Literature DB >> 33355537

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

Bum Chul Kwon1, Courtland VanDam2, Stephanie E Chiuve3,4, Hyung Wook Choi5, Paul Entler6, Pang-Ning Tan7, Jina Huh-Yoo5.   

Abstract

BACKGROUND: Diet-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.
OBJECTIVE: This 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.
METHODS: We 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.
RESULTS: Most 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 t29=-2.85; P=.008) and HHS (baseline: mean 22.94, SD 18.86; week 4: mean 22.15, SD 18.58; t29=2.41; P=.02) at week 5, although only 10 participants (10/38, 26%) checked their HHS risk scores more than once. Other outcomes, including weight, blood sugar, and blood pressure, did not show significant changes.
CONCLUSIONS: Our 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. ©Bum Chul Kwon, Courtland VanDam, Stephanie E Chiuve, Hyung Wook Choi, Paul Entler, Pang-Ning Tan, Jina Huh-Yoo. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 23.12.2020.

Entities:  

Keywords:  CVD; diet monitoring; diet tracking; food tracking; health risk communication; heart disease risk; human-computer interaction; mHealth; mobile phone; user study

Year:  2020        PMID: 33355537      PMCID: PMC7787891          DOI: 10.2196/21733

Source DB:  PubMed          Journal:  JMIR Mhealth Uhealth        ISSN: 2291-5222            Impact factor:   4.773


  33 in total

Review 1.  Comparisons of established risk prediction models for cardiovascular disease: systematic review.

Authors:  George C M Siontis; Ioanna Tzoulaki; Konstantinos C Siontis; John P A Ioannidis
Journal:  BMJ       Date:  2012-05-24

2.  Mobile applications for weight management: theory-based content analysis.

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Journal:  Am J Prev Med       Date:  2013-11       Impact factor: 5.043

3.  Mobile Health Initiatives to Improve Outcomes in Primary Prevention of Cardiovascular Disease.

Authors:  Bruno Urrea; Satish Misra; Timothy B Plante; Heval M Kelli; Sanjit Misra; Michael J Blaha; Seth S Martin
Journal:  Curr Treat Options Cardiovasc Med       Date:  2015-12

4.  Women's approaches to the use of new technology for cancer risk information.

Authors:  Heidi K Unruh; Deborah J Bowen; Hendrika Meischke; Nigel Bush; Jean A Wooldridge
Journal:  Women Health       Date:  2004

5.  The quantified patient of the future: Opportunities and challenges.

Authors:  Maulik D Majmudar; Lina Avancini Colucci; Adam B Landman
Journal:  Healthc (Amst)       Date:  2015-03-13

Review 6.  mHealth in Cardiovascular Health Care.

Authors:  Clara K Chow; Nilshan Ariyarathna; Sheikh Mohammed Shariful Islam; Aravinda Thiagalingam; Julie Redfern
Journal:  Heart Lung Circ       Date:  2016-05-11       Impact factor: 2.975

7.  Beyond Abandonment to Next Steps: Understanding and Designing for Life after Personal Informatics Tool Use.

Authors:  Daniel A Epstein; Monica Caraway; Chuck Johnston; An Ping; James Fogarty; Sean A Munson
Journal:  Proc SIGCHI Conf Hum Factor Comput Syst       Date:  2016-05

8.  Why is changing health-related behaviour so difficult?

Authors:  Michael P Kelly; Mary Barker
Journal:  Public Health       Date:  2016-05-13       Impact factor: 2.427

9.  Using mobile technology to support lower-salt food choices for people with cardiovascular disease: protocol for the SaltSwitch randomized controlled trial.

Authors:  Helen Eyles; Rebecca McLean; Bruce Neal; Robert N Doughty; Yannan Jiang; Cliona Ni Mhurchu
Journal:  BMC Public Health       Date:  2014-09-12       Impact factor: 3.295

10.  When Personal Tracking Becomes Social: Examining the Use of Instagram for Healthy Eating.

Authors:  Chia-Fang Chung; Elena Agapie; Jessica Schroeder; Sonali Mishra; James Fogarty; Sean A Munson
Journal:  Proc SIGCHI Conf Hum Factor Comput Syst       Date:  2017-05-02
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  1 in total

Review 1.  Using clinical prediction models to personalise lifestyle interventions for cardiovascular disease prevention: A systematic literature review.

Authors:  Anke Bruninx; Bart Scheenstra; Andre Dekker; Jos Maessen; Arnoud van 't Hof; Bas Kietselaer; Iñigo Bermejo
Journal:  Prev Med Rep       Date:  2021-12-16
  1 in total

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