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Subjectivity, cost-effectiveness, and inconsistent reporting limit monitoring after total knee arthroplasty (TKA). This prospective study leverages machine learning wearable technology to remotely monitor patients before and after TKA with fidelity and reliability, without sacrificing safe triage needing increased perioperative attention. Patients will download a mobile app that pairs with a "smart" knee sleeve to (1) monitor activity via daily step count, (2) solicit patient-reported outcomes, (3) calculate max flexion, and (4) provide physical therapy compliance data. The primary objective of this study is to determine validity and acceptability of the technology; secondary objectives include perioperative benchmarking with characterization of post-operative recovery trajectories.
Monitoring of pre-operative status and post-operative recovery from elective orthopaedic surgery is critical to delivering safe, value-based care. Measurement after TKA has traditionally been accomplished through clinician in-office assessments, validated surveys, or both; subjectivity, cost-effectiveness, and inconsistent reporting limit these assessments. Leveraging now ubiquitous smartphone technology and smart wearable technology with machine learning software offers the opportunity to remotely monitor patients before and after surgery. This provides surgeons, hospitals, and stakeholders the opportunity to objectively quantify (1) patient compliance, (2) value of a given surgical procedure with unprecedented benchmarking, and, more importantly, (3) the better triage of those needing increased perioperative attention. Regardless of the orthopaedic procedure, a motion-based machine learning software application to commercial mobile and wearable technology readily and inexpensively unlocks the potential of delivering value-based care through the low maintenance acquisition of both precision, small data that may then be extrapolated to population-level revelations from big data regardless of the joint or extremity. With the rise of telemedicine, clinical validation of the technology is of mutual interest to orthopaedic patients, surgeons, administrators, payers, and policymakers.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Primary TKA | A cohort of 25 patients undergoing primary TKA for osteoarthritis at our hospital will be enrolled into the study, which will receive IRB approval and be registered on ClinicalTrials.gov and RedCap. Patients will download the mobile application onto their personal smartphones (iOS) to record baseline activity and PROMs in the 2-4 weeks leading up to surgery. During the hospital admission, the knee sleeve will be fitted to the patient. The patient cohort will be followed for three months and four data points (both passive and active) will be extracted from the dashboard: PROMs, mean daily steps, ROM (particular attention to 2 weeks postoperatively), and home exercise plan (HEP) compliance. |
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| Measure | Description | Time Frame |
|---|---|---|
| Acceptability | Semi-structured interviewed | 3 months postoperative |
| Measure | Description | Time Frame |
|---|---|---|
| Step Count in steps per day | Passively collect steps for TKA patients before and after surgery. This will be used for comparing pre- and post-operative improvements. | Daily, from 4 weeks preoperatively to 3 months postoperatively totaling approximately 120 days |
| Maximum knee range of motion in degrees |
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Inclusion Criteria:
Exclusion criteria:
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A cohort of 25 patients undergoing primary TKA for osteoarthritis at our hospital will be enrolled into the study, which will receive IRB approval and be registered on ClinicalTrials.gov and RedCap. Patients will download the mobile application onto their personal smartphones (iOS) to record baseline activity and PROMs in the 2-4 weeks leading up to surgery. During the hospital admission, the knee sleeve will be fitted to the patient. The patient cohort will be followed for three months and four data points (both passive and active) will be extracted from the dashboard: PROMs, mean daily steps, ROM (particular attention to 2 weeks postoperatively), and home exercise plan (HEP) compliance.
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| Name | Affiliation | Role |
|---|---|---|
| Annabelle Visperas, BS | IRB Coordinator | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Cleveland Clinic | Cleveland | Ohio | 44195 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 27956125 | Background | Ramkumar PN, Muschler GF, Spindler KP, Harris JD, McCulloch PC, Mont MA. Open mHealth Architecture: A Primer for Tomorrow's Orthopedic Surgeon and Introduction to Its Use in Lower Extremity Arthroplasty. J Arthroplasty. 2017 Apr;32(4):1058-1062. doi: 10.1016/j.arth.2016.11.019. Epub 2016 Nov 17. | |
| 28455178 | Background |
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Actively collect max extension and flexion for TKA patients before and after surgery. This will be used for comparing pre- and post-operative improvements. |
| Weekly, from 4 weeks preoperatively to 3 months postoperatively totaling approximately 16 weeks |
| Patient Reported Outcome Measures (linear numeric scale from 0 to 28) | Actively collect KOOS Jr., VAS pain scales. This will be used for comparing pre- and post-operative improvements. | Weekly, from 4 weeks preoperatively to 3 months postoperatively totaling approximately 16 weeks |
| Home Exercise Plan compliance | Passively collect completion of at least one home exercise per day, as reported in binary fashion (yes or no) | Daily, from day of surgery to 3 months postoperatively totaling approximately 90 days |
| Ramkumar PN, Navarro SM, Chughtai M, La T Jr, Fisch E, Mont MA. The Patient Experience: An Analysis of Orthopedic Surgeon Quality on Physician-Rating Sites. J Arthroplasty. 2017 Sep;32(9):2905-2910. doi: 10.1016/j.arth.2017.03.053. Epub 2017 Apr 4. |
| 26220999 | Background | Ramkumar PN, Harris JD, Noble PC. Patient-reported outcome measures after total knee arthroplasty: a systematic review. Bone Joint Res. 2015 Jul;4(7):120-7. doi: 10.1302/2046-3758.47.2000380. |