Evidence map›Paper›PMID 41734142›Full record

ArticlePloS one2026

Reimagined diabetic care approach: A qualitative study on the acceptability of mhealth interventions in a LMIC.

Ola Sukkarieh, Leonard Egede, Mona Osman, Maya Bassil, Myrna A A Doumit

Abstract read
In one paragraph

Article in PloS one, 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

5 authors.

Ola SukkariehHariri School of Nursing-American University of Beirut, Lebanon.ORCID https://orcid.org/0000-0001-9971-1563
Leonard EgedeWisconsin College of Medicine, United States of America.
Mona OsmanDepartment of Family Medicine, American University of Beirut, Lebanon.
Maya BassilQatar University, Qatar.
Myrna A A DoumitHariri School of Nursing-American University of Beirut, Lebanon.ORCID https://orcid.org/0000-0001-6724-2862

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLebanon is a lower-middle income country in the MENA region that continues to be drained structurally by the socioeconomic upheaval. The estimated prevalence of T2DM in Lebanese adults is 9%. Despite the rapid growing use of mHealth and favorable health outcomes worldwide, the impact is understudied in Lebanon. PURPOSE: Our study aimed to assess the acceptability of the use of mHealth intervention delivered via mobile phones that promotes diabetes self-management behaviors for Lebanese patients with T2DM. DESIGN AND

methodsWe used a descriptive qualitative approach for the study. Nine study participants were recruited based on purposeful and maximum variation sampling. Interviews were analyzed using the conventional content analysis.

resultsAnalysis of the interviews revealed four major categories: (A) Transformative Approach to Care: Feeling Safe and Secure; (B) One Approach does not fit all; (C) Addressing psychological well-being; (D) Time and Economic gains.

conclusionThis study provides compelling evidence that mHealth is highly acceptable among Lebanese adults with T2DM and offers significant potential to enhance diabetes care in LMICs. Participants embraced mHealth as a complementary tool that enhances communication, supports psychological well-being, and reduces financial barriers.

Indexed as

Diabetes Mellitus, Type 2Patient Acceptance of Health CareTelemedicineAdultCell PhoneDigital HealthFemaleHumansLebanonMaleMiddle AgedQualitative Research

Identifiers

PMID41734142
PMCPMC12931804

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.