Evidence map›Paper›PMID 39702329›Full record

ArticleJournal of neuroengineering and rehabilitation2024

Evaluating inter- and intra-rater reliability in assessing upper limb compensatory movements post-stroke: creating a ground truth through video analysis?

Lena Sauerzopf, Celina G Chavez Panduro, Andreas R Luft, Benjamin Kühnis, Elena Gavagnin, Tim Unger, Christopher Easthope Awai, Josef G Schönhammer, Jürgen Degenfellner, Martina R Spiess

Abstract readEvaluation Study
In one paragraph

Article 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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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.

Lena Sauerzopf *Institute of Occupational Therapy, ZHAW School of Health Sciences, Winterthur, Switzerland. lena.sauerzopf@zhaw.ch.
Celina G Chavez Panduro *University for Continuing Education Krems, Krems, Austria.
Andreas R LuftNeuroscience of Motivation and Cognition in Rehabilitation (NeuroCoRe) Lab University of Zurich and University Hospital Zurich, Zurich, Switzerland.
Benjamin KühnisInstitute of Business Information Technology, ZHAW School of Management and Law, Winterthur, Switzerland.
Elena GavagninInstitute of Business Information Technology, ZHAW School of Management and Law, Winterthur, Switzerland.
Tim UngerData Analytics and Rehabilitation Technology (DART) Lab, Lake Lucerne Institute, Vitznau, Switzerland.
Christopher Easthope AwaiData Analytics and Rehabilitation Technology (DART) Lab, Lake Lucerne Institute, Vitznau, Switzerland.
Josef G SchönhammerNeuroscience of Motivation and Cognition in Rehabilitation (NeuroCoRe) Lab University of Zurich and University Hospital Zurich, Zurich, Switzerland.
Jürgen DegenfellnerInstitute of Physiotherapy, ZHAW School of Health Sciences, Winterthur, Switzerland.
Martina R SpiessInstitute of Occupational Therapy, ZHAW School of Health Sciences, Winterthur, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCompensatory movements frequently emerge in the process of motor recovery after a stroke. Given their potential for unfavorable long-term effects, it is crucial to assess and document compensatory movements throughout rehabilitation. However, clinically applicable assessment tools are currently limited. Deep learning methods have shown promising potential for assessing movement quality and addressing this gap. A crucial prerequisite for developing an accurate measurement tool is ensuring reliability in assessing compensatory movements, which is essential for establishing a valid ground truth.

objectiveThe study aimed to assess inter- and intra-rater reliability of occupational and physical therapists' visual assessment of compensatory movements based on video analysis.

methodsExperienced therapists evaluated video-recorded performances of a standardized drinking task through an online labeling system. The standardized drinking task was performed by seven individuals with mild to moderate upper limb motor impairments after a stroke. The therapists rated compensatory movements in predetermined body segments and movement phases using a slider with a continuous scale ranging from 0 (no compensation) to 100 (maximum compensation). The collected data were analyzed using a generalized-linear mixed effects model with zero-inflated beta regression to estimate variance components. Intraclass correlation coefficients (ICC) were calculated to assess inter- and intra-rater reliability.

resultsTwenty-two therapists participated in this study. Inter-rater reliability was good for the phases of reaching, drinking, and returning (ICC ≥ .0.75), and moderate for both phases of transporting. Intra-rater reliability was excellent for the drinking phase (ICC > 0.9) and moderate to good for the phases of reaching, transporting, and returning of our cohort. ICCs for smoothness and interjoint coordination were poor for both inter- and intra-rater reliability. The data analysis unveiled a wide range of credible intervals for the ICCs across all domains examined in this study.

conclusionsWhile this study shows promising inter- and intra-rater reliability for the drinking phases within our sample, the wide credible intervals raise the possibility that these results may have occurred by chance. Consequently, we cannot recommend the establishment of a ground truth for the automatic assessment of compensatory movements during a drinking task based on therapists' ratings alone.

Indexed as

Behavior Observation TechniquesDrinking BehaviorOccupational TherapistsPhysical TherapistsStrokeVideo RecordingAgedFemaleHumansLinear ModelsMaleMiddle AgedMotor ActivityObserver VariationReproducibility of ResultsStroke RehabilitationCompensatory movementsGround truthInter-rater reliabilityIntra-rater reliabilityMachine learningStrokeUpper extremity

Identifiers

PMID39702329
PMCPMC11660698

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.