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Article

Health Literacy and Active Transport in Austria: Results from a Rural Setting

1
Institute of Health and Tourism Management, FH JOANNEUM University of Applied Sciences, Bad Gleichenberg 8344, Austria
2
Institute of Sports Science, University of Graz, Graz 8010, Austria
3
Division of Endocrinology and Diabetology Department of Internal Medicine, Medical University of Graz, Graz 8036, Austria
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(4), 1404; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17041404
Received: 20 December 2019 / Revised: 11 February 2020 / Accepted: 17 February 2020 / Published: 21 February 2020

Abstract

Health literacy (HL) has been determined for the general population and for subgroups, though the relationship between HL and active transport in rural areas was not explored. The aim of our study is to investigate HL among citizens in an Austrian rural region and to explore the associations between HL and active transport. This cross-sectional telephone survey included 288 adults (171 women) with a mean age of 57.8 (SD 0.9). HL was assessed using the HLS-EU-Q16 questionnaire. Active transport was measured as the minutes per week spent on walking or cycling from A to B. After descriptive analysis, the association between HL and active transport was assessed using linear regression models. The mean HL score for all participants was 37.1 (SD 7.7). Among all subjects, 6.9% showed inadequate HL, 25.7% problematic HL, 38.9% sufficient HL, and 28.5% excellent HL. HL was significantly higher among citizens with high education (p = 0.04) and training/employment in healthcare (p = 0.001). Active transport was not associated with HL (p = 0.281). Active transport in rural areas might be influenced by other predictors like distance to work, street connectivity, and accessible facilities for walking and biking. This needs to be explored further for rural areas.
Keywords: health literacy; active transport; active mobility; rural area health literacy; active transport; active mobility; rural area

1. Introduction

Improving health literacy (HL) is a common public health approach and deals with the empowerment of individuals, organizations, and communities for more independent decision-making in health topics [1]. Sørensen et al. [2] defined HL as a comprehensive concept which is “people’s knowledge, motivation, and competences to access, understand, appraise, and apply health information”. An excellent HL enhances the quality of life due to active judgments and decisions concerning healthcare, disease prevention, and health promotion in everyday life [1,2,3].
Results for HL for the general population were published first in 2015 in eight European member states [4]. In this study, huge differences in HL between countries were shown. The proportion of respondents with inadequate or problematic HL was lowest in the Netherlands (29%) and highest for Bulgaria (62%). In Austria, 56.4% of respondents reported inadequate or problematic HL [5,6].
More recently, HL was measured for specific population groups in Germany [7,8], the Netherlands [9], or the European Union (EU) [10], e.g., children and adolescents, elderly people, or migrants. In addition, the influence of various factors, such as health behavior (physical activity, smoking, and eating habits) or health-related factors (body mass index (BMI), blood pressure, chronic diseases, and pain), of HL was assessed [5,7,8,11,12]. Previous studies showed a positive relationship between HL and physical activity [9,11,13,14]. The participation rate in moderate to vigorous physical activity as well as frequency and duration of physical activity during HL oriented type 2 diabetes (T2D) programs increased among patients with high HL [15,16]. On the other hand, low HL was associated with physical limitations, e.g., difficulty in performing everyday activities [17,18]. A recent study reported a positive correlation between some of the HL subscales and daily physical activity, but not of overall HL [19].
Little is known about the relationship between HL and active transport, which is physical activity for everyday transport by walking or cycling. Active transport is a strong determinant for health and important in health promotion. An increase in walking and cycling for transportation is related to positive health effects, e.g., better mental health, a lower body weight, a reduction of chronic diseases, and a better general cardiovascular health status [20,21,22].
Previous studies reported urban–rural differences for active transport. Residents in rural areas take fewer steps per day and spend fewer minutes per week walking (walking for leisure or transportation) compared to urban residents. Residents in rural areas also cycled less for transport [23,24]. As the body mass index (BMI) of a citizen in high-income countries (central and eastern Europe) is higher in rural than in urban areas, interventions promoting physical activity and active transport can contribute highly to general population health [25]. Besides health benefits, active transport can make a contribution to reducing greenhouse gas emissions and can bring economic benefits by a reduction of healthcare costs [26,27]. Although people with better HL are more physically active than those with low HL [13], currently it is unknown if they are also more likely to choose active transport over passive.
Thus, we designed the present study to examine HL among citizens in an Austrian rural region and to determine the associations between HL and active transport. This aim fits well with the Austrian Health Targets that focus on enhancing HL (target 3) as well as promoting physical activity in everyday life (target 8) [28].

2. Materials and Methods

2.1. Study Design

This cross-sectional study was conducted in July 2018 in a rural area with 13,100 inhabitants in southeast Austria. Rurality is defined by OECD [29] as an area of communities with a low population density (<150 inhabitants per km2) and which does not include an urban center. This definition applies to the rural area of the present study.
From 6300 households in the region, 300 were randomly selected from the telephone directory. For the first 200, the person who answered the telephone was interviewed, when aged >18 years. To make the study sample as representative as possible, in the last 100 households, the youngest eligible person (>18 years) in the household was asked to complete the interview. People who did not appear in the telephone directory, did not live in the region, or were aged under 18 years were excluded.
The sample size of 300 was based on feasibility. Assuming that associations between HL and active transport would be weak (effect size of 0.2), and with a two-sided alpha of 5%, a minimum of 55 participants was needed to achieve 90% statistical power.
The survey was developed on the basis of two validated health-related questionnaires (SF-12, HLS-EU-Q16) [4,30] and further questions on active transport and health taken from scientific literature [21,23]. Our questionnaire consisted of questions regarding HL and questions on active transport in everyday life, sociodemographic and anthropometric variables like weight and height, as well as self-perceived health status.
The study was approved by the local ethics committee of the University of Graz (GZ. 39/70/63 ex 2017/18).

2.2. Health Literacy (HL) Measures

HLS-EU-Q16 is the short version of HLS-EU-Q47 and measures health literacy (HL) based on the definition from Sørensen with three domains: healthcare, disease prevention, and health promotion [5]. It is a self-reported tool with responses in Likert-type (“very easy”, “fairly easy”, “fairly difficult”, “very difficult”). HL index score was computed based on the validated HLS-EU-Q16 questionnaire [4]. The index score was calculated for those respondents who answered ≥80% (i.e., ≥13 items) of the questionnaire. For comparison with other studies, the following formula was used to calculate the HL score [31]:
Index = ( mean   ( per   Item ) 1 ) 50 / 3
The index score has a minimum value of 0 and a maximum value of 50. In addition, four categories were formed on the basis of the score as defined by Sørensen et al. [6] (0–25 inadequate HL; >25–33 problematic HL; >33–42 sufficient HL, and >42–50 excellent HL).

2.3. Socio-Demographic and Health-Related Variables

Participants were asked about educational level, net household income, migration background, employment status, marital status, and whether they were trained or employed in healthcare.
The educational level was categorized according to the International Standard Classification of Education [32] as follows: low (primary school or lower secondary school without vocational training), medium (vocational training, upper secondary school, or professional school), high (any higher level, e.g., tertiary education). The net household income of participants (per month) was classified into three categories: <€1850 (low income), €1850 to €2950 (medium income), and >€2950 (high income). Participants were considered to have a migration background if they or one of their parents were not born in Austria. Employment status was classified into three categories: employed (fully employed or part-time), retired, or other (special-order contract, unemployed, maternity leave, student, or without income). Marital status was classified as married/living together, or single. Participants were considered to have training or employment in healthcare when they had an educational background or actual/former employment in the field of healthcare.
Self-reported height and weight of participants were assessed in order to calculate their body mass index (BMI) as weight in kg divided by height in meters squared. BMI was categorized into standard BMI categories: underweight (<18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30.0 kg/m2). Furthermore, the self-perceived health status was measured with SF-12 [30].

2.4. Active Transport

Based on previous work [21,23], participants were asked “Which of the following transport options did you choose for commuting in the last 12 months?” followed by seven subcategories: (1) foot; (2) car; (3) public transport; (4) bicycle or freight bicycle; (5) electric bicycle; (6) motorbike or moped; (7) taxi. Subcategories were answered with the following response options regarding frequency of active transport: daily, 3–4 days per week (daily/often), 1–2 days per week, 1–3 days per month (weekly/1–3 times a month), seldom, never (seldom/never), do not know.
In addition, we measured active transport as the minutes per week spent on walking or cycling from A to B. For walking, participants were asked “On how many days in a usual week do you walk more than 10 minutes from A to B?” and “For how long do you walk on such a day?”. Participants reported a number of days per week walking. For cycling (including electric bicycling) the same set of questions was asked.
Total minutes of walking per week was calculated by multiplying the number of hours reported with sixty and adding them to the minutes reported. Consequently, minutes of active walking per week was calculated by multiplying the reported number of days per week with the calculated minutes per day. This number of minutes of walking for active transport per week was used in the analyses. Minutes of cycling for active transport per week were calculated with the same steps.

2.5. Statistical Analysis

Continuous variables with a normal distribution are displayed as means with their standard deviation (SD). When distribution was not normal, median and interquartile range (IQR) are reported. Categorical variables are displayed as numbers (n) and percentages (%). Significance of differences between subgroups (based on gender, age, educational level, healthcare training/employment, active transport etc.) in HL score [31] was tested with independent t-test since HL was normally distributed. The association between active transport and HL was calculated with linear regression models, using the HL score as an independent variable and active transport as the dependent variable. Models were adjusted for variables that might influence the association between HL and active transport, and were chosen based on our own data (education, healthcare training/employment) and on literature (gender and age). Unadjusted models and adjusted models, including confounding variables, are presented. In the last step, the variable healthcare training/employment was added as another possible confounding variable. The regression coefficients resulting from linear regression are displayed with their 95% confidence interval (95% CI).
A p-value of <0.05 was considered statistically significant. All analyses were performed using IBM SPSS Statistics for Windows, Version 24.

3. Results

In total, 288 adults (171 women; 59.4%) could be included in the analysis. Twelve (4%) participants were excluded because they answered <80% of the items of the HLS-EU-Q16. The mean age of the subjects was 57.8 (SD 0.9) years. Demographic, socio-economic, anthropometric, and health-related characteristics are shown in Table 1.
Three-quarters of the study population had a medium level of education and 35% of women had a monthly household income under €1850. Only 4.5% had a migration background. Most participants were either employed (42%) or retired (47.2%). About two-thirds were married or living together with a partner and 18% showed a connection to healthcare because of training or former employment in this field. The mean BMI of the subjects was 25.9 (SD 4.1). In total, 41.7% of all participants were overweight (33.3% of women, 57.0% of men) and 13.6% were obese. The majority of participants rated their health status as very good (30.2%), or good (43.8%).

3.1. Active Transport Characteristics

Overall, participants reported a median of 180.0 (IQR 90; 410) minutes per week (min/wk) of active transport (walking 162.5 (IQR 60; 300) min/wk; cycling 105.0 (IQR 45; 240) min/wk; see Table 2). Men reported more active transport in general, for walking, cycling and electric cycling than women, however, this difference was not statistically significant. Participants walked on 4.3 (SD 2.3) days per week and cycled on 3.4 (SD 2.1) days per week on average. Seventy percent of men walked daily or often, while only 50% of the women did so. There were no differences between men and women for the frequency of cycling.

3.2. Health Literacy (HL) Score, Socio-Demographics, and Active Transport

The mean HL score of 288 study participants was 37.1 (SD 7.66), and 6.9% had inadequate HL, 25.7% problematic HL, 38.9% sufficient HL, and 28.5% had excellent HL (Table 3).
The HL score was not significantly different between men and women, between age groups, or between employed and retired participants. The HL score was related to education. Participants with high education had a 3.6 points higher score (95% CI 0.18; 7.11, p = 0.04) compared to participants with low education.
HL was related to training or employment in healthcare. Participants with training or employment in the health sector had a 3.8 points higher HL score (95% CI 1.51; 6.03, p = 0.001) compared to participants with no health training or employment.
Regarding the frequency of active transport by walking (p = 0.94) or cycling (p = 0.11), no significant differences between the groups were found. Respondents with sufficient HL had 34.5 min/wk more active transport compared to participants with problematic HL, but this was not statistically significant.

3.3. Health Literacy (HL) and Active Transport

The results of linear regression models for the association between HL and active transport are shown in Table 4. We did not observe an association between HL and active transport in terms of weekly minutes of walking and cycling. Adjustment for possible confounding variables did not change the results. The variables of gender, age, educational level, and training/employment in healthcare were not significantly associated with active transport. Also, the association between HL and active transport was not modified by age, gender, or education (p-value for interaction terms all >0.05).

4. Discussion

We aimed to describe HL among citizens in an Austrian rural region and to explore associations between HL and active transport. Standardized measurement tools were used, for HL from the HLS-EU project and for active transport from previous studies in this field. No association between HL and active mobility overall, nor by walking or cycling separately, was found.

4.1. Active Transport and HL

The study participants reported on average 180.0 min/wk (162.5 walking and 105.0 cycling) for active transport. Men had, in general, more active transport than women, also for walking and cycling separately, although this difference was not statistically significant. Respondents walked on 4.3 days/wk and cycled on 3.4 days/wk for more than 10 min from A to B. This reported level of active transport is surprisingly high. In comparison to an Austrian study conducted in the city of Graz (median 150 min/wk) our respondents reported more active transport [33]. In that study, men also had more active transport compared to women. The IPEN study also showed lower rates of active transport in Denmark (walking 3.3 days/wk; cycling 2.4 days/wk) and Belgium (walking 2.0 days/wk; cycling 1.7 days/wk) [34]. However, comparisons between countries are complex, because of differences between infrastructural and cultural factors.
Former studies reported a lower frequency of active transport in rural neighborhoods compared to urban [21,23,35], but with a higher daily duration [21]. A rural area may provide fewer opportunities for short-distance trips. This might lead to less active transport, since people are less likely to choose an active mode of transport if the destination is not within 800 m of walking distance [36,37].
We could not show that active transport in terms of weekly minutes of walking and cycling is influenced by HL. The result did not change after adjustment for confounding variables (gender, age, educational level, and training/employment in healthcare). Previous studies described street connectivity, bike lane connectivity, land-use mix, or walkable neighborhoods as predictors for walking and cycling, although they focused on urban areas [34,38,39]. Moreover, the configuration of the natural environment (ground conditions, hills) and aspects of traffic safety (high density and speed of traffic) also influence levels of active transport [36,37,40].
In rural areas, the built environment might be a barrier for active transport, due to a lack of connection between streets, long distances, and low walkability and bike-ability (caused by the absence of footpaths, cycle lanes, or streetlights at night). These barriers might be more important and preclude finding an association between HL and active transport in a rural area.
In our study population, we found an increase in HL in higher educated participants and a non-significantly higher HL in respondents who cycle “weekly/1–3 times a month” compared to those who cycle “daily/often”. The association between education and active transport shown in the literature is controversial [38,41,42]. In a rural context as in our study, many higher educated citizens might not find work in their living area, and thus, have a longer distance to work and need to commute by car. Therefore, they might not be able to walk or cycle much on working days, but can have active transport during the weekend. Unfortunately, we did not have information on commuting distance. The possible interaction between education and HL in the association with active transport, especially in rural areas, needs to be explored in further research.

4.2. HL and Baseline Characteristics

We found higher HL in our study population compared to former studies on EU and country-level using the same questionnaire (HLS-EU 16). The mean HL score in the HLS-EU study was 32.0 for Austria and 33.8 over eight countries [6], compared to 37.1 in our study population. However, the mean age in our study population was 57.8 years which is much higher than in the HLS-EU project. Previous studies in Germany and country-specific results of the HLS-EU survey (e.g., Netherlands) show an increase of HL score with age [8,43]. The rural setting might also be an explanation for the higher HL score in our study. The questionnaire measures, based on the definition from Sørensen, the ability of patients to navigate or negotiate the health care system [1]. Possibly, it could be rather easy for citizens in the rural region of our study to orient themselves in the system and navigate between the small number of health care providers (e.g., one hospital, a few doctors or only some other healthcare professionals). Therefore, the opportunities for healthcare choices or treatments are clearer and possibly more manageable. This assumption needs further investigation.
In our study population, no difference in HL score was found between men and women. Previous studies found controversial results regarding the association of HL and gender. A meta-analysis from 2005 concluded that there are no gender differences in HL [44]. However, Pelikan et al. found a slightly higher HL for women in the EU study population as well as in the Austrian sample [43].
We found an association between HL and education as well as with training/employment in healthcare. Participants with low education had a significantly lower HL score than subjects with high education level. In addition, the HL score of participants with an educational background or an actual/former employment in the field of healthcare was significantly higher. A strong connection for HL and educational level was also found in the HLS-EU survey [43] and several former studies [6,8,11,12,17,18,44]. Lorini et al. [12] also showed in their Italian HL survey that being trained or employed in the field of healthcare is associated with a higher HL score.

4.3. Limitations of the Study

To our knowledge, this is the first study analyzing HL in a rural area as well as exploring associations between HL and active mobility.
Due to the fact that it was a telephone survey, some limitations need to be recognized. Firstly, people who did not appear in the telephone directory could not be part of the study. This pertains especially to younger people who are often not registered with their mobile number. However, the study sample was representative of the Austrian population with regards to gender, education, and BMI. The comparison of study results with former studies could be influenced by the interviewing method. Former studies like HLS-EU used the method of computer-assisted personal interviewing (CAPI) [5]. For reasons of time, we used computer-assisted telephone interviewing (CATI). The questionnaire for HL consisted of 16 subquestions, all with the same Likert scale answer options. This set of questions was asked at the end of the interview, which could have influenced attention and motivation for answering each question accurately. Therefore, the instrument for measuring HL might need to be validated specifically as a telephone survey. The data for HL and active transport was self-reported and did not include any objective items to measure functional HL, which might impact its accuracy. Previous studies reported differences between self-reported and accelerometer-measured data on active mobility [45]. Another limitation of our study was the unavailability of data for distances for commuting.
It would have been interesting to compare our findings with other rural data. As it was the first study to explore HL in a rural setting this was not possible. Although previous studies analyzed active transport in rural areas, the comparison with studies from different countries is difficult due to a different understanding of the term “rural”.

5. Conclusions

This study shows a higher HL score for the rural study population compared to national data from the HLS-EU survey. HL was associated with education and being trained or employed in the field of healthcare. Previous studies showed a relationship between HL (or sub-dimensions of HL) and total physical activity. It could not be shown in our study that citizens with high HL are also more likely to choose active over passive transport. Active transport in rural areas might be influenced by other predictors like distance to work, street connectivity, and accessible facilities for walking and biking. This needs to be explored further for rural areas.

Author Contributions

Conceptualization, K.H.-F. and G.G.; methodology, K.H.-F., B.F.-N., and G.G.; formal analysis, K.H.-F., B.F.-N., and A.M.; investigation, K.H.-F., B.F.-N., and G.G.; data curation, B.F.-N. and A.M.; writing—original draft preparation, K.H.-F.; writing—review and editing, K.H.-F., B.F.-N., A.M., G.G., and M.N.M.v.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was embedded in the project “Auf Gesundheitskurs—gesundheitskompetent in Feldbach” and funded by “Gesundheitsfonds Steiermark” (http://www.gesundheitsfonds-steiermark.at/), the funding body had no roles in the study design, data collection, data analysis, and interpretation, or the report writing.

Acknowledgments

The authors would like to thank the Institute for Empirical Social Studies (IFES) for conducting the telephone interviews, MMag. Gerald Käfer-Schmid for statistical support, all volunteers for the participation in this study and the City of Feldbach for supporting the research project in this rural area.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Nutbeam, D. The evolving concept of health literacy. Soc. Sci. Med. 2008, 67, 2072–2078. [Google Scholar] [CrossRef] [PubMed]
  2. Sørensen, K.; van den Broucke, S.; Fullam, J.; Doyle, G.; Pelikan, J.; Slonska, Z.; Brand, H. Consortium Health Literacy Project European. Health literacy and public health: A systematic review and integration of definitions and models. BMC Public Health 2012, 12, 80. [Google Scholar] [CrossRef]
  3. Gugglberger, L. The multifaceted relationship between health promotion and health literacy. Health Promot. Int. 2019, 34, 887–891. [Google Scholar] [CrossRef]
  4. Sørensen, K.; van den Broucke, S.; Pelikan, J.M.; Fullam, J.; Doyle, G.; Slonska, Z.; Kondilis, B.; Stoffels, V.; Osborne, R.H.; Brand, H. Measuring health literacy in populations: Illuminating the design and development process of the European Health Literacy Survey Questionnaire (HLS-EU-Q). BMC Public Health 2013, 13, 948. [Google Scholar] [CrossRef]
  5. Pelikan, J.M.; Ganahl, K. Measuring Health Literacy in General Populations: Primary Findings from the HLS-EU Consortium’s Health Literacy Assessment Effort. Stud. Health Technol. Inform. 2017, 240, 34–59. [Google Scholar]
  6. Sørensen, K.; Pelikan, J.M.; Röthlin, F.; Ganahl, K.; Slonska, Z.; Doyle, G.; Fullam, J.; Kondilis, B.; Agrafiotis, D.; Uiters, E.; et al. Health literacy in Europe: Comparative results of the European health literacy survey (HLS-EU). Eur. J. Public Health 2015, 25, 1053–1058. [Google Scholar] [CrossRef]
  7. Okan, O.; Pinheiro, P.; Zamora, P.; Bauer, U. Health Literacy bei Kindern und Jugendlichen. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2015, 58, 930–941. [Google Scholar] [CrossRef]
  8. Tiller, D.; Herzog, B.; Kluttig, A.; Haerting, J. Health literacy in an urban elderly East-German population—Results from the population-based CARLA study. BMC Public Health 2015, 15, 883. [Google Scholar] [CrossRef]
  9. Geboers, B.; de Winter, A.F.; Luten, K.A.; Jansen, C.J.M.; Reijneveld, S.A. The association of health literacy with physical activity and nutritional behavior in older adults, and its social cognitive mediators. J. Health Commun. 2014, 19 (Suppl. S2), 61–76. [Google Scholar] [CrossRef]
  10. Ward, M.; Kristiansen, M.; Sørensen, K. Migrant health literacy in the European Union: A systematic literature review. Health Educ. J. 2019, 78, 81–95. [Google Scholar] [CrossRef]
  11. Jordan, S.; Hoebel, J. Gesundheitskompetenz von Erwachsenen in Deutschland. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2015, 58, 942–950. [Google Scholar] [CrossRef] [PubMed]
  12. Lorini, C.; Lastrucci, V.; Mantwill, S.; Vettori, V.; Bonaccorsi, G. Measuring health literacy in Italy: A validation study of the HLS-EU-Q16 and of the HLS-EU-Q6 in Italian language, conducted in Florence and its surroundings. Annali dell’Istituto Superiore di Sanità 2019, 55, 10–18. [Google Scholar] [CrossRef] [PubMed]
  13. Guntzviller, L.M.; King, A.J.; Jensen, J.D.; Davis, L.A. Self-Efficacy, Health Literacy, and Nutrition and Exercise Behaviors in a Low-Income, Hispanic Population. J. Immigr. Minor. Health 2017, 19, 489–493. [Google Scholar] [CrossRef] [PubMed]
  14. Plummer, L.C.; Chalmers, K.A. Health literacy and physical activity in women diagnosed with breast cancer. Psychooncology 2017, 26, 1478–1483. [Google Scholar] [CrossRef] [PubMed]
  15. Lam, M.H.S.; Leung, A.Y.-M. The Effectiveness of Health Literacy Oriented Programs on Physical Activity Behaviour in Middle Aged and Older Adults with Type 2 Diabetes: A Systematic Review. Health Psychol. Res. 2016, 4, 5595. [Google Scholar] [CrossRef]
  16. Kobayashi, L.C.; Wardle, J.; Wolf, M.S.; von Wagner, C. Health Literacy and Moderate to Vigorous Physical Activity During Aging, 2004–2013. Am. J. Prev. Med. 2016, 51, 463–472. [Google Scholar] [CrossRef]
  17. Garcia-Codina, O.; Juvinyà-Canal, D.; Amil-Bujan, P.; Bertran-Noguer, C.; González-Mestre, M.A.; Masachs-Fatjo, E.; Santaeugènia, S.J.; Magrinyà-Rull, P.; Saltó-Cerezuela, E. Determinants of health literacy in the general population: Results of the Catalan health survey. BMC Public Health 2019, 19, 1122. [Google Scholar] [CrossRef]
  18. Bostock, S.; Steptoe, A. Association between low functional health literacy and mortality in older adults: Longitudinal cohort study. BMJ 2012, 344, e1602. [Google Scholar] [CrossRef]
  19. Rudolf, K.; Biallas, B.; Dejonghe, L.A.L.; Grieben, C.; Rückel, L.-M.; Schaller, A.; Stassen, G.; Pfaff, H.; Froböse, I. Influence of Health Literacy on the Physical Activity of Working Adults: A Cross-Sectional Analysis of the TRISEARCH Trial. Int. J. Environ. Res. Public Health 2019, 16. [Google Scholar] [CrossRef]
  20. Xu, H.; Wen, L.M.; Rissel, C. The relationships between active transport to work or school and cardiovascular health or body weight: A systematic review. Asia Pac. J. Public Health 2013, 25, 298–315. [Google Scholar] [CrossRef]
  21. Lu, S.-R.; Su, J.; Xiang, Q.-Y.; Zhang, F.-Y.; Wu, M. Active transport and health outcomes: Findings from a population study in Jiangsu, China. J. Environ. Public Health 2013, 2013, 624194. [Google Scholar] [CrossRef] [PubMed]
  22. Pucher, J.; Buehler, R.; Bassett, D.R.; Dannenberg, A.L. Walking and cycling to health: A comparative analysis of city, state, and international data. Am. J. Public Health 2010, 100, 1986–1992. [Google Scholar] [CrossRef] [PubMed]
  23. Carlson, S.A.; Whitfield, G.P.; Peterson, E.L.; Ussery, E.N.; Watson, K.B.; Berrigan, D.; Fulton, J.E. Geographic and Urban–Rural Differences in Walking for Leisure and Transportation. Am. J. Prev. Med. 2018, 55, 887–895. [Google Scholar] [CrossRef] [PubMed]
  24. van Dyck, D.; Cardon, G.; Deforche, B.; de Bourdeaudhuij, I. Urban–Rural Differences in Physical Activity in Belgian Adults and the Importance of Psychosocial Factors. J. Urban Health 2011, 88, 154–167. [Google Scholar] [CrossRef]
  25. NCD Risk Factor Collaboration. Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature 2019, 569, 260–264. [Google Scholar] [CrossRef]
  26. Mizdrak, A.; Blakely, T.; Cleghorn, C.L.; Cobiac, L.J. Potential of active transport to improve health, reduce healthcare costs, and reduce greenhouse gas emissions: A modelling study. PLoS ONE 2019, 14, e0219316. [Google Scholar] [CrossRef]
  27. Kriit, H.K.; Williams, J.S.; Lindholm, L.; Forsberg, B.; Nilsson Sommar, J. Health economic assessment of a scenario to promote bicycling as active transport in Stockholm, Sweden. BMJ Open 2019, 9, e030466. [Google Scholar] [CrossRef]
  28. Federal Ministry of Health and Women’s Affairs. Health Targets Austria Relevance-Options-Contexts; Abbreviated version; Federal Ministry of Health and Women’s Affairs: Vienna, Austria, 2012. [Google Scholar]
  29. OECD. Regions at A Glance; OECD Publishing: Paris, France, 2005. [Google Scholar]
  30. Wilson, D.; Tucker, G.; Chittleborough, C. Rethinking and rescoring the SF-12. Sozial und Präventivmedizin 2002, 47, 172–177. [Google Scholar] [CrossRef]
  31. Pelikan, J.M.; Röthlin, F.; Ganahl, K. Introduction to HL measurement procedures of the HLS-EU study. In Proceedings of the 2nd European HL Conference, Aarhus, Denmark, 10–11 April 2014. [Google Scholar]
  32. UNESCO United Nations Educational, Scientific and Cultural Organization. International Standard Classification of Education, ISCED 1997. In Advances in Cross-National Comparison: A European Working Book for Demographic and Socio-Economic Variables; Hoffmeyer-Zlotnik, J.H.P., Wolf, C., Eds.; Springer: Boston, MA, USA, 2003; pp. 195–220. [Google Scholar]
  33. Grasser, G. Walkability and Public Health: Development of GIS-Based Indicators of Walkability for Surveillance and Planning Purposes in the City of Graz. Doctoral Thesis, Medical University of Graz, Graz, Austria, 2014. [Google Scholar]
  34. Christiansen, L.B.; Cerin, E.; Badland, H.; Kerr, J.; Davey, R.; Troelsen, J.; van Dyck, D.; Mitáš, J.; Schofield, G.; Sugiyama, T.; et al. International comparisons of the associations between objective measures of the built environment and transport-related walking and cycling: IPEN Adult Study. J. Transp. Health 2016, 3, 467–478. [Google Scholar] [CrossRef]
  35. Scheepers, E.; Wendel-Vos, W.; van Kempen, E.; Panis, L.I.; Maas, J.; Stipdonk, H.; Moerman, M.; den Hertog, F.; Staatsen, B.; van Wesemael, P.; et al. Personal and environmental characteristics associated with choice of active transport modes versus car use for different trip purposes of trips up to 7.5 km in The Netherlands. PLoS ONE 2013, 8, e73105. [Google Scholar] [CrossRef]
  36. Veitch, J.; Carver, A.; Salmon, J.; Abbott, G.; Ball, K.; Crawford, D.; Cleland, V.; Timperio, A. What predicts children’s active transport and independent mobility in disadvantaged neighborhoods? Health Place 2017, 44, 103–109. [Google Scholar] [CrossRef]
  37. Pocock, T.; Moore, A.; Keall, M.; Mandic, S. Physical and spatial assessment of school neighbourhood built environments for active transport to school in adolescents from Dunedin (New Zealand). Health Place 2019, 55, 1–8. [Google Scholar] [CrossRef]
  38. Owen, N.; Cerin, E.; Leslie, E.; duToit, L.; Coffee, N.; Frank, L.D.; Bauman, A.E.; Hugo, G.; Saelens, B.E.; Sallis, J.F. Neighborhood walkability and the walking behavior of Australian adults. Am. J. Prev. Med. 2007, 33, 387–395. [Google Scholar] [CrossRef] [PubMed]
  39. Titze, S.; Stronegger, W.J.; Janschitz, S.; Oja, P. Association of built-environment, social-environment and personal factors with bicycling as a mode of transportation among Austrian city dwellers. Prev. Med. 2008, 47, 252–259. [Google Scholar] [CrossRef] [PubMed]
  40. Aranda-Balboa, M.J.; Huertas-Delgado, F.J.; Herrador-Colmenero, M.; Cardon, G.; Chillón, P. Parental barriers to active transport to school: A systematic review. Int. J. Public Health 2019. [Google Scholar] [CrossRef] [PubMed]
  41. Ball, K.; Timperio, A.; Salmon, J.; Giles-Corti, B.; Roberts, R.; Crawford, D. Personal, social and environmental determinants of educational inequalities in walking: A multilevel study. J. Epidemiol. Community Health 2007, 61, 108–114. [Google Scholar] [CrossRef]
  42. Cerin, E.; Leslie, E.; Owen, N. Explaining socio-economic status differences in walking for transport: An ecological analysis of individual, social and environmental factors. Soc. Sci. Med. 2009, 68, 1013–1020. [Google Scholar] [CrossRef]
  43. Pelikan, J.M.; Röthlin, F.; Ganahl, K. Die Gesundheitskompetenz Der Österreichischen Bevölkerung-Nach Bundesländern Und Im Internationalen Vergleich. Abschlussbericht Der Österreichischen Gesundheitskompetenz (Health Literacy) Bundesländer-Studie; LBIHPR Forschungsbericht: Vienna, Austria, 2013. [Google Scholar]
  44. Paasche-Orlow, M.K.; Parker, R.M.; Gazmararian, J.A.; Nielsen-Bohlman, L.T.; Rudd, R.R. The prevalence of limited health literacy. J. Gen. Intern. Med. 2005, 20, 175–184. [Google Scholar] [CrossRef]
  45. Tully, M.A.; Panter, J.; Ogilvie, D. Individual characteristics associated with mismatches between self-reported and accelerometer-measured physical activity. PLoS ONE 2014, 9, e99636. [Google Scholar] [CrossRef]
Table 1. Baseline characteristics of the study population.
Table 1. Baseline characteristics of the study population.
N = 288 Female Male
Proportion in %
Mean (SD)
nProportion in %
Mean (SD)
nProportion in %
Mean (SD)
n
Age (yrs 1)57.8 (0.9)28857.9 (1.1)17157.7 (1.4)117
 <55 yrs41.011840.46941.949
 ≥55 yrs59.017059.610258.168
Educational level
 Low education9.42712.3215.16
 Medium education76.021974.312778.692
 High education 14.64213.52316.219
Household income (n = 228)
 <1850 EUR35.18040.55327.827
 1850 EUR–2950 EUR32.07330.64034.033
 >2950 EUR32.97529.03838.237
Migration background
 yes 4.5136.4111.72
Emloyment status
 Employed42.012138.66647.055
 Retired47.213647.48147.055
 Other (e.g. student, unemployed)10.73114.0246.07
Marital status
 Married or living together70.520373.112566.778
Trained or employed in healthcare
 yes 18.45323.44011.113
BMI 2 (kg/m2)25.9 (4.1)27925.5 (4.6)16526.7 (3.1)114
 Underweight0.721.2200
 Normal weight41.011852.18628.132
 Overweight41.712033.35557.065
 Obese13.63913.32214.917
Self-perceived health status
 very good30.28731.05329.134
 good43.812644.47642.750
 fair23.36722.23824.829
 poor2.882.343.44
1 years; 2 Body Mass Index.
Table 2. Active transport of the study population.
Table 2. Active transport of the study population.
Total SamplenFemalenMalen
Active transport min/wk 1, median (IQR 2)180.0 (90; 410)233180.0 (90; 379)134210.0 (100; 420)99
 Active walking min/wk162.5 (60; 300)214140.0 (60; 278)120180.0 (76; 300)94
 Active cycling min/wk105.0 (45; 240)116105.0 (45; 225)67120.0 (35; 240)49
 Active electric cycling min/wk120.0 (48; 285)25120.0 (45; 420)17135.0 (70; 203)8
Frequency of active transport/wk, mean (SD)
 frequency of walking days/wk 34.3 (2.3)2144.1 (2.4)1204.6 (2.2)94
 frequency of cycling days/wk3.4 (2.1)1163.4 (2.1)673.4 (2.1)49
General frequency of walking %
 daily/often58.016749.78570.182
 weekly/1–3 times a month20.86025.74413.716
 seldom/never21.26124.64216.219
General frequency of cycling, %
 daily/often27.47926.94628.233
 weekly/1–3 times a month22.96624.64220.524
 seldom/never49.314248.58350.459
1 minutes per week; 2 interquartile range, 3 days per week.
Table 3. Distribution of health literacy (HL) levels and HL scores of the study population.
Table 3. Distribution of health literacy (HL) levels and HL scores of the study population.
Number (%)
CategoriesInadequate
(0–25)
Problematic
(>25–33)
Sufficient
(>33–42)
Excellent
(>42)
Mean (SD)
HL Score
Total20 (6.9%)74 (25.7%)112 (38.9%)82 (28.5%)37.1 (7.66)
Sex
 Female10 (5.8%)44 (25.7%)64 (37.4%)53 (31.0%)37.5 (7.62)
 Male10 (8.5%)30 (25.6%)48 (41.0%)29 (24.8%)36.6 (7.74)
Age
 <55 years7 (5.9%)30 (25.4%)45 (38.1%)36 (30.5%)37.4 (7.94)
 ≥55 years13 (7.6%)44 (25.9%)67 (39.4%)46 (27.1%)37.0 (7.49)
Educational Level
 Low2 (7.4%)7 (25.9%)11 (40.7%)7 (25.9%)36.5 (7.25)
 Medium17 (7.8%)64 (27.9%)85 (38.8%)56 (25.6%)36.6 (7.75)
 High1 (2.4%)6 (14.3%)16 (38.1%)19 (45.2%)40.2 (6.90) *
Migration background
 Yes1 (7.7%)1 (7.7%)6 (46.2%)5 (38.5%)39.3 (8.06)
 No19 (6.9%)73 (26.5%)106 (38.5%)77 (28.0%)37.0 (7.64)
Employment status
 Employed8 (6.6%)36 (29.8%)39 (32.2%)38 (31.4%)37.3 (8.13)
 Retired10 (7.4%)34 (25.0%)54 (39.7%)38 (27.9%)37.1 (7.60)
Trained/employed in hc 1
 yes3 (5.7%)10 (18.9%)13 (34.5%)27 (50.9%)40.2 (8.12) *
 no17 (7.2%)64 (27.2%)99 (42.1%)55 (23.4%)36.4 (7.41)
Frequency of walking
 daily/often13 (7.8%)40 (24.0%)71 (42.5%)43 (25.7%)37.0 (7.75)
 weekly/1–3 times a month3 (5.0%)18 (30.0%)20 (33.3%)19 (31.7%)37.7 (7.56)
 seldom/never4 (6.6%)16 (26.2%)21 (34.4%)20 (32.8%)37.0 (7.63)
Frequency of cycling
 daily/often7 (8.9%)17 (21.5%)36 (45.6%)19 (24.1%)37.1 (7.44)
 weekly/1–3 times a month1 (1.5%)14 (21.2%)32 (48.5%)19 (28.8%)38.5 (6.73)
 seldom/never11 (7.7%)43 (30.3%)44 (31.0%)44 (31.0%)36.6 (8.12)
Active transport
 mean min/wk 2 (SD)210.3 (196.2)205.2 (189.8)239.7 (188.8)221.8 (177.8)-
1 healthcare; 2 minutes per week; * p < 0.05.
Table 4. Associations for total active transport as the dependent variable and HL as the independent variable, adjusted for gender, age, educational level, and training/employment in healthcare.
Table 4. Associations for total active transport as the dependent variable and HL as the independent variable, adjusted for gender, age, educational level, and training/employment in healthcare.
Total Sample
ß95% CIp
Model 1
HL Score0.012–0.010; 0.0340.281
Model 2
HL Score0.013–0.009; 0.0350.257
Gender
 femaleref.
 male–0.163–0.512; 0.1860.359
Age0.006–0.006; 0.0170.314
Educational Level
 lowref.
 medium0.070–0.537; 0.6770.820
 high0.101–0.619; 0.8210.783
Model 3
HL Score0.014–0.008; 0.0370.208
Gender
 femaleref.
 male–0.133–0.486; 0.2210.460
Age0.006–0.006; 0.0170.324
Educational Level
 lowref.
 medium0.087–0.521; 0.6940.779
 high0.167–0.563; 0.8970.653
Trained / Employed in hc 1
 yesref.
 no0.251–0.717; 0.2160.291
1 healthcare.
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