Evidence map›Paper›PMID 38175685›Full record

ArticleJournal of medical Internet research2024

Association Between the Composite Cardiovascular Risk and mHealth Use Among Adults in the 2017-2020 Health Information National Trends Survey: Cross-Sectional Study.

Yuling Chen, Ruth-Alma Turkson-Ocran, Binu Koirala, Patricia M Davidson, Yvonne Commodore-Mensah, Cheryl Dennison Himmelfarb

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.5field-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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

6 authors at 2 institutions in 2 countries.

Yuling ChenJohns Hopkins University School of Nursing, Baltimore, MD, United States.ORCID 0000-0001-8273-0442
Ruth-Alma Turkson-OcranBeth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States.ORCID 0000-0001-9932-052X
Binu KoiralaJohns Hopkins University School of Nursing, Baltimore, MD, United States.ORCID 0000-0001-7771-8422
Patricia M DavidsonJohns Hopkins University School of Nursing, Baltimore, MD, United States.ORCID 0000-0003-2050-1534
Yvonne Commodore-MensahJohns Hopkins University School of Nursing, Baltimore, MD, United States.ORCID 0000-0002-5054-3025
Cheryl Dennison HimmelfarbJohns Hopkins University School of Nursing, Baltimore, MD, United States.ORCID 0000-0003-1918-4316
Johns Hopkins University · USBeth Israel Deaconess Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNumerous studies have suggested that the relationship between cardiovascular disease (CVD) risk and the usage of mobile health (mHealth) technology may vary depending on the total number of CVD risk factors present. However, whether higher CVD risk is associated with a greater likelihood of engaging in specific mHealth use among US adults is currently unknown.

objectiveWe aim to assess the associations between the composite CVD risk and each component of mHealth use among US adults regardless of whether they have a history of CVD or not.

methodsThis study used cross-sectional data from the 2017 to 2020 Health Information National Trends Survey. The exposure was CVD risk (diabetes, hypertension, smoking, physical inactivity, and overweight or obesity). We defined low, moderate, and high CVD risk as having 0-1, 2-3, and 4-5 CVD risk factors, respectively. The outcome variables of interest were each component of mHealth use, including using mHealth to make health decisions, track health progress, share health information, and discuss health decisions with health providers. We used multivariable logistic regression models to examine the association between CVD risk and mHealth use adjusted for demographic factors.

resultsWe included 10,531 adults, with a mean age of 54 (SD 16.2) years. Among the included participants, 50.2% were men, 65.4% were non-Hispanic White, 41.9% used mHealth to make health decisions, 50.8% used mHealth to track health progress toward a health-related goal, 18.3% used mHealth to share health information with health providers, and 37.7% used mHealth to discuss health decisions with health providers (all are weighted percentages). Adults with moderate CVD risk were more likely to use mHealth to share health information with health providers (adjusted odds ratio 1.49, 95% CI 1.24-1.80) and discuss health decisions with health providers (1.22, 95% CI 1.04-1.44) compared to those with low CVD risk. Similarly, having high CVD risk was associated with higher odds of using mHealth to share health information with health providers (2.61, 95% CI 1.93-3.54) and discuss health decisions with health providers (1.56, 95% CI 1.17-2.10) compared to those with low CVD risk. Upon stratifying by age and gender, we observed age and gender disparities in the relationship between CVD risk and the usage of mHealth to discuss health decisions with health providers.

conclusionsAdults with a greater number of CVD risk factors were more likely to use mHealth to share health information with health providers and discuss health decisions with health providers. These findings suggest a promising avenue for enhancing health care communication and advancing both primary and secondary prevention efforts related to managing CVD risk factors through the effective usage of mHealth technology.

Indexed as

Cardiovascular DiseasesTelemedicineAdultCross-Sectional StudiesFemaleHeart Disease Risk FactorsHumansMaleMiddle AgedRisk Factorsassociationcardiovascular riskmobile healthmobile phoneusage

Identifiers

PMID38175685
PMCPMC10797506
OpenAlexW4390589602

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.