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Latent profile analysis of sleep quality and associated factors in Chinese adolescents with major depressive disorder

BackgroundSleep disturbances are pervasive among adolescent patients with major depressive disorder (MDD), yet the heterogeneity of sleep phenotypes within this population remains underexplored. This study aimed to identify latent profiles of sleep quality in Chinese adolescent patients with MDD and to examine their associations with sociodemographic, clinical, and psychosocial factors.MethodsA total of 1,467 adolescent patients with MDD were assessed using the Pittsburgh Sleep Quality Index (PSQI), Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), the Borderline Personality Features Scale for Children (BPFS-C), and a self-designed sociodemographic and clinical inventory. Latent profile analysis (LPA) was conducted using the seven PSQI component scores as profile indicators. Multinomial logistic regression was subsequently performed to determine factors associated with profile membership.ResultsA five-profile solution was identified: Normal Sleep Group (13%), Mild Disturbance-Low Hypnotic Use (36%), Mild Disturbance-High Hypnotic Use (22%), Severe Disturbance-Low Hypnotic Use (16%), and Severe Disturbance-High Hypnotic Use (13%). Logistic regression revealed that the presence of non-suicidal self-injury (NSSI) and exposure to negative life events significantly increased the odds of belonging to more severe sleep disturbance profiles (OR = 2.92-8.37 and 2.33-2.60, respectively; all p < 0.05). Notably, patients without left-behind experiences exhibited higher odds of being classified into the Severe Disturbance-High Hypnotic Use profile compared to those with such histories (OR = 0.46, p < 0.001). Furthermore, profiles characterized by poorer sleep quality and profiles characterized by more frequent self-reported sleep medication use were cross-sectionally associated with greater anxiety and borderline features in this study.ConclusionSleep quality among adolescent patients with MDD exhibits distinct typological heterogeneity, characterized primarily by severity and hypnotic use patterns. These findings provide preliminary exploratory evidence for the development of personalized, profile-informed clinical interventions for this vulnerable population.
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Compassion-Focused Implementation of Smartwatches in Acute Mental Health Care: Mixed Method Replicated Single-Case Study

Background: Smartwatches can support mental health care by providing insights into activity and sleep, which are closely linked to mental health and daily functioning. However, their implementation in mental health care practice remains limited. Professionals can feel resistance toward digital mental health interventions if they feel their use is not aligned with therapeutic values, and report a need for guidance on how to use technologies in ways that do align with such values. Compassion, a core value in mental health care, may offer a meaningful frame for implementation. Therefore, we previously co-designed compassion-focused implementation materials: a card set offering practical suggestions of how smartwatch data can support group treatments in ways that counter the self-optimization logic of commercial devices and instead align with compassion. Objective: The current study evaluated the compassion-focused card set in practice, to explore whether the introduction of the card set influenced the use of smartwatches in treatment sessions, and mental health professionals’ experienced compassion when working with smartwatches, the therapeutic alliance, and acceptance of smartwatches. Methods: The card set was evaluated in a mixed methods replicated single-case design with 5 social psychiatric nurses from an acute mental health care team. Data collection included pre- and post-questionnaires, repeated measures, a focus group, and interviews. Results: For smartwatch use, no significant intervention effects were found, though several participants did report the start or increase of use of the smartwatch in sessions during the intervention phase. For compassion, only P2 showed a significant effect of the intervention on experienced compassion when using smartwatches (Tau-U=0.53, 95% CI −0.11 to 0.86=.02). This participant reported that the card set helped to make the use of smartwatches more concrete and insightful. For therapeutic alliance and acceptance of the smartwatches, no intervention effects were found. These scores were rather stable over time for most participants. The card set was experienced as flexible and easy to use, supporting session structure and enabling more in-depth, compassionate conversations. At the same time, barriers to sustained smartwatch integration included low patient uptake, challenges in mixed groups in which only some patients wore the smartwatch, and varying digital affinity among professionals. Conclusions: These findings suggest that compassion-focused materials may trigger initial adoption and help reframe smartwatch use in line with therapeutic values. Broader implementation strategies, including further training and tailoring to patient readiness, are required for sustainable integration.
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The Effect of Bipolar Disorder Illness Course on Physiological Digital Signals: Observational Study

Background: Although bipolar disorder (BD) lacks objective biomarkers of illness progression, wearable devices enable continuous, real-world collection of physiological digital biosignals, offering a promising approach to address this gap. Measures such as electrodermal activity (EDA) and heart rate variability (HRV) fluctuate with mood state and symptom severity. Previous studies have reported reduced EDA during depressive episodes and increased HRV following symptom resolution after acute mood episodes. Furthermore, greater illness burden in BD has been associated with lower HRV, suggesting that digital biosignals may capture not only state-related changes but also aspects of illness course. Objective: This study investigated whether naturalistic EDA and heart rate (HR) profiles differ according to illness course variables in BD—including diagnosis, predominant polarity, years since onset, and lifetime number of episodes—in euthymic patients, to evaluate their potential as illness course biomarkers. Methods: This cross-sectional post hoc analysis used data from 80 euthymic patients on the BD spectrum from the INTREPIBD-TIMEBASE cohort. Participants were recruited at Hospital Clínic de Barcelona (Spain) between 2021 and 2024 and wore an Empatica E4 wristband for 48 hours in naturalistic settings. Inclusion criteria comprised a current or past (, Fifth Edition) diagnosis of BD confirmed by structured interview and sustained euthymia. We examined the effects of 4 illness course variables (diagnosis, predominant polarity, years since onset, and number of lifetime episodes) on 3 biosignals (mean EDA, EDA peaks per minute, and HR) using generalized additive models. Interactions between illness course variables and circadian patterns were also assessed. All models were adjusted for age, sex, and current antipsychotic use and included a participant-specific random effect. Results: Of the 80 participants, 49 (61.3%) were female; participants’ mean age was 46.9 (SD 14.1) years. After adjustment for covariates and multiple comparisons, no significant associations were observed between illness course variables and EDA or HR. In sensitivity analyses excluding patients with schizoaffective disorder (n=75), years since illness onset and predominant polarity were associated with EDA peaks per minute after multiple comparison correction. Specifically, patients with more than 22 years since illness onset had fewer EDA peaks compared with other groups (β=−1.06, 95% CI −1.71 to −0.41; =.001). No significant interactions were found between illness course variables and circadian patterns of EDA or HR. Conclusions: To our knowledge, this is the first study to examine the association between illness course variables and EDA and HR recorded in naturalistic settings in BD. In contrast to prior findings linking illness burden with HRV, our results suggest that years since illness onset may show modest associations with phasic EDA activity. These findings indicate that digital biosignals may be more sensitive to state-related changes than to illness course characteristics, limiting their use as longitudinal biomarkers of disease progression.
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Mental health outcomes following COVID-19- associated caregiver loss in children and adolescents: a six-year evidence gap

By May 2022, an estimated 10.5 million children worldwide had lost a parent or caregiver to COVID-19. We found only two cohorts with quantitative mental-health data: one in Iran and the South African Orphanhood and Caregiver Loss Among Adolescents and Youth study. Both reported more psychiatric and psychosocial difficulties among bereaved children than controls. In Iran, internalizing symptoms, post-traumatic stress, and suicidal ideation increased during follow-up, and no participant received mental-health care. In South Africa, likely mental disorder declined in both groups but remained more common among bereaved participants; suicidal ideation or attempts rose slightly in the bereaved group while falling among controls. Social support, food insecurity, and domestic violence were associated with outcomes. These children were bereaved during school closures, social isolation, disrupted mourning, and financial strain, when usual sources of support were often reduced. Yet few were identified and followed over time. Research funding priorities and fragmented health, education, mortality, and child-protection systems may have contributed to this gap. Many children bereaved in the first pandemic waves are now adolescents or young adults, but their longer-term mental-health course remains largely unknown. They should be identified, followed, and offered support according to clinical and family need.
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Note2Narr: a neuro-symbolic multi-agent system for synthesizing high-fidelity training narratives from sparse counselor notes

IntroductionEarly identification of mental health risks among university students requires high-quality, privacy-conscious dialogue data for developing and evaluating computational screening models. Existing synthetic datasets often lack the contextual nuance and diagnostic consistency needed to preserve clinically relevant psychological signals.MethodsWe introduce Note2Narr, a neuro-symbolic framework for synthesizing high-fidelity training narratives from sparse counselor notes. A Dynamic Counseling Tree guides inquiry from broad topic exploration to in-depth probing of personal experiences. A Supervisor Agent equipped with a Chain of Psychological Clues audits the safety and consistency of generated narratives, while a Fictional Detail Generator incorporates diverse, disorder-neutral personal experiences. A Vendi Score-guided 1-to-X strategy adaptively allocates the number of narrative variants. Evaluation combines blinded human assessment, LLM-based assessment, student-disjoint downstream prediction, and a label-leakage audit with masking ablation.ResultsThe framework produced 981 multi-turn counseling dialogues comprising 11,145 question-answer turns from 224 source students. Blinded human comparisons with MDD-5K identified statistically significant improvements in Professionalism, Communication, Participation, Repetition, and Authenticity after Holm correction. Downstream text classifiers achieved macro-AUROC and macro-AUPRC values of up to 0.739 and 0.405, respectively. Masking explicit diagnostic cues changed macro-AUPRC by at most 0.005, suggesting that predictive utility was not driven solely by direct diagnostic wording.DiscussionThese findings support Note2Narr as a framework for constructing synthetic counseling corpora for privacy-conscious dialogue modeling and screening-oriented research. The results demonstrate learnable associations in synthetic narratives; prospective and external validation remains necessary before applied use.
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Factors associated with medication experience in patients with schizophrenia: a mixed-methods study across three post-discharge time points

BackgroundSchizophrenia requires sustained antipsychotic therapy, yet medication adherence remains suboptimal in over 50% of patients due to suboptimal medication experiences, often leading to self-directed dose reduction or discontinuation. This non-adherence contributes to disease relapse, functional impairment, and substantial familial economic burdens, while recurrent hospitalizations strain healthcare systems. We investigated correlates of medication experiences in patients with schizophrenia using a mixed-methods design with repeated assessments across three post-discharge time points.MethodsOur cohort comprised 425 clinically stable patients with schizophrenia from two tertiary psychiatric hospitals in Heilongjiang Province. Using a mixed-methods design with repeated assessments at three post-discharge time points, we collected concurrent quantitative and qualitative data at three timepoints: pre-discharge (T1), 1-month (T2), and 3-months post-discharge (T3). Quantitative measures included standardized assessments of medication experience (SWN), burden (LMQ), beliefs (BMQ), symptomatology (PANSS), social support (SSRS), and family functioning (APGAR). Machine learning models (logistic regression, random forest, support vector machine) identified key factors associated with medication experience. Qualitative data from 15 patients with suboptimal experiences were analyzed thematically, with mixed-methods integration.ResultsRandom Forest models consistently demonstrated superior predictive performance, identifying the top predictors for each time-point: at T1 – medication beliefs, medication burden and psychotic symptoms (in descending importance); at T2 – medication burden, beliefs, family functioning, and symptoms; at T3 – medication beliefs, social support, burden, symptoms, and family functioning. Qualitative analysis revealed four core themes at T1 and three at T2/T3, showing complementary quantitative-qualitative relationships in benefit-risk perceptions and treatment valuation.ConclusionsThese findings suggest psychiatric nurses may consider interventions that address modifiable factors associated with medication experience: mitigating medication burdens while enhancing perceived treatment benefits during hospitalization (T1); strengthening medication beliefs and family support early post-discharge (T2); and bolstering social support networks during community reintegration (T3). These findings help identify potential intervention targets for future practice to optimize medication experience, and may have implications for medication-related clinical outcomes in patients with schizophrenia.