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首页> 外文期刊>JMIR mHealth and uHealth >Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App
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Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App

机译:通过以质量为中心的饮食测量提高心脏病风险:饮食质量跟踪应用的后期研究

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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.
机译:背景技术饮食跟踪移动应用程序从学术和临床领域获得了更多的兴趣。然而,以数额为中心的饮食跟踪(例如,卡路里计数)可能是耗时和繁琐的,导致不稳定的采用。饮食质量专注于高质量的饮食模式,而不是将饮食量化到卡路里 - 已经显示出改善心脏病风险的有效性。健康的心脏评分(HHS)基于从优质的食品类别的食物消费,而不是详细的服务尺寸,预测了20年的心血管风险。任何研究都没有审查了关注饮食质量的移动健康(MHEATH)应用程序可以带来有希望的卫生结果和易于采用的结果。目的本研究旨在通过使用户能够记录简化的饮食质量,并观察对未来心脏病风险的实时影响,设计一项移动应用程序,以支持HHS知识的优质饮食方法,并以对未来的心脏病风险观察其实时影响。要求用户注销HHS的主要预测因子的食物类别。我们以改善影响未来心脏病风险和应用程序使用的个人临床和行为因素来衡量了应用的可行性和疗效。方法招聘了38名参与者,患有高患有高患者或肥胖的患者,患有高患者的风险,均使用该应用5周,测量重量,血糖,血压,HHS和饮食评分(DS) - 饮食质量 - 在基线上的测量和第5周的干预。结果大多数参与者(30/38,79%)每周使用该应用,并在DS中显示出显着的改进(基线:平均1.31,SD 1.14;第5周:平均2.36,SD 2.48; 2尾T检测T29 =?2.85; P = .008)和HHS(基线:平均22.94,SD 18.86;第4周:第5周的平均22.15,SD 18.58; T29 = 2.41; P = .02)虽然只有10名参与者(10/38,26%)检查他们的HHS风险不止一次。其他结果,包括重量,血糖和血压,没有显着变化。结论我们的研究表明,我们的伐木工具显着改善了饮食选择。与会者对看到HHS并感知伐木饮食类别无关紧要,以改善HHS的重要性。我们讨论了解决健康风险和数量与质量的健康监测的复杂性,并将次要行为改变目标纳入用户在设计MHECHEATH应用程序时。

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