James J. McClain

Sedentary Activity Associated With Metabolic Syndrome Independent of Physical Activity

OBJECTIVE This study examined the association between objectively measured sedentary activity and metabolic syndrome among older adults. RESEARCH DESIGN AND METHODS Data were from 1,367 men and women, aged ≥60 years who participated in the 2003–2006 National Health and Nutrition Examination Survey (NHANES). Sedentary time during waking hours was measured by an accelerometer (<100 counts per minute). A sedentary bout was defined as a period of time >5 min. A sedentary break was defined as an interruption in sedentary time (≥100 counts per minute). Metabolic syndrome was defined according to the Adult Treatment Panel (ATP) III criteria. RESULTS On average, people spent 9.5 h (65% of wear time) as sedentary. Compared with people without metabolic syndrome, people with metabolic syndrome spent a greater percentage of time as sedentary (67.3 vs. 62.2%), had longer average sedentary bouts (17.7 vs. 16.7 min), had lower intensity during sedentary time (14.8 vs. 15.8 average counts per minute), and had fewer sedentary breaks (82.3 vs. 86.7), adjusted for age and sex (all P < 0.01). A higher percentage of time sedentary and fewer sedentary breaks were associated with a significantly greater likelihood of metabolic syndrome after adjustment for age, sex, ethnicity, education, alcohol consumption, smoking, BMI, diabetes, heart disease, and physical activity. The association between intensity during sedentary time and metabolic syndrome was borderline significant. CONCLUSIONS The proportion of sedentary time was strongly related to metabolic risk, independent of physical activity. Current results suggest older people may benefit from reducing total sedentary time and avoiding prolonged periods of sedentary time by increasing the number of breaks during sedentary time.

Evolution of accelerometer methods for physical activity research

The technology and application of current accelerometer-based devices in physical activity (PA) research allow the capture and storage or transmission of large volumes of raw acceleration signal data. These rich data not only provide opportunities to improve PA characterisation, but also bring logistical and analytic challenges. We discuss how researchers and developers from multiple disciplines are responding to the analytic challenges and how advances in data storage, transmission and big data computing will minimise logistical challenges. These new approaches also bring the need for several paradigm shifts for PA researchers, including a shift from count-based approaches and regression calibrations for PA energy expenditure (PAEE) estimation to activity characterisation and EE estimation based on features extracted from raw acceleration signals. Furthermore, a collaborative approach towards analytic methods is proposed to facilitate PA research, which requires a shift away from multiple independent calibration studies. Finally, we make the case for a distinction between PA represented by accelerometer-based devices and PA assessed by self-report.

Field Validation of the MTI Actigraph and BodyMedia Armband Monitor Using the IDEEA Monitor

Objective: Accelerometers offer considerable promise for improving estimates of physical activity (PA) and energy expenditure (EE) in free‐living subjects. Differences in calibration equations and cut‐off points have made it difficult to determine the most accurate way to process these data. The objective of this study was to compare the accuracy of various calibration equations and algorithms that are currently used with the MTI Actigraph (MTI) and the Sensewear Pro II (SP2) armband monitor.

Epoch length and accelerometer outputs in children: Comparison to direct observation

PURPOSE To determine the effects of epoch length and activity count cutpoints on ActiGraph (AG; ActiGraph Health Services, Pensacola, FL) accelerometer estimates of time in moderate-to-vigorous physical activity (MVPA) in fifth-grade children monitored during physical education (PE) compared with a direct observation (DO) criterion standard. METHODS A sample of 32 fifth-grade males and females (mean age = 10.3 +/- 0.5 yr) wore an AG attached at the waist for a 30-min PE class. Participants were concurrently videotaped, and the Computerized System for Observing Fitness Instruction Time (C-SOFIT) was used to create a DO measure of MVPA time (DO_MVPA). AG data were collected in 5-s epochs then integrated into 10-, 15-, 20-, 30-, and 60-s epochs. AG activity counts were converted into time (in s) in MVPA using validated (and epoch-adjusted) childrens activity count cutpoints established by Treuth et al. (AG_T), Mattocks et al. (AG_M), and Freedson et al. (AG_F). RESULTS All AG_T and AG_M epoch detected significantly lower time in MVPA than DO_MVPA. The percentage of DO_MVPA detected by AG_T and AG_M epochs ranged from 46% to 61% and from 26% to 47%, respectively. All AG_F epochs yielded similar (i.e., nonsignificant) mean estimates of MVPA versus DO_MVPA, with modest increases in root mean squared error (RMSE) with increasing epoch length. The percentage of DO_MVPA detected by AG_F epochs ranged from 93% to 100%. CONCLUSIONS All AG_F epoch lengths provide comparable mean estimates to DO-detected MVPA time in fifth-grade children during PE. To minimize error among individual estimates, shorter epoch lengths should be used, with 5-s epochs yielding the lowest RMSE in the current study. Considerations of both epoch length and activity count cutpoint are important to improved detection of intermittent bouts of MVPA among fifth-grade children.

Expected Values for Pedometer-Determined Physical Activity in Youth

This review assembles pedometry literature focused on youth, with particular attention to expected values for habitual, school day, physical education class, recess, lunch break, out-of-school, weekend, and vacation activity. From 31 studies published since 1999, we constructed a youth habitual activity step-curve that indicates: (a) from ages 6 to 18 years, boys typically take more steps per day than girls; (b) for both sexes the youngest age groups appear to take fewer steps per day than those immediately older; and (c) from a young age, boys decline more in steps per day to become more consistent with girls at older ages. Additional studies revealed that boys take approximately 42–49% of daily steps during the school day; girls take 41–47%. Steps taken during physical education class contribute to total steps per day by 8.7–23.7% in boys and 11.4–17.2% in girls. Recess represents 8–11% and lunch break represents 15–16% of total steps per day. After-school activity contributes approximately 47–56% of total steps per day for boys and 47–59% for girls. Weekdays range from approximately 12,000 to 16,000 steps per day in boys and 10,000 to 14,000 steps per day in girls. The corresponding values for weekend days are 12,000–13,000 steps per day in boys and 10,000–12,000 steps per day in girls.

Employment and physical activity in the U.S.

BACKGROUND Physical inactivity is a risk factor for obesity, cardiovascular disease, hypertension, and other chronic diseases that are increasingly prevalent in the U.S. and worldwide. Time at work represents a major portion of the day for employed people. PURPOSE To determine how employment status (full-time, part-time, or not employed) and job type (active or sedentary) are related to daily physical activity levels in American adults. METHODS Cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) were collected in 2003-2004 and analyzed in 2010. Physical activity was measured using Actigraph uniaxial accelerometers, and participants aged 20-60 years with ≥4 days of monitoring were included (N=1826). Accelerometer variables included mean counts/minute during wear time and proportion of wear time spent in various intensity levels. RESULTS In men, full-time workers were more active than healthy nonworkers (p=0.004), and in weekday-only analyses, even workers with sedentary jobs were more active (p=0.03) and spent less time sedentary (p<0.001) than nonworkers. In contrast with men, women with full-time sedentary jobs spent more time sedentary (p=0.008) and had less light and lifestyle intensity activity than healthy nonworkers on weekdays. Within full-time workers, those with active jobs had greater weekday activity than those with sedentary jobs (22% greater in men, 30% greater in women). CONCLUSIONS In men, full-time employment, even in sedentary occupations, is positively associated with physical activity compared to not working, and in both genders job type has a major bearing on daily activity levels.

Protocols for evaluating equivalency of accelerometry-based activity monitors.

A wide array of accelerometer-based activity monitors has been developed to facilitate objective monitoring of physical activity behaviors, but it has proven difficult to equate outputs from different monitors. On the surface, commercially available monitors seem to be performing the same basic task-monitoring total body acceleration. However, differences in sensor properties and internal data processing have made it difficult to directly compare output from different monitors. In recent years, many new competing technologies have been released into the market, compounding the challenge of evaluating monitor equivalency and the relative strengths and limitations of different monitors. To advance physical activity assessment and improve our ability to compare results across studies using different monitors, it is important to conduct functional equivalency studies in a standardized and systematic way. This article summarizes issues associated with monitor equivalency and proposes methods for standardization and quality control in future research.

Pedometry Methods for Assessing Free-Living Youth

The purpose of this review is to integrate and summarize specific measurement topics (instrument and metric choice, validity, reliability, how many and what types of days, reactivity, and data treatment) appropriate to the study of youth physical activity. Research quality pedometers are necessary to aid interpretation of steps per day collected in a range of young populations under a variety of circumstances. Steps per day is the most appropriate metric choice, but steps per minute can be used to interpret time-in-intensity in specifically delimited time periods (e.g., physical education class). Reported intraclass correlations (ICC) have ranged from .65 over 2 days (although higher values also have been reported for 2 days) to .87 over 8 days (although higher values have been reported for fewer days). Reported ICCs are lower on weekend days (.59) versus weekdays (.75) and lower over vacation days (.69) versus school days (.74). There is no objective evidence of reactivity at this time. Data treatment includes (a) identifying and addressing missing values, (b) identifying outliers and reducing data appropriately if necessary, and (c) transforming the data as required in preparation for inferential analysis. As more pedometry studies in young populations are published, these preliminary methodological recommendations should be modified and refined.

Accelerometer-based physical activity: total volume per day and standardized measures.

The use of accelerometers in physical activity (PA) research has increased exponentially over the past 20 yr. The first commercially available accelerometer for assessing PA, the Caltrac, was worn on the waist and estimated PA energy expenditure in kilocalories. Around 1995, the emphasis shifted to measuring minutes of moderate-to-vigorous PA (MVPA), especially for bouts of 10 min or longer. Recent studies, however, show that light-intensity PA and intermittent (nonbout) MVPA also have important health benefits. The total volume of PA performed is an important variable because it takes the frequency, intensity, and duration of activity bouts and condenses them down into a single metric. The total volume of PA is appropriate for many research applications and can enhance comparisons between studies. In the future, machine learning algorithms will provide improved accuracy for activity type recognition and estimation of PA energy expenditure. However, in the current landscape of objectively measured PA, total activity counts per day (TAC/d) is a proxy for the total volume of PA. TAC/d percentiles for age- and gender-specific groups have been developed from the National Health and Nutrition Examination Survey ActiGraph data (2003-2006), providing a novel way to assess PA. The use of TAC/d or standardized units of acceleration could harmonize PA across studies. TAC/d should be viewed as an additional metric, not intended to replace other metrics (e.g., sedentary time, light-intensity PA, moderate PA, and vigorous PA) that may also be related to health. As future refinements to wearable monitors occur, researchers should continue to consider metrics that reflect the total volume of PA in addition to existing PA metrics.

Objective monitoring of physical activity in children: considerations for instrument selection.

There has been a rapid recent increase in both the number and type of objective physical activity (PA) assessment instruments which are commercially available to researchers, practitioners, and consumers. Although this has provided improved capacity for PA assessment, it also presents a somewhat bewildering range of options related to instrument selection for users of these technologies. The purpose of this review is to provide a primer to guide selection of instruments for the objective monitoring of childrens PA. In an effort to inform without overwhelming, it is not intended to be exhaustive in terms of all available instruments. A general overview is provided of two primary categories of objective monitors: pedometers and accelerometers. Within each category we focus on distinctly relevant options and features important to consider during instrument selection. In general, the desired outcome measure will determine the specific instrument category, options, and features from which the ultimate instrument choice is made. Other considerations include evidence of validity and reliability, cost, computer interface and download options, memory capacity, data aggregation and storage methods, and general participant and researcher burden associated with instrument use. There is no single objective PA assessment instrument that is appropriate for all situations, populations, and research questions. Further, we can anticipate that the commercial nature of these instruments will drive an even greater range of features and options in the future, increasing both the opportunity and the challenge for objectively assessing PA in children.