Emily Mire

Fully automated waist-worn accelerometer algorithm for detecting children’s sleep-period time separate from 24-h physical activity or sedentary behaviors

Analysis of 24-h waist-worn accelerometer data for physical activity and sedentary behavior requires that sleep-period time (from sleep onset to the end of sleep, including all sleep epochs and wakefulness after onset) is first identified. To identify sleep-period time in children in this study, we evaluated the validity of a published automated algorithm that requires nonaccelerometer bed- and wake-time inputs, relative to a criterion expert visual analysis of minute-by-minute waist-worn accelerometer data, and validated a refined fully automated algorithm. Thirty grade 4 schoolchildren (50% girls) provided 24-h waist-worn accelerometry data. Expert visual inspection (criterion), a published algorithm (Algorithm 1), and 2 additional automated refinements (Algorithm 2, which draws on the instruments inclinometer function, and Algorithm 3, which focuses on bedtime and wake time points) were applied to a standardized 24-h time block. Paired t tests were used to evaluate differences in mean sleep time (expert criterion minus algorithm estimate). Compared with the criterion, Algorithm 1 and Algorithm 2 significantly overestimated sleep time by 43 min and 90 min, respectively. Algorithm 3 produced the smallest mean difference (2 min), and was not significantly different from the criterion. Relative to expert visual inspection, our automated Algorithm 3 produced an estimate that was precise and within expected values for similarly aged children. This fully automated algorithm for 24-h waist-worn accelerometer data will facilitate the separation of sleep time from sedentary behavior and physical activity of all intensities during the remainder of the day.

Identifying children's nocturnal sleep using 24-h waist accelerometry.

PURPOSE The purposes of this study were 1) to add layers and features to a previously published fully automated algorithm designed to identify childrens nocturnal sleep and to exclude episodes of nighttime nonwear/wakefulness and potentially misclassified daytime sleep episodes and 2) to validate this refined sleep algorithm (RSA) against sleep logs. METHODS Forty-five fourth-grade school children (51% female) participants were asked to log evening bedtime and morning wake time and wear an ActiGraph GT3X+ (ActiGraph LLC, Pensacola, FL) accelerometer at their waist for seven consecutive days. Accelerometers were distributed through a single school participating in the Baton Rouge, USA, site of the International Study of Childhood Obesity, Lifestyle, and the Environment. We compared log-based variables of sleep period time (SPT), bedtime, and wake time to corresponding accelerometer-determined variables of total sleep episode time, sleep onset, and sleep offset estimated with the RSA. In addition, SPT and sleep onset estimated using standard procedures combining sleep logs and accelerometry (Log + Accel) were compared to the RSA-derived values. RESULTS RSA total sleep episode time (540 ± 36 min) was significantly different from Log SPT (560 ± 24 min), P = 0.003, but not different from Log + Accel SPT (549 ± 24 min), P = 0.15. Significant and moderately high correlations were apparent between RSA-determined variables and those using the other methods (r = 0.61 to 0.74). There were no differences between RSA and Log + Accel estimates of sleep onset (P = 0.15) or RSA sleep offset and log wake time (P = 0.16). CONCLUSIONS The RSA is a refinement of our previous algorithm, allowing researchers who use a 24-h waist-worn accelerometry protocol to distinguish childrens nocturnal sleep (including night time wake episodes) from daytime activities.

Reconsidering the Sedentary Behaviour Paradigm

Aims Recent literature has posed sedentary behaviour as an independent entity to physical inactivity. This study investigated whether associations between sedentary behaviour and cardio-metabolic biomarkers remain when analyses are adjusted for total physical activity. Methods Cross-sectional analyses were undertaken on 4,618 adults from the 2003/04 and 2005/06 U.S. National Health and Nutrition Examination Survey. Minutes of sedentary behaviour and moderate-to-vigorous physical activity (MVPA), and total physical activity (total daily accelerometer counts minus counts accrued during sedentary minutes) were determined from accelerometry. Associations between sedentary behaviour and cardio-metabolic biomarkers were examined using linear regression. Results Results showed that sedentary behaviour was detrimentally associated with 8/11 cardio-metabolic biomarkers when adjusted for MVPA. However, when adjusted for total physical activity, the associations effectively disappeared, except for C-reactive protein, which showed a very small, favourable association (β = −0.06) and triglycerides, which showed a very small, detrimental association (β = 0.04). Standardised betas suggested that total physical activity was consistently, favourably associated with cardio-metabolic biomarkers (9/11 biomarkers, standardized β = 0.08–0.30) while sedentary behaviour was detrimentally associated with just 1 biomarker (standardized β = 0.12). Conclusion There is virtually no association between sedentary behaviour and cardio-metabolic biomarkers once analyses are adjusted for total physical activity. This suggests that sedentary behaviour may not have health effects independent of physical activity.

The independent and combined associations of physical activity and sedentary behavior with obesity in adults: NHANES 2003‐06

To examine the combined influence of moderate‐to‐vigorous physical activity (MVPA) and sedentary behavior on obesity in US adults.

Abdominal obesity and mortality: The Pennington Center Longitudinal Study

The purpose of this study was to determine the association between visceral adipose tissue (VAT) and all-cause mortality. The sample included 1089 white men and women 18–84 years of age from the Pennington Center Longitudinal Study, a prospective cohort of participants assessed between 1995 and 2008, and followed for mortality until 31 December 2009. Abdominal VAT was measured at the L4–L5 vertebral level using computed tomography. There were 27 deaths during an average of 9.1 years of follow-up. Abdominal VAT was significantly associated with mortality after adjustment for age, sex and year of examination (hazards ratio (HR) 1.46; 95% confidence interval 1.05–2.05). The association was stronger after the inclusion of abdominal subcutaneous adipose tissue (SAT), smoking status, alcohol consumption and leisure-time physical activity as additional covariates (HR 1.74; 1.17–2.59). Limiting the sample to participants who were free of stroke, heart disease and cancer at baseline reduced the strength of the relationship slightly (HR 1.62; 1.07–2.47). Abdominal SAT was not associated with mortality, either alone or in combination with VAT and other covariates. The results support the assertion that abdominal VAT is an important therapeutic target for obesity reduction efforts.

The Importance of Waist Circumference and BMI for Mortality Risk in Diabetic Adults

OBJECTIVE We aimed to determine the associations of waist circumference (WC) and BMI with all-cause mortality among patients with diabetes. RESEARCH DESIGN AND METHODS The sample included 847 white and 553 African American patients (18–69 years of age) with diabetes. Height, weight, and WC were measured, and the BMI (kg/m2) was calculated. Cox regression was used to analyze the associations of BMI and WC with mortality, adjusting for age, sex, race, examination year, smoking status, alcohol consumption, and physical activity. Hazard ratios (HRs) are expressed per standard deviation of each independent variable. RESULTS A total of 86 deaths occurred during 6.7 years of follow-up. After adjustment for covariates, WC (HR 1.40 [95% CI 1.14–1.72]) and BMI (1.29 [1.04–1.61]) demonstrated significant relationships with mortality. CONCLUSIONS The results indicate that maintaining a healthy WC and BMI are both important for individuals living with diabetes.

Body composition indices of a load-capacity model: gender- and BMI-specific reference curves

OBJECTIVE Fat mass (FM) and fat-free mass (FFM) are frequently measured to define body composition phenotypes. The load-capacity model integrates the effects of both FM and FFM to improve disease-risk prediction. We aimed to derive age-, gender- and BMI-specific reference curves of load-capacity model indices in an adult population (≥18 years). DESIGN Cross-sectional study. Dual-energy X-ray absorptiometry was used to measure FM, FFM, appendicular skeletal muscle mass (ASM) and truncal fat mass (TrFM). Two metabolic load-capacity indices were calculated: ratio of FM (kg) to FFM (kg) and ratio of TrFM (kg) to ASM (kg). Age-standardised reference curves, stratified by gender and BMI (<25.0 kg/m2, 25.0-29.9 kg/m2, ≥30.0 kg/m2), were constructed using an LMS approach. Percentiles of the reference curves were 5th, 15th, 25th, 50th, 75th, 85th and 95th. SETTING Secondary analysis of data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES). SUBJECTS The population included 6580 females and 6656 males. RESULTS The unweighted proportions of obesity in males and females were 25.5 % and 34.7 %, respectively. The average values of both FM:FFM and TrFM:ASM were greater in female and obese subjects. Gender and BMI influenced the shape of the association of age with FM:FFM and TrFM:ASM, as a curvilinear relationship was observed in female and obese subjects. Menopause appeared to modify the steepness of the reference curves of both indices. CONCLUSIONS This is a novel risk-stratification approach integrating the effects of high adiposity and low muscle mass which may be particularly useful to identify cases of sarcopenic obesity and improve disease-risk prediction.

Anthropometric markers of obesity and mortality in white and African American adults: the pennington center longitudinal study.

The purpose of this study was to determine the association between anthropometric measures of obesity and all‐cause mortality in white and African American men and women.

Socioeconomic status indicators, physical activity, and overweight/obesity in Brazilian children

Abstract Objective: To analyze the associations between socioeconomic status (SES) indicators and physical activity and overweight/obesity in children. Methods: 485 children wore accelerometers for 7 days. Variables included time in sedentary behavior and moderate-to-vigorous physical activity (MVPA), and steps/day. Children were further categorized as meeting or not meeting guidelines of ≥60min/day MVPA and ≥12,000 steps/day. Body mass index (BMI) and body fat percentage (BF%) were measured using bioelectrical impedance. Overweight/obesity was defined as BMI >+1 SD and BF% ≥85th percentile. Parents answered questionnaires that questioned total annual household income, parental education level, parental employment status and automobile ownership. Results: Children averaged 59.5min/day in MVPA (44.1% met MVPA guidelines), and 9639 steps/day (18.4% met steps/day guidelines). 45.4% and 33% were overweight/obese classified by BMI and BF% respectively. Higher relative total annual household income level (Odds Ratio 0.31; 95% confidence interval=0.15-0.65), and relatively higher maternal (OR=0.38; 95%CI=0.20-0.72) and paternal (OR=0.36; 95%CI=0.17-0.75) education levels were associated with lower odds of children meeting MVPA guidelines. Household automobile ownership was associated with lower odds of children meeting MVPA (OR=0.48; 95%CI=0.31-0.75) and steps/day guidelines (OR=0.44; 95%CI=0.26-0.74). Conclusions: SES indicators were not associated with overweight/obesity, but higher SES was associated with lower odds of children meeting MVPA guidelines.

Associations between breakfast frequency and adiposity indicators in children from 12 countries

OBJECTIVES Reports of inverse associations between breakfast frequency and indices of obesity are predominantly based on samples of children from high-income countries with limited socioeconomic diversity. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE), the present study examined associations between breakfast frequency and adiposity in a sample of 9-11-year-old children from 12 countries representing a wide range of geographic and socio-cultural variability. METHODS Multilevel statistical models were used to examine associations between breakfast frequency (independent variable) and adiposity indicators (dependent variables: body mass index (BMI) z-score and body fat percentage (BF%)), adjusting for age, sex, and parental education in 6941 children from 12 ISCOLE study sites. Associations were also adjusted for moderate-to-vigorous physical activity, healthy and unhealthy dietary patterns and sleep time in a sub-sample (n=5710). Where interactions with site were significant, results were stratified by site. RESULTS Adjusted mean BMI z-score and BF% for frequent breakfast consumers were 0.45 and 20.5%, respectively. Frequent breakfast consumption was associated with lower BMI z-scores compared with occasional (P<0.0001, 95% confidence intervals (CI): 0.10-0.29) and rare (P<0.0001, 95% CI: 0.18-0.46) consumption, as well as lower BF% compared with occasional (P<0.0001, 95% CI: 0.86-1.99) and rare (P<0.0001, 95% CI: 1.07-2.76). Associations with BMI z-score varied by site (breakfast by site interaction; P=0.033): associations were non-significant in three sites (Australia, Finland and Kenya), and occasional (not rare) consumption was associated with higher BMI z-scores compared with frequent consumption in three sites (Canada, Portugal and South Africa). Sub-sample analyses adjusting for additional covariates showed similar associations between breakfast and adiposity indicators, but lacked site interactions. CONCLUSIONS In a multinational sample of children, more frequent breakfast consumption was associated with lower BMI z-scores and BF% compared with occasional and rare consumption. Associations were not consistent across all 12 countries. Further research is required to understand global differences in the observed associations.