Design and participants
One hundred and six physically active women aged 60+, the participants of a program run at the University of Third Age (U3A), volunteered to be was screened for the study. Of those, 51 failed to meet the study inclusion criteria which required the participants to be able to walk without a prosthetic aid, to not use medications for metabolic disorders, to not smoke cigarettes, and to submitting a participation consent form. Therefore, the 2009 study group consisted of 89 women.
The analysis of participants’ physical activity and biochemical parameters was conducted in 2009 and 2016. The 2016 study group was smaller (59 women) because 2 participants died, 7 could not be located, 5 could not be reached for other reasons, and 12 refused to participate in the study again.
The mean age of women assessed in 2016 was 62.9±4.3 years. Twenty-one of them (35.6%) were university graduates, 29 (59.2%) had secondary education, and 9 (15.2%) had basic vocational education. The participant questionnaires completed in 2016 showed that the women did not had change their dietary habits, start smoking cigarettes, or receive treatment for metabolic disorders between the measurements.
The research protocols of studies conducted in 2009 and 2016 were approved by the Ethics Commission at The Jerzy Kukuczka Academy of Physical Education (resolution no. 3/2009).
Biochemical and anthropometric measurements
Between 8:00 and 10:00 a.m., fasting blood samples were taken from participants and their systolic / diastolic blood pressure (SDP and DBP, respectively) was measured using a standard mercury sphygmomanometer. The results of two measurements taken at an interval of 15 minutes were averaged for analysis. The serum concentrations of glucose, high-density lipoprotein cholesterol (HDL-C), and serum triglycerides (TG) were determined using enzymatic assays and the commercially available diagnostic kits (Randox UK, cat. no. GL 2623, CH 200, CH 203, TR 1697). Serum was separated in the usual manner and analyzed immediately or kept frozen at –80oC until analyzed.
Waist circumference (WC) was determined to the nearest 0.5 cm using an anthropometric tape at midway between the lowest rib and the iliac crest in a standing position. PBF and VFA at the umbilical level were determined using an InBody 720 analyzer [L4-L5] [17, 18] as per the manufacturer’s instructions (Biospace Co., Ltd., Seoul, Korea).
The presence of MetS was determined in line with the NCEP/ATP III revised guidelines . According to the guidelines, MetS occurs when three or more of the following criteria are met: (1) WC ≥88 cm; (2) TG ≥150 mg/dl; 3) HDL-C <50 mg/dL; 4) systolic blood pressure (SBP) ≥130 mm Hg and diastolic blood pressure (DBP) ≥85 mm Hg; 5) fasting glucose level ≥100 mg/dl.
Physical activity assessment
In both 2009 and 2016, participants’ physical activity (PA) was measured using the accelerometers (ActiGraph GT1M, Manufacturing Technology Inc., FL, USA) after their PBF and VFA were determined. The accelerometers were worn by the participants in the small pockets of the elastic belts positioned near the right iliac crest at least 12h each day over a period of 8 days and were only removed for water exercises and before bedtime. The first day’s readings were excluded from analysis to be sure that the potential reactivity of participants did not compromise the reliability of measurements . All participants were instructed to record before going to bed the duration and type of each physical activity they performed during the day (e.g. sitting and watching TV, using the computer, sitting at school, commuting).
The time sampling interval of the accelerometers was set at 1 min, an epoch commonly used to measure free-living physical activity (PA) and in epidemiological research , and the step mode was activated. The accelerometers’ readings were processed in ActiLife v6.13.1 (Pensacola, FL, USA).
Data and statistical analysis
The statistical analysis of the data was performed in STATISTICA 12.5 (StatSoft, USA). The descriptive statistics below represent means and their 95% confidence intervals.
The data subjected to statistical analysis were the numbers and percentages of women who met, or did not meet, the diagnostic criteria for each MetS component in 2009 and 2016, as well as their daily numbers of steps as a measure of physical activity [21–24].
In order to divide women aged <65 and ≥65 years into physically high active and physically low active, the threshold values of 10.000 and 9.000 steps per day, respectively, were used. The first number was adopted from Bassett et al. , Freak-Poli et al. , Harris et al. , Tudor-Locke et al.  who recommend that adults take at least 10.000 steps per day to stay healthy. For women aged 65+, it was lowered to account for the likely effect of their age on their activity.
Using the number of steps the participants took each day, they were divided into four physical activity groups. Women in the LL group (low-low) were below the recommended activity thresholds in both 2009 and 2016. Women in the HL group (high-low) reached their activity thresholds in 2009 and those in the LH group (low-high) in 2016. The HH group (high-high) included women who reached or exceeded their activity thresholds in both 2009 and 2016.
Differences between participants’ anthropometric parameters, MetS components, and physical activity levels measured in 2009 and 2016, as well as between-group differences in the number of steps, were assessed for statistical significance using a paired t-test. The longitudinal changes in MetS components (the effect of time, TE), the between-group differences in MetS components (the effect of physical activity, PAE), and the associations between the groups’ physical activity and changes in MetS components (the interaction effect, INT) were assessed by a multivariate repeated-measures ANOVA (MANOVA). The effect size was determined by calculating eta squared (η2) as per the following formula: η2= SS effect/SS total, where SS effect is the sum of squares for a given effect and SS total is the total of squares for all effects, interactions, and errors . The 95% confidence intervals calculated for individual MetS components were also analyzed [29, 30].