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Precision Rehabilitation: Optimizing Function, Adding Value to Health Care

Published:February 15, 2022DOI:https://doi.org/10.1016/j.apmr.2022.01.154

      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

      1. “Precision medicine.” OED Online.
        Oxford University Press, 2021
        • National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease
        Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease.
        National Academies Press, Washington, DC2011
        • Hamburg MA
        • Collins FS.
        The path to personalized medicine.
        N Engl J Med. 2010; 363: 301-304
        • Confavreux C
        • Vukusic S.
        The clinical course of multiple sclerosis.
        Handb Clin Neurol. 2014; 122: 343-369
        • Doogan C
        • Playford ED.
        Supporting work for people with multiple sclerosis.
        Mult Scler. 2014; 20: 646-650
        • O'Connor RJ
        • Cano SJ
        • Ramió i Torrentà L
        • Thompson AJ
        • Playford ED.
        Factors influencing work retention for people with multiple sclerosis: cross-sectional studies using qualitative and quantitative methods.
        J Neurol. 2005; 252: 892-896
        • Kobelt G
        • Langdon D
        • Jönsson L.
        The effect of self-assessed fatigue and subjective cognitive impairment on work capacity: the case of multiple sclerosis.
        Mult Scler. 2019; 25: 740-749
        • Messmer Uccelli M
        • Specchia C
        • Battaglia MA
        • Miller DM.
        Factors that influence the employment status of people with multiple sclerosis: a multi-national study.
        J Neurol. 2009; 256: 1989-1996
        • Bothwell LE
        • Greene JA
        • Podolsky SH
        • Jones DS.
        Assessing the gold standard—lessons from the history of RCTs.
        N Engl J Med. 2016; 374: 2175-2181
        • Atkins D
        • Best D
        • Briss PA
        • et al.
        Grading quality of evidence and strength of recommendations.
        BMJ. 2004; 328: 1490-1494
        • Burns PB
        • Rohrich RJ
        • Chung KC.
        The levels of evidence and their role in evidence-based medicine.
        Plast Reconstr Surg. 2011; 128: 305-310
        • Bartlett C
        • Doyal L
        • Ebrahim S
        • et al.
        The causes and effects of socio-demographic exclusions from clinical trials.
        Health Technol Assess. 2005; 9 (iii-iv, ix-x): 1-152
        • Caplan A
        • Friesen P.
        Health disparities and clinical trial recruitment: is there a duty to tweet?.
        PLOS Biology. 2017; 15e2002040
        • Frieden TR.
        Evidence for health decision making—beyond randomized, controlled trials.
        N Engl J Med. 2017; 377: 465-475
        • Klonoff DC.
        The expanding role of real-world evidence trials in health care decision making.
        J Diabetes Sci Technol. 2020; 14: 174-179
        • Krause JH
        • Saver RS.
        Real-world evidence in the real world: beyond the FDA.
        Am J Law Med. 2018; 44: 161-179
        • Blackstone EH.
        Precision medicine versus evidence-based medicine: individual treatment effect versus average treatment effect.
        Circulation. 2019; 140: 1236-1238
        • Barnish MS
        • Turner S.
        The value of pragmatic and observational studies in health care and public health.
        Pragmat Obs Res. 2017; 8: 49-55
        • Delitto A
        • Erhard RE
        • Bowling RW.
        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
        • George SZ
        • Delitto A.
        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
        • de Oliveira IO
        • de Vasconcelos RA
        • Pilz B
        • et al.
        Prevalence and reliability of treatment-based classification for subgrouping patients with low back pain.
        J Man Manip Ther. 2018; 26: 36-42
        • Fritz JM
        • Delitto A
        • Erhard RE.
        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
        • Delitto A
        • Patterson CG
        • Stevans JM
        • et al.
        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
        • Hall H
        • McIntosh G
        • Boyle C.
        Effectiveness of a low back pain classification system.
        Spine J. 2009; 9: 648-657
        • Thackeray A
        • Fritz JM
        • Childs JD
        • Brennan GP.
        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
        • Bekkering GE
        • van Tulder MW
        • Hendriks EJ
        • et al.
        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
        • Beneciuk JM
        • George SZ.
        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
        • Bjoernshave B
        • Korsgaard J
        • Jensen C
        Vinther Nielsen C. Participation in pulmonary rehabilitation in routine clinical practice.
        Clin Respir J. 2011; 5: 235-244
        • Min HH
        • Hile E
        • Croarkin E
        • et al.
        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
        • Kahn JH
        • Tappan R
        • Newman CP
        • Palma P
        • Romney W
        • Tseng Stultz E
        • et al.
        Outcome measure recommendations from the spinal cord injury EDGE task force.
        Phys Ther. 2016; 96: 1832-1842
        • Lang CE
        • Bland MD
        • Connor LT
        • et al.
        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
        • Bland MD
        • Sturmoski A
        • Whitson M
        • et al.
        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
        • Lang CE
        • Barth J
        • Holleran CL
        • Konrad JD
        • Bland MD.
        Implementation of wearable sensing technology for movement: pushing forward into the routine physical rehabilitation care field.
        Sensors (Basel). 2020; 20: 5744-5764
        • Nascimento LMSD
        • Bonfati LV
        • Freitas MB
        • Mendes Junior JJA
        • Siqueira HV
        • Stevan SL
        Sensors and systems for physical rehabilitation and health monitoring—a review.
        Sensors (Basel). 2020; 20: 4063-4091
        • Dobkin BH
        • Martinez C.
        Wearable sensors to monitor, enable feedback, and measure outcomes of activity and practice.
        Curr Neurol Neurosci Rep. 2018; 18: 87-95
        • Nussbaum R
        • Kelly C
        • Quinby E
        • Mac A
        • Parmanto B
        • Dicianno BE.
        Systematic review of mobile health applications in rehabilitation.
        Arch Phys Med Rehabil. 2019; 100: 115-127
        • Dicianno BE
        • Parmanto B
        • Fairman AD
        • et al.
        Perspectives on the evolution of mobile (mHealth) technologies and application to rehabilitation.
        Phys Ther. 2015; 95: 397-405
        • Peters DM
        • O’Brien ES
        • Kamrud KE
        • et al.
        Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review.
        J Neuroeng Rehabil. 2021; 18: 67-85
        • Cao Z
        • Hidalgo G
        • Simon T
        • Wei SE
        • Sheikh Y.
        OpenPose: realtime multi-person 2D pose estimation using part affinity fields.
        IEEE Trans Pattern Anal Mach Intell. 2021; 43: 172-186
        • Insafutdinov E
        • Pishchulin L
        • Andres B
        • Andriluka M
        • Schiele B.
        DeeperCut: a deeper, stronger, and faster multi-person pose estimation model.
        Springer International, Cham, Switzerland2016
      2. Fang H-S, Xie S, Tai Y-W, Lu C. RMPE: regional multi-person pose estimation. 2016. arXiv:1612.00137.

        • Germine L
        • Nakayama K
        • Duchaine BC
        • Chabris CF
        • Chatterjee G
        • Wilmer JB.
        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
        • Kaye J
        • Mattek N
        • Dodge HH
        • et al.
        Unobtrusive measurement of daily computer use to detect mild cognitive impairment.
        Alzheimers Dement. 2014; 10: 10-17
        • Seelye A
        • Hagler S
        • Mattek N
        • et al.
        Computer mouse movement patterns: a potential marker of mild cognitive impairment.
        Alzheimers Dement (Amst). 2015; 1: 472-480
        • Dodge HH
        • Mattek NC
        • Austin D
        • Hayes TL
        • Kaye JA.
        In-home walking speeds and variability trajectories associated with mild cognitive impairment.
        Neurology. 2012; 78: 1946-1952
        • Hayes TL
        • Abendroth F
        • Adami A
        • Pavel M
        • Zitzelberger TA
        • Kaye JA.
        Unobtrusive assessment of activity patterns associated with mild cognitive impairment.
        Alzheimers Dement. 2008; 4: 395-405
        • Adans-Dester C
        • Hankov N
        • O’Brien A
        • et al.
        Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery.
        NPJ Digit Med. 2020; 3: 121-131
        • Lee SI
        • Adans-Dester CP
        • O'Brien AT
        • et al.
        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
        • Haendel MA
        • Chute CG
        • Bennett TD
        • et al.
        The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment.
        J Am Med Inform Assoc. 2021; 28: 427-443
        • Hopp WJ
        • Li J
        • Wang G.
        Big data and the precision medicine revolution.
        Prod Oper Manag. 2018; 27: 1647-1664
        • Hulsen T
        • Jamuar SS
        • Moody AR
        • et al.
        From big data to precision medicine.
        Front Med (Lausanne). 2019; 6: 1-14
        • Wu PY
        • Cheng CW
        • Kaddi CD
        • Venugopalan J
        • Hoffman R
        • Wang MD.
        Omic and electronic health record big data analytics for precision medicine.
        IEEE Trans Biomed Eng. 2017; 64: 263-273
        • Garza M
        • Del Fiol G
        • Tenenbaum J
        • Walden A
        • Zozus MN.
        Evaluating common data models for use with a longitudinal community registry.
        J Biomed Inform. 2016; 64: 333-341
        • Klann JG
        • Joss MAH
        • Embree K
        • Murphy SN.
        Data model harmonization for the All Of Us Research Program: transforming i2b2 data into the OMOP common data model.
        PLoS One. 2019; 14e0212463
        • Ogunyemi OI
        • Meeker D
        • Kim HE
        • Ashish N
        • Farzaneh S
        • Boxwala A.
        Identifying appropriate reference data models for comparative effectiveness research (CER) studies based on data from clinical information systems.
        Med Care. 2013; 51: S45-S52
        • Feng Z.
        Classification versus association models: should the same methods apply?.
        Scand J Clin Lab Invest Suppl. 2010; 242: 53-58
        • Poldrack RA
        • Huckins G
        • Varoquaux G.
        Establishment of best practices for evidence for prediction: a review.
        JAMA Psychiatry. 2020; 77: 534-540
        • Varga TV
        • Niss K
        • Estampador AC
        • Collin CB
        • Moseley PL.
        Association is not prediction: a landscape of confused reporting in diabetes—a systematic review.
        Diabetes Res Clin Pract. 2020; 170108497
        • Lo A
        • Chernoff H
        • Zheng T
        • Lo SH.
        Why significant variables aren't automatically good predictors.
        Proc Natl Acad Sci U S A. 2015; 112: 13892-13897
        • Pepe MS
        • Janes H
        • Longton G
        • Leisenring W
        • Newcomb P.
        Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker.
        Am J Epidemiol. 2004; 159: 882-890
        • Obermeyer Z
        • Emanuel EJ.
        Predicting the future—big data, machine learning, and clinical medicine.
        N Engl J Med. 2016; 375: 1216-1219
        • Ngiam KY
        • Khor IW.
        Big data and machine learning algorithms for health-care delivery.
        Lancet Oncol. 2019; 20: e262-ee73
        • Stinear CM
        • Barber PA
        • Petoe M
        • Anwar S
        • Byblow WD.
        The PREP algorithm predicts potential for upper limb recovery after stroke.
        Brain. 2012; 135: 2527-2535
        • Stinear CM
        • Byblow WD
        • Ackerley SJ
        • Smith MC
        • Borges VM
        • Barber PA.
        PREP2: a biomarker-based algorithm for predicting upper limb function after stroke.
        Ann Clin Transl Neurol. 2017; 4: 811-820