Plausible self-reported dietary intakes in a residential facility are not necessarily reliable

Snack and Nutrient Intake Status of Top-Level Female University Athletes: A Cross-Sectional Study

Ensuring proper energy, nutrient intake, and sleep is vital for athlete health and competitiveness. Despite previous studies investigating the nutrient intake among top-level collegiate female athletes in Japan, the status of snack consumption remains unclear. This study addressed this gap by surveying 70 top-level female university athletes. The survey included a self-administered diet history questionnaire, a qualitative food intake frequency survey, and a survey on snack and dietary supplement use. The results revealed a low frequency of snack intake (2.1 ± 2.3 days/week), with 55.7% of athletes reporting snack consumption. The energy intake in the snack-intake group was significantly higher than that in the without-snack-intake group (31.5 ± 10.0 vs. 26.6 ± 9.92 kcal/kg of BM, p = 0.047). Similarly, carbohydrate intake was significantly higher in the snack-intake group than in the without-snack-intake group (4.84 ± 1.71 vs. 3.96 ± 1.65 g/kg of BM/day, p = 0.035). However, neither group reached the recommended value of 5-8 g/kg of BM/day during the medium training period. Overall, this study emphasizes inadequate energy intake even among athletes with a high snack intake frequency, highlighting the necessity to enhance overall food consumption and underscoring the importance of nutritional education for incorporating appropriate complementary meals to improve performance.

Comparing Self-Reported Dietary Intake to Provided Diet during a Randomized Controlled Feeding Intervention: A Pilot Study

Systematic and random errors based on self-reported diet may bias estimates of dietary intake. The objective of this pilot study was to describe errors in self-reported dietary intake by comparing 24 h dietary recalls to provided menu items in a controlled feeding study. This feeding study was a parallel randomized block design consisting of a standard diet (STD; 15% protein, 50% carbohydrate, 35% fat) followed by either a high-fat (HF; 15% protein, 25% carbohydrate, 60% fat) or a high-carbohydrate (HC; 15% protein, 75% carbohydrate, 10% fat) diet. During the intervention, participants reported dietary intake in 24 h recalls. Participants included 12 males (seven HC, five HF) and 12 females (six HC, six HF). The Nutrition Data System for Research was utilized to quantify energy, macronutrients, and serving size of food groups. Statistical analyses assessed differences in 24 h dietary recalls vs. provided menu items, considering intervention type (STD vs. HF vs. HC) (Student's t-test). Caloric intake was consistent between self-reported intake and provided meals. Participants in the HF diet underreported energy-adjusted dietary fat and participants in the HC diet underreported energy-adjusted dietary carbohydrates. Energy-adjusted protein intake was overreported in each dietary intervention, specifically overreporting beef and poultry. Classifying misreported dietary components can lead to strategies to mitigate self-report errors for accurate dietary assessment.

Micronutrient intake from three popular diet patterns in the United States: modeled replacement of foods highest in added sugar and sodium using the National Health and Nutrition Examination Survey, 2005-2018

Introduction

Fifty-two percent of adults in the United States reported following a popular diet pattern in 2022, yet there is limited information on daily micronutrient intakes associated with these diet patterns. The objective of the present study was to model the impact on micronutrient intake when foods highest in added sugar and sodium were replaced with healthier alternatives to align with the Dietary Guidelines for Americans recommendations.

Methods

Dietary data were acquired from 34,411 adults ≥ 20 y in the National Health and Nutrition Examination Survey, 2005-2018. The National Cancer Institute methodology was used to estimate usual dietary intake at baseline of 17 micronutrients using information from up to two dietary recalls per person. A food substitution model was used to evaluate the impact on micronutrient intake when three servings of foods highest in added sugar and sodium were substituted with healthier alternatives.

Results

Dietary modeling to replace foods highest in added sugar with healthier alternatives increased the mean intake of fat-soluble vitamins (0.15% for vitamin A to 4.28% for vitamin K), most water-soluble vitamins (0.01% for vitamin B1 to 12.09% for vitamin C), and most minerals (0.01% for sodium to 4.44% for potassium) across all diet patterns. Replacing foods highest in sodium had mixed effects on the mean intake of micronutrients. The intake of most fatsoluble vitamins increased by 1.37-6.53% (particularly vitamin A and D), yet while the intake of some water-soluble vitamins and minerals increased by 0.18-2.64% (particularly vitamin B2, calcium, and iron) others decreased by 0.56-10.38% (notably vitamin B3 and B6, magnesium, sodium, and potassium).

Discussion

Modeled replacement of foods highest in added sugar led to more favorable changes in mean micronutrient intake compared to modeled replacement of foods highest in sodium. Due to the composite nature of mixed dishes that include multiple ingredients, food substitutions may result in both favorable and unfavorable changes in micronutrient intake. These findings highlight the challenges of making singleitem food substitutions to increase micronutrient intake and call for further research to evaluate optimal combinations of replacement foods to maximize the intake of all micronutrients simultaneously.

Identification of psychological correlates of dietary misreporting under laboratory and free-living environments

Errors inherent in self-reported measures of energy intake (EI) are substantial and well documented, but correlates of misreporting remain unclear. Therefore, potential predictors of misreporting were examined. In Study One, fifty-nine individuals (BMI = 26·1 (sd 3·8) kg/m2, age = 42·7 (sd 13·6) years, females = 29) completed a 14-d stay in a residential feeding behaviour suite where eating behaviour was continuously monitored. In Study Two, 182 individuals (BMI = 25·7 (sd 3·9) kg/m2, age = 42·4 (sd 12·2) years, females = 96) completed two consecutive days in a residential feeding suite and five consecutive days at home. Misreporting was directly quantified by comparing covertly measured laboratory weighed intakes (LWI) with self-reported EI (weighed dietary record (WDR), 24-h recall, 7-d diet history, FFQ). Personal (age, sex and %body fat) and psychological traits (personality, social desirability, body image, intelligence quotient and eating behaviour) were used as predictors of misreporting. In Study One, those with lower psychoticism (P = 0·009), openness to experience (P = 0·006) and higher agreeableness (P = 0·038) reduced EI on days participants knew EI was being measured to a greater extent than on covert days. Isolated associations existed between personality traits (psychoticism and openness to experience), eating behaviour (emotional eating) and differences between the LWI and self-reported EI, but these were inconsistent between dietary assessment techniques and typically became non-significant after accounting for multiplicity of comparisons. In Study Two, sex was associated with differences between LWI and the WDR (P = 0·009), 24-h recall (P = 0·002) and diet history (P = 0·050) in the laboratory, but not home environment. Personal and psychological correlates of misreporting identified displayed no clear pattern across studies or dietary assessment techniques and had little utility in predicting misreporting.

Two Sides of the Same Coin: Environmental and Health Concern Pathways Toward Meat Consumption

The dramatic increase of meat production in the last decades has proven to be one of the most impacting causes of negative environmental outcomes (e.g., increase of greenhouse emissions, pollution of land and water, and biodiversity loss). In two studies, we aimed to verify the role of key socio-psychological dimensions on meat intake. Study 1 (N = 198) tested the predictive power of an extended version of the Value-Belief-Norm (VBN) model on individual food choices in an online supermarket simulation. In an online survey, participants were directed to a virtual shop and asked to buy food within a set amount of money. Subsequently, they completed measures of behavioral intention, the VBN constructs (values, general pro-environmental beliefs, awareness of consequences, ascription of responsibility, and personal norm), and social norms (injunctive and descriptive). The outcome variable was operationalized in terms of percentage of expenses dedicated to meat and processed meat items, which provided a more robust behavioral measure than the common self-reported ones. Results confirmed the VBN sequential path, showing direct effects of biospheric values and descriptive norm on personal norm. Furthermore, a proof of validity for the new behavioral measure was provided (medium-sized correlation with behavioral intention). Study 2 (N = 218) aimed at verifying whether the meat consumption could be also motivated by a health concern, reflecting individual (cost/benefit) considerations, besides pro-environmental drivers. Results showed the direct impact of health concern and confirmed the indirect role of biospheric values and descriptive norm (via personal norm) on meat intake. This evidence would suggest the use of multiple-frame messages, highlighting both pro-environmental and health consequences, for meat consumption reduction. Nevertheless, the different implications of moral (e.g., environmental concern) vs. non-moral motivators (e.g., health concern) for reducing meat intake need to be stressed: indeed, the first drivers are more central for self-identity and for engaging in environmental citizenship behaviors.

Food for thought: Comparing self-reported versus curbside measurements of household food wasting behavior and the predictive capacity of behavioral determinants

A survey, based on an expanded Theory of Planned Behavior (TPB), was used to indirectly measure self-reported food wasting and its behavioral determinants. This was complemented with directly and objectively measured food waste in curbside garbage samples. Households (n = 189) reported throwing out avoidable food waste a mean of 5.48 times (SD = 5.58) and 6.63 portions (SD = 6.61) the week prior to completing the survey. These same households threw out a mean of 2,783 g/week of food waste (SD = 2,664) in a curbside garbage sample, with 63.27% of this consisting of avoidable food waste. There were weak to fair correlations between self-reported and curbside food waste samples. The direction and level of significance of all correlations of TPB behavioral determinants with self-reported and curbside food waste samples were similar, although the correlation coefficients were higher for self-reported food wasting. A linear regression (R² = 0.34, p < 0.001) on self-reported avoidable food waste frequency demonstrated that it was significantly (p < 0.05) associated with perceived behavioral control, personal attitude, number of people per household, gender and employment status. This was contrasted with a linear regression (R² = 0.19, p < 0.001) on curbside avoidable food waste which was also significantly (p < 0.05) associated with perceived behavioral control and number of people per household, but also housing tenure type (owner-occupancy vs tenancy) and the good provider identity. In general, self-reported results should be used with caution as they may underestimate food waste disposal and consideration should be given to supplement, if not replace, them with direct measurement of food waste disposal.

Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor

Accurate and objective assessment of energy intake remains an ongoing problem. We used features derived from annotated video observation and a chewing sensor to predict mass and energy intake during a meal without participant self-report. 30 participants each consumed 4 different meals in a laboratory setting and wore a chewing sensor while being videotaped. Subject-independent models were derived from bite, chew, and swallow features obtained from either video observation or information extracted from the chewing sensor. With multiple regression analysis, a forward selection procedure was used to choose the best model. The best estimates of meal mass and energy intake had (mean ± standard deviation) absolute percentage errors of 25.2% ± 18.9% and 30.1% ± 33.8%, respectively, and mean ± standard deviation estimation errors of −17.7 ± 226.9 g and −6.1 ± 273.8 kcal using features derived from both video observations and sensor data. Both video annotation and sensor-derived features may be utilized to objectively quantify energy intake.

Negative Consequences of Low Energy Availability in Natural Male Bodybuilding: A Review

Energy availability (EA) is a scientific concept describing how much energy is available for basic metabolic functions such as reproduction, immunity, and skeletal homeostasis. Carefully controlled studies on women have shown pathological effects of EA < 30 kcal/kg fat-free mass (FFM), and this state has been labeled low EA (LEA). Bodybuilding is a sport in which athletes compete to show muscular definition, symmetry, and low body fat (BF). The process of contest preparation in bodybuilding includes months of underfeeding, thus increasing the risk of LEA and its negative health consequences. As no well-controlled studies have been conducted in natural male bodybuilders on effects of LEA, the aim of this review was to summarize what can be extrapolated from previous relevant research findings in which EA can be calculated. The reviewed literature indicates that a prolonged EA < 25 kcal/kg FFM results in muscle loss, hormonal imbalances, psychological problems, and negatively affects the cardiovascular system when approaching the lower limits of BF (∼4%-5%) among males. Case studies on natural male bodybuilders who prepare for contest show muscle loss (>40% of total weight loss) with EA < 20 kcal/kg FFM, and in the study with the lowest observed BF (∼4 kg), major mood disturbance and hormonal imbalances co-occurred. Studies also underline the problem of BF overshoot during refeeding after extremes of LEA among males. A more tempered approach (EA > 25 kcal/kg FFM) might result in less muscle loss among natural male bodybuilders who prepare for contest, but more research is needed.

Feasibility and relative validity of a digital photo-based dietary assessment: results from the Nutris-Phone study

Objective

For dietary assessment, mobile devices with a camera can be used as an alternative to hand-written paper records. The Nutritional Tracking Information Smartphone (Nutris-Phone) study aimed to examine relative validity and feasibility of a photo-based dietary record in everyday life.

Design

Parallel to the photo-based technique, a weighed record was performed. Participant satisfaction was assessed by questionnaire. A trained nutrition scientist evaluated portion sizes and nutrient content was calculated (DGExpert). Spearman correlation and Bland–Altman analyses were applied.

Setting

Healthy, non-pregnant volunteers (≥18 years) without intent to lose weight recruited at Ulm University, Germany.

Subjects

Sixty-six participants (36 % males, median age 22·0 (interquartile range 20·0–25·0) years) took pictures of foods and beverages consumed with a commercially available mobile phone.

Results

Significant correlation between the photo-based and weighed record was observed: energy (r=0·991), carbohydrate (r=0·980), fat (r=0·972), protein (r=0·988), fibre (r=0·941). Bland–Altman analyses indicated comparable means and acceptable 95 % limits of agreement (energy: −345·2 to 302·9 kJ (−82·5 to 72·4 kcal); carbohydrate: −15·2 to 13·1 g; fat: −6·4 to 6·4 g; protein: −5·9 to 5·6 g; fibre: −2·7 to 2·5 g). However, with increasing intake level, underestimation by the digital method was present (except for fat, all P&lt;0·01). Over 80 % of participants were satisfied with the photo-based record. In nearly 90 %, technical implementation was without major problems.

Conclusions

Compared with a weighed record, the photo-based dietary record seems to be valid, feasible and user-friendly to estimate energy, macronutrient and fibre intakes, although a systematic bias with increasing levels of intake should be kept in mind.