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Objectives: Assess the effect of water dousing on heat strain and performance during self- and fixed-paced exercise in the heat. Design: Crossover, block-randomized controlled trial. Methods: Thirteen trained runners completed a 10-km time trial (TT) and 60-minute fixed-pace run (60% velocity of $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ ) in a 30.4 °C, 47.4% relative humidity environment using either water dousing (DOUSE) or no dousing (CON). Results: Ten-kilometer TT performance was faster in DOUSE compared to CON (44:11 [40:48, 47:34] vs 44:38 [41:21, 47:56] min:s; P = .033). Change in core temperature (T _c ) was not different between groups during the TT (+0.02 [−0.04, 0.07] °C in DOUSE; P = .853) or fixed-pace run (+0.02 [−0.15, 0.18] °C; P = .848). Change in mean skin temperature was lower in DOUSE during the TT (−1.80 [−2.15, −1.46] °C; P < .001) and fixed-pace run (−1.38 [−1.81, −0.96] °C; P < .001). Heart rate was lower for DOUSE during the fixed-pace run (−3.5 [−6.8, −0.2] beats/min; P = .041) but not during the TT (−0.2 [−2.5, 2.1] beats/min; P = .853). Thermal sensation was lower for DOUSE during the TT (−49.3 [−72.1, −26.1] mm; P < .001) and fixed-pace run (−44.7 [−59.7, −29.6] mm; P < .001). Rating of perceived exertion was not different between groups for the TT (−0.2 [−0.7, 0.3]; P = .390) or fixed-pace run (−0.2 [−0.8, 0.4]; P = .480). Sweat rate was lower for DOUSE for the TT (−0.37 [−0.53, −0.22] L/h; P < .001) and fixed-pace run (−0.37 [−0.48, −0.26] L/h; P < .001). Conclusion: Water dousing improves 10-km TT performance in the heat but does not affect T _c . The positive change in thermal perception (via lower skin temperature) during the TT likely drives this benefit.

Purpose: We investigated the effects of manipulating running velocity and hypoxic exposure on vastus lateralis muscle oxygenation levels during treadmill running. Methods: Eleven trained male distance runners performed 7 randomized runs at different velocities (8, 10, 12, 14, 16, 18, and 20 km·h⁻¹), each lasting 45 seconds on an instrumented treadmill in normoxia (fraction of inspired oxygen [FiO₂] = 20.9%), moderate hypoxia (FiO₂ = 16.1%), high hypoxia (FiO₂ = 14.1%), and severe hypoxia (FiO₂ = 13.0%). Continuous assessment of Tissue Saturation Index (TSI) in the vastus lateralis muscle was conducted using near-infrared spectroscopy. Subsequently, changes in TSI (ΔTSI) data over the final 20 seconds of each run were compared between velocities and conditions. Results: There was a significant velocity × condition interaction for ΔTSI% (P < .001, ${\eta }_{\mathrm{p}}^2=.19$ ), with a smaller ΔTSI% decline in normoxia compared with high hypoxia and severe hypoxia at 8 km·h⁻¹ (g = 1.30 and 1.91, respectively), 10 km·h⁻¹ (g = 0.75 and 1.43, respectively), and 12 km·h⁻¹ (g = 1.47 and 1.95, respectively) (pooled values for all conditions: P < .037). The ΔTSI% decline increased with each subsequent velocity increment from 8 km·h⁻¹ (−9.2% [3.7%]) to 20 km·h⁻¹ (−22.5% [4.1%]) irrespective of hypoxia severity (pooled values for all conditions: P < .048). Conclusions: Running at slower velocities in conjunction with high and severe hypoxia reduces vastus lateralis muscle oxygenation levels. Muscle ΔTSI% proves to be a sensitive indicator, underscoring the potential use of near-infrared spectroscopy as a reference index of internal load during treadmill runs.

Background: The aim of this study was to systematically review the findings for validity, reliability, and acceptability of device-based measures of office-based physical activity and/or sedentary time in an office context to evaluate workplace interventions. Methods: The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Analysis guidelines. Five electronic databases (PubMed, EMBASE, CINAHL, Cochrane, and Web of Science) were searched (inception to December 2023). Keywords included population (e.g., workers), type of measure (e.g., device-based), measurement constructs (e.g., validity), context (e.g., office), and behavior (e.g., sitting). Two authors screened titles, abstracts, and full texts independently with disagreements resolved by a third author. Findings were reported using narrative synthesis, and COnsensus-based standards for the Selection of health status Measurement INstruments was used for quality assessment. Results: In total, 2,299 articles were identified, with 16 articles retained. These reported 21 measurement protocols (nine in free-living settings) assessing eight worn, four remote, and one combined method. Sixteen protocols assessed office sitting, with standing (n = 8), moving (n = 11), postural transitions (n = 7), and location (n = 2) also assessed. Participant sample sizes ranged from one to 42 (median = 13). Criterion validity was assessed in all 21 protocols, with lower limb–worn measures of sitting, and worn and remote measures of location reporting the highest validity/accuracy compared with the ground truth (good to excellent). Only two articles reported acceptability (good acceptability), with none reporting reliability. Conclusions: There is evidence of valid device-based measures of office behavior (particularly sitting and location of workers), but this has largely been obtained in laboratory settings and/or with small samples. Larger studies in more varied free-living settings, potentially using multiples sources of data and assessing acceptability, are required.

Measuring infants’ (0–2 years) physical activity is a growing area of research globally. Accelerometers have been widely used to measure older children’s and adults’ physical activity. An increasing number of studies have used accelerometers as a way to measure infant physical activity, which has resulted in the application of a variety of methods. The purpose of this scoping review is to synthesize the published literature on accelerometer methodology to measure daytime physical activity among infants (0–2 years). A systematic search of five online databases using carefully selected key terms was conducted to compile relevant literature. The results of the online database searches were screened for inclusion in the scoping review. In total, 105 articles met the inclusion criteria of using accelerometers to measure infants’ physical activity. The methodologies used in the included studies were categorized by age groups: <1 month, 1–6 months, >6–12 months, >12–18 months, >18–24 months, and longitudinal (i.e., multiple measurements taken across the previously mentioned age groups). Accelerometry methodologies (e.g., wear location, number of devices, device initialization) and study design qualities (e.g., outcome of interest and location of data collection) varied widely between and within the various age groups. Accelerometer brand or type of device demonstrated greatest variation across included studies. However, ActiGraph devices to measure physical activity within free-living environments were the most common. This review provides evidence of the need for researchers to ensure the methodology used is reported in detail in order to help develop methodology that can accurately assess infant daytime movement.

Accelerometers have been at the forefront of free-living activity capture for decades, and accordingly ActiGraph the largest distributor. Historically, limitations in data storage and battery power led to the use of summary metrics, which have been termed activity counts. Recently, ActiGraph publicly released their count-based algorithm, marking a notable development in the field. This study aimed to assess and compare activity counts generated through different processing techniques (ActiLife and open-source), filters that are available through ActiGraph count generation (normal- and low-frequency extension), and data from various ActiGraph models and GENEActiv devices. We evaluated ActiGraph GT3X+ (n = 8), ActiGraph wGT3X-BT (n = 10), ActiGraph GT9X (n = 8; primary and secondary sensors), OPAL (n = 6), and GENEActiv (n = 5), subjected to oscillations across their full dynamic range (0.005–8 G) using a multiaxis shaker table. Results indicated that the low-frequency extension produced significantly higher counts compared to the normal frequency across the devices and processing techniques. Notably, open-source counts (R and Python) were statistically equivalent to ActiLife-generated counts (p < .05) for the GT9X, wGT3X-BT, and the GT3X+. Overall, many of the counts generated by different ActiGraph models were statistically equivalent or had mean differences <5.03 counts. Conversely, the GENEActiv, OPAL, and GT9X secondary monitor exhibited significantly higher responses than the other ActiGraph models at higher frequencies with mean differences ranging from 55.50 to 104.91 counts. This study provides insights into accelerometer data processing methods and highlights the comparability of counts across different devices and techniques.

Activity counts have been used for over two decades with over 22,000 published scientific papers in public health and clinical research. ActiGraph recently released the algorithm for computing counts from raw accelerometer data as an open-source Python library, which is now ported by researchers to other languages, notably R. The current commentary presents historical overview of ActiGraph counts, and its development and evolution as a measure of physical activity. Further, we provide general recommendations on extracting counts from raw accelerometer data and discuss specific considerations with respect to device types, resampling, nonwear, axes orientations, and epoch length that may influence counts. Last, we provide a tutorial on how to use ActiGraph’s open-source Python library, agcounts, for consistent, accurate, and reproducible count. We expect this commentary will provide familiarity and transparency needed to adopt and produce activity counts in a consistent manner, allowing researchers to conduct statistical comparisons across multiple data sets and studies.