Compendium Of Physical Activities Research Articles

Physical activity measurement instruments for children with cerebral palsy: a systematic review

this paper is a systematic review of physical activity measurement instruments for field-based studies involving children with cerebral palsy (CP). database searches using PubMed Central, MEDLINE, CINAHL Plus, PsycINFO, EMBASE, Cochrane Library, and PEDro located 12 research papers, identifying seven instruments that met the inclusion criteria of (1) having been developed for children aged 0 to 18 years, (2) having been used to evaluate a physical activity dimension, and (3) having been used in a field-based study involving children with CP. The instruments reviewed were the Activities Scale for Kids - Performance version (ASKp), the Canada Fitness Survey, the Children's Assessment of Participation and Enjoyment/Preferences for Activities of Children (CAPE/PAC), the Compendium of Physical Activities, the Physical Activity Questionnaire - Adolescents (PAQ-A), StepWatch, and the Uptimer. Second-round searches yielded 11 more papers, providing reliability and validity evidence for the instruments. the instruments measure physical activity frequency, mode, domain, and duration. Although most instruments demonstrated adequate reliability and validity, only the ASKp and CAPE/PAC have established reliability and validity for children with physical disabilities; the Uptimer has established concurrent validity. No instrument measuring intensity in free-living has been validated or found reliable for children with CP. the findings suggest that further studies are needed to examine the methodological properties of physical activity measurement in children with CP. Combining subjective and objective instruments is recommended to achieve better understanding of physical activity participation.

“Active Living” Related to the Rural-Urban Continuum: A Time-Use Perspective

This paper assesses the degree to which "active living" varies along the rural-urban continuum, within the county-sized regional municipality of Halifax, Nova Scotia. Time-diary data from the Halifax Space-Time Activity Research project were used to compute daily participation rates (PRs) and time durations, at various physical effort levels, in the 4 activity domains of leisure, active transportation (AT), chores, and occupation. Geographic zones were based on the rural-urban fringe concept: the 4 zones are the Inner City (IC), Suburbs, Inner Commuter Belt (ICB), and Outer Commuter Belt (OCB). The Compendium of Physical Activities was employed to classify activity episodes into 5 effort levels. Light through maximum effort levels indicate "healthy" activities, while moderate through maximum levels indicate "aerobic" activities. Two threshold levels of "active living" were defined and calculated. Mann-Whitney tests show that significant interzonal differences in activity-level durations exist for all domains. The IC contrasts strongly with the suburbs (more AT and active leisure, but less occupational activity), and the suburbs with the ICB, but there are fewer significant differences between the ICB and OCB. The percentage of respondents meeting "maintenance" and "enhanced" thresholds of active living is significantly higher in the OCB, and there is also significant urban-rural variation by sociodemographic characteristics. Position along the rural-urban continuum is significantly related to PRs and mean durations for levels of physical activity, and to the proportion of respondents meeting threshold levels of active living.

Accelerometer Output and MET Values of Common Physical Activities

This article 1) provides the calibration procedures and methods for metabolic and activity monitor data collection, 2) compares measured MET values to the MET values from the compendium of physical activities, and 3) examines the relationship between accelerometer output and METs for a range of physical activities. Participants (N = 277) completed 11 activities for 7 min each from a menu of 23 physical activities. Oxygen consumption (V O2) was measured using a portable metabolic system, and an accelerometer was worn. MET values were defined as measured METs (V O2/measured resting metabolic rate) and standard METs (V O2/3.5 mL.kg.min). For the total sample and by subgroup (age [young < 40 yr], sex, and body mass index [normal weight < 25 kg.m]), measured METs and standard METs were compared with the compendium, using 95% confidence intervals to determine statistical significance (alpha = 0.05). Average counts per minute for each activity and the linear association between counts per minute and METs are presented. Compendium METs were different than measured METs for 17/21 activities (81%). The number of activities different than the compendium was similar between subgroups or when standard METs were used. The average counts for the activities ranged from 11 counts per minute (dishes) to 7490 counts per minute (treadmill: 2.23 m.s, 3%). The r between counts and METs was 0.65. This study provides valuable information about data collection, metabolic responses, and accelerometer output for common physical activities in a diverse participant sample. The compendium should be updated with additional empirical data, and linear regression models are inappropriate for accurately predicting METs from accelerometer output.

Energy expended by adults with and without intellectual disabilities during activities of daily living

The aims of this study were to (1) determine the energy expenditure of adults with and without intellectual disabilities during common activities of daily living (ADL), (2) use these values to evaluate the accuracy of equivalent activity values reported in the Compendium of Physical Activities (CPA), and (3) identify ADL that may confer a health benefit for adults with intellectual disabilities when undertaken regularly. Energy expenditure was measured for adults with intellectual disabilities (N=31; 29.0±8.6 yr) and adults without intellectual disabilities (N=15; 30.4±9.6 yr) while undertaking each of seven ADL: sitting quietly (SitQ); sitting watching television (SitTV); sitting and standing while completing an assembly task (SitAT, StaAT); and walking at a slow (WalkS, 3.0kmh−1), quick (WalkQ, 6.0kmh−1) and fast (WalkF, 9.0kmh−1) speed, under laboratory conditions. Adults with intellectual disabilities were found to expend significantly more energy than adults without intellectual disabilities for SitQ, WalkS, WalkQ and WalkF (p<0.05). Energy expended by both populations was significantly more than CPA values for SitQ, SitTV, SitAT, WalkS, and WalkQ (p<0.02) and significantly less for WalkF (p<0.01). Walking at the speed of 3.0kmh−1 (50mmin−1) was found to be sufficient to achieve moderate-intensity energy expenditure, surpassing the intensity threshold for conferring a health benefit. Energy expenditure inaccuracies of the CPA have important consequences when estimating prevalence of engagement in health enhancing physical activities among population sub-groups. The identification of slow walking as a moderate-intensity physical activity offers significant health promotion opportunities for adults with intellectual disabilities through active transport and leisure.

Accuracy And Consistency Of Examining Energy Expenditure In Guo-lin Qi-gong Using The Compendium Of Physical Activity

PURPOSE: While the Compendium of Physical Activity (PA; Ainsworth et al., 2002) has been widely used in estimating PA energy expenditure (EE), the accuracy and consistency of this practice has not been examined. The purpose of this study was to determine the accuracy and consistency of the Compendium in estimating EE during Guo-Lin Qi-gong, an exercise has been used for cancer care in China since the 1970s. METHOD: Twenty (10 males/females) long-term (> 10 yr.) cancer survivors (59.9± 5.5 yr. old; 59.9±5.5 kg body weight) were recruited for the study. They performed Guo- Lin Qi-gong twice on two consecutive days and their oxygen consumption and practice times for the corresponding Guo-Lin Qi-gong components were collected using the K4b2 portable gas metabolism apparatus (Cosmed, Italy). According to the practice time and corresponding MET values of the components determined in a previous study (Zhu et al., 2008), their EE were estimated and compared with those measured by K4b2 using both correlation and paired t-test. RESULTS: While correlations between estimated and measured EE were high (r=.76 for Trial 1 and r =.82 for Trial 2, EE during Guo-Lin Qi-gong were over-estimated, with mean differences between these EE being 84.13 ml/kg/min (SD = 19.37; 13.52%; p <.05) for Trial 1 and 58.1 (SD = 16.42; 9.32%; p <.05) for Trial 2, respectively. There is no trial difference (p>.05)in both estimated and measured EE. CONCLUSION: While related accuracy and reliability of the Compendium method was high, about a -11.42% absolute difference was noticed. The Compendium thus can be employed for estimating group's EE, but should be used with caution for individuals' PA EE assessment.

Determining metabolic equivalent values of physical activities for persons with paraplegia

Purpose. The compendium of physical activity (CPA) may not be appropriate for persons with paraplegia (PP) because of their possible low resting metabolic rate (RMR), or 1 MET, and a lack of physical activities (PAs) engaged by PP in the CPA. A CPA supplement, therefore, is needed. The purpose of this study was to examine the feasibility of constructing a short supplement for PP with two specific aims: to determine whether PP need an alternative 1-MET value and if selected PA can be classified into corresponding categories based on their ‘engagement’ and ‘energy expenditure (EE)’ characteristics.Method. Thirty-one PP (161.26 cm ± 22.99 cm, 60.52 kg ± 15.32 kg, and 24.16 ± 6.25 years) were recruited. RMR and 10 PA with different intensities were measured using indirect calorimetry. The z- and t-tests were employed to examine MET difference between the measured values and those of CPA (α = 0.05).Results. One-MET for PP (3.1 mL/kg/min) was lower than that of the CPA (3.5 mL/kg/min). Although some MET values were found to be similar to those in the CPA, others were statistically significantly different.Conclusions. To be able to measure PA-related EE of a disability subpopulation using the CPA accurately, a supplement that accounts for the impact of different types of activities and the EE characteristics of the subpopulation must be developed.

RESPONSE

Dear Editor-in-Chief: In their recent letter, Jehn et al. (6) questioned our conclusions regarding measurement validity and reliability of the Omron HJ-151 and HJ-720ITC pedometers (5) and implied that our conclusions could not be extended to low-intensity ambulatory activity or for chronically ill populations. We agree that researchers cannot make generalizations across populations or conditions from a single piece of validity evidence. As a measurement tool is validated, its accuracy (or lack thereof) is specific to the population and parameters for which the device was originally assessed (7). Therefore, the use of the tool cannot be generalized to situations in which the environmental conditions, walking speeds, mounting positions, and populations are different from the context of the validation process. Our study was conducted among college students with a normal gait pattern. Considering that validity is context specific, the results of our study should not be generalized to other populations or conditions. Similarly, the concept of context specificity also extends to the physical activity monitors themselves. For this reason, although Jehn et al. (6) provide evidence that spring-levered pedometers may not be accurate at slow speeds, we caution the authors in comparing the validity of physical activity monitors for recording ambulatory activity at slow speeds among monitors with vastly different internal mechanisms. Moreover, previous authors have shown the superiority of piezoelectric pedometers when compared with spring-levered models (2,4) and that piezoelectric pedometers are fairly accurate at speeds as slow as 54 m·min−1 (2,3). In our study of college students, walking speeds were selected objectively from the compendium of physical activities (1) to illustrate walking at 2.0 mph ("slow"), 3.0 mph ("moderate"), and 4.0 mph ("very brisk"). The scripted protocol was used to allow natural variations in our participants' perceptions of "slow," "moderate," and "very brisk" speeds to be observed, enabling the pedometer to be assessed across a wide range of speeds (from 1.5 to 5.9 mph). In our study, the average speeds corresponding to "slow" and "moderate" were even slower than those elicited by older individuals (age = 71.9 ± 5.7 yr) at "slow" (3.1 mph) and "normal" (3.5 mph) walking speeds (4). Both models recorded steps with less than 3% absolute error, illustrating a high degree of accuracy among the tested intensities. Moreover, the incorporation of self-selected walking trials enabled validity and reliability evidence to be established for both pedometers at each participant's freely chosen walking speed, providing a "real-life" representation of the accuracy of these devices. In regard to the concept of context specificity, perhaps Shepard (8) described the process of establishing validity evidence best, as "being a never-ending process" (1993, p. 407). We agree with the authors that the Omron pedometers may not be appropriate for clinical populations with abnormal gait characteristics. We encourage such research to be performed because the unique possibility of the Omron pedometers to be mounted at various body positions may make these pedometers more attractive to special populations. Minsoo Kang Elizabeth A. Holbrook Tiago V. Barreira Department of Health and Human Performance Middle Tennessee State University Murfreesboro, TN

Novel daily energy expenditure estimation by using objective activity type classification: where do we go from here?

ACCURATE ESTIMATION of daily energy expenditure (DEE) in population studies is challenged by factors such as feasibility, cost, and validity of the method under consideration. The use of wearable sensors, in particular accelerometers, is possibly the most promising currently available method to estimate DEE. Numerous accelerometer-based prediction models for estimating DEE and physical activity energy expenditure (PAEE) have been developed (5). Most of the prediction models published so far are based on linear regression analysis in experimental populations. To increase precision of these regression models, group-specific or even individual-specific regression models may be necessary. An alternative and potentially more effective and feasible approach may be to assess other individually specific characteristics that explain those yet unexplained differences between populations and between individuals. Assessing the major types of physical activity usually performed in daily life may improve the explained variation in DEE. In their article in the Journal of Applied Physiology, Bonomi and colleagues (3) present results from a study aimed to develop a model for estimating energy expenditure from accelerometer-based classification of types of physical activity in combination with published data on the energy cost [metabolic equivalent (MET) compendium values] of these activities (1, 3). The method proposed is novel because it does not rely on the use of regression analysis for predicting energy expenditure, nor does it rely on manufacturer-dependent information such as activity counts, the usual output from activity monitors based on accelerometry. Bonomi and colleagues compare their newly developed method with the more traditional method of using activity counts for DEE prediction. The high level of transparency of the proposed method is likely to inspire future research in this area. However, there are some additional issues that remain to be elucidated. The importance of classifying activity types when estimating energy expenditure is not yet fully understood. Previous studies have shown that the slope of the linear relation between energy expenditure and body acceleration varies across types of physical activity (6, 9). Theoretically, knowledge about the slope for each type of activity explains the variation in energy expenditure between types of activity within individuals that cannot be explained by acceleration (i.e., counts) alone. Between-individual differences in time spent in each type of physical activity may therefore be related to the average individuals’ slope across activities over a day and thus explain between-individual differences in DEE. In the model proposed by Bonomi and colleagues the types of physical activity identified by accelerometry and an accelerometer-based estimation of the speed of movement are used to estimate the MET intensity level defined by the compendium (1). In contrast to the assessment of counts by accelerometry, the compendium, at least in theory, takes into account differences in slope between types of activity because the activities included are based on measured or estimated energy expenditure rather than body acceleration. Therefore, the activity classification from accelerometry may have accounted for differences in slope between types of activity via the compendium. Future research will have to evaluate whether the main contribution of activity classification truly lies in the ability to account for differences in slope, which would indicate that activity classification needs to focus on explaining differences in slope rather than the activity type itself. Additionally, the y-intercept of the linear relation may also be an important parameter.

Linking the American Time Use Survey (ATUS) and the Compendium of Physical Activities: Methods and Rationale

The 2003 Bureau of Labor Statistics American Time Use Survey (ATUS) contains 438 distinct primary activity variables that can be analyzed with regard to how time is spent by Americans. The Compendium of Physical Activities is used to code physical activities derived from various surveys, logs, diaries, etc to facilitate comparison of coded intensity levels across studies. This article describes the methods, challenges, and rationale for linking Compendium estimates of physical activity intensity (METs, metabolic equivalents) with all activities reported in the 2003 ATUS. The assigned ATUS intensity levels are not intended to compute the energy costs of physical activity in individuals. Instead, they are intended to be used to identify time spent in activities broadly classified by type and intensity. This function will complement public health surveillance systems and aid in policy and health-promotion activities. For example, at least one of the future projects of this process is the descriptive epidemiology of time spent in common physical activity intensity categories. The process of metabolic coding of the ATUS by linking it with the Compendium of Physical Activities can make important contributions to our understanding of American's time spent in health-related physical activity.

A Correction Factor Improves Met Estimates Of Physical Activities

The standard 1 MET = 3.5 ml·kg-1·min-1 generally overestimates resting metabolic rate (RMR) for low-fit, overweight, older and female individuals. It is unknown what effect this systematic error has on MET estimates of physical activities in self-report instruments (SRI). PURPOSE: To examine differences in activity METs estimated using measured RMR (measMET) compared to 3.5 ml·kg-1·min-1 (calcMET) and corrected METs (corrMET= mean calcMET *(3.5/Harris-Benedict RMR)) in sub-groups by age group, BMI category, gender and fitness. METHODS: 252 subjects were classified as: normal weight (NW = BMI < 25 kg·m-2 n=159) or overweight (OW= BMI ≥25 kg·m -2 n=93), male (n=118) or female (n=134), age group (by decade 20,30,40, or 50 y n=70, 69, 53 and 59, respectively) and fitness quintiles (low to high fit; n= 30, 31, 73, 88 and 39, respectively). RMR was measured after a 15 minute rest following a 4-hr restriction of food, caffeine and exercise. Subjects completed 6 treadmill activities at 3 speeds (1.34, 1.56, 2.23 m·sec-1) at 0% and 3% grade and 5 self-paced activities of daily living (ADL). Each activity was performed for 7 minutes while wearing a portable metabolic unit to measure VO2. Average MET differences (calcMET-measMET and corrMET-measMET) were computed by sub-group. RESULTS: The (mean ±SD) measMET was 6.1 ±2.64 (n=2555). CalcMET (Mean (95% CI)) was (0.51(0.59,0.42) METs) less than measMET. CorrMET was not statistically different from measMET (0.02 (-0.11,0.06) METs). calcMET underestimated measMET for most individuals who were low fit (-1.04(-1.38,-0.71) METs), OW (-0.65(-0.81,-0.49) METs), older (50-60yrs,-0.67(-0.87,0.47)METs) and female (-0.55(-0.68,-0.41) METs). Differences between corrMET and measMET were less than calcMET and measMET for all subgroups. CONCLUSION: SRI's that transform activity into MET metrics (e.g. MET hrs·day-1) using the Compendium of Physical Activities which assumes the 3.5 ml·kg-1·min-1 baseline, will lead to less precise estimates of self-reported physical activity particularly in individuals who are overweight, older, low fit and women. The Harris-Benedict based correction factor improves MET estimates and reduces the systematic error observed within sub-groups. Supported by NIH RO1 CA121005

SIX MONTH PHYSIOTHERAPY ASSESSMENTS ON THE PATIENTS EXPOSED TO THE TWO DIFFERENT OPERATIONS FOR THEIR LUMBAR DISC HERNIAS

Purpose: To compare the patients operated on for lumbar disc hernia with two different methods in view of pain, disability, physical performance, physical activity and aerobic capacity. Methods: Forty-one patients were included in the study (group I, nucleoplasty – n = 14), (group II, lumbar discectomy – n = 27). Visual Analog Scale (VAS) was performed for pain; Oswestry Disability Index for lumbar disability level; total rolling time, lie/sit, sit and stand, 50 foot walking and five-minute walk tests for physical performance measurements; the Compendium of Physical Activities (CPA) scale for activity level measurement, and VO2max measurements for aerobic capacity. Results: In the second month's controls, changes of pain and disability levels were insignificant in both groups, even though pain in nucleoplasty group and disability in discectomy group were found to be better. In performance measurements, group II showed better results in the second postoperative month, whereas group I had better results in the sixth postoperative month. The levels of physical activity in group I, and of aerobic capacity in group II increased significantly. Conclusion: Different physiotherapy approaches are necessary to obtain good results in terms of pain intensity, disability, physical performance, physical activity and aerobic capacity, since these parameters are found to be worse during the sixth month postoperative in patients who had undergone lumbar discectomy.

An Analysis Of The Association Between Leisure-time Physical Activity And Breast Cancer Risk

PURPOSE: The objective of this study was to review the methodology and analyze the existing data examining the relationship between leisure-time physical activity and breast cancer risk. METHODS: Methodological differences (subject characteristics, physical activity assessment instrumentation, and time frame of physical activity measurement) were assessed for all studies. The potential effect of confounding variables was estimated by calculating the percent difference between the multivariate-adjusted risk estimates from the original studies and the unadjusted risk estimates used in the present meta-analyses. A series of meta-analyses were performed, and only studies quantifying leisure-time physical activity using the Compendium of Physical Activities by assigning MET scores to activities were included in the analyses. Subjects from the studies were combined and categorized as either having a low, moderate, or high amount of total weekly physical activity, and the moderate and high activity groups were compared to the low activity reference group. RESULTS: The percent difference between the adjusted and unadjusted estimates ranged from 0-21% across all physical activity categories for 14 of 15 studies, with one study (Patel et al., 2003) reporting a difference of 64%. The combined risk estimate for high vs. low activity was 0.816 (95% CI = 0.721-0.924; p = .001) for pre- and postmenopausal females combined, 0.820 (95% CI = 0.584-1.151; p = .251) for premenopausal females, and 0.847 (95% CI = 0.727-0.987; p = 0.034) for postmenopausal females. Moderate activity was only significantly different (p = .001) among pre- and postmenopausal females combined. CONCLUSIONS: Combined risk estimates of studies using the Compendium report that high, but not moderate, amounts of leisure-time physical activity appear to reduce the risk for postmenopausal breast cancer, while the association between activity and premenopausal breast cancer risk remains unclear. Future studies assessing the relationship between physical activity and breast cancer should examine pre- and postmenopausal females separately, and should adhere to a standardized questionnaire for assessing types of physical activity as well as a standard time-frame for measuring physical activity.

Finding Physical Activity Patterns Using Cluster Analysis

Without fully understanding patterns of physical activity (PA) participation of a targeted population, population specific designed PA interventions may not work effectively. PURPOSE: Using cluster analysis, the purpose of this study is to identify PA patterns of urban women. METHOD: This study used data from a previous urban women PA study in which PA participation information was collected from 480 urban women using a questionnaire composed of 11 activity categories and 99 activities. PA participation was converted into energy expenditure using the Compendium of Physical Activities (Ainsworth et al., 2000). PA patterns were detected and the pattern characteristics were explained. Major activities that determined the PA patterns were investigated. Specifically, cluster analysis using dissimilarity distance between subjects was used to find PA participation clusters. Descriptive statistics were then used to provide an explanation of PA patterns by cluster. RESULTS: Among the 11 activity categories, the most energy was spent on working (29.5%), housework (28.4%), walking (10.8%) and moving (9.4%). Sports/exercise was only 6.1% of the total energy expenditure. 16 activities played key roles in differentiating the eight distinct PA patterns, identified by cluster analysis. Group 1 did the most housework activities and the least work related activities among all the groups, and had a high activity level in sports and walking. Group 2 was the most active group in sports/exercise, and was also active while working. Group 3 showed the lowest activity level in sports, walking up stairs and walking of all the groups, and the highest energy expenditure in activities while working and driving a car. Group 4 spent the most energy in caring for others of all the groups and had a high energy level while driving a car. Group 5 had rather high activity levels in housework and sports, and average levels in other activities compared to the other groups. Group 6 showed some unique activity patterns in arts and dancing in which they had higher activity levels than other groups. Groups 7 and 8 were different but did not show clearly different patterns. CONCLUSION: Cluster analysis proves to be a useful means to identifying PA patterns, which should help design more effective, population-specific, targeted PA interventions.

Estimating MET Values of Persons with SCI Using Heart Rate

MET values in the compendium of physical activities (PA) are frequently used to estimate energy expenditure (EE) of PA, but most MET values cannot be applied to persons with disabilities (PD) because the values may differ for a specific disability subpopulation and only a few disability-specific activities are included. Therefore, development of a supplement PA compendium (SPAC) for PD is urgently needed. A simple and accurate way to estimate METs is needed and heart rate (HR) may be a solution using the ratio of PA HR and resting HR. PURPOSE: To determine the accuracy of the HR ratio method estimating PA MET values of persons with spinal cord injury (PSCI). METHODS: Thirty one PSCI were recruited, including 19 males and 12 females with complete and incomplete SCI T3-T12; 161.26±22.99 cm; 60.52±15.32 kg; and 18-45 yrs old. Ten PAs used including reading (RD), watching TV (TV), working on a computer (CM), vacuuming (VM), mopping (MP), moving chairs (MC), pushing a wheelchair-tile floor-own pace (TL), -sidewalk-40% faster than TL (SW), -ramp (RP) and arm ergometer-60rpm/60W(EX). RMR and 10-min VO epochs of the 10 PAs were measured by indirect calorimetry. The MET values of each PA were calculated based on collected RMR and PA oxygen uptake and the HR ratio METs were calculated by dividing HR of the PA with the resting HR. Correlation and regression analysis were employed to determine the relationship between measured METs and those estimated by the HR ratio method. RESULTS: The directly measured and HR ratio estimated MET values (M±SD) were respectively: CM=1.3±0.2 / 1.2±0.1, TV=1.2±0.2 / 1.2±0.2, RD=1.3±0.3 / 1.2±0.2, TL=2.7±0.6 / 1.7±0.4, VM=3.3±0.5 / 1.8±0.3, MP=3.9±0.7 / 1.9±0.5, MC=4.2±0.8 / 1.9±0.5, SW=4.4±1.3 / 2.2±0.5, RP=4.4±0.9 / 2.1±0.5, & EX=5.8±0.9 / 2.4±0.5; Mean correlation between individual direct METs and HR ratio estimated METs is r = 0.93 (SD=.05, R2 = .87±.09 & SEE = .58±.19), but it decreased to r = 0.71 when using the above group mean values. CONCLUSION: The HR ratio method is a valid method for estimating PA METs and individual calibration may further improve prediction accuracy. Acknowledgement: Study supported by ACSM Foundation's Carl V. Gisolfi Memorial Research Fund

Daily step goal of 10,000 steps: A literature review

This review looks at ways to increase physical activity, by walking and other sports and home activities, to reach the daily 10,000 steps goal. It also looks at a number of issues associated with achieving the daily step goal, such as considerations in walking, step counting and physical activity. The review is based on MEDLINE (1982-2006) and Google searches using keywords "pedometer", "daily step goal", "physical activity", "exercise". Research has suggested a daily 10,000 step goal for maintaining a desirable level of physical activity for health. However, this is not normally achievable through routine daily activities. For many, there is a daily deficit of approximately 4000 steps (most from 3000 to 6000 steps), which must be gained from other more rigorous activities. This paper provides information based on the Compendium of Physical Activities, to help people to choose their physical activities to supplement their daily steps, through both sports activities and home activities. It thus helps people to better achieve the goals of Canada's Physical Activity Guide. There are issues to consider in counting steps. A pedometer is not an exact method to measure energy expenditure. Focusing on counting steps may lead to an obsessive attitude toward exercise. Excessive walking and physical activity may lead to certain health problems. Walking is a practical and fun way to change our sedentary life style and to improve the health of the nation. When there is a deficit in daily steps, both sports and home activities can be used to supplement the daily steps to reach the daily step goal. The user-friendly table provided in this paper helps people to identify the sports and home activities, and estimate the durations needed, to meet the daily step goal.

Energy Expenditure of Daily Tasks Among Adults With Intellectual Disability

Little is known about the enrgy expenditure associated with activities of daily living (ADL) among adults with an intellectual disability (AWID) nor is it known whether energy expenditure estimates derived for non-AWID are accurate for this group. PURPOSE To measure the energy expenditure of AWID during 7 ADL and establish if the MET values reported in the compendium of physical activities (Ainsworthetal., 2000) are accurate for use with this group. METHOD Thirty ambulatory, community living AWID (males, N= 20; females, N = 10), aged 18–53 years completed 7 ADL; sitting quietly (SitQ), sitting watching television (SitTV), sitting assembly task (SitAT), standing assembly task (StaAT), walking at 3.0 km/hr (WalkS), 6.0 km/hr (WalkQ), and 9.0 km/hr (WalkF). Ten of the 30 AWID have Down syndrome (DS). Analyses revealed no significant difference (p < .05) in energy expenditure for any ADL for those with and without DS; data was therefore pooled for subsequent analyses. Following a period of familiarisation, each AWID undertook each ADL for 10 minutes in a laboratory based setting. Calibrated instrumentation was used to collect expired air that was analysed for carbon dioxide (CO2), oxygen (O2) and volume expired. The data was converted to units of energy expenditure (METs). RESULTS Mean MET Values per ActivityTableCONCLUSION The MET values for each ADL engaged in by AWID were higher, ranging from 45% for WalkF to 378% for WalkS, than those reported in the Compendium of Physical Activities (Ainsworth et al., 2000). These findings suggest that energy expenditure of ADL in the compendium may not be accurate for use with AWID. SitAT and StaAT were found to be 'moderate intensity activities' (3–6 METs) and WalkS, WalkQ and WalkF 'vigorous intensity activities' (> 6 METs) for AWID suggesting that if these ADL were engaged in for appropriate time, they provide an energy expenditure sufficient to confer some health benefit.

Energy Expenditure of Daily Tasks Among Adults With Intellectual Disability

Little is known about the enrgy expenditure associated with activities of daily living (ADL) among adults with an intellectual disability (AWID) nor is it known whether energy expenditure estimates derived for non-AWID are accurate for this group. PURPOSE To measure the energy expenditure of AWID during 7 ADL and establish if the MET values reported in the compendium of physical activities (Ainsworthetal., 2000) are accurate for use with this group. METHOD Thirty ambulatory, community living AWID (males, N= 20; females, N = 10), aged 18–53 years completed 7 ADL; sitting quietly (SitQ), sitting watching television (SitTV), sitting assembly task (SitAT), standing assembly task (StaAT), walking at 3.0 km/hr (WalkS), 6.0 km/hr (WalkQ), and 9.0 km/hr (WalkF). Ten of the 30 AWID have Down syndrome (DS). Analyses revealed no significant difference (p < .05) in energy expenditure for any ADL for those with and without DS; data was therefore pooled for subsequent analyses. Following a period of familiarisation, each AWID undertook each ADL for 10 minutes in a laboratory based setting. Calibrated instrumentation was used to collect expired air that was analysed for carbon dioxide (CO2), oxygen (O2) and volume expired. The data was converted to units of energy expenditure (METs). RESULTS Mean MET Values per ActivityTableCONCLUSION The MET values for each ADL engaged in by AWID were higher, ranging from 45% for WalkF to 378% for WalkS, than those reported in the Compendium of Physical Activities (Ainsworth et al., 2000). These findings suggest that energy expenditure of ADL in the compendium may not be accurate for use with AWID. SitAT and StaAT were found to be 'moderate intensity activities' (3–6 METs) and WalkS, WalkQ and WalkF 'vigorous intensity activities' (> 6 METs) for AWID suggesting that if these ADL were engaged in for appropriate time, they provide an energy expenditure sufficient to confer some health benefit.

Energy Costs of Physical Activities in Children and Adolescents

The primary aim was to determine the energy expenditure (EE: kcal.kg(-1).h(-1)) in terms of caloric cost and metabolic equivalents of activities commonly performed by children and adolescents. Secondary aims were to determine at what age and pubertal developmental stage values approach those of adults. In this descriptive study, 295 volunteer youth 8-18 yr of age completed 18 common physical activities (including rest) while EE was measured continuously with a portable metabolic system. Three sets of activities were assigned in random order for each subject. Activities ranged from television viewing and video game play to running and rope skipping. Pubertal development was estimated from a self-report questionnaire. At rest, VO(2) and EE were highest in the youngest children and decreased with advancing age and higher pubertal stage in both genders. The age-adjusted and puberty-adjusted energy expenditure values were generally lower than the compendium MET values for sedentary and moderate activities but were more varied for high-intensity activities. However, the ratio of activity EE to REE was comparable in children and adults. Energy expenditure per kilogram of body mass at rest or during exercise is greater in children than adults and varies with pubertal status, thus using the definition of a MET in the compendium of physical activities without adjustment is inadequate for energy estimation in children, until a child reaches Tanner Stage 5. However, the ratio of activity EE to resting EE in children appears to be similar or slightly less than in the compendium, suggesting that the compendium MET increments used with our adjusted EE values more closely approximate the true EE of activities in children than present adult norms.

Leisure-time physical activity, sedentary behavior, and fitness of high school girls

Background: Childhood physical inactivity may exacerbate other behaviorally linked cardiovascular disease risk factors such as hypertension, and hyperlipidemia, which have been associated with subsequent morbidity and mortality later in life. Evidence suggests that physical activity as adults is related to their exercise patterns as children. Methods: Television watching and Internet use time were evaluated for one week, and their relationships to various physical fitness parameters among 129 high school girls were assessed. Leisure-time physical activity, quantified by a 21-day physical activity diary and a compendium of physical activities with metabolic equivalent (MET) intensities, and attitudinal belief to leisure-time physical activity were compared to all the aforementioned variables. Results: No significant correlations were found between television watching time or Internet use time with body mass index, one-mile run time, and three different operational definitions of leisure-time physical activity. One-mile run time correlated moderately with body mass index and negatively with leisure-time physical activity. Caloric expenditure during lei-sure-time physical activity was weakly related to body mass index. Attitudinal belief to leisure-time physical activity correlated significantly with leisure-time physical activity, weekly caloric expenditure, and inversely with one-mile run time. The foremost predictor of mile-run time was body mass index, followed by attitudinal belief to leisure-time physical activity and leisure-time physical activity. Conclusions: Leisure-time physical activity and attitudinal belief to leisure-time physical activity are significantly (and inversely) associated with physical fitness only when expressed by an actual performance measure, but not when represented by the sum of the skinfold measurements, body fat, or body mass index. Physical fitness is unrelated to Internet use time or television watching time. Furthermore, since these sedentary behaviors are unrelated to leisure-time physical activity, yet positively correlated with each other, it is suggested that time spent watching television or on the Internet does not occur at the expense of leisure-time physical activity.

Physical Activity and Perceived Health in a Sample of Active-duty US Air Force Members

2107 Literature examining the relationship between physical activity (PA) and perceived health in younger populations, specifically the military is limited. Given that many military members do not meet minimum ACSM PA guidelines—coupled with the effect inactivity has on health—examining the relationship between PA and perceived health may prove bene. cial in formulating broader messages to promote PA in the military population. Purpose: To examine difference in health perceptions among physically active and low-active Air Force (AF) members. Methods: Data were collected via interviews from 94 AF members (50 males (M), 44 females (F)) ages 19–52 y. Members were classified as physically active (A) if they responded “yes” to the PA identification question indicating they met minimum ACSM PA fitness guidelines (i.e., at least 3x/week, 20 min or >, at an intensity that causes you to breathe heavier); those not meeting guidelines were classified as low-active (LA). Estimated energy expenditure (kcal/week) was computed using the Compendium of PA (Ainsworth et al., 1993; 2000) guidelines for assessing PA data. Perception of health was assessed using Version 2 of the SF-36 Health Survey (Ware et al., 2001). Analyses were conducted on the Physical (PCS) and Mental (MCS) Component Summary scores. Results: 53% of members reported meeting ACSM guidelines. A and LA groups did not differ in gender (AM = 29, LAM = 21, AF = 21, LAF = 23) or age (A = 33.06, LA = 31.73). Independent samples t-tests (one-tailed; pp<.05) were used to compare A and LA group means. No significant differences existed on self-perceptions of physical health and both groups in general reported high PCS scores (A = 56.28, LA = 56.88). Significant differences existed for self-perceptions of mental health (MCS) and PA recall (kcal/week). Higher levels of PA (A = 1721.10, LA = 230.70) and mental health (A = 50.82, LA = 46.72) were reported by A than LA members. Conclusions: The greater MCS and PA scores reported by A members suggest that PA is an important variable in maintaining emotional health of AF members. However, support for the relationship between PA and physical health was not found in this population. This may be due to the measure used to assess physical health and suggests that the SF-36 PCS may be ineffective in differentiating physical health status in a younger, disease-free population. These findings af. rm the need for further research—improving our understanding of the physical and mental health benefits of PA in younger populations and suggest that health promotion messages promoting PA in military populations should consider targeting the mental benefits associated with being physically active.