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Inadequate physical activity is currently one of the leading risk factors for mortality worldwide. University students are a high-risk group in terms of rates of obesity and lack of physical activity. In recent years, activity trackers have become increasingly popular for measuring physical activity. The aim of the present study is to examine whether university students in Hungary meet the health recommendations (10,000 steps/day) for physical activity and investigate the impact of different variables (semester-exam period, days-weekdays, days, months, sex) on the level of physical activity in free-living conditions for 3 months period. In free-living conditions, 57 healthy university students (male: 25 female: 32 mean age: 19.50 SD = 1.58) wore MiBand 1S activity tracker for 3 months. Independent sample t-tests were used to explore differences between sexes. A One-way analysis of variance (ANOVA) was used to explore differences in measures among different grouping variables and step count. A Two-way ANOVA was conducted to test for differences in the number of steps by days of the week, months, seasons and for sex differences. Tukey HSD post-hoc tests were used to examine significant differences. Students in the study achieved 10,000 steps per day on 17% of days (minimum: 0%; maximum: 76.5%; median: 11.1%). Unfortunately, 70% of the participants did not comply the 10,000 steps at least 80% of the days studied. No statistical difference were found between sexes. However, significant differences were found between BMI categories (underweight <18.50 kg/m2; normal range 18.50–24.99 kg/m2; overweight: 25.00–29.99 kg/m2 obese > 30 kg/m2, the number of steps in the overweight category was significantly lower (F = 72.073, p < 0.001). The average daily steps were significantly higher in autumn (t = 11.457, p < 0.001) than in winter. During exam period average steps/day were significantly lower than during fall semester (t = 13.696, p < 0.001). On weekdays, steps were significantly higher than on weekends (F = 14.017, p < 0.001), and even within this, the greatest physical activity can be done by the middle of the week. Our data suggest that university students may be priority groups for future physical activity interventions. Commercial activity trackers provide huge amount of data for relatively low cost therefore it has the potential to objectively analyze physical activity and plan interventions.

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The classic conceptual modeling around business processes followed by the 'bus matrix' methodology of designing the data cubes of data warehouses (Kimball & Ross 2013). For a serious system, such a quantity of management questions and dimensions, the bus matrix results a difficult-to-understand conceptual data model. The subject of automation and conceptual design-to which many individual methods already have been developed-are relevant topics in today's literature also. In the 2010s data warehouse projects were realized in Hungarian higher education to inform the decision makers of the universities about their own institutions. As we participated in this project in 2009-2010, we faced that our bus matrix at the end contained about 80-120 indicators with nearly 200 dimensions (dimensional attributes), therefore we worked on the early stenography to formalize the management question. We provide a kind of 'business intelligence problem solving thinking' and a kind of descriptive language that can serve it and present a method which has two novelties compared to formers: 1. It is based on the management questions and its visualization. 2. As a kind of stenography, it is always based on the terminology corresponding to the current problem, so it forms an intermediate language for the data model. We introduce our method through an example in a popular research area which is activity tracking.