Evidence map›Paper›PMID 40108565›Full record

ArticleBMC public health2025

Catenation between mHealth application advertisements and cardiovascular diseases: moderation of artificial intelligence (AI)-enabled internet of things, digital divide, and individual trust.

Syed Hassan Raza, Uzma Norin, Emenyeonu C Ogadimma, Umer Zaman, Amjad Ali Shah, Shumaila Khan, Hend Khalid Alkahtani, Mohammed Alkhowaiter, Sajid Ullah Khan

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In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

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

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

  1. Pooled it
  2. Review
  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

9 authors.

Syed Hassan RazaSchool of Media and Communication, Taylor's University, Subang Jaya, 47500, Malaysia.ORCID http://orcid.org/0000-0002-1318-7818
Uzma NorinInstitute of Media and Communication Studies, Bahauddin Zakariya University, Multan, 66000, Pakistan.
Emenyeonu C OgadimmaCollege of Communication, University of Sharjah, Sharjah, 27272, UAE.ORCID http://orcid.org/0000-0002-8918-7419
Umer ZamanEndicott College of International Studies (ECIS), Woosong University, Daejeon, 34606, Korea.ORCID http://orcid.org/0000-0002-3729-3543
Amjad Ali ShahInstitute of Media and Communication Studies, Bahauddin Zakariya University, Multan, 66000, Pakistan.
Shumaila KhanInstitute of Computer Science & IT, University of Science and Technology, Bannu, 28100, Pakistan.
Hend Khalid AlkahtaniDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia. Hkalqahtani@pnu.edu.sa.
Mohammed AlkhowaiterDepartment of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, 16278, Al-Kharj, KSA, Saudi Arabia.
Sajid Ullah KhanDepartment of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, 16278, Al-Kharj, KSA, Saudi Arabia.

Funding

Princess Nourah Bint Abdulrahman University PNURSP2025R513
6 · The paper itself

Abstract

backgroundWorld Health Organization (WHO) identified noncommunicable diseases as the foremost risk to public health globally and the cause of approximately 80% of premature deaths. However, Cardiovascular diseases account for most of these premature deaths and established threats to public health. Recently, digitization of health consultancy services through mHealth (e.g., mobile applications) using artificial intelligence (AI)-enabled Internet of Things has notably facilitated the accessibility of expert advice and continuous monitoring of Cardiac health on public health matters, making it more convenient for adoption in recent times. Nonetheless, scant attention has been paid to how digital media might encourage the adoption of digitally based health consulting services while accounting for the current IoT, Digital Divide, and Individual Trust conditions. Given this status quo, this study contributes to the body of literature by delving into an unexplored evaluation of the role digital media advertising plays in outlining the characteristics and effectiveness of AI-enabled IoT mHealth.

methodThe study used a cross-sectional online survey to collect data from 495 middle-aged adults.

resultsThe findings showed that social influence, performance expectation, effort expectancy, and facilitating conditions are critical factors in predicting intention to use mobile health applications. While the advertisements of mobile health are effective in ingraining the perception of their better performance regarding evading cardiovascular diseases added with the Internet of Things is an effective tool in adopting mobile health applications.

conclusionThe utilization of digital media to promote emerging elements like the AI-enabled Internet of Things in health consulting to counter noncommunicable diseases, e.g., cardiovascular disease, has not yet been thoroughly explored. Consequently, the effectiveness of these communication features promotion among middle-aged adults remains largely uncharted. This research examines how middle-aged adults respond to advertisements showcasing the AI-enabled Internet of Things in digital media. Drawing an analogy with a UTAUT-3 theory, this research investigates how digital media advertisement can effectively promote recently integrated AI-enabled Internet of Things mHealth consultation services. Remarkably, findings identified that trust in AI-enabled IoTs-based mHealth applications remained insignificant. However, the digital divide resulting from low digital literacy in the Global South, especially Pakistan, is the primary obstacle to adopting mHealth apps for digital health services. This study suggests organizations such as WHO, mHealth apps, and telemedicine service providers employ health messaging and instructional advertising to educate the public.

Indexed as

AdvertisingArtificial IntelligenceCardiovascular DiseasesDigital DivideInternet of ThingsMobile ApplicationsTelemedicineTrustAdultCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and QuestionnairesArtificial intelligenceDigital mediaInternet of thingsmHealthNoncommunicable diseaseSocial mediaUTUAT3

Identifiers

PMID40108565
PMCPMC11921525

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.