The Impact of Social Media Use on Sleep and Mental Health in Youth: a Scoping Review.

PURPOSE OF REVIEW: Social media use (SMU) and other internet-based technologies are ubiquitous in today's interconnected society, with young people being among the commonest users. Previous literature tends to support that SMU is associated with poor sleep and mental health issues in youth, despite some conflicting findings. In this scoping review, we summarized relevant studies published within the past 3 years, highlighted the impacts of SMU on sleep and mental health in youth, while also examined the possible underlying mechanisms involved. Future direction and intervention on rational use of SMU was discussed. RECENT FINDINGS: Both cross-sectional and longitudinal cohort studies demonstrated the negative impacts of SMU on sleep and mental health, with preliminary evidence indicating potential benefits especially during the COVID period at which social restriction was common. However, the limited longitudinal research has hindered the establishment of directionality and causality in the association among SMU, sleep, and mental health. Recent studies have made advances with a more comprehensive understanding of the impact of SMU on sleep and mental health in youth, which is of public health importance and will contribute to improving sleep and mental health outcomes while promoting rational and beneficial SMU. Future research should include the implementation of cohort studies with representative samples to investigate the directionality and causality of the complex relationships among SMU, sleep, and mental health; the use of validated questionnaires and objective measurements; and the design of randomized controlled interventional trials to reduce overall and problematic SMU that will ultimately enhance sleep and mental health outcomes in youth.

Hypersomnolence is associated with non-remission of major depressive disorder.

OBJECTIVE: This study aimed to investigate the prevalence, clinical correlates and the relationship between hypersomnolence and clinical outcomes in a cohort of MDD patients. METHODS: This is a cross-sectional study of a MDD cohort in an university-affiliated adult psychiatric outpatient clinic. The diagnosis of MDD and severity of depression were ascertained by the clinician with structured clinical interviews. Each participant completed the Epworth Sleepiness Scale (ESS), 1-week sleep diary, and a battery of questionnaires that assessed usual sleep pattern, insomnia, anxiety, depression, fatigue and circadian preference. Hypersomnolence was defined as ESS score ≥14 among those reported ≥7 h of nighttime sleep. Univariate analysis and multiple logistic regression were used to analyze the relationships between the variables. RESULTS: Among 252 recruited subjects, 11 % met the criteria of hypersomnolence as defined by a ESS score ≥14 despite ≥7 h of nighttime sleep. Patients with hypersomnolence had greater depression ratings, higher rates of suicidal ideations over the past week, and more likely to meet a diagnosis of atypical depression (p < 0.05) than those without hypersomnolence. Step-wise logistic regression demonstrated that hypersomnolence was an independent risk factor associated with a 3-fold increase in the risk of depression non-remission (adjusted OR 3.13; 95 % CI 1.10-8.95; p = 0.034). CONCLUSION: Patients with hypersomnolence despite seemingly adequate sleep represent a subgroup of MDD patients who have a more severe illness profile with higher non-remission rate and suicidality. The findings highlight the importance of addressing both sleep and mood symptoms in the management of MDD.

Exploring the Role of First-Person Singular Pronouns in Detecting Suicidal Ideation: A Machine Learning Analysis of Clinical Transcripts.

Linguistic features, particularly the use of first-person singular pronouns (FPSPs), have been identified as potential indicators of suicidal ideation. Machine learning (ML) and natural language processing (NLP) have shown potential in suicide detection, but their clinical applicability remains underexplored. This study aimed to identify linguistic features associated with suicidal ideation and develop ML models for detection. NLP techniques were applied to clinical interview transcripts (n = 319) to extract relevant features, including four cases of FPSP (subjective, objective, dative, and possessive cases) and first-person plural pronouns (FPPPs). Logistic regression analyses were conducted for each linguistic feature, controlling for age, gender, and depression. Gradient boosting, support vector machine, random forest, decision tree, and logistic regression were trained and evaluated. Results indicated that all four cases of FPSPs were associated with depression (p < 0.05) but only the use of objective FPSPs was significantly associated with suicidal ideation (p = 0.02). Logistic regression and support vector machine models successfully detected suicidal ideation, achieving an area under the curve (AUC) of 0.57 (p < 0.05). In conclusion, FPSPs identified during clinical interviews might be a promising indicator of suicidal ideation in Chinese patients. ML algorithms might have the potential to aid clinicians in improving the detection of suicidal ideation in clinical settings.

Multimodal digital assessment of depression with actigraphy and app in Hong Kong Chinese.

There is an emerging potential for digital assessment of depression. In this study, Chinese patients with major depressive disorder (MDD) and controls underwent a week of multimodal measurement including actigraphy and app-based measures (D-MOMO) to record rest-activity, facial expression, voice, and mood states. Seven machine-learning models (Random Forest [RF], Logistic regression [LR], Support vector machine [SVM], K-Nearest Neighbors [KNN], Decision tree [DT], Naive Bayes [NB], and Artificial Neural Networks [ANN]) with leave-one-out cross-validation were applied to detect lifetime diagnosis of MDD and non-remission status. Eighty MDD subjects and 76 age- and sex-matched controls completed the actigraphy, while 61 MDD subjects and 47 controls completed the app-based assessment. MDD subjects had lower mobile time (P = 0.006), later sleep midpoint (P = 0.047) and Acrophase (P = 0.024) than controls. For app measurement, MDD subjects had more frequent brow lowering (P = 0.023), less lip corner pulling (P = 0.007), higher pause variability (P = 0.046), more frequent self-reference (P = 0.024) and negative emotion words (P = 0.002), lower articulation rate (P < 0.001) and happiness level (P < 0.001) than controls. With the fusion of all digital modalities, the predictive performance (F1-score) of ANN for a lifetime diagnosis of MDD was 0.81 and 0.70 for non-remission status when combined with the HADS-D item score, respectively. Multimodal digital measurement is a feasible diagnostic tool for depression in Chinese. A combination of multimodal measurement and machine-learning approach has enhanced the performance of digital markers in phenotyping and diagnosis of MDD.

Efficacy and safety of transcranial pulse stimulation in young adolescents with attention-deficit/hyperactivity disorder: a pilot, randomized, double-blind, sham-controlled trial.

Background: This is the first study to evaluate the efficacy and safety of transcranial pulse stimulation (TPS) for the treatment of attention-deficit/hyperactivity disorder (ADHD) among young adolescents in Hong Kong. Methods: This double-blind, randomized, sham-controlled trial included a TPS group and a sham TPS group, encompassing a total of 30 subjects aged 12-17 years who were diagnosed with ADHD. Baseline measurements SNAP-IV, ADHD RS-IV, CGI and executive functions (Stroop tests, Digit Span) and post-TPS evaluation were collected. Both groups were assessed at baseline, immediately after intervention, and at 1-month and 3-month follow-ups. Repeated-measures ANOVAs were used to analyze data. Results: The TPS group exhibited a 30% reduction in the mean SNAP-IV score at postintervention that was maintained at 1- and 3-month follow-ups. Conclusion: TPS is an effective and safe adjunct treatment for the clinical management of ADHD. Clinical trial registration: ClinicalTrials.Gov, identifier NCT05422274.

Withdrawal behaviors and mental health among college students / Comportamientos de aislamiento y salud mental en estudiantes universitarios

Youth social withdrawal has raised clinical concerns, and prevention of withdrawal behavior is important yet difficult. While human evaluation of withdrawal behavior can be subjective, technology provides objective measurement for withdrawal behavior. This study aims to examine the association between withdrawal behaviors (home-stay and non-communication) and mental health status (stress, depression and loneliness). The open-access StudentLife dataset, including the location and conversation information derived from the sensor data, stress levels, and pre- and post-questionnaires of depression (PHQ-9) and loneliness (RULS) of 47 college students over 10 weeks was used. Multilevel modeling and functional regression were employed for data analysis. Daily duration of home-stay was negatively associated with daily stress levels, and the interaction effect of daily duration of home-stay and non-communication were positively associated with daily stress levels and changes in PHQ-9 and RULS scores. Smartphone data is useful to provide adjunct information to the professional clinical judgement and early detection on withdrawal behavior

El aislamiento social de los jóvenes ha generado preocupaciones clínicas y prevenir estos comportamientos es importante pero difícil. Aunque la evaluación del aislamiento puede ser subjetiva, la tecnología proporciona medidas objetivas de este comportamiento. El objetivo de este estudio es examinar la asociación entre los comportamientos de aislamiento (permanecer en casa y no comunicarse) y el estado de la salud mental (estrés, depresión y soledad). Se utilizó la base de datos de libre acceso StudentLife, incluyendo información sobre la ubicación y la conversación registrada por un sensor de datos, los niveles de estrés y medidas de autoinforme pre y pos sobre depresión (PHQ-9) y soledad (RULS) de 47 estudiantes universitarios durante 10 semanas. Para el análisis de datos se utilizaron modelos multinivel y la regresión funcional. La duración diaria de la permanencia en casa estaba negativamente asociada con los niveles diarios de estrés y el efecto de interacción de la duración diaria de la permanencia en casa y la falta de comunicación estaban positivamente relacionados con los niveles diarios de estrés y los cambios en las puntuaciones en PHQ-9 y RULS. Los datos del teléfono inteligente son útiles para obtener información complementaria al juicio clínico profesional y para la detección temprana de los comportamientos de aislamiento