprised of 15 questions administered 4 times per day and a daily questionnaire of 22 items related to sleep-related impairment and disruption, as well as racism-related stress. Study recruitment is currently underway. We anticipate the study will be completed in February 2023. Dissemination will be conducted through peer-reviewed publications and conference presentations.

CONCLUSIONS

This protocol will address gaps in our understanding of Black men's suicide outcomes in the social contexts that they regularly navigate and will clarify the temporal role of racism-related stressors that influence suicidal outcomes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/31241.

SWAY: Associations between sleep disturbance and suicidality in psychiatric inpatients transitioning to the community: an ecological momentary assessment study protocol (Preprint)

Background

Patients are at high risk of suicidal behavior and death by suicide immediately following discharge from inpatient psychiatric hospitals. Furthermore, there is a high prevalence of sleep problems in inpatient settings, which is associated with worse outcomes following hospitalization. However, it is unknown whether poor sleep is associated with suicidality following initial hospital discharge.

Objective

Our study objective is to describe a protocol for an ecological momentary assessment (EMA) study that aims to examine the relationship between sleep and suicidality in discharged patients.

Methods

Our study will use an EMA design based on a wearable device to examine the sleep-suicide relationship during the transition from acute inpatient care to the community. Prospectively discharged inpatients 18 to 35 years old with mental disorders (N=50) will be assessed for eligibility and recruited across 2 sites. Data on suicidal ideation, behavior, and imagery; nonsuicidal self-harm and imagery; defeat, entrapment, and hopelessness; affect; and sleep will be collected on the Pro-Diary V wrist-worn electronic watch for up to 14 days. Objective sleep and daytime activity will be measured using the inbuilt MotionWare software. Questionnaires will be administered face-to-face at baseline and follow up, and data will also be collected on the acceptability and feasibility of using the Pro-Diary V watch to monitor the transition following discharge. The study has been, and will continue to be, coproduced with young people with experience of being in an inpatient setting and suicidality.

Results

South Birmingham Research Ethics Committee (21/WM/0128) approved the study on June 28, 2021. We expect to see a relationship between poor sleep and postdischarge suicidality. Results will be available in 2022.

Conclusions

This protocol describes the first coproduced EMA study to examine the relationship between sleep and suicidality and to apply the integrated motivational volitional model in young patients transitioning from a psychiatric hospital to the community. We expect our findings will inform coproduction in suicidology research and clarify the role of digital monitoring of suicidality and sleep before and after initial hospital discharge.

International Registered Report Identifier (IRRID)

PRR1-10.2196/33817