Health and nutrition literacy in adults: Links with lifestyle factors and obesity

Abstract

BACKGROUND:

Low Health Literacy (HL) and Nutrition Literacy (NL) are associated with serious negative health outcomes.

OBJECTIVES:

The aim of this study was to investigate certain lifestyle factors and obesity, in relation to HL and NL.

METHODS:

This cross-sectional study was conducted in the urban area of the Attica region, in Greece. The sample consisted of 1281 individuals, aged ≥18 years. HL, NL sociodemographic characteristics and lifestyle factors (physical activity, smoking status, alcohol consumption,) were assessed. Mann-Whitney U, the Kruskall Wallis, Pearson chi-square tests and multiple linear regression models were used.

RESULTS:

Linear regression analysis has shown that smoking, alcohol consumption and physical activity, were associated with HL levels (–1.573 points for ex-smokers in comparison to smokers, p = 0.035, –1.349 points for alcohol consumers in comparison to non-consumers, p = 0.006 and 1.544 points for physically active individuals to non-active, p = 0.001). With respect to NL levels, it was also not associated with any of these factors. Obesity was not associated with HL and NL levels.

CONCLUSIONS:

Certain lifestyle factors, including physical activity, are predicting factors of HL levels, in Greek adults. The results contribute to the understanding of the relationship between lifestyle factors and HL and should be taken into account when HL policies are designed.

Keywords

Health literacy nutrition literacy lifestyle

1 Introduction

The concept of Health Literacy (HL), in recent years, has expanded from a simple awareness & understanding of health information to a more comprehensive meaning of health focusing at empowering people to live a healthier life. Studies seem to agree that health literacy is a stronger predictor of health than age, income, employment, education, and race [1] and is directly linked to premature death [2]. It is also a modifiable risk factor, and therefore there is an increasing research interest in this field [3]. Nutrition Literacy (NL), is an increasingly important concept in the health promotion sector [4] and it seems to be an important contributor to healthy eating behaviors [5]. There are many definitions of HL, but the most recent and comprehensive one is the following: ‘HL incorporates the knowledge, motivation and competences to access, understand, appraise and apply health information in order to make judgments and take decisions in everyday life concerning health care, disease prevention and health promotion to maintain or improve quality of life throughout the course of life’ [6]. NL, is defined as ‘the degree to which individuals can obtain, process, and understand basic nutrition information & nutrition services they need, to make appropriate nutritional decisions’ [7].

Unhealthy lifestyle factors, such as smoking, alcohol consumption, physical inactivity and poor diet, directly affect health [8]. Moreover, individuals with low socioeconomic status (SES), usually have increased rates of obesity, and the prevalence of smoking [9], alcohol intake [10] and physical inactivity [11] is higher in people with low SES, in comparison to the general population. Also, people with low SES, seem to have lower HL and NL levels [12].

Many studies have focused on investigating the possible interrelationships between excess body weight and certain lifestyle factors, with HL levels, worldwide. The majority of the studies tend to indicate that obesity and unhealthy lifestyle factors are usually associated with lower levels of HL [13 , 16]. A number of studies have found an association between low HL and obesity and unhealthy diet and physical inactivity, but not with smoking and alcohol consumption [13, 16]. On the other hand, other studies supported that limited HL was significantly associated with physical inactivity, fruit and vegetable consumption, alcohol use and smoking [14, 15]. In the context of NL, few studies are available investigating the impact of poor NL on excess body weight and certain lifestyle factors. A recent study in USA, using the Nutrition Literacy Assessment Instrument (NLit), supported that NL predicted adherence to healthy or unhealthy diet patterns [17] and the study conducted, in Taiwan, using an 8-item scale on capacities regarding nutritional information in five dimensions, showed that participants with a higher level of NL were more likely to engage in healthy-eating behaviors [18].

In the case of Greece, the prevalence of obesity and smoking rates are amongst the highest in Europe [19, 20] and adherence to the Mediterranean diet seems to deteriorate in all age groups [21]. As for the frequency of alcohol consumption in Greek adults, 6.9% consume alcohol every day, 23.7% every week and 25.8% every month [22]. In addition, 84.6% of the population meet the recommended physical activity levels for health [23]. Moreover, in Greece, chronic degenerative diseases associated with obesity and unhealthy lifestyle factors are major causes of ill health and premature death [24]. Therefore, given the fact that relevant studies on HL & NL in Southern Europe and especially in Greece, are lacking, more research on this field is necessary [25].

To the best of our knowledge, there are no studies that have investigated concurrently, the association between HL and NL in relation to obesity and lifestyle in Greece. The aim of the study was to investigate the effect of obesity and certain lifestyle factors as predictors of HL and NL levels, in Greek adults. We hypothesized that a normal body mass index (BMI), less alcohol consumption, less smoking and higher levels of physical activity would be positively related with HL and NL levels.

2 Materials & methods

2.1 Study population

This cross-sectional study took place in the urban area of the Attica region in Greece. The areas of recruitment were selected on a feasibility basis, among the municipalities of the greater metropolitan area of Athens. The recruitment period lasted from October 2017 to April 2018. Sampling was conducted according to the age and gender demographics of the Greek population (2011 census Hellenic Statistical Authority, ELSTAT) from the participants’ places of work or residence. Because no random sampling was performed, there were differences in sex and age distribution in women, but not in men, between the selected sample and the total Greek population. The final sample consisted of 1281 participants of both sexes, aged ≥18, of whom 59.4% women and 40.6% men (n = 760 and 520 respectively), with a mean age of 44.52 (±17.44) years. Inclusion criteria were: participants of both sexes, ≥18 years of age and the ability to read and write in Greek. No other exclusion criteria were used. The participation rate was 85.4%, with 14.6% dropping out. The main reasons for dropping out, as stated by the participants, were lack of time or lack of interest in the study. It was estimated that a sample size n = 1083 was adequate to evaluate effect size measures’ differences of 20% at a < 5% level of significance, achieving 85% statistical power.

2.2 Data Collection

Data collection was conducted using two separate validated questionnaires for the assessment of Health Literacy [26] and Nutrition Literacy [27]. Sociodemographic characteristics including gender, age and education in years, smoking status, alcohol consumption and physical activity levels were also assessed, together with anthropometric characteristics which were self-reported. All participants were informed about the aim of the study by a cover letter and after their written consent to participate in the study, they completed the questionnaires either in printed or in electronic form. The mean time of completion was about 25 minutes. To avoid sources of bias, efforts were made to ensure the recruitment of a relatively large sample of participants, to ensure that all instruments were previously tested and validated in the Greek language and population and all the procedures were conducted in an anonymous manner.

2.3 European Health Literacy Questionnaire (HLS_EU_Q47)

The original full version of the European Health Literacy Questionnaire (HLS_EU_Q47) was used to record HL levels [26]. It consists of 47 items which integrate questions relevant to three health-relevant sectors (health care, disease prevention and health promotion) and four information processing areas (accessing, understanding, appraising and applying). A general HL score (the comprehensive HL Index) and 7 sub-indices are calculated. The 7 sub-indices are: Health Care Index, Disease Prevention Index, Health Promotion Index, Access/Obtain Health Information Index, Understanding Health Information Index, Process/Appraise Health Information Index and Apply/Use Health Information Index. Thus, it is believed to be a more comprehensive tool in contrast to the other existing questionnaires and enables comparisons within and between countries [28].

Responses range from 1 to 4, using a four-point self-reported Likert-type scale. Total score varies from 0 to 50 and a score over 43 indicates excellent HL, between 34 and 42 shows adequate HL, between 25 and 33 indicates problematic HL and under 25 suggests inadequate HL.

2.4 Nutrition Literacy Scale-Greek (NLS-Gr)

Nutrition Literacy Scale (NLS) [27] was firstly validated in Greek [29] and then it was used for NL assessment. The aim of the tool is to assess reading comprehension and to measure individual’s ability to understand nutrition information. The NLS-Gr includes 29 sentences, in which one word is missing. Participants have four options where only one is the correct. Adding the correct answers, the total score (from 0 to 29) is calculated and a score >15 shows adequate NL, between 8 and 14 shows marginal NL and <8 shows inadequate NL.

2.5 Ethical standards disclosure

This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Institutional Ethics Review Board of the Harokopio University. The protocol number was 57 and the date of expedition was 15 09 2017. Written informed consent was obtained from all subjects/patients.

2.6 Statistical analysis

Data are presented as N (%) for qualitative variables (i.e. sex, obesity category, smoking, alcohol consumption, physical activity, the existence of chronic disease, HL and NL categories) and as mean (SD) for quantitative variables (i.e. age and education in years, HL and NL scores). In the case of the participants who did not answer all the questions of the questionnaires (missing values), the final scores were not calculated and were not included in the analysis. Due to the skewed distribution of the quantitative variables (i.e. age and education in years, HL and NL scores) the Mann-Whitney, non-parametric test was used to evaluate differences between men and women. Also, the Kruskall Wallis, non-parametric test was used to evaluate differences between obesity categories and HL and NL scores. Moreover, Pearson chi-square, was used to evaluate differences between men and women for the qualitative variables. Then, multiple linear regression analysis were further used to evaluate the association between obesity (evaluated as a categorical variable, underweight and normal weight vs overweight and obese), smoking status (evaluated as a categorical variable, smokers vs ex-smokers and smokers vs non-smokers), alcohol consumption and physical activity (evaluated as a categorical variable, yes vs no) (independent variables) and HL and NL scores (depended variables) after adjusting for sex, age and education (independent determinants). The inclusion of the independent variables was based on literature review made and the tested research hypothesis of the present study. Multicolinearity was evaluated using the Variance Inflation Factor (VIF; variables with value > 4 were not included at the same time in the model). The STATA software, version 14 (MP & Associates, Sparta, Greece) was used for all statistical analyses.

3 Results

3.1 Descriptive characteristics and levels of HL and NL of the participants

The participants’ descriptive characteristics and HL and NL levels, are presented in Table 1. Body Mass Index was calculated: BMI = weight/height² and obesity categories were defined as: Underweight (BMI below 18.5), Normal Weight (BMI:18.5 –24.9), Overweight (BMI: 25.0 –29.9) and Obese (BMI: 30.0 and above). The mean age of the sample was 44.52 (17.44) years and 59.4% of the sample were women. Women were statistically significant younger (p < 0.0001) and more educated (p = 0.002) than men. Men had higher rates of overweight and obesity in comparison to women (< 0.0001). More women were non-smokers than men (p = 0.002), more men were alcohol consumers in contrast to women (p < 0.0001).

Table 1
Sample’s Descriptive Characteristics and HL and NL for men and women and in total (n = 1281)

Men Women p Total

N (%) 520 (40.6) 760 (59.4) – 1281 (100)

Age Mean (SD) 46.92 (17.68) 42.88 (17.11) <0.001 44.52 (17.44)

Education in years Mean (SD) 6.86 (1.93) 7.13 (1.96) 0.002 7.02 (1.95)

Obesity Category N (%)

- Underweight^* 4 (0.8) 18 (2.4) 22 (1.7)

- Normal Weight^* 175 (33.9) 463 (61.5) <0.0001 639 (50.3)

- Overweight^* 237 (45.9) 197 (26.2) 434 (34.2)

- Obese^* 100 (19.4) 75 (10) 175 (13.8)

Smoking N (%)

- Yes 173 (33.5) 219 (29.1) 392 (30.9)

- No^* 252 (48.8) 439 (58.4) 0.002 693 (54.5)

- Ex-smoker^* 91 (17.6) 94 (12.5) 185 (14.6)

Alcohol Consumption N (%)

- Yes^* 380 (73.1) 397 (52.7) <0.0001 777 (60.9)

- No^* 140 (26.9) 357 (47.3) 498 (39.1)

Physical Activity N (%)

- Yes 304 (58.8) 471 (62.1) 0.254 776 (60.8)

- No 213 (41.2) 287 (37.9) 500 (39.2)

Chronic Disease N (%)

- Yes 138 (27) 179 (23.8) 0.227 317 (25.1)

- No 373 (73) 572 (76.2) 946 (74.9)

Health Literacy Score Mean (SD) 31.53 (8.88) 32.79 (7.82) 0.066 32.28 (8.28)

Health Literacy Categories N (%)

- Inadequate HL^* 93 (20.8) 87 (13.7) 180 (16.6)

- Problematic HL^* 170 (38) 277 (43.5) 0.017 447 (41.2)

- Sufficient HL^* 137 (30.6) 202 (31.7) 339 (31.3)

- Excellent HL 47 (10.5) 71 (11.1) 118 (10.9)

Nutrition Literacy Score Mean (SD) 21.35 (5.94) 22.62 (5.42) <0.001 22.11 (5.67)

Nutrition Literacy Categories N (%)

- Inadequate NL 31 (6) 29 (3.8) 60 (4.7)

- Marginal NL 37 (7.1) 41 (5.4) 0.079 78 (6.1)

- Adequate NL 451 (86.9) 689 (90.8) 1141 (89.2)

	Men	Women	p	Total
N (%)	520 (40.6)	760 (59.4)	–	1281 (100)
Age Mean (SD)	46.92 (17.68)	42.88 (17.11)	<0.001	44.52 (17.44)
Education in years Mean (SD)	6.86 (1.93)	7.13 (1.96)	0.002	7.02 (1.95)
Obesity Category N (%)
- Underweight^*	4 (0.8)	18 (2.4)		22 (1.7)
- Normal Weight^*	175 (33.9)	463 (61.5)	<0.0001	639 (50.3)
- Overweight^*	237 (45.9)	197 (26.2)		434 (34.2)
- Obese^*	100 (19.4)	75 (10)		175 (13.8)
Smoking N (%)
- Yes	173 (33.5)	219 (29.1)		392 (30.9)
- No^*	252 (48.8)	439 (58.4)	0.002	693 (54.5)
- Ex-smoker^*	91 (17.6)	94 (12.5)		185 (14.6)
Alcohol Consumption N (%)
- Yes^*	380 (73.1)	397 (52.7)	<0.0001	777 (60.9)
- No^*	140 (26.9)	357 (47.3)		498 (39.1)
Physical Activity N (%)
- Yes	304 (58.8)	471 (62.1)	0.254	776 (60.8)
- No	213 (41.2)	287 (37.9)		500 (39.2)
Chronic Disease N (%)
- Yes	138 (27)	179 (23.8)	0.227	317 (25.1)
- No	373 (73)	572 (76.2)		946 (74.9)
Health Literacy Score Mean (SD)	31.53 (8.88)	32.79 (7.82)	0.066	32.28 (8.28)
Health Literacy Categories N (%)
- Inadequate HL^*	93 (20.8)	87 (13.7)		180 (16.6)
- Problematic HL^*	170 (38)	277 (43.5)	0.017	447 (41.2)
- Sufficient HL^*	137 (30.6)	202 (31.7)		339 (31.3)
- Excellent HL	47 (10.5)	71 (11.1)		118 (10.9)
Nutrition Literacy Score Mean (SD)	21.35 (5.94)	22.62 (5.42)	<0.001	22.11 (5.67)
Nutrition Literacy Categories N (%)
- Inadequate NL	31 (6)	29 (3.8)		60 (4.7)
- Marginal NL	37 (7.1)	41 (5.4)	0.079	78 (6.1)
- Adequate NL	451 (86.9)	689 (90.8)		1141 (89.2)

p < 0.005, Mann-Whitney, Kruskal Wallis, x²

The mean score of HL was 32.28 (8.28) and the majority of the participants were classified in the problematic HL category (41.2%). Only a percentage of 10.9% had excellent levels of HL. As for NL levels, the mean score of NL was 22.11 (5.67) and 89.2% of the participants had adequate levels of NL. Women had statistically significant higher levels of NL compared to men (p < 0.0001).

Table 2 presents the total HL score, the 7 main subcategories of the HLS_EU_Q47 and the NL score, separate for each obesity category. A significant difference was observed between the obesity categories and the HL subcategory “health promotion sector” (knowledge and skills which enable a person to navigate to this sector as a citizen in relation to the health promotion efforts in the community, the work place, the educational system, the political arena and the market place) [6] where underweight participants had statistically significant higher scores than overweight and obese (p = 0.035) participants. Also, in the “assessing information area” subcategory of HL (which refers to the ability to seek, find and obtain health information) the difference between the obesity categories was marginally insignificant (p = 0.050) with underweight individuals having higher levels than rest of the participants. NL levels, were statistically significant higher levels in normal weight participants in comparison to overweight and obese (p < 0.0001).

Table 2

HL and its 7 main subcategories and NL scores by obesity category (N = 1281)

	Underweight	Normal weight	Overweight	Obese	p-value
	N = 22	N = 639	N = 434	N = 175
Health Literacy Score Mean (SD)	35.36 (6.67)	32.92 (7.51)	31.28 (9.07)	32.47 (8.17)	0.117
Health Care Score Mean (SD)	36.55 (6.19)	34.05 (7.72)	32.43 (9.38)	33.75 (8.39)	0.078
Disease Prevention Score Mean (SD)	34.85 (7.67)	33.02 (8.45)	31.48 (9.67)	33.33 (9.07)	0.335
Health Promotion Score Mean (SD)	34.66 (8.15)^*∧	31.70 (8.64)	29.94 (9.95)^*	30.39 (9.76)^∧	0.035
Assessing Information Score Mean (SD)	34.50 (7.65)	31.93 (8.37)	30.08 (9.73)	31.13 (9.22)	0.050
Understanding Information Score Mean (SD)	37.19 (7.16)	35.00 (8.05)	33.66 (9.72)	34.83 (8.60)	0.548
Appraising Information Score Mean (SD)	34.09 (7.75)	31.88 (8.61)	30.32 (9.99)	31.71 (9.23)	0.197
Applying Information Score Mean (SD)	35.95 (6.76)	33.15 (8.20)	31.35 (9.67)	32.53 (8.75)	0.068
Nutrition Literacy Score Mean (SD)	22.96 (5.26)	23.05 (5.08)^*∧	21.36 (5.82)^*	21.75 (5.75)^∧	<0.0001
Kruskal Wallis Test

P < 0.005, ^* & ^∧shows between which obesity subcategories exists the significant difference

3.2 Models of linear regression for HL

The results of a linear regression for HL when considering obesity, smoking status, alcohol consumption and physical activity, as predicting factors for HL, are presented in Table 3. In the first model, obesity category was included and the results showed that overweight and obese individuals had significantly lower HL with –1. 312 points (p = 0.005) in contrast to under and normal weight individuals. In model 2, smoking status was added. Firstly, obesity category was still significantly associated with HL, with the overweight and obese individuals having lower HL with –1.212 points (p = 0.010) in contrast to under and normal weight. Also, ex-smokers had significantly lower HL with –1.750 points (p = 0.020) in contrast to smokers. In model 3, alcohol consumption was added and the results remained the same for obesity (–1.162 points for overweight and obese individuals in contrast to under and normal weight, p = 0.014) and smoking status (–1.820 points for ex-smokers in contrast to smokers p = 0.016), but the alcohol consumption was not significantly associated. The model 4 included also physical activity. The results remained the same for obesity (–0.933 points for overweight and obese individuals in contrast to under and normal weight, p = 0.049) and for smoking status (–2.143 points for ex-smokers in contrast to smokers, p = 0.005) and alcohol consumption (no association) and physical activity was significantly positively associated with HL levels with 2.043 points (p < 0.0001). In the 5^th model where sex (p = 0.433), age in years (p = 0.282) and education in years (p < 0.0001) were added as confounders, obesity was no longer significantly associated with HL levels. Smoking status, alcohol consumption and physical activity were significantly associated with HL levels (–1.573 points for ex-smokers in contrast to smokers, p = 0.035, –1.349 points for alcohol consumers in contrast to non-consumers, p = 0.006 and 1.544 points for physical active individuals in contrast to non-active, p = 0.001).

Table 3
Results (b, SE) from Regression Analysis models that evaluated determinants of Health Literacy (n = 1281)

Model 1 Model 2 Model 3 Model 4 Model 5

b±SE, p b±SE, p b±SE, p b±SE, p b±SE, p

Obesity Category (Overweight and Obese/Under and Normal Weight) –1.312±0.470, 0.005 –1.212±0.472, 0.010 –1.162±0.472, 0.014 –0.933±0.474, 0.049 –0.029±0.499, 0.953

Smoking (Reference: Smokers)

- Non Smokers –0.167±0.526, 0.751 –0.149±0.530, 0.779 –0.510±0.534, 0.340 –0.444±0.524, 0.396

- Ex-Smokers –1.750±0.750, 0.020 –1.820±0.752, 0.016 –2.143±0.753, 0.005 –1.573±0.744, 0.035

Alcohol Consumption (Yes/No) –0.405±0.484, 0.403 –0.653±0.485, 0.178 –1.349±0.495, 0.006

Physical Activity (Yes/No) 2.043±0.490, <0.0001 1.544±0.479, 0.001

Sex (Men/Women) –0.393±0.501, 0.433

Age in years –0.018±0.016, 0.282

Education in years 0.959±0.142, <0.0001

	Model 1	Model 2	Model 3	Model 4	Model 5
Obesity Category (Overweight and Obese/Under and Normal Weight)	–1.312±0.470, 0.005	–1.212±0.472, 0.010	–1.162±0.472, 0.014	–0.933±0.474, 0.049	–0.029±0.499, 0.953
Smoking (Reference: Smokers)
- Non Smokers		–0.167±0.526, 0.751	–0.149±0.530, 0.779	–0.510±0.534, 0.340	–0.444±0.524, 0.396
- Ex-Smokers		–1.750±0.750, 0.020	–1.820±0.752, 0.016	–2.143±0.753, 0.005	–1.573±0.744, 0.035
Alcohol Consumption (Yes/No)			–0.405±0.484, 0.403	–0.653±0.485, 0.178	–1.349±0.495, 0.006
Physical Activity (Yes/No)				2.043±0.490, <0.0001	1.544±0.479, 0.001
Sex (Men/Women)					–0.393±0.501, 0.433
Age in years					–0.018±0.016, 0.282
Education in years					0.959±0.142, <0.0001

3.3 Models of linear regression for NL

Table 4 presents the results of a linear regression for NL when considering obesity, smoking status, alcohol consumption and physical activity as predicting factors for NL. Obesity category was the only factor in the first model and the results showed that overweight and obese individuals had significantly lower NL with –1.491 points (p < 0.0001) in contrast to under and normal weight. In model 2, smoking status was added. Firstly, obesity category was still significantly associated with NL, with the overweight and obese individuals having lower NL with –1.397 points (p < 0.0001) in contrast to under and normal weight. As for smoking status, ex-smokers had significantly lower NL with –1.699 points (p = 0.001) in contrast to smokers. When in model 3, alcohol consumption was added, the results remained the same for obesity (–1.441 points for overweight and obese in contrast to under and normal weight, p < 0.0001) and smoking status (–1.628 points for ex-smokers in contrast to smokers, p = 0.001). Also, alcohol consumers were significantly associated with NL levels with 1.079 points (p = 0.001) in contrast to non-consumers. In the 4^th model physical activity was also included. The results remained the same for obesity (–1.390 for overweight and obese in contrast to under and normal weight, p < 0.0001), smoking status (–1.706 points for ex-smokers in contrast to smokers, p = 0.001), and alcohol consumption (1.024 points for consumers in contrast to non-consumers, p = 0.001). As for physical activity, it was not significantly associated with NL levels. In the 5th model where sex (p = 0.003), age in years (p < 0.0001) and education in years (p < 0.0001) were added as confounders, obesity, smoking status, alcohol consumption and physical activity were no significantly associated with NL levels.

Table 4
Results (b, SE) from Regression Analysis models that evaluated determinants of Nutrition Literacy (n = 1281)

Model 1 Model 2 Model 3 Model 4 Model 5

b±SE, p b±SE, p b±SE, p b±SE, p b±SE, p

Obesity Category (Overweight and Obese/Under and Normal Weight) –1.491±0.315, <0.0001 –1.397±0.315, <0.0001 –1.441±0.311, <0.0001 –1.390±0.312, <0.0001 0.449±0.278, 0.107

Smoking (Reference: Smokers)

- Non Smokers –0.404±0.351, 0.250 –0.393±0.349, 0.260 –0.450±0.352, 0.201 –0.310±0.291, 0.286

- Ex-Smokers –1.699±0.499, 0.001 –1.628±0.494, 0.001 –1.706±0.495, 0.001 –0.317±0.412, 0.441

Alcohol Consumption (Yes/No) 1.079±0.319, 0.001 1.024±0.320, 0.001 –0.089±0.275, 0.745

Physical Activity (Yes/No) –0.572±0.324, 0.077 –0.086±0.267, 0.749

Sex (Men/Women) –0.819±0.278, 0.003

Age in years –0.086±0.009, <0.0001

Education in years 1.145±0.079, <0.0001

	Model 1	Model 2	Model 3	Model 4	Model 5
Obesity Category (Overweight and Obese/Under and Normal Weight)	–1.491±0.315, <0.0001	–1.397±0.315, <0.0001	–1.441±0.311, <0.0001	–1.390±0.312, <0.0001	0.449±0.278, 0.107
Smoking (Reference: Smokers)
- Non Smokers		–0.404±0.351, 0.250	–0.393±0.349, 0.260	–0.450±0.352, 0.201	–0.310±0.291, 0.286
- Ex-Smokers		–1.699±0.499, 0.001	–1.628±0.494, 0.001	–1.706±0.495, 0.001	–0.317±0.412, 0.441
Alcohol Consumption (Yes/No)			1.079±0.319, 0.001	1.024±0.320, 0.001	–0.089±0.275, 0.745
Physical Activity (Yes/No)				–0.572±0.324, 0.077	–0.086±0.267, 0.749
Sex (Men/Women)					–0.819±0.278, 0.003
Age in years					–0.086±0.009, <0.0001
Education in years					1.145±0.079, <0.0001

4 Discussion

The present study aimed to investigate the effect of obesity and certain lifestyle factors as predictors of HL and NL levels, in Greek adults. Fifty eight percent of the participants had inadequate or problematic HL levels and almost 90% had adequate NL levels. Regression analysis has shown that smoking, alcohol consumption and physical activity were predicting factors for HL levels, after adjustment for sex, age and education.

The inadequate and problematic HL levels presented in this study are higher in comparison to the only other similar study that has evaluated HL levels, in Greece in 2011, where 44.7% of the participants had inadequate and problematic HL levels [25]. This result could be partly attributed to the prolonged economic austerity period which had a significant negative impact on lifestyle determinants [30]. It is also plausible that the culture around health, particularly in terms of primary prevention, in Greece, has room for improvement [31].

In the case of NL, no other similar study has been conducted in the past, attempting to asses NL levels with a validated instrument in Greece. Most of the studies in other countries, investigated nutrition knowledge, attitudes and beliefs, not NL levels [32, 33].

The HL findings in this study are partially supported by other studies. The HLS-EU study, is the only study, till today, that has evaluated HL levels in Greek healthy adults. In the HLS-EU study Greeks with higher HL, exercised more often, were more likely to be of normal weight, they were more likely non-alcohol consumers and smoked more, than people with lower HL [34]. These findings are consistent with the results of the current study, except for the association of obesity with HL levels, which was not found to be significant in the current study. In the current study it was found that ex-smokers had lower levels of HL in comparison to non-smokers, which was an unexpected result, not supported by other studies, which warrants further investigation.

In a recent study which was conducted in Catalonia, aiming to identify social and health-related determinants of HL, results revealed that individuals in the sufficient HL group, reported more frequently light consumption of alcohol, healthy physical activity, and they had lower BMI, compared to those with inadequate or problematic HL. However, the regression analysis showed that only physical activity was a factor predicting an inadequate or problematic HL level [35]. Levin Zamir et al., 2016, conducted a similar study in 600 adult participants in Israel and the results showed that HL was positively associated with physical inactivity and body mass index, but not with cigarette smoking or alcohol consumption [13]. Another study which took place in the UK with 759 adult participants, concluded that higher HL increased the likelihood of eating better (at least five portions of fruit and vegetables a day) and of being a non-smoker [15].

One plausible explanation for the weak influence of obesity on HL levels, in Greek adults, could be partly attributed to different cultural perceptions of whether obesity truly constitutes a disease and as a result, is perceived as a real threat to ill health. Culture was found in the past, to influence significantly individual perceptions of obesity and the differences observed, were country specific [36]. Studies have shown that people, especially in developed countries, generally overestimate their height and underestimate their weight, resulting in an overall underestimation of BMI [37]. To the best of our knowledge, no study assessing obesity in relation to perceptions and cultural attitudes was conducted in Greece, till today. The cultural perception of obesity, as a serious risk to health, needs to be better investigated in Greece by future studies.

The limitations of the current study include: no random sampling which led to small differences in sex and age distribution in women but not in men, between the selected sample and the total Greek population, the cross-sectional design of the study, which cannot directly support the causal nature of the influencing factors, the fact that the instruments used were subjective measures, and that all the data were self-reported. Lastly, the participants were recruited only from the urban area of the Attica region.

This is the first study, to the best of our knowledge, attempting to concurrently measure HL and NL in Greece, using validated instruments, in relation to obesity and certain lifestyle factors. The findings could have an influential impact on health policy in Greece, since currently discussions on further primary care reform, are underway. HL and NL are necessary pre0-cursors to assist individuals in achieving a higher level of health, hence, more studies are needed to elucidate the above hypothesis.

Declaration of interest

None to declare.

Financial Support

The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under the HFRI PhD Fellowship grant (GA. no. 949).

Footnotes

Acknowledgments

Special thanks to all the participants for their valuable contribution to the study

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