Evidence map›Paper›PMID 42308475›Full record

ArticleJMIR rehabilitation and assistive technologies2026

Digital Platform to Provide Health Data Feedback for Neurorehabilitation Patients: User-Centered Development and Proof-of-Concept Usability Study.

Nadine Domnik, Katarzyna Krasnopolska, Ramona Sylvester, Jens Bansi, Jaeyong Song, Roman Gonzenbach, Olivier Lambercy

Abstract read
In one paragraph

Article in JMIR rehabilitation and assistive technologies, 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

7 authors.

Nadine DomnikDepartment of Health Science and Technology, Rehabilitation Engineering Laboratory, ETH Zurich, Gloriastrasse 37/39, Zurich, 8092, Switzerland, 41 044 632 72 33.ORCID http://orcid.org/0000-0003-1568-7426
Katarzyna KrasnopolskaDepartment of Health Science and Technology, Rehabilitation Engineering Laboratory, ETH Zurich, Gloriastrasse 37/39, Zurich, 8092, Switzerland, 41 044 632 72 33.ORCID http://orcid.org/0009-0006-0759-4069
Ramona SylvesterNeurology Department, Kliniken Valens, Valens, Switzerland.ORCID http://orcid.org/0009-0008-6981-6952
Jens BansiNeurology Department, Kliniken Valens, Valens, Switzerland.ORCID http://orcid.org/0000-0002-2953-2233
Jaeyong SongDepartment of Health Science and Technology, Rehabilitation Engineering Laboratory, ETH Zurich, Gloriastrasse 37/39, Zurich, 8092, Switzerland, 41 044 632 72 33.ORCID http://orcid.org/0000-0003-0712-2425
Roman GonzenbachNeurology Department, Kliniken Valens, Valens, Switzerland.ORCID http://orcid.org/0009-0004-1917-0613
Olivier LambercyDepartment of Health Science and Technology, Rehabilitation Engineering Laboratory, ETH Zurich, Gloriastrasse 37/39, Zurich, 8092, Switzerland, 41 044 632 72 33.ORCID http://orcid.org/0000-0002-0760-7054

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: An increasing amount of digital health data are being collected across rehabilitation settings, but their integration into routine clinical practice remains limited, despite its potential to motivate patients or inform clinical decision-making. Specifically, effective visualization and communication of assessment outcomes to both patients and health care practitioners (HCPs) represent a key gap in the neurorehabilitation practice. Objective: This study describes the development and evaluation of RehaLink (author ND, ETH Zürich), a proof-of-concept mobile app that delivers structured, interpretable feedback from conventional and technology-based assessments to neurorehabilitation patients and HCPs. Methods: The app was developed through a 3-step iterative co-design process involving 17 inpatients with multiple sclerosis and 15 HCPs from a single rehabilitation center. The app integrates a full battery of conventional assessments routinely conducted at the clinic, as well as digital health metrics from the Virtual Peg Insertion Test, a validated technology-based assessment of upper limb function, as a proof of concept for integrating technology-based assessment data into clinical workflows. Three structured feedback sessions were conducted, in which participants evaluated feedback types, visualization formats, and app usability using Likert-scale ratings, preference rankings, open-ended questions, and the System Usability Scale. Data were analyzed using descriptive statistics and directed content analysis. Results: Across all 3 sessions, progress bars and color-coded indicators were consistently preferred over text-heavy or abstract formats by both patients and HCPs. A persistent set of competing demands was observed, with participants requesting both visual simplicity and access to absolute values and normative comparisons. HCPs tended to underestimate patients' preference for informative visualizations. The perceived value of structured feedback increased over the course of the study; patients' median ratings rose from 4.0 to 5.0 and HCPs' from 4.0 to 4.5 on a 5-point Likert scale. The resulting mobile app prototype demonstrated high usability, with patients achieving a mean System Usability Scale score of 93.6 (mean 6.4; best imaginable) and HCPs 80.9 (SD 8.1; good), according to established benchmarks. Conclusions: These findings demonstrate the feasibility and value of a co-designed digital feedback tool for neurorehabilitation. By combining conventional and technology-based assessment outcomes in an accessible, user-centered format, the app has the potential to enhance patient engagement, support clinical decision-making, and advance the implementation of value-based, personalized care.

Indexed as

clinical decision-makingdigital feedback applicationdigital healthhealth data visualizationmobile health (mHealth)neurorehabilitationuser-centered designvalue based health care (VBHC)

Identifiers

PMID42308475
PMCPMC13274913

What OpenQuestion holds

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LicenceCC BY
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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.