Evidence map›Paper›PMID 41836102›Full record

ArticleProceedings of the ... Design of Medical Devices Conference. Design of Medical Devices Conference2025

HUMAN CENTERED DESIGN: VETERAN TESTING OF A MOBILE APP WITH AUGMENTED REALITY FOR PHANTOM LIMB PAIN.

Tonya L Rich, Katharyn G Cristan, Timothy P Truty, Andrew H Hansen, Princess E Ackland

Abstract read
In one paragraph

Article in Proceedings of the ... Design of Medical Devices Conference. Design of Medical Devices Conference, 2025. 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

5 authors.

Tonya L RichMinneapolis VA Health Care System; University of Minnesota, Minneapolis, MN, USA.
Katharyn G CristanMinneapolis VA Health Care System, Minneapolis, MN, USA.
Timothy P TrutyMinneapolis VA Health Care System, Minneapolis, MN, USA.
Andrew H HansenMinneapolis VA Health Care System; University of Minnesota, Minneapolis, MN, USA.
Princess E AcklandMinneapolis VA Health Care System; University of Minnesota, Minneapolis, MN, USA.

Funding

RRD VA IK2 RX004805
6 · The paper itself

Abstract

Phantom limb pain (PLP) after amputation is commonly experienced and can impact quality of life.[1] Management of PLP is challenging with few effective treatment options. One non-drug intervention is graded motor imagery (GMI) that includes 3-phases (i.e., limb laterality, motor imagery, and mirror therapy). Although clinicians report patient benefit from use of GMI, barriers exist to supporting at-home use of the intervention. This report focuses on refining a mobile app (VA-GMI) through serial cognitive interviews with 12 Veterans with transtibial amputation and PLP. Following each round of interviews, we summarized the major points to guide iterative changes in the mobile app. Our first round of interviews suggested that the Veterans find the VA-GMI app acceptable, but Veterans endorsed the need for added instructions. Concurrent with these enhancements, we worked to optimize the motor imagery and mirror therapy phases. We then invited Veterans to try the new version and repeated the process. Our second round of interviews suggested the need for additional education to support Veteran understanding of pain rehabilitation. This report describes the development and testing work to date using human centered design considerations. We anticipate we will reach consensus on the VA-GMI mobile app within the next iteration.

Indexed as

amputationgraded motor imagerypainrehabilitationtranstibial

Identifiers

PMID41836102
PMCPMC12981237

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.