Evidence map›Paper›PMID 42435105›Full record

ArticleJournal of medical systems2026

A Digital Cueing Intervention for Parkinsonian Gait: Laboratory-Based Clinical Validation and Acute Gait Responses.

Conor Wall, Victoria Hetherington, Rodrigo Vitorio, Peter McMeekin, Richard Walker, Jason Moore, Rosie Morris, Yunus Celik, Alan Godfrey

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Conor WallSchool of Computer Science, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK.
Victoria HetheringtonCumbria Northumberland Tyne and Wear NHS Foundation Trust, Newcastle upon Tyne, UK.
Rodrigo VitorioSchool of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, UK.
Peter McMeekinSchool of Healthcare and Nursing Sciences, Northumbria University, Newcastle Upon Tyne, UK.
Richard WalkerNorthumbria Healthcare NHS Foundation Trust, North Shields, UK.
Jason MooreSchool of Computer Science, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK.
Rosie MorrisSchool of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, UK.
Yunus CelikDepartment of Electrical and Electronics Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey.
Alan GodfreySchool of Computer Science, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK. alan.godfrey@northumbria.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Falls are common in people with Parkinson's (PwP). There is a need for a scalable, pragmatic, personalisable, and single device intervention to improve gait characteristics related to fall risk. CuePD is an app-only approach to deliver personalised auditory cues to retrain gait in PwP. This study aimed to clinically validate and evaluate effectiveness of CuePD in PwP and to explore their perceptions of CuePD. Sixty/60 PwP performed cued walks via metronome, instrumental and vocal music personalised to their baseline walking speed (cadence). PwP chose music from e.g., pop, rock genres. Cued walks were +10% on baseline cadence. We examined effectiveness of each cue and any carryover effects across non-cued walks. CuePD clinical validation (e.g., intraclass correlation coefficients, ICC

Indexed as

Accidental FallsCuesGaitGait Disorders, NeurologicMobile ApplicationsParkinson DiseaseAgedFemaleHumansMaleMiddle AgedClinical validationFall riskParkinson’s diseasePersonalised cueingSmartphone

Identifiers

PMID42435105
PMCPMC13356047

What OpenQuestion holds

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LicenceCC BY
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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.