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• Research article
• Open access
• Published: 06 February 2017

Blended learning effectiveness: the relationship between student characteristics, design features and outcomes

• Mugenyi Justice Kintu   ORCID: orcid.org/0000-0002-4500-1168 1 , 2 ,
• Chang Zhu 2 &
• Edmond Kagambe 1  

International Journal of Educational Technology in Higher Education volume  14 , Article number:  7 ( 2017 ) Cite this article

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This paper investigates the effectiveness of a blended learning environment through analyzing the relationship between student characteristics/background, design features and learning outcomes. It is aimed at determining the significant predictors of blended learning effectiveness taking student characteristics/background and design features as independent variables and learning outcomes as dependent variables. A survey was administered to 238 respondents to gather data on student characteristics/background, design features and learning outcomes. The final semester evaluation results were used as a measure for performance as an outcome. We applied the online self regulatory learning questionnaire for data on learner self regulation, the intrinsic motivation inventory for data on intrinsic motivation and other self-developed instruments for measuring the other constructs. Multiple regression analysis results showed that blended learning design features (technology quality, online tools and face-to-face support) and student characteristics (attitudes and self-regulation) predicted student satisfaction as an outcome. The results indicate that some of the student characteristics/backgrounds and design features are significant predictors for student learning outcomes in blended learning.

Introduction

The teaching and learning environment is embracing a number of innovations and some of these involve the use of technology through blended learning. This innovative pedagogical approach has been embraced rapidly though it goes through a process. The introduction of blended learning (combination of face-to-face and online teaching and learning) initiatives is part of these innovations but its uptake, especially in the developing world faces challenges for it to be an effective innovation in teaching and learning. Blended learning effectiveness has quite a number of underlying factors that pose challenges. One big challenge is about how users can successfully use the technology and ensuring participants’ commitment given the individual learner characteristics and encounters with technology (Hofmann, 2014 ). Hofmann adds that users getting into difficulties with technology may result into abandoning the learning and eventual failure of technological applications. In a report by Oxford Group ( 2013 ), some learners (16%) had negative attitudes to blended learning while 26% were concerned that learners would not complete study in blended learning. Learners are important partners in any learning process and therefore, their backgrounds and characteristics affect their ability to effectively carry on with learning and being in blended learning, the design tools to be used may impinge on the effectiveness in their learning.

This study tackles blended learning effectiveness which has been investigated in previous studies considering grades, course completion, retention and graduation rates but no studies regarding effectiveness in view of learner characteristics/background, design features and outcomes have been done in the Ugandan university context. No studies have also been done on how the characteristics of learners and design features are predictors of outcomes in the context of a planning evaluation research (Guskey, 2000 ) to establish the effectiveness of blended learning. Guskey ( 2000 ) noted that planning evaluation fits in well since it occurs before the implementation of any innovation as well as allowing planners to determine the needs, considering participant characteristics, analyzing contextual matters and gathering baseline information. This study is done in the context of a plan to undertake innovative pedagogy involving use of a learning management system (moodle) for the first time in teaching and learning in a Ugandan university. The learner characteristics/backgrounds being investigated for blended learning effectiveness include self-regulation, computer competence, workload management, social and family support, attitude to blended learning, gender and age. We investigate the blended learning design features of learner interactions, face-to-face support, learning management system tools and technology quality while the outcomes considered include satisfaction, performance, intrinsic motivation and knowledge construction. Establishing the significant predictors of outcomes in blended learning will help to inform planners of such learning environments in order to put in place necessary groundwork preparations for designing blended learning as an innovative pedagogical approach.

Kenney and Newcombe ( 2011 ) did their comparison to establish effectiveness in view of grades and found that blended learning had higher average score than the non-blended learning environment. Garrison and Kanuka ( 2004 ) examined the transformative potential of blended learning and reported an increase in course completion rates, improved retention and increased student satisfaction. Comparisons between blended learning environments have been done to establish the disparity between academic achievement, grade dispersions and gender performance differences and no significant differences were found between the groups (Demirkol & Kazu, 2014 ).

However, blended learning effectiveness may be dependent on many other factors and among them student characteristics, design features and learning outcomes. Research shows that the failure of learners to continue their online education in some cases has been due to family support or increased workload leading to learner dropout (Park & Choi, 2009 ) as well as little time for study. Additionally, it is dependent on learner interactions with instructors since failure to continue with online learning is attributed to this. In Greer, Hudson & Paugh’s study as cited in Park and Choi ( 2009 ), family and peer support for learners is important for success in online and face-to-face learning. Support is needed for learners from all areas in web-based courses and this may be from family, friends, co-workers as well as peers in class. Greer, Hudson and Paugh further noted that peer encouragement assisted new learners in computer use and applications. The authors also show that learners need time budgeting, appropriate technology tools and support from friends and family in web-based courses. Peer support is required by learners who have no or little knowledge of technology, especially computers, to help them overcome fears. Park and Choi, ( 2009 ) showed that organizational support significantly predicts learners’ stay and success in online courses because employers at times are willing to reduce learners’ workload during study as well as supervisors showing that they are interested in job-related learning for employees to advance and improve their skills.

The study by Kintu and Zhu ( 2016 ) investigated the possibility of blended learning in a Ugandan University and examined whether student characteristics (such as self-regulation, attitudes towards blended learning, computer competence) and student background (such as family support, social support and management of workload) were significant factors in learner outcomes (such as motivation, satisfaction, knowledge construction and performance). The characteristics and background factors were studied along with blended learning design features such as technology quality, learner interactions, and Moodle with its tools and resources. The findings from that study indicated that learner attitudes towards blended learning were significant factors to learner satisfaction and motivation while workload management was a significant factor to learner satisfaction and knowledge construction. Among the blended learning design features, only learner interaction was a significant factor to learner satisfaction and knowledge construction.

The focus of the present study is on examining the effectiveness of blended learning taking into consideration learner characteristics/background, blended learning design elements and learning outcomes and how the former are significant predictors of blended learning effectiveness.

Studies like that of Morris and Lim ( 2009 ) have investigated learner and instructional factors influencing learning outcomes in blended learning. They however do not deal with such variables in the contexts of blended learning design as an aspect of innovative pedagogy involving the use of technology in education. Apart from the learner variables such as gender, age, experience, study time as tackled before, this study considers social and background aspects of the learners such as family and social support, self-regulation, attitudes towards blended learning and management of workload to find out their relationship to blended learning effectiveness. Identifying the various types of learner variables with regard to their relationship to blended learning effectiveness is important in this study as we embark on innovative pedagogy with technology in teaching and learning.

Literature review

This review presents research about blended learning effectiveness from the perspective of learner characteristics/background, design features and learning outcomes. It also gives the factors that are considered to be significant for blended learning effectiveness. The selected elements are as a result of the researcher’s experiences at a Ugandan university where student learning faces challenges with regard to learner characteristics and blended learning features in adopting the use of technology in teaching and learning. We have made use of Loukis, Georgiou, and Pazalo ( 2007 ) value flow model for evaluating an e-learning and blended learning service specifically considering the effectiveness evaluation layer. This evaluates the extent of an e-learning system usage and the educational effectiveness. In addition, studies by Leidner, Jarvenpaa, Dillon and Gunawardena as cited in Selim ( 2007 ) have noted three main factors that affect e-learning and blended learning effectiveness as instructor characteristics, technology and student characteristics. Heinich, Molenda, Russell, and Smaldino ( 2001 ) showed the need for examining learner characteristics for effective instructional technology use and showed that user characteristics do impact on behavioral intention to use technology. Research has dealt with learner characteristics that contribute to learner performance outcomes. They have dealt with emotional intelligence, resilience, personality type and success in an online learning context (Berenson, Boyles, & Weaver, 2008 ). Dealing with the characteristics identified in this study will give another dimension, especially for blended learning in learning environment designs and add to specific debate on learning using technology. Lin and Vassar, ( 2009 ) indicated that learner success is dependent on ability to cope with technical difficulty as well as technical skills in computer operations and internet navigation. This justifies our approach in dealing with the design features of blended learning in this study.

Learner characteristics/background and blended learning effectiveness

Studies indicate that student characteristics such as gender play significant roles in academic achievement (Oxford Group, 2013 ), but no study examines performance of male and female as an important factor in blended learning effectiveness. It has again been noted that the success of e- and blended learning is highly dependent on experience in internet and computer applications (Picciano & Seaman, 2007 ). Rigorous discovery of such competences can finally lead to a confirmation of high possibilities of establishing blended learning. Research agrees that the success of e-learning and blended learning can largely depend on students as well as teachers gaining confidence and capability to participate in blended learning (Hadad, 2007 ). Shraim and Khlaif ( 2010 ) note in their research that 75% of students and 72% of teachers were lacking in skills to utilize ICT based learning components due to insufficient skills and experience in computer and internet applications and this may lead to failure in e-learning and blended learning. It is therefore pertinent that since the use of blended learning applies high usage of computers, computer competence is necessary (Abubakar & Adetimirin, 2015 ) to avoid failure in applying technology in education for learning effectiveness. Rovai, ( 2003 ) noted that learners’ computer literacy and time management are crucial in distance learning contexts and concluded that such factors are meaningful in online classes. This is supported by Selim ( 2007 ) that learners need to posses time management skills and computer skills necessary for effectiveness in e- learning and blended learning. Self-regulatory skills of time management lead to better performance and learners’ ability to structure the physical learning environment leads to efficiency in e-learning and blended learning environments. Learners need to seek helpful assistance from peers and teachers through chats, email and face-to-face meetings for effectiveness (Lynch & Dembo, 2004 ). Factors such as learners’ hours of employment and family responsibilities are known to impede learners’ process of learning, blended learning inclusive (Cohen, Stage, Hammack, & Marcus, 2012 ). It was also noted that a common factor in failure and learner drop-out is the time conflict which is compounded by issues of family , employment status as well as management support (Packham, Jones, Miller, & Thomas, 2004 ). A study by Thompson ( 2004 ) shows that work, family, insufficient time and study load made learners withdraw from online courses.

Learner attitudes to blended learning can result in its effectiveness and these shape behavioral intentions which usually lead to persistence in a learning environment, blended inclusive. Selim, ( 2007 ) noted that the learners’ attitude towards e-learning and blended learning are success factors for these learning environments. Learner performance by age and gender in e-learning and blended learning has been found to indicate no significant differences between male and female learners and different age groups (i.e. young, middle-aged and old above 45 years) (Coldwell, Craig, Paterson, & Mustard, 2008 ). This implies that the potential for blended learning to be effective exists and is unhampered by gender or age differences.

Blended learning design features

The design features under study here include interactions, technology with its quality, face-to-face support and learning management system tools and resources.

Research shows that absence of learner interaction causes failure and eventual drop-out in online courses (Willging & Johnson, 2009 ) and the lack of learner connectedness was noted as an internal factor leading to learner drop-out in online courses (Zielinski, 2000 ). It was also noted that learners may not continue in e- and blended learning if they are unable to make friends thereby being disconnected and developing feelings of isolation during their blended learning experiences (Willging & Johnson, 2009). Learners’ Interactions with teachers and peers can make blended learning effective as its absence makes learners withdraw (Astleitner, 2000 ). Loukis, Georgious and Pazalo (2007) noted that learners’ measuring of a system’s quality, reliability and ease of use leads to learning efficiency and can be so in blended learning. Learner success in blended learning may substantially be affected by system functionality (Pituch & Lee, 2006 ) and may lead to failure of such learning initiatives (Shrain, 2012 ). It is therefore important to examine technology quality for ensuring learning effectiveness in blended learning. Tselios, Daskalakis, and Papadopoulou ( 2011 ) investigated learner perceptions after a learning management system use and found out that the actual system use determines the usefulness among users. It is again noted that a system with poor response time cannot be taken to be useful for e-learning and blended learning especially in cases of limited bandwidth (Anderson, 2004 ). In this study, we investigate the use of Moodle and its tools as a function of potential effectiveness of blended learning.

The quality of learning management system content for learners can be a predictor of good performance in e-and blended learning environments and can lead to learner satisfaction. On the whole, poor quality technology yields no satisfaction by users and therefore the quality of technology significantly affects satisfaction (Piccoli, Ahmad, & Ives, 2001 ). Continued navigation through a learning management system increases use and is an indicator of success in blended learning (Delone & McLean, 2003 ). The efficient use of learning management system and its tools improves learning outcomes in e-learning and blended learning environments.

It is noted that learner satisfaction with a learning management system can be an antecedent factor for blended learning effectiveness. Goyal and Tambe ( 2015 ) noted that learners showed an appreciation to Moodle’s contribution in their learning. They showed positivity with it as it improved their understanding of course material (Ahmad & Al-Khanjari, 2011 ). The study by Goyal and Tambe ( 2015 ) used descriptive statistics to indicate improved learning by use of uploaded syllabus and session plans on Moodle. Improved learning is also noted through sharing study material, submitting assignments and using the calendar. Learners in the study found Moodle to be an effective educational tool.

In blended learning set ups, face-to-face experiences form part of the blend and learner positive attitudes to such sessions could mean blended learning effectiveness. A study by Marriot, Marriot, and Selwyn ( 2004 ) showed learners expressing their preference for face-to-face due to its facilitation of social interaction and communication skills acquired from classroom environment. Their preference for the online session was only in as far as it complemented the traditional face-to-face learning. Learners in a study by Osgerby ( 2013 ) had positive perceptions of blended learning but preferred face-to-face with its step-by-stem instruction. Beard, Harper and Riley ( 2004 ) shows that some learners are successful while in a personal interaction with teachers and peers thus prefer face-to-face in the blend. Beard however dealt with a comparison between online and on-campus learning while our study combines both, singling out the face-to-face part of the blend. The advantage found by Beard is all the same relevant here because learners in blended learning express attitude to both online and face-to-face for an effective blend. Researchers indicate that teacher presence in face-to-face sessions lessens psychological distance between them and the learners and leads to greater learning. This is because there are verbal aspects like giving praise, soliciting for viewpoints, humor, etc and non-verbal expressions like eye contact, facial expressions, gestures, etc which make teachers to be closer to learners psychologically (Kelley & Gorham, 2009 ).

Learner outcomes

The outcomes under scrutiny in this study include performance, motivation, satisfaction and knowledge construction. Motivation is seen here as an outcome because, much as cognitive factors such as course grades are used in measuring learning outcomes, affective factors like intrinsic motivation may also be used to indicate outcomes of learning (Kuo, Walker, Belland, & Schroder, 2013 ). Research shows that high motivation among online learners leads to persistence in their courses (Menager-Beeley, 2004 ). Sankaran and Bui ( 2001 ) indicated that less motivated learners performed poorly in knowledge tests while those with high learning motivation demonstrate high performance in academics (Green, Nelson, Martin, & Marsh, 2006 ). Lim and Kim, ( 2003 ) indicated that learner interest as a motivation factor promotes learner involvement in learning and this could lead to learning effectiveness in blended learning.

Learner satisfaction was noted as a strong factor for effectiveness of blended and online courses (Wilging & Johnson, 2009) and dissatisfaction may result from learners’ incompetence in the use of the learning management system as an effective learning tool since, as Islam ( 2014 ) puts it, users may be dissatisfied with an information system due to ease of use. A lack of prompt feedback for learners from course instructors was found to cause dissatisfaction in an online graduate course. In addition, dissatisfaction resulted from technical difficulties as well as ambiguous course instruction Hara and Kling ( 2001 ). These factors, once addressed, can lead to learner satisfaction in e-learning and blended learning and eventual effectiveness. A study by Blocker and Tucker ( 2001 ) also showed that learners had difficulties with technology and inadequate group participation by peers leading to dissatisfaction within these design features. Student-teacher interactions are known to bring satisfaction within online courses. Study results by Swan ( 2001 ) indicated that student-teacher interaction strongly related with student satisfaction and high learner-learner interaction resulted in higher levels of course satisfaction. Descriptive results by Naaj, Nachouki, and Ankit ( 2012 ) showed that learners were satisfied with technology which was a video-conferencing component of blended learning with a mean of 3.7. The same study indicated student satisfaction with instructors at a mean of 3.8. Askar and Altun, ( 2008 ) found that learners were satisfied with face-to-face sessions of the blend with t-tests and ANOVA results indicating female scores as higher than for males in the satisfaction with face-to-face environment of the blended learning.

Studies comparing blended learning with traditional face-to-face have indicated that learners perform equally well in blended learning and their performance is unaffected by the delivery method (Kwak, Menezes, & Sherwood, 2013 ). In another study, learning experience and performance are known to improve when traditional course delivery is integrated with online learning (Stacey & Gerbic, 2007 ). Such improvement as noted may be an indicator of blended learning effectiveness. Our study however, delves into improved performance but seeks to establish the potential of blended learning effectiveness by considering grades obtained in a blended learning experiment. Score 50 and above is considered a pass in this study’s setting and learners scoring this and above will be considered to have passed. This will make our conclusions about the potential of blended learning effectiveness.

Regarding knowledge construction, it has been noted that effective learning occurs where learners are actively involved (Nurmela, Palonen, Lehtinen & Hakkarainen, 2003 , cited in Zhu, 2012 ) and this may be an indicator of learning environment effectiveness. Effective blended learning would require that learners are able to initiate, discover and accomplish the processes of knowledge construction as antecedents of blended learning effectiveness. A study by Rahman, Yasin and Jusoff ( 2011 ) indicated that learners were able to use some steps to construct meaning through an online discussion process through assignments given. In the process of giving and receiving among themselves, the authors noted that learners learned by writing what they understood. From our perspective, this can be considered to be accomplishment in the knowledge construction process. Their study further shows that learners construct meaning individually from assignments and this stage is referred to as pre-construction which for our study, is an aspect of discovery in the knowledge construction process.

Predictors of blended learning effectiveness

Researchers have dealt with success factors for online learning or those for traditional face-to-face learning but little is known about factors that predict blended learning effectiveness in view of learner characteristics and blended learning design features. This part of our study seeks to establish the learner characteristics/backgrounds and design features that predict blended learning effectiveness with regard to satisfaction, outcomes, motivation and knowledge construction. Song, Singleton, Hill, and Koh ( 2004 ) examined online learning effectiveness factors and found out that time management (a self-regulatory factor) was crucial for successful online learning. Eom, Wen, and Ashill ( 2006 ) using a survey found out that interaction, among other factors, was significant for learner satisfaction. Technical problems with regard to instructional design were a challenge to online learners thus not indicating effectiveness (Song et al., 2004 ), though the authors also indicated that descriptive statistics to a tune of 75% and time management (62%) impact on success of online learning. Arbaugh ( 2000 ) and Swan ( 2001 ) indicated that high levels of learner-instructor interaction are associated with high levels of user satisfaction and learning outcomes. A study by Naaj et al. ( 2012 ) indicated that technology and learner interactions, among other factors, influenced learner satisfaction in blended learning.

Objective and research questions of the current study

The objective of the current study is to investigate the effectiveness of blended learning in view of student satisfaction, knowledge construction, performance and intrinsic motivation and how they are related to student characteristics and blended learning design features in a blended learning environment.

Research questions

What are the student characteristics and blended learning design features for an effective blended learning environment?

Which factors (among the learner characteristics and blended learning design features) predict student satisfaction, learning outcomes, intrinsic motivation and knowledge construction?

Conceptual model of the present study

The reviewed literature clearly shows learner characteristics/background and blended learning design features play a part in blended learning effectiveness and some of them are significant predictors of effectiveness. The conceptual model for our study is depicted as follows (Fig.  1 ):

Conceptual model of the current study

Research design

This research applies a quantitative design where descriptive statistics are used for the student characteristics and design features data, t-tests for the age and gender variables to determine if they are significant in blended learning effectiveness and regression for predictors of blended learning effectiveness.

This study is based on an experiment in which learners participated during their study using face-to-face sessions and an on-line session of a blended learning design. A learning management system (Moodle) was used and learner characteristics/background and blended learning design features were measured in relation to learning effectiveness. It is therefore a planning evaluation research design as noted by Guskey ( 2000 ) since the outcomes are aimed at blended learning implementation at MMU. The plan under which the various variables were tested involved face-to-face study at the beginning of a 17 week semester which was followed by online teaching and learning in the second half of the semester. The last part of the semester was for another face-to-face to review work done during the online sessions and final semester examinations. A questionnaire with items on student characteristics, design features and learning outcomes was distributed among students from three schools and one directorate of postgraduate studies.

Participants

Cluster sampling was used to select a total of 238 learners to participate in this study. Out of the whole university population of students, three schools and one directorate were used. From these, one course unit was selected from each school and all the learners following the course unit were surveyed. In the school of Education ( n  = 70) and Business and Management Studies ( n  = 133), sophomore students were involved due to the fact that they have been introduced to ICT basics during their first year of study. Students of the third year were used from the department of technology in the School of Applied Sciences and Technology ( n  = 18) since most of the year two courses had a lot of practical aspects that could not be used for the online learning part. From the Postgraduate Directorate ( n  = 17), first and second year students were selected because learners attend a face-to-face session before they are given paper modules to study away from campus.

The study population comprised of 139 male students representing 58.4% and 99 females representing 41.6% with an average age of 24 years.

Instruments

The end of semester results were used to measure learner performance. The online self-regulated learning questionnaire (Barnard, Lan, To, Paton, & Lai, 2009 ) and the intrinsic motivation inventory (Deci & Ryan, 1982 ) were applied to measure the constructs on self regulation in the student characteristics and motivation in the learning outcome constructs. Other self-developed instruments were used for the other remaining variables of attitudes, computer competence, workload management, social and family support, satisfaction, knowledge construction, technology quality, interactions, learning management system tools and resources and face-to-face support.

Instrument reliability

Cronbach’s alpha was used to test reliability and the table below gives the results. All the scales and sub-scales had acceptable internal consistency reliabilities as shown in Table  1 below:

Data analysis

First, descriptive statistics was conducted. Shapiro-Wilk test was done to test normality of the data for it to qualify for parametric tests. The test results for normality of our data before the t- test resulted into significant levels (Male = .003, female = .000) thereby violating the normality assumption. We therefore used the skewness and curtosis results which were between −1.0 and +1.0 and assumed distribution to be sufficiently normal to qualify the data for a parametric test, (Pallant, 2010 ). An independent samples t -test was done to find out the differences in male and female performance to explain the gender characteristics in blended learning effectiveness. A one-way ANOVA between subjects was conducted to establish the differences in performance between age groups. Finally, multiple regression analysis was done between student variables and design elements with learning outcomes to determine the significant predictors for blended learning effectiveness.

Student characteristics, blended learning design features and learning outcomes ( RQ1 )

A t- test was carried out to establish the performance of male and female learners in the blended learning set up. This was aimed at finding out if male and female learners do perform equally well in blended learning given their different roles and responsibilities in society. It was found that male learners performed slightly better ( M  = 62.5) than their female counterparts ( M  = 61.1). An independent t -test revealed that the difference between the performances was not statistically significant ( t  = 1.569, df = 228, p  = 0.05, one tailed). The magnitude of the differences in the means is small with effect size ( d  = 0.18). A one way between subjects ANOVA was conducted on the performance of different age groups to establish the performance of learners of young and middle aged age groups (20–30, young & and 31–39, middle aged). This revealed a significant difference in performance (F(1,236 = 8.498, p < . 001).

Average percentages of the items making up the self regulated learning scale are used to report the findings about all the sub-scales in the learner characteristics/background scale. Results show that learner self-regulation was good enough at 72.3% in all the sub-scales of goal setting, environment structuring, task strategies, time management, help-seeking and self-evaluation among learners. The least in the scoring was task strategies at 67.7% and the highest was learner environment structuring at 76.3%. Learner attitude towards blended learning environment is at 76% in the sub-scales of learner autonomy, quality of instructional materials, course structure, course interface and interactions. The least scored here is attitude to course structure at 66% and their attitudes were high on learner autonomy and course interface both at 82%. Results on the learners’ computer competences are summarized in percentages in the table below (Table  2 ):

It can be seen that learners are skilled in word processing at 91%, email at 63.5%, spreadsheets at 68%, web browsers at 70.2% and html tools at 45.4%. They are therefore good enough in word processing and web browsing. Their computer confidence levels are reported at 75.3% and specifically feel very confident when it comes to working with a computer (85.7%). Levels of family and social support for learners during blended learning experiences are at 60.5 and 75% respectively. There is however a low score on learners being assisted by family members in situations of computer setbacks (33.2%) as 53.4% of the learners reported no assistance in this regard. A higher percentage (85.3%) is reported on learners getting support from family regarding provision of essentials for learning such as tuition. A big percentage of learners spend two hours on study while at home (35.3%) followed by one hour (28.2%) while only 9.7% spend more than three hours on study at home. Peers showed great care during the blended learning experience (81%) and their experiences were appreciated by the society (66%). Workload management by learners vis-à-vis studying is good at 60%. Learners reported that their workmates stand in for them at workplaces to enable them do their study in blended learning while 61% are encouraged by their bosses to go and improve their skills through further education and training. On the time spent on other activities not related to study, majority of the learners spend three hours (35%) while 19% spend 6 hours. Sixty percent of the learners have to answer to someone when they are not attending to other activities outside study compared to the 39.9% who do not and can therefore do study or those other activities.

The usability of the online system, tools and resources was below average as shown in the table below in percentages (Table  3 ):

However, learners became skilled at navigating around the learning management system (79%) and it was easy for them to locate course content, tools and resources needed such as course works, news, discussions and journal materials. They effectively used the communication tools (60%) and to work with peers by making posts (57%). They reported that online resources were well organized, user friendly and easy to access (71%) as well as well structured in a clear and understandable manner (72%). They therefore recommended the use of online resources for other course units in future (78%) because they were satisfied with them (64.3%). On the whole, the online resources were fine for the learners (67.2%) and useful as a learning resource (80%). The learners’ perceived usefulness/satisfaction with online system, tools, and resources was at 81% as the LMS tools helped them to communicate, work with peers and reflect on their learning (74%). They reported that using moodle helped them to learn new concepts, information and gaining skills (85.3%) as well as sharing what they knew or learned (76.4%). They enjoyed the course units (78%) and improved their skills with technology (89%).

Learner interactions were seen from three angles of cognitivism, collaborative learning and student-teacher interactions. Collaborative learning was average at 50% with low percentages in learners posting challenges to colleagues’ ideas online (34%) and posting ideas for colleagues to read online (37%). They however met oftentimes online (60%) and organized how they would work together in study during the face-to-face meetings (69%). The common form of communication medium frequently used by learners during the blended learning experience was by phone (34.5%) followed by whatsapp (21.8%), face book (21%), discussion board (11.8%) and email (10.9%). At the cognitive level, learners interacted with content at 72% by reading the posted content (81%), exchanging knowledge via the LMS (58.4%), participating in discussions on the forum (62%) and got course objectives and structure introduced during the face-to-face sessions (86%). Student-teacher interaction was reported at 71% through instructors individually working with them online (57.2%) and being well guided towards learning goals (81%). They did receive suggestions from instructors about resources to use in their learning (75.3%) and instructors provided learning input for them to come up with their own answers (71%).

The technology quality during the blended learning intervention was rated at 69% with availability of 72%, quality of the resources was at 68% with learners reporting that discussion boards gave right content necessary for study (71%) and the email exchanges containing relevant and much needed information (63.4%) as well as chats comprising of essential information to aid the learning (69%). Internet reliability was rated at 66% with a speed considered averagely good to facilitate online activities (63%). They however reported that there was intermittent breakdown during online study (67%) though they could complete their internet program during connection (63.4%). Learners eventually found it easy to download necessary materials for study in their blended learning experiences (71%).

Learner extent of use of the learning management system features was as shown in the table below in percentage (Table  4 ):

From the table, very rarely used features include the blog and wiki while very often used ones include the email, forum, chat and calendar.

The effectiveness of the LMS was rated at 79% by learners reporting that they found it useful (89%) and using it makes their learning activities much easier (75.2%). Moodle has helped learners to accomplish their learning tasks more quickly (74%) and that as a LMS, it is effective in teaching and learning (88%) with overall satisfaction levels at 68%. However, learners note challenges in the use of the LMS regarding its performance as having been problematic to them (57%) and only 8% of the learners reported navigation while 16% reported access as challenges.

Learner attitudes towards Face-to-face support were reported at 88% showing that the sessions were enjoyable experiences (89%) with high quality class discussions (86%) and therefore recommended that the sessions should continue in blended learning (89%). The frequency of the face-to-face sessions is shown in the table below as preferred by learners (Table  5 ).

Learners preferred face-to-face sessions after every month in the semester (33.6%) and at the beginning of the blended learning session only (27.7%).

Learners reported high intrinsic motivation levels with interest and enjoyment of tasks at 83.7%, perceived competence at 70.2%, effort/importance sub-scale at 80%, pressure/tension reported at 54%. The pressure percentage of 54% arises from learners feeling nervous (39.2%) and a lot of anxiety (53%) while 44% felt a lot of pressure during the blended learning experiences. Learners however reported the value/usefulness of blended learning at 91% with majority believing that studying online and face-to-face had value for them (93.3%) and were therefore willing to take part in blended learning (91.2%). They showed that it is beneficial for them (94%) and that it was an important way of studying (84.3%).

Learner satisfaction was reported at 81% especially with instructors (85%) high percentage reported on encouraging learner participation during the course of study 93%, course content (83%) with the highest being satisfaction with the good relationship between the objectives of the course units and the content (90%), technology (71%) with a high percentage on the fact that the platform was adequate for the online part of the learning (76%), interactions (75%) with participation in class at 79%, and face-to-face sessions (91%) with learner satisfaction high on face-to-face sessions being good enough for interaction and giving an overview of the courses when objectives were introduced at 92%.

Learners’ knowledge construction was reported at 78% with initiation and discovery scales scoring 84% with 88% specifically for discovering the learning points in the course units. The accomplishment scale in knowledge construction scored 71% and specifically the fact that learners were able to work together with group members to accomplish learning tasks throughout the study of the course units (79%). Learners developed reports from activities (67%), submitted solutions to discussion questions (68%) and did critique peer arguments (69%). Generally, learners performed well in blended learning in the final examination with an average pass of 62% and standard deviation of 7.5.

Significant predictors of blended learning effectiveness ( RQ 2)

A standard multiple regression analysis was done taking learner characteristics/background and design features as predictor variables and learning outcomes as criterion variables. The data was first tested to check if it met the linear regression test assumptions and results showed the correlations between the independent variables and each of the dependent variables (highest 0.62 and lowest 0.22) as not being too high, which indicated that multicollinearity was not a problem in our model. From the coefficients table, the VIF values ranged from 1.0 to 2.4, well below the cut off value of 10 and indicating no possibility of multicollinearity. The normal probability plot was seen to lie as a reasonably straight diagonal from bottom left to top right indicating normality of our data. Linearity was found suitable from the scatter plot of the standardized residuals and was rectangular in distribution. Outliers were no cause for concern in our data since we had only 1% of all cases falling outside 3.0 thus proving the data as a normally distributed sample. Our R -square values was at 0.525 meaning that the independent variables explained about 53% of the variance in overall satisfaction, motivation and knowledge construction of the learners. All the models explaining the three dependent variables of learner satisfaction, intrinsic motivation and knowledge construction were significant at the 0.000 probability level (Table  6 ).

From the table above, design features (technology quality and online tools and resources), and learner characteristics (attitudes to blended learning, self-regulation) were significant predictors of learner satisfaction in blended learning. This means that good technology with the features involved and the learner positive attitudes with capacity to do blended learning with self drive led to their satisfaction. The design features (technology quality, interactions) and learner characteristics (self regulation and social support), were found to be significant predictors of learner knowledge construction. This implies that learners’ capacity to go on their work by themselves supported by peers and high levels of interaction using the quality technology led them to construct their own ideas in blended learning. Design features (technology quality, online tools and resources as well as learner interactions) and learner characteristics (self regulation), significantly predicted the learners’ intrinsic motivation in blended learning suggesting that good technology, tools and high interaction levels with independence in learning led to learners being highly motivated. Finally, none of the independent variables considered under this study were predictors of learning outcomes (grade).

In this study we have investigated learning outcomes as dependent variables to establish if particular learner characteristics/backgrounds and design features are related to the outcomes for blended learning effectiveness and if they predict learning outcomes in blended learning. We took students from three schools out of five and one directorate of post-graduate studies at a Ugandan University. The study suggests that the characteristics and design features examined are good drivers towards an effective blended learning environment though a few of them predicted learning outcomes in blended learning.

Student characteristics/background, blended learning design features and learning outcomes

The learner characteristics, design features investigated are potentially important for an effective blended learning environment. Performance by gender shows a balance with no statistical differences between male and female. There are statistically significant differences ( p  < .005) in the performance between age groups with means of 62% for age group 20–30 and 67% for age group 31 –39. The indicators of self regulation exist as well as positive attitudes towards blended learning. Learners do well with word processing, e-mail, spreadsheets and web browsers but still lag below average in html tools. They show computer confidence at 75.3%; which gives prospects for an effective blended learning environment in regard to their computer competence and confidence. The levels of family and social support for learners stand at 61 and 75% respectively, indicating potential for blended learning to be effective. The learners’ balance between study and work is a drive factor towards blended learning effectiveness since their management of their workload vis a vis study time is at 60 and 61% of the learners are encouraged to go for study by their bosses. Learner satisfaction with the online system and its tools shows prospect for blended learning effectiveness but there are challenges in regard to locating course content and assignments, submitting their work and staying on a task during online study. Average collaborative, cognitive learning as well as learner-teacher interactions exist as important factors. Technology quality for effective blended learning is a potential for effectiveness though features like the blog and wiki are rarely used by learners. Face-to-face support is satisfactory and it should be conducted every month. There is high intrinsic motivation, satisfaction and knowledge construction as well as good performance in examinations ( M  = 62%, SD = 7.5); which indicates potentiality for blended learning effectiveness.

Significant predictors of blended learning effectiveness

Among the design features, technology quality, online tools and face-to-face support are predictors of learner satisfaction while learner characteristics of self regulation and attitudes to blended learning are predictors of satisfaction. Technology quality and interactions are the only design features predicting learner knowledge construction, while social support, among the learner backgrounds, is a predictor of knowledge construction. Self regulation as a learner characteristic is a predictor of knowledge construction. Self regulation is the only learner characteristic predicting intrinsic motivation in blended learning while technology quality, online tools and interactions are the design features predicting intrinsic motivation. However, all the independent variables are not significant predictors of learning performance in blended learning.

The high computer competences and confidence is an antecedent factor for blended learning effectiveness as noted by Hadad ( 2007 ) and this study finds learners confident and competent enough for the effectiveness of blended learning. A lack in computer skills causes failure in e-learning and blended learning as noted by Shraim and Khlaif ( 2010 ). From our study findings, this is no threat for blended learning our case as noted by our results. Contrary to Cohen et al. ( 2012 ) findings that learners’ family responsibilities and hours of employment can impede their process of learning, it is not the case here since they are drivers to the blended learning process. Time conflict, as compounded by family, employment status and management support (Packham et al., 2004 ) were noted as causes of learner failure and drop out of online courses. Our results show, on the contrary, that these factors are drivers for blended learning effectiveness because learners have a good balance between work and study and are supported by bosses to study. In agreement with Selim ( 2007 ), learner positive attitudes towards e-and blended learning environments are success factors. In line with Coldwell et al. ( 2008 ), no statistically significant differences exist between age groups. We however note that Coldwel, et al dealt with young, middle-aged and old above 45 years whereas we dealt with young and middle aged only.

Learner interactions at all levels are good enough and contrary to Astleitner, ( 2000 ) that their absence makes learners withdraw, they are a drive factor here. In line with Loukis (2007) the LMS quality, reliability and ease of use lead to learning efficiency as technology quality, online tools are predictors of learner satisfaction and intrinsic motivation. Face-to-face sessions should continue on a monthly basis as noted here and is in agreement with Marriot et al. ( 2004 ) who noted learner preference for it for facilitating social interaction and communication skills. High learner intrinsic motivation leads to persistence in online courses as noted by Menager-Beeley, ( 2004 ) and is high enough in our study. This implies a possibility of an effectiveness blended learning environment. The causes of learner dissatisfaction noted by Islam ( 2014 ) such as incompetence in the use of the LMS are contrary to our results in our study, while the one noted by Hara and Kling, ( 2001 ) as resulting from technical difficulties and ambiguous course instruction are no threat from our findings. Student-teacher interaction showed a relation with satisfaction according to Swan ( 2001 ) but is not a predictor in our study. Initiating knowledge construction by learners for blended learning effectiveness is exhibited in our findings and agrees with Rahman, Yasin and Jusof ( 2011 ). Our study has not agreed with Eom et al. ( 2006 ) who found learner interactions as predictors of learner satisfaction but agrees with Naaj et al. ( 2012 ) regarding technology as a predictor of learner satisfaction.

Conclusion and recommendations

An effective blended learning environment is necessary in undertaking innovative pedagogical approaches through the use of technology in teaching and learning. An examination of learner characteristics/background, design features and learning outcomes as factors for effectiveness can help to inform the design of effective learning environments that involve face-to-face sessions and online aspects. Most of the student characteristics and blended learning design features dealt with in this study are important factors for blended learning effectiveness. None of the independent variables were identified as significant predictors of student performance. These gaps are open for further investigation in order to understand if they can be significant predictors of blended learning effectiveness in a similar or different learning setting.

In planning to design and implement blended learning, we are mindful of the implications raised by this study which is a planning evaluation research for the design and eventual implementation of blended learning. Universities should be mindful of the interplay between the learner characteristics, design features and learning outcomes which are indicators of blended learning effectiveness. From this research, learners manifest high potential to take on blended learning more especially in regard to learner self-regulation exhibited. Blended learning is meant to increase learners’ levels of knowledge construction in order to create analytical skills in them. Learner ability to assess and critically evaluate knowledge sources is hereby established in our findings. This can go a long way in producing skilled learners who can be innovative graduates enough to satisfy employment demands through creativity and innovativeness. Technology being less of a shock to students gives potential for blended learning design. Universities and other institutions of learning should continue to emphasize blended learning approaches through installation of learning management systems along with strong internet to enable effective learning through technology especially in the developing world.

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MJK conceived the study idea, developed the conceptual framework, collected the data, analyzed it and wrote the article. CZ gave the technical advice concerning the write-up and advised on relevant corrections to be made before final submission. EK did the proof-reading of the article as well as language editing. All authors read and approved the final manuscript.

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Kintu, M.J., Zhu, C. & Kagambe, E. Blended learning effectiveness: the relationship between student characteristics, design features and outcomes. Int J Educ Technol High Educ 14 , 7 (2017). https://doi.org/10.1186/s41239-017-0043-4

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Received : 13 July 2016

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DOI : https://doi.org/10.1186/s41239-017-0043-4

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Intake of sugar sweetened beverages among children and adolescents in 185 countries between 1990 and 2018: population based study

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• Peer review
• Renata Micha , professor 1 3 ,
• Frederick Cudhea , biostatistician 1 ,
• Victoria Miller , research fellow 1 4 5 ,
• Peilin Shi , biostatistician 1 ,
• Jianyi Zhang , biostatistician 6 ,
• Julia R Sharib , researcher 1 ,
• Josh Erndt-Marino , researcher 1 ,
• Sean B Cash , professor 7 ,
• Simon Barquera , director 8 ,
• Dariush Mozaffarian , professor 1 9 10
• on behalf of the Global Dietary Database
• 1 Food is Medicine Institute, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
• 2 Institute of Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
• 3 University of Thessaly, Volos, Greece
• 4 Department of Medicine, McMaster University, Hamilton, ON, Canada
• 5 Population Health Research Institute, Hamilton, ON, Canada
• 6 Center for Surgery and Public Health, Brigham and Women’s Hospital Boston, MA, USA
• 7 Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
• 8 Research Center on Nutrition and Health, National Institute of Public Health, Cuernavaca, Morelos, Mexico
• 9 Tufts University School of Medicine, Boston, MA, USA
• 10 Division of Cardiology, Tufts Medical Center, Boston, MA, USA
• Correspondence to: L Lara-Castor lauralac{at}uw.edu
• Accepted 11 June 2024

Objective To quantify global intakes of sugar sweetened beverages (SSBs) and trends over time among children and adolescents.

Design Population based study.

Setting Global Dietary Database.

Population Children and adolescents aged 3-19 years in 185 countries between 1990 and 2018, jointly stratified at subnational level by age, sex, parental education, and rural or urban residence.

Results In 2018, mean global SSB intake was 3.6 (standardized serving=248 g (8 oz)) servings/week (1.3 (95% uncertainly interval 1.0 to 1.9) in south Asia to 9.1 (8.3 to 10.1) in Latin America and the Caribbean). SSB intakes were higher in older versus younger children and adolescents, those resident in urban versus rural areas, and those of parents with higher versus lower education. Between 1990 and 2018, mean global SSB intakes increased by 0.68 servings/week (22.9%), with the largest increases in sub-Saharan Africa (2.17 servings/week; 106%). Of 185 countries included in the analysis, 56 (30.3%) had a mean SSB intake of ≥7 servings/week, representing 238 million children and adolescents, or 10.4% of the global population of young people.

Conclusion This study found that intakes of SSBs among children and adolescents aged 3-19 years in 185 countries increased by 23% from 1990 to 2018, parallel to the rise in prevalence of obesity among this population globally. SSB intakes showed large heterogeneity among children and adolescents worldwide and by age, parental level of education, and urbanicity. This research should help to inform policies to reduce SSB intake among young people, particularly those with larger intakes across all education levels in urban and rural areas in Latin America and the Caribbean, and the growing problem of SSBs for public health in sub-Saharan Africa.

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Introduction

In 2015, obesity was estimated to affect more than 100 million children and adolescents, in line with observed increases in body mass index among this population from 1975 to 2016 in most world regions. 1 43 Among the main risk factors for obesity, unhealthy diets play a crucial role. 2 In particular, intake of sugar sweetened beverages (SSBs) has been consistently reported to increase the risk of obesity among children and adolescents. 2 3 This is especially concerning because obesity in childhood tends to persist into adulthood, increasing the risk of type 2 diabetes, cardiovascular disease, and premature mortality. 4 Explanations for the increase in intake of SSBs include globalization of markets, transformation of food systems, aggressive marketing strategies directed at children and adolescents, and lack of (or poor) regulatory measures to limit intake. 5 6 In studies at national and subnational level, policies and strategies such as taxation on sugar sweetened drinks, restrictions on food marketing, regulations for front-of-package labeling, and restrictions at school level have proven to curb the intake of SSBs among children and adolescents. 6 7 8

Although quantifying the intake of SSBs among children and adolescents is critical to further evaluate the impact of these beverages on disease and the effectiveness of policies to control intake, recent national estimates among young people are unavailable for most countries. 6 The lack of such data prevents an analysis of the trends in SSB intake over time, as well as the role of key sociodemographic factors such as age, sex, education, and urbanicity to more accurately inform current and future policies. In this study we present SSB intakes among children and adolescents aged 3-19 years at global, regional, and national level and trends over time from 1990 to 2018, jointly stratified at subnational level by age, sex, parental level of education, and area of residence.

Study design

This investigation is based on a serial cross sectional analysis of SSB intakes from the Global Dietary Database 2018 for 185 countries. Details on the methods and standardized data collection protocol are described in detail elsewhere. 9 10 11 12 13 Compared with the Global Dietary Database 2010, innovations include major expansion of individual level dietary surveys and global coverage up to 2018; inclusion of new data jointly stratified at subnational level by age, sex, education level, and urban or rural residence; and updated modeling methods, covariates, and validation to improve prediction of stratum specific mean intakes and uncertainty. This present analysis focused on children and adolescents aged 3-19 years.

Data sources

The approach and results of our survey search strategy by dietary factor, time, and region are reported in detail elsewhere. 11 We performed systematic online searches for individual level dietary surveys in global and regional databases: PubMed, Embase, Web of science, LILACS, African Index Medicus, and the South-east Asia Index Medicus, using search terms “nutrition” or “diet” or “food habits” or “nutrition surveys” or “diet surveys” or “food habits”[mesh] or “diet”[mesh] or “nutrition surveys”[mesh] or “diet surveys”[mesh] and (“country of interest”). Additionally, we identified surveys through extensive personal communications with researchers and government authorities throughout the world, inviting them to be corresponding members of the Global Dietary Database. The search included surveys that collected data on at least one of 54 foods, beverages, nutrients, or dietary indices, including SSBs. A single reviewer screened identified studies by title and abstract, a random subset of articles was screened by a second reviewer to ensure consistency and accuracy, and a third reviewer screened studies to ensure that survey inclusion criteria were met. Surveys were prioritized if they were performed at national or subnational level and used individual level dietary assessments with standardized 24 hour recalls, food frequency questionnaires, or short standardized questionnaires (eg, Demographic Health Survey questionnaires). When national or subnational surveys at individual level were not identified for a country, we searched for individual level surveys from large cohorts, the World Health Organization (WHO) Global Infobase, and the WHO Stepwise Approach to Surveillance database. When individual level dietary surveys were not identified for a particular country, we considered household budget surveys. We excluded surveys focused on special populations (eg, exclusively pregnant or nursing mothers, individuals with a specific disease) or cohorts (eg, specific occupations or dietary patterns). Supplementary methods 1-3, supplementary tables 1-2, and supplementary figure 1 provide additional details on the methods. The final Global Dietary Database model incorporated 1224 dietary surveys from 185 countries, with 89% representative at national or subnational level, thus covering about 99.0% of the global population in 2018. Among these, 450 surveys reported data on SSBs, 85% of which provided individual level data. These 450 originated from 118 countries and surveyed a total of 2.9 million individuals, with 94% being representative at national or subnational level (see supplementary tables 4 and 5). Supplementary data 1 provides details on the characteristics of the survey.

Data extraction

For each survey, we used standardized methods to extract data on survey characteristics and dietary metrics, units, and mean and standard deviation of intake by age, sex, education level, and urban or rural residence (see supplementary methods 1). 12 All intakes are reported adjusted to 5439 kilojoules (kJ) daily (1300 kilocalories (kcal) daily) for ages 3-5 years, 7113 kJ/day (1700 kcal/day) for ages 6-10 years, and 8368 kJ/day (2000 kcal/day) for ages 11-19 years. SSBs were defined as any beverages with added sugars and ≥209 kJ (50 kcal) for each 237 g serving, including commercial or homemade beverages, soft drinks, energy drinks, fruit drinks, punch, lemonade, and aguas frescas. This definition excluded 100% fruit and vegetable juices, non-caloric artificially sweetened drinks, and sweetened milk. All included surveys used this definition.

Data modeling

Our model estimates intakes of SSBs for years for which we have survey data available. To incorporate and deal with differences in data comparability and sampling uncertainty, we used a bayesian model with a nested hierarchical structure (with random effects by country and region) to estimate the mean consumption of SSBs and its statistical uncertainty for each of 264 population strata across 185 countries for 1990, 1995, 2000, 2005, 2010, 2015, and 2018. Our model incorporated seven world regions: central and eastern Europe and central Asia, high income countries, Latin America and the Caribbean, the Middle East and north Africa, south Asia, southeast and east Asia, and sub-Saharan Africa. Our team and others (eg, the Global Burden of Disease study) have previously used this (or similar) classification for world regions, which aims to group nations by general similarities in risk profiles and disease outcomes. Although the current analysis only focuses on children and adolescents aged 3-19 years, the model used all age data to generate the strata predictions. Modeling all age groups jointly allows the use of the full set of available data and covariates to inform estimates, including age patterns, relationships between predictors and SSB intakes, and influence of covariates (eg, dietary assessment methods).

Primary inputs were the survey level quantitative data on SSB intakes (by country, time, age, sex, education level, and urban or rural residence), survey characteristics (dietary assessment method, type of dietary metric), and country-year specific covariates (see supplementary methods 2). The model included overdispersion of survey level variance for surveys that were not nationally representative or not stratified by smaller age groups (≤10 years), sex, education level, or urbanicity. Survey level covariates addressed potential survey bias, and the overdispersion parameter non-sampling variation due to survey level error (from imperfect study design and quality). The model then estimated intakes jointly stratified by age (<1, 1-2, 3-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, ≥95 years), sex, education (≤6 years, >6-12 years, >12 years), and urbanicity (urban, rural). For children and adolescents (age <20 years) the stratification by education refers to parental education.

The uncertainty of each stratum specific estimate was quantified using 4000 Monte Carlo iterations to determine posterior predictive distributions of mean intake jointly by country, year, and sociodemographic subgroup. We computed the median intake and the 95% uncertainty interval (UI) for each stratum as the 50th, 2.5th, and 97.5th percentiles of the 4000 draws, respectively. For model selection and validation, we compared results from fivefold cross validation (randomly omitting 20% of the survey data at the stratum level and using that to evaluate predictive ability, run five times), compared predicted country intakes with survey observed intakes, assessed implausible estimates (see supplementary table 2), and visually assessed global and national mean intakes using heat maps.

A second bayesian model was used to strengthen time trend estimates for dietary factors (including SSBs) with corresponding available date on food or nutrients from the Food and Agriculture Organization’s food balance sheets 14 or the Global Expanded Nutrient Supply dataset. 15 No time component was formally included in the model; rather, time was captured by the underlying time variation in the model covariates. This second model incorporated country level intercepts and slopes, along with their correlation estimated across countries. The model is commonly referred to as a varying slopes model structure, and it leverages two dimensional partial pooling between intercepts and slopes to regularize all parameters and minimize the risk of overfitting. 16 17 The final presented results are a combination of these two bayesian models, as detailed in supplementary methods 3.

Statistical analysis

Global, regional, national, and within country population subgroup intakes of SSBs and their uncertainty were calculated as population weighted averages using all 4000 posterior predictions for each of the 264 demographic strata in each country-year. Population weights for each year were derived from the United Nations (UN) Population Division, 18 supplemented with data for education and urban or rural status from Barro and Lee 19 and the UN. 20

Intakes were calculated as 248 g (8 oz) servings weekly, or two thirds of a common 355 mL (12 oz) can of a sugar sweetened drink weekly. Absolute changes and percentage changes in consumption between 1990 and 2005, 2005 and 2018, and 1990 and 2018 were calculated at the stratum specific prediction level to account for the full spectrum of uncertainty and standardized to the proportion of individuals within each stratum in 2018 to account for changes in population characteristics over time. Stratum specific predictions were summed to calculate the differences in intake between all children and adolescents aged 3-19 years, high and low parental education (>12 years and ≤6 years, respectively), and urban and rural residence, further stratified by sex, age, parental education, and area of residence, as appropriate.

National intakes of SSBs and trends were assessed by sociodemographic development index, including trends over time between 1990 and 2005, 2005 and 2018, and 1990 and 2018. The sociodemographic development index is a measure of the development of a country or region, ranging from 0 to 1, with 0 representing the minimum level and 1 the maximum level of development of a given nation, and it is based on income per capita, average educational attainment, and fertility rates. 21 Our UIs are derived from a bayesian model and can be interpreted as at least 95% probability that the true mean is contained within the interval. For comparisons between groups (or over time), if the 95% UI of the difference (or change over time) does not include zero, this can be interpreted as at least 95% probability of a true difference. No hypothesis testing was conducted, as estimation with uncertainty has been recognized as a more informative approach. 22

Patient and public involvement

No patients or members of the public were involved in the study as we did not collect data directly from individuals, the funding source did not provide support for direct patient and public involvement, and the study was initiated before patient and public involvement was common. The present analysis used modeled data derived from dietary data that had been previously collected, and we engaged with a diverse set of 320 corresponding members in nations around the world.

Global, regional, and national SSB intakes in 2018

In 2018, the mean global intake of SSBs among children and adolescents was 3.6 (standardized serving=248 g (8 oz)) servings/week (95% UI 3.3 to 4.0), with wide (sevenfold) variation across world regions, from 1.3 servings/week (1.0 to 1.9) in south Asia to 9.1 (8.3 to 10.1) in Latin America and the Caribbean ( table 1 ). Among the 25 countries with the largest population of children and adolescents worldwide, mean highest intakes were in Mexico (10.1 (9.1 to 11.3)), followed by Uganda (6.9 (4.5 to 10.6)), Pakistan (6.4 (4.3 to 9.7)), South Africa (6.2 (4.7 to 8.1)), and the US (6.2 (5.9 to 6.6)); while the lowest intakes were in India and Bangladesh (0.3 servings/week each) ( fig 1 , also see supplementary figure 9). Of the 185 countries included in the analysis, 56 (30.3%) had mean SSB intakes of ≥7 servings/week, representing 238 million young people aged 3-19 years, or 10.4% of the global population for this age group.

Global and regional mean intake of SSBs (248 g (8 oz) serving/week) in children and adolescents aged 3-19 years, by age, sex, parental education, and area of residence across 185 countries in 2018

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National mean intakes of SSBs (standardized 248 g (8 oz) serving/week for this analysis) in children and adolescents aged 3-19 years across 185 countries in 2018. SSBs were defined as any beverage with added sugars and ≥209 kJ (50 kcal) per 237 g serving, including commercial or homemade beverages, soft drinks, energy drinks, fruit drinks, punch, lemonade, and aguas frescas. This definition excludes 100% fruit and vegetable juices, non-caloric artificially sweetened drinks, and sweetened milk. For this visual representation, values were truncated at 21 servings/week to better reflect the distribution of intakes globally. The figure was created using the rworldmap package (v1.3-6). SSB=sugar sweetened beverage

SSB intake by sex and age in 2018

Globally, regionally, and nationally, SSB intakes between male and female children and adolescents aged 3-19 years did not differ noticeably, as observed by the 95% UI of the differences including zero ( table 1 , also see supplementary tables 7 and 8). Intake of SSBs in young people was greater with increasing age globally and regionally, although with varying magnitude of these differences by region ( table 1 and fig 2 ). For instance, intakes of SSBs exceeded 9 servings/week among children aged ≥10 years in Latin America and the Caribbean and in the Middle East and north Africa but were just over 1 serving/week among young people of the same age in south Asia. Regionally, patterns of intake by age were similar between young people (see supplementary figure 2). Considering both age and region, the highest weekly intakes of SSBs were in Latin America and the Caribbean in 15-19 year olds (11.5 servings/week) and lowest in southeast and east Asia in 3-4 year olds (0.9 servings/week) ( table 1 ). Among the 25 most populous countries, the highest intakes of SSBs were in Mexico among 10-14 year olds (11.9 servings/week) and 15-19 year olds (12.8 servings/week) and lowest in Kenya and China among 3-4 year olds (0.2 servings/week each) (supplementary table 6).

Global and regional intakes of SSBs (standardized 248 g (8 oz) serving/week for this analysis) by age in children and adolescents aged 3-19 years in 2018. SSBs were defined as any beverage with added sugars and ≥209 kJ (50 kcal) per 237 g serving, including commercial or homemade beverages, soft drinks, energy drinks, fruit drinks, punch, lemonade, and aguas frescas. This definition excludes 100% fruit and vegetable juices, non-caloric artificially sweetened drinks, and sweetened milk. The filled circles represent the mean SSBs intake (248 g (8 oz) serving/week) and the error bars the 95% UIs. In previous Global Dietary Database reports, the region central and eastern Europe and central Asia was referred to as the former Soviet Union, and southeast and east Asia was referred to as Asia. SSBs=sugar sweetened beverages; UI=uncertainty interval

SSB intake by parental education and urbanicity in 2018

Intakes of SSBs were greater in children and adolescents from urban areas than those from rural areas (4.6 servings/week (4.2 to 5.0) v 2.7 servings/week (2.4 to 3.1); table 1 ). When parental education and area of residence was assessed jointly, globally the highest intakes of SSBs were among children and adolescents of parents with high education in urban areas (5.15 servings/week (4.76 to 5.64)), representing 11.2% of the global population of children and adolescents ( fig 3 ). Regionally, a similar pattern was observed in Latin America and the Caribbean, south Asia, and sub-Saharan Africa, with the largest intakes of SSBs in children and adolescents of parents with high and medium education in urban and rural areas in Latin America and the Caribbean (≥9 servings/week each), representing 56% of the population of children and adolescents in that region. Intakes of SSBs by area of residence and education were inverted in the Middle East and north Africa, with larger intakes among children and adolescents from rural areas and of parents with lower education, and little variation was observed in other world regions. See supplementary tables 7, 9, and 10, supplementary figures 3 and 4, and supplementary results for further details on SSB intakes by parental education and area of residence.

Global and regional mean SSB intakes (standardized 248 g (8 oz) serving/week for this analysis) in children and adolescents aged 3-19 years by area of residence and parental education level in 2018. SSBs were defined as any beverage with added sugars and ≥209 kJ (50 kcal) per 237 g serving, including commercial or homemade beverages, soft drinks, energy drinks, fruit drinks, punch, lemonade, and aguas frescas. This definition excludes 100% fruit and vegetable juices, non-caloric artificially sweetened drinks, and sweetened milk. Error bars represent 95% UIs. Values were truncated at 11.5 servings/week to better reflect the distribution of intakes. Upper 95% UIs above that value are shown with a dashed line. In previous Global Dietary Database reports, the region central and eastern Europe and central Asia was referred to as the former Soviet Union, and southeast and east Asia was referred to as Asia. SSBs=sugar sweetened beverages; UI=uncertainty interval

Trends in SSB intake during 1990-2005, 2005-18, and 1990-2018

Supplementary tables 11-14 and supplementary figures 5-8 show absolute global, regional, and national intakes of SSBs for 1990 and 2005. Globally, from 1990 to 2018, intakes among children and adolescents increased by 0.68 servings/week (95% UI 0.54 to 0.85; 22.9%) ( fig 4 , also see supplementary data 2). The magnitude of global increase was similar from 1990 to 2005 (0.33 (0.25 to 0.43); 11.0%) and from 2005 to 2018 (0.35 (0.26 to 0.47); 10.7%). However, regionally, changes did not follow the same global pattern. Between 1990 and 2005, increases in intakes of SSBs were observed in most regions, with the largest increase in high income countries (1.48 (1.37 to 1.60); 29.1%), little change in central and eastern Europe and central Asia and in south Asia, and a decrease in Latin America and the Caribbean (−1.20 (−1.54 to −0.88); −12.7%). More recently, from 2005 to 2018, increases continued in most regions, with the largest in sub-Saharan Africa (1.38 (1.01 to 1.85); 49.2%), except for south Asia where little change was evident and high income countries where intakes decreased (−1.59 (−1.71 to −1.47); −24.1%). In the overall period from 1990 to 2018, the largest regional increase was in sub-Saharan Africa (2.17 (1.60 to 2.88); 106%), with other world regions showing steady, more modest increases over time. Exceptions were high income countries and Latin America and the Caribbean, where intakes increased after 1990 and then decreased close to 1990 levels by 2018. The supplementary results and supplementary table 15 describe regional trends over time by age, sex, parental education, and urbanicity.

(Top panel) Mean SSB intakes (standardized 248 g (8 oz) serving/week for this analysis) by world region in 1990, 2005, and 2018, and absolute changes from 1990 to 2005, 2005-18, and 1990-2018 in children and adolescents aged 3-19 years. (Bottom panel) Absolute changes in SSB intakes from 1990-2005, 2005-18, and 1990-2018. SSBs were defined as any beverage with added sugars and ≥209 kJ (50 kcal) per 237 g serving, including commercial or homemade beverages, soft drinks, energy drinks, fruit drinks, punch, lemonade, and aguas frescas. This definition excludes 100% fruit and vegetable juices, non-caloric artificially sweetened drinks, and sweetened milk. Error bars represent 95% UIs. In previous Global Dietary Database reports, the region central and eastern Europe and central Asia was referred to as the former Soviet Union, and southeast and east Asia was referred to as Asia. SSBs=sugar sweetened beverages; UI=uncertainty interval

Among the 25 most populous countries, the largest increase from 1990 to 2005 was in the US (2.95 (2.73 to 3.17); 43.2%) and the largest decrease was in Brazil (−3.42 (−3.95 to −2.97); −40.6%) (see supplementary data 2 and supplementary figure 9). From 2005 to 2018, the largest increase was in Uganda (4.30 (2.31 to 7.39); 173%), and the largest decrease was in the US (−3.55 (−3.81 to −3.30); −36.4%). Overall, between 1990 and 2018, the largest increased was in Uganda (6.73 (4.38 to 10.39); 5573%) and the largest decrease was in Brazil (−3.29 (−3.79 to −2.86); −39.0%) (see supplementary data 2 and supplementary figure 10). The supplementary results and supplementary tables 16-19 show trends over time within the 25 most populous countries by age, sex, parental education, and urbanicity.

SSB intakes and trends by sociodemographic development index and obesity

In 1990 and 2005 a positive correlation was evident between national intakes of SSBs and sociodemographic development index, with greater intakes observed in countries with a higher sociodemographic development index (see supplementary figures 11 and 12). However, this correlation was no longer present in 2018 (r=−0.001, P=0.99). Intakes of SSBs and prevalence of obesity were positively correlated in both 1990 (r=0.28, P<0.001) and 2018 (r=0.23, P<0.001) (see supplementary figure 13).

Intakes of SSBs among children and adolescents aged 3-19 years in 185 countries increased by 23% (0.68 servings/week (0.54 to 0.85)) from 1990 to 2018, parallel to the rise in prevalence of obesity among this population globally. 23 We found a positive correlation between intake of SSBs and prevalence of obesity among children and adolescents in all years. This finding needs particular attention given the incremental economic costs associated with overweight and obesity globally, which are projected to increase from about $2.0tn (£1.6tn; €1.9tn) in 2020 to $18tn by 2060, exceeding 3% of the world’s gross domestic product. 24 Chronic diet related conditions such as obesity have been recognized as part of a global syndemic along with undernutrition given their interaction and shared underlying societal drivers. 25 Tackling drivers of obesity and other diet related diseases among children and adolescents is also critical to be better equipped for potential future pandemics, as cardiometabolic conditions such as obesity, diabetes, and hypertension were top drivers of increased risk of hospital admission and death with covid-19. 26 The increase in intakes of SSBs among children and adolescents corresponded to nearly twice the absolute increase in intake observed among the adult population from 1990 to 2018, for which policies targeting specifically children and adolescents are critical. 13 Young people are particularly appealing to the food industry as they are easily influenced by food marketing, having an effect on not only their current intakes but also their preferences as they develop into adulthood. 27 Their susceptibility to marketing, rising trends in obesity, and accelerated increases in intakes of SSBs underline the necessity for interventions such as taxes, regulations on front-of-package labeling, and regulations in the school environment to curb intakes of SSBs. 6 8 27 28

Changes in intakes of SSBs in children and adolescents from 1990 to 2018 varied substantially by world region. As with the adult population, the largest increase from 1990 to 2018 was in sub-Saharan Africa, emphasizing the need for prompt interventions in this region. Young people in the Middle East and north Africa and in southeast and east Asia showed a more accelerated increase in SSB consumption compared with adults, underlining the importance of policies targeting young people in these regions. The Middle East and north Africa had the second highest intakes of SSBs among children and adolescents in 2018, which differed from our findings among adults, where the Middle East and north Africa occupied third place after sub-Saharan Africa.

Latin America and the Caribbean experienced an overall decrease in intakes of SSBs from 1990 to 2005, which could be attributed to the economic crisis experienced among most of the major economies in the region during this period, 29 in addition to potential greater health awareness as a result of healthy eating campaigns in several countries in the region. 30 In contrast, the increases in intakes in this region from 2005 to 2018 may relate to economic recovery, increased marketing campaigns, and industry opposition to public policies to reduce the intake of SSBs. 31 These findings align with findings in the adult population of this region. 13 Over the past 30 years, Latin America and the Caribbean has undergone an accelerated transformation in the food systems, resulting in wider availability of unhealthy foods, including SSBs, that could explain the large intakes in this region. 7 Moreover, the influence of multinational corporations responsible for ultra-processed foods, marketing strategies targeted at young people, lack of (or poor) regulatory measures to limit the intake of SSBs have also been consistently observed in Latin America and other regions with improving economies. 1 6 7 The use of social media and TV to target advertising at young people has been identified as being especially high in Latin America as well as in the Middle East. 6 27

High income countries experienced an overall decrease in intakes of SSBs from 2005 to 2018. This might be explained by the increasing scientific and public health attention on the harms of SSBs as well as obesity in these nations during this period, which may have led to increased media and public awareness about the harms to health associated with SSBs, wider formulation, promotion, and availability of non-caloric sweetened beverage substitutes, and, more recently, taxation on SSBs in several of these nations. 32

The potential role of sociodemographic factors on intakes of SSBs was evidenced by the large variations in intake by parental education and urbanicity, particularly in south Asia and sub-Saharan Africa, evidencing the need to account for these factors in the design of policies and interventions. At national level, the correlation between intake of SSBs and sociodemographic development index changed from positive in 1990 to null in 2018 (see supplementary figure 11), suggesting that the association between the two might be reversing. This is similar to what was observed in adults, where the association between intake of SSBs and sociodemographic index changed from null to negative from 1990 to 2018. 13 Our new findings show similar directional trends in national and subnational intakes of SSBs among young people compared with adults, 13 although with generally higher absolute intakes among young people, suggesting nation specific influences on SSB intakes are at least partly shared across the lifespan. Further efforts are needed to incorporate data on other social determinants of health, such as income, access to water, access to healthcare, and race/ethnicity to elucidate additional potential heterogeneities.

Strengths and limitations of this study

Our study has several strengths. We assessed and reported global, regional, and national estimates of SSB intakes jointly stratified by age, sex, parental education, and urbanicity among children and adolescents. Compared with previous estimates, our current model included a larger number of dietary surveys, additional demographic subgroups, and years of assessment. Our updated bayesian hierarchical model better incorporated survey and country level covariates—and addressed heterogeneity and uncertainty about sampling and modeling. 13 33 Intakes were estimated from 450 surveys—mostly representative at national and subnational levels and collected at individual level—and represented 87.1% of the world’s population. Other recent estimates for global intakes of SSBs relied mostly on national per capita estimates of food availability (eg, Food and Agriculture Organization food balance sheets) or sales data. 34 Such estimates can substantially overestimate and underestimate intake compared with individual level data 35 and are less robust for characterizing differences across population subgroups. Our estimates are informed by dietary data at individual level collected from both 24 hour recalls (24% of surveys), considered the ideal method for assessing nutritional intakes of populations), and food frequency questionnaires (61% of surveys), a validated approach for measuring intakes of SSBs 36 (see supplementary table 4).

Overall, our findings should be taken as the best currently available, but nonetheless imperfect, estimates of SSB intakes worldwide. Even with systematic searches for all relevant surveys, we identified limited availability of data for several countries (particularly lower income nations) and time periods. 11 Thus, estimated findings in countries with no primary individual level surveys have higher corresponding uncertainty, informing surveillance needs to assess SSBs nationally and in populations at subnational level. Particularly, we identified limited surveys for south Asia (n=9) and sub-Saharan Africa (n=22), which might have affected the accuracy of our estimates in those regions (see supplementary table 4). This finding emphasizes the critical need for further efforts in data collection and surveillance, particularly in these regions. Categorization by age, parental education, and urbanicity were in groups rather than in more nuanced classifications, balancing the interest in subgroup detail versus the realities required from a global demographic effort of de novo harmonized analyses of individual level dietary data from hundreds of different dietary surveys and corresponding members globally. All types of dietary assessments include some errors, whether from individual level surveys, national food availability estimates, or other sources. Our model’s incorporation of multiple types and sources of dietary assessments provided the best available estimates of global diets, as well as the uncertainty of these estimates. For instance, self-reported data rely on the memory and personal biases of the respondents, thus introducing potential bias from underreporting or overreporting of actual intakes. Furthermore, assumptions relating to standardization of serving sizes, SSB definitions, energy adjustment, and disaggregation at household level, as well as of no interaction between sociodemographic variables in our model, could have impacted our estimates. To minimize these limitations, we used standardized approaches and carefully documented each survey’s methods and standardization processes to maximize transparency.

Our definition and data collection on SSBs excluded 100% fruit juice, sugar sweetened milk, tea, and coffee, given that evidence for health effects of these beverages is inconsistent and does not achieve at least probable evidence for causal harms. 37 38 These differences may relate to additional nutrients, such as calcium, vitamin D, fats, and protein in milk, caffeine and polyphenols in coffee and tea, and fiber and vitamins in 100% juice; or to differences in rapidity of consumption and drinking patterns. Each of these beverages is generally also excluded in policy and surveillance efforts around SSBs. A recent meta-analysis suggested a modest positive association between 100% fruit juices and body mass index in children (0.03 units higher for each daily serving), 39 highlighting the need for more research on the health impacts of these and other beverages in children. Sweetened milks are mostly targeted at children and adolescents, and in some regions are mostly consumed by the youngest children. 40 Given that our SSBs definition excluded sweetened milk, this could partially explain the low intakes observed in our study among the youngest age categories. Future studies should also look into characterizing intakes of sweetened milks, especially in countries such as the US, Australia, Pakistan, and Chile where high intakes among children and adolescents have been reported. 40 41 Home sweetened teas and coffees were not explicitly excluded from the definition of SSBs at the time of data collection, but tea and coffee were collected as separate variables and thus most likely excluded by data owners from the SSBs category. SSBs were defined as beverages with added sugars and ≥209 kJ (50 kcal) per 237g serving, capturing most of the SSBs during the time period of our investigation that typically contained about 418 kJ (100 kcal) per serving. More recently, some SSBs with slightly less than 10 g of added sugar have entered the market. As these are a relatively recent addition, their exclusion is unlikely to meaningfully alter our findings, but future research should focus on more refined surveillance of SSBs to allow flexibility in beverage group definitions—for example, similar to the data harmonized in our collaboration with the FAO/WHO GIFT food consumption data tool. 42 Our current definition leveraging product name and caloric content to identify beverages with added sugar across the world ensures consistency in reporting.

Intakes of SSBs among children and adolescents aged 3-19 years in 185 countries increased by almost a quarter from 1990 to 2018, parallel to the rise in prevalence of obesity among this population globally. Policies and approaches at both a national level and a more targeted level are needed to reduce intakes of SSBs among young people worldwide, highlighting the larger intakes across all education levels in urban and rural areas in Latin America and the Caribbean, and the growing problem of SSBs for public health in sub-Saharan Africa. Our findings are intended to inform current and future policies to curb SSB intakes, adding to the UN’s 2030 Agenda for Sustainable Development for improving health and wellbeing, reducing inequities, responsible consumption, poverty, and access to clean water.

What is already known in this topic

The intake of sugar sweetened beverages (SSBs) has been consistently reported to increase the risk of obesity among children and adolescents

This is especially concerning given that obesity in childhood tends to persist into adulthood, increasing the risk of type 2 diabetes, cardiovascular disease, and premature mortality

Quantification of SSB intakes among children and adolescents is therefore critical, yet recent estimates among children and adolescents are unavailable for most nations

What this study adds

Intakes of SSBs among children and adolescents aged 3-19 years in 185 countries increased by almost a quarter from 1990 to 2018, parallel to the rise in prevalence of obesity among this population globally

Larger intakes were identified across all education levels in urban and rural areas in Latin America and the Caribbean, along with the growing problem of SSBs for public health in sub-Saharan Africa

Intake of SSBs among children and adolescents showed large heterogeneity by region and population characteristics, informing the need for national and targeted policies and approaches to reduce SSB intake among this population worldwide

Ethics statements

Ethical approval.

This investigation was exempt from ethical review board approval because it was based on published deidentified nationally representative data, without personally identifiable information. Individual surveys underwent ethical review board approval required for the applicable local context.

Data availability statement

The individual SSB intake estimate distribution data used in this as means and uncertainty (SE) for each strata in the analysis are available freely online at the Global Dietary Database (Download 2018 Final Estimates: https://www.globaldietarydatabase.org/data-download ). Global Dietary Database data were utilized in agreement with the database guidelines. Absolute and relative differences by strata and by year presented in this analysis were calculated using the 4000 simulations corresponding to the stratum level intake data derived from the bayesian model. The 4000 simulations files can be made available to researchers upon request. Eligibility criteria for such requests include utilization for non-profit purposes only, for appropriate scientific use based on a robust research plan, and by investigators from an academic institution. If you are interested in requesting access to the data, please submit the following documents: (1) proposed research plan (please download and complete the proposed research plan form: https://www.globaldietarydatabase.org/sites/default/files/manual_upload/research-proposal-template.pdf ), (2) data-sharing agreement (please download this form https://www.globaldietarydatabase.org/sites/default/files/manual_upload/tufts-gdd-data-sharing-agreement.docx and complete the highlighted fields, have someone who is authorized to enter your institution into a binding legal agreement with outside institutions sign the document. Note that this agreement does not apply when protected health information or personally identifiable information are shared), (3) email items (1) and (2) [email protected]. Please use the subject line “GDD Code Access Request.” Once all documents have been received, the Global Dietary Database team will be in contact with you within 2-4 weeks about subsequent steps. Data will be shared as .csv or .xlsx files, using a compressed format when appropriate. Population weights for each strata and year were derived from the United Nations Population Division ( https://population.un.org/wpp/ ), supplemented with data for education and urban or rural status from Barro and Lee (doi: 10.3386/w15902 ) and the United Nations ( https://population.un.org/wup/Download/ ).

Acknowledgments

We acknowledge the Tufts University High Performance Computing Cluster ( https://it.tufts.edu/high-performance-computing ), which was used for the research reported in this paper.

Members of the Global Dietary Database (see supplementary text 1 for affiliations)

Antonia Trichopoulou, Murat Bas, Jemal Haidar Ali, Tatyana El-Kour, Anand Krishnan, Puneet Misra, Nahla Hwalla, Chandrashekar Janakiram, Nur Indrawaty Lipoeto, Abdulrahman Musaiger, Farhad Pourfarzi, Iftikhar Alam, Celine Termote, Anjum Memon, Marieke Vossenaar, Paramita Mazumdar, Ingrid Rached, Alicia Rovirosa, María Elisa Zapata, Roya Kelishadi, Tamene Taye Asayehu, Francis Oduor, Julia Boedecker, Lilian Aluso, Emanuele Marconi, Laura D’Addezio, Raffaela Piccinelli, Stefania Sette, Johana Ortiz-Ulloa, J V Meenakshi, Giuseppe Grosso, Anna Waskiewicz, Umber S Khan, Kenneth Brown, Lene Frost Andersen, Anastasia Thanopoulou, Reza Malekzadeh, Neville Calleja, Anca Ioana Nicolau, Cornelia Tudorie, Marga Ocke, Zohreh Etemad, Mohannad Al Nsour, Lydiah M Waswa, Maryam Hashemian, Eha Nurk, Joanne Arsenault, Patricio Lopez-Jaramillo, Abla Mehio Sibai, Albertino Damasceno, Pulani Lanerolle, Carukshi Arambepola, Carla Lopes, Milton Severo, Nuno Lunet, Duarte Torres, Heli Tapanainen, Jaana Lindstrom, Suvi Virtanen, Cristina Palacios, Noel Barengo, Eva Roos, Irmgard Jordan, Charmaine Duante, Corazon Cerdena (retired), Imelda Angeles-Agdeppa (retired), Josie Desnacido, Mario Capanzana (retired), Anoop Misra, Ilse Khouw, Swee Ai Ng, Edna Gamboa Delgado, Mauricio T Caballero, Johanna Otero, Hae-Jeung Lee, Eda Koksal, Idris Guessous, Carl Lachat, Stefaan De Henauw, Ali Reza Rahbar, Alison Tedstone, Annie Ling, Beth Hopping, Catherine Leclercq, Christian Haerpfer, Christine Hotz, Christos Pitsavos, Coline van Oosterhout, Debbie Bradshaw, Dimitrios Trichopoulos, Dorothy Gauci, Dulitha Fernando, Elzbieta Sygnowska, Erkki Vartiainen, Farshad Farzadfar, Gabor Zajkas, Gillian Swan, Guansheng Ma, Hajah Masni Ibrahim, Harri Sinkko, Isabelle Sioen, Jean-Michel Gaspoz, Jillian Odenkirk, Kanitta Bundhamcharoen, Keiu Nelis, Khairul Zarina, Lajos Biro, Lars Johansson, Leanne Riley, Mabel Yap, Manami Inoue, Maria Szabo, Marja-Leena Ovaskainen, Meei-Shyuan Lee, Mei Fen Chan, Melanie Cowan, Mirnalini Kandiah, Ola Kally, Olof Jonsdottir, Pam Palmer, Philippos Orfanos, Renzo Asciak, Robert Templeton, Rokiah Don, Roseyati Yaakub, Rusidah Selamat, Safiah Yusof, Sameer Al-Zenki, Shu-Yi Hung, Sigrid Beer-Borst, Suh Wu, Widjaja Lukito, Wilbur Hadden, Xia Cao, Yi Ma, Yuen Lai, Zaiton Hjdaud, Jennifer Ali, Ron Gravel, Tina Tao, Jacob Lennert Veerman, Mustafa Arici, Demosthenes Panagiotakos, Yanping Li, Gülden Pekcan, Karim Anzid, Anuradha Khadilkar, Veena Ekbote, Irina Kovalskys, Arlappa Nimmathota, Avula Laxmaiah, Balakrishna Nagalla, Brahmam Ginnela, Hemalatha Rajkumar, Indrapal Meshram, Kalpagam Polasa, Licia Iacoviello, Marialaura Bonaccio, Simona Costanzo, Yves Martin-Prevel, Nattinee Jitnarin, Wen-Harn Pan, Yao-Te Hsieh, Sonia Olivares, Gabriela Tejeda, Aida Hadziomeragic, Le Tran Ngoan, Amanda de Moura Souza, Daniel Illescas-Zarate, Inge Huybrechts, Alan de Brauw, Mourad Moursi, Augustin Nawidimbasba Zeba, Maryam Maghroun, Nizal Sarrafzadegan, Noushin Mohammadifard, Lital Keinan-Boker, Rebecca Goldsmith, Tal Shimony, Gudrun B Keding, Shivanand C Mastiholi, Moses Mwangi, Yeri Kombe, Zipporah Bukania, Eman Alissa, Nasser Al-Daghri, Shaun Sabico, Rajesh Jeewon, Martin Gulliford, Tshilenge S Diba, Kyungwon Oh, Sihyun Park, Sungha Yun, Yoonsu Cho, Suad Al-Hooti, Chanthaly Luangphaxay, Daovieng Douangvichit, Latsamy Siengsounthone, Pedro Marques-Vidal, Peter Vollenweider, Constance Rybak, Amy Luke, Nipa Rojroongwasinkul, Noppawan Piaseu, Kalyana Sundram, Jeremy Koster, Donka Baykova, Parvin Abedi, Sandjaja Sandjaja, Fariza Fadzil, Noriklil Bukhary Ismail Bukhary, Pascal Bovet, Yu Chen, Norie Sawada, Shoichiro Tsugane, Lalka Rangelova, Stefka Petrova, Vesselka Duleva, Ward Siamusantu, Lucjan Szponar, Hsing-Yi Chang, Makiko Sekiyama, Khanh Le Nguyen Bao, Sesikeran Boindala, Jalila El Ati, Ivonne Ramirez Silva, Juan Rivera Dommarco, Luz Maria Sanchez-Romero, Simon Barquera, Sonia Rodríguez-Ramírez, Nayu Ikeda, Sahar Zaghloul, Anahita Houshiar-rad, Fatemeh Mohammadi-Nasrabadi, Morteza Abdollahi, Khun-Aik Chuah, Zaleha Abdullah Mahdy, Alison Eldridge, Eric L Ding, Herculina Kruger, Sigrun Henjum, Milton Fabian Suarez-Ortegon, Nawal Al-Hamad, Veronika Janská, Reema Tayyem, Bemnet Tedla, Parvin Mirmiran, Almut Richter, Gert Mensink, Lothar Wieler, Daniel Hoffman, Benoit Salanave, Shashi Chiplonkar, Anne Fernandez, Androniki Naska, Karin De Ridder, Cho-il Kim, Rebecca Kuriyan, Sumathi Swaminathan, Didier Garriguet, Anna Karin Lindroos, Eva Warensjo Lemming, Jessica Petrelius Sipinen, Lotta Moraeus, Saeed Dastgiri, Sirje Vaask, Tilakavati Karupaiah, Fatemeh Vida Zohoori, Alireza Esteghamati, Sina Noshad, Suhad Abumweis, Elizabeth Mwaniki, Simon G Anderson, Justin Chileshe, Sydney Mwanza, Lydia Lera Marques, Samuel Duran Aguero, Mariana Oleas, Luz Posada, Angelica Ochoa, Alan Martin Preston, Khadijah Shamsuddin, Zalilah Mohd Shariff, Hamid Jan Bin Jan Mohamed, Wan Manan, Bee Koon Poh, Pamela Abbott, Mohammadreza Pakseresht, Sangita Sharma, Tor Strand, Ute Alexy, Ute Nöthlings, Indu Waidyatilaka, Ranil Jayawardena, Julie M Long, K Michael Hambidge, Nancy F Krebs, Aminul Haque, Liisa Korkalo, Maijaliisa Erkkola, Riitta Freese, Laila Eleraky, Wolfgang Stuetz, Laufey Steingrimsdottir, Inga Thorsdottir, Ingibjorg Gunnarsdottir, Lluis Serra-Majem, Foong Ming Moy, Corina Aurelia Zugravu, Elizabeth Yakes Jimenez, Linda Adair, Shu Wen Ng, Sheila Skeaff, Regina Fisberg, Carol Henry, Getahun Ersino, Gordon Zello, Alexa Meyer, Ibrahim Elmadfa, Claudette Mitchell, David Balfour, Johanna M Geleijnse, Mark Manary, Laetitia Nikiema, Masoud Mirzaei, Rubina Hakeem

Contributors: LLC, RM, and DM conceived the study. FC, PS, JZ, JRS, JEM, VM, LLC, RM, DM curated the data. FC, LLC, RM, and DM were responsible for the methodology. LLC, JRS, VM, and RM collected the data. FC, PS, JZ, JEM, VM, and LLC developed the software. FC, PS, JZ, VM, LLC, RM, and DM validated the data. LLC, SBC, SB, RM, and DM performed the formal analysis. LLC prepared the original draft of the manuscript. LLC, FC, PS, JZ, JRS, JEM, VM, SBC, SB, RM, and DM wrote, reviewed, and edited the manuscript. LLC generated the original figures and tables; SBC, SB, RM, and DM supervised the analysis, manuscript draft, and generation of figures and tables. LLC, RM, and DM acquired funding. They are the guarantors. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This research was supported by the Bill & Melinda Gates Foundation (grant OPP1176682 to DM), the American Heart Association (grant 903679 to LLC), and Consejo Nacional de Ciencia y Tecnología in Mexico (to LLC). This material is based upon work supported by the National Science Foundation under grant number 2018149. The computational resource is under active development by Research Technology, Tufts Technology Services. The funding agencies had no role in the design of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare the following: support from the Bill & Melinda Gates Foundation, American Heart Association, and Consejo Nacional de Ciencia y Tecnología in Mexico. LLC reports research funding from the Bill & Melinda Gates Foundation, the American Heart Association, and Consejo Nacional de Ciencia y Tecnología in Mexico (CONACyT), outside of the submitted work. RM reports research funding from the Bill & Melinda Gates Foundation; and (ended) the US National Institutes of Health, Danone, and Nestle. She also reports consulting from Development Initiatives and as IEG chair for the Global Nutrition Report, outside of the submitted work. FC, JZ, and PS report research funding from the Bill & Melinda Gates Foundation, as well as the National Institutes of Health, outside of the submitted work. VM reports research funding the Canadian Institutes of Health Research and from the American Heart Association, outside the submitted work. JRS reports research funding from the Bill & Melinda Gates Foundation, as well as the National Institutes of Health, Nestlé, Rockefeller Foundation, and Kaiser Permanent Fund at East Bay Community Foundation, outside of the submitted work. SBC reports research funding from the US. National Institutes of Health, US. Department of Agriculture, the Rockefeller Foundation, US. Agency for International Development, and the Kaiser Permanente Fund at East Bay Community Foundation, outside the submitted work. SB reports funding from Bloomberg Philanthropies, CONACyT, United Nations International Children’s Emergency Fund (Unicef), and Fundación Rio Arronte, outside the submitted work. DM reports research funding from the US National Institutes of Health, the Bill & Melinda Gates Foundation, the Rockefeller Foundation, Vail Innovative Global Research, and the Kaiser Permanente Fund at East Bay Community Foundation; personal fees from Acasti Pharma, Barilla, Danone, and Motif FoodWorks; is on the scientific advisory board for Beren Therapeutics, Brightseed, Calibrate, Elysium Health, Filtricine, HumanCo, Instacart, January, Perfect Day, Tiny Organics, and (ended) Day Two, Discern Dx, and Season Health; has stock ownership in Calibrate and HumanCo; and receives chapter royalties from UpToDate, outside the submitted work. The investigators did not receive funding from a pharmaceutical company or other agency to write this report, and declare no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The lead author (LLC) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as originally planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: Our research will be disseminated to the scientific community in a scientific conference and scientific publications; to the public through our website and social media; and to funders and interested ministries in various nations through presentations and brief reports.

Provenance and peer review: Not commissioned; externally peer reviewed.

Publisher’s note: Published maps are provided without any warranty of any kind, either express or implied. BMJ remains neutral with regard to jurisdictional claims in published maps.

Editor’s note: The visual abstract was included in this article on 9 August 2024 post-publication.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

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