Evidence map›Paper›PMID 34591026›Full record

ArticleJMIR public health and surveillance2021

Trends in Health Information Technology Use Among the US Population With and Without Cardiovascular Risk Factors, 2012-2018: Evidence From the National Health Interview Survey.

Nikhila Gandrakota, Mohammed K Ali, Megha K Shah

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.5field-weighted citation impact, top 7% 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

8 citing papers in PubMed, 14 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

3 authors at 1 institution in 1 country.

Nikhila GandrakotaDepartment of Family & Preventive Medicine, Emory University School of Medicine, Atlanta, GA, United States.ORCID 0000-0002-4267-7533
Mohammed K AliHubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States.ORCID 0000-0001-7266-2503
Megha K ShahDepartment of Family & Preventive Medicine, Emory University School of Medicine, Atlanta, GA, United States.ORCID 0000-0002-6722-7846
Emory University · US

Funding

Translational Research Core - Engagement and Behavior ChangeP30DK111024 · NIDDK · EMORY UNIVERSITY · PI Mohammed Kumail Ali · 2016 to 2026
$13.4M
Better Together: Leveraging primary care and social network resources to create a patient-centered approach to improve diabetes among immigrant communitiesK23MD015088 · NIMHD · EMORY UNIVERSITY · PI SHAH, MEGHA · 2020 to 2024
$753k
NIDDK NIH HHS P30 DK111024NIMHD NIH HHS K23 MD015088
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has required clinicians to pivot to offering services via telehealth; however, it is unclear which patients (users of care) are equipped to use digital health. This is especially pertinent for adults managing chronic diseases, such as obesity, hypertension, and diabetes, which require regular follow-up, medication management, and self-monitoring.

objectiveThe aim of this study is to measure the trends and assess factors affecting health information technology (HIT) use among members of the US population with and without cardiovascular risk factors.

methodsWe used serial cross-sectional data from the National Health Interview Survey for the years 2012-2018 to assess trends in HIT use among adults, stratified by age and cardiovascular risk factor status. We developed multivariate logistic regression models adjusted for age, sex, race, insurance status, marital status, geographic region, and perceived health status to assess the likelihood of HIT use among patients with and without cardiovascular disease risk factors.

resultsA total of 14,304 (44.6%) and 14,644 (58.7%) participants reported using HIT in 2012 and 2018, respectively. When comparing the rates of HIT use for the years 2012 and 2018, among participants without cardiovascular risk factors, the HIT use proportion increased from 51.1% to 65.8%; among those with one risk factor, it increased from 43.9% to 59%; and among those with more than one risk factor, it increased from 41.3% to 54.7%. Increasing trends in HIT use were highest among adults aged >65 years (annual percentage change [APC] 8.3%), who had more than one cardiovascular risk factor (APC 5%) and among those who did not graduate from high school (APC 8.8%). Likelihood of HIT use was significantly higher in individuals who were younger, female, and non-Hispanic White; had higher education and income; were married; and reported very good or excellent health status. In 2018, college graduates were 7.18 (95% CI 5.86-8.79), 6.25 (95% CI 5.02-7.78), or 7.80 (95% CI 5.87-10.36) times more likely to use HIT compared to adults without high school education among people with multiple cardiovascular risk factors, one cardiovascular risk factor, or no cardiovascular risk factors, respectively.

conclusionsOver 2012-2018, HIT use increased nationally, with greater use noted among younger and higher educated US adults. Targeted strategies are needed to engage wider age, racial, education, and socioeconomic groups by lowering barriers to HIT access and use.

Indexed as

Heart Disease Risk FactorsAdolescentAdultAgedCardiovascular DiseasesCross-Sectional StudiesFemaleHealth SurveysHumansMaleMedical InformaticsMiddle AgedUnited StatesYoung Adultcardiovascular risk factorsdigital healthhealth information technologypublic healthsurveillancetelehealthtelemedicine

Identifiers

PMID34591026
PMCPMC8517814
OpenAlexW3192987167

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.