Rethinking Data Collection Methods During the Pandemic: Development and Implementation of CATI for the All of Us Research Program.

The All of Us Research Program is a longitudinal cohort study aiming to build a diverse database to advance precision medicine. The COVID-19 pandemic hindered the ability of participants to receive in-person assistance at enrollment sites to complete digital surveys. Therefore, the program implemented Computer-Assisted Telephone Interviewing (CATI) to facilitate survey completion remotely to combat the disrupted data collection procedures. In January 2021, All of Us implemented a 1-year CATI Pilot supporting 9399 participants and resulting in 16 337 submitted surveys. The pilot showed that CATI was successful in increasing survey completion and retention activities for the All of Us Research Program, given the additional remote support offered to participants. Given the success of the CATI Pilot, multimodal survey administration will continue.

Use of population-referenced total activity counts percentiles to assess and classify physical activity of population groups.

OBJECTIVES: Population-referenced total activity counts per day (TAC/d) percentiles provide public health practitioners a standardized measure of physical activity (PA) volume obtained from an accelerometer that can be compared across populations. The purpose of this study was to describe the application of TAC/d population-referenced percentiles to characterize the PA levels of population groups relative to US estimates. METHODS: A total of 679 adults participating in the 2011 NYC Physical Activity Transit survey wore an ActiGraph accelerometer on their hip for seven consecutive days. Accelerometer-derived TAC/d was classified into age- and gender-specific quartiles of US population-referenced TAC/d to compare differences in the distributions by borough (N=5). RESULTS: Males in Brooklyn, Manhattan, and Staten Island had significantly greater TAC/d than US males. Females in Brooklyn and Queens had significantly greater levels of TAC/d compared to US females. The proportion of males in each population-referenced TAC/d quartile varied significantly by borough (χ(2)(12)=2.63, p=0.002), with disproportionately more men in Manhattan and the Bronx found to be in the highest and lowest US population-referenced TAC/d quartiles, respectively. For females, there was no significant difference in US population-reference TAC/d quartile by borough (χ(2)(12)=1.09, p=0.36). CONCLUSIONS: These results demonstrate the utility of population-referenced TAC/d percentiles in public health monitoring and surveillance. These findings also provide insights into the PA levels of NYC residents relative to the broader US population, which can be used to guide health promotion efforts.

Associations between physical activity, sedentary time, sleep duration and daytime sleepiness in US adults.

OBJECTIVE: To examine the associations between objectively measured physical activity (PA) or sedentary behavior and self-reported sleep duration or daytime sleepiness in a nationally representative sample of healthy US adults (N=2128). METHODS: We report analyses of four aspects of sedentary behavior and PA derived from accelerometry data (minutes of sedentary time, activity counts/minute, Minutes of Moderate and Vigorous PA [MVPA], and MVPA in 10-minute bouts) versus self-report of sleep duration and frequency of daytime sleepiness from the 2005-2006 National Health and Nutrition Examination Survey. RESULTS: Age and sex dependence of associations between PA and sleep were observed. Aspects of PA were significantly lower in adults reporting more frequent daytime sleepiness in younger (20-39) and older (≥ 60) age groups, but not in middle-aged (40-59), respondents. In younger respondents, PA increased with sleep duration, but in middle aged and older respondents PA was either unrelated to sleep duration or lower in those reporting ≥ 8 h of sleep. Objectively measured sedentary time showed limited evidence of associations with sleep duration. CONCLUSIONS: Further research delineating the relationships between sleep and PA is important because both activities have been implicated in diverse health outcomes as well as in the etiology of obesity.

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.

Reliability and convergent validity of the past-week Modifiable Activity Questionnaire.

OBJECTIVE: To examine the reliability and convergent validity of physical activity (PA) and inactivity estimates obtained with the past-week Modifiable Activity Questionnaire (PWMAQ). DESIGN: The PWMAQ, an interviewer-administered questionnaire, was administered twice, one week apart, during visits 3 and 4 of six total visits. Intra-class correlation coefficients (ICC) between administrations of the PWMAQ were used to assess the reliability of summary estimates. Spearman rank-order correlation coefficients (ρ) were used to examine the associations of PWMAQ summary estimates with temporally matched and averaged accelerometer data in all participants and then stratified by whether the data were reflective of usual PA. SETTING: Data were obtained from the Evaluation of Physical Activity Measures in Middle-Aged Women (PAW) study. SUBJECTS: Sixty-six women, mean age 52·6 (sd 5·4) years. RESULTS: The reliability of the PWMAQ physical inactivity estimate suggested substantial agreement over one week (ICC = 0·77, 95 % CI 0·57, 0·82; P < 0·0001). With the exception of light-intensity PA, the PWMAQ leisure PA estimate was significantly associated with averaged accelerometer data (ρ = 0·33-0·76; P < 0·05). For both temporally matched and averaged accelerometer data, correlation coefficients were higher between the PWMAQ estimate and moderate-walk- to vigorous-intensity PA in those who indicated that reported activity was reflective of usual PA; however, the association with moderate-lifestyle-intensity PA was higher in those reporting that data were not reflective. CONCLUSIONS: The PWMAQ is a reliable and valid measure of leisure PA levels in middle-aged women and supports subsequent studies evaluating this questionnaire in other population subgroups.

Issues in accelerometer methodology: the role of epoch length on estimates of physical activity and relationships with health outcomes in overweight, post-menopausal women.

BACKGROUND: Current accelerometer technology allows for data collection using brief time sampling intervals (i.e., epochs). The study aims were to examine the role of epoch length on physical activity estimates and subsequent relationships with clinically-meaningful health outcomes in post-menopausal women. METHODS: Data was obtained from the Woman On the Move through Activity and Nutrition Study (n = 102). Differences in activity estimates presented as 60s and 10s epochs were evaluated using paired t-tests. Relationships with health outcomes were examined using correlational and regression analyses to evaluate differences by epoch length. RESULTS: Inactivity, moderate- and vigorous-intensity activity (MVPA) were significantly higher and light-intensity activity was significantly lower (all P < 0.001) when presented as 10s epochs. The correlation between inactivity and self-reported physical activity was stronger with 10s estimates (P < 0.03); however, the regression slopes were not significantly different. Conversely, relationships between MVPA and body weight, BMI, whole body and trunk lean and fat mass, and femoral neck bone mineral density was stronger with 60s estimates (all P < 0.05); however, regression slopes were similar. CONCLUSION: These findings suggest that although the use of a shorter time sampling interval may suggestively reduce misclassification error of physical activity estimates, associations with health outcomes did not yield strikingly different results. Additional studies are needed to further our understanding of the ways in which epoch length contributes to the ascertainment of physical activity in research studies. TRIAL REGISTRATION: Clinical Trials Identifier: NCT00023543.

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 children's 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.

Campus walkability, pedometer-determined steps, and moderate-to-vigorous physical activity: a comparison of 2 university campuses.

OBJECTIVE AND PARTICIPANTS: At 2 Arizona State University (ASU) campuses, the authors measured student activity and distance walked on campus, as well as student-reported walkability around the student union. METHODS: Students from ASU-Polytechnic (n = 20, 33% male) and ASU-Tempe (n = 20, 60% male) recorded distance walked on campus and wore physical activity monitors for 5 days. RESULTS: Polytechnic students spent an average of 36.9 minutes in moderate-to-vigorous physical activity each week; Tempe students spent 69.5 minutes (p < .001). At Polytechnic, students walked an average of 7,674 steps per weekday; at Tempe, 11,294 steps (p = .003). Female students at Polytechnic walked an average of 1.3 km/d; at Tempe, 4.3 km/d (p < .001). At Polytechnic, men walked an average of 1.4 km/d; at Tempe, 3.1 km/d (p = .03). Tempe students rated campus walk-ability as very good, whereas Polytechnic students rated it fair (p < .001). CONCLUSIONS: Students at both campuses met activity recommendations; noted differences may be attributed to the built environment's contribution to walkability.

Actigraph accelerometer interinstrument reliability during free-living in adults.

UNLABELLED: The Actigraph (AG; model 7164 v. 2.2) accelerometer is widely accepted as a valid means of physical activity (PA) assessment. However, no studies have investigated interinstrument reliability for data collection in a free-living condition. PURPOSE: To determine agreement (on raw outputs and derived variables) between AG accelerometers worn concurrently on the right hip (RH) and left hip (LH) during 24 h of free-living. METHODS: Ten distinct pairings of AG were evaluated on 10 participants (four males, six females; age=30.1+/-3.8 yr). AG were worn during waking hours (other than water activities). Raw outputs were activity counts and steps. Derived variables were time (minutes) in sedentary, light, moderate, vigorous, and MVPA (moderate+vigorous) intensity using published cut points. Intraclass correlations (ICC), absolute percent error (APE), coefficient of variation (CV), and paired t-tests were evaluated between accelerometers. RESULTS: Raw variables of total activity counts and steps, and derived variables of time in sedentary, light, vigorous, and MVPA intensity displayed high interinstrument reliability. The ICC, APE, and CV values for moderate intensity were 0.98, 18.8%, and 13.5% and, for MVPA, 0.99, 3.7%, and 4.9%, respectively. Only MVPA displayed a statistically significant mean difference (2 min) between RH versus LH AG, but the effect size was small (ES=0.07). CONCLUSION: Results display evidence of decreased interinstrument reliability of moderate-intensity time assessed during free-living. If separate moderate and vigorous categories are not necessary for specific research questions, MVPA may be the optimal intensity range to report, at least in terms of interinstrument reliability.

Comparison of Kenz Lifecorder EX and ActiGraph accelerometers in 10-yr-old children.

UNLABELLED: A new accelerometer, the Kenz Lifecorder EX (LC; Suzuken Co. Ltd, Nagoya, Japan), offers promise as a feasible monitor alternative to the commonly used Actigraph (AG: Actigraph LLC, Fort Walton Beach, FL). PURPOSE: This study compared the LC and AG accelerometers and the Yamax SW-200 pedometer (DW) under free-living conditions with regard to children's steps taken and time in light-intensity physical activity (PA) and moderate to vigorous PA (MVPA). METHODS: Participants (N=31, age=10.2 +/- 0.4 yr) wore LC, AG, and DW monitors from arrival at school (7:45 a.m.) until they went to bed. Time in light and MVPA intensities were calculated using two separate intensity classifications for the LC (LC_4 and LC_5) and four classifications for the AG (AG_Treuth, AG_Puyau, AG_Trost, and AG_Freedson). Both accelerometers provided steps as outputs. DW steps were self-recorded. Repeated-measures ANOVA was used to assess overlapping monitor outputs. RESULTS: There was no difference between DW and LC steps (Delta=200 steps), but a nonsignificant trend was observed in the pairwise comparison between DW and AG steps (Delta=1001 steps, P=0.058). AG detected significantly greater steps than the LC (Delta=801 steps, P=0.001). Estimates of light-intensity activity minutes ranged from a low of 75.6 +/- 18.4 min (LC_4) to a high of 309 +/- 69.2 min (AG_Treuth). Estimates of MVPA minutes ranged from a low of 25.9 +/- 9.4 min (LC_5) to a high of 112.2 +/- 34.5 min (AG_Freedson). No significant differences in MVPA were seen between LC_5 and AG_Treuth (Delta=4.9 min) or AG_Puyau (Delta=1.7 min). CONCLUSION: The LC detected a comparable number of steps as the DW but significantly fewer steps than the AG in children. Current results indicate that the LC_5 and either AG_Treuth or AG_Puyau intensity derivations provide similar mean estimates of time in MVPA during-free living activity in 10-yr-old children.

Number of accelerometer monitoring days needed for stable group-level estimates of activity.

To determine the number and distribution of days required to produce stable group-level estimates of a 7 d mean for common accelerometer-derived activity measures. Data from the 2003-2006 NHANES were used in this analysis. The sample included 986 youth (6-19 year) and 2532 adults (⩾20 year) with 7 d of ⩾10 h of wear. Accelerometer measures included minutes of inactive, light physical activity, moderate-to-vigorous physical activity (MVPA); and total activity counts/d. Twenty-five alternative protocols were bootstrapped with 50 000 samples drawn for each protocol. Alternative protocols included: 1-6 random days, Saturday plus 1-5 random weekdays (WD), Sunday plus 1-5 random WD, 1 random weekend day (WE) plus 1-5 WD, and both WE plus 1-4 random WD. Relative difference was calculated between the 7 d mean and alternative protocol mean (((alternative protocol mean - 7 d mean)/7 d mean) (*) 100). Adult MVPA is used as an example; however, similar trends were observed across age groups and variables except adult inactive time, which was stable across protocols. The 7 d mean for adult MVPA was 44.1(0.9) min d(-1). The mean bias for any 1-6 random days ranged from -0.0(0.3) to 0.0(0.2) min d(-1) with a relative difference of -0.1 to 0.0%. For protocols with non-random components, bias ranged from -1.4(0.2) to 0.6(0.1) min d(-1) with relative difference ranging from -7.2 to 3.1%. Simulation data suggest that stable estimates of group-level means can be obtained from as few as one randomly selected monitoring day from a sampled week. On the other hand, estimates using non-random selection of weekend days may be significantly biased. Purposeful sampling that disproportionally forces inclusion of weekend data in analyses should be discouraged.

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.

Ability of thigh-worn ActiGraph and activPAL monitors to classify posture and motion.

PURPOSE: This study compared sitting, standing, and stepping classifications from thigh-worn ActiGraph and activPAL monitors under laboratory and free-living conditions. METHODS: Adults wore both monitors on the right thigh while performing activities (six sitting, two standing, nine stepping, and one cycling) and writing on a whiteboard with intermittent stepping under laboratory conditions (n = 21) and under free-living conditions for 3 d (n = 18). Percent time correctly classified was calculated under laboratory conditions. Between-monitor agreement and weighted κ were calculated under free-living conditions. RESULTS: In the laboratory, both monitors correctly classified 100% of standing time and >95% of the time spent in four of six sitting postures. Both monitors demonstrated misclassification of laboratory stool sitting time (ActiGraph 14% vs. activPAL 95%). ActivPAL misclassified 14% of the time spent sitting with legs outstretched; ActiGraph was 100% accurate. Monitors were >95% accurate for stepping, although ActiGraph was less so for descending stairs (86%), ascending stairs (92%), and running at 2.91 m·s(-1) (93%). Monitors classified whiteboard writing differently (ActiGraph 83% standing/15% stepping vs. activPAL 98% standing/2% stepping). ActivPAL classified 93% of cycling time as stepping, whereas ActiGraph classified <1% of cycling time as stepping. During free-living wear, monitors had substantial agreement (86% observed; weighted κ = 0.77). Monitors classified similar amounts of time as sitting (ActiGraph 64% vs. activPAL 62%). There were differences in time recorded as standing (ActiGraph 21% vs. activPAL 27%) and stepping (ActiGraph 15% vs. activPAL 11%). CONCLUSIONS: Differences in data processing algorithms may have resulted in the observed disagreement in posture and activity classification between thigh-worn ActiGraph and activPAL. Despite between-monitor agreement in classifying sitting time under free-living conditions, ActiGraph appears to be more sensitive to free-living upright walking motions than activPAL.

Effect of temporal domain on self-reported walking behaviors in the California Health Interview Survey.

BACKGROUND: Question design can influence the validity and reliability of physical activity (PA) self-report instruments. This study assesses the effect of temporal domain ("days" walked versus "times" walked) on survey questions about walking behavior. METHODS: A 2005 California Health Interview Survey (CHIS) subsample (n = 6332) reported the number of days or times they walked for leisure or transportation in the past 7 days and the usual time spent per day or per time. Question order was randomized by temporal domain. Minutes walked per week (mean ± SE) and adherence to PA guidelines (≥150 min/wk) were assessed. RESULTS: Estimates of leisure walking remained stable across temporal domain (days = 71.4 ± 2.5 min; times = 73.4 ± 2.4 min), but transportation walking differed depending on domain (days = 70.4 ± 3.2 min; times = 52.5 ± 2.6 min). Adherence to PA guidelines based on leisure walking was stable across temporal domain (days = 14.9 ± 0.6%; times = 14.9 ± 0.6%), but again differed by domain for transportation walking (days = 10.4 ± 0.6%; times = 7.8 ± 0.5%). A large order effect (number-of-days versus number-of-times asked first) was observed for reports of days walking for transportation (days first = 87.8 ± 2.9 min; times first = 52.3 ± 2.5 min). CONCLUSION: Temporal domain influences estimates of self-reported transportation walking behavior. Current efforts to capture PA from both transportation and leisure activities in health research appear to present distinct methodological challenges.

Patterns of accelerometer-derived estimates of inactivity in middle-age women.

PURPOSE: The study's purpose was to characterize accelerometer-derived estimates of physical inactivity collected during five consecutive weeks in middle-age women. METHODS: Data were obtained from 63 participants (95.5%) enrolled in the Evaluation of Physical Activity Measures in Middle-Age Women Study. Inactive time (min · d(-1)) was estimated as the sum of activity counts <100, and inactive-to-active transitions were defined as an interruption in which a period of inactivity was immediately followed by a minute or more above 100 counts. A repeated-measures ANOVA using PROC MIXED (SAS/STAT software, v. 9.2) was used to describe hourly, daily, and weekly variation in estimates of physical inactivity. RESULTS: Participants were 52.7 ± 5.5 yr, 85.7% non-Hispanic white, and 63.5% postmenopausal, with a body mass index of 26.7 ± 5.1 kg · m(-2). Inactive time gradually increased as the day continued, particularly on weekend days. When compared with weekdays, average inactive time was lower on Saturday and Sunday (all P < 0.01 except for Saturday vs Monday, P < 0.10); Saturdays were not significantly different from Sundays. Breaks in inactive time were significantly lower on Sunday when compared with weekdays and Saturday (all P < 0.05), and fewer breaks were noted on Saturday when compared with Wednesday and Friday (both P < 0.01). After adjustment for total wear time or inactive time, most day-to-day differences were attenuated. Week-by-week differences in physical inactivity estimates were also not statistically significant. CONCLUSIONS: The results of this study suggest that inactive time increases as the day continues and that daily physical inactivity estimates are more stable after 1) adjustment for wear time or 2) when averaged over the week. Researchers should carefully consider the intended application of physical inactivity estimates before data collection and processing, analysis, and final data reporting.

Controlled and free-living evaluation of objective measures of sedentary and active behaviors.

BACKGROUND: Emerging interest in the health impacts of sedentary behaviors has driven the exploration of objective instrumentation capable of capturing these behaviors. The purpose was to compare (under laboratory conditions) outputs from ActiGraph (AG), Intelligent Device for Energy Expenditure and Physical Activity (IDEEA), and activPAL Professional (AP) against direct observation (DO) in sedentary, standing, and active behaviors; and assess convergent validity of instrument outputs under free-living conditions. METHODS: Participants (13 males/16 females; 28.9 ± 6.2 years) wore instruments concurrently during laboratory and free-living studies. AG cutpoints of ≤50, <100, and ≤259 counts/minute were used to determine time in sedentary behaviors. Laboratory data were evaluated using mean percent error. Free-living data were analyzed using dependent t tests and RM ANOVA. RESULTS: AP precisely measured all identified DO behaviors under laboratory conditions; IDEEA precisely identified sitting and standing. For the free-living study, there was no difference in sedentary time detected by AP and IDEEA but a significant difference was observed in standing time. No difference was apparent between AP and AG259 in sit/lie/stand or ambulatory activity time. CONCLUSIONS: In a laboratory setting, the utility of all instruments to classify activities into behavioral categories was confirmed. This may enhance research on sedentary behaviors and health-related outcomes.

Pedometry methods for assessing free-living adults.

BACKGROUND: The purpose of this review is to update the methodological aspects of pedometry to encourage the consistent use of pedometers for assessment, to decrease sources of error, and to facilitate comparison and interpretation of results. METHODS: The specific measurement topics addressed include: instrument choice, metric choice, validity, reliability, data collection and retrieval, time worn, day-to-day variability, monitoring time frame, reactivity, and data treatment. RESULTS: A wide variety of valid and reliable instruments are commercially available and we can expect continued evolutions in value-added features as supporting technology improves. Data collection and retrieval has been achieved through various methods, including face-to-face contact, fax, e-mail, website, and conventional mail, and sometimes a combination of these. Day-to-day variation is not random, as would be expected from inconsistent pedometer performance, but rather exposes true behavior instability that can be explained by other factors and described using a coefficient of variation. Data reduction should be conducted cautiously and only after a full discovery (and disclosure) of its impact on aggregated group statistics and their relationship with other parameters. CONCLUSIONS: We have no doubt that research with pedometers will continue to yield new and important insights in the coming years.

Evaluation of low-cost, objective instruments for assessing physical activity in 10-11-year-old children.

This study compared step counts detected by four, low-cost, objective, physical-activity-assessment instruments and evaluated their ability to detect moderate-to-vigorous physical activity (MVPA) compared to the ActiGraph accelerometer (AG). Thirty-six 10-11-year-old children wore the NL-1000, Yamax Digiwalker SW 200, Omron HJ-151, and Walk4Life MVP concurrently with the AG during school hours on a single day. AG MVPA was derived from activity count data using previously validated cut points. Two of the evaluated instruments provided similar group mean MVPA and step counts compared to AG (dependent on cut point). Low-cost instruments may be useful for measurement of both MVPA and steps in children's physical activity interventions and program evaluation.

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.