With increased professionalism in sport there has been a greater interest in the scientific approach to training and recovery of athletes. Applying appropriate training loads along with adequate recovery, is essential in gaining maximal adaptation in athletes, while minimizing harm such as overreaching, overtraining, injury and illness. Although appropriate physical stress is essential, stress for many athletes may come from areas other than training. Stress from may arise from social or environmental pressure, and for many athletes who combine elite athletic training with university study, academic workloads create significant stress which adds to the constant pressure to perform athletically. This research aimed to determine if subjective stressors were associated with counterproductive training adaptations in university athletes. Moreover, it aimed to elucidate if, and when, such stressors are most harmful (i.e., certain times of the academic year or sports training season). We monitored subjective (mood state, energy levels, academic stress, sleep quality/quantity, muscle soreness, training load) and objective (injury and illness) markers in 182 young (18–22 years) elite athletes over a 4-year period using a commercially available software package. Athletes combined full-time university study with elite sport and training obligations. Results suggest athletes were relatively un-stressed with high levels of energy at the beginning of each university semester, however, energy levels deteriorated along with sleep parameters toward the examination periods of the year. A logistical regression indicated decreased levels of perceived mood (0.89, 0.85–0.94, Odds Ratio and 95% confidence limits), sleep duration (0.94, 0.91–0.97) and increased academic stress (0.91, 0.88–0.94) and energy levels (1.07, 1.01–1.14) were able to predict injury in these athletes. Examination periods coincided with the highest stress levels and increased likelihood of illness. Additionally, a sudden and high increase in training workload during the preseason was associated with an elevated incidence of injury and illness (r = 0.63). In conclusion, young elite athletes undertaking full-time university study alongside their training and competition loads were vulnerable to increased levels of stress at certain periods of the year (pre-season and examination time). Monitoring and understanding these stressors may assist coaches and support staff in managing overall stress in these athletes.

In research clinic settings, overweight adults undertaking HIIT (high intensity interval training) improve their fitness as effectively as those undertaking conventional walking programs but can do so within a shorter time spent exercising. We undertook a randomized controlled feasibility (pilot) study aimed at extending HIIT into a real world setting by recruiting overweight/obese, inactive adults into a group based activity program, held in a community park. Participants were allocated into one of three groups. The two interventions, aerobic interval training and maximal volitional interval training, were compared with an active control group undertaking walking based exercise. Supervised group sessions (36 per intervention) were held outdoors. Cardiorespiratory fitness was measured using VO₂max (maximal oxygen uptake, results expressed in ml/min/kg), before and after the 12 week interventions. On ITT (intention to treat) analyses, baseline (N = 49) and exit (N = 39) O₂ was 25.3±4.5 and 25.3±3.9, respectively. Participant allocation and baseline/exit VO₂max by group was as follows: Aerobic interval training N = 16, 24.2±4.8/25.6±4.8; maximal volitional interval training N = 16, 25.0±2.8/25.2±3.4; walking N = 17, 26.5±5.3/25.2±3.6. The post intervention change in VO₂max was +1.01 in the aerobic interval training, −0.06 in the maximal volitional interval training and −1.03 in the walking subgroups. The aerobic interval training subgroup increased VO₂max compared to walking (p = 0.03). The actual (observed, rather than prescribed) time spent exercising (minutes per week, ITT analysis) was 74 for aerobic interval training, 45 for maximal volitional interval training and 116 for walking (p = 0.001). On descriptive analysis, the walking subgroup had the fewest adverse events. In contrast to earlier studies, the improvement in cardiorespiratory fitness in a cohort of overweight/obese participants undertaking aerobic interval training in a real world setting was modest. The most likely reason for this finding relates to reduced adherence to the exercise program, when moving beyond the research clinic setting. ACTR.org.au ACTRN12610000295044

Background: Validation of instruments used to measure physical activity patterns is essential when attempting to assess the effectiveness of physical activity interventions.Objectives: To assess the validity of two self-report physical activity questionnaires on a representative sample of New Zealand adults.Methods: 70 adults aged 18–65 years from around Christchurch, New Zealand were required to wear an ActiGraph GT1M accelerometer during all waking hours for 7 consecutive days. Immediately following the 7 day accelerometer period participants were required to complete the long forms of both the New Zealand Physical Activity Questionnaire (NZPAQ-LF) and the International Physical Activity Questionnaire (IPAQ-LF).Results: Both the NZPAQ-LF and the IPAQ-LF questionnaires showed small to moderate correlations with ActiGraph data for time spent in moderate-intensity physical activity (r=0.19–0.30) and total physical activity (sum of moderate and vigorous-intensity physical activity, r=0.30–0.32). In comparison with the ActiGraph data, both self-report questionnaires tended to overestimate activity levels by approximately 165%. Total physical activity levels gathered from both questionnaires were strongly correlated with each other (r=0.79) and showed good levels of agreement in the Bland–Altman plots.Conclusions: The long forms of the NZPAQ and IPAQ were found to have acceptable validity when detecting participants' ability to meet activity guidelines based on exercise duration, but a significant amount of overestimation was evident. This presents a need for both instruments to be further developed and tested in order to increase validity.

We examined the relationship between changes in cardiorespiratory and cerebrovascular function in 14 healthy volunteers with and without hypoxia [arterial O₂ saturation (SaO₂) ∼80%] at rest and during 60–70% maximal oxygen uptake steady-state cycling exercise. During all procedures, ventilation, end-tidal gases, heart rate (HR), arterial blood pressure (BP; Finometer) cardiac output (Modelflow), muscle and cerebral oxygenation (near-infrared spectroscopy), and middle cerebral artery blood flow velocity (MCAV; transcranial Doppler ultrasound) were measured continuously. The effect of hypoxia on dynamic cerebral autoregulation was assessed with transfer function gain and phase shift in mean BP and MCAV. At rest, hypoxia resulted in increases in ventilation, progressive hypocapnia, and general sympathoexcitation (i.e., elevated HR and cardiac output); these responses were more marked during hypoxic exercise ( P < 0.05 vs. rest) and were also reflected in elevation of the slopes of the linear regressions of ventilation, HR, and cardiac output with SaO₂ ( P < 0.05 vs. rest). MCAV was maintained during hypoxic exercise, despite marked hypocapnia (44.1 ± 2.9 to 36.3 ± 4.2 Torr; P < 0.05). Conversely, hypoxia both at rest and during exercise decreased cerebral oxygenation compared with muscle. The low-frequency phase between MCAV and mean BP was lowered during hypoxic exercise, indicating impairment in cerebral autoregulation. These data indicate that increases in cerebral neurogenic activity and/or sympathoexcitation during hypoxic exercise can potentially outbalance the hypocapnia-induced lowering of MCAV. Despite maintaining MCAV, such hypoxic exercise can potentially compromise cerebral autoregulation and oxygenation.