Evidence map›Paper›PMID 42699143›Full record

ArticleDiabetology2026

Can a Chatbot Help Heal a Wound? Context-Aware Prompts for Boosting Adherence in Diabetic Foot Ulcers.

Aria Rabet, Aminreza Khandan, Arian Rabet, Mohammad Dehghan Rouzi, Fabiola Rodriguez, Adriana Garibay, David G Armstrong, Bijan Najafi

Abstract read
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In one paragraph

Article in Diabetology, 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

8 authors.

Aria RabetCenter for Advanced Surgical & Interventional Technology (CASIT), Department of Surgery, University of California, Los Angeles, CA 90024, USA.ORCID 0009-0000-4245-4009
Aminreza KhandanCenter for Advanced Surgical & Interventional Technology (CASIT), Department of Surgery, University of California, Los Angeles, CA 90024, USA.ORCID 0000-0001-5045-1414
Arian RabetCenter for Advanced Surgical & Interventional Technology (CASIT), Department of Surgery, University of California, Los Angeles, CA 90024, USA.
Mohammad Dehghan RouziCenter for Advanced Surgical & Interventional Technology (CASIT), Department of Surgery, University of California, Los Angeles, CA 90024, USA.
Fabiola RodriguezSouthwestern Academic Limb Salvage Alliance (SALSA), Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Adriana GaribaySouthwestern Academic Limb Salvage Alliance (SALSA), Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
David G ArmstrongSouthwestern Academic Limb Salvage Alliance (SALSA), Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Bijan NajafiCenter for Advanced Surgical & Interventional Technology (CASIT), Department of Surgery, University of California, Los Angeles, CA 90024, USA.ORCID 0000-0002-0320-8101

Funding

Improving the science of adherence reinforcement and safe mobility in people with diabetic foot ulcers using smart offloadingR01DK124789 · NIDDK · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ARMSTRONG, DAVID GEORGE, NAJAFI, BIJAN · 2021 to 2025
$2.0M
NIDDK NIH HHS R01 DK124789
6 · The paper itself

Abstract

Background: Smart offloading technologies enable the real-time, objective monitoring of adherence in patients with diabetic foot ulcers (DFUs). Although remote tracking may reinforce adherence and improve wound healing, effectiveness depends on sustained device use, particularly as devices are often removed during rest periods. Real-time, behavior-contingent feedback informed by sensor data, including AI-supported messaging capable of detecting nonadherence, may enhance reinforcement. However, the feasibility and behavioral impact of such strategies remain unclear. Methods: We conducted a prospective feasibility case series nested within a larger DFU cohort of 210 participants, enrolling eight adults with active DFUs. Participants used a sensor-integrated offloading device paired with a smartwatch (SmartBoot) and a mobile application (CORA) that delivered notifications to their smartphones. Notifications were either schedule-based or context-aware, using real-time SmartBoot data to generate personalized messages. The primary outcome was a sensor-detected transition from nonadherent to adherent offloading within 60 min. Results: A total of 130 notifications were delivered, with 125 included in the behavioral response analysis. Context-aware notifications demonstrated higher transition rates than schedule-based notifications. Adaptive Reinforcement yielded the highest response rate (77.4%, 24/31), followed by Clinical Course Correction (71.4%, 20/28), whereas Safety and Technical Assurance (40.7%, 11/27) and Motivational Coaching (30.8%, 12/39) showed lower response rates. Conclusions: Real-time, context-aware feedback is feasible and associated with improved short-term adherence, supporting evaluation in larger trials.

Indexed as

context-aware notificationsdiabetic foot ulcerfeasibilitymobile healthoffloadingremote monitoringwearable sensors

Identifiers

PMID42699143
PMCPMC13543935

What OpenQuestion holds

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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.