Evidence map›Paper›PMID 36633891›Full record

ReviewJMIR perioperative medicine2023

The Role of Wearable Technology in Measuring and Supporting Patient Outcomes Following Total Joint Replacement: Review of the Literature.

Gregory Iovanel, David Ayers, Hua Zheng

Registry-linked trialAbstract readReview
In one paragraph

Review in JMIR perioperative medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05992064 (Investigating the Potential of Wearable Sensors for Monitoring the Postoperative Recovery of Total Knee Arthroplasty Patients), which is not on this map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05992064 recruitingnot on this mapstarted 2024, after this paper: background citation

Investigating the Potential of Wearable Sensors for Monitoring the Postoperative Recovery of Total Knee Arthroplasty Patients: A Protocol for Prospective Longitudinal Observational Study

TypeobservationalSponsorAalborg University HospitalRan2024 to 2026Enrolled30ConditionsArthroplasty, Replacement, KneeArmsMeasuring gait
3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. An Interactive Vision-Based 3D Augmented Reality System for In-Home Physical Rehabilitation: A Qualitative Inquiry to Inform System Development.Health expectations : an international journal of public participation in health care and health policy · 2024
    Article
  9. Review
  10. Article
  11. Observational
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gregory IovanelDepartment of Orthopedics and Physical Rehabilitation, UMass Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-6352-5627
David AyersDepartment of Orthopedics and Physical Rehabilitation, UMass Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-5458-7052
Hua Zheng *Department of Orthopedics and Physical Rehabilitation, UMass Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-0632-161X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe incidence rate of total joint replacement (TJR) continues to increase due to the aging population and the surgery that is very successful in providing pain relief to and improving function among patients with advanced knee or hip arthritis. Improving patient outcomes and patient satisfaction after TJR remain important goals. Wearable technologies provide a novel way to capture patient function and activity data and supplement clinical measures and patient-reported outcome measures in order to better understand patient outcomes after TJR.

objectiveWe examined the current literature to evaluate the potential role of wearable devices and compare them with existing methods for monitoring and improving patient rehabilitation and outcomes following TJR.

methodsWe performed a literature search by using the research databases supported by the University of Massachusetts Chan Medical School's Lamar Soutter Library, including PubMed and Scopus, supplemented with the Google Scholar search engine. A specific search strategy was used to identify articles discussing the use of wearable devices in measuring and affecting postoperative outcomes of patients who have undergone TJR. Selected papers were organized into a spreadsheet and categorized for our qualitative literature review to assess how wearable data correlated with clinical measures and patient-reported outcome measures.

resultsA total of 9 papers were selected. The literature showed the impact of wearable devices on evaluating and improving postoperative functional outcomes. Wearable-collected data could be used to predict postoperative clinical measures, such as range of motion and Timed Up and Go times. When predicting patient-reported outcomes, specifically Hip Disability and Osteoarthritis Outcome Scores/Knee Injury and Osteoarthritis Outcome Scores and Veterans RAND 12-Item Health Survey scores, strong associations were found between changes in sensor-collected data and changes in patient-reported outcomes over time. Further, the step counts of patients who received feedback from a wearable improved over time when compared to those of patients who did not receive feedback.

conclusionsThese findings suggest that wearable technology has the potential to remotely measure and improve postoperative orthopedic patient outcomes. We anticipate that this review will facilitate further investigation into whether wearable devices are viable tools for guiding the clinical management of TJR rehabilitation.

Indexed as

mobilityosteoarthritisrehabilitationtotal joint replacementwearables

Identifiers

PMID36633891
PMCPMC9880809

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.