Telemedicine in Total Knee Arthroplasty Using Wearable Technology
Condition: Knee Arthritis
MeSH terms: Arthritis
Study type: Observational

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.
    

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.
      
Keywords: wearable; mhealth