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

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.

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

“… CONNECT ( Redfern et al, 2020 ) AU RCT n = 934 Primary care ≥ 18 years Inclusion based on CVD risk Access to application with electronic health record connection displaying current diagnoses and medications, educational materials, risk calculator, lifestyle change support concerning smoking, physical activity, nutrition, psychological well-being, and / or medication adherence, medications possible via encouragement discussion with GP, social media, possibility to receive additional advice via e-mail and / or SMS, support service TAU FRS Anderson ( Anderson et al, 1991 ) Suggested action: determine intervention type Insufficient detail in reporting Length of follow-up: 1 year Lifestyle (other: medication adherence) No statistically significant difference 32. – ( Kwon et al, 2020 ) USA Single-arm study n = 38 Weight management clinic patients ≥ 18 years Inclusion based on CVD risk Access to application to track and improve nutrition Healthy heart score ( Chiuve et al, 2014 ) Suggested action: determine intervention type Length of follow-up: 5 weeks Not specified ACTIVATE = a coaching by telephone intervention for veterans and care team engagement; ANCHOR = a novel approach to cardiovascular health by optimizing risk management; ARRIBA-Herz = Aufgabe gemeinsam definieren, Risiko subjektiv, Risiko objektiv, Information über Präventionsmöglichkeiten, Bewertung der Präventionsmöglichkeiten und Absprache über weiteres Vorgehen – Herz (define task together, subjective risk, objective risk, information about prevention options and agreement on further action - heart); ATP = adult treatment panel; AU = Australia; AZM = Academisch Ziekenhuis Maastricht (Maastricht University Medical Centre +); BE = Belgium; BMI = body mass index; CA = Canada; CHARLAR = community heart health actions for Latinos at risk; CMD = cardiometabolic disease; CN = China; COHRT = community outreach heart health and risk reduction trial; CONNECT = consumer navigation of electronic cardiovascular tools; CPM = clinical prediction model; CVD = cardiovascular disease; DE = Deutschland (Germany); DECADE = decision-aid, action planning, and follow-up support for patients to reduce the 10-year risk of CVD; DK = Denmark; EU = European Union...…”

“…Providing intermediate feedback of the effect of the participant’s behavioural changes on the CPM-based estimated risk may aid the participant to understand this dynamic. This was done in the publications by Edelman et al, 2006 , Wister et al, 2007 , Khanji et al, 2019 , Redfern et al, 2020 , and Kwon et al (2020) .…”

Ifood: Development and usability study of a social media-based applet for dietary monitoring

“…The good Median system usability scale (SUS) scores in adolescents and adults for the live version have proved the usability of this tool. A smartphone application based on behavior change strategies was created by Kwon et al 25 Users could enter up to 4 food categories for each diet they eat every day and see the real-time effect on their risk of developing heart disease. Kong et al 26 developed a dietary monitoring smartphone application, MyDietCam, which could record dietary intake through food image recognition and provide nutrient analyses through visuals.…”