Telemedicine in Total Knee Arthroplasty Using Wearable Technology

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Telemedicine in Total Knee Arthroplasty Using Wearable Technology

Conditions
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Knee Arthritis

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: patients undergoing primary TKA for osteoarthritis patients who have an iOS smartphone and carry it with them daily patients who reside in a home and not a facility or rehabilitation center patients under the age of 80 years patients who preoperatively have not used any assist devices for more than a year due to the non-affected joint or other functional reasons, including back pain. Exclusion criteria: patients receiving treatment for inflammatory arthritis patients receiving active or maintenance treatment for cancer or solid organ and/or marrow transplant patients with any other medical issues limiting mobility and function, including cardiopulmonary, gastrointestinal, and hematologic comorbidities patients indicated for TKA for post-traumatic or inflammatory arthritis patients who have ever had a periprosthetic joint infection of any joint patients who have a history of native septic arthritis in the operative joint patients who were functionally immobilized or residing anywhere other than a home (nursing facility, rehabilitation centers) patients who preoperatively used an assist device for more than a year (i.e. cane, walker) patients over the age of 80 years.

Ages Eligible for Study
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Minimum Age: 60 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Primary TKA<br>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. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Acceptability | Semi-structured interviewed | 3 months postoperative | 

Secondary Outcome Measures

| Outcome Measure | 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 | 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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wearable, mhealth