Sociodemographic determinants of the level of physical activity in residents of eastern Poland

doi:10.21203/rs.3.rs-4944590/v1

Background

Post-industrial societies that benefit from the development of science, technology and subsequent inventions that relieve people of everyday duties, on the one hand, have more free time, but on the other hand face a greater temptation of laziness and lack of physical activity. Common diseases increasingly resulting from limited physical activity (PA) mean that physical activity is no longer just a way to spend free time, but a necessity in the field of health care. This necessity obliges us to undertake research that allows for recognizing factors influencing the level of physical activity in individual societies.

Objective

The conducted research aimed at identifying sociodemographic factors that would determine the level of physical activity in women and men from the Biała Podlaska district in eastern Poland.

Participants

The group consisted of 173 adults, including 71 women and 102 men from eastern Poland.

Methods

The presented research was conducted in the years 2018–2020 as part of the international EUPASMOS Plus project. The collected sociodemographic data of the respondents and the results of physical activity monitoring with the use of the GPAQ questionnaire and the RM42 accelerometer − 24/7 allowed for an analysis of the factors determining the physical activity undertaken by the respondents, as well as the comparison of the obtained data with the use of the above-mentioned tools.

Results

The results of the research on sociodemographic factors conditioning the physical activity of the examined persons, obtained from the objective tool (Acceleromete RM42) and a subjective one for measuring physical activity (GPAQ questionnaire) showed some discrepancies. However, the established consistency of the research results using the above-mentioned tools allows for formulating the following conclusions: women from the Biała Podlaska district are more active than men. Older people more often undertake PA of lower intensity, giving up high-intensity efforts. The respondents declaring a higher subjective assessment of their health are more physically active.

Conclusions

The results obtained are varied and depend on the used tool. They indicate an enormous importance of the tool used in the study on physical activity.

physical activity

health

RM42 accelerometer

GPAQ

The World Health Organization (WHO) warns that global estimates from 2010 indicated that 27.5% of adults [1] and 81% of youth [2] do not meet the recommendations for health-promoting physical activity [3]. This trend has also continued over the last decade. Meanwhile, regular physical activity is considered a preventive and therapeutic behavior in the case of non-communicable diseases, including type 2 diabetes, cancer, circulatory system diseases and mental illnesses. Among the many factors determining physical activity, the WHO distinguished, among others, gender - showing that in most countries women are less active than men [4]. Another important factor is economic conditions, the analysis of which indicated differences in the undertaking of physical activity among people with a higher and lower economic status [4]. The importance of physical activity in the prevention of non-communicable diseases, as well as the relatively small percentage of people undertaking health-promoting physical activity, encourages conducting research in this area of research groups from particular countries, as well as searching for such research methods and tools that will allow for collecting reliable results comparable with other populations. The results of studies conducted around the world confirm the beneficial effect of physical activity on the broadly understood health of respondents [5–11].

The following paper presents the results of research that is part of the international EUPASMOS Plus project, carried out in the years 2018–2020. This project covered 18 European countries, and its main goal was to develop an integrated system for monitoring physical activity, recreational and sports activity, and sedentary behavior based on innovative tools for tracking population changes in the above areas. The following paper presents sociodemographic factors that influence the undertaking of physical activity by women and men living in the Biała Podlaska district in eastern Poland.

Aim of the study and research questions

Physical activity, if undertaken regularly, can be an excellent tool for health prevention. This is evidenced by the recommendations regarding health-promoting physical activity published by the World Health Organization. However, the problem of today's societies is very often a sedentary lifestyle and, as a consequence, insufficient physical activity. Lack of exercise (hypokinesia) together with other negative factors cause many non-communicable diseases. The aim of the analyses was to indicate the sociodemographic factors that determine the level of physical activity of people living in the Biała Podlaska district, in the area of eastern Poland.

Based on the formulated objective, the following research question was posed:

What sociodemographic factors determine the level of physical activity of the residents in the Biała Podlaska district in eastern Poland?

The study material concerned the results on a group of 173 people of both sexes (71 women and 102 men) aged 18+, participants of the EUPASMOS plus study. The selection of the group was made in a purposeful manner:

- the inclusion criteria included:

• submitting a declaration of consent to participate in the study;

• age 18 years or more;

• no health contraindications to undertaking health-promoting physical activity.

- exclusion criteria included:

• refusal to participate in the study;

• age below 18;

• health condition precluding participation in any physical activity.

The above presented study was approved of by resolution no. 6/2018, the bioethics committee of the PSW in Biała Podlaska.

The research conducted in the years 2018–2020 was part of the international EUPASMOS PLUS program, financed by the European Commission under the Erasmus + Program grant no. 603328-EPP-1-2018-1-PT-SPO-SCP. The aim of the research was to develop a harmonized system for monitoring physical activity, sedentary behavior and participation in sports. The research process was carried out based on a strictly defined methodology including: anthropometric and sociodemographic studies and monitoring of physical activity using the RM42 accelerometer. The subjects were trained in how to put on and wear the accelerometer and document cases of not wearing the device. Each person also kept a physical activity diary, in which recreational and sports activity was reported. The subjects were required to wear the accelerometer for 7 consecutive days (24/7). On the eighth day, the study participants were asked to report to the research laboratory to submit accelerometers and physical activity diaries. Interviews using physical activity questionnaires were also conducted that day. Participation in the study was free of charge and voluntary.

The data collected during the research process were coded in a database that included both quantitative and categorical variables, and then subjected to in-depth statistical analysis using TIBCO Software Inc. (2017). The Statistica data analysis software system, version 13.

In order to answer the research question, statistical analyses were conducted using the IBM SPSS Statistics 26 package. It was used to perform basic descriptive statistics analysis with the Shapiro-Wilk test, the Mann-Whitney test and Spearman's rho correlation analysis. The significance level was considered to be α = 0.05.

The result of the Shapiro-Wilk test for most of the entered variables turned out to be statistically significant, which means that their distributions significantly deviate from the normal distribution. It should also be noted that the skewness of the distribution of these variables often exceeded the absolute value of 2, which means that their distributions are asymmetric to a significant extent. Therefore, it was considered justified to conduct an analysis based on nonparametric tests.

Sociodemographic characteristics

The group of the respondents involved people aged 19 to 78 (M = 46.07; SD = 18.19), the majority of whom were men (59.0%). The surveyed persons lived together with 1–8 people (Me = 2.00) (Table 1).

Table 1

*Basic descriptive statistics of quantitative sociodemographic variables*
	M	Me	SD	Min.	Maks.
Age	46,07	47,00	18,19	19,00	78,00
Number of persons in the household	2,80	2,00	1,36	1,00	8,00

The majority of the respondents lived in houses (57.8%) located in small towns or suburbs (78.0%). Almost one third of them declared higher education degree, master's degree (35.3%), or secondary education (33.5%). The declared health status of most people (54.9%) was "good" (Table 2).

Table 2

*Percentage distributions of nominal and ordinal sociodemographic variables*
		N	%
Gender	Men	102	59,0
Gender	Women	71	41,0
Documented education	Secondary	58	33,5
	Post-Secondary	30	17,3
	Bachelor's or equivalent	11	6,4
	Master's or equivalent	61	35,3
	PhD or equivalent	13	7,5
Type of residence	Flat	73	42,2
Type of residence	House	100	57,8
Place of residence	Big city	1	0,6
	Small town	135	78,0
	Countryside	37	21,4
Health condition	Very good	29	16,8
	Good	95	54,9
	Moderate	48	27,7
	Bad	1	0,6
Age group	< 30 years	43	24,9
	30–39 years	23	13,3
	40–49 years	28	16,2
	≥ 50 years	79	45,7

Sociodemographic factors influencing the level of physical activity in the respondents assessed using the RM42 accelerometer.

In order to search for a relationship between the accelerometer data and sociodemographic variables, the Mann-Whitney test was performed to check the relationship with gender and place of residence, and Spearman's rho correlation analysis was performed to check the relationship with other variables.

First, the differences between the genders in terms of accelerometer variables were verified. The results of the analysis are presented in Table 3.

Table 3

*Comparison of women and men’s physical activity with regard to the data from accelerometers*
	Men (n = 102)			Women (n = 71)
	average rank	Mdn	IQR	average rank	Mdn	IQR	Z	p	η2
LPA	84,92	4:20:51	01:35:57	89,99	4:18:27	02:23:08	-0,66	0,512	< 0,01
MVPA	77,04	0:45:05	00:27:55	101,31	1:01:56	00:59:00	-3,14	0,002	0,06
VPA	82,19	0:00:08	00:01:02	93,92	0:00:20	00:04:27	-1,56	0,120	0,01
PA0-5	90,85	2:52:39	00:57:05	81,46	2:42:51	01:12:05	-1,21	0,225	0,01
PA5-10	83,83	0:56:46	00:30:34	91,55	0:58:59	00:36:59	-1,00	0,319	0,01
PA10+	78,29	1:05:23	01:02:23	99,51	1:28:33	01:28:51	-2,74	0,006	0,04
Steps	81,01	7170,57	3250,47	95,61	8358,86	5091,43	-1,89	0,059	0,02

LPA – low intensity physical activity; MVPA – moderate intensity physical activity; VPA – high intensity physical activity; PA0-5 – physical activity from 0 to 5 minutes; PA5-10 – physical activity up to 10 minutes; PA10+ – physical activity over 10 minutes; Steps – number of steps.

The analysis showed statistically significant differences between women and men in terms of moderate-intensity exercise (MVPA) and physical activity lasting more than 10 minutes (PA10+). It turned out that women were characterized by significantly longer daily time of moderately intensive physical activity, and longer time of physical activity above 10 minutes compared to men. In terms of the remaining variables, no statistically significant differences between the sexes were noted.

The next correlation that was checked was whether there was a difference between the physical activity undertaken by rural and urban residents with regard to the accelerometer data. The results of the analysis are presented in Table 4.

Table 4

*Comparison of the physical activity of rural and urban residents with regard to the accelerometer data*
	City (n = 135)			Countryside (n = 37)
	Average rank	Mdn	IQR	Average rank	Mdn	IQR	Z	p	η2
LPA	89,09	4:20:47	01:54:47	77,04	3:59:44	01:48:10	-1,30	0,192	0,01
MVPA	86,58	0:50:01	00:40:23	86,20	0:47:16	00:38:33	-0,04	0,967	< 0,01
VPA	85,77	0:00:09	00:02:49	89,16	0:00:15	00:01:44	-0,38	0,706	< 0,01
PA0-5	88,81	2:52:32	00:58:42	78,05	2:42:18	01:11:19	-1,16	0,244	0,01
PA5-10	89,75	0:58:59	00:30:13	74,65	0:52:25	00:25:51	-1,63	0,102	0,02
PA10+	88,48	1:18:21	01:06:42	79,27	1:07:19	01:12:58	-1,00	0,319	0,01
Steps	88,13	7661,14	3936,00	80,54	7357,29	3073,58	-0,82	0,411	< 0,01

LPA – low intensity physical activity; MVPA – moderate intensity physical activity; VPA – high intensity physical activity; PA0-5 – physical activity from 0 to 5 minutes; PA5-10 – physical activity up to 10 minutes; PA10+ – physical activity over 10 minutes; Steps – number of steps.

The analysis did not show any statistically significant differences between city dwellers and rural dwellers with regard to the accelerometer measurement results, which indicates that, regardless of the place of residence, the subjects showed similar physical activity parameters.

The next examined correlation was the connection between physical activity and age, education and health status of the respondents. The results of the analysis are presented in Table 5.

Table 5

*Correlation of the accelerometer data with regard to age, education and health status*
	Age	Documented education	Health condition
LPA	0,16*	0,10	-0,05
MVPA	-0,36***	0,04	-0,28***
VPA	-0,51***	0,09	-0,38***
PA0-5	0,17*	0,17*	-0,02
PA5-10	< 0,01	0,08	-0,11
PA10+	-0,09	0,03	-0,19*
Steps	-0,26***	0,07	-0,23**
* p < 0,050; p < 0,010; * p < 0,001

LPA – low intensity physical activity; MVPA – moderate intensity physical activity; VPA – high intensity physical activity; PA0-5 – physical activity from 0 to 5 minutes; PA5-10 – physical activity up to 10 minutes; PA10+ – physical activity over 10 minutes; Steps – number of steps.

The analysis showed statistically significant negative correspondence between age and moderate and vigorous exercise (MVPA, VPA), as well as the number of steps (Steps). The results also indicated statistically significant positive correlation between age and light physical exercise (LPA). Weak positive correlations were observed between the education degree and physical activity up to 5 min (PA0-5). It also turned out that with the increase in the subjective assessment of health, moderate and vigorous physical activity (MVPA, VPA), physical activity above 10 min (PA10+) and the number of steps decreased.

Sociodemographic factors influencing the level of physical activity of respondents in the studies conducted using the GPAQ questionnaire

In order to compare the results of the objective (accelerometer) and subjective (GPAQ) studies, it was checked whether there was any correspondence between the data from the GPAQ questionnaire and the sociodemographic variables of the examined persons.

For this purpose, the Mann-Whitney test was used to check the relationship with gender and place of residence, and Spearman's rho correlation analysis was used to check the relationship with other variables.

First, the differences between the genders in the questionnaire variables were verified. The results of the analysis are presented in Table 6.

Table 6

*Comparison of physical activity of women and men with regard to the variables from the GPAQ questionnaire*
	Men (n = 95)			Women (n = 65)
	average rank	Mdn	IQR	average rank	Mdn	IQR	Z	p	η2
GPAQ
WorkVigDays	67,12	0,00	0,00	81,33	0,00	1,00	-2,62	0,009	0,05
WorkVigMinDay	67,91	0,00	0,00	80,21	0,00	30,00	-2,56	0,011	0,05
WorkModDays	65,45	0,00	4,00	83,70	3,00	5,00	-2,72	0,007	0,05
WorkModMinDay	63,95	0,00	60,00	85,82	60,00	180,00	-3,24	0,001	0,07
TravelDays	72,30	3,00	5,00	73,99	3,00	5,00	-0,24	0,808	< 0,01
TravelMinDay	69,03	30,00	60,00	78,63	40,00	86,25	-1,38	0,169	0,01
SportsVigDays	65,79	0,50	3,00	81,90	2,00	4,00	-2,39	0,017	0,04
SportsVigMinDay	67,05	0,00	55,00	81,43	30,00	60,00	-2,17	0,030	0,03
SportsModDays	70,33	1,00	3,00	76,78	2,00	3,00	-0,94	0,346	0,01
SportsModMinDay	69,18	20,00	60,00	78,41	30,00	60,00	-1,36	0,175	0,01
MET (min/week)	64,12	1800,00	3440,00	85,58	4680,00	7900,00	-3,03	0,002	0,06

WorkVigDays - frequency of intense physical effort at work; WorkVigMinDay - length of intense physical effort at work; WorkModDays - frequency of moderate physical effort at work; WorkModMinDay - length of moderate physical effort at work; TravelDays - frequency of getting around/travel; TravelMinDay - time spent on getting around/travel; SportsVigDays - frequency of intense physical effort while doing sports; SportsVigMinDay - length of intense physical effort while doing sports; SportsModDays - frequency of moderate physical effort while doing sports; SportsModMinDay - length of moderate physical effort while doing sports; MET (min/week) - metabolic equivalent value.

The analysis of the results obtained from the GPAQ questionnaires showed statistically significant differences between women and men in terms of such variables as the length and frequency of intensive and moderate physical effort at work (WorkVigDays, WorkVigMinDay, WorkModDays, WorkModMinDay), the length and frequency of intensive physical effort during sports (SpostrVigDays, SportsVigMinDay), as well as MET-min/week. It turned out that in each of the recorded cases, women were characterized by significantly higher values of the indicators compared to men.

Next, it was checked whether there were any differences between rural and urban residents in terms of physical activity declared in the GPAQ questionnaire. The results of the analysis are presented in Table 7.

Table 7

*Comparison of physical activity of urban and rural residents with regard to the variables from the GPAQ questionnaire*
	City (n = 124)			Countryside (n = 36)
	average rank	Mdn	IQR	average rank	Mdn	IQR	Z	p	η2
GPAQ
WorkVigDays	71,90	0,00	0,00	76,74	0,00	1,00	-0,76	0,446	< 0,01
WorkVigMinDay	72,29	0,00	0,00	75,41	0,00	0,00	-0,55	0,581	< 0,01
WorkModDays	74,96	1,50	5,00	66,35	0,00	5,00	-1,09	0,275	0,01
WorkModMinDay	75,03	30,00	90,00	66,11	0,00	65,00	-1,13	0,260	0,01
TravelDays	76,67	4,00	5,00	60,55	3,00	4,50	-1,97	0,049*	0,03
TravelMinDay	76,87	30,00	57,50	59,86	30,00	50,00	-2,08	0,038*	0,03
SportsVigDays	73,29	1,00	3,00	69,85	1,00	3,00	-0,43	0,664	< 0,01
SportsVigMinDay	75,28	10,00	60,00	65,26	0,00	55,00	-1,29	0,198	0,01
SportsModDays	73,05	2,00	3,00	72,82	1,00	3,50	-0,03	0,977	< 0,01
SportsModMinDay	74,70	30,00	60,00	67,24	20,00	60,00	-0,93	0,351	0,01
MET (min/week)	71,90	0,00	0,00	76,74	0,00	1,00	-0,76	0,446	< 0,01
* p < 0,050

WorkVigDays - frequency of intense physical effort at work; WorkVigMinDay - length of intense physical effort at work; WorkModDays - frequency of moderate physical effort at work; WorkModMinDay - length of moderate physical effort at work; TravelDays - frequency of getting around/travel; TravelMinDay - time spent on getting around/travel; SportsVigDays - frequency of intense physical effort while doing sports; SportsVigMinDay - length of intense physical effort while doing sports; SportsModDays - frequency of moderate physical effort while doing sports; SportsModMinDay - length of moderate physical effort while doing sports; MET (min/week) - metabolic equivalent value.

The analysis showed statistically significant differences between the physical activity of urban and rural residents. It turned out that urban residents were characterized by significantly higher values in terms of the frequency and length (duration) of active getting around/movement, compared to rural residents. In the case of the remaining analyzed variables, no statistically significant differences were noted, which means that regardless of the place of residence, the respondents showed similar declared parameters of physical activity.

The next verified correlation was the existence of relationship between the physical activity results from the GPAQ questionnaire and age, education and health status. The results of the analysis are presented in Table 9.

Table 9

*Correlation of variables from the GPAQ questionnaire with age, education and health status.*
	Age	Documented education	Health condition
GPAQ
WorkVigDays	-0,15	-0,06	-0,11
WorkVigMinDay	-0,11	-0,03	-0,08
WorkModDays	0,05	-0,27***	0,12
WorkModMinDay	0,10	-0,29***	0,09
TravelDays	-0,04	-0,12	-0,02
TravelMinDay	0,03	-0,09	-0,01
SportsVigDays	-0,21**	0,27**	-0,23**
SportsVigMinDay	-0,23**	0,18*	-0,24**
SportsModDays	0,01	0,13	-0,15
SportsModMinDay	-0,04	0,15	-0,13
MET (min/week)	0,03	-0,19*	-0,11
* p < 0,050; p < 0,010; * p < 0,001

WorkVigDays - frequency of intense physical effort at work; WorkVigMinDay - length of intense physical effort at work; WorkModDays - frequency of moderate physical effort at work; WorkModMinDay - length of moderate physical effort at work; TravelDays - frequency of getting around/travel; TravelMinDay - time spent on getting around/travel; SportsVigDays - frequency of intense physical effort while doing sports; SportsVigMinDay - length of intense physical effort while doing sports; SportsModDays - frequency of moderate physical effort while doing sports; SportsModMinDay - length of moderate physical effort while doing sports; MET (min/week) - metabolic equivalent value.

The results revealed statistically significant negative relationships between age and the duration and frequency of intense physical activity (SportsVigDays, SportsVigMinDay). A similar trend was observed between documented education and the frequency and duration of moderate physical activity at work (WorkModDays, WorkModMinDay) and the metabolic equivalent value (MET).

The analysis also showed statistically significant positive relationships between documented education and the frequency and duration of intense physical activity during sports (SportsVigDays, SportsVigMinDay).

It also turned out that with an increase in the subjective assessment of health, the frequency and duration of intense physical activity increased (SportsVigDays, SportsVigMinDay).

In the case of the remaining pairs of variables, no statistically significant relationships were noted. It is also worth noting that the observed correlations mostly turned out to be weak and moderately strong.

The aim of the study was to indicate the sociodemographic factors that determine the level of physical activity of people living in the Biała Podlaska district, in the eastern part of Poland. Physical activity was analyzed in relation to factors such as: gender, education, place of residence, subjective assessment of health, age.

The analyses of the research results indicated discrepancies between the results obtained from the objective research tool, which was the RM42 accelerometer, and the subjective tool, i.e. the GPAQ questionnaire.

GENDER

The analyses of the data collected with the use of the RM42 accelerometer showed that women living in the Biała Podlaska district spend more time on moderate-intensity physical activity during the day and engage in physical activity lasting more than 10 minutes for a longer period of time compared to men.

The analyses of the data obtained from the GPAQ questionnaire also indicated greater physical activity of the examined women compared to men. However, statistically significant differences in this case concerned other variables. Female respondents predominated in the duration and frequency of intensive and moderate physical effort at work (WorkVigDays, WorkVigMinDay, WorkModDays, WorkModMinDay). They more often undertook intensive physical effort while practicing sports which they did for a longer time (SpostrVigDays, SportsVigMinDay). Women also achieved a more favorable result in the analyses of the MET-min/week indicator compared to men. The obtained results contradict previous reports suggesting that adult men are more physically active than women [4, 12, 13, 14]. Similar results were shown by the studies of the physical activity of respondents from Portugal [15]. Also, the results presented above are worth comparing with Jędrzejczyk's study published in 2021 [16]. In his work, the author presented a review of the published research results on the assessment of the level of physical activity of adults. The results of the cited studies show that men declared a higher level of physical activity compared to women in all studies included in the sample. However, the author clearly pointed out the need for detailed verification of the data he referred to, which shows the need for further research in this area.

PLACE OF RESIDENCE

Further differences in the results of analyses of data obtained from the RM42 accelerometer and the GPAQ questionnaire were shown by the analysis of the impact of place of residence on physical the undertaken activity

The analysis of the data obtained from the MR42 accelerometer did not show statistically significant differences between city and village residents. Regardless of the place of residence, the subjects were characterized by similar parameters of physical activity. The analysis of the results obtained from the data collected with the use of the GPAQ questionnaire led to other conclusions. According to these subjective studies, people from the Biała Podlaska district, from the area of eastern Poland, living in the city and in the countryside significantly differ in the level of the undertaken physical activity. Significantly higher values were shown in city residents, compared to people living in the countryside. This was confirmed by the analysis of the frequency and duration of physical activity related to moving around.

Similar conclusions were obtained from the studies by Biernat and Buchholtz [17]. The results of their research conducted using the IPAQ questionnaire in the years 2014–2015 on a representative sample of Polish women and men aged 15–69 clearly showed that the smaller the town the respondents lived in, the lower their physical activity was.

The conclusions from Biernat and Bucholtz's research were also confirmed by the CBOS research from 2018 [18], which showed that living in the countryside was among the factors characterising physically passive people, These conclusions are also confirmed by the research from the Multi Sport program from 2019, which showed that physically active people are primarily those living in large cities [19].

AGE

Another analyzed factor was age. The analysis of the data collected using the RM42 accelerometer showed statistically significant negative relationships between age, moderately intensive and vigorous exercise (MVPA, VPA) and the number of steps (Steps). It was observed that older people are, the less physically active the become (the older the person, the lower the AF).

Positive, statistically significant relationships were found between age and light physical activity (LPA) undertaken by the respondents. Thus, older people more often chose low-intensity activity as opposed to moderate and high-intensity activity. Similar trends were shown in the analysis of the data from the GPAQ. Again, in this case, it was shown that the older the examined person, the less often they undertook intensive physical activity, and more often they undertook lower-intensity effort. This relationship also concerned age in comparison with the length and frequency of intensive effort (SportsVigDays, SportsVigMinDay).

Similar conclusions were presented in the Report of the Public Health Committee of the Polish Academy of Sciences from 2021 [20], which showed that the results of many studies indicate that the lowest level of physical activity was visible in people over 50 years of age. Much the same conclusions were obtained in the previously cited studies by Biernat and Buchholtz from 2014–2015, the results of which showed that Polish women and men aged 15–69 become more physically passive with the increase in the age of their children. The least physically active were parents of children aged 0–6 years, while the highest PA was recorded in parents of children of high school or university age [17].

EDUCATION

The analysis of the relationship between education and undertaken physical activity examined using the RM42 accelerometer did not show any statistically significant relationships between these variables. Only weak, positive relationships were noted between education and physical activity lasting up to 5 minutes (PA0-5).

However, the analyses of the data collected using the GPAQ questionnaire led to completely different conclusions. In this case, statistically significant relationships, both negative and positive, between physical activity and education were shown. Thus, the higher the educational degree of the respondents, the less frequent and shorter their declared moderate-intensity physical activity at work (WorkModDays, WorkModMinDay). This also results in a lower value of the metabolic equivalent MET.

In the case of intensive efforts undertaken during sports (SportsVigDays, SportsVigMinDay), their frequency and duration increase with education.

The above results may indicate that people with higher educational degrees do not have the opportunity to participate in physical activity during their work (e.g. office work). However, they make up for this deficit in a conscious way in their free time, undertaking high-intensity efforts, thus meeting the recommendations for health-promoting physical activity.

Similar conclusions are suggested by the results of the CBOS study from 2018, according to which people with lower educational degrees are characterized by a lower level of physical activity [18]. The above trends are also confirmed by the analyses of studies conducted among residents of Elbląg, according to which people with lower educational degrees engage in physical activity less often than those with higher degrees. The authors of the study explained this relationship assuming that the respondents with higher degrees are more aware of the positive impact of physical activity on their health [21]. Also in the case of studies conducted among respondents from the Mielec district, the analyses showed that the level of physical activity of the respondents increased with the increase in the declared degree of education [22].

However, the opposite conclusions were presented in the work of Puciato et al., who studied physical activity of the residents of Katowice. The data analysis showed that the lower the degree of education, the greater the probability of undertaking health-promoting physical activity [23]. This example may be related to the specificity of employment related to this region, which is specific on a national scale.

SUBJECTIVE HEALTH ASSESSMENT

Another aspect that was analyzed was the relationship between the subjective assessment of health status and physical activity. According to the results obtained from RM42 accelerometers, it was shown that with the increase in the subjective assessment of health status, moderate and intense physical activity (MVPA, VPA), physical activity over 10 min (PA10+) and the number of steps also increased.

The analysis of the data from GPAQ showed similar relationships in the case of intensive efforts undertaken by the examined persons - with the increase in the subjective assessment of health status, the frequency and duration of intensive physical effort increased (SportsVigDays, SportsVigMinDay). The presented results of the analyzed data constitute further evidence that the respondents declaring themselves as physically active people assess their subjective health status better.

Over the years, more and more researchers have proven that physical activity is one of the most important factors influencing not only health but also the quality and length of life - our life [24, 25, 26]. Subsequent studies have shown the beneficial effect of physical activity, among others, in the prevention of such groups of diseases as cardiovascular diseases [27–31], cancer diseases [32–37], diabetes [38–41], diseases of the skeletal system [42–44], as well as depression [45–47].

The results obtained from the RM42 accelerometer and the GPAQ questionnaire are varied and dependent on the used tool. Still, they indicate the great importance of the tool used in the study of physical activity.

The analysis of the data collected with the use of the RM42 accelerometer allowed for the formulating the following conclusions regarding the sociodemographic factors influencing the level of physical activity of residents of the Biała Podlaska district in eastern Poland:

Women living in the Biała Podlaska district are more active than men - they spend more time during the day on moderately intensive physical activity, and they engage in physical activity lasting more than 10 minutes for a longer period of time compared to men.

Regardless of the place of residence, the respondents show similar parameters of physical activity.

Older people showed lower physical activity, but the frequency of low-intensity activity increased in their case, as opposed to moderate and high-intensity activity.

No statistically significant differences were found between the educational degree and the undertaken physical activity by the respondents.

The higher the subjective assessment of health, the higher the recorded physical activity.

The data collected through the GPAQ questionnaire allowed for the formulation of the following conclusions regarding the above-mentioned factors:

Women living in the Biała Podlaska district are more active than men (more favorable results in analyses of the length and frequency of intensive and moderate physical effort at work (WorkVigDays, WorkVigMinDays, WorkModDays, WorkModMinDays), the length and frequency of intensive physical effort during sports (SpostrVigDays, SportsVigMinDays) and in the analyses of the MET-min/week indicator compared to men).

People from the Biała Podlaska district living in cities engage in physical activity more often than the respondents living in the countryside

Older people showed lower physical activity, while the frequency of low-intensity activity increased in their case as opposed to moderate and high-intensity activity. Older people also had lower values in the length and frequency of intensive effort while doing sports. sports (SportsVigDays, SportsVigMinDay).

The respondents with higher educational degree declared less frequent and shorter moderate-intensity physical activity at work (WorkModDays, WorkModMinDay). With higher educational experience. Furhtermore, with the higher the educational degree, the more frequent and longer the undertaken intensive efforts when doing sports (SportsVigDays, SportsVigMinDay) increases.

With the increase in the subjective assessment of health, the frequency and duration of intensive physical effort (SportsVigDays, SportsVigMinDay) undertaken by people from the Biała Podlaska district increases.

The presented results of the analyses of the data collected with the use of the RM42 accelerometer and the GPAQ questionnaire allow for the formulation of generalized conclusions based on the results that were consistent in both objective and subjective studies:

Women from the Biała Podlaska district are more active than men;
As age increases, the surveyed individuals more often undertake physical activity of lower

intensity, while giving up high-intensity efforts;

The respondents declaring a higher subjective assessment of their health are more

physically active.

EUPASMOS – European Union Physical Activity and Sport Monitoring System
GPAQ – Global Physical Activity Questionnaire
PA – Physical Activity
LPA – Light physical activity
MVPA - Moderate and vigorous physical activity
VPA - Vigorous physical activity
Steps - Number of steps
WHO – World Health Organization
MET - Metabolic Equivalent of Task
WorkVigDays – Number of days with vigorous physical activity at work
WorkVigMinDay – Number of days with vigorous physical activity at work
WorkModDays - Number of days with Moderate physical activity at work
WorkModMinDay – range of time with Moderate physical activity at work
TravelDays – Number of days with travel to and from places
TravelMinDay – Time spend for travel to and from places
SportsVigDays – Number of days with vigorous-intensity sports, fitness or recreational (leisure) activities
SportsVigMinDay – Duration of time spent on vigorous-intensity sports, fitness or recreational (leisure) activities
SportsModDays - Number of days with moderate-intensity sports, fitness or recreational (leisure) activities
SportsModMinDay - Duration of time spent on moderate-intensity sports, fitness or recreational (leisure) activities
CBOS - Centrum Badania Opinii Społecznej (Public Opinion Research Center)

Author contributions MB and BB conceptualized the study and was involved in design, analysis, interpretation, and manuscript writing. All authors made a substantial contribution with a specific task MB, BB, AS and PR the extraction of data, EN, DT, JB-K and MS analysis and interpretation, MB and BB drafting of the manuscript, and JB-K, MS, and DT critical revision of important intellectual content.

All the authors read and approved the final manuscript.

Funding Not applicable.

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

In this study each and every participant meets the same inclusion/exclusion criteria:
inclusion criteria: submitting a declaration of consent to participate in the study, age 18 or over, no health contraindications to health-promoting physical activities.
Exclusion criteria: failure to consent to participate in the study, age under 18, health condition that precludes participation in any physical activities.

The authors declare no competing interests

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No competing interests reported.

Sociodemographic determinants of the level of physical activity in residents of eastern Poland

Status:

Version 1

Abstract

Background

Objective

Participants

Methods

Results

Conclusions

Introduction

Aim of the study and research questions

Materials and Methods

Sociodemographic characteristics

Analysis of the research results

Discussion

GENDER

PLACE OF RESIDENCE

AGE

EDUCATION

SUBJECTIVE HEALTH ASSESSMENT

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1