Walter R. Frontera, Jonathan F. Bean, Diane Damiano, Linda Ehrlich-Jones, Melanie Fried-Oken, Alan Jette, Ranu Jung, Rick L. Lieber, James F. Malec, Michael J. Mueller, Kenneth J. Ottenbacher, Keith E. Tansey, Aiko Thompson; Rehabilitation Research at the National Institutes of Health: Moving the Field Forward (Executive Summary). Am J Occup Ther 2017;71(3):7103320010P1-7103320010P12. doi: 10.5014/ajot.2017.713003.
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© 2017 American Occupational Therapy Association
Approximately 53 million Americans live with a disability. For decades, the National Institutes of Health (NIH) has been conducting and supporting research to discover new ways to minimize disability and enhance the quality of life of people with disabilities. After the passage of the Americans With Disabilities Act, NIH established the National Center for Medical Rehabilitation Research, with the goal of developing and implementing a rehabilitation research agenda. Currently, 17 institutes and centers at NIH invest more than $500 million per year in rehabilitation research. Recently, the director of NIH, Francis Collins, appointed a Blue Ribbon Panel to evaluate the status of rehabilitation research across institutes and centers. As a follow-up to the work of that panel, NIH recently organized a conference, “Rehabilitation Research at NIH: Moving the Field Forward.” This report is a summary of the discussions and proposals that will help guide rehabilitation research at NIH in the near future.
Participatory action research is a critical element of rehabilitation research. Individuals with disabilities must be included in all stages of hypothesis testing and analysis to ensure content validity. Participatory action research is sensitive to group as well as individual differences (e.g., cultural, ethnic, lifestyle diversity) and leads to people having increased control over their lives.
The utility of AT for value added to end users and professionals must become a priority for rehabilitation science. Utility measures such as task performance (e.g., efficiency and effectiveness of task completion), user satisfaction, and quality of life must become standard. It is challenging to measure value because the user population is extremely heterogeneous in terms of needs, abilities, and preferences. Researchers must determine whether it is better to assess utility for a narrow population who is most likely to benefit from AT or for a broad population, where only a subset of individuals is likely to benefit. The variability of user population and task conditions can make it very hard and/or costly to get good statistics on utility. Although statistical success is easier to obtain under controlled laboratory conditions, the laboratory conditions do not translate to real-world conditions. Measurement of user satisfaction (or dissatisfaction) and quality of life, constructs that are often used for outcomes, has challenges as well.
AT must be scaled, in terms of sustainability and accessibility, to the population. As technology is rapidly advancing, we must try to get at the back end of it even as it gets more complex. For example, as infrared sensors became wireless, laboratories and smart homes needed to adjust so that our tools are sustainable. For the biggest impact, one goal in technology research and development must include keeping products and services affordable so they can be accessed by the population who needs them. Likewise, we must increase awareness and benefits of ATs for the general public. The AT must meet the environmental and personal demands of the end users while protecting privacy and maintaining confidentiality and security of personal information.
The importance of developing a consolidated infrastructure, be that through industry partnerships or academic hubs;
Using that infrastructure to develop systems that integrate mHealth, wearables, and patient-reported outcomes in efficient ways so that they complement each other to optimize assessment and monitoring;
Developing strategies to incorporate these integrated data elements into measurement systems with which patients and clinicians can optimally engage and interact; and
Integration of the resulting data into the electronic medical record.
Developing standards or best practices for wearable sensor technology akin to what the Patient-Reported Outcomes Measurement Information System had done for patient-reported outcomes;
Developing strategies for extracting the most important data from wearable sensors and presenting them in a way that is appropriate for the given stakeholder (patients, practitioners, payers); and
Using these approaches for more optimal management of self-care and thus relieving clinicians of the burden created by interpreting and processing high volumes of data.
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