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Record W4414212490 · doi:10.2196/73423

User Experiences of Limb-Worn Wearable Devices for Monitoring Parkinson Disease Motor Function and Blood Pressure: Usability Study

2025· article· en· W4414212490 on OpenAlexvenueno aff
Lorna Kenny, Marco Sica, Colum Crowe, Lauren O’Mahony, Brendan O’Flynn, David Scott Mueller, Salvatore Tedesco, John Barton, Suzanne Timmons

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityWearable computerWearable technologyDiseaseFunction (biology)Motor function

Abstract

fetched live from OpenAlex

BACKGROUND: Wearable devices have the potential to provide reliable and objective assessment and monitoring of Parkinson disease (PD). During design, there is often a focus on technical performance, accuracy, and reliability, with less emphasis on the user experience. OBJECTIVE: This study explored the user experience of a novel prototype wearable device (wrist- and ankle-worn) to record limb movements (accelerations and angular velocities), and physiological data (eg, photoplethysmography and heart rate information for estimation of blood pressure). METHODS: This qualitative study used internet-based semistructured interviews with people with PD, following wearing prototype devices for 24 hours at home. Interviews were audio-recorded and transcripts analyzed using a hybrid deductive and inductive approach. RESULTS: Six people with PD, 3 male and 3 female, aged 52-83 years, with mild-to-moderate PD (Hoehn and Yahr scale score ≤3; median MDS-UPDRS [Movement Disorder Society - Unified Parkinson's Disease Rating Scale] score of 72/199) and cognition within normal limits (6-item Cognitive Impairment Test median score 0), with an average disease duration of 8 years took part in the study. Participants were overall positive toward the device, finding it generally comfortable, light in weight and noninvasive. Five out of 6 participants reported minor problems related to strap adjustability and challenges specific to PD. The prototype was comfortable, but this was a lesser priority than robustness, the device not hindering their usual clothing choices (device size or outward projection, or both), and adjustability for fit, including the need to switch to an elasticated strap. In particular, a discreet design was important, as some individuals may feel self-conscious about wearing visible condition-specific products, due to stigma. The ankle-worn device was perceived as unfamiliar and nondiscreet, with some participants likening it to a prisoner tracking system and dressing specifically to conceal it. The wrist-worn device was considered more user-friendly, especially if designed discreetly and resembling more familiar devices. Five of the 6 participants believed their health care teams should have access to the data, particularly relating to their symptoms, fluctuations, and medication. Three also wished to access these data for self-management. One participant was hesitant regarding the potential benefits of technology to support PD management, preferring to use feedback data personally and relying on their health care team's usual assessment to guide decisions. Across the group, desirable device technical features included symptom prediction, reminder prompts, and support for medication management. Despite concerns about stigma, most participants were willing to wear PD-specific devices, believing that they could aid better symptom management. CONCLUSIONS: In summary, wearable devices must be discreet, robust, comfortable, and easily applied to promote adherence regardless of technical specifications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.391
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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