Obesity in Mediterranean Islands
Supervisor: Triantafyllos Pliakas
Candidate number: 108693
Word count: 9700
Project length: Standard
Submitted in part fulfilment of the requirements for the degree of MSc in Public Health (Health Promotion)
September 2015
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CONTENTS
1 INTRODUCTION ...... 1 1.1 Background on Obesity ...... 1 1.2 Negative Impact of Obesity ...... 1 1.2.1 The Physical and Psychological ...... 1 1.2.2 Economic Burden ...... 2 1.3 Obesity in Mediterranean Islands ...... 2 1.3.1 Obesity in Europe and the Mediterranean region ...... 2 1.3.2 Obesogenic Islands ...... 3 1.4 Rationale ...... 3 2 AIMS AND OBJECTIVES ...... 4 3 METHODS ...... 4 3.1 Search Strategy ...... 4 3.1.1 Databases ...... 6 3.1.2 Inclusion/Exclusion criteria ...... 6 3.2 Data Selection ...... 7 3.2.1 Snowballing ...... 7 3.3 Quality Assessment and Data Extraction ...... 7 3.4 Data Extraction ...... 8 3.5 Data Synthesis ...... 8 4 RESULTS ...... 9 4.1 Search Results ...... 9 4.2 Quality of included studies ...... 10 4.3 Data Extraction ...... 11 4.3.1 Study Characteristics ...... 11 4.3.2 Methods of study design & methodology ...... 11 4.3.3 Definitions of weight status and measurement ...... 13 4.4 Narrative Synthesis ...... 15 4.4.1 Inductive Content Analysis ...... 15 4.4.2 Educational level ...... 16 4.4.3 Socio-economic status ...... 16 4.4.4 Physical activity & sedentary behaviour ...... 17 4.4.5 Gender...... 17
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4.4.6 Parental obesity ...... 18 4.4.7 Birth weight ...... 18 4.4.8 Diet ...... 18 4.4.9 Living Environment ...... 19 4.4.10 Alcohol & smoking ...... 19 4.4.11 Blood lipids levels ...... 20 4.4.12 Marital Status ...... 20 4.4.13 Others ...... 20 5 DISCUSSION ...... 21 5.1 CRITICAL ANALYSIS ...... 21 5.1.1 Studies included ...... 21 5.1.2 Inductive content analysis ...... 22 5.2 Strengths ...... 24 5.3 Limitations ...... 24 5.4 Comparison to other literature ...... 25 5.5 Implications ...... 25 5.5.1 Implications for policy ...... 25 5.5.2 Implications for research ...... 26 6 Conclusion ...... 27 7 Reference list ...... 28 8 Appendix 1 – Search strings (Search till 2nd of August 2015) ...... 36 9 Appendix 2 – Quality appraisal tool ...... 46 10 Appendix 3 – Quality Appraisal Results ...... 54 11 Appendix 4 – Data Extraction Table ...... 56 12 Appendix 5 – Inductive Content Analysis Results ...... 80 13 Appendix 6: CARE form ...... 89
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ABSTRACT
Background: Obesity is a complex public health problem worldwide which imposes a financial burden on states. A study incorporating seventeen Mediterranean countries indicated higher rates of obesity when compared to central and northern European countries. Moreover, evidence hinted that this could also be said for isolated populations specifically located in the Mediterranean Sea. This comes with the fact that although the Mediterranean diet is known for its benefits to health, evidence shows a regressive shift towards the Western dietary patterns, which are considered unhealthy and potentially cause weight gain, among other cardiovascular health problems. However, literature is dispersed and thus the claim remains inconclusive. Hence, this review aims to contribute to the „missing gap‟ of information by compiling and analysing literature on the possible risk factors which are associated with obesity in Mediterranean islands.
Methods: Searches were performed on four online databases for epidemiological observational studies, which adhered to an inclusion and exclusion criteria. Only articles discussing potential risk factors for obesity in Mediterranean islands in any age group were included. Studies which weren‟t carried out specifically in isolated Mediterranean countries, no risk factors for obesity, and participants whom were unhealthy were excluded. A quality appraisal tool was utilised to assess and critically appraise methodological rigour of the studies eligible for inclusion. Moreover, an inductive content analysis was performed following the framework suggested by Elo & Kyngäs in 2008 .
Results: A total of forty studies were included through which the quality of methodology was rated either „Weak‟ or‟ Moderate‟. Following Dahlgren and Whitehead‟s model of health (1991) as guidelines, risk factors reporting positive associations for obesity were found to be behavioural, environmental, genetic and social.
Conclusion: This review brought to light the myriad of risk factors of obesity in Mediterranean islands. The information found was aggregated together, analysed and discussed and the strengths, limitations and recommendations were also highlighted to put further insight to investigate the matter further.
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LIST OF ACRONYMS & ABBREVIATIONS
BMI Body Mass Index
CI Confidence Interval
CINAHL PLUS Cumulative Index of Nursing and Allied Health Literature
EMBASE Excerpta Medica Database
IOTF International Obesity Task Force
LSHTM London School of Hygiene and Tropical Medicine
MEDLINE The National Library of Medicine‟s MEDLINE Database
NCD Non-Communicable Diseases
MeSH Medical Subject Heading
OW Overweight
OB Obesity
OR Odds Ratio
WHO World Health Organisation
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ACKNOWLEDGEMENTS
Foremost, I would like to express my sincere gratitude to my supervisor Triantafyllos Pliakas for his knowledge, guidance and patience he shared with me in this project through thick and thin.
I would also like to greatly thank Daniel Cauchi, a PhD student at LSHTM for his genuine advice, never-ending support and encouragement through-out the project.
Another heartfelt thank you goes for my year tutor, Sarah Milton, who always listened to me in times of difficulty and need.
My appreciation also goes to the librarian, Claire, who helped me build a comprehensive search strategy and Hannah Babad, the Faculty Course Director, for her understanding to make this project possible.
Last but not least I would like to express my eternal appreciation to my loving parents, for their unfailing support and to my lovely sister for being my pillar of support in my academic ambitions.
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1 INTRODUCTION
1.1 Background on Obesity
Obesity, a public health issue, has soared to worrying rates over the past few decades and is considered as a costly global epidemic in developed and developing countries (2–5). It is considered as the abnormal or excessive fat accumulation that may result into ill effects on one‟s health through the imbalance of energy consumed and energy expended (6). Even though there are other indices for obesity measurement, such as Fat Mass Index (FMI) and skinfold thickness (7), waist circumference (8) waist-to-hip ratio (9), and bio-impedance (10), Body Mass Index (BMI) is the one most arguably used (10). Therefore, it is defined as BMI 25.0-30kg/m2 and BMI> 30 kg/m2 stating that being obese needs to be over 30kg/m2 (6). Furthermore, the situation is exacerbated due to the abundance of high energy dense foods, a sedentary lifestyle, and the increasing urbanization (6). Obesity has more than doubled worldwide since 1980 increasing to a number of over 600 million and it kills more people than being underweight (6). Moreover, it is also predicted that by the year 2030, 50-60% of the world population are on target to be considered as having an obese status (11).
1.2 Negative Impact of Obesity
1.2.1 The Physical and Psychological
Obesity is a precursor for various non-communicable diseases (NCDs) such as cardiovascular disease, diabetes, and cancer (3). The disease is also a detriment to wellbeing and health through obstructive sleep apnoea, need for medication, asthma and others (12). Furthermore, it is also evident that people who are obese also experience negative social consequences, caused by discrimination, stigmatization and judgemental behaviour (13). These elements leave a negative impact on the individual by affecting his/her quality of life and also reducing life expectancy (11).
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1.2.2 Economic Burden
The condition also puts a significant strain on the coffers of health systems and countries world-wide through the decrease of productivity and the cost of medical treatment (14). According to a systematic review on the economic burden impinged by obesity, it reported that it accounts for between 0.7% to 2.8% of a country‟s entire healthcare costs (15). The expenses in medical and health care of an obese individual are 40% higher when compared to an individual with a healthy weight (16). Furthermore, the economic weight comes from direct and also indirect medical costs. Direct medical costs have to do with the treatment of NCDs; being illnesses which are all a repercussion of obesity whilst indirect costs are due to the absenteeism from work and premature deaths (17). These statistical figures could be overwhelmingly costly and unaffordable for the future health, social and economic structures due to the growing obesity levels in all ages of a population and therefore is required that the precarious situation is treated with urgency (16).
1.3 Obesity in Mediterranean Islands
1.3.1 Obesity in Europe and the Mediterranean region
The prevalence of obesity in continental Europe has reached epidemic proportions (18) which are caused by various factors such as an unhealthy diet (7), socioeconomic factors (19) and lack of physical activity (20), to name a few. Interestingly, southern Europeans (ranging from pre-school years to late adulthood), have a higher rate of obesity than their western and northern European counterparts (18,21–24). Although the high obesity prevalence in Southern Europe and the Mediterranean area could be explained by the different lifestyle, nutritional aspects and socioeconomic environments when compared to the other regions (18), it still can be considered to be as a phenomenon since the Mediterranean diet and lifestyle is known for its benefits to health (25,26).
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1.3.2 Obesogenic Islands
Evidence suggests that the higher obesity prevalence also includes the countries which are specifically isolated and constitute of an island population (27,28). Some of the islands situated in the Mediterranean Sea which report high prevalence of obesity from various ages are Sicily (29), Sardinia (30,31), Cyprus (32,33) Crete (34), the Balearic Islands (35), Malta (17,36,37) and the Adriatic Islands (38). Factors which might offer a possible explanation for the alarming rates of obesity in these islands could be that they are known to have populations which are diverse both ethnically and in the exchange of food production methods which have altered throughout the centuries (39). Providing that globalization, urbanization, nutritional transitions and lifestyle changes are dynamics currently taking place in the Mediterranean, the current and future generations are facing an arduous threat when it comes to health status (39–41).
1.4 Rationale
Coincidentally, Malta holds the highest prevalence of obesity among 10-11 year olds in the world, to the exception of Greece, which is also a Mediterranean country (36). It is unclear what is causing such severely high rates of obesity in the Mediterranean basin and even more specifically, in an island like Malta which has a relatively small population (416,055 overall and 66,447 children under the age of 16) (36).
A systematic review regarding the prevalence of obesity in the Mediterranean region was published in 2008 by Papandreou et al.,(22) yielding important results to obesity reviews and also reveals similar characteristics to this current study. However, it does not specifically focus on islands, which is the aim of this research. In addition, no former study has put focus to gather and evaluate the obesity issue among Mediterranean islands and the risk factors causing it.
This systematic literature review will thus contribute to the missing gap of information which needs to be relayed to understand which factors are causing such a high prevalence of obesity specifically in Mediterranean islands.
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2 AIMS AND OBJECTIVES
This review aims to add on the missing gap of information in obesity reviews as it will identify and evaluate the evidence to understand the risk factors that are related to obesity in isolated populations ranging in ages from children to older adults in the Mediterranean Sea. To the best of the author‟s knowledge, this is the first systematic review to collate all the findings of obesity in Mediterranean islands.
The objectives are;
To construct a search strategy and identify papers that are relevant to the topic To examine the prevalence of obesity in the whole Mediterranean island population To identify the risk factors associated with obesity in Mediterranean islands To critically assess, synthesize and evaluate the evidence in order to provide future research priorities or policy recommendations.
3 METHODS
3.1 Search Strategy
This review of literature pursues to answer the question; “What are the risk factors of obesity specifically in Mediterranean islands?” and thus important concepts which needed to be identified and evaluated due to their complexity were “risk factors”, “obesity” and “Mediterranean islands”. With risk factors, one could refer to the potential elements which contribute to the obesity prevalence in the Mediterranean islands. The Mediterranean islands included in the search were all the islands situated in the Mediterranean Sea which came in different sizes of populations. Besides having people who are relatively obese, the term of obesity was broadened to include individuals who were overweight, as these subjects are also considered to have an abnormally heavy weight, which puts them at risk of chronic diseases (42). Being aware that obesity is a complex condition and could incorporate branches of other related conditions it was essential to include medical terms which have an association with obesity such as „adiposity‟, „fatty disposition‟, „lipoproteins‟, „atherosclerosis‟ (shown as Athero* in Appendix 1 for search terms) and „metabolic 4 syndrome‟. Furthermore, since obesity was also the outcome of interest, measurements of weight status needed to be included and thus BMI, hip-to-waist-ratios, waist circumference and skin-fold thickness were identified.
The process of the search was rigorous and repetitive with keywords and subject headings where applicable going through frequent refinement. Terms for the concepts of „obesity‟ and „risk factors‟ emerged by utilising synonyms and/or associated words. The search terms affiliated with obesity further refine the search, were „excess weight‟, „weight status‟, „weight gain‟, „weight loss‟, „body fatness, „body weight increase‟ ,‟body weight change‟, „ body composition‟, „body size‟, „abdominal obesity‟, „plasma lipids‟, „visceral fat‟ and „subcutaneous fat‟. For the terms affiliated with risk factors, „physical activity/inactivity‟, „sedentary behaviour‟, „nutritional status‟, „overnutrition‟, „calorie intake‟, „energy intake‟, and „nutrient intake.‟ In regards to the islands, this involved listing and attempting to include all the inhabited islands which are geographically located in the Mediterranean Sea; both by including the clusters of islands and the name of the individual islands themselves. Major island names include Sicily, Sardinia, Corsica, Cyprus, Crete, Malta, Balearic islands and Adriatic islands. This procedure was important to minimize the risk of missing out on potential studies of interest for this review. Subject heading (MeSH) were specifically ticked through terms and adapted for each database (example; EMBASE). The concepts, terms and subject headings were at first conducted in a separate search and then pooled with the Boolean operator „OR‟. Thereafter, the results of each concept of interest was then pooled with “AND”.
The searches were not limited to any language or year of publishing. This resulted coming also across a small minority of articles in Italian and Spanish besides English ones which were read and evaluated due to the sound language knowledge which I possess.
Where the electronic addresses were available, emails were sent to the respective authors whose articles emerged through the search and did not grant full text access. The majority of authors complied and granted full-text access via email. This is shown in the flowchart (Figure 1).
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3.1.1 Databases
The four databases which were searched during the specified period were MEDLINE, EMBASE, Global Health and CINAHL Plus. The executed searches took place from late July till 2nd of August 2015 and the databases were selected due to their range and width of articles available on obesity following recommendations from the school librarian. Moreover, EMBASE was chosen due to being biased of having research based in Europe and a focus on clinical and health policy articles with the overlapping search of MEDLINE. Global Health covered all aspects of international public health whilst CINAHL Plus covers topics ranging from consumer health, law and health and health promotion.
This study was not open to grey literature and thus no searches were executed. In addition, the search was restricted to peer-reviewed articles.
All sources were exported through reference manager Endnote X7 which allowed for duplicates to be removed. This is shown in the flowchart (Figure 1).
3.1.2 Inclusion/Exclusion criteria
The PICOS (participants, intervention, comparison group, outcomes, study design) structure (43) will be utilised to assess whether the study and the appropriate design are relevant. From the PICOS framework, the comparison group was not used as a directive as we were only interested in observational studies.
Participants: Studies which involved participants from any age group (preschool children, children, adolescents, adults and the elderly) were open for inclusion. The islands of different sizes in both area and population based in the Mediterranean Sea were included. Studies which specifically focus on islands in the Mediterranean were included. Other countries which are not isolated and not in the Mediterranean Sea will be excluded. Studies which are based on multiple countries (European and/or global) which include Mediterranean islands in Europe and specifically not focused on them will be excluded. To explain further, studies which aggregated estimates across multiple countries (European or globally) were excluded. Furthermore, studies which reported risk factors and prevalence on a national level rather than on the islands specifically were also excluded. For example, studies which included mainland Greece and some of the Greek islands combined were identified, and thus excluded from the review.
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Intervention: Only epidemiological observational studies will be included and these could be delivered in any form of data collection and analysis. Studies with an intervention approach will be excluded.
Outcome: Since the outcomes of interest usually refer to ones of clinical nature, the primary outcome of interest is the potential risk factors of obesity in Mediterranean island populations. The odds ratios (OR) and confidence intervals (CI) of the risk factors, if reported in the studies, will be included. The prevalence rates (percentages) of both overweight and obesity, if assessed in studies, will be considered as secondary measures. Studies with no risk factors of obesity will be excluded.
(A flowchart of the search strategy with results and stages of selected studies could be found in Figure 1)
3.2 Data Selection
Following the removal of duplicates, the articles remaining needed to go through various stages. These articles needed to be screened through the title and abstract according to the inclusion/ exclusion criteria which is elaborated below. This further meant that the screening for texts with and without access had to be stratified. Moreover, the total articles which had access to full-texts, including the full text access from snowballing and personally requesting the articles had to be screened to make up the total records which did not meet the inclusion criteria.
3.2.1 Snowballing
The reading through the reference list of the related full texts which were identified through the inclusion/exclusion criteria was carried out to further the search.
3.3 Quality Assessment and Data Extraction
The quality appraisal tool used for this review was the Effective Public Health Practice Project (EPHPP), which is a quality assessment tool for quantitative studies (44). Through the tool, the validity, quality and heterogeneity of the chosen studies made it possible to be assessed, evaluated and understood. (Appendix 2: Quality Appraisal Tool and Dictionary)
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The main aspects utilised from the appraisal tool were selection bias, study design, confounders, blinding, data collection method, withdrawal and drop-outs. Since the studies reviewed were observational by nature, not all the aspects of the tool were measured. The features of the tool which involved intervention integrity and analyses were excluded. Selection bias, withdrawals and drop-outs were aimed at to assess the generalizability of the studies (external validity) while the other features were to evaluate quality and validity. Each study was rated „Weak‟, „Moderate‟ or „Strong‟ on each mentioned aspect and added up to give an global rating from „Weak, „Moderate‟ or „Strong‟.
3.4 Data Extraction
Important features of the studies which included study characteristics (location, sample size, study design and population), aim and objectives of the study, obesity definitions and measurements, data collection and analysis, the main findings of the study and the risk factors were the main things the author was interested in. The full data extraction sheet could be found in
Appendix 5a: Social Educational level Low parental Low maternal High education Low level of High parental educational educational level education educational level level level
Ferra et al. (2012) No.3 (+) ≥65 yrs Loviselli et al. OW & OB (2010) No.13 (-) Ramón et al. (2012) (1969) 18 yrs No.1 (-) 10-16 yrs
OW & OB OW & OB Baroudi et al. Sanna et al. (2010) No.4 (+) Bibiloni et al.(2012) Bibiloni et al. (2006) No.36 (+) (32-64 yrs) OW & No.2 (-) 12-12-17 (2010) No.12 (-) 6-10 yrs OW & Loviselli et al. OB yrs OW & OB 12-17 yrs OB OB (2010) No.13 (+) (1998) 18 yrs Manios et al. (2011) OW & OB No.30 (+) 10-12 yrs Vardavas et al. OW & OB (2009) No.31 (+) (18-79 yrs) OW & OB
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Tur et al. (2005) No.32 (+) (20-60 yrs) OW & OB
Low level of Maternal awareness of Maternal awareness of education in mother health Maternal education child health promotion women promotion
Coll et al. (2015) No Buttigieg et al. (2012) Savva et al. (2014) 11. (+) (18-35 yrs; Buttigieg et al. (2012) No. No. 6 (-) 3 yrs OW & No10. (-) 6-17.9 yrs 36-55 years) OW & 6 (-) 3 yrs OW & OB OB OW & OB OB
Socio-economic status
High Low High parental Low parental Low socio- maternal maternal Unemployment socio- socio- economic socio- socio- economic economic status (adults) economic economic status status (adults) status status
Bibiloni et al. Ramón et al. (2010) No.12 (2012) No.1 (-) (-) 10-16 yrs 10-16 yrs OW OW & OB & OB Ferra et al. (2012) No.3
Athanasopoul Athanasopoul (+ ≥65 yrs Sidoti et al. Bibiloni et al. os et al. os et al. OW & OB (2009) No.19 (2012) No. 2 (- (2011) (2011) Coll et al. (2015) (+) 8-11 yrs ) 12-17 yrs No 11. (+) 18-55 OW & OB No.20 (+) 8- No.20 (+) 8- yrs OW & OB OW & OB 16 yrs OW & 16 yrs OW Smoljanović et al. (2007) OB & OB No.24 (+) Sanna et al. Sidoti et al. (Adults) (2006) No.36 (2009) No.19 (+) 6-10 (-) 8-11 yrs years OW & OW & OB OB
Parental Obesity
Obesity in both parents Maternal obesity
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Athanasopolous et al. (2011) No.20 (+) 8-16 yrs OW & OB Buttigieg et al. (2012) No. 6 (+) 3 yrs OW & OB
Lazarou et al. (2008) No.27 (+) (Girls Sidoti et al. (2009) (+) 8-11 yrs OW & OB 3 specifically) 9-13 OW & OB
Savva et al. (2002) No.26 (+) 6-17 yrs OW & OB Savva et al. (2005) No.29 (+) 2-6 yrs OB
Manios et al. (2011) No. 30 (+) 10-12 yrs OW & Manios et al. (2011) No. 30 (+) 10-12 yrs OW & OB OB
Marital status
Married Free union Widowed
Tur et al. (2005) Coll et al. (2015) No.11 Married men Calamusa et al. (2012) No.17 (+) 18 (+) 18-55 yrs OW & OB No.32 (+) 20-60 yrs yrs + OW & OB OW & OB
.
3.5 Data Synthesis
A narrative synthesis through content analysis was identified and implemented, an approach described by (43,45). The reason is due that the studies possessed heterogenic risk factors for the outcome of interest, and thus the use of a meta-analysis was inappropriate (43). Furthermore, the use of inductive content analysis was the most suitable since this review aims to narrow the gap of fragmented information of the topic at hand (1). This approach involved open coding, forming categories and abstracting the risk factors of the outcome of interest (1). Additionally, the social model of health was used to present the risk factors according to „themes‟ in the results (46). These methods were thus attempting to explore and assess the associations within the findings; understanding what and how the risk factors of obesity are affecting populations in Mediterranean islands.
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4 RESULTS
4.1 Search Results
In total, forty studies were selected for inclusion for this review through database searching, requesting articles from authors and snowballing. This is explained in the flowchart portrayed in Figure 1. The search result from the 4 databases generated 12,163 articles and getting to 9937 after duplicates were removed. Following the screening of titles and abstracts through adherence of the inclusion and exclusion criteria depicted in the Methods section, sending emails to respective authors and through snowballing, a total of 172 articles were assessed for eligibility under the same criteria. The remaining 134 articles were firstly searched for through the attempt of sending an email to the author/s, where the address was specified in the „Author Information‟ section in the abstract, and secondly via internet browsing. One hundred and thirty-two articles were excluded for the reasons mentioned in Figure 1, and thus leaving 40 articles to be included in the review.
Embase (9111) MEDLINE (1831) Cinahl Plus (259) Global Health (962)
12,163 total number of articles from databases
9937 after duplicates were removed and screened via 9691 articles excluded title and abstracts
246 potential 112 full-text access articles for 132 full-text articles excluded: articles screening Other related diseases (21) Outcome not obesity (41) 15 articles received No risk factors (9) from authors via email 172 full-text No Mediterranean islands (22) 45 articles with full- articles for Not an observational study (7) text through Individuals who are not healthy eligibility snowballing (4) Study not on Mediterranean island/s only (28)
40 articles included11
Figure 1. Search Strategy Results
4.2 Quality of included studies
The quality appraisal was carried out through using the EPHPP tool (44) and all studies were tested on their global rating to either be of „Weak‟, „Moderate‟, or „Strong‟ quality. In short, out of 40 studies, thirty-two studies scored a „Weak‟ rating, whilst only eight were rated as „Moderate‟. No studies managed to obtain a „Strong‟ rating. The quality appraisal table with all the ratings could be found in
Appendix 5a: Social Educational level Low parental Low maternal High education Low level of High parental educational educational level education educational level level level
Ferra et al. (2012) No.3 (+) ≥65 yrs OW & OB
Loviselli et al. Baroudi et al. (2010) No.13 (-) (2010) No.4 (+) Ramón et al. (2012) (1969) 18 yrs (32-64 yrs) OW & No.1 (-) 10-16 yrs
OW & OB OB OW & OB Sanna et al. Bibiloni et al.(2012) Bibiloni et al. (2006) No.36 (+) No.2 (-) 12-12-17 (2010) No.12 (-) 6-10 yrs OW & Loviselli et al. Vardavas et al. yrs OW & OB 12-17 yrs OB OB (2010) No.13 (+) (2009) No.31 (+) (1998) 18 yrs (18-79 yrs) OW & Manios et al. (2011) OW & OB OB No.30 (+) 10-12 yrs OW & OB
Tur et al. (2005) No.32 (+) (20-60 yrs) OW & OB
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Low level of Maternal awareness of Maternal awareness of education in mother health Maternal education child health promotion women promotion
Coll et al. (2015) No Buttigieg et al. (2012) Savva et al. (2014) 11. (+) (18-35 yrs; Buttigieg et al. (2012) No. No. 6 (-) 3 yrs OW & No10. (-) 6-17.9 yrs 36-55 years) OW & 6 (-) 3 yrs OW & OB OB OW & OB OB
Socio-economic status
High Low High parental Low parental Low socio- maternal maternal Unemployment socio- socio- economic socio- socio- economic economic status (adults) economic economic status status (adults) status status
Bibiloni et al. Ramón et al. (2010) No.12 (2012) No.1 (-) (-) 10-16 yrs 10-16 yrs OW OW & OB & OB Ferra et al. (2012) No.3
Athanasopoul Athanasopoul (+ ≥65 yrs
Sidoti et al. os et al. os et al. Bibiloni et al. OW & OB (2009) No.19 (2012) No. 2 (- (2011) (2011) Coll et al. (2015) (+) 8-11 yrs ) 12-17 yrs No 11. (+) 18-55 OW & OB No.20 (+) 8- No.20 (+) 8- OW & OB yrs OW & OB 16 yrs OW & 16 yrs OW Smoljanović OB & OB et al. (2007)
No.24 (+) Sanna et al. Sidoti et al. (Adults) (2006) No.36 (2009) No.19 (+) 6-10 (-) 8-11 yrs years OW & OW & OB OB
Parental Obesity
Obesity in both parents Maternal obesity
Buttigieg et al. (2012) No. 6 (+) 3 yrs OW & OB Athanasopolous et al. (2011) No.20 (+) 8-16 yrs OW & OB
Sidoti et al. (2009) (+) 8-11 yrs OW & OB 3 Lazarou et al. (2008) No.27 (+) (Girls 13
specifically) 9-13 OW & OB
Savva et al. (2002) No.26 (+) 6-17 yrs OW & OB
Savva et al. (2005) No.29 (+) 2-6 yrs OB
Manios et al. (2011) No. 30 (+) 10-12 yrs OW & OB Manios et al. (2011) No. 30 (+) 10-12 yrs OW & OB
Marital status
Married Free union Widowed
Tur et al. (2005) Coll et al. (2015) No.11 Married men Calamusa et al. (2012) No.17 (+) 18 (+) 18-55 yrs OW & OB No.32 (+) 20-60 yrs yrs + OW & OB OW & OB
.
Thirty-two of the studies included were cross-sectional, and three were of cohort design as described in their own literature. However, five of the studies did not clearly indicate this, even though that the characteristics indicated that they were cross-sectional by nature. Only 5 studies were rated as having a weak study design, while the rest were rated either „Moderate‟ or „Strong‟. Furthermore, 3 studies adopted a Cohort study design (47); (33); (17)). This meant that the ratings varied from weak to strong throughout. Twenty-two studies (55%) reported participation rates, which varied from 49% (48) to 93% (35), where 13 of them reported having a strong response rate over 80% (7–9,35,49–55); Decelis et al. 2013; (56)). This proceeded to selection bias which was marked on representativeness where 17 (42.5%) studies scored a „Moderate‟ rating whilst 16 (40%) achieving a „Strong‟ rating. The remaining 7 (17.5%) were rated as weak. Assessing confounders was based on the percentages of them being controlled through which only one (2.5%) study obtained a „Moderate‟ score, whilst the remaining 39 (97.5%) studies all scored a „Weak‟ rating as no percentages of controlled confounders were reported. Blinding was measured through the awareness of the outcome assessors and participants of the study and of the research question of. This resulted that 31 (77.5%) studies obtained a „Moderate‟ rating as they did not report anything related, whilst the rest of the studies (22.5%) scored „Weak‟ because
14 they were either outcome assessors or participants were aware of the study and research question. The validity and reliability of the data collection methods yielded very different ratings. Six (15%) of the studies scored a robust „Strong‟ rating as both validity and reliability were reported, twenty (50%) of studies had either one validity or reliability described. The remaining 14 (35%) studies reported that either the methods were not shown to be valid or both reliability and validity were not described. Withdrawals and drop-outs were dependant on the follow-up rate reported in the study and also resulted into different scores. The sixteen (40%) studies which merited a „Weak‟ score meant that the follow-up rate was less than 60% or weren‟t described. The 9 (22.5%) studies which were rated as „Moderate‟ had a follow-up rate of 60-79% or not applicable, whilst the fifteen (37.5%) „Strong‟ studies had a follow-up rate of 80% or over.
4.3 Data Extraction
The description and the quality appraisal ratings extracted from the forty studies included in the review could be found in Appendix 6. The number of included studies in this review was quite high, but this was normal when considering that risk factors for obesity in different areas and populations differ greatly from one another. In the following subsections, the quality appraisal rating of the characteristics of the studies and the methods will be discussed.
4.3.1 Study Characteristics
As part of the inclusion criteria, the geographical representation of all studies was focused specifically on Mediterranean islands. This comprised of coming across populations with various age groups and areas. Studies varied from different islands across the Mediterranean Sea namely, Cyprus, Malta, the Balearic Islands, Sicily, Sardinia, Crete, Jerba Island, Rab, Vis, Lastovo, Mljet, Vis, Majorca, Menorca, Samos and Corfu. Thirty-two (80%) of the studies specifically focused on one island (Cyprus, Malta, Sicily, Sardinia, Crete, Jerba, Vis, Majorca and Menorca) whilst seven studies included islands which were combined together as an archipelago, (Balearic Islands and Adriatic Islands) (17.5%) and a single study which included two Greek islands, Samos and Corfu respectively (2.5%).
The sample sizes also varied significantly ranging from 42 (57) to 48,897 participants (29). This variation is explained by the different sampling settings the investigators utilised for data collection, which stretched from a school-setting to national archives. Studies which reported response rates were at 55% of all studies; where 32.5% of them could be considered as having a high response rate (over 80%), the highest being 93% (35).
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Furthermore, 22.5% of the these studies had a response rate ranging from 49% (48) to 78% (29). The remaining 45% did not describe clearly the response rates.
4.3.2 Methods of study design & methodology
Similar data collection tools were utilised frequently from one study to another and indicated that the methodological rigour and quality could have been high standard. However, the quality appraisal looked specifically at whether validity and reliability were described, and thus granted a „Strong‟ rating to only six studies for their methodology (29,55,59–63). In further analysis, information such as participation rates were missing in 21 studies; (8,17,28,29,31,33,36,41,47,57–59,62,64–70). These were at times calculated by the authors of this systematic review (where possible) to give an approximate percentage of the participation rate, but were considered as null when considering the number of studies who did not report any response rates. These calculated rates were inserted in the data extraction sheet.
Similarities could be observed in data collection sources from which the studies gathered their data. 24 of studies (60%) used schools as a source for participants, which ranged from kindergarten, primary and secondary. Furthermore, eight studies (8,9,28,31,41,50,64,71) (22.5%) obtained their population samples from any type of registry list which consisted and varied from an electoral/military register, population and scholar census and telephone directory, whilst 2 studies (5%) acquired their samples from a medical centre. One particular study (66) (2.5%) reported of having garnered the sample population both from school sources in Samos and from acquaintances residing in Corfu, possibly leading to selection bias. The remaining three (7.5%) did not clearly describe the population source.
To attain data on lifestyles and socio-demographics, which could possibly indicate the risk factors for obesity, were measured from a range of differently structured questionnaires, them being; self-reported questionnaires (53,55,69,70), interview questionnaires (35,59), semi-quantitative questionnaires (72,73), general questionnaires surveys (7,50,57); structured interviews (64), and 3-day next-day interviews (66), which both reported no response rates. For the studies which used self-reported questionnaires, two studies did not describe any response rates, whilst Decelis et al. (2012) and Athanasopoulos (2011) both reported 80% and 91.6% response rates. Moreover, studies which involved interview questionnaires had one reporting a high response rate of 93% (35) whilst the other (59) failed to describe it. Studies by Lazarou et al. (73) and Lazarou & Soteriades (72) both reported 72% response rates as they were both taken from the same population sample for the CYKIDS studies. The vast majority of the studies consisted of general questionnaires 16 and the ones which reported response rates were as follows; (49) – 89%, (50) – 82%, (51) – 82%, as they were 2 studies based on the same population sample, (74) – 65% in the 2009- 2010 sample and 95% in the 1999-2000 sample, (48) – 49%, (71) – 70%, (32) – 72%, (63) – 80%, (75) – 75%, (54) – 92%, (8) – 90.5%, (76) – 72%. All but one of the mentioned studies had a relatively low response rate, which was 49% (48).
These were combined with anthropometric measurements which varied from waist circumference, weight and height, skinfold thickness, body fat percentage, to gather data for BMI, other overweight, obesity definitions and measurements.
The majority of studies (7–9,28,29,35,36,41,50–55,63,58,62,64,66,67,74) reported utilising randomisation for their sampling procedures. This meant that chances for potential bias were minimized in these respective studies.
Even though all studies but one (76), obtained a „Weak‟ rating for confounders in the quality appraisal, which is explained by the absence of percentages reported in the studies for controlled confounders, tools like logistic regression analysis were still evidently used. Some of the studies described in their text that potential confounders were controlled, but no percentages were recorded and described, hence the „Weak‟ score. Studies which reported of having used such tools were twenty-three in total; and these varied from logistic, linear and poisson regression analyses. Logistic regression analysis was the most commonly used as eighteen (45%) reported so. Furthermore, linear regression analyses were utilised by 3 studies (8,58,59) and the poisson regression analysis was used by (56). This accounted for the use of data analysis methods to be considered as adequate to carry out the study in a proficient manner.
Interestingly, some studies were carried out from the same collected data. Namely, the three CYKIDS studies (32,72,73), which are spread throughout four years are based in Cyprus on children varying with age from 9-12 years. These studies consist of the same sample population (1140) and participation rate (72%). However, these studies portrayed time lag of at least three to seven years since data collection took place in 2004-2005, revealing a flaw of the data mining method. Even though obesity has risen to be one of the most prominent public health issues for the past few decades, old socio-demographics and lifestyle characteristics from mining data could thus weaken the validity and strength of the information described.
4.3.3 Definitions of weight status and measurement
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All but five studies (50,57,62,69,71) reported a clear definition for the measurement of obesity status. The study of Ramón et al.(69) did not indicate any specific definition or refer to any form of guideline even though having analysed weight status among the participants, whilst (57), did not define clearly the definition of overweight and obesity in the literature but did indicate reference of BMI measurements in the study‟s nutritional assessment tool. In the remaining studies, (62,71) no trace of a definition was available; but still managing to report obesity rates which are of interest to this review. Even though the rest of the studies provided measurements which varied, the BMI measurement was the most commonly used, which in itself also proved to be subjective through evident discrepancies from studies. Seven studies (28,31,32,35,58,65,76) described both overweight and obese under the category of BMI 25.0-29.9kg/m2 and BMI ≥30 kg/m2 respectively, which adheres to the guidelines of the WHO (77). Four studies (41,64,70,78) defined overweight and obesity as BMI 25.0-30kg/m2 and BMI> 30 kg/m2 stating that being obese needs to be specifically over the 30kg/m2 mark, unlike the previous seven mentioned studies. Two studies, (29,54) vaguely defined overweight as BMI 25kg/m2 and obesity as BMI 30kg/m2, failing to describe them in a specific manner whereas the study of Ferra et al. (49) described the status of overweight and obese as 27.0≤BMI<30.0kg/m2 and BMI≥30.0kg/m2. Two studies (53,75) which specifically focused on children as their target population, also measured the weight status of parents and defined BMI>25kg/m2 for overweight and BMI>30kg/m2 for obesity to study possible links which could be related with childhood obesity. In contradiction, despite mentioning WHO‟s definition, Vardavas et al.(8), still deviated from adhering to it as it still used a measurement of BMI 25.1-30 for overweight and BMI≥30kg/m2 which also additionally described waist circumference cut-offs of >102cm for males and >88cm for females; contributing to finding the conicity index specifically for abdominal adiposity and large skinfold calliper for body fat percentage. So far, despite the differences shown, the mentioned studies apart for (53,75), had shown a focus on adults and the elderly, bringing the range of BMI measurements on a similar level.
Childhood weight status was measured differently from the adult one and this was the case in every study. Furthermore, like definitions for adults, inconsistency was also the case for children weight status definition. Seven studies (33,48,53–56,63,67,73,75) followed the International Obesity Task Force (IOTF) age and sex-specific BMI cut-off criteria which is specifically tailored for childhood overweight and obesity, however, no exact measurement in numbers were described in these studies. In Buttigieg et al's (67) study, child overweight and obesity were defined as between the 75th – 97th and >97th weight-for-height percentiles respectively. Similarly, Bibiloni et al. (50) defined it as BMI ≥I p85
4.4 Narrative Synthesis
After analysing the information from the data extraction table (Appendix 5) from all forty studies, potential risk factors of obesity were found to be vast. Due to the vast heterogenic nature of the findings of the studies, the systematic construction of categories and frequency count through inductive content analysis (1) described in the Methods section was imperative to aid the understanding of the obesity patterns in the Mediterranean islands.
4.4.1 Inductive Content Analysis
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An inductive content analysis was extracted from Appendix 5 where the results which emerged from the studies were grouped together and categorized through their frequency. The categories which materialized ranged from lifestyle, age, socio-economic status level, education level, socio-demographics, diet, physical activity and inactivity, sedentary behaviour, gender, parental obesity, ethnicity, birth weight, marital status, smoking, environmental, blood lipids, having one child, metabolic syndrome, short sleep, unemployment and underestimation of excess weight.
Following the assortment of the groupings of risk factors, mixed results were identified and were a common occurrence throughout. Furthermore, the majority of studies reported more than one risk factor, and this meant that they were mentioned multiple times underlining the importance to keep a track record for their frequency. The risk factors found are evaluated below.
4.4.2 Educational level
A total of 6 studies (7,31,51,54,68,69) reported positive associations with level of education, which defined as being from high, medium and low throughout. However, only the high and low education categories were reported as potential risk factors. Ramón et al.(69) and Bibiloni et al.(7) found that parents of adolescents who had a high level of education indicated no association. One study (51) indicated that this was also the case for low parental education level. Furthermore, two studies found that the level of education in fathers of adolescents (54) and mothers of children (68) each reported a positive association. An inverse relationship was found in 18-year old conscripts with high level of education but this was out-dated as it was reported from 1969 and when compared with the 1998 sample, a positive correlation for obesity was found (31). Moreover, 4 studies found a positive association for overweight and obesity ranging from young to elderly adults (18-79 years) (8,9,49,70) and low level of education in women was also positively correlated (35).
4.4.2.1 Maternal awareness and education
Mothers being aware of the health of their children and their own maternal wellbeing reported a negative correlation for overweight and obesity of their children, as found by Buttigieg and her colleagues (64). Another study (74), insinuated that educated mothers had a negative association for overweight and obesity in their children aged from six till eighteen years of age. Comparably, the findings of Gelpi-méndez et al. (65) correspond greatly with the previously mentioned study (74) as it also described a negative relationship for
20 overweight and obesity specifically among males aged from sixteen years till twenty-five. No positive relationship was reported to be found for this aspect.
4.4.3 Socio-economic status
Four studies found that high parental socio-economic status shows a negative association for obesity in children (59,68) and adolescents (7,69). Mixed results were found for low parental socio-economic status with one study reporting a negative association (51) for children and the previously mentioned study (59) also showing a positive one for adolescents. Contrastingly, the same study (53) showed a positive and negative correlation for both high and low maternal socio-economic status as a risk factor for obesity in 8-16 year olds. Two studies on low socio-economic status amongst the elderly (49) and adults (71) both reported a positive correlation as a risk factor for obesity. Moreover, a related study (35), reported that unemployment was a risk factor for overweight and obesity among adults.
4.4.4 Physical activity & sedentary behaviour
Seven studies (7,8,29,55,63,59,72) found that sedentary behaviour is a risk factor for overweight and obesity in children, early adolescents and the elderly. In addition, Ferra et al. (49) reported in their study on the elderly that even a low rate of physical activity is enough to build a negative association for overweight and obesity. Ferra et al‟s (49) argument is backed by five studies (7,35,41,58,59), whose studies give evidence on populations from as young as eight years of age (59) and as old till eighty years old (41). Interestingly, for children‟s participation on the type of sport (competitive or non-competitive), a study (69) found that there was a negative association for obesity with children participating in competitive sport when compared with others who took part in friendly competition.
4.4.5 Gender
Gender was one of the most risk factors for obesity indicating a great majority that the male gender is more at risk than their female counterparts. A total of 11 studies (7,29,35,36,52,54,63,58,65,68,74) found that the male gender is a risk factor positively associated with obesity. The age ranges from children of six years (68,74) till late adulthood of fifty-five years (35). Mixed results were found for the female gender as a risk factor as three studies (8,57,70) indicated a positive association for obesity, whilst only one study (56) reported a negative relationship. However, this was only reported for the weight status of overweight only amongst 11-15 year olds. Another study (32) which had 9-13 year olds as their target population reported that fathers and mothers who worked more than nine hours 21 per day were more positively prone to be obese. The study adds that fathers who worked of a maximum of eight hours per day had a negative association for obesity whilst the higher the number of children, the more of a positive association could be found. Furthermore, Gelpi-méndez et al. (65) found a positive association for females (aged at 16-25), who were obese during their childhood and adolescence as a risk factor for maintaining the same weight status.
4.4.6 Parental obesity
A total of 8 studies (32,47,53,54,59,64,67,75) all found that parental overweight and obesity has a positive association on the weight status of their children, which ranged from two year olds (75) till late adolescence (67). These studies overlapped reporting on either both parents (54,59,64,67), the mother (32,53,54,75) or the father (32,47,75). Furthermore, Lazarou et al's study (32) found that overweight and obese weight status in mothers were most likely to influence their daughters to be overweight and obese. Similarly, a positive association of overweight and obese fathers influencing their boys was also indicated (32). No study was reported to have found a negative association in this matter, suggesting further the possible influence of overweight and obese parents on the weight status of their children. Furthermore, one final study (35), reported that a positive correlation for overweight and obesity was found for parents who have one child.
4.4.7 Birth weight
Studies which reported on birth weight were only two (54,75), and both had mixed results. One of the studies (75) found that newborns who had a birth weight between 2501g and 3000g were less likely to be obese thus hinting a negative association. Contrastingly, the same study of (75) implied that a birth weight of equal or over to 4000g, was a risk factor for obesity in toddlers and thus indicating a positive association, which was also backed by Manios and his colleagues (54).
4.4.8 Diet
Mixed results were found when it came to diet as a potential risk factor for overweight and obesity through thirteen studies (7,9,29,48,50,51,53,57,59,66,70,73,80). These factors were categorically heterogeneous and little overlapping was noted. Bibiloni et al. (82) found that 22 an unhealthy diet and lifestyle is a risk factor for overweight and obesity in adolescents. Moreover, another study (66) added that inadequacy of micronutrients in one‟s diet had a positive association specifically for overweight twelve year olds. Having a western diet (29) consuming calorie-dense foods (fried food, delicatessen, meat, sweets, junk food) (73), saturated fat (9), sugar added-beverages (48,59), soft drinks daily (53) all indicated a positive association for overweight and obesity in populations ranging from pre-schoolers to adulthood. In addition, three studies found that overnutrition among the elderly (57), eating disorders among adolescents (80), distraction during mealtime and skipping meals, also among adolescents (51) are all also positively associated with overweight and obesity. Furthermore, a study also suggested a positive relationship for overweight and obesity for the consumption of the Western diet among adolescents. In comparison, a negative relationship was found in three studies; two of them (50,73) indicated the adherence to the Mediterranean diet, which consists of fish, milk and cereals whilst the third study (59) stated that breakfast consumption also had a protective factor against overweight and obesity. One last study (70), specified that an ethnic group between Arabs and Berbers in Jerba could pose as a potential risk factor for obesity in adults and elderly. This is explained by the cultural effect on dietary intake, as the Berbers had shown a positive association for overweight and obesity more than the Arabs. However, this could be also interpreted as a negative association for overweight and obesity as the Arabs could have had a better type of diet when compared to their Berber counterparts.
4.4.9 Living Environment
A total of ten studies reported factors affiliated with the living environment through which overlapped between living in urban, rural, hilly and mountainous regions. Six studies (29,31,36,52,54,56) indicated that living in urban areas is a risk factor for overweight and obesity in adolescents. Living in rural areas had four studies (31,33,74,75) also showing a positive association for overweight and obesity among adolescents and obesity specifically among pre-school children in another study (75). Contrastingly, Parrino et al. (52) reported a negative association for overweight and obesity for 11-13 year olds living in rural areas. Different results were found for people living in mountainous regions as (32,56) found a negative association for overweight and obesity among adolescents, whilst Loviselli et al. (31) described it a risk factor for overweight and obesity. One should note that Loviselli et al‟s (31) study consisted of two samples from 1969 and 1998 and was the only one who showed a consistent positive association for overweight and obesity across living in urban (1998 sample), rural (1969), hilly (1969 & 1998) and mountainous areas (1969).
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Furthermore, one study (32) showed that living in an apartment had a negative association for overweight and obesity among mothers and fathers of 9-13 year olds.
4.4.10 Alcohol & smoking
Two studies (9,51), found a positive relationship for overweight and obesity in smokers among adolescents and adults. Another study (58), reported that former smoking was a risk factor for overweight and obesity among adults. Another study (41), showed that the consumption of alcohol is positively correlated with overweight and obesity in early adulthood to elderly years. This was further underlined by Calamusa et al's (60) findings as they found that drinking 14 units of alcohol per week positively contributed to overweight and obesity.
4.4.11 Blood lipids levels
One study (28), found a positive association for overweight and obesity adults and the elderly when levels of cholesterol, LDL, triglycerides and glucose were high. Similarly, another study (47), reported that high HDL-cholesterol, triglyceride levels and systolic blood pressure were also reported as risk factors for overweight and obesity among adolescents.
4.4.12 Marital Status
A total of three studies reported on marital status as a possible risk factor for overweight and obesity. One study (35) found that married men had a positive association for overweight and obesity. On the other hand, the second study (9) indicated that widowed persons also recorded a positive association as a risk factor for overweight and obesity. This was also the case for people who practiced free union relationships, as Calamusa et al. (58) also found a positive correlation for overweight and obesity.
4.4.13 Others
To sum up all the results which were produced in this study, a further three studies were still not added in any of the constructed categories and thus will be discussed here. One study (50) found that the condition of metabolic syndrome has a positive association with overweight and obesity among adolescents. The underestimation of excess weight among twenty to sixty year olds also reported a positive relationship for overweight and obesity, describing it as a potential risk factor (9). Another study (51) reported short sleep as a risk factor for overweight and obesity among adolescents as a positive association was found.
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5 DISCUSSION
5.1 CRITICAL ANALYSIS
5.1.1 Studies included
With reference to section 4.3.1, thirteen of the studies reported a presumably strong response rate, from 80% (9,36,55) till 93% (35), whilst the ones which were rated as moderate ranged from 65% (74) till 78% (63). These high response rates minimize the risk of selection bias which positively affirms the generalizability of the results. Through the analysis of the data collection methods from the included studies, the frequent use of questionnaires was evident. The reason for the high response rates might come from the fact of the practicality to complete which questionnaires by their nature possess (82). Furthermore, since the approach adopted by twenty-four of the studies was placed in schools (7,17,29,32,33,36,47,48,52–55,59,63,66–69,72–75,80), public registries (8,28,31,50,64,65,71), population censuses (9,35,51), and centres (57,64,70) it was thus more accessible for individuals to participate, resulting into the recorded high response rates. Only one study (41), reported using a telephone directory through randomisation for the study sample whilst (76) obtained the participants from a rural village; but no clear description was available on the recruitment procedure, which was also the case for a further of two studies (49,58). Moreover, one study (66), obtained the samples from two Greek islands. The random selection from four differnet public schools were carried out in Samos; whilst in Corfu, participants were randomly selected through friends and acquaintances from cities, which increases the chances of selection bias.
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General, self-reported and structured questionaires were the most common amongst studies, which managed to obtain a high percentage of participation. Furthermore, the study of Coll et al.(35) was the only one which remarked using interview questionnaires, topped the highest participation rate with 93%, showing the effectiveness of this method due to the presence of the researcher which can aid the participants to comply even further (83). Moreover, studies (53,55,69,70) which utlised self-reported questionnaires on lifestyle and BMI could possibly have experienced biases such as recall or social-desirability bias; and thus reduces their relability. This is often the case among obese individuals (42).
Even though the prevalence rates of overweight and obesity were not the primary interest in this review, it was still of relevance to extract data from the articles included. Only three studies (71,73,80) failed to report prevalence rates. The rest of the studies all included some sort of calculated prevalence from the target population varying from the representation of the overall prevalence of obesity only (74), the prevalence of both overweight and obesity (58), the prevalence of overweight and obesity in both genders (28), overall obesity at baseline with comparison to follow-up (47), abdominal obesity (35) and overweight and obesity prevalence according to different standard definitions for overweight and obesity (63).
5.1.2 Inductive content analysis
This research attempted to identify and assess the risk factors which are possibly causing a high prevalence of obesity in Mediterranean islands in all age groups. From the inductive content analysis carried out, a variety of risk factors were identified and evaluated, which reported of either having a negative or positive association. The risk factors which reported a positive association were level of education, maternal awareness and education, socio- economic status, sedentary behaviour, gender, age, parental obesity, birth weight, diet, the living environment, alcohol & smoking, level of blood lipids, marital status, metabolic syndrome, underestimation of excess weight and short sleep. These were all grouped according to social, behavioural, environmental and genetic categories following the social model of health (46).
Despite the determined associations, one needs to take into consideration the methodological weaknesses which the studies entailed. Eighteen of the studies (17,28,29,31,41,47,57–59,62,64–70), which almost totals to half of the included studies in this review did not indicate any participation rates and one study (48), reported a low response rate, being 49%. Therefore, this undermines the generalizability of the articles being assessed. In addition, definitions for overweight and obesity were not consistent in 26 the studies as mentioned in Section 4.3.3. Four studies (57,62,69,71) did not adhere to any guidelines for overweight and obesity and therefore this could result in an over or under- estimation of the real association reported. Furthermore, another study (69) was self- reported, meaning that responder bias (recall bias or social desirability) might also deviate the findings of the study from the truth.
The study of Loviselli et al. (31) consisted of two samples of Sardinian conscripts from 1969 and 1998 respectively, amounting to a gap of twenty-nine years between the studied samples. Furthermore, the year which the study was published had a further 12 year gap, showing severe time lag not only among the public health issue of obesity but also among a time lag between data collection and publication, which also affects the generalizability of the findings. Since the obesity pandemic has surfaced around the 1980‟s, one could question the inclusion of the 1969 cohort in the study whether it is really of relevance or not. However, taking this in consideration, the 1969 sample still reported 4.33% overweight and 0.55% obese; whilst the 1998 sample recorded 9.8% overweight and 3% obese conscripts. This indicates more than a double increase in percentages from 1969 to 1998. On a similar note, Vardavas et al. (8) also took four years to publish their results following their data collection in 2005, displaying time lag.
The prevalence percentages of overweight and/or obesity were reported from all studies but three (62,71,73). These however, still contributed by reporting risk factors positively associated with obesity. Moreover, the highest rates in this review were found amongst the elderly age group, scoring 66.8% for men and 85.1% in women for central obesity prevalence (49). Furthermore, 50.7% and 26.8% overall overweight and obesity percentages were reported amongst samples of adults to elderly years (28). Similarly, a study in Sicily, reported 45% or overweight and 27.7% for obese prevalence in adults (76). Going further down the age spectrum, 12-17 year olds from the Balearic Islands reported having the highest prevalence among adolescents, being 30.9% and 37.5% of boys being overweight and obese. Among girls, it was reported a much higher percentage of overweight (59.8%) but marginally smaller among obese (37%) (7). The highest rates among preschool children were overweight (26%) and obese (12.5%) in Malta (64). This shows the strong influence of parents who were 37.5% (overweight) and 16.5% (obese) mothers, and 46.5% overweight and 21% obese fathers, on the weight status of their children. Following the percentages above gives an indication of the fact that progressive aging is positively correlated with gaining weight, since the elderly in Menorca (49) had the highest percentages.
There was also a noticeable pattern of studies from the same primary authors, namely being (7,50,51), all based in the Balearic islands,(36,55,63), all based in Malta, (33,47,67,74,75), 27 and (32,72,73) all based in Cyprus. These studies did not only focus upon the prevalence rates and risk factors of overweight and obesity but adopted a more generalised approach to socio-demographic associations with the issue of weight status. This pattern implores that the results identified from these studies of the same pattern suggest a strong notion of reliability due to the overlapping and scrutinizing of possible variables which have to do with overweight and obese populations. However, the studies of Lazarou, termed as the CYKIDS study, used the same population sample throughout the years, which might raise the issue of both time lag and generalizability of Cypriot children, whilst two studies of Decelis et al. (36) Decelis et al. (63), used the same sample for both of their studies. Moreover, the studies of Savva et al. (74)and Bibiloni et al. (51) portrayed a different approach as the sample sizes differed means that the studies had arguably reached a larger proportion of a population when compared to the others, thus enhancing generalizability of them grouped altogether.
Even though risk factors were identified to be the case for overweight and obesity prevalence in Mediterranean islands, and positive associations seem to be evident, one should approach the narrative in a critical manner due to the heterogenic nature of the studies. In addition, reverse causality, was one of the numerous limitations due to the cross- sectional design of the majority of the studies, through which no causality can be concluded.
5.2 Strengths This review attempted to narrow the research gap in the obesity issue in the Mediterranean region, by putting a focus specifically on Mediterranean islands. The fragmented studies needed to be grouped together to have a more robust indication for the risk factors possibly explaining the prevalence of obesity in isolated Mediterranean populations. This aggregation could thus contribute to obesity reviews to further help measure the precarious obesity situation.
One noticeable strength of this review was the comprehensive search strategy which detected all the important studies. Snowballing contributed to the further addition of other related articles which were important for inclusion. In an additional attempt to gather as many studies as possible for comprehensiveness, a request through email to the respective authors for their study was carried out, which gave an additional fifteen studies to the review.
5.3 Limitations
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Limitations were present and thus need to be recognised. One main weakness is the nature of the cross-sectional studies in the review which amounted to thirty-two. In addition, five studies did not clearly describe the study design used and therefore no causality can be determined, even though the studies seemed like entailing cross-sectional design characteristics. Furthermore, reverse causality and validity are two limitations among many others, and thus what the studies aim to measure might not be conclusive (84). This is because both the outcome (obesity) and the exposure (risk factors), involve time for the exposure and for the measurement of the exertion of influence respectively. This is not what a cross-sectional study entails, since it takes a „snapshot‟ of the situation at hand and thus the associations may not be clarified comprehensively (85). Nonetheless, cross-sectional designs may hint of any associations and thus hypotheses can be generated for further research (85).
Taking the nature of cross-sectional studies into account, weak ratings were unsurprisingly found among the majority of studies. This was also due to the low rating which almost all studies had scored. This might be due that the rating tool was not thorough enough in its scoring standards (ie. the weighting in confounders was just based on two questions; one of them being very dependent on the percentages for confounders, which in fact, if judged correctly, only one study scored a „Moderate‟ rating). If improvements were to be made, a number of questions which aren‟t reliant on each other would be constructed. Furthermore, having the tool validated before use could improve precision. Nonetheless, one cannot negate the fact that it was useful as a medium to assess the literature through the weaknesses and strengths of the articles.
The heterogeneous nature of all the studies which included a wide range of definitions of obesity, different risk factors and several age groups makes comparing an arduous task (86). The attempt of this review was to combine and evaluate the data to the best of our knowledge through an inductive content analysis. Although the analysis achieved important results, one still needs to exercise care when looking to understand the information, as excessive interpretation could pose a threat to a successful content analysis (1).
5.4 Comparison to other literature The systematic review of Papandreou et al.(22) had similarities with this study as it aimed to assess the obesity situation in the Mediterranean region. However, this was not limited to islands only, which is a unique characteristic of this review. One main finding in the mentioned study was that adults were at high risk of obesity irrespective of the geographical location or income (22). Although what they found is also hinted, this review adds that adults
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(35,72) and the elderly (49) in the lower socio-economic strata, had a positive association when compared to individuals in the higher-class.
5.5 Implications
5.5.1 Implications for policy
The results of the review could be taken into consideration aimed at obesity reduction as guidelines to contribute to awareness interventions promoting the urgency to adopt a lifestyle with a healthy diet and increase physical activity to help mitigate the situation through policy implementation. Moreover, social inequalities among groups of low education and income should also be tackled as higher obesity rates are commonly observed (87), especially due to the last years which saw the world experience a global financial crisis, making individuals more prone to experience financial hardships (39). This could be executed through welfare policies for low-income families as the supply of cash-benefits for incentives in nutrition and physical activity programmes could encourage participation (88). Key settings like schools and workplaces to address childhood and adult obesity are imperative for prevention programmes. However, these are not enough since obesity is a multifaceted condition, hence the commitment and lead from governments through substantial investment and contribution from all stakeholders at all levels is imperative for prevention campaigns to be successful (88).
5.5.2 Implications for research
A 2013 study demonstrating the mapping of the global rise in overweight and obesity has seen strong indications of confounding by the simultaneously aging populations (89). This phenomenon was also confirmed by the results from this review, firstly, the highest prevalence rate for all included studies was found among the elderly (49) and secondly, aging was also identified as a potential independent risk factor across multiple studies, which are found in the Inductive Content Analysis results in Appendix 5 – Inductive Content Analysis Results. However, it is impossible to confirm causality just from cross-sectional studies. This would require follow-up of the participants over time, for instance in a cohort study as was conducted by Sant‟Angelo & Grech (17). Age-adjusted prevalence for overweight and obesity needs to be explored in order to contribute to the comparisons of risk factors and prevalence across countries through nationally representative surveys in order to give further light on this issue (89).
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In spite of all the research directed in past years, the need for the continuation of information gathering based on body weight and height in children and adults across countries is vital to improve the precision of estimates of prospective obesity percentages (89).
Furthermore, there needs to be a more consistent definition of obesity in children less than five years of age as both the IOTF and WHO guidelines produce different results, often overestimating obesity, particularly in girls (89). This was evident among the included articles in this study as different overweight and obesity definitions were utilised.
Appreciating the fact that research has already been conducted in the whole Mediterranean region (22) the Eastern Mediterranean (90), and European regions (18) a systematic review on risk factors and obesity prevalence specifically in the Western Mediterranean basin would help further group together the findings and be able to generate evidence-based recommendations for policies.
6 Conclusion
This review concluded that there are a number of risk factors in Mediterranean island populations which all contribute to high obesity percentages in all age groups and particularly among the elderly. These results could thus be utilised for the consideration of implementing policies on a national level, such as financial incentives from the government for the adults and elderly to participate in any form of physical activity, implement school- based health education programmes for the parents of primary and secondary students, varying from adopting a nutritious diet and healthy lifestyle practices.
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8 Appendix 1 – Search strings (Search till 2nd of August 2015)
Appendix 1a: Cinahl Plus
S1 obes* or overweight or adipos* or physical* activ* or physical* inactiv* or excess weight or metabolic syndrome or sedentary behaviour or fatty disposition or weight gain or dyslipidaemia or central obes* or body weight increase or Body mass index or body composition or body fatness or Athero* or nutritional status or abdominal obesity or waist circumference or waist-hip ratio or weight to height ratio or weight loss or body size or body weight change or BMI or weight status or overnutrition or calorie intake or energy intake or nutrient intake or plasma lipids or lipoproteins or anthropometric measurements or skin-fold thickness or visceral fat or subcutaneous fat2
S2 (MH “Obesity, Morbid”) OR (MH “Pediatric Obesity”) OR (MH “Weight Gain”)
S3 (MH “Adipose Tissue”) OR (MH “Adipose Tissue”) OR (MH “Adipose Tissue Distribution”) OR (MH”Abdominal Fat”) OR (MH “Waist-Hip Ratio”)
S4 (MH ”Metabolic Syndrome X”)
S5 (MH “Hyperlipidemia+”)
S6 (MH “Adipose Tissue Distribution”) OR (MH “Adipose Tissue”) OR (MH “Body Weights and Measures”) OR (MH “Body Weight Changes”) OR (MH “Dietary Fats”) OR (MH “Body Weight”) OR (MH “Body Surface Area”) OR (MH “Body Size”) OR (MH “Body Composition”) OR (MH “Body Mass Index”) OR (MH “Nutritional Status: Body Mass (Iowa NOC)”)
S7 (MH “Energy Intake”) OR (MH “Food Intake”) OR (MH “Energy Density”) OR (MH “Nutritional Status”)
S8 (MH “Skinfold Thickness”) 40
S9 (MH “Lipoproteins, LDL Cholesterol”) OR (MH “Lipoproteins”)
S10 abdominal fat
S11 S1 OR S2 OR S3 OR S4 OR S5 OR S6 OR S7 OR S8 OR S9 OR S10
S12 Balearic or Adriatic or Ionian or Aeolian or Phlegraean or Greek Island* or Croatian Island* or Italian Island* or Spanish Island* or French Island* or Island* of Greece or Island* of Croatia or Island* of Italy or Island* of France or Aegean Island* or Islands of Spain or Mediterranean islands or Malta or Maltese or Sicily or Sicilian or Sardinia or Sardinian or Cyprus or Crete* or Pelagie Islands or Rhodes or Ibiza or Lesbos or Euboea or Evia or Ischia or Salamis or Salamina or Elba or Kos or Zakynthos or Samos or Djerba or Ibiza or Corfu or Minorca or Kefalonia or Chios or Majorca or Corsica or Saronic or Argo Saronic or Sporades or Dodecanese or Cyclades or Cretan Islands or Islands of Crete
S13 Arwad or Kerkennah Islands or Chergui or Gharbi or Alboran or Cabrera Chafarinas or Espalmadora or Formentera or Ibiza or Mallora or Menorca or Corsica or Cavallo or Hyeres or Lavezzi or Porquerolles
S14 Aeloian Islands or Lipari Islands or Alicudi or Filicudi or Panarea or Salina or Aegadian Islands or Favignana or Levanzo or Marettimo or Bergeggi or San Pietro Island or Phlegraean Islands or Capri or Procida or La Maddalena or Molara or Pelagian Islands or Lampedusa or Linosa or Pantelleria or Ventotene or Sant Antioco or Tuscan Archipelago or Giglio or Capraia or Gorgona or Ustica or Pontine Islands or San Nicola or San Domino or Tremiti islands or Ponza or giglio island or Ciovo or Cres or Dugi Otok or Elaphiti Islands or Hvar or Ist or Kolocep or Korcula or Krapanj or Krk or Lastovo or Losinj or Lopud or Mljet or Murter or Pasman or Pag or Prvic or Rab or Silba or Susak or Sipan or solta or Ugljan or Vir or Vis or Vrgada or Zlarin or zirje or Brac or Rava or Premuda or olib or kaprije or ilovik or drvenik mali or drvenik veli or Salamina or Aegina or Angistri or Hydra or Poros or Spetses or Dokos or Agathonissi or Alimia or Arkoi or Astypalaia or Farmakonisi or Halki or Kalymnos or Karphatos or Kasos or Kastellorizo or Kos or Lepsoi or Leros or Nisyros or Patmos or Pserimos or Symi or Syme or Simi or Telendos or Tilos or elafonissos or Chalki
S15 Amorgos or Anafi or Andros or Antiparos or Donousa or Folegandros or Ios or Irakleia or Iraklaia or Kea or Kimolos or Koufonisia or Kythnos or Milos or Mykonos or Naxos or Paros or Santorini or Schoinousa or Serifos or Sifnos or Sikinos or Syros or Therasia or Tinos or karpathos or Alonnisos or Skiathos or Skopelos or Skyros or Agia Varvara OR Crete OR Gavdos OR Sfakia or Cunda Island or Alibey Island or Ayvalik Islands or Baba Island or Buyuk Ada or Doganbey or Gokceada or Gerence or Guvercin Island or Sivriada or Hayirsizada or Princes Islands or Incir Ada or Kara Ada or Kargi Adasi or Presa or Metallic Island or Salih Ada or Sedir Island or Cleopatra Island or Tenedos or Bozcaada or Uzunada or Yassi Ada or Besadalar or Gemiler Island or St Nicholas Island or Agios Efstratios or Chios or Oinousses or Psara or Thymaina or Icaria or Ikaria or Lemnos or Samnos or Samothraki or Thasos or Fournoi Korseon or Samothrace or Kerkyra OR Paxi OR Paxos OR Lefkada OR Lefkas OR Ithaki OR Ithaca OR Kefalonia OR Cephalonia OR Kefallinia OR Zante OR Kythira OR Cythera OR Cerigo OR Elafonisos OR Kalamos OR Kastos OR Mathraki OR Meganisi or Othonoi OR Skorpios OR Strofades OR Strofadia
S16 S12 OR S13 OR S14 OR S15
41
S17 S11 AND S16
259 results
Appendix 1b: Embase
1 obes* or overweight or adipos* or physical* activ* or physical* inactiv* or excess weight or metabolic syndrome or sedentary behaviour or fatty disposition or weight gain or dyslipidaemia or central obes* or body weight increase or Body mass index or body composition or body fatness or Athero* or nutritional status or abdominal obesity or waist circumference or waist-hip ratio or weight to height ratio or weight loss or body size or body weight change or BMI or weight status or overnutrition or calorie intake or energy intake or nutrient intake or plasma lipids or lipoproteins or anthropometric measurements or skin-fold thickness or visceral fat or subcutaneous fat).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword]
2 overnutrition/ or obesity/
3 obesity/ or body weight disorder/ or overnutrition/ or abdominal obesity/ or childhood obesity/ or diabetic obesity/ or metabolic syndrome x/or morbid obesity/
4 adipose tissue/ or abdominal fat/ or body fat/ or body fat distribution/ or epididymis fat/or body fat/ or subcutaneous fat/ or white adipose tissue/
5 metabolic syndrome x/ or “disorders of lipid and lipoprotein metabolism”/ or obesity/
6 dyslipidemia/
7 body mass/ or body size/ or body surface/
8 caloric intake/ or dietary intake/ or caloric restriction/
9 lipid/ or fat/
10 lipoprotein/ or lipid/
11 waist circumference/ or waist hip ratio/ or anthropometric parameters/ or “weight, mass and size”/ or waist to height ratio/
12 abdominal fat/ or adipose tissue/ or abdominal subcutaneous fat/ or intraabdominal fat/ 42
13 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12
14 southern Europe/ or malta/
15 (Balearic or Adriatic or Ionian or Aeolian or Phlegraean or Greek Island* or Croatian Island* or Italian Island* or Spanish Island* or French Island* or Island* of Greece or Island* of Croatia or Island* of Italy or Island* of France or Aegean Island* or Islands of Spain or Mediterranean islands or Malta or Maltese or Sicily or Sicilian or Sardinia or Sardinian or Cyprus or Crete* or Pelagie Islands or Rhodes or Ibiza or Lesbos or Euboea or Evia or Ischia or Salamis or Salamina or Elba or Kos or Zakynthos or Samos or Djerba or Ibiza or Corfu or Minorca or Kefalonia or Chios or Majorca or Corsica or Saronic or Argo Saronic or Sporades or Dodecanese or Cyclades or Cretan Islands or Islands of Crete).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword]
16 (Arwad or Kerkennah Islands or Chergui or Gharbi or Alboran or Cabrera Chafarinas or Espalmadora or Formentera or Ibiza or Mallorca or Menorca or Corsica or Cavallo or Hyeres or Lavezzi or Porquerolles).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword]
17 (Aeloian Islands or Lipari Islands or Aliudi or Filicudi or Panarea or Salina or Aegadian Islands or Favignana or Levanzo or Marettimo or Bergeggi or San Pietro Island or Phlegraean Islands or Capri or Procida or LA Maddalena or Molara or Pelagian Islands or Lampedusa or Linosa or Pantelleria or Ventotene or Sant Antioco OR Tuscan Archipelago or Giglio or Capraia or Gorgona or Ustica or Pontine Islands OR San Nicola OR San Domino OR Tremiti islands OR Ponza or giglio island Ciovo or Cres or Dugi Otok or Elaphiti Islands or Hvar or Ist or Kolocep or Korcula or Krapanj or Krk or Lastovo or Losinj or Lopud or Mljet or Murter or Pasman or Pag or Prvic or Rab or Silba or Susak or Sipan or solta or Ugljan or Vir or Vis or Vrgada or Zlarin or zirje or Brac or Rava or Premuda or olib or kaprije or ilovik or drvenik mali or drvenik veli or Salamina or Aegina or Angistri or Hydra or Poros or Spetses or Dokos or Agathonissi or Alimia or Arkoi or Astypalaia or Farmakonisi or Halki or Kalymnos or Karphatos or Kasos or Kastellorizo or Kos or Lepsoi or Leros or Nisyros or Patmos or Pserimos or Symi or Syme or Simi or Telendos or Tilos or elafonissos or Chalki).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword]
18 (Amorgos or Anafi or Andros or Antiparos or Donousa or Folegandros or Ios or Irakleia or Iraklaia or Kea or Kimolos or Koufonisia or Kythnos or Milos or Mykonos or Naxos or Paros or Santorini or Schoinousa or Serifos or Sifnos or Sikinos or Syros or Therasia or Tinos or karpathos or Alonnisos or Skiathos or Skopelos or Skyros or Agia Varvara or Crete or Gavdos or Sfakia).mp [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword]
19 (Cunda Island or Alibey Island or Ayvalik Islands or Baba Island or Buyuk Ada or Doganbey or Gokceada or Gerence or Guvercin Island or Sivriada or Hayirsizada or Princes Islands or Incir Ada or Kara Ada or Kargi Adasi or Presa or Metallic Island or Salih Ada or Sedir Island or Cleopatra Island or Tenedos or Bozcaada or Uzunada or Yassi Ada or Besadalar or Gemiler Island or St Nicholas Island or Agios Efstratios or Chios or Oinousses
43 or Psara or Thymaina or Icaria or Ikaria or Lemnos or Samnos or Samothraki or Thasos or Fournoi Korseon or Samothrace or Kerkyra OR Paxi OR Paxos OR Lefkada OR Lefkas OR Ithaki OR Ithaca OR Kefalonia OR Cephalonia OR Kefallinia OR Zante OR Kythira OR Cythera OR Cerigo OR Elafonisos OR Kalamos OR Kastos OR Mathraki OR Meganisi or Othonoi OR Skorpios OR Strofades OR Strofadia).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, device trade name, keyword]
20 14 or 15 or 16 or 17 or 18 or 19
21 13 and 20
22 (Balearic or Adriatic or Ionian or Aeolian or Phlegraean or Greek Island* or Croatian Island* or Italian Island* or Spanish Island* or French Island* or Island* Greece or Island* or Croatia or Aegean Island* or Mediterranean islands or Malta or Maltese or Sicily or Sicilian or Sardinia or Sardinian or Cyprus or Crete* or Pelagie Islands or Rhodes or Ibiza or Lesbos or Euboea or Evia or Ischia or Salamis or Salamina or Elba or Kos or Zakynthos or Samos or Djerba or Ibiza or Corfu or Minorca or Kefalonia or Chios or Majorca or Corsica or Saronic or Argo Saronic or Sporades or Dodecanese or Cyclades or Cretan Islands).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, device trade name, keyword]
23 1 or 14 or 16 or 17 or 18 or 19 or 22
24 13 and 23
25 16 or 17 or 18 or 19 or 22
26 13 and 25
9111 results
44
Appendix 1c: Medline
1 (obes* or overweight or adipos* or physical* activ* or physical* inactiv* or excess weight or metabolic syndrome or sedentary behaviour or fatty disposition or weight gain or dyslipidaemia or central obes* or body weight increase or Body mass index or body composition or body fatness or Athero* or nutritional status or abdominal obesity or waist circumference or waist-hip ratio or weight to height ratio or weight loss or body size or body weight change or BMI or weight status or overnutrition or calorie intake or energy intake or nutrient intake or plasma lipids or lipoproteins or anthropometric measurements or skin-fold thickness or visceral fat or subcutaneous fat).mp. [mp=title, abstract, original title, name of substance word, subject heading, word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]
2 Overnutrition/ or obesity/ or obesity, abdominal/ or obesity, morbid/ or pediatric obesity/
3 Overweight/
4 Adipose tissue/ or adipose tissue, white/ or abdominal fat/ or intra-adbdominal fat/ or subcutaneous fat, abdominal/ or subcutaneous fat/
5 Metabolic Syndrome X/
6 Exp dyslipidemias/ or hyperlipidemias/
7 Body fat distribution/ or body mass index/or body size/ or body weight/ or ideal body weight/ or overweight/ or waists circumference/ or lipid accumulation product/ or waist- height-ratio/ or body surface area/ or skinfold thickness/ or waist-hip ratio/
8 1 or 2 or 3 or 4 or 5 or 6 or 7
9 Mediterranean islands/ or Cyprus/ or malta/ or sicily
10 8 and 9
45
11 Overnutrition/
12 Energy intake/ or portion size/ or serving size/ or diet,western/
13 Lipids/ or cholesterol, dietary/
14 Lipoproteins/
15 Skinfold thickness/ or waist-hip ratio/
16 Abdominal fat/ or intra-abdominal fat/or subcutaneous fat, abdominal/ or subcutaneous fat/
17 8 and 9
18 11 or 12 or 13 or 14 or 15 or 16
19 1 or 2 or 3 or 4 or 5 or 6 or 7 or 18
20 9 and 19
21 (Balearic or Adriatic or Ionian or Aeolian or Phlegraean or Greek Island* or Croatian Island* or Italian Island* or Spanish Island* or French Island* or Island* of Greece or Island* of Croatia or Island* of Italy or Island* of France or Aegean Island* or Islands of Spain or Mediterranean islands or Malta or Maltese or Sicily or Sicilian or Sardinia or Sardinian or Cyprus or Crete* or Pelagie Islands or Rhodes or Ibiza or Lesbos or Euboea or Evia or Ischia or Salamis or Salamina or Elba or Kos or Zakynthos or Samos or Djerba or Ibiza or Corfu or Minorca or Kefalonia or Chios or Majorca or Corsica or Saronic or Argo Saronic or Sporades or Dodecanese or Cyclades or Cretan Islands or Islands of Crete).mp. [mp=title, abstract, original title, name of substance word, subject heading, word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]
22 (Arwad or Kerkennah Islands or Chergui or Gharbi or Alboran or Cabrera Chafarinas or Espalmadora or Formentera or Ibiza or Mallora or Menorca).mp. [mp=title, abstract, original title, name of substance word, subject heading, word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]
23 (Aeloian Islands or Lipari Islands or Alicudi or Filicudi or Panarea or Salina or Aegadian Islands or Favignana or Levanzo or Marettimo or Bergeggi or San Pietro Island or Phlegraean Islands or Capri or Procida or LA Maddalena or Molara or Pelagian Islands or Lampedusa or Linosa or Pantelleria or Ventotene or Sant Antioco or Tuscan Archipelago or Giglio or Capraia or Gorgona or Ustica or Pontine Islands or San Nicola or San Domino or Tremiti islands or Ponza or giglio island).mp. [mp=title, abstract, original title, name of substance word, subject heading, word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]
46
24 (Ciovo or Cres or Dugi Otok or Elaphiti Islands or Hvar or Ist or Kolocep or Korcula or Krapanj or Krk or Lastovo or Losinj or Lopud or Mljet or Murter or Pasman or Pag or Prvic or Rab or Silba or Susak or Sipan or solta or Ugljan or Vir or Vis or Vrgada or Zlarin or zirje OR Brac OR Rava OR Premuda or olib or kaprije or ilovik or drvenik mali OR drvenik veli OR Salamina or Aegina or Angistri or Hydra or Poros or Spetses or Dokos OR Agathonissi or Alimia or Arkoi or Astypalaia or Farmakonisi or Halki or Kalymnos or Karphatos or Kasos or Kastellorizo or Kos or Lepsoi or Leros or Nisyros or Patmos or Pserimos or Symi or Syme or Simi or Telendos or Tilos or elafonissos or Chalki).mp. [mp=title, abstract, original title, name of substance word, subject heading, word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]
25 (Amorgos or Anafi or Andros or Antiparos or Donousa or Folegandros or Ios or Irakleia or Iraklaia or Kea or Kimolos or Koufonisia or Kythnos or Milos or Mykonos or Naxos or Paros or Santorini or Schoinousa or Serifos or Sifnos or Sikinos or Syros or Therasia or Tinos or karpathos or Alonnisos or Skiathos or Skopelos or Skyros or Agia Varvara OR Crete OR Gavdos OR Sfakia).mp. [mp=title, abstract, original title, name of substance word, subject heading, word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]
26 Cunda Island or Alibey Island or Ayvalik Islands or Baba Island or Buyuk Ada or Doganbey or Gokceada or Gerence or Guvercin Island or Sivriada or Hayirsizada or Princes Islands or Incir Ada or Kara Ada or Kargi Adasi or Presa or Metallic Island or Salih Ada or Sedir Island or Cleopatra Island or Tenedos or Bozcaada or Uzunada or Yassi Ada or Besadalar or Gemiler Island or St Nicholas Island or Agios Efstratios or Chios or Oinousses or Psara or Thymaina or Icaria or Ikaria or Lemnos or Samnos or Samothraki or Thasos or Fournoi Korseon or Samothrace).mp. [mp=title, abstract, original title, name of substance word, subject heading, word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]
27 1 or 2 or 3 or 4 or 5 or 6 or 7 or 11 or 12 or 13 or 14 or 15 or 16
28 21 and 27
29 21 or 22 or 23 or 24 or 25 or 26
30 27 and 29
1831 results
47
Appendix 1d: Global Health
1 (obes* or overweight or adipos* or physical* activ* or physical* inactiv* or excess weight or metabolic syndrome or sedentary behaviour or fatty disposition or weight gain or dyslipidaemia or central obes* or body weight increase or Body mass index or body composition or body fatness or Athero* or nutritional status or abdominal obesity or waist circumference or waist-hip ratio or weight to height ratio or weight loss or body size or body weight change or BMI or weight status or overnutrition or calorie intake or energy intake or nutrient intake or plasma lipids or lipoproteins or anthropometric measurements or skin-fold thickness or visceral fat or subcutaneous fat).mp. [mp=abstract, title, original title, broad terms, heading words, identifiers, cabicodes]
2 adipose tissue/ or body fat/ or subcutaneous fat/
3 overeating/ or overfeeding/
4 overweight/ or obesity/
5 metabolic syndrome/ or metabolic disorders/
6 hyperlipaemia/ or lipid metabolism disorders/ or blood liids/ or cholesterol/
7 body fat/ or body composition/ or abdominal fat/ or fat thickness/
8 body mass index/ or body measurements/ or body weight/ or height-weight ratio/
9 energy expenditure/ or energy intake/ or caloric intake/ or food intake/
10 energy balance/ or energy consumption/
11 skin fold thickness/
12 lipoproteins/
13 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12
14 (Balearic or Adriatic or Ionian or Aeolian or Phlegraean or Greek Island* or Croatian Island* or Italian Island* or Spanish Island* or French Island* or Island* of Greece or Island*
48 of Croatia or Island* of Italy or Island* of France or Aegean Island* or Islands of Spain or Mediterranean islands or Malta or Maltese or Sicily or Sicilian or Sardinia or Sardinian or Cyprus or Crete* or Pelagie Islands or Rhodes or Ibiza or Lesbos or Euboea or Evia or Ischia or Salamis or Salamina or Elba or Kos or Zakynthos or Samos or Djerba or Ibiza or Corfu or Minorca or Kefalonia or Chios or Majorca or Corsica or Saronic or Argo Saronic or Sporades or Dodecanese or Cyclades or Cretan Islands or Islands of Crete).mp. [mp=abstract, title, original title, broad terms, heading words, identifiers, cabicodes]
15 (Corsica or Cavallo or Hyeres or Lavezzi or Porquerolles or Alboran or Cabrera Chafarinas or Espalmadora or Formentera or Ibiza or Mallora or Menorca or Kerkennah Islands or Chergui or Gharbi or Arwad).mp. [mp=abstract, title, original title, broad terms, heading words, identifiers, cabicodes]
16 (Aeloian Islands or Lipari Islands or Alicudi or Filicudi or Panarea or Salina or Aegadian Islands or Favignana or Levanzo or Marettimo or Bergeggi or San Pietro Island or Phlegraean Islands or Capri or Procida or LA Maddalena or Molara or Pelagian Islands or Lampedusa or Linosa or Pantelleria or Ventotene or Sant Antioco OR Tuscan Archipelago or Giglio or Capraia or Gorgona or Ustica or Pontine Islands OR San Nicola OR San Domino OR Tremiti islands OR Ponza or giglio island OR Ciovo or Cres or Dugi Otok or Elaphiti Islands or Hvar or Ist or Kolocep or Korcula or Krapanj or Krk or Lastovo or Losinj or Lopud or Mljet or Murter or Pasman or Pag or Prvic or Rab or Silba or Susak or Sipan or solta or Ugljan or Vir or Vis or Vrgada or Zlarin or zirje OR Brac OR Rava OR Premuda or olib or kaprije or ilovik or drvenik mali OR drvenik veli OR Salamina or Aegina or Angistri or Hydra or Poros or Spetses or Dokos OR Agathonissi or Alimia or Arkoi or Astypalaia or Farmakonisi or Halki or Kalymnos or Karphatos or Kasos or Kastellorizo or Kos or Lepsoi or Leros or Nisyros or Patmos or Pserimos or Symi or Syme or Simi or Telendos or Tilos or elafonissos or Chalki).mp. [mp=abstract, title, original title, broad terms, heading words, identifiers, cabicodes]
17 (Alonnisos or Skiathos or Skopelos or Skyros or Amorgos or Anafi or Andros or Antiparos or Donousa or Folegandros or Ios or Irakleia or Iraklaia or Kea or Kimolos or Koufonisia or Kythnos or Milos or Mykonos or Naxos or Paros or Santorini or Schoinousa or Serifos or Sifnos or Sikinos or Syros or Therasia or Tinos or karpathos or Agia Varvara OR Crete OR Gavdos OR Sfakia).mp. [mp=abstract, title, original title, broad terms, heading words, identifiers, cabicodes]
18 (Cunda Island or Alibey Island or Ayvalik Islands or Baba Island or Buyuk Ada or Doganbey or Gokceada or Gerence or Guvercin Island or Sivriada or Hayirsizada or Princes Islands or Incir Ada or Kara Ada or Kargi Adasi or Presa or Metallic Island or Salih Ada or Sedir Island or Cleopatra Island or Tenedos or Bozcaada or Uzunada or Yassi Ada or Besadalar or Gemiler Island or St Nicholas Island or Agios Efstratios or Chios or Oinousses or Psara or Thymaina or Icaria or Ikaria or Lemnos or Samnos or Samothraki or Thasos or Fournoi Korseon or Samothrace).mp. [mp=abstract, title, original title, broad terms, heading words, identifiers, cabicodes]
19 (Kerkyra or Paxi or Paxos or Lefkada or Lefkas or Ithaki or Ithaca or Kefalonia or Cephalonia or Kefallinia or Zante or Kythira or Cythera or Cerigo or Elafonisos or Kalamos or Kastos or Mathraki or Meganisi or Othonoi or Skorpios or Strofades or Strofadia).mp. [mp=abstract, title, original title, broad terms, heading words, identifiers, cabicodes] 49
20 14 or 15 or 16 or 17 or 18 or 19
21 13 and 20
962 results
9 Appendix 2 – Quality appraisal tool
Appendix 2a
COMPONENT RATINGS
A) SELECTION BIAS
(Q1) Are the individuals selected to participate in the study likely to be representative of the target population?
1 Very likely
2 Somewhat likely
3 Not likely
4 Can‟t tell
(Q2) What percentage of selected individuals agreed to participate?
1 80 - 100% agreement
2 60 – 79% agreement
3 less than 60% agreement
4 Not applicable
5 Can‟t tell
Section Rating STRONG MODERATE WEAK
According to 1 2 3 Dictionary
50
B) STUDY DESIGN
Indicate the study design
1 Randomized controlled trial
2 Controlled clinical trial
3 Cohort analytic (two group pre + post)
4 Case-control
5 Cohort (one group pre + post (before and after))
6 Interrupted time series
7 Other specify ______
8 Can‟t tell
Was the study described as randomized? If NO, go to Component C.
No Yes
If Yes, was the method of randomization described? (See dictionary)
No Yes
If Yes, was the method appropriate? (See dictionary)
No Yes
Section Rating STRONG MODERATE WEAK
According to 1 2 3 Dictionary
C) CONFOUNDERS
(Q1) Were there important differences between groups prior to the intervention?
1 Yes
2 No
3 Can‟t tell
The following are examples of confounders:
1 Race
2 Sex 51
3 Marital status/family
4 Age
5 SES (income or class)
6 Education
7 Health status
8 Pre-intervention score on outcome measure
(Q2) If yes, indicate the percentage of relevant confounders that were controlled (either in the design (e.g. stratification, matching) or analysis)?
1 80 – 100% (most)
2 60 – 79% (some)
3 Less than 60% (few or none)
4 Can‟t Tell
Section Rating STRONG MODERATE WEAK
According to 1 2 3 Dictionary
D) BLINDING
(Q1) Was (were) the outcome assessor(s) aware of the intervention or exposure status of participants?
1 Yes
2 No
3 Can‟t tell
(Q2) Were the study participants aware of the research question?
1 Yes
2 No
3 Can‟t tell
Section Rating STRONG MODERATE WEAK
According to 1 2 3 Dictionary
52
E) DATA COLLECTION METHODS
(Q1) Were data collection tools shown to be valid?
1 Yes
2 No
3 Can‟t tell
(Q2) Were data collection tools shown to be reliable?
1 Yes
2 No
3 Can‟t tell
Section Rating STRONG MODERATE WEAK
According to 1 2 3 Dictionary
F) WITHDRAWALS AND DROP-OUTS
(Q1) Were withdrawals and drop-outs reported in terms of numbers and/or reasons per group?
1 Yes
2 No
3 Can‟t tell
4 Not Applicable (i.e. one time surveys or interviews)
(Q2) Indicate the percentage of participants completing the study. (If the percentage differs by groups, record the lowest).
1 80 -100%
2 60 - 79%
3 less than 60%
4 Can‟t tell
5 Not Applicable (i.e. Retrospective case-control)
Section Rating STRONG MODERATE WEAK
According to 1 2 3
53
Dictionary
G) INTERVENTION INTEGRITY
(Q1) What percentage of participants received the allocated intervention or exposure of interest?
1 80 -100%
2 60 - 79%
3 less than 60%
4 Can‟t tell
(Q2) Was the consistency of the intervention measured?
1 Yes
2 No
3 Can‟t tell
(Q3) Is it likely that subjects received an unintended intervention (contamination or co-intervention) that may influence the results?
4 Yes
5 No
6 Can‟t tell
H) ANALYSES
(Q1) Indicate the unit of allocation (circle one) community organization/institution practice/office individual
(Q2) Indicate the unit of analysis (circle one) community organization/institution practice/office individual
(Q3) Are the statistical methods appropriate for the study design?
1 Yes
2 No
3 Can‟t tell
(Q4) Is the analysis performed by intervention allocation status (i.e. intention to treat) rather than the actual intervention received?
54
1 Yes
2 No
3 Can‟t tell
GLOBAL RATING
COMPONENT RATINGS
Please transcribe the information from the gray boxes on pages 1-4 onto this page. See dictionary on how to rate this section.
Selection Bias STRONG (1) MODERATE (2) WEAK (3)
Study Design STRONG (1) MODERATE (2) WEAK (3)
Confounders STRONG (1) MODERATE (2) WEAK (3)
Blinding STRONG (1) MODERATE (2) WEAK (3)
Data Collection STRONG (1) MODERATE (2) WEAK (3) Method
Withdrawals and STRONG (1) MODERATE (2) WEAK (3) Dropouts
GLOBAL RATING FOR THIS PAPER (circle one):
1 STRONG (no WEAK ratings)
2 MODERATE (one WEAK rating)
3 WEAK (two or more WEAK ratings)
Final decision of reviewer (circle one):
1 STRONG
2 MODERATE
3 WEAK
55
Appendix 2b – Quality Assessment Tool for Quantitative Studies Dictionary
A) SELECTION BIAS
(Q1) Participants are more likely to be representative of the target population if they are randomly selected from a comprehensive list of individuals in the target population (score very likely). They may not be representative if they are referred from a source (e.g. clinic) in a systematic manner (score somewhat likely) or self-referred (score not likely).
(Q2) Refers to the % of subjects in the control and intervention groups that agreed to participate in the study before they were assigned to intervention or control groups.
B) STUDY DESIGN
In this section, raters assess the likelihood of bias due to the allocation process in an experimental study. For observational studies, raters assess the extent that assessments of exposure and outcome are likely to be independent.
Generally, the type of design is a good indicator of the extent of bias. In stronger designs, an equivalent control group is present and the allocation process is such that the investigators are unable to predict the sequence.
Randomized Controlled Trial (RCT)
An experimental design where investigators randomly allocate eligible people to an intervention or control group. A rater should describe a study as an RCT if the randomization sequence allows each study participant to have the same chance of receiving each intervention and the investigators could not predict which intervention was next. If the investigators do not describe the allocation process and only use the words „random‟ or „randomly‟, the study is described as a controlled clinical trial.
See below for more details.
Was the study described as randomized?
Score YES, if the authors used words such as random allocation, randomly assigned, and random assignment.
56
Score NO, if no mention of randomization is made.
Was the method of randomization described?
Score YES, if the authors describe any method used to generate a random allocation sequence.
Score NO, if the authors do not describe the allocation method or describe methods of allocation such as alternation, case record numbers, dates of birth, day of the week, and any allocation procedure that is entirely transparent before assignment, such as an open list of random numbers of assignments.
If NO is scored, then the study is a controlled clinical trial.
C) CONFOUNDERS
By definition, a confounder is a variable that is associated with the intervention or exposure and causally related to the outcome of interest. Even in a robust study design, groups may not be balanced with respect to important variables prior to the intervention. The authors should indicate if confounders were controlled in the design (by stratification or matching) or in the analysis. If the allocation to intervention and control groups is randomized, the authors must report that the groups were balanced at baseline with respect to confounders (either in the text or a table).
D) BLINDING
(Q1) Assessors should be described as blinded to which participants were in the control and intervention groups. The purpose of blinding the outcome assessors (who might also be the care providers) is to protect against detection bias.
(Q2) Study participants should not be aware of (i.e. blinded to) the research question. The purpose of blinding the participants is to protect against reporting bias.
E) DATA COLLECTION METHODS
Tools for primary outcome measures must be described as reliable and valid. If „face‟ validity or „content‟ validity has been demonstrated, this is acceptable. Some sources from which data may be collected are described below:
Self-reported data includes data that is collected from participants in the study (e.g. completing a questionnaire, survey, answering questions during an interview, etc.).
Assessment/Screening includes objective data that is retrieved by the researchers. (e.g. observations by investigators).
Medical Records/Vital Statistics refers to the types of formal records used for the extraction of the data.
Reliability and validity can be reported in the study or in a separate study. For example, some standard assessment tools have known reliability and validity.
F) WITHDRAWALS AND DROP-OUTS
57
Score YES if the authors describe BOTH the numbers and reasons for withdrawals and drop-outs.
Score NO if either the numbers or reasons for withdrawals and drop-outs are not reported.
The percentage of participants completing the study refers to the % of subjects remaining in the study at the final data collection period in all groups (i.e. control and intervention groups).
Intervention Integrity and Analysis to appropriate to question were left out since only observational studies were of interest.
10 Appendix 3 – Quality Appraisal Results
Selection Study Data Collection Withdrawals Final Global Study Confounders Blinding Bias design Method and Drop-outs rating Ramón et al. (2012) Moderate Weak Weak Weak Moderate Weak Weak Bibiloni et al. (2012) Strong Strong Weak Moderate Moderate Strong Weak Ferra et al. (2012) Strong Strong Weak Moderate Strong Strong Weak Baroudi et al. Moderate Weak Weak Weak Moderate Weak Weak (2010) Baratta et al. (2005) Weak Strong Weak Weak Strong Weak Weak Buttigieg et al. Moderate Strong Weak Moderate Moderate Weak Weak (2012) Bibiloni et al. (2009) Strong Strong Weak Moderate Moderate Strong Weak Gelpi-Méndez et al. Moderate Weak Weak Moderate Moderate Weak Weak (2012) Decelis et al. (2012) Strong Strong Weak Moderate Strong Strong Moderate Koh (2005) Weak Weak Weak Weak Moderate Weak Weak Savva et al. (2014) Moderate Strong Weak Moderate Moderate Moderate Weak Coll et al. (2015) Strong Strong Weak Weak Moderate Strong Weak Bibiloni et al. (2010) Strong Strong Weak Weak Moderate Strong Weak Loviselli et al. Moderate Weak Weak Weak Moderate Strong Weak (2009) Andreou et al. Moderate Strong Weak Weak Moderate Weak Weak (2012) Parrino et al. (2012) Strong Strong Weak Moderate Moderate Strong Moderate Linardakis et al. Moderate Moderate Weak Moderate Moderate Weak Weak (2008) Calamusa et al. Weak Strong Weak Moderate Strong Weak Weak (2012 Pucarin-Cvetković Weak Strong Weak Moderate Moderate Weak Weak (2006) Sidoti et al. (2009) Weak Moderate Weak Moderate Strong Strong Weak Athanasopoulos et Strong Strong Weak Moderate Weak Strong Weak al. (2011) Lazarou et al. Moderate Strong Weak Moderate Strong Moderate Moderate (2009)
58
Grammatikaopoulou Moderate Strong Weak Moderate Weak Moderate Weak et al. (2008) Lazarou et al. Strong Strong Weak Moderate Moderate Weak Weak (2012) Smoljanović et al. Moderate Strong Weak Moderate Weak Weak Weak (2007) Hadjigeorgiou et al. Weak Strong Weak Moderate Moderate Moderate Weak (2012) Savva et al. (2002) Weak Strong Weak Moderate Moderate Moderate Weak Lazarou et al. Moderate Strong Weak Moderate Moderate Moderate Moderate (2008) Decelis et al. (2014) Moderate Strong Weak Moderate Moderate Moderate Moderate Savva et al. (2005) Moderate Strong Weak Moderate Weak Moderate Weak Manios et al. (2011) Strong Strong Weak Moderate Weak Weak Weak Vardavas et al. Strong Strong Weak Moderate Weak Strong Weak (2009) Tur et al. (2005) Strong Strong Weak Moderate Weak Strong Weak Savva et al. (2004) Moderate Moderate Weak Weak Weak Weak Weak Sant‟ Angelo & Strong Moderate Weak Moderate Weak Weak Weak Grech (2011) Sanna et al. (2006) Moderate Strong Weak Moderate Weak Weak Weak Decelis et al. (2013) Strong Strong Weak Moderate Weak Strong Weak Savva et al. (2008) Strong Moderate Weak Moderate Weak Strong Weak Barbagallo et al. Moderate Moderate Moderate Moderate Weak Moderate Moderate (2001) Velluzzi et al. Strong Moderate Weak Moderate Weak Strong Weak (2007)
59
11 Appendix 4 – Data Extraction Table Study Study Study Aim & Obesity Definition Data Collection Main Findings Risk Factors Quality Characteristics Objectives and Measurements and Analysis Appraisal Rating (response rate) Database Location: Majorca Identification of No definition Self-reported Parents‟ education and Parents‟ high education Weak Searching Initial Sample: N/A causes of obesity questionnaire socio-economic status are level (-) Ramón et al. Sample: 4135 (N/A) and overweight in Self-Reported BMI statistically associated with Parents‟ high socio- (2012) Design: N/A adolescents Multistage an increase of obesity rates economic level (-) Population: 10-16 sampling in their children. Participation in (Spanish year olds competitive Sport (-) language) Socio-economic status Lower class (5.5% obese; No. 1 12.7%overweight)
Lower-middle class (6.9% obese; 16% overweight)
Middle class (5% obese; 10.3% overweight)
Middle-high class (3% obese; 9.6% overweight)
High class (4.2% obese; 11.5% overweight) Bibiloni et al. Location: Balearic Assessing Overweight Survey Sedentary behaviour, Sedentary behaviour (+) Weak 2 (2012) Islands association FMI≥4.58kg/m socioeconomic factors, diet Physical activity (-) Initial Sample: between (boys) Anthropometric and lifestyle are statistically Male gender (+) No. 2 2400 sedentary measurements associated with an increase 2 High parental Sample (*): 1961 behaviour, FMI≥7.76kg/m (girls) (waist in the odds of obesity. educational and (82%) socioeconomic circumference, hip professional level (-) Design: Cross- factors, diet, and Obesity circumference, Childhood obesity Unhealthy diet and sectional lifestyle in FMI≥4.58kg/m2 in triceps and prevalence: 2 lifestyle (+) Population: 12-17 Balearic Island boys; FMI≥7.76kg/m subscapular Boys year olds adolescents in girls skinfold thickness, 30.9% Overweight OR= 0.96. 95% CI (0.47 – body fat (%), 37.5% Obese 1.48) Measured FMI weight and height) Girls (overweight boys) 59.8% Overweight Random Sampling 37.0% Obese OR= 1.00 (obese boys) 60
Multiple logistic OR=2.91. 95% CI ( 1.29- regression 6.57) analyses (overweight girls
OR=1.00 (obese girls) Ferra et al. Location: Menorca Assessing the Overweight Questionnaire Low level of education, low Age (+) Weak 2 (2012) Initial Sample: 450 BMI, lifestyle, and 27.0≤BMI<30.0kg/m socioeconomic status and Low level of education (- Sample (*): 402 healthy status and Obesity Anthropometric low physical activity rates are ) 2 No. 3 (89%) explore the BMI≥30.0kg/m measurements statistically associated with Low socioeconomic Design: Cross- relationships (weight and height) the prevalence of overweight status (-) sectional between them Measured BMI and obesity amongst the Low physical activity (-) Population: ≥65 among Menorca‟s elderly. years elderly Prevalence of central obesity was 66.8% in men and 85.1% in women. Baroudi et Location: Jerba Investigating Overweight Self-reported Men Low level of education (-) Weak 2 al. (2010) Island dietary intake and BMI 25–30 kg/m questionnaire Among the Arabs group, 12 Effect of ethnicity on Initial Sample: N/A food sources Obesity men (54.5%) were dietary intake (+) (-) 2 No. 4 Sample: 94 (N/A) among two BMI >30 kg/m Anthropometric overweight and 4 (18.2%) Female gender(+) Design: N/A Tunisian ethnic measurements were obese. Population: 32-64 groups of Measured BMI (waist years old moderate circumference, Among the Berbers group, 8 socioeconomic body height) men (50%) were overweight status in Jerba and 8 (32%) were obese. Island Single 24 hour Women dietary recall Among the Arabs group, 9 women (36%) were overweight and 8 (32%) were obese.
Among the Berbers group, 5 women (16%) were overweight and 24 (77%) were obese.
Prevalence of overweight and obesity are not significantly greater for Arab men compared with Berber men. 61
Prevalence of obesity significantly greater for Berber women.
Significant effect of ethnicity on dietary intakes Baratta et al. Location: Sicily To evaluate the Overweight Data collected The prevalence of Sedentary behaviour (+) Weak (2006) Initial Sample: N/A prevalence of BMI 25kg/m2 within an overweight and obesity in Western diet (+) Sample: 48,897 overweight and epidemiological 11-15 year olds is one of the Urban areas (+) No. 5 (N/A) obesity of Sicilian Obese project for goitre highest ever reported. Male gender (+) Design: Cross- children and BMI 30kg/m2 prevalence in Age (-) sectional adolescents school-children Prevalence of overweight Population: 11-15 and obesity in Sicilian years old Anthropometric schoolchildren is highest at measurements age 11 when compared with (weight and height) other geographic areas (Northern Italy & USA)
Children (boys 12-15 years old, girls 11 years old) living in urban areas had a higher prevalence of overweight and obesity than their urban counterparts
Prevalence is much higher at a younger age
Childhood obesity prevalence: Boys 3.1% severely obese (higher than 30kg/m2)
Girls 2.6% severely obese (higher than 30kg/m2)
Overall overweight and obesity prevalence Nearly 40% Buttigieg et Location: Malta To investigate the Children Face-to-face The protective effect of Overweight & obese parents Weak 62 al. (2012) Initial Samples: relationship Overweight structured maternal awareness of (+) 200 main sample between parental 75th and 97th interviews health promotion on No. 6 200 replacement and preschool percentile maternal and preschool (r=0.2; p<0.001) sample childhood obesity Anthropometric childhood obesity was Sample: 200 3-year and maternal Obese (weight and height) shown. Maternal awareness of child old children, 200 awareness of >97th weight-for- measurements for health promotion (-) mothers, 193 health promotion height percentiles both children and There is an association that fathers (N/A) on healthy eating parents overweight and obese OR = 0.38, 95% CI=0.20-0.70 Design: Cross- with parental and Parents preschool children had sectional preschool Overweight parents who were Maternal awareness of Population: 3-year childhood obesity BMI 25-30kg/m2 overweight and obese mother health promotion (-) olds and the parents Obese Preschool child overweight & OR=0.51, 95% CI=0.28-0.95 BMI 30kg/m2 obesity prevalence
26% overweight 12.5% obese
Parents overweight and obesity prevalence
Mothers 37.5% overweight 16.5% obese
Fathers 46.5% overweight 21% obese Bibiloni et al. Location: Balearic To assess the Overweight Survey Metabolic syndrome was Prevalence of metabolic Weak (2009) Islands prevalence of BMI ≥I p85 Boys (BMI status) 18.9% overweight 63 7.7% obese Girls 15.1% overweight 5% obese Location: Balearic To evaluate the Overweight Data collected from Prevalence of obesity is Female obesity Weak 2 Gelpi- Islands prevalence of BMI 25.0-29.9 kg/m Spanish greater in the young worker Socio-demographic factors (-) Méndez et Initial Sample: N/A overweight & association population than the general al. (2010) Sample: 42,086 obesity in the Obese (Sociedad de population of Spain Child and adolescent obesity (total sample of young worker BMI ≥ 30 kg/m2 Prevencion de (+) study) population in Ibermutuamur) Overweight and obesity (Spanish Design: Cross- Spanish regions (SPI) prevalence for the Balearic Male obesity language) sectional and to compare Islands Maternal education (-) Population: 16-17, with the Anthropometric 18-25 years old prevalence of the (weight & height) Males Socio-demographic factors (-) general population measurements 17.42% overweight of the same age 5.06% obese General No.8 Male gender (+) Females Age under 25 (+) 3.37% overweight 7.30% obese Both Genders 20.79% overweight 7.30% obese Koh (2005) Location: Malta To evaluate the No definition Quantitative survey Over nutrition is a problem Over nutrition (+) Weak No.9 Initial Sample: status of nutrition among nursing home 80 of Maltese nursing Nutritional Nutritional residents in St.Vincent de Female gender (+) Sample (*): 42 home residents to assessment tool assessment tool Paule Residence. (calculated 52.5%) find the (includes BMI (includes BMI Design: Cross- prevalence of measurement) measurement) High proportion of residents sectional underweight, at risk of undernutrition Population: >65 overweight and year olds obesity Overweight and obesity prevalence Males 36.8% overweight 10.5% obese 64 Females 34.8% overweight 39.1% obese Both Genders 35.7% overweight 26.2% obese Savva et al. Location: Cyprus To evaluate the Measured BMI Multiple logistic Prevalence of obesity and Male gender (+) Weak (2014) Initial Sample: prevalence of regression overweight among Cypriot Living in rural areas (+) No.10 4800 (for sample 1) overweight and Overweight children and adolescents has Maternal education (-) Sample 1 (2009- obesity in 2010 (between 25 and <30 Questionnaire greatly increased over a 2010 study): 3090 and assess cutoffs) decade. OR = 0.55 (95% CI: 0.31, (65%) prevalence trends Anthropometric 0.98), p=0.042 (secondary Sample 2 (1999- between 2000 and Obesity (weight and height) A greater tendency of school) 2000 study): 2467 2010 among (≥30 measurements obesity prevalence being (95%) school-children in higher in rural areas and OR = 0.35 (95% CI: 0.17, Design: Cross- Cyprus among school-aged boys. 0.69), p=0.003 sectional (college/university Population: 6 Overall prevalence of obesity graduates compared to years – 17.9 years 20.1% (95% CI:18.7, 21.5) mothers with lowest education level) Coll et al. Location: Balearic To evaluate the Measured BMI Multiple logistic Men had higher prevalence Age (+) Weak (2015) Islands prevalence and regression of overweight and abdominal Leisure time physical activity No.11 Initial Sample: risk factors of Overweight obesity than women. (-) 1388 overweight (OW) BMI 25.0-29.9 kg/m2 Interview One child (+) Sample: and obesity (OB) questionnaire Age was the main risk Male gender (+) 1081(93%) by BMI and Obese factors associated with Men Design: Cross- abdominal obesity BMI ≥ 30 kg/m2 Anthropometric overweight/obesity and OW 35.9%,( 95% CI 31.6- sectional (AO) among the measurements abdominal obesity. 40.5%) and AO (37.9%, 95% Population: 18-35 Balearic Islands‟ Abdominal Obesity (weight and height) CI 33.6-42.5%) years (young adult population (Waist-to-height ratio) Absence of leisure-time Women adults) and 36-55 WHtR ≥ 0.5 physical activity (LTPA) OW 24.9%, (95% CI 21.7- years (middle-aged 28.4%); AO 29.7 (95% CI adults) Defined according to Overall prevalence of 8.6-13.5%) WHO criteria overweight/obesity & abdominal obesity Married men (+) Unemployed (+) 29.4% OW (95% CI: 26.9- Born in South America (+) ?? 65 32.3%) Low education level in women (-) 11.2% OB (95% CI 9.5- 13.2%) 33.1% AO (95% CI 30.4- 36.0%) Bibiloni et al. Location: Balearic To calculate the Measured BMI Multiple logistic Main risk factors related with Low parental education level Weak (2010) Islands prevalence and regression prevalence of obesity in (-) No.12 Initial Sample: risk factors of Overweight adolescents are age, 1500 obesity in the BMI ≥ P85 < P97 General parental education level, Males Sample (*): 1231 Balearic Islands‟ questionnaire skipping meals, distraction OR: 3.47; 95% CI: 1.58 ,7.62 (82%) adolescents. Obesity during mealtime, lack of Design: Cross- BMI ≥ P97 Anthropometric sleep and smoking.. Females sectional measurements OR: 3.29; 95% CI:1.38, 7.89 Population: 10-16 WHO growth (weight and height) Prevalence of overweight & year olds standards obesity Skipping meals (+) Males Males 19.9% overweight OR: 4.99; 95% CI: 2.1, 11.54 12.7% obese Females OR: 2.20; 95% CI:0.99, 4.89 Females 15.5% overweight Age (+) 8.5% obese Males OR: 2.75; 95% CI: 1.14, 6.64 Distraction during mealtime (+) Males OR: 1.50; 95% CI:0.81, 2.84 Females OR: 2.06; 95% CI 0.91, 4.68 Short sleep (+) Males OR: 3.42; 95% CI: 66 0.88,13.26 Low parental socioeconomic status (-) Females OR: 3.24, 95% CI: 1.04, 10.05 Smoking (+) Females OR: 2.51; 95% CI: 0.88, 7.13 Loviselli et Location: Sardinia To assess the Measured BMI Data collected from Northern Sardinia had the Rural areas (+) (1969) Weak al. (2010) Initial Sample (*) prevalence of military registers highest incidence of No.13 (**): 1969 overweight and Overweight overweight and obesity in Urban areas (+) (1998) sample:14,200 obesity in the BMI 25.0-29.9 kg/m2 Means and 1969 Initial Sample (*) male Sardinian standard deviation Educational level (-) (1969) (**): 1998 sample: population. Obese Central Sardinia had the 8125 BMI ≥ 30 kg/m2 Anthropometric highest incidence in 1998. Educational level (+) (1998) Sample: 1969 measurements sample – 10,576 Defined according to (weight and height) The prevalence of obesity Overweight (N/A) WHO criteria (1995) increased during the past 30 Hilly zones (+) (1969) 1998 sample – years 6928 (N/A) Obese Design: N/A Prevalence of overweight & Mountainous zones (+) Population: 18 obesity (1969) year old military conscripts at the 1969 years of data 4.33% overweight Obese collected (1969 & 0.55% obese Hilly zones (+) (1998) 1998) 1998 9.8% overweight 3% obese Andreou et Location: Cyprus To evaluate the Measured BMI Questionnaire Prevalence of overweight Waist circumference (+) Weak al. (2012) Initial Sample: N/A prevalence of and obesity is very high in No.14 Sample: 1001 (N/A) overweight and Overweight Laboratory Cypriot adults. High blood glucose levels (+) Design: Cross- obesity in adults in BMI 25.0-30 kg/m2 examinations sectional Cyprus. A significant positive relation Increased alcohol 67 Population: 18 - 80 Obese 24-hour recall to of OW and OB with waist consumption (+) years old To assess and BMI > 30 kg/m2 collect dietary circumference, high blood relate obesity risk information glucose levels and increased Physical activity (-) factors of the adult alcohol consumption. Cypriot Anthropometric population. measurements A negative one with (weight and height decreased levels of exercise Prevalence of overweight & obesity Males 46.9% overweight 28.8% obese Females 26% overweight 27% obese Parrino et al. Location: Sicily To identify the Measured BMI Multiple logistic Children who were part of Male gender (+) Moderate (2012) Initial Sample: trends in the regression the 2009-1020 group had OR: 1..63; 95% CI: 1.24-2.15 No.15 48,897 (sample 1 & prevalence of Obesity Task Force higher BMI, BMI z-scores 2) overweight and (IOTF) age and sex- Anthropometric and waist circumferences Urban area (+) Sample 1: 924 obesity in relation specific BMI cut-off measurements than the 1999-2001 cohort. Rural area (-) (1999-2001) (90%) to gender and are criteria (weight and height) Sample 2: of residence Prevalence of obesity was 915 (2009-2010) Overweight higher in males in 2009-2010 (90%) From 85th -94th than 1999-2001. Design: Cross- sectional Obese Prevalence of obesity higher Population: 11-13 Equal to or above the in urban areas than rural year old school 95th areas children Overall prevalence of obesity 1999-2001 7.9% 2009-2010 13.7% 68 Genders Males (1999-2001 vs 2009- 2010) 9.7% vs 17.6% Females (1999-2001 vs 2009-2010) 6.3% vs 9.8% Rural vs Urban Rural (1999-2001 vs 2009- 2010) 7.8% vs 13.0% Urban (1999-2001 vs 2009- 2010) 8.8% vs 14.3% Linardakis et Location: Crete To evaluate the Measured BMI Logistic regression High intake of sugar-added Sugar-added beverages (+) Weak al. (2008) Initial sample: intake of sugar- beverages among No.16 1988 added beverages Obesity Task Force Questionnaire kindergarten children is BMI Sample (**): 856 such as soft (IOTF) age and sex- correlated with poor eating OR – 2.35, (p=0.0234) (49%) drinks and fruit specific BMI cut-off Anthropometric habits and poor nutrition, Design: Cross- juices among criteria for childhood measurements increasing the risk for Waist circumference sectional kindergarten overweight and (weight and height) developing childhood obesity OR – 2.07, 0.028 Population: 4-7 children and to obesity years old examine its association with Children above 90th Overall prevalence of obesity, physical percentile – overweight & obesity activity and diet. overweight and obese 19% overweight 10.8% obese Snowballing Location: Sicily To measure BMI Measured BMI Linear regression In small urban areas, Male gender (+) Weak Sample Initial sample:802 and factors analysis demographic factors and OR=3.17; 95% CI=2.07-4.89 Calamusa et Sample (*): 411 correlated with Overweight socio-economics deprivation al. (2012) (calculated 51%) overweight and BMI≥ 25.0 < 30 kg/m2 Structured can be seen as a risk factor Age (40-59; 60-79 years old) No.17 Design: Cross- obesity in a questionnaire for increased BMI also after (+) sectional population of a Obese controlling confounding OR=3.10; 95% CI=1.80-5.31 Population: 18 small city of BMI ≥ 30 kg/m2 Anthropometric factors. and OR=3.02; 95% CI=1.77- years + Western Sicily measurements 5.14 69 (weight and height) Increase risk of BMI over 25 Married/free union in subjects 40-59 years old participants (+) (OR=2.3; 95% CI=1.2-4.4) in OR=2.74; 95% CI=1.67-4.49 comparison with 18-39 year old males (OR=2.8;95% CI= Former smokers (+) 1.6-4.7) OR=2.01; 95% CI=1.03-3.86 This is further compared with Subjects drinking 14 units of females and subjects with a alcohol or more per week (+) higher socio-economic OR=3.89; 95% CI=1.09- deprivation score 13.79 (OR=1.3;95% CI= 1.1-17) Regular physical activity (-) Overall prevalence of OR=0.50; 95% CI=0.28-0.90 overweight & obesity 43.8% overweight 18.3% obese Pucarin- Location: Adriatic To examine the Measured BMI Logistic regression Prevalence of obesity and Level of cholesterol, LDL, Weak Cvetković Islands (Rab, Vis, relationships analysis related health problems were triglycerides, glucose (+) (2006) Lastovo, Mljet) between BMI, Overweight high for the studied No.18 Initial sample: dietary habits and BMI 25.0-29.9 kg/m2 Validated populations. Diastolic pressure (+) (N/A) cardiovascular questionnaire Sample: 1001 (N/A) risk factors in the Obese An association between BMI, Design: N/A Adriatic islands of BMI ≥30 kg/m2 Anthropometric dietary habits and CV risk Population: 18-88 Croatia measurements factors was found. years (weight and height) Overall prevalence of overweight & obesity 50.7% overweight (48.7% men, 51.3% women) 26.8% obese (45.5% men and 54.5% women) Sidoti et al. Location: Sicily To identify an Measured BMI Multi linear A strong association was Obese parents (+) Weak (2009) Initial sample:316 association regression analysis identified saying the No.19 Sample: 294 between BMI, Overweight percentage of students Family cultural level (-) (calculated 93%) physical activity BMI ≥19,84 ≤24,05 Interview classified as having a high Design: N/A and eating questionnaire BMI and a sedentary Regural physical activity (-) Population: 8-11 behaviour. Obese lifestyle/incorrect eating BMI >24,05 Anthropometric behaviour. Breakfast consumption (-) 70 To identify risks, measurements protective factors (weight and height) A statistical association was Sugar-added beverages (+) and suggestions also found between the for future weight of parents of Sedentary behaviour (+) interventions overweight/obese children and daily lifestyles. Family cultural level was significantly associated with having breakfast, fruit and vegetable consumption and increased physical activity. Overall prevalence of overweight & obesity 23% overweight 18% obese Athanasopo Location: To evaluate the Measured BMI Multivariable The high prevalence present Maternal obesity (+) Weak ulos et al. Kalymnos, Greek prevalence and logistic regression of increased weight in OR 2.46; 95% CI 1.34-4.51 (2011) island factors of Obesity Task Force analysis children and adolescents is No.20 Initial Sample:253 increased weight (IOTF) age and sex- due of the major global Working class mothers (+) Sample (*): 232 in children and specific BMI cut-off Self-reported problem. OR 2.76; 95% CI 1.07-7.15 (91.6%) adolescents in a criteria for childhood questionnaire Design: Cross- remote Greek overweight and Maternal nutritional status & Business class mothers (+) sectional island. obesity Anthropometric occupation, together with OR 2.82; 95% CI 1.10-7.34 Population: 8-16 measurements children‟s dietary habits, are years old Overweight (weight and height) associated with childhood Soft drinks daily (+) (Parents) body weight. OR 2.89; 95% CI 1.05-7.94 BMI > 25kg/m Overall prevalence of Obese overweight & obesity BMI > 30 kg/m2 20.6% overweight 8.1% obese Lazarou & Location: Cyprus To assess the Measured BMI Linear and logistic Sedentary behaviour and tv TV viewing time (both Moderate Soteriades Initial sample: relationship regression watching may be a predictor genders) (+) (2009) 1589 between physical Obesity Task Force analyses of the various obesity indices OR 2.84; 1.08-7.47 No.21 Sample (*): 1140 activity, sedentary (IOTF) age and sex- in children than physical (72%) behaviour, and specific BMI cut-off Semi-quantitative activity behaviours. 71 Design: Cross- obesity indices. on age- and sex- questionnaire sectional specific BMI An inverse association Population: 9-13 Anthropometric between more afternoon years Percentage of body measurements sleep and fewer private fat (weight and height) lessons and obesity status was observed. Overweight & Obesity (i) obesity status = (normal weight) NW and WC< 75th percentile; (ii) either condition, obesity status = overweight (OW)/obese (OB) or WC_75th percentile; and (iii) both conditions apply obesity status =OW/OB and WC_75th percentile. Grammatika Location: Greek To assess the Measured BMI 3-day food recall Inadequacies in Samos Weak opoulou et islands; Samos and differences in via three next-day micronutrient intake in the Inadequacy of micronutrients al. (2008) Corfu dietary intake and CDC growth charts interviews diet of Samos, similar to (+) No.22 Initial Sample: 330 growth of pre- (weight-for-age Z- Turkish cuisine. (165 from both schoolers and score) Corfu islands) schoolchildren Anthropometric Corfu dietary intake was Mediterranean diet Sample (**): 248 living in Samos measurements more similar to the overall adherence (-) (calculated 75%) and Corfu. (weight and height) Mediterranean diet, similar to Design: Cross- Italian cuisine. sectional Population: 3-12 Prevalence of overweight years old Samos – 10.7% Corfu – 6.5% None of the participants were obese. Lazarou et Location: Cyprus To evaluate the Measured BMI Semi-quantitative Frequent intake of fried food, Intake of fried food, Weak al. (2012) Initial Sample: dietary patterns questionnaire delicatessen meat, sweets, delicatessen meat, sweets, No.23 1589 using data mining Obesity Task Force junk food and soft drinks junk food, soft drinks, soft Sample (*): 1140 method from the (IOTF) age and sex- Logistic regression were attributed with an drinks (+) 72 (72%) CYKIDS study specific BMI cut-off analysis increased risk for obesity. Sub-sample: 634 among 10-12 on age- and sex- Intake of fish, milk, cereals (-) Design: Cross- years olds in specific BMI Anthropometric The consumption of fish sectional Cyprus and to see measurements helps to keep a normal Population: 10-12 the relationship of (weight and height weight. year old their weight and waist status. circumference) Smoljanović Location: Vis To examine N/A Comprehensive Age and gender were mostly Age (+) Weak et al. (2007) island, Croatia whether questionnaire associated with the presence No.24 Initial Sample: N/A socioeconomic of major health risk factors. Low socioeconomic status (+) Sample: 1024 inequalities at a Logistic regression (70%) micro-scale, analysis Level of education did not Design: Cross- through their show a high association with sectional effect on health risk factors, supplements, or Population: N/A risk factors dietary habits. contribute to health status in Household socioeconomic Vis. status was significantly correlated with excessive alcohol intake, obesity, and a high-fat diet. Hadjigeorgio Location: Cyprus To compare the Measured BMI Questionnaire Mean waist circumference of Eating disorders (+) Weak u et al. Initial Sample prevalence of 2010 cohort was higher in (2012) (2003): 2017 overweight/obesit N/A Anthropometric boys and higher in girls than No.25 Sample: 2017 (N/A) y among Cypriot measurements the 2003 cohort. Initial Sample adolescents (weight and height (2010): 2302 between 2003 and and waist Mean BMI was higher in Sample: 2302 (N/A) 2010 cohorts and circumference) 2010 only in 16-18 year old Design: Cross- to assess whether age group. sectional BMI was Population: 10-18 associated with An increasing number of year olds psychological adolescents had pathological traits linked to scores on the eating disorder eating disorders. scales. Obesity and maladaptive eating attitudes are common in Cypriot adolescents. Savva et al. Location: Cyprus To evaluate the Measured BMI Questionnaire Prevalence of childhood and Parental obesity (+) Weak (2002) Initial Sample: N/A prevalence of adolescent obesity was No.26 Sample: 2467(N/A) childhood and National Health and Logistic regression calculated and indicate need OR 18.09 (95% CI 2.06 – 73 Design: Cross- adolescent Nutrition Examination analysis for individual and population 158.81) in males 12-17 year sectional obesity in Cyprus Survey I (NHANES intervention to treat olds Population: 6-17 and identify I) Anthropometric paediatric obesity. years old possible risk measurements OR 11.34 (95% CI 1.83- factors. Children‟s (weight and height Most significant factor for 75.50) in females 6-11 year measurements and skinfold obesity was parental obesity olds Overweight thickness) status BMI 85th – 95th percentiles Prevalence of obesity and overweight Obese BMI >95th percentile NHANES I definition Males 16.9% overweight Obesity Task Force 10.3% obese (IOTF) age and sex- specific BMI cut-off Females on age- and sex- 13.1% overweight specific BMI 9.1%% obese Overweight IOTF definition 25kg/m2 < BMI Males <30kg/m2 18.8% overweight 6.9% obese Obese BMI ≥30kg/m2 Females 17.0% overweight Parents 5.7% obese Overweight BMI 25-30kg/m2 Obese BMI ≥30kg/m2 Lazarou et Location: Cyprus To evaluate Measured BMI Questionnaire Socio-demographic Moderate al. (2008) Initial Sample: overweight and predictors of obesity status Mothers No.27 1589 obesity Obesity Task Force Logistic regression are factors of built Age (+) Sample: 1140 prevalence among (IOTF) age and sex- analysis environment in both children 9 working hours per day (+) (72%) Cypriot children specific BMI cut-off and parents. Number of children (+) Design: Cross- and adults and on age- and sex- Anthropometric Living in an apartment (-) sectional identify related specific BMI measurements A higher prevalence of Population: 9-13 socio- (weight and height) OW/OB was found among Fathers years old demographic Overweight adults living in a house. 9 working hours per day (+) 74 factors. (Parents) Older adults, younger 8 working hours per day (-) BMI > 25-29.9kg/m children and men, Living in an apartment (-) irrespective of age in Living in mountainous areas Obese apartment resulted in a lower (-) BMI ≥30 kg/m2 rate of OW/OB prevalence. Girls Severe obesity problem in OB mother (+) Cyprus (one in two adults OR=1.87, 95% CI=1.00-3.52 and 1 in 4 preadolescent children are OW or OB). Boys OW/OB father (+) Prevalence of obesity and OR=3.14 95% CI=1.54-6.40 overweight Children Male] 19.0% overweight 6.0% obese Females 18.3% overweight 2.9% obese Parents Males 47.1% overweight 14.1% obese Females 22.6% overweight 5.8%% obese Decelis et al. Location: Malta To assess the Measured BMI Questionnaire Low percentage of 10-11 Male gender (+) Moderate (2014) Initial Sample: physical activity year olds, girls in particular, Low PA levels (-) No.28 1126 (PA) levels and Obesity Task Force Anthropometric reached the recommended Sedentary behaviour (+) Sample (provided sedentary time (IOTF) age and sex- measurements levels of daily MVPA and consent): 901 and screen time specific BMI cut-off (weight and height) sedentary time. (80%) (ST) patterns of on age- and sex- Sample (met boys and girls in specific BMI Obese children were less inclusion criteria): Malta and to active than non-obese 811 (78% of total compare with children. sample) children in other 75 Design: Cross- countries. Children spend most of their sectional walking time at school, which Population: 10-11 is one third of daily years old requirements. Prevalence of obesity and overweight Overall 20.4% overweight 14.2% obese Boys 24.4% overweight 14.8% obese Girls 16.4% overweight 13.6% obese Savva et al. Location: Cyprus To calculate the Measured BMI Questionnaire Prevalence of undernutrition Age (+) Weak (2005) Initial Sample: prevalence of among preschool children No.29 2000 undernutrition and Obesity Task Force Logistic regression was low. Rural areas (+) Sample (provided obesity in (IOTF) age and sex- analysis consent and preschool children specific BMI cut-off Obesity prevalence was Paternal obesity (+) questionnaire): in Cyprus and on age- and sex- Anthropometric higher. OR 3.24 (95% CI: 1.59, 6.61) 1503 (75%) identify possible specific BMI measurements Sample (present factors. (weight and height) Parental obesity and high Maternal obesity (+) on day of Overweight birth weight were 95%: 1.78, 8.59 measurements): 25kg/m2 significantly correlated with 1412 (calculated obesity while low birth weight Birthweight > 4000g (+) 70.6%) Obese was associated with Design: Cross- 30kg/m2 undernutrition in preschool Birthweight between 2501 – sectional children. 3000g (-) Population: 2-6 OR 7.63 (95% CI: 1.91, years old Prevalence of obesity 30.52) Overall 1.3% in 2 year olds 10.4% in 6 year olds 76 Manios et al. Location: Crete To assess the Measured BMI Structured Prevalence of overweight Male gender (+) Weak (2011) Initial Sample: 522 prevalence of questionnaire and obesity was very high. 95% CI: 0.21 – 0.56 No.30 Sample: 481 (92%) overweight and (IOTF) age and sex- Design: Cross- obesity and specific BMI cut-off Logistic regression Several physiological, High birth weight (+) sectional investigate factors on age- and sex- analysis behavioural and social 95% CI: 1.33-3.46 Population: 10-12 associated with specific BMI factors were shown to affect years old increased Anthropometric children‟s BMI status Parental overweight/obesity adiposity in measurements (+) Cretan (weight and height) Prevalence of obesity and 95%CI: 1.11 – 6.5 (for one schoolchildren. overweight parent) 95% CI: 4.37-30.7 (for both Overall parents) 28% overweight 13% obese Maternal obesity (+) High paternal education level (+) 95%CI: 0.89-3.48 and 95%CI: 1.49-6.13 (low to high educational level) Cardiovascular fitness levels (-) Urban areas (+) Vardavas et Location: Crete To investigate the Measured BMI Questionnaire In comparison to middle Female gender (+) Weak al. (2009) Initial Sample: 622 prevalence of aged male Cretan farmers in No.31 Sample (agreed to obesity and its WHO classification Linear regression the 1960s, the mean weight Physical inactivity (+) participate): 599 indexes among analysis has increased which has led (90.5%) Cretan farmers in Overweight to a 7kg/m2 in mean BMI Low education level (+) Sample 2005. (Parents) Anthropometric (22.9kg/m2 to 29.8kg/m2). (examined): 505 BMI > 25-29.9kg/m measurements (calculated 81%) (weight, height and The prevalence of obesity Design: Cross- Obese waist has risen significantly over sectional BMI > 30 kg/m2 circumference) the years, even among Population: 18-79 farmers from Crete, which years old Waist circumference are known for having a cut-offs healthy lifestyle. >102cm for males >88cm for females Prevalence of obesity and overweight 77 Conicity index to measure abdominal Overall adiposity 86.1% were overweight and/or obese Lange skinfold calliper (body fat 42.9% BMI 25.1-30kg/m2 percentage) (overweight) 43.2% BMI > 30kg/m2 (obese) Males 27.3% body fat Females 39.3% body fat Tur et al. Location: Balearic To determine the Measured BMI Multiple logistic Almost one-half of the Males Weak (2005) Islands prevalence of regression analysis Balearic Islands population is Age (+) No.32 Initial Sample: overweight and Waist circumference above normal weight range AOR 0.78 (95% CI: 0.24- 1500 obesity and the and waist-to-hip ratio Anthropometric (BMI > 25). 2.57) Sample: 1200 environment risk cut-off limits for men measurements Widowed (+) (80%) factors in the and women (weight, height, Predictors of Adjusted OR (AOR) 2.76 Design: Cross- Balearic Islands. triceps skinfold overweight/obesity are to be (95% CI: 0.26-7.73) sectional WHO classification thickness, body fat married, over 40 years old, Married (+) Population: 20-60 percentage, have a low level of education AOR 3.16 (95% CI: 1.93- years old midupper arm and sedentary lifestyle 6.43) circumference, Low educational level (+) midupper arm Large percentage of AOR 4.15 (95% CI: 1.33- muscle area, body overweight/obese individuals 12.94) learn mass, waist underestimate their BMI, not Fat and saturated fat and hip concerned with weight consumption (+) circumferences and status, snack more often and Smoking occasionally (+) waist-to-hip ratio) never dieted. AOR 2.91 (95% CI: 0.53- 15.87 Prevalence of obesity and Underestimation of excess overweight weight (+) Overall 27.8% overweight Females 13.1% obese Alcohol consumption (+) AOR 1.24 (95% CI: 0.46- Overall BMI > 25kg/m2 3.31) Males Low socioeconomic status 78 48.8% (+) AOR 1.39 (95% CI: 0.89- Females 2.17) 38.8% Leisure-time physical activity (-) AOR 1.00 Fat and saturated fat consumption (-) Underestimation of excess weight (+) Savva et al. Location: Cyprus To identify short- Measured BMI Logistic regression Triglycerides and HDL- Fathers high BMI (+) Weak (2004) Initial Sample: term predictors of analysis cholesterol levels have been OR: 7.1 (95% CI:1.3, 38.0) No.33 9000 risk for overweight (IOTF) age and sex- associated as risk factors for Sample: 357 (N/A) in early specific BMI cut-off Anthropometric overweight in early Systolic blood pressure (+) Design: Cohort adolescence in a on age- and sex- measurements adolescence. OR: 8.9 (95% CI: 1.9, 41.7) Population: 11.5( Cypriot sample. specific BMI (weight and height) +-) 0.4 years at Different gender pattern in HDL-Cholesterol & high baseline. BMI and environmental triglyceride levels (+) 1.6+- 0.5 years after influences were recorded OR: 24.6 (95% CI: 4.0, re-evaluation 149.8) More males overweight or obese than females. High triglyceride levels & high systolic blood pressure (+) Prevalence of obesity and OR:18.3 (95% CI: 2.4, 140.5) overweight Overall obesity (baseline vs follow-up) 3.7% baseline vs 4.2% follow-up Males (baseline vs follow- up) 4.0% baseline vs 6.7% follow-up Females (baseline vs follow-up) 3.4% baseline vs 1.7% follow-up Overall overweight (baseline vs follow-up) 79 20.2% baseline vs 19.0% follow-up Sant‟ Angelo Location: Malta To compare body Measured BMI Anthropometric The same cohort rose to Age (+) Weak & Grech Initial Sample mass index at 7 measurements over 40% when it was (2011) (2008): 3734 years and 9 years WHO Child Growth (weight and height) measured again in 2010. Independent schools (-) No.34 Sample (*) (2008): of age in a Reference BMI-for- 3435 (calculated Maltese children age 5-19 charts Significant prevalence of Living in Gozo (+) 92%) cohort who were overweight and obese boys Initial Sample born in 2001. found in Gozo for both (2010): 3723 studies. Sample: 3090 To compare the Children attending (calculated 83%) results carried out independent schools were Design: Cohort in 2007 in the the least overweight and Population (2008): same cohort. obese. 7 years old Population (2010) : 9 years old Decelis et al. Location: Malta To identify levels Measured BMI Self-reported Physical activity levels were Physical inactivity (+) Moderate (2012) Initial Sample: 234 of physical activity questionnaire very low when compared to Sedentary behaviour (+) No.35 Sample (**): 187 and sedentary (IOTF) age and sex- recommended levels. (80%) time and assess specific BMI cut-off Accelerometer Design: Cross- how they differ by on age- and sex- (measuring Prevalence of sedentary time sectional weight status in specific BMI physical activity) and overweight and obesity Population: 11-12 Maltese boys and were high. year olds girls. Anthropometric Girls are significantly less measurements active than boys throughout (weight and height the week. Activity levels patterns and differences across the day and week were related to weight status with obese children showing less activity. Prevalence of overweight and obesity Overall overweight and obesity 27% overweight and 18.6% 80 obese Boys 25.7% overweight 20.0% obese Girls 27% overweight 17.2% obese Sanna et al. Location: Sardinia To evaluate Measured BMI Questionnaire Percentages of overweight Age (+) Weak (2006) Initial Sample: N/A overweight and and obese children increase 11.5% and 14.0% at 6 years No.36 Sample: 1000 (N/A) obesity Overweight & obese Multiple regression with age. to 15.4% and 22.7% at 10 Design: Cross- prevalence among 10-20% and >20$ analysis years sectional children in higher than weight Higher number of overweight Population: 6-10 Cagliari, Sardinia value of the 50th Anthropometric and obese boys than girls in Male gender (+) year olds with different centile for age, sex, measurements all age groups. socio-economic and height (Tanner et (weight and height) Low socioeconomic status (+) status al‟s charts) As socioeconomic level 18.68% and 26.46% in Sum of two trunk decreases, increasing rates males skinfolds of OW and OB increase. 13.60% and 23.62% in (subscapular, females suprailac) , sum of Males indicate a higher OW three limb skinfolds and OB prevalence than Low level of maternal (biceps, triceps, females in the same social schooling medial calf) level. (+) Overall prevalence of overweight & obesity 22.70% Decelis et al. Location: Malta To identify the Measured BMI Anthropometric Prevalence of overweight Living in urban south east Weak (2013) Initial Sample: prevalence of measurements and obesity among Maltese regions (+) No.37 1126 overweight and (IOTF) age and sex- (weight and height) 10-11 year-old children are Sample (*): 901 obesity in a specific BMI cut-off higher than previously Male gender (+) (80%) sample of Maltese on age- and sex- calculated. Sample (*): 874 children using specific BMI (calculated 97%) different It is higher than all other Design: Cross- international WHO classification countries in the world except sectional standards for the CDC standards Greece. Population: 10-11 measurement of years old obesity. UK Department of Prevalence of overweight 81 Health standards and obesity IOTF standards (similar to CDC) 20.4% overweight 14.2% obese WHO standards 23.1% overweight 20.9% obese UK standards (overall prevalence) 12.3% overweight 28.4% obese Savva et al. Location: Cyprus To assess 5-year Measured BMI Logistic regression Overall prevalence of Males in rural areas Weak (2008) Initial Sample: changes in the models overweight children was (overweight increase) (+) No.38 18,792 prevalence of (IOTF) age and sex- slightly higher in the second OR 1.22 (95% CI: 1.07, 1.38) Sample: 14,090 overweight and specific BMI cut-off Anthropometric period. (calculated 75%) obesity amongst on age- and sex- measurements 5-year change (statistically Design: Cohort 11-year old specific BMI (weight and height) Significant increase in significant in Males in rural Population: 11- children in overweight boys in rural areas (+) year old Cyprus. areas. OR 1.51 (1.14, 2.00) Prevalence of obesity Females in urban areas (+) OR 2.19 (1.01, 1.64) Increase in obesity (from 1997-8 to 2002-3) Rural areas (+) Overall increase of 17.9% OR 1.46 (95% CI: 1.17, 1.81) Overall obesity increase in rural areas vs urban areas 35.9% vs 8.7% Increase in rural areas for both genders Males (37.3%) Females (30.8%) Barbagallo Location: Sicily The evaluate the Measured BMI Medical High overall prevalence of Age (until 50-59 years) (+) Moderate et al. (2001) Initial Sample: prevalence of questionnaire overweight and obesity, with No.39 1351 (75%) overweight and Overweight differences evident in age. Female postmenopausal age Sample: 835 (72%) obesity and their BMI 25.0-2.99 kg/m2 Blood pressure, (+) 82 Design: Cross- relationships with biochemical Body weight and fat sectional cardiovascular Obese measurements and distribution were associated Population: 20-69 risk factors in a BMI equal or higher electrocardiogram with hypertension, diabetes, years olds rural village in than 30 kg/m2 recording. dyslipidemia and high lipid Sicily. profiles. Anthropometric measurements Cardiovascular mortality (weight and height) risks were associated with high BMI. Overall prevalence of overweight or obesity 45.0% overweight 27.7% obese Velluzzi et Location: Sardinia To calculate the Measured BMI Poisson regression Female gender resulted into Age (12-14 years) (-) (for al. (2007) Initial Sample: N/A prevalence of analysis a significant protection overweight & obesity) No.40 Sample: 3946 overweight and (IOTF) age and sex- against. (82.8%) obesity among specific BMI cut-off Anthropometric Female gender (-) (for Design: Cross- Sardinian on age- and sex- measurements obesity only) sectional adolescents and specific BMI (weight and height) Population: 11-15 to identify the Overall prevalence of Boys living in urban areas (+) year olds association of overweight or obesity (only overweight) several biological and geographic 14.9% overweight Living in mountainous area (-) factors. 3.7% obese (for overweight & obesity) 83 12 Appendix 5 – Inductive Content Analysis Results Abbreviations OW – Overweight ; OB – Obesity. (+): Refers to a positive association (in this case, risk factor with outcome) (-): Refers to a negative association (in this case, risk factor with outcome) The ‘No.’ specified in the cells are coming from the data extraction table, and were used to keep track when aggregating the results into the respective categories. Appendix 5a: Social Educational level Low parental Low maternal High education Low level of High parental educational educational level education educational level level level Ferra et al. (2012) No.3 (+) ≥65 yrs OW & OB Loviselli et al. Baroudi et al. (2010) No.13 (-) (2010) No.4 (+) Ramón et al. (2012) (1969) 18 yrs (32-64 yrs) OW & No.1 (-) 10-16 yrs OW & OB OB OW & OB Sanna et al. Bibiloni et al.(2012) Bibiloni et al. (2006) No.36 (+) No.2 (-) 12-12-17 (2010) No.12 (-) 6-10 yrs OW & Loviselli et al. Vardavas et al. yrs OW & OB 12-17 yrs OB OB (2010) No.13 (+) (2009) No.31 (+) (1998) 18 yrs (18-79 yrs) OW & Manios et al. (2011) OW & OB OB No.30 (+) 10-12 yrs OW & OB Tur et al. (2005) No.32 (+) (20-60 yrs) OW & OB 84 Low level of Maternal awareness of Maternal awareness of education in mother health Maternal education child health promotion women promotion Coll et al. (2015) No Buttigieg et al. (2012) Savva et al. (2014) 11. (+) (18-35 yrs; Buttigieg et al. (2012) No. No. 6 (-) 3 yrs OW & No10. (-) 6-17.9 yrs 36-55 years) OW & 6 (-) 3 yrs OW & OB OB OW & OB OB Socio-economic status High Low High parental Low parental Low socio- maternal maternal Unemployment socio- socio- economic socio- socio- economic economic status (adults) economic economic status status (adults) status status Bibiloni et al. Ramón et al. (2010) No.12 (2012) No.1 (-) (-) 10-16 yrs 10-16 yrs OW OW & OB & OB Ferra et al. (2012) No.3 Athanasopoul Athanasopoul (+ ≥65 yrs Sidoti et al. os et al. os et al. Bibiloni et al. OW & OB (2009) No.19 (2012) No. 2 (- (2011) (2011) Coll et al. (2015) (+) 8-11 yrs ) 12-17 yrs No 11. (+) 18-55 OW & OB No.20 (+) 8- No.20 (+) 8- OW & OB yrs OW & OB 16 yrs OW & 16 yrs OW Smoljanović OB & OB et al. (2007) No.24 (+) Sanna et al. Sidoti et al. (Adults) (2006) No.36 (2009) No.19 (+) 6-10 (-) 8-11 yrs years OW & OW & OB OB Parental Obesity Obesity in both parents Maternal obesity Buttigieg et al. (2012) No. 6 (+) 3 yrs OW & OB Athanasopolous et al. (2011) No.20 (+) 8-16 yrs OW & OB Sidoti et al. (2009) (+) 8-11 yrs OW & OB 3 Lazarou et al. (2008) No.27 (+) (Girls 85 specifically) 9-13 OW & OB Savva et al. (2002) No.26 (+) 6-17 yrs OW & OB Savva et al. (2005) No.29 (+) 2-6 yrs OB Manios et al. (2011) No. 30 (+) 10-12 yrs OW & OB Manios et al. (2011) No. 30 (+) 10-12 yrs OW & OB Marital status Married Free union Widowed Tur et al. (2005) Coll et al. (2015) No.11 Married men Calamusa et al. (2012) No.17 (+) 18 (+) 18-55 yrs OW & OB No.32 (+) 20-60 yrs yrs + OW & OB OW & OB 86 Appendix 5b: Behavioural Physical Activity Leisure-time Physical Physical Inactivity Low Physical Activity Activity 1. Bibiloni et al. (2012) No.2 (-) (12-17 yrs) OW & OB 1. Coll et al. (2015) 1. Vardavas et al. 2. Andreou et al. No.11 (-) 18-55 yrs OW (2009) No.31 (+) 18- (2012) No.14 (-) (18- & OB 79 yrs OW & OB 80 yrs) OW & OB Ferra et al. (2012) No.3 (-): ≥65 yrs OW & 2. Ramón et al. (2012) 2. Decelis et al. OB 3. Calamusa et al. No.1 (-) OW & OB (2012) (2012) No.17 (-) (18 (participation in yrs +) OW & OB competitive sport) 10- No.35 (+) 11-12 yrs 16 yrs OW & OB OW & OB 4. Sidoti et al. (2009) No.19 (-) (8-11 yrs) OW & OB Alcohol drinking Smoking Former smokers Alcohol 14 units per week 1. Bibiloni et al. (2010) No.12 (+) 10-16 yrs OW & OB Andreaou et al. Calamusa et al. Calamusa et al. (2012) (2012) No.14 (+) 18- (2012) No.17 (+) No.17 (+) 18 + yrs OW 80 yrs OW & OB 18 + yrs OW & & OB 2. Tur et al. (2005) OB No.32 (+) 20-60 yrs OW & OB) One child Coll et al. (2015) No.11 (+) 18-55 yrs OW & OB 87 Short sleep Bibiloni et al. (2010) No.12 (+) 10-16 yrs OW & OB Underestimation of excess weight Tur et al. (2005) No.32 (+) 20-60 yrs OW & OB 88 Appendix 5c: Genetic Male gender Female gender 1. Bibiloni et al. (2012) No.2 (+) 12-17 yrs OW & OB 2. Baratta et al. (2006) No.5 (+) 11-15 yrs OW & OB 3. Gelpi-Méndez et al. (2010) No.8 (+) 16-25 yrs 4. Savva et al. OW & OB 5. Coll et al. (2015) No.11 (+) 18-55 yrs OW & 1. Baroudi et al. (2010) No.4 (+) 32-64 yrs OW OB & OB 6. Parrino et al. (2012) No.15 (+) 11-13 yrs OW 2. Koh (2005) No.9 (+) >65 yrs OW & OB & OB 3. Vardavas et al. (2009) No.31 (+) 18-79 yrs 7. Calamusa et al. (2012) No.17 (+) 18 yrs + OW & OB OW & OB 4. Velluzzi et al. (2007) No.40 (-) (for 8. Decelis et al. (2014) No.28 (+) 10-11 yrs OW overweight only) 11-15 yrs OW & OB & OB 9. Manios et al. (2011) No.30 (+) 10-12 yrs 10- 12 yrs OW & OB 10. Sanna et al. (2006) No.36 (+) 6-10 yrs OW & OB 11. Decelis et al. (2013) No.37 (+) 10-11 yrs OW & OB Fathers Mothers Lazarou et al. (2008) No.27 9-13 yrs OW & OB Lazarou et al. (2008) No.27 9-13 yrs OW & OB 9 working hours per day (+) 9 working hours per day (+) 8 working hours per day (-) Number of children (+) Specifically for Male Obesity Specifically for Female Obesity Gelpi-Méndez et al. (2010) No.8 (Maternal Gelpi-Méndez et al. (2010) No.8 (Child and education) (-) 16-25 yrs OW & OB adolescent obesity) (+) 16-25 yrs OW& OB 89 Age 1. Ferra et al. (2012) No.3 (+) ≥65 yrs OW & OB 2. Baratta et al. (2006) No.5 (-) 11-15 yrs OW & OB (specific age) 3. Coll et al. (2015) No. 11 (+) 18-55 yrs OW & OB & CO 4. Bibiloni et al. (2010) No. 12 (+) 12-17 OB (specific age) 5. Smoljanović et al. (2007) No.24 (+) Adults OB 6. Lazarou et al. (2008) No.27 (+) (Mothers specifically) 9-13 yrs OW & OB 7. Savva et al. (2005) No.29 (+) 2-6 yrs OB (increases with age) 8. Sant‟ Angelo & Grech (2011) No.34 (+) 7 & 9 yrs OW & OB (rose on follow-up) 9. Sanna et al. (2006) No.36 (+) 6-10 yrs OW & OB (increases from 6 to 10) Age under 25 Age 40-59; 60-79 1.Gelpi-Méndez et al. No.8 (2010) (+) 16-17, 1. Calamusa et al. (2012) No.17 (+) 18 yrs + OW 18-25 yrs OW & OB (greater in young & OB pop’n) 2. Barbagallo et al. (2001) No.39 (+) 20-69 2. Velluzzi et al. (2007) No.40 (-) (Female postmenopausal age & until 50-59 (Age (12-14 years) (OW & OB) (specifically years) OW & OB for this age 3. Tur et al. (2005) No.32 (+) (Males specifically) 20-60 yrs OW & OB) 40+ yrs (+) Prevalence of Metabolic Syndrome Bibiloni et al. (2009) No.7 (+) 12-17 yrs OW & OB High triglyceride levels & Level of cholesterol, LDL, HDL-Cholesterol & high high systolic blood Triglycerides, glucose triglyceride levels pressure Savva et al. (2004) No.33 Savva et al. (2004) No.33 (+) Pucarin-Cvetković (2006) No.18 (+)11.5( +-) 0.4 years at 11.5( +-) 0.4 years at baseline. (+) 18-88 yrs OW & OB baseline. 1.6+- 0.5 years after re- 1.6+- 0.5 years after re- evaluation OW & OB evaluation OW & OB 90 Birthweight between 2501 – High birth weight Birthweight ≥ 4000g 3000g Savva et al. (2005) No.29 (+) Savva et al. (2005) No.29 (- ) 2- Manios et al. (2011) No.30 2-6 yrs OB 6 yrs OB (+) 10-12 yrs OW & OB 91 Appendix 5d: Environmental Living in urban areas Living in rural areas Hilly zones Mountainous zones 1. Baratta et al. (2006) No.5 (+) 11-15 OW & OB (specific age) 2. Loviselli et al. (2010) No.13 (+) (1998) 18 yrs OW & OB 1. Savva et al. (2014) 3. Parrino et al. (2012) No.10 (+) 6-17.9 yrs No.15 (+) 11-13 yrs OW OW & OB & OB 1. Loviselli et al. 2010) 4. Manios et al. (2011) No.13 (+) (1969) 18 2. Loviselli et al. (2010) No.30 (+) 10-12 yrs OW yrs OW & OB No.13 (+) (1969) 18 yrs & OB OW & OB 5. Decelis et al. (2013) 2. Lazarou et al. (2008) Loviselli et al. 2010) No.37 (+) 10-11 yrs No.27 (-) 9-13 yrs OW 3. Parrino et al. (2012) No.13 (+) (1969 & OW & OB & OB (Fathers No.15 (-) 11-13 yrs OW 1998) 18 yrs OW & 6. Velluzzi et al. (2007) specifically) & OB OB No.40 (+) 11-15 yrs (Boys specifically) (only 3. Velluzzi et al. (2007) 4. Savva et al. (2005) overweight) No.40 (-) 11-15 yrs No.29 (+) 2-6 yrs OB (for overweight & obesity) Living in an apartment 5. Savva et al. (2008) No.38 (+) 11-year old OW & OB Lazarou et al. (2008) No.27 (-) (Specifically fathers and mothers) 9- 13 yrs OW & OB 92 13 Appendix 6: CARE form 93 94 95 96 97 98 99 100 101 102