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Background: The goal of this study was to establish preliminary criterion-referenced cut points for adult pedometer-determined physical activity (PA) related to weight status defined by body mass index (BMI). Methods: Researchers contributed directly measured BMI and pedometer data that had been collected (1) using a Yamax-manufactured pedometer, (2) for a minimum of 3 days, (3) on ostensibly healthy adults. The contrasting groups method was used to identify age- and gender-specific cut points for steps/d related to BMI cut points for normal weight and overweight/obesity (defined as BMI <25 and ≥25 kg/m², respectively). Results: Data included 3127 individuals age 18 to 94 years (976 men, age = 46.8 ± 15.4 years, BMI = 27.3 ± 4.9; 2151 women, age = 47.4 ± 14.9 years, BMI = 27.6 ± 6.4; all gender differences NS). Best estimated cut points for normal versus overweight/obesity ranged from 11,000 to 12,000 steps/d for men and 8000 to 12,000 steps/d for women (consistently higher for younger age groups). Conclusions: These steps/d cut points can be used to identify individuals at risk, or the proportion of adults achieving or falling short of set cut points can be reported and compared between populations. Cut points can also be used to set intervention goals, and they can be referred to when evaluating program impact, as well as environmental and policy changes. [ABSTRACT FROM AUTHOR]

Abstract Background This study examined the independent association of objectively measured physical activity on insulin resistance while controlling for confounding variables including: cardiorespiratory fitness, adiposity, sex, age, and smoking status. Methods Data were obtained from National Health and Nutrition Examination Survey 2003–2004, a cross-sectional observational study conducted by the National Center for Health Statistics of the Centers for Disease Control that uses a stratified, multistage probability design to obtain a nationally representative sample of the U.S. population. The analysis included 402 healthy U.S. adults with valid accelerometer, cardiorespiratory fitness, and fasting plasma glucose and insulin concentrations. After controlling for relevant confounding variables we performed a multiple linear regression to predict homeostatic model of insulin resistance (HOMA-IR) based on average daily minutes of moderate-to-vigorous physical activity (MVPA). Results In our bivariate models, MVPA, cardiorespiratory fitness and body fat percentage were all significantly correlated with log HOMA-IR. In the complete model including MVPA and relevant confounding variables, there were strong and significant associations between MVPA and log HOMA-IR (β= −0.1607, P=0.004). In contrast the association between cardiorespiratory fitness and log HOMA-IR was not significant. Conclusion When using an objective measure of physical activity the amount of time engaged in daily physical activity was associated with lower insulin resistance, whereas higher cardiorespiratory fitness was not. These results suggest that the amount of time engaged in physical activity may be an important determinant for improving glucose metabolism.

Abstract Purpose The purpose of this study was to examine the accuracy of uploadable pedometers to accurately count steps during treadmill (TM) and overground (OG) walking, and during a 24 hour monitoring period (24 hr) under free living conditions in young and older adults. Methods One hundred and two participants (n=53 aged 20–49 yrs; n=49 aged 50–80 yrs) completed a TM protocol (53.6, 67.0, 80.4, 93.8, and 107.2 m/min, five minutes for each speed) and an OG walking protocol (self-determined “< normal”, “normal”, and “> normal” walking speeds) while wearing two waist-mounted uploadable pedometers (Omron HJ-720ITC [OM] and Kenz Lifecorder EX [LC]). Actual steps were manually tallied by a researcher. During the 24 hr period, participants wore a New Lifestyles-1000 (NL) pedometer (standard of care) attached to a belt at waist level over the midline of the left thigh, in addition to the LC on the belt over the midline of the right thigh. The following day, the same procedure was conducted, replacing the LC with the OM. One-sample t-tests were performed to compare measured and manually tallied steps during the TM and OG protocols, and between steps quantified by the NL with that of the OM and LC during the 24 hr period. Mean error step scores (MES, criterion – device) and 95% Limits of Agreement (LoA) were calculated. Results There were no significant differences between the OM and tallied steps for any of the TM speeds for either the young or older adult groups. The LC significantly underestimated steps for the young adult group during the 53.6 m/min TM speed (MES 31.4 [14.5, 48.3]) and during the OG < normal walking speed (MES 12.0 [0.9, 23.1] (ppppp Conclusion Both the OM and LC pedometers were more accurate as TM and OG walking speed increased. The OM significantly underestimated steps during the 24 hr compared with a standard of care evaluation. Overall, both uploadable pedometers appear acceptable to use in young or old age groups to measure walking behavior.

Abstract Background Few studies have investigated both the self-perceived and measured environment with objectively determined physical activity in older adults. Accordingly, the aim of this study was to examine measured and perceived environmental associations with physical activity of older adults residing across different neighborhood types. Methods One-hundred and forty-eight older individuals, mean age 64.3 ± 8.4, were randomly recruited from one of four neighborhoods that were pre-determined as either having high- or low walkable characteristics. Individual residences were geocoded and 200 m network buffers established. Both objective environment audit, and self-perceived environmental measures were collected, in conjunction with accelerometer derived physical activity behavior. Using both perceived and objective environment data, analysis consisted of a macro-level comparison of physical activity levels across neighborhood, and a micro-level analysis of individual environmental predictors of physical activity levels. Results Individuals residing in high-walkable neighborhoods on average engaged in 11 min of moderate to vigorous physical activity per day more than individuals residing in low-walkable neighborhoods. Both measured access to non-residential destinations (b = .11, p < .001) and self-perceived access to non-residential uses (b = 2.89, p = .031) were significant predictors of time spent in moderate to vigorous physical activity. Other environmental variables significantly predicting components of physical activity behavior included presence of measured neighborhood crime signage (b = .4785, p = .031), measured street safety (b = 26.8, p = .006), and perceived neighborhood satisfaction (b = .5.8, p = .003). Conclusions Older adult residents who live in high-walkable neighborhoods, who have easy and close access to nonresidential destinations, have lower social dysfunction pertinent to crime, and generally perceive the neighborhood to a higher overall satisfaction are likely to engage in higher levels of physical activity behavior. Efforts aimed at promoting more walkable neighborhoods could influence activity levels in older adults.

Abstract Background Advances in technology, social influences and environmental attributes have resulted in substan-tial portions of the day spent in sedentary pursuits. Sedentary behavior may be a cause of many chronic diseases including obesity, insulin resistance, type 2 diabetes and the metabolic syndrome. Research demonstrated that breaking up sedentary time was beneficially associated with markers of body composition, cardiovascular health and type 2 diabetes. Therefore, the purpose of this study was to quantify the total energy expenditure of three different durations of physical activity within a 30-minute sedentary period and to examine the potential benefits of interrupting sedentary behavior with physical activity for weight control. Methods Participants completed four consecutive 30-minute bouts of sedentary behavior (reading, working on the computer, or doing other desk activities) with and without interruptions of walking at a self-selected pace. Bout one contained no walking interruptions. Bout two contained a 1-minute walking period. Bout three contained a 2-minute walking period. Bout four contained a 5-minute walking period. Body composition and resting metabolic rate were assessed. Result Twenty males and females (18-39 years) completed this study. Results of the repeated measures analysis of variance with post-hoc testing showed that significantly more energy was expended during each 30 minute sedentary bout with a walking break than in the 30 minute sedentary bout (p < 0.05 for all comparisons). On average, participants expended an additional 3.0, 7.4, and 16.5 additional net or activity kilocalories during bouts 2, 3, and 4, respectively compared with bout 1. When extrapolated for a full eight-hour working day, this data shows that an individual would theoretically expend an additional 24, 59 or 132 kilocalories per day, if they stood up and walked at a normal, self selected pace for one, two or five minutes every hour, respectively, compared with sitting for the 8-hour period. Conclusions This study demonstrated that making small changes, such as taking a five minute walking break every hour could yield beneficial weight control or weight loss results. Therefore, taking breaks from sedentary time is a potential outlet to prevent obesity and the rise of obesity in developed countries.

Abstract Background The number of days of pedometer or accelerometer data needed to reliably assess physical activity (PA) is important for research that examines the relationship with health. While this important research has been completed in young to middle-aged adults, data is lacking in older adults. Further, data determining the number of days of self-reports PA data is also void. The purpose of this study was to examine the number of days needed to predict habitual PA and sedentary behaviour across pedometer, accelerometer, and physical activity log (PA log) data in older adults. Methods Participants (52 older men and women; age = 69.3 ± 7.4 years, range= 55-86 years) wore a Yamax Digiwalker SW-200 pedometer and an ActiGraph 7164 accelerometer while completing a PA log for 21 consecutive days. Mean differences each instrument and intensity between days of the week were examined using separate repeated measures analysis of variance for with pairwise comparisons. Spearman-Brown Prophecy Formulae based on Intraclass Correlations of .80, .85, .90 and .95 were used to predict the number of days of accelerometer or pedometer wear or PA log daily records needed to represent total PA, light PA, moderate-to-vigorous PA, and sedentary behaviour. Results Results of this study showed that three days of accelerometer data, four days of pedometer data, or four days of completing PA logs are needed to accurately predict PA levels in older adults. When examining time spent in specific intensities of PA, fewer days of data are needed for accurate prediction of time spent in that activity for ActiGraph but more for the PA log. To accurately predict average daily time spent in sedentary behaviour, five days of ActiGraph data are needed. Conclusions The number days of objective (pedometer and ActiGraph) and subjective (PA log) data needed to accurately estimate daily PA in older adults was relatively consistent. Despite no statistical differences between days for total PA by the pedometer and ActiGraph, the magnitude of differences between days suggests that day of the week cannot be completely ignored in the design and analysis of PA studies that involve < 7-day monitoring protocols for these instruments. More days of accelerometer data were needed to determine typical sedentary behaviour than PA level in this population of older adults.

Abstract Background Accelerometers were incorporated in the 2003–2004 National Health and Nutritional Examination Survey (NHANES) study cycle for objective assessment of physical activity. This is the first time that objective physical activity data are available on a nationally representative sample of U.S. residents. The use of accelerometers allows researchers to measure total physical activity, including light intensity and unstructured activities, which may be a better predictor of health outcomes than structured activity alone. The aim of this study was to examine objectively determined physical activity levels by sex, age and racial/ethnic groups in a national sample of U.S. adults. Methods Data were obtained from the 2003–2004 NHANES, a cross-sectional study of a complex, multistage probability sample of the U.S. population. Physical activity was assessed with the Actigraph AM-7164 accelerometer for seven days following an examination. 2,688 U.S. adults with valid accelerometer data (i.e. at least four days with at least 10 hours of wear-time) were included in the analysis. Mean daily total physical activity counts, as well as counts accumulated in minutes of light, and moderate-vigorous intensity physical activity are presented by sex across age and racial/ethnic groups. Generalized linear modeling using the log link function was performed to compare physical activity in sex and racial/ethnic groups adjusting for age. Results Physical activity decreases with age for both men and women across all racial/ethnic groups with men being more active than women, with the exception of Hispanic women. Hispanic women are more active at middle age (40–59 years) compared to younger or older age and not significantly less active than men in middle or older age groups (i.e. age 40–59 or age 60 and older). Hispanic men accumulate more total and light intensity physical activity counts than their white and black counterparts for all age groups. Conclusion Physical activity levels measured objectively by accelerometer demonstrated that Hispanic men are, in general, more active than their white and black counterparts. This appears to be in contrast to self-reported physical activity previously reported in the literature and identifies the need to use objective measures in situations where the contribution of light intensity and/or unstructured physical activity cannot be assumed homogenous across the populations of interest.

To develop and evaluate the validity of the Pregnancy Physical Activity Questionnaire-Short Form (PPAQ-SF). A prospective cohort of participants (N=50) completed the updated PPAQ (long-form) and wore an ActiGraph for 7 days on the nondominant wrist in early, mid, and late pregnancy. The top ten questions with the highest relative contribution to the between-person variance in PPAQ-assessed total MET-hours/day were selected. Internal validity was evaluated using the ActiGraph and the updated PPAQ (long-form) within the current dataset. Validity was also assessed in an external validation dataset of 222 pregnant participants using the updated and original PPAQ (long-forms). Spearman correlations between the PPAQ-SF and ActiGraph estimates of MVPA MET-hours/day were r = 0.34 (95% CI 0.18-0.48) for overall pregnancy and ranged from 0.21 (95% CI -0.07-0.47) in early pregnancy to 0.49 (95% CI 0.23-0.69) in mid-pregnancy. In the external validation dataset, correlations between the PPAQ-SF and the PPAQ (long forms) estimates of MVPA MET-hours/day were statistically significant across pregnancy (r=0.94-0.99). Reproducibility ranged from 0.35 in early pregnancy to 0.77 in mid pregnancy. In summary, the PPAQ-SF can provide a time-efficient measure of MVPA during pregnancy with acceptable validity; reproducibility was reasonable in mid-pregnancy., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The aim of this study was to update and validate the Pregnancy Physical Activity Questionnaire (PPAQ), using novel and innovative accelerometer and wearable camera measures in a free-living setting, to improve the measurement performance of this method for self-reporting physical activity. A prospective cohort of 50 eligible pregnant women were enrolled in early pregnancy (mean = 14.9 weeks' gestation). In early, middle, and late pregnancy, participants completed the updated PPAQ and, for 7 days, wore an accelerometer (GT3X-BT; ActiGraph, Pensacola, Florida) on the nondominant wrist and a wearable camera (Autographer; OMG Life (defunct)). At the end of the 7-day period, participants repeated the PPAQ. Spearman correlations between the PPAQ and accelerometer data ranged from 0.37 to 0.44 for total activity, 0.17 to 0.53 for moderate- to vigorous-intensity activity, 0.19 to 0.42 for light-intensity activity, and 0.23 to 0.45 for sedentary behavior. Spearman correlations between the PPAQ and wearable camera data ranged from 0.52 to 0.70 for sports/exercise and from 0.26 to 0.30 for transportation activity. Reproducibility scores ranged from 0.70 to 0.92 for moderate- to vigorous-intensity activity and from 0.79 to 0.91 for sports/exercise, and were comparable across other domains of physical activity. The PPAQ is a reliable instrument and a valid measure of a broad range of physical activities during pregnancy., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Cardiac patients would benefit from increasing their physical activity (PA) levels. Understanding of factors that influence cardiac patients' PA participation would benefit the development of effective interventions. Therefore, the purpose of this study was to determine correlates of objectively-measured PA in cardiac patients. Participants were 65 cardiac patients (74% male, 95% white), age 58.6±10.6 years. The amount of time spent in PA was measured by ActiGraph GT3X accelerometers for 7 days prior to joining cardiac rehabilitation programs (CRP). A total of 25 potential determinants of PA across multiple domains (demographic, clinical, psychosocial, and behavioral) were measured via self-reported questionnaire and clinical examinations. Backward elimination model selection procedures were performed to examine associations of potential determinants with total PA (min/day) and moderate-to-vigorous PA (MVPA) (min/day). Patients spent 153.8±62.8 and 8.4±8.1 min/day in total PA and MVPA, respectively. Across four domains, ten and five potential correlates were found to be significant in univariate analyses for MVPA and total PA, respectively. In the final model, functional capacity, PA readiness, and participation in regular exercise were positively associated with MVPA (R(2) =26.6%). Functional capacity and PA readiness were also positively associated with total PA (R(2) =15.9%). Future initiatives to increase PA levels in cardiac patients could be improved by considering patients' functional capacity, PA readiness, and exercise history in designing interventions.

Portable amplifiers that record electromyograms (EMGs) for longer than four hours are commonly priced over $20,000 USD. This cost, and the technical challenges associated with recording EMGs during free-living situations, typically restrict EMG use to laboratory settings. A low-cost system (μEMG; OT Bioelecttronica, 100€), using specialized concentric bipolar electrodes, has been developed specifically for free-living situations. The purpose of this study was to validate the μEMG system by comparing EMGs from μEMG with a laboratory-based alternative (Telemyo 900; Noraxon USA, Inc.). Surface EMGs from biceps brachii (BB) and tibialis anterior (TA) of ten subjects were recorded simultaneously with both systems as subjects performed maximal voluntary contractions (MVCs), submaximal contractions at 25%, 50%, and 75% MVC, seven simulated activities of daily living (ADLs), and >60min of simulated free-living inside the laboratory. In general, EMG parameters (e.g., average full-wave rectified EMG amplitude) derived from both systems were not significantly different for all outcome variables, except there were small differences across systems in baseline noise and absolute EMG amplitudes during MVCs. These results suggest that μEMG is a valid approach to the long-term recording of EMG., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Background: Little data exists describing the impact that walking has on metabolic syndrome (MetS) in a multicultural sample of older adults., Methods: Walking was measured via pedometer in 150 older adults from 4 different ethnic categories. Steps per day were classified as low (< 3100 steps/d) or high (> or = 3100 steps/d) for statistical analyses., Results: Occurrence of MetS was lower in the white (33%) versus non-white population (50%). Low steps/d were related to an increase in MetS for both white (OR = 96.8, 95% CI 12.3-764.6) and non-white individuals (OR = 4.5, 95% CI 1.8-11.3). Low steps/d also increased the odds for selected components of MetS in both the white and non-white groups., Conclusion: Low levels of walking increase the likelihood of having MetS in both white and nonwhite older adults. Efforts to increase walking in older adults may decrease the likelihood of developing this clustering of disease risk factors.

This qualitative study describes environmental supports and barriers to physical activity in an older adult sample drawn from low- and high-walkable neighborhoods. Thirty-seven individuals age 55 and over were recruited and answered open-ended survey questions, with a subsample invited back to partake in a semistructured interview. Content analysis identified categories and themes linking perceptions of neighborhood-environment characteristics to activity. Emerging categories and themes did not differ across neighborhood walkability, so results are presented for both groups combined. Infrastructure was the most common category identified to encourage activity, specifically, well-maintained sidewalks, bike paths or lanes, and traffic control. Other categories of land use, landscape, and aesthetics were reported. Poorly maintained or missing sidewalks, crosswalks, bike paths or lanes, and traffic safety were categories that discouraged activity. In conclusion, the information obtained is helpful in solidifying which environmental characteristics are important to measure as they relate to activity behavior in an older adult population.

Background: The purpose of this study was to examine the extent to which physical activity (PA) is related to obesity in older adults when accounting for race/ethnicity., Methods: Cross-sectional data were collected on 214 older adults (72.3 +/- 8.9 y; body mass index [BMI] 28.9 +/- 6.0; 151 females; 96 non-White). Measures of body height and mass were collected; BMI was calculated. PA was assessed via an electronic pedometer worn for seven consecutive days., Results: "White" subjects accumulated 5036 +/- 286 steps/d. "Non-White" subjects accumulated significantly fewer steps/d (3671 +/- 253 steps/d; z = -3.45, P = 0.001). Race/ethnicity, income, age, gender, and steps/d accounted for 27.4% (P < 0.001) of the variance in BMI, with steps/d accounting for 21.2% (P < 0.001). The most influential factor in this model was PA level (beta = -0.510), followed by age (beta = -0.220), and finally gender being the least influential, but still a significant factor (beta = 0.168)., Conclusion: Although race/ethnicity and income have been associated with obesity levels, this study shows that older adults who accumulate more ambulatory activity tend to have healthier levels of BMI irrespective of race/ethnicity or income.

Purpose: Heart rate (HR) and motion sensors represent promising tools for physical activity (PA) assessment, as each provides an estimate of energy expenditure (EE). Although each has inherent limitations, the simultaneous use of HR and motion sensors may increase the accuracy of EE estimates. The primary purpose of this study was to establish the accuracy of predicting EE from the simultaneous HR-motion sensor technique. In addition, the accuracy of EE estimated by the simultaneous HR-motion sensor technique was compared to that of HR and motion sensors used independently., Methods: Thirty participants (16 men: age, 33.1 +/- 12.2 yr; BMI, 26.1 +/- 0.7 kg.m(-2); and 14 women: age, 31.9 +/- 13.1 yr; BMI, 27.2 +/- 1.1 kg.m(-2) (mean +/- SD)) performed arm and leg work in the laboratory for the purpose of developing individualized HR-VO2 regression equations. Participants then performed physical tasks in a field setting for 15 min each. CSA accelerometers placed on the arm and leg were to discriminate between upper and lower body movement, and HR was then used to predict EE (METs) from the corresponding arm or leg laboratory regression equation. A hip-mounted CSA accelerometer and Yamax pedometer were also used to predict EE. Predicted values (METs) were compared to measured values (METs), obtained via a portable metabolic measurement system (Cosmed K4b(2))., Results: The Yamax pedometer and the CSA accelerometer on the hip significantly underestimated the energy cost of selected physical activities, whereas HR alone significantly overestimated the energy cost of selected physical activities. The simultaneous HR-motion sensor technique showed the strongest relationship with VO(2) (R(2) = 0.81) and did not significantly over- or underpredict the energy cost (P = 0.341)., Conclusion: The simultaneous HR-motion sensor technique is a good predictor of EE during selected lifestyle activities, and allows researchers to more accurately quantify free-living PA.

The accuracy of a computerized metabolic system, using inspiratory and expiratory methods of measuring ventilation, was assessed in eight male subjects. Gas exchange was measured at rest and during five stages on a cycle ergometer. Pneumotachometers were placed on the inspired and expired side to measure inspired (VI) and expired ventilation (VE). The devices were connected to two systems sampling expired O(2) and CO(2) from a single mixing chamber. Simultaneously, the criterion (Douglas bag, or DB) method assessed VE and fractions of O(2) and CO(2) in expired gas (FE(O(2)) and FE(CO(2))) for subsequent calculation of O(2) uptake (VO(2)), CO(2) production (VCO(2)), and respiratory exchange ratio. Both systems accurately measured metabolic variables over a wide range of intensities. Though differences were found between the DB and computerized systems for FE(O(2)) (both inspired and expired systems), FE(CO(2)) (expired system only), and VO(2) (inspired system only), the differences were extremely small (FE(O(2)) = 0.0004, FE(CO(2)) = -0.0003, VO(2) = -0.018 l/min). Thus a computerized system, using inspiratory or expiratory configurations, permits extremely precise measurements to be made in a less time-consuming manner than the DB technique.

We provide an updated version of the Compendium of Physical Activities, a coding scheme that classifies specific physical activity (PA) by rate of energy expenditure. It was developed to enhance the comparability of results across studies using self-reports of PA. The Compendium coding scheme links a five-digit code that describes physical activities by major headings (e.g., occupation, transportation, etc.) and specific activities within each major heading with its intensity, defined as the ratio of work metabolic rate to a standard resting metabolic rate (MET). Energy expenditure in MET-minutes, MET-hours, kcal, or kcal per kilogram body weight can be estimated for specific activities by type or MET intensity. Additions to the Compendium were obtained from studies describing daily PA patterns of adults and studies measuring the energy cost of specific physical activities in field settings. The updated version includes two new major headings of volunteer and religious activities, extends the number of specific activities from 477 to 605, and provides updated MET intensity levels for selected activities.

Purpose: This study tested the validity of four motion sensors for measuring energy expenditure (EE) during moderate intensity physical activities in field and laboratory settings. We also evaluated the accuracy of the EE values for selected moderate activities listed in the 1993 Compendium of Physical Activities., Methods: A total of 81 participants (age 19-74 yr) completed selected tasks from six general categories: yardwork, housework, occupation, family care, conditioning, and recreation. Twelve individuals performed each of the 28 activities examined. During each activity, EE was measured using a portable metabolic measurement system. Participants also wore three accelerometers (Computer Science and Applications [CSA], Inc. model 7164; Caltrac; and Kenz Select 2) and the Yamax SW-701 electronic pedometer. For the CSA device, three previously developed regression equations were used to convert accelerometer scores to EE., Results: The mean error scores (indirect calorimetry minus device) across all activities were: CSA1, 0.97 MET; CSA2, 0.47 MET, CSA3, 0.05 MET; Caltrac, 0.83 MET; Kenz, 0.96 MET; and Yamax, 1.12 MET. The correlation coefficients between indirect calorimetry and motion sensors ranged from r = 0.33 to r = 0.62. The energy cost for power mowing and sweeping/mopping was higher than that listed in the 1993 Compendium (P < 0.05), and the cost for several household and recreational activities was lower (P < 0.05)., Conclusion: Motion sensors tended to overpredict EE during walking. However, they underpredicted the energy cost of many other activities because of an inability to detect arm movements and external work. These findings illustrate some of the limitations of using motion sensors to predict EE in field settings.

Purpose: This study was designed to establish prediction models that relate hip and wrist accelerometer data to energy expenditure (EE) in field and laboratory settings. We also sought to determine whether the addition of a wrist accelerometer would significantly improve the prediction of EE (METs), compared with a model that used a hip accelerometer alone., Methods: Seventy participants completed one to six activities within the categories of yardwork, housework, family care, occupation, recreation, and conditioning, for a total of 5 to 12 participants tested per activity. EE was measured using the Cosmed K4b2 portable metabolic system. Simultaneously, two Computer Science and Applications, Inc. (CSA) accelerometers (model 7164), one worn on the wrist and one worn on the hip, recorded body movement. Correlations between EE measured by the Cosmed and the counts recorded by the CSA accelerometers were calculated, and regression equations were developed to predict EE from the CSA data., Results: The wrist, hip, and combined hip and wrist regression equations accounted for 3.3%, 31.7%, and 34.3% of the variation in EE, respectively. The addition of the wrist accelerometer data to the hip accelerometer data to form a bivariate regression equation, although statistically significant (P = 0.002), resulted in only a minor improvement in prediction of EE. Cut points for 3 METs (574 hip counts), 6 METs (4945 hip counts), and 9 METs (9317 hip counts) were also established., Conclusion: The small amount of additional accuracy gained from the wrist accelerometer is offset by the extra time required to analyze the data and the cost of the accelerometer.

Purpose: Three methods for measuring time spent in daily physical activity (PA) were compared during a 21-d period among 83 adults (38 men and 45 women)., Methods: Each day, participants wore a Computer Science and Applications, Inc. (CSA) monitor and completed a 1-page, 48-item PA log that reflected time spent in household, occupational, transportation, sport, conditioning, and leisure activities. Once a week, participants also completed a telephone survey to identify the number of minutes spent each week in nonoccupational walking and in moderate intensity and hard/very hard-intensity PA. Data were analyzed using descriptive statistics and Spearman rank-order correlations. Three equations developed to compute CSA cut points for moderate and hard/very hard PA were also compared with the PA logs and PA survey., Results: There was modest to good agreement for the time spent in different PA intensity categories between the three CSA cut point methods (r = 0.43-0.94, P < 0.001). Correlations between the CSA and PA logs ranged from r = 0.22 to r = 0.36, depending on the comparisons. Correlations between the survey items and PA logs were r = 0.26-0.54 (P < 0.01) for moderate and walking activities and r < 0.09 (P > 0.05) for hard/very hard activities. Correlations between the survey items and the CSA min per day varied according to the method used to compute the CSA intensity cut points., Conclusions: The results were consistent with findings from other PA validation studies that show motion sensors, PA logs, and surveys reflect PA; however, these methods do not always provide similar estimates of the time spent in resting/light, moderate, or hard/very hard PA.

Unlabelled: To further develop our understanding of the relationship between habitual physical activity and health, research studies require a method of assessment that is objective, accurate, and noninvasive. Heart rate (HR) monitoring represents a promising tool for measurement because it is a physiological parameter that correlates well with energy expenditure (EE). However, one of the limitations of HR monitoring is that training state and individual HR characteristics can affect the HR-VO2 relationship., Purpose: The primary purpose of this study was to examine the relationship between HR (beats x min(-1)) and VO2 (mL x kg(-1 x -1) min(-1)) during field- and laboratory-based moderate-intensity activities. In addition, we examined the validity of estimating EE from HR after adjusting for age and fitness. This was done by expressing the data as a percent of heart rate reserve (%HRR) and percent of VO2 reserve (%VO2R)., Methods: Sixty-one adults (18-74 yr) performed physical tasks in both a laboratory and field setting. HR and VO2 were measured continuously during the 15-min tasks. Mean values over min 5-15 were used to perform linear regression analysis on HR versus VO2. HR data were then used to predict EE (METs), using age-predicted HRmax and estimated VO2max., Results: The correlation between HR and VO2 was r = 0.68, with HR accounting for 47% of the variability in VO2. After adjusting for age and fitness level, HR was an accurate predictor of EE (r = 0.87, SEE = 0.76 METs)., Conclusion: This method of analyzing HR data could allow researchers to more accurately quantify physical activity in free-living individuals.