Patty S. Freedson

Calibration of the Computer Science and Applications, Inc. accelerometer

PURPOSE We established accelerometer count ranges for the Computer Science and Applications, Inc. (CSA) activity monitor corresponding to commonly employed MET categories. METHODS Data were obtained from 50 adults (25 males, 25 females) during treadmill exercise at three different speeds (4.8, 6.4, and 9.7 km x h(-1)). RESULTS Activity counts and steady-state oxygen consumption were highly correlated (r = 0.88), and count ranges corresponding to light, moderate, hard, and very hard intensity levels were < or = 1951, 1952-5724, 5725-9498, > or = 9499 cnts x min(-1), respectively. A model to predict energy expenditure from activity counts and body mass was developed using data from a random sample of 35 subjects (r2 = 0.82, SEE = 1.40 kcal x min(-1)). Cross validation with data from the remaining 15 subjects revealed no significant differences between actual and predicted energy expenditure at any treadmill speed (SEE = 0.50-1.40 kcal x min(-1)). CONCLUSIONS These data provide a template on which patterns of activity can be classified into intensity levels using the CSA accelerometer.

Amount of Time Spent in Sedentary Behaviors in the United States, 2003–2004

Sedentary behaviors are linked to adverse health outcomes, but the total amount of time spent in these behaviors in the United States has not been objectively quantified. The authors evaluated participants from the 2003-2004 National Health and Nutrition Examination Survey aged >/=6 years who wore an activity monitor for up to 7 days. Among 6,329 participants with at least one 10-hour day of monitor wear, the average monitor-wearing time was 13.9 hours/day (standard deviation, 1.9). Overall, participants spent 54.9% of their monitored time, or 7.7 hours/day, in sedentary behaviors. The most sedentary groups in the United States were older adolescents and adults aged >/=60 years, and they spent about 60% of their waking time in sedentary pursuits. Females were more sedentary than males before age 30 years, but this pattern was reversed after age 60 years. Mexican-American adults were significantly less sedentary than other US adults, and White and Black females were similarly sedentary after age 12 years. These data provide the first objective measure of the amount of time spent in sedentary behavior in the US population and indicate that Americans spend the majority of their time in behaviors that expend very little energy.

Age and gender differences in objectively measured physical activity in youth.

PURPOSE The purpose of this study was to evaluate age and gender differences in objectively measured physical activity (PA) in a population-based sample of students in grades 1-12. METHODS Participants (185 male, 190 female) wore a CSA 7164 accelerometer for 7 consecutive days. To examine age-related trends, students were grouped as follows: grades 1-3 (N = 90), grades 4-6 (N = 91), grades 7-9 (N = 96), and grades 10-12 (N = 92). Bouts of PA and minutes spent in moderate-to-vigorous PA (MVPA) and vigorous PA (VPA) were examined. RESULTS Daily MVPA and VPA exhibited a significant inverse relationship with grade level, with the largest differences occurring between grades 1-3 and 4-6. Boys were more active than girls; however, for overall PA, the magnitudes of the gender differences were modest. Participation in continuous 20-min bouts of PA was low to nonexistent. CONCLUSION Our results support the notion that PA declines rapidly during childhood and adolescence and that accelerometers are feasible alternatives to self-report methods in moderately sized population-level surveillance studies.

Using objective physical activity measures with youth: How many days of monitoring are needed?

PURPOSE The purpose of this study was to establish the minimal number of days of monitoring required for accelerometers to assess usual physical activity in children. METHODS A total of 381 students (189 M, 192 F) wore a CSA 7164 uniaxial accelerometer for seven consecutive days. To examine age-related trends students were grouped as follows: Group I: grades 1-3 (N = 92); Group II: grades 4-6 (N = 98); Group III: grades 7-9 (N = 97); Group IV: grades 10-12 (N = 94). Average daily time spent in moderate-to-vigorous physical activity (MVPA) was calculated from minute-by-minute activity counts using the regression equation developed by Freedson et al. (1997). RESULTS Compared with adolescents in grades 7 to 12, children in grades 1 to 6 exhibited less day-to-day variability in MVPA behavior. Spearman-Brown analyses indicated that between 4 and 5 d of monitoring would be necessary to a achieve a reliability of 0.80 in children, and between 8 and 9 d of monitoring would be necessary to achieve a reliability of 0.80 in adolescents. Within all grade levels, the 7-d monitoring protocol produced acceptable estimates of daily participation in MVPA (R = 0.76 (0.71-0.81) to 0.87 (0.84-0.90)). Compared with weekdays, children exhibited significantly higher levels of MVPA on weekends, whereas adolescents exhibited significantly lower levels of MVPA on weekends. Principal components analysis revealed two distinct time components for MVPA during the day for children (early morning, rest of the day), and three distinct time components for MVPA during the day for adolescents (morning, afternoon, early evening). CONCLUSIONS These results indicate that a 7-d monitoring protocol provides reliable estimates of usual physical activity behavior in children and adolescents and accounts for potentially important differences in weekend versus weekday activity behavior as well as differences in activity patterns within a given day.

Validity of accelerometry for the assessment of moderate intensity physical activity in the field

PURPOSE This study was undertaken to examine the validity of accelerometry in assessing moderate intensity physical activity in the field and to evaluate the metabolic cost of various recreational and household activities. METHODS Twenty-five subjects completed four bouts of overground walking at a range of self-selected speeds, played two holes of golf, and performed indoor (window washing, dusting, vacuuming) and outdoor (lawn mowing, planting shrubs) household tasks. Energy expenditure was measured using a portable metabolic system, and motion was recorded using a Yamax Digiwalker pedometer (walking only), a Computer Science and Application, Inc. (CSA) accelerometer, and a Tritrac accelerometer. Correlations between accelerometer counts and energy cost were examined. In addition, individual equations to predict METs from counts were developed from the walking data and applied to the other activities to compare the relationships between counts and energy cost. RESULTS Observed MET levels differed from values reported in the Compendium of Physical Activities, although all activities fell in the moderate intensity range. Relationships between counts and METs were stronger for walking (CSA, r = 0.77; Tritrac, r = 0.89) than for all activities combined (CSA, r = 0.59; Tritrac, r = 0.62). Metabolic costs of golf and the household activities were underestimated by 30-60% based on the equations derived from level walking. CONCLUSION The count versus METs relationship for accelerometry was found to be dependent on the type of activity performed, which may be due to the inability of accelerometers to detect increased energy cost from upper body movement, load carriage, or changes in surface or terrain. This may introduce error in attempts to use accelerometry to assess point estimates of physical activity energy expenditure in free-living situations.

Compliance with physical activity guidelines: Prevalence in a population of children and youth

PURPOSE To use objective monitoring of physical activity to determine the percentages of children and youth in a population that met physical activity guidelines. METHODS A total of 375 students in grades 1-12 wore an accelerometer (CSA 7164) for seven consecutive days. Bouts of continuous activity and accumulation of minutes spent in physical activity at various intensities were calculated to determine how many students met three physical activity guidelines. RESULTS Over 90% of students met Healthy People 2010, Objective 22.6 and nearly 70% met the United Kingdom Expert Consensus Group guideline, both of which recommend daily accumulation of moderate physical activity. Less than 3% met Healthy People 2010, Objective 22.7, which calls for bouts of continuous vigorous physical activity. For the United Kingdom Expert Consensus Group guideline, compliance decreased markedly with age, but gender differences were not statistically significant. CONCLUSIONS Prevalence estimates for compliance with national physical activity guidelines varied markedly for the three guidelines examined. Objective monitoring of physical activity in youth appears to be feasible and may provide more accurate prevalence rates than self-report measures.

Validation of Wearable Monitors for Assessing Sedentary Behavior

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.

Estimation of VO2max from a one-mile track walk, gender, age, and body weight.

The purpose of this investigation was to explore an alternative field test to estimate maximal oxygen consumption (VO2max) using a one-mile walk test. VO2max was determined in 343 healthy adult (males = 165, females = 178) subjects 30 to 69 yr using a treadmill protocol (mean +/- SD: VO2max = 37.0 +/- 10.7 ml X kg-1 X min-1). Each subject performed a minimum of two, one-mile track walks as fast as possible. The two fastest walks (T1, T2) with elapsed times within 30 s were used for subsequent analyses. Heart rates were monitored continuously and recorded every one-quarter mile. Multiple regression analysis (best sub-sets) to estimate VO2max (l X min-1) yielded the following predictor variables: track walk-1 time (T1); fourth quarter heart rate for track walk-1 (HR 1-4); age (yr); weight (lb); and sex (1 = male, 0 = female). The best equation (N = 174) was: VO2max = 6.9652 + (0.0091*WT) - (0.0257*AGE) + (0.5955*SEX) - (0.2240*T1) - (0.0115*HR1-4); r = 0.93, SEE = 0.325 l X min-1. Comparing observed and estimated VO2max values in a cross-validation group (N = 169) resulted in r = 0.92, SEE = 0.355 l X min-1. Generalized and sex-specific equations to estimate VO2max (ml X kg-1 X min-1) were also generated. The accuracy of estimation as expressed by SEE was similar among the equations. The results indicate that this one-mile walk test protocol provides a valid sub-maximum assessment for VO2max estimation.

Validity of the Computer Science and Applications, Inc. (CSA) activity monitor.

The validity of the Computer Science and Applications, Inc. (CSA) accelerometer in assessing physical activity was assessed during treadmill walking and running at three different grades. Energy expenditure (EE) served as the criterion measure. CSA data were compared to data collected with the Caltrac accelerometer. Both accelerometers were sensitive to changes in treadmill speed, but neither discriminated changes in treadmill grade. Caltrac and CSA activity counts were significantly and similarly correlated with EE (r = 0.66-0.82), relative VO2 (r = 0.77-0.89), heart rate (r = 0.66-0.80), treadmill speed (r = 0.82-0.92), and with each other (r = 0.77-0.82). CSA data were used to develop models to predict EE (kcal.min-1). Cross-validation resulted in a mean difference between actual and predicted EE of 0.02 kcal.min-1 (SEE = 0.85 kcal.min-1). The range of individual differences in the validation group was large for both the CSA model (-2.86 to +3.86 kcal.min-1) and Caltrac (-4.17 to +2.04 kcal.min-1). It is concluded that the CSA and Caltrac accelerometers have similar validity and that either instrument can be used to estimate EE of groups.

Validation and comparison of ActiGraph activity monitors

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 ANOVAs. Bland-Altman plots were used to assess agreement between activity counts from the GT3X and GT1M. Linear regression analysis between VM3 countsmin(-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<0.01) than those from the GT3X at 4.8, 9.7 and 12 km h(-1). High inter-monitor agreement was found for VT activity counts but not for AP and VM2 activity counts. VM3 cut-points for moderate, hard, and very hard PA intensities were 2690-6166, 6167-9642, >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.