Evidence map›Paper›PMID 33355537›Full record

ArticleJMIR mHealth and uHealth2020

Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App.

Bum Chul Kwon, Courtland VanDam, Stephanie E Chiuve, Hyung Wook Choi, Paul Entler, Pang-Ning Tan, Jina Huh-Yoo

Abstract read
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Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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.

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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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

7 authors.

Bum Chul KwonIBM Research, Cambridge, MA, United States.ORCID 0000-0002-9391-6274
Courtland VanDamMIT Licoln Laboratory, Lexington, MA, United States.ORCID 0000-0003-0027-4151
Stephanie E ChiuveAbbVie Inc, Chicago, IL, United States.ORCID 0000-0002-3524-8917
Hyung Wook ChoiCollege of Computing and Informatics, Drexel University, Philadelphia, PA, United States.ORCID 0000-0002-4075-0768
Paul EntlerSparrow Health System, Lansing, MI, United States.ORCID 0000-0002-6134-5219
Pang-Ning TanDepartment of Computer Science and Engineering, Michigan State University, East Lansing, MI, United States.ORCID 0000-0003-3205-0339
Jina Huh-YooCollege of Computing and Informatics, Drexel University, Philadelphia, PA, United States.ORCID 0000-0001-5811-9256

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiet-tracking mobile apps have gained increased interest from both academic and clinical fields. However, quantity-focused diet tracking (eg, calorie counting) can be time-consuming and tedious, leading to unsustained adoption. Diet quality-focusing on high-quality dietary patterns rather than quantifying diet into calories-has shown effectiveness in improving heart disease risk. The Healthy Heart Score (HHS) predicts 20-year cardiovascular risks based on the consumption of foods from quality-focused food categories, rather than detailed serving sizes. No studies have examined how mobile health (mHealth) apps focusing on diet quality can bring promising results in health outcomes and ease of adoption.

objectiveThis study aims to design a mobile app to support the HHS-informed quality-focused dietary approach by enabling users to log simplified diet quality and view its real-time impact on future heart disease risks. Users were asked to log food categories that are the main predictors of the HHS. We measured the app's feasibility and efficacy in improving individuals' clinical and behavioral factors that affect future heart disease risks and app use.

methodsWe recruited 38 participants who were overweight or obese with high heart disease risk and who used the app for 5 weeks and measured weight, blood sugar, blood pressure, HHS, and diet score (DS)-the measurement for diet quality-at baseline and week 5 of the intervention.

resultsMost participants (30/38, 79%) used the app every week and showed significant improvements in DS (baseline: mean 1.31, SD 1.14; week 5: mean 2.36, SD 2.48; 2-tailed t test t

conclusionsOur study showed that our logging tool significantly improved dietary choices. Participants were not interested in seeing the HHS and perceived logging diet categories irrelevant to improving the HHS as important. We discuss the complexities of addressing health risks and quantity- versus quality-based health monitoring and incorporating secondary behavior change goals that matter to users when designing mHealth apps.

Indexed as

DietHeart DiseasesMobile ApplicationsBody WeightHumansObesityCVDdiet monitoringdiet trackingfood trackinghealth risk communicationheart disease riskhuman-computer interactionmHealthmobile phoneuser study

Identifiers

PMID33355537
PMCPMC7787891

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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.