*Manuscript Click here to view linked References

1 2 3 4 It’s craving time: Time of day effects on momentary and food craving in 5 6 7 daily life 8 9 10 11 12 1, 2 13 Julia Reichenberger 14 15 Anna Richard1, 2 16 17 3 18 Joshua M. Smyth 19 20 Dana Fischer4 21 22 Olga Pollatos4 23 24 1, 2 25 Jens Blechert 26 27 1 Centre for Cognitive Neuroscience, University of Salzburg 28 29 30 ² Department of Psychology, University of Salzburg 31 32 3 Departments of Biobehavioral Health and Medicine, Pennsylvania State University 33 34 4 35 Department of Clinical & Health Psychology, Ulm University 36 37 38 39 40 Address correspondence and reprint requests to: 41 42 Julia Reichenberger 43 44 Department of Psychology, Centre for Cognitive Neuroscience 45 46 47 University of Salzburg 48 49 Hellbrunnerstrasse 34 50 51 52 5020 Salzburg, Austria 53 54 Email: [email protected] 55 56 57 58 59 Keywords: food craving; food categories; hunger; ecological momentary assessment; 60 61 62 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 Abstract 5 6 7 Objective. A key determinant of food intake besides hunger is food craving, which refers to an 8 9 intense desire to consume a specific food. Although both frequently co-occur, they are 10 11 conceptually different and their dissociation is thought to underlie unhealthy eating (e.g., eating 12 13 14 in the absence of hunger). To date, we know almost nothing about their coherence (or 15 16 dissociation) in daily life, or about the role of time of the day and different food types. 17 18 19 Research Methods & Procedure. The present investigation assessed both hunger and food 20 21 craving for several food categories in daily life using smartphone-based ecological momentary 22 23 24 assessment. Across three independent studies (n = 50, n = 51 and n = 59), participants received 25 26 five/six prompts a day and reported their momentary hunger and desire for tasty food (a 27 28 29 subcomponent of food craving). 30 31 Results. Consistent across studies, hunger and desire for tasty food exhibited largely similar 32 33 patterns throughout the day with two peaks (roughly corresponding to lunch and dinner). 34 35 36 Examining more specific food categories, study 3 showed that while desire for main meal-type 37 38 foods showed a two peak pattern in coherence with hunger, this pattern was different for snack- 39 40 41 type foods: desire for fruits decreased, whereas desire for sweets and salty snacks increased 42 43 throughout the day with less coherence with hunger. 44 45 46 Conclusions. These findings suggest that dissociations between hunger and craving are seen only 47 48 for snack-type foods while hunger and general food cravings cohere strongly. Interventions 49 50 addressing snacking may take these circadian patterns of food cravings into account. 51 52 53 54 55 56 57 58 59 60 61 62 2 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 Introduction 5 6 7 In affluent, urban societies, food intake not only serves energy balancing but has also 8 9 adopted psychological functions (Cleobury & Tapper, 2014; Verhoeven, Adriaanse, de Vet, 10 11 Fennis, & de Ridder, 2015). Studies show that the hedonic appeal inherent in ‘hyperpalatable 12 13 14 foods’ plays an important role in food intake, particularly in regard to unhealthy snack 15 16 consumption in the absence of hunger (Cleobury & Tapper, 2014; Verhoeven et al., 2015). An 17 18 19 overreliance on hedonic eating motives, however, is likely to result in an unfavorable energy 20 21 balance and potential weight gain over time. Hence, investigating reasons for food consumption 22 23 24 seems necessary to optimally tackle the rising prevalence of overweight and obesity (Ng et al., 25 26 2014). 27 28 29 Homeostatic hunger is defined as a “biological state of acute energy deprivation or the 30 31 subjective state presumably reflecting an actual or impending state of energy deprivation” (Lowe 32 33 & Butryn, 2007; p. 433). Others define hunger as the absence of fullness (Rogers & Brunstrom, 34 35 36 2016). Although hunger has a physiological basis, it may be influenced by environmental or 37 38 cognitive factors like availability or habitual meal patterns (for an overview see Lowe & Levine, 39 40 41 2005). Thus, homeostatic hunger does not necessarily lead to food intake (e.g., a person is 42 43 hungry, however, no food is currently available, or the person is on a diet) (Mattes, 1990). Apart 44 45 46 from such homeostatic processes, food intake can also be driven by hedonic process. In this 47 48 context, the concept of food craving has gained prominence. Food craving has been defined as an 49 50 intense desire or urge to consume specific foods (Hormes & Rozin, 2010; Weingarten & Elston, 51 52 53 1991) and is sometimes compared with drug cravings in addiction (Pelchat, 2002). Although 54 55 hunger and food craving often co-occur, food cravings can also occur without being hungry; that 56 57 58 is, without an energy deficit (e.g., craving a desert after a satiating meal). This dissociation has 59 60 also been shown repeatedly in experimental, laboratory-based studies (e.g., Gibson & Desmond, 61 62 3 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 1999; Meule & Hormes, 2015; Pelchat & Schaefer, 2000) or retrospective psychometric 5 6 7 measures (Hill, Weaver, & Blundell, 1991; Meule, Hermann, & Kübler, 2014). This research has 8 9 revealed a general and robust positive correlation between the two, unless specific conditions are 10 11 created – such as exposure to attractive foods under conditions of satiety or ‘hedonic 12 13 14 deprivation’, that is, the selective withholding of craved foods alongside adequate homeostatic 15 16 feeding (Blechert, Naumann, Schmitz, Herbert, & Tuschen-Caffier, 2014; Pelchat & Schaefer, 17 18 19 2000; Richard, Meule, Friese, & Blechert, 2017). Of note, food cravings have been prospectively 20 21 associated with low dieting success, increased food intake, and weight gain (Boswell & Kober, 22 23 24 2016; Meule, Richard, & Platte, 2017) and, thus, are an important target of most comprehensive 25 26 weight loss approaches. 27 28 29 This conceptual and empirical distinction between hunger and food craving has 30 31 implication for their respective measurements: Whereas hunger is not directed towards a specific 32 33 food category, as any energy-containing or filling food can satisfy hunger, food craving is 34 35 36 typically directed at a particular food type and taste and can only be satisfied by consumption of 37 38 the craved or very similar food (Hill, 2007). Previous studies showed that food cravings are 39 40 41 mainly directed at energy-dense, high-caloric foods such as or salty snacks (e.g., 42 43 Martin, O’Neil, Tollefson, Greenway, & White, 2008; Rozin, Levine, & Stoess, 1991), but 44 45 46 healthy foods (e.g., fruits and vegetables) are also sometimes reported (Hill & Heaton-Brown, 47 48 1994; Richard, Meule, Reichenberger, & Blechert, 2017). Thus, although momentary hunger can 49 50 be measured with simple ‘how hungry are you at the moment?” questions, the assessment of food 51 52 53 cravings may require reference to specific foods / food categories, or to the tastiness of food. 54 55 Thus, as reviewed above, although the distinction between hunger and food craving has 56 57 58 been shown, what is missing almost entirely are studies on when and how often this dissociation 59 60 occurs in everyday life. Furthermore, studies in the natural environments could reveal the 61 62 4 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 common and distinct trajectories of the hunger and food craving and characterize their circadian 5 6 7 rhythms. The methodology of Ecological Momentary Assessment (EMA), consisting of repeated 8 9 assessments within individuals over time, is ideally suited for this purpose (see Smyth, Juth, Ma, 10 11 & Sliwinski, 2017). To close this gap, we employed EMA in a naturalistic study of hunger and 12 13 14 craving. We hypothesized that subjective hunger ratings exhibit two clear circadian peaks, 15 16 approximately around midday and evening meal times (Huh, Shiyko, Keller, Dunton, & 17 18 19 Schembre, 2015; Mattes, 1990). Furthermore, some incidental evidence indicates that cravings 20 21 gain prominence during afternoon and early evening (Pelchat, 1997), leading to the expectations 22 23 24 that concordance between hunger and craving will drop during those times. In addition, as food 25 26 cravings seems specific to distinct foods, the role of specific food categories (, 27 28 29 fatty, salty and sweet foods, fruits and vegetables) in this relationship will be explored in a more 30 31 exploratory fashion. To increase the generalizability and rigor of findings, a three-study design in 32 33 three independent samples was used. 34 35 36 Material and methods 37 38 Study 11 39 40 41 Participants 42 43 Fifty-three participants, predominantly students, were recruited into the study. Three 44 45 46 individuals were excluded after the data collection phase because of overall low compliance rates 47 48 (<50%). The remaining 50 participants (mean age of 23.6 years, SD = 2.75 years) were 49 50 predominantly female (66%) with a mean Body Mass Index (BMI) of 22.1 kg/m² (SD = 3.27 51 52 53 kg/m²; range: 16.6 – 34.9 kg/m²). Participants signed an informed consent form approved by the 54 55 ethics committee of the University of Salzburg and were paid for their participation (€ 30 to 50 56 57 58 depending on compliance). 59 60 61 1 Other data from this sample have been reported in Reichenberger, Smyth, and Blechert (2017). 62 5 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 Procedure 5 6 7 Initially, participants completed several diagnostic measures and demographic 8 9 information (e.g. weight/height) at an online survey platform. Afterwards, individuals were 10 11 personally instructed on the installation and usage of a smartphone app and received a user 12 13 14 manual. After one practice day (data not used in the study), participants completed six days of 15 16 EMA with data completeness being monitored closely by study staff. At the end of this period, 17 18 19 participants completed a second block of online questionnaires. A second week of EMA 20 21 assessment (again one practice day and six days of data collection) was conducted to increase 22 23 24 contextual variability and thereby generalizability. After a last questionnaire block, participant 25 26 were debriefed and compensated. 27 28 29 The study used signal-, event-, and interval-contingent sampling (see Shiffman, Stone, & 30 31 Hufford, 2008), prompting individuals at five equidistant times (10 a.m., 1 p.m., 4 p.m., 7 p.m., 32 33 10 p.m.). Immediately after waking up and shortly before going to bed, as well as before every 34 35 36 eating episode, participants self-initiated questionnaires on their smartphone that are not of 37 38 relevance for the present study. Diary entries could be delayed for one hour with or without 39 40 41 reminders every 10 minutes with entries provided any later resulting in missing values. 42 43 Participants completed 87.1% (SD = 11.6%) of their signal-contingent daily signals. Delayed 44 45 46 entries were delayed, on average, for 11.8 minutes (SD = 15.2 minutes). 47 48 Study 22 49 50 Participants 51 52 53 Fifty-nine participants (mean age 39.9 years, SD = 11.9 years), predominantly female 54 55 (78%) and with a mean BMI of 26.7 kg/m² (SD = 5.76 kg/m²; range: 17.5 – 38.6 kg/m²), signed 56 57 58 an informed consent form approved by the ethics committee of the University of Salzburg and 59 60 61 2 Other findings from this sample have been reported in Reichenberger et al. (2018) and Reichenberger et al. (2017). 62 6 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 were compensated for their participation with their choice of individualized feedback report or a 5 6 7 compliance dependent remuneration of € 35 - 60. 8 9 Procedure 10 11 Participants first completed several questionnaires and demographic information (e.g. 12 13 14 weight/height) via an online survey platform. Afterwards, individuals were contacted via 15 16 telephone to instruct participants on the installation and usage of a smartphone app; participants 17 18 19 were also sent a user manual via email. After one to two practice days (data not used in the 20 21 study), participants completed 10 days of EMA with data completeness being monitored closely 22 23 24 by the study staff. At the end of this period, participants completed several questionnaires via an 25 26 online platform and were compensated for their participation. 27 28 29 The study used signal-contingent sampling, prompting individuals at five equidistant 30 31 times (9 a.m., 12 a.m., 3 p.m., 6 p.m., 9 p.m.). Because of a higher percentage (49%) of 32 33 employees with regular work shifts the sampling times started with an earlier first prompt. 34 35 36 Participants completed 83.6 % (SD = 12.3%) of their daily signals and delayed entries were 37 38 delayed, on average, for 15.7 minutes (SD = 15.1 minutes). 39 40 41 Study 3 42 43 Participants 44 45 46 Fifty-one participants (mean age 23.5 years, SD = 2.61 years), predominantly female 47 48 (78%) and with a mean BMI of 22.4 kg/m² (SD = 3.21 kg/m²; range: 17.3 – 33.2 kg/m²), signed 49 50 an informed consent form approved by the ethics committee of the University of Ulm and were 51 52 53 compensated for their participation with their choice of € 25 or 3.5 course credits. 54 55 Procedure 56 57 58 Participants initially completed several diagnostic measures and demographic information 59 60 using an online survey platform. Individuals were then scheduled for a short laboratory testing at 61 62 7 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 the University of Ulm (data not reported here; measurement of weight and height, application of 5 6 7 a mobile heartrate monitor and physical activity tracker) that included the installation of the 8 9 smartphone app and detailed instruction on its usage. Participants were handed a user manual and 10 11 completed one practice day. Afterwards, individuals completed seven days of EMA with data 12 13 14 completeness being monitored closely by the study staff. At the end of this period, participants 15 16 completed several questionnaires via an online platform, returned study equipment, and were 17 18 19 compensated for their participation. 20 21 The study used signal-contingent sampling for the EMA. Because of a shorter sampling 22 23 24 duration, the frequency of daily prompts was increased. Specifically, participants were prompted 25 26 at six equidistant times (9 a.m., 11.30 a.m., 2 p.m., 4.30 p.m., 7 p.m., 9.30 p.m.) each day for one 27 28 29 week. Participants completed 85.0 % (SD = 11.2%) of their daily signals. When entries were 30 31 delayed, they were delayed, on average, for 17.3 minutes (SD = 16.0 minutes). 32 33 EMA measures in Study 1, 2, and 3 34 35 36 At each of the intraday signals, participants completed questions about affect, stress as 37 38 well as several eating-related behaviors. Among them, participants rated ‘How hungry are you 39 40 41 right now?’ from 0 – 100 (not at all – very much). One subcomponent of food craving – desire to 42 43 eat tasty foods – appears to tap into the intensity of food craving (Cepeda-Benito, Gleaves, 44 45 46 Williams, & Erath, 2000). As this intensity seems to be conceptualized as a state-like (rather than 47 48 trait-like) aspect of food craving (Cepeda-Benito et al., 2000), desire for tasty foods might be best 49 50 suited for assessing momentary changes of food craving in daily life. Hence, in study 1 and 2, 51 52 53 food craving was assessed by asking participants ‘Do you have a desire to eat something tasty 54 55 right now?’ from 0 – 100 (not at all – very much). Similarly, study 3 asked participants ‘What do 56 57 58 you feel like eating right now?’ followed by a list of food categories and desire for each food 59 60 category was answered from 0 – 100 (nothing – very much). Food categories were initially 61 62 8 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 derived from the Yale Scale 2.0 (Gearhardt, Corbin, & Brownell, 2016; Meule, 5 6 7 2016), comprising specifically high- and high-sugar foods, and extended by healthy food 8 9 categories (also supported by previous research; Richard, Meule, Reichenberger, et al., 2017). 10 11 The assessment thus included: salty snacks (e.g. chips, pretzel sticks), sweets (e.g., chocolate, 12 13 14 cookies, ice cream), fatty snacks (e.g., burger, pizza, fries), carbohydrates (e.g., bread, pasta, 15 16 rice), vegetables and salads (e.g., tomatoes, carrots), as well as fruits (e.g., apples, berries). 17 18 19 Calculation 20 21 For visualization, hunger and desire for tasty food were averaged over days separately for 22 23 24 each signal and each study. In addition, each of the six food categories was also separately 25 26 averaged for each signal but across days in order to compare daily courses for different food 27 28 29 categories with hunger. To statistically assess coherence between hunger and desire for tasty food 30 31 (in general in study 1 and 2, or food category specific in study 3), within-person correlations were 32 33 calculated. Time of day effects in study 3 were tested using hierarchical multilevel models with 34 35 36 signals (Level 1) nested in participants (Level 2) using the software HLM7 (Raudenbush, Byrk, 37 38 & Congdon, 2011). Food categories of study 3 were used as separate outcomes, whereas signals 39 40 41 were used as predictors on Level 1 (uncentered; fixed slopes). After linear trends, higher-order 42 43 trends (quadratic trends with signals*signals) were tested, however, these were only retained and 44 45 46 presented when statistically significant. The alpha level for all analyses was set to .05. In 47 48 addition, we controlled for BMI (grand-mean centered) and weekday (weekday = 0 versus 49 50 weekend-day = 1). 51 52 53 Results 54 55 Momentary hunger and desire for tasty food over the day 56 57 58 As expected study 1 and study 2 showed a relatively consistent pattern of two hunger 59 60 peaks – roughly corresponding with lunch and dinner – over the day (see Figure 1). Desire for 61 62 9 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 tasty food and hunger time series increased and decreased simultaneously, thus demonstrating a 5 6 7 relatively high coherence: The within-person correlations between desire for tasty food and 8 9 hunger were r = .80, p < .001 in study 13 and r = .83, p < .001 in study 24. Desire for tasty food 10 11 mean levels were slightly higher than hunger in both studies.5 12 13 14 15 16 << insert Figure 1 here >> 17 18 19 20 21 Momentary desire for different food categories over the day 22 23 24 When plotting desire for different food categories separately, other time-of-day effects 25 26 emerge (see Figure 2). Typical main meal food categories (left panel in Figure 2) like 27 28 carbohydrates, fatty foods (like pizza, fries) and vegetables exhibit two peaks, similar to hunger, 29 30 31 at usual lunch and dinner times. Supporting this impression, within-person correlations between 32 33 hunger and desire for tasty food like carbohydrates (r = .68, p < .001), fatty foods (r = .49, p < 34 35 36 .001) and vegetables (r = .54, p < .001) were high and statistically significant. This contrasts with 37 38 the time courses of typical snack-type foods: desire for sweets and salty snacks gradually 39 40 41 increased over the day (sweets: linear β10 = 1.12, p < .001; quadratic β10 = -.068, p = .002, salty 42 43 snacks: linear β10 = 1.34, p < .001), see right panel in Figure 2. In contrast, desire for fruits 44 45 46 gradually decreased over the day (linear β10 = -2.82, p < .001). Consistent with this, the within- 47 48 person correlation between hunger and desire for sweets (r = .15, p = .043), salty snacks (r = .20, 49 50 51 52 53 54 55 56 57 3 The within-person correlation between desire for tasty food and hunger was r = .81, p < .001 at signal 1, r = .80, p 58 < .001 at signal 2, r = .80, p < .001 at signal 3, r = .81, p < .001 at signal 4, and r = .75, p < .001 at signal 5. 59 4 The within-person correlation between desire for tasty food and hunger was r = .86, p < .001 at signal 1, r = .81, p 60 < .001 at signal 2, r = .84, p < .001 at signal 3, r = .86, p < .001 at signal 4, and r = .78, p < .001 at signal 5. 61 5 Adding weekday or BMI as covariates did not change the pattern of results. 62 10 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 p < .001) and fruits (r = .37, p < .001), albeit statistically significant, were of much smaller 5 6 6 7 absolute magnitude as the ones observed for main meal foods. 8 9 10 11 << insert Figure 2 here >> 12 13 14 Discussion 15 16 The aim of the present study was to characterize the daily course of hunger and food 17 18 19 craving with a focus on their coherence in a naturalistic environment during daily life, using three 20 21 independent samples. Previous work using other methods, notably laboratory and questionnaire 22 23 24 studies, have often demonstrated the dissociation between hunger and food craving. In contrast, 25 26 these three studies using EMA in daily life demonstrated a relatively high concordance between 27 28 29 hunger and food craving. In addition, similarities and differences between the two constructs as a 30 31 function of food categories were explored. This work has the potential to help inform theory and 32 33 empirical understanding of food cravings as they are experienced in daily life, and suggest a 34 35 36 number of lines of future research 37 38 Replicating and extending previous naturalistic studies, our three studies demonstrated 39 40 41 that hunger exhibited two peaks, roughly around lunch and dinner times (Huh et al., 2015; 42 43 Mattes, 1990). This indicates a high correspondence of hunger with traditional main meal times 44 45 46 (Warde & Yates, 2017), displaying a cyclical pattern with about 6 hours between lunch and 47 48 dinner. Similarly, the timing of daily hunger ratings was consistent with socially entrained and 49 50 normative patterns of eating episodes (de Castro, 2004; Leech, Worsley, Timperio, & 51 52 53 McNaughton, 2017; Lhuissier et al., 2013). Across the three studies, food craving in general 54 55 closely followed hunger ratings (i.e., indicated by strong positive correlations). This strong 56 57 58 association could indicate that a) craving and hunger are more closely associated as assumed and 59 60 6 Adding weekday or BMI as covariates did not change the pattern of results. Higher BMI significantly predicted 61 stronger desire for sweets (β01 = 1.25, p = .038). 62 11 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 diverge only under particular circumstances such as experimental satiety or deprivation, and/or b) 5 6 7 participants have difficulties differentiating and reporting on the two constructs in naturalistic 8 9 settings. This might be particularly difficult during and around main meals where hunger can be 10 11 expected to mirror cravings: hardly anyone likes to satisfy hunger with tasteless and unattractive 12 13 14 foods that would then show this dissociation with cravings. 15 16 Study 3 further qualified this interpretation of generic craving patterns by exploring 17 18 19 cravings for several more specific food types. Whereas the pattern of craving for typical meal- 20 21 type foods (carbohydrates, vegetables, and fatty foods) was consistent with hunger patterns, the 22 23 24 snack-type food categories (sweets, salty snacks, fruits) (Warde & Yates, 2017) deviated from the 25 26 M-shaped hunger-pattern and revealed increasing (sweets, salty snacks) or linearly decreasing 27 28 29 (fruits) trajectories across the day. Although food craving does not necessarily lead to 30 31 consumption of the respective food (Hill, 2007), these results are in line with studies on actual 32 33 food intake. Warde and Yates (2017) examined snack patterns and found that confectionary 34 35 36 (sweets) and crisps/potato chips (salty) were more popular during the evenings, and fruits earlier 37 38 in the day compared to the rest of the day. Moreover, a study by Haynes, Kemps, and Moffitt 39 40 41 (2016) proposed that unhealthy food categories increase in appeal in the afternoon/evening 42 43 relative to healthy foods because of decreasing self-control over the day. Insofar as we are aware, 44 45 46 this is the first naturalistic demonstration of these patterns in daily life using EMA. Importantly, 47 48 although these observed patterns are consistent with theoretical explanations (e.g., loss of 49 50 regulatory control), additional work will need to be done to explicitly test mechanisms as our data 51 52 53 is fundamentally correlational in nature. For example, and alternative (non-self-control-related) 54 55 account might suggest that certain food types are more frequently eaten during certain times of 56 57 58 the day due to social modeling, advertising, etc.; and that this in turn establishes eating habits and 59 60 leads to respective cravings. 61 62 12 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 A notable methodological implication of study 3 is that it may be essential to separate 5 6 7 craving reports by food category in order to understand the differentiated set of craving vs. 8 9 hunger trajectories. In other words, if cravings are averaged across several food categories, the 10 11 resultant average trajectory obscures the specific temporal patterns we observed. This not only 12 13 14 provides insight into important timing issues in intervention (e.g., reducing snack intake in the 15 16 afternoon, maintaining fruit consumption across the day) but also represents an elegant 17 18 19 demonstration of the hunger-craving difference: If hunger was the only driver of cravings, all 20 21 food categories should have shown the same general trajectory. Future studies on eating patterns 22 23 24 in various populations or conditions or during/after interventions should therefore consider a per- 25 26 category assessment of cravings and should not rely solely on hunger ratings or generic craving 27 28 29 ratings. This call for additional specification dovetails with approaches taken in the broader 30 31 craving literature; for example, that relates cravings for specific foods to physiological or 32 33 nutritional deficiencies (e.g., during menstrual cycling; e.g., Bruinsma & Taren, 1999). Relatedly, 34 35 36 future research might profit from an examination of the coherence of hunger and food craving in 37 38 populations with disordered eating behaviors as these individuals might be less susceptible to 39 40 41 homeostatic and more susceptible to hedonic processes (Berridge, 2009). Likewise, while we 42 43 focused on one component of food craving – desire for tasty food – due to its relationship with 44 45 46 momentary craving intensity, other food craving dimensions remain to be explored (such as 47 48 environmental cues triggering food cravings or intention and plans to consume food) and might 49 50 well reveal different temporal trajectories (or even more complex interactions, such as with 51 52 53 specific contexts or contexts by time of day or day of week). 54 55 Despite the strengths of each of the present studies, and the general consistency of the 56 57 58 results across three independent studies, we note several important issues and limitations. The 59 60 first smartphone prompt each day was relatively late (9 am), thus resulting in very few hunger 61 62 13 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 ratings before breakfast, as previous studies showed that breakfast typically occurs around 7 to 8 5 6 7 am (Leech et al., 2017; Lhuissier et al., 2013). Another limitation is the omission of behavioral 8 9 measures of actual food intake, and this would be a promising avenue for future research. 10 11 However, typical challenges with food intake reports (Blechert, Liedlgruber, Lender, 12 13 14 Reichenberger, & Wilhelm, 2017) and availability (Blechert, Klackl, Miedl, & Wilhelm, 2016) 15 16 arise and have to be addressed. Similarly, the exact time when participants ate could not be 17 18 19 obtained from the current data. Future studies might profit from a time-stamping of eating 20 21 episodes in order to more precisely study their dynamic influence on subsequent hunger/food 22 23 24 craving reports. 25 26 Conclusions 27 28 29 To conclude, this work provides evidence that hunger and food craving show concurrence 30 31 in some domains but that, especially with regard to different food categories, should be distinctly 32 33 measured in daily life. The varying trajectories for these food categories provide empirical 34 35 36 evidence that may help inform the tailoring of interventions for diet control (e.g., those with 37 38 components addressing snack food and fruit intake) may benefit by taking circadian patterns of 39 40 41 food cravings into account. 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 14 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 Acknowledgements 5 6 7 JB, JR and AR were supported by the European Research Council (ERC) under the European 8 9 Union’s Horizon 2020 research and innovation program (ERC-StG-2014 639445 NewEat). This 10 11 work was supported by the Austrian Science Fund (FWF): [I 02130-B27]. Study 3 was supported 12 13 14 by the Peer-Mentoring Team-Program (Line A) of the German Psychology Society (DGPs, 15 16 section Health Psychology). 17 18 19 20 21 The authors declare no conflict of interest. 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 15 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 References 5 6 7 8 Berridge, K. C. (2009). 'Liking' and 'wanting' food rewards: Brain substrates and roles in eating 9 10 disorders. Physiology & Behavior, 97(5), 537–550. doi:10.1016/j.physbeh.2009.02.044 11 Blechert, J., Klackl, J., Miedl, S. F., & Wilhelm, F. H. (2016). To eat or not to eat: Effects of food 12 availability on reward system activity during food picture viewing. Appetite, 99, 254-261. 13 Blechert, J., Liedlgruber, M., Lender, A., Reichenberger, J., & Wilhelm, F. H. (2017). 14 15 Unobtrusive electromyography-based eating detection in daily life: A new tool to address 16 underreporting? Appetite, 118, 168-173. doi:10.1016/j.appet.2017.08.008 17 Blechert, J., Naumann, E., Schmitz, J., Herbert, B. M., & Tuschen-Caffier, B. (2014). Startling 18 sweet temptations: hedonic chocolate deprivation modulates experience, eating behavior, 19 and eyeblink startle. PLoS ONE, 9, e85679. 20 21 Boswell, R. G., & Kober, H. (2016). Food cue reactivity and craving predict eating and weight 22 gain: a metaǦanalytic review. Obesity Reviews, 17, 159-177. 23 Bruinsma, K., & Taren, D. L. (1999). Chocolate: Food or drug? Journal of the American Dietetic 24 Association, 99, 1249-1256. 25 26 Cepeda-Benito, A., Gleaves, D. H., Williams, T. L., & Erath, S. A. (2000). The development and 27 validation of the state and trait Food-Cravings Questionnaires. Behavior Therapy, 31, 28 151-173. 29 Cleobury, L., & Tapper, K. (2014). Reasons for eating ‘unhealthy’ snacks in overweight and 30 obese males and females. Journal of Human Nutrition and Dietetics, 27(4), 333-341. 31 32 doi:10.1111/jhn.12169 33 de Castro, J. M. (2004). The time of day of food intake influences overall intake in humans. 34 Journal of Nutrition, 134(1), 104-111. 35 Gearhardt, A. N., Corbin, W. R., & Brownell, K. D. (2016). Development of the Yale Food 36 Addiction Scale Version 2.0. Psychology of Addictive Behaviors, 30, 113–121. 37 38 Gibson, E. L., & Desmond, E. (1999). Chocolate craving and hunger state: implications for the 39 acquisition and expression of appetite and food choice. Appetite, 32, 219-240. 40 Haynes, A., Kemps, E., & Moffitt, R. (2016). Is cake more appealing in the afternoon? Time of 41 day is associated with control over automatic positive responses to unhealthy food. Food 42 43 Quality and Preference, 54, 67-74. 44 Hill, A. J. (2007). The psychology of food craving. Proceedings of the Nutrition Society, 66, 277- 45 285. 46 Hill, A. J., & Heaton-Brown, L. (1994). The experience of food craving - A prospective 47 investigation in healthy women. Journal of Psychosomatic Research, 38, 801-814. 48 49 Hill, A. J., Weaver, C. F. L., & Blundell, J. E. (1991). Food craving, dietary restraint and mood. 50 Appetite, 17, 187-197. 51 Hormes, J. M., & Rozin, P. (2010). Does "craving" carve nature at the joints? Absence of a 52 synonym for craving in many languages. Addictive Behaviors, 35, 459-463. 53 54 Huh, J., Shiyko, M., Keller, S., Dunton, G., & Schembre, S. M. (2015). The time-varying 55 association between perceived stress and hunger within and between days. Appetite, 89, 56 145-151. doi:http://dx.doi.org/10.1016/j.appet.2015.02.001 57 Leech, R. M., Worsley, A., Timperio, A., & McNaughton, S. A. (2017). Temporal eating 58 patterns: a latent class analysis approach. International Journal of Behavioral Nutrition 59 60 and Physical Activity, 14(1), 3. doi:10.1186/s12966-016-0459-6 61 62 16 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 Lhuissier, A., Tichit, C., Caillavet, F., Cardon, P., Masullo, A., Martin-Fernandez, J., . . . 5 Chauvin, P. (2013). Who still eats three meals a day? Findings from a quantitative survey 6 7 in the Paris area. Appetite, 63, 59-69. doi:http://dx.doi.org/10.1016/j.appet.2012.12.012 8 Lowe, M. R., & Butryn, M. L. (2007). Hedonic hunger: a new dimension of appetite? Physiology 9 and Behavior, 91, 432-439. 10 Lowe, M. R., & Levine, A. S. (2005). Eating motives and the controversy over dieting: eating 11 less than needed versus less than wanted. Obesity Research, 13(5), 797-806. 12 13 doi:10.1038/oby.2005.90 14 Martin, C. K., O’Neil, P. M., Tollefson, G., Greenway, F. L., & White, M. A. (2008). The 15 association between food cravings and consumption of specific foods in a laboratory taste 16 test. Appetite, 51, 324-326. 17 18 Mattes, R. (1990). Hunger ratings are not a valid proxy measure of reported food intake in 19 humans. Appetite, 15(2), 103-113. doi:http://dx.doi.org/10.1016/0195-6663(90)90043-8 20 Meule, A. (2016). German version of the Yale Food Addiction Scale 2.0: Associations with 21 impulsivity, trait food craving, binge eating, dieting success, and body mass. German 22 Medical Science. doi:10.3205/16dgess080 23 24 Meule, A., Hermann, T., & Kübler, A. (2014). A short version of the Food Cravings 25 Questionnaire-Trait: The FCQ-T-reduced. Frontiers in Psychology, 5, 190. 26 doi:10.3389/fpsyg.2014.00190 27 Meule, A., & Hormes, J. M. (2015). Chocolate versions of the Food Cravings Questionnaires. 28 29 Associations with chocolate exposure-induced salivary flow and ad libitum chocolate 30 consumption. Appetite, 91, 256-265. 31 Meule, A., Richard, A., & Platte, P. (2017). Food cravings prospectively predict decreases in 32 perceived self-regulatory success in dieting. Eating Behaviors, 24, 34-38. 33 doi:10.1016/j.eatbeh.2016.11.007 34 35 Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., . . . Abera, S. F. 36 (2014). Global, regional, and national prevalence of overweight and obesity in children 37 and adults during 1980–2013: a systematic analysis for the Global Burden of Disease 38 Study 2013. The Lancet, 384(9945), 766-781. 39 40 Pelchat, M. L. (1997). Food Cravings in Young and Elderly Adults. Appetite, 28(2), 103-113. 41 doi:https://doi.org/10.1006/appe.1996.0063 42 Pelchat, M. L. (2002). Of human bondage: Food craving, obsession, compulsion, and addiction. 43 Physiology & Behavior, 76(3), 347-352. doi:http://dx.doi.org/10.1016/S0031- 44 9384(02)00757-6 45 46 Pelchat, M. L., & Schaefer, S. (2000). Dietary monotony and food cravings in young and elderly 47 adults. Physiology and Behavior, 68, 353-359. 48 Raudenbush, S. W., Byrk, A. S., & Congdon, R. (2011). HLM7 for Windows [Computer 49 software]. Skokie, IL: Scientific Software International, Inc. 50 Reichenberger, J., Kuppens, P., Liedlgruber, M., Wilhelm, F. H., Tiefengrabner, M., Ginzinger, 51 52 S., & Blechert, J. (2018). No haste, more taste: An EMA study of the effects of stress, 53 negative and positive on eating behavior. Biological Psychology, 131, 54-62. 54 doi:https://doi.org/10.1016/j.biopsycho.2016.09.002 55 Reichenberger, J., Smyth, J. M., & Blechert, J. (2017). Fear of evaluation unpacked: day-to-day 56 57 correlates of fear of negative and positive evaluation. Anxiety, Stress, & Coping, 1-16. 58 doi:10.1080/10615806.2017.1396826 59 60 61 62 17 63 64 65 1 CRAVING IN DAILY LIFE 2 3 4 Richard, A., Meule, A., Friese, M., & Blechert, J. (2017). Effects of chocolate deprivation on 5 implicit and explicit evaluation of chocolate in high and low trait chocolate cravers. 6 7 Frontiers in Psychology, 8(1591), 1-11. 8 Richard, A., Meule, A., Reichenberger, J., & Blechert, J. (2017). Food cravings in everyday life: 9 An EMA study on snack-related thoughts, cravings, and consumption. Appetite, 113, 215- 10 223. 11 Rogers, P. J., & Brunstrom, J. M. (2016). Appetite and energy balancing. Physiology and 12 13 Behavior, 164(Part B), 465-471. doi:https://doi.org/10.1016/j.physbeh.2016.03.038 14 Rozin, P., Levine, E., & Stoess, C. (1991). Chocolate craving and liking. Appetite, 17, 199-212. 15 Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annu 16 Rev Clin Psychol, 4, 1-32. 17 18 Smyth, J. M., Juth, V., Ma, J., & Sliwinski, M. (2017). A slice of life: Ecologically valid methods 19 for research on social relationships and health across the life span. Social and Personality 20 Psychology Compass, 11(10), e12356-n/a. doi:10.1111/spc3.12356 21 Verhoeven, A. A. C., Adriaanse, M. A., de Vet, E., Fennis, B. M., & de Ridder, D. T. D. (2015). 22 It's my party and I eat if I want to. Reasons for unhealthy snacking. Appetite, 84, 20-27. 23 24 doi:http://dx.doi.org/10.1016/j.appet.2014.09.013 25 Warde, A., & Yates, L. (2017). Understanding Eating Events: Snacks and Meal Patterns in Great 26 Britain. Food, Culture & Society, 20(1), 15-36. doi:10.1080/15528014.2016.1243763 27 Weingarten, H. P., & Elston, D. (1991). Food cravings in a college population. Appetite, 17, 167- 28 29 175. 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 18 63 64 65 Figure(s)

Study 1 15

10

5

0 10.00 1.00 4.00 7.00 10.00 -5 a.m. p.m.

-10 not at all - very at all much - very not

-15 Signal time

Hunger - 32.7 Desire - 35.7

Study 2 15

10

5

0 9.00 12.00 3.00 6.00 9.00 -5 a.m. p.m.

not at all at much not - very -10

-15 Signal time

Hunger - 28.3 Desire - 35.3

Figure 1. Course of subjective hunger and desire for tasty foods ratings (mean centered across

the day) in study 1 and study 2 across the day.

Figure(s)

Figure 2. Desire for different food categories (centered by M=10.9 for sweets, M=17.8 for carbohydrates, M=6.50 for salty snacks, M=11.7 for fatty foods, M=21.5 for fruits and M=16.0 for vegetables) and general hunger (centered by M=26.7) across six daily signals.