Evidence map›Paper›PMID 39068424›Full record

Observational studyJournal of neuroengineering and rehabilitation2024

Influence of virtual reality and task complexity on digital health metrics assessing upper limb function.

Christoph M Kanzler, Tom Armand, Leonardo Simovic, Ramona Sylvester, Nadine Domnik, Antonia M Eilfort, Carola Rohner, Roger Gassert, Roman Gonzenbach, Olivier Lambercy

Abstract readObservational Study
In one paragraph

Observational study in Journal of neuroengineering and rehabilitation, 2024. 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. Review
  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

10 authors.

Christoph M KanzlerRehabilitation Engineering Laboratory, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland. c-m.kanzler@gmx.de.
Tom ArmandRehabilitation Engineering Laboratory, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Leonardo SimovicRehabilitation Engineering Laboratory, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Ramona SylvesterRehabilitation Center Valens, Valens, Switzerland.
Nadine DomnikRehabilitation Engineering Laboratory, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Antonia M EilfortRehabilitation Engineering Laboratory, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Carola RohnerRehabilitation Engineering Laboratory, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Roger GassertRehabilitation Engineering Laboratory, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
Roman Gonzenbach *Rehabilitation Center Valens, Valens, Switzerland.
Olivier Lambercy *Rehabilitation Engineering Laboratory, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.

Funding

National Research Foundation Singapore CREATESchweizerische Multiple Sklerose Gesellschaft 2021-06Singapore-ETH Centre Future Health Technologies
6 · The paper itself

Abstract

backgroundTechnology-based assessments using 2D virtual reality (VR) environments and goal-directed instrumented tasks can deliver digital health metrics describing upper limb sensorimotor function that are expected to provide sensitive endpoints for clinical studies. Open questions remain about the influence of the VR environment and task complexity on such metrics and their clinimetric properties.

methodsWe aim to investigate the influence of VR and task complexity on the clinimetric properties of digital health metrics describing upper limb function. We relied on the Virtual Peg Insertion Test (VPIT), a haptic VR-based assessment with a virtual manipulation task. To evaluate the influence of VR and task complexity, we designed two novel tasks derived from the VPIT, the VPIT-2H (VR environment with reduced task complexity) and the PPIT (physical task with reduced task complexity). These were administered in an observational longitudinal study with 27 able-bodied participants and 31 participants with multiple sclerosis (pwMS, VPIT and PPIT only) and the value of kinematic and kinetic metrics, their clinimetric properties, and the usability of the assessment tasks were compared.

resultsIntra-participant variability strongly increased with increasing task complexity (coefficient of variation + 56%) and was higher in the VR compared to the physical environment (+ 27%). Surprisingly, this did not translate into significant differences in the metrics' measurement error and test-retest reliability across task conditions (p > 0.05). Responsiveness to longitudinal changes in pwMS was even significantly higher (effect size + 0.35, p < 0.05) for the VR task with high task complexity compared to the physical instrumented task with low task complexity. Increased inter-participant variability might have compensated for the increased intra-participant variability to maintain good clinimetric properties. No significant influence of task condition on concurrent validity was present in pwMS. Lastly, pwMS rated the PPIT with higher usability than the VPIT (System Usability Scale + 7.5, p < 0.05).

conclusionThe metrics of both the VR haptic- and physical task-based instrumented assessments showed adequate clinimetric properties. The VR haptic-based assessment may be superior when longitudinally assessing pwMS due to its increased responsiveness. The physical instrumented task may be advantageous for regular clinical use due to its higher usability. These findings highlight that both assessments should be further validated for their ideal use-cases.

Indexed as

Upper ExtremityVirtual RealityAdultBiomechanical PhenomenaDigital HealthFemaleHumansLongitudinal StudiesMaleMiddle AgedMultiple SclerosisPsychomotor Performance

Identifiers

PMID39068424
PMCPMC11282591

What OpenQuestion holds

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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.