ADP is the dominant controller of AMP-activated protein kinase activity dynamics in skeletal muscle during exercise.

Exercise training elicits profound metabolic adaptations in skeletal muscle cells. A key molecule in coordinating these adaptations is AMP-activated protein kinase (AMPK), whose activity increases in response to cellular energy demand. AMPK activity dynamics are primarily controlled by the adenine nucleotides ADP and AMP, but how each contributes to its control in skeletal muscle during exercise is unclear. We developed and validated a mathematical model of AMPK signaling dynamics, and then applied global parameter sensitivity analyses with data-informed constraints to predict that AMPK activity dynamics are determined principally by ADP and not AMP. We then used the model to predict the effects of two additional direct-binding activators of AMPK, ZMP and Compound 991, further validating the model and demonstrating its applicability to understanding AMPK pharmacology. The relative effects of direct-binding activators can be understood in terms of four properties, namely their concentrations, binding affinities for AMPK, abilities to enhance AMPK phosphorylation, and the magnitudes of their allosteric activation of AMPK. Despite AMP's favorable values in three of these four properties, ADP is the dominant controller of AMPK activity dynamics in skeletal muscle during exercise by virtue of its higher concentration compared to that of AMP.

Development and field validation of an omni-domain power-duration model.

Purpose: To validate and compare a novel model based on the critical power (CP) concept that describes the entire domain of maximal mean power (MMP) data from cyclists.Methods: An omni-domain power-duration (OmPD) model was derived whereby the rate of W' expenditure is bound by maximum sprint power and the power at prolonged durations declines from CP log-linearly. The three-parameter CP (3CP) and exponential (Exp) models were likewise extended with the log-linear decay function (Om3CP and OmExp). Each model bounds W' using a different nonconstant function, W'eff (effective W'). Models were fit to MMP data from nine cyclists who also completed four time-trials (TTs).Results: The OmPD and Om3CP residuals (4 ± 1%) were smaller than the OmExp residuals (6 ± 2%; P < 0.001). W'eff predicted by the OmPD model was stable between 120-1,800 s, whereas it varied for the Om3CP and OmExp models. TT prediction errors were not different between models (7 ± 5%, 8 ± 5%, 7 ± 6%; P = 0.914).Conclusion: The OmPD offers similar or superior goodness-of-fit and better theoretical properties compared to the other models, such that it best extends the CP concept to short-sprint and prolonged-endurance performance.

Normalization and statistical analysis of multiplexed bead-based immunoassay data using mixed-effects modeling.

Multiplexed bead-based flow cytometric immunoassays are a powerful experimental tool for investigating cellular communication networks, yet their widespread adoption is limited in part by challenges in robust quantitative analysis of the measurements. Here we report our application of mixed-effects modeling for the normalization and statistical analysis of bead-based immunoassay data. Our data set consisted of bead-based immunoassay measurements of 16 phospho-proteins in lysates of HepG2 cells treated with ligands that regulate acute-phase protein secretion. Mixed-effects modeling provided estimates for the effects of both the technical and biological sources of variance, and normalization was achieved by subtracting the technical effects from the measured values. This approach allowed us to detect ligand effects on signaling with greater precision and sensitivity and to more accurately characterize the HepG2 cell signaling network using constrained fuzzy logic. Mixed-effects modeling analysis of our data was vital for ascertaining that IL-1α and TGF-α treatment increased the activities of more pathways than IL-6 and TNF-α and that TGF-α and TNF-α increased p38 MAPK and c-Jun N-terminal kinase (JNK) phospho-protein levels in a synergistic manner. Moreover, we used mixed-effects modeling-based technical effect estimates to reveal the substantial variance contributed by batch effects along with the absence of loading order and assay plate position effects. We conclude that mixed-effects modeling enabled additional insights to be gained from our data than would otherwise be possible and we discuss how this methodology can play an important role in enhancing the value of experiments employing multiplexed bead-based immunoassays.

Intramuscular determinants of the ability to recover work capacity above critical power.

PURPOSE: The primary purpose of this investigation was to compare the recovery of the W' to the recovery of intramuscular substrates and metabolites using (31)P- and (1)H-magnetic resonance spectroscopy. METHODS: Ten healthy recreationally trained subjects were tested to determine critical power (CP) and W' for single-leg-extensor exercise. They subsequently exercised in the bore of a 1.5-T MRI scanner at a supra-CP work rate. Following exhaustion, the subjects rested in place for 1, 2, 5 or 7 min, and then repeated the effort. The temporal course of W' recovery was estimated, which was then compared to the recovery of creatine phosphate [PCr], pH, carnosine content, and to the output of a novel derivation of the W' BAL model. RESULTS: W' recovery closely correlated with the predictions of the novel model (r = 0.97, p = 0.03). [PCr] recovered faster [Formula: see text] than W' [Formula: see text] The W' available for the second exercise bout was directly correlated with the difference between [PCr] at the beginning of the work bout and [PCr] at exhaustion (r = 0.99, p = 0.005). Nonlinear regression revealed an inverse curvilinear relationship between carnosine concentration and the W' t 1/2 (r (2) = 0.55). CONCLUSION: The kinetics of W' recovery in single-leg-extensor exercise is comparable to that observed in whole-body exercise, suggesting a conserved mechanism. The extent to which the recovery of the W' can be directly attributed to the recovery of [PCr] is unclear. The relationship of the W' to muscle carnosine content suggests novel future avenues of investigation.

Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling.

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118-310, targeting ß-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.

Kinetic modeling of leucine-mediated signaling and protein metabolism in human skeletal muscle.

Skeletal muscle protein levels are governed by the relative rates of muscle protein synthesis (MPS) and breakdown (MPB). The mechanisms controlling these rates are complex, and their integrated behaviors are challenging to study through experiments alone. The purpose of this study was to develop and analyze a kinetic model of leucine-mediated mTOR signaling and protein metabolism in the skeletal muscle of young adults. Our model amalgamates published cellular-level models of the IRS1-PI3K-Akt-mTORC1 signaling system and of skeletal-muscle leucine kinetics with physiological-level models of leucine digestion and transport and insulin dynamics. The model satisfactorily predicts experimental data from diverse leucine feeding protocols. Model analysis revealed that total levels of p70S6K are a primary determinant of MPS, insulin signaling substantially affects muscle net protein balance via its effects on MPB, and p70S6K-mediated feedback of mTORC1 signaling reduces MPS in a dose-dependent manner.

Attenuated thermoregulatory, metabolic, and liver acute phase protein response to heat stroke in TNF receptor knockout mice.

Tumor necrosis factor (TNF) is considered an adverse mediator of heat stroke (HS) based on clinical studies showing high serum levels. However, soluble TNF receptors (sTNFR; TNF antagonists) were higher in survivors than nonsurvivors, and TNFR knockout (KO) mice showed a trend toward increased mortality, suggesting TNF has protective actions for recovery. We delineated TNF actions in HS by comparing thermoregulatory, metabolic, and inflammatory responses between B6129F2 (wild type, WT) and TNFR KO mice. Before heat exposure, TNFR KO mice showed ~0.4°C lower core temperature (T(c); radiotelemetry), ~10% lower metabolic rate (M(r); indirect calorimetry), and reduced plasma interleukin (IL)-1α and sIL-1RI than WT mice. KO mice selected warmer temperatures than WT mice in a gradient but remained hypothermic. In the calorimeter, both genotypes showed a similar heating rate, but TNFR KO maintained lower T(c) and M(r) than WT mice for a given heat exposure duration and required ~30 min longer to reach maximum T(c) (42.4°C). Plasma IL-6 increased at ~3 h of recovery in both genotypes, but KO mice showed a more robust sIL-6R response. Higher sIL-6R in the KO mice was associated with delayed liver p-STAT3 protein expression and attenuated serum amyloid A3 (SAA3) gene expression, suggesting the acute phase response (APR) was attenuated in these mice. Our data suggest that the absence of TNF signaling induced a regulated hypothermic state in the KO mice, TNF-IL-1 interactions may modulate T(c) and M(r) during homeostatic conditions, and TNF modulates the APR during HS recovery through interactions with the liver IL-6-STAT3 pathway of SAA3 regulation.

Rationale and resources for teaching the mathematical modeling of athletic training and performance.

A number of professions rely on exercise prescription to improve health or athletic performance, including coaching, fitness/personal training, rehabilitation, and exercise physiology. It is therefore advisable that the professionals involved learn the various tools available for designing effective training programs. Mathematical modeling of athletic training and performance, which we henceforth call "performance modeling," is one such tool. Two models, the critical power (CP) model and the Banister impulse-response (IR) model, offer complementary information. The CP model describes the relationship between work rates and the durations for which an individual can sustain them during constant-work-rate or intermittent exercise. The IR model describes the dynamics by which an individual's performance capacity changes over time as a function of training. Both models elegantly abstract the underlying physiology, and both can accurately fit performance data, such that educating exercise practitioners in the science of performance modeling offers both pedagogical and practical benefits. In addition, performance modeling offers an avenue for introducing mathematical modeling skills to exercise physiology researchers. A principal limitation to the adoption of performance modeling is a lack of education. The goal of this report is therefore to encourage educators of exercise physiology practitioners and researchers to incorporate the science of performance modeling in their curricula and to serve as a resource to support this effort. The resources include a comprehensive review of the concepts associated with the development and use of the models, software to enable hands-on computer exercises, and strategies for teaching the models to different audiences.

Augmented-reality swim goggles accurately and reliably measure swim performance metrics in recreational swimmers.

Background: Swimmers commonly access performance metrics such as lap splits, distance, and pacing information between work bouts while they rest. Recently, a new category of tracking devices for swimming was introduced with the FORM Smart Swim Goggles (FORM Goggles). The goggles have a built-in see-through display and are capable of tracking and displaying distance, time splits, stroke, and pace metrics in real time using machine learning and augmented reality through a heads-up display. The purpose of this study was to assess the validity and reliability of the FORM Goggles compared with video analysis for stroke type, pool length count, pool length time, stroke rate, and stroke count in recreational swimmers and triathletes. Method: A total of 36 participants performed mixed swimming intervals in a 25-m pool across two identical 900-m swim sessions performed at comparable intensities with 1 week interval. The participants wore FORM Goggles during their swims, which detected the following five swim metrics: stroke type, pool length time, pool length count, stroke count, and stroke rate. Four video cameras were positioned on the pool edges to capture ground truth video footage, which was then manually labeled by three trained individuals. Mean (SD) differences between FORM Goggles and ground truth were calculated for the selected metrics for both sessions. The absolute mean difference and mean absolute percentage error were used to assess the differences of the FORM Goggles relative to ground truth. The test-retest reliability of the goggles was assessed using both relative and absolute reliability metrics. Results: Compared with video analysis, the FORM Goggles identified the correct stroke type at a rate of 99.7% (N = 2,354 pool lengths, p < 0.001), pool length count accuracy of 99.8%, and mean differences (FORM Goggles-ground truth) for pool length time: -0.10 s (1.49); stroke count: -0.63 (1.82); and stroke rate: 0.19 strokes/min (3.23). The test-retest intra-class correlation coefficient (ICC) values between the two test days were 0.793 for pool length time, 0.797 for stroke count, and 0.883 for stroke rate. Overall, for pool length time, the residuals were within ±1.0s for 65.3% of the total pool lengths, for stroke count within ±1 stroke for 62.6% of the total pool lengths, and for stroke rate within ±2 strokes/min for 66.40% of the total pool lengths. Conclusion: The FORM Goggles were found valid and reliable for the tracking of pool length time, pool length count, stroke count, stroke rate, and stroke type during freestyle, backstroke, and breaststroke swimming in recreational swimmers and triathletes when compared with video analysis. This opens perspectives for receiving real-time information on performance metrics during swimming.

Minimal Number of Events Required for Acceleration-Speed Profiling in Elite Women's Soccer.

PURPOSE: To determine the minimum number of events (training or matches) for producing valid acceleration-speed (AS) profiles from global navigation satellite system (GNSS) data. METHODS: Nine elite female soccer players participated in a 4-week training camp consisting of 19 events. AS profile metrics calculated from different combinations of athlete events were compared to force-velocity (FV) profile metrics from 2 × 40-m stand-alone sprint effort trials, using the same GNSS 10-Hz technology. Force-velocity profiles were calculated, from which AS profiles were obtained. AS profiles from training and matches were generated by plotting acceleration and speed points and performing a regression through the maximal points to obtain the AS metrics (theoretical maximal speed, x-intercept [in meters per second], theoretical maximal acceleration, y-intercept [in meters per second squared], and the slope per second). A linear mixed model was performed with the AS metrics as the outcome variables, the number of events as a fixed effect, and the participant identifier as a mixed effect. Dunnett post hoc multiple comparisons were used to compare the means of each number of event grouping (1-19 events) to those estimated from the dedicated sprint test. RESULTS: Theoretical maximal speed and theoretical maximal acceleration means were no longer significantly different from the isolated sprint reference with 9 to 19 (small to trivial differences = -0.31 to -0.04 m·s-1, P = .12-.99) and 6 to 19 (small differences = -0.4 to -0.28 m·s-2, P = .06-.79) events, and the slopes were no longer different with 1 to 19 events (trivial differences = 0.06-0.03 s-1, P = .35-.99). CONCLUSIONS: AS profiles can be estimated from a minimum of 9 days of tracking data. Future research should investigate methodology resulting in AS profiles estimated from fewer events.

Comparing the reliability of muscle oxygen saturation with common performance and physiological markers across cycling exercise intensity.

Introduction: Wearable near-infrared spectroscopy (NIRS) measurements of muscle oxygen saturation (SmO2) demonstrated good test-retest reliability at rest. We hypothesized SmO2 measured with the Moxy monitor at the vastus lateralis (VL) would demonstrate good reliability across intensities. For relative reliability, SmO2 will be lower than volume of oxygen consumption (V̇O2) and heart rate (HR), higher than concentration of blood lactate accumulation ([BLa]) and rating of perceived exertion (RPE). We aimed to estimate the reliability of SmO2 and common physiological measures across exercise intensities, as well as to quantify within-participant agreement between sessions. Methods: Twenty-one trained cyclists completed two trials of an incremental multi-stage cycling test with 5 min constant workload steps starting at 1.0 watt per kg bodyweight (W·kg-1) and increasing by 0.5 W kg-1 per step, separated by 1 min passive recovery intervals until maximal task tolerance. SmO2, HR, V̇O2, [BLa], and RPE were recorded for each stage. Continuous measures were averaged over the final 60 s of each stage. Relative reliability at the lowest, median, and highest work stages was quantified as intraclass correlation coefficient (ICC). Absolute reliability and within-subject agreement were quantified as standard error of the measurement (SEM) and minimum detectable change (MDC). Results: Comparisons between trials showed no significant differences within each exercise intensity for all outcome variables. ICC for SmO2 was 0.81-0.90 across exercise intensity. ICC for HR, V̇O2, [BLa], and RPE were 0.87-0.92, 0.73-0.97, 0.44-0.74, 0.29-0.70, respectively. SEM (95% CI) for SmO2 was 5 (3-7), 6 (4-9), and 7 (5-10)%, and MDC was 12%, 16%, and 18%. Discussion: Our results demonstrate good-to-excellent test-retest reliability for SmO2 across intensity during an incremental multi-stage cycling test. V̇O2 and HR had excellent reliability, higher than SmO2. [BLa] and RPE had lower reliability than SmO2. Muscle oxygen saturation measured by wearable NIRS was found to have similar reliability to V̇O2 and HR, and higher than [BLa] and RPE across exercise intensity, suggesting that it is appropriate for everyday use as a non-invasive method of monitoring internal load alongside other metrics.

The effect of severe intensity bouts on muscle oxygen saturation responses in trained cyclists.

Near-infrared spectroscopy (NIRS) quantifies muscle oxygenation (SmO2) during exercise. Muscle oxygenation response to self-paced, severe-intensity cycling remains unclear. Observing SmO2 can provide cycling professionals with the ability to assess muscular response, helping optimize decision-making. We aimed to describe the effect of self-paced severe intensity bouts on SmO2, measured noninvasively by a wearable NIRS sensor on the vastus lateralis (VL) muscle, and examine its reliability. We hypothesized a greater desaturation response with each bout, whereas, between trials, good reliability would be observed. Fourteen recreationally trained, and trained cyclists completed a ramp test to determine the power output (PO) at the respiratory compensation point (RCP). Athletes completed two subsequent visits of 50-minute sessions that included four severe-intensity bouts done at 5% above RCP PO. Muscle oxygenation in the VL was monitored using a wearable NIRS device. Measures included mean PO, heart-rate (HR), cadence, and SmO2 at bout onset, during work (work SmO2), and ΔSmO2. The bouts were compared using a one-way repeated measures ANOVA. For significant differences, a Fisher's least square difference post-hoc analysis was used. A two-way repeated measures ANOVA was used using trial and bout as main factors. Intraclass correlations (ICC) were used to quantify relative reliability for mean work, and standard error of the measurement (SEM) was used to quantify absolute agreement of mean work SmO2. Both PO and cadence showed no effect of bout or trial. Heart-rate at bout 2 (168 ± 8 bpm) and 4 (170 ± 7 bpm) were higher than bout 1 (160 ± 6 bpm). Onset SmO2 (%) response significantly increased in the final two bouts of the session. Mean work SmO2 increased across bouts, with the highest value displayed in bout 4 (36 ± 22%). ΔSmO2 showed a smaller desaturation response during bout 4 (27 ± 10%) compared to bout 3 (31 ± 10%). Mean work SmO2 ICC showed good reliability (ICC = 0.87), and SEM was 12% (CI 9-15%). We concluded that a non-invasive, affordable, wearable NIRS sensor demonstrated the heterogeneous muscle oxygenation response during severe intensity cycling bouts with good reliability in trained cyclists.

Multi-pathway network analysis of mammalian epithelial cell responses in inflammatory environments.

Inflammation is a key physiological response to infection and injury and, although usually beneficial, it can also be damaging to the host. The liver is a prototypical example in this regard because inflammation helps to resolve liver injury, but it also underlies the aetiology of pathologies such as fibrosis and hepatocellular carcinoma. Liver cells sense their environment, including the inflammatory environment, through the activities of receptor-mediated signal transduction pathways. These pathways are organized in a complex interconnected network, and it is becoming increasingly recognized that cellular adaptations result from the quantitative integration of multi-pathway network activities, rather than isolated pathways causing particular phenotypes. Therefore comprehending liver cell signalling in inflammation requires a scientific approach that is appropriate for studying complex networks. In the present paper, we review our application of systems analyses of liver cell signalling in response to inflammatory environments. Our studies feature broad measurements of cell signalling and phenotypes in response to numerous experimental perturbations reflective of inflammatory environments, the data from which are analysed using Boolean and fuzzy logic models and regression-based methods in order to quantitatively relate the phenotypic responses to cell signalling network states. Our principal biological insight from these studies is that hepatocellular carcinoma cells feature uncoupled inflammatory and growth factor signalling, which may underlie their immune evasion and hyperproliferative properties.

Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli.

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.

The W' Balance Model: Mathematical and Methodological Considerations.

Since its publication in 2012, the W' balance model has become an important tool in the scientific armamentarium for understanding and predicting human physiology and performance during high-intensity intermittent exercise. Indeed, publications featuring the model are accumulating, and it has been adapted for popular use both in desktop computer software and on wrist-worn devices. Despite the model's intuitive appeal, it has achieved mixed results thus far, in part due to a lack of clarity in its basis and calculation. Purpose: This review examines the theoretical basis, assumptions, calculation methods, and the strengths and limitations of the integral and differential forms of the W' balance model. In particular, the authors emphasize that the formulations are based on distinct assumptions about the depletion and reconstitution of W' during intermittent exercise; understanding the distinctions between the 2 forms will enable practitioners to correctly implement the models and interpret their results. The authors then discuss foundational issues affecting the validity and utility of the model, followed by evaluating potential modifications and suggesting avenues for further research. Conclusions: The W' balance model has served as a valuable conceptual and computational tool. Improved versions may better predict performance and further advance the physiology of high-intensity intermittent exercise.

Formative Evaluation of Consumer-Grade Activity Monitors Worn by Older Adults: Test-Retest Reliability and Criterion Validity of Step Counts.

BACKGROUND: To assess whether commercial-grade activity monitors are appropriate for measuring step counts in older adults, it is essential to evaluate their measurement properties in this population. OBJECTIVE: This study aimed to evaluate test-retest reliability and criterion validity of step counting in older adults with self-reported intact and limited mobility from 6 commercial-grade activity monitors: Fitbit Charge, Fitbit One, Garmin vívofit 2, Jawbone UP2, Misfit Shine, and New-Lifestyles NL-1000. METHODS: For test-retest reliability, participants completed two 100-step overground walks at a usual pace while wearing all monitors. We tested the effects of the activity monitor and mobility status on the absolute difference in step count error (%) and computed the standard error of measurement (SEM) between repeat trials. To assess criterion validity, participants completed two 400-meter overground walks at a usual pace while wearing all monitors. The first walk was continuous; the second walk incorporated interruptions to mimic the conditions of daily walking. Criterion step counts were from the researcher tally count. We estimated the effects of the activity monitor, mobility status, and walk interruptions on step count error (%). We also generated Bland-Altman plots and conducted equivalence tests. RESULTS: A total of 36 individuals participated (n=20 intact mobility and n=16 limited mobility; 19/36, 53% female) with a mean age of 71.4 (SD 4.7) years and BMI of 29.4 (SD 5.9) kg/m2. Considering test-retest reliability, there was an effect of the activity monitor (P<.001). The Fitbit One (1.0%, 95% CI 0.6% to 1.3%), the New-Lifestyles NL-1000 (2.6%, 95% CI 1.3% to 3.9%), and the Garmin vívofit 2 (6.0%, 95 CI 3.2% to 8.8%) had the smallest mean absolute differences in step count errors. The SEM values ranged from 1.0% (Fitbit One) to 23.5% (Jawbone UP2). Regarding criterion validity, all monitors undercounted the steps. Step count error was affected by the activity monitor (P<.001) and walk interruptions (P=.02). Three monitors had small mean step count errors: Misfit Shine (-1.3%, 95% CI -19.5% to 16.8%), Fitbit One (-2.1%, 95% CI -6.1% to 2.0%), and New-Lifestyles NL-1000 (-4.3%, 95 CI -18.9% to 10.3%). Mean step count error was larger during interrupted walking than continuous walking (-5.5% vs -3.6%; P=.02). Bland-Altman plots illustrated nonsystematic bias and small limits of agreement for Fitbit One and Jawbone UP2. Mean step count error lay within an equivalence bound of ±5% for Fitbit One (P<.001) and Misfit Shine (P=.001). CONCLUSIONS: Test-retest reliability and criterion validity of step counting varied across 6 consumer-grade activity monitors worn by older adults with self-reported intact and limited mobility. Walk interruptions increased the step count error for all monitors, whereas mobility status did not affect the step count error. The hip-worn Fitbit One was the only monitor with high test-retest reliability and criterion validity.

Self-initiated lifestyle interventions lead to potential insight into an effective, alternative, non-surgical therapy for mitochondrial disease associated multiple symmetric lipomatosis.

BACKGROUND: A 56-year-old female, diagnosed as a carrier of the mitochondrial DNA mutation (MTTK c.8344A > G) associated with the MERRF (myoclonic epilepsy with ragged red fibers) syndrome, presented with a relatively uncommon but well-known phenotypic manifestation: severe multiple symmetric lipomatosis (MSL). After surgical resection of three kilograms of upper mid-back lipomatous tissue, the patient experienced a significant decline in her functional capacity and quality of life, which ultimately resulted in her placement on long-term disability. METHODS: Dissatisfied with the available treatment options centered on additional resection surgeries, given the high probability of lipoma regrowth, the patient independently researched and applied alternative therapies that centred on a carbohydrate-restricted diet and a supervised exercise program. RESULTS: The cumulative effect of her lifestyle interventions resulted in the reversal of her MSL and her previously low quality of life. She met all her personal goals by the one-year mark, including reduced size of the residual post-surgical lipomas, markedly enhanced exercise tolerance, and return to work. She continues to maintain her interventions and to experience positive outcomes at the two-year mark. INTERPRETATION: This case report documents the timing and nature of lifestyle interventions in relation to the reversal in growth pattern of her previously expanding and debilitating lipomas. The profound nature of the apparent benefit on lipoma growth demonstrates the intervention's potential as a new feasible non-surgical therapy for mitochondrial-disease-associated MSL, and justifies its systematic study. We also describe how this case has inspired the care team to re-examine its approach to involved patients.

The Critical Power Model as a Potential Tool for Anti-doping.

Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W'. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation.

Assessing recent warming using instrumentally homogeneous sea surface temperature records.

Sea surface temperature (SST) records are subject to potential biases due to changing instrumentation and measurement practices. Significant differences exist between commonly used composite SST reconstructions from the National Oceanic and Atmospheric Administration's Extended Reconstruction Sea Surface Temperature (ERSST), the Hadley Centre SST data set (HadSST3), and the Japanese Meteorological Agency's Centennial Observation-Based Estimates of SSTs (COBE-SST) from 2003 to the present. The update from ERSST version 3b to version 4 resulted in an increase in the operational SST trend estimate during the last 19 years from 0.07° to 0.12°C per decade, indicating a higher rate of warming in recent years. We show that ERSST version 4 trends generally agree with largely independent, near-global, and instrumentally homogeneous SST measurements from floating buoys, Argo floats, and radiometer-based satellite measurements that have been developed and deployed during the past two decades. We find a large cooling bias in ERSST version 3b and smaller but significant cooling biases in HadSST3 and COBE-SST from 2003 to the present, with respect to most series examined. These results suggest that reported rates of SST warming in recent years have been underestimated in these three data sets.