Showing papers by "Patty S. Freedson published in 2011"

Validation of wearable monitors for assessing sedentary behavior.

Abstract: Purpose: A primary barrier to elucidating the association between sedentary behavior (SB) and health outcomes is the lack of valid monitors to assess SB in a free-living environment. The purpose of this study was to examine the validity of commercially available monitors to assess SB. Methods: Twenty overweight (mean ± SD: body mass index = 33.7 ± 5.7 kg·m-2) inactive, office workers age 46.5 ± 10.7 yr were directly observed for two 6-h periods while wearing an activPAL (AP) and an ActiGraph GT3X (AG). During the second observation, participants were instructed to reduce sitting time. We assessed the validity of the commonly used cut point of 100 counts per minute (AG100) and several additional AG cut points for defining SB. We used direct observation (DO) using focal sampling with duration coding to record either sedentary (sitting/lying) or nonsedentary behavior. The accuracy and precision of the monitors and the sensitivity of the monitors to detect reductions in sitting time were assessed using mixed-model repeated-measures analyses. Results: On average, the AP and the AG100 underestimated sitting time by 2.8% and 4.9%, respectively. The correlation between the AP and DO was R2 = 0.94, and the AG100 and DO sedentary minutes was R2 = 0.39. Only the AP was able to detect reductions in sitting time. The AG 150-counts-per-minute threshold demonstrated the lowest bias (1.8%) of the AG cut points. Conclusions: The AP was more precise and more sensitive to reductions in sitting time than the AG, and thus, studies designed to assess SB should consider using the AP. When the AG monitor is used, 150 counts per minute may be the most appropriate cut point to define SB.

A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations

Abstract: Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. The purpose of this study was to evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Two hundred and seventy-seven participants completed an average of six treadmill (TRD) (1.34, 1.56, 2.23 ms−1 each at 0 and 3% grade) and five self-paced activities of daily living (ADLs). EE estimates were compared with indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Across all activities, each equation underestimated EE (bias −0.1 to −1.4 METs and −0.5 to −1.3 kcal, respectively). For ADLs EE was underestimated by all prediction models (bias −0.2 to −2.0 and −0.2 to −2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias −0.8 to 0.2 METs and −0.4 to 0.5 kcal, respectively). Misclassification rates ranged from 21.7 (95% CI 20.4, 24.2%) to 34.3% (95% CI 32.3, 36.3%), with vigorous intensity activities being most often misclassified. Prediction equations did not yield accurate point estimates of EE across a broad range of activities nor were they accurate at classifying activities across a range of intensities (light <3 METs, moderate 3–5.99 METs, vigorous ≥6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.

Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample

Abstract: Previous work from our laboratory provided a "proof of concept" for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330-1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.

Calibrating a novel multi-sensor physical activity measurement system.

Abstract: Advancing the field of physical activity (PA) monitoring requires the development of innovative multi-sensor measurement systems that are feasible in the free-living environment. The use of novel analytical techniques to combine and process these multiple sensor signals is equally important. This paper describes a novel multi-sensor 'integrated PA measurement system' (IMS), the lab-based methodology used to calibrate the IMS, techniques used to predict multiple variables from the sensor signals, and proposes design changes to improve the feasibility of deploying the IMS in the free-living environment. The IMS consists of hip and wrist acceleration sensors, two piezoelectric respiration sensors on the torso, and an ultraviolet radiation sensor to obtain contextual information (indoors versus outdoors) of PA. During lab-based calibration of the IMS, data were collected on participants performing a PA routine consisting of seven different ambulatory and free-living activities while wearing a portable metabolic unit (criterion measure) and the IMS. Data analyses on the first 50 adult participants are presented. These analyses were used to determine if the IMS can be used to predict the variables of interest. Finally, physical modifications for the IMS that could enhance the feasibility of free-living use are proposed and refinement of the prediction techniques is discussed.

SVM-based multi-sensor fusion for free-living physical activity assessment

Abstract: This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on the support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multi-sensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which the activity types and related energy expenditures are derived. The result shows that the method correctly recognized the 13 activity types 84.7% of the time, which is 26% higher than using a hip accelerometer alone. Also, the method predicted the associated energy expenditure with a root mean square error of 0.43 METs, 43% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor was added to the fusion model. These results demonstrate that the multi-sensor fusion technique presented is more effective in assessing activities of varying intensities than the traditional accelerometer-alone based methods.

Physical Activity and Risk of Small-for-Gestational-Age Birth Among Predominantly Puerto Rican Women

Abstract: To estimate the association between multiple domains of physical activity and risk of small-for-gestational-age (SGA) birth. We utilized data from 1,040 participants in the Latina Gestational Diabetes Mellitus Study, a prospective cohort of predominantly Puerto Rican prenatal care patients in Massachusetts. Physical activity was assessed by bilingual interviewers using a modified version of the Kaiser physical activity survey in early (mean = 15 weeks) and mid pregnancy (mean = 28 weeks). Physical activity (i.e., sports/exercise, household, occupational, and active living) in pre, early and mid pregnancy was categorized in quartiles. SGA was classified as <10th percentile of birth weight for gestational age. Pre- and early-pregnancy physical activity were not associated with SGA. In multivariable analyses, women with high total activity in mid-pregnancy had a decreased risk of SGA [risk ratio (RR) = 0.42; 95% confidence interval (CI) 0.21-0.82; p(trend) = 0.003] as compared to those with low total activity. Findings were similar for high household activity (RR = 0.69; 95% CI = 0.34-1.40; p(trend) = 0.26), active living (RR = 0.63; 95% CI = 0.35-1.13; p(trend) = 0.04), and occupational activity (RR = 0.79, 95% CI = 0.47-1.34; p(trend) = 0.26). High levels of sports/exercise were associated with an increased SGA risk without a significant dose-response association (RR = 2.14, 95% CI 1.04-4.39; p(trend) = 0.33). Results extend prior studies of physical activity and SGA to the Hispanic population.

Non-invasive respiration and ventilation prediction using a single abdominal sensor belt

Abstract: On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the abdomen of the test object. This paper first presents a signal decomposition technique for tissue artifact removal from respiratory signals and respiratory signal reconstruction, based on the Empirical Mode Decomposition (EMD). Methods based on spectral analysis and multiple linear regressions were then developed to predict the respiration rate and minute ventilation, respectively. Performance of the algorithms was evaluated through real-life experiments of 105 subjects engaged in 14 types of physical activities. The predictions were compared to the criterion respiration measurements using a bidirectional digital volume transducer housed in a respiratory gas exchange system. Results have verified reasonably good performance of the algorithms and the applicability of the wearable sensing system for respiratory parameter prediction during physical activity.

Original research Validation and comparison of ActiGraph activity monitors

Abstract: Objective: To compare activity counts from the ActiGraph GT3X to those from the ActiGraph GT1M during treadmill walking/running. A secondary aim was to develop tri-axial vector magnitude (VM3) cut-points to classify physical activity (PA) intensity. Methods: Fifty participants wore the GT3X and the GT1M on the non-dominant hip and exercised at 4 treadmill speeds (4.8, 6.4, 9.7, and 12 km h −1 ). Vertical (VT) and antero-posterior (AP) activity counts (counts min −1 ) as well as the vector magnitudes of the two axes (VM2) from both monitors were tested for significant differences using two-way ANOVA’s. Bland–Altman plots were used to assess agreement between activity counts from the GT3X and GT1M. Linear regression analysis between VM3 counts min −1 and oxygen consumption data was conducted to develop VM3 cut-points for moderate, hard and very hard PA. Results: There were no significant inter-monitor differences in VT activity counts at any speed. AP and VM2 activity counts from the GT1M were significantly higher (p 9642 counts min −1 . Conclusion: Due to the lack of congruence between the AP and VM2 activity counts from the GT1M and the GT3X, comparisons of data obtained with these two monitors should be avoided when using more than just the VT axis. VM3 cut-points may be used to classify PA in future studies. © 2011 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

ZigBee-based wireless multi-sensor system for physical activity assessment

Abstract: Physical activity (PA) is important for assessing human exposure to the environment. This paper presents a ZigBee-based Wireless wearable multi-sensor Integrated Measurement System (WIMS) for in-situ PA measurement. Two accelerometers, a piezoelectric displacement sensor, and an ultraviolet (UV) sensor have been used for the physical activity assessment. Detailed analysis was performed for the hardware design and embedded program control, enabling efficient data sampling and transmission, compact design, and extended battery life to meet requirements for PA assessment under free-living conditions. Preliminary testing of the WIMS has demonstrated the functionality of the design, while performance comparison of the WIMS with a wired version on an electromagnetic shaker has demonstrated the signal validity.

Psychological Responses to Acute Exercise in Sedentary Black and White Individuals

Abstract: Background: Racial differences in psychological determinants of exercise exist between non-Hispanic blacks (blacks) and non-Hispanic whites (whites). To date, no study has examined racial differences in the psychological responses during and after exercise. The objective of this study was to compare psychological outcomes of single exercise bouts in blacks and whites. Methods: On 3 separate occasions, sedentary black (n = 16) and white (n = 14) participants walked on a treadmill at 75%max HR for 75 minutes. Questionnaires assessing mood, state anxiety, and exercise task self-efficacy were administered before and after each exercise bout. In-task mood and rating of perceived exertion (RPE) were measured every 5 minutes during exercise. Results: Exercise self-efficacy and psychological distress significantly improved in both blacks and whites. However during exercise blacks reported more positive in-task mood and lower RPE compared with whites. Conclusions: These data suggest that racial differences exist i...