Langford, R; Bonell, CP; Jones, HE; Pouliou, T; Murphy, SM (PDF)

L a n gf or d , R ; B on e l l , C P ; J on e s , HE ; P ou l i ou , T; M u r p h y , S M ; W a - t e r s , E ; K om r o, K A ; G i b b s , L F ; M a gn u s , D ; C a m p b e l l , R ( 2014) Th e W HO He a l t h P r om ot i n g S c h ool f r a m e w or k f or i m p r ov i n g t h e h e a l t h a n d w e l l - b e i n g of s t u d e n t s a n d t h e i r a c a d e m i c a c h i e v e m e n t . C oc h r a n e D a t a b a s e S y s t R e v , 4 ( 4) . C D 008958. I S S N 1469- 493X D O I : h t t p s : //d oi . or g/10. 1002/14651858. C D 008958. p u b 2 D ow n l oad e d f r om : h t t p : //r e s e ar c h on l i n e . l s h t m . ac . u k /2548662/ D O I : 10. 1002/14651858. C D 008958. p u b 2 U s a g e G u i d e l i n e s P l e a s e re f e r t o u s a g e g u i d e l i n e s a t h t t p : / / re s e a rc h o n l i n e . l s h t m . a c . u k / p o l i c i e s . h t m l o r a l t e rn a - t i v e l y c o n t a c t re s e a rc h o n l i n e @ l s h t m . a c . u k . A v a i l a b l e u n d e r l i c e n s e : h t t p : / / c re a t i v e c o m m o n s . o rg / l i c e n s e s / b y - n c - n d / 2 . 5 /  Cochrane Database of Systematic Reviews The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Langford R, Bonell CP, Jones HE, Pouliou T, Murphy SM, Waters E, Komro KA, Gibbs LF, Magnus D, Campbell R Langford R, Bonell CP, Jones HE, Pouliou T, Murphy SM, Waters E, Komro KA, Gibbs LF, Magnus D, Campbell R. The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement. Cochrane Database of Systematic Reviews 2014, Issue 4. Art. No.: CD008958. DOI: 10.1002/14651858.CD008958.pub2. www.cochranelibrary.com The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  T A B L E O F C O N T E N T S 1 HEADER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 PLAIN LANGUAGE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Figure 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 6 OBJECTIVES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Figure 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Figure 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Figure 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Figure 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Figure 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 29 DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 AUTHORS’ CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 CHARACTERISTICS OF STUDIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 DATA AND ANALYSES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis 1.1. Comparison 1 Overweight or obesity, Outcome 1 BMI. . . . . . . . . . . . . . . . . . 189 Analysis 1.2. Comparison 1 Overweight or obesity, Outcome 2 zBMI. . . . . . . . . . . . . . . . . 190 Analysis 2.1. Comparison 2 Physical activity, Outcome 1 Physical activity. . . . . . . . . . . . . . . . 191 Analysis 2.2. Comparison 2 Physical activity, Outcome 2 Physical fitness. . . . . . . . . . . . . . . . 192 Analysis 3.1. Comparison 3 Nutrition, Outcome 1 Fat intake. . . . . . . . . . . . . . . . . . . . 193 Analysis 3.2. Comparison 3 Nutrition, Outcome 2 Fruit and vegetable intake. . . . . . . . . . . . . . . 194 Analysis 4.1. Comparison 4 Tobacco use, Outcome 1 Tobacco use. . . . . . . . . . . . . . . . . . . 195 Analysis 5.1. Comparison 5 Alcohol use, Outcome 1 Alcohol use. . . . . . . . . . . . . . . . . . . 196 Analysis 6.1. Comparison 6 Substance use, Outcome 1 Substance use. . . . . . . . . . . . . . . . . 197 Analysis 7.1. Comparison 7 Mental health, Outcome 1 Depression. . . . . . . . . . . . . . . . . . 198 Analysis 8.1. Comparison 8 Violence, Outcome 1 Violence. . . . . . . . . . . . . . . . . . . . . 199 Analysis 9.1. Comparison 9 Bullying, Outcome 1 Being bullied. . . . . . . . . . . . . . . . . . . 200 Analysis 9.2. Comparison 9 Bullying, Outcome 2 Bullying others. . . . . . . . . . . . . . . . . . . 201 201 ADDITIONAL TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 HISTORY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 CONTRIBUTIONS OF AUTHORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 DECLARATIONS OF INTEREST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 SOURCES OF SUPPORT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 DIFFERENCES BETWEEN PROTOCOL AND REVIEW . . . . . . . . . . . . . . . . . . . . . 268 INDEX TERMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  [Intervention Review] The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement Rebecca Langford1, Christopher P Bonell2, Hayley E Jones1, Theodora Pouliou1, Simon M Murphy3, Elizabeth Waters4, Kelli A Komro5, Lisa F Gibbs4, Daniel Magnus1, Rona Campbell1 1School of Social and Community Medicine, University of Bristol, Bristol, UK. 2Social Science Research Unit, Institute of Education, University of London, London, UK. 3Cardiff School of Social Sciences, Cardiff University, Cardiff, UK. 4Jack Brockhoff Child Health and Wellbeing Program, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia. 5Health Outcomes and Policy and Institute for Child Health Policy, University of Florida, Gainesville, Florida, USA Contact address: Rebecca Langford, School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK. [email protected] Editorial group: Cochrane Developmental, Psychosocial and Learning Problems Group. Publication status and date: New, published in Issue 4, 2014. Review content assessed as up-to-date: 15 September 2013. Citation: Langford R, Bonell CP, Jones HE, Pouliou T, Murphy SM, Waters E, Komro KA, Gibbs LF, Magnus D, Campbell R. The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement. Cochrane Database of Systematic Reviews 2014, Issue 4. Art. No.: CD008958. DOI: 10.1002/14651858.CD008958.pub2. Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd. A B S T R A C T Background The World Health Organization’s (WHO’s) Health Promoting Schools (HPS) framework is an holistic, settings-based approach to promoting health and educational attainment in school. The effectiveness of this approach has not been previously rigorously reviewed. Objectives To assess the effectiveness of the Health Promoting Schools (HPS) framework in improving the health and well-being of students and their academic achievement. Search methods We searched the following electronic databases in January 2011 and again in March and April 2013: Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO, CINAHL, Campbell Library, ASSIA, BiblioMap, CAB Abstracts, IBSS, Social Science Citation Index, Sociological Abstracts, TRoPHI, Global Health Database, SIGLE, Australian Education Index, British Education Index, Education Resources Information Centre, Database of Education Research, Dissertation Express, Index to Theses in Great Britain and Ireland, ClinicalTrials.gov, Current controlled trials, and WHO International Clinical Trials Registry Platform. We also searched relevant websites, handsearched reference lists, and used citation tracking to identify other relevant articles. Selection criteria We included cluster-randomised controlled trials where randomisation took place at the level of school, district or other geographical area. Participants were children and young people aged four to 18 years, attending schools or colleges. In this review, we define HPS interventions as comprising the following three elements: input to the curriculum; changes to the school’s ethos or environment or both; and engagement with families or communities, or both. We compared this intervention against schools that implemented either no intervention or continued with their usual practice, or any programme that included just one or two of the above mentioned HPS elements. 1 The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  Data collection and analysis At least two review authors identified relevant trials, extracted data, and assessed risk of bias in the trials. We grouped different types of interventions according to the health topic targeted or the approach used, or both. Where data permitted, we performed random- effects meta-analyses to provide a summary of results across studies. Main results We included 67 eligible cluster trials, randomising 1443 schools or districts. This is made up of 1345 schools and 98 districts. The studies tackled a range of health issues: physical activity (4), nutrition (12), physical activity and nutrition combined (18), bullying (7), tobacco (5), alcohol (2), sexual health (2), violence (2), mental health (2), hand-washing (2), multiple risk behaviours (7), cycle-helmet use (1), eating disorders (1), sun protection (1), and oral health (1). The quality of evidence overall was low to moderate as determined by the GRADE approach. ’Risk of bias’ assessments identified methodological limitations, including heavy reliance on self-reported data and high attrition rates for some studies. In addition, there was a lack of long-term follow-up data for most studies. We found positive effects for some interventions for: body mass index (BMI), physical activity, physical fitness, fruit and vegetable intake, tobacco use, and being bullied. Intervention effects were generally small but have the potential to produce public health benefits at the population level. We found little evidence of effectiveness for standardised body mass index (zBMI) and no evidence of effectiveness for fat intake, alcohol use, drug use, mental health, violence and bullying others; however, only a small number of studies focused on these latter outcomes. It was not possible to meta-analyse data on other health outcomes due to lack of data. Few studies provided details on adverse events or outcomes related to the interventions. In addition, few studies included any academic, attendance or school-related outcomes. We therefore cannot draw any clear conclusions as to the effectiveness of this approach for improving academic achievement. Authors’ conclusions The results of this review provide evidence for the effectiveness of some interventions based on the HPS framework for improving certain health outcomes but not others. More well-designed research is required to establish the effectiveness of this approach for other health topics and academic achievement. P L A I N L A N G U A G E S U M M A R Y The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement Background Health and education are strongly connected: healthy children achieve better results at school, which in turn are associated with improved health later in life. This relationship between health and education forms the basis of the World Health Organization’s (WHO’s) Health Promoting Schools (HPS) framework, an approach to promoting health in schools that addresses the whole school environment. Although the HPS framework is used in many schools, we currently do not know if it is effective. This review aimed to assess whether the HPS framework can improve students’ health and well-being and their performance at school. Study characteristics We searched 20 health, education, and social science databases, as well as trials registries and relevant websites, for cluster-randomised controlled trials of school-based interventions aiming to improve the health of young people aged four to 18 years. We only included trials of programmes that addressed all three points in the HPS framework: including health education in the curriculum; changing the school’s social or physical environment, or both; and involving students’ families or the local community, or both. Key results We found 67 trials, comprising 1345 schools and 98 districts, that fulfilled our criteria. These focused on a wide range of health topics, including physical activity, nutrition, substance use (tobacco, alcohol, and drugs), bullying, violence, mental health, sexual health, hand-washing, cycle-helmet use, sun protection, eating disorders, and oral health. For each study, two review authors independently extracted relevant data and assessed the risk of the study being biased. We grouped together studies according to the health topic(s) they focused on. We found that interventions using the HPS approach were able to reduce students’ body mass index (BMI), increase physical activity and fitness levels, improve fruit and vegetable consumption, decrease cigarette use, and reduce reports of being bullied. However, we 2 The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  found little evidence of an effect on BMI when age and gender were taken into account (zBMI), and no evidence of effectiveness on fat intake, alcohol and drug use, mental health, violence, and bullying others. We did not have enough data to draw conclusions about the effectiveness of the HPS approach for sexual health, hand-washing, cycle-helmet use, eating disorders, sun protection, oral health or academic outcomes. Few studies discussed whether the health promotion activities, or the collection of data relating to these, could have caused any harm to the students involved. Quality of the evidence Overall, the quality of evidence was low to moderate. We identified some problems with the way studies were conducted, which may have introduced bias, including many studies relying on students’ accounts of their own behaviours (rather than these being measured objectively) and high numbers of students dropping out of studies. These problems, and the small number of studies included in our analysis, limit our ability to draw clear conclusions about the effectiveness of the HPS framework in general. Conclusions Overall, we found some evidence to suggest the HPS approach can produce improvements in certain areas of health, but there are not enough data to draw conclusions about its effectiveness for others. We need more studies to find out if this approach can improve other aspects of health and how students perform at school. B A C K G R O U N D Promoting health in schools The influence of childhood experiences on health status later in life is well documented (Felitti 1998; Galobardes 2006; Kessler 2010; Poulton 2002; Wadsworth 1997; Wright 2001). There is evidence to suggest that attitudes, beliefs, and behaviours learned during these early years - for example, those relating to smoking, physical activity, and food choices - show strong ‘tracking’ into adulthood (Kelder1994; Singh 2008; Whitaker1997). Promoting healthy habits during these early formative years is therefore of key importance. Recognitionof thishasledtoaninterestin using schoolsas ameans of promoting healthy behaviours in children and young people. Children spend a large proportion of their time at school and thus schools have the potential to be a powerful domain of influence on children’s health. Additionally, there is a strong link between children’s health status and their capacity to learn (Powney 2000; Singh 2008). Creating positive and healthy school environments, therefore, can have numerous benefits in improving health, well- being, and academic achievement, and reducing inequities. Promoting health has long been an important role of schools, but traditionally activities have focused on health education, whereby information about health topics is imparted to students via the formal school curriculum, or on the development of specific skills such as communication skills or refusal techniques (Lynagh 1997). While a few programmes appear to have had some short-term impact, there is little evidence to demonstrate that such approaches can effect sustainable behavioural change in the long term (Brown 2009; Faggiano 2005; Foxcroft 2011; Waters 2011). The WHO Health Promoting Schools Framework In recognition of the limited success of these interventions, a new holistic approach to school health promotion was developed in the late 1980s, influenced and underpinned by the values set out in the World Health Organization’s Ottawa Charter (WHO 1986). This charter marked a significant shift in WHO public health policy, from a focus on individual behaviour to recognition of the wider social, political, and environmental influences on health. The application of these principles to the educational setting led to the idea of the ‘Health Promoting School’ (HPS) whereby health is promoted through the whole school environment and not just through ‘health education’ in the curriculum. Thus, a Health Pro- moting School aims to: • Promote the adoption of lifestyles conducive to good health • Provide an environment that supports and encourages healthy lifestyles • Enable students and staff to take action for a healthier community and healthier living conditions (Health Education Boards 1996). No strict definition of a Health Promoting School exists and it has been described in various ways in different documents (Denman 1999; IUHPE 2008; Lister-Sharp 1999; Lynagh 1997; Nutbeam 1992; Parsons 1996; St Leger 1998; WHO 1997; Young 1989). 3 The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  The International Union for Health Promotion and Education, for example, provide a six-point definition of Health Promoting Schools (school health policies; physical environment; social envi- ronment; individual health skills and action competencies; com- munity links; and health services) (IUHPE 2008). Elsewhere in the literature a simpler, three-point definition is employed, which subsumes the six points above (Denman 1999; Deschesnes 2003; Lister-Sharp 1999; Marshall 2000; M koma 2004; Nutbeam 1992; Parsons 1996; Rogers 1998; Young 1989). Additionally, whilst some interventions are explicitly labelled as adopting a HPS approach, others do not use this name but nonetheless are implic- itly based upon HPS principles. In the United States, for exam- ple, this type of approach is commonly known as ’Comprehensive School Health Education’. For the purposes of this review, we use the broad, three-point definition of the HPS model in our selection criteria to ensure the review is inclusive of the somewhat varied and earlier approaches to HPS. According to this model, Health Promoting Schools require change in three areas of school life: 1. Formal health curriculum Health education topics are given specific time allocation within the formal school curriculum in order to help students develop the knowledge, attitudes, and skills needed for healthy choices; 2. Ethos and environment of the school Health and well-being of students and staff are promoted through the ‘hidden’ or ‘informal’ curriculum, which encompasses the val- ues and attitudes promoted within the school, and the physical environment and setting of the school; and 3. Engagement with families or communities or both Schools seek to engage with families, outside agencies, and the wider community in recognition of the importance of these other spheres of influence on children’s attitudes and behaviours. How Health Promoting Schools might influence health We developed a logic model to capture the ways in which the Health Promoting Schools framework might influence health and educational outcomes (Figure 1). We identified important pol- icy documents relevant to the intervention (HPS framework, Ot- tawa Charter) to inform the logic model, outlining key inputs and mechanisms of action, and providing examples of hypothesised changes in health behaviours or outcomes or both. The review authors refined and agreed the logic model. 4 The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  Figure 1. Logic model The Health Promoting Schools framework is based on an eco- holistic model, recognising the physical, social, mental, emotional, and environmental dimensions of health and well-being (Parsons 1996). The three domains described above recognise different lev- els of influence upon health - moving from the individual, to the school environment, to the wider community context - and em- phasise the need to act upon all three levels in order to successfully influence health. At the individual level, health education, through the formal cur- riculum, remains an important part of the HPS approach. Recog- nising that “to lead a healthy life is, to some degree, a matter of making the right choices” (Young 1989), students need accurate informationabouthealth issuesinordertomake informedchoices. Thus, health education can increase knowledge and help establish positive attitudes and health behaviours. Developing the neces- sary skills in order to be able to act upon such information is also key; programmes may therefore emphasise communication skills, refusal techniques, and ways to promote self confidence and self efficacy. Ultimately improvements in knowledge, attitudes, and skills can enhance psychosocial health and help establish new pos- itive social norms within the student population regarding health behaviours. What children learn about health within the formal curriculum must be endorsed and promoted within the wider school environ- ment to have credibility. The ‘hidden’ or ‘informal’ curriculum promoted within the school can help create a safe and support- ive atmosphere that is conducive to healthy behaviours. Schools might, for example, provide secure cycle racks to promote active transport to school; implement a ‘no smoking’ policy; increase pro- vision of healthy foods through the school catering service; develop peer mentoring approaches to tackle bullying; or increase stu- dent participation and engagement within schools through school councils. Finally, it is important to recognise that the school environment is only one of the many domains of influence on children’s health. Families and the wider community in which children live also have an enormous impact on children’s health. It is necessary, therefore, to engage with the community beyond the school. To achieve this, schools should take into account the views and opinions of the families and communities they serve, and encourage their support and participation in health-promoting activities. Health messages 5 The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  promoted at school need to be reinforced within the family and wider community settings if they are to have a significant impact on physical and social exposures and children’s behaviours. Why it is important to do this review A systematic review conducted in 1999 examined the impact of the HPS approach on a variety of student health outcomes (Lister- Sharp 1999). However, the conclusions of this review were limited by the small number of studies available and weaknesses in their study designs. Results from these studies varied, but improvements in dietary intake, measures of physical fitness, self esteem, and rates of bullying were observed, and the authors concluded that there was “limited but promising” data to suggest that the HPS approach could have a positive impact on health (Lister-Sharp 1999). In the years since the Lister-Sharp 1999 review was completed, interest in the HPS framework has continued to grow, with this approach being used in many countries in the absence of clear ev- idence of its effectiveness or potential harm. Focusing on studies with rigorous evaluation designs, we sought to re-assess the cur- rent evidence of effectiveness of the Health Promoting Schools framework in order to inform future policy and research recom- mendations. O B J E C T I V E S To assess the effectiveness of the Health Promoting Schools (HPS) framework in improving the health and well-being of students and their academic achievement. M E T H O D S Criteria for considering studies for this review Types of studies Cluster-randomised controlled trials (RCTs), where clusters were atthe level of school, districtorothergeographical area. Asthe HPS framework is an holistic, whole-school approach, we excluded any studies where clusters were at the classroom level.We also excluded feasibility and pilot RCTs and any trials where only one school was allocated to intervention and control groups. Public health interventions are often highly complex and con- text-dependent (Rychetnik 2002), and as such may require differ- ent types of evaluative approaches. Many evaluations of the HPS framework have not been conducted using RCT methodology and offer important insights into both process and implementa- tion. While we acknowledge the value of this body of evidence, we focus this review on cluster-randomised trials as the most re- liable form of evidence for evaluating the relative effects of inter- ventions (Green 2011). For an overview of other evidence on the HPS framework (including non-randomised study designs), see IUHPE 2010, Stewart-Brown 2006 and Lister-Sharp 1999. Types of participants Children and young people aged four to 18 years attending schools or colleges (including special schools). We excluded studies which covered both pre-school and school-aged students. We made a post hoc change to the types of participants focused on in this review. We had originally intended to examine the impact of the Health Promoting Schools framework on staff as well as student health (Langford 2011). However, the definition of HPS interventions (as described in the published literature, referenced above) requires there to be curricular input as an essential criterion. This therefore eliminated any studies that focus on staff health, as they would not contain any curricular element. Consequently, this review is focused exclusively on students’ health and well-being. Types of interventions Interventions (of any duration) based upon the HPS framework that demonstrate active engagement of the school in health pro- motion activities ineach of the following areas. • School curriculum; • Ethos or environment of the school or both; • Engagement with families or communities or both. We present more specific inclusion criteria for these three cate- gories in Appendix 1. Interventions did not have to explicitly state that they were based upon the HPS framework to be eligible for inclusion. If they addressed the three domains of the intervention we included them. It was not an eligibility requirement that stud- ies reported academic outcomes. Control schools were schools that implemented either no inter- vention or continued with their usual practice, or schools that im- plemented an alternative intervention that included only one or two of the HPS criteria. Types of outcome measures The HPS frameworkisahighlycomplex, multi-dimensional inter- vention, which presented particular methodological challenges for this systematic review. The intervention seeks to improve ‘health’ in general, and does not restrict itself to specific health issues; the focus of each intervention is determined by the schools and re- searchers according to need. Thus, while individual studies may focus on a specific health topic (for example, obesity or substance misuse), the range of topics included in the review is very broad. Consequently this review defined its primary outcome - health - to reflect the broad focus of the HPS framework (improving health in its widest sense) as well as educational outcomes. 6 The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  Primary outcomes Health For each health topic, we identified both positive and potentially adverse outcomes (where reported). We categorised health out- comes into the following topic areas: • Obesity or overweight or body size: body mass index or standardised body mass index (BMI or zBMI), height-for-age, weight-for-age, and weight-for-height z-scores, skin-fold thickness measures, waist circumference • Physical activity or sedentary behaviours: accelerometry, multi-stage fitness tests (for example, shuttle runs, step tests), self-reported levels of physical activity or sedentary behaviours • Nutrition: self-reported food intake (particularly focusing on consumption of fruits and vegetables, water, high fat or sugar foods), indicators of specific nutritional deficiencies (for example, iron, iodine, and vitamin A deficiencies) • Tobacco use: salivary cotinine, carbon monoxide levels, self- reported use of cigarettes or other tobacco products • Alcohol use: self-reported use of alcohol • Other drug use: self-reported use of other drugs (legal or illegal) • Sexual health: incidence of sexually transmitted infections, pregnancy or abortion, self-reported use of condoms or other contraception, abstinence or delaying of sexual intercourse • Mental health and emotional well-being: validated scales of well-being or quality of life or both, incidence of self harm or suicide, use of validated scales such as Rosenberg’s self esteem scale, Beck Depression Inventory, Strengths and Difficulties Questionnaire • Violence: self-reported violence (for example, carried weapon, got into a fight) • Bullying: self-reported incidence of being bullied or bullying others • Infectious diseases: incidence of diseases such as diarrhoea, cold or influenza, skin disease, worms, head lice; observation or self report of hand-washing with soap after visiting toilet or before handling food • Safety and accident prevention: incidence of traffic accidents or other accidents or injuries in school or at home; observation or self report of cycle-helmet use • Body image or eating disorders: student (or teacher or parent) reports of disordered eating habits, body size acceptance, self esteem • Skin or sun safety: observation or self report of sunscreen, behaviours to reduce exposure to the sun (for example, wearing hat, seeking shade, covering up) • Oral health: decayed, missing or filled teeth index; self- reported dental hygiene behaviours such as regular tooth brushing, dental check-ups; self-reported consumption of sugary snacks or drinks Within each health topic, we measured outcomes using: a. Objective measures of health or health behaviours, for example, validated methods or techniques such as BMI, accelerometry. b. Subjective measures of health or health behaviours, for example, observation or self reports of behaviour or subjective ratings of health. c. Measures of knowledge or attitudes or self efficacy (for example, knowledge of causes or consequences of specific health issues; at- titudes towards behaviours that are known risk or protective fac- tors for health; perceptions of one’s ability to perform a certain behaviour). Where studies presented an outcome measured in more than one way(forexample, smokinginlastsevendaysand smokinginlast30 days), we chose the category that indicated the highest frequency of the (harmful) behaviour within each respective study, assuming that this would be of the greatest public health importance. Academic outcomes Academic outcomes focused on: student-standardised academic test scores, IQ tests or other validated scales; school academic per- formance. Secondary outcomes Secondary outcomes focused on: 1. School attendance outcomes. 2. Non-academic school outcomes: for example, ratings of school climate, attachment to school, satisfaction with school. 3. Process outcomes: fidelity, acceptability, reach, and intensity of the intervention delivery. 4. Curriculum outcomes: evidence of health education topics within the formal school curriculum. 5. School environment outcomes: evidence of changes to the school’s social or physical environment or both. Examples might include: implementing no-smoking policies, improving school catering services, developing peer mentoring programmes to tackle bullying, playground redesign. 6. Engagement with families or communities or both: participation of parents or families in relevant school-based activities; evidence of engagement with local community organisations. Timing of outcome assessment The primary end point for outcome data extraction was immedi- ately postintervention (or the closest time point to this, up to a maximum of six months postintervention). We then categorised follow-up data after the end of the intervention (if presented) as be- ing either short- (12 months or less), medium- (12 to 24 months) or long-term (24 months or more). 7 The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  Economic data Where provided, we extracted data on the costs and cost effective- ness of studies. Search methods for identification of studies Electronic searches We searched the following databases in January 2011. We con- ducted updated searches in 2013, beginning on 15 March 2013 and completed on 22 April 2013. We did not apply any date or language restrictions to our searches. Studies were not excluded on the basis of publication status. Abstracts, conference proceedings, and other ’grey’ literature were included if they met the inclusion criteria. • Cochrane Central Register of Controlled Trials (CENTRAL) 2013, Issue 3, part of The Cochrane Library. • Ovid MEDLINE, 1950 to 15 March 2013. • EMBASE,1980 to 2013 week 16. • ASSIA - Applied Social Science Index and Abstracts, 1987 to 2011. • Australian Education Index, 1979 to current. • BEI - British Education Index, 1975 to current. • BiblioMap - Database of Health Promotion Research ( eppi.ioe.ac.uk/cms/). • CAB Abstracts, 1973 to 2013 week 11. • Campbell Library of Systematic Reviews ( campbellcollaboration.org/lib/). • CINAHL - Cumulative Index to Nursing and Allied Health Literature, 1982 to current. • Clinical Trials.gov (clinicaltrials.gov/). • Current Controlled Trials (controlled-trials.com/mrct/) • Database of Abstracts of Reviews of Effects 2013, Issue 1, part of The Cochrane Library. • Database of Education Research (eppi.ioe.ac.uk/cms/). • Dissertation Express (dissexpress.umi.com/dxweb/ search.html). • ERIC - Education Resources Information Centre, 1966 to current. • Global Health Database. • IBSS - International Bibliography of Social Sciences, 1950 to current. • International Clinical Trials Registry Platform (ICTRP) ( who.int/ictrp/en/). • Index to Theses in Great Britain and Ireland. • PsycINFO, 1806 to 2013 week 10. • SIGLE - System for Information on Grey Literature in Europe (now known as OpenGrey) (www.opengrey.eu/). • Social Science Citation Index, 1956 to current. • Sociological Abstracts, 1952 to current. • TRoPHI - Trials Register of Promoting Health Interventions (eppi.ioe.ac.uk/cms/). The search strategies and search dates for these databases are shown in Appendix 2. Searching other resources We handsearched the reference lists of relevant articles and used citation tracking to identify and obtain relevant articles. In addi- tion, we searched the following websites for relevant publications, including grey literature: • Australian Health Promoting Schools Association ( www.ahpsa.org.au). • Barnardo’s (www.ahpsa.org.au). • Center for Disease Control and Prevention (www.cdc.gov). • Communities and Schools Promoting Health ( www.safehealthyschools.org). • International Union for Health Promotion and Education ( www.iuhpe.org). • International School Health Network ( www.internationalschoolhealth.org). • National Centre for Social Research (www.natcen.ac.uk/). • National Children’s Bureau (www.ncb.org.uk). • National College for School Leadership ( www.nationalcollege.org.uk). • National Foundation for Education Research ( www.nfer.ac.uk). • National Healthy Schools Programme ( home.healthyschools.gov.uk). • National Youth Agency (www.nya.org.uk). • Schools for Health in Europe (www.schoolsforhealth.eu). • School Health Education Unit (sheu.org.uk). • UNAIDS (www.unaids.org/). • UNFPA (www.unfpa.org). • UNICEF (www.unfpa.org). • World Bank (www.worldbank.org). • World Health Organization (www.who.int). Several of the databases and the majority of websites that we searched in January 2011 yielded no or very few studies eligible for inclusion. The few eligible studies identified via these databases or websites were also identified through searches of MEDLINE, EMBASE, and PsycINFO. We therefore chose to exclude the fol- lowing from our updated search in 2013: Global Health Database, Index to Theses in Great Britain and Ireland, Dissertation Ex- press, SIGLE, Database of Educational Research, Bibliomap, and all websites. In addition, we no longer had access to ASSIA and therefore could not update our search of this database. Data collection and analysis 8 The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  Selection of studies The initial search strategy produced over 35,000 reports, after removing duplicate records. A further 12,750 were retrieved in March and April 2013 after deduplication. One review author (RL) conducted an initial title screen to remove those which were obviously not pertinent to the review. For quality assurance pur- poses, a second review author (RC) double-screened a random selection of 10% of these titles, yielding a kappa score of 0.88, reflecting excellent agreement. Thereafter, two authors indepen- dently screened all abstracts and full-texts to determine eligibility. We resolved any disagreements regarding eligibility through dis- cussion and, when necessary, in consultation with a third review author (usually RC). Data extraction and management For each study, two review authors (RL, and shared between LG, CB, SM, DM, and KK) independently completed data extraction forms created for the purposes of this review. We extracted data pertaining to: basic study details (participant characteristics, study location, sample size, rates of attrition); study design and duration; intervention characteristics (including health focus, theoretical framework, content and activities, and details of any intervention offered to the control group); process evalu- ation of the intervention (including fidelity, acceptability, reach, intensity, and context of intervention); outcome measures postin- tervention and subsequent follow-up; and costs of intervention. We used the PROGRESS PLUS check list to collect data relevant for equity (Kavanagh 2008). Assessment of risk of bias in included studies We assessed risk of bias within each included study using the tool outlined in the Cochrane Handbook for Systematic Reviews of In- terventions (Higgins 2011a). For each study two review authors (RL and DP) independently judged the likelihood of bias in the following domains: selection (sequence generation and allocation concealment), blinding (performance and detection bias), attri- tion (incomplete outcome data), reporting (selective outcome re- porting), and any other potential sources of bias. For each domain, we rated studies as being at ‘high’, ‘low’ or ‘unclear’ risk of bias. We resolved any disagreements on categorisation through discussion, referring to a third review author when necessary (HJ). Selection bias included an assessment of both adequate sequence generation and allocation concealment. We assessed sequence gen- eration to be at low risk of bias when studies clearly specified a method for generating a truly random sequence. As all studies included in this review were cluster-RCTs, we assessed studies as being at low risk of bias for allocation concealment if allocation was performed for all clusters at the start of the study. The blinding domain covers both performance and detection bias. It was rarely (if ever) possible to blind students or staff to the fact that they were taking part in an intervention; we therefore assessed studies as being at high risk of performance bias unless authors explicitly stated that students were blind to group allocation. We assessed studies as being at low risk of detection bias if they clearly described the blinding of outcome assessors. If outcomes were assessed by self report, we rated the studies as being at high risk of bias where students were unlikely to have been adequately blinded. In order to assess attrition bias we considered rates of attrition both overall and between groups, and considered whether this was likely to be related to intervention outcomes. We assessed studies as being at low risk of reporting bias when a published protocol or study design paper was available and all prespecified outcomes were presented in the report. Where no protocol was available, we assessed studies as being at unclear risk of bias. If an outcome was specified in the study protocol but was not reported in any subsequent outcome papers, we assessed the study as being at high risk of bias. We used the ‘other bias’ domain to note any additional concerns relating to study quality that did not fit into any of the previous five domains. For example, in this domain we included concerns about recruitment bias, baseline imbalances between groups, or selective reporting of subgroup analyses. We assessed the overall quality of the body of evidence for each outcome using the GRADE approach (Schünemann 2011). Us- ing this method, randomised trial evidence can be downgraded from high to moderate, low or very low quality on the basis of five factors: limitations in design or implementation (often indicative of high risk of bias); indirectness of evidence; unexplained hetero- geneity; imprecision of results; or high probability of publication bias. Measures of treatment effect For dichotomous (binary) data, we used odds ratios (ORs) with 95% confidence intervals (CIs) to summarise results within each study. We summarised continuous outcomes using a mean dif- ference (MD) with standard error. We extracted mean differences (adjusted for baseline) from an analysis of covariance (ANCOVA) model when these were presented. When ANCOVA results were not available we instead extracted or calculated mean differences based on final value measurements. We calculated a pooled stan- dard deviation (SD) from intervention and control SDs at follow- up. Where studies used different scales to measure what we consid- ered to represent the same underlying outcome, we first standard- ised results to a uniform scale by calculating standardised mean differences (SMDs). This involves dividing the estimated mean difference by the standard deviation of outcome measurements. Regardless of the method used to estimate the mean difference (ANCOVA or final values), standardisation was always performed using the standard deviation of outcome measurements at follow- up. This was to avoid the problem of computed SMDs not being 9 The WHO Health Promoting School framework for improving the health and well-being of students and their academic achievement (Review) Copyright © 2014 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.  combinable across studies using different approaches to estimate the mean difference. Where some studies reported an outcome as dichotomous and others provided a continuous measure, we converted results to the most commonly reported scale, assuming the underlying continu- ous measurement had an approximate logistic distribution, using methods described in Borenstein 2009 (Chapter seven). Where data were presented separately by gender or age group, we combined these data using methods described in Borenstein 2009 (Chapter 23). Unit of analysis issues Interventions employing a ’whole school’ approach require ran- domisation at the group (rather than individual) level. Where anal- ysis took place at the school level (for example, school academic performance) no special statistical analysis is required. However, where studies reported results at the individual level, we deter- mined whether or not the authors had accounted for the effect of clustering using appropriate statistical techniques such as multi- level modelling. Where this had not been done (or it was not clear if it had been done), we attempted to contact the study authors to ask for the intra-cluster correlation coefficient (ICC) and mean cluster size. This information allowed us to make an adjustment for clustering to their results before inclusion in the meta-analyses (Higgins 2011b). If these data were not available, we examined the ICCs in similar studies. To be conservative, we selected the largest of these to adjust results prior to inclusion in the meta-analyses. When performing a meta-analysis of SMDs from cluster-RCTs, we had to decide whether to use the standard deviation of out- come measurements within clusters or the overall (‘total’) standard deviation across all individuals in a study (Grieve 2012; White 2005). The latter will be larger, since it also incorporates between- cluster variability (specifically, Variance [total] = Variance [within clusters] + Variance [between clusters], White 2005), although the difference between the two measures is lessened if ICCs are small. Since within-cluster standard deviations are rarely reported, we used the total standard deviation. It is useful to have estimates of ICCs for different outcomes within different population groups to inform future research. Additional Table 1 presents the ICCs that were either reported in the included studies, or obtained via correspondence with study authors. Dealing with missing data In the event of missing or unclear data within published stud- ies, we attempted to contact the study authors. Where multi-level model data were presented but authors did not provide standard errors or specific P values (and we were unable to obtain these from authors), we used final value outcome measurements and adjusted for clustering as described above (three cases). To calcu- late standardised mean differences, we needed to divide the effect estimate by the standard deviation of the sample. Where this was not available, we imputed the standard deviation from baseline or from another similar study (Higgins 2011b). Assessment of heterogeneity We assessed statistical heterogeneity among studies initially by vi- sual inspection of forest plots. We performed Chi² tests to assess evidence of variation in effect estimates beyond that expected by chance. However, since this test has low power to detect hetero- geneity when studies have small sample sizes or are few in number, we calculated I², which is an estimate of the percentage of vari- ation due to heterogeneity rather than sampling error or chance, where a value greater than 50% indicates moderate to substan- tial heterogeneity (Deeks 2011). For meta-analyses where I² was greater than 50%, we performed subgroup analyses to explore this heterogeneity. Assessment of reporting biases Where possible, we drew funnel plots to assess the presence of possible publication bias or small study effects (Sterne 2011). Data synthesis Quantitative data The HPS framework is a flexible intervention, which can be used to target a wide range of health behaviours. We identified a num- ber of different types of HPS interventions based broadly on the health topic(s) that the studies sought to tackle. However, we also differentiated between the different approaches that were taken to tackling specific he...