Distressing Aspects of Elder Abuse Victimization: Perspective of Survivors.

Objectives: Our understanding of elder abuse (EA) phenomena has largely been shaped from the perspective of researchers and professionals whose conceptualizations often differ from the perceptions of older adults who experience mistreatment. This study sought to understand the most distressing aspects of EA victimization from the perspective of survivors.

Methods: Using a descriptive phenomenological approach, individual interviews were conducted with a diverse sample ( = 32) of EA survivors, recruited from EA support and Adult Protective Services programs in New York City and Los Angeles.

Understanding Cognitive Aging Through White Matter: A Fixel-Based Analysis.

Diffusion-weighted imaging (DWI) has been frequently used to examine age-related deterioration of white matter microstructure and its relationship to cognitive decline. However, typical tensor-based analytical approaches are often difficult to interpret due to the challenge of decomposing and (mis)interpreting the impact of crossing fibers within a voxel. We hypothesized that a novel analytical approach capable of resolving fiber-specific changes within each voxel (i.

Brain Activation Associated With Response to Psychotherapies for Late-Life Depression: A Task-Based fMRI Study.

Background: The course of late-life depression is associated with functioning of multiple brain networks. Understanding the brain mechanisms associated with response to psychotherapy can inform treatment development and a personalized treatment approach. This study examined how activation of key regions of the salience network, default mode network and reward systems is associated with response to psychotherapies for late-life depression.

2SpamH: A Two-Stage Pre-Processing Algorithm for Passively Sensed mHealth Data.

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices.

A digital intervention for cognitive deficits following COVID-19: a randomized clinical trial.

Post-COVID-19 cognitive deficits are common, persistent, and disabling. Evidence on effective treatments is limited. The goal of this study was to investigate the efficacy of a digital intervention to reduce cognitive and functional deficits in adults with persistent post-COVID-19 cognitive dysfunction.

Predictors of attrition during acute pharmacotherapy of psychotic depression in a clinical trial.

Little is known about factors that contribute to attrition in clinical trials of the pharmacotherapy of psychotic depression. The purpose of this study was to identify factors associated with attrition during acute pharmacotherapy in the Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II) clinical trial. Sociodemographic and clinical variables were assessed at baseline in 269 men and women, aged 18-85 years, who were treated with up to 12 weeks of open-label sertraline plus olanzapine.

Effects of open-label transdermal nicotine antidepressant augmentation on affective symptoms and executive function in late-life depression.

Background: Late-life depression (LLD) is characterized by a poor response to antidepressant medications and diminished cognitive performance, particularly in executive functioning. There is currently no accepted pharmacotherapy for LLD that effectively treats both mood and cognitive symptoms. This study investigated whether transdermal nicotine augmentation of standard antidepressant medications benefitted mood and cognitive symptoms in LLD.

Improved outcomes for depressed elder abuse victims with video-delivered psychotherapy during COVID-19.

Objective: There is a lack of evidence-based scalable therapies for elder abuse victims, with no current remotely delivered tailored psychotherapy. The purpose of this manuscript is to (a) examine the effectiveness of a brief therapy for depression for elder abuse victims, and (b) to compare remote intervention delivery via phone or video to the traditional in-person delivery.

Method: PROTECT, , is a brief therapy developed in collaboration with partners at the Department for the Aging (DFTA) of New York City.

Trajectories of remitted psychotic depression: identification of predictors of worsening by machine learning.

Background: Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory.

Method: One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine.

Improving social reward responsivity and social connectedness in psychotherapies for late-life depression: Engage & Connect as an example.

Psychotherapies are effective in reducing late-life depression. Yet, about half of patients remain depressed at treatment end. Advances in neuroscience can inform simplified interventions that target key brain networks impacted by depression.

Risk stratification models for predicting preventable hospitalization in commercially insured late middle-aged adults with depression.

Background: A significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods.