Evidence map›Paper›PMID 41410978›Full record

ArticleMedical & biological engineering & computing2026

Variability-Adaptive IV insertion training with dual haptic feedback in mixed reality.

Jin Woo Kim, Kwangtaek Kim, Jeremy Jarzembak, Robert Clements, John Dunlosky, Ann James, Jennifer Biggs

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Article in Medical & biological engineering & computing, 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

7 authors.

Jin Woo KimDepartment of Computer Science, Kent State University, Kent, OH, 44243, USA.
Kwangtaek KimDepartment of Computer Science, Kent State University, Kent, OH, 44243, USA. kkim17@kent.edu.
Jeremy JarzembakCollege of Nursing, Kent State University, Kent, OH, 44243, USA.
Robert ClementsDepartment of Biological Science, Kent State University, Kent, OH, 44243, USA.
John DunloskyDepartment of Psychological Science, Kent State University, Kent, OH, 44243, USA.
Ann JamesCollege of Nursing, Kent State University, Kent, OH, 44243, USA.
Jennifer BiggsCollege of Nursing, Kent State University, Kent, OH, 44243, USA.

Funding

National Science Foundation RETTL-2118380
6 · The paper itself

Abstract

Intravenous (IV) needle insertion is a crucial nursing skill that can be challenging to master. This study introduces a variability-adaptive IV needle insertion simulation integrating dual haptic feedback and mixed reality (MR) to create a realistic IV training environment. Unlike traditional training tools, our system incorporates graphic and haptic variable factors to simulate varying IV difficulty levels, reflecting real clinical conditions. Four variability-based difficulty levels, tuned and validated by nursing faculty, were designed and automatically rendered during training sessions to implement variability-adaptive training. To measure the effectiveness of this training, training impact study was conducted with 54 nursing students at Kent State University after the refinement from iterative usability studies(53 participants). The students were randomly assigned to one of two groups (variability and static). The performances were analyzed using objective measurements (completion time, insertion angles, success rate, and hand grip position), as well as a proposed objective performance evaluation metric, IV-score. The results showed learning improvement in both groups, with the variable group achieving higher learning gains (Variable: success rate + 22.4%, IV-score + 8.15; Static: success rate + 19.3%, IV-score + 6.6). These findings demonstrate the potential of variability-based training in our system as an early step towards achieving real clinic-like variability-adaptive training.

Indexed as

Administration, IntravenousFeedbackClinical CompetenceFemaleHumansMaleStudents, NursingHaptic gloveHapticsIntravenousMixed realitySimulation

Identifiers

What OpenQuestion holds

Textmetadata
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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.