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.

Validation of an ICD-Code-Based Case Definition for Psychotic Illness Across Three Health Systems.

BACKGROUND AND HYPOTHESIS: Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis. STUDY DESIGN: Using EHRs at 3 health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into 5 higher-order groups. 1133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings. STUDY RESULTS: PPVs across all diagnostic groups and hospital systems exceeded 70%: Mass General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62). CONCLUSIONS: We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the case definitions used in the development of risk prediction models designed to predict or detect undiagnosed psychosis.

Validation of an ICD-code-based case definition for psychotic illness across three health systems.

Background and Hypothesis: Early detection of psychosis is critical for improving outcomes. Algorithms to predict or detect psychosis using electronic health record (EHR) data depend on the validity of the case definitions used, typically based on diagnostic codes. Data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis. Study Design: Using EHRs at three health systems, ICD codes comprising primary psychotic disorders and mood disorders with psychosis were grouped into five higher-order groups. 1,133 records were sampled for chart review using the full EHR. PPVs (the probability of chart-confirmed psychosis given ICD psychosis codes) were calculated across multiple treatment settings. Study Results: PPVs across all diagnostic groups and hospital systems exceeded 70%: Massachusetts General Brigham 0.72 [95% CI 0.68-0.77], Boston Children's Hospital 0.80 [0.75-0.84], and Boston Medical Center 0.83 [0.79-0.86]. Schizoaffective disorder PPVs were consistently the highest across sites (0.80-0.92) and major depressive disorder with psychosis were the most variable (0.57-0.79). To determine if the first documented code captured first-episode psychosis (FEP), we excluded cases with prior chart evidence of a diagnosis of or treatment for a psychotic illness, yielding substantially lower PPVs (0.08-0.62). Conclusions: We found that the first documented psychosis diagnostic code accurately captured true episodes of psychosis but was a poor index of FEP. These data have important implications for the development of risk prediction models designed to predict or detect undiagnosed psychosis.

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.

Examining tradeoffs between cognitive effort and relief among adults with self-injurious behavior.

BACKGROUND: People engage in nonsuicidal self-injury (NSSI) to reduce negative affect, but it is not clear why they engage in this harmful type of behavior instead of using healthier strategies. The primary goal of this study was to evaluate whether people choose NSSI to reduce negative affect because they perceive it to be less cognitively costly than other available strategies. METHOD: In experiment one, 43 adults completed a novel, relief-based effort discounting task designed to index preferences about exerting cognitive effort to achieve relief. In experiment two, 149 adults, 52 % with a history of NSSI, completed our effort discounting task. RESULTS: Our main results suggest that people will accept less relief from an aversive experience if doing so requires expending less effort, i.e. they demonstrate effort discounting in the context of decisions about relief. We also found and that effort discounting is stronger among those with a history of NSSI, but this association became nonsignificant when simultaneously accounting for other conditions associated with aberrant effort tradeoffs. LIMITATIONS: The use of a control group without NSSI or other potentially harmful relief-seeking behaviors limits our ability to draw specific conclusions about NSSI. The ecological validity of our task was limited by a modestly effective affect manipulation, and because participants made hypothetical choices. CONCLUSIONS: This study demonstrates that preferences about exerting cognitive effort may be a barrier to using healthier affect regulation strategies. Further, the preference not to exert cognitive effort, though present in NSSI, is likely not unique to NSSI. Instead, effort discounting may be a transdiagnostic mechanism promoting an array of harmful relief-seeking behaviors.

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.

Sleep Quality Impairment Is Associated With Pandemic Attitudes During the Coronavirus Disease 2019 (COVID-19) Circuit Breaker Lockdown in England: A Cross-Sectional Study.

Objectives: The COVID-19 pandemic has been associated with sleep quality impairment and psychological distress, and the general public has responded to the pandemic and quarantine requirements in a variety of ways. We aimed to investigate whether sleep quality is low during a short-term (circuit break) quarantine restriction, and whether sleep quality is associated with respondents' overall attitudes to the pandemic using a validated scale. Design and Setting: Online cross-sectional study in England in November 2020. Participants: The study included 502 respondents over the age of 18. Measurements: Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), and pandemic attitudes were assessed using the Oxford Pandemic Attitudes Scale-COVID-19 (OPAS-C), a validated 20-item, 7-domain scale that assesses pandemic-related stress, fear, loneliness, sense of community, sense of exaggerated concern, non-pharmaceutical interventions, and vaccine hesitancy. Unadjusted and multivariable logistic regression odds ratios of association were assessed between the dependent variable of poor sleep quality (PSQI>5) and risk factors, including OPAS-C score, age, sex, educational status, and income. Results: The mean (SD) PSQI score was 7.62 (3.49). Overall, 68.9% of respondents met criteria for poor sleep quality using the PSQI cutoff of >5. The mean (SD) OPAS-C score was 60.3 (9.1). There was a significantly increased odds of poor sleep quality in the highest vs. lowest OPAS-C quartiles (OR 4.94, 95% CI [2.67, 9.13], p < 0.0001). Age, sex, income, political leaning, employment status, and education attainment were not associated with poor sleep quality. Conclusions: More than two-thirds of respondents met criteria for poor sleep quality. The odds of poor sleep quality increased in a dose-response relationship with pandemic attitudes (such as higher levels of pandemic-related stress, fear, or loneliness). The association between poor sleep quality and pandemic attitudes suggests opportunities for public health and sleep medicine interventions, and highlights the need for further research.

Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers.

BACKGROUND: Interest in developing machine learning models that use electronic health record data to predict patients' risk of suicidal behavior has recently proliferated. However, whether and how such models might be implemented and useful in clinical practice remain unknown. To ultimately make automated suicide risk-prediction models useful in practice, and thus better prevent patient suicides, it is critical to partner with key stakeholders, including the frontline providers who will be using such tools, at each stage of the implementation process. OBJECTIVE: The aim of this focus group study is to inform ongoing and future efforts to deploy suicide risk-prediction models in clinical practice. The specific goals are to better understand hospital providers' current practices for assessing and managing suicide risk; determine providers' perspectives on using automated suicide risk-prediction models in practice; and identify barriers, facilitators, recommendations, and factors to consider. METHODS: We conducted 10 two-hour focus groups with a total of 40 providers from psychiatry, internal medicine and primary care, emergency medicine, and obstetrics and gynecology departments within an urban academic medical center. Audio recordings of open-ended group discussions were transcribed and coded for relevant and recurrent themes by 2 independent study staff members. All coded text was reviewed and discrepancies were resolved in consensus meetings with doctoral-level staff. RESULTS: Although most providers reported using standardized suicide risk assessment tools in their clinical practices, existing tools were commonly described as unhelpful and providers indicated dissatisfaction with current suicide risk assessment methods. Overall, providers' general attitudes toward the practical use of automated suicide risk-prediction models and corresponding clinical decision support tools were positive. Providers were especially interested in the potential to identify high-risk patients who might be missed by traditional screening methods. Some expressed skepticism about the potential usefulness of these models in routine care; specific barriers included concerns about liability, alert fatigue, and increased demand on the health care system. Key facilitators included presenting specific patient-level features contributing to risk scores, emphasizing changes in risk over time, and developing systematic clinical workflows and provider training. Participants also recommended considering risk-prediction windows, timing of alerts, who will have access to model predictions, and variability across treatment settings. CONCLUSIONS: Providers were dissatisfied with current suicide risk assessment methods and were open to the use of a machine learning-based risk-prediction system to inform clinical decision-making. They also raised multiple concerns about potential barriers to the usefulness of this approach and suggested several possible facilitators. Future efforts in this area will benefit from incorporating systematic qualitative feedback from providers, patients, administrators, and payers on the use of these new approaches in routine care, especially given the complex, sensitive, and unfortunately still stigmatized nature of suicide risk.

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.

Can passive measurement of physiological distress help better predict suicidal thinking?

There has been growing interest in using wearable physiological monitors to passively detect the signals of distress (i.e., increases in autonomic arousal measured through increased electrodermal activity [EDA]) that may be imminently associated with suicidal thoughts. Before using these monitors in advanced applications such as creating suicide risk detection algorithms or just-in-time interventions, several preliminary questions must be answered. Specifically, we lack information about whether: (1) EDA concurrently and prospectively predicts suicidal thinking and (2) data on EDA adds to the ability to predict the presence and severity of suicidal thinking over and above self-reports of emotional distress. Participants were suicidal psychiatric inpatients (n = 25, 56% female, M age = 33.48 years) who completed six daily assessments of negative affect and suicidal thinking duration of their psychiatric inpatient stay and 28 days post-discharge, and wore on their wrist a physiological monitor (Empatica Embrace) that passively detects autonomic activity. We found that physiological data alone both concurrently and prospectively predicted periods of suicidal thinking, but models with physiological data alone had the poorest fit. Adding physiological data to self-report models improved fit when the outcome variable was severity of suicidal thinking, but worsened model fit when the outcome was presence of suicidal thinking. When predicting severity of suicidal thinking, physiological data improved model fit more for models with non-overlapping self-report data (i.e., low arousal negative affect) than for overlapping self-report data (i.e., high arousal negative affect). These findings suggest that physiological data, under certain contexts (e.g., when combined with self-report data), may be useful in better predicting-and ultimately, preventing-acute increases in suicide risk. However, some cautious optimism is warranted since physiological data do not always improve our ability to predict suicidal thinking.

Ringing the Alarm on Suicide Prevention: A Call to Action.

Suicide is among the leading causes of death in the US and worldwide. Devastatingly, it disproportionately affects youth, making it a leading contributor to years of life lost as well. Whereas the US federal government has prioritized the study and prevention of other causes of death, causing mortality rates from them to drop precipitously (e.g., cancer, heart disease, HIV/AIDS), this is not true of suicide. Funding for suicide prevention research is less than one-third of that allocated to other leading causes of death, and as a result the US suicide rate now is virtually identical to what it was 100 years ago. This situation is alarming and requires immediate action.

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.

Do Patterns and Types of Negative Affect During Hospitalization Predict Short-Term Post-Discharge Suicidal Thoughts and Behaviors?

We still have little understanding of short-term predictors of suicidal thoughts and behaviors (STBs). Prior research links increased negative affect to STBs, but the vast majority of earlier work is limited by measuring negative affect at one time point and aiming to predict STBs months or years in the future. Recently, intensive longitudinal studies have shown that negative affect is associated with suicidal thoughts over relatively short, clinically useful time periods; however, the specific patterns and types of negative affect that predict STBs remain unclear. Using ecological momentary assessment (EMA) data from psychiatric inpatients hospitalized for suicide risk (N = 83), this study sought to test whether the patterns (means and variability) of two types of negative affect (anxiety/agitation and shame/self-hatred, which were derived from a larger EMA battery) during hospitalization predict STBs in the four weeks after discharge: an extremely high-risk time for suicidal behavior. The mean - but not the variability - of both anxiety/agitation and shame/self-hatred during hospitalization predicted the number of days with suicidal thoughts after discharge. The mean and the variability of shame/self-hatred - but not anxiety/agitation - predicted post-discharge suicide attempt. We discuss implications for assessment and treatment of suicidal individuals and propose key directions for future research.

Increase in Suicidal Thinking During COVID-19.

There is concern that the COVID-19 pandemic may cause increased risk of suicide. In the current study, we tested whether suicidal thinking has increased during the COVID-19 pandemic and whether such thinking was predicted by increased feelings of social isolation. In a sample of 55 individuals recently hospitalized for suicidal thinking or behaviors and participating in a 6-month intensive longitudinal smartphone monitoring study, we examined suicidal thinking and isolation before and after the COVID-19 pandemic was declared a national emergency in the United States. We found that suicidal thinking increased significantly among adults (odds ratio [OR] = 4.01, 95% confidence interval [CI] = [3.28, 4.90], p < .001) but not adolescents (OR = 0.84, 95% CI = [0.69, 1.01], p = .07) during the onset of the COVID-19 pandemic. Increased feelings of isolation predicted suicidal thinking during the pandemic phase. Given the importance of social distancing policies, these findings support the need for digital outreach and treatment.

Schizophrenia symptom severity and motivations for gambling in individuals with schizophrenia or schizoaffective disorder.

Individuals with schizophrenia are over three times more likely to have problem and pathological gambling (PPG) than the general population (Cunningham-Williams et al., 1998; Desai and Potenza, 2009), but little is known about this co-occurrence and how PPG relates to specific symptom dimensions of psychotic disorders. Although cognitive distortions in PPG have been linked to gambling motivations (e.g., distorted thoughts about odds of winning), how psychotic symptoms in schizophrenia or related disorders relate to gambling motivations have not been examined systematically to date. Individuals with schizophrenia or schizoaffective disorder (n = 170) completed structured face-to-face interviews regarding problem-gambling severity, gambling motivations, and five symptom factors of psychosis (Positive, Negative, Disorganized/Concrete, Depressed, and Excited). Different symptom dimensions of psychosis showed distinct patterns of relationships with motivations to gamble and gambling onset. PPG in schizophrenia was associated with elevated scores on the Depressed factor. Psychotic symptom severity was associated with increased motivation to gamble for financial reasons and decreased motivations to gamble for service, and possibly social or interpersonal, reasons. Age of gambling onset was inversely associated with psychotic symptom severity, particularly positive features. Our findings suggest that motivations for gambling may differ in the context of schizophrenia and relate to specific symptom clusters.

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.

Cognitive effort and amotivation in first-episode psychosis.

Impaired cognition and amotivation are considered core features of psychotic disorders. Amotivation may manifest as reduced willingness to expend effort on cognitive tasks. It remains unclear whether reduced effort is responsible for any of the observed cognitive deficits in these patients, as we do not generally assess continuous effort during testing. In the current study, we tested whether disengagement of effort is greater during cognitive performance in individuals with first-episode psychosis (FEP) compared with healthy community members. We used a novel task called Cognitive Effort and DisEngagement (CEDE), which increases in difficulty and permits skipping any trial without penalty. No additional monetary incentives were used, and skips were used as an index of effort disengagement. We found that FEP patients had lower overall accuracy on the CEDE, but they also skipped significantly more, specifically on difficult trials. Self-reported amotivation significantly predicted skips among patients. The present results suggest that disengagement of effort may account for a portion of cognitive test performance among individuals with FEP. This possibility is relevant to cognitive remediation, as effort and ability may optimally be targeted by different interventions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).