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| Cluster themes | Author | Purpose | Finding |
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| Attitude to improved dietary behaviors | Joshua H West et al., 2017 | The purpose of this study was to identify which behavior change mechanisms are associated with the use of diet and nutrition-related health apps and whether the use of diet- and nutrition-related apps is associated with health behavior change. | Study findings indicate that diet/nutrition apps are associated with diet-related behavior change. Hence, diet- and nutrition-related apps that focus on improving motivation, desire, self-efficacy, attitudes, knowledge, and goal-setting may be useful [23]. |
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| Parameters for disease diagnosis | Den Braber et al., 2019 | An ideal application would track food intake, physical activity, glucose levels, and medication use and then combine the data to give patients and healthcare providers insight into these elements and the impact of lifestyle on glucose levels in everyday life. | This research focuses on the needs for the initial iteration of the diameter, which are focused on gathering data and providing insight to patients. It is critical to collect lifestyle and glucose data rapidly to construct future versions of the diameter, including a personalized data-driven coaching module [28]. |
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| Noncommunicable diseases (NCD) | Richardson et al., 2021 | This study aims to see if an abridged dietary self-monitoring method in T2D patients, in which only carbohydrate-containing foods are recorded in a diet tracker, is feasible. | A simplified dietary self-monitoring strategy may not be possible, especially for people unfamiliar with carbohydrate-containing meals. Despite these findings, this study contributes to the little literature that examines alternatives to more intense dietary self-monitoring for T2D management [29]. |
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| Methods | Prudhon et al., 2011 | It provides an algorithm for analyzing nutritional and mortality survey reports using systematic and comparable criteria to identify a wide range of errors that could lead to sample, response, or measurement biases and rate the overall quality of the survey. | The intra-class correlation coefficient for mortality surveys was 0.79, while for nutrition surveys, it was 0.78. For mortality and nutrition surveys, the total median quality score and range of around 100 surveys completed in Darfur were 0.60 (0.12–0.93) and 0.675 (0.23–0.86), respectively. They vary depending on the surveying organization, with no discernible trend over time [30]. |
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| Nutrition algorithms | Sun et al., 2012 | It contributes to understanding the human factors that determine diets, eating patterns, and lifestyle choices by describing specific task force actions and findings in Asian countries. It also discusses the impact of transcultural factors on the adaptability of current evidence-based CPGs for diabetes-specific nutrition therapy and their implementation in Asian nations. | An international task team created a transcultural diabetes-specific nutrition algorithm that breaks down complex diabetes rules into a simple, customizable structure. To address the demands and preferences of afflicted patients, the Asian adaption integrates regional variances in lifestyles, diets, and customs [31]. |
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| Mobile health applications | Turner-McGrievy et al., 2017 | The purpose of this study was to compare traditional and mobile app self-monitoring of physical activity and dietary intake. | The study findings point to the potential benefits of mobile monitoring methods during behavioral weight loss trials. Future studies should examine ways to predict which self-monitoring method works best for an individual to increase adherence [32]. |
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| Body mass index | Chen and Tseng, 2010 | It aims to determine the marginal effects of food intakes, health behaviors, and nutrition knowledge on the overall BMI distribution across individuals. | Evidence suggests that calories, oleic acid, and cholesterol raise BMI, but fiber, calcium, and sodium have the opposite impact. Protein decreases BMI in females who are overweight or obese. Vitamin C lowers BMI in underweight and mildly to severely obese males. Jogging reduces BMI. However, drinking enhances BMI in nonobese people. Nutrition knowledge lowers BMI in males whose BMI is in the optimal weight to slightly overweight ranges, whereas this effect is minor in females [33]. |
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