Our Peers - Empowerment and Navigational Support - Community Living (OP-ENS-CL): A Peer Navigator Intervention for Adults With Acquired Physical Disabilities

Overview

The goal of this clinical trial is to learn if a remote peer navigator intervention (OP-ENS - CL) for people with newly acquired physical disabilities returning to community living after rehabilitation improves self-reported social support, health, and community participation. The main questions it aims to answer are: Do people with acquired physical disabilities who receive the remote peer navigator intervention experience greater social support and self-efficacy than people in the control group? Do people with acquired physical disabilities who receive the remote peer navigator intervention have better self-reported health and social participation outcomes than people in the control group? Is the OP-ENS - CL intervention acceptable to people with newly acquired physical disabilities returning to community living?

The purpose of this study is to evaluate the efficacy and social validity of the OP-ENS - CL (Our Peers - Empowerment and Navigational Support - Community Living) a remote 12-month peer navigator intervention using a clinical trial design in a sample of adults with newly acquired with physical disabilities transitioning to community living. Participants will be recruited through community and clinical networks and screened for eligibility. Participants will be randomly assigned to either the intervention or usual care groups. Participants in the intervention group will be matched with a trained peer health navigator (who is also a person with a physical disability). Using a structure process of barrier identification, goal setting and action planning peer health navigators will work with participants to help address their healthcare needs and concerns, including such things as patient-provider communication, transportation, access to durable medical equipment. It is important to note that peer health navigators are not healthcare providers and do not provide medical or health advice. Participants in the intervention trial are asked to meet with their peer navigator at least once a month during the study period. To encourage participation in this intervention, progressive micro-incentives are integrated into the study protocol. To promote equipoise, a similar micro-incentive schedule is created for the usual care group. Data Collection - All participants, regardless of group assignment, will be interviewed at 3 time points (baseline, 6 months, 13 months) using a self-report measures of healthcare access, quality, and outcomes as well as measures of social support and patient activation. Data collectors will be blinded to group assignment and not involved in the delivery of the OP-ENS - CL intervention. Data Analysis - The investigators will employ mixed effects model using each study outcome as a time-varying dependent variable and treatment group (PHN vs. usual care) as the main fixed effect. Baseline characteristics including race/ethnicity, gender, and socioeconomic status will be entered as time-invariant covariates if baseline group differences are observed (in spite of randomization). Subject intercept will be modeled as a random effect. The null hypothesis will be rejected if a significant group\*time interaction effect is observed. The investigators hypothesize that PN will have a more favorable trajectory slope of study outcomes than the matched control group. For each hypothesis, post-hoc analysis will be conducted to assess difference in each outcome variable between the two groups at different time points. The mixed models will run using PROC MIXED from of SAS 9.3 (Cary, NC). The method of estimation will be maximum likelihood (ML). A variety of covariance structures (first order regressive, compound symmetry, toeplitz, variance components, unstructured) will be carefully examined and compared for best model fit, Akaike's Information (AIC) and Bayesian Information Criteria (BIC). These statistics are functions of the log likelihood and can be compared across models. As missing data are inevitable in a longitudinal study, values will be imputed where possible using either mean (median) substitute or formal imputation procedures such as EM algorithm if missing data are MCAR (missing completely at random) or MAR (missing at random). If missing data are NMAR (not missing at random), the "pattern mixture" approach will be used to compute a "weighted average" of the parameters that are associated with the missing data to estimate what the data would have been.