Highlights
- •Precision rehabilitation delivers targeted interventions that optimize function.
- •Heterogeneous study designs and databases are critical to precision rehabilitation.
- •Standardized and accurate measures of function advance precision rehabilitation.
- •System and team science are critical to the success of precision rehabilitation.
- •Deliberate action by researchers, professional organizations, and funders is needed.
Abstract
Precision medicine efforts are underway in many medical disciplines; however, the
power of precision rehabilitation has not yet been explored. Precision medicine aims
to deliver the right intervention, at the right time, in the right setting, for the
right person, ultimately bolstering the value of the care that we provide. To date,
precision medicine efforts have rarely focused on function at the level of a person,
but precision rehabilitation is poised to change this and bring the focus on function
to the broader precision medicine enterprise. To do this, subgroups of individuals
must be identified based on their level of function via precise measurement of their
abilities in the physical, cognitive, and psychosocial domains. Adoption of electronic
health records, advances in data storage and analytics, and improved measurement technology
make this shift possible. Here we detail critical components of the precision rehabilitation
framework, including (1) the synergistic use of various study designs, (2) the need
for standardized functional measurements, (3) the importance of precise and longitudinal
measures of function, (4) the utility of comprehensive databases, (5) the importance
of predictive analyses, and (6) the need for system and team science. Precision rehabilitation
has the potential to revolutionize clinical care, optimize function for all individuals,
and magnify the value of rehabilitation in health care; however, to reap the benefits
of precision rehabilitation, the rehabilitation community must actively pursue this
shift.
Keywords
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References
- “Precision medicine.” OED Online.Oxford University Press, 2021
- Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease.National Academies Press, Washington, DC2011
- The path to personalized medicine.N Engl J Med. 2010; 363: 301-304
- The clinical course of multiple sclerosis.Handb Clin Neurol. 2014; 122: 343-369
- Supporting work for people with multiple sclerosis.Mult Scler. 2014; 20: 646-650
- Factors influencing work retention for people with multiple sclerosis: cross-sectional studies using qualitative and quantitative methods.J Neurol. 2005; 252: 892-896
- The effect of self-assessed fatigue and subjective cognitive impairment on work capacity: the case of multiple sclerosis.Mult Scler. 2019; 25: 740-749
- Factors that influence the employment status of people with multiple sclerosis: a multi-national study.J Neurol. 2009; 256: 1989-1996
- Assessing the gold standard—lessons from the history of RCTs.N Engl J Med. 2016; 374: 2175-2181
- Grading quality of evidence and strength of recommendations.BMJ. 2004; 328: 1490-1494
- The levels of evidence and their role in evidence-based medicine.Plast Reconstr Surg. 2011; 128: 305-310
- The causes and effects of socio-demographic exclusions from clinical trials.Health Technol Assess. 2005; 9 (iii-iv, ix-x): 1-152
- Health disparities and clinical trial recruitment: is there a duty to tweet?.PLOS Biology. 2017; 15e2002040
- Evidence for health decision making—beyond randomized, controlled trials.N Engl J Med. 2017; 377: 465-475
- The expanding role of real-world evidence trials in health care decision making.J Diabetes Sci Technol. 2020; 14: 174-179
- Real-world evidence in the real world: beyond the FDA.Am J Law Med. 2018; 44: 161-179
- Precision medicine versus evidence-based medicine: individual treatment effect versus average treatment effect.Circulation. 2019; 140: 1236-1238
- The value of pragmatic and observational studies in health care and public health.Pragmat Obs Res. 2017; 8: 49-55
- A treatment-based classification approach to low back syndrome: identifying and staging patients for conservative treatment.Phys Ther. 1995; 75 (discussion 85-9): 470-485
- Clinical examination variables discriminate among treatment-based classification groups: a study of construct validity in patients with acute low back pain.Phys Ther. 2005; 85: 306-314
- Prevalence and reliability of treatment-based classification for subgrouping patients with low back pain.J Man Manip Ther. 2018; 26: 36-42
- Comparison of classification-based physical therapy with therapy based on clinical practice guidelines for patients with acute low back pain: a randomized clinical trial.Spine (Phila Pa 1976). 2003; 28 (discussion 72): 1363-1371
- Stratified care to prevent chronic low back pain in high-risk patients: the TARGET trial. A multi-site pragmatic cluster randomized trial.EClinicalMedicine. 2021; 34100795
- Effectiveness of a low back pain classification system.Spine J. 2009; 9: 648-657
- The effectiveness of mechanical traction among subgroups of patients with low back pain and leg pain: a randomized trial.J Orthop Sports Phys Ther. 2016; 46: 144-154
- Implementation of clinical guidelines on physical therapy for patients with low back pain: randomized trial comparing patient outcomes after a standard and active implementation strategy.Phys Ther. 2005; 85: 544-555
- Pragmatic implementation of a stratified primary care model for low back pain management in outpatient physical therapy settings: two-phase, sequential preliminary study.Phys Ther. 2015; 95: 1120-1134
- Vinther Nielsen C. Participation in pulmonary rehabilitation in routine clinical practice.Clin Respir J. 2011; 5: 235-244
- Academy of Oncologic Physical Therapy EDGE task force: a systematic review of measures of balance in adult cancer survivors.Rehabil Oncol. 2019; 37: 92-103
- Outcome measure recommendations from the spinal cord injury EDGE task force.Phys Ther. 2016; 96: 1832-1842
- The brain recovery core: building a system of organized stroke rehabilitation and outcomes assessment across the continuum of care.J Neurol Phys Ther. 2011; 35: 194-201
- Clinician adherence to a standardized assessment battery across settings and disciplines in a poststroke rehabilitation population.Arch Phys Med Rehabil. 2013; 94 (e1): 1048-1053
- Implementation of wearable sensing technology for movement: pushing forward into the routine physical rehabilitation care field.Sensors (Basel). 2020; 20: 5744-5764
- Sensors and systems for physical rehabilitation and health monitoring—a review.Sensors (Basel). 2020; 20: 4063-4091
- Wearable sensors to monitor, enable feedback, and measure outcomes of activity and practice.Curr Neurol Neurosci Rep. 2018; 18: 87-95
- Systematic review of mobile health applications in rehabilitation.Arch Phys Med Rehabil. 2019; 100: 115-127
- Perspectives on the evolution of mobile (mHealth) technologies and application to rehabilitation.Phys Ther. 2015; 95: 397-405
- Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review.J Neuroeng Rehabil. 2021; 18: 67-85
- OpenPose: realtime multi-person 2D pose estimation using part affinity fields.IEEE Trans Pattern Anal Mach Intell. 2021; 43: 172-186
- DeeperCut: a deeper, stronger, and faster multi-person pose estimation model.Springer International, Cham, Switzerland2016
Fang H-S, Xie S, Tai Y-W, Lu C. RMPE: regional multi-person pose estimation. 2016. arXiv:1612.00137.
- Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments.Psychon Bull Rev. 2012; 19: 847-857
- Unobtrusive measurement of daily computer use to detect mild cognitive impairment.Alzheimers Dement. 2014; 10: 10-17
- Computer mouse movement patterns: a potential marker of mild cognitive impairment.Alzheimers Dement (Amst). 2015; 1: 472-480
- In-home walking speeds and variability trajectories associated with mild cognitive impairment.Neurology. 2012; 78: 1946-1952
- Unobtrusive assessment of activity patterns associated with mild cognitive impairment.Alzheimers Dement. 2008; 4: 395-405
- Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery.NPJ Digit Med. 2020; 3: 121-131
- Predicting and monitoring upper-limb rehabilitation outcomes using clinical and wearable sensor data in brain injury survivors.IEEE Trans Biomed Eng. 2021; 68: 1871-1881
- The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment.J Am Med Inform Assoc. 2021; 28: 427-443
- Big data and the precision medicine revolution.Prod Oper Manag. 2018; 27: 1647-1664
- From big data to precision medicine.Front Med (Lausanne). 2019; 6: 1-14
- Omic and electronic health record big data analytics for precision medicine.IEEE Trans Biomed Eng. 2017; 64: 263-273
- Evaluating common data models for use with a longitudinal community registry.J Biomed Inform. 2016; 64: 333-341
- Data model harmonization for the All Of Us Research Program: transforming i2b2 data into the OMOP common data model.PLoS One. 2019; 14e0212463
- Identifying appropriate reference data models for comparative effectiveness research (CER) studies based on data from clinical information systems.Med Care. 2013; 51: S45-S52
- Classification versus association models: should the same methods apply?.Scand J Clin Lab Invest Suppl. 2010; 242: 53-58
- Establishment of best practices for evidence for prediction: a review.JAMA Psychiatry. 2020; 77: 534-540
- Association is not prediction: a landscape of confused reporting in diabetes—a systematic review.Diabetes Res Clin Pract. 2020; 170108497
- Why significant variables aren't automatically good predictors.Proc Natl Acad Sci U S A. 2015; 112: 13892-13897
- Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker.Am J Epidemiol. 2004; 159: 882-890
- Predicting the future—big data, machine learning, and clinical medicine.N Engl J Med. 2016; 375: 1216-1219
- Big data and machine learning algorithms for health-care delivery.Lancet Oncol. 2019; 20: e262-ee73
- The PREP algorithm predicts potential for upper limb recovery after stroke.Brain. 2012; 135: 2527-2535
- PREP2: a biomarker-based algorithm for predicting upper limb function after stroke.Ann Clin Transl Neurol. 2017; 4: 811-820
Article info
Publication history
Published online: February 15, 2022
Accepted:
January 31,
2022
Received in revised form:
January 7,
2022
Received:
July 26,
2021
Footnotes
Disclosure: none.
The Johns Hopkins Precision Rehabilitation Group
Identification
Copyright
© 2022 by the American Congress of Rehabilitation Medicine.