Dataset Information

Measuring Sedentary Behavior by Means of Muscular Activity and Accelerometry.

ABSTRACT: Sedentary Behavior (SB) is among the most frequent human behaviors and is associated with a plethora of serious chronic lifestyle diseases as well as premature death. Office workers in particular are at an increased risk due to their extensive amounts of occupational SB. However, we still lack an objective method to measure SB consistent with its definition. We have therefore developed a new measurement system based on muscular activity and accelerometry. The primary aim of the present study was to calibrate the new-developed 8-CH-EMG+ for measuring occupational SB against an indirect calorimeter during typical desk-based office work activities. In total, 25 volunteers performed nine office tasks at three typical workplaces. Minute-by-minute posture and activity classification was performed using subsequent decision trees developed with artificial intelligence data processing techniques. The 8-CH-EMG+ successfully identified all sitting episodes (AUC = 1.0). Furthermore, depending on the number of electromyography channels included, the device has a sensitivity of 83?98% and 74?98% to detect SB and active sitting (AUC = 0.85?0.91). The 8-CH-EMG+ advances the field of objective SB measurements by combining accelerometry with muscular activity. Future field studies should consider the use of EMG sensors to record SB in line with its definition.

SUBMITTER: Kuster RP

PROVIDER: S-EPMC6263709 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Measuring Sedentary Behavior by Means of Muscular Activity and Accelerometry.

Kuster Roman P RP Huber Mirco M Hirschi Silas S Siegl Walter W Baumgartner Daniel D Hagströmer Maria M Grooten Wim W

Sensors (Basel, Switzerland) 20181117 11

Sedentary Behavior (SB) is among the most frequent human behaviors and is associated with a plethora of serious chronic lifestyle diseases as well as premature death. Office workers in particular are at an increased risk due to their extensive amounts of occupational SB. However, we still lack an objective method to measure SB consistent with its definition. We have therefore developed a new measurement system based on muscular activity and accelerometry. The primary aim of the present study was ...[more]

PMID: 30453605

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Project description:BackgroundThe constructs and interdependency of physical behaviors are not well described and the complexity of physical activity (PA) data analysis remains unexplored in COPD. This study examined the interrelationships of 24-hour physical behaviors and investigated their associations with participant characteristics for individuals with mild-moderate airflow obstruction and healthy control subjects.Patients and methodsVigorous PA (VPA), moderate-to-vigorous PA (MVPA), light PA (LPA), stationary time (ST), average movement intensity (vector magnitude counts per minute), and sleep duration for 109 individuals with COPD and 135 healthy controls were obtained by wrist-worn accelerometry. Principal components analysis (PCA) examined interrelationships of physical behaviors to identify distinct behavioral constructs. Using the PCA component loadings, linear regressions examined associations with participant (+, positive correlation; -, negative correlation), and were compared between COPD and healthy control groups.ResultsFor both groups PCA revealed ST, LPA, and average movement intensity as distinct behavioral constructs to MVPA and VPA, labeled "low-intensity movement" and "high-intensity movement," respectively. Sleep was also found to be its own distinct behavioral construct. Results from linear regressions supported the identification of distinct behavioral constructs from PCA. In COPD, low-intensity movement was associated with limitations with mobility (-), daily activities (-), health status (+), and body mass index (BMI) (-) independent of high-intensity movement and sleep. High-intensity movement was associated with age (-) and self-care limitations (-) independent of low-intensity movement and sleep. Sleep was associated with gender (0= female, 1= male; [-]), lung function (-), and percentage body fat (+) independent of low-intensity and high-intensity movement.ConclusionDistinct behavioral constructs comprising the 24-hour day were identified as "low-intensity movement," "high-intensity movement," and "sleep" with each construct independently associated with different participant characteristics. Future research should determine whether modifying these behaviors improves health outcomes in COPD.

Project description:BackgroundDescribing correlates of physical activity (PA) and sedentary behavior (SB) among postmenopausal cancer survivors can help identify risk profiles and can be used to support development of targeted interventions to improve PA and reduce SB in this population.ObjectiveTo describe PA/SB and identify correlates of PA/SB among cancer and cancer-free post-menopausal women.MethodsWomen from the Women's Health Study (N = 16,629) and Women's Health Initiative/Objective Physical Activity and Cardiovascular Health Study (N = 6,079) were asked to wear an accelerometer on the hip for 7 days. Multiple mixed-effects linear regression models were used to identify sociodemographic-, health-, and chronic condition-related correlates (independent variables) associated with PA and SB (dependent variables) among women with (n = 2,554) and without (n = 20,154) a history of cancer. All correlates were mutually adjusted for each other.ResultsIn unadjusted analyses, women with a history of cancer took fewer mean daily steps (4,572 (standard deviation 2557) vs 5,029 (2679) steps/day) and had lower mean moderate-to-vigorous PA (74.9 (45.0) vs. 81.6 (46.7) minutes/day) than cancer-free women. In adjusted analyses, for cancer and cancer-free women, age, diabetes, overweight, and obesity were inversely associated with all metrics of PA (average vector magnitude, time in moderate-to-vigorous PA, step volume, time at ≥40 steps/minutes, and peak 30-minute step cadence). In unadjusted analyses, mean SB was similar for those with and without cancer (529.7 (98.1) vs. 521.7 (101.2) minutes/day). In adjusted analyses, for cancer and cancer-free women, age, diabetes, cardiovascular disease, current smoking, overweight, and obesity were positive correlates of SB, while Black or Hispanic race/ethnicity, weekly/daily alcohol intake, and excellent/very good/good self-rated health were inverse correlates of SB.ConclusionSeveral sociodemographic, health, and chronic conditions were correlates of PA/SB for postmenopausal women with and without cancer. Future studies should examine longitudinal relationships to gain insight into potential determinants of PA/SB.

Dataset Information

Measuring Sedentary Behavior by Means of Muscular Activity and Accelerometry.

Publications

Measuring Sedentary Behavior by Means of Muscular Activity and Accelerometry.

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