Clinical trial NCT03406455: Telemedicine in Total Knee Arthroplasty Using Wearable Technology
Official title: Telemedicine in Total Knee Arthroplasty Using Wearable Technology
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Knee Arthritis
Keywords: wearable
mhealth
Lead sponsor: The Cleveland Clinic
Sponsor class: OTHER
First posted: 2018-01-23
Enrollment: 25
Sex: ALL
Minimum age (years): 60
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: 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.

Brief summary: 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: 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.

Eligibility criteria: Inclusion Criteria:

1. patients undergoing primary TKA for osteoarthritis
2. patients who have an iOS smartphone and carry it with them daily
3. patients who reside in a home and not a facility or rehabilitation center
4. patients under the age of 80 years
5. 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:

1. patients receiving treatment for inflammatory arthritis
2. patients receiving active or maintenance treatment for cancer or solid organ and/or marrow transplant
3. patients with any other medical issues limiting mobility and function, including cardiopulmonary, gastrointestinal, and hematologic comorbidities
4. patients indicated for TKA for post-traumatic or inflammatory arthritis
5. patients who have ever had a periprosthetic joint infection of any joint
6. patients who have a history of native septic arthritis in the operative joint
7. patients who were functionally immobilized or residing anywhere other than a home (nursing facility, rehabilitation centers)
8. patients who preoperatively used an assist device for more than a year (i.e. cane, walker)
9. patients over the age of 80 years.

Primary outcomes:
measure: Acceptability
timeFrame: 3 months postoperative
Secondary outcomes:
measure: Step Count in steps per day
timeFrame: Daily, from 4 weeks preoperatively to 3 months postoperatively totaling approximately 120 days
measure: Maximum knee range of motion in degrees
timeFrame: Weekly, from 4 weeks preoperatively to 3 months postoperatively totaling approximately 16 weeks
measure: Patient Reported Outcome Measures (linear numeric scale from 0 to 28)
timeFrame: Weekly, from 4 weeks preoperatively to 3 months postoperatively totaling approximately 16 weeks
measure: Home Exercise Plan compliance
timeFrame: Daily, from day of surgery to 3 months postoperatively totaling approximately 90 days
Other outcomes:
Locations:
city: Cleveland
country: United States
facility: Cleveland Clinic
state: Ohio
Study officials:
affiliation: IRB Coordinator
name: Annabelle Visperas, BS
role: STUDY_DIRECTOR
References:
citation: 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.
citation: 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.
citation: 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.