Explorative Characterization of GI Complaints, General Physical and Mental Wellbeing, and Gut Microbiota in Trained Recreative and Competitive Athletes with or without Self-Reported Gastrointestinal Symptoms.

The current state of the literature lacks a clear characterization of gastrointestinal (GI) symptoms, gut microbiota composition, and general physical and mental wellbeing in well-trained athletes. Therefore, this study aimed to characterize differences in self-reported symptoms, gut microbiota composition, and wellbeing (i.e., sleep quality, mood, and physical (PHQ) and mental wellbeing) between athletes with and without GI symptoms. In addition, we assessed the potential impact of a 3-week multi-ingredient fermented whey supplement in the GI complaints group, without a control group, on the gut microbiota and self-reported GI symptoms and wellbeing. A total of 50 athletes (24.7 ± 4.5 years) with GI issues (GI group at baseline, GI-B) and 21 athletes (25.4 ± 5.3 years) without GI issues (non-GI group, NGI) were included. At baseline, there was a significant difference in the total gastrointestinal symptom rating scale (GSRS) score (24.1 ± 8.48 vs. 30.3 ± 8.82, p = 0.008) and a trend difference in PHQ (33.9 ± 10.7 vs. 30.3 ± 8.82, p = 0.081), but no differences (p > 0.05) were seen for other outcomes, including gut microbiota metrics, between groups. After 3-week supplementation, the GI group (GI-S) showed increased Bifidobacterium relative abundance (p < 0.05), reported a lower number of severe GI complaints (from 72% to 54%, p < 0.001), and PHQ declined (p = 0.010). In conclusion, well-trained athletes with GI complaints reported more severe GI symptoms than an athletic reference group, without showing clear differences in wellbeing or microbiota composition. Future controlled research should further investigate the impact of such multi-ingredient supplements on GI complaints and the associated changes in gut health-related markers.

Collegiate student-athletes use and knowledge of third-party tested nutritional supplements: An exploratory study.

CONTEXT: Nutritional supplement use in athletes is common, accompanied by potential doping risk. OBJECTIVE: Determine athletes' nutritional supplement and third-party tested (TPT) supplement use, supplement knowledge as well as factors influencing their behavior. DESIGN: Cross-Sectional Study. SETTING: NCAA DI athletic departments. PARTICIPANTS: Student-athletes (n=410, 53% female, age 21.4±1.6 years). MAIN OUTCOME MEASURES: Survey questions addressed topics including nutritional supplement knowledge and use, TPT supplement knowledge, use and logo recognition and data were 11 stratified for sex differences. RESULTS: Athletes (91%) report the use of supplements, but the total number of supplements used (median and interquartile range, IQR) is lower in females (7, 4-11), than males (9, 4-12), with U=17960, p=0.01. A total of 48% (n=191, out of 402 responses) reported purchasing supplements outside of their athletic department, with significantly fewer females (40%, n=84) than males (56%, n=107) reporting this behavior (χ2=11.20, p<0.001). No association between TPT logo recognition and TPT use was seen (χ2=0.238, p=0.63). Of all athletes using supplements, 38% (n=140) reported "consistent TPT use", while females (36%, n=70) reported this less often than males (41%, n=70, χ2=0.952, p<0.32). No sex differences were seen for receiving nutritional counseling (89%, p=0.37), or the low nutritional supplement knowledge (<50%, p=0.38), however, males had 2.5 times greater odds at recognizing a TPT organization logo than females (OR=2.45, with 95% CI=1.58-3.79). CONCLUSIONS: Most athletes use nutritional supplements. Females report slightly fewer supplements than males, while also less frequently purchasing them outside their athletic department, potentially explaining the lower TPT logo recognition in female athletes.

Development of a screener to assess athlete risk behavior of not using third-party tested nutritional supplements.

Introduction: The aim of this cross-sectional study was to develop an algorithm to predict athletes use of third-party tested (TPT) supplements. Therefore, a nutritional supplement questionnaire was used with a section about self-reported TPT supplement use. Methods: Outcomes were randomly assigned to a training dataset to identify predictors using logistic regression models, or a cross-validation dataset. Training data were used to develop an algorithm with a score from 0 to 100 predicting use or non-use of TPT nutritional supplements. Results: A total of n = 410 NCAA Division I student-athletes (age: 21.4 ± 1.6 years, 53% female, from >20 sports) were included. Then n = 320 were randomly selected, of which 34% (n = 109) of users consistently reported that all supplements they used were TPT. Analyses resulted in a 10-item algorithm associated with use or non-use of TPT. Risk quadrants provided the best fit for classifying low vs. high risk toward inconsistent TPT-use resulting in a cut-off ≥60% (χ2(4) = 61.26, P < 0.001), with reasonable AUC 0.78. There was a significant association for TPT use (yes/no) and risk behavior (low vs. high) defined from the algorithm (χ2(1)=58.6, P < 0.001). The algorithm had a high sensitivity, classifying 89% of non-TPT users correctly, while having a low specificity, classifying 49% of TPT-users correctly. This was confirmed by cross-validation (n = 34), reporting a high sensitivity (83%), despite a lower AUC (0.61). Discussion: The algorithm classifies high-risk inconsistent TPT-users with reasonable accuracy, but lacks the specificity to classify consistent users at low risk. This approach should be useful in identifying athletes that would benefit from additional counseling.

Validity of combined hydration self-assessment measurements to estimate a low vs. high urine concentration in a small sample of (tactical) athletes.

PURPOSE: Relationships between body weight, urine color (Uc), and thirst level (WUT) have been proposed as a simple and inexpensive self-assessment method to predict dehydration. This study aimed to determine if this method also allowed us to accurately identify a low vs. high urine concentration in (tactical) athletes. METHODS: A total of n = 19 Army Reserve Officer Training Corps cadets and club sports athletes (22.7 ± 3.8 years old, of which 13 male) were included in the analysis, providing morning body weight, thirst sensation, and Uc for five consecutive days. Each item received a score 0 or 1, resulting in a WUT score ranging from 0 (likely hydrated) to 3 (very likely dehydrated). WUT model and individual item outcomes were then compared with a ≥ 1.020 urine specific gravity (USG) cut-off indicating a high urine concentration, using descriptive comparisons, generalized linear mixed models, and logistic regression (to calculate the area under the curve (AUC)). RESULTS: WUT score was not significantly predictive of urine concentration, z = 1.59, p = 0.11. The AUC ranged from 0.54 to 0.77 for test days, suggesting a fair AUC on most days. Only Uc was significantly related to urine concentration, z = 2.49, p = 0.01. The accuracy of the WUT model for correctly classifying urine samples with a high concentration was 68% vs. 51% of samples with a low concentration, resulting in an average accuracy of 61%. CONCLUSION: This study shows that WUT scores were not predictive of urine concentration, and the method did not substantially outperform the accuracy of Uc scoring alone.

Curiosity and intentions to use myblu e-cigarettes and an examination of the 'gateway' theory: Data from cross-sectional nationally representative surveys.

Encouraging adult smokers who are uninterested or unwilling to quit, and would otherwise continue to smoke, to transition to potentially less harmful nicotine products such as electronic nicotine delivery systems (ENDS) may positively impact population health. However, counterbalancing this benefit is the societal concern that ENDS may be used by never smokers and youth and serve as a 'gateway' into cigarette smoking. Data were analysed from two independent surveys of the prevalence and perceptions of myblu ENDS use in the United States. Total sample size was 22,232 young adults and 23,264 adults. Being curious to use myblu was 1.6-2.0 times more likely in young adult current smokers than young adult never smokers. This likelihood was 2.8 times greater for adult current smokers compared with adult never smokers in the perceptions survey, while in the prevalence survey, there was no difference between adult current and never smokers. Intentions to use myblu were significantly greater in young adult current smokers compared with young adult never smokers in both surveys and in adults in the prevalence survey. In all surveys and age cohorts, 124 of 45,496 participants (0.1% of the total survey population) reported first using myblu prior to smoking cigarettes and went on to become established smokers. Curiosity and intentions to use myblu were generally higher in current smokers compared with never smokers. There was minimal evidence to suggest the existence of a 'gateway' effect to established cigarette smoking among never-smoking myblu users.

Examination of the impact of myblu electronic nicotine delivery system e-liquid nicotine strength on self-reported measures of dependence.

BACKGROUND: Greater nicotine delivery is associated with higher nicotine concentrations in electronic nicotine delivery system (ENDS) liquids. However, there is a current debate as to whether this leads to increased dependence and mitigates ENDS public health potential. METHODS: Self-reported dependence among users of myblu ENDS containing different nicotine concentrations was examined with data from a multiwave cross-sectional survey of US young adults and adults. Questions examined responses related to dependence measures and participants' most often used myblu ENDS nicotine concentration (low: 0%, 1% and 1.2%; medium: 2%, 2.4% and 2.5%; or high: 3.6% and 4%). RESULTS: A global general linear model using nicotine concentration, age and days myblu that was used in the past 30 revealed a significant difference in PROMIS scores among nicotine concentration groups (F = 4.07, p = 0.02). However, pairwise comparisons to examine which specific groups differed significantly from others showed no significant differences. Logistic regression demonstrated that strong past 30-day cravings to use myblu among participants using high or medium nicotine concentrations were not significantly different from those using a low concentration (ORs 0.66 [0.42, 1.03], p = 0.07 and 0.95 [0.49, 1.82], p = 0.98, respectively). Time to daily first use for high or medium nicotine concentration users was not significantly different from those using a low concentration (ORs 0.89 [0.70, 1.14], p = 0.35 and 0.84 [0.57, 1.25], p = 0.40, respectively). CONCLUSIONS: Use of myblu ENDS with different nicotine concentrations is not associated with differing levels of dependence. Our findings contradict the notion that high ENDS e-liquid nicotine levels generate increased dependence.

A randomised, crossover, clinical study to assess nicotine pharmacokinetics and subjective effects of the BIDI® stick ENDS compared with combustible cigarettes and a comparator ENDS in adult smokers.

BACKGROUND: Nicotine pharmacokinetic assessments of electronic nicotine delivery systems (ENDS) are crucial to understand their ability to provide an alternative to cigarette smoking. Subjective effects data also strongly contribute to this understanding. The BIDI® Stick is a disposable ENDS product which contains 59 mg/ml nicotine benzoate salt and various flavours. METHODS: In this study, we assessed nicotine pharmacokinetics and subjective effects of 6 flavour variants of BIDI® Stick ENDS in adult smokers, compared to cigarettes and a comparator ENDS product. During each of eight study visits, 18 volunteer smoker subjects randomly used one of either their usual brand (UB) of cigarette, a BIDI® Stick ENDS, or a comparator ENDS (JUUL 59 mg/ml nicotine with Virginia Tobacco flavour), during both controlled (10 puffs, 30 s apart) and ad libitum (60 min) puffing sessions. Blood samples were collected at various time points and subjective effects questionnaires were administered. RESULTS: Mean [SD] plasma nicotine Cmax 0-120 was not significantly different between BIDI® Stick ENDS with any flavour (range 15.3 [9.90] ng/ml for BIDI® Stick Winter to 17.6 [9.00] ng/ml for BIDI® Stick Classic) and UB cigarettes (16.2 [9.17] ng/ml). Mean [SD] AUC0-120 (range 569.7 [327.29] to 628.6 [408.99]  min*ng/ml for BIDI® Stick ENDS and 747.1 [325.48]  min*ng/ml for UB cigarettes) and median Tmax 0-120 (range 5-7 min for all BIDI® Stick ENDS and UB cigarettes) values were also not significantly different between BIDI® Stick ENDS and UB cigarettes, while subjective effects measures were also similar between BIDI® Stick ENDS and UB cigarettes. Mean [SD] plasma nicotine Cmax 0-120, AUC0-120, and median Tmax 0-120 were 6.8 [4.13] ng/ml, 243.6 [179.04] min*ng/ml, and 5 min, respectively, for JUUL ENDS. These values were significantly different compared with those for all BIDI® Stick ENDS and UB cigarettes for both Cmax 0-120 and AUC0-120 but not for Tmax 0-120. CONCLUSIONS: BIDI® Stick ENDS delivered nicotine to users comparably to their UB combustible cigarette and higher than JUUL ENDS, and also elicited similar subjective effects such as satisfaction and relief. Thus, the BIDI® Stick ENDS may be a satisfying alternative to cigarettes among current smokers and may support their transitioning away from cigarette smoking. TRIAL REGISTRATION: ClinicalTrials.gov (identifier number NCT05072925).

Automated Generation of Personalized Shock Wave Lithotripsy Protocols: Treatment Planning Using Deep Learning.

BACKGROUND: Though shock wave lithotripsy (SWL) has developed to be one of the most common treatment approaches for nephrolithiasis in recent decades, its treatment planning is often a trial-and-error process based on physicians' subjective judgement. Physicians' inexperience with this modality can lead to low-quality treatment and unnecessary risks to patients. OBJECTIVE: To improve the quality and consistency of shock wave lithotripsy treatment, we aimed to develop a deep learning model for generating the next treatment step by previous steps and preoperative patient characteristics and to produce personalized SWL treatment plans in a step-by-step protocol based on the deep learning model. METHODS: We developed a deep learning model to generate the optimal power level, shock rate, and number of shocks in the next step, given previous treatment steps encoded by long short-term memory neural networks and preoperative patient characteristics. We constructed a next-step data set (N=8583) from top practices of renal SWL treatments recorded in the International Stone Registry. Then, we trained the deep learning model and baseline models (linear regression, logistic regression, random forest, and support vector machine) with 90% of the samples and validated them with the remaining samples. RESULTS: The deep learning models for generating the next treatment steps outperformed the baseline models (accuracy = 98.8%, F1 = 98.0% for power levels; accuracy = 98.1%, F1 = 96.0% for shock rates; root mean squared error = 207, mean absolute error = 121 for numbers of shocks). The hypothesis testing showed no significant difference between steps generated by our model and the top practices (P=.480 for power levels; P=.782 for shock rates; P=.727 for numbers of shocks). CONCLUSIONS: The high performance of our deep learning approach shows its treatment planning capability on par with top physicians. To the best of our knowledge, our framework is the first effort to implement automated planning of SWL treatment via deep learning. It is a promising technique in assisting treatment planning and physician training at low cost.

The Perilous Use of Proxy Variables.

It is often not stated or quantified how well measured proxy variables account for the variance in latent constructs they are intended to represent. A sensitivity analysis was run using data from the Survey of Health, Ageing and Retirement in Europe to estimate models varying in the degree to which proxy variables represent intended constructs. Results showed that parameter estimates differ substantially across different levels of variable representation. When variables are used with poor construct validity, an insufficient amount of variance is removed from the observed spurious relationship between design variable and outcome. The findings from this methodological demonstration underscore the importance of selecting proxy variables that accurately represent the underlying construct for which control is intended.

Relationship between smokers' modes of entry into quitlines and treatment outcomes.

OBJECTIVES: To assess the relationship between the mode of entry into a quitline service and subsequent tobacco use treatment outcomes. METHODS: A retrospective study using logistic regression analysis of 11,040 Arizona Smokers' Helpline (ASHLine) clients was conducted to determine whether self- or medical referrals were related to 7- and 30-day point prevalence tobacco treatment outcomes at 7 months postquit. RESULTS: Smokers referred to the ASHLine by a health care provider were more likely to quit smoking than were those who self-referred. CONCLUSIONS: Mode of entry into a quitline service for smoking cessation is related to treatment outcomes. Reasons for this outcome are uncertain and require additional research.