Dataset Information

A method to estimate free-living active and sedentary behavior from an accelerometer.

ABSTRACT: INTRODUCTION:Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings. PURPOSE:The 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. METHODS:Participants 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. RESULTS:Compared 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. CONCLUSIONS:Compared 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.

SUBMITTER: Lyden K

PROVIDER: S-EPMC4527685 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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A method to estimate free-living active and sedentary behavior from an accelerometer.

Lyden Kate K Keadle Sarah Kozey SK Staudenmayer John J Freedson Patty S PS

Medicine and science in sports and exercise 20140201 2

<h4>Introduction</h4>Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings.<h4>Purpose</h4>The 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.<h4>Methods</h4>Participants were directly observed in their natural environment for 10 consecutive hours on three separate occasions. Physical activity and ...[more]

PMID: 23860415

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Project description:This study aimed to examine the validity and accuracy of wrist accelerometers for classifying sedentary behavior (SB) in children.Fifty-seven children (5-8 and 9-12 yr) completed an ~170-min protocol, including 15 semistructured activities and transitions. Nine ActiGraph (GT3X+) and two GENEActiv wrist cut points were evaluated. Direct observation was the criterion measure. The accuracy of wrist cut points was compared with that achieved by the ActiGraph hip cut point (?25 counts per 15 s) and the thigh-mounted activPAL3. Analyses included equivalence testing, Bland-Altman procedures, and area under the receiver operating curve (ROC-AUC).The most accurate ActiGraph wrist cut points (Kim; vector magnitude, ?3958 counts per 60 s; vertical axis, ?1756 counts per 60 s) demonstrated good classification accuracy (ROC-AUC = 0.85-0.86) and accurately estimated SB time in 5-8 yr (equivalence P = 0.02; mean bias = 4.1%, limits of agreement = -20.1% to 28.4%) and 9-12 yr (equivalence P < 0.01; -2.5%, -27.9% to 22.9%). The mean bias of SB time estimates from Kim were smaller than ActiGraph hip (5-8 yr: 15.8%, -5.7% to 37.2%; 9-12 yr: 17.8%, -3.9% to 39.5%) and similar to or smaller than activPAL3 (5-8 yr: 12.6%, -39.8% to 14.7%; 9-12 yr: -1.4%, -13.9% to 11.0%), although classification accuracy was similar to ActiGraph hip (ROC-AUC = 0.85) but lower than activPAL3 (ROC-AUC = 0.92-0.97). Mean bias (5-8 yr: 6.5%, -16.1% to 29.1%; 9-12 yr: 10.5%, -13.6% to 34.6%) for the most accurate GENEActiv wrist cut point (Schaefer: ?0.19 g) was smaller than ActiGraph hip, and activPAL3 in 5-8 yr, but larger than activPAL3 in 9-12 yr. However, SB time estimates from Schaefer were not equivalent to direct observation (equivalence P > 0.05) and classification accuracy (ROC-AUC = 0.79-0.80) was lower than for ActiGraph hip and activPAL3.The most accurate SB ActiGraph (Kim) and GENEActiv (Schaefer) wrist cut points can be applied in children with similar confidence as the ActiGraph hip cut point (?25 counts per 15 s), although activPAL3 was generally more accurate.

Project description:Accelerometer-based activity monitors are widely used in research and surveillance applications for quantifying sedentary behavior (SB) and physical activity (PA). Considerable research has been done to refine methods for assessing PA, but relatively little attention has been given to operationalizing SB parameters (i.e., sedentary time and breaks) from accelerometer data - particularly in relation to health outcomes. This study investigated: (a) the accrued patterns of sedentary time and breaks; and (b) the associations of sedentary time and breaks in different bout durations with cardiovascular risk factors.Accelerometer data on 5,917 adults from the National Health Examination and Nutrition Survey (NHANES) 2003-2006 were used. Sedentary time and breaks at different bout durations (i.e., 1, 2-4, 5-9, 10-14, 15-19, 20-24, 25-29, and ≥ 30-min) were obtained using a threshold of < 100 counts per minute. Sedentary time and breaks were regressed on cardiovascular risk factors (waist circumference, triglyceride, and high-density lipoprotein cholesterol) and body mass index across bout durations.The results revealed that the majority of sedentary time occurred within relatively short bout durations (≈ 70% and ≈ 85% for < 5-min and < 10-min, respectively). The associations of sedentary time and breaks with health outcomes varied depending on how bout time was defined. Estimates of SB parameters based on bout durations of 5 min or shorter were associated with reduced cardiovascular risk factors while durations longer than 10-min were generally associated with increased risk factors.The present study demonstrates that the duration of sedentary bouts should be further considered when operationalizing the SB parameters from accelerometer data. The threshold of 5 minutes to define a bout is defensible, but a 10 minute threshold would provide a more conservative estimate to clearly capture the prolonged nature of sedentary behavior. Additional research is needed to determine the relative sensitivity and specificity of these thresholds.

Project description:MotionSense HRV is a wrist-worn accelerometery-based sensor that is paired with a smartphone and is thus capable of measuring the intensity, duration, and frequency of physical activity (PA). However, little information is available on the validity of the MotionSense HRV. Therefore, the purpose of this study was to assess the concurrent validity of the MotionSense HRV in estimating sedentary behavior (SED) and PA. A total of 20 healthy adults (age: 32.5 ± 15.1 years) wore the MotionSense HRV and ActiGraph GT9X accelerometer (GT9X) on their non-dominant wrist for seven consecutive days during free-living conditions. Raw acceleration data from the devices were summarized into average time (min/day) spent in SED and moderate-to-vigorous PA (MVPA). Additionally, using the Cosemed K5 indirect calorimetry system (K5) as a criterion measure, the validity of the MotionSense HRV was examined in simulated free-living conditions. Pearson correlations, mean absolute percent errors (MAPE), Bland-Altman (BA) plots, and equivalence tests were used to examine the validity of the MotionSense HRV against criterion measures. The correlations between the MotionSense HRV and GT9X were high and the MAPE were low for both the SED (r = 0.99, MAPE = 2.4%) and MVPA (r = 0.97, MAPE = 9.1%) estimates under free-living conditions. BA plots illustrated that there was no systematic bias between the MotionSense HRV and criterion measures. The estimates of SED and MVPA from the MotionSense HRV were significantly equivalent to those from the GT9X; the equivalence zones were set at 16.5% for SED and 29% for MVPA. The estimates of SED and PA from the MotionSense HRV were less comparable when compared with those from the K5. The MotionSense HRV yielded comparable estimates for SED and PA when compared with the GT9X accelerometer under free-living conditions. We confirmed the promising application of the MotionSense HRV for monitoring PA patterns for practical and research purposes.

Project description:Focused on the accelerometer-determined physical activity and sedentary time metrics in 9-11-year-old children, we sought to determine the following: (i) number of days that are necessary to achieve reliable estimates (G⩾0.8); (ii) proportion of variance attributed to different facets (participants and days) of reliability estimates; and (iii) actual reliability of data as collected in The International Study of Childhood Obesity, Lifestyle and Environment (ISCOLE).The analytical sample consisted of 6025 children (55% girls) from sites in 12 countries. Physical activity and sedentary time metrics measures were assessed for up to 7 consecutive days for 24 h per day with a waist-worn ActiGraph GT3X+. Generalizability theory using R software was used to investigate the objectives i and ii. Intra-class correlation coefficients (ICC) were computed using SAS PROC GLM to inform objective iii.The estimated minimum number of days required to achieve a reliability estimate of G⩾0.8 ranged from 5 to 9 for boys and 3 to 11 for girls for light physical activity (LPA); 5 to 9 and 3 to 10, for moderate-to-vigorous physical activity (MVPA); 5 to 10 and 4 to 10 for total activity counts; and 7 to 11 and 6 to 11 for sedentary time, respectively. For all variables investigated, the 'participant' facet accounted for 30-50% of the variability, whereas the 'days' facet accounted for ⩽5%, and the interaction (P × D) accounted for 50-70% of the variability. The actual reliability for boys in ISCOLE ranged from ICCs of 0.78 to 0.86, 0.73 to 0.85 and 0.72 to 0.86 for LPA, MVPA and total activity counts, respectively, and 0.67 to 0.79 for sedentary time. The corresponding values for girls were 0.80-0.88, 0.70-0.89, 0.74-0.86 and 0.64-0.80.It was rare that only 4 days from all participants would be enough to achieve desirable reliability estimates. However, asking participants to wear the device for 7 days and requiring ⩾4 days of data to include the participant in the analysis might be an appropriate approach to achieve reliable estimates for most accelerometer-derived metrics.

Project description:BackgroundAlthough higher sedentary behavior (SB) with low light intensity (LPA) and moderate-to-vigorous intensity physical activity (MVPA) are thought to increase risk for obesity, other data suggest excess weight may precede these behaviors in the causal pathway. We aimed to investigate 10-year bidirectional associations between SB and activity with weight.MethodsAnalysis included 886 CARDIA participants (aged 38-50 years, 62% female, 38% black) with weight and accelerometry ( ≥ 4 days with ≥ 10 h/day) collected in 2005-6 (ActiGraph 7164) and 2015-6 (ActiGraph wGT3X-BT). Accelerometer data were calibrated, harmonized, and expressed as counts per minute (cpm) and time-dependent intensity categories (min/day of SB, LPA, and MVPA; SB and MVPA were also separated into long-bout and short-bout categories). Linear regression models were constructed to estimate adjusted associations of baseline activity with 10-year change in weight and vice versa. When activity categories were the independent variables, standardized regression coefficients (βstd.) estimated associations of replacing SB with a one SD increase in other categories, adjusted for accelerometer wear time.ResultsOver 10-years, weight increased by a mean 2.55 ± 8.05 kg and mean total activity decreased by 50 ± 153 cpm. In adjusted models, one SD higher baseline mean total activity (βstd. = -1.4 kg, p < 0.001), LPA (βstd. = -0.80 kg, p = 0.013), total MVPA (βstd. = -1.07 kg, p = 0.001), and long-bout MVPA (βstd. = -1.20 kg, p < 0.001) were associated with attenuated 10-year weight gain. Conversely, a one SD higher baseline weight was associated with unfavorable 10-year changes in daily activity profile including increases in SB (βstd. = 12.0 min, p < 0.001) and decreases in mean total activity (βstd. = 14.9 cpm, p = 0.004), LPA (βstd. = 8.9, p = 0.002), and MVPA (βstd. = 3.5 min, p = 0.001). Associations varied by race and gender.ConclusionsHigher SB with lower activity and body weight were bidirectionally related. Interventions that work simultaneously to replace SB with LPA and long-bout MVPA while also using other methods to address excess weight may be optimal.

Dataset Information

A method to estimate free-living active and sedentary behavior from an accelerometer.

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A method to estimate free-living active and sedentary behavior from an accelerometer.

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