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通过以质量为重点的饮食记录改善心脏病风险:饮食质量跟踪应用的前后研究。

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

机构信息

IBM Research, Cambridge, MA, United States.

MIT Licoln Laboratory, Lexington, MA, United States.

出版信息

JMIR Mhealth Uhealth. 2020 Dec 23;8(12):e21733. doi: 10.2196/21733.

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 t=-2.85; P=.008) and HHS (baseline: mean 22.94, SD 18.86; week 4: mean 22.15, SD 18.58; t=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.

摘要

背景

饮食追踪移动应用在学术和临床领域都受到了越来越多的关注。然而,以数量为重点的饮食追踪(例如,计算卡路里)可能既耗时又乏味,导致无法持续采用。关注高质量饮食模式而不是将饮食量化为卡路里的饮食质量,已被证明在改善心脏病风险方面是有效的。健康心脏评分(HHS)基于高质量食物类别的食物消耗来预测 20 年的心血管风险,而不是详细的份量。目前还没有研究探讨专注于饮食质量的移动健康(mHealth)应用如何在健康结果和采用便利性方面带来有希望的结果。

目的

本研究旨在设计一个移动应用程序,通过使用户能够记录简化的饮食质量并实时查看其对未来心脏病风险的影响,从而支持基于 HHS 的注重质量的饮食方法。要求用户记录 HHS 的主要预测因素的食物类别。我们测量了该应用程序在改善个体影响未来心脏病风险的临床和行为因素以及应用程序使用方面的可行性和功效。

方法

我们招募了 38 名超重或肥胖且心脏病风险高的参与者,他们使用该应用程序 5 周,并在基线和干预的第 5 周测量体重、血糖、血压、HHS 和饮食评分(DS)-饮食质量的衡量标准。

结果

大多数参与者(30/38,79%)每周都使用该应用程序,并且 DS 有显著改善(基线:均值 1.31,标准差 1.14;第 5 周:均值 2.36,标准差 2.48;双尾 t 检验 t=-2.85;P=.008)和 HHS(基线:均值 22.94,标准差 18.86;第 4 周:均值 22.15,标准差 18.58;t=2.41;P=.02)在第 5 周,但只有 10 名参与者(10/38,26%)检查了他们的 HHS 风险评分超过一次。其他结果,包括体重、血糖和血压,没有显示出显著变化。

结论

我们的研究表明,我们的记录工具显著改善了饮食选择。参与者对查看 HHS 不感兴趣,并认为记录饮食类别与提高 HHS 无关,认为这很重要。当设计 mHealth 应用程序时,我们讨论了应对健康风险以及基于数量和质量的健康监测的复杂性,并纳入了对用户重要的次要行为改变目标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a6a/7787891/9c740817a019/mhealth_v8i12e21733_fig1.jpg

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