Association of Problematic Internet Use With Psychotic Experiences and Depression in Adolescents: A Cohort Study.

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.

Optimizing smartphone psychotherapy for depressive symptoms in patients with cancer: Multiphase optimization strategy using a decentralized multicenter randomized clinical trial (J-SUPPORT 2001 Study).

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.

Shortening the Alzheimer's disease assessment scale cognitive subscale.

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, ADAS-Cog 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.

Uncovering associations between gender nonconformity, psychosocial factors, and mental health in adolescents: a birth cohort study.

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 (ß = 2.02, 95% confidence interval [CI] 0.85 to 3.19) and psychotic experiences (ß = 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.

Development of a model to predict antidepressant treatment response for depression among Veterans.

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.

Psychosocial interventions for the prevention of self-harm repetition: protocol for a systematic review and network meta-analysis.

INTRODUCTION: Suicide is an important public health problem. Providing evidence-based psychosocial interventions to individuals presenting with self-harm is recognised as an important suicide prevention strategy. Therefore, it is crucial to understand which intervention is most effective in preventing self-harm repetition. We will evaluate the comparative efficacy of psychosocial interventions for the prevention of self-harm in adults. METHODS AND ANALYSIS: We will perform a systematic review and network meta-analysis (NMA) of randomised controlled trials (RCTs) testing psychosocial interventions for the prevention of self-harm repetition. We will include RCTs in adults (mean age: 18 years or more) who presented with self-harm in the 6 months preceding enrolment in the trial. Interventions will be categorised according to their similarities and underpinning theoretical approaches (eg, cognitive behavioural therapy, case management). A health sciences librarian will update and adapt the search strategy from the most recent Cochrane pairwise systematic review on this topic. The searches will be performed in MEDLINE (Ovid), Embase (Ovid), PsycInfo (Ovid), CINAHL (EBSCO), Cochrane Central (Wiley), Cochrane Protocols (Wiley), LILACS and PSYNDEX from 1 July 2020 (Cochrane review last search date) to 1 September 2023. The primary efficacy outcome will be self-harm repetition. Secondary outcomes will include suicide mortality, suicidal ideation and depressive symptoms. Retention in treatment (ie, drop-outs rates) will be analysed as the main acceptability outcome. Two reviewers will independently assess the study eligibility and risk of bias (using RoB-2). An NMA will be performed to synthesise all direct and indirect comparisons. Ranked forest plots and Vitruvian plots will be used to represent graphically the results of the NMA. Credibility of network estimates will be evaluated using Confidence in NMA (CINeMA). ETHICS AND DISSEMINATION: As this is the protocol for an aggregate-data level NMA, ethical approval will not be required. Results will be disseminated at national/international conferences and in peer-review journals. TRIAL REGISTRATION NUMBER: CRD42021273057.

Imiquimod for Cervical and Vaginal Intraepithelial Neoplasia: A Systematic Review and Meta-analysis.

OBJECTIVE: To evaluate the treatment efficacy and the risk of adverse events of imiquimod for cervical intraepithelial neoplasia (CIN) and vaginal intraepithelial neoplasia (VAIN), compared with placebo or no intervention. DATA SOURCES: We searched Cochrane, PubMed, ISRCTN registry, ClinicalTrials.gov , and the World Health Organization International Clinical Trials Registry Platform up to November 23, 2022. METHODS OF STUDY SELECTION: We included randomized controlled trials and prospective nonrandomized studies with control arms that investigated the efficacy of imiquimod for histologically confirmed CIN or VAIN. The primary outcomes were histologic regression of the disease (primary efficacy outcome) and treatment discontinuation due to side effects (primary safety outcome). We estimated pooled odds ratios (ORs) of imiquimod, compared with placebo or no intervention. We also conducted a meta-analysis of the proportions of patients with adverse events in the imiquimod arms. TABULATION, INTEGRATION, AND RESULTS: Four studies contributed to the pooled OR for the primary efficacy outcome. An additional four studies were available for meta-analyses of proportions in the imiquimod arm. Imiquimod was associated with increased probability of regression (pooled OR 4.05, 95% CI 2.08-7.89). Pooled OR for CIN in the three studies was 4.27 (95% CI 2.11-8.66); results of one study were available for VAIN (OR, 2.67, 95% CI 0.36-19.71). Pooled probability for primary safety outcome in the imiquimod arm was 0.07 (95% CI 0.03-0.14). The pooled probabilities (95% CI) of secondary outcomes were 0.51 (0.20-0.81) for fever, 0.53 (0.31-0.73) for arthralgia or myalgia, 0.31 (0.18-0.47) for abdominal pain, 0.28 (0.09-0.61) for abnormal vaginal discharge or genital bleeding, 0.48 (0.16-0.82) for vulvovaginal pain, and 0.02 (0.01-0.06) for vaginal ulceration. CONCLUSION: Imiquimod was found to be effective for CIN, whereas data on VAIN were limited. Although local and systemic complications are common, treatment discontinuation is infrequent. Thus, imiquimod is potentially an alternative therapy to surgery for CIN. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, CRD42022377982.

Optimization of smartphone psychotherapy for depression and anxiety among patients with cancer using the multiphase optimization strategy (MOST) framework and decentralized clinical trial system (SMartphone Intervention to LEssen depression/Anxiety and GAIN resilience: SMILE AGAIN project): a protocol for a randomized controlled trial.

BACKGROUND: Cancer patients experience various forms of psychological distress. Their distress, mainly in the form of depression and anxiety, leads to poor quality of life, increased medical spending due to frequent visits, and decrease in treatment adherence. It is estimated that 30-50% among them would require support from mental health professionals: in reality, much less actually receive such support partly due to a shortage of qualified professionals and also due to psychological barriers in seeking such help. The purpose of the present study is to develop the easily accessible and the most efficient and effective smartphone psychotherapy package to alleviate depression and anxiety in cancer patients. METHODS: Based on the multiphase optimization strategy (MOST) framework, the SMartphone Intervention to LEssen depression/Anxiety and GAIN resilience project (SMILE-AGAIN project) is a parallel-group, multicenter, open, stratified block randomized, fully factorial trial with four experimental components: psychosocial education (PE), behavioral activation (BA), assertion training (AT), and problem-solving therapy (PS). The allocation sequences are maintained centrally. All participants receive PE and then are randomized to the presence/absence of the remaining three components. The primary outcome of this study is the Patient Health Questionnaire-9 (PHQ-9) total score, which will be administered as an electronic patient-reported outcome on the patients' smartphones after 8 weeks. The protocol was approved by the Institutional Review Board of Nagoya City University on July 15, 2020 (ID: 46-20-0005). The randomized trial, which commenced in March 2021, is currently enrolling participants. The estimated end date for this study is March 2023. DISCUSSION: The highly efficient experimental design will allow for the identification of the most effective components and the most efficient combinations among the four components of the smartphone psychotherapy package for cancer patients. Given that many cancer patients face significant psychological hurdles in seeing mental health professionals, easily accessible therapeutic interventions without hospital visits may offer benefits. If an effective combination of psychotherapy is determined in this study, it can be provided using smartphones to patients who cannot easily access hospitals or clinics. TRIAL REGISTRATION: UMIN000041536, CTR. Registered on 1 November 2020  https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000047301 .

Behavioral change stage might moderate the impact of multifaceted interventions on non-attendance from medical care among patients with type 2 diabetes: The Japan Diabetes Outcome Intervention Trial-2 Large-Scale Trial 007 (J-DOIT2-LT007).

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.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.

BACKGROUND: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. RESULTS: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. LIMITATIONS: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. CONCLUSIONS: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Measuring the performance of prediction models to personalize treatment choice.

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.

Utility of a novel tablet computer software for memory impairment in participants with brain injuries: A randomized control trial.

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.

Trends in Prescribing Antipsychotics for Children and Adolescents in Japan: A Descriptive Epidemiological Study Using a Large-Scale Pharmacy Dataset.

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.

Personalized Prediction of Alzheimer's Disease and Its Treatment Effects by Donepezil: An Individual Participant Data Meta-Analysis of Eight Randomized Controlled Trials.

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.

Development and validation of the Japanese version of the uMARS (user version of the mobile app rating system).

BACKGROUND: Although the global market of Mobile Health Apps (mHealth apps) continues to grow dramatically, most mHealth apps still not only lack evidence base but have even not been evaluated for the basic usability or functionality. The User Version of the Mobile App Rating Scale (uMARS) was developed to allow end users to assess mHealth apps objectively and subjectively. However, there is no Japanese version of uMARS to date. OBJECTIVE: The purpose of this study is (1) to develop a validated Japanese version of uMARS and (2) to assess the translated version's reliability and validity in evaluating mHealth apps. METHODS: The original uMARS was adapted for Japanese use by four specialists using universalist cross-cultural methods. Translation/back-translation was reviewed by the author of the original version of uMARS, and confirmed. Its reliability and validity were further evaluated as part of a prospective cohort study of postoperative patients using a new mHealth app. RESULTS: Conceptual equivalence was analyzed and all items in all subcategories of the original uMARS were included in the Japanese version. Internal consistency was deemed acceptable for all subscales of objective and subjective quality with a Cronbach's alpha of 0.75-0.85. Test-retest reliability of all subscales was also acceptable with intraclass correlation coefficients (ICCs) of 0.57-0.88. Convergent/divergent validity and concurrent validity were also considered acceptable. CONCLUSION: A Japanese version of uMARS was cross-culturally validated and found to be as reliable as the original uMARS. This Japanese version of uMARS is expected to become a standard tool in assessing the quality of mHealth apps in Japan.

Early- and subsequent- response of cognitive functioning in Alzheimer's disease: Individual-participant data from five pivotal randomized clinical trials of donepezil.

The association between early improvement and subsequent change in cognition is unexamined in antidementia clinical trials. We aimed to examine the consequences of early-response to antidementia medication in Alzheimer's disease. Participant-level data were analyzed from five pivotal clinical trials of donepezil for Alzheimer's disease lasting up to 24 weeks (N = 1917). Early-response was based on Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) change scores under minus four from baseline to week six, otherwise classified non-response; then subgrouped by donepezil or placebo. The primary analysis tested the group differences in ADAS-Cog change from baseline for the interval after week six up to 24, based on a three-level mixed-effects model repeated measures (MMRM) model. Four models of increasing complexity were tested, and the most parsimonious model was examined in the primary analysis. The remaining models were tested in sensitivity analysis. In the analytic sample, 32.09% (N = 396/1234) of donepezil and 24.01% (N = 164/683) of placebo participants were classified as early responders, and 67.91% donepezil (N = 838/1234), 75.99% (N = 519/683) placebo participants were not. MMRM identified a statistically significant (P < 0.05) responder group effect. Marginal means (MM) demonstrated more improvement for the early responders (donepezil: MM = -4.13, 95% CI = -5.93, -2.32; placebo MM = 1.81, 95% CI = -4.12, 0.50), compared to non-early responders (donepezil MM = 0.05, 95% CI = -1.40, 1.51; placebo MM = 2.59, 95% CI = 0.99, 4.19). Results replicated in sensitivity analysis. Our results inform clinicians regarding the extent and consequences of early improvement in cognitive functioning and potentially contribute to treatment monitoring and the design of clinical trials for Alzheimer's disease.

Cognitive impairment networks in Alzheimer's disease: Analysis of three double-blind randomized, placebo-controlled, clinical trials of donepezil.

Psychometric network analysis is an alternative theoretically-driven analytic approach that has the potential to conceptualize cognitive impairment in Alzheimer's disease differently than was previously assumed and consequently detect unknown treatment effects. Based on individual participant data, extracted from three double-blind, randomized placebo-controlled clinical trials, psychometric networks were computed on observed Alzheimer's Disease Assessment Scale Cognitive Subscale scores at baseline (N=1,554) and on predicted change scores at 24 weeks of follow-up for participants who received donepezil (N=797) or placebo (N=484). A novel conceptualization of cognitive impairment in Alzheimer's disease was displayed through the baseline network, that had 90% (n=27) positive statistically significant (p<0.05) associations, and a most central aspect of ideational praxis. Following 24 weeks, treatment effects emerged via the differences between the change score networks. The donepezil network had more statistically significant (p<0.05) positive associations and a higher global strength (n=15; S=1.22; p=0.03), than the placebo network (n=8; S=0.57). This suggests that for those who were treated with donepezil compared with placebo, cognition is a more unified construct. The main aspects of change in cognitive impairment were comprehension of spoken language for the donepezil network and spoken language ability for the placebo network. Comprehension of spoken language apears to be most sensitive to psychopharmaceutical interventions and should therefore be closely monitored. Overall, our psychometric network analysis presents a new conceptualization of cognitive impairment in Alzheimer's disease, points to previously unknown treatment effects and highlights well-defined aspects of cognitive impairment  that may translate into future treatment targets.

Association of Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Traits with Depression and Empathy Among Medical Students.

PURPOSE: This study aimed to investigate the associations of the traits of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) with depression and empathy among medical students. PATIENTS AND METHODS: A cross-sectional survey was conducted with 202 fifth-year students at a Japanese medical school for 10 months during their clinical clerkship. The survey included sociodemographic questions and validated tools to measure depressive symptoms (Hospital Anxiety and Depression Scale [HADS]), medical students' empathy for patients (Jefferson Scale of Empathy-Student version [JSE]), ADHD traits (ADHD Self-Report Scale Screener [ASRS Screener]), and ASD traits (Autism-Spectrum Quotient Japanese version-21 [AQ-J-21]). RESULTS: A total of 151 students (response rate: 74.7%) participated in the survey. Of these, 41 (27.2%) reported a total score of ≥ 20 on the HADS and were categorized as depressed. Depressed students reported significantly lower and higher rates of having a part-time job and a history of enrolment in other faculties, respectively, than non-depressed students. According to the cutoff criteria of the ASRS Screener and AQ-J-21, 31 (20.5%) and 42 (27.8%) students reported ADHD and ASD traits, respectively. Multivariate regression analysis, controlling for age and sex, reported that higher age, ASRS Screener scores, and AQ-J-21 scores were significant predictors of higher HADS total scores. Additionally, higher AQ-J-21 scores significantly predicted lower JSE scores. CONCLUSION: The degree of ADHD and ASD traits was significantly associated with depression. Moreover, the degree of ASD traits was significantly associated with lower empathy for their patients. It is important to consider that about 20-30% of medical students have these neurodevelopmental traits and to develop intervention strategies for improving depression and empathy.

The GAD-7 and the PHQ-8 exhibit the same mathematical pattern of item responses in the general population: analysis of data from the National Health Interview Survey.

BACKGROUND: Recent studies have shown that, among the general population, responses to depression-rating scales follow a common mathematical pattern. However, the mathematical pattern among responses to the items of the Generalized Anxiety Disorder-7 (GAD-7) is currently unknown. The present study investigated whether item responses to the GAD-7, when administered to the general population, follow the same mathematical distribution as those of depression-rating scales. METHODS: We used data from the 2019 National Health Interview Survey (31,997 individuals), which is a nationwide survey of adults conducted annually in the United States. The patterns of item responses to the GAD-7 and the Patient Health Questionnaire-8 (PHQ-8), respectively, were analyzed inductively. RESULTS: For all GAD-7 items, the frequency distribution for each response option ("not at all," "several days," "more than half the days," and "nearly every day," respectively) was positively skewed. Line charts representing the responses to each GAD-7 item all crossed at a single point between "not at all" and "several days" and, on a logarithmic scale, showed a parallel pattern from "several days" to "nearly every day." This mathematical pattern among the item responses was identical to that of the PHQ-8. This characteristic pattern of the item responses developed because the values for the "more than half the days" to "several days" ratio were similar across all items, as were the values for the "nearly every day" to "more than half the days" ratio. CONCLUSIONS: Our results suggest that the symptom criteria of generalized anxiety disorder and major depression have a common distribution pattern in the general population.