Evidence map›Paper›PMID 34935506›Full record

ArticlePopulation health management2022

The Impact of Expanding Telehealth-Delivered Dietary Interventions on Long-Term Cardiometabolic Health.

Yan Li, Donglan Zhang, Janani Thapa, Weixin Li, Zhuo Chen, Lan Mu, Junxiu Liu, José A Pagán

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Population health management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 28% 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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Review
  4. Article
  5. Article
  6. Review
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

8 authors at 3 institutions in 1 country.

Yan LiDepartment of Population Health Science and Policy and Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Donglan ZhangDepartment of Health Policy and Management, College of Public Health, University of Georgia, Athens, Georgia, USA.
Janani ThapaDepartment of Health Policy and Management, College of Public Health, University of Georgia, Athens, Georgia, USA.
Weixin LiDepartment of Population Health Science and Policy and Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Zhuo ChenDepartment of Health Policy and Management, College of Public Health, University of Georgia, Athens, Georgia, USA.
Lan MuDepartment of Geography, University of Georgia, Athens, Georgia, USA.
Junxiu LiuDepartment of Population Health Science and Policy and Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
José A PagánDepartment of Public Health Policy and Management, School of Global Public Health, New York University, New York, New York, USA.
University of Georgia · USIcahn School of Medicine at Mount Sinai · USNew York University · US

Funding

New York Regional Center for Diabetes Translation Research - Translational Intervention Methodology CoreP30DK111022 · NIDDK · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI JEFFREY GONZALEZ · 2016 to 2026
$7.8M
Assessment of Policies through Prediction of Long-term Effects on Cardiovascular Disease Using Simulation (APPLE CDS)R01HL141427 · NHLBI · NEW YORK ACADEMY OF MEDICINE · PI LIU, BIAN, YI, STELLA S · 2018 to 2021
$3.5M
Using machine learning and causal inference approaches to investigate disparities in Alzheimer’s disease and related dementias outcomesR01MD013886 · NIMHD · UNIVERSITY OF GEORGIA · PI ZHANG, DONGLAN · 2019 to 2022
$2.4M
NHLBI NIH HHS R01 HL141427NIDDK NIH HHS P30 DK111022NIMHD NIH HHS R01 MD013886
6 · The paper itself

Abstract

A healthy diet is an important protective factor to prevent cardiometabolic disease. Traditional face-to-face dietary interventions are often episodic, expensive, and may have limited effectiveness, particularly among older adults and people living in rural areas. Telehealth-delivered dietary interventions have proven to be a low-cost and effective alternative approach to improve dietary behaviors among adults with chronic health conditions. In this study, we developed a validated agent-based model of cardiometabolic health conditions to project the impact of expanding telehealth-delivered dietary interventions among older adults in the state of Georgia, a state with a large rural population. We projected the incidence of major cardiometabolic health conditions (type 2 diabetes, hypertension, and high cholesterol) with the implementation of telehealth-delivered dietary interventions versus no intervention among all older adults and 3 subpopulations (older adults with diabetes, hypertension, and high cholesterol, separately). The results showed that expanding telehealth-delivered dietary interventions could avert 22,774 (95% confidence interval [CI]: 22,091-23,457) cases of type 2 diabetes, 19,732 (19,145-20,329) cases of hypertension, and 18,219 (17,672-18,766) cases of high cholesterol for 5 years among older adults in Georgia. The intervention would have a similar effect in preventing cardiometabolic health conditions among the 3 selected subpopulations. Therefore, expanding telehealth-delivered dietary interventions could substantially reduce the burden of cardiometabolic health conditions in the long term among older adults and those with chronic health conditions.

Indexed as

Diabetes Mellitus, Type 2HypertensionTelemedicineAgedCholesterolChronic DiseaseDiet, HealthyHumansCholesterolagingcardiovascular diseasefood and nutritiontelehealth

Identifiers

PMID34935506
PMCPMC9232230
OpenAlexW4200447190

What OpenQuestion holds

Textmetadata
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.