Brief digital psychological intervention to prevent relapse of non-suicidal self-injury behavior in adolescents: A randomized controlled trial / Chang ZHANG in Journal of Child Psychology and Psychiatry, 67-3 (March 2026)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 67-3 (March 2026) . - p.380-389 Titre : Brief digital psychological intervention to prevent relapse of non-suicidal self-injury behavior in adolescents: A randomized controlled trial Type de document : texte imprimé Auteurs : Chang ZHANG, Auteur ; Diyang QU, Auteur ; Dennis CHONG, Auteur ; Chang LEI, Auteur ; Yidong SHEN, Auteur ; Xilong CUI, Auteur ; Yuqiong HE, Auteur ; Yamin LI, Auteur ; Jianjun OU, Auteur ; Runsen CHEN, Auteur Article en page(s) : p.380-389 Langues : Anglais (eng) Mots-clés : Non-suicidal self-injury  adolescents  short message service  intervention Index. décimale : PER Périodiques Résumé : Background Non-suicidal self-injury (NSSI) poses a significant mental health challenge among adolescents, necessitating accessible and effective interventions. While the development of technology offers new opportunities, higher costs remain a concern. In this context, digital psychological interventions such as text message intervention (SMS) present a convenient and low-cost delivery method that requires no face-to-face contact. However, the extent to which this method could function as a viable strategy remains underexplored. Objective To evaluate the effectiveness of an SMS intervention specifically developed for NSSI among adolescents when combined with treatment as usual (TAU), compared to TAU alone. Methods A randomized controlled trial (RCT) was conducted with 86 Chinese adolescents, randomly assigned to either the SMS intervention plus TAU or TAU alone. The SMS intervention, consisting of text messages addressing NSSI-related knowledge, distress tolerance skills, and emotion regulation strategies, was administered over 8?weeks. Assessments were conducted at baseline, 4?weeks, and 8?weeks. Results Participants in the intervention group showed a significant reduction in NSSI behavior at 4?weeks (RR?=?0.43, p? En ligne : https://doi.org/10.1111/jcpp.70054 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=580 [article] Brief digital psychological intervention to prevent relapse of non-suicidal self-injury behavior in adolescents: A randomized controlled trial [texte imprimé] / Chang ZHANG, Auteur ; Diyang QU, Auteur ; Dennis CHONG, Auteur ; Chang LEI, Auteur ; Yidong SHEN, Auteur ; Xilong CUI, Auteur ; Yuqiong HE, Auteur ; Yamin LI, Auteur ; Jianjun OU, Auteur ; Runsen CHEN, Auteur . - p.380-389. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 67-3 (March 2026) . - p.380-389 Mots-clés : Non-suicidal self-injury  adolescents  short message service  intervention Index. décimale : PER Périodiques Résumé : Background Non-suicidal self-injury (NSSI) poses a significant mental health challenge among adolescents, necessitating accessible and effective interventions. While the development of technology offers new opportunities, higher costs remain a concern. In this context, digital psychological interventions such as text message intervention (SMS) present a convenient and low-cost delivery method that requires no face-to-face contact. However, the extent to which this method could function as a viable strategy remains underexplored. Objective To evaluate the effectiveness of an SMS intervention specifically developed for NSSI among adolescents when combined with treatment as usual (TAU), compared to TAU alone. Methods A randomized controlled trial (RCT) was conducted with 86 Chinese adolescents, randomly assigned to either the SMS intervention plus TAU or TAU alone. The SMS intervention, consisting of text messages addressing NSSI-related knowledge, distress tolerance skills, and emotion regulation strategies, was administered over 8?weeks. Assessments were conducted at baseline, 4?weeks, and 8?weeks. Results Participants in the intervention group showed a significant reduction in NSSI behavior at 4?weeks (RR?=?0.43, p? En ligne : https://doi.org/10.1111/jcpp.70054 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=580

Development and validation of a machine learning-based screening tool for early detection of adolescent suicide risk / Weijia LI in Journal of Child Psychology and Psychiatry, 67-9 (September 2026)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 67-9 (September 2026) . - p.1576-1589 Titre : Development and validation of a machine learning-based screening tool for early detection of adolescent suicide risk Type de document : texte imprimé Auteurs : Weijia LI, Auteur ; Yongtian CHENG, Auteur ; Zeming ZHANG, Auteur ; Yingying YANG, Auteur ; Yanpeng JIN, Auteur ; Zhenhua CUI, Auteur ; Juan WANG, Auteur ; Yinan DUAN, Auteur ; Runsen CHEN, Auteur Article en page(s) : p.1576-1589 Langues : Anglais (eng) Mots-clés : Adolescent  suicide risk screening  machine learning  school  longitudinal validation Index. décimale : PER Périodiques Résumé : Background Adolescent suicide remains a significant public health concern, yet existing suicide screening instruments primarily focus on already manifested suicidal phenomena, underscoring the need for reliable and practical tools to enable early identification and intervention. Methods Based on a large-scale school-based cohort study conducted in Southern China in 2022, this study aimed to develop and preliminarily validate two machine learning-based tools (a 51-item full version and an 11-item abbreviated version) designed to help identify adolescents at risk of developing suicide risk. The dataset was divided into two samples for tool development and longitudinal interview validation. During the tool development phase, LASSO regression was employed to select items with optimal contributions for recent suicide attempts from a multidimensional set of risk factors, followed by model training with multiple machine learning algorithms. The developed models were subsequently evaluated for their ability to predict suicide risk as assessed by the follow-up interview in the longitudinal validation phase. Results Both versions of the screening tool demonstrated adequate discriminative ability, with the CatBoost algorithm outperforming others (AUROC?≥?0.87). The abbreviated tool showed a slight trade-off between model precision and practicality, with a 0.02 reduction in AUROC, while still maintaining appropriate discrimination. Longitudinal validation using follow-up interview outcomes supported the predictive validity of both tools. These findings provide preliminary evidence for the utility of machine learning-based suicide risk screening tools among adolescents. Conclusions This study provides evidence supporting the machine learning-based screening tools for early suicide risk detection in adolescents that integrates multidimensional vulnerabilities. The tools show promise in facilitating early identification and targeted interventions in school settings, addressing a critical need in adolescent mental health care. Nonetheless, further research is warranted to confirm their efficacy and support broader implementation. En ligne : https://doi.org/10.1111/jcpp.70160 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=592 [article] Development and validation of a machine learning-based screening tool for early detection of adolescent suicide risk [texte imprimé] / Weijia LI, Auteur ; Yongtian CHENG, Auteur ; Zeming ZHANG, Auteur ; Yingying YANG, Auteur ; Yanpeng JIN, Auteur ; Zhenhua CUI, Auteur ; Juan WANG, Auteur ; Yinan DUAN, Auteur ; Runsen CHEN, Auteur . - p.1576-1589. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 67-9 (September 2026) . - p.1576-1589 Mots-clés : Adolescent  suicide risk screening  machine learning  school  longitudinal validation Index. décimale : PER Périodiques Résumé : Background Adolescent suicide remains a significant public health concern, yet existing suicide screening instruments primarily focus on already manifested suicidal phenomena, underscoring the need for reliable and practical tools to enable early identification and intervention. Methods Based on a large-scale school-based cohort study conducted in Southern China in 2022, this study aimed to develop and preliminarily validate two machine learning-based tools (a 51-item full version and an 11-item abbreviated version) designed to help identify adolescents at risk of developing suicide risk. The dataset was divided into two samples for tool development and longitudinal interview validation. During the tool development phase, LASSO regression was employed to select items with optimal contributions for recent suicide attempts from a multidimensional set of risk factors, followed by model training with multiple machine learning algorithms. The developed models were subsequently evaluated for their ability to predict suicide risk as assessed by the follow-up interview in the longitudinal validation phase. Results Both versions of the screening tool demonstrated adequate discriminative ability, with the CatBoost algorithm outperforming others (AUROC?≥?0.87). The abbreviated tool showed a slight trade-off between model precision and practicality, with a 0.02 reduction in AUROC, while still maintaining appropriate discrimination. Longitudinal validation using follow-up interview outcomes supported the predictive validity of both tools. These findings provide preliminary evidence for the utility of machine learning-based suicide risk screening tools among adolescents. Conclusions This study provides evidence supporting the machine learning-based screening tools for early suicide risk detection in adolescents that integrates multidimensional vulnerabilities. The tools show promise in facilitating early identification and targeted interventions in school settings, addressing a critical need in adolescent mental health care. Nonetheless, further research is warranted to confirm their efficacy and support broader implementation. En ligne : https://doi.org/10.1111/jcpp.70160 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=592

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