Literature DB >> 31128890

Remote Patient Monitoring Using Mobile Health for Total Knee Arthroplasty: Validation of a Wearable and Machine Learning-Based Surveillance Platform.

Prem N Ramkumar1, Heather S Haeberle2, Deepak Ramanathan3, William A Cantrell3, Sergio M Navarro4, Michael A Mont5, Michael Bloomfield3, Brendan M Patterson1.   

Abstract

BACKGROUND: Recent technologic advances capable of measuring outcomes after total knee arthroplasty (TKA) are critical in quantifying value-based care. Traditionally accomplished through office assessments and surveys with variable follow-up, this strategy lacks continuous and complete data. The primary objective of this study was to validate the feasibility of a remote patient monitoring (RPM) system in terms of the frequency of data interruptions and patient acceptance. Second, we report pilot data for (1) mobility; (2) knee range of motion, (3) patient-reported outcome measures (PROMs); (4) opioid use; and (5) home exercise program (HEP) compliance.
METHODS: A pilot cohort of 25 patients undergoing primary TKA for osteoarthritis was enrolled. Patients downloaded the RPM mobile application preoperatively to collect baseline activity and PROMs data, and the wearable knee sleeve was paired to the smartphone during admission. The following was collected up to 3 months postoperatively: mobility (step count), range of motion, PROMs, opioid consumption, and HEP compliance. Validation was determined by acquisition of continuous data and patient tolerance at semistructured interviews 3 months after operation.
RESULTS: Of the 25 enrolled patients, 100% had uninterrupted passive data collection. Of the 22 available for follow-up interviews, all found the system motivating and engaging. Mean mobility returned to baseline within 6 weeks and exceeded preoperative baseline by 30% at 3 months. Mean knee flexion achieved was 119°, which did not differ from clinic measurements (P = .31). Mean KOOS improvement was 39.3 after 3 months (range: 3-60). Opioid use typically stopped by postoperative day 5. HEP compliance was 62% (range: 0%-99%).
CONCLUSIONS: In this pilot study, we established the ability to remotely acquire continuous data for patients undergoing TKA, who found the application to be engaging. RPM offers the newfound ability to more completely evaluate the patients undergoing TKA in terms of mobility and rehabilitation compliance. Study with more patients is required to establish clinical significance.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  mHealth; machine learning; remote patient monitoring; telemedicine; total knee arthroplasty (TKA); wearable technology

Year:  2019        PMID: 31128890     DOI: 10.1016/j.arth.2019.05.021

Source DB:  PubMed          Journal:  J Arthroplasty        ISSN: 0883-5403            Impact factor:   4.757


  22 in total

Review 1.  Artificial Intelligence and Orthopaedics: An Introduction for Clinicians.

Authors:  Thomas G Myers; Prem N Ramkumar; Benjamin F Ricciardi; Kenneth L Urish; Jens Kipper; Constantinos Ketonis
Journal:  J Bone Joint Surg Am       Date:  2020-05-06       Impact factor: 5.284

2.  Two-year continuous data capture using a wearable sensor to remotely monitor the surgical spine patient: a case report.

Authors:  R Dineth Fonseka; Pragadesh Natarajan; Monish M Maharaj; Kaitlin Rooke; Ralph J Mobbs
Journal:  J Spine Surg       Date:  2022-03

Review 3.  Artificial intelligence in orthopedic surgery: evolution, current state and future directions.

Authors:  Andrew P Kurmis; Jamie R Ianunzio
Journal:  Arthroplasty       Date:  2022-03-02

Review 4.  Applications of Digital Health Technologies in Knee Osteoarthritis: Narrative Review.

Authors:  Nirali Shah; Kerry Costello; Akshat Mehta; Deepak Kumar
Journal:  JMIR Rehabil Assist Technol       Date:  2022-06-08

5.  Adoption of Telemedicine: A Debrief for the Orthopedic Practitioner.

Authors:  Karim A Shafi; Katherine Fortson; Sravisht Iyer
Journal:  HSS J       Date:  2021-02-21

Review 6.  Smart Technology and Orthopaedic Surgery: Current Concepts Regarding the Impact of Smartphones and Wearable Technology on Our Patients and Practice.

Authors:  Neil V Shah; Richard Gold; Qurratul-Ain Dar; Bassel G Diebo; Carl B Paulino; Qais Naziri
Journal:  Curr Rev Musculoskelet Med       Date:  2021-11-03

Review 7.  Potential benefits of integrating ecological momentary assessment data into mHealth care systems.

Authors:  Jinhyuk Kim; David Marcusson-Clavertz; Kazuhiro Yoshiuchi; Joshua M Smyth
Journal:  Biopsychosoc Med       Date:  2019-08-09

8.  Current clinical utilisation of wearable motion sensors for the assessment of outcome following knee arthroplasty: a scoping review.

Authors:  Scott R Small; Garrett S Bullock; Sara Khalid; Karen Barker; Marialena Trivella; Andrew James Price
Journal:  BMJ Open       Date:  2019-12-29       Impact factor: 2.692

9.  Smartphone App with an Accelerometer Enhances Patients' Physical Activity Following Elective Orthopedic Surgery: A Pilot Study.

Authors:  Hanneke C van Dijk-Huisman; Anouk T R Weemaes; Tim A E J Boymans; Antoine F Lenssen; Rob A de Bie
Journal:  Sensors (Basel)       Date:  2020-08-02       Impact factor: 3.576

10.  A data-driven performance dashboard for surgical dissection.

Authors:  Amir Baghdadi; Sanju Lama; Rahul Singh; Hamidreza Hoshyarmanesh; Mohammadsaleh Razmi; Garnette R Sutherland
Journal:  Sci Rep       Date:  2021-07-22       Impact factor: 4.379

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