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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]

Background. A short yet reliable cognitive measure is needed that separates treatment and placebo for treatment trials for Alzheimer's disease. Hence, we aimed to shorten the Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS-Cog) and test its use as an efficacy measure. Methods. Secondary data analysis of participant-level data from five pivotal clinical trials of donepezil compared with placebo for Alzheimer's disease (N = 2,198). Across all five trials, cognition was appraised using the original 11-item ADAS-Cog. Statistical analysis consisted of sample characterization, item response theory (IRT) to identify an ADAS-Cog short version, and mixed models for repeated-measures analysis to examine the effect sizes of ADAS-Cog change on the original and short versions in the placebo versus donepezil groups. Results. Based on IRT, a short ADAS-Cog was developed with seven items and two response options. The original and short ADAS-Cog correlated at baseline and at weeks 12 and 24 at 0.7. Effect sizes based on mixed modeling showed that the short and original ADAS-Cog separated placebo and donepezil comparably (ADAS-Cog original ES = 0.33, 95% CI = 0.29, 0.40, ADASCog short ES = 0.25, 95% CI =0.23, 0.34). Conclusions. IRT identified a short ADAS-cog version that separated donepezil and placebo, suggesting its clinical potential for assessment and treatment monitoring. [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]

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. [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]

Administered Parental Bonding Instrument (PBI), Maudsley Personality Inventory, and General Health Questionnaire (GHQ) to 177 Japanese adolescents enrolled in foreign exchange student program before departure. Found that parental practices influenced personality features, which in turn contributed to mental health status. No direct significant relationship emerged between PBI and GHQ measures. (Author/NB)

Background: Patient characteristics may predict the progression of Alzheimer's disease (AD) and may moderate the effects of donepezil.Objective: To build a personalized prediction model for patients with AD and to estimate patient-specific treatment effects of donepezil, using individual patient characteristics.Methods: We systematically searched for all double-masked randomized controlled trials comparing oral donepezil and pill placebo in the treatment of AD and requested individual participant data through its developer, Eisai. The primary outcome was cognitive function at 24 weeks, measured with the Alzheimer's Disease Assessment Scale-cognitive component (ADAS-cog). We built a Bayesian meta-analytical prediction model for patients receiving placebo and we performed an individual patient data meta-analysis to estimate patient-level treatment effects.Results: Eight studies with 3,156 participants were included. The Bayesian prediction model suggested that more severe cognitive and global function at baseline and younger age were associated with worse cognitive function at 24 weeks. The individual participant data meta-analysis showed that, on average, donepezil was superior to placebo in cognitive function (ADAS-cog scores, -3.2; 95% Credible Interval (CrI) -4.2 to -2.1). In addition, our results suggested that antipsychotic drug use at baseline might be associated with a lower effect of donepezil in ADAS-cog (2.0; 95% CrI, -0.02 to 4.3).Conclusion: Although our results suggested that donepezil is somewhat efficacious for cognitive function for most patients with AD, use of antipsychotic drugs may be associated with lower efficacy of the drug. Future research with larger sample sizes, more patient covariates, and longer treatment duration is needed. [ABSTRACT FROM AUTHOR]