Pamela Z. Cacchione

Purpose: Robot-assisted therapy predominantly targets motor impairment, although post-stroke motor and cognitive impairments co-occur. We report user experience in a longitudinal deployment of a Rehab Community-based Affordable Robot Exercise System (RehabCARES) for delivering personalized motor/cognitive training. Methods: Six adults with chronic stroke (FM-UE 20-61; MoCA 22-28) completed 12 therapy visits with THEAbot, a reconfigurable 1-DoF robot. Rehab CARES also included a haptic/biometric dynamic difficulty adjustment controller and eight games characterized by cognitive processes and motor/cognitive demand. We report gaming preference, usage, and user experience (pain, exertion, usability, affect, workload) across therapy. Results: User experience varied, but generally, self-reported pain was low, perceived exertion was light to moderate, enjoyment was strong and stable, and sense of control increased while excitement plateaued. Workload showed decreasing frustration and effort. Game selection during therapy was partially consistent with motor/cognitive clinical profiles; preferences and perceptual abilities may have influenced choices. Usability scores exceeded acceptability at all time points, peaking at the final visit (86 +/- 4.5 out of 100). Conclusions: RehabCARES provided personalized motor/cognitive training to a heterogeneous cohort in a community-based setting, delivering a positive user experience. Hardware reconfigurability and motor/cognitive game variety likely enabled this success.

Background: Following stroke, motor and cognitive impairments often limit functional independence. Personalized rehabilitation requires comprehensive clinical assessments, which are costly and time-consuming. Imaging scans are routinely collected, but their predictive value in determining post-stroke function remains unclear. Objectives: To determine the ability of clinical assessments, lesion features with medical chart information, and a combined clinical and biomedical model to predict kinematic outcomes from a robot-based tracking task in chronic stroke individuals. Methods: Eighteen participants (70±6 years) completed a robot-based tracking task and a battery of motor and cognitive assessments. Three elastic net regression models were developed: 1) clinical assessments only; 2) lesion metrics and chart information only; and 3) a combined model. Models were evaluated using leave-one-subject-out cross-validation, bootstrapping, and permutation testing with false discovery rate correction. Robot-based outcomes included tracking error, normalized distance, velocity error response, and delay. Results: The clinical assessment model significantly predicted tracking error (R²=0.45), delay (R²=0.22), velocity error response (R²=0.18), and normalized distance (R²=0.12). The combined model significantly predicted tracking error (R²=0.37), delay (R²=0.19), and velocity error response (R²=0.11). The imaging model did not significantly predict any outcome. Box and Blocks Test and Color Trails 1 were stable features. The right dorsal attention network was a stable predictor in several combined models. Conclusion: Clinical assessments outperformed lesion features in predicting tracking performance after chronic stroke. Attention- and motor-related measures were the strongest predictors, highlighting the importance of cognitive-motor interactions in function. A larger sample and more granular lesion features may improve prediction from imaging biomarkers.

Though individuals have the capacity to innovate, their actions may not correlate with willingness to engage in innovative behaviors, identifying as an innovator, or diffusing their innovations. The goal of this study was to create a phenotype and taxonomy of nurses engaging in innovative behaviors by determining how the scores from our sample aligned with the Diffusion of Innovation adopter categories. Using a cross-sectional design, we performed a survey of nurses in the United States who engage in innovative behaviors. Nurses (n = 329) completed the Survey for the Measurement of Innovativeness tool. Results indicated that 9 (2.74%) respondents had an innovativeness score representing an innovator, 201 (61.09%) of Early Adopter, 117 (35.56%) Early Majority, and 2 (0.61%) Late Majority. There were no Laggards. We compared their scores with the normal distribution of the Diffusion of Innovation theory adopter categories. Using latent class analysis (LCA) with data from the survey, we determined four groups: class 1 (non-innovator), class 2 (uncreative), class 3 (optimal innovator), and class 4 (wary innovator). In our sample, engaging in innovative behaviors differed somewhat from the Diffusion of Innovation adopter categories; a greater percentage were categorized as the Early Adopter group. Using these results, a new phenotype and taxonomy was created to better understand the different types and characteristics of nurses engaging in innovative behaviors. Results of this study can be used to develop and support innovation by nurses across the continuum of learning from students enrolled in nursing programs to nurses employed in the healthcare workforce.

Aims: To better understand perceived stress and burnout levels among registered nurses, certified nursing assistants, certified nurse leaders, and nurse practitioners and describe clinician characteristics associated with their perceived stress and burnout during the COVID‐19 pandemic and concurrent racial unrest in Philadelphia. Design: A descriptive, cross‐sectional design was used to collect data on demographics, perceived stress, and burnout levels among a convenience sample of nursing clinicians. Methods: Data were collected using the 4‐item Perceived Stress Scale (PSS‐4) and Copenhagen Burnout Inventory (CBI) to assess perceived stress and burnout. SAS was used to tabulate and calculate PSS‐4 and CBI scores and descriptive data. Chi‐squared and Fisher Exact tests were used to measure statistical association between variables. Results: The study revealed a significant association between gender and self‐reported tiredness, with women reporting greater fatigue than men. African American participants reported that things rarely went their way on Item 3 of the PSS‐4. Approximately, 70% of nursing clinicians experienced work‐related burnout and 85% of experienced clinicians, 5 or more years, expressed more doubts about their capacity to continue to work with patients. Conclusions: Nurses experienced the stress of caring for COVID‐19 patients and in some cases victims of the racial unrest in their communities. Nursing clinicians are not immune to the turmoil outside of the hospitals within their own communities. Relevance to Clinical Practice: This study underscores the major impact the pandemic, racial unrest, and perceived stress had on nurse clinicians, emphasizing the need for proactive strategies to support their well‐being and decrease burnout.

Background: Economically disadvantaged patients diagnosed with serious mental illness (SMI) experience post-hospitalizations disparities due to fragmented care transitions. Purpose: To describe the pre-implementation strategies used to adapt and implement a nurse-led transitional care intervention (Thrive) to meet the needs of economically disadvantaged patients diagnosed with an SMI. Methods: Two pre-implementation strategies, Evidence Based Quality Improvement (EBQI) meetings and Formative Evaluation (FE) research, were used to adapt intervention components. FE data included semi-structured interviews analyzed using Rapid Qualitative Analysis. Findings: Adaptations were made to core components of Thrive and strategies to support implementation were identified. Conclusions: Participatory strategies help to adapt interventions that are person-centered and tailored to the organizational context. Trial: NCT06203509.

Background: People diagnosed with a co-occurring serious mental illness (SMI; ie, major depressive disorder, bipolar disorder, or schizophrenia) but hospitalized for a nonpsychiatric condition experience higher rates of readmissions and other adverse outcomes, in part due to poorly coordinated care transitions. Current hospital-to-home transitional care programs lack a focus on the integrated social, medical, and mental health needs of these patients. The Thrive clinical pathway provides transitional care support for patients insured by Medicaid with multiple chronic conditions by focusing on posthospitalization medical concerns and the social determinants of health. This study seeks to evaluate an adapted version of Thrive that also meets the needs of patients with co-occurring SMI discharged from a nonpsychiatric hospitalization. Objective: This study aimed to (1) engage staff and community advisors in participatory implementation processes to adapt the Thrive clinical pathway for all Medicaid-insured patients, including those with SMI; (2) examine utilization outcomes (ie, Thrive referral, readmission, emergency department [ED], primary, and specialty care visits) for Medicaid-insured individuals with and without SMI who receive Thrive compared with usual care; and (3) evaluate the acceptability, appropriateness, feasibility, and cost-benefit of an adapted Thrive clinical pathway that is tailored for Medicaid-insured patients with co-occurring SMI. Methods: This study will use a prospective, type I hybrid effectiveness-implementation, stepped-wedge, cluster randomized controlled trial design. We will randomize the initiation of Thrive referrals at the unit level. Data collection will occur over 24 months. Inclusion criteria for Thrive referral include individuals who (1) are Medicaid insured, dually enrolled in Medicaid and Medicare, or Medicaid eligible; (2) reside in Philadelphia; (3) are admitted for a medical diagnosis for over 24 hours at the study hospital; (4) are planned for discharge to home; (5) agree to receive home care services; and (6) are aged ≥18 years. Primary analyses will use a mixed-effects negative binomial regression model to evaluate readmission and ED utilization, comparing those with and without SMI who receive Thrive to those with and without SMI who receive usual care. Using a convergent parallel mixed methods design, analyses will be conducted simultaneously for the survey and interview data of patients, clinicians, and health care system leaders. The cost of Thrive will be calculated from budget monitoring data for the research budget, the cost of staff time, and average Medicaid facility fee payments. Results: This research project was funded in October 2023. Data collection will occur from April 2024 through December 2025. Results are anticipated to be published in 2025-2027. Conclusions: We anticipate that patients with and without co-occurring SMI will benefit from the adapted Thrive clinical pathway. We also anticipate the adapted version of Thrive to be deemed feasible, acceptable, and appropriate by patients, clinicians, and health system leaders. Trial Registration: ClinicalTrials.gov NCT06203509; https://clinicaltrials.gov/ct2/show/NCT06203509 International Registered Report Identifier (IRRID): DERR1-10.2196/64575

Background: Maintaining balanced blood product ratios during damage control resuscitation (DCR) is independently associated with improved survival. We hypothesized that real‐time performance improvement (RT‐PI) would increase adherence to DCR best practice.Study Design and Methods: From December 2020–August 2021, we prospectively used a bedside RT‐PI tool to guide DCR in severely injured patients surviving at least 30 min. RT‐PI study patients were compared to contemporary control patients at our institution and historic PROMMTT study patients. A subset of patients transfused ≥6 U red blood cells (RBC) in 6 h (MT+) was also identified. The primary endpoint was percentage time in a high ratio range (≥3:4) of plasma (PLAS):RBC and platelet (PLT):RBC over 6 h. Secondary endpoints included time to massive transfusion protocol activation, time to calcium and tranexamic acid (TXA) dosing, and cumulative 6‐h ratios.Results: Included patients (n = 772) were 35 (24–51) years old with an Injury Severity Score of 27 (17–38) and 42% had penetrating injuries. RT‐PI (n = 10) patients spent 96% of the 6‐h resuscitation in a high PLAS:RBC range, no different versus CONTROL (n = 87) (96%) but more than PROMMTT (n = 675) (25%, p < .001). In the MT+ subgroup, optimal PLAS:RBC and PLT:RBC were maintained for the entire 6 h in RT‐PI (n = 4) versus PROMMTT (n = 391) patients for both PLAS (p < .001) and PLT ratios (p < .001). Time to TXA also improved significantly in RT‐PI versus CONTROL patients (27 min [22–31] vs. 51 min [29–98], p = .035).Conclusion: In this prospective study, RT‐PI was associated with optimized DCR. Multicenter validation of this novel approach to optimizing DCR implementation is warranted.

BACKGROUND People diagnosed with a co-occurring serious mental illness (SMI; ie, major depressive disorder, bipolar disorder, or schizophrenia) but hospitalized for a nonpsychiatric condition experience higher rates of readmissions and other adverse outcomes, in part due to poorly coordinated care transitions. Current hospital-to-home transitional care programs lack a focus on the integrated social, medical, and mental health needs of these patients. The Thrive clinical pathway provides transitional care support for patients insured by Medicaid with multiple chronic conditions by focusing on posthospitalization medical concerns and the social determinants of health. This study seeks to evaluate an adapted version of Thrive that also meets the needs of patients with co-occurring SMI discharged from a nonpsychiatric hospitalization. OBJECTIVE This study aimed to (1) engage staff and community advisors in participatory implementation processes to adapt the Thrive clinical pathway for all Medicaid-insured patients, including those with SMI; (2) examine utilization outcomes (ie, Thrive referral, readmission, emergency department [ED], primary, and specialty care visits) for Medicaid-insured individuals with and without SMI who receive Thrive compared with usual care; and (3) evaluate the acceptability, appropriateness, feasibility, and cost-benefit of an adapted Thrive clinical pathway that is tailored for Medicaid-insured patients with co-occurring SMI. METHODS This study will use a prospective, type I hybrid effectiveness-implementation, stepped-wedge, cluster randomized controlled trial design. We will randomize the initiation of Thrive referrals at the unit level. Data collection will occur over 24 months. Inclusion criteria for Thrive referral include individuals who (1) are Medicaid insured, dually enrolled in Medicaid and Medicare, or Medicaid eligible; (2) reside in Philadelphia; (3) are admitted for a medical diagnosis for over 24 hours at the study hospital; (4) are planned for discharge to home; (5) agree to receive home care services; and (6) are aged ≥18 years. Primary analyses will use a mixed-effects negative binomial regression model to evaluate readmission and ED utilization, comparing those with and without SMI who receive Thrive to those with and without SMI who receive usual care. Using a convergent parallel mixed methods design, analyses will be conducted simultaneously for the survey and interview data of patients, clinicians, and health care system leaders. The cost of Thrive will be calculated from budget monitoring data for the research budget, the cost of staff time, and average Medicaid facility fee payments. RESULTS This research project was funded in October 2023. Data collection will occur from April 2024 through December 2025. Results are anticipated to be published in 2025-2027. CONCLUSIONS We anticipate that patients with and without co-occurring SMI will benefit from the adapted Thrive clinical pathway. We also anticipate the adapted version of Thrive to be deemed feasible, acceptable, and appropriate by patients, clinicians, and health system leaders. CLINICALTRIAL ClinicalTrials.gov NCT06203509; https://clinicaltrials.gov/ct2/show/NCT06203509 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/64575

Background: We sought to understand the innovativeness of nurses engaging in innovative behaviors and quantify the associated characteristics that make nurses more able to innovate in practice. We first compared the innovativeness scores of our population; then we examined those who self-identified as an innovator versus those who did not to explore differences associated with innovativeness between these groups. Methods: A cross-sectional survey study of nurses in the US engaging in innovative behaviors was performed. We performed an exploratory factor analysis (EFA) to determine the correlates of innovative behavior. Results: Three-hundred and twenty-nine respondents completed the survey. Respondents who viewed themselves as innovators had greater exposure to HCD/DT workshops in the past year (55.8% vs. 36.6%, p = 0.02). The mean innovativeness score of our sample was 120.3 ± 11.2 out of a score of 140. The mean innovativeness score was higher for those who self-identified as an innovator compared with those who did not (121.3 ± 10.2 vs. 112.9 ± 14.8, p =< 0.001). The EFA created four factor groups: Factor 1 (risk aversion), Factor 2 (willingness to try new things), Factor 3 (creativity and originality) and Factor 4 (being challenged). Conclusion: Nurses who view themselves as innovators have higher innovativeness scores compared with those who do not. Multiple individual and organizational characteristics are associated with the innovativeness of nurses.