Translation from Concept to Clinic

Join us as this presenter discusses this poster live on Tuesday, August 11, 2020 | Track D at 4:45 PM Mountain

PRESENTER
JEREMY GRABER
PhD Student, University of Colorado Anschutz Medical Campus
BACKGROUND
Rehabilitation after total knee arthroplasty (TKA) is typically generic and based on population-level estimates of recovery. Individualized, patient-centered care improves orthopedic outcomes1 and is desired by patients after TKA.2, 3 However, clinicians lack the necessary tools to deliver this kind of care consistently. We developed a novel approach for generating individualized recovery trajectories in rehabilitation to improve patient-centered care for patients with TKA.4 We worked closely with relevant stakeholders to incorporate our approach into a clinical support tool—the Knee Recovery App—designed for TKA rehabilitation. The purpose of this project is to evaluate the effectiveness of the Knee Recovery App while gathering information about its implementation using the RE-AIM framework.5
SETTING
The Knee Recovery App will be implemented as standard of care in two physical therapy clinics in Greenville, SC, owned by ATI Physical Therapy. The app will be used directly by physical therapists to generate individualized recovery information for all patients between the ages of 40 and 90 seeking rehabilitation after TKA (n=30).
METHODS
We will use a Hybrid Type 1 design to test the effectiveness of the Knee Recovery App while gathering information about its implementation potential.6 Physical therapists will use the app to generate patient-specific recovery estimates to (1) inform patients of their expected recovery and (2) develop a patient-centered plan of care. Outcomes will be collected prospectively throughout the episode of care and compared to a retrospective cohort of age and sex-matched patients (n=60) in ATI’s quality improvement database of patients with TKA. Group differences will be examined using linear models and effect size will be calculated with Cohen’s d. Effectiveness outcomes will include functional measures and surveys related to patient-centered care (Table 1). We will evaluate the implementation potential of our app using a mixed-methods approach informed by the RE-AIM framework (Table 1).7 Our qualitative approach will consist of directed content analysis of semi-structured interviews with representation from all stakeholders (n=30).8 Quantitative implementation outcomes will be extracted from three sources: (1) ATI’s quality improvement database, (2) information stored on the Knee Recovery App, and (3) survey administered to clinicians.
CONCLUSIONS
Our novel approach for individualized rehabilitation has the potential to improve patient-centered care and outcomes in TKA rehabilitation. The RE-AIM framework informed our implementation strategy and will provide the structure to examine barriers and facilitators to clinical use of the Knee Recovery App. We anticipate the results of this project will inform a future cluster randomized trial in the ATI system guided by the PRISM framework.
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VIDEO


How Understanding Change Experience In Smoking Cessation Might Inform Treatment Development

Join us as this presenter discusses this poster live on Tuesday, August 11, 2020 | Track C at 4:35 PM Mountain

PRESENTER
ADRIENNE L. JOHNSON, PhD
Postdoctoral Fellow, University of Wisconsin Center for Tobacco Research and Intervention; William S. Middleton Memorial Veteran’s Hospital
BACKGROUND
Cigarette smoking is the leading preventable cause of death and disability in the U.S., accounting for almost half a million deaths per year1,2. Despite ongoing efforts to improve cessation rates through treatment development and dissemination of evidence-based practices, less than one-third of the population use proven cessation methods and the average quit rate is 7.4%3. Qualitative research methodologies have the potential to highlight limitations and identify novel approaches or adaptations to existing behavioral treatments4 that may increase engagement, adherence, and success. Using a mixed methods approach, we examine whether smokers have insight into changes needed to quit smoking and how this insight affects actual changes made during a smoking cessation attempt as well as cessation success.
METHODS
Stratified random sampling5 (Figure 1) was used to select 100 current cigarette smokers participating in an aided smoking cessation attempt as part of a larger comparative effectiveness trial. Bachelors level Health Counselors completed brief individual qualitative interviews at baseline and two-week post-quit visits. Interviews were audio-recorded and transcribed, then entered into a MS-excel file for coding purposes. Rapid data analytic methods 6,7 are being used to examine three separate domains: planned changes, used changes, and consistency in changes.
RESULTS
Initial themes for planned changes prior to quitting include: changing routines (no plan), change alcohol consumption, limiting smoking urges, focus on benefits of quitting, reduce exposure to social smoking cues, identify other coping mechanisms for stress, get support from friends/loved ones, make other health changes, reduce stress (no plan), get partner to quit, identify problem (no plan), planned changes unknown. Initial used changes themes include: reduce exposure to non-social paraphernalia/smoking cues, distraction/keep busy, make other health changes, changed daily routine, change mindset, reduce exposure to social smoking cues, get support from friends/loved ones. Consistency between planned and used changes revealed participants use both planned and unplanned changes, while some reported not knowing what to do prior to quitting and others identified additional changes needed.
DISCUSSION
Ongoing rapid content analysis6 revealed multiple themes for smoker-identified planned and used changes to improve cessation success, but analyses need to be completed. We will then examine distribution of themes based on race, gender, psychiatric history, and nicotine dependence as well as the ability of smokers to identify and make changes. Exploratory analyses will descriptively examine differences between smokers who did and did not identify changes on cessation success. Findings will guide treatment development and adaptations for behavioral smoking cessation treatments.
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VIDEO


Web-based Sample Size Calculator for Cluster-Randomized and Stepped-Wedge Designs

Join us as this presenter discusses this poster live on Tuesday, August 11, 2020 | Track D at 5:25 PM Mountain

PRESENTER
KRITHIKA SURESK, JOHN RICE
Assistant Professor – Research, University of Colorado
BACKGROUND
Cluster-randomized and stepped-wedge are pragmatic trial designs that have become increasingly popular in recent years. Due to feasibility or logistical constraints, individual-level randomization is often not possible, and interventions must be implemented at the cluster (e.g., site, clinic) level. Power/sample size calculations are used to identify whether a proposed design s feasible for detecting a clinically meaningful effect of an intervention. Tools that perform these calculations are thus essential in the planning of an effective study and for assessing various design options.
SETTING
Power calculations for cluster-randomized and stepped-wedge designs incorporate the correlation between multiple observations in the same cluster. They also require additional consideration such as the number of clusters, and individuals per cluster. There are trade-offs when evaluating each of these two designs, and often one is considered when the other is not feasible and/or does not provide sufficient power. With a free, web-based applet, we unify the power/sample size calculations for these two clustered study designs in a single application, allowing for easy comparison and evaluation of alternative designs.
METHODS
Using an R Shiny application, we implement methodology developed for cluster-randomized and stepped-wedge designs for sample size/power calculations. We incorporate recent extensions, such as cluster auto-correlation, washout effects, and hybrid designs. The application will use a guided step-by-step process, where users will specify the parameters of their trial design. Users will provide inputs related to the outcome of interest and study design, such as number of clusters, individuals per cluster, desired power, type I error rate, outcome distribution, effect size, and the intraclass correlation coefficient. Outputs will include a visualization of the study design, and a summary statement describing the design, assumptions, and power/sample size values.
RESULTS
The R Shiny calculator will be hosted online as a web applet that can be used by clinicians and statisticians to help plan their trial design. A range of examples will be presented to demonstrate the use of the calculator. Documentation for the methods and references be provided. Code for the application and power calculations will be shared using Github, where users can provide feedback and request modifications or extensions.
CONCLUSIONS
With this online calculator, we aim to increase the accessibility of current and emerging sample size methodology for researchers who are considering pragmatic design alternatives to answer their research question.
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