Adolescent suicide: what can pediatricians do? DOI

Kirsten Breslin,

Julie Balaban,

Catherine D. Shubkin

et al.

Current Opinion in Pediatrics, Journal Year: 2020, Volume and Issue: 32(4), P. 595 - 600

Published: July 2, 2020

Suicide is a major public health concern and the second leading cause of death for adolescents. Faced with an already-high prevalence increasing rates over past decade, pediatricians feel inadequately prepared to manage suicidal patient. This article will review changing suicide, discuss recent literature on risk factors, identify methods screen thoughts suggest approach counseling Finally, there be brief discussion safety planning measures help reduce suicide rates.Rates attempted by have been more than decade. Risk assessment potential suicidality remains very challenging, as factors are multifactorial. However, some common persist including sexual minority identification family or personal history mental issues. Although keeping these other in mind, regular screening adolescents depression self-harm important. best plans treatment appear team-based.It responsibility stay aware regularly prioritize collaborative patients.

Language: Английский

Suicide Risk in Emerging Adulthood: Associations with Screen Time over 10 years DOI Open Access
Sarah M. Coyne, Jeffrey L. Hurst, W. Justin Dyer

et al.

Journal of Youth and Adolescence, Journal Year: 2021, Volume and Issue: 50(12), P. 2324 - 2338

Published: Feb. 2, 2021

Language: Английский

Citations

49
Ecological Momentary Assessments and Passive Sensing in the Prediction of Short-Term Suicidal Ideation in Young Adults DOI Creative Commons
Ewa K. Czyz, Cheryl A. King, Nadia Al‐Dajani

et al.

JAMA Network Open, Journal Year: 2023, Volume and Issue: 6(8), P. e2328005 - e2328005

Published: Aug. 8, 2023

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 which EMA data, particularly combination, utility detecting risk everyday life remains poorly understood. Objective To examine whether what combinations of self-reported sensor-based next-day suicidal ideation. Design, Setting, Participants In this intensive longitudinal prognostic study, participants completed EMAs 4 times daily wore a sensor wristband (Fitbit Charge 3) for 8 weeks. Multilevel machine learning methods, penalized generalized estimating equations classification regression trees (CARTs) with repeated 5-fold cross-validation, were used optimize prediction ideation based on time-varying features from (affective, cognitive, behavioral factors) data (sleep, activity, heart rate). Young adult patients who visited an emergency department recent and/or attempt recruited. Identified via electronic health record screening, eligible individuals contacted remotely complete enrollment procedures. (aged 18 25 years) 14 708 observations (64.4% adherence) approximately half time (55.6% adherence). Data collected between June 2020 July 2021. Statistical analysis was performed January March 2023. Main Outcomes Measures The outcome presence Results Among 102 enrolled participants, 83 (81.4%) female; 6 (5.9%) Asian, 5 (4.9%) Black or African American, 9 (8.8%) more than 1 race, 76 (74.5%) White; mean (SD) age 20.9 (2.1) years. best-performing model incorporated showed good predictive accuracy (mean [SE] cross-validated area under receiver operating characteristic curve [AUC], 0.84 [0.02]), whereas that alone poor AUC, 0.56 [0.02]). Sensor-based did not improve when combined EMAs. Suicidal ideation-related strongest predictors When excluded, alternative had acceptable 0.76 Both models included at different timescales reflecting within-day, end-of-day, cumulative effects. Conclusions Relevance factors identifying near-term thoughts. Best-performing required information, derived EMAs, negligible accuracy. These results may implications developing decision algorithms thoughts guide monitoring intervention delivery life.

Language: Английский

Citations

23
Screen time and suicidal behaviors among U.S. children 9–11 years old: A prospective cohort study DOI Creative Commons
Jonathan Chu, Kyle T. Ganson, Fiona C. Baker

et al.

Preventive Medicine, Journal Year: 2023, Volume and Issue: 169, P. 107452 - 107452

Published: Feb. 17, 2023

Suicide is a leading cause of death among adolescents. Emerging literature has described relationships between excessive screen time and suicidal behaviors, though findings have been mixed. The objective this study to determine the prospective associations behaviors two-years later in national (U.S.) cohort 9-11-year-old-children. We analyzed data from Adolescent Brain Cognitive Development (ABCD) Study (N = 11,633). Logistic regression analyses were estimated baseline self-reported (exposure) (outcome) based on Kiddie Schedule for Affective Disorders Schizophrenia (KSADS-5) at two-year-follow-up. Participants reported an average 4.0 h total per day baseline. At two-year-follow-up, 1.38% sample least one behavior. Each additional hour was prospectively associated with 1.09 higher odds 2-year-follow-up (95% CI 1.03-1.14), after adjusting covariates. For specific modalities, each texting (aOR 1.36, 95% 1.06-1.74), video chatting 1.30, 1.03-1.65), watching videos 1.21, 1.04-1.39), playing games 1.18, 1.01-1.38) subsequent behaviors. Higher reporting Future research should seek identify how experiences may influence

Language: Английский

Citations

22
Machine learning-based prediction for self-harm and suicide attempts in adolescents DOI Creative Commons

Raymond Su,

James Rufus John, Ping‐I Lin

et al.

Psychiatry Research, Journal Year: 2023, Volume and Issue: 328, P. 115446 - 115446

Published: Aug. 29, 2023

This study aimed to use machine learning (ML) models predict the risk of self-harm and suicide attempts in adolescents. We conducted secondary analysis cross-sectional data from Longitudinal Study Australian Children dataset. Several key variables at age 14-15 years were used or attempt 16-17 years. Random forest classification select optimal subset predictors subsequently make predictions. Among 2809 participants, 296 (10.54%) reported an act 145 (5.16%) attempting least once past 12 months. The area under receiver operating curve was fair for (0.7397) (0.7220), which outperformed prediction strategy solely based on prior (AUC: 0.6). most important factors identified similar, included depressed feelings, strengths difficulties questionnaire scores, perceptions self, school- parent-related factors. random algorithm, ML technique, can effectively hundreds forecast risks among Further research is needed validate utility scalability techniques mental health research.

Language: Английский

Citations

17
Adolescent suicide: what can pediatricians do? DOI

Kirsten Breslin,

Julie Balaban,

Catherine D. Shubkin

et al.

Current Opinion in Pediatrics, Journal Year: 2020, Volume and Issue: 32(4), P. 595 - 600

Published: July 2, 2020

Suicide is a major public health concern and the second leading cause of death for adolescents. Faced with an already-high prevalence increasing rates over past decade, pediatricians feel inadequately prepared to manage suicidal patient. This article will review changing suicide, discuss recent literature on risk factors, identify methods screen thoughts suggest approach counseling Finally, there be brief discussion safety planning measures help reduce suicide rates.Rates attempted by have been more than decade. Risk assessment potential suicidality remains very challenging, as factors are multifactorial. However, some common persist including sexual minority identification family or personal history mental issues. Although keeping these other in mind, regular screening adolescents depression self-harm important. best plans treatment appear team-based.It responsibility stay aware regularly prioritize collaborative patients.

Language: Английский

Citations

47