The effect of a community-based e-health program to promote the role of physical activity among healthy adults in Hungary

Our study was an interventional pre-post design research using purposive sampling, representative for gender and age based on country statistics of the Hungarian Central Statistical Office. The target group was selected with the aim of examining the physical activity patterns and effectiveness of a community-based e-health program among healthy adults according to socioeconomic status. Baseline data collection occurred in June to September 2014 and the follow-up data derived from June to September 2015 in Baranya County, Hungary, among healthy 18–64-year-old adults.

A total sample of 750 participants was recruited and 633 individuals agreed to participate in the “E-Harmony” Study (response rate was 84.4%). Eligible participants reported good or very good health status and Baranya county residency, were not hampered in physical activities, and were aged 41.03 ± 13.25 years. Data were collected by trained researchers using standard tablet-assisted personal interviews, covering physical activity and socioeconomic parameters. Participating researchers were trained before the examination by a senior researcher team. All participants provided written informed consent prior to the survey. There was no incentive to participate in this examination.

The first part of the data collection tool contained a demographic questionnaire with additional queries about health status.

The second part consisted of the International Physical Activity Questionnaire (IPAQ) – long version. Its Hunagrian version (IPAQ-HL) was validated among healthy adult population. The IPAQ long version contains 31 questions; the examined 5 domains were work, active transportation, household work, leisure time activitites, and sitting time. All activities were measured over the 7 days prior to data provision. We applied the IPAQ data truncation rule, and all activities exceeding 3 h were recoded as 180 min. Evaluation of physical activity has two different methods, using the frequency values as min/week scores or MET min/week values [25]. For summarizing physical activities, we calculated domain scores (MET min/week), and intensity scores as moderate, vigorous and MVPA values (MET min/week). The MET values are given in the study by Ainsworth et al. (2000) and the IPAQ scoring protocol. One MET is equal to energy expenditure during rest, and is approximately equal to 3.5 ml O2/kg min in healthy adults [26].

To measure antrophometric data we used the OMRON BF511 body composition monitor.

While measuring body weight (kg), participants were dressed in light clothing without shoes. Height (cm) was measured and recorded to the nearest 0.5 cm, in standing position, without shoes, using an anthropometric tape measure.

E-Harmony is a community-based e-health program organized to encourage and guide active lifestyle. It is based on two popular and frequently used theoretical frameworks for health programs aimed at increasing physical activity. One is Bandura’s Social Cognitive Theory and the other is Pender’s Health Promotion Model [23, 24]. Health promotion is aimed at changing social, environmental, and economic conditions to improve the health of the target group or community (WHO, 2017). In 1973, Bandura’s experiments underpinned the theory of social learning, based on many interventions aimed at increasing physical activity today (Bandura, 1986). The theory claimed that most human behaviour is learned observationally through modelling, observing others and on later occasions this coded information serves as a guide of action. The theory explains human behaviour in terms of continuous reciprocal interaction between cognitive, behavioral, and environmental influences [27].

The Health Promotion Model was developed by Pender in 1996. It claims that health behaviour is driven by behaviour-specific conditions and affect interpersonal influences [24].

It focuses on helping people achieve higher levels of health-related quality of life and identifies background factors influencing that [28].

We evaluated our community-based e-health program based on Bandura and Pender’s theories. The first part of our research was a quantitative analysis of participants, where they completed a demographic and physical activity questionnaire. After the first data collection the research team summerized the results and evaluated the information dissemination and physical activity programs.

The program highlighted freely available outdoor, home, and work activitites and active transportation options. E-Harmony was developed by our research group and designed especially for the targeted population of healthy adults taking into account their socio-cultural specificities.

The program contained two elements as web-based video tools to increase physical activity level of the participants with 10 sessions and web-based educational contents to increase the physical literacy levels. Program elements were compiled by a research team consisting of an internal medicine specialist, physiotherapists, sport experts, and psychologists.

In the program videos we highlighted special interest groups like different age groups (middle aged and ageing adults), current sport trends for younger people and water activitites. Furthermore, we provided information on a training program applicable during healthy pregnancy and after childbirth.

In our health promotion program the physical activity videos were supplemented by training notes to increase the physical literacy of participants. Through our research website and social network sites our physiotherapist staff provided useful information on sports and physical activity topics. The purpose of the various educational contents was to draw attention to the importance of physical activity, especially on the tools and forms of exercise that are easily accessible and free of charge. Furthermore, every participant was mentored by a trained interviewer deriving from academic field of health sciences.

The major socioeconomic parameters of the study population are presented in Table 1. Half of the sample was married, two third of them had a job, and only 25% of the sample had a physically demanding job. Most of them had urban residence, and their educational level was secondary. We found significant differences in gender according to the marital status, work form, and financial situations (Table 1).

Key element of our study was the examination of the antrophometric properties. The results are shown in Table 2. We found significant difference by gender according to all parameters except for waist circumference (Table 2).

The physical activity patterns of the sample were measured by IPAQ-HL. After summarizing the scores, the main results were scored in continuous variables. These indicators were total physical activity, domain scores (as work, transportation, household activities, and leisure time actitivites), and intensity scores (moderate and vigorous physical activity and walking scores), all of them expressed in MET min/week. Respective results are presented in Table 3.

The total activity was 5129.9 (SD = 4488.1) MET min/week. Male participants scored higher in total activity but the results did not show statistical significance. The total scores contained all examined domains of the IPAQ-HL as listed before. Two third of the activities were work and household activities and only one third of them were leisure time activitites and active transportation (i.e. walking and cycling).

Another indicator in examining the active lifestyle is sedentary behaviour and time spent sitting. On average, participants were sitting 2211.6 (SD = 1592.8) min/week. The daily average time spent sitting was 315.9 (SD = 227.6) minutes. The results did not show statistically significant difference among genders.

Physical activity patterns were described for the whole sample and for male and female participants separately. Male adults were more active in work and in leisure time activitites, while female adults spent more time on household activitites and active transportation. According to the intensity of the activitites, male participant spent more time with vigorous activitites than females (Table 3).

The results in Table 4 showed correlations between antrophometric properties and physical activity patterns. We found weak but significant relationship between the active lifestyle and antrophometric data, especially according to the leisure time activities and sedentary behaviour. The physically active lifestyle has a positive effect on body composition scales (Table 4).

In our study we examined the effect of the sociodemographic parameters on physical activity (PA) patterns with multivariate linear regression analysis. We analysed 5 regression models where the dependent variables were total PA, work PA, active transportation, household activitites, and leisure time activitites. We found significant relationship between the physical activity pattens and sociodemographic variables such as age, education, work type and financial status, results of which are shown in Table 5.

We found significant positive effect of active lifestyle on anthropometric properties and our results showed that socio-demographic parameters significantly affected the physical activity level of Hungarian healthy adults.

We examined changes after the 12-month web-based health promotion program. 10.34% changes were shown in total physical activity level. The different subdomains showed the details of changes in PA patterns: the vigorous activity values did not increase (−4.5%), while the highest increase was found in walking (36.5%), transportation (27.7%), and leisure time activities (16.1%). The increased level of PA and the decreased level of sitting time (23.4% weekly sitting time in total) was evaluated (Table 6).