Childhood maltreatment exposure is differentially associated with transdiagnostic perinatal depression symptoms.

History of childhood maltreatment (CM) is common and robustly associated with prenatal and postpartum (perinatal) depression. Given perinatal depression symptom heterogeneity, a transdiagnostic approach to measurement could enhance understanding of patterns between CM and perinatal depression. METHODS: In two independently collected samples of women receiving care at perinatal psychiatry clinics (n = 523 and n = 134), we categorized longitudinal symptoms of perinatal depression, anxiety, stress, and sleep into transdiagnostic factors derived from the Research Domain Criteria and depression literatures. We split the perinatal period into four time points. We conducted a latent profile analysis of transdiagnostic factors in each period. We then used self-reported history of CM (total exposure and subtypes of abuse and neglect) to predict class membership. RESULTS: A three-class solution best fit our data. In relation to positive adaptive functioning, one class had relatively more positive symptoms (high adaptive), one class had average values (middle adaptive), and one class had fewer adaptive symptoms (low adaptive). More total CM and specific subtypes associated with threat/abuse increased an individual's likelihood of being in the Low Adaptive class in both samples (ORs: 0.90-0.97, p < .05). LIMITATIONS: Generalizability of our results was curtailed by 1) limited racial/ethnic diversity and 2) missing data. CONCLUSIONS: Our results support taking a person-centered approach to characterize the relationship between perinatal depression and childhood maltreatment. Given evidence that increased exposure to childhood maltreatment is associated with worse overall symptoms, providers should consider incorporating preventative, transdiagnostic interventions for perinatal distress in individuals with a history of childhood maltreatment.

Determinants of per- and polyfluoroalkyl substances (PFAS) exposure among Wisconsin residents.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) include thousands of manufactured compounds with growing public health concerns due to their potential for widespread human exposure and adverse health outcomes. While PFAS contamination remains a significant concern, especially from ingestion of contaminated food and water, determinants of the variability in PFAS exposure among regional and statewide populations in the United States remains unclear. OBJECTIVES: The objective of this study was to leverage The Survey of the Health of Wisconsin (SHOW), the only statewide representative cohort in the US, to assess and characterize the variability of PFAS exposure in a general population. METHODS: This study sample included a sub-sample of 605 adult participants from the 2014-2016 tri-annual statewide representative sample. Geometric means for PFOS, PFOA, PFNA, PFHxS, PFPeS, PFHpA, and a summed measure of 38 analyzed serum PFAS were presented by demographic, diet, behavioral, and residential characteristics. Multivariate linear regression was used to determine significant predictors of serum PFAS after adjustment. RESULTS: Overall, higher serum concentrations of long-chain PFAS were observed compared with short-chain PFAS. Older adults, males, and non-Hispanic White individuals had higher serum PFAS compared to younger adults, females, and non-White individuals. Eating caught fish in the past year was associated with elevated levels of several PFAS. DISCUSSION: This is among the first studies to characterize serum PFAS among a representative statewide sample in Wisconsin. Both short- and long-chain serum PFAS were detectable for six prominent PFAS. Age and consumption of great lakes fish were the most significant predictors of serum PFAS. State-level PFAS biomonitoring is important for identifying high risk populations and informing state public health standards and interventions, especially among those not living near known contamination sites.

The association between attitudes and the provision of medications for opioid use disorder (MOUD) in United States jails.

Background: Opioid use disorder is prevalent among individuals who are incarcerated, yet medications for opioid use disorder (MOUD) are not widely available in United States jails and prisons. Negative staff attitudes across the criminal legal system may prevent MOUD from being provided. We sought to determine if staff attitudes are associated with the provision of MOUD in prisons or jails. Methods: 227 staff members of 43 jails and partnering community-based treatment providers answered questions on the effectiveness and acceptability of methadone, buprenorphine, and naltrexone. Response patterns were summarized with principal component analysis. Mixed-effects regression was performed to determine if attitudes toward MOUD were associated with the number of individuals screened and diagnosed with an OUD, referred to treatment, provided MOUD and referred to treatment after release. Results: Sites whose staff had negative attitudes towards methadone and positive attitudes towards naltrexone were associated with fewer people being screened (Mean ratio [MR] = 0.84, 95 % CI: [0.72, 0.97]), diagnosed (MR = 0.85, 95 % CI: [0.73, 0.99]), referred (MR = 0.76, 95 % CI: [0.65, 0.89]), provided MOUD (MR = 0.70, 95 % CI: [0.58, 0.84]), and referred after release (MR = 0.82, 95 % CI: [0.72, 0.94]). Sites with overall positive attitudes towards all MOUD were associated with more people being screened (MR = 1.16, 95 % CI: [1.01, 1.34]), diagnosed (MR = 1.37, 95 % CI: [1.18, 1.60]), and referred to treatment (MR = 1.41, 95 % CI: [1.20, 1.65]). Conclusions: Attitudinal barriers exist in the criminal legal system and are associated with the provision of MOUD.

Dashboarding to Monitor Machine-Learning-Based Clinical Decision Support Interventions.

BACKGROUND: Existing monitoring of machine-learning-based clinical decision support (ML-CDS) is focused predominantly on the ML outputs and accuracy thereof. Improving patient care requires not only accurate algorithms but also systems of care that enable the output of these algorithms to drive specific actions by care teams, necessitating expanding their monitoring. OBJECTIVES: In this case report, we describe the creation of a dashboard that allows the intervention development team and operational stakeholders to govern and identify potential issues that may require corrective action by bridging the monitoring gap between model outputs and patient outcomes. METHODS: We used an iterative development process to build a dashboard to monitor the performance of our intervention in the broader context of the care system. RESULTS: Our investigation of best practices elsewhere, iterative design, and expert consultation led us to anchor our dashboard on alluvial charts and control charts. Both the development process and the dashboard itself illuminated areas to improve the broader intervention. CONCLUSION: We propose that monitoring ML-CDS algorithms with regular dashboards that allow both a context-level view of the system and a drilled down view of specific components is a critical part of implementing these algorithms to ensure that these tools function appropriately within the broader care system.

Quantifying and reducing inequity in average treatment effect estimation.

BACKGROUND: Across studies of average treatment effects, some population subgroups consistently have lower representation than others which can lead to discrepancies in how well results generalize. METHODS: We develop a framework for quantifying inequity due to systemic disparities in sample representation and a method for mitigation during data analysis. Assuming subgroup treatment effects are exchangeable, an unbiased sample average treatment effect estimator will have lower mean-squared error, on average across studies, for subgroups with less representation when treatment effects vary. We present a method for estimating average treatment effects in representation-adjusted samples which enables subgroups to optimally leverage information from the full sample rather than only their own subgroup's data. Two approaches for specifying representation adjustment are offered-one minimizes average mean-squared error for each subgroup separately and the other balances minimization of mean-squared error and equal representation. We conduct simulation studies to compare the performance of the proposed estimators to several subgroup-specific estimators. RESULTS: We find that the proposed estimators generally provide lower mean squared error, particularly for smaller subgroups, relative to the other estimators. As a case study, we apply this method to a subgroup analysis from a published study. CONCLUSIONS: We recommend the use of the proposed estimators to mitigate the impact of disparities in representation, though structural change is ultimately needed.

Placing Approach-Avoidance Conflict Within the Framework of Multi-objective Reinforcement Learning.

Many psychiatric disorders are marked by impaired decision-making during an approach-avoidance conflict. Current experiments elicit approach-avoidance conflicts in bandit tasks by pairing an individual's actions with consequences that are simultaneously desirable (reward) and undesirable (harm). We frame approach-avoidance conflict tasks as a multi-objective multi-armed bandit. By defining a general decision-maker as a limiting sequence of actions, we disentangle the decision process from learning. Each decision maker can then be identified as a multi-dimensional point representing its long-term average expected outcomes, while different decision making models can be associated by the geometry of their 'feasible region', the set of all possible long term performances on a fixed task. We introduce three example decision-makers based on popular reinforcement learning models and characterize their feasible regions, including whether they can be Pareto optimal. From this perspective, we find that existing tasks are unable to distinguish between the three examples of decision-makers. We show how to design new tasks whose geometric structure can be used to better distinguish between decision-makers. These findings are expected to guide the design of approach-avoidance conflict tasks and the modeling of resulting decision-making behavior.

Effectiveness of an Emergency Department-Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls Among Older Adults: Protocol for a Quasi-Experimental Study.

BACKGROUND: Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients. OBJECTIVE: The goal of this paper is to describe a research protocol for evaluating the effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of ML and artificial intelligence to augment medical decision-making. METHODS: To assess the effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rates at 3 different EDs. We will use a quasi-experimental design known as a sharp regression discontinuity with regard to intent-to-treat, since the intervention is administered to patients whose risk score falls above a threshold. A conditional logistic regression model will be built to describe 6-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of a return visit for a fall and the 95% CI will be estimated by comparing those identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile. RESULTS: The ML-CDS tool under study has been implemented at 2 of the 3 EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 15% (45/339) of patients have scheduled an appointment with the clinic. CONCLUSIONS: This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end data set allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multisite implementation plan will demonstrate applicability to a broad population and the possibility to adapt the intervention to other EDs and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and a staggered implementation strategy allows for the identification of secular trends that could affect causal associations and allow mitigation as necessary. TRIAL REGISTRATION: ClinicalTrials.gov NCT05810064; https://www.clinicaltrials.gov/study/NCT05810064. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48128.

Prediction of rehospitalization and mortality risks for skilled nursing facilities using a dimension reduction approach.

BACKGROUND: Hospitals are incentivized to reduce rehospitalization rates, creating an emphasis on skilled nursing facilities (SNFs) for post-hospital discharge. How rehospitalization rates vary depending on patient and SNF characteristics is not well understood, in part because these characteristics are high-dimensional. We sought to estimate rehospitalization and mortality risks by patient and skilled nursing facility (SNF) leveraging high-dimensional characteristics. METHODS: Using 1,060,337 discharges from 13,708 SNFs of Medicare patients residing or visiting a provider in Wisconsin, Iowa, and Illinois, factor analysis was performed to reduce the number of patient and SNF characteristics. K-means clustering was applied to SNF factors to categorize SNFs into groups. Rehospitalization and mortality risks within 60 days of discharge was estimated by SNF group for various values of patient factors. RESULTS: Patient and SNF characteristics (616 in total) were reduced to 12 patient factors and 4 SNF groups. Patient factors reflected broad conditions. SNF groups differed in beds and staff capacity, off-site services, and physical and occupational therapy capacity; and in mortality and rehospitalization rates for some patients. Patients with cardiac, orthopedic, and neuropsychiatric conditions are associated with better outcomes when assigned to SNFs with greater on-site capacity (i.e. beds, staff, physical and occupational therapy), whereas patients with conditions related to cancer or chronic renal failure are associated with better outcomes when assigned to SNFs with less on-site capacity. CONCLUSIONS: Risks of rehospitalization and mortality appear to vary significantly by patient and SNF, with certain SNFs being better suited for some patient conditions over others.

Differential Effects of an Emergency Department-to-Home Care Transitions Intervention in an Older Adult Population: A Latent Class Analysis.

BACKGROUND: Older adults frequently return to the emergency department (ED) within 30 days of a visit. High-risk patients can differentially benefit from transitional care interventions. Latent class analysis (LCA) is a model-based method used to segment the population and test intervention effects by subgroup. OBJECTIVES: We aimed to identify latent classes within an older adult population from a randomized controlled trial evaluating the effectiveness of an ED-to-home transitional care program and test whether class membership modified the intervention effect. RESEARCH DESIGN: Participants were randomized to receive the Care Transitions Intervention or usual care. Study staff collected outcomes data through medical record reviews and surveys. We performed LCA and logistic regression to evaluate the differential effects of the intervention by class membership. SUBJECTS: Participants were ED patients (age 60 y and above) discharged to a community residence. MEASURES: Indicator variables for the LCA included clinically available and patient-reported data from the initial ED visit. Our primary outcome was ED revisits within 30 days. Secondary outcomes included ED revisits within 14 days, outpatient follow-up within 7 and 30 days, and self-management behaviors. RESULTS: We interpreted 6 latent classes in this study population. Classes 1, 4, 5, and 6 showed a reduction in ED revisit rates with the intervention; classes 2 and 3 showed an increase in ED revisit rates. In class 5, we found evidence that the intervention increased outpatient follow-up within 7 and 30 days (odds ratio: 1.81, 95% CI: 1.13-2.91; odds ratio: 2.24, 95% CI: 1.25-4.03). CONCLUSIONS: Class membership modified the intervention effect. Population segmentation is an important step in evaluating a transitional care intervention.

Positive affect improves a transdiagnostic model of perinatal depression symptoms.

INTRODUCTION: Accurate measurement of perinatal depression is vital. We aimed to 1) test whether a factor that measured positive affect (PA) bettered a transdiagnostic model of depression symptoms and 2) replicate the model in a second sample. METHODS: We conducted secondary analyses from two samples (n's = 657 and 142) of women in treatment at perinatal psychiatric clinics. Data were derived from items from seven commonly used measures. We compared fit indices from our original factor model-one general and six specific factors derived from the Research Domain Criteria (Loss, Potential Threat, Frustrative Nonreward, and Sleep-Wakefulness) and depression literatures (Somatic and Coping)-to our novel factor model with a PA factor. The PA factor was created by recategorizing items that measured affective states with a positive valence into a new factor. Sample 1 data were split into six perinatal periods. RESULTS: In both samples, the addition of a PA factor improved model fit. At least partial metric invariance was found between perinatal periods, with the exception of trimester 3 - postpartum period 1. LIMITATIONS: Our measures did not operationalize PA in the same way as in the positive valence system in RDoC and we were unable to perform longitudinal analyses on our cross-validation sample. CONCLUSIONS: Clinicians and researchers are encouraged to consider these findings as a template for understanding symptoms of depression in perinatal patients, which can be used to guide treatment planning and the development of more effective screening, prevention, and intervention tools to prevent deleterious outcomes.

Mobile Acceptance and Commitment Therapy With Distressed First-Generation College Students: Microrandomized Trial.

BACKGROUND: Extant gaps in mental health services are intensified among first-generation college students. Improving access to empirically based interventions is critical, and mobile health (mHealth) interventions are growing in support. Acceptance and commitment therapy (ACT) is an empirically supported intervention that has been applied to college students, via mobile app, and in brief intervals. OBJECTIVE: This study evaluated the safety, feasibility, and effectiveness of an ACT-based mHealth intervention using a microrandomized trial (MRT) design. METHODS: Participants (N=34) were 18- to 19-year-old first-generation college students reporting distress, who participated in a 6-week intervention period of twice-daily assessments and randomization to intervention. Participants logged symptoms, moods, and behaviors on the mobile app Lorevimo. After the assessment, participants were randomized to an ACT-based intervention or no intervention. Analyses examined proximal change after randomization using a weighted and centered least squares approach. Outcomes included values-based and avoidance behavior, as well as depressive symptoms and perceived stress. RESULTS: The findings indicated the intervention was safe and feasible. The intervention increased values-based behavior but did not decrease avoidance behavior. The intervention reduced depressive symptoms but not perceived stress. CONCLUSIONS: An MRT of an mHealth ACT-based intervention among distressed first-generation college students suggests that a larger MRT is warranted. Future investigations may tailor interventions to contexts where intervention is most impactful. TRIAL REGISTRATION: ClinicalTrials.gov NCT04081662; https://clinicaltrials.gov/show/NCT04081662. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/17086.

Mobile Acceptance and Commitment Therapy in Bipolar Disorder: Microrandomized Trial.

BACKGROUND: Mobile interventions promise to fill in gaps in care with their broad reach and flexible delivery. OBJECTIVE: Our goal was to investigate delivery of a mobile version of acceptance and commitment therapy (ACT) for individuals with bipolar disorder (BP). METHODS: Individuals with BP (n=30) participated in a 6-week microrandomized trial. Twice daily, participants logged symptoms in the app and were repeatedly randomized (or not) to receive an ACT intervention. Self-reported behavior and mood were measured as the energy devoted to moving toward valued domains or away from difficult emotions and with depressive d and manic m scores from the digital survey of mood in BP survey (digiBP). RESULTS: Participants completed an average of 66% of in-app assessments. Interventions did not significantly impact the average toward energy or away energy but did significantly increase the average manic score m (P=.008) and depressive score d (P=.02). This was driven by increased fidgeting and irritability and interventions focused on increasing awareness of internal experiences. CONCLUSIONS: The findings of the study do not support a larger study on the mobile ACT in BP but have significant implications for future studies seeking mobile therapy for individuals with BP. TRIAL REGISTRATION: ClinicalTrials.gov NCT04098497; https://clinicaltrials.gov/ct2/show/NCT04098497.

Detection of differential depressive symptom patterns in a cohort of perinatal women: an exploratory factor analysis using a robust statistics approach.

Background: Postpartum depression can take many forms. Different symptom patterns could have divergent implications for how we screen, diagnose, and treat postpartum depression. We sought to utilise a recently developed robust estimation algorithm to automatically identify differential patterns in depressive symptoms and subsequently characterise the individuals who exhibit different patterns. Methods: Depressive symptom data (N = 548) were collected from women with neuropsychiatric illnesses at two U.S. urban sites participating in a longitudinal observational study of stress across the perinatal period. Data were collected from Emory University between 1994 and 2012 and from the University of Arkansas for Medical Sciences between 2012 and 2017. We conducted an exploratory factor analysis of Beck Depression Inventory (BDI) items using a robust expectation-maximization algorithm, rather than a conventional expectation-maximization algorithm. This recently developed method enabled automatic detection of differential symptom patterns. We described differences in symptom patterns and conducted unadjusted and adjusted analyses of associations of symptom patterns with demographics and psychiatric histories. Findings: 53 (9.7%) participants were identified by the algorithm as having a different pattern of reported symptoms compared to other participants. This group had more severe symptoms across all items-especially items related to thoughts of self-harm and self-judgement-and differed in how their symptoms related to underlying psychological constructs. History of social anxiety disorder (OR: 4.0; 95% CI [1.9, 8.1]) and history of childhood trauma (for each 5-point increase, OR: 1.2; 95% CI [1.1, 1.3]) were significantly associated with this differential pattern after adjustment for other covariates. Interpretation: Social anxiety disorder and childhood trauma are associated with differential patterns of severe postpartum depressive symptoms, which might warrant tailored strategies for screening, diagnosis, and treatment to address these comorbid conditions. Funding: There are no funding sources to declare.

Quantifying aberrant approach-avoidance conflict in psychopathology: A review of computational approaches.

Making effective decisions during approach-avoidance conflict is critical in daily life. Aberrant decision-making during approach-avoidance conflict is evident in a range of psychological disorders, including anxiety, depression, trauma-related disorders, substance use disorders, and alcohol use disorders. To help clarify etiological pathways and reveal novel intervention targets, clinical research into decision-making is increasingly adopting a computational psychopathology approach. This approach uses mathematical models that can identify specific decision-making related processes that are altered in mental health disorders. In our review, we highlight foundational approach-avoidance conflict research, followed by more in-depth discussion of computational approaches that have been used to model behavior in these tasks. Specifically, we describe the computational models that have been applied to approach-avoidance conflict (e.g., drift-diffusion, active inference, and reinforcement learning models), and provide resources to guide clinical researchers who may be interested in applying computational modeling. Finally, we identify notable gaps in the current literature and potential future directions for computational approaches aimed at identifying mechanisms of approach-avoidance conflict in psychopathology.

Ecological Study of Urbanicity and Self-reported Poor Mental Health Days Across US Counties.

Geography may influence mental health by inducing changes to social and physical environmental and health-related factors. This understanding is largely based on older studies from Western Europe. We sought to quantify contemporary relationships between urbanicity and self-reported poor mental health days in US counties. We performed regression on US counties (n = 3142) using data from the County Health Rankings and Roadmaps. Controlling for state, age, income, education, and race/ethnicity, large central metro counties reported 0.24 fewer average poor mental health days than small metro counties (t = - 5.78, df = 423, p < .001). Noncore counties had 0.07 more average poor mental health days than small metro counties (t = 3.06, df = 1690, p = 0.002). Better mental health in large central metro counties was partly mediated by differences in the built environment, such as better food environments. Poorer mental health in noncore counties was not mediated by considered mediators.

Addressing heterogeneous populations in latent variable settings through robust estimation.

Individuals routinely differ in how they present with psychiatric illnesses and in how they respond to treatment. This heterogeneity, when overlooked in data analysis, can lead to misspecified models and distorted inferences. While several methods exist to handle various forms of heterogeneity in latent variable models, their implementation in applied research requires additional layers of model crafting, which might be a reason for their underutilization. In response, we present a robust estimation approach based on the expectation-maximization (EM) algorithm. Our method makes minor adjustments to EM to enable automatic detection of population heterogeneity and to recognize individuals who are inadequately explained by the assumed model. Each individual is associated with a probability that reflects how likely their data were to have been generated from the assumed model. The individual-level probabilities are simultaneously estimated and used to weight each individual's contribution in parameter estimation. We examine the utility of our approach for Gaussian mixture models and linear factor models through several simulation studies, drawing contrasts with the EM algorithm. We demonstrate that our method yields inferences more robust to population heterogeneity or other model misspecifications than EM does. We hope that the proposed approach can be incorporated into the model-building process to improve population-level estimates and to shed light on subsets of the population that demand further attention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The electrophysiological masking level difference: effects of age and mediation of hearing and cognition.

OBJECTIVE: Evaluate the conceptual framework that age effects on the electrophysiological binaural masking level difference (MLD) are partially mediated by age-related hearing loss and/or global cognitive function via mediation analysis. DESIGN: Participants underwent a series of audiometric tests. The MLD was measured via cortical auditory evoked potentials using a speech stimulus (/ɑ/) in speech-weighted background noise. We used mediation analyses to determine the total effect, natural direct effects, and natural indirect effects, which are displayed as regression coefficients ([95% CI]; p value). STUDY SAMPLE: Twenty-eight individuals aged 19-87 years (mean [SD]: 53.3 [25.2]), recruited from the community. RESULTS: Older age had a significant total effect on the MLD (-0.69 [95% CI: -0.96, -0.45]; p < 0.01). Neither pure tone average (-0.11 [95% CI: -0.43, 0.24; p = 0.54] nor global cognitive function (-0.02 [95% CI: -0.13, 0.02]; p = 0.55) mediated the relationship of age and the MLD and effect sizes were small. Results were insensitive to use of alternative hearing measures or inclusion of interaction terms. CONCLUSIONS: The electrophysiological MLD may be an age-sensitive measure of binaural temporal processing that is minimally affected by age-related hearing loss and global cognitive function.

Multimethod Process Evaluation of a Community Paramedic Delivered Care Transitions Intervention for Older Emergency Department Patients.

OBJECTIVE: We assessed fidelity of delivery and participant engagement in the implementation of a community paramedic coach-led Care Transitions Intervention (CTI) program adapted for use following emergency department (ED) visits. METHODS: The adapted CTI for ED-to-home transitions was implemented at three university-affiliated hospitals in two cities from 2016 to 2019. Participants were aged ≥60 years old and discharged from the ED within 24 hours of arrival. In the current analysis, participants had to have received the CTI. Community paramedic coaches collected data on program delivery and participant characteristics at each transition contact via inventories and assessments. Participants provided commentary on the acceptability of the adapted CTI. Using a multimethod approach, the CTI implementation was assessed quantitatively for site- and coach-level differences. Qualitatively, barriers to implementation and participant satisfaction with the CTI were thematically analyzed. RESULTS: Of the 863 patient participants, 726 (84.1%) completed their home visits. Cancellations were usually patient-generated (94.9%). Most planned follow-up visits were successfully completed (94.6%). Content on the planning for red flags and post-discharge goal setting was discussed with high rates of fidelity overall (95% and greater), while content on outpatient follow-up was lower overall (75%). Differences in service delivery between the two sites existed for the in-person visit and the first phone follow-up, but the differences narrowed as the study progressed. Participants showed a 24.6% increase in patient activation (i.e., behavioral adoption) over the 30-day study period (p < 0.001).Overall, participants reported that the program was beneficial for managing their health, the quality of coaching was high, and that the program should continue. Not all participants felt that they needed the program. Community paramedic coaches reported barriers to CTI delivery due to patient medical problems and difficulties with phone visit coordination. Coaches also noted refusal to communicate or engage with the intervention as an implementation barrier. CONCLUSIONS: Community paramedic coaches delivered the adapted CTI with high fidelity across geographically distant sites and successfully facilitated participant engagement, highlighting community paramedics as an effective resource for implementing such patient-centered interventions.

Scalar Assessment of the Family Caregiver Activation in Transitions Tool: An Exploratory Factor Analysis.

The Family Caregiver Activation in Transitions (FCAT) tool in its current, non-scalar form is not pragmatic for clinical use as each item is scored and intended to be interpreted individually. The purpose of the current study was to create a scalar version of the FCAT to facilitate better care communications between hospital staff and family caregivers. We also assessed the scale's validity by comparing the scalar version of the measure against patient health measures. Data were collected from 463 family caregiver-patient dyads from January 2016 to July 2019. An exploratory factor analysis was performed on the 10-item FCAT, resulting in a statistically homogeneous six-item scale focused on current caregiving activation factors. The measure was then compared against patient health measures, with no significant biases found. The six-item scalar FCAT can provide hospital staff insight into the level of caregiver activation occurring in the patient's health care and help tailor care transition needs for family caregiver-patient dyads. [Journal of Gerontological Nursing, 48(12), 35-42.].