Conor Jordan Murphy

Background: Previous research has attempted to determine the minimum number of days of accelerometry required to reliably reflect an individual’s physical activity. However, human behaviors on a day-to-day basis can be highly variable. As a consequence, the number of days required to reliably predict habitual physical activity is dependent on the variability that exists within an individual. There is a concern that adopting generic recommendations from previous research could provide unreliable estimates by failing to represent individuals with specific physical activity patterns. Objective: The main aim of this study was to identify clusters of individuals with distinct physical activity patterns and to determine if the number of days of accelerometry data required to reliably estimate short- (7 days) and medium-term (28 days) physical activity differed between each unique cluster. Methods: Accelerometry data were retrieved from 2 independent research studies. Participants during each study had their physical activity recorded using a Withings Scanwatch (Withings Health Solutions). Following a data eligibility process, agglomerative hierarchical clustering was used to identify clusters of individuals based on their physical activity. The clusters were determined using 4 dimensions; mean, SD, skewness, and kurtosis of the step count data. Intraclass correlation coefficients (ICCs) of step count were then calculated within each physical activity cluster. A series of ICCs were computed by separately comparing the average step count across the full periods (7 and 28, for the short- and medium-term analysis, respectively) to a series of averaged subsamples (ranging from 1-6 days and 1-27 days, for the short- and medium-term analysis, respectively). For each subsample, 500 random combinations were generated and compared, providing a distribution of ICCs for each subsample. An ICC of ≥0.80 identified when the subsample of days was sufficient to achieve appropriate reliability. Results: Of 258 participant datasets, 149 were eligible for the short-term analysis and 64 were eligible for the medium-term analysis. Following agglomerative hierarchical clustering, 4 and 3 clusters of sufficient size (n≥12) were identified in the short-term and medium-term analyses, respectively. When considering the short-term analysis, to achieve a mean ICC score greater than or equal to 0.80, using all randomized combinations, the number of days ranged from 2 to 6 days depending on the physical activity cluster. For the medium-term analysis, the number of days required to achieve a mean ICC score greater than or equal to 0.80 ranged from 6 to 11 days. The short-term analysis clusters displayed more diversity in physical activity patterns than the medium-term analysis. Conclusions: Physical activity patterns influence the number of days required to estimate habitual physical activity. Thus, to avoid unreliable estimates of physical activity, which could significantly impact the interpretation of results, researchers should be mindful of the physical activity patterns of their sample before adopting generic recommendations.

Objectives. The objectives of this study were (i) to design in detail an exercise program for individuals with sleep-disordered breathing (SDB) that would be reproducible, and (ii) to present a system for monitoring training load (volume × intensity) within such a program. Methods. A comprehensive exercise program was developed for individuals with SDB, detailing not only the session structure (warm-up, main part—circuit training and brisk walking—and cool-down) but also the specific exercises, training volume (actual exercise time excluding rest), intensity (Borg Rating of Perceived Exertion—RPE), and training load (calculated as time × RPE, in arbitrary units). This detailed program was previously implemented in a RCT (ISRCTN16974764). A comparison was also made between the planned and performed intensity, and training load through a paired t-test. Results. A fully replicable program was presented. No significant difference was found between the planned and performed training load (p = 0.482). When analyzed by week, a significant difference was found only for overestimation in weeks 9–12 (p < 0.001). Conclusions. In general terms, it can be concluded that a detailed exercise program was described for individuals with SDB. The program is reproducible in terms of content, training volume, intensity, and load. Moreover, the RPE proved to be a valid parameter for quantifying intensity, allowing for the integration of all parts of the session, as well as various types of content. The planned and performed programs (as quantified via participants’ RPE) matched appropriately. Therefore, this program can be reproduced and applied to this type of population.

Wheelchair sprint performance varies by sports classification. Yet, it is unclear how spatio-temporal, kinetic and kinetic asymmetries of wheelchair sprinting differ among wheelchair rugby players of different performance standard. The study purpose was to examine the associations between 30s sprint performances and spatio-temporal, kinetic and kinetic asymmetries on a dual-roller ergometer in elite and sub-elite wheelchair rugby players (n = 20). Kinetic differences between groups were investigated using statistical parametric mapping. Peak velocities were associated with the acceleration phase and higher peak power (r = 0.62, p = 0.003) and lower push times (r=-0.50, p = 0.020). Greater distance travelled during the acceleration phase were correlated with lower asymmetries in peak power (r=-0.58, p = 0.005). Overall, both peak velocity and total distance covered during the entire sprint was correlated with lower push times (r=-0.61, p = 0.003 and r=-0.62, p = 0.003) but greater peak power (r=-0.61, p = 0.003 and r=-0.62, p = 0.003). Elite players had lower power asymmetries between 1 to 15% and 95 to 100% of the push phase during the acceleration phase, accompanied by a lower initial contact asymmetry (p = 0.011). While power is an essential feature of sprinting, our findings show that how it is applied, in terms of asymmetry and coordination might differentiate the performance in wheelchair rugby.

BACKGROUND Previous research has attempted to determine the minimum number of days of accelerometry required to reliably reflect an individual’s physical activity. However, human behaviors on a day-to-day basis can be highly variable. As a consequence, the number of days required to reliably predict habitual physical activity is dependent on the variability that exists within an individual. There is a concern that adopting generic recommendations from previous research could provide unreliable estimates by failing to represent individuals with specific physical activity patterns. OBJECTIVE The main aim of this study was to identify clusters of individuals with distinct physical activity patterns and to determine if the number of days of accelerometry data required to reliably estimate short- (7 days) and medium-term (28 days) physical activity differed between each unique cluster. METHODS Accelerometry data were retrieved from 2 independent research studies. Participants during each study had their physical activity recorded using a Withings Scanwatch (Withings Health Solutions). Following a data eligibility process, agglomerative hierarchical clustering was used to identify clusters of individuals based on their physical activity. The clusters were determined using 4 dimensions; mean, SD, skewness, and kurtosis of the step count data. Intraclass correlation coefficients (ICCs) of step count were then calculated within each physical activity cluster. A series of ICCs were computed by separately comparing the average step count across the full periods (7 and 28, for the short- and medium-term analysis, respectively) to a series of averaged subsamples (ranging from 1-6 days and 1-27 days, for the short- and medium-term analysis, respectively). For each subsample, 500 random combinations were generated and compared, providing a distribution of ICCs for each subsample. An ICC of ≥0.80 identified when the subsample of days was sufficient to achieve appropriate reliability. RESULTS Of 258 participant datasets, 149 were eligible for the short-term analysis and 64 were eligible for the medium-term analysis. Following agglomerative hierarchical clustering, 4 and 3 clusters of sufficient size (n≥12) were identified in the short-term and medium-term analyses, respectively. When considering the short-term analysis, to achieve a mean ICC score greater than or equal to 0.80, using all randomized combinations, the number of days ranged from 2 to 6 days depending on the physical activity cluster. For the medium-term analysis, the number of days required to achieve a mean ICC score greater than or equal to 0.80 ranged from 6 to 11 days. The short-term analysis clusters displayed more diversity in physical activity patterns than the medium-term analysis. CONCLUSIONS Physical activity patterns influence the number of days required to estimate habitual physical activity. Thus, to avoid unreliable estimates of physical activity, which could significantly impact the interpretation of results, researchers should be mindful of the physical activity patterns of their sample before adopting generic recommendations.

Overreaching is often linked to a deterioration in sleep quality, yet a comprehensive review is lacking. The aim of this systemic review and meta-analysis was to synthesise the literature and quantify the effect of overreaching from endurance-based training on sleep quality. The review was conducted following the PRISMA guidelines. The final search was conducted in May 2023 using four electronic databases (Web of Science Core Collection, MEDLINE, Cochrane Central Database, SPORTDiscus). Studies were included for a qualitative review, while random-effects meta-analyses were conducted for objective and subjective sleep. The search returned 805 articles. Fourteen studies were included in the systematic review; Three and eight articles were eligible for the meta-analyses (objective and subjective, respectively). On average, the overreaching protocols were sixteen days in length (6 to 28 days) and included exercise modalities such as cycling (number of studies [k] = 5), rowing (k = 4), triathlon (k = 3), running (k = 2), and swimming (k = 1). Actigraphy was the only form of objective sleep measurement used across all studies (k = 3), while various instruments were used to capture subjective sleep quality (k = 13). When comparing objective sleep quality following the overreaching intervention to baseline (or a control), there was a significant reduction in sleep efficiency (mean difference = -2.0%; 95% CI -3.2, -0.8%; Glass’ Δ = -0.83; p < 0.01). In contrast, when comparing subjective sleep quality following the overreaching intervention to baseline (or a control), there was no effect on subjective sleep quality (Glass’ Δ = -0.27; 95% CI -0.79, 0.25; p = 0.08). Importantly, none of the included studies were judged to have a low risk of bias. While acknowledging the need for more high-quality studies, it appears that overreaching from endurance-based training can deteriorate objective sleep without influencing the perception of sleep quality. This protocol was registered in The International Prospective Register of Systematic Reviews (PROSPERO) on 21^st November 2022, with the registration number CRD42022373204.

Sleep disturbances are common in athletes with a cervical spinal cord injury (cSCI) and may be associated with circadian alterations. Therefore, the purpose of this study was to compare physiological circadian outputs between athletes with a cSCI and non-disabled controls (CON). Eight male wheelchair athletes with a cSCI and eight male CON (30±4 and 30±6 yrs, respectively) had their core body temperature (T_core), skin temperature (T_skin), and salivary melatonin measured during a 24 h period. In the cSCI group, daytime T_core was significantly lower (36.5 (0.2) vs 36.9 (0.3)°C; p=0.02) and time of the T_core sleep minimum was significantly earlier (23:56±00:46 vs 02:39 ± 02:57; p=0.04). The athletes with a cSCI had significantly lower T_core values during the beginning of the night compared with the CON group, but their T_core increased at a greater rate, thereafter, indicated by a significant time/group interaction (p=0.04). Moreover, the cSCI group did not display a salivary melatonin response and exhibited significantly lower concentrations at 22:00 (p=0.01) and 07:00 (p=0.01) compared with the CON group. Under natural living conditions, athletes with a cSCI displayed circadian changes in the T_core rhythm and nocturnal melatonin production. Received: 25 October 2021 Accepted: 08 August 2022 Article published online: 11 November 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany