A novel method for using accelerometer data to predict energy expenditure.

The purpose of this study was to develop a new two-regression model relating Actigraph activity counts to energy expenditure over a wide range of physical activities. Forty-eight participants [age 35 yr (11.4)] performed various activities chosen to represent sedentary, light, moderate, and vigorous intensities. Eighteen activities were split into three routines with each routine being performed by 20 individuals, for a total of 60 tests. Forty-five tests were randomly selected for the development of the new equation, and 15 tests were used to cross-validate the new equation and compare it against already existing equations. During each routine, the participant wore an Actigraph accelerometer on the hip, and oxygen consumption was simultaneously measured by a portable metabolic system. For each activity, the coefficient of variation (CV) for the counts per 10 s was calculated to determine whether the activity was walking/running or some other activity. If the CV was 10, a lifestyle/leisure time physical activity regression was used. In the cross-validation group, the mean estimates using the new algorithm (2-regression model with an inactivity threshold) were within 0.75 metabolic equivalents (METs) of measured METs for each of the activities performed (P >or= 0.05), which was a substantial improvement over the single-regression models. The new algorithm is more accurate for the prediction of energy expenditure than currently published regression equations using the Actigraph accelerometer.     

Prediction Models Discriminating between Nonlocomotive and Locomotive Activities in Children Using a Triaxial Accelerometer with a Gravity-removal Physical Activity Classification Algorithm

The aims of our study were to examine whether a gravity-removal physical activity classification algorithm (GRPACA) is applicable for discrimination between nonlocomotive and locomotive activities for various physical activities (PAs) of children and to prove that this approach improves the estimation accuracy of a prediction model for children using an accelerometer. Japanese children (42 boys and 26 girls) attending primary school were invited to participate in this study. We used a triaxial accelerometer with a sampling interval of 32 Hz and within a measurement range of ±6 G. Participants were asked to perform 6 nonlocomotive and 5 locomotive activities. We measured raw synthetic acceleration with the triaxial accelerometer and monitored oxygen consumption and carbon dioxide production during each activity with the Douglas bag method. In addition, the resting metabolic rate (RMR) was measured with the subject sitting on a chair to calculate metabolic equivalents (METs). When the ratio of unfiltered synthetic acceleration (USA) and filtered synthetic acceleration (FSA) was 1.12, the rate of correct discrimination between nonlocomotive and locomotive activities was excellent, at 99.1% on average. As a result, a strong linear relationship was found for both nonlocomotive (METs = 0.013×synthetic acceleration +1.220, R² = 0.772) and locomotive (METs = 0.005×synthetic acceleration +0.944, R² = 0.880) activities, except for climbing down and up. The mean differences between the values predicted by our model and measured METs were −0.50 to 0.23 for moderate to vigorous intensity (>3.5 METs) PAs like running, ball throwing and washing the floor, which were regarded as unpredictable PAs. In addition, the difference was within 0.25 METs for sedentary to mild moderate PAs (<3.5 METs). Our specific calibration model that discriminates between nonlocomotive and locomotive activities for children can be useful to evaluate the sedentary to vigorous PAs intensity of both nonlocomotive and locomotive activities.     

Separating Bedtime Rest from Activity Using Waist or Wrist-Worn Accelerometers in Youth

Recent interest in sedentary behavior and technological advances expanded use of watch-size accelerometers for continuous monitoring of physical activity (PA) over extended periods (e.g., 24 h/day for 1 week) in studies conducted in natural living environment. This approach necessitates the development of new methods separating bedtime rest and activity periods from the accelerometer recordings. The goal of this study was to develop a decision tree with acceptable accuracy for separating bedtime rest from activity in youth using accelerometer placed on waist or wrist. Minute-by-minute accelerometry data were collected from 81 youth (10–18 years old, 47 females) during a monitored 24-h stay in a whole-room indirect calorimeter equipped with a force platform covering the floor to detect movement. Receiver Operating Characteristic (ROC) curve analysis was used to determine the accelerometer cut points for rest and activity. To examine the classification differences, the accelerometer bedtime rest and activity classified by the algorithm in the development group (n = 41) were compared with actual bedtime rest and activity classification obtained from the room calorimeter-measured metabolic rate and movement data. The selected optimal bedtime rest cut points were 20 and 250 counts/min for the waist- and the wrist-worn accelerometer, respectively. The selected optimal activity cut points were 500 and 3,000 counts/min for waist and wrist-worn accelerometers, respectively. Bedtime rest and activity were correctly classified by the algorithm in the validation group (n = 40) by both waist- (sensitivity: 0.983, specificity: 0.946, area under ROC curve: 0. 872) and wrist-worn (0.999, 0.980 and 0.943) accelerometers. The decision tree classified bedtime rest correctly with higher accuracy than commonly used automated algorithm for both waist- and wrist-warn accelerometer (all p<0.001). We concluded that cut points developed and validated for waist- and wrist-worn uniaxial accelerometer have a good power for accurate separation of time spent in bedtime rest from activity in youth.     

Classification accuracy of a single tri-axial accelerometer for training background and experience level in runners

Accelerometers are increasingly used tools for gait analysis, but there remains a lack of research on their application to running and their ability to classify running patterns. The purpose of this study was to conduct an exploratory examination into the capability of a tri-axial accelerometer to classify runners of different training backgrounds and experience levels, according to their 3-dimensional (3D) accelerometer data patterns. Training background was examined with 14 competitive soccer players and 12 experienced marathon runners, and experience level was examined with 16 first-time and the same 12 experienced marathon runners. Discrete variables were extracted from 3D accelerations during a short run using root mean square, wavelet transformation, and autocorrelation procedures. A principal component analysis (PCA) was conducted on all variables, including gait speed to account for covariance. Eight PCs were retained, explaining 88% of the variance in the data. A stepwise discriminant analysis of PCs was used to determine the binary classification accuracy for training background and experience level, with and without the PC of Speed. With Speed, the accelerometer correctly classified 96% of runners for both training background and experience level. Without Speed, the accelerometer correctly classified 85% of runners based on training background, but only 68% based on experience level. These findings suggest that the accelerometer is effective in classifying athletes of different training backgrounds, but is less effective for classifying runners of different experience levels where gait speed is the primary discriminator.     

构象性B细胞表位预测的免疫信息学方法及其网络资源

抗原表位预测是免疫信息学研究的重要方向之一,可以给实验提供重要的线索。B细胞表位或抗原决定簇是抗原中可被B细胞受体或抗体特异性识别并结合的部位。实际上,近90%的B细胞表位是构象性的。即使抗原蛋白质三级结构已知,B细胞表位预测仍然是一大挑战。该文结合实例阐述当今主要的构象性B细胞表位预测方法和算法:机器学习预测、非机器学习的计算预测、基于噬菌体展示数据的识别方法,以及一些也可用于构象性B细胞表位预测的通用蛋白质-蛋白质界面预测方法;介绍最新相关预测软件和Web服务资源,说明未来的研究趋势。     

Accelerometer-Determined Physical Activity and Its Comparison with the International Physical Activity Questionnaire in a Sample of Nigerian Adults

Introduction

Accurate assessment of physical activity to identify current levels and changes within the population is dependent on the precision of the measurement tools. The aim of this study was to compare components of physical activity measured with an adapted version of the International Physical Activity Questionnaire (Hausa IPAQ-SF) and the accelerometer in a sample of Nigeria adults.

Methods

One hundred and forty-four participants (Mean age = 32.6±9.9 years, 40.3% women) in a cross-sectional study wore an accelerometer for seven consecutive days and completed the Hausa IPAQ-SF questionnaire on the eighth day. Total physical activity, time spent in moderate-to-vigorous activity (MVPA) and sedentary time assessed by Hausa IPAQ-SF and accelerometer were compared. The absolute and criterion- related validity of the Hausa IPAQ-SF was assessed by Bland-Altman analysis and Spearman Correlation Coefficients, respectively. Specificity and sensitivity were calculated to classify individuals according to the global standard guideline for sufficient physical activity.

Results

Compared with the accelerometer, higher time in MVPA and total physical activity were reported on the Hausa IPAQ-SF (p<0.001), while low to moderate correlations (Rs = 0.03–0.38) were found between the two methods. The 95% limits of agreement were wide between methods for total physical activity (−23019 to 20375 METmin.d⁻¹) and sedentary time (−510 to 150 min.d⁻¹). The sensitivity (76.2%) of Hausa IPAQ-SF to identify insufficiently active people was good, but its specificity (33.3%) to correctly classify sufficiently active people was low.

Conclusions

The Hausa IPAQ-SF overestimated components of physical activity among Nigerian adults, and demonstrated poor to moderate evidence of absolute and criterion validity. Further evaluation of IPAQ and other self-report physical activity instruments in other Africa populations could enhance accurate evaluation of physical activity data in the region countries.     

Population-Referenced Percentiles for Waist-Worn Accelerometer-Derived Total Activity Counts in U.S. Youth: 2003 – 2006 NHANES

Background

The total activity volume performed is an overall measure that takes into account the frequency, intensity, and duration of activities performed. The importance of considering total activity volume is shown by recent studies indicating that light physical activity (LPA) and intermittent moderate-to-vigorous physical activity (MVPA) have health benefits. Accelerometer-derived total activity counts (TAC) per day from a waist-worn accelerometer can serve as a proxy for an individual''s total activity volume. The purpose of this study was to develop age- and gender-specific percentiles for daily TAC, minutes of MVPA, and minutes of LPA in U.S. youth ages 6 – 19 y.

Methods

Data from the 2003 – 2006 NHANES waist-worn accelerometer component were used in this analysis. The sample was composed of youth aged 6 – 19 years with at least 4 d of ≥ 10 hours of accelerometer wear time (N = 3698). MVPA was defined using age specific cutpoints as the total number of minutes at ≥4 metabolic equivalents (METs) for youth 6 – 17 y or minutes with ≥2020 counts for youth 18 – 19 y. LPA was defined as the total number of minutes between 100 counts and the MVPA threshold. TAC/d, MVPA, and LPA were averaged across all valid days.

Results

For males in the 50^th percentile, the median activity level was 441,431 TAC/d, with 53 min/d of MVPA and 368 min/d of LPA. The median level of activity for females was 234,322 TAC/d, with 32 min/d of MVPA and 355 min/d of LPA.

Conclusion

Population referenced TAC/d percentiles for U.S. youth ages 6-19 y provide a novel means of characterizing the total activity volume performed by children and adolescents.     

Arm exercise stress perfusion imaging predicts clinical outcome

Treadmill exercise capacity in resting metabolic equivalents (METs) and stress hemodynamic, electrocardiographic (ECG), and myocardial perfusion imaging (MPI) responses are independently predictive of adverse clinical events. However, limited data exist for arm ergometer stress testing (AXT) in patients who cannot perform leg exercise because of lower extremity disabilities. We sought to determine the extent to which AXT METs, hemodynamic, ECG, and MPI responses to arm exercise add independent incremental value to demographic and clinical variables for prediction of all-cause mortality, myocardial infarction (MI), or late coronary revascularization, individually or as a composite. A prospective cohort of 186 patients aged 64 ± 10 (SD) yr, unable to perform lower extremity exercise, underwent AXT MPI for clinical reasons between 1997 and 2002, and were followed for 62 ± 23 mo, to an endpoint of death or 12/31/2006. Average annual rates were 5.4% for mortality, 2.2% for MI, 2.5% for late coronary revascularization, and 8.0% for combined events. After adjustment for age and clinical variables, AXT METs [P < 0.05; hazard ratio (HR) = 0.59; confidence interval (CI) = 0.35-0.84] and abnormal MPI (P < 0.01; HR = 2.48; CI = 2.15-2.81) were independently predictive of mortality. A positive AXT ECG (P < 0.05; HR = 2.61; CI = 2.13-3.10) was predictive of MI. Death and MI combined were prognosticated by METs (P < 0.05; HR = 0.63; CI = 0.41-0.85), MPI (P < 0.05; HR = 1.77; CI = 1.49-2.05), and a positive AXT ECG (P < 0.05; HR = 1.86; CI = 1.55-2.17). In conclusion, for high risk older patients who cannot perform leg exercise because of lower extremity disabilities, AXT METs are as important as MPI for prediction of mortality alone and death and MI combined, and a positive AXT ECG prognosticates MI alone and death and MI combined.     

Use of ordinal categorical variables in skeletal assessment of sex from the cranium

In anthropological studies, visual indicators of sex are traditionally scored on an ordinal categorical scale. Logistic and probit regression models are commonly used statistical tools for the analysis of ordinal categorical data. These models provide unbiased estimates of the posterior probabilities of sex conditional on observed indicators, but they do so only under certain conditions. We suggest a more general method for sexing using a multivariate cumulative probit model and examine both single indicator and multivariate indicator models on a sample of 138 crania from a Late Mississippian site in middle Tennessee. The crania were scored for five common sex indicators: superciliary arch form, chin form, size of mastoid process, shape of the supraorbital margin, and nuchal cresting. Independent assessment of sex for each individual is based on pubic indicators. The traditional logistic regressions are cumbersome because of limitations imposed by missing data. The logistic regression correctly classified 66/74 males and 46/64 females, with an overall correct classification of 81%. The cumulative probit model classified 64/74 males correctly and 51/64 females correctly for an overall correct classification rate of 83%. Finally, we apply parameters estimated from the logit and probit models to find posterior probabilities of sex assignment for 296 additional crania for which pubic indicators were absent or ambiguous. Am J Phys Anthropol 107:97–112, 1998. © 1998 Wiley-Liss, Inc.     

A smartphone based real-time daily activity monitoring system

A real-time activity monitoring system within an Android based smartphone is proposed and evaluated. Motion and motionless postures may be classified using principles of kinematical theory, which underpins hierarchical rule-based algorithms, based on accelerometer and orientation data. Falls detection was implemented by analyzing whether the postures classified as ‘lying’ or ‘sit-tilted’ posture are deemed normal or abnormal, based on the analysis of time, users’ current position and posture transition. Experimental results demonstrate that the approach can detect various types of falls efficiently (i.e., in real-time within a smart phone processor) and also correctly (95 % and 93 % true positives for falls ending with ‘lying’ and ‘sit-tilted’ respectively). The approach is reliable for different subjects and different situations, since it is not only based on empirical thresholds and subject-based training models, but in addition it is underpinned by theory.     

Lifestyle Behaviors in Metabolically Healthy and Unhealthy Overweight and Obese Women: A Preliminary Study

Background

Few studies have examined dietary data or objective measures of physical activity (PA) and sedentary behavior among metabolically healthy overweight/obese (MHO) and metabolically unhealthy overweight/obese (MUO). Thus, the purpose is to determine whether PA, sedentary behavior and/or diet differ between MHO and MUO in a sample of young women.

Methods

Forty-six overweight/obese (BMI ≥25 kg/m²) African American and Caucasian women 19–35 years were classified by cardiometabolic risk factors, including elevated blood pressure, triglyceride, glucose and C-reactive protein, low high density lipoprotein, and insulin resistance (MUO ≥2; MHO, <2). Time (mins/day) in light, moderate, vigorous PA, and sedentary behavior were estimated using an accelerometer (≥3 days; ≥8 hrs wear time). Questionnaires were used to quantify sitting time, TV/computer use and usual daily activity. The Block Food Frequency Questionnaire assessed dietary food intake. Differences between MHO and MUO for lifestyle behaviors were tested with linear regression (continuous data) or logistic regression (categorical data) after adjusting for age, race, BMI, smoking and accelerometer wear and/or total kilocalories, as appropriate.

Results

Women were 26.7±4.7 years, with a mean BMI of 31.1±3.7 kg/m², and 61% were African American. Compared to MUO (n = 9), MHO (n = 37; 80%) spent less mins/day in sedentary behavior (difference: -58.1±25.5, p = 0.02), more mins/day in light PA (difference: 38.2±16.1, p = 0.02), and had higher daily METs (difference: 0.21±0.09, p = 0.03). MHO had higher fiber intakes (g/day of total fiber, soluble fiber, fruit/vegetable fiber, bean fiber) and daily servings of vegetables; but lower daily dairy servings, saturated fat, monounsaturated fat and trans fats (g/day) compared to MUO.

Conclusion

Compared to MUO, MHO young women demonstrate healthier lifestyle habits with less sedentary behavior, more time in light PA, and healthier dietary quality for fat type and fiber. Future studies are needed to replicate findings with larger samples that include men and women of diverse race/ethnic groups.     

Prediction of in vivo radiation dose status in radiotherapy patients using ex vivo and in vivo gene expression signatures

After a large-scale nuclear accident or an attack with an improvised nuclear device, rapid biodosimetry would be needed for triage. As a possible means to address this need, we previously defined a gene expression signature in human peripheral white blood cells irradiated ex vivo that predicts the level of radiation exposure with high accuracy. We now demonstrate this principle in vivo using blood from patients receiving total-body irradiation (TBI). Whole genome microarray analysis has identified genes responding significantly to in vivo radiation exposure in peripheral blood. A 3-nearest neighbor classifier built from the TBI patient data correctly predicted samples as exposed to 0, 1.25 or 3.75 Gy with 94% accuracy (P < 0.001) even when samples from healthy donor controls were included. The same samples were classified with 98% accuracy using a signature previously defined from ex vivo irradiation data. The samples could also be classified as exposed or not exposed with 100% accuracy. The demonstration that ex vivo irradiation is an appropriate model that can provide meaningful prediction of in vivo exposure levels, and that the signatures are robust across diverse disease states and independent sample sets, is an important advance in the application of gene expression for biodosimetry.     

Developing and applying a benthic index of estuarine condition for the Virginian Biogeographic Province

A benthic index of estuarine condition was constructed for the Virginian Biogeographic Province (from Cape Cod, Massachusetts, to the mouth of Chesapeake Bay, Virginia) with data collected during summers of 1990 through 1993 by the US EPA’s Environmental Monitoring and Assessment Program (EMAP). Forty-eight metrics, based on attributes of the macrobenthos, were considered for the index, including measures of biodiversity, community condition, individual health, functional organization, and taxonomic composition. Salinity was correlated significantly with some of the metrics. Therefore, some metrics were normalized for salinity. The data used to develop the index (the calibration data) included equal numbers of reference and degraded sites, distributed equally across three salinity zones (<5, 5–18, >18‰). An independent set of data was used for validation. Linear discriminant analysis identified combinations of metrics that could best discriminate reference from degraded sites. The targets for correct classification were 90% of the sites for the calibration data and 80% for the validation data. Six combinations of metrics were identified. The final index was based on the ecological interpretation and relevance of the individual metrics and the ability to meet the calibration and validation targets. The final index consisted of three metrics: a positive contribution from salinity-normalized Gleason’s D (a biodiversity metric), and negative contributions from two taxonomic composition metrics, abundances of spionid polychaetes and of salinity-normalized tubificid oligochaetes. The index correctly classified 87% of reference and 90% of degraded sites in the calibration data and 88% of reference and 81% of degraded sites in the validation data. The index correctly classified sites over the full range of salinity (tidal-fresh to marine waters) and across grain sizes (silt–clay to sand).     

Large Sample Size,Wide Variant Spectrum,and Advanced Machine-Learning Technique Boost Risk Prediction for Inflammatory Bowel Disease

We performed risk assessment for Crohn’s disease (CD) and ulcerative colitis (UC), the two common forms of inflammatory bowel disease (IBD), by using data from the International IBD Genetics Consortium’s Immunochip project. This data set contains ∼17,000 CD cases, ∼13,000 UC cases, and ∼22,000 controls from 15 European countries typed on the Immunochip. This custom chip provides a more comprehensive catalog of the most promising candidate variants by picking up the remaining common variants and certain rare variants that were missed in the first generation of GWAS. Given this unprecedented large sample size and wide variant spectrum, we employed the most recent machine-learning techniques to build optimal predictive models. Our final predictive models achieved areas under the curve (AUCs) of 0.86 and 0.83 for CD and UC, respectively, in an independent evaluation. To our knowledge, this is the best prediction performance ever reported for CD and UC to date.     

Prediction of the location and type of beta-turns in proteins using neural networks.

A neural network has been used to predict both the location and the type of beta-turns in a set of 300 nonhomologous protein domains. A substantial improvement in prediction accuracy compared with previous methods has been achieved by incorporating secondary structure information in the input data. The total percentage of residues correctly classified as beta-turn or not-beta-turn is around 75% with predicted secondary structure information. More significantly, the method gives a Matthews correlation coefficient (MCC) of around 0.35, compared with a typical MCC of around 0.20 using other beta-turn prediction methods. Our method also distinguishes the two most numerous and well-defined types of beta-turn, types I and II, with a significant level of accuracy (MCCs 0.22 and 0.26, respectively).     

Identification and validity of accelerometer cut-points for toddlers

The purpose of this study was to derive ActiGraph cut-points for sedentary (SED), light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) in toddlers and evaluate their validity in an independent sample. The predictive validity of established preschool cut-points were also evaluated and compared. Twenty-two toddlers (mean age = 2.1 years ± 0.4 years) wore an ActiGraph accelerometer during a videotaped 20-min play period. Videos were subsequently coded for physical activity (PA) intensity using the modified Children's Activity Rating Scale (CARS). Receiver operating characteristic (ROC) curve analyses were conducted to determine cut-points. Predictive validity was assessed in an independent sample of 18 toddlers (mean age = 2.3 ± 0.4 years). From the ROC curve analyses, the 15-s count ranges corresponding to SED, LPA, and MVPA were 0-48, 49-418, and >418 counts/15 s, respectively. Classification accuracy was fair for the SED threshold (ROC-AUC = 0.74, 95% confidence interval = 0.71-0.76) and excellent for MVPA threshold (ROC-AUC = 0.90, 95% confidence interval = 0.88-0.92). In the cross-validation sample, the toddler cut-point and established preschool cut-points significantly overestimated time spent in SED and underestimated time in spent in LPA. For MVPA, mean differences between observed and predicted values for the toddler and Pate cut-points were not significantly different from zero. In summary, the ActiGraph accelerometer can provide useful group-level estimates of MVPA in toddlers. The results support the use of the Pate cut-point of 420 counts/15 s for MVPA.     

Validation and calibration of an accelerometer in preschool children

Objective: Obesity rates in young children are increasing, and decreased physical activity is likely to be a major contributor to this trend. Studies of physical activity in young children are limited by the lack of valid and acceptable measures. The purpose of this study was to calibrate and validate the ActiGraph accelerometer for use with 3‐ to 5‐year‐old children. Research Methods and Procedures: Thirty preschool children wore an ActiGraph accelerometer (ActiGraph, Fort Walton Beach, FL) and a Cosmed portable metabolic system (Cosmed, Rome, Italy) during a period of rest and while performing three structured physical activities in a laboratory setting. Expired respiratory gases were collected, and oxygen consumption was measured on a breath‐by‐breath basis. Accelerometer data were collected at 15‐second intervals. For cross‐validation, the same children wore the same instruments while participating in unstructured indoor and outdoor activities for 20 minutes each at their preschool. Results: In calibrating the accelerometer, the correlation between V?o ₂ (ml/kg per min) and counts was r = 0.82 across all activities. The only significant variable in the prediction equation was accelerometer counts (R² = 0.90, standard error of the estimate = 4.70). In the cross‐validation, the intraclass correlation coefficient between measured and predicted V?o ₂ was R = 0.57 and the Spearman correlation coefficient was R = 0.66 (p < 0.001). Cut‐off points for moderate‐ and vigorous‐intensity physical activity were identified at 420 counts/15 s (V?o ₂ = 20 mL/kg per min) and 842 counts/15 s (V?o ₂ = 30 mL/kg per min), respectively. When these cutpoints were applied to the cross‐validation data, percentage agreement, kappa, and modified kappa for moderate activity were 0.69, 0.36, and 0.38, respectively. For vigorous activity, the same measures were 0.81, 0.13, and 0.62. Discussion: Accelerometer counts were highly correlated with V?o ₂ in young children. Accelerometers can be appropriately used as a measure of physical activity in this population.     

High-performance liquid chromatography-based multivariate analysis to predict the estrogenic activity of an Epimedium koreanum extract

This study characterizes the correlation between the chemical fingerprint and estrogenic activity of an Epimedium koreanum extract. The estrogenic activity of 31 E. koreanum extract samples was evaluated by a luciferase reporter gene assay, and the samples were classified into 3 groups based on their bioactivity. A chemical fingerprint analysis was performed on each sample by high-performance liquid chromatography (HPLC), and 44 common peaks were selected from the chromatogram and used as a dataset for a pattern recognition analysis. A canonical discriminant analysis performed on this dataset determined a distinct distribution of the samples according to their estrogenic activity on the scoring plot. The classification results showed that 90.3% of the original grouped cases had been correctly classified. The total content of the 4 major extract compounds, epimedin A, epimedin B, epimedin C, and icariin, exhibited good correlation (r=0.784) with the estrogenic activities of the respective extracts. This chromatographic fingerprint-chemometric analysis system could be useful for predicting the E. koreanum pharmacological activity and consequent biological activity-relevant quality control assessment.     

Comparison of Self-Reported and Accelerometer-Assessed Physical Activity in Older Women

Background

Self-reported physical activity measures continue to be validated against accelerometers; however, the absence of standardized, accelerometer moderate-to-vigorous physical activity (MVPA) definitions has made comparisons across studies difficult. Furthermore, recent accelerometer models assess accelerations in three axes, instead of only the vertical axis, but validation studies have yet to take incorporate triaxial data.

Methods

Participants (n = 10 115) from the Women’s Health Study wore a hip-worn accelerometer (ActiGraph GT3X+) for seven days during waking hours (2011–2014). Women then completed a physical activity questionnaire. We compared self-reported with accelerometer-assessed MVPA, using four established cutpoints for MVPA: three using only vertical axis data (760, 1041 and 1952 counts per minute (cpm)) and one using triaxial data (2690 cpm).

Results

According to self-reported physical activity, 66.6% of women met the US federal physical activity guidelines, engaging in ≥150 minutes per week of MVPA. The percent of women who met guidelines varied widely depending on the accelerometer MVPA definition (760 cpm: 50.0%, 1041 cpm: 33.0%, 1952 cpm: 13.4%, and 2690 cpm: 19.3%).

Conclusions

Triaxial count data do not substantially reduce the difference between self-reported and accelerometer-assessed MVPA.