Several studies have shown the differences among chronotypes in the circadian rhythm of different physiological variables. Individuals show variation in their preference for the daily timing of ...activity; additionally, there is an association between chronotype and sleep duration/sleep complaints. Few studies have investigated sleep quality during the week days and weekends in relation to the circadian typology using self-assessment questionnaires or actigraphy. The purpose of this study was to use actigraphy to assess the relationship between the three chronotypes and the circadian rhythm of activity levels and to determine whether sleep parameters respond differently with respect to time (weekdays versus the weekend) in Morning-types (M-types), Neither-types (N-types) and Evening-types (E-types). The morningness-eveningness questionnaire (MEQ) was administered to 502 college students to determine their chronotypes. Fifty subjects (16 M-types, 15 N-types and 19 E-types) were recruited to undergo a 7-days monitoring period with an actigraph (Actiwacth® actometers, CNT, Cambridge, UK) to evaluate their sleep parameters and the circadian rhythm of their activity levels. To compare the amplitude and the acrophase among the three chronotypes, we used a one-way ANOVA followed by the Tukey-Kramer post-hoc test. To compare the Midline Estimating Statistic of Rhythm (MESOR) among the three chronotypes, we used a Kruskal-Wallis non-parametric test followed by pairwise comparisons that were performed using Dunn's procedure with a Bonferroni correction for multiple comparisons. The analysis of each sleep parameter was conducted using the mixed ANOVA procedure. The results showed that the chronotype was influenced by sex (χ2 with p = 0.011) and the photoperiod at birth (χ2 with p < 0.05). Though the MESOR and amplitude of the activity levels were not different among the three chronotypes, the acrophases compared by the ANOVA post-hoc test were significantly different (p < 0.001). The ANOVA post-hoc test revealed the presence of a significant difference (p < 0.001) between the M-types (14:32 h) and E-types (16:53 h). There was also a significant interaction between the chronotype and four sleep parameters: Sleep end, Assumed Sleep, Immobility Time and Sleep Efficiency. Sleep Efficiency showed the same patterns as did Assumed Sleep and Immobility Time: the Sleep Efficiency of the E-types was poorer than that of the M- and N-types during weekdays (77.9% ± 7.0 versus 84.1% ± 4.9 and 84.1% ± 5.2) but was similar to that measured in the M- and N-types during the weekend. Sleep Latency and Movement and Fragmentation Index were not different among the three chronotypes and did not change on the weekend compared with weekdays. This study highlights two key findings: first, we observed that the circadian rhythm of activity levels was influenced by the chronotype; second, the chronotype had a significant effect on sleep parameters: the E-types had a reduced sleep quality and quantity compared with the M- and N-types during weekdays, whereas the E-types reached the same levels as the other chronotypes during the weekends. These findings suggest that E-types accumulate a sleep deficit during weekdays due to social and academic commitments and that they recover from this deficit during "free days" on the weekend.
Actigraphy is the reference objective method to measure circadian rhythmicity. One simpler subjective approach to assess the circadian typology is the Morningness-Eveningness Questionnaire (MEQ) by ...Horne and Ostberg. In this study, we compared the MEQ score against the actigraphy-based circadian parameters MESOR, amplitude and acrophase in a sample of 54 students of the University of Milan in Northern Italy. MEQ and the acrophase resulted strongly and inversely associated (r = −0.84, p < 0.0001), and their relationship exhibited a clear-cut linear trend. We thus used linear regression to develop an equation enabling us to predict the value of the acrophase from the MEQ score. The parameters of the regression model were precisely estimated, with the slope of the regression line being significantly different from 0 (p < 0.0001). The best-fit linear equation was: acrophase (min) = 1238.7-5.49·MEQ, indicating that each additional point in the MEQ score corresponded to a shortening of the acrophase of approximately 5 min. The coefficient of determination, R
2
, was 0.70. The residuals were evenly distributed and did not show any systematic pattern, thus indicating that the linear model yielded a good, balanced prediction of the acrophase throughout the range of the MEQ score. In particular, the model was able to accurately predict the mean values of the acrophase in the three chronotypes (Morning-, Neither-, and Evening-types) in which the study subjects were categorized. Both the confidence and prediction limits associated to the regression line were calculated, thus providing an assessment of the uncertainty associated with the prediction of the model. In particular, the size of the two-sided prediction limits for the acrophase was about ±100 min in the midrange of the MEQ score. Finally, k-fold cross-validation showed that both the model's predictive ability on new data and the model's stability to changes in the data set used for parameter estimation were good. In conclusion, the actigraphy-based acrophase can be predicted using the MEQ score in a population of college students of North Italy.
Heat dissipation during sport exercise is an important physiological mechanism that may influence athletic performance. Our aim was to test the hypothesis that differences exist in the dynamics of ...exercise-associated skin temperature changes between trained and untrained subjects. We investigated thermoregulation of a local muscle area (muscle–tendon unit) involved in a localized steady-load exercise (standing heels raise) using infrared thermography. Seven trained female subjects and seven untrained female controls were studied. Each subject performed standing heels raise exercise for 2 min. Thermal images were recorded prior to exercise (1 min), during exercise (2 min), and after exercise (7 min). The analysis of thermal images provided the skin temperature time course, which was characterized by a set of descriptive parameters. Two-way ANOVA for repeated measures detected a significant interaction (
p
= 0.03) between group and time, thus indicating that athletic subjects increased their skin temperature differently with respect to untrained subjects. This was confirmed by comparing the parameters describing the speed of rise of skin temperature. It was found that trained subjects responded to exercise more quickly than untrained controls (
p
< 0.05). In conclusion, physical training improves the ability to rapidly elevate skin temperature in response to a localized exercise in female subjects.
Low intensity resistance training with slow movement and tonic force generation has been shown to create blood flow restriction within muscles that may affect thermoregulation through the skin. We ...aimed to investigate the influence of two speeds of exercise execution on skin temperature dynamics using infrared thermography. Thirteen active males performed randomly two sessions of squat exercise (normal speed, 1s eccentric/1s concentric phase, 1s; slow speed, 5s eccentric/5s concentric phase, 5s), using ~50% of 1 maximal repetition. Thermal images of ST above muscles quadriceps were recorded at a rate of 0.05Hz before the exercise (to determine basal ST) and for 480s following the initiation of the exercise (to determine the nonsteady-state time course of ST). Results showed that ST changed more slowly during the 5s exercise (p=0.002), whereas the delta (with respect to basal) excursions were similar for the two exercises (p>0.05). In summary, our data provided a detailed nonsteady-state portrait of ST changes following squat exercises executed at two different speeds. These results lay the basis for further investigations entailing the joint use of infrared thermography and Doppler flowmetry to study the events taking place both at the skin and the muscle level during exercises executed at slow speed.
•Two speeds of exercise execution on skin temperature dynamics were studied.•The skin temperature dynamics were different in the two exercises.•Skin temperature changed more slowly in the exercise with slow speed.•Similar temperature excursions were found in the two exercises.
Rest-Activity circadian Rhythm (RAR) can be used as a marker of the circadian timing system. Recent studies investigated the relationship between irregular circadian rhythms and cardiovascular risk ...factors such as hypertension, obesity, and dyslipidemia. These factors are related to the Metabolic Syndrome (MS), a clustering of metabolic risk factors that increases the risk of several cardiovascular and metabolic diseases. This cross-sectional analysis aimed to explore the RAR characteristics by actigraphy in subjects with MS, particularly in relation to sex and MS parameters, using parametric and non-parametric analyses. Distinguishing the characteristics of RAR based on sex could prove useful as a tool to improve the daily level of activity and set up customized activity programs based on each person's circadian activity profile. This study showed that female participants exhibited higher values than male participants in the Midline Estimating Statistic of Rhythm (MESOR) (243.3 ± 20.0 vs 197.6 ± 17.9 activity count), Amplitude (184.5 ± 18.5 vs 144.2 ± 17.2 activity count), which measures half of the extent of the rhythmic variation in a cycle, and the most active 10-h period (M10) (379.08 ± 16.43 vs 295.13 ± 12.88 activity count). All these parameters are indicative of a higher daily activity level in women. Female participants also had lower Intradaily Variability (IV) than male participants (0.75 ± 0.03 vs 0.85 ± 0.03 activity count), which indicates a more stable and less fragmented RAR. These preliminary data provide the first experimental evidence of a difference in RAR parameters between male and female people with MS.
Hypotheses. Sleep disorders are associated with an increased risk of cancer, including breast cancer (BC). Physical activity (PA) can produce beneficial effects on sleep. Study design. We designed a ...randomized controlled trial to test the effect of 3 months of physical activity on sleep and circadian rhythm activity level evaluated by actigraphy. Methods. 40 BC women, aged 35-70 years, were randomized into an intervention (IG) and a control group (CG). IG performed a 3 month of aerobic exercise. At baseline and after 3 months, the following parameters were evaluated both for IG and CG: anthropometric and body composition measurements, energy expenditure and motion level; sleep parameters (Actual Sleep Time-AST, Actual Wake Time-AWT, Sleep Efficiency-SE, Sleep Latency-SL, Mean Activity Score-MAS, Movement and Fragmentation Index-MFI and Immobility Time-IT) and activity level circadian rhythm using the Actigraph Actiwatch. Results. The CG showed a deterioration of sleep, whereas the IG showed a stable pattern. In the CG the SE, AST and IT decreased and the AWT, SL, MAS and MFI increased. In the IG, the SE, IT, AWT, SL, and MAS showed no changes and AST and MFI showed a less pronounced change in the IG than in the CG. The rhythmometric analysis revealed a significant circadian rhythm in two groups. After 3 months of PA, IG showed reduced fat mass %, while CG had improved weight and BMI. Conclusion. Physical activity may be beneficial against sleep disruption. Indeed, PA prevented sleep worsening in IG. PA can represent an integrative intervention therapy able to modify sleep behaviour.
ABSTRACTVitale, JA, Caumo, A, Roveda, E, Montaruli, A, La Torre, A, Battaglini, CL, and Carandente, F. Physical attributes and NFL Combine performance tests between Italian National League and ...American football playersa comparative study. J Strength Cond Res 30(10)2802–2808, 2016—The purpose of this study was to examine anthropometric measurements and the results of a battery of performance tests administered during the National Football League (NFL) Combine between American football players who were declared eligible to participate in the NFL Combine and football players of a top Italian team (Rhinos Milan). Participants (N = 50) were categorized by position into 1 of 3 groups based on playing positionskill players (SP) included wide receivers, cornerbacks, free safeties, strong safeties, and running backs; big skill players (BSP) consisted of fullbacks, linebackers, tight ends, and defensive ends; lineman (LM) included centers, offensive guards, offensive tackles, and defensive tackles. A 1-way analysis of variance followed by the Tukey-Kramer post hoc test was used for comparisons between Italian players by playing position. Ninety-five percent CIs were used for comparisons between American and Italian football for the NFL Combine performance tests. Significant differences for all the variables between the 3 playing categories were observed among the Italian players; LM had higher anthropometric and body composition values than SP (p < 0.001) and BSP (p < 0.001), whereas LM performed significantly worse in the physical tests, except for the 225-lb bench press test when compared with SP (p < 0.002). American football players presented significantly higher anthropometric values and test performance scores when compared with Italian players. Administrators of professional football teams in Italy need to improve the playerʼs physical attributes, so the gap that currently exists between American and Italian players can be reduced, which could significantly improve the quality of American football in Italy.
OBJECTIVE: Considerable evidence suggests a connection between panic disorder and respiration, but the nature of the respiratory abnormalities in panic disorder remains unclear. The authors ...investigated the breath-by-breath complexity of respiration dynamics in panic disorder. METHOD: Respiratory physiology was assessed in 40 patients with panic disorder and 31 healthy comparison subjects by using a breath-by-breath stationary system for testing cardiorespiratory function. Irregularity in the breathing pattern was determined by applying the approximate entropy index, which is an indicator of the irregularity and the "disorder" of the measure. RESULTS: The patients with panic disorder showed significantly higher approximate entropy indexes than the healthy subjects for the measured respiratory parameters. Sighs contributed to the irregularity of breathing patterns but did not account for all the differences in approximate entropy between the patients with panic disorder and the comparison subjects. Anxiety state, severity of illness, and somatic and individual variables such as participation in sports and cigarette smoking did not seem to influence the results. CONCLUSIONS: Patients with panic disorder showed greater entropy in baseline respiratory patterns, indicating higher levels of irregularity and complexity in their respiratory function. Greater respiratory entropy could be a factor in vulnerability to panic attacks.
Several simple measures of graft function after islet transplantation have been proposed but a comparative evaluation is lacking. Here, we compared the performance of five indices of β-cell function: ...β-score, transplant estimated function (TEF), homeostasis model assessment (HOMA) 2-B%, C-peptide/glucose ratio, and Secretory Units of Islets in Transplantation (SUIT).
Two cohorts of transplanted patients were analyzed. Cohort 1 consisted of 14 recipients with type 1 diabetes of islet transplantation whereas cohort 2 consisted of 21 recipients with type 1 diabetes of cultured islet cell graft. The five surrogate indices were compared against the first- and second-phase insulin response to arginine in cohort 1, and against the C-peptide response to a hyperglycemic clamp in cohort 2.
We found that the performances of the five surrogate indices were close one to each other in cohort 1. The correlation coefficients ranged 0.62 to 0.67 and 0.62 to 0.68 against the first- and second-phase insulin response to arginine, respectively. In cohort 2, we found that the β-score, TEF, C-peptide/glucose ratio, and SUIT were reasonably well correlated with the clamp response (correlation coefficients were in the range 0.71-0.81), whereas HOMA2-B% showed a modest performance (r=0.54). HOMA2-B% could not be evaluated in one patient whose fasting glucose concentration level was below the lower bound indicated by the HOMA calculator (3 mmol/L). SUIT could not be evaluated in three patients whose fasting glucose concentration was below the glucose threshold of the SUIT formula (3.43 mmol/L).
In summary, no single index outperformed the others. Nevertheless, when the benefit to cost ratio is considered, TEF stands out for its good performance at a very low cost.
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