Faculty Bibliography

Background/UNASSIGNED:Depression, generalized anxiety, fatigue, diminished physical function, reduced social participation, and pain are common for many older adults and negatively impact quality of life. The purpose of the overall trial was to compare the effects of cognitive-behavioral therapy (CBT) and yoga on late-life worry, anxiety, and sleep; and examine preference and selection effects on these outcomes. Objective/UNASSIGNED:The present analyses compared effects of the 2 interventions on additional outcomes (depressive symptoms, generalized anxiety symptoms, fatigue, pain interference/intensity, physical function, social participation); and examined whether there are preference and selection effects for these treatments. Methods/UNASSIGNED:A randomized preference trial of CBT and yoga was conducted in adults ≥60 years who scored ≥26 on the Penn State Worry Questionnaire-Abbreviated (PSWQ-A), recruited from outpatient medical clinics, mailings, and advertisements. Cognitive-behavioral therapy consisted of 10 weekly telephone sessions. Yoga consisted of 20 bi-weekly group yoga classes. Participants were randomized to(1): a randomized controlled trial (RCT) of CBT or yoga (n = 250); or (2) a preference trial in which they selected their treatment (CBT or yoga; n = 250). Outcomes were measured at baseline and post-intervention. Results/UNASSIGNED:< .01]. Depressive symptoms, generalized anxiety, and fatigue showed clinically meaningful within-group changes in both groups. There were no changes in or difference between physical function or social participation for either group. No preference or selection effects were found. Conclusion/UNASSIGNED:Both CBT and yoga may be useful for older adults for improving psychological symptoms and fatigue. Cognitive-behavioral therapy may offer even greater benefit than yoga for decreasing pain.

Objectives/UNASSIGNED:We aim to assess the influence of COVID-19 on the social needs of emergency department (ED) patients, and assess patients' access to social services. Methods/UNASSIGNED:We conducted a cross-sectional survey of 175 purposively sampled adult ED patients. Results/UNASSIGNED:Approximately half of participants stated that COVID-19 negatively impacted their social needs with statistically significant differences observed for race, ethnicity, and insurance status. Many participants did not know of available social services, and a majority welcomed assistance from the ED. Conclusion/UNASSIGNED:This study suggests that unmet social needs have risen because of COVID-19, and EDs may be positioned to identify and assist affected patients.

BACKGROUND:High-throughput metagenomic sequencing technologies have shown prominent advantages over traditional pathogen detection methods, bringing great potential in clinical pathogen diagnosis and treatment of infectious diseases. Nevertheless, how to accurately detect the difference in microbiome profiles between treatment or disease conditions remains computationally challenging. RESULTS:In this study, we propose a novel test for identifying the difference between two high-dimensional microbiome abundance data matrices based on the centered log-ratio transformation of the microbiome compositions. The test p-value can be calculated directly with a closed-form solution from the derived asymptotic null distribution. We also investigate the asymptotic statistical power against sparse alternatives that are typically encountered in microbiome studies. The proposed test is maximum-type equal-covariance-assumption-free (MECAF), making it widely applicable to studies that compare microbiome compositions between conditions. Our simulation studies demonstrated that the proposed MECAF test achieves more desirable power than competing methods while having the type I error rate well controlled under various scenarios. The usefulness of the proposed test is further illustrated with two real microbiome data analyses. The source code of the proposed method is freely available at https://github.com/Jiyuan-NYU-Langone/MECAF. CONCLUSIONS:MECAF is a flexible differential abundance test and achieves statistical efficiency in analyzing high-throughput microbiome data. The proposed new method will allow us to efficiently discover shifts in microbiome abundances between disease and treatment conditions, broadening our understanding of the disease and ultimately improving clinical diagnosis and treatment.

Background/UNASSIGNED:Mobile health (mHealth) tools offer an effective and personalized approach to enhance chronic disease management and may partially offset provider-level barriers to increasing buprenorphine prescribing in primary care. This study assessed the feasibility of integrating a text messaging-based medical management tool (TeMeS) in primary care among patients initiating buprenorphine. Methods/UNASSIGNED:TeMeS messages are categorized per the medical management model, programed in a HIPAA-compliant texting software (Apptoto©), and delivered in a tiered fashion over 8-weeks to patients. This mixed-methods evaluation of TeMeS utilized key stakeholder feedback (patients, physicians, administrators, nursing), text messaging software process measures, thematic analysis of patient participant text message content, and electronic administrative data (eg, appointment adherence, treatment retention) at 2-months. Results/UNASSIGNED:The study team approached 65 patients and n = 14 (21%) were ineligible or declined to participate in the study. Most eligible participants owned a smartphone (90%), responded to at least one text query (88%) over an average of 24 days, and few requested to stop receiving texts (6%). Participant text replies included responses to cognitive behavioral therapy-based queries (13.8%), confirming or rescheduling appointments (6.1%), and insurance, pharmacy, or clinical issues pertaining to buprenorphine dispensation or dosing (2%). Suggestions for design modifications included personalizing message content and adjusting message frequency per patient risk of illicit opioid reuse, use of video-based informational content, and real-time provider and staff support for emergent issues. Conclusion/UNASSIGNED:Our findings highlight the acceptability, feasibility, and high rates of engagement of utilizing text messaging to enhance self-management among patients initiating buprenorphine treatment.

Aims/UNASSIGNED:We examined the effects of a statewide New York (NY) care management (CM) program for substance use disorder (SUD), Managed Addiction Treatment Services (MATS), on SUD treatment services' utilization and spending among patients with a recent history of high Medicaid spending and among those for whom a predictive algorithm indicates a higher probability of outlier spending in the following year. Methods/UNASSIGNED:We applied difference-in-difference analyses with propensity score matching using NY Medicaid claims data and a state registry of SUD-treatment episodes from 2006 to 2009. A total of 1263 CM enrollees with high SUD treatment spending (>$10K) in the prior year and a matched comparison group were included in the analysis. Crisis care utilization for SUD (detoxification and hospitalizations), outpatient SUD treatment, and Medicaid spending were examined over 12 months among both groups. CM effects among predicted high-future-spending patients (HFS) were also analyzed. Results/UNASSIGNED:CM increased outpatient SUD treatment visits by approximately 10.5 days (95% CI = 0.9, 20.0). CM crisis care and spending outcomes were not statistically different from comparison since both conditions had comparable pre-post declines. Conversely, CM significantly reduced SUD treatment spending by approximately $955 (95% CI = -1518, -391) and reduced days of detox utilization by about 1.0 days (95% CI = -1.9, -0.1) among HFS. Conclusion/UNASSIGNED:Findings suggest that CM can reduce SUD treatment spending and utilization when targeted at patients with a greater likelihood of high future spending, indicating the potential value of predictive models to select CM patients.

[This corrects the article DOI: 10.1371/journal.pone.0245064.].

We present a hierarchical integrated model of self-regulation in which executive function is the cognitive component of the model, together with emotional, behavioral, physiological, and genetic components. These five components in the model are reciprocally and recursively related. The model is supported by empirical evidence, primarily from a single longitudinal study with good measurement at each level of the model. We also find that the model is consistent with current thinking on related topics such as cybernetic theory, the theory of allostasis and allostatic load, and the theory of skill development in harsh and unpredictable environments, referred to as "hidden talents." Next, we present literature that the integrative processes are susceptible to environmental adversity, poverty-related risk in particular, while positive social interactions with caregivers (e.g., maternal sensitivity) would promote self-regulatory processes or mitigate the adverse effect of early risk on the processes. A hierarchical integrative model of self-regulation advances our understanding of self-regulatory processes. Future research may consider broader social contexts of the integrative self-regulation system, such as neighborhood/community contexts and structural racism. This can be an integral step to provide children with equitable opportunities to thrive, even among children living in socioeconomically and psychosocially disadvantaged environments.