Evidence mapPaperPMID 38400321Full record

ArticleSensors (Basel, Switzerland)2024

Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement.

Julien Lebleu, Kim Daniels, Andries Pauwels, Lucie Dekimpe, Jean Mapinduzi, Hervé Poilvache, Bruno Bonnechère

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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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

7 authors.

Julien LebleumoveUp, 1000 Brussels, Belgium.ORCID 0000-0001-6737-4830
Kim DanielsDepartment of PXL-Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium.ORCID 0000-0002-4222-4518
Andries PauwelsmoveUp, 1000 Brussels, Belgium.
Lucie DekimpemoveUp, 1000 Brussels, Belgium.
Jean MapinduziREVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium.
Hervé PoilvacheOrthopedic Surgery Department, CHIREC, 1420 Braine-l'Alleud, Belgium.ORCID 0000-0001-8859-578X
Bruno BonnechèreDepartment of PXL-Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium.ORCID 0000-0002-7729-4700

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients' dynamic activity profiles.

Indexed as

Arthroplasty, Replacement, KneeOsteoarthritis, KneeTelemedicineWearable Electronic DevicesAgedHumansKnee Jointactivity trackermHealthosteoarthritispersonalized carerehabilitationrehabilomicswearable sensors

Identifiers

PMID38400321
PMCPMC10892564

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.