Telemedicine in Total Knee Arthroplasty Using Wearable Technology

NCT ID: NCT03406455

Last Updated: 2019-07-24

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

25 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-07-02

Study Completion Date

2019-05-15

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.

Conditions

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Knee Arthritis

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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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.

No interventions assigned to this group

Eligibility Criteria

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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.
Minimum Eligible Age

60 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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The Cleveland Clinic

OTHER

Sponsor Role lead

Responsible Party

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Prem N Ramkumar

Orthopaedic Surgery Resident

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Annabelle Visperas, BS

Role: STUDY_DIRECTOR

IRB Coordinator

Locations

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Cleveland Clinic

Cleveland, Ohio, United States

Site Status

Countries

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United States

References

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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.

Reference Type BACKGROUND
PMID: 27956125 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 28455178 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 26220999 (View on PubMed)

Other Identifiers

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ClevelandCF02

Identifier Type: -

Identifier Source: org_study_id

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