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  • The present investigation is a cross-sectional analysis on baseline data within the frame of the PREDIMED-PLUS study, a 6-year multicenter, randomized, parallel-group, primary prevention clinical trial conducted in Spain to assess the effect on CVD morbimortality of an intensive weight loss intervention program based on an energy-restricted traditional Mediterranean diet, PA promotion and behavioral support, in comparison with an usual care intervention only with energy-unrestricted Mediterranean diet (control group). A more detailed description of the PREDIMED-PLUS study is available at http://predimedplus.com/. This study was registered at the International Standard Randomized Controlled Trial (ISRCT; http://www.isrctn.com/ISRCTN89898870) with number 89898870. Registration date: 24 July 2014. From October 2013 to October 2016, a total of 5776 participants were recruited and randomized in 22 centres from different universities, hospitals and research institutes of Spain. Each of these centres recruited participants from several Primary Care Health Facilities belonging to the National Health System. The eligible participants were community-dwelling adults (aged 55–75 in men; 60–75 in women) with overweight/obesity [body mass index (BMI) ≥27 and <40 kg/m2], who met at least three components of the MetS according to the updated harmonized criteria of the International Diabetes Federation and the American Heart Association and National Heart, Lung and Blood Institute [32]. All participants included in the current analysis presented data on PA, sedentary behaviors and sleeping time. All participants provided written informed consent, and the study protocol and procedures were approved according to the ethical standards of the Declaration of Helsinki by all the participating institutions: CEI Provincial de Málaga, CEI de los Hospitales Universitarios Virgen Macarena y Virgen del Rocío, CEI de la Universidad de Navarra, CEI de las Illes Balears, CEIC del Hospital Clínic de Barcelona, CEIC del Parc de Salut Mar, CEIC del Hospital Universitari Sant Joan de Reus, CEI del Hospital Universitario San Cecilio, CEIC de la Fundación Jiménez Díaz, CEIC Euskadi, CEI en Humanos de la Universidad de Valencia, CEIC del Hospital Universitario de Gran Canaria Doctor Negrín, CEIC del Hospital Universitario de Bellvitge, CEI de Córdoba, CEI de Instituto Madrileño De Estudios Avanzados, CEIC del Hospital Clínico San Carlos, CEI Provincial de Málaga, CEI de las Illes Balears, CCEI de la Investigación Biomédica de Andalucía and CEIC de León. Sedentary behaviours were evaluated on weekdays and weekends with the validated Nurses’ Health Study questionnaire for sedentary behaviours [33], consisting of a set of open-ended questions assessing the average daily time spent over the last year in watching TV, sitting while using computer, sitting on journeys (for work purposes or leisure time, as driver or passenger car, subway, bus, etc) and total sitting. Answers included 12 categories ranging from 0 to ≥9 h/day of sitting time for the corresponding activity. Because TV time is the most prevalent sedentary behavior, for which previous investigations among aged population have suggested to fairly capture total sedentary time [34] and to consistently associate to higher risk of various cardiometabolic risk factors and cardiovascular mortality in a dose-response fashion [8], the present study has evaluated TV time as a proxy for sedentary behaviors. Leisure-time PA was assessed using the validated REGICOR questionnaire [35] (including questions to collect information the type of activity, frequency (number of days) and duration (min/day). The intensity was assigned based on the compendium of PA [36]. A trained interviewer collected the required information about 6 types of activities performed during a conventional month: brisk walking (5 MET), walking at a slow/normal pace (4 MET), walking in the countryside (6 MET), climbing stairs (7 MET), working in the garden (5 MET), exercise or play sports at home, outdoors or in a gym (11 MET). According to PA intensity, activities were categorized into light PA <4.0 MET, moderate PA 4–5.5 MET and vigorous PA ≥6.0 MET. Total leisure-time PA-related energy expenditure was estimated as the summed product of frequency, duration and intensity of each activity divided by 30 days/month (MET·min/day). For the present study, leisure-time PA was categorized in light PA (including leisurely stroll or walk) and MVPA (including the sum for any activity of moderate or greater intensity). Finally, PA time was computed as the sum of frequency*duration of each activity divided by 30 to obtain the number of min/day. Regarding sleep, participants reported their average daily sleeping time for both weekdays and weekends, using the non-validated open question “How many hours do you sleep on average per day on weekdays and weekends?” Study outcomes were obesity, T2D and individual components of the MetS. Obesity was defined as BMI ≥30 kg/m2. T2D was defined as previous clinical diagnosis of diabetes, or HbA1c levels ≥6.5% or use of antidiabetic medication at baseline. Individual components of the MetS were defined as follows: abdominal obesity (waist circumference ≥102 cm in men; ≥88 cm in women), high blood pressure (systolic and/or diastolic ≥130/85 mmHg or using antihypertensive drugs), hyperglycaemia (glucose ≥5.5 mmol/L or taking medication for elevated glucose), hypertriglyceridemia (triglycerides ≥1.7 mmol/L or taking triglyceride-lowering medication), low HDL-cholesterol (HDL-c <1.03 mmol/L in men and <1.3 mmol/L in women or taking HDL-c raising medication) [32]. The covariates were evaluated using self-reported questionnaires about socio-demographic factors (sex, age, education, and marital and employment status), smoking habits, personal and family history of illness, medical conditions, medication use and a 17-item screening questionnaire assessing adherence to an energy-restricted Mediterranean diet. Anthropometric variables and blood pressure were determined by trained staff and in accordance with the PREDIMED-PLUS operations protocol. Weight and height were measured with calibrated scales and a wall-mounted stadiometer, respectively. BMI was calculated as the weight in kilograms divided by the height in meters squared. Waist circumference was measured midway between the lowest rib and the iliac crest, after normal exhalation, using an anthropometric tape. Blood pressure was measured in triplicate with the use of a validated semiautomatic oscillometer (Omron HEM-705CP, Netherlands) while the participant was in a seated position after 5 minutes of rest. Blood samples were collected after 12 hours overnight fast and biochemical analyses were performed on fasting plasma glucose, HDL-c and triglycerides concentrations in local laboratories using standard enzymatic methods. In order to provide with more detailed information, baseline characteristics are presented according to categories of total leisure-time PA in min/day (<15, from 15 to < 30, from 30 to < 60, from 60 to < 120 and ≥120) as means ± SD and number (%) by using one-way ANOVA or chi-square tests as appropriate. Given the cross-sectional design, Cox regression models with constant time of follow-up for all individuals and robust variance estimates were fitted to estimate RR and 95% confidence intervals (CI) for each study outcome (obesity, T2D, and individual components of the MetS, all as dichotomous variables) per 1-h/day increase in time spent in each activity separately (TV-viewing, light PA and MVPA and sleeping, all as continuous variables). Correspondingly, the time t was set to a constant (t = 1). According to methodologists, this model is better suited than logistic regression for cross-sectional studies with frequent prevalent outcomes, such as the present study, since it avoids the overestimation of the prevalence ratios derived from the odds ratios when logistic regression is applied in analysis with frequent outcomes [37,38]. A crude model and three multivarible-adjusted Cox regression models were fitted as follows: a) model 1 [adjusted for age (continuous), sex, education level (illiterate/primary education, secondary education and academic/graduate), smoking status (never smoker, past smoker and current smoker), marital status (single/divorced, married and widower), family history of coronary heart disease (yes or no) and energy-restricted Mediterranean diet adherence (score 0 to 17 items, in categories of <8 or ≥9 items)], b) model 2 [model 1 plus the time spent on the rest of the activities to precisely assess the independent effect of an activity]and c) model 3 [model 2 plus each of the other components of the MetS, only for the associations with each component of the MetS]. All models were stratified by recruiting center. In order to correct for multiple testing, the Benjamini-Hochberg approach was applied to calculate false discovery rate q values [39]. Effect modification by sex, age (≤65, >65 years) and the exposure variables (time spent in sleeping time, TV-viewing, light PA and MVPA) on each outcome was evaluated by calculating te likelihood ratio test between the fully adjusted model and the same model adding the interaction product-term. All analysis testing for effect modifications by sex and age showed no statistical significance (P >.40 for all interactions). Taking advantage of the interpretation and the relevance to public health recommendations, we employed isotemporal substitution modeling to estimate the theoretical association of replacing 1-h/day from one activity for 1-h/day of another activity on the prevalence of each outcome, adjusting for potential confounders as detailed previously in models 2 and 3. For these analyses, all activity variables (e.g., time spent in TV-viewing, light PA, and MVPA), except the activity of interest which was dropped (e.g., sleeping time), were entered simultaneously into the models, along with a total discretionary time and covariates as follows: h(t) = h0(t) exp [β1(TV-viewing) + β2(light PA) + β3(MVPA) + β4(total discretionary time) + β5(covariates)], where t = 1. Total discretionary time was computed as a result of the sum of hours spent in TV-viewing, light PA, MVPA and sleeping. Therefore, it is assumed that the model was isotemporal when including the total discretionary time variable herein. Thus, the Cox regression estimates for the included activities variables reflects the RR for each outcome observed when the time spent in these activities increases 1-h/day because the time spent in the omitted activity (e.g., sleeping) decreases 1-h/day. Finally, because PA and TV-viewing are two closely-related lifestyle behaviours concerning to cadiometabolic outcomes such as obesity and T2D [40] we explored the joint associations of combining MVPA and time spent watching TV on obesity and T2D. For this purpose, MVPA was first dichotomized into meeting or not meeting current WHO recommendations [41] for MVPA set in ≥2.5 h/week (yes/no). Time spent in watching TV (in hours) was categorized in three approximately equally distributed groups: low TV (≤2h/day), medium TV (>2 to ≤4h/day) and high TV (>4h/day). Therefore, each participant was cross-allocated to one of the six joint categories and meeting MVPA recommendations and low TV group was considered as the reference category. The interaction between meeting or not meeting the recommendations for MVPA and time spent watching TV in their associations with each outcome was examined by calculating the likelihood ratio test between the fully adjusted model and the same model including the interaction product-term (P>.30 for all the interactions). Significance for all statistical tests was P < .05 for bilateral contrast. All analyses were cross-sectional, and performed using Stata (14.0, StataCorp LP, Tx. USA).
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