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Background: We aimed to evaluate the comparative efficacy and acceptability of cognitive behavioral therapy for insomnia (CBT‐I), pharmacotherapy, and their combination in the long and short terms among adults with chronic insomnia disorder. Methods: We searched multiple databases to December 27, 2023. We included trials in hypnotic‐free adults with chronic insomnia comparing at least two of CBT‐I, pharmacotherapy, or their combination. We assessed the confidence in evidence using CINeMA. The primary outcome was long‐term remission. Secondary outcomes included all‐cause dropout and self‐reported sleep continuity measures in the long term, and the same outcomes in the short term. We performed frequentist random‐effects network meta‐analyses (CRD42024505519). Findings: We identified 13 trials including 823 randomized participants (mean age, 47.8 years; 60% women). CBT‐I was more beneficial than pharmacotherapy in the long term (median duration, 24 weeks [range, 12 to 48 weeks]; remission odds ratio, 1.82 [95% confidence interval (CI), 1.15–2.87]; [certainty of evidence: high]), while there was weaker evidence of benefit of combination against pharmacotherapy (1.71 [95% CI, 0.88–3.30: moderate]) and no clear difference of CBT‐I against combination (1.07 [95% CI, 0.63–1.80: moderate]). CBT‐I was associated with fewer dropouts than pharmacotherapy. Short‐term outcomes favored CBT‐I over pharmacotherapy except total sleep time. Given the average long‐term remission rate in the pharmacotherapy‐initiating arms of 28%, CBT‐I resulted in a long‐term remission rate of 41% (95% CI, 31%–53%) and combination 40% (95% CI, 25%–56%). Interpretation: The current study found that starting with CBT‐I for chronic insomnia leads to better outcomes than pharmacotherapy. Combination may be better than pharmacotherapy alone, but unlikely to be worth the additional burden over CBT‐I alone. [ABSTRACT FROM AUTHOR]

Aim: Patients with cancer experience various forms of psychological distress, including depressive symptoms, which can impact quality of life, elevate morbidity risk, and increase medical costs. Psychotherapy and pharmacotherapy are effective for reducing depressive symptoms among patients with cancer, but most patients prefer psychotherapy. This study aimed to develop an efficient and effective smartphone psychotherapy component to address depressive symptom. Methods: This was a decentralized, parallel‐group, multicenter, open, individually randomized, fully factorial trial. Patients aged ≥20 years with cancer were randomized by the presence/absence of three cognitive‐behavioral therapy (CBT) skills (behavioral activation [BA], assertiveness training [AT], and problem‐solving [PS]) on a smartphone app. All participants received psychoeducation (PE). The primary outcome was change in the patient health questionnaire‐9 (PHQ‐9) total score between baseline and week 8. Secondary outcomes included anxiety. Results: In total, 359 participants were randomized. Primary outcome data at week 8 were obtained for 355 participants (99%). The week 8 PHQ‐9 total score was significantly reduced from baseline for all participants by −1.41 points (95% confidence interval [CI] −1.89, −0.92), but between‐group differences in change scores were not significant (BA: −0.04, 95% CI −0.75, 0.67; AT: −0.16, 95% CI −0.87, 0.55; PS: −0.19, 95% CI −0.90, 0.52). Conclusion: As the presence of any of the three intervention components did not contribute to a significant additive reduction of depressive symptoms, we cannot make evidence‐based recommendations regarding the use of specific smartphone psychotherapy. [ABSTRACT FROM AUTHOR]

Background: Little information is available on the association between gender nonconformity during adolescence and subsequent mental health. While the distress related to gender nonconformity may be socially produced rather than attributed to individual-level factors, further research is needed to better understand the role of psychosocial factors in this context. Method: We analyzed data from the Tokyo Teen Cohort, obtained through random sampling of adolescents born between 2002 and 2004. We used inverse probability weighting to examine the association of gender nonconformity at ages 12 and 14 as a time-varying variable with subsequent mental health at age 16, while accounting for time-fixed and time-varying confounders. Furthermore, we used a weighting approach to investigate the mediating role of modifiable psychosocial factors in this association, addressing exposure-mediator and mediator–mediator interactions. Results: A total of 3171 participants were analyzed. Persistent gender nonconforming behavior at ages 12 and 14 was associated with subsequent depression (β = 2.02, 95% confidence interval [CI] 0.85 to 3.19) and psychotic experiences (β = 0.33, 95% CI 0.14 to 0.52) at age 16. The results remained robust in sensitivity analyses. Approximately 30% of the association between gender nonconformity and depression was consistently mediated by a set of psychosocial factors, namely loneliness, bullying victimization, and relationships with mother, father, and friends. Conclusions: Persistent gender nonconformity during adolescence is associated with subsequent mental health. Psychosocial factors play a vital mediating role in this association, highlighting the essential need for social intervention and change to reduce stigmatization and ameliorate mental health challenges. [ABSTRACT FROM AUTHOR]

Little is known about antipsychotic prescription patterns among children and adolescents in Japan, particularly in outpatient settings. We investigated the prevalence and trends of antipsychotic prescription for outpatients aged ≤ 17 years receiving a first antipsychotic prescription from 2006 to 2012 based on a large-scale dispensation dataset. Measurements included age, sex, department of diagnosis and treatment, type of prescription (monotherapy or polytherapy), antipsychotic dosage, and concomitant psychotropic drugs. Of the 10,511 patients, 65.1% were aged 13–17 years, and 52.9% were males. Second-generation antipsychotic monotherapy prescriptions increased from 53.8% in 2006 to 78.3% in 2012. Risperidone was the most frequently prescribed antipsychotic, followed by aripiprazole and olanzapine. Approximately 25.0% of patients were prescribed an initial dose less than recommended. Second-generation antipsychotic monotherapy is currently the most frequent prescription pattern among outpatients aged ≤ 17 years receiving an initial antipsychotic prescription. [ABSTRACT FROM AUTHOR]

Aims/Introduction: Non‐attendance from regular medical care is a major problem in diabetes patients. This study aimed to examine the impact of a multifaceted lifestyle intervention by face‐to‐face approach (FFA) on non‐attendance from regular medical care in comparison with that by telephone from the technical support center (TSC). Materials and Methods: This was secondary analysis from a 1‐year, prospective, cluster randomized, intervention study. Patients with type 2 diabetes, who were regularly visiting primary care physicians cluster‐randomized into the control or intervention (TSC or FFA according to resource availability of the district medical associations) groups, were consecutively recruited. The primary end‐point was non‐attendance from regular medical care. The interaction between the type of intervention (TSC vs FFA) and behavioral change stage (pre‐ vs post‐action stage) in diet and exercise for the dropout rate was assessed. Results: Among the 1,915 participants (mean age 56 ± 6 years; 36% women) enrolled, 828, 564 and 264 patients belonged to the control, TSC and FFA groups, respectively. We found evidence suggestive of an interaction between the intervention type and behavioral change stage in diet (P = 0.042) and exercise (P = 0.038) after adjusting for covariates. The hazard ratios (95% confidence interval) of FFA to TSC were 0.21 (0.05–0.93) and 7.69 (0.50–117.78) in the pre‐action and post‐action stages for diet, respectively, whereas they were 0.20 (0.05–0.92) and 4.75 (0.29–73.70) in the pre‐action and post‐action stages for exercise. Conclusions: Among diabetes patients, the impact of multifaceted intervention on non‐attendance from medical care might differ by the behavioral change stage. [ABSTRACT FROM AUTHOR]

This study examined the effectiveness of a novel information and communication technology (ICT) tool developed for external memory compensation to improve memory function in participants with brain injuries. In this 3-month randomized control study, participants with memory impairment secondary to brain injury were randomly assigned on a 1:1 basis to either intervention (the ICT tool [ARATA]) or 3-month waitlist control groups. This study's primary outcome measure was memory-related difficulties in everyday life, assessed using the Everyday Memory Checklist (EMC). Secondary outcomes included tests for memory function and psychosocial status, all of which were administered by blinded assessors. Seventy-eight participants (53 males, 25 females; mean age, 43.5 ± 12.7 [SD] years) were enrolled and 39 participants were allocated to each group (intervention and control). There was no significant difference in EMC scores between the two groups throughout the study (mean 0.26; 95% CI: −2.55–3.07; p=0.853); however, the intervention group scored significantly higher on the Rivermead Behavioural Memory and General Self-Efficacy tests compared to the control group. While the ICT tool did not improve the primary study outcome, evidence suggests that the ICT tool can improve memory functions related to activities of daily living. [ABSTRACT FROM AUTHOR]

Background and Hypothesis: Problematic internet use (PIU) is prevalent among adolescents. Past research suggested cross-sectional associations between PIU and psychotic experiences, but little information is available on the longitudinal association. We hypothesized that PIU in adolescence may be longitudinally associated with psychotic experiences, adjusting for confounders., Study Design: We analyzed a random sample of adolescents in the Tokyo Teen Cohort to examine how PIU at ages 10 (2012-2015), 12 (2014-2017), and 16 (2019-2021) was associated with mental health issues at age 16. PIU was evaluated by the modified Compulsive Internet Use Scale, psychotic experiences by the Adolescent Psychotic-like Symptom Screener, and depression by the Short Mood and Feelings Questionnaire. We also examined the mediating role of social withdrawal., Study Results: We analyzed 3171 adolescents; 151 reported psychotic experiences and 327 reported depression at age 16. Compared with the lowest tertile PIU group, the highest tertile PIU group at age 12 showed an increased adjusted risk of psychotic experiences (RD 3.3%, 95% CI 2.9%-3.7%; RR 1.65, 95% CI 1.55-1.73) and depression (RD 5.9%, 95% CI 5.5%-6.3%; RR 1.61, 95% CI 1.55-1.68) at age 16. PIU at age 16 showed analogous results, while PIU at age 10 suggested a smaller impact. Social withdrawal mediated 9.4%-29.0% of the association between PIU and psychotic experiences., Conclusions: PIU is longitudinally associated with psychotic experiences and depression in adolescents. Further longitudinal and intervention studies are warranted to provide robust public health implications and foster a safer digital future., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA)., Methods: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample., Results: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors., Conclusions: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration for benefit. We then amalgamate these two dimensions into an additional concept, decision accuracy, which quantifies the model's ability to identify patients for whom the benefit from treatment exceeds a given threshold. Subsequently, we propose a series of performance measures related to these dimensions and discuss estimating procedures, focusing on randomized data. Our methods are applicable for continuous or binary outcomes, for any type of prediction model, as long as it uses baseline covariates to predict outcomes under treatment and control. We illustrate all methods using two simulated datasets and a real dataset from a trial in depression. We implement all methods in the R package predieval. Results suggest that the proposed measures can be useful in evaluating and comparing the performance of competing models in predicting individualized treatment effect., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)