Evidence map›Paper›PMID 35976767›Full record

ReviewJournal of rehabilitation medicine2022

Compliance with Upper Limb Home-Based Exergaming Interventions for Stroke Patients: A Narrative Review.

Axelle Gelineau, Anaick Perrochon, Jean-Christophe Daviet, Stephane Mandigout

Abstract readReview
In one paragraph

Review in Journal of rehabilitation medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Axelle GelineauHAVAE Laboratory UR 20217, University of Limoges, Limoges, France. axelle.gelineau@unilim.fr.
Anaick PerrochonHAVAE Laboratory UR 20217, University of Limoges, Limoges, France.
Jean-Christophe DavietHAVAE Laboratory UR 20217, University of Limoges, Limoges, France; Department of Physical Medicine and Rehabilitation, University Hospital Center of Limoges, Limoges, France.
Stephane MandigoutHAVAE Laboratory UR 20217, University of Limoges, Limoges, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTelerehabilitation and follow-up techniques have been developed in recent years to assess the effectiveness of diverse intervention programmes that include exergaming technologies. For patients with upper limb impairment after stroke, motion-gaming technologies can provide effective and amusing training. Beyond efficiency, professionals must analyse patient compliance with the system for self-use at home, because patients may or may not independently perform the exercises prescribed by the therapist. Questions on the sustainable use of this type of home exercise also arise.

objectiveThis review examines user compliance with exercise programmes, measured according to the training rate (percentage of prescribed sessions and minutes completed) and completion rate (number of drop-outs and discontinued interventions) reported or calculable according to the data collected. RESULTS AND DISCUSSION: Rates of compliance with training were relatively high. No group effect on compliance was found. Drop-out and discontinued intervention rates were either due to external causes or directly related to the technologies. Some studies have reported the use of supervision, most of them through home visits and remote support. Few studies performed long-term follow-up, which could provide information to help broaden practices. This narrative review considers how this field of research may evolve in the future.

Indexed as

StrokeStroke RehabilitationExercise TherapyExergamingHumansPatient ComplianceUpper Extremity

Identifiers

PMID35976767
PMCPMC9706874

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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.