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Dori E. Rosenberg, Fiona C. Bull, Alison L. Marshall, James F. Sallis, and Adrian E. Bauman

Purpose:

This study explored definitions of sedentary behavior and examined the relationship between sitting time and physical inactivity using the sitting items from the International Physical Activity Questionnaire (IPAQ).

Methods:

Participants (N = 289, 44.6% male, mean age = 35.93) from 3 countries completed self-administered long- and short-IPAQ sitting items. Participants wore accelero-meters; were classified as inactive (no leisure-time activity), insufficiently active, or meeting recommendations; and were classified into tertiles of sitting behavior.

Results:

Reliability of sitting time was acceptable for men and women. Correlations between total sitting and accelerometer counts/min <100 were significant for both long (r = .33) and short (r = .34) forms. There was no agreement between tertiles of sitting and the inactivity category (kappa = .02, P = .68).

Conclusion:

Sedentary behavior should be explicitly measured in population surveillance and research instead of being defined by lack of physical activity.

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Dori E. Rosenberg, Gregory J. Norman, Nicole Wagner, Kevin Patrick, Karen J. Calfas, and James F. Sallis

Background:

Sedentary behavior is related to obesity, but measures of sedentary behaviors are lacking for adults. The purpose of this study was to examine the reliability and validity of the Sedentary Behavior Questionnaire (SBQ) among overweight adults.

Methods:

Participants were 49 adults for the 2 week test-retest reliability study (67% female, 53% white, mean age = 20) and 401 overweight women (mean age = 41, 61% white) and 441 overweight men (mean age = 44, 81% white) for the validity study. The SBQ consisted of reports of time spent in 9 sedentary behaviors. Outcomes for validity included accelerometer measured inactivity, sitting time (International Physical Activity Questionnaire), and BMI. Intraclass correlation coefficients (ICCs) assessed reliability and partial correlations assessed validity.

Results:

ICCs were acceptable for all items and the total scale (range = .51–.93). For men, there were significant relationships of SBQ items with IPAQ sitting time and BMI. For women, there were relationships between the SBQ and accelerometer inactivity minutes, IPAQ sitting time, and BMI.

Conclusions:

The SBQ has acceptable measurement properties for use among overweight adults. Specific measures of sedentary behavior should be included in studies and population surveillance.

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Dori E. Rosenberg, Jacqueline Kerr, James F. Sallis, Gregory J. Norman, Karen Calfas, and Kevin Patrick

The authors tested the feasibility and acceptability, and explored the outcomes, of 2 walking interventions based on ecological models among older adults living in retirement communities. An enhanced intervention (EI) was compared with a standard walking intervention (SI) among residents in 4 retirement facilities (N = 87 at baseline; mean age = 84.1 yr). All participants received a walking intervention including pedometers, printed materials, and biweekly group sessions. EI participants also received phone counseling and environmental-awareness components. Measures included pedometer step counts, activities of daily living, environment-related variables, physical function, depression, cognitive function, satisfaction, and adherence. Results indicated improvements among the total sample for step counts, neighborhood barriers, cognitive function, and satisfaction with walking opportunities. Satisfaction and adherence were high. Both walking interventions were feasible to implement among facility-dwelling older adults. Future studies can build on this multilevel approach.

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Rachel A. Millstein, Katherine D. Hoerster, Dori E. Rosenberg, Karin M. Nelson, Gayle Reiber, and Brian E. Saelens

Background:

Sedentary behavior is an increasingly recognized health risk factor, independent of physical activity. Although several correlates of sedentary behavior are known, little research has identified them among U.S. veterans, a population that faces disproportionate chronic disease burden.

Methods:

A survey was mailed to 1997 randomly selected veterans at a large urban Veterans Affairs medical center in 2012 and remailed in 2013 to nonresponders, resulting in a 40% response rate. We examined individual-, social-, and neighborhood-level factors in association with self-reported sitting time. Factors correlated with sitting time at P < .05 were included in a multiple linear regression model.

Results:

In the multivariate model, higher depression (B = 7.8), body mass index (B = 5.1), functional impairment (B = 4.2), and self-rated health (B = 68.5) were significantly associated with higher sitting time, and leisure time physical activity (B = –0.10) and being employed (B = –71.3) were significantly associated with lower sitting time.

Conclusions:

Individual-level, but not social- and neighborhood-level, variables were associated with sitting time in this population. This study identified individual-level targets for reducing sitting time and improving overall health among veterans.

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Basia Belza, Christina E. Miyawaki, Peg Allen, Diane K. King, David X. Marquez, Dina L. Jones, Sarah Janicek, Dori Rosenberg, and David R. Brown

Mall walking has been a popular physical activity for decades. However, little is known about why mall managers support these programs or why adults choose to walk. Our study aim was to describe mall walking programs from the perspectives of walkers, managers, and leaders. Twenty-eight walkers, 16 walking program managers, and six walking program leaders from five states participated in a telephone or in-person semi-structured interview (N = 50). Interview guides were developed using a social-ecological model. Interviews were recorded, transcribed verbatim, and analyzed thematically. All informants indicated satisfaction with their program and environmental features. Differences in expectations were noted in that walkers wanted a safe, clean, and social place whereas managers and leaders felt a need to provide programmatic features. Given the favorable walking environments in malls, there is an opportunity for public health professionals, health care organizations, and providers of aging services to partner with malls to promote walking.

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Mikael Anne Greenwood-Hickman, Rod Walker, John Bellettiere, Andrea Z. LaCroix, Boeun Kim, David Wing, KatieRose Richmire, Paul K. Crane, Eric B. Larson, and Dori E. Rosenberg

Neighborhood walkability has been associated with self-reported sedentary behavior (SB) and self-reported and objective physical activity. However, self-reported measures of SB are inaccurate and can lead to biased estimates, and few studies have examined how associations differ by gender and age. The authors examined the relationships between perceived neighborhood walkability measured with the Physical Activity Neighborhood Environment Scale (scored 1.0–4.0) and device-based SB and physical activity in a cohort of community-dwelling older adults (N = 1,077). The authors fit linear regression models adjusting for device wear time, demographics, self-rated health, and accounting for probability of participation. The Higher Physical Activity Neighborhood Environment Scale was associated with higher steps (+676 steps/point on the Physical Activity Neighborhood Environment Scale, p = .001) and sit-to-stand transitions (+2.4 transitions/point, p = .018). Though not statistically significant, stratified analyses suggest an attenuation of effect for those aged 85 years and older and for women. Consistent with previous literature, neighborhood walkability was associated with more steps, though not with physical activity time. The neighborhood environment may also influence SB.

Open access

Supun Nakandala, Marta M. Jankowska, Fatima Tuz-Zahra, John Bellettiere, Jordan A. Carlson, Andrea Z. LaCroix, Sheri J. Hartman, Dori E. Rosenberg, Jingjing Zou, Arun Kumar, and Loki Natarajan

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

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Dori E. Rosenberg, Melissa L. Anderson, Anne Renz, Theresa E. Matson, Amy K. Lee, Mikael Anne Greenwood-Hickman, David E. Arterburn, Paul A. Gardiner, Jacqueline Kerr, and Jennifer B. McClure

Background: The authors tested the efficacy of the “I-STAND” intervention for reducing sitting time, a novel and potentially health-promoting approach, in older adults with obesity. Methods: The authors recruited 60 people (mean age = 68 ± 4.9 years, 68% female, 86% White; mean body mass index = 35.4). The participants were randomized to receive the I-STAND sitting reduction intervention (n = 29) or healthy living control group (n = 31) for 12 weeks. At baseline and at 12 weeks, the participants wore activPAL devices to assess sitting time (primary outcome). Secondary outcomes included fasting glucose, blood pressure, and weight. Linear regression models assessed between-group differences in the outcomes. Results: The I-STAND participants significantly reduced their sitting time compared with the controls (–58 min per day; 95% confidence interval [–100.3, –15.6]; p = .007). There were no statistically significant changes in the secondary outcomes. Conclusion: I-STAND was efficacious in reducing sitting time, but not in changing health outcomes in older adults with obesity.

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Michael J. LaMonte, I-Min Lee, Eileen Rillamas-Sun, John Bellettiere, Kelly R. Evenson, David M. Buchner, Chongzhi Di, Cora E. Lewis, Dori E. Rosenberg, Marcia L. Stefanick, and Andrea Z. LaCroix

Background: Limited data are available regarding the correlation between questionnaire and device-measured physical activity (PA) and sedentary behavior (SB) in older women. Methods: We evaluated these correlations in 5,992 women, aged 63 and older, who completed the Women’s Health Initiative (WHI) and Community Healthy Activities Model Program for Seniors (CHAMPS) PA questionnaires and the CARDIA SB questionnaire prior to wearing a hip-worn accelerometer for 7 consecutive days. Accelerometer-measured total, light, and moderate-to-vigorous PA (MVPA), and total SB time were defined according to cutpoints established in a calibration study. Spearman coefficients were used to evaluate correlations between questionnaire and device measures. Results: Mean time spent in PA and SB was lower for questionnaire than accelerometer measures, with variation in means according to age, race/ethnicity, body mass index, and functional status. Overall, correlations between questionnaires and accelerometer measures were moderate for total PA, MVPA, and SB (r ≈ 0.20–0.40). Light intensity PA correlated weakly for WHI (r ≈ 0.01–0.06) and was variable for CHAMPS (r ≈ 0.07–0.22). Conclusion: Questionnaire and accelerometer estimates of total PA, MVPA, and SB have at best moderate correlations in older women and should not be assumed to be measuring the same behaviors or quantity of behavior. Light intensity PA is poorly measured by questionnaire. Because light intensity activities account for the largest proportion of daily activity time in older adults, and likely contribute to its health benefits, further research should investigate how to improve measurement of light intensity PA by questionnaires.

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John Bellettiere, Fatima Tuz-Zahra, Jordan A. Carlson, Nicola D. Ridgers, Sandy Liles, Mikael Anne Greenwood-Hickman, Rod L. Walker, Andrea Z. LaCroix, Marta M. Jankowska, Dori E. Rosenberg, and Loki Natarajan

Little is known about how sedentary behavior (SB) metrics derived from hip- and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL (AP) micro monitors were concurrently worn with hip-worn ActiGraph (AG) GT3X+ accelerometers (with SB measured using the 100 counts per minute [cpm] cut point; AG100cpm) by 953 older adults (age 77 ± 6.6, 54% women) for 4–7 days. Device agreement for sedentary time and five SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with four health outcomes using standardized (i.e., z scores) and unstandardized SB metrics. Mean errors (AP − AG100cpm) and 95% limits of agreement were: sedentary time −54.7 [−223.4, 113.9] min/day; time in 30+ min bouts 77.6 [−74.8, 230.1] min/day; mean bout duration 5.9 [0.5, 11.4] min; usual bout duration 15.2 [0.4, 30] min; breaks in sedentary time −35.4 [−63.1, −7.6] breaks/day; and alpha −.5 [−.6, −.4]. Respective Pearson correlations were: .66, .78, .73, .79, .51, and .40. Concordance correlations were: .57, .67, .40, .50, .14, and .02. The statistical significance and direction of associations were identical for AG100cpm and AP metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 13 of 24 tests for unstandardized and five of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from AG100cpm due to the tendency for it to overestimate breaks in sedentary time relative to AP. However, high correlations between AP and AG100cpm measures and similar standardized associations with health outcomes suggest that studies using AG100cpm are useful, though not ideal, for studying SB in older adults.