Effectiveness of tailored multichannel interventions on weight loss among adolescents: a randomized controlled trial.

Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Front Public Health . 2026 Mar 27;14:1733027. doi: 10.3389/fpubh.2026.1733027 Search in PMC Search in PubMed View in NLM Catalog Add to search Effectiveness of tailored multichannel interventions on weight loss among adolescents: a randomized controlled trial Mahmoud Ahmed Elsheikh Mahmoud Ahmed Elsheikh 1 Nursing Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Wadi Addawasir, Riyadh, Saudi Arabia Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing Find articles by Mahmoud Ahmed Elsheikh 1, *, † , Mohamed Ali Zoromba Mohamed Ali Zoromba 2 College of Nursing, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh, Saudi Arabia Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing Find articles by Mohamed Ali Zoromba 2 , Tarek Selim Tarek Selim 1 Nursing Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Wadi Addawasir, Riyadh, Saudi Arabia Conceptualization, Investigation, Methodology, Supervision, Visualization, Writing – original draft Find articles by Tarek Selim 1 , Mohamed Hussein Ramdan Atta Mohamed Hussein Ramdan Atta 1 Nursing Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Wadi Addawasir, Riyadh, Saudi Arabia Find articles by Mohamed Hussein Ramdan Atta 1 , Narges Fathi Mohamed Narges Fathi Mohamed 3 Community Health Nursing, Faculty of Nursing, Mansoura University, Mansoura, Dakahlia, Egypt 4 Faculty of Nursing, Mansoura National University, Faculty of Nursing, Mansoura National University, Dakahlia, Egypt Conceptualization, Data curation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft Find articles by Narges Fathi Mohamed 3, 4 , Samar Othman Abdelazim Samar Othman Abdelazim 5 Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan 6 Faculty of Nursing, the British University in Egypt, Cairo, Egypt Data curation, Investigation, Project administration, Supervision, Writing – original draft Find articles by Samar Othman Abdelazim 5, 6 , Heba Emad El-Gazar Heba Emad El-Gazar 7 College of Nursing, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia Conceptualization, Methodology, Project administration, Writing – original draft Find articles by Heba Emad El-Gazar 7 , Osama Albasheer Osama Albasheer 8 Family and Community Medicine Department, College of Medicine, Jazan University, Jazan, Saudi Arabia Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – review & editing Find articles by Osama Albasheer 8 , Ali Daif Abousoliman Ali Daif Abousoliman 2 College of Nursing, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh, Saudi Arabia Conceptualization, Methodology, Resources, Validation, Writing – review & editing Find articles by Ali Daif Abousoliman 2 , Lamiaa Saad Abdallah Lamiaa Saad Abdallah 9 Community Health Nursing, Faculty of Nursing, Cairo University, Cairo, Egypt 6 Faculty of Nursing, the British University in Egypt, Cairo, Egypt Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing Find articles by Lamiaa Saad Abdallah 9, 6 Author information Article notes Copyright and License information 1 Nursing Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Wadi Addawasir, Riyadh, Saudi Arabia 2 College of Nursing, Prince Sattam bin Abdulaziz University, Al Kharj, Riyadh, Saudi Arabia 3 Community Health Nursing, Faculty of Nursing, Mansoura University, Mansoura, Dakahlia, Egypt 4 Faculty of Nursing, Mansoura National University, Faculty of Nursing, Mansoura National University, Dakahlia, Egypt 5 Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan 6 Faculty of Nursing, the British University in Egypt, Cairo, Egypt 7 College of Nursing, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia 8 Family and Community Medicine Department, College of Medicine, Jazan University, Jazan, Saudi Arabia 9 Community Health Nursing, Faculty of Nursing, Cairo University, Cairo, Egypt * Correspondence: Mahmoud Ahmed Elsheikh, [email protected] † ORCID: Mahmoud Ahmed Elsheikh, orcid.org/0000-0002-4256-9516 Roles Mahmoud Ahmed Elsheikh : Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing Mohamed Ali Zoromba : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing Tarek Selim : Conceptualization, Investigation, Methodology, Supervision, Visualization, Writing – original draft Narges Fathi Mohamed : Conceptualization, Data curation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft Samar Othman Abdelazim : Data curation, Investigation, Project administration, Supervision, Writing – original draft Heba Emad El-Gazar : Conceptualization, Methodology, Project administration, Writing – original draft Osama Albasheer : Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – review & editing Ali Daif Abousoliman : Conceptualization, Methodology, Resources, Validation, Writing – review & editing Lamiaa Saad Abdallah : Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing Received 2025 Oct 27; Revised 2026 Feb 2; Accepted 2026 Feb 23; Collection date 2026. Copyright © 2026 Elsheikh, Zoromba, Selim, Atta, Mohamed, Abdelazim, El-Gazar, Albasheer, Abousoliman and Abdallah. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. PMC Copyright notice PMCID: PMC13067464  PMID: 41971264 Abstract Background Several weight loss interventions have been provided for adolescents. However, these interventions were often not adequately tailored to their perception. This study aims to investigate the effectiveness of tailored multichannel interventions on weight loss among adolescents. Methods This study is a randomized controlled trial. A total of 279 adolescents, whose Body Mass Index (BMI) was≥25 kg/m 2 , were recruited in January 2025 from eight non-medical colleges at Mansoura University, Mansoura, Egypt. For the intervention group (IG; n = 133), an intervention was developed based on the Health Belief Model (HBM). The intervention has been designed and administered by a multidisciplinary team for 5 months, until June 2025, via in-person meetings, telephone calls, and digital messages. The control group (CG; n = 146) received basic education over two face-to-face sessions. The primary outcome is the mean difference in BMI. Secondary outcomes include self-administered HBM constructs. All outcomes were assessed at baseline (T0), 2 months (T1), and 5 months (T2). Results The independent t -test revealed statistically significant differences between the groups in the scores of the BMI at T2 (−1.75, 95% CI: −2.48 to −1.02, p < 0.001, Cohen’s d = −0.57). Additionally, two-way repeated measures ANOVA demonstrated a statistically significant interaction effect (Group × Time), F (1.24, 342.36) = 49.72, p = <0.001, partial η 2 = 0.15, as well as statistically significant differences within-group, F (1.24, 342.36) = 29.73, p = <0.001, partial η 2 = 0.10. Regarding HBM, the intervention produced statistically significant main effects between and within groups over time for perceived susceptibility, benefits, self-efficacy, and internal cues to action (all p < 0.05). Conclusion Adolescents in the IG experienced significant weight loss and a positive change in their perception of weight management. The study findings suggest that such tailored multichannel interventions be integrated with other community-based participatory approaches to enhance external cues to action and overcome the perceived barriers in the long term. Clinical trial registration https://clinicaltrials.gov/study/NCT06767072 , NCT06767072 . Keywords: adolescents, digital intervention, college-based telephone calls, health belief model, multichannel intervention, tailored, weight loss, WhatsApp text messages 1. Introduction Adolescent overweight is a global public health issue with rapidly increasing prevalence ( 1 ) and negative health consequences ( 2 ). Although it is preventable, rates have quadrupled worldwide since 1990 ( 3 ). This significant rise has been observed across high-income, middle-income, and low-income countries, indicating global shifts in dietary practices, physical activity, and living conditions ( 4 ). Being overweight in adolescence often leads to negative health effects in adulthood ( 5 ). It is associated with a higher risk of cardiovascular disease, psychiatric issues, type II diabetes, fatty liver, and other metabolic syndromes ( 6 ). Overweight adolescents also face emotional disorders ( 7 ), social barriers, and economic burdens ( 8 ). Adolescent overweight results from an interaction of multiple relevant factors. High intake of sugar-sweetened beverages, calorie-dense, nutrient-poor diets, inadequate physical activity, and extended screen time are major individual contributors ( 9 ). Low self-efficacy, emotional eating disorders, perceived body image, and peer influences further affect adolescents’ healthy choices and weight management-related practices ( 10 ). Broader environmental and social contexts, such as urbanization, lack of access to healthy foods, marketing of unhealthy foods, family dietary patterns, socioeconomic circumstances, and school setting environments, all of which reinforce individual-level factors and contribute to an obesogenic environment ( 11 ). Adolescence is a critical period. Dietary habits and lifestyle behaviors are shaped, making it an ideal time for interventions that aim at promoting healthy weight management ( 12 ). Several interventions have been conducted to enhance weight management behavior among adolescents in many countries ( 13 ). However, the effectiveness of past interventions has shown inconsistent outcomes ( 14 ). Compliance with weight-loss interventions varies due to multiple factors, mostly associated with the design and implementation of the interventions ( 15 ). Systematic reviews reveal debate regarding the best way to motivate adolescents to manage their ideal body weight ( 15 ). Empirical evidence shows that tailored interventions are often more effective than standardized interventions for reducing overweight among adolescents. Tailored interventions account for individual differences in lifestyle, preferences, perceptions about health, self-efficacy, and challenges ( 16 ). While standardized interventions can provide a solid foundation, tailoring strategies to individual needs often leads to more sustainable behavior changes and better health outcomes ( 17 ). The literature suggests that interventions delivered through multiple channels—such as social media, face-to-face interactions, and telephone calls—are often more effective in addressing overweight among adolescents ( 18 ). Adolescents are highly engaged with digital platforms, making them an effective channel and providing daily reminders or motivation ( 19 ). Incorporating face-to-face interactions into digital platforms establishes personal connections and offers in-depth discussions ( 20 ). While telephone calls provide a direct and accessible line of communication ( 5 ). The literature highlighted that tailored interventions could achieve positive outcomes. However, there is a lack of rigorous research incorporating weight management strategies within the scope of practice ( 21 ). Ongoing support and encouragement can significantly motivate adolescents to achieve weight loss objectives ( 22 ). Assessing adolescents’ unique perceptions and challenges and designing tailored plans fosters sustainable weight loss ( 23 ). Improving research in these ways would provide a strong evidence base to expand the role of tailored strategies in the prevention and management of overweight ( 24 ). Despite literature recommendations, the effectiveness of interventions that adopted tailored and multichannel approaches has not been adequately assessed in developed and developing countries. For instance, Egypt currently ranks among the countries with the highest rates of overweight worldwide ( 25 ). The prevalence of overweight among Egyptians is 39.82% ( 26 ). According to the updated systematic review, the prevalence of overweight among adolescents is 13.3% ( 27 ). Moreover, overweight-related health consequences among adolescents keep rising. According to a national study, adolescents’ overweight is associated with cardiometabolic risks and social and psychological negative consequences ( 28 ). The literature concerning the effectiveness of such interventions is limited. There is a lack of knowledge regarding their usefulness across various contexts ( 17 ). Therefore, the current study suggests that a tailored, multichannel intervention is likely to have a positive effect on weight loss among adolescents in Egypt. 1.1. Conceptual framework of tailored multichannel intervention: applying health belief model to weight loss The Health Belief Model (HBM) is a framework that analyzes and predicts health behaviors by addressing individual beliefs ( 29 ). HBM has been successfully adapted to topics across various disciplines ( 30 , 31 ). For weight loss interventions, HBM constructs can provide a structured approach to understanding how individuals perceive weight loss ( 32 ) and what intervention motivates them to adopt healthier behaviors ( 33 ). The conceptual framework of the current study ( Figure 1 ) outlines the relationship between HBM constructs and weight loss among adolescents, as well as how the proposed intervention influences the adolescents’ perception and beliefs to motivate them to adopt weight loss behaviors. The fundamental constructs of the HBM are perceived severity, perceived susceptibility, perceived benefits, perceived barriers, perceived self-efficacy, and cues to action ( 29 ). Prior research showed that the aforementioned variables directly affect the behavioral intention of weight management among adolescents ( 34 ) and are crucial to the effectiveness of weight-loss programs ( 35 ). Figure 1. Open in a new tab Conceptual framework. A systematic review reported that although many behavioral weight loss interventions demonstrated evidence-based strategies, only a few studies employed a theoretical base ( 36 ). Based on HBM, the current study hypothesizes that fostering positive health beliefs regarding a healthy weight through education and training positively influences weight management behavior ( 37 ). Considering individual characteristics of adolescents and their perceptions, the intervention addresses tailored intervention strategies. These strategies include providing educational information, reducing barriers, skill-building, and addressing behavioral triggers. The components of the intervention are explained clearly in Table 1 . Table 1. The framework of the intervention contents. In-person, Face-to-face college meeting (60 min) Telephone call (20–30 min) WhatsApp interactive message (15–20 min) Objectives (Educational information): Provide basic information on balanced nutrition. Discuss the individual nutritional requirements and risks of being overweight. Objectives (Skill building and barrier reduction): Mitigate perceived obstacles to weight management. Build weight management skills and enhance self-efficacy to sustain weight loss. Objectives (Behavioral triggers): Initiate regular behavioral triggers. Remember monthly tips to maintain healthy behavior. Severity of being overweight Susceptibility of being overweight Benefits of adopting weight loss behavior Perceived barriers to adopting weight loss behavior Solutions to overcome barriers Remember your weight loss goal should be time limited (time frame). Self-monitoring: Weekly Calculation of BMI and interpreting results (manual and using BMI calculators). How many calories do you need using the BMR calculator Acceptable Macronutrient Distribution Ranges (AMDR) Essential Vitamins Water and Minerals Change your eating habits; be more aware of how much you eat and choose affordable diet/meal options. Preparing low-calorie, high-nutrition meals. Personalized beginner-level exercise plans; adopting affordable exercise options Remember: The types of foods eaten regularly are related to whether weight gain or loss occurs over time. Remember: regular physical activity alone is not likely to lead to weight loss, but it has numerous health benefits. Review the diet and physical activity personalized plan Revise your created diet and exercise plan. Reading food labels and making healthier choices. Using rewards for achieving goals Identify what causes you to feel the need to eat. Rewarding proper eating behavior can help in developing better habits. The aim is to reward yourself for replacing undesirable habits with positive ones, not for losing weight. The current behavior and the progress toward behavioral goals. Review the successful strategies used and challenges experienced. Be aware of family and peer influence: getting support for healthy behavior change Discuss challenges and improvement strategies. Remember: the types of foods associated with weight gain. Remember: the types of foods associated with maintaining a healthy weight. Enforcement of strategies found beneficial Where to get reliable and valid information Substitute negative thoughts with positive thinking. Avoid temptations, handle slips, and stay motivated Reduce stress Organized and adequate sleep/rest Drink an adequate amount of water. Unnecessary dietary supplements are not recommended Include exercise in everyday schedules. Track the activity and food log. Open in a new tab In brief, educational information aims to enhance the perception of adolescents about the health risks of being overweight (severity and susceptibility) and the benefits of weight loss. Perceived severity indicates an adolescent’s belief about the seriousness of the negative health effects of being overweight, ranging from illness to death ( 38 ). The current intervention aims to emphasize the severe health outcomes of being overweight (e.g., chronic diseases and reduced life expectancy) and the impact on quality of life (e.g., physical limitations and social stigma) ( 39 ). This can enhance perceived severity and create greater behavioral intention of weight management ( 40 ). Perceived susceptibility refers to adolescents’ beliefs about the probability of developing health problems due to being overweight ( 32 ). The intervention proposes that adolescents who perceive themselves as being at high risk of overweight-related complications may be more motivated to lose weight and more likely to engage in preventive measures ( 10 ). Barrier reduction as a component of the current intervention aims to help adolescents identify and mitigate perceived barriers. Perceived barriers to a healthy lifestyle significantly influence the behavioral intentions to weight management ( 41 ) and often hinder adherence to healthy behaviors ( 42 ). Addressing these barriers with solutions enhances adherence to healthy nutrition and active exercise ( 43 ). The intervention focuses on weight management skill-building, aiming to improve adolescents’ self-efficacy, which may be observed in their ability to lose weight successfully and maintain healthy behaviors ( 44 ). Self-efficacy in the scope of weight management refers to an individual’s belief regarding their ability to comply with a balanced diet and perform regular physical activities, even under challenging conditions ( 45 ). The current study suggests that building weight management skills increases adolescents’ self-efficacy, leading to higher engagement in consistent behavior ( 40 ). The intervention also includes behavioral triggers, incorporating cues to action. It is proposed that behavioral triggers will motivate the perception of external or internal cues to action. Subsequently, this will prompt an adolescent to initiate and maintain weight loss behaviors ( 10 ). The current study suggests a direct effect of cues to action on engagement in health behavior. Additionally, cues to action mediate the effects of perceived severity, susceptibility, benefits, barriers, and self-efficacy on weight management behavior ( 43 ). 1.2. Aim and hypothesis The proposed study aimed to evaluate the effectiveness of tailored multichannel interventions on weight loss among adolescents. The primary hypothesis is that adolescents who receive a tailored multichannel intervention (intervention group) will experience a reduced BMI compared to those who receive basic education on weight management (control group). 1.2.1. Secondary objective The proposed study assesses the change in adolescents’ perception regarding weight management and loss, in terms of perceived severity, perceived susceptibility, perceived benefits, perceived barriers, perceived self-efficacy, and cues to action. 2. Methods 2.1. Trial design The study design is a prospective, randomized controlled trial (RCT). The study was registered at ClinicalTrials.gov (number NCT06767072 ). 2.2. Setting and participants The recruitment setting for this study is eight non-medical colleges at Mansoura University (commerce, education, arts, rights, engineering, computer and information, fine arts, and special education). Mansoura University is located in Mansoura City, the capital of Dakahlia, Egypt. Students in the first and second grades of college who were 17–19 years old and had a BMI equal to 25 or higher at the baseline assessment were eligible to participate in the study. Students were excluded from the study if they had one of the following criteria. (1) Students who disclose a physical condition, chronic disease, or handicap that hinders their ability to engage in physical activity or reduce extra body weight, (2) Students with a history of mental health disorders, (3) Students taking medication that causes weight gain, or (4) Students who participate in another weight loss program. 2.3. Recruitment Participants were approached by teachers at the selected eight colleges in January 2025. Teachers were contacted, and the aim of the study was explained. The teachers announced the study to the students. Second, the researchers visited the colleges and explained the purpose of the study to students. Students who agreed to participate were asked to provide written informed consent. Participants’ weight and height were measured, and BMIs were calculated. Eligible participants with a BMI of 25 or higher were enrolled in the study. 2.4. Randomization and allocation A randomized method has been used to allocate participants to the IG or the CG. Firstly, the study was announced at the selected schools. Within each selected college, students who expressed interest in participation were screened for the inclusion criteria. Eligible participants were randomly assigned to IG or CG by an independent researcher using computer-generated random numbers. The study employs an open-label design without concealment, given the nature of the study. Regarding blinding, neither care providers nor outcome assessors were blinded. 2.5. Intervention tailoring and fidelity The intervention was formulated by researchers from various fields, including specialists in community health, psychiatry, and nutrition (interdisciplinary team). The designing procedure for each individual involved the following consecutive steps: (1) the nurses asked the participants to complete HBM constructs, which are used to collect data on each unique perception regarding overweight and weight loss ( 10 ); (2) data of adolescent health beliefs are summarized by the interdisciplinary team.; (3) the interdisciplinary team then creates an intervention plan; (4) the intervention plan, outlined by realistic goals and applicable strategies, and shared with the participant. The completed Template for Intervention Description and Replication checklist (TIDieR) checklist has been completed and provided as Supplementary file 1 . The intervention modules are fundamentally consistent with the contents of the nutritional behavior program published elsewhere ( 46 , 47 ), which corresponds with the guidelines of the National Nutrition Institute. The current intervention also takes into account evidence-based practice and expert recommendations for treating adolescents’ overweight ( 48 – 50 ). It considers the guidelines of successful weight loss ( 51 ) and guidelines of the American Heart Association (AHA) ( 52 ). Table 1 shows a summary of the content framework and the schedule of intervention delivery. Ten nurses administered the proposed intervention over a five-month period, comprising five face-to-face meetings, five phone calls, and five WhatsApp text messages. Each month included a 60-min college educational session, a 20–30 min telephone counseling call focused on skills building and barrier reduction, and a 15–20 min WhatsApp interactive message serving as a behavioral trigger. The intervention sessions were administered at a 10-day interval. The intervention sessions were strategically planned to build knowledge, enhance self-efficacy, reduce perceived barriers, and sustain motivation for long-term behavior change. First month: introduction to weight-related health beliefs and self-monitoring. The first month focused on foundational health beliefs by addressing the severity and susceptibility associated with being overweight, as well as the perceived benefits and barriers to adopting weight loss behaviors. Strategies were introduced to participants to overcome these barriers and engage in weekly self-monitoring practices, including the calculation and interpretation of BMI using both manual and digital tools. This month aims to raise awareness and motivation for behavior change. Second month: nutrition and exercise awareness. In the second month, the focus shifted to personalized nutrition education. Adolescents learned how to calculate the number of required calories using the Basal Metabolic Rate (BMR) calculator and received guidance on Acceptable Macronutrient Distribution Ranges (AMDR). Adolescents were encouraged to follow weight loss goals, improve their eating habits, and incorporate more physical activity. Furthermore, the participants were taught practical components to develop personalized exercise plans and meal preparation strategies that emphasized low-calorie, high-nutrition choices. The importance of consistent behavior and establishing time-bound goals was highlighted. Third month: making informed dietary decisions and using positive reinforcement. During the third month, the participants engaged in individualized plans for a healthy diet and physical exercise. They were educated on how to interpret food labels and make healthier food choices. The behavioral focus was on identifying eating triggers and using non-food rewards to reinforce positive changes. Emphasis was placed on rewarding healthy behavior modifications rather than weight loss outcomes, promoting intrinsic motivation. Fourth month: navigating external influences and assessing weight loss. During this month, the nurses addressed the influence of family and peers, emphasizing the need for a supportive environment. Participants were instructed to assess the accomplishment of behavioral goals. They were advised to review the challenges they experienced and the strategies they used. Educational content helped differentiate between foods associated with weight gain and those that support a healthy weight, reinforcing the value of evidence-based dietary choices. Fifth month: sustaining long-term behavior change. The final month aimed to reinforce healthy lifestyle habits and build resilience against common challenges. Adolescents were taught how to seek reliable information sources, manage temptations, and adopt strategies for stress reduction, positive thinking, and sufficient rest. They were also advised to maintain hydration and avoid unnecessary dietary supplements. These components supported the transition to long-term maintenance of healthy behaviors. 2.6. Procedures to improve the adherence level The nurses underwent a comprehensive training. The training employed various methodologies, such as demonstrations and redemonstrations, group discussions, role-plays, and both in-person and virtual lectures. Training emphasized data collection techniques and instrument utilization, along with the development of a tailored plan with shared objectives. The study included several methods to improve adherence. First, the intervention provided to adolescents. It is assumed adolescents are willing to maintain healthy body weight and body image, thus a lower dropout rate is anticipated. Second, tailored interventions to the perceived beliefs may ensure compliance and raise the intervention effectiveness. Third, different modes of delivery were used in short time intervals (10 days) to improve adherence levels. Each intervention nurse was assigned to a group of 13 or 14 participants in the IG. The nurse is accountable for administering the intervention, following up with the participants, answering the participants’ questions, or providing continuous feedback. Fourth, the interdisciplinary team evaluated the progress of the intervention and offered continuous mentoring of participants’ attendance, call completion, WhatsApp engagement, risks, or harms. There were no risks, adverse events, or negative psychological symptoms of the intervention reported. 2.7. Control group The CG was provided with general information on being overweight and general advice on managing their weight. These were explained to them during two college visits. CG participants did not receive the designed multichannel intervention. 2.8. Outcomes and instruments 2.8.1. Primary outcome: anthropometric measurement The primary outcome is the between-group difference in change in body mass index, reflecting the clinical objective of assessing intervention-attributable weight change. This approach is commonly used in randomized trials and offers a direct and interpretable measure of effectiveness. Weight and height were assessed using a numerical electronic scale and a wall-mounted tape measure, respectively. BMI was calculated [BMI = weight (in kg)/height (in m) 2 ] and interpreted based on WHO guidelines and using BMI charts ( 53 ). Overweight is considered when a BMI is 25 kg/m 2 or over ( 10 , 54 ). 2.8.2. Secondary outcome: health belief model construct The key constructs of the HBM include 89 statements ( 55 ). Thirteen questions assessed perceived severity, 7 questions assessed perceived susceptibility, 14 questions addressed perceived barriers, 13 questions on perceived benefits, 12 questions on cues to action, 18 questions evaluated self-efficacy in dieting, 7 questions on self-efficacy in exercise, and 5 questions on the behavioral intention of weight management (dieting and exercising). Between-group differences in change in these constructs are the secondary outcomes of the study. All items are self-reported and scored on a 5-point Likert scale, with 1 representing “strongly disagree” and 5 indicating “strongly agree.” Higher scores denoted a more positive belief toward overweight preventive behaviors. The tool was translated into Arabic and adapted to the culture. Based on Cronbach’s alpha, the overall reliability of the tool was 0.92 ( 56 ). The current study demonstrates high overall reliability of the tool, with a Cronbach’s alpha of 0.93. Each construct and its subscale also show strong reliability (0.84–0.94). 2.9. Data collection All data related to primary and secondary outcomes were collected at baseline (T0), at 2 months (T1), and at 5 months (T2) after the start of the intervention. BMI was calculated and documented by the nurses at three time-points. Also, participants were required to complete the HBM construct. 2.10. Ethical considerations Institutional Review Board approval was obtained from the Faculty of Nursing at Mansoura University (IRB 0689/30/12/2024). The study adhered to ethical principles outlined in the Declaration of Helsinki, protecting participants’ rights and wellbeing. All participants were provided with oral and written details on the study before being enrolled. The study participants were informed that all data collected is for research purposes only and that they have the right to withdraw from the study at any time. Informed written consents had been obtained from the enrolled participants. Data collection is fully anonymized and only accessed by authorized study staff. 2.11. Sample size Using G Power software V.3.1.9.4 (Psychonomic Society, Madison, Wisconsin, United States) ( 57 ), the sample size was estimated. Assuming a difference between two independent means (two groups), a statistical power of 0.80, a confidence level of 0.95, and an effect size of 0.386, which was based on a similar previous study on weight loss using BMI ( 58 ), the sample size was 214 participants. An additional 30 participants were recruited to compensate for an estimated dropout rate of 30% ( 59 ). The total sample size was 279 participants. 2.12. Statistical analyses Intention-to-treat analysis was applied in this study ( 60 ). All statistical analyses were conducted using Statistical Package for Social Sciences (SPSS) V.29.0 (IBM). Statistical significance was set at p < 0.05. The baseline characteristics of the two groups were compared using the t -test, Pearson’s χ 2 test, or Fisher’s exact test, according to the variable type. The baseline characteristics include participants’ demographic data, such as age, sex, marital status, academic semester, residence, and family income. It also includes questions related to obesity among family members and experience in weight loss through behavior change, diet therapy, physical exercises, or medical treatment. Independent t -tests were conducted to measure the efficacy of the intervention by comparing the means of the outcomes (BMI and HBM subscales) between the two groups at separate time points: T0, T1, and T2. In addition, to evaluate changes in outcomes within the groups over time (T0–T2), a two-way repeated measures analysis of variance (ANOVA) was conducted. The assumptions of the applied statistical tests were fulfilled (i.e., normality, homogeneity, and sphericity of variance) ( 61 ). Occasionally, the assumption of normality and sphericity was violated, so the Greenhouse–Geisser correction was applied ( 62 ). The last observation carried forward method was used to compensate for missing data. LOCF was specified as a conservative method consistent with an intention-to-treat analysis, proposing no future progress following dropout and thereby reducing the bias of overestimating intervention effects. Significantly, attrition was low and similar between groups across time points, reducing the differential attrition bias. Sex and age-stratified analyses were conducted. In addition, analysis of covariance (ANCOVA) was performed to investigate the effect of the intervention on the primary outcome after adjusting for confounding variables such as age and sex of the participants. Bias in evaluating the effects of the intervention is expected because of the study’s open-label design. A researcher who did not participate in the intervention or allocation of participants carried out the statistical analysis. 3. Results Figure 2 shows the CONSORT (Consolidated Standards of Reporting Trials) diagram of the study. During January 2025, a total of 1,580 participants were assessed to verify their eligibility for the study from eight colleges (education: 105, arts: 183, engineering: 170, fine arts: 254, commerce: 266, rights: 220, computer and information: 100, and specific education: 282). Of them, 279 participants from eight colleges were enrolled in the study and allocated to either the IG ( n = 133) or the CG ( n = 146), as illustrated in Table 2 . A total of 245 (118 in the IG and 127 in the CG) completed the study within 5 months by June 2025. The data of all 279 participants were analyzed. The reasons for dropout are displayed in Figure 2 , where all dropouts within the two groups occur early, before the first assessment (T1). Supplementary file 2 shows the CONSORT checklist of reporting results. Figure 2. Open in a new tab Diagram according to consolidated standards of reporting trials (CONSORT). Table 2. Baseline assessment of adolescents (IG; n = 133) and (CG; n = 146). Variables Total ( n = 279) IG ( n = 133) CG ( n = 146) Test value p -value Demographic Age 17.88 ± 0.78 17.9474 ± 0.77 17.8151 ± 0.78787 1.415 0.158 Sex 0.001 0.979 Male 124 (44.4%) 59 (44.4%) 65 (44.5%) Female 155 (55.6%) 74 (55.6%) 81 (55.5%) Marital status 1.631 0.274 Single 265 (95.0%) 124 (93.2%) 141 (96.6%) Married 14 (5.0%) 9 (6.8%) 5 (3.4%) Income 7530.46 ± 3478.34 7345.86 ± 3531.34 7698.63 ± 3432.84 −0.846 0.398 Academic year 0.029 0.864 First year 163 (58.4%) 77 (57.9%) 86 (58.9%) Second year 116 (41.6%) 56 (42.1%) 60 (41.1%) College Fine arts 38 (13.6%) 38 (28.6%) Education 32 (11.5%) 32 (24.0%) Arts 36 (12.9%) 36 (27.1%) Engineering 27 (9.7%) 27 (20.3%) Commerce 35 (12.5%) 35 (24.0%) Rights 40 (14.3%) 40 (27.4%) Computer and information 29 (10.4%) 29 (19.9%) Specific education 42 (15.1%) 42 (28.7%) Anthropometric measurements Weight 77.20 ± 10.80 76.79 ± 10.50 77.58 ± 11.08 −0.612 0.541 Height 168.04 ± 9.19 168.22 ± 8.51 167.88 ± 9.80 0.309 0.757 BMI 27.33 ± 3.21 27.11 ± 3.01 27.54 ± 3.37 −1.104 0.271 Experience and history Family history of overweight 1.312 0.277 Yes 73 (26.2%) 39 (29.3%) 34 (23.3%) No 206 (73.8%) 94 (70.7%) 112 (76.7%) Experience of behavioral therapy 1.408 0.495 Yes 48 (17.2%) 21 (15.8%) 27 (18.5%) No 172 (61.6%) 80 (60.2%) 92 (63.0%) Do not know 59 (21.1%) 32 (24.1%) 27 (18.5%) Experience of calculated calories 1.485 0.476 Yes 90 (32.3%) 43 (32.3%) 47 (32.2%) No 140 (50.2%) 63 (47.4%) 77 (52.7%) Do not know 49 (17.6%) 27 (20.3%) 22 (15.1%) Experience of physical exercises 6.438 0.012 Yes 127 (45.5%) 50 (37.6%) 77 (52.7%) No 152 (54.5%) 83 (62.4%) 69 (47.3%) Experience of medical therapy 0.432 0.593 Yes 36 (12.9%) 19 (14.3%) 17 (11.6%) No 243 (87.1%) 114 (85.7%) 129 (88.4%) Motivator/desire to control weight 1.989 0.370 Healthy 32 (11.5%) 17 (12.8%) 15 10.3% Appearance 18 (6.5%) 11 (8.3%) 7 4.8% Appearance and health 229 (82.1%) 105 (78.9%) 124 84.9% HBM constructs Perceived severity 3.35 ± 1.08 3.39 ± 1.11 3.32 ± 1.06 0.534 0.594 Emotional/mental health 3.22 ± 1.34 3.21 ± 1.39 3.23 ± 1.30 −0.138 0.890 Physical health/fitness 3.65 ± 1.24 3.69 ± 1.28 3.61 ± 1.21 0.550 0.583 Social professional 3.11 ± 1.20 3.18 ± 1.24 3.04 ± 1.15 1.010 0.313 Perceived susceptibility 3.55 ± 0.93 3.59 ± 0.92 3.50 ± 0.94 0.770 0.442 Lifestyle 3.64 ± 1.03 3.71 ± 0.99 3.58 ± 1.05 1.049 0.295 Environmental 3.30 ± 1.27 3.29 ± 1.27 3.31 ± 1.28 −0.143 0.887 Perceived barriers 3.13 ± 1.32 3.10 ± 1.38 3.15 ± 1.27 −0.310 0.757 Practical concerns 3.17 ± 1.43 3.11 ± 1.49 3.22 ± 1.37 −0.651 0.516 Emotional/mental health 3.23 ± 1.43 3.22 ± 1.48 3.25 ± 1.40 −0.214 0.831 Awareness 2.99 ± 1.43 2.99 ± 1.47 3.00 ± 1.39 −0.071 0.943 Perceived benefits 3.78 ± 0.95 3.71 ± 1.00 3.84 ± 0.90 −1.076 0.283 Emotional/mental health 3.76 ± 1.07 3.64 ± 1.12 3.87 ± 1.00 −1.808 0.072 Physical health/fitness 3.77 ± 1.04 3.74 ± 1.06 3.81 ± 1.02 −0.587 0.557 Social/professional 3.81 ± 1.11 3.77 ± 1.15 3.85 ± 1.09 −0.585 0.559 Cues to action 3.61 ± 0.95 3.61 ± 0.98 3.61 ± 0.93 −0.030 0.976 Internal cues 3.57 ± 1.05 3.53 ± 1.10 3.61 ± 1.00 −0.687 0.493 External cues 3.67 ± 0.98 3.69 ± 0.99 3.66 ± 0.97 0.261 0.794 Perceived self-efficacy 3.57 ± 0.92 3.62 ± 0.94 3.53 ± 0.90 0.838 0.403 Perceived self-efficacy in dieting 3.53 ± 0.96 3.56 ± 1.01 3.51 ± 0.92 0.421 0.674 Habits, preferences 3.55 ± 0.96 3.57 ± 1.00 3.53 ± 0.92 0.393 0.695 Emotional/mental health 3.49 ± 1.09 3.52 ± 1.14 3.46 ± 1.04 0.441 0.660 Perceived self-efficacy in exercise 3.67 ± 0.99 3.77 ± 0.96 3.57 ± 1.01 1.737 0.084 Behavioral intention of weight management 3.18 ± 1.15 3.27 ± 1.15 3.10 ± 1.14 1.272 0.204 Dieting 3.17 ± 1.17 3.26 ± 1.17 3.08 ± 1.17 1.272 0.205 Exercising 3.20 ± 1.31 3.29 ± 1.29 3.12 ± 1.33 1.080 0.281 Open in a new tab 3.1. Baseline characteristics Except for the experience of physical exercise ( p = 0.012), there were no statistically significant differences between the IG and CG at T0 regarding demographic characteristics, anthropometric measurements, and the mean score of HBM constructs ( Table 2 ). 3.2. Body mass index It is observed that the mean scores of BMI decreased in the IG compared to the CG at both T1 and T2, as displayed in Figure 3 . The independent t -test shows no statistically significant difference in BMI between groups at T1 ( p = 0.091) ( Table 3 ). The statistically significant reduction in BMI was demonstrated in the IG compared to the CG at T2 (−1.75, CI: −2.48, −1.02, p < 0.001) and moderate effect size (Cohen’s d = −0.568) ( Table 3 ). Also, two-way repeated measures ANOVA reveals a statistically significant small effect between groups: F (1, 277) = 6.45, p = 0.012, partial η 2 = 0.023. Interaction effect (Group × Time) is statistically significant: F (1.236, 342.358) = 49.72, p = <0. 001, partial η 2 = 0.152. Within-group differences are also statistically significant: F (1.236, 342.358) = 29.73, p = <0. 001, partial η 2 = 0.097 ( Table 4 ). Figure 3. Open in a new tab Mean scores of BMI with SE bars (95% CI and ±2 SE); T0, baseline; T1, 2 months; T2, 5 months. Intervention group ( n = 133); control group ( n = 146). Table 3. Mean differences of BMI and HBM constructs between IG ( n = 133) and CG ( n = 146) at different time points. Outcomes Time points T value Mean difference 95% CI P- value Cohen’s d (95% CI) BMI T0 −1.10 −0.42 −1.18 to 0.33 0.271 T1 −1.70 −0.66 −1.43 to 0.11 0.091 T2 −4.73 −1.75 −2.48 to −1.02 <0.001 −0.568 HBM constructs Perceived severity T0 0.53 0.07 −0.18 to 0.32 0.594 T1 0.99 0.12 −0.12 to 0.35 0.324 T2 1.63 0.16 −0.03 to 0.35 0.105 Emotional/mental health T0 −0.14 −0.02 −0.34 to 0.29 0.890 T1 0.35 0.05 −0.24 to 0.34 0.729 T2 1.46 0.17 −0.06 to 0.4 0.146 Physical health/fitness T0 0.55 0.08 −0.21 to 0.38 0.583 T1 0.45 0.06 −0.2 to 0.33 0.653 T2 0.94 0.11 −0.12 to 0.34 0.350 Social professional T0 1.01 0.14 −0.13 to 0.43 0.313 T1 1.87 0.25 −0.01 to 0.52 0.063 T2 1.79 0.21 −0.02 to 0.44 0.075 Perceived susceptibility T0 0.77 0.08 −0.13 to 0.3 0.442 T1 2.59 0.26 0.06–0.46 0.010 0.310 T2 3.95 0.40 0.2–0.6 <0.001 0.474 Lifestyle T0 1.05 0.13 −0.11 to 0.37 0.295 T1 2.48 0.27 0.05–0.48 0.014 0.297 T2 4.49 0.46 0.26–0.67 <0.001 0.539 Environmental T0 −0.14 −0.02 −0.32 to 0.28 0.887 T1 1.64 0.25 −0.05 to 0.55 0.102 T2 1.59 0.23 −0.05 to 0.52 0.112 Perceived barriers T0 −0.31 −0.05 −0.36 to 0.26 0.757 T1 −1.30 −0.19 −0.49 to 0.09 0.195 T2 −3.19 −0.45 −0.73 to −0.17 0.002 −0.382 Practical concerns T0 −0.65 −0.11 −0.45 to 0.22 0.516 T1 −1.98 −0.33 −0.65 to −0.01 0.049 −0.237 T2 −4.43 −0.70 −1.01 to −0.39 <0.001 −0.531 Emotional/mental health T0 −0.21 −0.04 −0.38 to 0.3 0.831 T1 −1.23 −0.20 −0.52 to 0.12 0.221 T2 −3.30 −0.52 −0.84 to −0.21 0.001 −0.395 Awareness T0 −0.07 −0.01 −0.35 to 0.32 0.943 T1 −0.51 −0.08 −0.39 to 0.23 0.613 T2 −1.23 −0.19 −0.49 to 0.11 0.218 Perceived benefits T0 −1.08 −0.12 −0.35 to 0.1 0.283 T1 2.97 0.34 0.11–0.56 0.003 0.357 T2 4.61 0.48 0.28–0.69 <0.001 0.553 Emotional/mental health T0 −1.81 −0.23 −0.48 to 0.02 0.072 T1 2.62 0.31 0.08–0.54 0.009 0.314 T2 5.16 0.57 0.35–0.79 <0.001 0.618 Physical health/fitness T0 −0.59 −0.07 −0.32 to 0.17 0.557 T1 2.78 0.35 0.1–0.6 0.006 0.333 T2 3.88 0.45 0.23–0.68 <0.001 0.465 Social/professional T0 −0.58 −0.08 −0.34 to 0.18 0.559 T1 2.75 0.35 0.1–0.6 0.006 0.330 T2 3.35 0.40 0.17–0.64 <0.001 0.401 Cues to action T0 −0.03 −0.03 −0.23 to 0.22 0.976 T1 2.17 0.24 0.02–0.45 0.030 0.261 T2 3.77 0.40 0.19–0.6 <0.001 0.452 Internal cues T0 −0.69 −0.09 −0.33 to 0.16 0.493 T1 2.32 0.28 0.04–0.52 0.021 0.278 T2 5.24 0.64 0.4–0.88 <0.001 0.628 External cues T0 0.26 0.03 −0.2 to 0.26 0.794 T1 1.58 0.19 −0.05 to 0.43 0.116 T2 1.27 0.16 −0.08 to 0.4 0.102 Perceived self-efficacy T0 0.84 0.09 −0.12 to 0.31 0.403 T1 2.40 0.26 0.05–0.47 0.017 0.288 T2 3.83 0.41 0.2–0.62 <0.001 0.459 Perceived self-efficacy in dieting T0 0.42 0.05 −0.18 to 0.28 0.674 T1 2.33 0.26 0.04–0.48 0.021 0.279 T2 3.41 0.4 0.17–0.63 <0.001 0.409 Habits, preferences T0 0.39 0.04 −0.18 − 0.27 0.695 T1 2.10 0.24 0.01–0.47 0.037 0.251 T2 3.29 0.4 0.16–0.64 0.001 0.394 Emotional/mental health T0 0.44 0.06 −0.2 to 0.31 0.660 T1 2.48 0.31 0.06–0.56 0.014 0.297 T2 3.08 0.4 0.14–0.66 0.002 0.370 Perceived self-efficacy in exercise T0 1.74 0.2 −0.03 to 0.44 0.084 T1 2.40 0.26 0.05–0.47 0.017 0.288 T2 3.38 0.43 0.18–0.69 0.001 0.405 Behavioral intention of weight management T0 1.27 0.17 −0.09 to 0.44 0.204 T1 3.22 0.48 0.19–0.77 0.001 0.387 T2 4.90 0.71 0.43–1 <0.001 0.588 Dieting T0 1.27 0.18 −0.09 to 0.45 0.205 T1 3.27 0.49 0.2–0.79 0.001 0.362 T2 4.85 0.73 0.43–1.02 <0.001 0.582 Exercising T0 1.08 0.17 −0.14 to 0.48 0.281 T1 2.81 0.45 0.13–0.77 0.005 0.337 T2 4.38 0.69 0.38–1 <0.001 0.525 Open in a new tab Table 4. Results of two-way repeated measures analysis of variance of BMI and HBM constructs at different time points (IG; n = 133) and (CG; n = 146). Outcomes Time points IG ( n = 133) CG ( n = 146) Two-way repeated measures ANOVA Interaction group × time Within-groups Between-groups Mean ± SD Mean ± SD F -value P -value partial η 2 F -value P -value partial η 2 F -value P -value partial η 2 BMI T0 27.11 ± 3.01 27.54 ± 3.37 49.72 <0.001 0.152 29.73 <0.001 0.097 6.45 0.012 0.023 T1 27.03 ± 3.03 27.69 ± 3.45 T2 26.94 ± 2.61 28.69 ± 3.47 HBM constructs Perceived severity T0 3.39 ± 1.11 3.32 ± 1.06 0.762 0.439 0.003 28.45 <0.001 0.093 1.13 0.289 0.004 T1 3.59 ± 1.04 3.47 ± 0.94 T2 3.70 ± 0.83 3.54 ± 0.80 Emotional/mental health T0 3.21 ± 1.39 3.23 ± 1.30 2.786 0.072 0.01 46.25 <0.001 0.143 0.24 0.623 0.001 T1 3.46 ± 1.30 3.41 ± 1.17 T2 3.70 ± 0.98 3.53 ± 0.97 Physical health/fitness T0 3.69 ± 1.28 3.61 ± 1.21 0.185 0.782 0.001 8.29 0.001 0.029 0.44 0.506 0.002 T1 3.81 ± 1.18 3.75 ± 1.08 T2 3.85 ± 0.97 3.75 ± 0.97 Social professional T0 3.18 ± 1.24 3.04 ± 1.15 0.849 0.404 0.003 25.22 <0.001 0.083 2.71 0.101 0.01 T1 3.45 ± 1.19 3.20 ± 1.08 T2 3.50 ± 0.99 3.29 ± 0.96 Perceived susceptibility T0 3.59 ± 0.92 3.50 ± 0.94 8.003 0.001 0.028 26.55 <0.001 0.087 6.93 0.009 0.024 T1 3.85 ± 0.82 3.59 ± 0.87 T2 4.03 ± 0.68 3.63 ± 0.96 Lifestyle T0 3.71 ± 0.99 3.58 ± 1.05 8.550 0.001 0.03 18.03 <0.001 0.061 8.00 0.005 0.028 T1 3.93 ± 0.81 3.66 ± 0.98 T2 4.12 ± 0.62 3.66 ± 1.03 Environmental T0 3.29 ± 1.27 3.31 ± 1.28 2.531 0.097 0.009 16.31 <0.001 0.056 1.43 0.234 0.005 T1 3.66 ± 1.33 3.41 ± 1.22 T2 3.80 ± 1.27 3.57 ± 1.16 Perceived barriers T0 3.10 ± 1.38 3.15 ± 1.27 36.57 <0.001 0.117 1.24 0.280 0.004 2.47 0.117 0.009 T1 2.99 ± 1.30 3.19 ± 1.19 T2 2.89 ± 1.33 3.34 ± 1.05 Practical concerns T0 3.11 ± 1.49 3.22 ± 1.37 33.06 <0.001 0.107 7.53 0.002 0.026 5.64 0.018 0.02 T1 3.02 ± 1.44 3.35 ± 1.32 T2 2.95 ± 1.50 3.65 ± 1.12 Emotional/mental health T0 3.22 ± 1.48 3.25 ± 1.40 26.34 <0.001 0.087 1.64 0.202 0.006 2.52 0.114 0.009 T1 3.13 ± 1.42 3.33 ± 1.31 T2 3.02 ± 1.48 3.55 ± 1.17 Awareness T0 2.99 ± 1.47 3.00 ± 1.39 9.78 <0.001 0.034 46.21 <0.001 0.143 0.35 0.555 0.001 T1 2.84 ± 1.32 2.92 ± 1.32 T2 2.70 ± 1.26 2.89 ± 1.31 Perceived benefits T0 3.71 ± 1.00 3.84 ± 0.90 25.46 <0.001 0.084 28.01 <0.001 0.092 5.618 0.018 0.02 T1 4.14 ± 0.86 3.80 ± 1.01 T2 4.35 ± 0.75 3.86 ± 0.97 Emotional/mental health T0 3.64 ± 1.12 3.87 ± 1.00 31.07 <0.001 0.101 16.91 <0.001 0.058 4.442 0.036 0.016 T1 4.18 ± 0.87 3.88 ± 1.07 T2 4.29 ± 0.72 3.72 ± 1.10 Physical health/fitness T0 3.74 ± 1.06 3.81 ± 1.02 17.39 <0.001 0.059 28.77 <0.001 0.094 4.922 0.027 0.017 T1 4.11 ± 0.97 3.76 ± 1.12 T2 4.36 ± 0.87 3.90 ± 1.06 Social/professional T0 3.77 ± 1.15 3.85 ± 1.09 13.22 <0.001 0.046 28.37 <0.001 0.093 4.012 0.046 0.014 T1 4.14 ± 0.95 3.79 ± 1.16 T2 4.40 ± 0.88 4.00 ± 1.12 Cues to action T0 3.61 ± 0.98 3.61 ± 0.93 29.70 <0.001 0.097 23.80 <0.001 0.079 3.995 0.047 0.014 T1 3.86 ± 0.86 3.63 ± 0.95 T2 3.98 ± 0.73 3.58 ± 0.99 Internal cues T0 3.53 ± 1.10 3.61 ± 1.00 34.55 <0.001 0.111 36.98 <0.001 0.118 6.106 0.014 0.022 T1 3.99 ± 1.02 3.71 ± 1.01 T2 4.24 ± 0.83 3.61 ± 1.16 External cues T0 3.69 ± 0.99 3.66 ± 0.97 5.30 0.013 0.019 1.30 0.266 0.005 1.16 0.281 0.004 T1 3.74 ± 0.96 3.55 ± 1.06 T2 3.71 ± 0.96 3.56 ± 1.08 Perceived self-efficacy T0 3.62 ± 0.94 3.53 ± 0.90 24.05 <0.001 0.08 14.13 <0.001 0.049 5.825 0.016 0.021 T1 3.65 ± 0.92 3.39 ± 0.89 T2 3.85 ± 0.89 3.44 ± 0.90 Perceived self-efficacy in dieting T0 3.56 ± 1.01 3.51 ± 0.92 18.12 <0.001 0.061 15.44 <0.001 0.053 4.635 0.032 0.016 T1 3.62 ± 0.98 3.36 ± 0.89 T2 3.85 ± 1.02 3.45 ± 0.94 Habits, preferences T0 3.57 ± 1.00 3.53 ± 0.92 14.42 <0.001 0.049 15.39 <0.001 0.053 4.247 0.040 0.015 T1 3.61 ± 1.01 3.37 ± 0.92 T2 3.87 ± 1.05 3.47 ± 0.97 Emotional/mental health T0 3.52 ± 1.14 3.46 ± 1.04 17.46 <0.001 0.059 7.99 0.001 0.028 4.30 0.039 0.015 T1 3.66 ± 1.11 3.35 ± 1.01 T2 3.81 ± 1.15 3.40 ± 1.03 Perceived self-efficacy in exercise T0 3.77 ± 0.96 3.57 ± 1.01 5.97 0.004 0.021 9.60 <0.001 0.034 7.23 0.008 0.025 T1 3.65 ± 0.92 3.39 ± 0.89 T2 3.84 ± 1.10 3.41 ± 1.05 Behavioral intention of weight management T0 3.27 ± 1.15 3.10 ± 1.14 20.06 <0.001 0.068 0.826 0.407 0.003 11.35 <0.001 0.039 T1 3.43 ± 1.31 2.95 ± 1.17 T2 3.59 ± 1.27 2.88 ± 1.15 Dieting T0 3.26 ± 1.17 3.08 ± 1.17 18.834 <0.001 0.064 1.414 0.244 0.005 11.468 <0.001 0.04 T1 3.41 ± 1.34 2.91 ± 1.19 T2 3.59 ± 1.32 2.87 ± 1.17 Exercising T0 3.29 ± 1.29 3.12 ± 1.33 15.652 <0.001 0.053 0.240 0.725 0.001 8.618 0.004 0.03 T1 3.45 ± 1.36 3.00 ± 1.32 T2 3.59 ± 1.36 2.89 ± 1.28 Open in a new tab ANCOVA models adjusting for sex and age were performed, and revealed that the variables were not statistically significant confounders. Additionally, no statistically significant sex-by-intervention or age-by-intervention interactions were observed. 3.3. Perceived severity Figure 4 shows that perceived severity increases in both groups at different time points. This progress in the two groups did not differ statistically between groups at T1 ( p = 0.324) or T2 ( p = 0.105) as revealed in Table 3 for the perceived severity and subscales. However, there is a statistically significant difference within-group: F (1.589, 440.089) = 28.45, p = <. 001, partial η 2 = 0.093. Similar results were revealed in the subscales of perceived severity (emotional/mental health, physical health/fitness, and social professional) [ F (1.69, 469.35) = 46.25, p = <0.001, partial η 2 = 0.0143], [ F (1.60, 442.40) = 8.29, p = 0.001, partial η 2 = 0.029], and [ F (1.57, 435.83) = 25.22, p = <0.001, partial η 2 = 0.083] respectively ( Table 4 ). Figure 4. Open in a new tab Mean scores of HBM constructs with SE bars (95% CI and ±2 SE); T0, baseline; T1, 2 months; T2, 5 months. Intervention group ( n = 133); control group ( n = 146). 3.4. Perceived susceptibility Conversely, Figure 3 shows increased perceived susceptibility in the IG versus the CG. The intervention has a statistically significant positive effect on the adolescents in the intervention group to perceive their susceptibility to overweight consequences, compared to the adolescents in the control group at T1 (0.26, CI: 0.06–0.46, p = 0.010) and T2 (0.40, CI: 0.2–0.6, p < 0.001) ( Table 3 ). Furthermore, there are statistical differences within-group over time and a statistically significant interaction effect (Group × Time) [ F (1.68, 466.47) = 26.55, p < 0.001, partial η 2 = 0.087], and [ F (1.68, 466.47) = 8.00, p = 0.001, partial η 2 = 0.028] respectively ( Table 4 ). What is noted, the statistically significant progress in the perceived susceptibility in IG is attributed to statistically significant change in lifestyle subscale at T1 (0.27, CI: 0.05–0.48, p = 0.014) and T2 (0.46, CI: 0.26–0.67, p < 0.001) compared to CG, and the statistically significant main effect within groups overtime [ F (1.57, 435.81) = 18.03, p < 0.001, partial η 2 = 0.061] ( Table 4 ). The environmental subscale did not differ statistically between groups at different time points (all p > 0.05) ( Table 4 ). 3.5. Perceived barriers The intervention group (IG) experiences a reduction in perceived barriers and subscales compared to the control group (CG), as shown in Figure 4 ; Table 3 . Still, this difference is not statistically significant at T1 (all p > 0.05) ( Table 3 ). However, the difference becomes statistically significant at T2 (−0.45, CI: −0.73 to −0.17, p = 0.002), though with a small effect size (Cohen’s d = −0.382) ( Table 3 ). In addition, it is observed that there are no statistically significant changes within groups over time for the total perceived barriers ( Table 4 ). The statistically significant difference of total perceived barriers between IG and CG at T2 may be attributed to the statistically significant interaction effect (Group × Time) for the total perceived barriers: F (1.37, 379.48) = 36.57, p < 0.001, partial η 2 = 0.117 and subscales; practical concerns: F (1.44,399.82) = 33.06, p < 0.001, partial η 2 = 0.107, emotional mental health: F (1.229, 340.37) = 26.34, p < 0.001, partial η 2 = 0.087, awareness: F (1.435, 397.46) = 9.78, p < 0.001, partial η 2 = 0.034. 3.6. Perceived benefits Perceived benefits increased in the IG, compared to the CG, as shown in Figure 4 . There are statistically significant differences between groups at T1 and T2 regarding total perceived benefits and subscales (all p < 0.05) ( Table 3 ). In addition, there are statistically significant effects of Interaction (Group × Time) and within-group (all p < 0.05) ( Table 4 ). 3.7. Perceived cues to action Concerning the differences in perceived internal and external cues to action between the IG and CG. Specifically, internal cues were statistically significantly different between the IG and CG at both T1 (0.28, CI: 0.04–0.52, p = 0.021) and T2 (0.64, CI: 0.40–0.88, p < 0.001) ( Table 3 ). External cues, however, were not statistically different between the two groups at T1 ( p = 0.116) or T2 ( p = 0.102) ( Table 3 ), and neither did they change statistically within-groups over time ( p = 0.266) ( Table 4 ). The overall total change in cues to action was statistically significant between groups at T1 (0.24, CI: 0.02–0.45, p = 0.03) and T2 (0.40, CI: 0.19–0.60, p < 0.001) ( Table 3 ), within groups: F (1.69, 467.69) = 23.80, p < 0.001, partial η 2 = 0.079, and interaction: F (1.69, 467.69) = 3.99, p < 0.047, partial η 2 = 0.014 ( Table 4 ). This change in the total cues to action is mainly related to the statistically significant improvement in internal cues. 3.8. Perceived efficacy for dieting and exercise Table 3 presents the participants in the intervention group (IG) showed a statistically significant increase in total perceived efficacy for dieting and exercise, compared to the control group (CG) at T1 (0.26, CI:0.05–0.47, p = 0.017) and T2 (0.41, CI:0.20–0.62, p < 0.00). Also, it is revealed statistically significant effect within-groups: F (1.42, 393.29) = 14.13, p < 0.001, partial η 2 = 0.049, and statistically significant interaction effect between group and time: F (1.42, 393.29) = 24.05, p < 0.001, partial η 2 = 0.08. At the same time, there are statistically significant changes for all subscales of perceived efficacy (all p < 0.0.05). 3.9. Perceived behavioral intention of weight management in dieting and exercise The means scores of behavioral intention of weight management decreased from T0 to T2, while the means increased obviously in the IG over timepoints ( Figure 4 ). Compared to the CG, the participants in the IG perceived statistically significant behavioral intention in dieting and exercise at T1 (0.48, CI: 0.19–0.77, p = 0.001) and T2 (0.71, CI: 0.43–1.00, p < 0.001) ( Table 3 ). However, these improvements did not demonstrate a statistically significant effect within groups over time ( p = 0.407) ( Table 4 ). This indicates a positive impact on behavioral intention, but this impact was not sustained among participants over time. 4. Discussion This RCT investigated the effectiveness of a tailored multichannel intervention in promoting weight loss and enhancing health beliefs among overweight adolescents, informed by HBM. The findings demonstrate that the intervention had a statistically significant impact on BMI reduction and several HBM constructs, validating the hypothesis that tailored multichannel interventions can effectively influence weight loss and management behaviors among adolescents. In line with the current trial, previous research and systematic reviews have reported significant decreases in BMI when interventions are grounded in theory, tailored to individuals, and provided through various channels over a sufficient period ( 19 ). 4.1. BMI The primary outcome—BMI—decreased significantly in the IG compared to the CG by the end of the intervention at 5 months, with a moderate effect size. This finding aligns with previous research that proves sustained tailored interventions targeting specific cognitive and behavioral determinants can produce clinically meaningful changes in body weight ( 16 ). The statistically significant interaction effect between group and time further supports the assertion that sustained, multichannel engagement over time contributes to positive outcomes. In contrast, the lack of a statistically significant difference between groups at 2 months suggests that early intervention phases affect awareness and motivation, rather than resulting in immediate weight changes ( 63 ). This interpretation is consistent with previous studies. For example, the trial aimed to determine the effectiveness of 3 months of nutritional education based on the HBM on self-esteem and BMI of overweight adolescent girls. The study revealed no significant difference in mean BMI score between the IG and CG. There was no statistically significant difference between groups at 2 months [IG (26.82 ± 1.42), CG (27.19 ± 1.55), p = 0.170] or at 3 months from the intervention [IG (26.70 ± 1.38), CG (27.13 ± 1.56), p = 0.090] ( 64 ). Similarly, the RCT, which evaluated the effectiveness of a 10-week theory-based intervention to manage overweight among adolescents, demonstrated a reduction in BMI among overweight adolescents. Mean scores of BMI of the participants in the IG were reduced from 28.10 to 27.93 kg/m 2 ( p = 0.001) from baseline to immediate post-intervention and further decreased to 27.80 kg/m 2 ( p < 0.001) from baseline to 3-month follow-up. However, there were no statistically significant differences between groups, neither post-intervention ( p = 0.175) nor follow-up ( p = 0.093) ( 65 ). These findings suggest that short-term intervention may not be sufficient to achieve statistically significant outcomes. Supporting this, a systematic review assessing weight loss trajectories in a 12-month randomized crossover study found that participants experienced statistically significant weight loss between three and 6 months of intervention ( 66 ). 4.2. HBM The intervention effectively improved several key HBM constructs. Similar to the current results, a trial was conducted to assess the impact of educational intervention, based on HBM and collaborative learning techniques, on diet quality in adolescents. The study elaborated that there were statistically significant improvements regarding all subscales of HBM (perceived severity, perceived susceptibility, perceived barriers, perceived benefits, perceived self-efficacy, cues to action, and behavioral intention of weight management). HBM’s constructs were significantly improved in the IG, and mean differences were increased after the intervention ( p < 0.001), which was statistically significantly different from the CG ( p = 0.001) ( 67 ). Another trial was conducted to investigate the efficacy of a training program for overweight women based on the HBM and social support approach. It was reported “After the intervention, a statistically significant difference was observed between the two groups on perceived benefits [ t (71) = 3.55, p < 0.001], perceived barriers [ t (71) = 3.60, p = 0.001], cue to action [ t (70) = 1.91, p = 0.03], and self-efficacy [ t (71) = 3.45, p < 0.001] with very large to small effect sizes.” In addition, the participants within the IG experienced statistically significant improvements. “Post-test scores on perceived benefits [ t (33) = 2.82, p = 0.02], cue to action [ t (33) = 2.17, p = 0.03], and self-efficacy [ t (33) = 2.46, p = 0.01] differed significantly from pre-test scores with medium effect sizes” ( 68 ). The results of the present study are consistent with those of Rabiei et al., who assessed the effectiveness of 3 months of nutritional education based on the HBM on self-esteem and BMI in overweight adolescent girls. They discovered that the intervention group’s mean score in knowledge, perceived susceptibility, perceived severity, perceived benefits, and, finally, self-esteem before and after the intervention differed significantly among adolescents in the IG from the CG (all p < 0.001) ( 64 ). This finding is congruent with the results reported by Salem and Said, who investigated the effect of HBM-based nutrition education on the dietary habits of adolescent girls. It was found that nutrition education changes adolescents’ views regarding healthy eating habits. It was revealed that the perceived susceptibility, severity, benefits, barriers, self-efficacy, and cues to action were statistically significantly improved after intervention ( p < 0.001) ( 69 ). Perceived severity increased significantly in both groups. It indicates a growing awareness of the risks of being overweight. This statistically significant progress within groups is likely due to the general education provided to both groups. Perceived susceptibility significantly increased in the IG at different timepoints, especially in the lifestyle subscale, suggesting that adolescents in the IG became more aware of their risk of overweight-related health issues. This is critical as higher perceived susceptibility has been linked to increased motivation for health behavior change ( 70 ). Perceived benefits, internal cues to action, and self-efficacy in dieting and exercise significantly improved in the IG compared to the CG. The observed improvements are in agreement with prior HBM-based interventions targeting adolescent weight management ( 68 ). These findings reinforce that altering health beliefs is central and essential for promoting long-term adherence to health-promoting behaviors. The increase in internal cues (e.g., emotional triggers or self-awareness) suggests that adolescents became more attuned to their internal motivators for change, a critical factor for sustainable weight management ( 71 ). Saghafi-Asl et al. highlighted that cues to action and perceived self-efficacy were significantly associated with weight loss behavioral intention ( 10 ). The multichannel delivery method employed in this study may account for the more significant enhancements in cognitive and motivational constructs when compared to single-channel or solely educational interventions documented in other research ( 19 ). Consistent interaction through in-person meetings, telephone counseling, and WhatsApp communications likely bolstered learning, improved self-monitoring, and offered repeated internal prompts to take action ( 16 ). Conversely, certain findings differ from those of comparable studies. External cues to action showed no statistically significant changes between groups or within groups over time. Furthermore, the perceived barriers construct demonstrated a statistically significant reduction only at the end of the intervention in the IG, indicating a delayed effect of the intervention on overcoming obstacles to behavior change. This contrasts with certain interventions that included family participation or wider community involvement, which have shown more significant decreases in perceived obstacles and more robust external motivational factors ( 72 ). Despite the adolescents’ awareness of overweight-related health consequences, overweight management is the main challenge facing this vulnerable group ( 73 ). Several lifestyle interventions were provided without revealing positive outcomes ( 74 ). There were various barriers to implementation. Barriers include personal factors, the unavailability of healthy foods, and a lack of community support. Furthermore, the healthcare system is designed to focus on disease management instead of promoting health ( 73 ). The review, which aimed to summarize the evidence on the effectiveness of interventions for adolescent overweight in limited socioeconomic resource settings, concluded that no definite intervention approaches were particularly successful in managing overweight among disadvantaged adolescents ( 74 ). However, the evidence suggests that integrating community support in intervention delivery seems to be a useful strategy ( 72 ). Engagement of educational institutions or family could enhance external motivational sources ( 75 ). The educational institution is a familiar environment for enhancing healthy lifestyles ( 76 ). Furthermore, it was suggested that school programs include weight management strategies ( 65 ). Finally, behavioral intention for weight management in dieting and exercise significantly improved in the IG at both T1 and T2 compared to the CG. However, this improvement was not sustained within the groups over time. This indicates that the intervention initially increases intentions to change, but these intentions may not have been maintained. Highlighting the need for continued reinforcement or follow-up beyond the intervention period. 5. Limitations The results of this RCT are consistent with current evidence indicating that tailored, multichannel lifestyle interventions can successfully encourage weight loss and foster positive behavioral changes in adolescents. However, several limitations should be noted. First, BMI, while widely used, does not account for body composition variability such as muscle mass or fat distribution. Also, although guidelines support a BMI cutoff (BMI ≥ 25 kg/m 2 ) for late adolescents, BMI-for-age sensitivity analysis may be considered a limitation of the study. Second, a long questionnaire of HBM constructs may present a burden on adolescents. Third, the progress evaluation, intervention adherence, and individual understanding of the intervention were not assessed adequately. Fourth, the college-based setting may limit the generalizability of findings to other age groups or non-academic populations. Fifth, the absence of family participation may have influenced the outcomes, given the importance of familial support in adolescent health behaviors. Sixth, improvements in behavioral intention were not sustained over time, suggesting a need for longer intervention or follow-up periods. Finally, although the statistical analysis was performed by an independent researcher, a bias in estimating the effectiveness of the intervention is anticipated due to the open-label design. Also, the study may be underpowered to detect cluster-level effects. Moreover, LOCF may have methodological limitations. Future research with prolonged follow-up and larger samples may consider multiple-imputation or mixed-effects approaches. 6. Recommendations The results of the study could not be generalized. However, the findings of this research carry significant implications for health policy and clinical practice. Policymakers must consider weight management initiatives within school systems, supported by digital health platforms, to ensure ongoing engagement. Incorporating such evidence-based strategies into national adolescent health policies could reduce obesity-related morbidity and nurture lifelong healthy habits. Furthermore, promoting intersectoral collaboration among healthcare systems, educational entities, and families is vital for maintaining behavioral changes beyond the duration of the intervention. Expanding these initiatives, with the support of community resources and policy measures, would contribute to adolescent wellbeing and public health objectives. 7. Conclusion This research highlights various positive methodological and conceptual aspects that add to the expanding evidence base regarding adolescent weight management. First, it stands out as one of the limited randomized controlled trials that thoroughly assesses a tailored multichannel intervention specifically designed for adolescents and based on the Health Belief Model (HBM). The study provides strong evidence that a tailored multichannel intervention based on the theoretical framework HBM significantly improves BMI and multiple health beliefs related to weight loss among overweight adolescents. Second, the application of a theoretical framework improved the intervention’s ability to address cognitive, affective, and behavioral factors influencing weight-related behaviors. Third, the multichannel strategy—combining in-person sessions, telephone counseling, and WhatsApp follow-ups—promoted ongoing engagement and reinforced behavioral changes through repeated prompts for action. Fourth, the design of the intervention was backed by a multidisciplinary team, which ensured the content’s validity and its relevance to the psychosocial and lifestyle contexts of adolescents. Fifth, the randomization process reduced contamination bias among participants. The inclusion of several assessment points (baseline, mid-intervention, and post-intervention) allowed for a thorough evaluation of changes over time. Moreover, the adherence to CONSORT guidelines further strengthens methodological rigor, internal validity, and reproducibility. In summary, these strengths highlight the methodological robustness of the study. Acknowledgments We would like to acknowledge the adolescents with overweight for agreeing to participate. We also express our gratitude to all staff at Mansoura University who collaborated with the research team. The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2024/03/31782). Funding Statement The author(s) declared that financial support was received for this work and/or its publication.The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2024/03/31782). Footnotes Edited by: Danilo Silva , Federal University of Sergipe, Brazil Reviewed by: Ahmed Kadhim Jawad , Middle Technical University, Iraq Mustafa Shouli , Nablus University for Vocational and Technical Education, Palestine Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. The data were submitted to Dryad platform data repository, doi: 10.5061/dryad.sj3tx96k5 . Ethics statement Institutional Review Board approval was obtained from the faculty of nursing at Mansoura University (IRB 0689/30/12/2024). The study adhered to ethical principles outlined in the Declaration of Helsinki, protecting participants' rights and well-being. All participants were provided with oral and written details on the study before being enrolled. The study participants were informed that all data collected is for research purposes only and that they have the right to withdraw from the study at any time. Informed written consents were obtained from the enrolled participants. Author contributions ME: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing. MZ: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing. TS: Conceptualization, Investigation, Methodology, Supervision, Visualization, Writing – original draft. MA: Methodology, Data curation, Validation, Formal analysis, Writing – original draft. NM: Conceptualization, Data curation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft. SA: Data curation, Investigation, Project administration, Supervision, Writing – original draft. HE-G: Conceptualization, Methodology, Project administration, Writing – original draft. OA: Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – review & editing. AA: Conceptualization, Methodology, Resources, Validation, Writing – review & editing. LA: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing. Conflict of interest The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Generative AI statement The author(s) declared that Generative AI was not used in the creation of this manuscript. Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2026.1733027/full#supplementary-material Data_Sheet_1.pdf (733.2KB, pdf) Table_1.pdf (49.5KB, pdf) References 1. Zhang X, Liu J, Ni Y, Yi C, Fang Y, Ning Q, et al. Global prevalence of overweight and obesity in children and adolescents: a systematic review and meta-analysis. JAMA Pediatr. (2024) 178:800–13. doi: 10.1001/jamapediatrics.2024.1576, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Phelps NH, Singleton RK, Zhou B, Heap RA, Mishra A, Bennett JE, et al. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet. (2024) 403:1027–50. doi: 10.1016/S0140-6736(23)02750-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. World Health Organization. Obesity and Overweight. Available online at: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (Accessed April 2, 2025). 4. Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. (2020) 396:1223–49. doi: 10.1016/S0140-6736(20)30752-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Napolitano MA, Whiteley JA, Mavredes MN, Faro J, DiPietro L, Hayman LL, et al. Using social media to deliver weight loss programming to young adults: design and rationale for the healthy body healthy U (HBHU) trial. Contemp Clin Trials. (2017) 60:1–13. doi: 10.1016/j.cct.2017.06.007, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Centers for Disease Control and Prevention. How Overweight and Obesity Impacts your Health | Healthy Weight and Growth | CDC. Available online at: https://www.cdc.gov/healthy-weight-growth/food-activity/overweight-obesity-impacts-health.html?CDC_AAref_Val=https://www.cdc.gov/healthyweight/effects/index.html (Accessed April 18, 2025). 7. Rankin J, Matthews L, Cobley S, Han A, Sanders R, Wiltshire HD, et al. Psychological consequences of childhood obesity: psychiatric comorbidity and prevention. Adolesc Health Med Ther. (2016) 7:125–46. doi: 10.2147/AHMT.S101631 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. French SA, Wall M, Corbeil T, Sherwood NE, Berge JM, Neumark-Sztainer D. Obesity in adolescence predicts lower educational attainment and income in adulthood: the project EAT longitudinal study. Obesi Sil Spr. (2018) 26:1467. doi: 10.1002/oby.22273 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Carson V, Hunter S, Kuzik N, Gray CE, Poitras VJ, Chaput JP, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth: an update. Appl Physiol Nutr Metab. (2016) 41:S240–65. doi: 10.1139/apnm-2015-0630 [ DOI ] [ PubMed ] [ Google Scholar ] 10. Saghafi-Asl M, Aliasgharzadeh S, Asghari-Jafarabadi M. Factors influencing weight management behavior among college students: an application of the health belief model. PLoS One. (2020) 15. doi: 10.1371/journal.pone.0228058 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Vandevijvere S, Chow CC, Hall KD, Umali E, Swinburn BA. Increased food energy supply as a major driver of the obesity epidemic: a global analysis. Bull World Health Organ. (2015) 93:446–56. doi: 10.2471/BLT.14.150565 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Arafa A, Yasui Y, Kato Y, Teramoto M, Nosaka S, Kogirima M. Lifestyle behaviors of childhood and adolescence: contributing factors, health consequences, and potential interventions. Am J Lifestyle Med. (2024). doi: 10.1177/15598276241245941 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Cox JS, Iturbe I, Searle A, Maiz E, Hinton EC. The acceptability, feasibility and preliminary efficacy of acceptance and commitment therapy for adolescents in the management of overweight or obesity: a scoping review. J Context Behav Sci. (2024) 31:100713. doi: 10.1016/j.jcbs.2023.11.004 [ DOI ] [ Google Scholar ] 14. Smith JD, Fu E, Kobayashi MA. Prevention and management of childhood obesity and its psychological and health comorbidities. Annu Rev Clin Psychol. (2024) 16:30. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Ahuja L, Schneider J, Budhraja M, Diedrichs PC, Williamson H. A systematic review exploring the effectiveness of body image interventions among boys. Adolesc Res Rev. (2024) 10:97–143. doi: 10.1007/s40894-024-00246-y [ DOI ] [ Google Scholar ] 16. Bannink R, Broeren S, Joosten-Van Zwanenburg E, Van As E, Van De Looij-Jansen P, Raat H. Effectiveness of a web-based tailored intervention (E-health4Uth) and consultation to promote adolescents’ health: randomized controlled trial. J Med Internet Res. (2014) 16:e143. doi: 10.2196/jmir.3163, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Kim MT, Heitkemper EM, Hébert ET, Hecht J, Crawford A, Nnaka T, et al. Redesigning culturally tailored intervention in the precision health era: self-management science context. Nurs Outlook. (2022) 70:710–24. doi: 10.1016/j.outlook.2022.05.015, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Kwasnicka D, Luszczynska A, Hagger MS, Quested E, Pagoto SL, Verboon P, et al. Theory-based digital intervention to promote weight loss and weight loss maintenance (choosing health): protocol for a randomised controlled trial. BMJ Open. (2020) 10:e040183. doi: 10.1136/bmjopen-2020-040183, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. De Luca V, Virgolesi M, Vetrani C, Aprano S, Cantelli F, Di Martino A, et al. Digital interventions for weight control to prevent obesity in adolescents: a systematic review. Front Public Health. (2025) 13:1584595. doi: 10.3389/fpubh.2025.1584595 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Wong CA, Merchant RM, Moreno MA. Using social media to engage adolescents and young adults with their health. Healthcare. (2014) 2:220–4. doi: 10.1016/j.hjdsi.2014.10.005, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Petit Francis L, Spaulding E, Turkson-Ocran RA, Allen J. Randomized trials of nurse-delivered interventions in weight management research: a systematic review. West J Nurs Res. (2017) 39:1120–50. doi: 10.1177/0193945916686962, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Coutts A. The nurse’s role in providing strategies and advice on weight management. Br J Nurs. (2021) 30:S20–7. doi: 10.12968/bjon.2021.30.21.S20 [ DOI ] [ PubMed ] [ Google Scholar ] 23. Hartman SJ, Risica PM, Gans KM, Marcus BH, Eaton CB. Tailored weight loss intervention in obese adults within primary care practice: rationale, design, and methods of choose to lose. Contemp Clin Trials. (2014) 38:409–19. doi: 10.1016/j.cct.2014.06.001, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Piwowarczyk E, MacPhee M, Howe J. Nurses’ role in obesity management in adults in primary healthcare settings worldwide: a scoping review. Healthcare. (2024) 12:1700. doi: 10.3390/healthcare12171700, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Abd El-aty NS, Osman SR, Ahmed ES, GadAllah MA. Overweight and obesity prevalence among upper Egypt primary schools’ children using Egyptian and CDC growth charts. Appl Nurs Res. (2020) 56:151346. doi: 10.1016/j.apnr.2020.151346 [ DOI ] [ PubMed ] [ Google Scholar ] 26. Aboulghate M, Elaghoury A, Elebrashy I, Elkafrawy N, Elshishiney G, Abul-Magd E, et al. The burden of obesity in Egypt. Front Public Health. (2021) 9:718978. doi: 10.3389/fpubh.2021.718978, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Alruwaili BF, Bayyumi DF, Alruwaili OS, Alsadun RS, Alanazi AS, Hadi A, et al. Prevalence and determinants of obesity and overweight among children and adolescents in the Middle East and north African countries: an updated systematic review. Diabetes Metab Syndr Obes. (2024) 17:2095–103. doi: 10.2147/DMSO.S458003 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Aljaadi AM, Alharbi M. "Overweight and obesity among Saudi children: prevalence, lifestyle factors, and health impacts". In: Handbook of Healthcare in the Arab World. Cham, Switzerland: Springer International Publishing; (2020). p. 1–25. [ Google Scholar ] 29. Rosenstock IM. The health belief model and preventive health behavior. Health Educ Behav. (1977) 2:354–86. doi: 10.1177/109019817400200405 [ DOI ] [ Google Scholar ] 30. Snetselaar LG. Nutrition intervention: lessons from clinical trials. Nutrition in the prevention and treatment of disease 4th ed. San Diego, CA: Elsevier; (2001). p. 95–104. [ Google Scholar ] 31. Haller DM, Sanci LA, Sawyer SM, Patton G. Do young people’s illness beliefs affect healthcare? A systematic review. J Adolesc Health. (2008) 42:436–49. doi: 10.1016/j.jadohealth.2007.09.013, [ DOI ] [ PubMed ] [ Google Scholar ] 32. Jorvand R, Ghofranipour F, HaeriMehrizi A, Tavousi M. Evaluating the impact of HBM-based education on exercise among health care workers: the usage of mobile applications in Iran. BMC Public Health. (2020) 20:546. doi: 10.1186/s12889-020-08668-8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Khodaveisi M, Azizpour B, Jadidi A, Mohammadi Y. Education based on the health belief model to improve the level of physical activity. Phys Act Nutr. (2021) undefined) 25:17–23. doi: 10.20463/pan.2021.0022 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 34. Lazzeretti L, Rotella F, Pala L, Rotella CM. Assessment of psychological predictors of weight loss: how and what for? World J Psychiatr. (2015) 5:56–67. doi: 10.5498/wjp.v5.i1.56 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Nezami BT, Lang W, Jakicic JM, Davis KK, Polzien K, Rickman AD, et al. The effect of self-efficacy on behavior and weight in a behavioral weight-loss intervention. Health Psychol. (2016) 35:714–22. doi: 10.1037/hea0000378, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Lv N, Azar KMJ, Rosas LG, Wulfovich S, Xiao L, Ma J. Behavioral lifestyle interventions for moderate and severe obesity: a systematic review. Prev Med Baltim. (2017) 100:180–93. doi: 10.1016/j.ypmed.2017.04.022 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Ma P-S, So W-Y, Choi H, et al. Using the health belief model to assess the physical exercise behaviors of international students in South Korea during the pandemic. Healthcare. (2023) 11:469. doi: 10.3390/healthcare11040469 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Cheema S, Maisonneuve P, Abraham A, Chaabna K, Yousuf W, Mushannen T, et al. Dietary patterns and associated lifestyle factors among university students in Qatar. J Am Coll Health. (2023) 71:2795–803. doi: 10.1080/07448481.2021.1996374 [ DOI ] [ PubMed ] [ Google Scholar ] 39. Park DY. Utilizing the health belief model to predicting female middle school students’ behavioral intention of weight reduction by weight status. Nutr Res Pract. (2011) 5:337–48. doi: 10.4162/nrp.2011.5.4.337, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Hoseini H, Maleki F, Moeini M, Sharifirad GR. Investigating the effect of an education plan based on the health belief model on the physical activity of women who are at risk for hypertension. Iran J Nurs Midwifery Res. 19:647–52. [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Mogeni BK, Ouma LO. Dietary patterns, behaviours, and their associated factors among university students in coastal Kenya. Cogent Food Agric. (2022) 8:2132873. doi: 10.1080/23311932.2022.2132873 [ DOI ] [ Google Scholar ] 42. Park JH, Sherman LD, Smith ML, Patterson MS, Prochnow T. The association between health belief model components and self-care practices among black/African American men with type 2 diabetes. Int J Env Res Public Health. (2025) 22:414. doi: 10.3390/ijerph22030414 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Szabó K, Pikó B. Likelihood of healthy eating among adolescents based on the health belief model. Dev Health Sci. (2019) 2:22–7. doi: 10.1556/2066.2.2019.004 [ DOI ] [ Google Scholar ] 44. Hays LM, Finch EA, Saha C, Marrero DG, Ackermann RT. Effect of self-efficacy on weight loss: a psychosocial analysis of a community-based adaptation of the diabetes prevention program lifestyle intervention. Diabetes Spectr. (2014) 27:270–5. doi: 10.2337/diaspect.27.4.270, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Kaur R, Kumar R, Gupta M. Predicting risk of obesity and meal planning to reduce the obese in adulthood using artificial intelligence. Endocrine. (2022) 78:458–69. doi: 10.1007/s12020-022-03215-4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Habib NS, Shair EH, Mansour MSAM. Effect of educational program utilizing braille method on nutritional behavior among vision loss children at Cairo Governorate. Neuroquantology. (2022) 20:530–47. [ Google Scholar ] 47. Details for: effect of educational program utilizing braille method on nutritional behavior among vision loss children at Cairo Governorate/new central library. Available online at: https://lis.cl.cu.edu.eg/cgi-bin/koha/opac-detail.pl?biblionumber=168407&shelfbrowse_itemnumber=301166 (Accessed October 2, 2024). 48. Cornier MA. A review of current guidelines for the treatment of obesity. Am J Manag Care. (2022) 28:S288–96. doi: 10.37765/ajmc.2022.89292 [ DOI ] [ PubMed ] [ Google Scholar ] 49. Barlow SE. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatr Int. (2007) 120. doi: 10.1542/peds.2007-2329C [ DOI ] [ PubMed ] [ Google Scholar ] 50. De Sanctis V, Soliman AT, Fiscina B, Elalaily R, Yassin M. A practical approach to adolescent health care: A brief overview. Ain Shams University repository. (2023). https://research.asu.edu.eg/handle/123456789/187160 (Accessed November 1, 2024). [ Google Scholar ] 51. Steps for Losing Weight | Healthy Weight and Growth | CDC. Available online at: https://www.cdc.gov/healthy-weight-growth/losing-weight/index.html#cdc_healthy_habits_tips-step-3-set-specific-goals-you-can-reach-in-a-realistic-time (Accessed October 15, 2024). 52. Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American college of cardiology/American Heart Association task force on practice guidelines and the obesity society. Circulation. (2014) 129:S102–38. doi: 10.1161/01.cir.0000437739.71477.ee [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 53. WHO (2024). Available online at: https://www.who.int/tools/growth-reference-data-for-5to19-years/indicators/bmi-for-age (Accessed January 2 2025). 54. Weir B, Jan A. Available online at: https://www.ncbi.nlm.nih.gov/books/NBK541070/ (Accessed January 10, 2025). 55. Jones CL, Jensen JD, Scherr CL, Brown NR, Christy K, Weaver J. The health belief model as an explanatory framework in communication research: exploring parallel, serial, and moderated mediation. Health Commun. (2015) 30:566–76. doi: 10.1080/10410236.2013.873363, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 56. Albasheer O, Hakami N, Abdelwahab SI, Alqassim AY, Alharbi A, Abdelmola AO, et al. Utilisation of the health belief model to study the behavioural intentions relating to obesity management among university students: a cross-sectional study. BMJ Open. (2024) 14:e079783. doi: 10.1136/bmjopen-2023-079783, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 57. Faul F, Erdfelder E, Lang AG, Buchner A. "G*power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences". Behav. Res. Methods. (2007). p. 175–91. doi: 10.3758/BF03193146 [ DOI ] [ PubMed ] [ Google Scholar ] 58. Bondyra-Wiśniewska B, Harton A. Effect of a low-glycemic index nutritional intervention on body weight and selected cardiometabolic parameters in children and adolescents with excess body weight and dyslipidemia. Nutrients. (2024) 16:2127. doi: 10.3390/nu16132127, [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 59. Davies CA, Spence JC, Vandelanotte C, Caperchione CM, Mummery WK. Meta-analysis of internet-delivered interventions to increase physical activity levels. Int J Behav Nutr Phys Act. (2012) 9:52. doi: 10.1186/1479-5868-9-52 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 60. Detry MA, Lewis RJ. The intention-to-treat principle how to assess the true effect of choosing a medical treatment. JAMA. (2020) 312:85–6. doi: 10.1001/jama.2014.7523 [ DOI ] [ PubMed ] [ Google Scholar ] 61. Doane DP, Seward LE. Measuring skewness: a forgotten statistic? J Stat Educ. (2011) 19:1–18. doi: 10.1080/10691898.2011.11889611 [ DOI ] [ Google Scholar ] 62. Mohd Razali N, Bee Wah Y. Power comparisons of Shapiro-wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J Statisti Mod Anal. (2011) 2:21–33. [ Google Scholar ] 63. Spiga F, Tomlinson E, Davies AL, Moore THM, Dawson S, Breheny K, et al. Interventions to prevent obesity in children aged 12 to 18 years old. Cochrane Database Syst Rev. (2024) 2024:CD015330. doi: 10.1002/14651858.CD015330.pub2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 64. Rabiei L, Masoudi R, Lotfizadeh M. Evaluation of the effectiveness of nutritional education based on health belief model on self-esteem and BMI of overweight and at risk of overweight adolescent girls. J Pediat Perspect. (2017) 5:5417–28. doi: 10.22038/IJP.2017.24196.2037 [ DOI ] [ Google Scholar ] 65. Lai WK, Mohd Sidik S, Rampal L, Gan WY, Ismail SIF. Effectiveness of a school-based intervention to manage overweight and obesity among adolescents in Seremban, Malaysia: a cluster randomized controlled trial. Hum Nutr Metabol. (2023) 34:200229. doi: 10.1016/j.hnm.2023.200229 [ DOI ] [ Google Scholar ] 66. Landry MJ, Ward CP, Cunanan KM, Fielding-Singh P, Crimarco A, Gardner CD. Switching diets after 6-months does not result in renewed weight loss: a secondary analysis of a 12-month crossover randomized trial. Sci Rep. (2024) 14:1–11. doi: 10.1038/s41598-024-60547-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 67. Keshani P, Kaveh MH, Faghih S, Salehi M. Improving diet quality among adolescents, using health belief model in a collaborative learning context: a randomized field trial study. Health Educ Res. (2019) 34:279–88. doi: 10.1093/her/cyz009 [ DOI ] [ PubMed ] [ Google Scholar ] 68. Faghih M, Kaveh MH, Nazari M, Khademi K, Hasanzadeh J. Effect of health belief model-based training and social support on the physical activity of overweight middle-aged women: a randomized controlled trial. Front Public Health. (2024) 12:1250152. doi: 10.3389/fpubh.2024.1250152 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 69. Salem GM, Said RM. Effect of health belief model based nutrition education on dietary effect of health belief model based nutrition education on dietary habits of secondary school adolescent girls in Sharkia Governorate. Egy J Comm Med. (2018) 36:35–47. doi: 10.21608/ejcm.2018.16330 [ DOI ] [ Google Scholar ] 70. McArthur LH, Riggs A, Uribe F, Spaulding TJ. Health belief model offers opportunities for designing weight management interventions for college students. J Nutr Educ Behav. (2018) 50:485–93. doi: 10.1016/j.jneb.2017.09.010 [ DOI ] [ PubMed ] [ Google Scholar ] 71. Aoun A, Ghoussoub C, Farsoun C, Al Mallah A, Ayoub F, Trezia N, et al. Examining the efficacy of mindfulness-based interventions in treating obesity, obesity-related eating disorders, and diabetes mellitus. J. Am. Nutr. Associ. (2025):581–93. doi: 10.1111/obr.12519 [ DOI ] [ PubMed ] [ Google Scholar ] 72. van der Kornet- Aa DA, Altenburg TM, Ranaad- Zee C d H, Chinapaw MJM. The effectiveness and promising strategies of obesity prevention and treatment programmes among adolescents from disadvantaged backgrounds: a systematic review. Obes Rev. (2017) 18:581–93. doi: 10.1111/obr.12519 [ DOI ] [ PubMed ] [ Google Scholar ] 73. Jebeile H, Kelly AS, O’Malley G, Baur LA. Obesity in children and adolescents: epidemiology, causes, assessment, and management. Lancet Diabetes Endocrinol. (2022) 10:351. doi: 10.1016/S2213-8587(22)00047-X [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 74. Martin A, Saunders DH, Shenkin SD, Sproule J. Lifestyle intervention for improving school achievement in overweight or obese children and adolescents. Cochrane Database Syst Rev. (2014) 2014:CD009728. doi: 10.1002/14651858.CD009728.pub2 [ DOI ] [ PubMed ] [ Google Scholar ] 75. Méndez-Hernández P, Dosamantes-Carrasco D, Lamure M, López-Loyo P, Hernández-Palafox C, Pineda-Pérez D, et al. Weight-loss practices among university students in Mexico. Int J Public Health. (2009) 55:221. doi: 10.1007/s00038-009-0104-0 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 76. safavi M, Yahyavi SH, Pourrahimi M. Impact of dietary behaviors and exercise activities education on the self- efficacy of middle school students. Med Sci J Islam. Azad Univ. (2012). 10) 22:143–51. [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Data_Sheet_1.pdf (733.2KB, pdf) Table_1.pdf (49.5KB, pdf) Data Availability Statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. The data were submitted to Dryad platform data repository, doi: 10.5061/dryad.sj3tx96k5 . Articles from Frontiers in Public Health are provided here courtesy of Frontiers Media SA ACTIONS View on publisher site PDF (1.8 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top