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Health Influencers: Wellness Gains, Emotional Strains

Posted on November 25, 2024November 26, 2024 by CNP Staff

A study of young adults in New Zealand published in CyberPsychology showed that followers of health influencers tend to exercise more vigorously, eat more fruits and vegetables, and report better well-being
However, they also reported greater distress compared to non-followers
Levels of distress were the highest among followers of health and diet-related influencers

While we scroll through social media now and then (or more often, depending on our browsing habits), a video will come up where a person recommends an exercise regimen, specific lifestyle habits, or nutritional choices. These individuals often claim that the modes of behavior they advocate will improve the viewers’ health or be beneficial in some way. They may also describe their personal experiences in overcoming health issues. Some of these individuals devote most or all of their online posts to sharing or promoting health-related information. We call these individuals health influencers.

Health influencers

Health influencers are individuals who share health-related information, advice, and lifestyle tips, primarily through social media platforms like Instagram, YouTube, or TikTok. They focus on areas such as nutrition, fitness, mental health, beauty, skincare, and wellness, reaching large audiences with personal stories, recommendations, and routines (Pilgrim & Bohnet-Joschko, 2019; Zou et al., 2021).

Many collaborate with brands to promote products, from supplements to fitness equipment, blending marketing with lifestyle advice. Their influence stems from their ability to connect with followers on a personal level, offering relatable and accessible health tips. However, not all have formal training or credentials. While they can inspire positive changes, some may inadvertently spread misinformation or promote unrealistic health standards (Byrne et al., 2017).

Not all health influencers have formal training or credentials. While they can inspire positive changes, some health influencers may inadvertently spread misinformation or promote unrealistic health standards

Following health influencers

People follow health influencers for various reasons. They may prompt positive exercise and dietary habits in their followers, and people might follow them, planning to improve their lifestyle habits. Some individuals might find their content interesting by itself.

Studies indicate that messaging from these figures might be more influential in affecting health behavior engagement than traditional channels because they are often perceived as more credible, trustworthy, knowledgeable, authentic, and attractive. Sometimes, individuals may identify with them and form parasocial bonds (Cooper et al., 2024).

Sometimes, individuals may identify with influencers and form parasocial bonds with them (Cooper et al., 2024)

However, following health influencers can sometimes be harmful. Their content may reinforce the “fit-ideal” —the concept that an athletic or fit body is the ideal body type. This may, in turn, increase body dissatisfaction and depressive symptoms in their followers (Pilgrim & Bohnet-Joschko, 2019).

The current study
Study author Jack R. H. Cooper and his colleagues wanted to investigate the physical and mental health of young adults who follow health influencers on Instagram. They focused on individuals between 18 and 25 years of age because they use social media more than other demographic groups, and problematic use of these platforms has the potential to disrupt their mental health (Cooper et al., 2024).

The study participants were 1022 young adults participating in the “Lifestyles of Young Adults” survey. They were university students from New Zealand, U.S. Mturk workers, and individuals from the United Kingdom recruited through Prolific. The participants’ average age was 22 years. 55% of them were women. 411 of them were classified as followers of health influencers.

Study participants completed an online survey that included assessments of social media usage (“How often do you use the following social networking sites or apps?”), physical activity (the International Physical Activity Questionnaire – Short Form), dietary habits (a modification of the 2008/2009 New Zealand Adult Nutrition Survey), distress (PROMIS Emotional Distress – Depression and the PROMIS Emotional Distress – Anxiety scales), psychological well-being (the Flourishing Scale), and positive and negative mood (an 18-item measure of the affective circumplex) (see Figure 1).

Figure 1. Study procedure (Cooper et al., 2024)

Followers of health influencers tend to eat more fruits and vegetables
Results showed that the followers were more often female than male. They tended to have higher socioeconomic status and education levels than non-followers. These individuals were more likely to exercise vigorously and eat more fruits and vegetables than participants who did not follow health influencers. The followers also reported better overall well-being.

Followers of health influencers showed higher levels of distress
The followers showed higher levels of distress. Individuals following diet/food influencers showed higher levels of distress than those following exercise/fitness influencers. However, the observed differences between followers and non-followers in the level of distress were of minimal magnitude. Additional analyses showed that the association between health behaviors like vigorous exercise and lower distress also appeared to be disrupted in the followers.

There were no differences between followers and non-followers in body mass index, minutes spent walking (per day), negative mood, or anxiety. Instagram was the most widely used social network, with only 7% of participants reporting not using it (see Figure 2).

Figure 2. Research Findings (Cooper et al., 2024)

Conclusions
Overall, the study showed that followers of health influencers are more likely to engage in exercise and tend to have healthier eating habits (e.g., eating more fruits and vegetables). These individuals tended to report better well-being but also higher levels of distress, indicating that following the influencers might not be exclusively beneficial for young adults.

On the other hand, the design of this study does not allow cause-and-effect conclusions to be drawn from the findings. Because of this, it remains unknown whether the act of following the influencers is the cause behind the observed differences. It is also possible that individuals eating more fruits and vegetables and exercising more often but also experiencing higher distress are simply more likely to be drawn to the influencers’ content.

The paper “Healthier But Not Happier? The Lifestyle Habits of Health Influencer Followers” was authored by Jack R. H. Cooper, Quinn Campbell, and Tamlin S. Conner.

References

Byrne, E., Kearney, J., & MacEvilly, C. (2017). The Role of Influencer Marketing and Social Influencers in Public Health. Proceedings of the Nutrition Society, 76(OCE3), E103. https://doi.org/10.1017/S0029665117001768

Cooper, J., Campbell, Q., & Conner, T. (2024). Healthier but not happier? The lifestyle habits of health influencer followers. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 18(2). https://doi.org/10.5817/CP2024-2-4

Pilgrim, K., & Bohnet-Joschko, S. (2019). Selling health and happiness how influencers communicate on Instagram about dieting and exercise: Mixed methods research. BMC Public Health, 19(1), 1054. https://doi.org/10.1186/s12889-019-7387-8

Zou, W., Zhang, W. J., & Tang, L. (2021). What Do Social Media Influencers Say about Health? A Theory-Driven Content Analysis of Top Ten Health Influencers’ Posts on Sina Weibo. Journal of Health Communication, 26(1), 1–11. https://doi.org/10.1080/10810730.2020.1865486

Does sleep duration affect how much fruit and vegetables we eat?

Posted on November 12, 2024November 25, 2024 by CNP Staff

An analysis of data from the National FinHealth 2017 study published in the Frontiers in Nutrition reported a link between sleep duration and consumption of fruits and vegetables.
Both short and long sleepers consumed less fruits and vegetables than normal sleepers.
People sleeping less than 7 hours per day consumed 37 grams of fruits and vegetables less than those sleeping 7-9 hours.
Individuals sleeping more than 9 hours per day consumed 73 grams per day, less than those sleeping 7-9 hours per day.

Sleep and diet are critical factors in our overall health and well-being. When we lack enough food or the food we access lacks the nutrients we need, our health gradually becomes compromised. Similarly, if one does not sleep, health will start to suffer. Prolonged lack of sleep can even lead to death.

The importance of sleep
Humans need between 7 and 9 hours of sleep per night. However, the modern lifestyle often puts individuals in situations where they cannot get enough sleep. The need to fulfill demands from different spheres of life (family, work, connecting with friends, personal activities…) is often satisfied at the cost of sleep time. Studies indicate that a substantial share of the population reports problems with sleep (Wang et al., 2023). These problems lead to issues such as daytime fatigue, sleepiness, irritability, and many others, creating a substantial well-being and even economic burden for society (Hillman & Lack, 2013).

Studies indicate that a substantial share of the population reports problems with sleep.

Sleep problems are also associated with changes to our dietary habits. A study has shown that just one night of reduced sleep increased subsequent food intake in healthy men (Brondel et al., 2010). A meta-analysis of 12 studies reported that individuals with short sleep duration had 41% higher odds of developing obesity than those with a normal sleep period (Bacaro et al., 2020). Another meta-analysis reported 89% increased odds of developing obesity for children who are short sleepers and 55% increased odds for adults (Cappuccio et al., 2008).

Studies have also linked short sleep duration to an increased risk of contracting or dying from cardiovascular diseases (Sofi et al., 2014) and type 2 diabetes (Vgontzas et al., 2009).

A study has shown that just one night of reduced sleep increased subsequent food intake in healthy men.

Lack of sleep increases the desire for unhealthy foods
A neuroimaging study of healthy adults found that sleep deprivation significantly decreases the activity in regions within the frontal cortex and insular cortex of the brain that evaluate food choices while increasing the activity in the amygdala region (Greer et al., 2013).

This indicates that, after sleep deprivation, an individual’s ability to evaluate food choices rationally likely decreases (frontal and insular cortex) while emotional experiences related to food increase (amygdala). This neural mechanism potentially explains why insufficient sleep makes individuals more likely to choose high-calorie foods, foods that promote weight gain (see Figure 1).

Figure 1. Effects of sleep deprivation in the brain related to diet

The current study
Study author Anupa Thapa and her colleagues wanted to investigate whether sleep duration is associated with how much fruit and vegetables one consumes (Thapa et al., 2024). They hoped to discover whether sleep duration influences fruit and vegetable consumption and vice versa.

These researchers analyzed data from the National FinHealth 2017 Study, a population-based health survey conducted in Finland. The study included 5043 adults who responded to a letter inviting them to a health examination and to complete a self-administered questionnaire by mail.

The mean age of participants was 55. 56% were women, and 71% were in a marital, cohabiting, or registered relationship.

Study authors analyzed data about participants’ eating habits (the Food Frequency Questionnaire) and sleep duration (“How many hours do you sleep in 24 hours?”). In data about eating habits, the study authors focused on the responses about participants’ consumption of fruits (including citrus fruits, apples, berries, and other fresh and canned fruits) and vegetables (including green leafy vegetables, root vegetables, cabbages, mushrooms, legumes, fruit vegetables, other fresh and canned vegetables) over the past 12 months.

Using participants’ responses and specialized software (the FINESSI software of THL and the Finish National Food Consumption Database), study authors estimated participants’ average daily food consumption (in grams per day). They used sleep duration responses to classify participants into short sleepers (less than 7 hours per day), normal sleepers (7-9 hours per day), and long sleepers (more than 9 hours per day).

In addition to these data, the study authors analyzed participants’ age, gender, education, income, employment, cohabitation, body-mass index, physical activity level, and chronotype (whether they are morning types, evening types, or in-between) (see Figure 2).

Figure 2. Study procedure (Thapa et al., 2024)

21% of participants were short sleepers
Results showed that 21% of participants were short-sleepers, sleeping 6 hours per night on average. 3% were long sleepers (10 hours per night on average). 22% were morning types, 16% were evening types, and most participants (62%) reported their chronotype as intermediate.

Short and long sleepers eat less fruits and vegetables than normal sleepers
Further analysis revealed that short- and long-sleepers eat less fruit and vegetables than normal sleepers. Short sleepers consumed, on average, 37 grams of fruits and vegetables per day less than normal sleepers. Long sleepers consumed 73 grams of fruits and vegetables per day less than individuals sleeping between 7 and 9 hours (see Figure 3).

Figure 3. Study Results (Thapa et., 2024)

The study authors checked whether some of the demographic variables they studied could explain this difference. However, the differences in fruit and vegetable consumption remained even after adjusting for demographic differences.

Conclusion
This study on a large national sample discovered that deviation from normal sleep duration is consistently associated with decreased consumption of fruits and vegetables. Fruits and vegetables are rich in many important micronutrients, as well as antioxidants and fibers. These substances are essential for maintaining various bodily functions and preventing different chronic diseases. Because of this, they are necessary components of a healthy diet (e.g., Mithril et al., 2012), suggesting the need to take sleep patterns into account when planning dietary interventions.

The paper “Consumption of fruits and vegetables and its association with sleep duration among Finnish adult population: a nationwide cross-sectional study” was authored by Anupa Thapa, Tuuli Lahti, Mirkka Maukonen, and Timo Partonen.

References
Bacaro, V., Ballesio, A., Cerolini, S., Vacca, M., Poggiogalle, E., Donini, L. M., Lucidi, F., & Lombardo, C. (2020). Sleep duration and obesity in adulthood: An updated systematic review and meta-analysis. Obesity Research & Clinical Practice, 14(4), 301–309. https://doi.org/10.1016/j.orcp.2020.03.004

Brondel, L., Romer, M. A., Nougues, P. M., Touyarou, P., & Davenne, D. (2010). Acute partial sleep deprivation increases food intake in healthy men. The American Journal of Clinical Nutrition, 91(6), 1550–1559. https://doi.org/10.3945/ajcn.2009.28523

Cappuccio, F. P., Taggart, F. M., Kandala, N.-B., Currie, A., ChB, M., Peile, E., & Miller, M. A. (2008). Meta-Analysis of Short Sleep Duration and Obesity in Children and Adults. 31(5).

Greer, S. M., Goldstein, A. N., & Walker, M. P. (2013). The impact of sleep deprivation on food desire in the human brain. Nature Communications, 4. https://doi.org/10.1038/ncomms3259

Hillman, D. R., & Lack, L. C. (2013). Public health implications of sleep loss: The community burden. Medical Journal of Australia, 199(8), S7–S10. https://doi.org/10.5694/mja13.10620

Mithril, C., Dragsted, L., Meyer, C., Blauert, E., Holt, M., & Astrup, A. (2012). Guidelines for the New Nordic Diet. Public Health Nutrition, 15, 1941–1947. https://doi.org/10.1017/S136898001100351X

Sofi, F., Cesari, F., Casini, A., Macchi, C., Abbate, R., & Gensini, G. (2014). Insomnia and risk of cardiovascular disease: A meta-analysis. European Journal of Preventive Cardiology, 21, 57–64. https://doi.org/10.1177/2047487312460020

Thapa, A., Lahti, T., Maukonen, M., & Partonen, T. (2024). Consumption of fruits and vegetables and its association with sleep duration among Finnish adult population: A nationwide cross-sectional study. Frontiers in Nutrition, 11, 1319821. https://doi.org/10.3389/fnut.2024.1319821

Vgontzas, A. N., Liao, D., Pejovic, S., Calhoun, S., Karataraki, M., & Bixler, E. O. (2009). Insomnia With Objective Short Sleep Duration Is Associated With Type 2 Diabetes. Diabetes Care, 32(11), 1980–1985. https://doi.org/10.2337/dc09-0284

Wang, S., Rossheim, M. E., & Nandy, R. R. (2023). Trends in prevalence of short sleep duration and trouble sleeping among US adults, 2005–2018. Sleep, 46(1), zsac231. https://doi.org/10.1093/sleep/zsac231

Stress Changes Feeding Behavior in Mice. What About People?

Posted on October 28, 2024November 25, 2024 by CNP Staff

A study on mice published in the Frontiers in Neuroscience found that their feeding behavior changes when exposed to stress.
Stressed mice tended to fixate on a single source of food, deviating from the natural tendency of mice to try different sources when they are available.
This behavior could be artificially caused by inhibiting the activity of dopaminergic neurons going from the ventral tegmental area to the nucleus accumbens shell area of the brain while injecting dopamine in the later area would eliminate it.

Many people experience stress that can affect their behavior related to food. Extreme stress can cause us to lose our appetite and “forget to eat.” However, stress can sometimes also make us want to eat, very often snacks and different highly palatable foods (e.g., sweets, chocolates). To describe this and similar phenomena, scientists coined the term emotional eating.

Stress can sometimes also make us want to eat, very often snacks and different highly palatable foods.

Emotional eating
Emotional eating is a “tendency to eat in response to negative emotions with the chosen foods primarily being energy-dense and palatable” (Konttinen, 2020; Ljubičić et al., 2023). People engage in emotional eating in response to feelings like stress, sadness, boredom, or anxiety. Sometimes, even positive emotions like happiness can trigger the desire to eat. This behavior often involves craving high-calorie comfort foods (such as sweets or junk food), leading to unhealthy eating patterns and potential weight gain (Dakanalis et al., 2023).

People engage in emotional eating in response to feelings like stress, sadness, boredom, or anxiety. Sometimes, even positive emotions like happiness can trigger the desire to eat.

However, the link between food consumption, emotions, and mood in general is much broader. For example, a recent study reported that individuals tend to report greater anger, irritability, and decreased pleasure when hungry (Swami et al., 2022). In animals, hunger increases their motivation to engage in escalated and persistent aggression to acquire food. Keepers of animals know very well that the animals are the most dangerous when hungry. This link between negative emotions and hunger gave rise to the term “hangry,” describing a state in which one experiences hunger and anger due to hunger (Hedrih, 2023; Swami et al., 2022).

In animals, hunger increases their motivation to engage in escalated and persistent aggression to acquire food.

The neural basis of food-seeking behaviors
Food-seeking behaviors are complex activities regulated by various areas of the brain, including those responsible for the most complex behaviors. However, studies indicate that the hypothalamus is the primary area of the brain regulating these behaviors.

It is the part of the brain where agouti-related peptide (AgRP) neurons, also known as “hunger neurons,” are located. Experiments on rodents revealed that triggering the activity of these neurons makes the animals start eating but also produces anti-inflammatory effects (Hedrih, 2024a; Klima et al., 2023). Another more recent study identified a group of neurons in the midbrain projecting to the hypothalamus (vesicular GABA transporter-expressing GABAergic neurons), the activity of which alone triggers food-seeking behaviors in rodents (Hedrih, 2024b; Reis et al., 2024).

Another component regulating this complex behavioral mechanism is neurons utilizing the neurotransmitter dopamine located in the nucleus accumbens region of the brain and interacting with the hypothalamus. These neurons are part of the brain’s reward system, processing feelings of reward, pleasure, or cravings related to food (Aitken et al., 2016) (see Figure 1).

Figure 1. Neural Pathways Driving Food-Seeking Behavior

The current study
Study author Yusuke Fujioka and his colleagues wanted to investigate how stress affects feeding behaviors in mice and identify physiological and neural mechanisms responsible for these changes (Fujioka et al., 2024).

These authors conducted a series of experiments on wild-type C57BL/6J strain mice. This is an inbred strain of laboratory mice commonly used in research studies due to its consistent genetic background and well-documented physiology. They also used two other strains of mice, genetically engineered to allow researchers to control and track the activity of neurons in their brains that produce the neurotransmitter dopamine (Dopamine transporter (DAT)-Cre mice and Tyrosine hydroxylase (TH)-Cre mice).

In each experiment, mice were divided into an experimental and a control group. The experimental groups were subjected to various stressors, including social isolation (through solitary housing), limited access to food (access to a high-fat diet for 2 hours per day only), and restraint (mice were completely immobilized in a special device for 2 hours on five consecutive days). Control groups lived with free access to food and water without exposure to these stressors.

After the treatments, the authors ran behavioral experiments that monitored these mice’s feeding behaviors. They also monitored levels of the neurotransmitter dopamine in the nucleus accumbens shell regions of their brains and performed surgery on some of the mice from the experimental groups, allowing them to administer dopamine to this region of their brains (see Figure 2).

Figure 2. Study Procedure (Fujioka et al., 2024)

Stress leads to fixated eating
Normally, when exposed to multiple food sources, mice try food from all of them and explore the available food sources. However, mice exposed to stress tended to fixate on one of the food sources, eating mainly from it while devoting little time to exploring the other sources.

Stress impairs the release of dopamine in the nucleus accumbens shell
10-30 minutes after feeding, dopamine levels in the nucleus accumbens shell of mice from the control groups would increase. However, this increase was absent in mice exposed to stress (socially isolated mice) or much lower (other experimental groups). The nucleus accumbens shell is a subregion of the nucleus accumbens that plays a critical role in regulating motivation for food (among other things).

When the study authors injected dopamine directly into the nucleus accumbens shells of the experimental group mice, fixated eating stopped, and their feeding behavior returned to normal. This confirmed that the reduced release of dopamine produced changes in feeding behavior.

Finally, the study authors used specific drugs to inhibit the activity of neurons that produce and release dopamine from the brain’s ventral tegmental area to the nucleus accumbens shell in their genetically engineered mice. This reduced dopamine levels in the nucleus accumbens shells of these mice and changed their feeding behavior so that they also displayed fixated eating (see Figure 3).

Figure 3. Stress impairs the release of dopamine in the nucleus accumbens shell

Conclusion
The study discovered a mechanism through which stress affects feeding behaviors in mice. Feeding behaviors in humans are more complex than in mice, but neural mechanisms are often not too dissimilar in the two species. Because of this, mapping neural circuits and mechanisms controlling feeding behavior in mice will help scientists better understand and study food-related behaviors in humans.

The paper “Stress-impaired reward pathway promotes distinct feeding behavior patterns” was authored by Yusuke Fujioka, Kaori Kawai, Kuniyuki Endo, Minaka Ishibashi, Nobuyuki Iwade, Dilina Tuerde, Kozo Kaibuchi, Takayuki Yamashita, Akihiro Yamanaka, Masahisa Katsuno, Hirohisa Watanabe, Gen Sobue, and Shinsuke Ishigaki.

References

Aitken, T. J., Greenfield, V. Y., & Wassum, K. M. (2016). Nucleus accumbens core dopamine signaling tracks the need-based motivational value of food-paired cues. Journal of Neurochemistry, 136(5), 1026–1036. https://doi.org/10.1111/jnc.13494

Dakanalis, A., Mentzelou, M., Papadopoulou, S. K., Papandreou, D., Spanoudaki, M., Vasios, G. K., Pavlidou, E., Mantzorou, M., & Giaginis, C. (2023). The Association of Emotional Eating with Overweight/Obesity, Depression, Anxiety/Stress, and Dietary Patterns: A Review of the Current Clinical Evidence. Nutrients, 15(5), 1173. https://doi.org/10.3390/nu15051173

Fujioka, Y., Kawai, K., Endo, K., Ishibashi, M., Iwade, N., Tuerde, D., Kaibuchi, K., Yamashita, T., Yamanaka, A., Katsuno, M., Watanabe, H., Sobue, G., & Ishigaki, S. (2024). Stress-impaired reward pathway promotes distinct feeding behavior patterns. Frontiers in Neuroscience, 18, 1349366. https://doi.org/10.3389/fnins.2024.1349366

Hedrih, V. (2023). Food and Mood: Is the Concept of ‘Hangry’ Real? CNP Articles in Nutritional Psychology. https://www.nutritional-psychology.org/food-and-mood-is-the-concept-of-hangry-real/

Hedrih, V. (2024a, March 4). Researchers Identify Neural Pathways Transmitting Anti-Inflammatory Effects of Hunger. Nutritional Psychology. https://www.nutritional-psychology.org/researchers-identify-neural-pathways-transmitting-anti-inflammatory-effects-of-hunger/

Hedrih, V. (2024b, September 16). Study Identifies Neurons Controlling Food-Seeking Behaviors in Mice. CNP Articles in Nutritional Psychology. https://www.nutritional-psychology.org/study-identifies-neurons-controlling-food-seeking-behaviors-in-mice/

Klima, M. L., Kruger, K. A., Goldstein, N., Pulido, S., Low, A. Y. T., Assenmacher, C. A., Alhadeff, A. L., & Betley, J. N. (2023). Anti-inflammatory effects of hunger are transmitted to the periphery via projection-specific AgRP circuits. Cell Reports, 42(11). https://doi.org/10.1016/j.celrep.2023.113338

Konttinen, H. (2020). Emotional eating and obesity in adults: The role of depression, sleep and genes. Proceedings of the Nutrition Society, 79(3), 283–289. https://doi.org/10.1017/S0029665120000166

Ljubičić, M., Matek Sarić, M., Klarin, I., Rumbak, I., Colić Barić, I., Ranilović, J., Dželalija, B., Sarić, A., Nakić, D., Djekic, I., Korzeniowska, M., Bartkiene, E., Papageorgiou, M., Tarcea, M., Černelič-Bizjak, M., Klava, D., Szűcs, V., Vittadini, E., Bolhuis, D., & Guiné, R. P. F. (2023). Emotions and Food Consumption: Emotional Eating Behavior in a European Population. Foods, 12(4), Article 4. https://doi.org/10.3390/foods12040872

Reis, F. M. C. V., Maesta-Pereira, S., Ollivier, M., Schuette, P. J., Sethi, E., Miranda, B. A., Iniguez, E., Chakerian, M., Vaughn, E., Sehgal, M., Nguyen, D. C. T., Yuan, F. T. H., Torossian, A., Ikebara, J. M., Kihara, A. H., Silva, A. J., Kao, J. C., Khakh, B. S., & Adhikari, A. (2024). Control of feeding by a bottom-up midbrain-subthalamic pathway. Nature Communications, 15(1), 2111. https://doi.org/10.1038/s41467-024-46430-5

Swami, V., Hochstöger, S., Kargl, E., & Stieger, S. (2022). Hangry in the field: An experience sampling study on the impact of hunger on anger, irritability, and affect. PLOS ONE, 17(7), e0269629. https://doi.org/10.1371/JOURNAL.PONE.0269629

Are Food Choices More Indulgent When People Are Hungry?

Posted on October 14, 2024November 3, 2024 by CNP Staff

A study published in the Frontiers in Nutrition reported an association between how hungry a person is and the likelihood of making indulgent rather than virtuous food choices.
This association was only present in individuals not concerned about healthy eating.
The association was also present in individuals who did not practice a healthy lifestyle.

Having enough fresh air, food, and water is essential for survival. Living organisms are generally hard-wired to prioritize these resources. Anyone who has observed animals while eating is very familiar with how excited they can get when given food, particularly if they have not eaten for some time. In humans, the need for food, water, shelter, and other resources needed to survive physically is at the very foundation of the classic Maslow’s hierarchy of needs (e.g., Ryan et al., 2020). This hierarchy proposes that all higher needs, such as love, respect, self-actualization, and even basic safety, become inactive when the basic needs required for physical survival are not satisfied. Because of this, it should not come as a surprise that our behavior changes when we are hungry.

All higher needs, such as love, respect, self-actualization, and even basic ***safety*, become inactive when the basic needs are required for physical survival**

Hunger changes the way we behave
There are ample examples from disaster or war-stricken areas of people exposing their lives to extreme risk or completely forfeiting dignity or material wealth to procure food when they do not have it. There are many historical reports of people even resorting to cannibalism in the face of starvation, people from cultures where cannibalism is one of the strongest taboos (e.g., Herrmann, 2011; MacKenzie, 2023).

However, these are situations of extreme hunger and starvation. But do mild, everyday levels of hunger we experience from time to time in modern, developed societies with sufficient access to food also change our behavior? For example, what hunger levels do we experience when we skip or postpone a meal due to work obligations or because we spend time doing things we are interested in and have not yet found time to eat? Research studies indicate that they do.

How do everyday levels of hunger change behavior?
So far, studies in humans show that, when hungry, people tend to become restless, nervous, and irritable. Feelings of hunger are associated with behavioral difficulties in children (Hedrih, 2023). Low blood glucose levels, known to trigger the sensation of hunger, are associated with increased impulsivity, anger, and aggression (Swami et al., 2022).

To explain these behavioral changes, scientists have proposed the concept of ego depletion. This concept suggests that the human capacity for self-regulation and active volition is limited (Baumeister et al., 1998), so when a person is hungry, he/she may struggle to exercise self-regulation and self-control making displays of negative and high-arousal emotions more likely (Hedrih, 2023).

When a person is hungry, he/she may struggle to exercise self-regulation and self-control

These findings and casual observations have even given rise to the term “hangry.” “Hangry” is a combination of “hungry” and angry” and describes a state when a person is hungry and angry due to hunger (Hedrih, 2023; Swami et al., 2022).

The current study
Author and his colleagues wanted to explore whether hungry people are more prone to indulgent food choices. Previous studies indicate this might be the case, but these researchers also wanted to identify some of the factors that this link might depend on (Otterbring et al., 2024). They hypothesized that hungrier individuals would be more inclined to make indulgent food choices, but this would depend on their concern for healthy eating and lifestyle.

The study participants were 461 undergraduate students from a Northern European university. Their average age was 22, and 49% were female. The study authors recruited them in the vicinity of a university cafeteria. They conducted interviews at different times of day to obtain higher individual differences in how hungry the students were when they answered the researchers’ questions.

In an interview, these researchers would present a participating student (verbally) with a set of 8 binary food choices and ask them to select the food item that appeals to them the most. In each choice, students had to choose between a “virtuous” and an “indulgent” food option. Virtuous food options were foods considered to be healthy rather than tasty. Indulgent food options were foods that were primarily consumed for their immediate pleasure but that were typically unhealthy. For example, one such choice was between a food salad and a chocolate cake.

Students made their choice on a 7-point rating scale ranging from definitive preference for the first item (virtuous, i.e., healthy) to definitive preference for the second item (indulgent). They also completed a brief assessment of their current hunger. At the end of the survey, students completed brief assessments of their concerns about healthy eating and adherence to a healthy lifestyle (see Figure 1).

Figure 1. Study Procedure (Otterbring, 2024).

Hungry students tended to make more indulgent choices
Results showed a weak link between hunger and indulgent food choices. Students who were hungrier at the time of the interview were more likely to choose indulgent food options. Those more concerned about healthy eating chose indulgent foods less often.

Additional analyses revealed that only the students who were not as concerned with healthy eating showed a heightened preference for indulgent foods when hungry. This link was absent in students who were highly concerned about healthy eating. Similarly, the link between hunger and the preference for indulgent food options was only present in students who reported not practicing a healthy lifestyle (see Figure 2).

Figure 2. The link between hunger and food choice

Conclusion
The study confirmed a link between hunger and the propensity to choose indulgent (i.e., unhealthy) foods. However, it also added to the existing scientific knowledge by showing that this link was produced primarily by individuals who were not very concerned about the healthiness of their food choices and did not adhere to a healthy lifestyle.

These findings indicate that raising awareness about the importance of healthy eating and adherence to a healthy lifestyle could make people less likely to choose unhealthy foods and prevent them from switching to indulgent foods when experiencing hunger (at everyday levels).

However, additional studies are needed to verify the feasibility of this mechanism, as the current study’s design does not allow cause-and-effect conclusions to be derived from the findings.

The paper “The impact of hunger on indulgent food choices is moderated by healthy eating concerns” was authored by Tobias Otterbring, Michał Folwarczny, and Agata Gasiorowska.

You can also watch our Diet-Mental Health Break #43 video on YouTube: Are People More Likely to Choose Indulgent Foods When Hungry?

References

Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74(5), 1252–1265. https://doi.org/10.1037/0022-3514.74.5.1252

Hedrih, V. (2023). Food and Mood: Is the Concept of ‘Hangry’ Real? CNP Articles in Nutritional Psychology. https://www.nutritional-psychology.org/food-and-mood-is-the-concept-of-hangry-real/

Herrmann, R. B. (2011). The “tragicall historie”: Cannibalism and Abundance in Colonial Jamestown. The William and Mary Quarterly, 68(1), 47–74. https://doi.org/10.5309/willmaryquar.68.1.0047

MacKenzie, H. L. (2023). ‘Desperate with hunger’: Food, Eating, and Cannibalism in. The University of Leeds, Institute for Medieval Studies.

Otterbring, T., Folwarczny, M., & Gasiorowska, A. (2024). The impact of hunger on indulgent food choices is moderated by healthy eating concerns. Frontiers in Nutrition, 11, 1377120. https://doi.org/10.3389/fnut.2024.1377120

Ryan, B. J., Coppola, D., Canyon, D. V., Brickhouse, M., & Swienton, R. (2020). COVID-19 Community Stabilization and Sustainability Framework: An Integration of the Maslow Hierarchy of Needs and Social Determinants of Health. Disaster Medicine and Public Health Preparedness, 14(5), 623–629. https://doi.org/10.1017/dmp.2020.109

Social Isolation is Associated With Altered Neural Reactivity to the Sight of Food

Posted on August 20, 2024August 20, 2024 by CNP Staff

A study of women published in JAMA Network Open found that women with higher perceived social isolation tend to have altered neural activity in response to pictures of food
This altered activity was detected in brain regions responsible for appetite and food-related motivation and included the default mode, executive control, and visual attention networks
More socially isolated women also tended to be more overweight and obese, to have lower diet quality, more maladaptive eating behaviors, and poorer mental health

Sometimes, eating something will make us feel better when we are sad or experience strong negative emotions. Some foods are so tasty that eating them feels like a rewarding experience. People associate some foods with positive memories, so eating them will improve their emotional state by invoking them. But what happens when negative emotions are persistent? For example, when we generally feel lonely?

Social isolation
Perceived social isolation, also known as loneliness, is one’s subjective appraisal of his/her available social relationships and community support (Zhang et al., 2024). Loneliness is entirely subjective and does not necessarily rely on how many social connections one objectively has. A person can feel lonely while being surrounded by people.

Loneliness is entirely subjective and does not necessarily rely on how many social connections one objectively has.

Loneliness is not a pleasant feeling. Feeling lonely adversely impacts our well-being. However, studies indicate that loneliness might have a broader adverse impact on our health. Lonely individuals seem to be at an increased risk of cardiovascular diseases, cognitive decline, and unhealthy eating behaviors (Zhang et al., 2024), but also dying of cancer (Hawkley & Cacioppo, 2003; Park et al., 2020). Some authors suggest that health risks created by loneliness are on par with those created by chronic high blood pressure, obesity, or smoking (Singer, 2018).

However, it should be noted that these observations are just associations. While loneliness may lead to these adverse health effects, health problems can make socialization difficult or impossible, creating an association with loneliness.

Social isolation and eating behaviors
Loneliness may increase the risk of obesity. It may also worsen eating behaviors and eating disorders (Zhang et al., 2024). Previous studies established that negative emotions make individuals more likely to overeat (Zeeck et al., 2011). When intense emotions overwhelm an individual’s capacity to self-regulate their own emotional well-being, binge eating behaviors may emerge as a coping mechanism.

Loneliness may increase the risk of obesity. It may also worsen eating behaviors and eating disorders.

There is both scientific and anecdotal evidence of the phenomenon of being “hangry,” i.e., becoming angry while hungry (Hedrih, 2023; Swami et al., 2022). A similar link may exist between loneliness and hunger as well.

The current study
Study author Xiaobei Zhang and his colleagues note that there is emerging evidence that the brains of individuals experiencing chronic loneliness undergo functional changes that may contribute to obesity, altered eating behaviors, and associated psychological symptoms. They wanted to explore the links between loneliness and the brain’s responses to the sight of food better.

These authors hypothesized that several brain networks will show increased activation in lonely individuals when viewing foods. The size of this increase would likely be higher in obese individuals who already have altered eating behaviors and worsened mental health. These authors also hypothesized that reactions would be particularly strong to sweet foods, given their highly rewarding nature.

The study participants were 93 women of reproductive age from Los Angeles, California. Their average age was 25, and they ranged between 18 and 50. The study authors recruited them through advertisements.

The study authors took participants’ weight and height measurements (to calculate body mass index) and estimated their body composition. Participants provided data on their diet style and quality (the UCLA Diet Checklist and the Healthy Eating Index), age, marital status, and socioeconomic status. They completed an assessment of perceived social isolation (the Perceived Isolation Scale). Based on this assessment, the study authors divided participants into two groups – the high-isolation group and the low-isolation group.

Aside from this, participants completed assessments of food craving (the General Food Craving Questionnaire), eating behaviors (the Reward-based Eating Drive, the Three-Factor Eating Questionnaire), food addiction (the Yale Food Addiction Scale), resilience (the Connor-Davidson Resilience scale), anxiety and depression symptoms (the Hospital Anxiety and Depression Scale), and affect (the Positive Affect – Negative Affect Schedule). Participants also underwent functional magnetic resonance imaging while viewing a slideshow with pictures of different types of food – unhealthy savory, unhealthy sweet, healthy savory, healthy sweet, and non-food images (i.e., pixelated images created from food pictures to serve as controls) (see Figure 1).

Figure 1. Study Procedure (Zhang et al., 2024)

Lonely individuals had higher fat mass percentage, ate lower quality diets, and had worse mental health
Results showed that the high loneliness group of participants tended to have higher fat mass percentage and ate diets of lower quality. They showed more maladaptive eating behaviors (cravings, reward-based eating, uncontrolled eating, and food addiction scores) and more anxiety and depression symptoms. They also tended to have lower psychological resilience.

The high loneliness group of participants tended to have a higher fat mass percentage and eat lower-quality diets.

Lonely individuals tended to have altered brain reactivity to food pictures
Participants in the high social isolation group also tended to have altered brain reactivity to the pictures of food in regions of the brain belonging to the default mode, executive control, and visual attention networks compared to the low social isolation group (see Figure 2).

Figure 2. Study results (Zhang et al., 2024)

The default mode network is a brain network active during rest and involved in self-referential thoughts, mind-wandering, and daydreaming. The executive control network is responsible for high-level cognitive functions such as decision-making, problem-solving, and maintaining attention to tasks. The visual attention network directs attention to visual stimuli, enabling the processing and prioritization of visual information in the environment.

Neural responses to sweet foods were associated with altered eating behaviors and psychological symptoms. Statistical analysis showed that these altered brain responses may mediate the relationship between loneliness and maladaptive eating behaviors, increased body fat composition, and diminished positive emotions.

The study confirmed the link between social isolation and obesity.

Conclusions
The study confirmed the link between social isolation and obesity. It identified altered brain responses to the sight of food in women who reported higher feelings of loneliness. These findings underscore the need for overweight and obesity treatments to take into account the whole psychological and social situation of an individual rather than focusing on eating behaviors alone.

The paper “Social Isolation, Brain Food Cue Processing, Eating Behaviors, and Mental Health Symptoms” was authored by Xiaobei Zhang, Soumya Ravichandran, Gilbert C. Gee, Tien S. Dong, Hiram Beltrán-Sánchez, May C. Wang, Lisa A. Kilpatrick, Jennifer S. Labus, Allison Vaughan, and Arpana Gupta.

References

Hawkley, L. C., & Cacioppo, J. T. (2003). Loneliness and pathways to disease. Brain, Behavior, and Immunity, 17(1, Supplement), 98–105. https://doi.org/10.1016/S0889-1591(02)00073-9

Hedrih, V. (2023). Food and Mood: Is the Concept of ‘Hangry’ Real? CNP Articles in Nutritional Psychology. https://www.nutritional-psychology.org/food-and-mood-is-the-concept-of-hangry-real/

Park, C., Majeed, A., Gill, H., Tamura, J., Ho, R. C., Mansur, R. B., Nasri, F., Lee, Y., Rosenblat, J. D., Wong, E., & McIntyre, R. S. (2020). The Effect of Loneliness on Distinct Health Outcomes: A Comprehensive Review and Meta-Analysis. Psychiatry Research, 294, 113514. https://doi.org/10.1016/j.psychres.2020.113514

Singer, C. (2018). Health Effects of Social Isolation and Loneliness. Journal of Aging and Care, 28(1), 4–8.

Zeeck, A., Stelzer, N., Linster, H. W., Joos, A., & Hartmann, A. (2011). Emotion and eating in binge eating disorder and obesity. European Eating Disorders Review, 19(5), 426–437. https://doi.org/10.1002/erv.1066

Zhang, X., Ravichandran, S., Gee, G. C., Dong, T. S., Beltrán-Sánchez, H., Wang, M. C., Kilpatrick, L. A., Labus, J. S., Vaughan, A., & Gupta, A. (2024). Social Isolation, Brain Food Cue Processing, Eating Behaviors, and Mental Health Symptoms. JAMA Network Open, 7(4), e244855. https://doi.org/10.1001/jamanetworkopen.2024.4855

Can Dietary Prebiotics Reduce the Rewarding Effects of High-Calorie Foods?

Posted on November 30, 2023January 12, 2024 by CNP Staff

An experimental study of overweight young adults published in Gut demonstrated that dietary prebiotics can reduce the rewarding effects of high-calorie food
After 14 days of using prebiotics, fMRI scans showed reduced reward-related brain activation in response to high-calorie food compared to the placebo group
Occurrence of short-chain-fatty-acid-producing Bifidobacteriaceae increased in participants taking prebiotics

We are all familiar with the intense feelings of pleasure we get after consuming highly tasty and palatable food – a piece of chocolate cake, for example. We will remember these feelings and associate them with this food. When we decide what to eat in the future, we will recall the memories of that food and the feelings we experienced when eating it —and likely develop a desire to eat the same food again. That is an important part of how we create our food preferences.

Consuming highly palatable foods, especially those high in sugar and fat, can increase dopamine in the brain, creating a sense of pleasure and satisfaction

Rewarding effects of food
The feelings of pleasure we experience when eating highly palatable foods are created by our brain’s reward system. The brain’s reward system comprises a complex network of neural circuits that play a fundamental role in motivation, reinforcement, and pleasure. This process involves the release of neurotransmitters, particularly dopamine, in response to rewarding stimuli, such as food, sex, or positive social interactions. This dopamine release reinforces behaviors associated with the reward, creating a drive to seek out and repeat those behaviors (Lewis et al., 2021) (see Figure 1).

Consuming highly palatable foods, especially those high in sugar and fat, can increase dopamine in the brain, creating a sense of pleasure and satisfaction. This activation of reward neural pathways reinforces the desire to eat, making us more likely to seek those foods in the future.

Figure 1. Food and the brain’s reward system

How is the brain’s reward system linked to obesity?
Repeated exposure to highly rewarding foods, especially in excess, can lead to desensitization of the reward system. When this happens, an individual will need more and more food to achieve the expected pleasant feelings, i.e., the rewarding effects of food. This might make the individual increase the intake of the food in question, potentially leading to overeating and an increase in body weight in the long run (see Figure 2).

Figure 2. Exposure to highly rewarding foods can lead to increased food intake

The described mechanism is similar to that which occurs with addictions. This is the reason why some authors propose that an individual can develop an addiction to certain foods and that food addiction should be recognized as a distinct mental health condition. Studies these researchers carried out indicate that consumption of foods rich in both sugar and fats, such as (many types of) ultra-processed foods, is particularly likely to result in food addiction (Gearhardt et al., 2011, 2023; Hedrih, 2023; Thanarajah et al., 2023).

Studies of these neural mechanisms in humans found functional alterations in the areas of the brain involved in the reward system in overweight individuals (e.g., Pujol et al., 2021; Seabrook et al., 2023), while studies on mice clearly show that high-fat diets can indeed induce obesity by disrupting the functioning of neural mechanisms regulating food intake (e.g., Ikemoto et al., 1996).

This leads many researchers to search for methods of influencing the brain’s reward system to prevent obesity or help individuals regain healthy body mass.

The current study
Study author Evelyn Medawar and her colleagues hypothesized that consuming high doses of prebiotic fiber as a supplement would change the patterns of neural activity related to food in the brain reward system in individuals at risk for weight gain and developing diabetes. Prebiotic fiber is a type of dietary fiber that promotes the growth and activity of beneficial bacteria in the gut, supporting a healthy microbiota. While it is indigestible for humans, it serves as food for various bacteria living in the gut, promoting their growth.

The authors of this study expected that an intervention using prebiotics would alter the composition of the gut microbiome, which would, in turn, alter the food-related patterns of brain activation (Medawar et al., 2023).

The gut microbiome or gut microbiota is the community of trillions of bacteria, fungi, viruses, and other microorganisms that live in the human gut. These microorganisms are critical for digestion, allowing the human digestive system to digest substances it would not be able to digest without them. Still, they also serve a number of other metabolic roles.

High doses of prebiotic fiber as a supplement would change the patterns of neural activity related to food in the brain reward system

How can gut bacteria affect the brain reward system?
Scientists studying human life processes in recent decades discovered a system in the human body that allows communication between gut microorganisms and the brain. This bidirectional communication system is called the microbiota-gut-brain axis (MGBA). It links the gut microbiota, the gastrointestinal tract, and the central nervous system (CNS).

Aside from their role in digestion, gut microorganisms produce various signaling molecules that can influence neural, endocrine, and immune functions. These molecules enable communication between the gut and the brain through neural, hormonal, and immunological pathways. Studies have already identified various ways in which processes in the gut can affect brain functioning, but much still remains to be discovered (Qu et al., 2023; Valles-Colomer et al., 2019; Zhu et al., 2023).

Recent findings indicate that the microbiota-gut-brain axis MGBA can affect and regulate the reward system of the brain as well (Carbia et al., 2023; García-Cabrerizo et al., 2021). Given this, it is possible that gut microbiota changes could also affect the brain’s reward system (see Figure 3).

Figure 3. Signaling molecules enabling communication between the gut and brain

Recent findings indicate that the microbiota-gut-brain axis (MGBA) can affect and regulate the reward system of the brain

The procedure
Study participants were 59 overweight adults (body mass index values between 25 and 30). Their ages ranged between 18 and 42 years. Nineteen of them were females. Participants received 9-10 EUR per hour for their activities during testing days and another 30 EUR for completing the study.

The study’s authors prepared two different treatment substances for participants: a prebiotic fiber supplement and a placebo. The daily dose of prebiotic fiber consisted of 30 grams of inulin, a carbohydrate found in plants such as chicory root. Humans lack the enzymes needed to digest inulin, but many beneficial bacteria in the gut can ferment it. The product of this fermentation is short-chain fatty acids. Among other things, these substances are involved in the functioning of the MGBA (learn more about short-chain fatty acids in NP 120 Parts I and II through CNP) (see Figure 4).

Figure 4. Research procedure using prebiotic fiber vs. placebo

A daily placebo treatment substance consisted of 16g of maltodextrin, a carbohydrate used as a food additive and a thickening agent derived from starch. The placebo treatment was matched on calories with the prebiotic fiber treatment. Both substances were packed in identical sachets, so participants could not tell which substance a sachet contained. In both cases, participants were supposed to take two sachets of the treatment substance daily.

Each participant was supposed to take each of the two treatment substances for 14 days in a row, with two weeks between the treatments. The order in which each participant would take the treatments was randomized – some participants first took the prebiotic fiber for 14 days, paused for 14 days, and then took a placebo for another 14 days. Other participants would start with 14 days of placebo and finish with 14 days of using the prebiotic fiber. This was a blind study, so study participants and staff working with them did not know when they were taking a placebo and when it was prebiotic fiber.

Neuroimaging and measurements
Before and after each 14-day treatment, participants underwent functional magnetic resonance imaging (fMRI). During imaging, they viewed pictures of food and art and rated how much they wanted the item shown in the picture. Researchers told them they would receive a reward based on their ratings – either a food item that could be eaten immediately after fMRI or a carton print of an art piece.

Participants gave blood samples to assess short-chain fatty acid concentrations, gastrointestinal hormones, glucose, lipids, and inflammatory markers. They also gave stool samples for measuring gut microbiota composition and short-chain fatty acids.

Brain reactions to high-calorie foods decreased after prebiotic treatment

On average, during fMRI scans, participants wanted food more than they wanted art. The prebiotic treatment did not change the relationship between these wanting scores. However, after 14 days of taking prebiotics, participants had lower overall wanting scores than after 14 days of taking a placebo. In particular, after prebiotics treatment, participants expressed very little desire for very low and very high-calorie foods and plants (see Figure 5).

Figure 5. Brain reactions to high-calorie foods decreased after prebiotic treatment

When brain activity was examined, regional brain activity in reaction to food and art pictures remained the same. Evaluation of food pictures activated large parts of the reward network, including the ventral tegmental area, hypothalamus, nucleus accumbens, basal ganglia, and ventromedial thalamus, as well as anterior insula, amygdala, cingulate, ventromedial prefrontal cortex, and distinct parts of the orbitofrontal cortex.

However, brain reactions to pictures of wanted, high-calorie food decreased after prebiotics (compared to placebo) in three brain regions – ventral tegmental area, right and right medial the orbitofrontal cortex. Additionally, participants reported feeling less subjective hunger during fMRI after the prebiotics treatment.

Brain reactions to pictures of wanted, high-calorie food decreased after prebiotics compared to placebo

Prebiotic treatment altered gut microbiota composition
Analysis of stool samples indicated that the treatment with prebiotics led to significant shifts in gut microbiota composition. Overall richness, evenness, and diversity (the number of different species of microorganisms) decreased. The amounts of Bifidobacteria in samples increased. Increases in Bifidobacteria were associated with increases in the activity of a number of metabolic pathways.

Participants who had a more substantial decrease in Subdoligranulum bacteria after prebiotics treatment tended to show a decreased activation of the brain’s ventral tegmental area in response to high-calorie food. Participants who had a decrease in Lactiplantibacillus, a lactic acid-producing bacteria, after prebiotics treatment tended to have decreased activation in the right medial orbitofrontal cortex.

Conclusion
The study showed that intervention with a common prebiotic attenuated reactions of the brain’s reward network in response to high-calorie food. It also induced changes to the gut microbiota, through which this effect was likely achieved.

The study showed that intervention with a common prebiotic attenuated reactions of the brain’s reward network in response to high-calorie food

Since the discovery of the microbiota-gut-brain axis, scientists have considered using prebiotics or other interventions aimed at gut microbiota to achieve beneficial changes in brain activity and behavior. The results of this study indicate that this idea holds merit. While it used a common type of prebiotic, it proved that brain functioning can be affected in this way. This opens a path for future researchers to develop more effective and specific ways to achieve mental health outcomes through interventions aimed at gut microbiota.

The paper “Prebiotic diet changes neural correlates of food decision-making in overweight adults: a randomized controlled within-subject cross-over trial” was authored by Evelyn Medawar, Frauke Beyer, Ronja Thieleking, Sven-Bastiaan Haange, Ulrike Rolle-Kampczyk, Madlen Reinicke, Rima Chakaroun, Martin von Bergen, Michael Stumvoll, Arno Villringer, A Veronica Witte.

References

Carbia, C., Bastiaanssen, T. F. S., Iannone, F., García-cabrerizo, R., Boscaini, S., Berding, K., Strain, C. R., Clarke, G., Stanton, C., Dinan, T. G., & Cryan, J. F. (2023). The Microbiome-Gut-Brain axis regulates social cognition & craving in young binge drinkers. EBioMedicine, (In press), 104442. https://doi.org/10.1016/j.ebiom.2023.104442

García-Cabrerizo, R., Carbia, C., O´Riordan, K. J., Schellekens, H., & Cryan, J. F. (2021). Microbiota-gut-brain axis as a regulator of reward processes. Journal of Neurochemistry, 157(5), 1495–1524. https://doi.org/10.1111/JNC.15284

Gearhardt, A. N., Bueno, N. B., DiFeliceantonio, A. G., Roberto, C. A., Jiménez-Murcia, S., & Fernandez-Aranda, F. (2023). Social, clinical, and policy implications of ultra-processed food addiction. BMJ, e075354. https://doi.org/10.1136/bmj-2023-075354

Gearhardt, A. N., Yokum, S., Orr, P. T., Stice, E., Corbin, W. R., & Brownell, K. D. (2011). Neural Correlates of Food Addiction. Archives of General Psychiatry, 68(8), 808–816. https://doi.org/10.1001/ARCHGENPSYCHIATRY.2011.32

Hedrih, V. (2023). Scientists Propose that Ultra-Processed Foods be Classified as Addictive Substances. CNP Articles in Nutritional Psychology. https://www.nutritional-psychology.org/scientists-propose-that-ultra-processed-foods-be-classified-as-addictive-substances/

Ikemoto, S., Takahashi, M., Tsunoda, N., Maruyama, K., Itakura, H., & Ezaki, O. (1996). High-fat diet-induced hyperglycemia and obesity in mice: Differential effects of dietary oils. Metabolism, 45(12), 1539–1546. https://doi.org/10.1016/S0026-0495(96)90185-7

Lewis, R. G., Florio, E., Punzo, D., & Borrelli, E. (2021). The Brain’s Reward System in Health and Disease. In Advances in Experimental Medicine and Biology (Vol. 1344, pp. 57–69). Springer. https://doi.org/10.1007/978-3-030-81147-1_4

Medawar, E., Beyer, F., Thieleking, R., Haange, S.-B., Rolle-Kampczyk, U., Reinicke, M., Chakaroun, R., von Bergen, M., Stumvoll, M., Villringer, A., & Witte, A. V. (2023). Prebiotic diet changes neural correlates of food decision-making in overweight adults: a randomised controlled within-subject cross-over trial. Gut, gutjnl-2023-330365. https://doi.org/10.1136/gutjnl-2023-330365

Pujol, J., Blanco-Hinojo, L., Martínez-Vilavella, G., Deus, J., Pérez-Sola, V., & Sunyer, J. (2021). Dysfunctional Brain Reward System in Child Obesity. Cerebral Cortex, 31, 4376–4385. https://doi.org/10.1093/cercor/bhab092

Qu, Y., Eguchi, A., Wan, X., Ma, L., Chang, L., Shan, J., Yang, Y., Mori, C., & Hashimoto, K. (2023). Repeated use of 3,4-methylenedioxymethamphetamine is associated with the resilience in mice after chronic social defeat stress: A role of gut–microbiota–brain axis. Psychiatry Research, 320(October 2022), 0–9. https://doi.org/10.1016/j.psychres.2022.115020

Seabrook, L. T., Naef, L., Baimel, C., Judge, A. K., Kenney, T., Ellis, M., Tayyab, T., Armstrong, M., Qiao, M., Floresco, S. B., & Borgland, S. L. (2023). Disinhibition of the orbitofrontal cortex biases decision-making in obesity. Nature Neuroscience, 26(1), 92–106. https://doi.org/10.1038/s41593-022-01210-6

Thanarajah, S. E., Difeliceantonio, A. G., Albus, K., Br, J. C., Tittgemeyer, M., Small, D. M., Thanarajah, S. E., Difeliceantonio, A. G., Albus, K., Kuzmanovic, B., & Rigoux, L. (2023). Habitual daily intake of a sweet and fatty snack modulates reward processing in humans. Cell Metabolism, 35, 1–14. https://doi.org/10.1016/j.cmet.2023.02.015

Valles-Colomer, M., Falony, G., Darzi, Y., Tigchelaar, E. F., Wang, J., Tito, R. Y., Schiweck, C., Kurilshikov, A., Joossens, M., Wijmenga, C., Claes, S., Van Oudenhove, L., Zhernakova, A., Vieira-Silva, S., & Raes, J. (2019). The neuroactive potential of the human gut microbiota in quality of life and depression. Nature Microbiology, 4(4), 623–632. https://doi.org/10.1038/s41564-018-0337-x

Zhu, X., Sakamoto, S., Ishii, C., Smith, M. D., Ito, K., Obayashi, M., Unger, L., Hasegawa, Y., Kurokawa, S., Kishimoto, T., Li, H., Hatano, S., Wang, T. H., Yoshikai, Y., Kano, S. ichi, Fukuda, S., Sanada, K., Calabresi, P. A., & Kamiya, A. (2023). Dectin-1 signaling on colonic γδ T cells promotes psychosocial stress responses. Nature Immunology. https://doi.org/10.1038/s41590-023-01447-8

Children Prone to Overeating are More Likely to be Overweight as Adults

Posted on September 30, 2023October 3, 2023 by CNP Staff

Body weight and the body mass index
When we want to track whether our body has accumulated (unwanted) body fat, we usually assess this by weighing ourselves. This is adequate because, after we stop growing, changes in our body mass will most likely be due to changes in our body composition (e.g., accumulating or losing body fat). However, when we need to compare different persons, their differences in height come into play. People with very different weights can have similar body compositions if they are of different height. To solve this issue, scientists use the body mass index.

Body Mass Index (BMI) is a statistical indicator calculated by dividing an individual’s weight (in kilograms) by the square of their height (in meters). It serves as a straightforward and widely accepted method of estimating body fat and determining whether an individual falls into categories such as underweight, normal weight, overweight, or obese. BMI provides a convenient way to screen for potential weight-related health issues. However, it has limitations, notably because it does not account for factors like muscle mass, body composition, or fat distribution. An individual with a BMI above 30 is considered obese, while values between 25 and 30 indicate that a person is overweight (Bhatt et al., 2023).

Food intake and obesity
Most obviously, obesity is caused by excessive food intake over a prolonged period. However, the causes of such excessive intake are much more complex. Our body uses a complex mechanism to regulate our food-related behavior. We shift between states of hunger and satiety multiple times every day. While hunger motivates us to seek and eat food, satiety tells us that we should stop eating. Both are linked to our emotions in very complex ways (Swami et al., 2022).

Hunger motivates us to seek and eat food; satiety tells us we should stop eating

However, recent studies indicate that neither hunger nor satiety strictly depend on our body’s nutritional needs. Scientists differentiate between two processes of hunger. One is triggered by a lack of specific nutrients (homeostatic hunger), while the other arises from learned associations between various cues for food (appetite) (Hedrih, 2023) (see Figure 1).

Figure 1. Mechanisms to regulate food-related behavior.

Causes of obesity
Food intake can often not result in satiety (feeling full or satisfied). Studies showed that mice that became obese because they were fed high-fat diets* do not seem to experience satiety and decreased motivation to consume food after eating.

Food intake can often not result in satiety

Researchers have tracked the cause of this disruption to the lateral orbitofrontal cortex region of the brain. They found that reduced inhibition of neurons produces this effect (Seabrook et al., 2023) (see Figure 2).

Figure 2. Prolonged high-fat diet disrupts the food intake regulation mechanism

Due to all this, disruption of the hunger-satiety mechanism in the brain is now seen as one of the primary causes of obesity. However, the exact ways this disruption develops in humans are unknown. Scientists have linked the functioning of this mechanism to various genetic and environmental factors, but also to diet (Changizi et al., 2002; Dubois et al., 2022; Stevenson et al., 2023). Of these, diet is the most easily modifiable.

Disruption of the hunger-satiety mechanism in the brain is now seen as one of the primary causes of obesity

The current study
Study author Lise Dubois and her colleagues wanted to explore the links between young adults’ eating behaviors, dietary patterns, and weight status. They also wanted to know whether eating behaviors in childhood are associated with eating habits and weight status in adulthood.

Their study participants were young adults in the Quebec Longitudinal Study of Child Development. The Quebec Longitudinal Study of Child Development is an ongoing study that has been running since 1998. It started with 2,120 singleton babies who entered the study at 5 months old. Researchers conducting the study periodically collect various data from these individuals, now adults.

At the time of the last data collection for the current study, these individuals were 22 years of age, and 698 participated.

The study procedure
Participants completed assessments of eating behaviors (the Adult Eating Behavior Questionnaire) and reported how often they ate items from each of the 60 food groups. They also reported their height and weight and several other pieces of demographic data. The study authors used data on height and weight to calculate participants’ body mass index values.

These researchers also used data on participants’ eating behaviors that persons “most knowledgeable” about the participant (who was a child at that time) provided when participants were 2, 3, 4, 5, and 6 years old. These persons were most often mothers. From these data, researchers derived information on whether the child was prone to overeating and fussy/picky eating (see Figure 3).

Figure 3. Research Procedure

The Eating behavior assessment
The Adult Eating Behavior Questionnaire measures multiple aspects of adult eating behavior. Hunger assesses an individual’s perception of their hunger levels. Questions inquire about how often one feels hungry and how strong that hunger is. Food responsiveness assesses an individual’s sensitivity and responsiveness to the sight and smell of palatable foods and how much these influence eating behaviors. Enjoyment of food evaluates how much an individual enjoys the taste and experience of consuming food.

Emotional overeating and undereating assess the extent to which an individual tends to overeat or undereat as a coping mechanism for emotional distress or negative emotions. Satiety responsiveness examines how often and how strongly an individual experiences the feeling of fullness during and after meals. Food fussiness is the extent to which a respondent is picky about what foods to eat. Slowness in eating assesses the tendency to eat slowly.

Associations between eating behaviors
Results showed that all food approach behaviors and tendencies to enjoy and eat lots of food were associated. The same was the case with different food avoidance behaviors. Food approach measures were negatively correlated with food avoidance measures. For example, individuals who reported enjoying the taste of food more also reported feeling stronger hunger and being more responsive to the sight and smell of food (food responsiveness).

Individuals who reported enjoying the taste of food more also reported feeling stronger hunger and being more responsive to the sight and smell of food

Participants who were slow eaters tended to experience satiety more easily and more strongly. They were also more prone to emotional undereating. Participants who experienced satiety more easily were more likely to be picky about their food (food fussiness). On the other hand, people who responded more easily to the sight and smell of food were less likely to be slow eaters (see Figure 4).

Figure 4. Food approach and avoidance behaviors

Overweight and obese individuals tend to be more positive about food
Results showed that overweight and obese individuals were more prone to emotional overeating and enjoyed the taste of food and the experience of eating more (Enjoyment of food) than their normal-weight peers. On the other hand, they were less prone to emotional undereating, did not experience satiety as easily, and were less likely to be slow eaters.

Males had higher average values on all food behavior measures than females, except food fussiness. In other words, men tended to report experiencing every food behavior, both positive and negative, more strongly than females, except for picky eating. Men and women, on average, reported being picky eaters equally often.

Four food consumption patterns
Analysis of data on food item consumption revealed 4 different dietary patterns. Study authors named them healthy (eats legumes, nuts & seeds, whole-grain products, vegetables, and fruit), beverage-rich (a tendency to drink sugar-sweetened beverages and unsweetened milk & plant-based drinks), high energy density (a tendency to eat processed meat, alcohol, cheese and fatty/salty snacks), and protein-rich (a tendency to eat meat, poultry, fish, shellfish, and eggs).

These patterns indicate groups of food items associated in the sense that individuals who consume one food item from the pattern are also more likely to consume other items in that pattern. For example, people who report eating meat are more likely to consume poultry, fish, shellfish, and eggs. Patterns are not different groups of people but tendencies that each individual can have differently (see Figure 5).

Figure 5. Four food consumption dietary patterns

Participants more prone to the healthy dietary pattern tended to be less picky about their food, enjoyed food more, and had a slightly stronger feeling of hunger than those less prone to this pattern. Individuals more often drinking drinks from the beverage-rich pattern tended to report being somewhat fussier about their food and enjoying food a bit less. The protein-rich pattern was very weakly associated with the enjoyment of food. Still, individuals prone to this pattern experienced satiety a bit less easily. Finally, those consuming more foods from the high energy density pattern also experienced satiety less easily. They tended to be more fussy about their food (see Figure 6).

Figure 6. Food preferences and eating habits

Individuals who were prone to overeating as children were more likely to be overweight or obese as adults
Participants who were prone to overeating in childhood were 1.44 times more likely to be overweight or obese as adults than individuals who were not prone to overeating as children. These individuals also tended to experience satiety less easily than adults compared to those who were not overeating as children.

Individuals who were fussy eaters in childhood tended to be slightly more prone to emotional undereating as adults. They were also more prone to be fussy about their food and were slightly less likely to consume the foods constituting the healthy dietary pattern. There was no association between picky eating in childhood and weight status in adulthood.

Women, but not men, who were fussy eaters in childhood, were more prone to emotional overeating and enjoyed the sight and smell of food as adults. In contrast, men who were picky eaters in childhood tended to be enjoying food a bit less than adults (see Figure 7).

Figure 7. Eating habits in childhood and adulthood

Conclusion
Overall, the study reported several associations between eating behaviors and dietary patterns. Individuals more prone to one positive behavior towards food tended to be likelier to show other positive-approaching behaviors (enjoyment of food, responsiveness…). Such individuals were less likely to manifest negative behaviors towards food (be picky or slow eaters, feel satiety easily). Results also showed numerous associations between eating behaviors and dietary patterns, i.e., the food individuals eat.

Most importantly, they showed that individuals prone to overeating in childhood were more likely to be obese or overweight as adults. Additionally, associations, although very weak, were found between eating behaviors in childhood and current eating behaviors over a time gap spanning more than a decade.

These links support the notion that eating behaviors take root in early childhood

These links support the notion that eating behaviors take root in early childhood. Still, the fact that they are generally weak indicates that eating behaviors are very modifiable. Given this, early interventions to establish healthy eating habits in childhood can make it more likely that a person will continue with such habits into adulthood. However, they also indicate that it is never too late to change unhealthy eating behaviors as the repercussions of childhood tendencies on adult eating behaviors appear limited.

The paper “Eating behaviors, dietary patterns and weight status in emerging adulthood and longitudinal associations with eating behaviors in early childhood” was authored by Lise Dubois, Brigitte Bédard, Danick Goulet, Denis Prud’homme, Richard E. Tremblay, and Michel Boivin.

*The study authors do not specify their definition of types of fats in the “high-fat diet” described in this paper (for example, polyunsaturated vs. monounsaturated vs. trans fats).

References

Bhatt, R. R., Todorov, S., Sood, R., Ravichandran, S., Kilpatrick, L. A., Peng, N., Liu, C., Vora, P. P., Jahanshad, N., Gupta, A., & Bhatt, R. R. (2023). Integrated multi-modal brain signatures predict sex-specific obesity status. Brain Communications, 5(2), 1–14. https://doi.org/10.1093/BRAINCOMMS/FCAD098

Changizi, M. A., McGehee, R. M. F., & Hall, W. G. (2002). Evidence that appetitive responses to dehydration and food deprivation are learned. Physiology and Behavior, 75(3), 295–304. https://doi.org/10.1016/S0031-9384(01)00660-6

Dubois, L., Bédard, B., Goulet, D., Prud’homme, D., Tremblay, R. E., & Boivin, M. (2022). Eating behaviors, dietary patterns and weight status in emerging adulthood and longitudinal associations with eating behaviors in early childhood. International Journal of Behavioral Nutrition and Physical Activity, 19(1). https://doi.org/10.1186/s12966-022-01376-z

Hedrih, V. (2023). Are Hunger Cues Learned in Childhood? CNP Articles. https://www.nutritional-psychology.org/are-hunger-cues-learned-in-childhood/

Stevenson, R. J., Bartlett, J., Wright, M., Hughes, A., Hill, B. J., Saluja, S., & Francis, H. M. (2023). The development of interoceptive hunger signals. Developmental Psychobiology, 65(2), 1–11. https://doi.org/10.1002/dev.22374

Are Hunger Cues Learned in Childhood?

Posted on September 16, 2023November 10, 2023 by CNP Staff

A study on a group of Australian students and their caregivers examined whether hunger cues our body uses to create the subjective feeling of hunger, might be something that is learned in childhood. Results showed a substantial association between how students and their primary caregivers experience hunger. This might indicate that how we experience hunger is indeed learned in childhood by caregivers (Stevenson et al., 2023). The study was published in Developmental Psychobiology.

The way we experience hunger is learned in childhood from caregivers

When do we get hungry?
People eat when they feel hungry. The sensation of hunger motivates individuals to seek food and ingest it. For a long time, scientists and the general public believed that we feel hungry when our brain detects that certain nutrients are lacking in the body. Views such as these are called the energy-deficit models of hunger (Stevenson et al., 2023).

However, studies in the past century, particularly those in the last decades, revealed that experiences of hunger need not be a consequence of lacking nutrients. Humans and many animals can experience hunger when they smell or see tasty food, when they feel bored or desire sensory stimulation (McKiernan et al., 2008), or when they are under stress (Levine & Morley, 1981) (see Figure 1).

Figure 1. Energy-deficit model of hunger vs. other reasons for hunger

Hunger can be learned
Studies indicate that individuals (but also animals) can learn to expect food at certain places and at certain times. Human daily rhythms of psychological processes (circadian rhythms) are typically adjusted to having three meals per day. However, our body can also learn to expect a different number of daily meals at different times. This expectation will then trigger hunger at those times without much link to energy needs (Isherwood et al., 2023).

Our body can learn to expect a different number of daily meals and at different times

Two processes of hunger
Some scientists propose that there might be two different processes responsible for hunger. According to this concept, one of these processes is centered around acquiring nutrients the body requires. Lack of specific nutrients triggers specific signals leading to the experience of hunger that motivates the organism to seek and eat foods containing those nutrients. This process is called homeostatic hunger. It describes the traditional view about how hunger develops (see Figure 2).

Figure 2. Homeostatic hunger (Process 1)

The other process is appetite (see Figure 3). It arises from the learned associations between various cues for food and their consequences. For example, I see a package of chocolate. I know from previous experience that if I open it, there will be chocolate inside. I also know that if I eat the chocolate, I will feel its pleasant taste in my mouth. Due to all this, when I see chocolate, I start feeling an appetite for chocolate.

Figure 3. Appetite (Process 2)

Is homeostatic hunger real?
While scientific studies thoroughly explored and documented the processes of appetite, several authors in recent decades expressed skepticism about the workings of homeostatic hunger and its very existence. These authors note that the energy intake of a single meal is practically negligible compared to the body’s total energy reserves.

The body utilizes a complex regulation system that ensures body tissues receive enough nutrients even if a meal or several meals are missed. Hunger regulation mechanisms, on the other hand, often seem to be more about accounting for the limited capacity of the gut and adapting to the physiological challenges and hindrance of other activities that digesting food represents than about the provision of energy (Rogers & Brunstrom, 2016; Stevenson et al., 2023). For example, we will likely not feel hungry while doing intense physical work. The feeling of hunger will come only after we take a break or reduce the activity level. With these and other arguments in mind, these authors claim that the feeling of hunger might not have anything to do with the body’s short-term energy needs (Rogers & Brunstrom, 2016).

The feeling of hunger might not have anything to do with the body’s short-term energy needs at all (Rogers & Brunstrom, 2016)

The current study
Study author Richard J. Stevenson and his colleagues wanted to test the hypothesis that food sensations are learned. A recent study found that rat pups cannot respond adequately to food deprivation until they have encountered food and eaten in that state (Changizi et al., 2002). In other words, rat pups learn that eating food will produce rewarding consequences when they feel the sensations we interpret as hunger.
But does that work similarly in humans? Study authors believe so. They state that parenting might be important for teaching children the meaning of hunger. This likely happens during the weaning period and onward.

Parenting might be important for teaching children the meaning of hunger

The weaning period
The weaning period is when caregivers gradually introduce solid foods into a baby’s diet and reduce its dependency on breast milk or formula as the primary source of nutrition. This transition typically begins when a baby is around six months old, although the timing can vary depending on the baby’s development and the recommendations of healthcare professionals.

During the weaning period, parents and caregivers start offering the baby a variety of soft, mashed, or pureed foods in addition to breast milk or formula. The goal is to expose the baby to different tastes and textures while ensuring it receives the nutrients needed for healthy growth and development. Over time, solid foods gradually replace some of the milk feeds.

Learning to interpret hunger
It is in this period that children likely learn the meaning of hunger. Occasionally, they experience hunger-related feelings (e.g., a tummy rumble). On some occasions, this feeling will be followed by food. On others, it would not be. The child will note that when these sensations are followed by food, the food will taste good, and they will feel good after eating it (see Figure 4).

Figure 4. Weaning and learning to interpret hunger

Once they have learned this, they will likely be resistant to change because of the nature of the learning process. This learning would likely persist into adulthood as a pattern of internal signals linked to eating. These patterns of signals would differ between individuals but are likely more similar to the pattern of one’s primary caregiver (the person they learned it from) than to that of a stranger.

The procedure
To test this, the study authors asked 116 caregiver-offspring pairs to complete a hunger survey. The “offspring” were first-year psychology students, and the “caregiver” was the person who primarily cared for the student when he/she was a child. The study authors excluded five pairs of participants because they failed the check questions in the survey or suffered from an eating disorder. Data from a total of 111 pairs remained for the analysis. The average age of student participants (“the offspring”) was 22 years. It was 52 for the primary caregivers. One hundred and five primary caregivers were mothers, and 6 were fathers.
After they agreed to participate in the study, researchers asked both offspring and their caregivers to complete a 30-minute online survey. They instructed them to do this 30 minutes before eating a main meal. The survey first asked respondents about their current level of hunger and continued with questions focusing on different internal states that participants associated with hunger.
Additionally, participants completed assessments of hunger beliefs (a questionnaire created by study authors) and eating attitudes (the Three-Factor Eating Questionnaire). Before taking the survey, students reported their age, height, weight, gender, and whether they were currently dieting. Students completed the survey before their caregivers (See Figure 5).

Figure 5. Research Procedure

Participants were moderately hungry while they were doing the survey
The median time since the last meal during the survey was 2-4 hours. This was the case both with offspring and their caregivers. Both groups of participants reported feeling a moderate urge to eat at the time of the survey. This urge was a bit more pronounced in offspring (students). Both groups reported that they could eat moderate food at that point. Overall, offspring and their caregivers were similarly hungry when they were doing the survey.

Caregivers and offspring tended to give similar responses to the hunger survey
The study authors found a medium-strength association between the responses of offspring and their caregivers on the food survey. While their responses were far from identical, there was quite a bit of similarity—much more than could be expected based on random chance.

Offspring report experiencing hunger signals more intensely than caregivers
The researchers used statistical procedures to divide hunger survey questions into several groups based on hunger signals participants tended to report similarly. They then calculated the reported intensity of those groups of signals. Results showed that the rankings of intensities of these signals were the same for offspring and caregivers.

However, on average, offspring reported experiencing the same hunger signals more intensely than their caregivers. The most intensely experienced hunger signals were empty stomachs and fatigue. Full stomach and positive anticipation were the least often seen as signaling hunger.

Offspring reported being more prone to uncontrolled and emotional eating and less prone to restrained eating than their caregivers. Additional analysis showed that when offspring and their caregivers believed more in homeostatic hunger (i.e., hunger being an indicator that the body needs energy), when offspring was more prone to uncontrolled eating, and when the caregiver had a greater body mass index, the responses of offspring and their caregiver to the hunger survey tended to be more similar (see Figure 6).

Figure 6. Different parental beliefs about hunger

Conclusion
Overall, the study supported the hypothesis that hunger sensations, what sensations to interpret as hunger, are indeed learned. The fact that responses about the sensations one interprets as hunger were similar between offspring and their caregiver shows that this might indeed be something that individuals learn from their caregivers in childhood and persists without much change into adulthood.

Parent beliefs about the cause of hunger influenced what they teach their children, implying that genetics are not the sole driver of parent-child similarity

These findings may have important implications for preventing eating disorders and obesity. If interpreting sensations from the body as hunger is learned in early childhood along with what to do about those sensations, there might be a link between these learnings and later eating disorders, including the current worldwide obesity pandemic (Wong et al., 2022). It might also turn out that the development of these disorders can be mitigated or even completely prevented by simply changing what the current and future children learn about interpreting hunger sensations and how to deal with them.

The paper “The development of interoceptive hunger signals” was authored by Richard J. Stevenson, Johanna Bartlett, Madeline Wright, Alannah Hughes, Brayson J. Hill, Supreet Saluja, and Heather M. Francis.

References
Changizi, M. A., McGehee, R. M. F., & Hall, W. G. (2002). Evidence that appetitive responses for dehydration and food-deprivation are learned. Physiology and Behavior, 75(3), 295–304. https://doi.org/10.1016/S0031-9384(01)00660-6\

Isherwood, C. M., van der Veen, D. R., Hassanin, H., Skene, D. J., & Johnston, J. D. (2023). Human glucose rhythms and subjective hunger anticipate meal timing. Current Biology, 33(7), 1321-1326.e3. https://doi.org/10.1016/j.cub.2023.02.005

Levine, A. S., & Morley, J. E. (1981). Stress-induced eating in rats. American Journal of Physiology – Regulatory, Integrative and Comparative Physiology, 241(1), R72–R76.

McKiernan, F., Houchins, J. A., & Mattes, R. D. (2008). Relationships between human thirst, hunger, drinking, and feeding. Physiology & Behavior, 94(5), 700. https://doi.org/10.1016/J.PHYSBEH.2008.04.007

Rogers, P. J., & Brunstrom, J. M. (2016). Appetite and energy balancing. Physiology & Behavior, 164, 465–471. https://doi.org/10.1016/J.PHYSBEH.2016.03.038

Stevenson, R. J., Bartlett, J., Wright, M., Hughes, A., Hill, B. J., Saluja, S., & Francis, H. M. (2023). The development of interoceptive hunger signals. Developmental Psychobiology, 65(2), 1–11. https://doi.org/10.1002/dev.22374

Wong, M. C., Mccarthy, C., Fearnbach, N., Yang, S., Shepherd, J., & Heymsfield, S. B. (2022). Emergence of the obesity epidemic: 6-decade visualization with humanoid avatars. The American Journal of Clinical Nutrition, 115(4), 1189–1193. https://doi.org/10.1093/AJCN/NQAC00

How Your Gut Microbiota is Linked to Both Positive and Negative Aspects of Mental Health

Posted on September 2, 2023November 10, 2023 by CNP Staff

Microbiota composition is linked to both positive and negative aspects of mental health

A large-scale study in Belgium and the Netherlands found links between the abundance of certain groups of gut bacteria species and mental health outcomes. Faecalibacterium and Coprococcus bacteria that produce a short-chain fatty acid called butyrate were consistently more abundant in individuals with higher quality of life. In contrast, Dialister, Coprococcus spp, tended to be depleted in individuals with depression. Social functioning tended to be better in individuals with many bacteria capable of producing 3,4-dihydroxyphenylacetic acid in their gut. 3,4-dihydroxyphenylacetic acid is a substance our body produces when processing dopamine, a neurotransmitter associated with experiencing good feelings (Valles-Colomer et al., 2019). The study was published in Nature Microbiology.

Social functioning was better in those with bacteria capable of producing a substance our body produces (3,4-dihydroxyphenylacetic acid) when processing dopamine

Humans have known for centuries that there is a link between how our digestive system works and how we feel. Everyone senses from experience that our mental state also deteriorates when our digestive system doesn’t work well. However, in the past century, medical and biological science has advanced enough to allow scientists to examine the gut microbiota in our digestive system and study the interaction between them and the human body in detail.

A large-scale study found links between the abundance of certain gut bacteria species and mental health outcomes

What is gut microbiota?

The human gut microbiome, often called gut microbiota or gut flora, is a complex community of trillions of microorganisms that reside in the digestive tract, primarily in the colon. These microorganisms include bacteria, viruses, fungi, and other microbes. Gur microbiota is critical in digesting food, absorbing nutrients, and aiding our metabolic activity.

Humans have known for centuries there is a link between our digestive system and how we feel

Gut microbiota helps maintain a balanced and healthy immune system. The composition and diversity of gut microbiota can vary significantly among individuals and can be influenced by factors such as diet, genetics, and lifestyle. It is increasingly recognized as a crucial factor in overall health and well-being (Heiss et al., 2021; Zhu et al., 2023).

Microbiota-gut-brain-axis

A key pathway through which the link between gut microbiota and well-being is achieved is the microbiota-gut-brain axis (MGBA). The microbiota-gut-brain axis is a bidirectional communication system connecting the gut, microbiota, and brain. This axis regulates physiological and psychological processes (Carbia et al., 2023; Zhu et al., 2023).

Gut microbiota can vary among individuals and is recognized as a crucial factor in overall health and well-being (Heiss et al., 2021; Zhu et al., 2023)

The microbiota-gut-brain axis (MGBA) is based on small proteins called cytokines and several other biomolecules, including the hormone cortisol, short-chain fatty acids (SCFAs), tryptophan, neurotransmitters, and others (see Figure 1).

Figure 1. Some of the Biomolecules involved in MGBA

Emerging studies reveal that the gut microbiota produces substances that can influence the brain’s activity and its responses to stress and emotions. Additionally, the microbiota-gut-brain axis is closely tied to the immune system, influencing the body’s inflammatory responses and potentially contributing to neuroinflammation (Zhu et al., 2023).

Gut microbiota produces substances that influence the brain’s activity and its responses to stress and emotions

These scientific findings suggest that interventions targeting the gut microbiota, such as probiotics and dietary changes, may positively impact mental health and neurological disorders. This can open a new avenue of treatment for mental health issues and possibly other disorders.

The current study

Study author Mireia Valles-Colomer and her colleagues wanted to examine the association between gut microbiota composition and quality of life indicators in the general population. They also wanted to examine links between gut microbiota composition and depression (Valles-Colomer et al., 2019).

They note that recent advances in genetic sequencing technology allowed researchers to start studying the role of the gut microbiota in a broad range of neurological and psychiatric disorders and diseases. Advancements in the field of metagenomics are a particularly important part of this as it allows relatively easy and noninvasive exploration of human gut microbiota composition.

Recent advances in genetic sequencing technology allows researchers to study the role of microbiota in neurological and psychiatric disorders

Metagenomics

Metagenomics is a field of genetics and microbiology that involves the study of genetic material collected directly from environmental samples, like soil, water, or the human gut, without the need for isolating and cultivating individual organisms. It employs advanced DNA sequencing techniques to analyze and characterize collective genomes of microorganisms in studied samples and their genetic diversity.

In the case of human gut microbiota studies, researchers typically collect stool samples for this purpose. They then use metagenomics techniques to determine the presence, absence, and abundance of different species of microorganisms in the gut microbiota (see Figure 2).

Figure 2. Metagenomics

The procedure – the Belgian Flemish Gut Flora project data

The authors of this study analyzed data from two large-group longitudinal studies in Europe. The first set came from the Belgian Flemish Gut Flora Project (FGFP). It contained data from 1054 individuals on gut microbiota and depression as reported by general medical practitioners. The quality of life of these study participants was assessed using the RAND 36-Item Health Survey 1.0. This assessment covers eight health concepts – role limitations caused by emotional health problems, social functioning, emotional well-being, vitality, physical functioning, role limitations caused by physical health, body pain, and general health perception. Participants who were using antidepressants but were not diagnosed with depression were excluded from the analysis.

From this group, study authors selected 80 participants with clinically diagnosed depression (40 were using antidepressants) and 70 healthy participants as controls, matched with them on age, sex, body mass index, and stool consistency for in-depth analysis using shotgun metagenomic sequencing. Shotgun metagenomic sequencing is a method that involves sequencing all the genetic material present in a microbial community sample, providing a comprehensive view of the genes and organisms within that community.

The Lifelines Cohort data and controls

Researchers used another sample to verify their findings – the Lifelines Cohort. The Lifelines Cohort is a large-scale, three-generation longitudinal study in the Netherlands. It contains a large amount of medical and psychological data from over 167,000 participants so far. The Lifelines cohort study was started in 2006 and aimed to include 10% of the northern population of the Netherlands of all ages. The authors of the Lifelines Cohort study hope to be able to follow these individuals for 30 years and collect follow-up data during this time.

In this study, the authors used data from 1063 individuals from the Lifeline Cohort. The quality of life of this group was assessed in the same way as in the Belgian sample. Participants self-reported depression. Researchers asked participants to indicate the disorders they have or have had, and depression was on the list. Participants also reported on their use of antidepressants in the last three months.

The study authors used gas chromatography-mass spectrometry (GC-MS) to determine butyrate levels in stool samples from this dataset. Butyrate is a short-chain fatty acid produced by certain species of bacteria in the gut during the fermentation of dietary fiber (see Figure 3). It is an important energy source for the cells lining the colon and helps maintain their integrity and function. Additionally, it has anti-inflammatory properties and has been associated with various health benefits. Butyrate potentially reduces the risk of inflammatory bowel diseases and promotes overall gut health.

Figure 3. The Short-Chain Fatty Acid Butyrate

Additionally, study authors collected and sequenced seven stool samples from patients suffering from major depressive disorder resistant to treatment. Participants in this sample were diagnosed with moderate-to-severe depression and inadequate response to at least two therapies with antidepressants. Inadequate response means that symptoms of depression persist after treatment.

Gut microbiota composition was related to quality of life

Results revealed multiple associations between microbiome characteristics and all aspects of quality of life (see Figure 4). Study participants with better quality of life indicators tended to have more Faecalibacterium and Coprococcus bacteria in their guts. Those with better physical functioning tended to have fewer bacteria from the Flavonifractor group of species (genus). This group of bacterial species was also increased in individuals suffering from major depressive disorder (MDD).

Figure 4. Associations between microbiome characteristics and all aspects of quality of life (as outlined earlier)

Study authors note that Faecalibacterium and Coprococcus bacteria produce the short-chain fatty acid butyrate. Butyrate levels in the gut are generally reduced in individuals with inflammatory bowel disease and those with depression. They examined the Lifelines cohort data to verify this finding, and the results showed that the abundance of these bacteria is indeed associated with butyrate concentrations in the stool.

Butyrate levels in the gut are reduced in those with inflammatory bowel disease and depression

Coprococcus and Dialister bacteria are depleted in the guts of individuals suffering from depression

Study authors identified 4 groups of bacterial species that were consistently depleted in individuals suffering from depression (depleted in this case, means that they are present in numbers significantly lower than those found in typical healthy individuals).

However, further analyses revealed that antidepressants can substantially affect the composition of gut bacteria. When study authors controlled for the use of antidepressants, only Coprococcus and Dialister groups of species remained associated with depression. There were significantly fewer bacteria from these groups in the guts of individuals suffering from depression than healthy individuals (see Figure 5). This finding was held in the Flemish Gut Flora and the Lifeline Cohort data.

Figure 5. Microorganism abundances linked to Quality of Life and Depression

Bacteria producing 3,4-dihydroxyphenylacetic acid are more abundant in individuals with better social functioning

Next, the study authors examined the gut-brain modules, i.e., they looked for groups of bacteria that produce substances that could affect mental states and their links to quality-of-life indicators. These analyses showed that bacteria producing 3,4-dihydroxyphenylacetic acid (DOPAC) were more abundant in participants with better social functioning scores.

The potential for producing this substance was the most strongly associated with Coprococcus group of bacteria. DOPAC is produced from dopamine, an important neurotransmitter, and researchers believe it can reduce the proliferation of colon cancer cells. Reduced DOPAC levels are a potential biomarker of Parkinson’s disease (Valles-Colomer et al., 2019).

Bacteria involved in the degradation of glutamate and production of GABA tended to be depleted in participants with depression

Additionally, bacteria involved in glutamate degradation tended to be depleted in participants with depression. Glutamate is an amino acid that plays a role in various metabolic and signaling pathways in the body. However, it is also the primary excitatory neurotransmitter in the central nervous system. This means that it increases the likelihood of neurons generating a nerve impulse.

Microorganisms involved in synthesizing gamma-aminobutyric acid (GABA) also tended to be depleted in participants with depression. GABA is an important inhibitory neurotransmitter. It makes neurons less likely to fire a nerve impulse (see Figure 6).

Figure 6. Link of microbial substance to the quality of life and depression

Conclusion

Overall, the analysis of two large sets of gut microbiome samples from two different (although neighboring) countries confirmed specific links between gut microbiota composition and mental health indicators. Individuals with better quality of life indicators tended to have more Faecalibacterium and Coprococcus bacteria in their gut. Those with better physical functioning tended to have fewer bacteria from the Flavonifractor species group. Bacteria from Coprococcus and Dialister groups of species tended to be much less present in the guts of individuals suffering from depression.

Bacteria capable of producing 3,4-dihydroxyphenylacetic acid or DOPAC were more abundant in participants with better social functioning scores. DOPAC is produced from dopamine, an important neurotransmitter in the human body, and it plays various important roles in the body’s functioning. Bacteria involved in the degradation of glutamate and the production of GABA, two important neurotransmitters, tended to be depleted in individuals with depression (see Figure 7).

Figure 7. Summary

While these findings are only correlational and do not allow for cause-and-effect conclusions, future research can be expected to map causal pathways responsible for the observed associations. This could open a new avenue of mental health treatments to achieve improved mental outcomes by affecting the gut or adjusting gut microbiota composition. It is also not hard to imagine scientists in the future using genetic techniques to create microorganisms that could influence mental health or mental states when placed in the gut.

The paper “The neuroactive potential of the human gut microbiota in quality of life and depression” was authored by Mireia Valles-Colomer, Gwen Falony, Youssef Darzi, Ettje F. Tigchelaar, Jun Wang , Raul Y. Tito, Carmen Schiweck, Alexander Kurilshikov , Marie Joossens, Cisca Wijmenga, Stephan Claes, Lukas Van Oudenhove, Alexandra Zhernakova, Sara Vieira-Silva , and Jeroen Raes.

To learn more about this topic,, CNP has developed two university-level continuing education courses exploring the evidence based interconnections in the microbiota-gut-brain axis diet-mental health relationship (MGBA-DMHR). See our course pages here.

References

Heiss, C. N., Mannerås-Holm, L., Lee, Y. S., Serrano-Lobo, J., Håkansson Gladh, A., Seeley, R. J., Drucker, D. J., Bäckhed, F., & Olofsson, L. E. (2021). The gut microbiota regulates hypothalamic inflammation and leptin sensitivity in Western diet-fed mice via a GLP-1R-dependent mechanism. Cell Reports, 35(8). https://doi.org/10.1016/j.celrep.2021.109163

Restrained Eating Leads to Hunger and Food Craving

Posted on August 15, 2023January 24, 2025 by CNP Staff

Study Overview

A study in Israel followed changes in food craving, restrained eating, hunger, and negative emotions within a representative sample of the population over a span of 10 days. The findings revealed that restrained eating is the central link between food-related sensations and negative emotions. The participants who engaged in restrained eating exhibited heightened food cravings and increased hunger at later times. Notably, stress emerged as a pivotal factor in the correlation between eating behaviors and negative emotional states, as outlined by Dicker-Oren and colleagues (2022). The study was published in Appetite.

Introduction

Food is one of the most fundamental needs of all living organisms. In the classical hierarchy of human needs proposed in the mid-20th century by Abraham Maslov, food is one of the primary needs that must be satisfied if other, higher-level needs are to be activated (Lester, 2013). This is why humans (and most other organisms) have evolved several psychological and behavioral mechanisms to ensure that securing enough food is given top priority.

Humans have evolved psychological and behavioral mechanisms to ensure that securing enough food is given top priority

These mechanisms induce changes to perception, attention, emotions, and various physiological parameters when we are hungry (Swami et al., 2022) (see Figure 1).

Figure 1. Psychological and behavioral mechanisms activated in hunger (Swami et al., 2022)

These mechanisms aim to focus us on food and prepare us for food intake, allowing us to survive and flourish for millennia of human existence. However, around the mid-20th century, a crucial change in the human dietary landscape happened – industrially processed foods became widely available and cheap. While the widespread availability of high-calorie and addictive processed foods has addressed food insecurity in many parts of the world, it has also given rise to a new challenge — obesity.

The obesity pandemic

Obesity is a medical condition characterized by excessive body fat accumulation, negatively affecting host health and well-being. The share of obese individuals in the population has been on the rise in recent decades in most world countries (Wong et al., 2022), reaching levels where many are talking about a global obesity pandemic.

While it is fairly obvious that excessive intake of food is a necessary component in the development of obesity, this excessive intake of food is caused by a combination of various biological, psychological, and physiological factors (Ulrich-Lai et al., 2015). Among these, emotions and food-related sensations are thought to be important.

While it is fairly obvious that excessive intake of food is a necessary component in the development of obesity, this excessive intake of food is caused by a combination of various biological, psychological, and physiological factors

Food craving and hunger

Food craving is a strong desire to consume specific foods, such as chocolate or potato chips (van Kleef et al., 2013). It differs from hunger by its specificity and intensity. People can crave food even when they are not hungry (Reichenberger et al., 2018). On the other hand, the sensation of hunger makes one motivated to eat, but not necessarily a specific type of food.

Food craving is a strong desire to consume specific foods or food types. It differs from hunger by its specificity and intensity.

Food craving may be a response to physiological deficiencies or disturbances in the body, but they may also be initiated through psychological mechanisms without deficiencies of nutrients (Weingarten & Elston, 1990). Food cravings are considered to be one of the main factors of overeating.

Food craving may be a response to physiological deficiencies or disturbances in the body, but they may also be initiated through psychological mechanisms without deficiencies in nutrients

When individuals notice they are gaining excessive weight or if they are worried they may be eating too much, many will try to limit their food intake consciously. Rather than rely on the sensation of satiety to signal that they have eaten enough, or if the over-consumption of hyperpalatable foods distorts these cues, these individuals may consciously decide to limit how much food they will eat. The amount will often be such that the person still feels hungry after eating it.

This type of behavior is referred to in science as restrained eating. People who engage in restrained eating often set rules or restrictions on what and how much they can eat, and they may closely monitor their calorie intake. This is usually done in an effort to achieve a specific body image or weight goal. However, restrained eating can sometimes lead to disordered eating patterns and psychological distress (Dicker-Oren et al., 2022).

The current study

Study author S.D. Dicker-Oren and colleagues aimed to examine the dynamic associations between food craving, restrained eating, hunger, and negative emotions. They wanted to know how these factors vary in the same individual over time. These researchers were particularly interested in determining whether negative emotions associated with food craving, restrained eating, and hunger differed (see Figure 2).

Figure 2. The study objective examined associations between food craving, restrained eating, hunger, and negative emotions.

They applied an analytic technique called network analysis to map the network of links between these factors and identify those that are central that can best predict others. The study authors also wanted to know which factors were most strongly associated with food cravings simultaneously and in subsequent assessments. They conducted a study using ecological momentary assessment.

Ecological momentary assessment

Ecological Momentary Assessment (EMA) is a research method to gather real-time data about individuals’ behaviors, thoughts, and emotions in their natural environments. It involves repeatedly sampling participants’ experiences over time, often through mobile devices like smartphones, to capture their moment-to-moment fluctuations and responses to various stimuli. Ecological momentary assessment provides valuable insights into how individuals’ psychological states and behaviors vary daily.

In this study, researchers wanted to observe whether food craving, hunger, restrained eating, and related emotions change within the same person. This procedure allowed them to observe, for example, what other sensations or emotions appear when a person experiences a craving for a certain type of food or whether participants tend to experience some specific emotions at the same time when they experience hunger or when they intentionally try to reduce their food intake, even though they still feel hungry.

Researchers wanted to observe whether food craving, hunger, restrained eating, and related emotions change within the same person

The procedure

Participants were 123 individuals from the general population of Israel. The study authors recruited them using social media posts and advertisements. To be included in the study, participants needed to be at least 18 years of age, with no severe psychiatric illness, not be underweight, have no eating disorders, and not have undergone obesity-related surgery. Female participants could not be pregnant or breastfeeding at the time of the study.

In the scope of the ecological momentary assessment procedure, participants answered questionnaires in the form of online surveys on the Qualtrics platform three times per day – in the morning, afternoon, and evening, over the course of 10 days. For each survey, the data collection software sent them a personal email message with the link to the survey and a WhatsApp notification on their smartphone. If a respondent missed four surveys, researchers would call him/her by phone to encourage him/her to continue participating in the study.

Participants answered questionnaires in the form of online surveys three times per day – in the morning, afternoon, and evening, over the course of 10 days

The surveys

At the start of the study, participants completed the baseline questionnaire. This questionnaire contained questions about various demographic characteristics of participants and about their weight and height. The study authors combined weight and height data to calculate body mass index values for each participant.

The questionnaires participants completed in the scope of the ecological momentary assessment asked them to indicate the extent to which they experienced each of the sensations and emotions from a list “since you woke up,” in the morning version of the questionnaire, or “over the last six hours” in the afternoon and evening versions. The questions were about experiencing seven different negative emotions – guilt, sadness, fear/ being afraid, stress, loneliness, boredom, and anger. It also asked about experiences of nervousness and anxiety and about restrained eating (“Did you deliberately try to limit the amount of food you eat?”) (see Figure 3). Additionally, participants completed assessments of food craving and hunger (“intense desire to eat” and “hunger” subscales from the Food Cravings Questionnaire-state, FCQ-S) in the scope of these surveys.

Figure 3. Sensations and emotions and restrained eating, assessments of food craving and hunger

Observed factors showed a degree of stability over time

Most of the factors measured tended to show certain stability over time. In later surveys, people who reported higher levels of negative emotions, hunger, craving, or restrained eating at one time point were also more likely to report these emotions and sensations.

Participants who reported consciously limiting their food intake were more likely to be hungry and crave food later

Individuals who reported restrained eating and hunger earlier were more likely to report food craving at the subsequent time. Individuals who reported restrained eating at one time were likelier to report being hungry later. Participants who reported sadness were likely to report loneliness and anger later. They were also more likely to report feeling stressed, afraid, and angry at later time points. When participants reported feeling stressed at one time, they were more likely to report craving food the next time.

When participants reported feeling stressed at one time, they were more likely to report craving food the next time

Certain participants experienced a frequent interplay of restrained eating, hunger, and food craving

Across all time points/surveys, participants who more often reported food craving were also more likely to report experiencing hunger and practicing restrained eating. Stress was the central emotion – participants who more often reported feeling stressed were more likely to report experiencing all other negative emotions as well. However, researchers found no association between mean levels of hunger, food craving, or restrained eating and average levels of negative emotions.

Individuals practiced restrained eating less when they were angry. They tended to crave food more when they were feeling bored.

Conclusion

The study revealed one piece of insight into the complex interplay between food-related sensations, negative emotions, and restrained eating. Restrained eating seemed to drive hunger and food craving. Individuals who exerted effort to consciously limit their food intake also more often experienced hunger and food craving. These two experiences often followed restrained eating. Hunger appeared to trigger food craving, but food craving did not seem to predict any overeating-related variables at later times (see Figure 4).

Figure 4. Study findings

The study results have important implications for beginning to understand some of the psychological dynamics of eating disorders and for planning interventions. The finding that restrained eating, which in itself represents an attempt by an individual to prevent excessive food intake (and perhaps the upregulation in brain and gut reward-based systems in the body), can, in this instance, actually have a reverse effect in the long run (by increased hunger and food craving) indicating that interventions aiming to reduce restrained eating might paradoxically have a positive effect on regulating food intake. The finding that stress can drive food craving indicates that it might also be useful for eating disorder interventions to address stress.

The paper “The dynamic network associations of food craving, restrained eating, hunger and negative emotions” was authored by S.D. Dicker-Oren, M. Gelkopf, and T. Greene.

More about dietary intake behavior, hedonic eating, and reward and gut-based mechanisms can be found in NP 110: Introduction to Nutritional Psychology Methods, and NP 120 Part I: Microbes in our Gut: An Evolutionary Journey into the World of the Microbiota Gut-Brain Axis and the DMHR and NP 120 Part II: Gut-Brain Diet-Mental Health Connection: Exploring the Role of Microbiota from Neurodevelopment to Neurodegeneration.

References

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Dicker-Oren, S. D., Gelkopf, M., & Greene, T. (2022). The dynamic network associations of food craving, restrained eating, hunger and negative emotions. Appetite, 175(March), 106019. https://doi.org/10.1016/j.appet.2022.106019

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Humans have evolved psychological and behavioral mechanisms to ensure that securing enough food is given top priority

While it is fairly obvious that excessive intake of food is a necessary component in the development of obesity, this excessive intake of food is caused by a combination of various biological, psychological, and physiological factors

Food craving is a strong desire to consume specific foods or food types. It differs from hunger by its specificity and intensity.

Food craving may be a response to physiological deficiencies or disturbances in the body, but they may also be initiated through psychological mechanisms without deficiencies in nutrients

Researchers wanted to observe whether food craving, hunger, restrained eating, and related emotions change within the same person

Participants answered questionnaires in the form of online surveys three times per day – in the morning, afternoon, and evening, over the course of 10 days

Participants who reported consciously limiting their food intake were more likely to be hungry and crave food later

When participants reported feeling stressed at one time, they were more likely to report craving food the next time

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