ABSTRACT
OBJECTIVES: This study aimed to describe the process of adapting an evidence-based patient engagement intervention, enhanced medical rehabilitation (E-MR), for inpatient spinal cord injury/disease (SCI/D) rehabilitation using an implementation science framework. DESIGN: We applied the collaborative intervention planning framework and included a community advisory board (CAB) in an intervention mapping process. SETTING: A rehabilitation hospital. PARTICIPANTS: Stakeholders from inpatient SCI/D rehabilitation (N=7) serving as a CAB and working with the research team (N=7) to co-adapt E-MR. INTERVENTIONS: E-MR. MAIN OUTCOME MEASURES: Logic model and matrices of change used in CAB meetings to identify areas of intervention adaptation. RESULTS: The CAB and research team implemented adaptations to E-MR, including (1) identifying factors influencing patient engagement in SCI/D rehabilitation (eg, therapist training); (2) revising intervention materials to meet SCI/D rehabilitation needs (eg, modified personal goals interview and therapy trackers to match SCI needs); (3) incorporating E-MR into the rehabilitation hospital's operations (eg, research team coordinated with CAB to store therapy trackers in the hospital system); and (4) retaining fidelity to the original intervention while best meeting the needs of SCI/D rehabilitation (eg, maintained core E-MR principles while adapting). CONCLUSIONS: This study demonstrated that structured processes guided by an implementation science framework can help researchers and clinicians identify adaptation targets and modify the E-MR program for inpatient SCI/D rehabilitation.
Subject(s)
Neurological Rehabilitation , Spinal Cord Injuries , Humans , Inpatients , Patient Participation , Implementation Science , Spinal Cord Injuries/rehabilitationABSTRACT
This review aimed to quantify correlations between heart rate variability (HRV) and functional outcomes after acquired brain injury (ABI). We conducted a literature search from inception to January 2020 via electronic databases, using search terms with HRV, ABI, and functional outcomes. Meta-analyses included 16 studies with 906 persons with ABI. Results demonstrated significant associations: Low frequency (LF) (r = -0.28) and SDNN (r = -0.33) with neurological function; LF (r = -0.33), High frequency (HF) (r = -0.22), SDNN (r = -0.22), and RMSSD (r = -0.23) with emotional function; and LF (r = 0.34), HF (r = 0.41 to 0.43), SDNN (r = 0.43 to 0.51), and RMSSD (r = 0.46) with behavioral function. Results indicate that higher HRV is related to better neurological, emotional, and behavioral functions after ABI. In addition, persons with stroke showed lower HF (SMD = -0.50) and SDNN (SMD = -0.75) than healthy controls. The findings support the use of HRV as a biomarker to facilitate precise monitoring of post-ABI functions.
Subject(s)
Brain Injuries , Emotions , Biomarkers , Heart Rate/physiology , HumansABSTRACT
Background: Older adults manage increasing numbers of everyday technologies to participate in home and community activities. Purpose: We investigated how assessing use of everyday technologies enhanced predictions of overall needed assistance among urban older adults. Method: We used a cross-sectional design to analyze responses from 114 participants completing the Everyday Technology Use Questionnaire, the Montreal Cognitive Assessment, and a sociodemographic questionnaire. We estimated overall needed assistance based on definitions in the Assessment of Motor and Process Skills. We created logistic regression models and receiver operator characteristic curves to analyze variables predicting overall needed assistance. Findings: With high specificity and sensitivity, the Everyday Technology Use Questionnaire and the Montreal Cognitive Assessment were the strongest predictors of overall needed assistance. Implications: Assessing everyday technology use enhanced predictions of overall needed assistance among urban older adults.
