The association of food cravings and preferences with food intake

An ubiquitous assumption about food intake behavior is that people eat the types of foods that they crave and prefer. Food preferences reflect hedonic ratings of the degree to which people like certain foods. The present study investigated the association of food cravings and hedonic ratings with food intake behavior, assessed in the laboratory with a Universal Eating Monitor (Kissileff, Klingsberg, & Van Itallie, 1980). The study sample consisted of 162 adults who completed the Food Craving Inventory (FCI; White, Whisenhunt, Williamson, Greenway, & Netemeyer, 2001), a questionnaire that measures craving, including cravings for specific types of foods (i.e., High Fats, Sweets, Carbohydrates/Starches, and Fast Food Fats). Also, participants completed the Food Preference Questionnaire (FPQ; Geiselman et al., 1998), which assesses preference for fat and provides hedonic ratings of foods that vary in fat and carbohydrate content. Finally, participants completed the Three Factor Eating Questionnaire (TFEQ; Stunkard & Messick,1985), which assesses Dietary Restraint, Disinhibition and Perceived Hunger. The subscales of the FCI and FPQ were correlated with intake of the test food (i.e., cheesecake) during a test lunch conducted in a laboratory. The results revealed that the correlations of food cravings and hedonic ratings with food intake were relatively small, indicating that people do not eat large quantities of the types of foods that they crave and rate as liking. To explore the construct of food cravings, the subscales of the FCI, FPQ, and TFEQ were correlated. The pattern of correlations provided support for the concurrent and discriminant validity of the FCI, and the data indicated that the construct of food craving is very similar to hunger. The present study investigated other aspects of food intake behavior, including cumulative food intake curves. Cumulative food intake curves represent food intake as a function of time and are categorized as either decelerated (i.e., eating rate decreases during the meal) or linear (i.e., eating rate remains steady during the meal). The present study failed to replicate the association of Dietary Restraint with linear cumulative food intake curves. Furthermore, a relation between Disinhibition and body mass with decelerated curves was detected

Ontology Design Rules Based On Comparability Via Particular Relations

International audienc

New fat free mass - fat mass model for use in physiological energy balance equations

<p>Abstract</p> <p>Background</p> <p>The Forbes equation relating fat-free mass (<it>FFM</it>) to fat mass (<it>FM</it>) has been used to predict longitudinal changes in <it>FFM </it>during weight change but has important limitations when paired with a one dimensional energy balance differential equation. Direct use of the Forbes model within a one dimensional energy balance differential equation requires calibration of a translate parameter for the specific population under study. Comparison of translates to a representative sample of the US population indicate that this parameter is a reflection of age, height, race and gender effects.</p> <p>Results</p> <p>We developed a class of fourth order polynomial equations relating <it>FFM </it>to <it>FM </it>that consider age, height, race and gender as covariates eliminating the need to calibrate a parameter to baseline subject data while providing meaningful individual estimates of <it>FFM</it>. Moreover, the intercepts of these polynomial equations are nonnegative and are consistent with observations of very low <it>FM </it>measured during a severe Somali famine. The models preserve the predictive power of the Forbes model for changes in body composition when compared to results from several longitudinal weight change studies.</p> <p>Conclusions</p> <p>The newly developed <it>FFM</it>-<it>FM </it>models provide new opportunities to compare individuals undergoing weight change to subjects in energy balance, analyze body composition for individual parameters, and predict body composition during weight change when pairing with energy balance differential equations.</p

A novel method to assess dietary intake and obtain real-time dietary adherence data: a pilot study

Objectives: We developed the PortionSizeTM app (PS) to estimate food intake and dietary adherence based on images of meals captured before and after eating occurs. The PS app provides real-time feedback to users about their dietary intake (energy, nutrients, and food groups) and adherence to specific diets. This pilot study provided initial tests of the validity of the PS app at assessing energy and nutrient intake from simulated meals in a laboratory setting. We also explored participants’ satisfaction with the PS app. Methods: Fifteen adult participants (aged 18–65 years) were trained to use the app. Participants then used the app to estimate food intake during simulated meals, where participants were provided with a plate of food to represent food provision as well as a plate of leftovers. The amount of food provided as food provision and waste was covertly weighed. Participants completed a six-point user satisfaction survey ranked from 1 (‘extremely dissatisfied’) to six ‘very much satisfied’. Dependent t-tests were performed to compare intake of energy, macronutrients, and food groups (fruits, vegetables, grain, dairy, and protein) from the 15 meals, where intake was estimated with the PS app and compared to directly weighed food. Alpha was set at 0.05. Results: Of the 15 participants, 73.3% (11) were female, and the mean (± SD) age and body mass index of the participants was 28.0 ± 12.2 years and 24.1 ± 6.6 kg/m2, respectively. Energy intake estimated by PS at the meal level (742.9 ± 328.2 kcal) was similar to directly weighed values (659.3 ± 190.7 kcal) and the difference (83.5 ± 287.5 kcal) was not significant (P &gt; .05). No significant differences were found between the two methods (PS and weigh back) for macronutrients (protein, total fat, and carbohydrate), and four food group servings (all P values &gt; .05) except total grains (P value &lt; .05). About 71% of the participants rated the app as a five or six in terms of satisfaction and ease of using the PS app. Conclusions: Results from this pilot provide preliminary support for the validity of, and user satisfaction with, the PS app. The pilot study identified ways to improve PS. Larger validation studies and further app refinement are ongoing

Validation of an integrated pedal desk and electronic behavior tracking platform

Background This study tested the validity of revolutions per minute (RPM) measurements from the Pennington Pedal Desk™. Forty-four participants (73 % female; 39 ± 11.4 years-old; BMI 25.8 ± 5.5 kg/m2 [mean ± SD]) completed a standardized trial consisting of guided computer tasks while using a pedal desk for approximately 20 min. Measures of RPM were concurrently collected by the pedal desk and the Garmin Vector power meter. After establishing the validity of RPM measurements with the Garmin Vector, we performed equivalence tests, quantified mean absolute percent error (MAPE), and constructed Bland–Altman plots to assess agreement between RPM measures from the pedal desk and the Garmin Vector (criterion) at the minute-by-minute and trial level (i.e., over the approximate 20 min trial period). Results The average (mean ± SD) duration of the pedal desk trial was 20.5 ± 2.5 min. Measures of RPM (mean ± SE) at the minute-by-minute (Garmin Vector: 54.8 ± 0.4 RPM; pedal desk: 55.8 ± 0.4 RPM) and trial level (Garmin Vector: 55.0 ± 1.7 RPM; pedal desk: 56.0 ± 1.7 RPM) were deemed equivalent. MAPE values for RPM measured by the pedal desk were small (minute-by-minute: 2.1 ± 0.1 %; trial: 1.8 ± 0.1 %) and no systematic relationships in error variance were evident by Bland–Altman plots. Conclusion The Pennington Pedal Desk™ provides a valid count of RPM, providing an accurate metric to promote usage

Exercise-induced changes in central adiposity during a RCT: effect of exercise dose and associations with compensation

Context: Exercise can decrease central adiposity, but the effect of exercise dose and the relationship between central adiposity and exercise-induced compensation is unclear. Objective: Test the effect of exercise dose on central adiposity change and the association between central adiposity and exercise-induced weight compensation. Methods: In this ancillary analysis of a 6-month randomized controlled trial, 170 participants with overweight or obesity (mean±SD BMI: 31.5±4.7 kg/m2) were randomized to a control group or exercise groups that reflected exercise recommendations for health (8 kcal/kg/week [KKW]) or weight loss and weight maintenance (20 KKW). Waist circumference was measured, and dual-energy X-ray absorptiometry assessed central adiposity. Predicted weight change was estimated and weight compensation (weight change minus predicted weight change) was calculated. Results: Between-group change in waist circumference (control: 0.0 cm [95% CI: -1.0,1.0], 8 KKW: -0.7 cm [95% CI: -1.7,0.4], 20 KKW: -1.3 cm [95% CI: -2.4, -0.2]) and visceral adipose tissue (VAT; control: -0.02 kg [95% CI: -0.07,0.04], 8 KKW: -0.01 kg [95% CI: -0.07,0.04], 20 KKW: -0.04 kg [95% CI: -0.10,0.02]) was similar (P≥0.23). Most exercisers (82.6%) compensated (predicted weight change lower than actual weight change). Exercisers who compensated exhibited a 2.5 cm (95% CI: 0.8,4.2) and 0.23 kg (95% CI: 0.14,0.31) increase in waist circumference and VAT, respectively, versus those who did not (P&lt;0.01). Desire to eat predicted VAT change during exercise (β=0.21; P=0.03). Conclusions: In the presence of significant weight compensation, exercise at doses recommended for health and weight loss and weight maintenance leads to negligible changes in central adiposity

Racial variations in appetite-related hormones, appetite, and laboratory-based energy intake from the E-MECHANIC randomized clinical trial

African Americans (AAs) have a higher obesity risk than Whites; however, it is unclear if appetite-related hormones and food intake are implicated. We examined differences in appetite-related hormones, appetite, and food intake between AAs (n = 53) and Whites (n = 111) with overweight or obesity. Participants were randomized into a control group or into supervised, controlled exercise groups at 8 kcal/kg of body weight/week (KKW) or 20 KKW. Participants consumed lunch and dinner at baseline and follow-up, with appetite and hormones measured before and after meals (except leptin). At baseline, AAs had lower peptide YY (PYY; p &lt; 0.01) and a blunted elevation in PYY after lunch (p = 0.01), as well as lower ghrelin (p = 0.02) and higher leptin (p &lt; 0.01) compared to Whites. Despite desire to eat being lower and satisfaction being higher in AAs relative to Whites (p ≤ 0.03), no racial differences in food intake were observed. Compared to Whites, leptin increased in the 8 KKW group in AAs (p = 0.01), yet no other race-by-group interactions were evident. Differences in appetite-related hormones between AAs and Whites exist; however, their influence on racial disparities in appetite, food intake, and obesity within this trial was limited

Associations between Cardiorespiratory Fitness and Health-Related Quality of Life

© 2009 Sloan et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

The Personalized Nutrition Study (POINTS): evaluation of a genetically informed weight loss approach, a randomized clinical trial

Weight loss (WL) differences between isocaloric high-carbohydrate and high-fat diets are generally small; however, individual WL varies within diet groups. Genotype patterns may modify diet effects, with carbohydrate-responsive genotypes losing more weight on high-carbohydrate diets (and vice versa for fat-responsive genotypes). We investigated whether 12-week WL (kg, primary outcome) differs between genotype-concordant and genotype-discordant diets. In this 12-week single-center WL trial, 145 participants with overweight/obesity were identified a priori as fat-responders or carbohydrate-responders based on their combined genotypes at ten genetic variants and randomized to a high-fat (n = 73) or high-carbohydrate diet (n = 72), yielding 4 groups: (1) fat-responders receiving high-fat diet, (2) fat-responders receiving high-carbohydrate diet, (3) carbohydrate-responders receiving high-fat diet, (4) carbohydrate-responders receiving high-carbohydrate diet. Dietitians delivered the WL intervention via 12 weekly diet-specific small group sessions. Outcome assessors were blind to diet assignment and genotype patterns. We included 122 participants (54.4 [SD:13.2] years, BMI 34.9 [SD:5.1] kg/m2, 84% women) in the analyses. Twelve-week WL did not differ between the genotype-concordant (−5.3 kg [SD:1.0]) and genotype-discordant diets (−4.8 kg [SD:1.1]; adjusted difference: −0.6 kg [95% CI: −2.1,0.9], p = 0.50). With the current ability to genotype participants as fat- or carbohydrate-responders, evidence does not support greater WL on genotype-concordant diets. ClinicalTrials identifier: NCT04145466