The UN Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs) have brought heightened global attention to the measurement of maternal mortality. It is imperative that new and novel approaches be used to measure maternal mortality and to better understand existing data. In this paper we present one approach: an epidemiologic framework for identifying the identification and quantification of systematic error , outline the necessary steps for investigators interested in conducting multiple-bias analyses in their own data, and suggest approaches for reporting such analyses in the literature.To conceptualize the systematic error present in studies of abortion-related deaths, we propose a bias framework. We posit that selection bias and misclassification are present in both verbal autopsy studies and facility-based studies. The multiple-bias analysis framework provides a relatively simple, quantitative strategy for assessing systematic error and resulting bias in any epidemiologic study.In our worked example of multiple-bias analysis on a study reporting 20.6 % of maternal deaths to be abortion related, after adjustment for selection bias, misclassification, and random error, the median increased, on average, to 0.308, approximately 20 % greater than the reported proportion of abortion-related deaths.Reporting results of multiple-bias analyses in estimates of abortion-related mortality, predictors of unsafe abortion, and other reproductive health questions that suffer from similar biases would not only improve reporting practices in the field, but might also provide a more accurate understanding of the range of potential impact of policies and programs that target the underlying causes of unsafe abortion and abortion-related mortality.The online version of this article (doi:10.1186/s12963-016-0075-3) contains supplementary material, which is available to authorized users. The launch of the UN Millennium Development Goals (MDGs) in 2000 brought heightened global attention to the importance of reducing levels of maternal mortality . The newDespite problems with data quality, existing evidence suggests that abortion-related policies may play a key role in maternal mortality reduction globally. Since the MDGs were established, precipitous nationwide reductions in maternal mortality have occurred following the liberalization of abortion laws in Nepal and South Africa , 10. It In order to better understand existing maternal mortality data, and improve the measurement of cause-specific maternal mortality, novel approaches to data analysis, reporting, and collection must be tested. Here we present a simple, theoretical framework for identifying bias in reproductive health studies , outline the steps for the quantification of bias in such studies, and suggest approaches for reporting such analyses in the literature.Abortion-related mortality is uniquely prone to bias for a number of reasons: 1) in countries where abortion is restricted or illegal altogether, women often seek abortion-related services outside of the formal medical system; 2) in such settings, due to social and cultural stigma, and fear of legal consequences, women are often reluctant to seek medical services in the event of complications or reveal to family members the underlying cause of the complications –18; and Selection bias arises when the study population is systematically different from the target population with respect to exposure or outcome. In the case of abortion-related mortality due to legal, social, and economic obstacles to safe abortion care, women who experience abortion-related deaths are less likely to come to health facilities relative to women who do not experience abortion-related death, and, because of the stigma surrounding abortion, are also less likely to have family members who can reliably participate in verbal autopsy studies of maternal deaths. Therefore, selection bias is likely to bias study results  .Misclassification arises when measurement of study variables is flawed, resulting in a study subject being incorrectly classified with respect to the outcome (or exposure) of interest. In the case of abortion-related mortality, women who experience abortion-related deaths are more likely to have their deaths be incorrectly classified as other causes of maternal death than women who do not experience abortion-related deaths. Misclassification is, therefore, likely to bias study results  .Techniques for the quantitative assessment of systematic error have existed for decades , and ranTo adjust the results of any study of abortion-related mortality using multiple-bias analysis techniques, eight straightforward steps can be implemented. Step 1: Specify probability distributions for the selection probabilities of abortion-related deaths and non-abortion-related deaths in each study. Step 2: Specify probability distributions for the sensitivity and specificity of classifying abortion-related deaths for each study. Step 3: Using crude data from each study of interest, calculate the proportion of abortion-related deaths in the study. Step 4: Construct 95 % confidence intervals for the reported proportion of abortion-related deaths for the study. Step 5: Adjust the reported proportion of abortion-related deaths for selection bias in the study. Step 6: Using the selection-bias adjusted proportion of abortion-related deaths, subsequently adjust for misclassification in the study. Step 7: Incorporate random error into the adjusted estimates for the study of interest and construct a range of possible values for the proportion of abortion-related deaths adjusted for selection bias, misclassification, and random error. Step 8: Model an a priori established quantity of Monte Carlo simulation trials for each simulation experiment under different probability distribution scenarios.Step 1: Specify the shape and width of probability distributions for the selection probabilities of abortion-related deaths and non-abortion-related deaths . Under ideal circumstances, selection probabilities would be determined via internal validation studies; however, given that few internal validation studies exist in the literature, selection probabilities should be approximated using 1) data from validation studies of maternal mortality conducted in similar regions/populations and 2) adjustment factors commonly used in the demographic literature to adjust for underestimation of maternal death in studies of maternal mortality and abortion-related mortality . While tStep 2: Specify shape and width of probability distributions for the sensitivity and specificity of classifying abortion-related deaths for each study. Specify probability distributions for the sensitivity and specificity of classifying abortion-related deaths for each study. As in Step 1, two sources of information should be used to specify these probability distributions: 1) data from validation studies of verbal autopsy algorithms conducted in the same country or in similar populations, and 2) data from validation studies conducted in the same country (or in similar populations) of cause of death classification from clinical case notes against autopsy diagnoses. While these two sources of data are imperfect, there is substantial validation literature testing the sensitivity and specificity of cause of death classification in different parts of the world that was used to inform our choices of bounds for the range of possible values of sensitivity and specificity –33.0 is the proportion of observed the number of maternal deaths abortion-related deaths (ARD), X0ARD is the number of abortion-related deaths identified by the study, and TotalMD is the total number of maternal deaths identified by the study of interest.Step 3: Using crude data from each study of interest, calculate the proportion of abortion-related deaths in each study with the following formula:SE = sqrt(Y0*(1-Y0)/), where SE is the standard error of Y0, and Y0 is the proportion observed abortion-related deaths in the study of interest.95 % CI = Y0 ± SEStep 4: Construct a probability distribution and a 95 % confidence interval of the proportion calculated in Step 3 using the following formulae:X1ARD is the number of abortion-related deaths adjusted for selection bias, X0ARD is the number of abortion-related deaths identified by the study of interest, and where W1 is the a priori specified trapezoidal distribution of all possible values for the selection probability for abortion-related deaths.X1NARD is the number of non-abortion-related maternal deaths adjusted for selection bias, X0NARD is the number of non-abortion-related maternal deaths identified by the study of interest, and where W2 is the selection probability for non-abortion-related maternal deaths.1 is the proportion of abortion-related deaths observed in the study of interest adjusted for selection bias, and other notation is as above.Step 5: Adjust for selection bias in the study using the following formulae:It should be noted that the proportion of abortion-related deaths should be adjusted in the order in which the biases occurred. Given that subjects must, by necessity, be selected into any study before misclassification can occur, adjustment for selection bias comes first, then misclassification bias.Y1 (the proportion of abortion-related deaths observed in the study adjusted for selection bias) should be utilized as the baseline for misclassification adjustment via the following formulae:X2ARD = [(XARD1 * W3) + (XNARD1 − (XNARD1*W4))] where X2ARD is the number of abortion-related deaths adjusted for selection bias and misclassification, X1ARD is the number of abortion-related deaths adjusted for selection bias, X1NARD is the number of non-abortion-related maternal deaths adjusted for selection bias, and where W3 is the sensitivity of classification of abortion-related death and W4 is the specificity of classification of abortion-related death.X2NARD = [(XNARD1 * W3) + (XARD1 − (XARD1 * W4)] where X2ARD is the number of non-abortion-related deaths adjusted for selection bias and misclassification, X1ARD is the number of abortion-related deaths adjusted for selection bias, X1NARD is the number of non-abortion-related maternal deaths adjusted for selection bias, and where W3 is the sensitivity of classification of abortion-related death and W4 is the specificity of classification of abortion-related death.Y2 is the proportion of abortion-related deaths adjusted for selection bias and misclassification, X2ARD is the number of abortion-related deaths adjusted for selection bias and misclassification, and X2NARD is the number of non-abortion-related maternal deaths adjusted for selection bias and misclassification.Step 6: Adjust for misclassification in the study of interest. Given that misclassification can only occur among subjects selected into any study, SE = sqrt(Y2*(1-Y2)/), where SE is the standard error of Y2 and Y2 is the proportion of observed abortion-related deaths in the study of interest, adjusted for selection bias and misclassification.95 % CI = Y2 ± SE.Step 7: After adjusting for both sources of bias (selection bias and misclassification) incorporate random error into the new estimate. Using the same formulae that were employed in Step 1, construct a probability distribution and a range of possible values for the proportion:Step 8: Model 50,000 Monte Carlo simulation trials for each simulation experiment under different probability distribution scenarios, with 21 scenarios in total.In order to test a working example of multiple-bias analysis and evaluate the influence of selection bias and misclassification in a study of abortion-related maternal mortality, we use a published study of maternal deaths from the maternity ward of a main referral hospital in a major urban center in East Africa over a seven-year period . This stIn this example, we applied a multiple-bias analysis framework to estimates of abortion-related mortality and performed multiple-bias analyses on estimates of the proportion of abortion-related mortality reported by Study A. We hypothesized that both selection bias and misclassification were present in Study A. We generated prior probability distributions for selection bias and misclassification from existing external validation studies –40 and cTable Under all 21 scenarios of multiple-bias analysis, our findings show that the increased median proportion of abortion-related deaths provides quantitative evidence that systematic error, specifically selection bias and misclassification, may indeed result in estimates of the proportion of abortion-related maternal deaths that underestimate the true proportion of abortion-related maternal deaths. For Study A, which initially found less than 20 % of maternal deaths to be abortion-related, after multiple-bias adjustment, the proportion of abortion-related mortality was, on average, closer to 30 %.Multiple-bias analysis provides authors with a set of statistical tools to estimate the influence of biases across a range of plausible magnitudes on the parameters estimated from the study data. In circumstances where systematic error is known to be present, some form of bias analysis should not only be considered a necessary analytic step, but also as a useful framework to help consumers of the literature interpret results vis-á-vis the magnitude and likelihood of potential biases. With some notable exceptions , 25, 42,Our finding that the range of possible values for the proportion of abortion-related deaths substantially increased with multiple-bias analysis is further evidence that the current estimates of abortion-related mortality lack both precision and validity. For those interested in quantifying the proportion of abortion-related deaths in any setting, this finding serves as a reminder that given the limitations of our data, we should report not only the observed proportion of abortion-related deaths but also their appropriate confidence intervals and, at the very least, statements about potential sources of bias.Despite their advantages, multiple-bias analysis techniques have their limitations. Some argue that the results of such analyses are themselves biased by the values chosen for each bias parameter . There iFindings from multiple-bias analyses of abortion-related mortality have broad reaching implications for the way we understand the distribution of cause of maternal death in a range of scenarios. If, as our worked example suggests, abortion-related deaths account for a larger proportion of maternal deaths than reported by the study, these methods could be used to more accurately estimate a potential range of abortion-related mortality in local- and country-specific contexts. Such data might also be useful for policymakers and program planners aiming to target funds towards increasing access to legal, safe abortion services at the community level. Such policies and programs will be fundamental to addressing the issue of mortality resulting from unsafe abortion and achieving the SDGs.With some fairly simple steps, reporting results of multiple-bias analyses in estimates of abortion-related mortality, predictors of unsafe abortion, and other reproductive health questions that suffer from similar biases, would improve reporting practices in the field, in addition to the possibility of providing a more accurate understanding of the range of potential impact of policies and programs that target the underlying causes of unsafe abortion and abortion-related mortality.


The reliability of accelerometry for measuring sedentary behavior in preschoolers has not been determined, thus we determined how many days of accelerometry monitoring are necessary to reliably estimate daily time spent in sedentary behavior in preschoolers. In total, 191 and 150 preschoolers (three to five years) wore ActiGraph accelerometers (15-s epoch) during the in-school (≥4 days) and the total-day (≥6 days) period respectively. Accelerometry data were summarized as time spent in sedentary behavior (min/h) using three different cutpoints developed for preschool-age children . The intraclass correlations (ICCs) and Spearman-Brown prophecy formula were used to estimate the reliability of accelerometer for measuring sedentary behavior. Across different cutpoints, the ICCs ranged from 0.81 to 0.92 for in-school sedentary behavior, and from 0.75 to 0.81 for total-day sedentary behavior, respectively. To achieve an ICC of ≥0.8, two to four days or six to nine days of monitoring were needed for in-school sedentary behavior and total-day sedentary behavior, respectively. These findings provide important guidance for future research on sedentary behavior in preschool children using accelerometry. Understanding the reliability of accelerometry will facilitate the conduct of research designed to inform policies and practices aimed at reducing sedentary behavior in preschool children. Childhood obesity is a significant public health concern because of its long-term detrimental health effects ,2,3,4. TContemporary children spend a significant amount of their waking hours in sedentary behavior and this may result in negative health consequences. Recent studies reported that the majority of North American children engage in sedentary behavior more than the recommended levels ,14,15,16Accelerometry has been considered the method of choice for objective measurement of physical activity and sedentary behavior in children ,33. Its Using an Intraclass Correlation Coefficient (ICC) of ≥0.8 as the standard for acceptable reliability, previous studies determined the reliability of accelerometery-derived physical activity (PA) in children (aged 3–17 years), and reported that the recommended number of days of accelerometry monitoring to reliably measure moderate-to-vigorous PA varies from 4 to 10 days ,36,37,38In addition to reliability, an important methodological issue in measuring sedentary behavior via accelerometry is the choice of cutpoints. Several cutpoints for preschool-age children have been used in previous studies, including <26 counts/15 s , <37.5 cN = 331) according to the activity monitored  as follows: children who had at least 4 valid days (weekdays) of in-school sedentary behavior data (N = 191), or at least 6 valid days (including weekend days) of total-day sedentary behavior data (N = 150), respectively. A total of 139 children were identified in both subsamples (overlap sample).A cross-sectional study design was employed, and analyses were performed using data from the Children’s Activity and Movement in Preschool Study (CHAMPS). Details regarding the design of CHAMPS are available elsewhere . BrieflyThe participants in this study were 3- to 5-year-olds preschool children. Using a stratified random sampling procedure, a total of 22 preschools  agreed to participate in CHAMPS. The 22 preschools were recruited from the greater Columbia, South Carolina, USA, an area that includes a wide range of ethnic and socioeconomic backgrounds. The number of participants per preschool ranged from 14 to 33 children. Written informed consent was obtained from children’s parents or guardians prior to collection of data. The study was approved by the Institutional Review Board at the University of South Carolina.Sedentary behavior was measured using ActiGraph accelerometers . Accelerometers were initialized to save data in 15-s intervals (epochs) in order to more effectively capture the spontaneous movement of 3- to 5-year-old children. Parents were instructed to help their child to wear the accelerometers on an elastic belt on the right hip (anterior to the iliac crest). Parents received information about the monitor and instructions for helping their child wear the monitor over a two-week period. Accelerometry data were summarized in terms of activity counts per 15 s (cts/15 s).i.e., monitor malfunction) were excluded from the analysis because those do not represent typical days.To determine whether the number of days necessary to reliably estimate accelerometry-derived sedentary behavior in preschool children differs by application of selected sets of cutpoints, the count data were reduced using three different activity intensity cutpoints developed specifically for 3- to 5-year old children to categorize each interval as sedentary. These three cutpoints are <37.5 cts/15 s , <200 ct2) from the averages of both height and weight.Children’s age, gender, race/ethnicity, and socioeconomic status were reported by a parent or guardian using a parent survey. Weight was measured to the nearest 0.1 kg using an electronic scale, and height was measured to the nearest 1 mm using a stadiometer, after the child had removed shoes and outer clothing. Parent education  was measured as a surrogate indicator of socioeconomic status. Body Mass Index (BMI) was calculated  were calculated for the subsets of the overall sample according to the activity monitored . The coefficient of variance (CoV = (SD/mean) × 100) was calculated to describe between-individual variability . A repeasMS is the mean square subject indicating the between-subject variability, iMS is the mean square interaction indicating variability interaction between the between-subject and within-subject variability, K is the number of days administered, K’ is the number of days for which R is estimated, and R is the ICC.The ICC was used to determine the reliability of the accelerometry-derived sedentary behavior and calculated using variance estimates from the two-way repeated ANOVA model and the formula below :R = (MSxx’ isr the estimated reliability (ICC) of the test, r*xx’ is the desired reliability (ICC) of the lengthened test, and K is the number of times (days) the length of the test has been increased. More specifically, the ICC of 0.8 was considered as the desired reliability cutoff to determine the minimum number of days necessary to reliably estimate sedentary behavior [The number of days of accelerometry monitoring required to reliably estimate sedentary behavior in preschool children was estimated using the Spearman-Brown prophecy formula below :r*xx’ behavior .xS is standard deviation of observed score, and xx’r is the estimated reliability (ICC) of the test. For all statistical analyses, the SAS statistical program, version 9.2  was used and the alpha level of 0.05 was considered as statistical significance.The Standard Error of Measurement (SEM) was calculated to examine the precision of measured sedentary behavior from the estimated ICCs. The following formula was used :SEM=Sx vs. total-day).Demographic characteristics of the participants and the average time the children wore the accelerometers are shown in The univariate analyses showed that the average time spent in sedentary behavior varied from 32 min/h to 51 min/h for both in-school and total-day depending upon the cutpoints used . RegardlP < 0.05).Gender and preschool-adjusted mean differences in sedentary behavior (min/h) across days of the week are presented in The ICCs for in-school sedentary behavior when applying three different accelerometry cutpoints are shown in The number of days of accelerometry monitoring required to achieve the given levels of ICC  was calculated for total day sedentary behavior . For totThe SEMs, the degree to which estimates of time spent in sedentary behavior (min/h) fluctuate as a result of measurement errors, are shown in This is the first study to determine the number of days of accelerometry needed to reliably measure sedentary behavior in preschool children. We found that the reliability of accelerometry-derived sedentary behavior varied when three age-specific sedentary behavior cutpoints were applied. Our results suggest that, to achieve a reliability of 0.8, 2–4 days of monitoring are required to reliably measure in-school sedentary behavior, and 6–9 days of monitoring are required to reliably measure total day sedentary behavior in preschool children. The observed differences in the number of days needed to reliably measure sedentary behavior possibly attributed to different estimates of sedentary behavior across three different cutpoints. These findings have important implications for the measurement of sedentary behavior in preschool children using accelerometry.A unique aspect of this study was that the reliability of accelerometry for measuring sedentary behavior was assessed separately for in-school and total day sedentary behavior. To our knowledge, only one previous study has determined the reliability of accelerometery for measuring children’s MVPA at specific times during the day  , and no We found that time spent in total-day sedentary behavior was significantly lower on weekend days compared to weekdays. This indicates that there was greater within-individual (day-to-day) variability in total-day sedentary behavior compared to in-school sedentary behavior ,57. By iIt is also important to discuss our finding regarding the markedly longer monitoring period (14–19 days across different cutpoints) required to achieve ≥90% reliability for total day sedentary behavior. The formula used to estimate the ICCs is the function of between- and within-individual variability. As indicated by the low between-individual variability in our sample of preschool children, the ICCs for total-day sedentary behavior may be underestimated, which resulted in an overestimation of the number of days required to reliably measure sedentary behavior. Supporting this, the standard deviation and the coefficient of variance (CoV) were lower for total day sedentary behavior compared to in-school sedentary behavior , suggesti.e., smaller SEM) for total-day compared to in-school sedentary behavior. This was due to the smaller standard deviation for total-day sedentary behavior as opposed to in-school sedentary behavior. Between different cutpoints, moreover, the precision determined by SEMs was not consistent with the reliability determined by ICCs. The disagreement between observed reliability (ICCs) and precision (SEMs) implies that understanding the reliability and precision of measurement of sedentary behavior is complex, and there is no single gold-standard statistical method to determine the number of days needed to reliably measure sedentary behavior. Nonetheless, both estimated ICCs and SEMs suggest that 2–4 days and 6–9 days of accelerometry monitoring provides a reliable and precise measure for in-school  and total-day sedentary behavior , respectively. In addition to the reliability estimates observed in this study, we also presented the precision of the accelerometry-derived sedentary behavior using SEM. Unlike the observed reliabilities, the precision determined by SEMs was greater , and <373 cts/15 s for total-day sedentary behavior (six days), respectively. These findings provide important guidance for the design of research in which accelerometry will be used to measure sedentary behavior in preschool children. In addition, knowing the reliability of accelerometry in measuring sedentary behavior will ultimately contribute to research designed to monitor and reduce population levels of sedentary behavior.


Neurotensin (NT) is an endogenous neuropeptide that exerts potent opioid-independent analgesic effects, most likely via the type 2 NT receptor (NTR2). Previous morphological and electrophysiological studies suggested that the NT-NTR2 system is primarily localized in structures that constitute the descending pain control pathway, such as the periaqueductal gray (PAG), the rostral ventromedial medulla (RVM), and the spinal dorsal horn (SDH). However, relevant morphological evidence for this neurotensinergic (NTergic) circuit is lacking. Thus, the aim of the present study was to morphologically elucidate the potential sites and connections in the NT-NTR2 system that are involved in the descending pain control pathway. Based on light and electron microscopy combined with anterograde and retrograde tracing, we found evidence that NTR2-immunoreactive (IR) neurons in the RVM receive NT-IR projections originating from the PAG; express NT, serotonin (5-HT), or both; and send projections that terminate in laminae I and II of the SDH. These results suggest that NTR2 may contribute to pain control by binding to NT in the PAG-RVM-SDH pathway. In conclusion, our data provide morphological evidence for an NTergic PAG-RVM-SDH pathway, implicating novel mechanisms of NT-induced analgesia. Pain is most likely one of the most prevalent human health problems, contributing to individual morbidity and mortality and imposing high societal costs Mogil, . HoweverThe periaqueductal gray (PAG) modulates nociception via a descending pathway that relays in the rostral ventromedial medulla (RVM) and terminates in superficial laminae (laminae I and II) of the spinal dorsal horn  rats (weighing 250–300 g) were used in the present study. The Ethics Committee for Animal Experiments of the Fourth Military Medical University  approved all animal experiments (Permit number: 10071). Efforts were made to minimize the number and suffering of the animals used.In the present study, rats were randomly separated into three groups for different purposes. In group 1, the anterograde tracer biotinylated dextran amine (BDA) was injected into the PAG and the retrograde tracer fluoro-gold (FG) was injected into the SDH, followed by immunofluorescence histochemical staining to reveal the distributions of the RVM-projecting fibers originating from the PAG, the SDH-projecting neurons located in the RVM, and the connections between the PAG-originating NTergic fibers and the SDH-projecting NTR2-immunoreactive (IR) neurons in the RVM. In group 2, the anterograde tracer BDA was injected into the PAG and the retrograde tracer horseradish peroxidase (HRP) was injected into the SDH, followed by electron microscopy analysis to further determine whether the connections between the neurons noted form actual synapses. In group 3, after injection of the retrograde tracer FG into the SDH, colchicine was administered to the animals, followed by immunofluorescence histochemical staining to identify the neurochemical properties of the SDH-projecting neurons in the RVM.BDA injection. In the first two groups, the anterograde tracer BDA was used to identify the RVM-projecting fibers and terminals originating from the PAG. The procedures for BDA injection were essentially the same as those previously described by our group  was injected into the lateral ventricle 2 days before perfusion. The purpose of intracerebroventricular administration of colchicine was to effectively block axoplasmic transport of the CNS neurons, thereby markedly increasing the somatic retention of neuropeptides, such as NT . The brains and/or spinal cords were transversely sliced into 30-µm-thick coronal sections using a freezing microtome , and the sections were serially collected into six dishes containing 0.01 M phosphate-buffered saline  as six sets of every sixth serial section.The sections in the first dish for both groups (groups 1 and 3) were used to evaluate the FG injection sites in the SDH and the distribution patterns of the retrogradely FG-labeled neurons in the RVM under an epifluorescence microscope  using an appropriate filter for FG . Then, these sections were processed for Nissl staining.To examine the BDA injection site and the distribution of anterogradely BDA-labeled fibers and terminals, the sections in the second dish of group 1 were treated with an Avidin/Biotin Kit . Then, BDA was visualized using the chromogen diaminobenzidine (DAB). Finally, the sections were observed under a light microscope .3, and 0.25% carrageenan  for 24 h. Then, the sections were incubated in fluorescein-labeled IgG (second antisera) for 6 h.The sections in the third, fourth, and fifth dishes of group 1 were used to evaluate the triple-labeling of FG/NTR2/BDA, FG/NT/BDA, and NTR2/NT/BDA, respectively. All of the antisera used in this study are presented in Table The sections in the second and third dishes of group 3 were processed for triple-labeling of FG/NT/NTR2 and NTR2/NT/5-HT, respectively. Different antisera Table  were useThe sections in the sixth dish were used for control experiments. The procedures were the same as those described above except that the primary antisera were omitted or were replaced with normal serum from the same species. No immunopositive products were detected.After immunofluorescence histochemical staining, the sections were observed and images were captured under a confocal laser-scanning microscope . Digital images were captured using FLUOVIEW software (Olympus).For immunofluorescence histochemical staining analysis of NTR2/NT/FG in the RVM, the neurons were counted to estimate the co-localization of the markers. Seven transverse sections selected from each animal were analyzed to provide an adequate sample size and to ensure that the entire RVM was examined. The sections were selected at the following distances caudal to bregma  at a 50-µm thickness and were serially collected into four dishes. All sections in the four dishes were processed for the histochemical detection of HRP using the tetramethylbenzidine-sodium tungstate (TMB-ST) method  osmification; and (6) counterstaining with uranyl acetate. Ultrathin sections at a 70-nm thickness were prepared from the superficial laminae of the SDH, mounted on single-slot grids, and examined under an electron microscope .The reacted sections of the lower medulla oblongata from the second, third, and fourth dishes were freeze-thawed in liquid nitrogen to enhance the penetration of the antibodies. Details of the immuno-electron microscopy procedures were described in our previous reports  of the NTR2-IR RVM neurons displayed 5-HT immunoreactivity, and approximately one-third (33.43%) of the NTR2/NT-IR neurons expressed 5-HT. These results indicated that NT and 5-HT are co-expressed in NTR2-IR neurons in the RVM and, thus, are most likely co-released simultaneously in the SDH to modulate nociceptive transmission. Our previous study revealed that 5-HT exerts an analgesic effect by potentiating glycine release in the superficial laminae neurons via the protein kinase C pathway  exerted a significant analgesic effect on NTR2−/− mice, indicating that both NTR1 and NTR2 are involved in NT-mediated analgesia in a visceral pain model using acetic acid-induced writhing tests (Smith et al., Previous studies have indicated that both NTR1 and NTR2 are required for NT-induced antinociception. The results of previous studies using NTR1 knockout mice have demonstrated an important role for NTR1 in NT-induced antinociception based on the hot plate test (Pettibone et al., Combining our current results with the previous findings, we conclude that an NTergic PAG-RVM-SDH descending pathway significantly contributes to NT-induced analgesia. Specifically, convergent stimulation activates NTergic neurons in the PAG, in turn exciting the spinally projecting NT/NTR2 co-expressing RVM neurons, which most likely simultaneously increases NT and 5-HT release in the spinal cord, thereby modulating pain transmission Figure . In contThere is an indirect NTergic PAG-RVM-SDH descending pathway from the PAG to the SDH. Both NT and NTR2 are critical for this pathway. This pathway may represent an important component of the descending system involved in the modulation of nociceptive transmission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The dual-task performance is associated with the functionality of the elderly andit becomes more complex with age.  To investigate the relationship between the Timed Up and Go dual task (TUG-DT)and cognitive tests among elderly participants who exercise regularly.  This study examined 98 non-institutionalized people over 60 years old whoexercised regularly. Participants were assessed using the TUG-DT (i.e. doing theTUG while listing the days of the week in reverse order), the Montreal CognitiveAssessment (MoCA), the Clock Drawing Test (CDT), and the Mini Mental StateExamination (MMSE). The motor (i.e. time and number of steps) and cognitive (i.e.number of correct words) data were collected from TUG-DT . We used a significancelevel of α=0.05 and SPSS 17.0 for all data analyses.  This current elderly sample featured a predominance of women (69.4%) who werehighly educated (median=10 years of education) compared to Brazilian populationand mostly non-fallers (86.7%). The volunteers showed a good performance on theTUG-DT and the other cognitive tests, except the MoCA, with scores below thecutoff of 26 points. Significant and weak correlations were observed between theTUG-DT (time) and the visuo-spatial/executive domain of the MoCA and the MMSE. Thecognitive component of the TUG-DT showed strong correlations between the totalMoCA performance score and its visuo-spatial/executive domain.  The use of the TUG-DT to assess cognition is promising; however, the use of morechallenging cognitive tasks should be considered when the study population has ahigh level of education.  According to the Brazilian Institute of Geography and Statistics, the proportion ofindividuals older than 65 years old, which represented 5.9% of the country's populationin 2000, will increase to 22.71% by 2050-,Cognitive screening enables the prevention, identification, diagnosis, and timelytreatment of early cognitive decline among older adults. Furthermore, this screeningenables practitioners to monitor small changes in cognitive function-Among older adults, cognitive deficits likely interfere with balance and gait. The automatic control of walking is impairedwith age, and walking becomes an activity that demands attention and is thereforecontrolled by a cortical region-The Timed Up and Go (TUG) task is a reliable and easy-to-apply assessment of mobility interms of the risk of falls; the TUG dual task (TUG-DT) is increasingly used to assessthe functionality of older adults attempting to combine a motor and cognitive taskDual-task studies have been conducted among individuals with Parkinson's diseaseTherefore, the present study investigated the relationships between the TUG-DT and theMoCA, CDT, and MMSE among older adults who exercise and live in the community. Wehypothesized that the TUG-DT and the cognitive tests would be correlated, therebyindicating the possible effectiveness of the TUG-DT as a quick, practical, andinexpensive screening tool in clinical practice for the early detection of cognitivedysfunctions among older adults who exercise.Universidade Federal de São Carlos(UFSCar), São Carlos, SP, Brazil approved the present cross-sectional correlation study. All of the participants signed an informed consent documentin compliance with the recommendations of National Health Council resolution no.196/96(4)The research ethics committee of the Programa deRevitalização de Adultos) and the Servant Quality of Life Program of UFSCar.Subjects were recruited from the Adult Revitalization Program  the study design; 2) atype I error rate of 5% (α=0.05); 3) a statistical power of 80% (1-β=0.80); and 4) amoderate effect size. The total estimated sample included 82 subjects.All 82 subjects were assessed on the same day. The assessments included the followingsequence of items: a clinical interview; a physical activity level evaluation usingthe Modified Baecke Questionnaire for Older Adults (MBQOA); the number of falls overthe last 3 months were recorded; and the TUG, the TUG-DT, cognitive tests  were administered.The MBQOA is a simple and easy-to-apply questionnaire that assesses level of physicalactivity over the past 12 months via questions concerning sports, leisure, andhousehold activities. This questionnaire has been validated for use among olderBrazilian adultsParticipants reported the number of times that they fell over the past 3 months. Afall was defined as "an event that results in a person coming to rest inadvertentlyon the ground or other lower level, other than as a consequence of a violent blow,loss of consciousness, sudden onset of paralysis or an epileptic seizure"The TUG was performed with a 45-cm high chair with a back and armrests. In this test,participants rise from the chair following the command "go" by using the arms forsupport, walk 3 meters at their usual speed, return to the chair, and sit down. Thetime needed to complete this task was measured using a chronometer. At the startingpoint, participants leaned back in the chair. The task duration was measured as up tothe moment when participants leaned back in the chair again. The cutoff point todefine the risk of falls inthe TUG among older Brazilian adults was 12.47secondsIn TUG-DT , participants completed a cognitive task  while performing the TUG. Theparticipants did not stop walking even when they made a mistake or forgot the days ofthe week. The task of reciting the days of the week in reverse order was selectedbecause Barbosa et al.,Two examiners conducted the TUG-DT: one recorded the task time and counted the numberof steps required to complete it, while the other registered the number of hits anderrors. To confirm the numbers of hits and errors, a tape recorder was usedthroughout the assessment. A pretest was performed to allow the participants toacquaint themselves with the TUG-DTThe MoCA was formulated to detect mild cognitive impairment and mild dementia, and ithas been validated for the Brazilian populationIn addition, the MMSE has been validated to assess cognitive functioning among theBrazilian populationTo perform the CDT, the participants drew a clock representing 2:45. The results wereanalyzed following Sunderland et al.'sThe cognitive tests were applied in a room that was as free from visual and auditorystimuli as possible. The examiners were trained to apply the tests before the testingwas begun.Descriptive analyses of the data and point-and-interval estimations of the parametersof interest were performed. The significance level was set as α=0.05. The statisticalanalyses were performed using SPSS 17.0.The data distribution was assessed first using the Kolmogorov-Smirnov normality test.Because the data were not normally distributed, Spearman's correlation was used toanalyze the relationships between the TUG-DT score and the MoCA , MMSE , and the CDT. The strength of thecorrelation was categorized using Munro's categoriesThe sample was composed of 98 participants (median=68 years old), most of whom werefemale (69.4%) non-fallers (86.7%) with a high level of education (median=10 years). Theparticipants did not engage in polypharmacy, and 81.6% did not use psychotropicmedications. The median body mass index was 27.23 kg/m², which is slightly above thenormal weight for their ageThe present study investigated the correlation between TUG-DT performance and MoCA, CDT,and MMSE among older adults living in the community who exercise. Compared with the TUGcutoff point established for older Brazilian adults (12.47 seconds), the performance ofthe present sample on the TUG-DT was satisfactory: 9.56 secondsDespite the satisfactory performance on the MMSE and CDT, the total MoCA score was lessthan the normal cutoff  for 81.6% of the participantsThe adequate performance on the TUG-DT notwithstanding, this score was significantlycorrelated with the MoCA total score and the visuospatial/executive function domain. Theestablishment of cutoff points for dual-task tests and the inclusion of more challengingcognitive tasks are necessary for physical therapists to be able to quickly assessseemingly healthy populations who, nevertheless, might exhibit cognitive decline.-Previous studies-Hausdorff et al.-The meta-analysis performed by Al-Yahya et al.,Unlike previous researchThe present study had limitations. The results could not be generalized to all olderBrazilian adults living in the community because the sample was composed of adults whoexercised regularly for at least 1 year and had higher levels of education.Nevertheless, cognitive assessments of this subpopulation of active and highly educatedolder adults (compared to Brazilian population) are highly relevant because (theirprofile notwithstanding) the performance of the participants on MoCA was below thatexpected. Importantly, the tests used in the present study are widely accessible andeasy to apply in clinical practice. The TUG-DT task is quick, practical, inexpensive,and functional. Additional studies combining different types of cognitive tasks with theTUG task are needed to facilitate the detection of early cognitive decline among olderadults who exercise. This detection will enable physical therapists to treat theseindividuals in a timely manner.To summarize, the correlation between the TUG-DT results  and the MoCA visuospatial/executive function domain was weak. The strongestcorrelations with the MoCA were exhibited by the TUG-DT variables not usually consideredsuch as the number of hits per unit of time. These findings suggest that the TUG-DTmight be used in clinical practice as a functional and practical test for the earlyscreening of cognitive dysfunction among older adults who exercise. However, additionalstudies including more challenging cognitive tasks in the TUG-DT task are needed toobtain stronger correlations with the cognitive tests.


We sought to measure HRQoL in all-cause encephalitis survivors and assess the impact of various socio-clinical factors on outcome.We used a prospective cohort study design, using the short-form 36 (SF-36) to measure the HRQoL in patients 15 years and older, and the short-form 10 (SF-10) for patients less than 15 years old. We posted questionnaires to individuals six months after discharge from hospital. All scores were normalised to the age- and sex-matched general population. We used multivariate statistical analysis to assess the relative association of clinical and socio-demographic variables on HRQoL in adults.Of 109 individuals followed-up, we received 61 SF-36 and twenty SF-10 questionnaires (response rate 74%). Patients scored consistently worse than the general population in all domains of the SF-36 and SF-10, although there was variation in individual scores. Infectious encephalitis was associated with the worst HRQoL in those aged 15 years and over, scoring on average 5.64 points less than immune-mediated encephalitis (95% CI −8.77– −2.89). In those aged less than 15 years the worst quality of life followed encephalitis of unknown cause. Immuno compromise, unemployment, and the 35–44 age group all had an independent negative association with HRQoL. A poor Glasgow Outcome Score was most strongly associated with a poor HRQoL. Less than half of those who had made a ‘good’ recovery on the score reported a HRQoL equivalent to the general population.Encephalitis has adverse effects on the majority of survivors’ wellbeing and quality of life. Many of these adverse consequences could be minimised by prompt identification and treatment, and with better rehabilitation and support for survivors. Encephalitis is a potentially life-threatening neurological syndrome characterised by inflammation of the brain parenchyma. It can be caused by infection or immune-mediated conditions. Its incidence in England has recently been estimated to be 5.23/100,000/year  The high morbidity and mortality associated with the illness result from inflammatory processes caused by microbial neuro virulence or direct immune-mediated damage. For survivors, the consequences of encephalitis can be severe. Three years after infectious encephalitis 51.7% seek help from general practitioners, for one or more symptoms What is less clear is the quality of life of patients who survive encephalitis. It is likely these individuals suffer subtle problems that are difficult to quantify. Research has concentrated on the long-term neurocognitive effects of encephalitis but the consequences for patients and their families have largely been neglected. The ability to measure the impact of a disease according to a patient’s perspective is important in identifying foci for treatment delivery as well as further research directions Our aim was to establish the health-related quality of life (HRQoL) in patients who survived an episode of encephalitis and compare this with a normative population. We identified any differences in HRQoL by aetiological category and assessed the effect of socio-clinical factors on post-encephalitic HRQoL.These analyses were built on a previous multi-centre, population-based, prospective study of encephalitis in England. We have reported details regarding recruitment and selection criteria elsewhere The North and East Devon Multicentre Research Ethics Committee granted overall approval for the study (05/Q2102/22). Local research ethics committee approval and Research and Development approval was also gained from all participating centres . We obtaWe used the Short Form 36-item survey (SF-36) version 2 to measure HRQoL in individuals aged ≥15 years and the Short Form 10-item (SF-10) Health Survey for Children in those aged 5–14 years The SF-36 is one of the most widely evaluated generic instruments of HRQoL, with demonstrably good validity and internal consistency Responses to the survey were transformed into norm based scores (NBS) as described in the scoring manual The SF-10 is a 10-item questionnaire designed to measure HRQoL in children. Unlike the SF-36 it should be completed by caregivers. The scoring method yields two summary measures: a physical summary score (PHS) and a psychosocial summary score (PSS), which are also norm-based and fluctuate around a mean of 50 with a standard deviation of 10 Study variables included patient demographics, occupation, immune competency, co-morbid illness and length of hospital stay. We categorised aetiologies as infectious, immune-mediated and unknown. We used occupation to indicate socio-economic status as defined by the National Statistics Socio-Economic Classification (NS-SEC) Three Class Categorisation We compared the demographic and clinical characteristics 1) across the three aetiological groups for individuals that completed the SF-36 and 2) between individuals that completed the HRQoL questionnaires and those that did not complete. A comparison of variables by a etiology was not possible for the SF-10 due to the small sample size. We carried out the descriptive data analysis using Stata v12 . Differences in proportions were assessed by Fisher’s exact or Chi-Squared test as appropriate. The nonparametric equality-of-medians test was used to assess any difference in medians.We carried out a multivariate regression analysis of SF-36 scores with exploration for pair-wise interactions to identify socio-demographic and prognostic factors influencing post-recovery HRQoL. We addressed incomplete responses using extrapolation techniques as laid out in the scoring manual We categorised HRQoL as ‘good’, ‘moderate’ or ‘poor’ based on the scoring manual recommendation that ‘group mean scores below 47 can be interpreted as being below the average range for the general population… [where] the standard deviations for each scale are equalized at 10′ 203 patients were identified with encephalitis in the initial prospective study. Of these, 55 were ineligible for either measure because they died pre-discharge (N = 20) or were less than 5 years old (N = 35) . We follMycobacterium tuberculosis, 4 other bacterial, 4 Varicella zoster virus (VZV), 1 Epstein-Barr virus, 1 measles virus – sclerosing subacute panencephalitis, 1 dual infection with bacteria and fungi) and 10 (24.4%) were immune-mediated , 7 vantibody-associated, 1 multiple-sclerosis associated).A definitive aetiology was identified in 41 (67.2%) individuals who completed the SF-36. Of these, 31 (75.6%) had an infectious cause (14 Herpes simplex virus (HSV), 6 Adult patients with immune-mediated encephalitis had a significantly longer duration of hospital stay than those with an infectious cause . There was little evidence patients differed by aetiology in terms of any other demographic or clinical characteristics . Of 63 pA definitive aetiological agent was identified in 11 (55%) of the 20 children for whom the SF-10 was successfully completed. Seven (63.6%) had immune-mediated encephalitis  and 4 (36.4%) had infectious encephalitis . SF-10 non-respondents were less likely to be white British (p = 0.04) than those for which the SF-10 was completed .The post-encephalitic population had a poorer HRQoL in all domains of the SF-36 compared to an age- and sex-matched general population . ResultsInferences on model parameters indicated having infective encephalitis, being immuno-compromised, being unemployed at point of admission, being 35–44 years-old at diagnosis or not showing good recovery six months after discharge all have a significant negative bearing on HRQoL independent of other factors . For insAs outcome on GOS deteriorated, the proportions of patients reporting good HRQoL decreased and those reporting a bad HRQoL increased . There wWith regard to aetiology, although the proportions reporting a good HRQoL after immune-mediated encephalitis was much higher than after infectious encephalitis and vice versa in all domains of the SF-36, the difference in proportions was only significant for the general health domain (p 0.026). The proportions reporting good or bad HRQoL after encephalitis of unknown cause were between those of immune-mediated and infectious encephalitis. Generally, few people reported a moderate HRQoL, with the greater proportions at the extremities.The children for whom the SF-10 was completed had a generally poor HRQoL compared to the age- and sex-matched US general population. Similar to the adult population that completed the SF-36 physical outcomes were more greatly impacted than psychosocial measures and there was variation in scores. . In contThis study provides compelling evidence that in addition to the significant mortality and morbidity associated with encephalitis, the illness has long-term adverse effects on quality of life for the majority of survivors. Mean scores were consistently reported to be worse in all domains of the SF-36 and SF-10 compared to the general population mean. Overall, HRQoL after encephalitis was in the poor or low-moderate range. Apart from in mental health perceptions, only 25–40% of patients assessed to have made a good recovery on the GOS reported a good HRQoL in the domains of the SF-36 .Infectious encephalitis was associated with the worst quality of life in adults, and encephalitis of unknown cause was associated with the worst quality of life in children, emphasising the need to identify aetiology early in both populations. Physical functioning was more severely impacted than mental health domains in both instruments. As a cohort, patients who responded were less ill and more affluent than those who were eligible but did not respond, indicating the results of this study may under-estimate the overall impact of encephalitis on HRQoL.There was great spread in the mean scores, which might be explained by the observation that individual scores appeared to aggregate either in the ‘good’ or ‘bad’ range, with only a small proportion reporting a moderate HRQoL after encephalitis ; suggestSeveral factors were associated with poor outcome overall. Being unemployed at the point of admission and being in the 35–44 year age group affected post-encephalitis HRQoL almost as much as aetiology or immunocompromise. There are a number of potential explanations. We do not have any measure of HRQoL for patients before they became ill. Although unemployment appeared to be associated with worse outcomes, the presence of significant confounding factors cannot be excluded and the correlation between employment at point of admission and post-discharge was not quantified. Regarding the effect of age on HRQoL, the worse outcomes in those aged 35–44 years may be because these individuals are at the peak of their financial and/or family lives, and many may also have young children to provide for (whereas the older age groups are more likely to have adult children as a source of support). An illness such as encephalitis therefore affects these individuals at their greatest level of socio-occupational functioning, and hence their perception of HRQoL may become negatively skewed. Similar effects have been demonstrated after stroke The strongest association with a fall in HRQoL (as measured by the SF-36) six months after encephalitis was found to be aetiology and GOS. We conducted further analyses to determine the association between these factors and the separate domains of the SF-36. The differences in the proportions reporting good, moderate or poor outcomes and aetiological category were only significant in the General Health domain . This doThe strongest determinant of HRQoL was GOS outcome so measures to improve GOS such as prompt diagnosis and treatment may have a great impact on post-encephalitic quality of life. In almost all domains, as GOS outcome worsened, proportions of patients reporting a good HRQoL decreased and those reporting a poor HRQoL increased. This was significant for the physical functioning, vitality, social functioning and role emotional domains, indicating that limitations in performing everyday physical activities such as bathing and dressing, and interference with social activities particularly from emotional problems and fatigue may be driving poor outcomes. Targeting these areas for post-encephalitis rehabilitation and support could achieve most gain.The sample size was not as large as other studies using validated tools to measure HRQoL, particularly for the younger age group. Encephalitis is rare and is associated with high morbidity and mortality; thus many patients will be excluded from an already small sample size if they cannot self-complete the SF-36, for example due to severe disability. There were differences in background characteristics and aetiologies among those who completed the SF- 10 compared with non-respondents and detailed analysis was difficult in this group. Although we were able to follow up >70% of the sample, there was a relatively high rate of attrition in those aged ≥15 years, with only 61 successfully completed SF-36 forms returned from the 118 eligible. A significant proportion of this non-response (35%) could be explained by mortality and severe morbidity. Non-respondents were more likely to be immunocompromised or have another co-morbid illness in both children and adults, and thus it is likely the population not captured by our study have even worse subjective quality of life compared to those that we were able to measure.Although we only measured HRQoL a median of 5.6 months after discharge from hospital, our results closely matched those of a similar study performed in members of the Encephalitis Society a median of 10.2 years after diagnosis suggesting that HRQoL remains poor in the long-term This study represents one of the most comprehensive attempts to measure the quality of life in a post-encephalitic population. It is the largest study to use a standardised case definition to identify encephalitis, and is the first to delineate quality of life by aetiology or measure HRQoL in children. Two highly validated instruments were used to measure the quality of life in individuals aged ≥5 years in an age-appropriate manner, with multiple clinical and socio-demographic factors taken into consideration. This is the most thorough attempt to reconcile the biological causes of encephalitis with its psycho-social impact of which we are aware.The mortality and morbidity associated with encephalitis have long been recognised as particularly grave. This study provides the first evidence of the variable quality of life of those who recover and the socio-clinical factors associated with good and bad outcomes. Aetiology and GOS outcome appear to be the two factors most associated with post-encephalitic quality of life, highlighting the need to distinguish aetiology at presentation, particularly as many causes can be treated effectively


The diagnosis and staging of lung cancer is an important process that identifies treatment options and guides disease prognosis. We aimed to assess endobronchial ultrasound-guided transbronchial needle aspiration as an initial investigation technique for patients with suspected lung cancer.ClinicalTrials.gov, number NCT00652769.In this open-label, multicentre, pragmatic, randomised controlled trial, we recruited patients who had undergone a CT scan and had suspected stage I to IIIA lung cancer, from six UK centres and randomly assigned them to either endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) or conventional diagnosis and staging (CDS), for further investigation and staging. If a target node could not be accessed by EBUS-TBNA, then endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) was allowed as an alternative procedure. Randomisation was stratified according to the presence of mediastinal lymph nodes measuring 1 cm or more in the short axis and by recruiting centre. We used a telephone randomisation method with permuted blocks of four generated by a computer. Because of the nature of the intervention, masking of participants and consenting investigators was not possible. The primary endpoint was the time-to-treatment decision after completion of the diagnostic and staging investigations and analysis was by intention-to-diagnose. This trial is registered with Between June 10, 2008, and July 4, 2011, we randomly allocated 133 patients to treatment: 66 to EBUS-TBNA and 67 to CDS (one later withdrew consent). Two patients from the EBUS-TBNA group underwent EUS-FNA. The median time to treatment decision was shorter with EBUS-TBNA  than with CDS  resulting in a hazard ratio of 1·98, . One patient in each group had a pneumothorax from a CT-guided biopsy sample; the patient from the CDS group needed intercostal drainage and was admitted to hospital.Transbronchial needle aspiration guided by endobronchial ultrasound should be considered as the initial investigation for patients with suspected lung cancer, because it reduces the time to treatment decision compared with conventional diagnosis and staging techniques.UK Medical Research Council. Lung cancer is the most common cause of cancer death across the world.318F-fluorodeoxyglucose (18F-FDG), the tumour is central, there is a PET-positive hilar lymph node, or any mediastinal node is larger than 1 cm in the short axis (irrespective of 18F-FDG uptake).Several invasive and non-invasive techniques are available to support the diagnosis and staging of lung cancer. Patients with suspected lung cancer undergo a CT scan of the lower neck, thorax, and upper abdomen. About 50% of patients present with metastatic disease that is evident outside the thoraxThe diagnosis and staging of patients with intrathoracic disease can therefore need several investigative procedures, including bronchoscopy, radiology-guided biopsy sampling, PET-CT, and mediastinoscopy. This process often takes several weeks and is a time of great anxiety for patients. Additionally, 26% of patients with lung cancer report that their health deteriorates while waiting for an hospital appointment.The present approach to mediastinal staging of non-small-cell lung cancer  can result in inaccurate nodal staging in 25% of operable patients,818F-FDG-avid hilar lymphadenopathy.Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a newer technique that allows minimally invasive sampling of all intrathoracic lymph nodes adjacent to the bronchial tree. A pooled analysis of 1299 patientsTherefore we aimed to investigate whether EBUS-TBNA could be used as an initial investigation for the diagnosis and staging of patients with suspected lung cancer because the procedure provides a tissue diagnosis and nodal staging in one investigation. Previous studies have shown that EBUS-TBNA might represent good value for money,We did this randomised controlled trial in six centres in the UK . Patients at these centres who were suspected to have stage I to IIIA lung cancer on the basis of CT scans of the neck, thorax, and upper abdomen were eligible for trial entry. For inclusion into the trial, patients had to be aged at least 18 years and fit enough to undergo thoracotomy and lung resection. Exclusion criteria were significant concurrent malignant disease or any condition or concurrent medicine that contraindicated EBUS-TBNA or mediastinoscopy. Patients with known extrathoracic malignant disease, supraclavicular lymphadenopathy, or pleural effusion were also excluded. The 7th edition of the tumour, node, metastasis (TNM) staging system in lung cancer was used throughout.This investigator-initiated pragmatic trial was approved by the UK national research ethics service (reference 07/H0711/127) and the ethics committees of the six participating centres. Patients provided written informed consent.We randomly assigned participants (1:1) to either conventional diagnosis and staging (CDS group) or EBUS-TBNA as an initial investigation after a staging CT scan followed by further diagnosis and staging techniques if needed (EBUS group). We used a telephone randomisation method with permuted computer-generated blocks of four. Randomisation was stratified according to the presence of mediastinal lymph nodes that measured 1 cm or more in the short axis and by recruiting centre. An investigator undertook the informed consent process, followed by the telephone randomisation process done by research assistants. The random allocation sequence was kept in the randomisation centre and concealed from participants and investigators until the interventions were assigned. Because of the nature of the intervention, masking of participants and consenting investigators was not possible. However, pathologists and radiologists were unaware that patients were enrolled into a clinical trial. Data were obtained on paper-based case forms and entered by an independent clerk onto a secured trial database on a dedicated trial computer.Participants allocated to CDS underwent investigations as determined by the local multidisciplinary team. We suggested an algorithm for CDS in the trial protocol based on the most recently available UK National Institute of Health and Clinical Excellence guidance (2005)Patients were given treatment according to the recommendations of the multidisciplinary team. PET-CT was recommended for all patients before a decision to treat with curative intent. In the absence of symptoms, we did not undertake surveillance for brain or bone metastases unless radical treatment was planned.The primary endpoint was the time from first outpatient appointment with the respiratory specialist to treatment decision by the multidisciplinary team, after completion of the diagnosis and staging procedures. We prespecified that the primary outcome measure would be analysed in the subgroup of patients with non-small-cell lung cancer. We prespecified four secondary endpoints: (1) UK National Health Service (NHS) costs of diagnosing and staging lung cancer, (2) the number of investigations and outpatient attendances per patient, (3) the proportion of patients diagnosed and staged with one procedure, and (4) the number of avoidable thoracotomies. An avoidable thoracotomy was defined as an open and close procedure, unexpected mediastinal nodal metastases (pN2/pN3), pT4 or pM1a/b disease, resection of benign disease or disease recurrence, or death within 1 year of thoracotomy. We also documented the sensitivity, negative predictive value, and diagnostic accuracy of EBUS-TBNA a priori, and overall survival  and complications from the different diagnostic and staging techniques.The incremental cost of EBUS-TBNA as an initial investigation compared with CDS was calculated from the perspective of the NHS. We included the costs associated with all diagnostic and staging investigations. We also calculated the costs of treating patients diagnosed with lung cancer. Resource use data were obtained prospectively in the trial. Unit costs were obtained from NHS reference costs,A priori, we expected that 80% of patients would be diagnosed and staged with only one investigation in the EBUS group, compared with 33% in the CDS group. Before the trial began, clinical practice was assessed in a retrospective analysis of diagnostic and staging procedures in five of the participating centres (data not shown). On the basis of this analysis, we estimated that patients in the CDS group of the trial would need a median time to treatment decision of 30 days, and patients in the EBUS group would need a median of 14 days. A sample size of at least 130 patients was planned to give 99% power, assuming a type 1 error of 5%.Analyses were done for the intention-to-diagnose population. The Kaplan-Meier method was used to analyse the primary endpoint (time-to-treatment decision). Hazard ratios (HRs) were calculated from a Cox model, and did not include adjustment for any baseline factors. We used standard definitions of sensitivity for the detection of nodal metastases. The final diagnosis of nodal staging was established in both groups by clinical follow-up of at least 1 year and pathological changes noted with EBUS-TBNA, conventional TBNA, EUS-FNA, mediastinoscopy, or dissection of mediastinal lymph nodes.t tests were used to compare groups of continuous normally distributed variables. All tests were two-sided and 5% was taken as the cutoff for statistical significance. The normal approximation method was used to calculate confidence intervals for the proportions. Final statistical analyses were done with STATA (version 10).The Fisher exact test was used to analyse categorical data, and unpaired ClinicalTrials.gov, number NCT00652769.This trial is registered on The funders of the study had no role in study design, data collection, data analysis, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.Between June 10, 2008, and July 4, 2011, we randomly assigned 133 patients with suspected lung cancer to the CDS group (n=67) and EBUS group (n=66) . One patLung cancer was diagnosed in 57 (86%) patients in the CDS group and 50 (76%) in the EBUS group (p=0·196), and clinical staging did not differ significantly between the groups in patients with non-small-cell lung cancer . The benvs 8 [12%], p<0·0001). In the subset of patients with non-small-cell lung cancer (The median time-to-treatment decision was longer after CDS (29 days [95% CI 23–35]), than after EBUS  in the intention-to-diagnose population. Therefore patients in the EBUS group of the trial were likely to receive a treatment decision twice as fast as patients in the CDS group . A greatg cancer , initialThe mean number of investigations per patient, PET scans , and avoIn the CDS group, 44 (67%) of 66 patients initially underwent a bronchoscopy and 29 (44%) had a radiology-guided biopsy sample taken; in the EBUS group, 64 (97%) of 66 underwent EBUS and two (3%) had EUS-FNA as an initial procedure. Five (8%) of 66 patients had a subsequent radiology-guided biopsy sample taken . The numIn a post-hoc analysis, the median survival of patients with non-small-cell lung cancer in the EBUS group of 503 days (95% CI 312–715) was longer than the median survival in the CDS group of 312 days . The sensitivity of EBUS-TBNA in this trial was 92% (95% CI 78–98). The negative predictive value of EBUS-TBNA was 90% (72–97) and diagnostic accuracy was 95% (86–99). Two patients randomised to the EBUS group of the trial underwent EUS-FNA instead of EBUS-TBNA, both of whom had station 5 lymph nodes. The procedure yielded a diagnosis of malignant disease in both patients (one adenocarcinoma and one large cell lung cancer). In the CDS group of the study, five patients underwent conventional TBNA. Two of these patients had a benign final diagnosis, and in one patient conventional TBNA provided a diagnosis of squamous cell lung cancer. In the remaining two patients undergoing conventional TBNA, a negative procedure was followed by a mediastinoscopy that showed mediastinal metastases.One patient in each group had a pneumothorax from a CT-guided biopsy sample; the patient in the CDS group needed intercostal drainage and was admitted to hospital.The mean cost per patient for diagnostic and staging investigations was £2407 (SD £180·50) in the EBUS group and £2348 (192·20) in the CDS group  were diagnosed with inoperable lung cancer by EBUS. Specimens obtained from EBUS-TBNA are accepted as suitable for the personalised approach to the management of non-small-cell lung cancer. In a multicentre study,The results of the cost analysis suggested that use of EBUS-TBNA as an initial investigation after a CT scan was not more expensive than CDS. Because patients in the EBUS group of the trial had an earlier treatment decision (the primary outcome), we can conclude that EBUS-TBNA was more effective for the same cost, and was therefore cost-effective.There were more avoidable thoracotomies at 1 year in the CDS group (13 [76%]) than in the EBUS group (5 [29%]). The broader definition of avoidable thoracotomy in this trial accounts for the high proportion compared with unnecessary thoracotomies in previous studies.In a post-hoc analysis, survival was higher in patients who underwent EBUS-TBNA as part of their diagnostic and staging strategy. Further post-hoc analysis suggests that this difference in survival might be due to superior selection  of candidates for surgery in patients undergoing preoperative EBUS-TBNA . We postWe recognise that the trial had several limitations. For example, the pragmatic nature of the trial meant that a consistent diagnostic and staging algorithm was not used across all the trial centres. However, the design of the study  gives the results strong external validity. Advances in radiotherapy techniques during the trial period could mean that more patients with mediastinal metastases might now suitable for radical treatment and therefore would need PET-CT. In this pragmatic trial of patients with suspected lung cancer, 19% of the participants had a final diagnosis of disorders other than lung cancer, including metastatic melanoma and lung abscess. Despite this, the primary endpoint was statistically significant for both all patients and also those with non-small-cell lung cancer only. Finally, in this trial, EBUS-TBNA was undertaken by clinicians who were skilled in the procedure—the sensitivity of EBUS-TBNA might not be immediately reproducible in other centres.In conclusion, when EBUS-TBNA is used as an initial investigation method after a CT scan in patients with suspected lung cancer confined to the thorax, it can provide a diagnosis and accurate nodal stage in one investigation. This results in a reduction in the time-to-treatment decision and might improve survival in patients with lung cancer when compared with a conventional diagnostic and staging strategy, at no additional cost.


We have developed a multi-array side-polished optical-fiber gas sensor for the detection of volatile organic compound (VOC) gases. The side-polished optical-fiber coupled with a polymer planar waveguide (PWG) provides high sensitivity to alterations in refractive index. The PWG was fabricated by coating a solvatochromic dye with poly(vinylpyrrolidone). To confirm the effectiveness of the sensor, five different sensing membranes were fabricated by coating the side-polished optical-fiber using the solvatochromic dyes Reinhardt's dye, Nile red, 4-aminophthalimide, 4-amino-N-methylphthalimide, and 4-(dimethylamino)cinnamaldehyde, which have different polarities that cause changes in the effective refractive index of the sensing membrane owing to evanescent field coupling. The fabricated gas detection system was tested with five types of VOC gases, namely acetic acid, benzene, dimethylamine, ethanol, and toluene at concentrations of 1, 2,…,10 ppb. Second-regression and principal component analyses showed that the response properties of the proposed VOC gas sensor were linearly shifted bathochromically, and each gas showed different response characteristics. It is important to monitor the vapor concentration to safeguard the health of workers by keeping atmospheric emissions in check and preventing environmental hazards ,2. With Several techniques for VOC gas detection have been reported, such as colorimetric gas sensors, semiconductor gas sensors, and optical sensors –6. The cRecently, side-polished optical-fiber sensors have been shown to be good candidates for monitoring environmental changes because they not only have mechanical stability, but also sensitivity control by changing the planar waveguide materials –17.In our previous work, we confirmed that side-polished optical-fiber sensors can be used to detect the VOC gas xylene using a solvatochromic dye . FurtherHere, we propose a simple multi-array VOC gas detection system based on changing the effective refractive index. The different polarities of the VOC gases cause changes in the effective refractive index of the sensing membrane owing to evanescent field coupling. We fabricated sensing membranes by incorporating five different solvatochromic dyes into poly(vinylpyrrolidone) (PVP) polymer. Upon contact between the VOC gases and the sensing membrane, the refractive index changes on the basis of the charge-transfer (CT) characteristics of the solvatochromic dye in the device's polymer planar waveguide (PWG). Our proposed optical-fiber gas sensor has a good linear response over a broad dynamic range and can detect approximately 10 ppb of VOC gas. Principal component analysis (PCA) was used to explore the data distribution and classify the VOCs ,19.2.2.1.3). The V-groove was fabricated using a mechanical slicer. A single-mode optical-fiber of core radius 3 μm and cladding radius 125 μm was placed in the V-groove. The bending radius of the optical-fiber inside the quartz block was approximately 50 cm. The final stage of the sensing device fabrication process was fiber polishing  of the wavelength shift exhibited a good dynamic range from 0.95344 to 0.99885 [The results indicate that the relative resonance wavelength shifts as the concentration of the VOCs gases increases. The dynamic range was approximately from 1 to 8 ppb and saturation level for VOC gas detection was ∼9 ppb. We repeated the experimental measurements about three times under VOC gas concentrations ranging from 1 to 10 ppb. Thus, we confirmed the performance of the proposed detection system by repeating similar experiments several times. As a result, the R-squared value [R 0.99885 .The average gas-sensing responses of the five different VOC gases for different sensing membranes are shown in We used PCA to determine the pattern of discrimination and separation of the proposed VOC gas-detection system. PCA is a Statistical Package for Social Science (SPSS) tool used to analyze, classify, and reduce the dimensionalities of numerical datasets in multivariate problems . The fir4.We have developed a new design for a highly sensitive optical gas sensor to detect VOC gases using evanescent-field coupling between a side-polished optical-fiber and a PWG. Five different solvatochromic dyes  and a PVP polymer were used to fabricate five different types of sensing membranes. The proposed VOC gas sensor response characteristics were reproducible, and the linear response of the detection system was good. The types and quantities of VOC gases were determined in real-time using the proposed detector. The response and recovery times were less than 10 s. PCA was used to distinguish different VOC gases on the basis of the responses of the five different sensing membranes. The proposed VOC gas sensor has many advantages, including easy fabrication, good selectivity, short response time, linear response over a practical range, low cost, and high sensitivity. We believe that sensor devices produced using the same fabrication process under the same conditions would show good stability. In the future, we plan to extend the analysis to include other VOC gases in mixed-gas experiments using different sensor array systems.


The majority of nutrition and physical activity assessments methods commonly used in scientific research are subject to recall and social desirability biases, which result in over- or under-reporting of behaviors. Real-time mobile-based ecological momentary assessments (mEMAs) may result in decreased measurement biases and minimize participant burden.The aim was to examine the validity of a mEMA methodology to assess dietary and physical activity levels compared to 24-hour dietary recalls and accelerometers.This study was a pilot test of the SPARC  study, which aimed to determine the mechanism by which friendship networks impact weight-related behaviors among young people. An mEMA app, devilSPARC, was developed to assess weight-related behaviors in real time. A diverse sample of 109 freshmen and community mentors attending a large southwestern university downloaded the devilSPARC mEMA app onto their personal mobile phones. Participants were prompted randomly eight times per day over the course of 4 days to complete mEMAs. During the same 4-day period, participants completed up to three 24-hour dietary recalls and/or 4 days of accelerometry. Self-reported mEMA responses were compared to 24-hour dietary recalls and accelerometry measures using comparison statistics, such as match rate, sensitivity and specificity, and mixed model odds ratios, adjusted for within-person correlation among repeated measurements.At the day level, total dietary intake data reported through the mEMA app reflected eating choices also captured by the 24-hour recall. Entrées had the lowest match rate, and fruits and vegetables had the highest match rate. Widening the window of aggregation of 24-hour dietary recall data on either side of the mEMA response resulted in increased specificity and decreased sensitivity. For physical activity behaviors, levels of activity reported through mEMA differed for sedentary versus non-sedentary activity at the day level as measured by accelerometers.The devilSPARC mEMA app is valid for assessing eating behaviors and the presence of sedentary activity at the day level. This mEMA may be useful in studies examining real-time weight-related behaviors. The majority of nutrition and physical activity (PA) assessments are subject to recall and social desirability biases, which can result in over- or under-reporting of behaviors ,2. For eEcological momentary assessments (EMAs) limit measurement biases associated with self-reported recall data. As described by Shiffman et al  and StonEmerging technologies and changes in how people use technologies have created opportunities to assess behaviors as the behaviors occur. With increasing use and ownership of mobile phones, mobile technologies are valuable assets in behavioral health research ,9,10. SedevilSPARC, in assessing dietary and PA behaviors with a college student sample.College students are an understudied population in regards to weight and weight-related behaviors and management . The NatThe SPARC  study was a large-scale NIH-funded study that aimed to determine the mechanisms by which friendship networks and interpersonal connections impact weight and weight-related outcomes. In the formative phase of the study, participants answered EMA prompts asking about their current nutrition and PA behaviors using the mEMA app, devilSPARC, as well as validated measures of diet  and/or aCollege freshmen and assigned community mentors (resident assistants) at Arizona State University from two residence halls were recruited for participation. Inclusion criteria were (1) enrollment at Arizona State University and (2) living in target residence halls. For those participants who were interested in participating but did not own an Android or iOS mobile phone, a Motorola Moto G was loaned to them for use during the duration of the study. The resulting sample was 109 participants: 68 participants who provided mEMA and 24-hour dietary recalls only, 17 students who provided mEMA PA reports and accelerometry assessments only, and 24 participants who completed both protocols .The mEMA software was designed specifically for this study and implemented on Android- or iOS-compatible mobile phones. For each 4-day period of data collection (Wednesday-Saturday), participants received a total of 32 short message service (SMS) text message prompts to complete mEMA surveys via the devilSPARC app. Each day, participants received seven “real-time” prompts per day  and one retrospective prompt per day (n=4). Real-time prompts asked participants what they were doing in the moment before they received the prompt and retrospective prompts asked participants to recall what they did in the past 3 hours. A random, interval-contingent schedule was used for the mEMA prompts. Twice during each of the four established time periods per day  the system prompted participants to complete a brief survey. In order to ensure the momentary nature of the mEMA, participants were allotted 35 minutes to respond to the prompt by completing a 1-minute survey, with the survey being available for 5 minutes prior to, and 30 minutes after, the text message prompt. Outside of these times, the mEMA surveys were not available to complete on the app. On average, the latency time (time between the sending of the SMS prompt to the completion of the survey on the mEMA app) was 7.25 minutes for participants completing the dietary validation and 6.90 minutes for participants completing the PA validation. Trained research assistants downloaded the devilSPARC mEMA app to each participant’s mobile phone and provided demonstrations on how to use the devilSPARC app.All SMS text prompts were sent directly to participants’ mobile phones using Web service application programming interfaces (APIs) provided by Twilio, a cloud communications company. Through Twilio’s API, a series of six local long codes (10-digit phone numbers) were used to send text messages to participants. Each long code was randomly assigned to a participant based on his or her participant identification number. Any failed text messages were sent two more times, for a total of three attempts. The text message included motivational text with an embedded link that would open a survey on the app. Data were transferred instantaneously to the study host server every time the user’s phone contacted the central server .The online version of the Automated Self-Administered 24-hour (ASA24) dietary recall system, a validated measure of self-reported dietary intake, was used to assess participants’ food and beverage intake over the previous 24 hours . The ASAActigraph, Inc (model GT3X+) accelerometer devices provided an objective measure for participants’ PA. A 60-second epoch for summing counts and the Freedson et al  cut poinAnalyses were specific to dietary and PA data. Given the varied distribution for each of the food choices across both the mEMA and ASA24, comparisons between the mEMA and ASA24 responses at the moment of the mEMA response were not possible. However, because accelerometers capture PA every 60 seconds, direct comparisons between the PA level reported in the mEMA and the PA recorded by the accelerometer at the moment of the mEMA response were possible. The specific analyses are described subsequently.Participants’ data were excluded (n=15) from the analysis for days without (1) mEMA data with at least one food entry and (2) a biologically plausible ASA24 dietary recall. This resulted in an analytical sample of 92 from the 107 participants with ASA information (86.0%). The percentage match between the ASA24 and mEMA at both the daily level, and for time windows around the mEMA  were determined for each food type .In day-level analyses, for each food type, the denominator for the match rate included the number of times the food type was reported via mEMA during the day; the numerator was the smaller of (1) the number of times the food type was reported in that day’s mEMA reports and (2) the corresponding ASA24 count for each participant. For example, if fruits and vegetables were reported four times via mEMA on a given day and three times in that day’s ASA24, this was recorded as three matches from a potential four (75% match rate). Conversely, if on a given day fruits and vegetables were reported three times via mEMA and four times in the corresponding ASA24, this would be recorded as three matches out of a potential three (100% match rate). For the time window analysis, the denominator for the match rate represented every mEMA response with the food type recorded; the numerator was the number of times the food type recorded in the mEMA was also in the ASA24 within the given time window.Chi-square tests were used to determine if match rates differed between males and females, white and nonwhite participants, and participants with and without a Pell grant. Sensitivity and specificity values were computed for the time windows to determine the impact of increased windows size on the match rates. For the sake of conciseness, positive and negative likelihood ratios are not reported for each food type. Mixed effects logistic regression models with random participant-level intercepts were used to determine how well the endorsement of a food type in mEMA reports could be predicted from the endorsement of the same food type in the ASA24.different PA level was reported via mEMA. To determine if the distribution of the accelerometer-derived activity levels differed systematically with respect to mEMA-reported PA level, two-sample Kolmogorov-Smirnov tests were run. This nonparametric test compares the maximum vertical distance between the cumulative distribution functions of two distributions, with the P value corresponding to the probability that the distributions are the same . Mixed effects linear regression models with random participant-level intercepts were used to determine how well mEMA-reported PA categories predicted log transformed accelerometer-derived activity levels. All analyses were conducted using R statistical software version 3.2.3 . Statistical significance was determined at P<.05.Participants’ mEMA responses were excluded (n=8) from the analysis if the accelerometer activity CPM values were zero for the 30 minutes before or after the mEMA response, or if the accelerometer had not been worn for at least 5 hours for the day of the mEMA. This resulted in an analytic sample of 41 from the 49 participants with accelerometer information (84%). Because accelerometer readings showed high minute-to-minute variability, the average accelerometer activity (CPM) value for the 5 minutes prior to the EMA response was used as the measure of accelerometer-derived activity. There were six parameters used to characterize agreement between the PA level as determined by accelerometer activity counts  and the PA level reported in the mEMA . These parameters were (1) odds ratio: the odds that a participant’s accelerometer-derived activity level and mEMA-reported PA level were both at a specific PA level, compared to the odds a participant’s accelerometer-derived activity level was at the specified level, but the mEMA-reported PA level was not; (2) match rates: the percentage of times the accelerometer-derived activity level and reported mEMA level were the same for each mEMA level; (3) sensitivity (true positive rate): the percentage of times both the mEMA-reported PA levels and the accelerometer-derived activity level were at the same PA level for each accelerometer-derived PA level; (4) specificity (true negative rate): the percentage of times the mEMA-reported PA levels and the accelerometer-derived activity level were not at the specific PA level for each accelerometer-derived PA level; (5) positive likelihood ratio: the increase in the likelihood that a particular accelerometer-measured activity level  was achieved, given that the same PA level was reported via mEMA; and (6) negative likelihood ratio: the decrease in the likelihood that a particular accelerometer-measured PA level  was achieved, given that a Data from 92 participants were used in analyses examining validity of mEMA-reported dietary behavior , and data from 41 participants were used in analyses aimed at examining the validity of mEMA-reported PA , but was the least-reported food type reported in the ASA24 , light PA , and moderate PA  Due to only one participant having vigorous accelerometer values, odds were not computed for vigorous activity. The match rates were highest for mEMA-reported sedentary and light PA , and lowest for moderate PA  and vigorous PA .We also conducted sensitivity and specificity between mEMA-reported activities and accelerometer-derived activities, for each respective PA intensity level. Specificity and positive likelihood ratio values were lower for mEMA-reported sedentary PA  than mEMA-reported light , moderate , and vigorous  PA. Sensitivity values were highest for mEMA-reported sedentary (91%) and vigorous PA (100%), and lowest for mEMA-reported light (13%) and moderate PA (24%); negative likelihood ratio values were lowest for mEMA-reported sedentary (0.29) and vigorous PA (0.00), and highest for mEMA-reported light (0.93) and moderate PA (0.79).lower on occasions when they reported light PA than on occasions when they reported being sedentary. Similarly, for six of the 14 participants who reported both light and moderate PA, on occasions when moderate PA was reported average, accelerometer-derived activity levels were lower than occasions when light PA was reported.The difference in participants’ average accelerometer-derived activity levels was not consistent across the mEMA levels. For example, as illustrated in P<.001; P=.02). No difference between the distribution of the accelerometer-derived activity counts were seen between mEMA-reported light and moderate PA occasions, or moderate and strenuous PA occasions.To examine the differences in distributions of accelerometer-derived activity levels for different reported mEMA PA levels, we conducted Kolmogorov-Smirnov tests. Results indicated a significant difference in the distribution of the accelerometer-derived activity counts for mEMA-reported sedentary and nonsedentary PA levels P<.001; . The difP<001) difference between logged activity count values for mEMA-reported sedentary and nonsedentary accelerometer occasions, but no significant difference in logged counts across mEMA-reported light, moderate, and strenuous PA occasions . For example, when comparing mEMA-reported sedentary versus light PA occasions, activity counts were higher for mEMA-reported light PA occasions than for sedentary occasions (P<.001). The estimated average increases in logged accelerometer activity counts between sedentary and nonsedentary occasions were 1.71, 1.81, and 2.79 for light, moderate, and vigorous PA, respectively, corresponding to differences of 178, 201, and 603 CPM, respectively, in raw count values.To more closely examine the association between mEMA-reported intensity of PA to accelerometer-measured levels, we estimated differences in distributions of logged accelerometer activity count values for pairs of mEMA-reported PA levels using mixed linear regression models (with repeated observations nested within participants) . There wThis study assessed the validity of the devilSPARC mEMA app as a tool for assessing eating and PA behaviors among young adults compared to online dietary recall and accelerometry methodologies. Diet and PA assessment methods commonly used in current research settings often require high levels of cost and personal effort for participants. Few objective assessments of dietary quality and intake are available; self-report remains the norm in observational studies. Objective assessments of PA tend to use expensive devices. The mEMA had high match rates with day-level reported dietary intake as measured by 24-hour recall. For PA behaviors, mEMA reports differentiated sedentary from nonsedentary activity, but these reports did not accurately distinguish among objectively measured PA levels. These findings suggest that the devilSPARC mEMA app had relatively high criterion validity with food choices and for distinguishing between sedentary versus nonsedentary activity.Research has demonstrated that methods for dietary recall are subject to significant compliance, self-reporting, and recall errors ,28. ThesReports of light, moderate, and vigorous levels of PA from the mEMA did not correspond to intensity of PA as measured objectively through accelerometry. The proportion of participants reporting an activity level that corresponded to the accelerometer decreased with increasing PA level. Social desirability and/or perception biases may be at play with these results. Social desirability is often related to over-reporting of activity duration and intensity ,33,34. OTo our knowledge, this is the first study to validate a mEMA tool assessing eating and PA behaviors among young people; however, several limitations should be considered. The devilSPARC app did not assess quantity of foods or specific details of the foods , as this would have added to the response burden; the devilSPARC tool assessed broad behaviors and was therefore not able to yield information about total caloric intake, macronutrients, or micronutrients. Given the relatively equal distribution of food choices captured, we were able to assess a variety of commonly consumed foods, including healthy and unhealthy food choices, for young adults in college. The devilSPARC mEMA tool was designed to assess behaviors in the moment; as such, it could not represent total dietary intake or total PA. In addition, because the mEMA and the 24-hour recall relied on self-reports, participants’ reporting biases and idiosyncratic interpretations of mEMA questions could have increased measurement error. Despite verbal and written directions to wear the accelerometer at all times except when swimming and bathing, there is a possibility that participants removed the accelerometer when participating in vigorous activities, such as contact sports, which would have improved our match rate between the mEMA and more vigorous activities. Also, although the sample was relatively diverse in terms of race/ethnicity, almost all participants were college freshmen; thus, these findings may not be generalizable beyond young adult populations.This new mEMA tool is valid for assessing eating behaviors and the presence of PA. With very brief surveys spaced through the day, this mEMA tool may reduce participant burden as compared to 24-hour dietary recall or PA recall instruments. The mEMA builds on previous measures of assessing eating and PA, including a wide range of foods.