Sample The inclusion criteria for this study were that the subjects needed selleckchem to be 18 years of age or older on the date of being approached regarding the interview, needed to be living in the home of the family that was drawn, could not be practicing any form of leisure-time physical activity during the month preceding the interview and could not Inhibitors,Modulators,Libraries be practicing physical activity for transportation (walking or cycling) of duration greater than or equal to 150 minutes in the week preceding the interview.

The exclusion criteria were as follows: a) type 2 diabetes; b) severe arterial Inhibitors,Modulators,Libraries hypertension or using beta-blockers for treating hypertension or cardiovascular disease; c) a health problem or disease that would Inhibitors,Modulators,Libraries make the individual incapable of leaving home and making the journey to practice physical activity at the time of the interview; d) diseases at advanced stages, such as cancer, cirrhosis, chronic kidney disease, Chagas disease, chronic obstructive pulmonary disease, chronic bronchitis, osteoporosis or severe depression (information gathered by questionnaires); e) a cognitive problem or disease that would prevent the individual from answering the questionnaire alone; f) morbidly obese, with a body mass index (BMI) greater than or equal to 40 kg.m-2; g) plans to move house over the two-year period subsequent to the date of being approached; and h) pregnancy. It was defined that all members of the family drawn who were not covered by any of the exclusion criteria would be invited to participate in the study.

According to published data, Inhibitors,Modulators,Libraries the adhesion to interventions (subjects who received the invitation and agreed to participate) was 63% [26]. Therefore, the challenge of both interventions was to stimulate a more active lifestyle for individuals who are not initially engaged in leisure-time Inhibitors,Modulators,Libraries physical activity, without any kind of chronic disease, and not considering becoming physically active as a priority. To calculate the sample size, Brefeldin_A results from previous representative population-based surveys among adults living in Ermelino Matarazzo were used [27]. For adults living in Ermelino Matarazzo who were not active in transportation, the mean time of leisure-time physical activity was 68.1 minutes per week (standard deviation=146.1 minutes.week-1) [28]. For the individuals targeted in this intervention study (adults who were physically inactive during leisure time and insufficiently active in transportation), the goal was to reach a mean 150 minutes of leisure-time or commuting physical activity per week. The goal of stimulating the practice of 150 minutes of physical activity during leisure time or commuting is in agreement with Brazilian studies published previously [29,30].

Our results have important selleck implications for the design and implementation of NCD prevention programs that aim to improve physical activity. The pooled RRs suggest that a well-designed mass media campaign may increase the likelihood of achieving sufficient walking by 53% which is equivalent to about 80 minutes per week. A recent meta-analysis of prospective studies found that an additional 150 minutes of walking over 5 days led to a 19% reduction in risk of coronary heart disease [23]. Applying this effect size to our results indicates a potential 11% reduction in risk of coronary heart disease following a well-designed mass media campaign. Results from four previous systematic reviews of mass media campaigns and physical activity were mixed.

The investigators qualitatively assessed the totality of evidence but did not conduct a meta-analysis. Two previous reviews concluded that mass media have either no effect or a very small effect on physical activity [8,9] and another review suggested a significant effect on physical activity levels without specifying the effect size or the type of activities that were influenced [7]. The original studies included in these four reviews reported different outcome measures and used widely different evaluation methods including sub-optimal designs such as post-campaign cross-sectional surveys [7,8]. Systematic reviews of mass media and other health behaviors have faced a similar challenge. For instance, mass media interventions were found effective in encouraging their audience to quit smoking, but the effects were derived from heterogeneous studies of variable quality [24,25].

Similarly, pooled analyses of mass media and diet found a beneficial effect but the pooled studies were widely different in design and quality [26-28]. Other interventions to promote physical activity have been systematically reviewed. Several prior meta-analyses have reported the effects of pedometers [3], internet-based interventions [4,5] and telephone calls [6] on physical activity. The pooled effects were generally larger than those we observed for media campaigns, but similar to those reported for exercise referral schemes [29] and computer-tailored interventions [3,30]. Strengths and limitations We selected 9 moderate to high-quality studies and extracted comparable metrics of effect.

When comparable metrics were not reported, we used the reported results to calculate a common metric for pooling. We explored the sources of heterogeneity across studies using meta-regression. Cilengitide However, our systematic review was still limited by the marked differences in the reported outcomes of the selected studies. We did not have sufficient power to detect differences across studies by study-level characteristics due to the small number of selected studies.

The vertical jump test was performed on an electronic mat connected to a digital timer that registered the total time in the air. From these data, the height of the jump in centimetres was automatically selleckchem calculated by the computer included in the standard equipment. All vertical jumps were performed from a standing position, and participants were first required to jump onto the mat with both feet, and then make a maximal vertical jump. Each subject performed three vertical jumps of which the highest jump (cm) was recorded. The intra-individual test variability, evaluated as the coefficient of variation for repeated measurements in 21 children, was 5.9%. Methodology of physical activity measurement has previously been presented in detail [1,15,16].

Physical activity was assessed using the MTI accelerometer, model 7164 for four consecutive days. Accelerometer data are averaged over a period of time called an epoch. A recording epoch of ten seconds was selected for this study. A SAS-based software was used to analyse all accelerometer data. This software automatically deletes missing data, defined as continuous sequences of 60 consecutive epochs (i.e. 10 minutes) or more with zero counts. This was done based on the assumption that all such sequences of zeroes lasting longer than ten minutes were caused by the accelerometer not being worn. In order to minimise inter-instrumental variation, all accelerometers were calibrated against a standardised vertical movement. Mean activity was considered to be the total accelerometer counts per valid minute of monitoring (mean counts/min).

Time spent performing above 3 METs was considered to reflect moderate to vigorous activity (MVPA), and time spent above 6 METs was considered to reflect vigorous activity (VPA). Cut-off points used for all children were > 167 counts/epoch for MVPA and > 583 counts/epoch for VPA [17,18]. Baseline measurements were performed at the commencement of school. Follow-up evaluations were done the same month one year later in the intervention group and two years later in the control group. Changes per 365 days were calculated. However, all the children stayed pre-pubertal in Tanner stage I during the study and pre-pubertal growth seems to be linear. One Swedish study reported that the growth rates in children are linear from age six to peak height velocity.

In girls peak height velocity is usually reached at a mean age of 11.7 years in Tanner stage III, whereas peak bone mineral accrual occurs at a mean age of 12.5 years in Tanner stage IV [19]. In boys both peak height velocity and peak bone mineral accrual are reached at an even higher age [19]. These observations are supported in Australian children Cilengitide [20], in which peak bone mass accrual and peak height velocity occurred in Tanner stage II or later, whereas the growth and bone mineral accrual were linear in Tanner stage I and during the ages followed in this study.