The test–retest reliability of physiological and perceptual responses during treadmill load carriage

Purpose: Understanding the test–retest reliability of physiological responses to load carriage influences the interpretation of those results. The aim of this study was to determine the test–retest reliability of physiological measures during loaded treadmill walking at 5.5 km h−1 using the MetaMax 3B. Methods: Fifteen Australian Army soldiers (9 male, 6 female) repeated two 12-min bouts of treadmill walking at 5.5 km h−1 in both a 7.2 kg Control condition (MetaMax 3B, replica rifle) and a 23.2 kg Patrol condition (Control condition plus vest) across three sessions, separated by one week. Expired respiratory gases and heart rate were continuously collected, with the final 3 min of data analysed. Ratings of Perceived Exertion and Omnibus-Resistance Exercise Scale were taken following each trial. Reliability was quantified by coefficient of variation (CV), intra-class correlation coefficients (ICC), smallest worthwhile change (SWC), and standard error of the measurement. Results: Metabolic and cardiovascular variables were highly reliable (≤ 5% CV; excellent-moderate ICC), while the respiratory variables demonstrated moderate reliability (< 8% CV; good-moderate ICC) across both conditions. Perceptual ratings had poorer reliability during the Control condition (12–45% CV; poor ICC) than the Patrol condition (7–16% CV; good ICC). Conclusions: The test–retest reliability of metabolic and cardiovascular variables was high and relatively consistent during load carriage. Respiratory responses demonstrated moderate test–retest reliability; however, as the SWC differed with load carriage tasks, such data should be interpreted independently across loads. Perceptual measures demonstrated poor to moderate reliability during load carriage, and it is recommended that they only be employed as secondary measures.</p

Physiological, Perceptual, and Biomechanical Responses to Load Carriage while Walking at Military-Relevant Speeds and Loads—Are There Differences between Males and Females?

This study aimed to investigate the physiological, perceptual, and biomechanical differences between male and female soldiers across several military-relevant load and walking speed combinations. Eleven female and twelve male soldiers completed twelve 12 min walking trials at varying speeds (3.5 km·h−1, 5.5 km·h−1, 6.5 km·h−1) and with varying external loads (7.2 kg, 23.2 kg, 35.2 kg). Physiological (indirect calorimetry, heart rate), perceptual (perceived exertion), and biomechanical (spatiotemporal, kinematic, kinetic) outcomes were measured throughout each trial. Females had a lower aerobic capacity and lower body strength than males, which resulted in them working at a greater exercise intensity (%VO2peak and heart rate) but with a lower oxygen pulse. Females demonstrated higher breathing frequency and perceived exertion with specific loads. At selected loads and speeds, frontal and sagittal pelvis, hip, and knee motions and forces were greater for females. Females consistently displayed greater relative stride length and step width. In conclusion, this study demonstrates the importance of tailored interventions, periodisation, and nutritional strategies for female military personnel, given their higher relative work rate and increased injury risk during load carriage tasks. Understanding these differences is crucial for preparing female soldiers for the physical demands of military service

Associations between app usage and behaviour change in a m-health intervention to improve physical activity and sleep health in adults: Secondary analyses from two randomised controlled trials

Background To examine associations between user engagement and activity-sleep patterns in a 12-week m-health behavioural intervention targeting physical activity and sleep. Methods This secondary analysis used data pooled from two Randomised Control Trials (RCT, [Synergy and Refresh]) that aimed to improve physical activity and sleep (PAS) among physically inactive adults with poor sleep. Both RCTs include a PAS intervention group (n = 190 [Synergy n = 80; Refresh n = 110]) and a wait list Control (CON n = 135 [Synergy n = 80; Refresh n = 55]). The PAS groups received a pedometer and accessed a smartphone/tablet “app” with behaviour change strategies, and email/SMS support. Activity-sleep patterns were quantified using the activity-sleep behaviour index (ASI) based on self-report measures. Intervention usage was quantified as a composite score of the frequency, intensity and duration of app usage during intervention (range: 0–30). Assessments were conducted at baseline, 3 and 6 months. Relationships between usage and ASI were examined using generalised linear models. Differences in ASI between the control group and intervention usage groups (Low [0–10.0], Mid [10.1–20.0], High [20.1–30.0]) were examined using generalised linear mixed models adjusted for baseline values of the outcome. Trial Registration: ACTRN12617000376347; ACTRN12617000680369. Results During the 3-month intervention, the mean (± sd) usage score was 18.9 ± 9.5. At 3 months (regression coefficient [95%CI]: 0.45 [0.22, 0.68]) and 6 months (0.48 [0.22, 0.74]) there was a weak association between usage score and ASI in the intervention group. At 3 months, ASI scores in the Mid (Mean [95%CI] = 57.51 [53.99, 61.04]) and High (60.09 [57.52, 62.67]) usage groups were significantly higher (better) than the control group (51.91 [49.58, 54.24]), but not the Low usage group (47.49 [41.87, 53.12]). Only differences between the high usage and control group remained at 6 months. Conclusion These findings suggests that while higher intervention usage is associated with improvements in behaviour, the weak magnitude of this association suggests that other factors are also likely to influence behaviour change in m-health interventions