Digital Interventions Targeting Sugar-Sweetened Beverage and Energy Drink Consumption in Adolescents: A Promising but Fragmented Field

Abstract

Background: Adolescence is a critical period for the development of beverage-related behaviors; however, the role of digital interventions in addressing sugar-sweetened beverage and energy drink consumption remains insufficiently characterized. Methods: This study examined the literature on digital interventions aimed at modifying beverage-related behaviors among adolescents through a structured search in Scopus, PubMed, and Web of Science. Eligible studies were analyzed descriptively and classified according to intervention focus, digital delivery mode, behavior change strategy, ecological level of delivery, and beverage outcome specificity. Results: Twenty-two studies were included. Most studies have focused on sugar-sweetened beverage outcomes, whereas energy drink consumption has rarely been addressed directly. The identified interventions were predominantly mobile- or web-based and were often embedded within broader multicomponent, school-based, or lifestyle-oriented approaches. Beverage-related outcomes varied substantially across studies. Conclusions: Digital interventions targeting beverage-related behaviors in adolescents represent a promising but fragmented field. These findings offer a structured analytical foundation for designing and implementing future digital interventions targeting beverage consumption in adolescents.

1. Introduction

1.1. Adolescence and Beverage Consumption Patterns

Adolescence is a critical period for the development of dietary behaviors that often extend into adulthood. Among the beverages of greatest concern during this stage are sugar-sweetened beverages (SSBs) and energy drinks. SSBs encompass a broad range of beverages with added sugars, including soft, fruit, and sports drinks. Energy drinks overlap with this category because many also contain added sugars; however, they constitute a distinct subgroup because of their stimulant content, particularly caffeine. Both beverage types are widely consumed during adolescence and have been consistently linked to adverse health outcomes, underscoring their relevance as targets for public health interventions [1,2].

Globally, SSB consumption remains high among adolescents. Available evidence indicates that adolescents aged 12–17 years consume SSBs almost daily, with no consistent improvement in intake trends over the past two decades. Consumption has increased in several high-income countries, and similar patterns have emerged in middle-income settings. In the United States, SSBs continue to account for a substantial proportion of added sugar intake, largely through soft drinks, fruit drinks, and sports and energy drinks [3]. Population-based estimates from 185 countries further showed that the intake of SSBs among children and adolescents increased substantially between 1990 and 2018, although with marked regional variation [4]. Globally, the median prevalence of soft drink consumption among adolescents across 140 countries has been estimated at 37.0%, although considerable heterogeneity exists across settings [5]. Modeling data further suggest that approximately 60–70% of children and adolescents consume more than one 355 mL SSB per day, indicating that these behaviors may become established early in life [6].

Energy drink consumption is common across all countries and age groups. Data from the European Food Safety Authority indicate that 68% of adolescents aged 10–18 years had consumed energy drinks in the previous year, compared with 18% of younger children. This pattern is particularly concerning because energy drink intake has been associated with a wide range of adverse physical, mental, behavioral, educational, and overall health outcomes [1].

These consumption patterns reflect more than beverage availability alone. Beverage choices during adolescence are shaped by the interaction of neurobehavioral, social, and environmental influences. Highly palatable ultra-processed foods and beverages may activate reward-related pathways involved in pleasure, motivation, and learning, and adolescents may be especially susceptible to these effects because reward sensitivity is heightened during this developmental period. Repeated exposure may also impair hunger and satiety regulation, thereby facilitating overconsumption over time [3]. Social and digital environments may further reinforce unhealthy choices. Exposure to branded food and beverage content on social media has been associated with higher consumption of unhealthy drinks, whereas prolonged or compulsive Internet use has been linked to less favorable nutritional behaviors, including more frequent SSB intake [7]. More broadly, beverage consumption in children and adolescents appears to be shaped by the interaction between individual, familial, economic, and environmental determinants [3].

Taken together, these findings identify adolescence as a key window for understanding unhealthy beverage consumption. SSBs remain highly prevalent and continue to contribute substantially to added sugar intake, whereas energy drinks warrant particular attention because they combine high sugar content with stimulant ingredients and are linked to multiple adverse outcomes. Clarifying the factors that shape these behaviors during adolescence is therefore essential for informing effective prevention and policy strategies.

1.2. Health Concerns Related to Sugar-Sweetened Beverages and Energy Drinks

Excess body weight during adolescence is a major public health concern because it is associated with adverse metabolic and cardiovascular outcomes and often persists into adulthood [8,9]. Among the dietary factors implicated in this process, SSBs have emerged as among the most consistently studied contributors to unhealthy weight gain. Their high palatability, widespread consumption, and low satiety value may facilitate excessive energy intake, particularly when consumed frequently and in large amounts [8,10].

A substantial body of evidence links SSB consumption to overweight and obesity in children and adolescents. Observational studies have reported positive associations between soft drink intake and body weight in both cross-sectional and prospective studies, supporting the view that the habitual consumption of these beverages contributes to excess weight gain over time [2,11]. Although not all studies have found statistically significant associations, the overall pattern of evidence remains consistent with the detrimental role of SSBs in the development of obesity [2,10]. More broadly, SSBs have been identified as important dietary drivers of childhood obesity and related cardiometabolic risks, reinforcing their relevance in adolescent health research [9].

Intervention studies strengthen this interpretation. Randomized controlled trials have shown that reducing sugar-containing beverage intake may help attenuate increases in body weight and adiposity in children and adolescents [12,13]. Mechanistic and longitudinal evidence also supports the role of SSBs in promoting positive energy balance, adiposity, and metabolic disturbances [10,14]. Taken together, these findings suggest that SSBs are not merely markers of unhealthy diets but are likely active contributors to weight-related and metabolic risk.

In addition to obesity-related outcomes, frequent consumption of sugary and acidic beverages is associated with poor oral health. Evidence suggests that SSBs contribute to dental caries and erosive tooth wear in children and adolescents, especially when intake is frequent and oral hygiene is suboptimal [15,16,17]. Although oral hygiene and fluoride are important protective factors, they may not completely offset the harmful effects of repeated exposure to sugary and acidic drinks [15,16]. Therefore, preventive oral health behaviors should be considered complementary to, rather than substitutes for, reducing beverage intake.

Compared with SSBs, the evidence base for energy drinks in adolescents is more limited; however, several concerns have been identified. Because energy drinks often combine high sugar content with caffeine and other stimulant ingredients, they may contribute not only to poor dietary quality but also to sleep-related problems, behavioral disturbances, and oral health effects [1,18,19,20]. Although the literature on energy drinks is less developed than that on SSBs, available evidence indicates that they deserve separate attention rather than simply being treated as another sugary beverage category.

Overall, the literature provides stronger and more consistent support for an association between SSB consumption and overweight, obesity, and related metabolic disturbances in adolescents than that for energy drinks [2,10,11]. At the same time, both beverage categories raise important concerns, including oral health consequences and stimulant-related effects in the case of energy drinks. These findings support the need for preventive strategies that specifically target unhealthy beverage consumption during adolescence.

1.3. Why Digital Interventions May Be Relevant for Adolescents

Digital interventions may be particularly relevant for adolescents because they are delivered through technologies already embedded in their daily lives. Digital media offers scalable and potentially cost-effective ways to support behavior change. In adolescent health promotion, digital interventions have shown promise [21], particularly when they include active components, such as health education, goal setting, self-monitoring, and parental involvement. Similar conclusions have been reported in broader health-promotion research, indicating that digital components can improve diet, physical activity, and related outcomes when incorporated into engaging multicomponent interventions [21]. Although the evidence base appears stronger for web-based interventions than for apps or social media-based approaches, digital delivery remains a promising avenue for adolescent health promotion.

Digital interventions may also be well suited to the developmental characteristics of adolescence. This period is marked by increasing autonomy, heightened sensitivity to peer influence, and the growing importance of social approval. Research on adolescents’ social media engagement suggests that online participation is shaped not only by individual interests but also by perceived peer norms and anticipated peer validation. Adolescents who perceive greater peer approval may be more likely to engage with content that they find relevant, potentially increasing subsequent exposure through algorithmic and social curation processes [22]. Therefore, digital environments are not merely neutral channels for intervention delivery; they are social contexts in which norms, motivation, and visibility may influence receptivity to health-related messages.

Simultaneously, environments that support health promotion may also reinforce unhealthy behaviors. Adolescents are routinely exposed to food and beverage marketing, peer-generated content, and other digital cues that may normalize unhealthy consumption patterns [7]. This dual role of digital environments strengthens the rationale for examining whether digital interventions have been used to address unhealthy beverage consumption in this population and, if so, how they have been designed.

This study examined the literature on digital interventions aimed at modifying beverage-related behaviors among adolescents, with a particular focus on sugar-sweetened beverage and energy drink consumption. Specifically, it sought to identify the types of digital interventions used, describe their main characteristics and delivery formats, examine the beverage-related outcomes assessed, classify interventions according to key analytical dimensions, and identify gaps in the current evidence base to inform future research and intervention development. The findings may also support the design and implementation of future beverage-focused digital interventions for adolescents.

2. Materials and Methods

2.1. Study Design

This study was designed as a structured analytical review of the literature on digital interventions aimed at modifying beverage-related behavior among adolescents. Rather than conducting a systematic review of intervention effectiveness, the purpose of this study was to identify, characterize, and interpret how digital interventions addressing sugar-sweetened beverage and energy drink consumption have been designed, delivered, and assessed across the existing evidence base. Eligible studies were identified, summarized, and analytically classified according to key dimensions, including intervention focus, digital delivery mode, behavior change strategy, ecological level of delivery, and beverage outcome specificity. This approach was considered appropriate given the heterogeneity of the field and the study’s emphasis on conceptual mapping and interpretive comparison rather than effect-size estimation.

2.2. Search Strategy

A comprehensive literature search was conducted in Scopus, PubMed, and Web of Science using database-specific search strategies. The search terms included those related to digital interventions, such as digital intervention, digital health, eHealth, mHealth, mobile apps, smartphones, text messaging, SMS, social media, web-based, Internet-based, gamified, and serious games. It also included terms related to the target beverages, such as sugar-sweetened beverages, sweetened beverages, soft drinks, soda, sugary drinks, energy drinks, and sports drinks; terms related to adolescent populations, such as adolescents, teens, young people, secondary school students, and high school students; and terms related to consumption- or behavior-related outcomes, such as consumption, intake, reduction, decrease, prevention, and behavior. Database-specific filters were applied to retain original articles published in English and, where applicable, to exclude review-type publications.

2.3. Eligibility Criteria

Studies were eligible if they met the following criteria: (1) included an adolescent population; (2) examined a digital intervention or an intervention with a substantial digital component; (3) assessed outcomes related to SSBs, soft drinks, sugary drinks, soda, or energy drinks; and (4) reported original research relevant to the study objectives.

2.4. Study Selection

The search yielded 46 records in Scopus, 135 in PubMed, and 182 in the Web of Science, for a total of 363 records. References were exported to EndNote 2025/X25 (Clarivate, Philadelphia, PA, USA) for organization and duplicate removal. After duplicate removal and manual verification, 207 unique records remained for analysis. These records were then preliminarily filtered in EndNote 2025/X25 using keywords related to the target population, digital intervention components, beverage-related terms, and age-related terms, reducing the dataset to 134 records for title and abstract screening. Screening was conducted in Abstrackr (Brown University, Providence, RI, USA; available at https://abstrackr.com/), using predefined eligibility questions based on the inclusion criteria described in Section 2.3. Records were excluded at this stage if they did not involve an adolescent population, did not include a digital intervention or substantial digital component, did not assess beverage-related outcomes of interest, or were not original research articles. After title and abstract screening, 30 records were retained for full-text assessments. One full-text article could not be retrieved from the database. The remaining articles were excluded if they did not meet the eligibility criteria upon full-text review. The final dataset included 22 studies.

2.5. Data Extraction and Analytical Classification

Data were extracted in a structured manner to support cross-study comparisons and analytical interpretations. The extracted variables included general study characteristics, study design and setting, participant demographics, type of digital intervention, and beverage-related outcomes. Because the aim of this review was to map, characterize, and analytically classify an emerging and fragmented field rather than to synthesize intervention effectiveness alone, study protocols, developmental studies, and secondary analyses were included when they provided relevant information on intervention design, delivery, implementation logic, or beverage-related outcomes. This broader inclusion strategy made it possible to capture not only completed interventions but also how the field is currently being conceptualized and operationalized.

Because beverage-related outcomes were not the primary focus of all the included studies, the extraction process distinguished between direct beverage intake outcomes and outcomes embedded within broader dietary, behavioral, or literacy-oriented frameworks. To support a structured comparison across heterogeneous studies, an author-developed analytical framework was created. The framework classifies interventions according to five dimensions: primary intervention focus, digital delivery mode, behavior change strategy, ecological level of delivery, and beverage outcome specificity. This framework was intended to generate interpretive comparisons across studies that would otherwise remain difficult to compare using conventional review categories. Each study was classified according to its predominant intervention characteristics to identify recurring patterns in intervention design and implementation.

2.6. Data Synthesis

The findings were synthesized both narratively and analytically. First, a descriptive synthesis was conducted to summarize the study characteristics, participant demographics, intervention formats, and beverage-related outcomes. Second, the analytical framework developed for this review was applied to identify patterns across interventions in terms of delivery mode, behavior change strategy, ecological level of delivery, and beverage outcome specificity. Because the included literature comprised heterogeneous study designs and publication types, this narrative and interpretive approach was considered more appropriate than a formal synthesis of effectiveness.

A formal quality appraisal or risk of bias assessment was not performed. This decision reflects the purpose of the review, which was analytical mapping rather than a comparative evaluation of intervention efficacy, as well as the heterogeneity of the included literature, which encompassed randomized trials, protocols, secondary analyses, and developmental studies that are not readily amenable to a single appraisal framework. Accordingly, the findings should be interpreted primarily as descriptive and conceptual rather than as a basis for firm conclusions regarding intervention effectiveness.

3. Results

3.1. Characteristics of the Included Studies

The 22 included studies comprised a heterogeneous body of literature, including randomized controlled trials, pilot studies, study protocols, secondary analyses, and development or evaluation studies. Most studies assessed outcomes related to SSBs, whereas only one specifically focused on energy drink consumption (Table 1).

Of the 22 included studies, 19 focused on outcomes related to SSBs, such as soft drinks, soda, or sugary drinks; only one study specifically examined energy drink consumption [23]. The remaining studies addressed beverage-related constructs more indirectly, such as knowledge of the sugar content of SSBs [24] or broader indicators of unhealthy dietary habits that included beverage-related behaviors [25]. Overall, these findings indicate a clear predominance of SSB-related outcomes in the literature, with much less attention being given to energy drinks (Table 1).

In terms of study design, the evidence base included 10 randomized studies reporting primary outcome data, including cluster randomized controlled trials, randomized controlled trials, and randomized pilot studies [26,27,28,29,30]. Five additional studies were trial protocols describing ongoing or planned randomized interventions [31,32,33,34,35]. Four studies were secondary analyses of intervention datasets or trial-derived data [36,37,38,39]. The remaining three studies consisted of development, evaluation, or non-randomized intervention research [23,24,40]. This distribution suggests that although randomized designs are well represented, a substantial portion of the literature remains in developmental or exploratory stages (Table 1).

School-based interventions were the most common, particularly in cluster-randomized trials and study protocols. Other studies were conducted in family, community, clinical, or home-based digital contexts. Across the included studies, the most frequently used digital formats were web-based platforms, mobile applications, smartphone-supported programs, SMS-based interventions, serious games, augmented reality tools, and digital dietary recording systems (Table 1).

In many cases, these digital strategies were integrated into multicomponent interventions that also included teacher-led activities, caregiver involvement, behavioral coaching, or self-monitoring support. Taken together, these findings show that the current evidence base is dominated by SSB-focused outcomes, school-based and multicomponent digital interventions, and a mix of completed trials, protocols, and secondary analyses, with comparatively limited beverage-specific evidence related to energy drinks.

Table 1. Characteristics of the included studies (n = 22).

Study	General Characteristics	Study Design and Setting	Participant Characteristics	Type of Digital Intervention	Beverage-Related Outcomes Assessed
Ezendam et al., 2011 [27]	School-based digital energy-balance intervention conducted in the Netherlands	Cluster randomized controlled trial; secondary school setting	Adolescents aged 12–13 years	Web-based computer-tailored intervention (FATaintPHAT)	SSB intake, specifically odds of consuming > 400 mL/day
Nollen et al., 2014 [28]	Pilot obesity-prevention study targeting at-risk youth in the United States	Randomized pilot trial; community-based setting	Low-income racial/ethnic-minority girls aged 9–14 years	Mobile technology-based intervention with real-time goal setting, self-monitoring, prompts, feedback, and reinforcement	SSB intake
Smith et al., 2014 [41]	Protocol of a school-based obesity-prevention intervention for adolescent boys in Australia	Group/cluster randomized controlled trial protocol; school setting	Adolescent boys from schools in low-income communities	Smartphone-assisted multicomponent intervention integrated with school-based delivery	Planned assessment of SSB intake within broader dietary outcomes
Quintiliani et al., 2014 [42]	Family-based obesity-prevention study conducted in public housing in the United States	Cluster randomized trial; community/public housing setting	Mother–daughter dyads living in public housing; daughters aged approximately 8–15 years	Multilevel intervention including mobile phone/text messaging support	Soft drink/soda consumption, including regular or diet soda, as part of broader dietary outcomes
Mâsse et al., 2015 [38]	Secondary analysis of adherence to a digital lifestyle intervention in Canada	Secondary analysis of a web-based intervention; home/family setting	160 adolescents with overweight/obesity and one parent	Web-based lifestyle behavior intervention	Sugar-sweetened beverage reduction goals and household soft drink availability within broader dietary behavior targets
Escárcega-Centeno et al., 2015 [24]	Educational digital study focused specifically on sugary drinks in Mexico	Development/intervention study in an educational context	Users/students in an educational intervention context; participant characteristics not extensively detailed	Augmented reality mobile application (Augmented-Sugar Intake)	Knowledge and understanding of sugar content in SSBs
Smith et al., 2014 [34]	Full trial of a smartphone-supported obesity-prevention intervention in Australia	Cluster randomized controlled trial; school setting	Adolescent boys from schools in low-income communities	Smartphone-supported multicomponent school-based intervention (ATLAS)	SSB intake assessed within broader lifestyle outcomes
Lubans et al., 2016 [32]	Protocol extending smartphone-supported school-based health interventions in Australia	Cluster randomized controlled trial protocol; school setting	Secondary school adolescents	School-based intervention including smartphone and web-based components, teacher delivery, and physical activity sessions	Planned assessment of sugar-sweetened beverage consumption alongside other lifestyle outcomes
Chen et al., 2017 [43]	Pilot weight-management study in adolescents in the United States	Pilot intervention study; clinical/community setting	40 adolescents with overweight/obesity	Mobile phone technology-based weight-management intervention	Servings of soda and sweetened drinks within a broader weight-management intervention
Jones et al., 2014 [40]	Web-based healthy weight regulation and eating disorder prevention study in US high school students	School-based intervention study; high school setting	High school students	Universal and targeted web-based intervention	Soda consumption
Kapitány-Fövény et al., 2018 [23]	Mobile prevention study in Hungary addressing health risk behaviors	Questionnaire/evaluation study; school setting	386 students	Interactive mobile phone application/serious game (Once Upon a High)	Energy drink consumption
Da Silva et al., 2019 [29]	Adapted computer-based healthy eating intervention conducted in Brazil	School cluster-randomized controlled trial	720 adolescents in grades 7–9	Computer-based/web-based intervention (StayingFit Brazil)	Soft drink consumption
Zoellner et al., 2019 [31]	Protocol of a beverage-focused intervention for rural adolescents in the United States	Cluster randomized controlled trial protocol; middle school setting	Appalachian middle-school students and caregivers	Multilevel school-based intervention with digital and SMS caregiver support (Kids SIPsmartER)	Planned assessment of SSB consumption and beverage-related literacy/behaviors
Mâsse et al., 2020 [33]	Protocol of an mHealth intervention for youth with overweight/obesity in Canada	Randomized controlled trial protocol; home/family digital setting	Children/adolescents with overweight or obesity and parents	Gamified mHealth application with health coaching and parent involvement (Aim2Be)	Planned assessment of SSB-related dietary behaviors
Teesson et al., 2020 [35]	Large-scale eHealth protocol targeting multiple lifestyle risk behaviors in Australia	Cluster randomized controlled trial protocol; school setting	Adolescents aged 12–17 years	School-based eHealth program with app/online components (Health4Life)	Planned assessment of SSB consumption as one of six lifestyle risk behaviors
Lin and Mâsse, 2021 [37]	Secondary analysis examining engagement with an mHealth intervention in Canada	Secondary engagement/profile analysis of app users; home/family setting	301 adolescents (mean age approximately 14.8 years) and their families	Lifestyle behavior modification app for adolescents and parents (Aim2Be)	SSB intake and related behavior change/self-efficacy targets
Caon et al., 2022 [36]	Digital dietary behavior study within a European adolescent health project	Secondary/descriptive analysis; school/community digital setting	Adolescents from three European countries	Mobile food record/e-diary app	SSB intake/recording and healthy drinking habits
Bjerregaard et al., 2024 [26]	Large digitally delivered dietary intervention embedded in a national cohort in Denmark	Multifactorial randomized controlled trial nested within the Danish National Birth Cohort	7890 adolescents from the Danish National Birth Cohort	SMS chatbot digital educational program	SSB intake
O’Dean et al., 2024 [39]	Mediation analysis of a school-based digital intervention targeting multiple lifestyle risks in Australia	Secondary analysis of a cluster randomized controlled trial; school setting	Year 7 students from Australian schools	School-based digital multiple health behavior intervention (Health4Life)	Sugar-sweetened beverage consumption/risk indicator within a six-risk-behavior framework
Zoellner et al., 2024 [30]	Outcomes paper of a beverage-focused intervention in rural United States	Cluster randomized controlled trial; middle school and caregiver setting	526 students and caregivers from Appalachian middle schools	School-based multilevel intervention with digital/SMS caregiver support (Kids SIPsmartER)	Student and caregiver SSB consumption
Proctor et al., 2025 [44]	Pilot study integrating an app into a community nutrition program in the United States	Randomized pilot study; school/community program setting	Adolescents in grades 6–8	Image-assisted dietary assessment and education app integrated into SNAP-Ed (PortionSize Ed)	Sugary drink intake within broader diet quality outcomes
Seiterö et al., 2025 [25]	Stand-alone mHealth multiple behavior change intervention in Sweden	Randomized controlled trial; school/digital setting	High school students aged approximately 15–20 years	Mobile phone-delivered multiple health behavior change intervention (LIFE4YOUth)	Beverage-related outcomes assessed within broader unhealthy dietary indicators

Abbreviations: mHealth, mobile health; SMS, short message service; SSB, sugar-sweetened beverage.

Table 1. Characteristics of the included studies (n = 22).

Study	General Characteristics	Study Design and Setting	Participant Characteristics	Type of Digital Intervention	Beverage-Related Outcomes Assessed
Ezendam et al., 2011 [27]	School-based digital energy-balance intervention conducted in the Netherlands	Cluster randomized controlled trial; secondary school setting	Adolescents aged 12–13 years	Web-based computer-tailored intervention (FATaintPHAT)	SSB intake, specifically odds of consuming > 400 mL/day
Nollen et al., 2014 [28]	Pilot obesity-prevention study targeting at-risk youth in the United States	Randomized pilot trial; community-based setting	Low-income racial/ethnic-minority girls aged 9–14 years	Mobile technology-based intervention with real-time goal setting, self-monitoring, prompts, feedback, and reinforcement	SSB intake
Smith et al., 2014 [41]	Protocol of a school-based obesity-prevention intervention for adolescent boys in Australia	Group/cluster randomized controlled trial protocol; school setting	Adolescent boys from schools in low-income communities	Smartphone-assisted multicomponent intervention integrated with school-based delivery	Planned assessment of SSB intake within broader dietary outcomes
Quintiliani et al., 2014 [42]	Family-based obesity-prevention study conducted in public housing in the United States	Cluster randomized trial; community/public housing setting	Mother–daughter dyads living in public housing; daughters aged approximately 8–15 years	Multilevel intervention including mobile phone/text messaging support	Soft drink/soda consumption, including regular or diet soda, as part of broader dietary outcomes
Mâsse et al., 2015 [38]	Secondary analysis of adherence to a digital lifestyle intervention in Canada	Secondary analysis of a web-based intervention; home/family setting	160 adolescents with overweight/obesity and one parent	Web-based lifestyle behavior intervention	Sugar-sweetened beverage reduction goals and household soft drink availability within broader dietary behavior targets
Escárcega-Centeno et al., 2015 [24]	Educational digital study focused specifically on sugary drinks in Mexico	Development/intervention study in an educational context	Users/students in an educational intervention context; participant characteristics not extensively detailed	Augmented reality mobile application (Augmented-Sugar Intake)	Knowledge and understanding of sugar content in SSBs
Smith et al., 2014 [34]	Full trial of a smartphone-supported obesity-prevention intervention in Australia	Cluster randomized controlled trial; school setting	Adolescent boys from schools in low-income communities	Smartphone-supported multicomponent school-based intervention (ATLAS)	SSB intake assessed within broader lifestyle outcomes
Lubans et al., 2016 [32]	Protocol extending smartphone-supported school-based health interventions in Australia	Cluster randomized controlled trial protocol; school setting	Secondary school adolescents	School-based intervention including smartphone and web-based components, teacher delivery, and physical activity sessions	Planned assessment of sugar-sweetened beverage consumption alongside other lifestyle outcomes
Chen et al., 2017 [43]	Pilot weight-management study in adolescents in the United States	Pilot intervention study; clinical/community setting	40 adolescents with overweight/obesity	Mobile phone technology-based weight-management intervention	Servings of soda and sweetened drinks within a broader weight-management intervention
Jones et al., 2014 [40]	Web-based healthy weight regulation and eating disorder prevention study in US high school students	School-based intervention study; high school setting	High school students	Universal and targeted web-based intervention	Soda consumption
Kapitány-Fövény et al., 2018 [23]	Mobile prevention study in Hungary addressing health risk behaviors	Questionnaire/evaluation study; school setting	386 students	Interactive mobile phone application/serious game (Once Upon a High)	Energy drink consumption
Da Silva et al., 2019 [29]	Adapted computer-based healthy eating intervention conducted in Brazil	School cluster-randomized controlled trial	720 adolescents in grades 7–9	Computer-based/web-based intervention (StayingFit Brazil)	Soft drink consumption
Zoellner et al., 2019 [31]	Protocol of a beverage-focused intervention for rural adolescents in the United States	Cluster randomized controlled trial protocol; middle school setting	Appalachian middle-school students and caregivers	Multilevel school-based intervention with digital and SMS caregiver support (Kids SIPsmartER)	Planned assessment of SSB consumption and beverage-related literacy/behaviors
Mâsse et al., 2020 [33]	Protocol of an mHealth intervention for youth with overweight/obesity in Canada	Randomized controlled trial protocol; home/family digital setting	Children/adolescents with overweight or obesity and parents	Gamified mHealth application with health coaching and parent involvement (Aim2Be)	Planned assessment of SSB-related dietary behaviors
Teesson et al., 2020 [35]	Large-scale eHealth protocol targeting multiple lifestyle risk behaviors in Australia	Cluster randomized controlled trial protocol; school setting	Adolescents aged 12–17 years	School-based eHealth program with app/online components (Health4Life)	Planned assessment of SSB consumption as one of six lifestyle risk behaviors
Lin and Mâsse, 2021 [37]	Secondary analysis examining engagement with an mHealth intervention in Canada	Secondary engagement/profile analysis of app users; home/family setting	301 adolescents (mean age approximately 14.8 years) and their families	Lifestyle behavior modification app for adolescents and parents (Aim2Be)	SSB intake and related behavior change/self-efficacy targets
Caon et al., 2022 [36]	Digital dietary behavior study within a European adolescent health project	Secondary/descriptive analysis; school/community digital setting	Adolescents from three European countries	Mobile food record/e-diary app	SSB intake/recording and healthy drinking habits
Bjerregaard et al., 2024 [26]	Large digitally delivered dietary intervention embedded in a national cohort in Denmark	Multifactorial randomized controlled trial nested within the Danish National Birth Cohort	7890 adolescents from the Danish National Birth Cohort	SMS chatbot digital educational program	SSB intake
O’Dean et al., 2024 [39]	Mediation analysis of a school-based digital intervention targeting multiple lifestyle risks in Australia	Secondary analysis of a cluster randomized controlled trial; school setting	Year 7 students from Australian schools	School-based digital multiple health behavior intervention (Health4Life)	Sugar-sweetened beverage consumption/risk indicator within a six-risk-behavior framework
Zoellner et al., 2024 [30]	Outcomes paper of a beverage-focused intervention in rural United States	Cluster randomized controlled trial; middle school and caregiver setting	526 students and caregivers from Appalachian middle schools	School-based multilevel intervention with digital/SMS caregiver support (Kids SIPsmartER)	Student and caregiver SSB consumption
Proctor et al., 2025 [44]	Pilot study integrating an app into a community nutrition program in the United States	Randomized pilot study; school/community program setting	Adolescents in grades 6–8	Image-assisted dietary assessment and education app integrated into SNAP-Ed (PortionSize Ed)	Sugary drink intake within broader diet quality outcomes
Seiterö et al., 2025 [25]	Stand-alone mHealth multiple behavior change intervention in Sweden	Randomized controlled trial; school/digital setting	High school students aged approximately 15–20 years	Mobile phone-delivered multiple health behavior change intervention (LIFE4YOUth)	Beverage-related outcomes assessed within broader unhealthy dietary indicators

Abbreviations: mHealth, mobile health; SMS, short message service; SSB, sugar-sweetened beverage.

3.2. Types of Digital Interventions Identified

The interventions included were categorized according to their predominant digital delivery format into five main groups: mobile app-based interventions, web-based interventions, social media-based approaches, gamified or serious game interventions, and multicomponent digital interventions (Figure 1). Because some studies combined more than one digital modality, the classification was based on the predominant format. Overall, the interventions identified were mainly mobile- or web-based and frequently multicomponent in nature, whereas social media-based interventions were not identified as primary delivery platforms. Gamified elements appeared in a small number of studies, usually as components of broader digital interventions rather than as standalone formats (Figure 1).

3.3. Analytical Classification of the Identified Interventions

To extend the descriptive summary presented in Section 3.1, an analytical framework was applied to classify the included interventions across five dimensions: primary intervention focus, digital delivery mode, behavior change strategy, ecological level of delivery, and beverage outcome specificity. This analytical step made it possible to identify cross-cutting patterns in intervention design that were not fully captured by the descriptive presentation of the study characteristics alone (

Table 2. Analytical framework for classifying the included digital interventions (n = 22).

Study	Primary Intervention Focus	Digital Delivery Mode	Behavior Change Strategy	Ecological Level of Delivery	Beverage Outcome Specificity
Ezendam et al., 2011 [27]	Beverage-inclusive dietary/energy-balance intervention	Web-based	Computer-tailored education and feedback	School-level	Direct beverage intake outcome
Nollen et al., 2014 [28]	Beverage-inclusive dietary intervention within obesity prevention	Mobile app-based –based	Goal setting, self-monitoring, prompts, and feedback	Individual-level	Direct beverage intake outcome
Smith et al., 2014 [41]	Multi-behavior lifestyle intervention	Multi-component digital	Education, self-monitoring, school-mediated support	School-level	Embedded beverage outcome within broader dietary measures
Quintiliani et al., 2014 [42]	Beverage-inclusive dietary intervention within family-based obesity prevention	Multi-component digital	Family support, education, and text-message reinforcement	Multilevel	Direct beverage intake outcome
Jones et al., 2014 [40]	Beverage-inclusive dietary intervention within healthy weight regulation	Web-based	Education and tailored behavior change support	School-level	Direct beverage intake outcome
Mâsse et al., 2015 [38]	Beverage-inclusive dietary intervention within obesity management	Web-based	Self-monitoring, behavioral goal setting, and family support	Family-level	Beverage-related behavioral outcome
Escárcega-Centeno et al., 2015 [24]	Knowledge/literacy-focused intervention	Mobile app–based	Education and awareness-building	Individual-level	Beverage-related knowledge/literacy outcome
Smith et al., 2014 [34]	Multi-behavior lifestyle intervention	Multi-component digital	Education, self-monitoring, school-mediated support	School-level	Embedded beverage outcome within broader dietary measures
Lubans et al., 2016 [32]	Multi-behavior lifestyle intervention	Multi-component digital	Education, goal setting, school-mediated support, and behavior change support	School-level	Embedded beverage outcome within broader dietary measures
Chen et al., 2017 [43]	Beverage-inclusive dietary intervention within weight management	Mobile app–based	Self-monitoring, goal setting, and feedback	Individual-level	Direct beverage intake outcome
Kapitány-Fövény et al., 2018 [23]	Beverage-specific/risk-behavior intervention	Gamified/serious game–based	Education and gamified engagement	School-level	Direct beverage intake outcome
Silva et al., 2019 [29]	Beverage-inclusive dietary intervention	Web-based	Education and structured behavior change activities	School-level	Direct beverage intake outcome
Zoellner et al., 2019 [31]	Beverage-specific intervention	Multi-component digital	Education, caregiver support, and digital reinforcement	Multilevel	Direct beverage intake outcome
Mâsse et al., 2020 [33]	Multi-behavior lifestyle intervention	Mobile app–based	Gamification, self-monitoring, health coaching, and family support	Family-level	Embedded beverage outcome within broader dietary measures
Teesson et al., 2020 [35]	Multi-behavior lifestyle intervention	Multi-component digital	Education and multiple risk-behavior prevention	School-level	Embedded beverage outcome within broader lifestyle measures
Lin & Mâsse, 2021 [37]	Multi-behavior lifestyle intervention	Mobile app–based	Engagement support, self-monitoring, and family-supported behavior change	Family-level	Beverage-related behavioral outcome
Caon et al., 2022 [36]	Beverage-inclusive dietary intervention	Digital self-monitoring/recording tool	Dietary recording, self-monitoring, and feedback support	Individual-level	Beverage-related behavioral outcome
Bjerregaard et al., 2024 [26]	Beverage-inclusive dietary intervention within healthy eating promotion	SMS/chatbot-based	Education, prompting, and digital reinforcement	Individual-level	Direct beverage intake outcome
O’Dean et al., 2024 [39]	Multi-behavior lifestyle intervention	Multi-component digital	Education and mediation of behavior change mechanisms	School-level	Embedded beverage outcome within broader lifestyle measures
Zoellner et al., 2024 [30]	Beverage-specific intervention	Multi-component digital	Education, caregiver support, and digital reinforcement	Multilevel	Direct beverage intake outcome
Proctor et al., 2025 [44]	Beverage-inclusive dietary intervention	Digital self-monitoring/recording tool	Dietary recording, education, and feedback	School-level	Direct beverage intake outcome
Seiterö et al., 2025 [25]	Multi-behavior lifestyle intervention	Mobile app–based	Behavior changes support, self-monitoring, and digital prompts	Individual-level	Embedded beverage outcome within broader unhealthy dietary indicators

).

The analytical classification highlighted several recurring patterns across the studies included. Most interventions were not explicitly beverage-focused but instead embedded beverage-related outcomes within broader dietary or multi-behavioral lifestyle interventions. In terms of delivery mode, mobile app-based, web-based, and multicomponent digital interventions predominated, whereas SMS/chatbot-based and gamified approaches were less common. The ecological level of delivery was most often school-based, individual, family-based, or multilevel, indicating that digital strategies were typically embedded within broader social or institutional contexts rather than implemented as standalone tools. Beverage outcome specificity also varied considerably across the literature, with some studies assessing direct beverage intake outcomes and others focusing on beverage-related behaviors, literacy, and broader dietary indicators.

4. Discussion

This study provides an analytical examination of the literature on digital interventions targeting beverage-related behaviors in adolescents, with a particular focus on SSBs and energy drinks. Overall, the evidence remains limited and heterogeneous. Most included studies focused on SSB-related outcomes, whereas energy drink consumption was rarely addressed directly. In addition, many interventions assessed beverage-related outcomes within broader dietary or lifestyle programs rather than as primary behavioral targets. These findings suggest that digital approaches in this field are promising but still fragmented.

An important contribution of this study lies in the analytical framework used to classify the interventions included. Beyond organizing the literature descriptively, the framework helps distinguish whether interventions are beverage-specific or beverage-inclusive, whether beverage outcomes are assessed directly or only as part of broader dietary or lifestyle measures, and whether intervention delivery is primarily individual-, school-, family-, or multilevel. These distinctions are important because they highlight several sources of fragmentation in the field that are not always apparent from conventional study summaries alone. Therefore, the framework may serve not only as an interpretive tool for the present review but also as a useful conceptual guide for future intervention design, reporting, and evidence synthesis.

A clear finding was the predominance of interventions focused on SSBs rather than energy drinks. This pattern is understandable given the stronger and more established evidence linking SSB consumption to adolescent overweight, obesity, and metabolic risk [2,10]. However, energy drinks have received limited attention in the literature. This is particularly important because these beverages combine high sugar content with stimulant exposure and have been associated with sleep-related, behavioral, educational, and oral health concerns among young people [1,18,19], Therefore, future intervention research should focus more explicitly on energy drinks rather than treating them as a secondary subset of sugary beverages.

A second important finding concerns intervention specificity. Most of the included studies did not target beverage behaviors in an explicitly beverage-centered way. Instead, beverage-related outcomes were frequently embedded within multi-behavioral obesity prevention, lifestyle, or dietary quality interventions. This broader framing may be appropriate, given that beverage choices are shaped by wider eating habits and social contexts [3,7]. However, this also reduces intervention specificity and makes it more difficult to determine which components are driving beverage-related change. Future studies would benefit from clearly distinguishing whether beverage behaviors are primary targets, secondary outcomes, or components within broader health-promotion models.

The results also highlight the importance of delivery context. School-based interventions were the most common, particularly in cluster-randomized trials and protocols. This is consistent with broader adolescent health promotion research, which identifies schools as strategic settings because they provide sustained access to adolescents and opportunities for curricular integration [21,45,46]. At the same time, school-based delivery alone is unlikely to be sufficient for beverage-related behavior change, since these behaviors are also shaped by family practices, peer norms, household availability, and digital environments [3,7,22]. The frequent use of multicomponent and multilevel interventions in the included studies therefore appears to reflect the ecological complexity of adolescent beverage behavior.

In terms of digital delivery, the identified interventions were mainly web-based, mobile app-based, smartphone-supported, and SMS-enabled. These formats are broadly compatible with adolescents’ everyday media environments and may support behavior change through self-monitoring, goal setting, feedback, and reinforcement [21,47]. However, the near absence of social media-based interventions as primary delivery platforms is striking. Given that adolescents encounter food and beverage messages in highly social and digitally mediated environments, this may represent an important gap. One possible interpretation is that the intervention literature has not yet fully engaged with these environments. Another is that developing interventions directly within social media raises challenges related to governance and the broader digital determinants of health [48]. Future research should consider whether private app- or website-based approaches are sufficient to address the social and environmental drivers of beverage choices.

Another issue emerging from this analysis is the substantial heterogeneity of beverage-related outcome assessments. Some studies measured direct beverage intake, whereas others assessed knowledge, literacy, availability, or broader dietary indicators in which beverages were embedded. This variability limits direct comparability across studies and constrains stronger conclusions about which digital strategies are most effective. It also suggests the need for greater conceptual clarity regarding what constitutes a meaningful beverage-related outcome in this field. Standardization of outcomes, particularly for direct intake measures and substitution behaviors, would strengthen future syntheses and improve the comparability of intervention studies. Although the present review was not designed to define a quantitative threshold of sugar intake reduction needed to improve health outcomes, broader evidence supports reducing free sugar intake to below 10% of total energy intake, with further benefits suggested below 5%, and indicates that lower sugar intake is relevant to obesity and cardiometabolic risk prevention in children and adolescents [49].

These findings have important implications for future intervention development. Based on the patterns identified in this review, digital strategies targeting adolescent beverage consumption may benefit from being more beverage-specific and context-sensitive while remaining feasible for implementation in real-world settings. Potentially relevant components include brief learning modules, self-monitoring, interactive challenges, label-reading activities, myth-correction approaches, media literacy elements, and links to school and family environments [45]. Such strategies may be especially relevant in contexts where adolescent energy drink consumption and unhealthy dietary patterns have been documented. From a policy perspective, these findings suggest that digital beverage interventions may be more impactful when integrated into broader school health promotion strategies and public health efforts that address adolescents’ digital food environments. In some settings, including Portugal, beverage-specific digital interventions for adolescents also appear limited [50,51,52,53,54]. Future research would also benefit from studies that directly compare intervention strategies, components, and delivery approaches to identify which configurations appear most promising for modifying beverage-related behaviors in adolescents.

Overall, digital interventions targeting beverage-related behaviors among adolescents represent a promising but fragmented field. The available literature is dominated by studies on SSB outcomes, multicomponent approaches, and mobile- or web-based delivery formats, whereas energy drinks, social-media-based strategies, and highly beverage-specific models remain underrepresented. The present study provides a structured basis for future research, intervention design, and feasibility testing aimed at reducing SSB and energy drink consumption in adolescents.

5. Limitations

This study had several limitations that should be considered when interpreting its findings. First, the search was restricted to three databases and original articles published in English, which may have resulted in the omission of relevant studies indexed elsewhere or published in other languages. Second, the included studies were highly heterogeneous in terms of study design, intervention format, delivery context, and beverage-related outcome assessment, which limited direct comparability across studies. Third, the review included not only completed intervention studies but also study protocols, developmental studies, and secondary analyses. Although this broader inclusion strategy was useful for mapping how the field was designed and operationalized, it limited the extent to which conclusions could be drawn about intervention efficacy. Fourth, no formal quality appraisal or risk-of-bias assessment was conducted. Given the analytical purpose of this review and the heterogeneity of the included publication types, the findings should be interpreted primarily as descriptive and conceptual rather than as definitive evidence regarding effectiveness. Finally, the interpretive scope of this review was constrained by the characteristics of the available literature, including the small number of beverage-specific interventions, limited focus on energy drinks, and frequent embedding of beverage-related outcomes within broader lifestyle or dietary interventions. Taken together, these limitations restrict the extent to which firm conclusions can be drawn regarding the digital approaches that appear most promising for modifying beverage-related behaviors in adolescents.

6. Conclusions

This study shows that digital interventions targeting beverage-related behaviors among adolescents represent a promising, but still fragmented, area of research. The existing literature is dominated by studies addressing SSB-related outcomes, whereas energy drink consumption has received comparatively little attention. The identified interventions were primarily mobile-based, web-based, or multicomponent digital approaches and were often embedded within broader school-based lifestyle or dietary programs rather than being explicitly designed as beverage-focused interventions. This review also highlights substantial heterogeneity in intervention design and outcome assessment, which limits comparability across studies and reduces the ability to draw firm conclusions regarding intervention effectiveness.

Simultaneously, the analytical framework applied in this study helps clarify recurring patterns and important sources of fragmentation within the field, including differences in intervention focus, delivery mode, ecological level, and beverage outcome specificity. In this sense, this study contributes not only a descriptive overview of the literature but also a conceptual structure that may support future intervention design, reporting, and evidence synthesis. Future research should prioritize beverage-specific and developmentally appropriate interventions, give greater attention to energy drinks, improve outcome standardization, and explore digital approaches that are responsive to adolescents’ actual social and media environments. Overall, this study provides a useful foundation for future co-design work, feasibility and pilot testing, and the development of more targeted digital strategies to reduce unhealthy beverage consumption among adolescents.