Evidence map›Paper›PMID 38400259›Full record

ArticleSensors (Basel, Switzerland)2024

Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST).

Valentino Šafran, Simon Lin, Jama Nateqi, Alistair G Martin, Urška Smrke, Umut Ariöz, Nejc Plohl, Matej Rojc, Dina Bēma, Marcela Chávez and 2 more

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

12 authors.

Valentino ŠafranFaculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia.ORCID 0000-0002-3664-3564
Simon LinScience Department, Symptoma GmbH, 1030 Vienna, Austria.ORCID 0000-0001-9870-2198
Jama NateqiScience Department, Symptoma GmbH, 1030 Vienna, Austria.
Alistair G MartinScience Department, Symptoma GmbH, 1030 Vienna, Austria.
Urška SmrkeFaculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia.ORCID 0000-0003-3516-0429
Umut AriözFaculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia.ORCID 0000-0002-6476-4494
Nejc PlohlDepartment of Psychology, Faculty of Arts, University of Maribor, 2000 Maribor, Slovenia.ORCID 0000-0001-9936-4039
Matej RojcFaculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia.
Dina BēmaInstitute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.ORCID 0000-0002-8353-7393
Marcela ChávezDepartment of Information System Management, Centre Hospitalier Universitaire de Liège, 4000 Liège, Belgium.
Matej HorvatDepartment of Oncology, University Medical Centre Maribor, 2000 Maribor, Slovenia.
Izidor MlakarFaculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia.ORCID 0000-0002-4910-1879

Funding

European Union Horizon 2020 Research and Innovation Program - project PERSIST 875406Slovenian Research Agency (Research Core Funding), Young Researcher Funding 0552-0796 P2-0069, 0733/2022/P157/522-KZ
6 · The paper itself

Abstract

The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app's usability as above satisfactory (i.e., 7.9 on 1-10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1-10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.

Indexed as

Breast NeoplasmsMobile ApplicationsFemaleHumansMalePalliative CareProspective StudiesRisk Assessmentchronic diseasesmultilingual frameworkpatient-centered carereal-world datarisk assessmentsymptom tracking

Identifiers

PMID38400259
PMCPMC10892413

What OpenQuestion holds

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LicenceCC BY
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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.