Exploring the effect of sedentary behavior on increased adiposity in middle-aged adults

doi:10.21203/rs.3.rs-3660699/v1

Background. Evidence about sedentary behaviors (SBS) and body adiposity association may be inconclusive due to potential recall bias errors in the SBS self-report questionnaires.

Objective. To assess the association between SBS and body adiposity. We also compared this association using noncorrected and corrected self-reported SBS data.

Methods. A total of 1,285 adults participating in the Health Worker Cohort Study (HWCS) were evaluated at baseline in 2004 and follow-up in 2010. Body adiposity was measured by dual X-ray absorptiometry (DXA). SBS were obtained with a self-administered questionnaire in the total sample and accelerometry in a subsample of 142 HWCS participants. Accelerometry was used to correct self-reported SBS with a generalized linear model. The agreement was assessed with a kappa analysis of terciles and Bland‒Altman for continuous values. After adjusting for confounders, we used a fixed effect model to evaluate the association between noncorrected and corrected SBS and body adiposity.

Results. The participants’ noncorrected self-reported SBS at baseline and follow-up were 2.8±1.8 and 2.3±1.6 hours/day, and adiposity was 24.9±8.1 and 26.8±8.5 kg, respectively. Corrected sedentary behavior was 7.6 hours/day at baseline and follow-up. Each additional hour of corrected SBS was associated with an 847-gram increase in adiposity during the 6.8 years from the baseline to the follow-up assessment. Conversely, noncorrected self-reported SBS were associated with a 97-gram reduction for every hour of increased SBS.

Conclusions. The increased SBS are associated with increased adiposity for the corrected self-reported SBS. It is imperative to implement public health strategies to reduce sedentary behavior.

Sedentary behaviors (SBS) are in the public health spotlight due to their association with adverse health outcomes. A dose‒response relationship exists between SBS and a greater risk of obesity, cardiovascular disease, DMT2, all-cause mortality, burdening populations, and health systems. (1–3) A forecast from half of the worldwide population revealed that energy expenditure from physical activity has decreased.(4) According to this prediction, physical activity will continue to decline in the coming years, accompanied by a significant increase in SBS. (4) This rise in sedentarism may worsen the global epidemiologic prospect, resulting in higher rates of excessive body fat and other chronic diseases. (4)

Recent studies show that uninterrupted sitting for over three hours is positively associated with increased adiposity and complications independent of physical activity. (5, 6) In 2016, an estimated 55% of the Mexican population reported spending more than 4 hours/day in SBS, which may explain an overweight and obesity prevalence of 84% in our population.(7) The complications associated with excessive body adiposity, which include disability and mortality from chronic diseases, are a significant public health issue in nearly 200 countries, including Mexico. (8, 9)

Despite the strong association between SBS and adiposity, few longitudinal studies have investigated their long-term consequences. While most studies have found that increasing SBS are associated with a rise in adiposity, the evidence is inconclusive. (5, 10–12) One of the main differences between the studies that have observed a positive association between SBS and adiposity and those that have not is accelerometer use vs. uncorrected self-reported SBS. There are diverse strategies to correct the measurement error of SBS obtained through self-reporting, such as generating an SB predictive equation with a more accurate method such as accelerometry.(13, 14) Correcting the error of SBS values obtained through self-report questionnaires can attenuate the bias and allow us to accurately determine the influence of this behavior on the growing obesity epidemic. (15–17)

In addition to not correcting self-reported SBS values, another limitation of the previously described studies is that they do not account for many variables that may confound the association between SBS and body adiposity.(18) Some relevant confounders include age, tobacco consumption, calorie intake, and physical activity.(18) The inconsistencies in the results reported by other authors that explored the relationship between SBS, and body adiposity may be due to the absence of confounders in the analysis and not correcting the errors in self-reported SBS.

Due to the lack of conclusive evidence regarding the association between SBS and adiposity, we aimed to evaluate changes in SBS over time and their association with increased body adiposity. We estimated the association of interest with noncorrected and corrected self-reported SBS to evaluate whether the correction decreases the questionnaire’s error and information bias. In addition to improving the accuracy of the self-reporting of SBS, we included the most relevant confounders in the association between SBS and body adiposity, which may improve the accuracy of our findings. Beyond this, estimating the association between SBS and adiposity might generate information for public health interventions to counteract the obesity epidemic and its burden on societies and health systems, especially in populations with high overweight and obesity prevalence, such as the Mexican adult population.

Study population and study design

The Health Workers Cohort Study (HWCS) is a cohort evaluating the relationship between lifestyles and chronic diseases among Mexican adults. Details of the study design and cohort characteristics are described elsewhere. (19) The HWCS participants include employees from three health systems and academic institutions in Cuernavaca and Toluca, Mexico. Of the 1,776 males and nonpregnant females aged 18 and older who participated in both the baseline and follow-up assessments, we excluded those who had missing adiposity (n=165) or SBS measure (n=103), participants without information or implausible values of calorie intake, inactivity, and tobacco use from the present analysis (n=491). The final sample size of 1,285 participants was used to conduct a longitudinal analysis with data from the baseline (2004) and follow-up (2010) (Figure 1). In a cross-sectional study, we used a subsample of 142 adults from the total HWCS population to correct the SBS from the self-administered questionnaire.

Calibration of self-reported SBS values with accelerometry

We used accelerometer based SBS to correct self-reported SBS answered by the participants when they returned the accelerometer. Sampling. The 142 participants were randomly split into two groups to cross-validate our corrected measure. One-half (n=71) was used to create a predictive generalized linear model (GLM) where accelerometer based SBS time was the dependent variable. The participants' second half or training sample (n=71) was used to test the correction equation obtained in the first half.

Measurements

Self-reported sedentary behaviors

We used an adapted and translated version of the Nurses' Health Study to capture self-reported SBS. (20)Study participants stated their time spent in specific activities during a typical week in the previous year. The instrument listed 21 activities with a metabolic equivalent (MET) value lower than 1.5 METs, including recreation (e.g., writing, using a computer for recreation, reading, watching TV, and seeing movies), household (activities such as sewing), and in a work setting (e.g., sitting time). Participants reported the frequency (days/week) and time spent daily on SB activities. We calculated the hours per day engaged in SBS by adding all weekly time invested in the mentioned activities and domains and dividing the result by seven. (19) We used self-reported SBS (hours/day) and categorical occupational SBS (<8 hours, 8-9 hours, <9 hours) to explore explicative variables in the correction or calibration model.

Accelerometer-based sedentary time

We used the ActiGraph GT3Xþ accelerometer to measure SBS in a subsample of the HWCS participants. The ACTi-Graph GT3Xþ is a triaxial device used as a reference method for movement assessment. (21) Accelerometers provided time spent in the different movement intensity spectra. Participants wore the accelerometer for seven days with an adjustable hip belt placed on the mid-axillary line of the dominant side. A set of accelerometry values was considered valid if it included at least 60 continuous minutes of nonzero values. We allowed a one- to two-minute series with values between 0 and 99 counts per minute (CPM), as performed by Actilife5 Software v5.7.4.12. We used counts <100 to classify SBS and included all subjects with at least four valid days of measurements with at least 10 hours. (22) Accelerometer data were processed using a MATLAB code developed by coauthors DS, UV, and other international studies.(23)

Adiposity

Dual X-ray absorptiometry (DXA) using a Lunar densitometer (model: DPX-GE 73735, serial number: 638405U77) (Lunar Radiation Corporation, Madison, WI, USA; software version.35, fast scan mode) was used for the adiposity measurements. Trained technicians carried out daily quality control checks using the manufacturer phantom.(19)

Other covariates

All participants completed a self-administered questionnaire that included sociodemographic information regarding education level and job position (retired, assistant, medical doctor, dietitian, nurse, manager, laboratory analyst, pharmacy attendant, researcher, etc.). and lifestyle (e.g., diet, smoking status, and physical activity) at the baseline and follow-up assessments. Participants visited our research center to undergo a physical examination and provide a venous fasting blood sample after 8 hours of fasting for laboratory testing. A standardized analyst measured glucose (mg/dL) and triglycerides (mg/dL) by chemiluminescence (Acces2; Beckman Coulter) with a routine enzymatic colorimetric method (Selectra XL instrument, Randox). Glucose and triglycerides were used as continuous values to calibrate self-reported SBS.

Dietary intake was assessed with a semiquantitative Food Frequency Questionnaire to record the consumption frequency and standard portion size of 116 food items during the previous year. Total calorie intake was calculated by multiplying the consumption frequency of each food by its nutrient and calorie content and used as a continuous variable in the association analysis as kcal/day. (19) Smoking status was classified as nonsmokers, ex-smokers, and smokers. Physical activity (PA) was measured through the self-administered questionnaire, which estimates the time (hours/week) and intensity (light, moderate, and vigorous) of activities carried out during a typical week in the past year. We calculated PA as hours/day by dividing the hours/week of moderate- and vigorous-intensity activities between seven. Participants were considered physically inactive if they performed < 150 min/week of moderate activity or < 75 min/week of vigorous activity as established by the World Health Organization (WHO) recommendations for adults aged 18-64. (24) Sex was defined as female=0 and male=1. We also included other variables, such as sleeping time (hours/day), back pain (yes=1, no=0), BMI (kg/height (m²)), chronic diseases (yes=1, no=0 for DMT2, hypertension, depression, overweight/obesity), age (in years as a continuous variable, or as categories of <30, 30-39, 40-49, 50-59, >60 years), and an education level variable for elementary school (where yes=1, no=0), middle school (where yes=1, no=0), high school (where yes=1, no=0), university and postgraduate (where yes=1, no=0).

Statistical analyses

Self-administered questionnaire calibration study. We performed variable selection for the correction SBS model. The prediction model had accelerometer based SBS as the dependent variable (hours/day). We used predictor variables that explained the error of self-reported SBS and its possible relationship with the SBS values obtained with accelerometry. (15,16,25–27) The following explanatory variables were included: self-reported SBS, occupation, caloric intake, sex, moderate/vigorous physical activity, sleep time, back pain, BMI, triglycerides, glucose, chronic diseases, age, and education. We evaluated the natural form of the distribution of each continuous explicative variable, possible mathematical transformations, and interactions that could improve the prediction model.

Estimation model. We graphically identified the best potential predictors of SBS measured with accelerometry and pooled them in a GLM. Each variable was withdrawn, and we tested its

contribution to the model with their beta values and significance (p<0.01) to identify the best predictor combination. We evaluated the Akaike criterion and kept the models with the lowest value.

Cross-validation and agreement. Once we generated the predictive model in the training sample of 71 subjects, we tested it in the second half of 71 subjects, our testing sample. We used the predicted SBS with the equation or corrected and noncorrected vs. accelerometry values for categorical concordance analyses of terciles. Finally, we employed Bland‒Altman analyses to evaluate the agreement between the questionnaire's SBS corrected, without correction, and accelerometry values as hours/day in the training and testing samples. We found a possible group of prediction models and calculated the SBS in the total sample of HWCS participants. We eliminated models with more than 30% implausible SB predicted values. After obtaining the calibration model, we calculated the sedentarism correction as hours per day with the values obtained in the baseline and follow-up assessments.

Association between SBS change and adiposity increment. We used a fixed-effects modeling approach to estimate the relationship between SBS and adiposity change. (28)After reviewing the literature to identify potential confounders, we obtained an acyclic diagram of causality (DAG) with age, physical inactivity, sex, tobacco, and calorie intake to avoid confounding bias from the statistical analysis (Daggity program, see Additional file 1 “DAG figure sedentary behavior”). We performed a linear regression analysis with deltas or the differences between the follow-up and baseline values of body adiposity (kg) and SBS (hrs./d) and included possible confounders. All analyses were performed using Stata version 14 (Stata Corp LLC).

At baseline, the mean age of the 1,285 adult participants was 45.4 ± 12.6 years, and most subjects were in the 30-39 (23%) and 40-49 (30%) year age groups. Nearly half of the study participants reported high education levels (university and postgraduate studies.) The average body adiposity measures were 24.9 ± 8.1 and 26.8 ± 8.5 kg in the baseline and follow-up assessments. (Table 1).

Self-reported SBS without calibration were 2.8 ± 1.8 hours/day at baseline and 2.3 ± 1.6 hours/day at follow-up in the complete HWCS population (n=1,285). Corrected SBS was 7.6 hours/day at both baseline and follow-up. The proportion of adults who self-reported physical activity decreased from 56.9% at baseline to 46.2% at follow-up. The average intake of calories was estimated to be 2,136 calories in 2004 and approximately 1,879 calories in 2010. Finally, the proportion of participants who reported using tobacco decreased from 17.3 at baseline to 13.3 at follow-up (Table 1).

SBS correction.

The best predictors of SBS obtained by accelerometry in the 142 participants were self-reported daily SBS (hours/day), time spent in SBS that were occupation-related (hours/day), sleep time and its quadratic term (hours/day), BMI and its quadratic and cubic terms, triglycerides as mg/dL and their quadratic and cubic terms, glucose as mg/dL and their quadratic and cubic terms, sex (female), and education level (elementary school, secondary school, high school) (see Additional file 2 “Equations and concordance analysis”). We used the formerly mentioned variables to correct the self-reported SBS questionnaire values.

The kappa value increased from 0.13 (p=0.11) to 0.37 (p=0.000) in the training sample. Furthermore, in the testing sample, the kappa value increased from 0.03 (p=0.739) to 0.11 (p=0.18). Furthermore, Bland‒Altman's analysis showed that the agreement improved in the training and testing samples. Thus, the agreement limits and bias diminished in the corrected self-reported SBS values with the prediction model in the training and testing samples (see Additional file 2 “Equations and concordance analysis”).

We found an association between SBS and aging differences with significant adiposity increment. Furthermore, aging was positively and significantly associated with SBS (corrected values) increment. The rest of the theoretical confounders showed no association with SBS or body adiposity (Table 2).

Association between SBS and adiposity.

We found a nonsignificant and negative association between self-reported noncorrected SBS and body adiposity. In contrast, the corrected self-reported SBS values indicated that each additional daily hour of SBS from baseline to follow-up was related to an increase of 847 grams of body fat during the same period after adjusting for age, calorie intake, and physical activity category changes (Table 3). The results of the linear regression model that indicates the difference between SBS at follow-up and baseline are shown in Table 4. During the six-year follow-up period, a three-hour per day rise in corrected self-reported SBS was associated with a 2.5 kg increase in body adiposity (p<0.0001).

Table 1 General characteristics of HWCS participants in baseline and follow-up.^a

	2004-2006	2010-2012
*Characteristics*	n=1,285	n=1,285
Sex
Female, n (%)	951 (73.7)	___
Age, years	45.4 ± 12.6	___
Time between measurements (years)	___	6.8 ± 1.1
Age categories, n (%) *
<30	139 (10.8)	38 (3.0)
30-39	290 (22.6)	156 (12.1)
40-49	380 (29.6)	333 (25.9)
50-59	285 (22.1)	385 (30.0)
>60	191 (14.9)	373 (29.0)
Education level, % (n)^b
Basic	326 (26.3)	___
Medium	297 (24.0)	___
High	614 (49.6)	___
Weight (kg)^g	66.6 ± 12.8**	67.8± 13.3**
BMI (kg/m2) ^g	26.3± 4.2***	26.9 ± 4.3***
BMI categories, n (%)^c
Overweight
Yes	538 (41.9) ***	571 (44.4) ***
Obesity
Yes	222 (17.3) ***	249 (19.4) ***
Body fat (kg)^g	24.9 ± 8.1*	26.8 ± 8.5*
Self-reported SBS (hrs./day)^g	2.8 ± 1.8***	2.3 ± 1.6***
Physical activity (hrs./day)^{e, g}	1.0 ± 0.9*	0.8 ± 0.8*
Active, n (%)
Yes	732 (56.9)	593 (46.2)
Energy intake (kcal/day) ^f,g	2136.1 ± 850.6*	1878.9 ± 786.0*
Smoking, n (%)
Yes	217 (16.9) **	166 (12.9) **

Health Workers Cohort (HWCS).
Basic education: elementary and middle school, medium level: high school / technical schools, high level: graduated and postgraduate education.
Overweight: IMC>25 y <30; Obesity: IMC>30.
Additional minutes invested in a week doing activities with a MET value less than<1.5 at work, household, and recreation.
Additional minutes invested in moderate and vigorous weekly recreation and work activities (run, walk, bike, etc.).
Calorie or energy intake by day.
Continuous variables are presented as mean ± standard deviation.

*p<0.0001 **p<0.001 ***p<0.05

Table 2 Adiposity and SBS change due to the difference in age, calorie intake, physical activity, and tobacco use in HWCS (n=1,285).^a

	Change in adiposity (kg)	b [IC 95%]²	p
SBS (hrs/d)	0.913	[0.490,1.336]	0.000
Physically active ^b	-0.135	[-0.593, 0.323]	0.563
Aging ^c	0.593	[0.348, 0.838]	0.000
Caloric intake increment (100 kcal/day)	0.013	[-0.018, 0.043]	0.415
Tobacco use
Quit smoking ^d	0.468	[-0.538, 1.473]	0.362
Current smoker^e	0.459	[- 0.828, 1.747]	0.484
	Change in SBS (hours/d)	b [IC 95%]²	p
Physically active ^b	0.038	[-0.020, 0.097]	0.201
Aging^c	0.051	[0.019 0.082]	0.002
Caloric intake increment (100 kcal/day)	-0.001	[-0.004, 0.003]	0.790
Tobacco use
Quit smoking^d	-0.096	[-0.222, 0.037]	0.160
Smoker^e	-0.083	[-0.245, 0.086]	0.346

^aHealth Workers Cohort (HWCS).

^b Physically active at follow-up (moderate and vigorous physical activity> 150 minutes/week)

^cAge change during the follow-up.

^dQuit smoking at follow-up.

^eBecoming a smoker at follow-up.

Table 3 Change in adiposity due to difference in time invested in SBS in participants of HWCS (n=1, 285). ^a

	Changes in body adiposity (kg)^b
		Without calibration	IC [95%]	p
1. SBS change (hours/day)		-0.085	[-0.243, 0.074]	0.295
2. SBS change (hours/day) and age change (years)^c		-0.096	[-0.254, 0.060]	0.227
3. SBS (hours/day), calories intake (100 kcal/day), age change (years)^c, and physically active (yes)^d		-0.097	[-0.254, 0.061]	0.228
		Calibrated	IC [95%]	p
4. SBS change (hours/day)		0.913	[0 .490, 1.336]	0.000
5. SBS change (hours/day) and age change (years)^c		0.830	[0.409, 1.252]	0.000
6. SBS (hours/day), calories intake (100 kcal/day), age change (years)^c, and physically active (yes)^d		0.847	[0.425, 1.270]	0.000

Health Workers Cohort (HWCS).
Fixed effects models.
Age change during the follow-up.
Becoming physically active at follow-up.

Table 4 Change in adiposity due to difference in time invested in SBS in participants of HWCS (n=1, 285). ^{a, b}

	Changes in body adiposity (kg)
	Without correction	IC [95%]	p
1. SBS (hours/day delta)	-0.236	[-0.396, -0.076]	0.004
2. SBS delta (hours/day) and age delta (years)^c	-0.080	[-0.240,0.079]	0.325
3. SBS (hours/day delta), caloric intake (kcal/day delta), physical activity (hours/day delta), and age (years delta)^d	-0.082	[-0.193, -0.029]	0.146
	Calibrated	IC [95%]	p
1. SBS (hours/day delta)	0.915	[0 .444, 1.385]	0.000
2. SBS delta (hours/day) and age (years delta)^d	0. 870	[0.407,1.333]	0.000
3. SBS (hours/day delta), caloric intake (kcal/day delta), physical activity (hours/day delta), and age (years delta)^d	0.778	[0.476, 1.080]	0.000

Health Workers Cohort (HWCS).
Linear regression model.
b coefficients
Age change during the follow-up.

In this longitudinal study, we found that body fat gain was positively associated with SBS corrected using accelerometers in a cohort of health workers compared with noncorrected values. Different authors have reported a positive association between SBS and adiposity. Staiano et al. found that each additional hour invested in SBS raised by day at baseline and body adiposity at follow-up in 71 U.S. adults aged 20-35. (5) Golubic et al. identified that the change in fat mass due to SBS was 1.5 kg for every 1.5 hours; this weight is equivalent to 990 grams of body fat for every increasing hour of SBS at seven years of follow-up. (29) Drenowatz et al. found that an increase of an hour in SBS was associated with a 0.848 kg gain in body fat after one year of follow-up. (10) Other researchers observed an association between SBS and other populations' body weight and fat mass gain. (30,31) In their studies, Drenowatz and Golubic measured SBS with accelerometers and obtained comparable results, which helps to support the validity of our correction of the self-reported SBS information.

However, there is still no consensus about the relationship between SBS and adiposity. Ekelund et al. found no association between SBS, evaluated using heart rate monitors (HRMs) and bioelectrical impedance (BIE), and an increase in body adiposity in 393 middle-aged adults after 5.6 years of follow-up. (12) Using BIE and HRMs may explain the difference between their findings and the previously mentioned studies. HRMs have a low validity in measuring SBS compared to other approaches, such as accelerometry (32) Furthermore, BIE requires specific and validated equations to obtain accurate estimations of body adiposity.

Although accelerometers accurately measure the time spent in sedentarism, the requirements to achieve valid accelerometry information limit their use in large population samples and surveillance. Therefore, correction or calibration of self-reported SBS may be an affordable tool to improve the accuracy in estimating this behavior in epidemiological settings. Cleland and colleagues described a poor correlation and agreement between the self-reported global physical activity questionnaire (GPAQ) and the accelerometry measures. (33) These authors determined an average difference between self-reported SBS and accelerometry of almost 6 hours, like the difference we found.

Other authors, such as Gupta et al. and Metcalf et al., used BMI and SBS to correct self-reported SBS with accelerometry values, and their results support our findings. (15,17) We also observed that self-reported SBS and occupation related SBS were relevant predictors of accelerometry values, as did Gupta et al. (16) In addition to the variables we previously described, we explored glucose and triglycerides as predictors of SBS in the correction model. A plausible justification for using triglycerides and glucose to predict SBS is that these biomarkers are associated with lifestyle factors, such as diet and physical activity. (25–27) Although biochemical parameters such as glucose and triglycerides may limit our correction model's usefulness, these variables are not cost-inaccessible and offer the advantage of having a more accurate lifestyle estimation.

This study has some limitations. First, the period between the baseline and follow-up assessments was over six years without intermediate measurements. We could not approximate the

SBS and body fat change trajectories, so future studies with more SBS and body adiposity measures at shorter intervals are necessary. A second limitation is the sample size for the correction study and the fact that it was not feasible to choose the sample study at random. However, the sample of 142 subjects is like the size of the sample used in Metcalf’s calibration study.

Nonetheless, future studies with larger sample sizes are recommended to ensure major representativeness and variability. (17) A third limitation is that the HWCS participants are health workers and their family members, who are more likely to be educated, middle-income adults, healthier than the general population and reside in an urban area such as Central Mexico.

Despite these limitations, this study has methodological strengths, including its longitudinal design to evaluate changes in adiposity over time due to SBS. It allows us to estimate the association more accurately than a cross-sectional study. Furthermore, calibrating self-reported SBS with accelerometry values reduces the possibility of information bias and improves the accuracy of self-reported SBS values. Another relevant strength was the inclusion of potential confounders such as calorie and tobacco consumption, physical activity, and age. A fixed effect model also eliminated confounders due to sociodemographic characteristics such as sex, education, and socioeconomic status, which did not change during the follow-up assessment. We used DXA to evaluate body adiposity, which is used as a reference method to measure body composition. (34) Finally, the results of the present study may be helpful to populations with similar occupation related SBS, such as health system workers. Our results' relevance lies in the possibility of using our findings in public health programs to weaken SBS and automatized lifestyle impacts on the population’s health.

In conclusion, an hour spent in SBS change was associated with a body adiposity increase of 843 grams during a 6.7 average follow-up. The magnitude of the association is like those obtained in studies that used accelerometry as a sedentarism determination method. The former makes evident the validation of our correction results. Our findings highlight the relevance of evaluating the relationship between SBS and excessive body fat in populations with a high prevalence of overweight and obesity, such as the Mexican population. Finally, future investigations into SBS and their determinants’ influence on the obesity epidemic are necessary to establish effective prevention strategies at the population level.

SBS: sedentary behaviors.

HWCS: Health Worker Cohort Study.

DXA: dual X-ray absorptiometry.

DMT”: Diabetes Mellitus Type 2.

MET: metabolic equivalent.

CPM: counts per minute.

WHO: World Health Organization.

BMI: Body mass index.

GLM: Generalized linear model.

DAG: acyclic causal diagram.

HRMs: heart rate monitors.

BIE: bioelectrical impedance.

GPAQ: Global physical activity questionnaire.

Ethics approval and Consent to Participate. The study protocol was conducted according to the guidelines in the Declaration of Helsinki. All procedures involving research study participants were approved by the IRBs of the Mexican Social Security Institute (2005-785-012), the National Institute of Public Health (13CEI 17 007 36), and the Autonomous University of the Mexico State (1233008X0236). Written informed consent was obtained from all participants.

Availability of Data and Materials. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Consent for Publication. “Not applicable”

Funding. This research was supported by the Mexican Science and Technology Council (CONAHCYT, by its acronym in Spanish). Grant numbers: 7876, 97783, 26267 M, SALUD-2010-1-139776, CB-2013-01-221628, SALUD-201-01-161930.

Competing Interests. The authors declare that they have no competing interests.

Authors' Contribution

NM: conceptualized this work, cured data, performed formal statistical analysis, SBS self-reported validation study performance, written original draft, creation of models.

EM: leadership responsibility to perform statistical analysis, reviewing and editing

JS: provided study materials and mentorship to the core team and reviewed and edited the manuscript.

JMO: cured data, reviewed and edited the manuscript.

YNF: conceptualization of the validation study, writing, reviewing, and editing the manuscript.

AJ: Reviewed the validation study, provided accelerometers, and reviewed and edited the manuscript.

DS: Reviewed the validation study, provided accelerometers, and reviewed and edited the manuscript.

UV: Accelerometer data processing reviewed and edited the manuscript.

AO: Accelerometer data processing reviewed and edited the manuscript.

KG: leadership responsibility for research activity planning and execution, including mentorship external to the core team and creation of models, reviewing, and editing the manuscript.

Acknowledgments

We thank Bernard Waldman for helping to edit this manuscript.

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No competing interests reported.

Exploring the effect of sedentary behavior on increased adiposity in middle-aged adults

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Status:

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