Heterogeneity in suicide risk: Evidence from personalized dynamic models.

Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction.

Do people know how suicidal they will be? Understanding suicidal prospection.

INTRODUCTION: Little research has been done on how people mentally simulate future suicidal thoughts and urges, a process we term suicidal prospection. METHODS: Participants were 94 adults with recent suicidal thoughts. Participants completed a 42-day real-time monitoring study and then a follow-up survey 28 days later. Each night, participants provided predictions for the severity of their suicidal thoughts the next day and ratings of the severity of suicidal thoughts over the past day. We measured three aspects of suicidal prospection: predicted levels of desire to kill self, urge to kill self, and intent to kill self. We generated prediction errors by subtracting participants' predictions of the severity of their suicidal thoughts from their experienced severity. RESULTS: Participants tended to overestimate (although the average magnitude was small and the modal error was zero) the severity of their future suicidal thoughts. The best fitting models suggested that participants used both their current suicidal thinking and previous predictions of their suicidal thinking to generate predictions of their future suicidal thinking. Finally, the average severity of predicted future suicidal thoughts predicted the number of days participants thought about suicide during the follow-up period. CONCLUSIONS: This study highlights prospection as a psychological process to better understand suicidal thoughts and behaviors.

Intervening on high-risk responses during ecological momentary assessment of suicidal thoughts: Is there an effect on study data?

Ecological momentary assessment (EMA) is increasingly used to study suicidal thoughts and behaviors (STBs). There is a potential ethical obligation for researchers to intervene when receiving information about suicidal thoughts in real time. A possible concern, however, is that intervening when receiving responses that indicate high risk for suicide during EMA research may impact how participants respond to questions about suicidal thoughts and thus affect the validity and integrity of collected data. We leveraged data from a study of adults and adolescents (N = 434) recruited during a hospital visit for STBs to examine whether monitoring and intervening on high-risk responses affects subsequent participant responding. Overall, we found mixed support for the notion that intervening on high-risk responses influences participants' ratings. Although we observed some evidence of discontinuity in subsequent responses at the threshold used to trigger response-contingent interventions, it was not clear that such discontinuity was caused by the interventions; lower subsequent responses could be due to effective intervention, participant desire to not be contacted again, or regression to the mean. Importantly, the likelihood of completing surveys did not change from before to after response-contingent intervention. Adolescents were significantly more likely than adults, however, to change their initial suicidal intent ratings from above to below the high-risk threshold after viewing automated response-contingent pop-up messages. Studies explicitly designed to assess the potential impact of intervening on high-risk responses in real-time monitoring research are needed, as this will inform effective, scalable strategies for intervening during moments of high suicide risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Suicidal thinking as affect regulation.

Nine percent of people worldwide report thinking about suicide at some point during their lives. A fundamental question we currently lack a clear answer to is: why do suicidal thoughts persist over time? One possibility is that suicidal thoughts serve adaptive functions for people who experience them. We tested whether suicidal thinking may serve as a form of affect regulation. In a real-time monitoring study among adults with recent suicidal thoughts (N = 105), we found that participants often endorsed using suicidal thinking as a form of affect regulation. The occurrence of suicidal thinking was followed by decreased negative affect. However, when assessing the direction of the relationship between suicidal thinking and negative affect, we also found positive bidirectional associations between them. Finally, using suicidal thinking as a form of affect regulation predicted the frequency and severity of suicidal thinking at later time points. These findings may help explain the persistence of suicidal thoughts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Mapping the timescale of suicidal thinking.

This study aims to identify the timescale of suicidal thinking, leveraging real-time monitoring data and a number of different analytic approaches. Participants were 105 adults with past week suicidal thoughts who completed a 42-d real-time monitoring study (total number of observations = 20,255). Participants completed two forms of real-time assessments: traditional real-time assessments (spaced hours apart each day) and high-frequency assessments (spaced 10 min apart over 1 h). We found that suicidal thinking changes rapidly. Both descriptive statistics and Markov-switching models indicated that elevated states of suicidal thinking lasted on average 1 to 3 h. Individuals exhibited heterogeneity in how often and for how long they reported elevated suicidal thinking, and our analyses suggest that different aspects of suicidal thinking operated on different timescales. Continuous-time autoregressive models suggest that current suicidal intent is predictive of future intent levels for 2 to 3 h, while current suicidal desire is predictive of future suicidal desire levels for 20 h. Multiple models found that elevated suicidal intent has on average shorter duration than elevated suicidal desire. Finally, inferences about the within-person dynamics of suicidal thinking on the basis of statistical modeling were shown to depend on the frequency at which data was sampled. For example, traditional real-time assessments estimated the duration of severe suicidal states of suicidal desire as 9.5 h, whereas the high-frequency assessments shifted the estimated duration to 1.4 h.

Real-time digital monitoring of a suicide attempt by a hospital patient.

Suicide is among the most devastating problems facing clinicians, who currently have limited tools to predict and prevent suicidal behavior. Here we report on real-time, continuous smartphone and sensor data collected before, during, and after a suicide attempt made by a patient during a psychiatric inpatient hospitalization. We observed elevated and persistent sympathetic nervous system arousal and suicidal thinking leading up to the suicide attempt. This case provides the highest resolution data to date on the psychological, psychophysiological, and behavioral markers of imminent suicidal behavior and highlights new directions for prediction and prevention efforts.

Inferring sleep disturbance from text messages of suicide attempt survivors: A pilot study.

OBJECTIVE: Identifying digital markers of sleep disturbance-a known suicide risk factor-may aid in the detection of imminent suicide risk. This study examined sleep-related communication and texting patterns in personal text messages (N = 86,705) of suicide attempt survivors. METHOD: Twenty-six participants provided dates of past suicide attempts and 2-week periods of positive mood, depressed mood, or suicidal ideation. Linguistic Inquiry Word Count was used to identify sleep-related texts via a custom dictionary. Mixed effect models were fitted to test the association between suicide/mood episode type (e.g., attempt versus ideation) and three outcomes: likelihood of a text including sleep-related content, nightly count of texts sent from midnight to 5:00 AM, and sum of unique hour bins from midnight to 5:00 AM with outgoing texts. RESULTS: Analyses with a sleep dictionary that was manually revised to be more accurate (but not the original unedited dictionary) showed sleep-related communication was more likely during depressed mood episodes than positive mood episodes. Otherwise, there were no significant differences in sleep-related communication or objective texting patterns across episode type. CONCLUSIONS: Although we did not detect differences in sleep-related communication tied to suicidal thoughts or behaviors, sleep-related communication may differ as a function of within-person mood level.

Just-in-Time Adaptive Interventions for Suicide Prevention: Promise, Challenges, and Future Directions.

The suicide rate (currently 14 per 100,000) has barely changed in the United States over the past 100 years. There is a need for new ways of preventing suicide. Further, research has revealed that suicidal thoughts and behaviors and the factors that drive them are dynamic, heterogeneous, and interactive. Most existing interventions for suicidal thoughts and behaviors are infrequent, not accessible when most needed, and not systematically tailored to the person using their own data (e.g., from their own smartphone). Advances in technology offer an opportunity to develop new interventions that may better match the dynamic, heterogeneous, and interactive nature of suicidal thoughts and behaviors. Just-In-Time Adaptive Interventions (JITAIs), which use smartphones and wearables, are designed to provide the right type of support at the right time by adapting to changes in internal states and external contexts, offering a promising pathway toward more effective suicide prevention. In this review, we highlight the potential of JITAIs for suicide prevention, challenges ahead (e.g., measurement, ethics), and possible solutions to these challenges.

Effect of frequent assessment of suicidal thinking on its incidence and severity: high-resolution real-time monitoring study.

Researchers, clinicians and patients are increasingly using real-time monitoring methods to understand and predict suicidal thoughts and behaviours. These methods involve frequently assessing suicidal thoughts, but it is not known whether asking about suicide repeatedly is iatrogenic. We tested two questions about this approach: (a) does repeatedly assessing suicidal thinking over short periods of time increase suicidal thinking, and (b) is more frequent assessment of suicidal thinking associated with more severe suicidal thinking? In a real-time monitoring study (n = 101 participants, n = 12 793 surveys), we found no evidence to support the notion that repeated assessment of suicidal thoughts is iatrogenic.

Variability in the Functions of Nonsuicidal Self-Injury: Evidence From Three Real-Time Monitoring Studies.

Despite functional models of nonsuicidal self-injury (NSSI) helping to explain why people engage in this perplexing behavior, we still lack an understanding of some of the key properties of NSSI functions. Here, we address three unanswered questions about NSSI functions: how much do distinct NSSI functions (1) vary between people over time, (2) vary within people over time, and (3) simultaneously co-occur over time? Data were drawn from three ecological momentary assessment (EMA) studies of self-injurious adult psychiatric outpatients (n = 7), community-based adolescents (n = 15), and community-based adults (n = 9). Across the three studies, there was a total of 271 NSSI episodes (all with corresponding functions captured by EMA). The vast majority (27 of 31; 87%) of participants exhibited unique patterns of NSSI functions during the monitoring periods, indicating high variability between people. The vast majority (26 of 31; 84%) of participants also showed changes in NSSI functions over time, indicating high variability within people. Although it was most common for only one function to be reported for a given NSSI episode, participants endorsed more than one function for 22% to 43% of NSSI episodes, indicating that different functions did simultaneously co-occur. These results underscore that reinforcement processes for NSSI differ from person-to-person, and are both time-varying and multifaceted, which has implications for personalized assessment and treatment of this clinical phenomenon.

A year in the social life of a teenager: Within-person fluctuations in stress, phone communication, and anxiety and depression.

Stressful life events (SLEs) are strongly associated with the emergence of adolescent anxiety and depression, but the underlying mechanisms remain poorly understood, especially at the within-person level. We investigated how adolescent social communication (i.e., frequency of calls and texts) following SLEs relates to changes in internalizing symptoms in a multi-timescale intensive year-long study (N=30; n=355 monthly observations; n=~5,000 experience-sampling observations). Within-person increases in SLEs were associated with receiving more calls than usual at both monthly- and momentary-levels, and making more calls at the monthly-level. Increased calls were prospectively associated with worsening internalizing symptoms at the monthly-level only, suggesting that SLEs rapidly influences phone communication patterns, but these communication changes may have a more protracted, cumulative influence on internalizing symptoms. Finally, increased incoming calls prospectively mediated the association between SLEs and anxiety at the monthly-level. We identify adolescent social communication fluctuations as a potential mechanism conferring risk for stress-related internalizing psychopathology.

A Pilot Study Using Frequent Inpatient Assessments of Suicidal Thinking to Predict Short-Term Postdischarge Suicidal Behavior.

Importance: The weeks following discharge from psychiatric hospitalization are the highest-risk period for suicide attempts. Real-time monitoring of suicidal thoughts via smartphone prompts may be more indicative of short-term risk than a single, cross-sectional assessment. Objective: To test whether modeling dynamic changes in real-time suicidal thoughts during psychiatric hospitalization can improve predictions of postdischarge suicide attempts vs using only baseline (ie, admission) data or using the mean level of real-time suicidal thoughts during hospitalization. Design, Setting, and Participants: In this prognostic study, 83 adults recruited from the inpatient psychiatric unit at Massachusetts General Hospital completed ecological momentary assessment surveys of suicidal thinking 4 to 6 times per day during hospitalization as well as brief follow-up surveys assessing suicide attempts at 2 and 4 weeks after discharge. Participants completed at least 3 real-time monitoring surveys. Inclusion criteria included hospitalization for suicidal thoughts and/or behaviors and English fluency. Data were collected from January 2016 to December 2018 and analyzed from January to December 2020. Main Outcomes and Measures: The primary outcome was suicide attempt in the month after discharge. Results: Of 83 participants (mean [SD] age, 38.4 [13.6] years; 43 [51.8%] male participants; 69 [83.1%] White individuals), 9 (10.8%) made a suicide attempt in the month after discharge. Mean cross-validated AUC for elastic net models revealed predictive accuracy was fair for the model using baseline data (area under the curve [AUC], 0.71; first to third quartile, 0.55-0.88), good for the model using the mean level of real-time suicidal thoughts during hospitalization (AUC, 0.81; first to third quartile, 0.67-0.91), and best for the model using dynamic changes in real-time suicidal thoughts during hospitalization (AUC, 0.89; first to third quartile, 0.81-0.97); this pattern of results held for other classification metrics (eg, accuracy, positive predictive value, Brier score) and when using different cross-validation procedures. Features assessing rapid fluctuations in suicidal thinking emerged as the strongest predictors of posthospital suicide attempts. A final set of models incorporating percentage missingness further improved both the mean (mean AUC, 0.93; first to third quartile, 0.90-1.00) and dynamic feature (mean AUC, 0.93; first to third quartile, 0.88-1.00) models. Conclusions and Relevance: In this study, collecting real-time data about suicidal thinking during the course of hospitalization significantly improved short-term prediction of posthospitalization suicide attempts. Models including dynamic changes in suicidal thinking over time yielded the best prediction; features that captured rapid changes in suicidal thoughts were particularly strong predictors. Survey noncompletion also emerged as an important predictor of posthospitalization suicide attempts.

A computational network perspective on pediatric anxiety symptoms.

BACKGROUND: While taxonomy segregates anxiety symptoms into diagnoses, patients typically present with multiple diagnoses; this poses major challenges, particularly for youth, where mixed presentation is particularly common. Anxiety comorbidity could reflect multivariate, cross-domain interactions insufficiently emphasized in current taxonomy. We utilize network analytic approaches that model these interactions by characterizing pediatric anxiety as involving distinct, inter-connected, symptom domains. Quantifying this network structure could inform views of pediatric anxiety that shape clinical practice and research. METHODS: Participants were 4964 youths (ages 5-17 years) from seven international sites. Participants completed standard symptom inventory assessing severity along distinct domains that follow pediatric anxiety diagnostic categories. We first applied network analytic tools to quantify the anxiety domain network structure. We then examined whether variation in the network structure related to age (3-year longitudinal assessments) and sex, key moderators of pediatric anxiety expression. RESULTS: The anxiety network featured a highly inter-connected structure; all domains correlated positively but to varying degrees. Anxiety patients and healthy youth differed in severity but demonstrated a comparable network structure. We noted specific sex differences in the network structure; longitudinal data indicated additional structural changes during childhood. Generalized-anxiety and panic symptoms consistently emerged as central domains. CONCLUSIONS: Pediatric anxiety manifests along multiple, inter-connected symptom domains. By quantifying cross-domain associations and related moderation effects, the current study might shape views on the diagnosis, treatment, and study of pediatric anxiety.

Ethical dilemmas posed by mobile health and machine learning in psychiatry research.

The application of digital technology to psychiatry research is rapidly leading to new discoveries and capabilities in the field of mobile health. However, the increase in opportunities to passively collect vast amounts of detailed information on study participants coupled with advances in statistical techniques that enable machine learning models to process such information has raised novel ethical dilemmas regarding researchers' duties to: (i) monitor adverse events and intervene accordingly; (ii) obtain fully informed, voluntary consent; (iii) protect the privacy of participants; and (iv) increase the transparency of powerful, machine learning models to ensure they can be applied ethically and fairly in psychiatric care. This review highlights emerging ethical challenges and unresolved ethical questions in mobile health research and provides recommendations on how mobile health researchers can address these issues in practice. Ultimately, the hope is that this review will facilitate continued discussion on how to achieve best practice in mobile health research within psychiatry.

L'application des technologies numériques à la recherche psychiatrique entraîne rapidement de nouvelles découvertes et capacités en matière de santé mobile. Cependant, la multiplication des opportunités de recueillir passivement d'immenses quantités d'informations détaillées sur les participants aux études combinée aux progrès des techniques statistiques permettant aux modèles d'apprentissage automatique de traiter de telles informations a soulevé de nouveaux dilemmes éthiques concernant l'obligation des chercheurs: (i) de surveiller les effets indésirables et d'intervenir en conséquence; (ii) d'obtenir un consentement pleinement éclairé et volontaire; (iii) de protéger la vie privée des participants; et enfin, (iv) d'améliorer la transparence des puissants modèles d'apprentissage automatique afin de garantir une application éthique et impartiale dans le domaine des soins psychiatriques. Ce rapport identifie les défis qui en découlent ainsi que les questions éthiques non résolues en matière de santé mobile. Il formule également des recommandations sur la façon dont les chercheurs en santé mobile peuvent résoudre ces problèmes dans la pratique. À terme, nous espérons que ce rapport favorisera la poursuite des discussions portant sur les moyens de définir des méthodes de recherche adéquates pour la santé mobile en psychiatrie.

La aplicación de la tecnología digital a la investigación en psiquiatría está conduciendo rápidamente a descubrimientos y capacidades nuevas en el ámbito de la salud móvil. No obstante, el incremento de las oportunidades para recopilar pasivamente grandes volúmenes de información detallada sobre los participantes en los estudios, junto con los avances en las técnicas de estadística que permiten a los modelos de aprendizaje automático procesar tal información, ha planteado nuevos dilemas éticos relativos a los deberes de los investigadores: (i) hacer un seguimiento de los eventos adversos e intervenir en consecuencia; (ii) obtener un consentimiento voluntario plenamente informado; (iii) proteger la privacidad de los participantes; y (iv) aumentar la transparencia de los modelos potentes de aprendizaje automático para asegurar que puedan aplicarse de manera ética y justa en la atención psiquiátrica. En este análisis se destacan tanto los desafíos éticos nuevos como las cuestiones éticas aún sin resolver en la investigación sobre la salud móvil y se formulan recomendaciones sobre cómo los investigadores de la salud móvil pueden abordar dichas cuestiones en la práctica. En última instancia, se espera que este análisis facilite un debate continuo sobre cómo lograr las mejores prácticas en la investigación de la salud móvil dentro de la psiquiatría.

Symptom structure of PTSD and co-morbid depressive symptoms - a network analysis of combat veteran patients.

BACKGROUND: Despite extensive research, symptom structure of posttraumatic stress disorder (PTSD) is highly debated. The network approach to psychopathology offers a novel method for understanding and conceptualizing PTSD. However, extant studies have mainly used small samples and self-report measures among sub-clinical populations, while also overlooking co-morbid depressive symptoms. METHODS: PTSD symptom network topology was estimated in a sample of 1489 treatment-seeking veteran patients based on a clinician-rated PTSD measure. Next, clinician-rated depressive symptoms were incorporated into the network to assess their influence on PTSD network structure. The PTSD-symptom network was then contrasted with the network of 306 trauma-exposed (TE) treatment-seeking patients not meeting full criteria for PTSD to assess corresponding network differences. Finally, a directed acyclic graph (DAG) was computed to estimate potential directionality among symptoms, including depressive symptoms and daily functioning. RESULTS: The PTSD symptom network evidenced robust reliability. Flashbacks and getting emotionally upset by trauma reminders emerged as the most central nodes in the PTSD network, regardless of the inclusion of depressive symptoms. Distinct clustering emerged for PTSD and depressive symptoms within the comorbidity network. DAG analysis suggested a key triggering role for re-experiencing symptoms. Network topology in the PTSD sample was significantly distinct from that of the TE sample. CONCLUSIONS: Flashbacks and psychological reactions to trauma reminders, along with their strong connections to other re-experiencing symptoms, have a pivotal role in the clinical presentation of combat-related PTSD among veterans. Depressive and posttraumatic symptoms constitute two separate diagnostic entities, but with meaningful between-disorder connections, suggesting two mutually-influential systems.

Sleep problems and suicide risk in youth: A systematic review, developmental framework, and implications for hospital treatment.

OBJECTIVE: Sleep problems are transdiagnostic symptoms that confer significant risk for suicidal thoughts and behaviors (STBs) in adults. However, less is known about the sleep-STB association in adolescence-a developmental period when rates of STBs increase drastically, and sleep problems may be particularly pernicious. This article provides a systematic review of research on the sleep-STB association in youth, an overview of changes in sleep regulation during adolescence that may make sleep problems particularly detrimental for youth, and a discussion of the clinical implications of the sleep-STB association for hospitalized youth. METHOD: The systematic review included all longitudinal studies in which sleep problems were examined as prospective predictors of STBs in adolescents (aged 10-24 years). The search was conducted on December 1, 2017 using PsychINFO, PubMed, and Web of Science databases. RESULTS: Ten studies qualified for inclusion in this review. Of these, seven studies found at least one type of sleep problem significantly predicted a STB outcome. CONCLUSIONS: Although findings are mixed, growing research suggests that sleep problems may be a unique risk factor for STBs in youth. Sleep problems may be particularly important intervention target because they are easily assessed across healthcare settings and are amenable to treatment.

The dynamics of social support among suicide attempters: A smartphone-based daily diary study.

Decades of research suggest that social support is an important factor in predicting suicide risk and resilience. However, no studies have examined dynamic fluctuations in day-by-day levels of perceived social support. We examined such fluctuations over 28 days among a sample of 53 adults who attempted suicide in the past year (992 total observations). Variability in social support was analyzed with between-person intraclass correlations and root mean square of successive differences. Multi-level models were conducted to determine the association between social support and suicidal ideation. Results revealed that social support varies considerably from day to day with 45% of social support ratings differing by at least one standard deviation from the prior assessment. Social support is inversely associated with same-day and next-day suicidal ideation, but not with next-day suicidal ideation after adjusting for same-day suicidal ideation (i.e., not with daily changes in suicidal ideation). These results suggest that social support is a time-varying protective factor for suicidal ideation.