Linda Ehrlich-Jones, RN, PhD

Namrata Grampurohit, PhD, OTR/L

INside the OUTcomes: A Rehabilitation Research Podcast

Episode 03: The Rehabilitation Measures Database 

The Rehabilitation Measures Database (RMD) is a free, online resource managed by the Center for Rehabilitation Outcomes Research at Shirley Ryan AbilityLab. With more than 500 measures supported by some of the world’s best doctors, therapists, researchers, and educators, the RMD is the go-to resource for measuring benchmarks and outcomes in physical medicine and rehabilitation. The database is visited more than 5 million times annually, and contains a range of instruments on stroke, spinal injuries, brain injuries, Parkinson’s disease, neuromuscular conditions, vestibular disorders, musculoskeletal conditions, cancers, arthritis or use our search feature to find what you’re looking for.  

On this episode, we will talk with Linda Ehrlich-Jones, RN, PhD, Associate Director, Center for Rehabilitation Outcomes Research and Research Associate Professor, Department of Physical Medicine & Rehabilitation, Northwestern University, Feinberg School of Medicine and Namrata Grampurohit, PhD, OTR/L, Associate Professor in the Department of Occupational Therapy at Thomas Jefferson University in Philadelphia, and a member of the Jefferson Center for Outcomes Measurement. Linda manages the RMD with help from collaborators across the country, including Dr. Grampurohit. Together, we will talk about how the database came to be, the measures it contains, how measures are reviewed and other resources offered by the RMD.

Join the Rehabilitation Measures Database Networking Group on LinkedIn and keep up with the #MeasureOfTheWeek posted each Thursday.

This podcast is funded by the National Institute on Disability, Independent Living, and Rehabilitation Research. (Grants 90DPKT0007, 90RTGE0004, 90RTEM0001, 90SIMS0015).  

Alex Wong, PhD, DPhil, OT, CRC

Developing Mobile Health Assessments

Dr. Wong and his research team are dedicated to advancing the field of mobile health assessments to improve the precision and ecological validity of outcome measurements that can be used in physical medicine and rehabilitation. One of their current projects, funded by the National Institutes of Health (NIH), builds upon their previous work in ecological momentary assessment. The team is currently engaged in the development, validation, and evaluation of a set of mobile cognitive assessments (resembling brain games) designed to monitor changes in cognitive functioning among individuals recovering from strokes. By combining patient-reported measures with these mobile cognitive assessments, they aim to gain a deeper understanding of the complex interplay between cognition, mood, somatic symptoms, and daily functioning. 

Furthermore, their research endeavors are supported by another project funded by the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR). This initiative aligns with their mission to utilize outcome measures to inform rehabilitation treatments and optimize patient outcomes. In this project, the team tested a smartphone application integrating principles from behavioral health, implementation science, and treatment optimization methodologies. The aim was to assist individuals with spinal cord injuries in adhering to physical activity guidelines effectively.

For more comprehensive insights into their research, you may refer to the following publication:

• Bui, Q., et al. (2022).  Smartphone assessment uncovers real-time relationships between depressed mood and daily functional behaviors after stroke. J Telemed Telecare, 1-14. 
• Bui, Q., et al. (2022). Ecological momentary assessment of real-world functional behaviors in individuals with stroke: A longitudinal observational study. Arch Phys Med Rehabil, 103(7), 1327-1337. 
• Chen, P., et al. (2021). Measuring activities of daily living in stroke patients with motion machine learning algorithms: A pilot study. Int J Environ Res Public Health, 18(4), 1634.

Chih-Ying “Cynthia” Li, PhD, OTR 

Non-Response Patterns in Medicare Data

Dr. Li’s research endeavors have centered around characterizing standardized functional data within inpatient rehabilitation facilities. Notably, Dr. Li and her research team conducted an analysis of 2018 Medicare administrative claims data to assess non-response patterns in the function domains of self-care and mobility (Section GG). The non-response patterns included categories such as refusal, non-attempt due to medical concerns or safety issues, not applicable, and completely blank responses with no recorded value.

The study involved an extensive analysis of 159,691 patients who had undergone inpatient rehabilitation for conditions such as stroke, brain dysfunction, neurologic conditions, orthopedic disorders, or debility. Four distinct methods were applied to generate imputed values for each non-response functional item of every patient, namely: Monte Carlo Markov Chains multiple imputations (MCMC), Fully Conditional Specification multiple imputations (FCS), Pattern-mixture model (PMM) multiple imputations, and the Centers for Medicare and Medicaid Services (CMS) approach.

The research team conducted a comprehensive comparison of changes in Spearman correlations and weighted kappa between Section GG and site-specific functional items across different impairments before and after the application of the four imputation methods. The results indicated that different non-response patterns were associated with varied functional statuses, particularly observing a high proportion of non-response patterns in mobility items. This raised concerns about potential biased quality reporting at inpatient rehabilitation facilities. Additionally, the study revealed that the four imputation methods produced similar imputed score results, adding to the validity of their findings. The implications of these findings are extensive, as they have the potential to contribute to the improvement of patient care, outcomes, quality reporting, and payment within post-acute settings.

Dr. Li is actively seeking collaborators who possess expertise in qualitative research, implementation science research, data science, virtual reality, and artificial intelligence, to further enrich and expand the scope of her research endeavors.

Trudy Mallinson, PhD, OTR/L, FAOTA,

Rasch Reporting Guideline for Rehabilitation Research (RULER): the RULER Statement

In recent years, the application of Rasch Measurement (RM) Theory to rehabilitation assessments has seen significant growth. RM Theory plays a crucial role in designing and refining assessments, ensuring that items represent a unidimensional construct with equal interval metrics, distinguishing among individuals with varying abilities consistently based on the underlying trait. However, with the rapid expansion of RM in rehabilitation assessment studies, there has been a lack of consistency in reporting results. Clear, transparent, and consistent reporting of RM Theory outcomes is essential to advance rehabilitation science and practice, as precise measures empower researchers, practitioners, patients, and stakeholders with effective decision-making tools. To address these reporting inconsistencies, a peer-reviewed and evidence-based Rasch Reporting Guideline for Rehabilitation Research (RULER) has been developed. This guideline aims to provide transparent and standardized recommendations for reporting studies that utilize RM Theory within a rehabilitation context.

For a comprehensive understanding of this research, the following publications serve as valuable resources:

• Website: https://acrm.org/acrm-communities/measurement/mng-task-forces/rasch-reporting-guidelines-task-force/
• Mallinson T, et al. Rasch Reporting Guideline for Rehabilitation Research (RULER): the RULER Statement. Arch Phys Med Rehabil. 2022 Jul;103(7):1477-1486. 
• Van de Winckel A, et al. Reporting Guideline for RULER: Rasch Reporting Guideline for Rehabilitation Research: Explanation and Elaboration. Arch Phys Med Rehabil. 2022 Jul;103(7):1487-1498.