The FOODLOOP diary study was executed using the mobile app Traqq. Traqq (available for Android and iOS) was developed for dietary research purposes by Wageningen University [46] and allows for descriptive data collection, has no commercial focus, ensures privacy and safe storage of users’ data, and enables customizations in line with this study’s objectives. In Traqq, participants registered their daily food and beverage intake by choosing what they consumed from the Traqq food database, which includes products from the NEVO (Nederlands Voedingsstoffenbestand) 2016/2019 Dutch Food Composition Database [47,48] and Dutch food stores’ assortments. Additional variables measured for consumed snacks were captured through questionnaires in Qualtrics XM (2022‐2023). These questionnaires were programmed such that they appeared integrated within Traqq itself to participants. At the start of the study, participants received log-in credentials for Traqq, which remained valid throughout the entire study period. Once logged in, participants stayed continuously logged in, enabling them to receive prompts for all data collection days. Access to Traqq was deactivated upon study completion. All additions to and changes in features in the existing Traqq app to align it with FOODLOOP study objectives can be found in Table S1 in Multimedia Appendix 1.

Given the objectives of this study, only data concerning products labeled as snacking occasions were included in the analyses. Data related to meals, incomplete collection days, and dropout participants were excluded. Therefore, there were no participants with missing data for any of the variables of interest (Table 1). After data collection, all snack products were classified into categories (Table 1; Table S4 in Multimedia Appendix 1), based on the Dutch Food Composition Database (versions 2016 and 2019; NEVO [47,48]), Dutch National Food Consumption Survey [51,52], and the Netherlands Nutrition Center (Voedingscentrum [53]). These snack product category variables were subsequently used in the analyses. In addition, the variables concerning physical context were merged into 2 categories, “At home“ and “Out of home,” and the social context variables into the categories “Alone” and “With others.” Table 1 shows the snacking determinants, corresponding variables, and response categories for analyses.

All analyses were performed with standardized snacking frequencies for each response category of all considered variables (Table 1) to correct for unequal participant weights, as some participants recorded more snacks than others. Each snack originally represented one case in the dataset. To ensure that all participants contributed equally to the analyses, absolute snacking frequencies for each response category were divided by the number of data collection days (12), resulting in the daily average frequency (DAF) of snacks consumed. The DAFs provided a uniform measure of comparison, with each row in the dataset representing one participant rather than one snack. To account for the differing number of data collection days across contexts, the DAFs were adjusted for the number of days per week (for “Type of day”) and per season (for “Season”). DAFs were calculated for all response categories of all variables (Table 1) separately for (1) beverages as snacks, (2) foods as snacks, and (3) all snacks combined (for the detailed calculation of DAFs and all DAFs for each of the response categories, consult the Data Preprocessing and Descriptive Data sections and Tables S5-S8 in Multimedia Appendix 1).

Data analyses were performed with Jamovi (Version 2.5; The jamovi Project), using R statistical packages (Version 4.1; R Core Team). Descriptive statistics and statistical tests were used to examine relationships between variables. In all analyses, a consumed snack represented a case (n) and the total number of participants was treated as the population (N). Descriptive statistics for snack category variables (Table S5 in Multimedia Appendix 1) demonstrated skewness toward nonalcoholic beverages (n=4204), indicating an imbalance between snack food and beverage categories. Given this imbalance and sufficient sample sizes for both beverages as snacks (n=5164) and foods as snacks (n=9148), analyses were conducted separately for these 2 groups.

Statistical tests were performed using DAFs of each response category (Table 1) to examine differences in snacking frequencies across determinants. Each variable was analyzed individually, aligning with the study’s aim to explore snacking determinants one-dimensionally, as these determinants were not mutually exclusive (eg, the same snack could be categorized as “At home,” “Alone,” and “In the morning”). For each variable, differences in DAFs were tested both between and within response categories for (1) beverages as snacks, (2) foods as snacks, and (3) all snacks combined (eg, comparing DAFs of “Alone” vs “With others” and subsequently beverages vs foods within each category).

Nonparametric tests were used given nonnormality (Shapiro-Wilk; P<.05). No sensitivity analyses or multivariable confounder-adjusted models were used, given the descriptive nature of the study. Differences in DAFs for product-, context-, and motivation-related variables were analyzed using Friedman ANOVA with Durbin-Conover pairwise comparisons. For motivation variables, McDonald ω was calculated to assess reliability [56,57], showing high internal consistency across all 20 FCM scales (beverages: ω=0.81; foods: ω=0.82; all snacks: ω=0.84). Subgroup analyses were conducted to explore differences in DAFs across sociodemographic and lifestyle variables and were analyzed using 2-tailed independent samples t tests, Mann-Whitney U, and Kruskal-Wallis tests. Depending on Levene test results, either Student t (P>.05) or Mann-Whitney U (P≤.05) was used for 2-category variables, while Kruskal-Wallis tests (Shapiro-Wilk, P≤.05) with Dwass-Steel-Critchlow-Fligner pairwise comparisons were applied for variables with more than 2 categories. Test values (except P values) were reported with 2 decimals, due to which minor rounding deviations may occur. For all statistical tests, a P value of ≤.05 was considered significant.

Most snacks were consumed at home rather than out of home. This was the case for both foods and beverages (Figure 6A). In both contexts, the DAFs of foods were higher than those of beverages, but at home, the difference was larger. This indicates that foods were relatively more frequently consumed than beverages at home compared to out of the home. All differences were statistically significant (Figure 6A; Table S10 in Multimedia Appendix 1).

Most foods and beverages as snacks were consumed with others, rather than alone (Figure 6B). In both contexts, the DAFs of foods were higher than those of beverages. However, this difference was larger when alone. This indicates that foods were relatively more frequently consumed than beverages alone, as compared to others. All differences were statistically significant (Figure 6B; Table S10 in Multimedia Appendix 1).

Data demonstrate that 10 out of the 20 FCMs played a prominent role in snacking (mean ≥1.5; Table S7 in Multimedia Appendix 1). Snacking was mainly motivated by liking and appetite (mean >2.5), followed by hunger and thirst, and convenience. Pleasure, visual appeal, health, habit, and food freshness were also found to belong to the core snacking drivers. The other 10 FCMs, including variety seeking, weight concerns, and affect regulation, did not often motivate participants to snack (mean <1.5). The role of most of the prominent FCM (mean ≥1.5) differed significantly between beverages and foods (Figure 9). These differences were largest for the FCM visual appeal, habit, and sociability. For beverages, the FCM habit, sociability, and appetite were more prominent, whereas visual appeal, health, pleasure, and food freshness were more prominent for foods. The other FCMs, liking, hunger and thirst, and convenience, were shown to be more universal snacking drivers, as they played a similar role in the consumption of both beverages and foods (Figure 9).

Regarding sociodemographic and lifestyle groups, the data demonstrate that there were statistically significant differences in DAFs for beverages and foods, given participants’ age and parental status. Participants belonging to the older millennials (born between 1980 and 1990) consumed more beverages and foods than participants belonging to the younger millennials (1990-2000) (Figure 10A). Participants with children consumed significantly more beverages than participants without children (Figure 10B). There were no statistically significant differences in DAFs for beverages and foods, given other sociodemographic and lifestyle factors of the participants, such as sex, education, urbanity of living area, and the activity and fit levels.

Examining the aggregated EMA data across all participants and snacking situations, this study found a daily average snacking frequency of 4.5. This is higher than reported in other studies, which range between 1.5 and 3.5 [58-62]. The higher frequency in our study might be explained by the composition of our cohort (1) millennials, who tend to snack more than other age groups, especially given the ongoing snackification trend [16], and (2) Dutch consumers, who likely adhere to the Dutch diet. As found by Geurts et al [14], Dutch adults (aged 19‐69 years) report more than 7 eating occasions per day, including 4 between-meal (ie, snacking) occasions, which aligns with our findings. Together, these findings suggest that snacking forms a substantial part of the daily diet of the Dutch, classifying them as heavy snackers (>3 snacks per day [21]).

Across all contexts, participants consumed relatively more foods than beverages as snacks. This contrasts with van Rossum et al [15], who found that the Dutch diet in general consists of more beverages than foods. However, our findings support existing snacking literature, showing that beverages are less prominent than foods in snack frequency, as beverages are less likely to be perceived as snacks by consumers [63,64].

Notwithstanding this smaller role of beverages in snacking, our findings do show that nonalcoholic beverages (mainly coffee and tea) were frequently consumed as snacks. This aligns with the Dutch diet in general being characterized by high consumption of nonalcoholic beverages [14,15] and the trend that an increasing proportion of beverages is consumed as snacks [21].

Concerning food categories, fruit, bread and cereals, cookies, bars and pastries, confectionery and ice creams, and savory snacks were among the top snacks for Dutch millennials. Vegetables, meat, and dairy (substitutes) were the least frequently consumed snacks. Most of these findings are consistent with previous studies [15,62,65,66]. Different from other studies is that our results show relatively high consumption of bread and cereal products as snacks. This may be explained by findings from other Dutch studies [14,15], showing that bread and cereal products are staple foods and important energy sources in the Dutch diet, especially for meal occasions. Our results suggest that these foods also play an important role in Dutch millennials’ snacking occasions.

Confirming our expectations, findings show that context plays a central role in shaping snacking behaviors. Findings regarding the physical context show that most snacks were consumed at home, aligning with previous studies [29,67-69] and national dietary data showing that over 85% of foods and beverages in the Netherlands are consumed at home [14,15]. This could be explained by the convenience of the accessibility to snacks at home and the cost-effectiveness of home snacking [70]. The timing of data collection, shortly after the COVID-19 pandemic (2022‐2023), may also have influenced these results. Postpandemic patterns show that consumers not only snack more frequently in general [71-73], but especially at home, a habit established during lockdowns [74-76]. Furthermore, the COVID-19 pandemic remnant of remote and hybrid working [77] may have increased participants’ time spent at home, thereby facilitating more home snacking.

Concerning the social context, our study shows that most snacks were consumed with others, rather than alone. Since information regarding social snacking consumption patterns of the Dutch is scarce [14], our findings offer new insights into the role of the social environment in Dutch millennials’ snacking. Our findings, however, contrast with social snacking consumption patterns in other Western countries, where snacking is considered a personal eating event and mostly done alone [3,50,78,79]. A possible explanation for the higher consumption of snacks with others in our study might be the interaction between the physical and social environment; most snacks were consumed at home, and since most of our cohort lived with partners, children, and/or family members, snacking at home may have naturally occurred in the presence of other inhabitants.

Concerning the temporal context, our study shows that snacking occurred throughout the day. This suggests that snacking is not necessarily confined to intervals between the regular Dutch mealtimes, but that any consumption at any time not perceived as (part of) a meal can be a snack. This is in line with findings of other studies [3,80,81] and the snackification trend [82]. Throughout the day, snacking was most prevalent in the afternoon, followed by the evening and morning, consistent with prior studies [60,69,83]. To the best of our knowledge, our study is one of the first to explore seasonal variations in consumers’ snacking behaviors. We show that snack consumption was the highest in spring and the lowest in autumn and winter. These seasonal variations in snacking frequency might be explained by the seasonal climate of the Netherlands, where variations in meteorological factors such as temperature, precipitation, sunshine hours, and humidity are more noticeable throughout the seasons. In such climates, seasonal variations are also more pronounced in food consumption [84]. Even though literature on the effect of seasonality on food behaviors is scarce, our findings support those of Girju and Ratchford [85], who also observed less snacking in winter and more snacking in spring and summer.

Concerning sociodemographic and lifestyle factors, we found some differences in snacking frequencies given age and parental status. Interestingly, older millennials snacked more frequently in general than younger millennials, and millennials with children consumed more beverages as snacks than those without. These 2 findings might be connected, as most of the participants with children were older millennials. Our findings concerning age align with those of Geurts et al [14], who reported that overall food consumption among the Dutch increases with age until middle age. The lack of expected differences in snacking frequencies for other sociodemographic and lifestyle factors, including sex, education, and activity levels, may reflect a possible type II error around the estimates, given minor subgroups of participants for factor levels [86].

Concerning FCMs, our study extends prior work by capturing within-person variations in snacking motives across different snacking contexts, thereby providing a deeper understanding of why consumers choose specific snacks in specific situations. We found that liking was the primary driver for snacking in all consumption situations, congruent with other studies [50,62]. Other core motivators for snacking in our study, including hunger and thirst, convenience, weight control, health, pleasure, and visual appeal, are also aligned with findings from other studies. Different from other studies is that we found other core FCM for snacking to entail sociability, food freshness, and habit [50,62]. The role of these FCMs in our study might be explained by the interaction of multiple snacking determinants, as the motives driving consumers’ snacking behaviors depend largely on the consumption context [29,50,87].

The role of FCM Sociability in our study could be explained by the finding that most snacks were consumed in the presence of others. The statement measuring this FCM included the Dutch word “gezellig.” The meaning of “gezellig” is not straightforward [88] but can be explained as the experience of togetherness in doing everyday activities together, often at specific times of the day and in one’s own home, implying collective enjoyment [88,89]. The “gezelligheid” of consuming snacks with others has played a noteworthy role in our study, possibly due to the social enjoyment and collectiveness of snacking. Even though “gezelligheid” is culturally embedded within the Dutch snacking context, the TEMS has demonstrated high cross-country and cultural group validity [26,30]. Therefore, our findings concerning FCM sociability might be translatable to other cultural contexts given sociability-related concepts similar to “gezelligheid,” but future studies are warranted.

The role of food freshness might be explained by our finding that mostly fruits, bread, and cereals were consumed as snacks. Food freshness includes the recency of production or harvest and the overall product quality [90-93]. It is a key determinant of consumers’ food choices, especially for fruits and baked products, which are highly susceptible to deterioration over time and perceived as having higher quality closer to the moment of harvest, production, or purchase [91,92]. This interaction of snack product categories and FCM might have played a role in our study.

The prominence of the motive habit in snacking has been established in other studies, but mainly for unhealthy snacking behaviors [94] and fruit intake in general [95]. The high consumption of fruit as a snack, alongside the cumulative consumption of cookies, bars, and pastries; savory snacks; and confectionery and ice creams in our study, might explain the prominent role of the FCM habit.

The prominence of the FCMs health and hunger and thirst in our study demonstrates that our cohort often selected snacks based on perceived healthfulness and satiating properties. Health- and hunger-driven snacking tends to be associated with the consumption of more healthful products [9,96]. This is observed in our study, as 64% of all consumed snacks consisted of coffee and tea, fruits, bread and cereal products, dairy products, and vegetables. This challenges the traditional view of snacking as a merely unhealthy dietary behavior characterized by underconsumption of nutrient- and fiber-rich products (such as fruits and grains) and excessive consumption of highly palatable traditional snack foods high in salt, saturated fat, trans fat, and sugar [97,98]. Our findings thus contrast with the traditional representation of snacking as merely unhealthy and harmful and support the growing scientific evidence that argues that snacking is not necessarily detrimental to but could also potentially benefit health [7,12,13,99]. We endorse the notion of Barnes et al [60] that snacking, like any other dietary behavior, can be practiced in a healthful or nonhealthful manner, depending on the product chosen, variety within the diet, and the underlying motives for consumption.