Evidence map›Paper›PMID 40337275›Full record

ArticleFrontiers in medicine2025

Perspectives of people with diabetes on AI-integrated wearable devices: perceived benefits, barriers, and opportunities for self-management.

Haitham Alzghaibi

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Review
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  12. Identifying Measurement Dimensions of Users' Benefit-Risk Perceptions of AI in Healthcare: A Scoping Review.Inquiry : a journal of medical care organization, provision and financing
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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

1 author.

Haitham AlzghaibiDepartment of Health Informatics, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Abstract: Wearable devices that incorporate artificial intelligence (AI) have become effective instruments for managing diabetes through real-time monitoring, improved adherence, and increased person with diabetes engagement. Person with diabetes perceptions, adoption barriers, and preferences critically impact the effectiveness and widespread utilisation of these technologies. Aim: The aim of study was to investigate the perceptions of people with diabetes regarding wearable devices, emphasising their perceived advantages, challenges, and potential role in facilitating diabetes self-management. Methods: A cross-sectional study involving 418 people with diabetes was conducted, with participants recruited via online platforms and people with diabetes groups. Data were gathered through a structured questionnaire that included Likert-scale items, multiple-choice questions, and open-ended responses. Descriptive statistics were employed to analyse quantitative data, whereas qualitative responses underwent thematic analysis to discern key trends. Results: Participants demonstrated significant awareness of the primary functions of wearable devices, with 83.9% acknowledging their utility in monitoring glucose levels and physical activity. The primary advantages comprised increased adherence to medication regimens (81.9%) and heightened confidence in diabetes management (82.1%). Significant barriers were identified, including data privacy concerns (79.7%), cost issues (77.0%), and usability challenges (75.1%). Thematic analysis of open-ended responses indicated a demand for features including actionable feedback, integration with healthcare providers, and enhanced usability. Despite these challenges, 81.9% of participants indicated a willingness to adopt AI-integrated wearable devices if recommended by healthcare providers. Conclusion: The findings indicate that people with diabetes regard wearable devices as effective instruments for managing their condition, especially in terms of real-time monitoring and adherence support. Concerns regarding privacy, cost, and device usability must be addressed to enhance adoption rates. These insights can inform the development of patient-centered wearable devices and guide healthcare strategies for the effective integration of these technologies into diabetes care.

Indexed as

artificial intelligencecross-sectional studydiabetes self-managementdigital healthhealth technology adoptionpatient perceptionswearable devices

Identifiers

PMID40337275
PMCPMC12055762

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Registered trials

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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.