Measurement of Active and Sedentary Behavior in Context of Large Epidemiologic Studies-Corrigendum.
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SUBMITTER: Matthews CE
PROVIDER: S-EPMC8375637 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Introduction/purposeTo assess the utility of measurement methods that may be more accurate and precise than traditional questionnaire-based estimates of habitual physical activity and sedentary behavior we compared the measurement properties of a past year questionnaire (AARP) and more comprehensive measures: an internet-based 24-h recall (ACT24), and a variety of estimates from an accelerometer (ActiGraph).MethodsParticipants were 932 adults (50-74 yr) in a 12-month study that included reference measures of energy expenditure from doubly labeled water (DLW) and active and sedentary time via activPAL.ResultsAccuracy at the group level (mean differences) was generally better for both ACT24 and ActiGraph than the AARP questionnaire. The AARP accuracy for energy expenditure ranged from -4% to -13% lower than DLW, but its accuracy was poorer for physical activity duration (-48%) and sedentary time (-18%) versus activPAL. In contrast, ACT24 accuracy was within 3% to 10% of DLW expenditure measures and within 1% to 3% of active and sedentary time from activPAL. For ActiGraph, accuracy for energy expenditure was best for the Crouter 2-regression method (-2% to -7%), and for active and sedentary time the 100 counts per minute cutpoint was most accurate (-1% to 2%) at the group level. One administration of the AARP questionnaire was significantly correlated with long-term average from the reference measures (ρTX = 0.16-0.34) overall, but four ACT24 recalls had higher correlations (ρTX = 0.48-0.60), as did 4 d of ActiGraph assessment (ρTX = 0.54-0.87).ConclusionsNew exposure assessments suitable for use in large epidemiologic studies (ACT24, ActiGraph) were more accurate and had higher correlations than a traditional questionnaire. Use of better more comprehensive measures in future epidemiologic studies could yield new etiologic discoveries and possibly new opportunities for prevention.
| S-EPMC5768470 | biostudies-literature
Project description:Sedentary behavior has already been associated with mortality, cardiovascular disease, and cancer. Questionnaires are an affordable tool for measuring sedentary behavior in large epidemiological studies. Here, we introduce and evaluate two statistical methods for quantifying measurement error in questionnaires. Accurate estimates are needed for assessing questionnaire quality. The two methods would be applied to validation studies that measure a sedentary behavior by both questionnaire and accelerometer on multiple days. The first method fits a reduced model by assuming the accelerometer is without error, while the second method fits a more complete model that allows both measures to have error. Because accelerometers tend to be highly accurate, we show that ignoring the accelerometer's measurement error, can result in more accurate estimates of measurement error in some scenarios. In this manuscript, we derive asymptotic approximations for the Mean-Squared Error of the estimated parameters from both methods, evaluate their dependence on study design and behavior characteristics, and offer an R package so investigators can make an informed choice between the two methods. We demonstrate the difference between the two methods in a recent validation study comparing Previous Day Recalls (PDR) to an accelerometer-based ActivPal.
| S-EPMC4915393 | biostudies-literature
Project description:Sedentary time (too much sitting) increasingly is being recognized as a distinct health risk behavior. This paper reviews the reliability and validity of self-reported and device-based sedentary time measures and provides recommendations for their use in population-based studies. The focus is on instruments that have been used in free-living, population-based research in adults. Data from the 2003-2006 National Health and Nutrition Examination Survey are utilized to compare the descriptive epidemiology of sedentary time that arises from the use of different sedentary time measures. A key recommendation from this review is that, wherever possible, population-based monitoring of sedentary time should incorporate both self-reported measures (to capture important domain- and behavior-specific sedentary time information) and device-based measures (to measure both total sedentary time and patterns of sedentary time accumulation).
| S-EPMC3179387 | biostudies-literature
Project description:In the present study, we investigated how overall and specific domains of physical activity and sedentary behavior at the age of 7 years were associated with cognition at the age of 11 years in 8,462 children from the Millennium Cohort Study. Data were collected from 2001 to 2013. Participation in domains of physical activity and sedentary behavior at 7 years of age were reported. Activity levels were also measured objectively. Cognition was assessed using the British Ability Scales. General linear models were used to assess longitudinal associations of physical activity and sedentary behavior, measured both objectively and via self-report, with cognition. Analyses were adjusted for prespecified covariates. Sports/physical activity club attendance (B = 0.6, 95% confidence interval (CI): 0.2, 1.1), doing homework (B = 0.5, 95% CI: 0.0, 0.9), and objectively measured sedentary time (B = 0.8, 95% CI: 0.1, 1.4) at age 7 years were positively associated with cognition at age 11 years in final the models. Television viewing was negatively associated with cognition (B = -1.7, 95% CI: -2.4, -1.0), although the association was attenuated to the null after adjustments for baseline cognition. Objectively measured light physical activity was inversely associated with cognition (B = -0.7, 95% CI: -1.3, -0.1). Moderate-to-vigorous physical activity was also inversely associated with cognition in girls only (B = -1.1, 95% CI: -2.0, -0.3). Associations of physical activity and sedentary behavior with cognition appear to be context-specific in young people.
| S-EPMC4908213 | biostudies-other
Project description:BackgroundWith aging, daily physical activity (PA) becomes less frequent and more fragmented. Accumulation patterns of daily PA-including transitions from active-to-sedentary behaviors-may provide important insights into functional status in older, less active populations.MethodsParticipants of the Baltimore Longitudinal Study of Aging (n = 680, 50% male, aged 27-94 years) completed a clinical assessment and wore an Actiheart accelerometer. Transitions between active and sedentary states were modeled as a probability (Active-to-Sedentary Transition Probability [ASTP]) defined as the reciprocal of the average PA bout duration. Cross-sectional associations between ASTP and gait speed (m/s), fatigability (rating-of-perceived-exertion [RPE]), 400 m time (seconds), and expanded short physical performance battery score were modeled using linear and logistic regression, adjusted for chronic conditions. Further analyses explored the utility of ASTP over-and-above total daily PA.ResultsIn continuous models, each 0.10-unit higher ASTP was associated slower gait (β = -0.06 m/s, SE = 0.01), higher fatigability (β = 0.60 RPE, SE = 0.12), slower 400 m time (β = 16.31 s, SE = 2.70), and lower functioning (β = -0.13 expanded short physical performance battery score, SE = 0.03; p < .001). In categorical analyses, those in the highest tertile of ASTP were >2 times more likely to have high fatigability (rating of perceived exertion ≥10), slow 400 m time (>300 seconds) and reduced functional performance (expanded short physical performance battery score < 3.07) than those in the lowest tertile (p < .01). Further analyses demonstrated ASTP provided additional insight into functional outcomes beyond total daily PA.ConclusionFragmented daily PA-as measured by ASTP-is strongly linked with measures of health and functional status and may identify those at risk of high fatigability and reduced functional performance over and above traditional PA metrics.
| S-EPMC6417447 | biostudies-literature
Project description:BackgroundSedentary behavior is ubiquitous in modern adults' daily lives and it has been suggested to be associated with incident cancer. However, the results have been inconsistent. In this study, we performed a systematic review and meta-analysis of prospective cohort studies to clarify the association between sedentary behavior and incident cancer.MethodPubMed and Embase databases were searched up to March 2014. All prospective cohort studies on the association between sedentary behavior and incident cancer were included. The summary relative risks (RRs) with 95% confidence intervals (CIs) were estimated using random effect model.ResultsA total of 17 prospective studies from 14 articles, including a total of 857,581 participants and 18,553 cases, were included in the analysis for sedentary behavior and risk of incident cancer. The overall meta-analysis suggested that sedentary behavior increased risk of cancer (RR = 1.20, 95%CI = 1.12-1.28), with no evidence of heterogeneity between studies (I(2) = 7.3%, P = 0.368). Subgroup analyses demonstrated that there were statistical associations between sedentary behavior and some cancer types (endometrial cancer: RR = 1.28, 95% CI = 1.08-1.53; colorectal cancer: RR = 1.30, 95%CI = 1.12-1.49; breast cancer: RR = 1.17, 95%CI = 1.03-1.33; lung cancer: RR = 1.27, 95%CI = 1.06-1.52). However, there was no association of sedentary behavior with ovarian cancer (RR = 1.26, 95%CI = 0.87-1.82), renal cell carcinoma (RR = 1.11, 95%CI = 0.87-1.41) or non-Hodgkin lymphoid neoplasms (RR = 1.09, 95%CI = 0.82-1.43).ConclusionThe present meta-analysis suggested that prolonged sedentary behavior was independently associated with an increased risk of incident endometrial, colorectal, breast, and lung cancers, but not with ovarian cancer, renal cell carcinoma or non-Hodgkin lymphoid neoplasms.
| S-EPMC4143275 | biostudies-literature
Project description:The interaction of genetics and environment play a major role in health and disease. Therefore, modeling experiments to investigate gene-environment interaction in a laboratory setting would provide the opportunity to gain mechanistic insight under controlled conditions. In laboratory research it is unclear if the gene-environment interaction of a sedentary lifestyle predisposes laboratory animals to hypokinetic diseases. Keywords: repeat sample
2005-06-24 | GSE2690 | GEO
Project description:The study's purpose was to describe longitudinal patterns of objectively measured sedentary behavior from age 12 to 16.Children participating in the Avon Longitudinal Study of Parents and Children wore accelerometers for 1 wk at ages 12, 14, and 16. Participants included boys (n = 2591) and girls (n = 2845) living in a single geographic location in the United Kingdom (Bristol). Total minutes per day spent in sedentary behavior and time spent in blocks of sedentary behavior lasting 10-19, 20-29, and ? 30 min are described. Growth curve models were used to determine the rate of change in sedentary behavior from age 12 to 16.At age 12, the boys and girls, on average, were sedentary for 418.0 ± 67.7 and 436.6 ± 64.0 min·d(-1), respectively, and sedentary behavior increased over time to 468.0 ± 74.3 and 495.6 ± 68.9 min·d(-1) at age 14 and to 510.4 ± 76.6 and 525.4 ± 67.4 min·d(-1) at age 16. Growth curve analyses found that total sedentary behavior increased at a rate of 19.5 ± 0.7 and 22.8 ± 0.7 min·d(-1)·yr for the boys and girls, respectively. The absolute mean increase in total sedentary behavior (+92.4 and +88.8 min·d(-1) for the boys and girls, respectively) closely matched the mean decrease in light physical activity (-82.2 and -82.9 min·d(-1) for the boys and girls, respectively) from age 12 to 16. Time spent in continuous sedentary behavior lasting ? 30 min increased by 121% from age 12 to 16.Sedentary behavior increased with age, at the expense of light physical activity. The increase in sedentary behavior lasting ? 30 min in duration contributed greatly to the increase in total sedentary behavior.
| S-EPMC3342456 | biostudies-literature
Project description: Not available
| S-EPMC8562323 | biostudies-literature
Project description:IntroductionMethods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings.PurposeThe purpose of this study was to develop and validate two novel machine-learning methods (Sojourn-1 Axis [soj-1x] and Sojourn-3 Axis [soj-3x]) in a free-living setting.MethodsParticipants were directly observed in their natural environment for 10 consecutive hours on three separate occasions. Physical activity and SB estimated from soj-1x, soj-3x, and a neural network previously calibrated in the laboratory (lab-nnet) were compared with direct observation.ResultsCompared with lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias [95% confidence interval] = 33.1 [25.9 to 40.4], root-mean-square error [RMSE] = 5.4 [4.6-6.2]; soj-1x: % bias = 1.9 [-2.0 to 5.9], RMSE = 1.0 [0.6 to 1.3]; soj-3x: % bias = 3.4 [0.0 to 6.7], RMSE = 1.0 [0.6 to 1.5]) and minutes in different intensity categories {lab-nnet: % bias = -8.2 (sedentary), -8.2 (light), and 72.8 (moderate-to-vigorous PA [MVPA]); soj-1x: % bias = 8.8 (sedentary), -18.5 (light), and -1.0 (MVPA); soj-3x: % bias = 0.5 (sedentary), -0.8 (light), and -1.0 (MVPA)}. Soj-1x and soj-3x also produced accurate estimates of guideline minutes and breaks from sedentary time.ConclusionsCompared with the lab-nnet algorithm, soj-1x and soj-3x improved the accuracy and precision in estimating free-living MET-hours, sedentary time, and time spent in light-intensity activity and MVPA. In addition, soj-3x is superior to soj-1x in differentiating SB from light-intensity activity.
| S-EPMC4527685 | biostudies-literature