Evidence map›Paper›PMID 41817821›Full record

SynthesisJournal of neurology2026

Technologies used in Parkinson's disease: a meta-analysis of their effect on health-related quality of life.

Cuma Fidan

Abstract readMeta-Analysis
In one paragraph

Synthesis in Journal of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Cuma FidanFaculty of Health Sciences, Department of Healthcare Management, Muş Alparslan University, 49250, Muş, Turkey. cmfdn91@gmail.com.ORCID http://orcid.org/0000-0002-8581-5940

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe results of randomised controlled trials (RCTs) and meta-analyses in the literature on whether technologies used in Parkinson's disease (PD) improve health-related quality of life (HRQoL) are varied, making it difficult to reach a definitive conclusion. Therefore, the aim was to investigate the effect of technologies used in PD on HRQoL according to moderator variables.

methodsThe mean effect size was calculated using either the fixed-effects or random-effects model. Assessment timepoints, technology and scale types were used as moderator variables. Technology types: Mobile health (mHealth), robotic-assisted, telerehabilitation, virtual reality and wearable technologies. The Egger's regression method was used to assess publication bias. The quality assessment used the risk of bias (RoB) 2 method. The results of the meta-analysis were evaluated clinically and statistically.

resultsThe meta-analysis included 34 RCTs. RCTs were published in the form of thesis and article publications between 2012 and 2025. The control group had 709 patients, the experimental group 759. The RoB 2 method indicates that the majority of RCTs are low risk of bias. According to the Egger's regression method, there is no publication bias. According to the results of the meta-analysis, mHealth, robotic-assisted, telerehabilitation, virtual reality and wearable technologies used in PD have the potential to improve patients' HRQoL.

conclusionThe technologies used in PD have the potential to improve patients' HRQoL. These results could provide significant opportunities for effectively managing the disease and its treatment, as well as improving patients' daily living activities.

Indexed as

Parkinson DiseaseQuality of LifeTelemedicineDigital HealthHumansRandomized Controlled Trials as TopicTelerehabilitationVirtual RealityWearable Electronic DevicesHealth-related quality of lifeHealth technologiesMeta-analysisParkinson’s disease

Identifiers

PMID41817821
PMCPMC12982199

What OpenQuestion 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 OpenQuestion graph.