Respirometry is an important tool for understanding whole-animal energy and water balance in relation to the environment. Consequently, the growing number of studies using respirometry over the last decade warrants reliable reporting and data sharing for effective dissemination and research synthesis. We provide a checklist guideline on five key sections to facilitate the transparency, reproducibility, and replicability of respirometry studies: 1) materials, set up, plumbing, 2) subject conditions/maintenance, 3) measurement conditions, 4) data processing, and 5) data reporting and statistics, each with explanations and example studies. Transparency in reporting and data availability has benefits on multiple fronts. Authors can use this checklist to design and report on their study, and reviewers and editors can use the checklist to assess the reporting quality of the manuscripts they review. Improved standards for reporting will enhance the value of primary studies and will greatly facilitate the ability to carry out higher quality research syntheses to address ecological and evolutionary theories.

While physical activity, sleep and sedentary behaviors are almost always considered independently, they should be considered as integrated human behaviors. The 24 h Movement approach proposes a concomitant consideration of these behaviors to promote overall health. Not only do these behaviors impact energy expenditure, but they have also been shown to separately impact energy intake, which should be further explored when considering the entire integration of these movement behaviors under the 24 h movement approach. After an evaluation of the prevalence of meeting the 24 h Movement and dietary recommendations, this review summarizes the available evidence (using English publications indexed in PubMed/MEDLINE) regarding the association between the 24 h Movement Guidelines and eating habits. Altogether, the results clearly show the beneficial impact of promoting the 24 h guidelines simultaneously, highlighting that the higher the number of respected movement recommendations, the better eating behaviors in both children and adults. Importantly, our results point out the importance of emphasizing the need to reach sedentary guidelines for better eating habits. Movement and dietary behaviors appear closely related, and giving recommendations on one might impact the other. Combining the 24 h Movement with dietary Guidelines might be more efficient than promoting them separately in public health strategies.

Critically ill patients with obesity have unique and complex nutritional needs, with clinical practice guidelines conflicting regarding recommended energy targets. The aim of this systematic review was to 1) describe measured resting energy expenditure (mREE) reported in the literature and; 2) compare mREE to predicted energy targets using the European (ESPEN) and American (ASPEN) guideline recommendations when indirect calorimetry is not available in critically ill patients with obesity.
The protocol was registered apriori and literature was searched until 17th March, 2022. Original studies were included if they reported mREE using indirect calorimetry in critically ill patients with obesity (BMI≥30 kg/m2). Group-level mREE data was reported as per the primary publication using mean ± standard deviation or median [interquartile range]. Where individual patient data was available, Bland-Altman analysis was used to assess mean bias (95% limits of agreement) between guideline recommendations and mREE targets (i.e. ASPEN for BMI 30-50, 11-14 kcal/kg actual weight compared to 70% mREE and ESPEN 20-25 kcal/kg adjusted weight compared to 100% mREE). Accuracy was assessed by the percentage (%) of estimates within ±10% of mREE targets.
After searching 8019 articles, 24 studies were included. mREE ranged from 1607 ± 385 to 2919 [2318-3362]kcal and 12-32kcal/actual body weight. For the ASPEN recommendations of 11-14 kcal/kg, a mean bias of -18% (-50% to +13%) and 4% (-36% to +44%) was observed, respectively (n = 104). For the ESPEN recommendations 20-25 kcal/kg, a bias of -22% (-51% to +7%) and -4% (-43% to +34%), was observed, respectively (n = 114). The guideline recommendations were able to accurately predict mREE targets on 30%-39% occasions (11-14 kcal/kg actual) and 15%-45% occasions (20-25 kcal/kg adjusted), for ASPEN and ESPEN recommendations, respectively.
Measured energy expenditure in critically ill patients with obesity is variable. Energy targets generated using predictive equations recommended in both the ASPEN and ESPEN clinical guidelines have poor agreement with mREE and are frequently not able to accurately predict within ±10% of mREE, most commonly underestimating energy needs.

The proliferation of approaches for analyzing accelerometer data using raw acceleration or novel analytic approaches like machine learning (\'novel methods\') outpaces their implementation in practice. This may be due to lack of accessibility, either because authors do not provide their developed models or because these models are difficult to find when included as supplementary material. Additionally, when access to a model is provided, authors may not include example data or instructions on how to use the model. This further hinders use by other researchers, particularly those who are not experts in statistics or writing computer code.Objective: We created a repository of novel methods of analyzing accelerometer data for the estimation of energy expenditure and/or physical activity intensity and a framework and reporting guidelines to guide future work.Approach: Methods were identified from a recent scoping review. Available code, models, sample data, and instructions were compiled or created.Main Results: Sixty-three methods are hosted in the repository, in preschoolers (n = 6), children/adolescents (n = 20), and adults (n = 42), using hip (n = 45), wrist (n = 25), thigh (n = 4), chest (n = 4), ankle (n = 6), other (n = 4), or a combination of monitor wear locations (n = 9). Fifteen models are implemented in R, while 48 are provided as cut-points, equations, or decision trees.Significance: The developed tools should facilitate the use and development of novel methods for analyzing accelerometer data, thus improving data harmonization and consistency across studies. Future advances may involve including models that authors did not link to the original published article or those which identify activity type.

Background: To evaluate the methodological quality of (1) clinical practice guidelines (CPGs) that inform nutrition care in critically ill adults using the AGREE II tool and (2) CPG recommendations for determining energy expenditure using the AGREE-REX tool. Methods: CPGs by a professional society or academic group, intended to guide nutrition care in critically ill adults, that used a systematic literature search and rated the evidence were included. Four databases and grey literature were searched from January 2011 to 19 January 2022. Five investigators assessed the methodological quality of CPGs and recommendations specific to energy expenditure determination. Scaled domain scores were calculated for AGREE II and a scaled total score for AGREE-REX. Data are presented as medians (interquartile range). Results: Eleven CPGs were included. Highest scoring domains for AGREE II were clarity of presentation (82% [76-87%]) and scope and purpose (78% [66-83%]). Lowest scoring domains were applicability (37% [32-42%]) and stakeholder involvement (46% [33-51%]). Eight (73%) CPGs provided recommendations relating to energy expenditure determination; scores were low overall (37% [36-40%]) and across individual domains. Conclusions: Nutrition CPGs for critically ill patients are developed using systematic methods but lack engagement with key stakeholders and guidance to support application. The quality of energy expenditure determination recommendations is low.

暂无摘要。

There has been substantial progress in the knowledge of exercise and type 1 diabetes, with the development of guidelines for optimal glucose management. In addition, an increasing number of people living with type 1 diabetes are pushing their physical limits to compete at the highest level of sport. However, the post-exercise recovery routine, particularly with a focus on sporting performance, has received little attention within the scientific literature, with most of the focus being placed on insulin or nutritional adaptations to manage glycaemia before and during the exercise bout. The post-exercise recovery period presents an opportunity for maximising training adaption and recovery, and the clinical management of glycaemia through the rest of the day and overnight. The absence of clear guidance for the post-exercise period means that people with type 1 diabetes should either develop their own recovery strategies on the basis of individual trial and error, or adhere to guidelines that have been developed for people without diabetes. This Review provides an up-to-date consensus on post-exercise recovery and glucose management for individuals living with type 1 diabetes. We aim to: (1) outline the principles and time course of post-exercise recovery, highlighting the implications and challenges for endurance athletes living with type 1 diabetes; (2) provide an overview of potential strategies for post-exercise recovery that could be used by athletes with type 1 diabetes to optimise recovery and adaptation, alongside improved glycaemic monitoring and management; and (3) highlight the potential for technology to ease the burden of managing glycaemia in the post-exercise recovery period.

暂无摘要。

Perturbations in resting energy expenditure (REE) among critically ill stroke patients are ill defined, and guidelines recommend weight-based dosing when indirect calorimetry (IC) is not feasible to estimate daily energy requirements. We aimed to determine whether guideline-recommended weight-based dosing provides adequate energy requirements compared with guidelines recommended IC target.
IC data was collected on stroke patients admitted to a neurocritical care unit. We compared low-weight-based dosing (25 kcal/kg) and high (30 kcal/kg) with the IC REE target. Subsequently, we analyzed the effect of stroke subtype on the differences among these measurements.
Seventy-two metabolic studies were performed (45.1% intracerebral hemorrhage [ICH], 18.3% aneurysmal subarachnoid hemorrhage [aSAH], and 36.6% acute ischemic stroke [AIS]). Energy needs, estimated using low-weight-based group, were significantly lower than IC REE target (1496 kcal/day [IQR, 1224-1850] vs 1770 kcal/day [IQR, 1400-2150]; P = .003). High weight-based group energy measurements were similar to IC REE target (1806 kcal/day [IQR, 1530-2236] vs 1770 kcal/day; P = .343). Subgroup analysis showed that low-weight-based calculations were significantly lower than those of IC in ICH and aSAH, but they were similar in AIS (P ≤ .001, .016, and .078, respectively). Linear regression analysis showed that weight, height, and hemorrhagic stroke subtype were associated with IC (P ≤ .001, .024, and .051, respectively).
Important differences between weight-based estimation of energy needs and guideline-recommended IC estimation exist for critically ill stroke patients. Low-weight-based calculations of REE underestimate energy needs in ICH and aSAH patients.

Pedelecs (e-bikes with electrical support up to 25 km·h-1) are important in active transportation. Yet, little is known about physiological responses during their everyday use. We compared daily pedelec (P) and bicycle (B) use to determine if pedelecs are a suitable tool to enhance physical activity. In 101 employees, cycling duration and intensity, heart rate (HR) during P and B were recorded via a smartphone app. Each recording period was a randomized crossover design and lasted two weeks. The ride quantity was higher in P compared to B (5.3 ± 4.3 vs. 3.2 ± 4.0 rides·wk-1; p < 0.001) resulting in a higher total cycling time per week for P (174 ± 146 min·wk-1) compared to B (99 ± 109 min·wk-1; p < 0.001). The mean HR during P was lower than B (109 ± 14 vs. 118 ± 17 bpm; p < 0.001). The perceived exertion was lower in P (11.7 ± 1.8 vs. 12.8 ± 2.1 in B; p < 0.001). The weekly energy expenditure was higher during P than B (717 ± 652 vs. 486 ± 557 metabolic equivalents of the task [MET]·min·wk-1; p < 0.01). Due to a sufficient HR increase in P, pedelecs offer a more active form of transportation to enhance physical activity.