Satiation And Satiety: An Overview And Clinical Calculator
Satiation and satiety are often confused and used interchangeably, though both are physiologically and behaviorally different sensations. Satiation describes the series of processes that cause one to stop eating. Governed by hormones and gastric stretch receptors, satiation occurs during an eating episode and is frequently associated with meal size (g or kcal). Satiety is described as a state of not eating, characterized by the physical feeling of fullness. Ideally, satiety is associated with measures of the inter-meal period (kcal).
This article summarizes the most commonly used as well as novel methods for quantifying satiation and satiety, and will introduce the benefits of satiety-specific clinical calculators.
Access our Basal Metabolic Rate Weight Loss Multi-Calculator HERE to quickly determine patient-specific weight and nutrition parameters.
Ad libitum meal is used to measure satiation, wherein total energy or the amount of food consumed to fullness is recorded and compared to control food. Ad libitum food intake is also used to capture food choice, eating patterns during the meal, temporal changes in appetite feelings, and the reasons for voluntary cessation of eating.
Satiety or between-meals Satiety captures the intensity and duration of the feeling of fullness between meals and the timing and extent of calorie intake as primary outcomes.
The measurement of satiety can be achieved by i) tracking changes in hunger, fullness, and desire to eat over time or ii) measuring the duration between the treatment and the next meal. Studies on satiety are complicated by socio-environmental factors, food palatability, and cognitive pressures. Since these factors jointly affect an individual’s eating behavior, multiple methods are used to study satiety.
Free-living vs. Laboratory studies
Human eating behavior is complex and multifaceted. Therefore, in appetite research, authors are likely to make compromises about the requirements for internal and external validity. Controlled laboratory studies, in general, offer a high level of sensitivity and control over external factors and outcome measures. Hence, they have high internal validity. On the other hand, while free-living studies have a theoretically high level of external validity, their internal validity is limited by several methodological issues. Data collection errors or biases are high, particularly when habitual dietary intake assessment under free-living conditions uses self-reported data. Furthermore, free-living studies are not subject to the same rigorous control as laboratory studies, which makes it difficult to interpret the effects of dietary components and the environment and yield meaningful results.
Though laboratory studies cannot replace studies in free-living circumstances, they can provide vital data to complement them. Therefore, there is a lot of scope for developing overlapping protocols that help circumvent the problems inherent in both approaches and bridge the gap between them.
The preload meal paradigm is one of the most influential methods used to study the short-term regulation of food intake and appetite. This study design uses preloads that vary along one dimension (e.g. ingredients, nutrients, energy content, weight, or volume) given on different occasions. A test meal is given to the subject ad libitum following the preload, and energy intake is measured after a variable time delay. In many of the preload studies, the subjects self-report their food intake for the remainder of the day. Several physiological mechanisms contribute to satiety during the sensory, cognitive, post-ingestive, and post-absorptive phases of the satiety cascade. Therefore, the time interval between the preload and the test meal is crucial to the study outcomes.
One of the major limitations of preload studies is that they are designed to minimize learning about the post-ingestive effects of eating. Another problem with these studies is that they are prone to type 2 errors, and evidence of the sensitivity of the preload study design should always be provided. Several extensions and adaptations to the preload studies have been made. Some studies have even manipulated the composition of both preload and test meals such that the effects of both on subsequent food intake can be measured.
Numerous self-reported scales are used to address feelings of hunger, fullness, satiety, somatic sensations, prospective consumption, etc. In general, these measures are completed before and after consumption of the test meal, and then at regular time intervals, usually for 4-5 hours, or until the start of the next meal. A good example is the Visual Analogue Scale (VAS), a 100-or 150-mm line with the extremes of a question being asked. For example, VAS for hunger may be labeled with “Not hungry at all” or “Never been more hungry”. Subjects are asked to mark across the line corresponding to their hunger at that time. Though the VAS line scale is cheap, easy to use, and simple to interpret, there are mixed conclusions as to the ability of the VAS to predict food intake. Rolls et al. and Kissileff reported that line scales do not accurately predict satiety or provide a sufficient means for food differentiation. A meta-analysis by Holt et al. concluded that self-reported appetite ratings do not predict energy intake accurately and emphasized the need for caution in interpreting later food intake from appetite ratings alone.
Feelings of hunger, fullness, or satiety can be measured with a 9-point category scale. Similar to VAS, category scales may go from the absence of a factor to the extremes of it. For example, if participants are asked about their hunger at a particular time, they can mark ‘1’ on a category scale to indicate ‘Not hungry at all’ or ‘9’ for ‘Extremely hungry’. As with VAS, category scales have interpretation issues. Furthermore, the distance between units 1 and 2 on a VAS line or category scale may not necessarily be perpetually equivalent to the distance between units 3 and 4.
Satiety-labeled intensity magnitude (SLIM) scale
Cardello et al. (2005) proposed the SLIM scale, a 100-mm bidirectional hunger-fullness scale to assess the satiety level reported by individuals following a meal. The SLIM offers good accuracy and enables better discrimination in comparison with line or VAS. As with other bipolar scales, SLIM does not allow for times when an individual could feel both slightly hungry and/or slightly full at the same time.
Measuring food intake
Measuring food intake is probably the most difficult aspect of nutritional assessment since even the same individual eats different foods prepared with different methods, at different places and times. Mattes et al. reported that psychosocial or environmental factors can cause a loss of appetite in some people. Furthermore, eating in the absence of hunger, consuming palatable foods when satiated, or stress eating complicates the accuracy of food intake data.
Given the net effect of these sources of variability, and the dubious accuracy, most satiety studies are done under laboratory conditions. The rate of eating either solids or liquids can be measured using a universal eating monitor (UEM).
Several procedures have been proposed to calculate the potency of foods to induce satiety. These include:
It uses a preloading method with many preloads given on different occasions, followed by an ad libitum test meal. The negative slope of the intake-preload equation represents satiating efficiency. The satiating efficiency provides a means to compare the ability of different foods to induce satiety, along any dimension (e.g. food composition, nutrition levels, energy, weight, or volume).
Satiety index (SI)
The satiety index (SI), designed by Holt et al. (1995), involves giving specific food to be measured as a preload, then obtaining satiety ratings every 15 minutes over the next 2 hours. After that, the subjects are free to eat ad libitum from a standard range of foods and drinks. The SI score calculated reflects the total amount of fullness produced by the test foods over two hours, i.e., short-term satiety.
Satiety quotient (SQ): Green et al. (1997) developed a satiety quotient (SQ) to assess the satiating effect of food or an eating episode. It is calculated by dividing the change in subjective appetite sensations in response to a meal by the weight or energy content of the food.
Confounders in satiety research
Different individuals have different responses to dietary manipulation, and these responses vary according to physiological and behavioral confounders.
Bodyweight: The measurement of an appetite response or energy test may vary according to the body weight or overall BMI of an individual. A study published in the International Journal of Obesity reported blunt responses to dietary manipulations in obese subjects as compared with lean ones. Barkeling et al. showed that obese females show similar hunger and fullness profiles in response to fixed-size meals when compared with lean women. There is an ongoing debate about the merit of using preloads or test meals as a function of body weight.
Gender and Age: Males, both adults and adolescents, experience hunger in a physical manner, while females experience it in a more diffuse and cerebral manner, says the literature. In extreme hunger, both adult men and women reported more intense sensations than adolescents. In Monello & Mayer’s research, adolescents described satiety as a gastric sensation, and they continued to feel mild hunger for longer after a meal than adults. Leahy et al. found that children adjusted for caloric density by eating lower energy-dense foods, while adults preferred a consistent volume of food with variable energy density.
Diet, alcohol, caffeine, and physical activity may be involved in boredom or satiation. The Three Factor Eating Questionnaire (TFEQ) or the Dutch Eating Behavior Questionnaire (DEBQ) is often used as a screening tool in appetite research. There is evidence that eating restraint levels can influence the outcomes of appetite studies. Lluch et al. reported that individuals stratified by their restraint scores showed different hunger responses when subjected to an exercise intervention.
The physiological state of the individuals, especially energy balance and physical activity, are potentially important confounders in satiety studies.
It has recently been demonstrated that allelic variation and individual differences can generate a wide range of responses.
In recent years, there has been an increased focus on the satiety index of foods and developing clinical calculators to establish enhanced satiety as a benefit to the ever increasing health-conscious market. And accurately measuring satiety and satiation is critical to understanding eating behavior, energy selection and intake.
Oncology Related Tools
- Prognostic Scoring for Myelofibrosis
- Opioid Conversion Calculator
- Updated Advanced Opioid Conversion Calculator
- Nonsteroidal anti-inflammatory drugs (NSAID) Selection Tool
- Absolute Neutrophil Count Calculator
- Body Surface Area (BSA) Multi-Calc
- Carboplatin AUC Calculator
- Carboplatin AUC – Updated Version
- Urinary Indices, Renal Failure Index (RFI) and Fractional Excretion of Sodium (FE-NA)
- Creatinine Clearance (CRCL) – Standard Calculator
- Creatinine Clearance Multi-Calc – All of the latest research
- Patient Controlled Analgesia (PCA) Settings
- Intravenous Antineoplastic Agents – Administration Guidelines
- Therapeutic Drug Levels
- Beers Criteria for potentially inappropriate medications
- Allergic response? 12-step desensitization protocol
- Protein requirements calculator
- Basal Metabolic Rate (BMR) Multi-calc (Estimate caloric requirements)
- Irritable Bowel Syndrome Treatment Options
- Common Anti-emetics
- Fall Assessment – Berg Balance Scale
- Cholesterol Screening To Aid In Glaucoma Detection
- Hearing Screening After Chemotherapy: A Study On Childhood Cancer Survivors
- Olfactory Dysfunction and Screening For Depression: A QOL Study
- Cancer Diagnosis And Mental Health
- Mental Health Screening In Psoriatic Arthritis Patients
- Prostate Cancer: A New Biopsy Risk Calculator Using MRI
- Mental Health Study in Cancer Survivors
- Mental Health Screening In The Community
- Bone Mineral Density Screening Combined With Mammography
- Bone Mineral Density In Type 1 Diabetes Mellitus
- Cochlear Implants and Vestibular Screening
- Aprocitentan In Resistant Hypertension
- Predicting Cardiovascular Disease With Body Mass Index
- Obesity Screening To Predict Hot Flashes
- Hypertension Screening For Cardiovascular Health
- Dental Screening For Cardiovascular Disease Risk
- Blood Pressure and CVD Risk Reduction
- Lifestyle Changes For Hypoglycemia Prevention
- Ovarian Adenocarcinoma With Glaucoma: A Case Report
- Community Hypertension and Atherosclerosis Risk
- Thyroid Malignancy and Serum Calcitonin
- Rare Schwannoma In Lateral Nasal Wall
- Pyrotinib Therapy In HER2+ Breast Cancer
- Osteopenia Predicts Outcomes in Pancreatic Cancer
- Outcomes of Physical Exercise Regimens in Advanced Cancer
- Penile Squamous Cell Carcinoma And HPV
- Radiation Therapy And VTE Risk
- Pseudouveitis With Pancreatic Carcinoma: A Case Study
- Cancer Prevention In Rural Communities
- Skeletal Muscle Mass and Cancer Patient Quality of Life: A Meta-Analysis
- Incidence of Secondary Cancers After CIRT VS RT
- Filanesib Combination Therapy in Multiple Myeloma
- Pediatric Leukemia Patients Utilizing Levofloxacin
- Breast Cancer And An Analysis Of Cardiovascular Events
- Monotherapy Or Chemotherapy Adjunct: Pembrolizumab in Advanced NSCLC
- Advanced Gastric Cancer: Prognosis with Nivolumab Monotherapy
- Sinonasal B‐Cell Lymphomas A Cohort Study On Progression And Recurrence
- Platinum Resistant Recurrent Ovarian Cancer Treatment+/-Bevacizumab
- Metastatic Melanoma and Follow-Up MRI Scans
- Isatuximab Treatment in Refractory T-Acute Lymphoblastic Leukemia
- Ocular Melanoma and Treatment with Metformin
- Gastric Neuroendocrine Neoplasms
- Lung Cancer with Brain Metastasis After Late-Onset Bipolar Disorder: A Case Report
- Anlotinib with Camrelizumab in Lung Cancer Treatment
- Sebaceous Carcinoma Treatment Outcomes: A Multicenter Study
- Diffuse-Type Tenosynovial Giant Cell Tumors: Treatment and Progression
- Lung Spindle Cell Carcinoma Responsive to Pembrolizumab: A Rare Case Report
- DNA Methylation Profiling in Sarcoma Classification
- Breast Tomosynthesis Simulator For Virtual Clinical Trials
- Renal Cell Carcinoma-Prognosis Via Albumin Levels
- Diagnostic Error Causing Cases of Cytopenia
- Hodgkin’s Lymphoma: A Case Study With Nystagmus and Diplopia
- Brugada Syndrome Treated with Lenalidomide: A Case Study
- Koolen-de Vries Case Study
- Suicidal Ideation and Somatic Treatments
- Study on Pavlovian Fear Conditioning and Fear Reversal in OCD
- Anxiety Scales in Lewy Body Disease
- Inoperable Locally Advanced Non-Small Cell Lung Cancer: Survival Rates of Endostar, CCRT
- Physician Practice Management and Private Equity
- Physician Spending And Its Association With Patient Outcomes
- Physician Burnout: Causes and Prevention
- LEAP-MS: Adaptations for Advanced Stages
- MS: Exercise Impacts on MRI
- The Role of Preretirement Job Complexity in Cognitive Performance
- Extrapontine Myelinolysis and PTA in Pregnancy
- Verbal Communication and Masks
- Sugammadex Versus Neostigmine in Thyroidectomy
- SGLT Inhibitors on Weight and Lipid Metabolism in Diabetes
- Saxagliptin: Obese Patients with Impaired Glucose Tolerance
- Levothyroxine Therapy and Depression
- Grave’s Disease and Risk of Systemic Lupus Erythematosus
- Benign Thyroid Removal and Patient Satisfaction
- MF- Biology, Management, and a Case Study of Ocular Manifestation
- Quality Of Life In Adolescent Cancer Survivors
- Cancer Opioid Risk Score
- Oncology-Specific Opioid Risk Calculator In Cancer Survivors
- 3D MRI for Non-invasive Ocular Proton Therapy of Uveal Melanomas
- Sexual Dysfunction in Prostate Cancer Patients
- 3-Day Surprise Question To Predict Survival Rates in Advanced Cancer Patients