Evaluation of Automated Anthropometrics Produced By Smartphone-Based Machine Learning: A Comparison With Traditional Anthropometric Assessments

Automated visual anthropometrics produced by mobile applications are accessible and cost-effective with the potential to assess clinically relevant anthropometrics without a trained technician present. Thus, the aim of this study was to evaluate the precision and agreement of smartphone-based automated anthropometrics against reference tape measurements. Waist and hip circumference (WC; HC), waist-to-hip ratio (WHR), and waist-to-height ratio (W:HT), were collected from 115 participants (69 F) using a tape measure and two smartphone applications (MeThreeSixty®, myBVI®) across multiple smartphone types. Precision metrics were used to assess test-retest precision of the automated measures. Agreement between the circumferences produced by each mobile application and the reference were assessed using equivalence testing and other validity metrics. All mobile applications across smartphone types produced reliable estimates for each variable with ICCs ≥0.93 (all

Time-restricted Feeding Plus Resistance Training in Active Females: A Randomized Trial

Background A very limited amount of research has examined intermittent fasting (IF) programs, such as time-restricted feeding (TRF), in active populations. Objective Our objective was to examine the effects of TRF, with or without β-hydroxy β-methylbutyrate (HMB) supplementation, during resistance training (RT). Methods This study employed a randomized, placebo-controlled, reduced factorial design and was double-blind with respect to supplementation in TRF groups. Resistance-trained females were randomly assigned to a control diet (CD), TRF, or TRF plus 3 g/d HMB (TRFHMB). TRF groups consumed all calories between 1200 h and 2000 h, whereas the CD group ate regularly from breakfast until the end of the day. All groups completed 8 wk of supervised RT and consumed supplemental whey protein. Body composition, muscular performance, dietary intake, physical activity, and physiological variables were assessed. Data were analyzed prior to unblinding using mixed models and both intention-to-treat (ITT) and per protocol (PP) frameworks. Results Forty participants were included in ITT, and 24 were included in PP. Energy and protein intake (1.6 g/kg/d) did not differ between groups despite different feeding durations (TRF and TRFHMB: ∼7.5 h/d; CD: ∼13 h/d). Comparable fat-free mass (FFM) accretion (+2% to 3% relative to baseline) and skeletal muscle hypertrophy occurred in all groups. Differential effects on fat mass (CD: +2%; TRF: −2% to −4%; TRFHMB: −4% to −7%) were statistically significant in the PP analysis, but not ITT. Muscular performance improved without differences between groups. No changes in physiological variables occurred in any group, and minimal side effects were reported. Conclusions IF, in the form of TRF, did not attenuate RT adaptations in resistance-trained females. Similar FFM accretion, skeletal muscle hypertrophy, and muscular performance improvements can be achieved with dramatically different feeding programs that contain similar energy and protein content during RT. Supplemental HMB during fasting periods of TRF did not definitively improve outcomes. This study was prospectively registered at clinicaltrials.gov as NCT03404271

Caffeine Supplementation Strategies Among Endurance Athletes

Caffeine is widely accepted as an endurance-performance enhancing supplement. Most scientific research studies use doses of 3–6 mg/kg of caffeine 60 min prior to exercise based on pharmacokinetics. It is not well understood whether endurance athletes employ similar supplementation strategies in practice. The purpose of this study was to investigate caffeine supplementation protocols among endurance athletes. A survey conducted on Qualtrics returned responses regarding caffeine supplementation from 254 endurance athletes (f = 134, m =120; age = 39.4 ± 13.9 y; pro = 11, current collegiate athlete = 37, recreational = 206; running = 98, triathlon = 83, cycling = 54, other = 19; training days per week = 5.4 ± 1.3). Most participants reported habitual caffeine consumption (85.0%; 41.2% multiple times daily). However, only 24.0% used caffeine supplements. A greater proportion of men (31.7%) used caffeine supplements compared with women (17.2%; p = 0.007). Caffeine use was also more prevalent among professional (45.5%) and recreational athletes (25.1%) than in collegiate athletes (9.4%). Type of sport (p = 0.641), household income (p = 0.263), education (p = 0.570) or working with a coach (p = 0.612) did not have an impact on caffeine supplementation prevalence. Of those reporting specific timing of caffeine supplementation, 49.1% and 34.9% reported consuming caffeine within 30 min of training and races respectively; 38.6 and 36.5% used caffeine 30–60 min before training and races. Recreational athletes reported consuming smaller amounts of caffeine before training (1.6 ± 1.0 mg/kg) and races (2.0 ± 1.2 mg/kg) compared with collegiate (TRG: 2.1 ± 1.2 mg/kg; RACE: 3.6 ± 0.2 mg/kg) and professional (TRG: 2.4 ± 1.1 mg/kg; RACE: 3.5 ± 0.6 mg/kg) athletes. Overall, participants reported minor to moderate perceived effectiveness of caffeine supplementation (2.31 ± 0.9 on a four-point Likert-type scale) with greatest effectiveness during longer sessions (2.8 ± 1.1). It appears that recreational athletes use lower caffeine amounts than what has been established as ergogenic in laboratory protocols; further, they consume caffeine closer to exercise compared with typical research protocols. Thus, better education of recreational athletes and additional research into alternative supplementation strategies are warranted

Caffeine Supplementation Strategies Among Endurance Athletes

Caffeine is widely accepted as an ergogenic aid for endurance performance. Many laboratory studies use doses of 3-6 mg/kg of caffeine 60 min prior to exercise. It is unclear if endurance athletes employ similar supplementation schemes in practice. Further, there is a paucity of data regarding caffeine consumption in this population. PURPOSE: The purpose of this study was to investigate caffeine supplementation strategies and consumption among endurance athletes. METHODS: A survey conducted on Qualtrics returned responses regarding caffeine supplementation from 247 endurance athletes (f = 129, m =118; age = 40.4 ± 18.4 y; pro = 11, current/former collegiate athlete = 67, recreational = 169; running = 95, triathlon = 80, cycling = 54, other = 18; training days per week = 5.4 ± 1.3). Descriptive statistics were calculated using SPSS V26. Pearson chi-square tests of independence were performed to investigate potential associations between a variety of grouping variables and caffeine use. Further, supplementation schemes were analyzed. Finally, athletes’ perception of the effectiveness of caffeine were examined. RESULTS: The majority of participants reported habitual caffeine consumption (84.2%; 34.8% multiple times daily). Yet, only 23.5% reported using caffeine supplements. A greater percentage of men (30.5%) used caffeine supplements compared with women (17.1%; p = .013). Athlete status was significantly associated with caffeine consumption (p = .004). Caffeine use was more prevalent among professional (36.4%) and recreational athletes (28.4%) compared with current/former collegiate athletes (9.0%). There were no significant differences in caffeine supplementation when comparing across type of sport (p = .505), household income (p = .191), education (p = .453) or working with a coach (p = .560). While not statistically significant (p = .064), 53.4% of those using caffeine supplements reported placing among the top 3 in their age group in the past year, compared with only 39.7% of those not using caffeine supplements. Sixty-eight athletes (27.5%) reported that they specifically timed caffeine supplementation around training (60.3% only before, 14.7% only during, 25.0% before and during sessions). Seventy-seven (31.2%) athletes reported timing caffeine intake around races (55.8% before, 13.0% during, 31.2% both). Of those reporting specific timing of caffeine use, 47.3% and 33.9% reported consuming caffeine within 30 min of training sessions and races respectively; 40.0% and 35.5% used caffeine 30-60 min before training and races; 12.7% and 36.6% reported taking caffeine \u3e60 min before training and races. The most frequently reported interval of supplementation during training (64.0%) and races (45.2%) was every 60-90 minutes. Those reporting specific amounts of caffeine consumed before training (n = 27) and races (n = 14), used 1.8 ± 1.0 mg/kg and 2.4 ± 1.3 mg/kg respectively. On average, 53.6% and 39.1% of athletes reported that caffeine exerted no effects to only minor effects during various types of training and racing respectively. A greater percentage of athletes reported moderate and major effects during more intense training as well as longer training sessions and races (52.7 - 72.7%). CONCLUSION: Most athletes in the present study did not follow typical laboratory protocols that have elicited ergogenic effects of caffeine. Better education among athletes and coaches or research into more diverse supplementation schemes are needed

Associations Between Visceral Adipose Tissue Estimates Produced By Near-Infrared Spectroscopy, Mobile Anthropometrics, and Traditional Body Composition Assessments and Estimates Derived From Dual-Energy X-Ray Absorptiometry

Assessments of visceral adipose tissue (VAT) are critical in preventing metabolic disorders; however, there are limited measurement methods that are accurate and accessible for VAT. The purpose of this cross-sectional study was to evaluate the association between VAT estimates from consumer-grade devices and traditional anthropometrics and VAT and subcutaneous adipose tissue (SAT) from dual-energy X-ray absorptiometry (DXA). Data were collected from 182 participants (female = 114; White = 127; Black/African-American (BAA) = 48) which included anthropometrics and indices of VAT produced by near-infrared reactance spectroscopy (NIRS), visual body composition (VBC) and multifrequency BIA (MFBIA). VAT and SAT were collected using DXA. Bivariate and partial correlations were calculated between DXAVAT and DXASAT and other VAT estimates. All VAT indices had positive moderate–strong correlations with VAT (all P \u3c 0·001) and SAT (all P \u3c 0·001). Only waist:hip (r = 0·69), VATVBC (r = 0·84), and VATMFBIA (r = 0·86) had stronger associations with VAT than SAT (P \u3c 0·001). Partial associations between VATVBC and VATMFBIA were only stronger for VAT than SAT in White participants (r = 0·67, P \u3c 0·001) but not female, male, or BAA participants individually. Partial correlations for waist:hip were stronger for VAT than SAT, but only for male (r = 0·40, P \u3c 0·010) or White participants (r = 0·48, P \u3c 0·001). NIRS was amongst the weakest predictors of VAT which was highest in male participants (r = 0·39, P \u3c 0·010) but non-existent in BAA participants (r = –0·02, P \u3e 0·050) after adjusting for SAT. Both anthropometric and consumer-grade VAT indices are consistently better predictors of SAT than VAT. These data highlight the need for a standardised, but convenient, VAT estimation protocol that can account for the relationship between SAT and VAT that differs by sex/race

The Impact of Dieting Culture Is Different Between Sexes In Endurance Athletes: A Cross-Sectional Analysis

Background: Frequent dieting is common in athletes attempting to achieve a body composition perceived to improve performance. Excessive dieting may indicate disordered eating (DE) behaviors and can result in clinical eating disorders. However, the current nutrition patterns that underly dieting culture are underexplored in endurance athletes. Therefore, the purpose of this study was to identify the sex differences in nutrition patterns among a group of endurance athletes. Methods: Two-hundred and thirty-one endurance athletes (females = 124) completed a questionnaire regarding their dieting patterns and associated variables. Results: The majority of athletes did not follow a planned diet (70.1%). For endurance athletes on planned diets (n = 69), males were more likely follow a balanced diet (p = 0.048) and females were more likely to follow a plant-based diet (p = 0.021). Female endurance athletes not on a planned diet (n = 162) were more likely to have attempted at least one diet (p \u3c 0.001). Male athletes attempted 2.0 ± 1.3 different diets on average compared to 3.0 ± 2.0 for females (p = 0.002). Female athletes were more likely to attempt ≥ three diets (p = 0.022). The most common diet attempts included carbohydrate/energy restrictive, plant-based, and elimination diets. Females were more likely to attempt ketogenic (p = 0.047), low-carbohydrate (p = 0.002), and energy restricted diets (p = 0.010). Females made up the entirety of those who attempted gluten-/dairy-free diets (F = 22.0%, M = 0.0%). Conclusions: Being a female athlete is a major determinant of higher dieting frequency and continual implementation of popular restrictive dietary interventions. Sports dietitians and coaches should prospectively assess eating behavior and provide appropriate programming, education, and monitoring of female endurance athletes

The Relationship Between Dietary Intake and Sleep Quality in Endurance Athletes

Many endurance athletes have poor sleep quality which may affect performance and health. It is unclear how dietary intake affects sleep quality among athletes. We examined if sleep quality in endurance athletes is associated with consumption of fruit, vegetables, whole grains, dairy milk, and caffeinated beverages. Two hundred thirty-four endurance athletes (39.5 ± 14.1 year) participated in a survey. Participants provided information on demographics, anthropometry, sleep behavior and quality, and dietary intake via questionnaires. Sleep quality was assessed using the Athlete Sleep Screening Questionnaire (ASSQ) with a global score (ASSQ-global) and subscales including sleep difficulty (ASSQ-SD), chronotype (ASSQ-C), and disordered breathing while sleeping (ASSQ-SDB). A general linear model (GLM), adjusted for age, body mass index, sleep discomfort, sleep behavior, gender, race, and ethnicity, showed that higher caffeinated beverage intake was related to poorer global sleep quality (p = 0.01) and increased risk for disordered breathing while sleeping (p = 0.03). Higher whole grain intake was associated with a morning chronotype and lower risk for sleep issues (p = 0.01). The GLM did not reveal a relationship between sleep quality and dairy milk, fruit, and vegetable intake. In conclusion, caffeinated beverages and whole grain intake may influence sleep quality. This relationship needs to be confirmed by further research

Nutrient Adequacy in Endurance Athletes

Adequate nutrition is critical to optimal performance in endurance athletes. However, it remains unclear if endurance athletes are consuming enough energy, macronutrients, and micronutrients. PURPOSE: The purpose of this study was to determine if endurance athletes are meeting their nutritional requirements and whether it varies by gender. METHODS: Endurance athletes (n=44), 39.0±14.2 y, participated in the study. Dietary intake was assessed using the five-step multiple-pass 24-hour recall method, a validated measure, that involved asking the participants to recall in detail the type and amount of foods and beverages they consumed the previous day. Energy, macronutrient, and micronutrient intakes were computed from the recalls using the ESHA Food Processor Diet Analysis Software. Nutritional adequacy was calculated by comparing the nutrient intakes of the participants with nutrient standards set by the Food and Nutrition Board, Institute of Medicine, the American College of Sports Medicine (ACSM), the Dietary Guidelines for Americans, and the American Heart Association (AHA). Fisher’s Exact test was used to compare the proportion of male and female endurance athletes that did not meet the requirements for energy, macronutrient, and micronutrient intakes. RESULTS: Over 50% of male athletes did not consume enough water, protein, carbohydrates, dietary fiber, linoleic acid, α-linolenic acid, eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), vitamins D, E, and K, pantothenic acid, biotin, manganese, chromium, zinc, molybdenum, choline, potassium, and magnesium. More than 50% of female athletes did not consume enough protein, carbohydrates, linoleic acid, α-linolenic acid, EPA, DHA, vitamins D, E, and B12, pantothenic acid, thiamine, biotin, manganese, chromium, zinc, molybdenum, choline, and potassium. About 50% of male and female athletes consumed more than the recommended amount of total fat, saturated fat, cholesterol, and sodium. Many athletes (male: 20%; female: 8%) did not meet the energy requirements. A significantly higher portion of male athletes compared to female athletes did not meet the nutrient requirements for dietary fiber (70.0% and 24.0%, respectively; p ≤ 0.001), α-linolenic acid (90.0% and 60.0%, respectively; p = 0.04), and total water (75.0% and 40.0%, respectively; p = 0.03). CONCLUSION: Many endurance athletes are not meeting the nutrient requirements for energy, water, and several macronutrients and micronutrients, with some differences by gender. These results need to be confirmed by a larger study. Endurance athletes would benefit from dietary counseling by a registered dietitian

The Relationship between Dietary Intake and Sleep Quality in Endurance Athletes

Athletes have a high prevalence of poor sleep quality. It is unknown if dietary intake affects sleep quality in athletes. PURPOSE: To examine if sleep quality in endurance athletes is related to dietary intake. METHODS: Endurance athletes (n=187), 42.0±13.7 y, participated in the study. Participants completed questionnaires on demographics, dietary intake, and sleep quality. Sleep quality was assessed using the Athlete Sleep Screening Questionnaire (ASSQ), a validated tool, with scores ranging from 0-40 (higher scores indicate poorer sleep quality). The ASSQ subscales included sleep difficulty (SD), chronotype (C), and sleep disordered breathing (SDB). ASSQ-SD was categorized as having none (0-4), mild (5-7), moderate (8-10), and severe (11-17) SD. ASSQ-C was categorized as morning (\u3e4) or evening (higher risk for sleep issues) (≤4) type. ASSQ-SDB was categorized as difficulty breathing (\u3e1) or not ( RESULTS: ASSQ score was 22.3±3.96, indicating average sleep quality among athletes. ASSQ-SD score showed that 33.7% of athletes had no SD, and 38.5%, 21.9%, and 5.9% had mild, moderate, and severe SD, respectively. ASSQ-C score was 9.4±2.82, and 93% of athletes were morning type and 7% were evening type. ASSQ-SDB score indicated that 79.1% of athletes had normal and 20.9% had disordered breathing. Preliminary analyses revealed that ASSQ scores were significantly related to vegetable (p=.038) and caffeinated beverage (p=0.034) intake, but not to the other dietary variables. Significantly higher ASSQ score, (i.e., poorer sleep quality) was found in athletes who consumed ≥5 servings/d (24.0±4.0) of vegetables compared with \u3c1 \u3e(20.9±3.18, p=.011) or 1-2 (21.6±4.11, p=.030) servings/d. Athletes who drank \u3e2.5 cups/d of caffeinated beverages had higher ASSQ score or poorer sleep quality versus those who consumed 3 cups/d of milk had a higher disordered breathing score (.69±.947) versus those who drank 1-2 (.18±.521, p=.009) and \u3c1 \u3e(.30±.641, p=.016) cups/d. Athletes who consumed /d of whole grains had a higher ASSQ-DBS score (.48±.79) versus those who consumed 3-4 servings/d (.09±.401, p=.029). ASSQ-SD was not related to any of the dietary variables. CONCLUSIONS: Increased vegetable and caffeinated beverage consumption were associated with decreased sleep quality. Less whole grains and fruits were associated with evening chronotype. Athletes who consumed more milk and less whole grains had increased disordered breathing

Training Modifications in Endurance Athletes due to COVID-19 Restrictions

The COVID-19 pandemic created a situation that abruptly altered the life of nearly every individual, forcing them to adjust their daily habits. Adults who regularly engaged in daily physical activity, either as recreational, collegiate, or professional athletes prior to lockdowns had to subsequently adapt and change their training regimens. PURPOSE: To determine which characteristics (age, sex, education level, socioeconomic status, primary endurance sport, whether the athlete is being coached or following a training program, and prior competition medaling) of recreational, collegiate, and professional endurance athletes were associated with training changes due to COVID-19 safety restrictions. METHODS: A cross-sectional study design was used for this study. Personal and training related descriptive statistics were collected using a Qualtrics survey that was distributed to endurance athletes around the world from June 2020 – February 2021. Significant differences between athlete characteristics and change in training status were assessed using a Chi-squared test (significance pRESULTS: Approximately 2 out of every 3 (66.2%) of the 331 endurance athletes, 38.8±14.0y, changed their training due to restrictions. Significant group differences were found for age, sex, current collegiate athlete status, prior coaching status, prior use of a training program, and based on athlete primary sport compared to the whole sample. Athletes aged 18-30y changed their training at a higher portion (74.6%), while those 31-40y (56%) changed their training a lower portion. A significantly higher portion of female athletes changed their training compared to males (72.8% and 60.0%, respectively). A majority of collegiate athletes (83%), athletes who have previously worked with a coach (70.8%), athletes who have followed a training program previously (72.4%) changed their training. A significantly smaller proportion of athletes who chose running as their primary sport (55%) changed their training and a significantly larger portion of those who chose triathlon (82.1%) changed their training due to pandemic-related safety restrictions. CONCLUSION: The majority of athletes changed their training with COVID-19 safety restrictions, with significant differences based on personal and training characteristics. This data can be of use to safety policy makers, athletes, and coaches to consider for training approach and return to sport. Analysis of factors that allowed athletes to maintain their training and understanding the changes in athlete training can help minimize or prevent the effects of detraining for a greater portion of athletes