Evidence map›Paper›PMID 34712983›Full record

ArticleArthroscopy, sports medicine, and rehabilitation2021

Self-Rehabilitation Guided by a Mobile Application After Anterior Cruciate Ligament Reconstruction Leads to Improved Early Motion and Less Pain.

Constant Foissey, Mathieu Thaunat, Louka Bondoux, Bertrand Sonnery-Cottet, Jean-Marie Fayard

Open access · goldAbstract read
In one paragraph

Article in Arthroscopy, sports medicine, and rehabilitation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 10% of its field
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
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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

5 authors at 1 institution in 1 country.

Constant FoisseyCentre Orthopédique Santy, Lyon, France.
Mathieu ThaunatCentre Orthopédique Santy, Lyon, France.
Louka BondouxCentre Orthopédique Santy, Lyon, France.
Bertrand Sonnery-CottetCentre Orthopédique Santy, Lyon, France.
Jean-Marie FayardCentre Orthopédique Santy, Lyon, France.
Hôpital Privé Jean Mermo · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the adherence rate and the contribution of self-rehabilitation (SR) guided by a mobile application after anterior cruciate ligament reconstruction (ACLR) in combination with physical therapy sessions on early knee function.

methodsThis study was a retrospective analysis of prospectively collected data from a single health care facility. All patients who underwent ACLR by a single surgeon from December 2019 to September 2020 were included. Two groups were formed and compared based on use of the mobile app: users (>10 days of use) and nonusers (≤10 days of use). Outcomes included physical examination at 3 and 6 weeks postoperatively.

resultsA total of 65 patients were analyzed: 19 in the nonuser group and 46 in the user group. Adherence rate was 91% at 10 days, 71% at 15 days, 62% at 21 days, and 44% at 45 days. At 3 weeks, the user group was 3.86 times [range 1.12 to 13.3] as likely to lock the quadriceps during gait with crutches and was 4.2 times [range 1.2 to 14.3] as likely to be pain free. There was a tendency to have less flexion contracture in the user group (17% versus 32%,

conclusionsSR guided by a mobile app combined with a standard rehabilitation protocol is correlated with better knee function at initial follow-up. LEVEL OF EVIDENCE: IV, therapeutic case series.

Identifiers

PMID34712983
PMCPMC8527319
OpenAlexW3190769432

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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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.