Can Popular High-Intensity Interval Training (HIIT) Models Lead to Impossible Training Sessions?

High-Intensity Interval Training (HIIT) is a time-efficient training method suggested to improve health and fitness for the clinical population, healthy subjects, and athletes. Many parameters can impact the difficulty of HIIT sessions. This study aims to highlight and explain, through logical deductions, some limitations of the Skiba and Coggan models, widely used to prescribe HIIT sessions in cycling. We simulated 6198 different HIIT training sessions leading to exhaustion, according to the Skiba and Coggan-Modified (modification of the Coggan model with the introduction of an exhaustion criterion) models, for three fictitious athlete profiles (Time-Trialist, All-Rounder, Sprinter). The simulation revealed impossible sessions (i.e., requiring athletes to surpass their maximal power output over the exercise interval duration), characterized by a few short exercise intervals, performed in the severe and extreme intensity domains, alternating with long recovery bouts. The fraction of impossible sessions depends on the athlete profile and ranges between 4.4 and 22.9% for the Skiba model and 0.6 and 3.2% for the Coggan-Modified model. For practitioners using these HIIT models, this study highlights the importance of understanding these models’ inherent limitations and mathematical assumptions to draw adequate conclusions from their use to prescribe HIIT sessions.

Central and Peripheral Oxygen Distribution in Two Different Modes of Interval Training

In high-intensity interval training the interval duration can be adjusted to optimize training results in oxygen uptake, cardiac output, and local oxygen supply. This study aimed to compare these variables in two interval trainings (long intervals HIIT3m: 3 min work, 3 min active rest vs. short intervals HIIT30s: 30 s work, 30 s active rest) at the same overall work rate and training duration. 24 participants accomplished both protocols, (work: 80% power output at VO2peak, relief: 85% power output at gas exchange threshold) in randomized order. Spirometry, impedance cardiography, and near-infrared spectroscopy were used to analyze the physiological stress of the cardiopulmonary system and muscle tissue. Although times above gas exchange threshold were shorter in HIIT3m (HIIT3m 1669.9 ± 310.9 s vs. HIIT30s 1769.5 ± 189.0 s, p = 0.034), both protocols evoked similar average fractional utilization of VO2peak (HIIT3m 65.23 ± 4.68% VO2peak vs. HIIT30s 64.39 ± 6.78% VO2peak, p = 0.261). However, HIIT3m resulted in higher cardiovascular responses during the loaded phases (VO2p < 0.001, cardiac output p < 0.001). Local hemodynamics were not different between both protocols. Average physiological responses were not different in both protocols owning to incomplete rests in HIIT30s and large response amplitudes in HIIT3m. Despite lower acute cardiovascular stress in HIIT30s, short submaximal intervals may also trigger microvascular and metabolic adaptions similar to HIIT3m. Therefore, the adaption of interval duration is an important tool to adjust the goals of interval training to the needs of the athlete or patient.

Impact of Anti-Hepatitis C Therapy on Cardiac rhythm

Background The use of direct-acting antiviral (DAA) has raised some concerns about the possibilities of cardiac toxicity after US Food and Drug Administration (FDA) issued a safety announcement of serious slowing of the heart rate when Amiodarone was used with hepatitis C treatment . Aim The aim of the present study was to assess the impact of Anti-Hepatitis C Therapy on cardiac rhythm using 24 hours Holter monitoring. Methods The current study was conducted on fifty consecutive adult patients with chronic hepatitis C infection eligible for DAA therapy supplied in the outpatient clinic of Ain shams University Virology Center .Patients have received Sofosbuvir 400 mg and Daclatasvir 60 mg / day for 12 weeks. All patients underwent 24 hour Holter monitoring before and after the completion of therapy. Both recordings were compared as regards: heart rate, minimum and maximum heart rate, the presence and frequency of ectopic atrial or ventricular activity. The measurement of QRS intervals such PR, corrected QT intervals in msec. Results The pre and post analysis of the Holter recording was done with comparing different baseline and post therapy parameters, The pre and post therapy mean average heart rates were 79.44±10.14 bpm vs.79.96±8.77 bpm respectively (p = 0.534) which are non-significant changes. There is also non-significant change was observed in mean maximum and mean minimum heart rate 129.08±20.07 bpm 128.98±16.89 bpm (p = 0.964), 55.88±9.20 bpm 56.66±9.45 bpm (p = 0.457) respectively. And as regard corrected QT duration and PR interval duration, the mean pre-therapy PR interval was 154.00 ± 25.95 ms as compared to the mean post-medication PR interval duration 151.40 ± 23.82 ms (p = 0.124) and The mean premedication Corrected QT duration was 397.34 ± 29.38ms vs. post therapy 395.04 ± 30.23ms ( p = 0.403) Which showing a non-significant change of both intervals. And as regard the median pre-therapy and post medication PACs and PVCs numbers and the median pre-therapy and post medication attacks of tachycardia and bradycardia, they showed non-significant changes with P-value &gt; 0.05 . Conclusions Novel DAAs are safe to use as regards its effect on cardiac rhythm

Monogenic and Polygenic Contributions to QTc Prolongation in the Population

Background: Rare sequence variation in genes underlying the long QT syndrome (LQTS) and common polygenic variation influence QT interval duration. It is unclear how rare and common variation contribute to QT interval duration in the general population. Objectives: Investigate monogenic and polygenic contributions to QT interval duration and the role of polygenic variation in modulating phenotypic expression of rare monogenic variation. Methods: We performed a genome wide association study (GWAS) of QTc duration in 44,979 United Kingdom Biobank (UKBB) participants and created a polygenic risk score (PRS). The PRS was validated in 39,800 independent UKBB participants. Among 26,976 participants with whole genome sequencing and ECG data in the TransOmics for Precision Medicine (TOPMed) program, we identified 160 carriers of putative pathogenic rare variants in 10 LQTS genes. We examined QTc associations with the PRS and with LQTS rare variants in TOPMed. Results: Twenty independent loci (4 novel) were identified by GWAS. The PRS comprising 565 common variants was significantly associated with QTc duration in TOPMed (p=1.1x10-64). Carriers of LQTS rare variants had longer QTc intervals than non-carriers (deltaQTc=10.9 ms [7.4-14.4] for all LQTS genes; deltaQTc=26.5 ms [20.7-32.3] for KCNQ1, KCNH2 and SCN5A). 16.7% of individuals with QTc>480 ms carried either a rare variant in a LQTS gene or had a PRS in the top decile (3.4% monogenic, 13.6% top decile of PRS). We observed a greater effect of rare variants on the QTc among individuals with a higher polygenic risk (lowest PRS tertile:deltaQTccarrier/non-carrier=4.8 ms [-1.2-10.7];highest PRS tertile:deltaQTccarrier/non-carrier=18.9 ms [12.8-25.1];p-interaction=0.001). Conclusions: QTc duration is influenced by both rare variants in established LQTS genes and polygenic risk. The phenotypic expression of monogenic variation is modulated by polygenic variation. Nevertheless, over 80% of individuals with prolonged QTc do not carry a rare monogenic variant or polygenic risk equivalent.

Perfect Timing: Associations Between Dietary Timing, Eating Intervals and Metabolic Health

Background: Metabolic disorders are on the rise resulting in an increased need for novel interventions to combat this growing concern. One such intervention is time-restricted eating, which consolidates caloric intake to a shortened eating duration, and has consistently demonstrated improvement in metabolic health. Additional interventions could harness circadian clocks, which help regulate daily rhythms of hormone release and maintain metabolic homeostasis. Circadian rhythms are influenced by timing of food intake and therefore meal timing may also impact metabolic health. Objective: To examine whether timing of eating was associated with metabolic health independently of eating duration. Methods: Data are from the National Health and Nutrition Examination Survey (NHANES), a nationally representative U.S. survey and physical exam. We analyzed two non-consecutive dietary recalls, fasting glucose and insulin from 10,575 adults (&gt;18) over four cycles (2005–2012). We calculated eating interval duration as the time between first and last eating occasion (&gt;10kcal) and formulated three groups: &lt;10h, 10-13h, &gt;13h. We then created 6 subgroups based on eating duration start time (before or after 0830 h) to analyze associations of eating interval timing. Linear regression analyses controlling for demographics were computed to determine whether eating interval duration and eating interval timing were associated with fasting glucose and estimated insulin resistance using HOMA-IR. Results: Fasting glucose did not differ significantly among eating interval groups. Adjusted mean HOMA-IR increased with shorter eating interval duration (2.23, 2.20, 1.97 for &lt;10h, 10-13h, &gt;13h; p&lt;0.05). When eating start time occurred before 0830 h compared to start time after 0830 h, mean adjusted HOMA-IR decreased across all eating interval subgroups (1.96 vs 2.28 for &lt;10h, 2.13 vs 2.28 for 10-13h, 1.94 vs 2.13 for &gt;13h; p &lt;0.05). Adjusted mean fasting glucose had a similar pattern (96.3 mg/dL vs 98.7 mg/dL for &lt;10h, 97.3 mg/dL vs 98.4 mg/dL for 10-13h, and 97.3 mg/dL vs 99.3 mg/dL for &gt;13h; p &lt; 0.05). In regression models where eating duration and timing were both entered as continuous variables, only timing was significantly associated with fasting glucose and HOMA-IR. Later time was associated with higher glucose and HOMA values (p&lt;.001) Conclusion: Shorter eating durations, i.e. time-restricted eating, was associated with worse metabolic outcomes, except when paired with an earlier start time. All subgroups with an early eating start time had better metabolic outcomes regardless of eating duration. These findings suggest that timing is more strongly associated with metabolic measures than duration and supports early eating strategies.

SYMBOL RATE. ESTIMATION BOUNDS

В статье обсуждаются проблемы оценивания символьной частоты и определения эффективности получаемых оценок. Предложенный подход позволяет определить границы оценивания символьной частоты для сигналов различных видов модуляции и спектральныххарактеристик. Получено аналитическое выражение для нормированной модифицированной нижней границы Крамера-Рао оценивания символьной частоты для сигналов с косинусным скруглением. Представлены зависимости соответствующих границ от коэффициента скругления, отношения сигнал/шум на символ и длительности интервала наблюдения. The article discusses the problems of symbol rate estimation and the estimation performance evaluation. The proposed approach allows us to determine the symbol rate estimation bounds for signals of various types of modulation and various spectral characteristics. An analytical expression for the symbol rate modified Kramer-Rao lower bound is obtained for signals with root raised cosine spectrum. The dependences of the corresponding boundaries on the roll-off factor, the signal-to-noise ratio per symbol, and the observation interval duration are presented.

Strength Abilities: Modeling of Immediate and Delayed Training Effect of Strength Loads in Boys Aged 8 Years

The purpose of the study was to obtain regression models of immediate and delayed training effect of strength loads in boys aged 8 years, based on a full factorial experiment. Materials and methods. The study participants were 48 boys aged 8 years. The experiment was performed using a 22 factorial design. The study materials were processed by the IBM SPSS 22 statistical analysis program. The study examined the impact of four variants of strength load on the immediate (ITE) and the delayed (DTE) training effect of orthogonal strength exercises modes and rest intervals in boys aged 8 years. Results. The study results show that in the proposed matrix of the 22 full factorial design, the chosen step of variation of factors is sufficient to study the influence of different modes of strength exercises on the dynamics of ITE in boys aged 8 years. Based on the data analysis, the study obtained regression models of load for calculating the ITE1, ITE2, and DTE. The obtained regression models make it possible to calculate the number of repetitions and rest interval to achieve the most rational load variant. Conclusions. The analysis of regression equations shows the interrelation between training effects: ITE1 —> ITE2 —> DTE. The value of ITE1, ITE2, and DTE at station I (exercises to strengthen arms and shoulders) and station II (exercises to strengthen abdominal muscles) depends on the increase in the number of repetitions in a set and the duration of the rest interval. The value of ITE1, ITE2 at station ІІІ (exercises to strengthen back muscles) depends on the increase in the number of repetitions in a set and the duration of the rest interval. The value of DTE – on the increase in the number of repetitions in a set and the reduction of the rest interval duration. The value of ITE1 at station IV (exercises to strengthen leg muscles) depends on the increase in the number of repetitions in a set and the reduction of the rest interval duration. To strengthen the DTE, it is necessary to reduce the number of repetitions in a set and the duration of the rest interval.