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Keith P. Gennuso, Charles E. Matthews, and Lisa H. Colbert

Background:

The purpose of this study was to examine the reliability and validity of 2 currently available physical activity surveys for assessing time spent in sedentary behavior (SB) in older adults.

Methods:

Fifty-eight adults (≥65 years) completed the Yale Physical Activity Survey for Older Adults (YPAS) and Community Health Activities Model Program for Seniors (CHAMPS) before and after a 10-day period during which they wore an ActiGraph accelerometer (ACC). Intraclass correlation coefficients (ICC) examined test-retest reliability. Overall percent agreement and a kappa statistic examined YPAS validity. Lin’s concordance correlation, Pearson correlation, and Bland-Altman analysis examined CHAMPS validity.

Results:

Both surveys had moderate test-retest reliability (ICC: YPAS = 0.59 (P < .001), CHAMPS = 0.64 (P < .001)) and significantly underestimated SB time. Agreement between YPAS and ACC was low (κ = −0.0003); however, there was a linear increase (P < .01) in ACC-derived SB time across YPAS response categories. There was poor agreement between ACC-derived SB and CHAMPS (Lin’s r = .005; 95% CI, −0.010 to 0.020), and no linear trend across CHAMPS quartiles (P = .53).

Conclusions:

Neither of the surveys should be used as the sole measure of SB in a study; though the YPAS has the ability to rank individuals, providing it with some merit for use in correlational SB research.

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Barbara E. Ainsworth, Carl J. Caspersen, Charles E. Matthews, Louise C. Mâsse, Tom Baranowski, and Weimo Zhu

Context:

Assessment of physical activity using self-report has the potential for measurement error that can lead to incorrect inferences about physical activity behaviors and bias study results.

Objective:

To provide recommendations to improve the accuracy of physical activity derived from self report.

Process:

We provide an overview of presentations and a compilation of perspectives shared by the authors of this paper and workgroup members.

Findings:

We identified a conceptual framework for reducing errors using physical activity self-report questionnaires. The framework identifies 6 steps to reduce error: 1) identifying the need to measure physical activity, 2) selecting an instrument, 3) collecting data, 4) analyzing data, 5) developing a summary score, and 6) interpreting data. Underlying the first 4 steps are behavioral parameters of type, intensity, frequency, and duration of physical activities performed, activity domains, and the location where activities are performed. We identified ways to reduce measurement error at each step and made recommendations for practitioners, researchers, and organizational units to reduce error in questionnaire assessment of physical activity.

Conclusions:

Self-report measures of physical activity have a prominent role in research and practice settings. Measurement error may be reduced by applying the framework discussed in this paper.

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Lisa H. Colbert, Charles E. Matthews, Dale A. Schoeller, Thomas C. Havighurst, and KyungMann Kim

This study examined the intensity of activity contributing to physical activity energy expenditure in older adults. In 57 men and women aged ≥ 65, total energy expenditure (TEE) was measured using doubly labeled water and resting metabolic rate was measured using indirect calorimetry to calculate a physical activity index (PAI). Sedentary time and physical activity of light and moderate to vigorous (mod/vig) intensity was measured using an accelerometer. The subjects were 75 ± 7 yrs (mean ± SD) of age and 79% female. Subjects spent 66 ± 8, 25 ± 5, and 9 ± 4% of monitor wear time in sedentary, light, and mod/vig activity per day, respectively. In a mixture regression model, both light (β = 29.6 [15.6–43.6, 95% CI]), p < .001) and mod/vig intensity activity (β = 28.7 [7.4−50.0, 95% CI]), p = .01) were strongly associated with PAI, suggesting that both light and mod/vig intensity activities are major determinants of their physical activity energy expenditure.

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Kathleen Y. Wolin, Daniel P. Heil, Sandy Askew, Charles E. Matthews, and Gary G. Bennett

Background:

The International Physical Activity Questionnaire-Short Form (IPAQ-S) has been evaluated against accelerometer-determined physical activity measures in small homogenous samples of adults in the United States. There is limited information about the validity of the IPAQ-S in diverse US samples.

Methods:

142 Blacks residing in low-income housing completed the IPAQ-S and wore an accelerometer for up to 6 days. Both 1- and 10-minute accelerometer bouts were used to define time spent in light, moderate, and vigorous physical activity.

Results:

We found fair agreement between the IPAQ-S and accelerometer-determined physical activity (r = .26 for 10-minute bout, r = .36 for 1-minute bout). Correlations were higher among men than women. When we classified participants as meeting physical activity recommendations, agreement was low (kappa = .04, 10-minute; kappa = .21, 1-minute); only 25% of individuals were classified the same by both instruments (10-minute bout).

Conclusions:

In one of the few studies to assess the validity of a self-reported physical activity measure among Blacks, we found moderate correlations with accelerometer data, though correlations were weaker for women. Correlations were smaller when IPAQ-S data were compared using a 10- versus a 1-minute bout definition. There was limited evidence for agreement between the instruments when classifying participants as meeting physical activity recommendations.

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Sarah Kozey Keadle, Shirley Bluethmann, Charles E. Matthews, Barry I. Graubard, and Frank M. Perna

Background:

This paper tested whether a physical activity index (PAI) that integrates PA-related behaviors (ie, moderate-to-vigorous physical activity [MVPA] and TV viewing) and performance measures (ie, cardiorespiratory fitness and muscle strength) improves prediction of health status.

Methods:

Participants were a nationally representative sample of US adults from 2011 to 2012 NHANES. Dependent variables (self-reported health status, multimorbidity, functional limitations, and metabolic syndrome) were dichotomized. Wald-F tests tested whether the model with all PAI components had statistically significantly higher area under the curve (AUC) values than the models with behavior or performance scores alone, adjusting for covariates and complex survey design.

Results:

The AUC (95% CI) for PAI in relation to health status was 0.72 (0.68, 0.76), and PAI-AUC for multimorbidity was 0.72 (0.69, 0.75), which were significantly higher than the behavior or performance scores alone. For functional limitations, the PAI AUC was 0.71 (0.67, 0.74), significantly higher than performance, but not behavior scores, while the PAI AUC for metabolic syndrome was 0.69 (0.66, 0.73), higher than behavior but not performance scores.

Conclusions:

These results provide empirical support that an integrated PAI may improve prediction of health and disease. Future research should examine the clinical utility of a PAI and verify these findings in prospective studies.

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Maciej S. Buchowski, Leena Choi, Karen M. Majchrzak, Sari Acra, Charles E. Matthews, and Kong Y. Chen

Background:

Environmental factors including seasonal changes are important to guide physical activity (PA) programs to achieve or sustain weight loss. The goal was to determine seasonal variability in the amount and patterns of free-living PA in women.

Methods:

PA was measured in 57 healthy women from metropolitan Nashville, TN, and surrounding counties (age: 20 to 54 years, body mass index: 17 to 48 kg/m2) using an accelerometer for 7 consecutive days during 3 seasons within 1 year. PA counts and energy expenditure (EE) were measured in a whole-room indirect calorimeter and used to model accelerometer output and to calculate daily EE and intensity of PA expressed as metabolic equivalents (METs).

Results:

PA was lower in winter than in summer (131 ± 45 vs. 144 ± 54 × 103 counts/d; P = .025) and in spring/fall (143 ± 48 × 103 counts/d; P = .027). On weekends, PA was lower in winter than in summer by 22,652 counts/d (P = .008). In winter, women spent more time in sedentary activities than in summer (difference 35 min/d; P = .007) and less time in light activities (difference −29 min/d, P = .018) and moderate or vigorous activities (difference −6 min/d, P = .051).

Conclusions:

Women living in the southeastern United States had lower PA levels in winter compared with summer and spring/fall, and the magnitude of this effect was greater on weekends than weekdays.

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Paul R. Hibbing, Nicholas R. Lamoureux, Charles E. Matthews, and Gregory J. Welk

Physical behavior can be assessed using a range of competing methods. The Free-Living Activity Study for Health (FLASH) is an ongoing study that facilitates the comparison of such methods. The purpose of this report is to describe the FLASH, with a particular emphasis on a subsample of participants who have consented to have their deidentified data released in a shared repository. Participants in the FLASH wear seven physical activity monitors for a 24-hr period and then complete a detailed recall using the Activities Completed Over Time in 24-hr online assessment tool. The participants can optionally agree to be video recorded for 30–60 min, which allows for direct observation as a criterion indicator of their behavior during that period. As of version 0.1.0, the repository includes data from 38 participants, and the sample size will grow as data are collected, processed, and released in future versions. The repository makes it possible to combine sensor data (e.g., from ActiGraph and SenseWear) with minute-by-minute contextual data (from the Activities Completed Over Time in 24-hr recall system), which enables the FLASH to generate benchmark data for a wide range of future research. The repository itself provides an example of how a powerful open-source tool (GitHub) can be used to share data and code in a way that encourages communication and collaboration among a variety of scientists (e.g., algorithm developers and end users). The FLASH data set will provide long-term benefits to researchers interested in advancing the science of physical behavior monitoring.

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Miguel A. Calabro, Gregory J. Welk, Alicia L. Carriquiry, Sarah M. Nusser, Nicholas K. Beyler, and Charles E. Matthews

Purpose:

The purpose of this study was to examine the validity of a computerized 24-hour physical activity recall instrument (24PAR).

Methods:

Participants (n = 20) wore 2 pattern-recognition activity monitors (an IDEEA and a SenseWear Pro Armband) for a 24-hour period and then completed the 24PAR the following morning. Participants completed 2 trials, 1 while maintaining a prospective diary of their activities and 1 without a diary. The trials were counterbalanced and completed within a week from each other. Estimates of energy expenditure (EE) and minutes of moderate-to-vigorous physical activity (MVPA) were compared with the criterion measures using 3-way (method by gender by trial) mixed-model ANOVA analyses.

Results:

For EE, pairwise correlations were high (r > .88), and there were no differences in estimates across methods. Estimates of MVPA were more variable, but correlations were still in the moderate to high range (r > .57). Average activity levels were significantly higher on the logging trial, but there was no significant difference in the accuracy of self-report on days with and without logging.

Conclusions:

The results of this study support the overall utility of the 24PAR for group-level estimates of daily EE and MVPA.

Open access

Emily N. Ussery, Geoffrey P. Whitfield, Janet E. Fulton, Deborah A. Galuska, Charles E. Matthews, Peter T. Katzmarzyk, and Susan A. Carlson

Background: High levels of sedentary behavior and physical inactivity increase the risk of premature mortality and several chronic diseases. Monitoring national trends and correlates of sedentary behavior and physical inactivity can help identify patterns of risk in the population over time. Methods: The authors used self-reported data from the National Health and Nutrition Examination Surveys (2007/2008–2017/2018) to estimate trends in US adults’ mean daily sitting time, overall, and stratified by levels of leisure-time and multidomain physical activity, and in the joint prevalence of high sitting time (>8 h/d) and physical inactivity. Trends were tested using orthogonal polynomial contrasts. Results: Overall, mean daily sitting time increased by 19 minutes from 2007/2008 (332 min/d) to 2017/2018 (351 min/d) (P linear < .05; P quadratic < .05). The highest point estimate occurred in 2013/2014 (426 min/d), with a decreasing trend observed after this point (P linear < .05). Similar trends were observed across physical activity levels and domains, with one exception: an overall linear increase was not observed among sufficiently active adults. The mean daily sitting time was lowest among highly active adults compared with less active adults when using the multidomain physical activity measure. Conclusions: Sitting time among adults increased over the study period but decreased in recent years.

Open access

Pedro F. Saint-Maurice, David Berrigan, Geoffrey P. Whitfield, Kathleen B. Watson, Shreya Patel, Erikka Loftfield, Joshua N. Sampson, Janet E. Fulton, and Charles E. Matthews

Background: Surveillance of domain-specific physical activity in the United States is lacking. Thus, the authors describe domain-specific moderate to vigorous physical activity (MVPA) in a nationwide sample of US adults. Methods: Participants from the AmeriSpeak panel (n = 2649; 20–75 y; 50% female) completed the Activities Completed Over Time in 24-Hours previous-day recall. The authors estimated average MVPA duration (in hours per day) overall and in major life domains by sex, age, race/ethnicity, and education. They also described the most commonly reported MVPAs and timing of MVPA during the day. Results: Across all life domains, participants reported an average of 2.5 hours per day in MVPA. Most MVPA was accumulated during work (50% of total, 1.2 h/d) and household activities (28%, 0.7 h/d) with less MVPA reported in leisure time (15%, 0.4 h/d). Time reported in MVPA varied by sex, and race/ethnicity (P < .05). Walking at work and for exercise, childcare, and walking for transportation were the most commonly reported domain-specific MVPAs. A greater proportion of MVPA took place in the morning (∼06:00 h) and evening (∼18:00 h). Conclusions: Work and household activities accounted for 78% of overall MVPA reported, while leisure-time MVPA accounted for only 15% of the total. Encouraging MVPA during leisure time and transportation remain important targets for promoting MVPA in US adults.