## 2 Materials and Methods
The control group followed a similar protocol, with the exception that the participants were not provided with wireless devices and access to the dietary logger and lifestyle app. Therefore, no detailed dietary or body weight logs were available for this group, only the demographic data and the body weights taken before and after the trial. Nevertheless, this group also took part in the startup consultation, and the participants were offered one personal consultation a month, in line with the regular treatment protocol for MetS patients. Aside from the daily measurement of the body weight, the blood pressure, plasma blood glucose level, LDL/HDL-cholesterol levels, and waist circumference values were also recorded at the startup and the end of the trial. The compilation of these clinical outcomes is still under way and will be published later. However, it is the weight loss outcome for which the most remarkable change could be expected, according to similar studies [19], and also the parameter for which detailed daily measurement data were available. Therefore, this paper focuses on the success of the dietary interventions based on the weight loss. The cornerstone of the dietary logging and analysis was an **expert database** that has been manually compiled by dietitian experts to match the target Hungarian culture. The database contains all popular dishes, foods and snacks with several natural units, along with their nutrient contents. The database also contains the weight equivalents of all natural units to support a weight-based dietary analysis. The source of the nutrient data was the public national database on the dietary content of foods, and the contents of dishes were calculated according to the recipe and the contents of the food ingredients. Missing nutrition data for ingredients were imported from the public USDA database [20] after a manual alignment of the food items. All database items have been manually assigned to one of 35 sets based on their content and dietary characteristics. Nine sets contained foods and dishes which were identified as dietary protective factors according to the relevant literature [8; 15; 16; 21; 22]. An example of such a set is 'Raw fruits'. Twelve of the sets were linked with dietary risk factors (e.g., 'High fat processed meat products, fast food'). The remaining 14 sets characterized items whose expected effect on nutritional therapy mainly depended on the amount consumed (e.g., 'pasta', or 'rice dishes'). For a complete list of the sets, see Appendix B. Due to the lack of detailed timeline data for the control group, the **data analysis** used the control group only for group-level comparisons to check the validity of the secondary hypotheses, and focused more on the primary hypothesis of the study; i.e., the exploration of temporal trends and sub-groups within the intervention group. As a first step, the dietary logs were systematically cleaned to filter out incomplete or erroneous entries, according to the following ordered scheme. 1. If the log contained an entry that was too general to be identified reliably, the whole day that contained the entry was excluded from the analysis
2. If the total energy logged for a day exceeded 2500 kcal, the logged items for the day were manually checked and corrected by a supervising experienced dietitian. Similarly, any single entries with a quantity above 9 (like e.g., 10 platelets of spaghetti) were considered a possible typo. Such entries were verified and corrected manually. A similar procedure was followed for repeated identical entries. 3. Incompletely logged days were excluded from further analysis. A logged day was considered incomplete if it had a total energy below 500 kcal, if it contained only one meal, or it contained only two meals and a total energy below 1000 kcal. 4. Outlier days were excluded due to a high probability of a logging error. A day was considered an outlier if its total energy differed more than 50% from the average of the two preceding and two succeeding days. 5.