Evidence map›Paper›PMID 37571682›Full record

ReviewSensors (Basel, Switzerland)2023

Adherence and the Diabetic Foot: High Tech Meets High Touch?

Hadia Srass, J Karim Ead, David G Armstrong

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 12% of its field
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Observational
  5. Article
  6. Review
  7. Article
  8. Article
  9. 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

3 authors at 2 institutions in 1 country.

Hadia SrassSouthwestern Academic Limb Salvage Alliance, Department of Surgery, Keck School of Medicine of University of Southern California, 1450 San Pablo St #6200, Los Angeles, CA 90033, USA.
J Karim EadSouthwestern Academic Limb Salvage Alliance, Department of Surgery, Keck School of Medicine of University of Southern California, 1450 San Pablo St #6200, Los Angeles, CA 90033, USA.
David G ArmstrongSouthwestern Academic Limb Salvage Alliance, Department of Surgery, Keck School of Medicine of University of Southern California, 1450 San Pablo St #6200, Los Angeles, CA 90033, USA.
University of Southern California · USUniversity of California, Riverside · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic foot ulcers, which are a common complication of diabetes, can have a negative impact on a person's physical and mental health, including an increased risk of depression. Patients suffering from depression are less likely to keep up with diabetic foot care, thus increasing the risk of developing ulcers. However, with the use of artificial intelligence (AI), at-home patient care has become easier, which increases adherence. To better understand how new technologies, including machine learning algorithms and wearable sensors, might improve patient adherence and outcomes, we conducted a literature review of several sensor technologies, including SmartMat© and Siren Care© socks for temperature, SurroSense Rx/Orpyx© for pressure, and Orthotimer© for adherence. An initial search identified 143 peer-reviewed manuscripts, from which we selected a total of 10 manuscripts for further analysis. We examined the potential benefits of personalized content and clinician support for those receiving mobile health interventions. These findings may help to demonstrate the current and future utility of advanced technologies in improving patient adherence and outcomes, particularly in the context of diabetes management and the link between behavior and complications in diabetes, such as diabetic foot ulcers.

Indexed as

Diabetes MellitusDiabetic FootArtificial IntelligenceHumansMachine Learningadherenceartificial intelligencedepressiondiabetesdiabetic foot ulcerstechnology

Identifiers

PMID37571682
PMCPMC10422535
OpenAlexW4385544049

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.