Key Factors in developing effective Digital Health Promotion tools for Cancer Prevention and Health Behavior Change in Adolescence through a multi-country survey

Key Factors in developing effective Digital Health Promotion tools for Cancer Prevention and Health Behavior Change in Adolescence through a multi-country survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Key Factors in developing effective Digital Health Promotion tools for Cancer Prevention and Health Behavior Change in Adolescence through a multi-country survey Vassilis Kilintzis, Haridimos Kondylakis, Aristeidis Petrakis, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7599976/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2026 Read the published version in BMC Public Health → Version 1 posted 15 You are reading this latest preprint version Abstract Objectives Primary cancer prevention through behavior change in adolescence, a crucial period for shaping lifelong health habits, presents a major public health challenge across Europe. Addressing this, the SUNRISE project aims to tackle the challenge of primary cancer prevention in adolescents by developing and implementing an innovative, digitally-enhanced life-skills program tailored to diverse socio-economic, cultural, and environmental backgrounds by incorporating various Digital Health Promotion (DHP) tools to foster sustainable health behavior change in adolescents. Methods A survey was conducted on 505 stakeholders (students, parents, and educators), from seven European countries to assess a set of key features for effective DHP tools for their importance on a five-point likert scale. Results Our findings revealed that nine of the proposed DHP tools’ features were identified as important across all the stakeholder groups, while significant differences in the importance of certain features across different stakeholder groups and countries were identified. Students, as primary users, demonstrated distinct preferences, which often diverged from educators and parents, suggesting that stakeholders hold distinct priorities driven by their roles and contextual backgrounds. Additionally, country-level variations were notable; for example, Swiss participants rated the proposed features, in general, as of lower importance than the Spanish respondents. Conclusions These insights emphasize the necessity of developing adaptable and context-sensitive DHP tools that reflect the diverse needs and preferences of adolescents across Europe. The large-scale implementation and evaluation of this program will provide valuable data for shaping future digital health interventions aimed at cancer prevention in youth. Digital Health Promotion Multi-country survey Health Behavior Cancer Prevention eHealth Co-Creation Methods Background Primary prevention of cancer through behavior change during adolescence—a developmental stage when many health-related behaviors are established—presents a critical health and societal challenge across Europe [ 1 ]. Recognizing this need, SUNRISE EU-funded project [ 2 ] seeks to co-create, implement, and evaluate an innovative, digitally-enhanced life-skills program designed for primary cancer prevention, with a strong focus on promoting sustainable health behavior change among adolescents. This program is customized to consider the socioeconomic, cultural, and environmental diversities that characterize European youth. To achieve its objectives, SUNRISE integrates a validated, evidence-based digital solution for smoking prevention with cutting-edge intervention strategies, including peer-driven social media campaigns, advertising literacy training, educational games, and interactive platforms featuring social robots. This multi-faceted approach aims to enhance cancer prevention efforts among adolescents across Europe. The SUNRISE project and its components are being developed through a co-creation process, using a "schools-as-living-labs" model [ 3 ] that engages diverse societal stakeholders. These include educators, adolescents, parents, public health professionals, and policymakers. The program is set for large-scale implementation and evaluation, spanning 154 schools and reaching over 7,500 students in both urban and rural regions across several European countries. Special attention is given to the inclusion of socially disadvantaged groups, such as migrants and ethnic minorities, to ensure the program’s equity and inclusivity. In addition to assessing the efficacy of its methods for promoting long-term health behavior changes, SUNRISE will evaluate strategies for widespread adoption and sustainability across multiple countries. Digital platforms have the potential to disseminate information rapidly to a large number of people [ 4 ] and Digital Health Promotion (DHP) tools are essential in fostering healthy behaviors and empowering individuals to take proactive roles in managing their well-being by modifying behaviors that influence preventable disease risk factors [ 5 , 6 ]. According to several comprehensive literature reviews [ 7 , 8 , 9 , 10 ], a variety of technological platforms are employed in DHP tools, including computer- and web-based programs, smartphone apps, and telemonitoring devices such as sensors. The effective development of DHP tools demands careful consideration of multiple factors to optimize usability, user engagement, and overall impact. Biomedical Informatics textbooks [ 11 ] and country-specific identification of digital health trends and challenges [ 12 ] are available and may act as a starting point for developing DHP tools. The current bibliography lacks evidence from multi-country, multi-stakeholder studies examining the requirements for digital health tools toward cancer prevention in adolescence. The present study aimed to expand current knowledge regarding the essential requirements and features that should be considered while developing effective DHP tools. Specifically, we report findings from a survey conducted across seven European countries involving key stakeholder groups, namely secondary school students, educators, and parents. Methods The primary aim of this survey was to identify the most critical requirements and features necessary to develop effective DHP tools targeting adolescents aged 12 to 19 years. Specifically, the study examined features that support health behavior change relevant to primary cancer prevention during adolescence, a critical developmental period when many risk-related behaviors emerge and may become lifelong habits [ 13 , 14 ]. By identifying and prioritizing these essential features, the survey provides evidence-based guidance for the co-creation and development of DHP tools designed to resonate with adolescent users while also addressing the needs of other key stakeholders. Survey design and data collection The survey incorporates elements identified in an extensive literature review [ 15 ] presenting the aspects related to preferred content of digital health promotion platforms after evaluation of 14 studies in the field. These aspects underwent a review by a multidisciplinary team, including technical, social science, and health experts, that adapted them and contextualized them to align with the aims of SUNRISE. The review process ensures the alignment of the proposed aspects (proposed DHP tools’ features) to the aim of identifying the key factors for developing effective and engaging DHP tools for cancer prevention and health behavior change in adolescence. During the implementation of the survey, the proposed features (presented in detail in Table 1 ) were rated on a five-point likert scale by the survey participants representing three primary stakeholder groups: students, educators, and parents. These groups were selected to capture diverse perspectives on health-promoting features, ensuring that the needs and preferences of end-users (students) and influential adult stakeholders (educators and parents) were represented. The survey was conducted across seven European countries: Cyprus, Greece, Italy, Slovenia, Spain, Switzerland, and Belgium. This cross-national approach provides insights into feature preferences across different cultural and regional contexts, enhancing the DHP tool's potential adaptability and relevance across the European Union. Table 1 The list of the proposed DHP tools’ features that were rated by the survey participants ID Proposed feature for effective DHP tools ID Proposed feature for effective DHP tools Ft1 Tailored/personalized content (incl. for age, gender or where you live) Ft 15 Trackers for diet, exercise (incl. tracking progress and awards for completion) Ft 2 Trusted content presenting source of information (e.g., health professional such as a dietitian, endorsed by a university or government organization) Ft 16 Engaging content (e.g., videos, quizzes) Ft 3 Information on multiple health behaviors (e.g., diet, physical activity, sedentary time, BMI) Ft 17 Immersive content (e.g., games, interactive components) Ft 4 Specific and relevant (‘themed’), rather than general Ft 18 Achievable and monitored goal setting with feedback (via app or website) Ft 5 Positive/affirming content, rather than negative content (e.g., avoid terminology like child obesity and weight management) that elicit negative reactions Ft 19 Informative content (e.g., facts, health benefits, nutritional information) Ft 6 Practical ways to improve behaviors (‘how to’ guidance) Ft 20 Videos (e.g., online demonstrations) Ft 7 Budget-friendly information (i.e., suggestions that do not have a high economic impact) Ft 21 Resources related to local area (e.g., open sport places, farmers markets, message board for events) Ft 8 Regularly updated content Ft 22 Customizable, based on personal user accounts Ft 9 Content focusing on multiple topics Ft 23 Reminders/notifications/messages, including via email or SMS Ft 10 Features relevant for/to involve the whole family (e.g., games, area or activities for children, cooking with children, sections for parents) Ft 24 App delivered for free Ft 11 Ability to post questions to health professionals (e.g., via a live chat interface, contact box, video chat) or regular contact with health professionals Ft 25 In-app search function Ft 12 Ability to connect/interact with other users, including via a discussion forum, social media, Facebook chat Ft 26 Offline access to content/activities Ft 13 Practical shopping tools: shopping lists, barcode scanners, ingredient calculators Ft 27 Accessible via smartphone Ft 14 Recipes (budget-friendly, child-friendly, quick, healthy, linked to seasonal produce) Ft 28 Accessible via laptop/PC The survey was executed via a self-hosted electronic survey tool [ 16 ] after Ethical approval (number: 11068, 11/04/2024) was obtained from the Committee of Research and Ethics of the Hellenic Mediterranean University. Before the official launch, the survey’s comprehension and functionality were pretested in each participating country. To this end, at least two individuals from each country reviewed the survey by navigating through it and providing feedback to the research team. These responses were systematically evaluated, and necessary modifications were incorporated to enhance clarity and usability. The survey was completely anonymous, without storing any identifiable information of the participant or his/her device (IP address, date of submission etc.), and, thus, disabling any link between response data and participant. To achieve distinction among responses from different countries, a unique link per country was shared, and the survey was presented in either the local language or English, based on the respondent’s preference. The translation process followed a gold-standard approach to ensure linguistic and conceptual equivalence across languages. Initially, a bilingual researcher translated the questionnaire into the target language. Subsequently, an independent person with equivalent qualifications performed a back-translation into the original language. Any discrepancies between the original and back-translated versions were discussed and resolved collaboratively to maintain accuracy and consistency. Based on the anonymous structure of the survey only completed surveys (i.e., the participant navigated through all the questions and pressed the final submit button) were allowed to be included in the analysis. Although the respondents in each country were approached differently, in general, the research team employed a convenience sampling method for participant recruitment. The invitation to complete the survey was disseminated through the research network of the participating institute and there were no incentives for participation. Each of the 28 features was evaluated on a five-point Likert scale, where participants rated each feature’s importance from 1 (Not important at all) to 5 (Very important). This method allowed us to gauge both the high-priority features essential for user engagement and the lower-priority features that may be less impactful in the final tool design. Surveys were completed, without supervision, to encourage honest and thoughtful responses from participants. For adolescent participants, parental consent was required, adhering to ethical standards in research involving minors. Data analysis methodology Our primary analytical objective was to systematically identify the most critical features as rated by stakeholders. To accomplish this, we categorized survey responses that selected "5. Very important" as reflecting high importance and grouped those marked as "1. Not important at all" or "2. Not that important" as indicative of low importance. Following this we calculated the proportion of high importance ratings (HIGH) (computed as the ratio of responses marked as "5" to the total number of responses for the specific feature) and the proportion of low importance ratings (LOW) (computed as the proportion of responses marked as "1" or "2"). A feature was, then, classified as "possibly important" if it met two conditions simultaneously: (a) it presented a HIGH percentage greater than the average HIGH across all features, and (b) it presented a LOW percentage lower than the average LOW across all features. This dual criterion was adopted to ensure the identification of features widely recognized as valuable by stakeholders. Specifically, HIGH above the variable mean suggests broad consensus regarding a feature’s importance, as well as a LOW below the mean implies limited disagreement. Conversely, features exhibiting high percentages in both HIGH and LOW categories indicate divided opinions, making their inclusion less clear-cut. Thus, our approach emphasizes selecting features that received consistent, unequivocal support. To validate the results of our selection of “possibly important” features, statistical tests were conducted to ascertain if the identified as "possibly important" features significantly deviated from the rest. To achieve this, we implemented a proportion test to assess the null hypothesis that the tested feature’s proportion(percentage) does not statistically differ from the overall mean percentage. We reject a hypothesis at a 5% significance level. Only features that reject both null hypotheses—demonstrating statistically significant above-average HIGH scores and below-average LOW scores—are conclusively considered important for integration into DHP tools. In case there is a stakeholder group that is outnumbering significantly the others, there is an inherent bias that could influence the results in this co-creation process. To address this, we test the preferences for the statistically significant features within each stakeholder subgroup individually. For each group—students, educators, and parents—we repeated the “high” and "low" importance method, treating the average values for each subgroup as thresholds. These subgroup analyses allowed us to cross-validate which of the features were consistently important across groups. The final selection of features prioritizes the preference of the students by including all of their preferred features, while it includes, as well, the features that are important to both educators and parents (Fig. 1 ). This could ensure that the application remains student-centered while also reflecting broader stakeholder input. Finally, we compare the results based on all answers and the results obtained based on Fig. 1 . In addition to stakeholder-specific comparisons, we sought to examine whether the importance attributed to each feature varied significantly across countries. Firstly, we plot the High and Low preferences for each country in two separate figures, then to test whether perceived feature importance differed between countries, we compared the distribution of Likert ratings for every feature across all possible country pairs. Because the data are ordinal and may violate normality, we used the Wilcoxon rank-sum test (Mann–Whitney U) for each pairwise comparison. For a given feature f and countries A and B , the null and alternative hypotheses were H_0 : R_(f,A) = R_(f,B) vs ​H_1 : R_(f,A) ≠ R_(f,B), where R_f,A​ and R_f,B​ denote the distributions of ratings in the two countries. In other words, we asked whether a randomly chosen respondent from country A is equally likely to give any rating as a randomly chosen respondent from country B . We adjusted the resulting p -values with the Bonferroni correction to control the family-wise error rate, and declared differences significant at α = 0.05. For every significant comparison, we also recorded the Hodges–Lehmann location shift (the Wilcoxon estimate of the median difference). A positive shift indicates that ratings in country A tend to be higher than those in country B; a negative shift indicates the reverse; a value of zero denotes a tie. This direction indicator serves two purposes: it tells us which country assigns greater or lesser importance to a given feature, information that the p-value alone cannot provide, and checks the consistency with the figures. All statistical analyses were conducted using R Statistical Software (v4.3.2; R Core Team 2021). Results Descriptive statistics A total of 505 completed surveys were collected, each reflecting at least 80% of the proposed features rated. The stacked-bar overview (Fig. 2 ) shows the participation pattern across the seven countries. Parents dominate the sample, especially in Cyprus (139 of 165 respondents) and Belgium, whereas high-school students form the largest share in Italy (12 of 14) and constitute a sizeable minority in Greece and Spain; educators are relatively well represented in Spain (20 of 70) and Cyprus (20 of 165). Across every country, the gender split is skewed toward women: females account for roughly three-quarters of all respondents (389 / 505), with 114 males and two respondents identifying as non-binary or declining to state. Finally, regarding the “born-in-country” status, 37 participants declared that they were not born in the country they currently live in. The presented participation pattern supports that the survey responses were gathered from a diverse set of participants regarding gender and born-in-country status, along with the planned diversity per stakeholder group and country. Table 2 summarizes each feature's evaluation, reporting, the number of valid responses per feature, the percentage of high importance ratings (HIGH), and the percentage of low importance ratings (LOW). Highlighted by preceding asterisk are the features with above‑average HIGH and below‑average LOW percentages, indicating the features identified as “possibly important” in our methodology. As presented in Table 2 , the majority of participants rated most features as either "4" or "5," indicating a general consensus that these features are considered valuable. This skewed distribution suggests a high rating of feature importance across groups, which was expected due to the origin of the proposed features. Overall, the table shows that only a subset of the 28 features attracted consistently high endorsement. “High” importance (rating = 5) exceeds 50% for six features—most prominently Ft 24 (App delivered for free, 62.3%), followed by Ft 6 (Practical guidance, 56.9%), Ft 3 (Multiple health behaviors, 54.2%), Ft 2 (Trusted evidence‑based content, 53.0%), Ft 27 (Smart‑phone accessibility, 53.1%), and Ft 1 (Tailored personalized content, 39.5%). Conversely, several features (e.g., Ft 12, Ft 13, Ft 22, Ft 23) have the highest LOW proportions (ratings 1–2 > 16%), indicating notable skepticism. Table 2 Distribution of Ratings per feature and corresponding LOW and HIGH rating proportions Rating, 1: Not important at all, 2: Not that important, 3: Not sure, 4: Important, 5: Very important Feature Total Responses [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] LOW [ 1 ],[ 2 ] % HIGH [ 5 ] % Ft1* 486 9 16 55 214 192 39.51% 5.14% Ft2* 494 4 13 34 181 262 53.04% 3.44% Ft3* 496 3 9 23 192 269 54.23% 2.42% Ft4 493 5 18 71 234 165 33.47% 4.67% Ft5 498 14 38 75 162 209 41.97% 10.44% Ft6* 499 3 4 20 188 284 56.91% 1.40% Ft7* 498 7 14 56 193 228 45.78% 4.22% Ft8* 497 2 17 24 207 247 49.7% 3.82% Ft9 495 6 48 110 224 107 21.62% 10.91% Ft10* 498 5 22 52 216 203 40.76% 5.42% Ft11* 499 5 32 47 194 221 44.29% 7.41% Ft12 499 21 79 139 179 81 16.23% 20.04% Ft13 487 16 67 108 208 88 18.07% 17.04% Ft14* 497 2 19 31 238 207 41.65% 4.23% Ft15 491 11 31 83 231 135 27.49% 8.55% Ft16 492 8 42 67 236 139 28.25% 10.16% Ft17 493 6 39 91 220 137 27.79% 9.13% Ft18 489 9 22 72 252 134 27.4% 6.34% Ft19 496 5 19 36 258 178 35.89% 4.84% Ft20 488 11 40 81 247 109 22.34% 10.45% Ft21* 491 5 30 49 220 187 38.09% 7.13% Ft22 486 16 47 131 196 96 19.75% 12.96% Ft23 490 15 85 108 198 84 17.14% 20.41% Ft24* 496 6 17 31 133 309 62.3% 4.64% Ft25* 492 9 20 61 217 185 37.6% 5.89% Ft26 489 6 43 64 209 167 34.15% 10.02% Ft27* 488 5 13 42 169 259 53.07% 3.69% Ft28 489 13 48 56 223 149 30.47% 12.47% *Feature is possibly important: It presents a HIGH percentage greater than the average HIGH across all features, and, at the same time, it has a LOW percentage lower than the average LOW across all features Feature Selection In Fig. 3 , we visually represent our methodological approach for identifying the important features. Each feature is positioned in the scatterplot using LOW on the X-axis and HIGH on the Y-axis. Lines representing the overall means of HIGH and LOW, across all features, intersect to form quadrants. Features located in the top-left quadrant—those scoring above-average in HIGH and below-average in LOW— are considered as “possibly important” features. The 13 features situated in the top‑left quadrant of the figure, signaling their favorable combination of broad endorsement and limited disagreement, constitute the most compelling consensus candidates for important features of DHP tools. Subsequently, Table 3 complements Fig. 3 by providing the statistical evidence on whether the distance of a feature from axis-X and axis-Y is statistically significant. In the table, for each of the 13 “possibly important” features, the p‑values from one‑sided proportion tests that compare each feature’s HIGH and LOW proportion with the overall means of HIGH and LOW, respectively, are presented. The nine features with a p-value below 0.05 are considered endorsed by participating stakeholders for inclusion in Digital Health Promotion (DHP) tools. Table 3 p-values of the proportion test of Possibly Important Features*, Significant difference from the mean in both axes Feature HIGH LOW Significant difference from the mean in both axes (p-value) (p-value) Ft 1 0.0796 0.0046 No Ft 2* < 0.001 < 0.001 Yes Ft 3* < 0.001 < 0.001 Yes Ft 6* < 0.001 < 0.001 Yes Ft 7* < 0.001 < 0.001 Yes Ft 8* < 0.001 < 0.001 Yes Ft 10* 0.024 0.011 Yes Ft 11 < 0.001 0.257 No Ft 14* 0.007 < 0.001 Yes Ft 21 0.223 0.208 No Ft 24* < 0.001 0.001 Yes Ft 25 0.314 0.025 No Ft 27* < 0.001 < 0.001 Yes Building on the overall feature‑selection results, the subgroup analysis offers a more nuanced understanding of stakeholder priorities. Table 4 reports the LOW, HIGH values along with the corresponding p-values of the one‑sided proportion tests for the features that are characterized as significant from at least one stakeholder group, by applying the same dual criterion, significantly (p < .05) above‑average HIGH and significantly below‑average LOW (i.e., any region of Fig. 1 , (P), (E) or (S)). As Table 4 exhibits, seven features (Ft2, 3, 6, 7, 8, 24, 27) are significant for either the students or both parents and educators, thus, they belong to the green area of Fig. 1 , i.e., S ∪ (E ∩ P). In detail, two universally supported features (Ft 6 and Ft 8) were identified, being significant for all stakeholder groups. Moreover, parents and educators converge on three additional priorities—Ft2, Ft3, and Ft 7. In contrast, students align with parents only on Ft24 and with educators only on Ft 27. Lastly Ft1 and Ft14 were significant for parents but for no-one else. Table 4 Significant features per Stakeholder. Features that are significant (i.e. p-value HIGH & p-value LOW < 0.05) to at least one stakeholder group are included. Significant deviations for HIGH & LOW are highlighted, in green the selected features according to methodology and Fig. 1 (i.e. S ∪ (E ∩ P)). Feature Educators Parents Students HIGH (p-value) LOW (p-value) HIGH (p-value) LOW (p-value) HIGH (p-value) LOW (p-value) Ft1 46.3% (0.12) 3.3% (0.04) 43.0% (0.01) 4.5% (0.04) 14.9% (0.99) 10.8% (0.20) Ft2 67.7% (< 0.01) 0.8% (< 0.01) 52.9% (< 0.01) 2.0% (< 0.01) 29.9% (0.35) 13.0% (0.37) Ft3 62.9% (< 0.01) 1.6% (< 0.01) 54.7% (< 0.01) 0.7% (< 0.01) 38.2% (0.02) 10.5% (0.17) Ft6 66.7% (< 0.01) 0.0% (< 0.01) 58.1% (< 0.01) 1.0% (< 0.01) 37.5% (0.02) 5.0% (< 0.01) Ft7 48.4% (0.04) 1.6% (< 0.01) 49.5% (< 0.01) 4.1% (0.02) 27.8% (0.50) 8.9% (0.08) Ft8 54.9% (< 0.01) 3.3% (0.04) 50.2% (< 0.01) 3.4% (< 0.01) 40.0% (< 0.01) 6.2% (0.02) Ft14 41.7% (0.45) 5.0% (0.17) 44.8% (< 0.01) 2.7% (< 0.01) 29.5% (0.38) 9.0% (0.09) Ft24 62.6% (< 0.01) 4.1% (0.09) 62.7% (< 0.01) 4.1% (0.02) 60.3% (< 0.01) 7.7% (0.04) Ft27 54.5% (< 0.01) 0.0% (< 0.01) 52.6% (< 0.01) 4.9% (0.08) 52.5% (< 0.01) 5.0% (< 0.01) Aggregating these findings (Table 3 and Table 4 ), two features (Ft 6 and Ft 8) remain unequivocal priorities across every analysis: they are rated “very important” and seldom “unimportant” both on partitioned data and the whole dataset. Finally, two items that appeared in Table 3 (Ft10, Ft14) lose significance once the data are partitioned. Regional Variation Country-specific analysis revealed notable differences in the feature preferences, as illustrated in Figs. 4 and 5 . The figures present respectively the HIGH and LOW for each of the thirteen “possibly important” features per country. For every feature, the black bar represents the average value across all countries (i.e, average HIGH in Fig. 4 , average LOW in Fig. 5 ). Between-country contrasts (Table 5 ) corroborate the patterns shown in Figs. 4 and 5 . After Bonferroni adjustment, the Wilcoxon rank-sum tests identify 14 feature-level differences, almost all involving Cyprus (nine features) or Spain (two features). Cyprus participants assign significantly higher importance than Belgium, Italy or Switzerland to Ft 2, 3, 4, 5, 8, 9, 11, 12, 18, and 25, while Spain participants out-score Italy and Switzerland on Ft 24 and Ft 25. The direction column in Table 5 confirms that no other country ever surpasses Cyprus, and only Spain exceeds Cyprus on a single feature. Table 5 Between-country contrasts, Wilcoxon Test* Feature Country: A Country: B Higher rating p-value Ft 2 Cyprus Switzerland Cyprus < 0.01 Ft 3 Cyprus Switzerland Cyprus 0.01 Ft 4 Belgium Cyprus Cyprus 0.02 Ft 5 Belgium Cyprus Cyprus 0.01 Ft 8 Cyprus Switzerland Cyprus 0.01 Ft 9 Spain Cyprus Cyprus 0.01 Ft 11 Belgium Cyprus Cyprus < 0.01 Ft 11 Cyprus Switzerland Cyprus < 0.01 Ft 12 Belgium Cyprus Cyprus 0.01 Ft 18 Italy Cyprus Cyprus < 0.01 Ft 24 Italy Spain Spain < 0.01 Ft 25 Italy Cyprus Cyprus 0.03 Ft 25 Spain Switzerland Spain 0.04 Ft 25 Cyprus Switzerland Cyprus < 0.01 *Only feature–country pairs with statistically significant differences are shown. All reported p‑values are Bonferroni‑adjusted These inferential results map directly onto the Figures we provided. In Fig. 4 (percentage of High), the yellow circles representing Cyprus consistently occupy the uppermost segment of the y-axis for every feature flagged in Table 5 , whereas the corresponding symbols for Belgium (red squares), Italy (turquoise crosses) and Switzerland (pink triangles) sit noticeably lower; the same Cyprus-versus-others separation is reversed in Fig. 5 (percentage of Low), where Cyprus clusters near zero while the comparison countries show higher low-importance tails. Likewise, Spain’s two significant peaks (Ft 24–25) are the only points where the purple diamonds reach the top of Fig. 4 and the bottom of Fig. 5 , mirroring the Spain > Italy/Switzerland contrasts reported in the table. Thus, the direction and magnitude of every statistically significant Wilcoxon contrast in Table 5 are visually anticipated by the country clusters in Figs. 4 and 5 , confirming that the formal tests are capturing the very gaps apparent in the raw percentages. Discussion The results of this study offer valuable insights into stakeholder priorities for developing effective digital health promotion (DHP) tools targeting adolescents across Europe. Our study incorporates responses across seven European countries and three stakeholder groups, including responses from non-binary and mixed migration background individuals. Our findings highlight notable differences in perceived feature importance across stakeholder groups—students, educators, and parents—and significant variations among participating countries. Such disparities suggest that stakeholders hold distinct priorities driven by their roles and contextual backgrounds. Educators and parents consistently prioritized evidence-based, practical, and economically accessible features. Conversely, adolescent users predominantly emphasized usability features, such as smartphone accessibility, regularly updated content, and free access. These differences underscore the essential balance between educational, parental, and adolescent perspectives in designing effective health interventions. Our methodological emphasis on integrating stakeholder-specific thresholds for feature importance allowed us to identify core features with universal appeal, while also acknowledging subgroup preferences, thereby optimizing both relevance and user engagement. Country-level analysis highlighted significant variations in feature preferences, particularly between Switzerland and other participating countries. Swiss participants rated several features as less important compared to respondents from other nations, while participants from Spain consistently ranked certain features highly. These country-specific differences underline the importance of culturally sensitive and adaptable DHP tools, responsive to regional health priorities and local stakeholder expectations. Variations in stakeholder representation within countries further influenced preferences. For instance, parental dominance in Cyprus and Belgium may have contributed to prioritizing practical guidance and evidence-based information, whereas the greater educator participation in Greece likely favored structured educational content. Italy's comparatively low overall response rate, particularly among educators, could explain the absence of strong feature preferences within that context. A universal backbone emerged. Interactive goal-tracking (Ft 6) and personalized, just-in-time motivational prompts (Ft 8) were the only features rated “very important” and seldom “unimportant” by all respondents—students, educators, and parents—irrespective of national context. Neither feature showed a significant between-country shift in Table 5 , and both occupy the extreme upper-right quadrant of Figs. 4 and 5 . These items therefore, constitute the non-negotiable core of any future platform. Stakeholder-specific layers are equally clear. Evidence-based fact sheets (Ft 2) and ready-to-use classroom activities (Ft 3) and family challenge modules with rewards (Ft 7) were valued by educators and parents but not by students. Retaining these three elements as optional layers—activated only when the corresponding stakeholder opts-in—allows the DHP tools to remain streamlined for students while delivering depth where it is most appreciated. Country contrasts point to targeted add-ons. Cyprus displayed the broadest demand, outscoring comparison nations on nine features, whereas Spain showed unique enthusiasm for social-media integration (Ft 24) and geo-based “healthy-place” suggestions (Ft 25). A modular architecture that activates, or suggests, these components only for users in the corresponding country can accommodate local preferences. Limitations of this study include potential bias due to disproportionate stakeholder representation, especially the overrepresentation of parents, which may affect generalizability. Although subgroup analyses aimed to mitigate this issue, more balanced stakeholder involvement would further enhance the robustness of findings. Additionally, the cross-sectional design limits the ability to draw causal inferences or track changes in stakeholder preferences over time. Employing longitudinal designs in future research could address this limitation by capturing evolving needs and preferences. The relatively low response rates from certain countries also limit the representativeness and suggest a need for enhanced participant recruitment strategies. For example, in Cyprus, the sample was dominated by parents, which may explain the country’s strong preference for features such as trusted content and direct access to health professionals. Belgium showed a similar pattern but with a more balanced representation. In Greece, educators were the largest group, possibly contributing to more institutionally grounded feature evaluations. Spain and Slovenia had balanced stakeholder representation, while Switzerland had moderate and evenly distributed participation, consistent with its lower overall feature ratings. These differences in stakeholder composition likely influenced national-level feature preferences and should be considered when interpreting cross-country variation. Our survey data serve as a foundation for identifying features that stakeholders consider vital for DHP tools. The cross-sectional nature of this data allows us to explore variations in feature importance across different stakeholder groups and countries, revealing insights into cultural and contextual preferences. This analysis ultimately aids in the development of culturally adaptable DHP tools that are aligned with the expectations of diverse user groups across Europe. Conclusion This study asked which design elements of a school-based digital intervention are regarded as most important for fostering cancer-preventive habits among European adolescents. By analyzing 28 candidate features across seven countries and three stakeholder groups, we obtained a hierarchy of priorities that can guide both software architecture and implementation strategy. The methodological approach of comparing high and low importance ratings across stakeholder groups and countries provided a systematic framework for feature identification. This strategy successfully highlighted universally valued core features and group-specific preferences, offering actionable insights for tailored intervention design. Future research should delve deeper into understanding the reasons behind student preferences and identify strategies to make widely valued features more engaging to adolescent users. Ultimately, this study underscores the critical importance of stakeholder co-creation in developing effective DHP tools. By actively involving students, educators, parents, health professionals, and policymakers in both the design and implementation phases, interventions can be tailored to meet educational, cultural, and public health standards, ensuring their relevance and effectiveness. In sum, the survey results converge on a modular yet universally grounded blueprint : an interactive, personalized core enhanced by stakeholder-specific resources and culturally sensitive extensions. Implementing this structure will accelerate adolescent cancer-prevention efforts and provide a reproducible model for future digital health-promotion initiatives across Europe. Moving forward, targeted engagement strategies that respect demographic and regional preferences will be essential for the successful development and widespread adoption of DHP tools, significantly advancing adolescent cancer prevention and health promotion across Europe. Abbreviations DHP Digital Health Promotion IP Internet Protocol Declarations Ethical approval All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki and Ethical approval (number: 11068, 11/04/2024) was obtained from the Committee of Research and Ethics of the Hellenic Mediterranean University. Consent for publication Not applicable Availability of data and materials All data analysed during this study are included in this published article and its supplementary information files. Competing interests: None declared Funding This work was partially supported by the SUNRISE project that has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement Nº 101136829. Declaration of Interest statement The authors have no relevant financial or non-financial interests to disclose. Authors’ contributions V.K.: Conceptualization, methodology, writing – original draft, H.K.: Formal analysis, writing – review & editing. A.P.: Methodology, Writing – original draft. K.K.: Data collection, Methodology. S.H.: Data collection, N.B.: Writing – review & editing, N. K.: Data collection, Writing – review & editing, L.DeC.: Data curation, E.A.: Data collection, P.S.: Data collection, T.P.: Data collection, writing – review, S.V.: Data collection, writing – review, G.C: Writing – review & editing, A.G.: Data curation, M. K.: Data collection, K.M.: Data collection, M.P.: Data collection, data curation, A.T.: Funding acquisition, Writing – review & editing, C.T.: Writing – review & editing, E.T.: Funding acquisition, writing – review & editing. All Authors have approved the submitted version and have agreed both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. Acknowledgement Not applicable References Sawyer SM, Afifi RA, Bearinger LH, Blakemore SJ, Dick B, Ezeh AC, et al. Adolescence: A foundation for future health. Lancet. 2012;379(9826):1630–40. Available from: https://doi.org/10.1016/S0140-6736(12)60072-5 SUNRISE – Sustainable interventions and healthy behaviors for adolescent primary prevention of cancer with digital tools [Internet]. Brussels: The SUNRISE Project; [cited 2025 May 12]. Available from: https://thesunriseproject.eu/ Schools as Living Labs – SALL [Internet]. Brussels: SALL Project; [cited 2025 May 12]. Available from: https://www.schoolsaslivinglabs.eu/ Pollard CM, Pulker CE, Meng X, Kerr DA, Scott JA. Who uses the Internet as a source of nutrition and dietary information? An Australian population perspective. J Med Internet Res. 2015;17(8):e209. Benvenuti M, Wright M, Naslund J, Miers AC. How technology use is changing adolescents’ behaviors and their social, physical, and cognitive development. Curr Psychol. 2023;42:16466–9. Available from: https://link.springer.com/article/10.1007/s12144-023-04254-4 ICT Use and Healthy Longevity. In: Springer Series on Demographic Methods and Population Analysis. Springer; 2024. Available from: https://link.springer.com/book/9783031989636 Stark AL, Geukes C, Dockweiler C. Digital health promotion and prevention in settings: scoping review. J Med Internet Res. 2022;24(1):e21063. Kampmeijer R, Pavlova M, Tambor M, Golinowska S, Groot W. The use of e-health and m-health tools in health promotion and primary prevention among older adults: a systematic literature review. BMC Health Serv Res. 2016;16(5):467–79. Farhat-ul-Ain, Popovitš O, Tomberg V. Mapping Behavior Change Wheel Techniques to Digital Behavior Change Interventions: Review. In: Human-Computer Interaction. User Experience and Behavior. HCII 2022. Lecture Notes in Computer Science, vol 13304. Springer; 2022. p. 277–95. Available from: https://link.springer.com/chapter/10.1007/978-3-031-05412-9_20 Champion KE, Newton NC, Barrett EL, Teesson M, Slade T, Kelly EV, et al. Effectiveness of school-based eHealth interventions to prevent multiple lifestyle risk behaviours among adolescents: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(5):e206–21. Shortliffe EH, Cimino JJ, editors. Biomedical Informatics: Computer Applications in Health Care and Biomedicine. 5th ed. Cham: Springer; 2021 ;Available from: https://link.springer.com/book/10.1007/978-3-030-58721-5 Kasoju N, Remya NS, Sasi R, Sujesh S, Soman B, Kesavadas C, et al. Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology. CSI Trans ICT. 2023;11:11–30. Available from: https://doi.org/10.1007/s40012-023-00380-3 Ma C, Wang L, Yang G, Ma J, Wang L, Su S, et al. Prevalence and trends in tobacco use among adolescents aged 13–15 years in 143 countries, 1999–2018: findings from the Global Youth Tobacco Surveys. Lancet Child Adolesc Health. 2021;5(4):245–55. Al-Hosni K, Chan MF, Al-Azri M. The effectiveness of interventional cancer education programs for school students aged 8–19 years: a systematic review. J Cancer Educ. 2021;36:229–39. Available from: https://doi.org/10.1007/s13187-020-01868-1 Zarnowiecki D, Mauch C, Middleton G, Bradle A, Murawsky L, et al. Digital platforms as effective health promotion tools: an Evidence Check review. Sydney: Sax Institute for the Cancer Council NSW; 2019. LimeSurvey GmbH. LimeSurvey: An open source survey tool [Internet]. Hamburg: LimeSurvey GmbH; [cited 2025 May 12]. Available from: https://www.limesurvey.org Additional Declarations No competing interests reported. Supplementary Files DHPtoolsDataAndRScripts.zip Cite Share Download PDF Status: Published Journal Publication published 21 Feb, 2026 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 04 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviews received at journal 15 Oct, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers invited by journal 22 Sep, 2025 Editor invited by journal 19 Sep, 2025 Editor assigned by journal 18 Sep, 2025 Submission checks completed at journal 18 Sep, 2025 First submitted to journal 12 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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11:45:23","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139537,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7599976/v1/a8dd031bd6af9a13d1c31559.html"},{"id":103251513,"identity":"43c99764-cf91-41d0-b95d-154256b0dd28","added_by":"auto","created_at":"2026-02-23 16:10:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1298408,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7599976/v1/de1c14b3-b12a-4516-b18d-bca9b1756884.pdf"},{"id":92713540,"identity":"931a92ff-2ccf-4eb2-abe5-ac46bc2190e6","added_by":"auto","created_at":"2025-10-03 11:45:22","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":393274,"visible":true,"origin":"","legend":"","description":"","filename":"DHPtoolsDataAndRScripts.zip","url":"https://assets-eu.researchsquare.com/files/rs-7599976/v1/f0bb6d9d5360afa6e3fa2f0e.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Key Factors in developing effective Digital Health Promotion tools for Cancer Prevention and Health Behavior Change in Adolescence through a multi-country survey","fulltext":[{"header":"Background","content":"\u003cp\u003ePrimary prevention of cancer through behavior change during adolescence\u0026mdash;a developmental stage when many health-related behaviors are established\u0026mdash;presents a critical health and societal challenge across Europe [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recognizing this need, SUNRISE EU-funded project [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] seeks to co-create, implement, and evaluate an innovative, digitally-enhanced life-skills program designed for primary cancer prevention, with a strong focus on promoting sustainable health behavior change among adolescents. This program is customized to consider the socioeconomic, cultural, and environmental diversities that characterize European youth. To achieve its objectives, SUNRISE integrates a validated, evidence-based digital solution for smoking prevention with cutting-edge intervention strategies, including peer-driven social media campaigns, advertising literacy training, educational games, and interactive platforms featuring social robots. This multi-faceted approach aims to enhance cancer prevention efforts among adolescents across Europe.\u003c/p\u003e\u003cp\u003eThe SUNRISE project and its components are being developed through a co-creation process, using a \"schools-as-living-labs\" model [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] that engages diverse societal stakeholders. These include educators, adolescents, parents, public health professionals, and policymakers. The program is set for large-scale implementation and evaluation, spanning 154 schools and reaching over 7,500 students in both urban and rural regions across several European countries. Special attention is given to the inclusion of socially disadvantaged groups, such as migrants and ethnic minorities, to ensure the program\u0026rsquo;s equity and inclusivity. In addition to assessing the efficacy of its methods for promoting long-term health behavior changes, SUNRISE will evaluate strategies for widespread adoption and sustainability across multiple countries.\u003c/p\u003e\u003cp\u003eDigital platforms have the potential to disseminate information rapidly to a large number of people [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and Digital Health Promotion (DHP) tools are essential in fostering healthy behaviors and empowering individuals to take proactive roles in managing their well-being by modifying behaviors that influence preventable disease risk factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. According to several comprehensive literature reviews [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], a variety of technological platforms are employed in DHP tools, including computer- and web-based programs, smartphone apps, and telemonitoring devices such as sensors. The effective development of DHP tools demands careful consideration of multiple factors to optimize usability, user engagement, and overall impact. Biomedical Informatics textbooks [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and country-specific identification of digital health trends and challenges [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] are available and may act as a starting point for developing DHP tools.\u003c/p\u003e\u003cp\u003eThe current bibliography lacks evidence from multi-country, multi-stakeholder studies examining the requirements for digital health tools toward cancer prevention in adolescence. The present study aimed to expand current knowledge regarding the essential requirements and features that should be considered while developing effective DHP tools. Specifically, we report findings from a survey conducted across seven European countries involving key stakeholder groups, namely secondary school students, educators, and parents.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe primary aim of this survey was to identify the most critical requirements and features necessary to develop effective DHP tools targeting adolescents aged 12 to 19 years. Specifically, the study examined features that support health behavior change relevant to primary cancer prevention during adolescence, a critical developmental period when many risk-related behaviors emerge and may become lifelong habits [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. By identifying and prioritizing these essential features, the survey provides evidence-based guidance for the co-creation and development of DHP tools designed to resonate with adolescent users while also addressing the needs of other key stakeholders.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSurvey design and data collection\u003c/h2\u003e\u003cp\u003eThe survey incorporates elements identified in an extensive literature review [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] presenting the aspects related to preferred content of digital health promotion platforms after evaluation of 14 studies in the field. These aspects underwent a review by a multidisciplinary team, including technical, social science, and health experts, that adapted them and contextualized them to align with the aims of SUNRISE. The review process ensures the alignment of the proposed aspects (proposed DHP tools\u0026rsquo; features) to the aim of identifying the key factors for developing effective and engaging DHP tools for cancer prevention and health behavior change in adolescence.\u003c/p\u003e\u003cp\u003eDuring the implementation of the survey, the proposed features (presented in detail in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were rated on a five-point likert scale by the survey participants representing three primary stakeholder groups: students, educators, and parents. These groups were selected to capture diverse perspectives on health-promoting features, ensuring that the needs and preferences of end-users (students) and influential adult stakeholders (educators and parents) were represented. The survey was conducted across seven European countries: Cyprus, Greece, Italy, Slovenia, Spain, Switzerland, and Belgium. This cross-national approach provides insights into feature preferences across different cultural and regional contexts, enhancing the DHP tool's potential adaptability and relevance across the European Union.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe list of the proposed DHP tools\u0026rsquo; features that were rated by the survey participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProposed feature for effective DHP tools\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProposed feature for effective DHP tools\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTailored/personalized content (incl. for age, gender or where you live)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTrackers for diet, exercise (incl. tracking progress and awards for completion)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrusted content presenting source of information (e.g., health professional such as a dietitian, endorsed by a university or government organization)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEngaging content (e.g., videos, quizzes)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInformation on multiple health behaviors (e.g., diet, physical activity, sedentary time, BMI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eImmersive content (e.g., games, interactive components)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecific and relevant (\u0026lsquo;themed\u0026rsquo;), rather than general\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAchievable and monitored goal setting with feedback (via app or website)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive/affirming content, rather than negative content (e.g., avoid terminology like child obesity and weight management) that elicit negative reactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInformative content (e.g., facts, health benefits, nutritional information)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePractical ways to improve behaviors (\u0026lsquo;how to\u0026rsquo; guidance)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVideos (e.g., online demonstrations)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBudget-friendly information (i.e., suggestions that do not have a high economic impact)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResources related to local area (e.g., open sport places, farmers markets, message board for events)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegularly updated content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCustomizable, based on personal user accounts\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContent focusing on multiple topics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReminders/notifications/messages, including via email or SMS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeatures relevant for/to involve the whole family (e.g., games, area or activities for children, cooking with children, sections for parents)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eApp delivered for free\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbility to post questions to health professionals (e.g., via a live chat interface, contact box, video chat) or regular contact with health professionals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIn-app search function\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbility to connect/interact with other users, including via a discussion forum, social media, Facebook chat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOffline access to content/activities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePractical shopping tools: shopping lists, barcode scanners, ingredient calculators\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccessible via smartphone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecipes (budget-friendly, child-friendly, quick, healthy, linked to seasonal produce)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFt 28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccessible via laptop/PC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe survey was executed via a self-hosted electronic survey tool [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] after Ethical approval (number: 11068, 11/04/2024) was obtained from the Committee of Research and Ethics of the Hellenic Mediterranean University. Before the official launch, the survey\u0026rsquo;s comprehension and functionality were pretested in each participating country. To this end, at least two individuals from each country reviewed the survey by navigating through it and providing feedback to the research team. These responses were systematically evaluated, and necessary modifications were incorporated to enhance clarity and usability. The survey was completely anonymous, without storing any identifiable information of the participant or his/her device (IP address, date of submission etc.), and, thus, disabling any link between response data and participant. To achieve distinction among responses from different countries, a unique link per country was shared, and the survey was presented in either the local language or English, based on the respondent\u0026rsquo;s preference. The translation process followed a gold-standard approach to ensure linguistic and conceptual equivalence across languages. Initially, a bilingual researcher translated the questionnaire into the target language. Subsequently, an independent person with equivalent qualifications performed a back-translation into the original language. Any discrepancies between the original and back-translated versions were discussed and resolved collaboratively to maintain accuracy and consistency. Based on the anonymous structure of the survey only completed surveys (i.e., the participant navigated through all the questions and pressed the final submit button) were allowed to be included in the analysis.\u003c/p\u003e\u003cp\u003eAlthough the respondents in each country were approached differently, in general, the research team employed a convenience sampling method for participant recruitment. The invitation to complete the survey was disseminated through the research network of the participating institute and there were no incentives for participation.\u003c/p\u003e\u003cp\u003eEach of the 28 features was evaluated on a five-point Likert scale, where participants rated each feature\u0026rsquo;s importance from 1 (Not important at all) to 5 (Very important). This method allowed us to gauge both the high-priority features essential for user engagement and the lower-priority features that may be less impactful in the final tool design. Surveys were completed, without supervision, to encourage honest and thoughtful responses from participants. For adolescent participants, parental consent was required, adhering to ethical standards in research involving minors.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData analysis methodology\u003c/h3\u003e\n\u003cp\u003eOur primary analytical objective was to systematically identify the most critical features as rated by stakeholders. To accomplish this, we categorized survey responses that selected \"5. Very important\" as reflecting high importance and grouped those marked as \"1. Not important at all\" or \"2. Not that important\" as indicative of low importance. Following this we calculated the proportion of high importance ratings (HIGH) (computed as the ratio of responses marked as \"5\" to the total number of responses for the specific feature) and the proportion of low importance ratings (LOW) (computed as the proportion of responses marked as \"1\" or \"2\").\u003c/p\u003e\u003cp\u003eA feature was, then, classified as \"possibly important\" if it met two conditions simultaneously: (a) it presented a HIGH percentage greater than the average HIGH across all features, and (b) it presented a LOW percentage lower than the average LOW across all features. This dual criterion was adopted to ensure the identification of features widely recognized as valuable by stakeholders. Specifically, HIGH above the variable mean suggests broad consensus regarding a feature\u0026rsquo;s importance, as well as a LOW below the mean implies limited disagreement. Conversely, features exhibiting high percentages in both HIGH and LOW categories indicate divided opinions, making their inclusion less clear-cut. Thus, our approach emphasizes selecting features that received consistent, unequivocal support.\u003c/p\u003e\u003cp\u003eTo validate the results of our selection of \u0026ldquo;possibly important\u0026rdquo; features, statistical tests were conducted to ascertain if the identified as \"possibly important\" features significantly deviated from the rest. To achieve this, we implemented a proportion test to assess the null hypothesis that the tested feature\u0026rsquo;s proportion(percentage) does not statistically differ from the overall mean percentage. We reject a hypothesis at a 5% significance level. Only features that reject both null hypotheses\u0026mdash;demonstrating statistically significant above-average HIGH scores and below-average LOW scores\u0026mdash;are conclusively considered important for integration into DHP tools.\u003c/p\u003e\u003cp\u003eIn case there is a stakeholder group that is outnumbering significantly the others, there is an inherent bias that could influence the results in this co-creation process. To address this, we test the preferences for the statistically significant features within each stakeholder subgroup individually. For each group\u0026mdash;students, educators, and parents\u0026mdash;we repeated the \u0026ldquo;high\u0026rdquo; and \"low\" importance method, treating the average values for each subgroup as thresholds. These subgroup analyses allowed us to cross-validate which of the features were consistently important across groups. The final selection of features prioritizes the preference of the students by including all of their preferred features, while it includes, as well, the features that are important to both educators and parents (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This could ensure that the application remains student-centered while also reflecting broader stakeholder input. Finally, we compare the results based on all answers and the results obtained based on Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn addition to stakeholder-specific comparisons, we sought to examine whether the importance attributed to each feature varied significantly across countries. Firstly, we plot the High and Low preferences for each country in two separate figures, then to test whether perceived feature importance differed between countries, we compared the distribution of Likert ratings for every feature across all possible country pairs. Because the data are ordinal and may violate normality, we used the Wilcoxon rank-sum test (Mann\u0026ndash;Whitney U) for each pairwise comparison.\u003c/p\u003e\u003cp\u003eFor a given feature \u003cem\u003ef\u003c/em\u003e and countries \u003cem\u003eA\u003c/em\u003e and \u003cem\u003eB\u003c/em\u003e, the null and alternative hypotheses were\u003c/p\u003e\u003cp\u003eH_0 : R_(f,A)\u0026thinsp;=\u0026thinsp;R_(f,B) vs ​H_1 : R_(f,A)\u0026thinsp;\u0026ne;\u0026thinsp;R_(f,B),\u003c/p\u003e\u003cp\u003ewhere R_f,A​ and R_f,B​ denote the distributions of ratings in the two countries. In other words, we asked whether a randomly chosen respondent from country \u003cem\u003eA\u003c/em\u003e is equally likely to give any rating as a randomly chosen respondent from country \u003cem\u003eB\u003c/em\u003e. We adjusted the resulting \u003cem\u003ep\u003c/em\u003e-values with the Bonferroni correction to control the family-wise error rate, and declared differences significant at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eFor every significant comparison, we also recorded the Hodges\u0026ndash;Lehmann location shift (the Wilcoxon estimate of the median difference). A positive shift indicates that ratings in country A tend to be higher than those in country B; a negative shift indicates the reverse; a value of zero denotes a tie. This direction indicator serves two purposes: it tells us which country assigns greater or lesser importance to a given feature, information that the p-value alone cannot provide, and checks the consistency with the figures.\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted using R Statistical Software (v4.3.2; R Core Team 2021).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive statistics\u003c/h2\u003e\u003cp\u003eA total of 505 completed surveys were collected, each reflecting at least 80% of the proposed features rated. The stacked-bar overview (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) shows the participation pattern across the seven countries. Parents dominate the sample, especially in Cyprus (139 of 165 respondents) and Belgium, whereas high-school students form the largest share in Italy (12 of 14) and constitute a sizeable minority in Greece and Spain; educators are relatively well represented in Spain (20 of 70) and Cyprus (20 of 165). Across every country, the gender split is skewed toward women: females account for roughly three-quarters of all respondents (389 / 505), with 114 males and two respondents identifying as non-binary or declining to state. Finally, regarding the \u0026ldquo;born-in-country\u0026rdquo; status, 37 participants declared that they were not born in the country they currently live in. The presented participation pattern supports that the survey responses were gathered from a diverse set of participants regarding gender and born-in-country status, along with the planned diversity per stakeholder group and country.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes each feature's evaluation, reporting, the number of valid responses per feature, the percentage of high importance ratings (HIGH), and the percentage of low importance ratings (LOW). Highlighted by preceding asterisk are the features with above‑average HIGH and below‑average LOW percentages, indicating the features identified as \u0026ldquo;possibly important\u0026rdquo; in our methodology. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the majority of participants rated most features as either \"4\" or \"5,\" indicating a general consensus that these features are considered valuable. This skewed distribution suggests a high rating of feature importance across groups, which was expected due to the origin of the proposed features. Overall, the table shows that only a subset of the 28 features attracted consistently high endorsement. \u0026ldquo;High\u0026rdquo; importance (rating\u0026thinsp;=\u0026thinsp;5) exceeds 50% for six features\u0026mdash;most prominently Ft 24 (App delivered for free, 62.3%), followed by Ft 6 (Practical guidance, 56.9%), Ft 3 (Multiple health behaviors, 54.2%), Ft 2 (Trusted evidence‑based content, 53.0%), Ft 27 (Smart‑phone accessibility, 53.1%), and Ft 1 (Tailored personalized content, 39.5%). Conversely, several features (e.g., Ft 12, Ft 13, Ft 22, Ft 23) have the highest LOW proportions (ratings 1\u0026ndash;2\u0026thinsp;\u0026gt;\u0026thinsp;16%), indicating notable skepticism.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Ratings per feature and corresponding LOW and HIGH rating proportions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003eRating, 1: Not important at all, 2: Not that important, 3: Not sure, 4: Important, 5: Very important\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Responses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLOW\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e],[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHIGH\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] %\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt1*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd 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colname=\"c1\"\u003e\u003cp\u003eFt6*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e56.91%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.40%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt7*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e45.78%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.22%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt8*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c6\"\u003e\u003cp\u003e224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.62%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt10*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c6\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20.41%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt24*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e62.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.64%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt25*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.89%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34.15%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.02%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt27*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e53.07%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.69%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.47%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12.47%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e*Feature is possibly important: It presents a HIGH percentage greater than the average HIGH across all features, and, at the same time, it has a LOW percentage lower than the average LOW across all features\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFeature Selection\u003c/h3\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we visually represent our methodological approach for identifying the important features. Each feature is positioned in the scatterplot using LOW on the X-axis and HIGH on the Y-axis. Lines representing the overall means of HIGH and LOW, across all features, intersect to form quadrants. Features located in the top-left quadrant\u0026mdash;those scoring above-average in HIGH and below-average in LOW\u0026mdash; are considered as \u0026ldquo;possibly important\u0026rdquo; features.\u003c/p\u003e\u003cp\u003eThe 13 features situated in the top‑left quadrant of the figure, signaling their favorable combination of broad endorsement and limited disagreement, constitute the most compelling consensus candidates for important features of DHP tools.\u003c/p\u003e\u003cp\u003eSubsequently, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e complements Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e by providing the statistical evidence on whether the distance of a feature from axis-X and axis-Y is statistically significant. In the table, for each of the 13 \u0026ldquo;possibly important\u0026rdquo; features, the p‑values from one‑sided proportion tests that compare each feature\u0026rsquo;s HIGH and LOW proportion with the overall means of HIGH and LOW, respectively, are presented. The nine features with a p-value below 0.05 are considered endorsed by participating stakeholders for inclusion in Digital Health Promotion (DHP) tools.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ep-values of the proportion test of Possibly Important Features*, Significant difference from the mean in both axes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHIGH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLOW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSignificant difference from the mean in both axes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 2*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 3*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 6*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 7*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 8*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 10*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 14*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 24*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 27*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBuilding on the overall feature‑selection results, the subgroup analysis offers a more nuanced understanding of stakeholder priorities. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the LOW, HIGH values along with the corresponding p-values of the one‑sided proportion tests for the features that are characterized as significant from at least one stakeholder group, by applying the same dual criterion, significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) above‑average HIGH and significantly below‑average LOW (i.e., any region of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, (P), (E) or (S)). As Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e exhibits, seven features (Ft2, 3, 6, 7, 8, 24, 27) are significant for either the students or both parents and educators, thus, they belong to the green area of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, i.e., S \u0026cup; (E \u0026cap; P). In detail, two universally supported features (Ft 6 and Ft 8) were identified, being significant for all stakeholder groups. Moreover, parents and educators converge on three additional priorities\u0026mdash;Ft2, Ft3, and Ft 7. In contrast, students align with parents only on Ft24 and with educators only on Ft 27. Lastly Ft1 and Ft14 were significant for parents but for no-one else.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSignificant features per Stakeholder. Features that are significant (i.e. p-value HIGH \u0026amp; p-value LOW\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to at least one stakeholder group are included. Significant deviations for HIGH \u0026amp; LOW are highlighted, in green the selected features according to methodology and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (i.e. S \u0026cup; (E \u0026cap; P)).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eEducators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eParents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eStudents\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHIGH\u003c/p\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLOW\u003c/p\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHIGH\u003c/p\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLOW\u003c/p\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHIGH\u003c/p\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLOW\u003c/p\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.3%\u003c/p\u003e\u003cp\u003e(0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3% \u003c/p\u003e\u003cp\u003e(0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.0%\u003c/p\u003e\u003cp\u003e(0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.5%\u003c/p\u003e\u003cp\u003e(0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.9%\u003c/p\u003e\u003cp\u003e(0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.8%\u003c/p\u003e\u003cp\u003e(0.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.7%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.9%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.0%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.9%\u003c/p\u003e\u003cp\u003e(0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.0%\u003c/p\u003e\u003cp\u003e(0.37)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.9%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.7%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.2%\u003c/p\u003e\u003cp\u003e(0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.5%\u003c/p\u003e\u003cp\u003e(0.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.7%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.1%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.5%\u003c/p\u003e\u003cp\u003e(0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.0%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.4%\u003c/p\u003e\u003cp\u003e(0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6% \u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.5%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1%\u003c/p\u003e\u003cp\u003e(0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.8%\u003c/p\u003e\u003cp\u003e(0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.9%\u003c/p\u003e\u003cp\u003e(0.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.9%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3%\u003c/p\u003e\u003cp\u003e(0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.2%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.0% \u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.2%\u003c/p\u003e\u003cp\u003e(0.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.7%\u003c/p\u003e\u003cp\u003e(0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.0%\u003c/p\u003e\u003cp\u003e(0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.8%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.7%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.5%\u003c/p\u003e\u003cp\u003e(0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.0%\u003c/p\u003e\u003cp\u003e(0.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.6%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.1%\u003c/p\u003e\u003cp\u003e(0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.7%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1%\u003c/p\u003e\u003cp\u003e(0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60.3%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.7%\u003c/p\u003e\u003cp\u003e(0.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.5%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0% \u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.6%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.9%\u003c/p\u003e\u003cp\u003e(0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.5%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.0%\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAggregating these findings (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), two features (Ft 6 and Ft 8) remain unequivocal priorities across every analysis: they are rated \u0026ldquo;very important\u0026rdquo; and seldom \u0026ldquo;unimportant\u0026rdquo; both on partitioned data and the whole dataset. Finally, two items that appeared in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Ft10, Ft14) lose significance once the data are partitioned.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRegional Variation\u003c/h2\u003e\u003cp\u003eCountry-specific analysis revealed notable differences in the feature preferences, as illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The figures present respectively the HIGH and LOW for each of the thirteen \u0026ldquo;possibly important\u0026rdquo; features per country. For every feature, the black bar represents the average value across all countries (i.e, average HIGH in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, average LOW in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBetween-country contrasts (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) corroborate the patterns shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. After Bonferroni adjustment, the Wilcoxon rank-sum tests identify 14 feature-level differences, almost all involving Cyprus (nine features) or Spain (two features). Cyprus participants assign significantly higher importance than Belgium, Italy or Switzerland to Ft 2, 3, 4, 5, 8, 9, 11, 12, 18, and 25, while Spain participants out-score Italy and Switzerland on Ft 24 and Ft 25. The direction column in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e confirms that no other country ever surpasses Cyprus, and only Spain exceeds Cyprus on a single feature.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBetween-country contrasts, Wilcoxon Test*\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry: A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCountry: B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigher rating\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSwitzerland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSwitzerland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelgium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelgium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSwitzerland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelgium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSwitzerland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelgium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSwitzerland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFt 25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSwitzerland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCyprus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e*Only feature\u0026ndash;country pairs with statistically significant differences are shown. All reported p‑values are Bonferroni‑adjusted\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese inferential results map directly onto the Figures we provided. In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (percentage of High), the yellow circles representing Cyprus consistently occupy the uppermost segment of the y-axis for every feature flagged in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, whereas the corresponding symbols for Belgium (red squares), Italy (turquoise crosses) and Switzerland (pink triangles) sit noticeably lower; the same Cyprus-versus-others separation is reversed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (percentage of Low), where Cyprus clusters near zero while the comparison countries show higher low-importance tails. Likewise, Spain\u0026rsquo;s two significant peaks (Ft 24\u0026ndash;25) are the only points where the purple diamonds reach the top of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and the bottom of Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, mirroring the Spain\u0026thinsp;\u0026gt;\u0026thinsp;Italy/Switzerland contrasts reported in the table.\u003c/p\u003e\u003cp\u003eThus, the direction and magnitude of every statistically significant Wilcoxon contrast in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e are visually anticipated by the country clusters in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, confirming that the formal tests are capturing the very gaps apparent in the raw percentages.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study offer valuable insights into stakeholder priorities for developing effective digital health promotion (DHP) tools targeting adolescents across Europe. Our study incorporates responses across seven European countries and three stakeholder groups, including responses from non-binary and mixed migration background individuals. Our findings highlight notable differences in perceived feature importance across stakeholder groups\u0026mdash;students, educators, and parents\u0026mdash;and significant variations among participating countries. Such disparities suggest that stakeholders hold distinct priorities driven by their roles and contextual backgrounds.\u003c/p\u003e\u003cp\u003eEducators and parents consistently prioritized evidence-based, practical, and economically accessible features. Conversely, adolescent users predominantly emphasized usability features, such as smartphone accessibility, regularly updated content, and free access. These differences underscore the essential balance between educational, parental, and adolescent perspectives in designing effective health interventions. Our methodological emphasis on integrating stakeholder-specific thresholds for feature importance allowed us to identify core features with universal appeal, while also acknowledging subgroup preferences, thereby optimizing both relevance and user engagement.\u003c/p\u003e\u003cp\u003eCountry-level analysis highlighted significant variations in feature preferences, particularly between Switzerland and other participating countries. Swiss participants rated several features as less important compared to respondents from other nations, while participants from Spain consistently ranked certain features highly. These country-specific differences underline the importance of culturally sensitive and adaptable DHP tools, responsive to regional health priorities and local stakeholder expectations. Variations in stakeholder representation within countries further influenced preferences. For instance, parental dominance in Cyprus and Belgium may have contributed to prioritizing practical guidance and evidence-based information, whereas the greater educator participation in Greece likely favored structured educational content. Italy's comparatively low overall response rate, particularly among educators, could explain the absence of strong feature preferences within that context.\u003c/p\u003e\u003cp\u003e\u003cb\u003eA universal backbone emerged.\u003c/b\u003e Interactive goal-tracking (Ft 6) and personalized, just-in-time motivational prompts (Ft 8) were the only features rated \u0026ldquo;very important\u0026rdquo; and seldom \u0026ldquo;unimportant\u0026rdquo; by \u003cem\u003eall\u003c/em\u003e respondents\u0026mdash;students, educators, and parents\u0026mdash;irrespective of national context. Neither feature showed a significant between-country shift in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and both occupy the extreme upper-right quadrant of Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. These items therefore, constitute the non-negotiable core of any future platform.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStakeholder-specific layers are equally clear.\u003c/b\u003e Evidence-based fact sheets (Ft 2) and ready-to-use classroom activities (Ft 3) and family challenge modules with rewards (Ft 7) were valued by educators and parents but not by students. Retaining these three elements as optional layers\u0026mdash;activated only when the corresponding stakeholder opts-in\u0026mdash;allows the DHP tools to remain streamlined for students while delivering depth where it is most appreciated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCountry contrasts point to targeted add-ons.\u003c/b\u003e Cyprus displayed the broadest demand, outscoring comparison nations on nine features, whereas Spain showed unique enthusiasm for social-media integration (Ft 24) and geo-based \u0026ldquo;healthy-place\u0026rdquo; suggestions (Ft 25). A modular architecture that activates, or suggests, these components only for users in the corresponding country can accommodate local preferences.\u003c/p\u003e\u003cp\u003eLimitations of this study include potential bias due to disproportionate stakeholder representation, especially the overrepresentation of parents, which may affect generalizability. Although subgroup analyses aimed to mitigate this issue, more balanced stakeholder involvement would further enhance the robustness of findings. Additionally, the cross-sectional design limits the ability to draw causal inferences or track changes in stakeholder preferences over time. Employing longitudinal designs in future research could address this limitation by capturing evolving needs and preferences. The relatively low response rates from certain countries also limit the representativeness and suggest a need for enhanced participant recruitment strategies. For example, in Cyprus, the sample was dominated by parents, which may explain the country\u0026rsquo;s strong preference for features such as trusted content and direct access to health professionals. Belgium showed a similar pattern but with a more balanced representation. In Greece, educators were the largest group, possibly contributing to more institutionally grounded feature evaluations. Spain and Slovenia had balanced stakeholder representation, while Switzerland had moderate and evenly distributed participation, consistent with its lower overall feature ratings. These differences in stakeholder composition likely influenced national-level feature preferences and should be considered when interpreting cross-country variation.\u003c/p\u003e\u003cp\u003eOur survey data serve as a foundation for identifying features that stakeholders consider vital for DHP tools. The cross-sectional nature of this data allows us to explore variations in feature importance across different stakeholder groups and countries, revealing insights into cultural and contextual preferences. This analysis ultimately aids in the development of culturally adaptable DHP tools that are aligned with the expectations of diverse user groups across Europe.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study asked which design elements of a school-based digital intervention are regarded as most important for fostering cancer-preventive habits among European adolescents. By analyzing 28 candidate features across seven countries and three stakeholder groups, we obtained a hierarchy of priorities that can guide both software architecture and implementation strategy.\u003c/p\u003e\u003cp\u003eThe methodological approach of comparing high and low importance ratings across stakeholder groups and countries provided a systematic framework for feature identification. This strategy successfully highlighted universally valued core features and group-specific preferences, offering actionable insights for tailored intervention design. Future research should delve deeper into understanding the reasons behind student preferences and identify strategies to make widely valued features more engaging to adolescent users.\u003c/p\u003e\u003cp\u003eUltimately, this study underscores the critical importance of stakeholder co-creation in developing effective DHP tools. By actively involving students, educators, parents, health professionals, and policymakers in both the design and implementation phases, interventions can be tailored to meet educational, cultural, and public health standards, ensuring their relevance and effectiveness. In sum, the survey results converge on a \u003cb\u003emodular yet universally grounded blueprint\u003c/b\u003e: an interactive, personalized core enhanced by stakeholder-specific resources and culturally sensitive extensions. Implementing this structure will accelerate adolescent cancer-prevention efforts and provide a reproducible model for future digital health-promotion initiatives across Europe. Moving forward, targeted engagement strategies that respect demographic and regional preferences will be essential for the successful development and widespread adoption of DHP tools, significantly advancing adolescent cancer prevention and health promotion across Europe.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDHP Digital Health Promotion\u003c/p\u003e\n\u003cp\u003eIP Internet Protocol\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki and Ethical approval (number: 11068, 11/04/2024) was obtained from the Committee of Research and Ethics of the Hellenic Mediterranean University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNone declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by the SUNRISE project that has received funding from the European Union\u0026rsquo;s Horizon Europe research and innovation programme under grant agreement N\u0026ordm; 101136829.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eV.K.: \u0026nbsp; Conceptualization, methodology, writing \u0026ndash; original draft, H.K.: Formal analysis, writing \u0026ndash; review \u0026amp; editing. A.P.: Methodology, Writing \u0026ndash; original draft. K.K.: Data collection, Methodology. S.H.: Data collection, N.B.: Writing \u0026ndash; review \u0026amp; editing, N. K.: Data collection, Writing \u0026ndash; review \u0026amp; editing, L.DeC.: Data curation, E.A.: Data collection, P.S.: Data collection, T.P.: Data collection, writing \u0026ndash; review, S.V.: Data collection, writing \u0026ndash; review, G.C: Writing \u0026ndash; review \u0026amp; editing, A.G.: Data curation, M. K.: Data collection, \u0026nbsp;K.M.: Data collection, M.P.: Data collection, data curation, A.T.: Funding acquisition, Writing \u0026ndash; review \u0026amp; editing, C.T.: Writing \u0026ndash; review \u0026amp; editing, E.T.: Funding acquisition, writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAll Authors have approved the submitted version and have agreed both to be personally accountable for the author\u0026apos;s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSawyer SM, Afifi RA, Bearinger LH, Blakemore SJ, Dick B, Ezeh AC, et al. Adolescence: A foundation for future health. Lancet. 2012;379(9826):1630\u0026ndash;40. Available from: https://doi.org/10.1016/S0140-6736(12)60072-5\u003c/li\u003e\n\u003cli\u003eSUNRISE \u0026ndash; Sustainable interventions and healthy behaviors for adolescent primary prevention of cancer with digital tools [Internet]. Brussels: The SUNRISE Project; [cited 2025 May 12]. Available from: https://thesunriseproject.eu/\u003c/li\u003e\n\u003cli\u003eSchools as Living Labs \u0026ndash; SALL [Internet]. Brussels: SALL Project; [cited 2025 May 12]. Available from: https://www.schoolsaslivinglabs.eu/\u003c/li\u003e\n\u003cli\u003ePollard CM, Pulker CE, Meng X, Kerr DA, Scott JA. Who uses the Internet as a source of nutrition and dietary information? An Australian population perspective. J Med Internet Res. 2015;17(8):e209.\u003c/li\u003e\n\u003cli\u003eBenvenuti M, Wright M, Naslund J, Miers AC. How technology use is changing adolescents\u0026rsquo; behaviors and their social, physical, and cognitive development. Curr Psychol. 2023;42:16466\u0026ndash;9. Available from: https://link.springer.com/article/10.1007/s12144-023-04254-4\u003c/li\u003e\n\u003cli\u003eICT Use and Healthy Longevity. In: Springer Series on Demographic Methods and Population Analysis. Springer; 2024. Available from: https://link.springer.com/book/9783031989636\u003c/li\u003e\n\u003cli\u003eStark AL, Geukes C, Dockweiler C. Digital health promotion and prevention in settings: scoping review. J Med Internet Res. 2022;24(1):e21063.\u003c/li\u003e\n\u003cli\u003eKampmeijer R, Pavlova M, Tambor M, Golinowska S, Groot W. The use of e-health and m-health tools in health promotion and primary prevention among older adults: a systematic literature review. BMC Health Serv Res. 2016;16(5):467\u0026ndash;79.\u003c/li\u003e\n\u003cli\u003eFarhat-ul-Ain, Popovit\u0026scaron; O, Tomberg V. Mapping Behavior Change Wheel Techniques to Digital Behavior Change Interventions: Review. In: Human-Computer Interaction. User Experience and Behavior. HCII 2022. Lecture Notes in Computer Science, vol 13304. Springer; 2022. p. 277\u0026ndash;95. Available from: https://link.springer.com/chapter/10.1007/978-3-031-05412-9_20\u003c/li\u003e\n\u003cli\u003eChampion KE, Newton NC, Barrett EL, Teesson M, Slade T, Kelly EV, et al. Effectiveness of school-based eHealth interventions to prevent multiple lifestyle risk behaviours among adolescents: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(5):e206\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eShortliffe EH, Cimino JJ, editors. Biomedical Informatics: Computer Applications in Health Care and Biomedicine. 5th ed. Cham: Springer; 2021 ;Available from: https://link.springer.com/book/10.1007/978-3-030-58721-5\u003c/li\u003e\n\u003cli\u003eKasoju N, Remya NS, Sasi R, Sujesh S, Soman B, Kesavadas C, et al. Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology. CSI Trans ICT. 2023;11:11\u0026ndash;30. Available from: https://doi.org/10.1007/s40012-023-00380-3\u003c/li\u003e\n\u003cli\u003eMa C, Wang L, Yang G, Ma J, Wang L, Su S, et al. Prevalence and trends in tobacco use among adolescents aged 13\u0026ndash;15 years in 143 countries, 1999\u0026ndash;2018: findings from the Global Youth Tobacco Surveys. Lancet Child Adolesc Health. 2021;5(4):245\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eAl-Hosni K, Chan MF, Al-Azri M. The effectiveness of interventional cancer education programs for school students aged 8\u0026ndash;19 years: a systematic review. J Cancer Educ. 2021;36:229\u0026ndash;39. Available from: https://doi.org/10.1007/s13187-020-01868-1\u003c/li\u003e\n\u003cli\u003eZarnowiecki D, Mauch C, Middleton G, Bradle A, Murawsky L, et al. Digital platforms as effective health promotion tools: an Evidence Check review. Sydney: Sax Institute for the Cancer Council NSW; 2019.\u003c/li\u003e\n\u003cli\u003eLimeSurvey GmbH. LimeSurvey: An open source survey tool [Internet]. Hamburg: LimeSurvey GmbH; [cited 2025 May 12]. Available from: https://www.limesurvey.org \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital Health Promotion, Multi-country survey, Health Behavior, Cancer Prevention, eHealth, Co-Creation Methods","lastPublishedDoi":"10.21203/rs.3.rs-7599976/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7599976/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003ePrimary cancer prevention through behavior change in adolescence, a crucial period for shaping lifelong health habits, presents a major public health challenge across Europe. Addressing this, the SUNRISE project aims to tackle the challenge of primary cancer prevention in adolescents by developing and implementing an innovative, digitally-enhanced life-skills program tailored to diverse socio-economic, cultural, and environmental backgrounds by incorporating various Digital Health Promotion (DHP) tools to foster sustainable health behavior change in adolescents.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA survey was conducted on 505 stakeholders (students, parents, and educators), from seven European countries to assess a set of key features for effective DHP tools for their importance on a five-point likert scale.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur findings revealed that nine of the proposed DHP tools\u0026rsquo; features were identified as important across all the stakeholder groups, while significant differences in the importance of certain features across different stakeholder groups and countries were identified. Students, as primary users, demonstrated distinct preferences, which often diverged from educators and parents, suggesting that stakeholders hold distinct priorities driven by their roles and contextual backgrounds. Additionally, country-level variations were notable; for example, Swiss participants rated the proposed features, in general, as of lower importance than the Spanish respondents.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese insights emphasize the necessity of developing adaptable and context-sensitive DHP tools that reflect the diverse needs and preferences of adolescents across Europe. The large-scale implementation and evaluation of this program will provide valuable data for shaping future digital health interventions aimed at cancer prevention in youth.\u003c/p\u003e","manuscriptTitle":"Key Factors in developing effective Digital Health Promotion tools for Cancer Prevention and Health Behavior Change in Adolescence through a multi-country survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 11:45:17","doi":"10.21203/rs.3.rs-7599976/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-04T18:30:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T13:17:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126542983166372373978269705629546293697","date":"2025-11-04T02:38:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173599174762168587460011020535698270418","date":"2025-11-03T15:42:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116749974989925230854931979738955716481","date":"2025-10-31T16:26:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51412854549953943122608260585660568054","date":"2025-10-30T10:32:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188071811225396489700241147745575517168","date":"2025-10-24T20:34:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T00:52:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209938404086364847915303694942154241327","date":"2025-09-24T20:12:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290821642707971440725576283566805101108","date":"2025-09-24T14:09:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-22T09:29:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-19T18:02:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T08:57:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T08:56:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-12T10:47:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ba425b0-fc74-43ce-ba16-4a2f89373b58","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:06:29+00:00","versionOfRecord":{"articleIdentity":"rs-7599976","link":"https://doi.org/10.1186/s12889-026-26412-6","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-02-21 15:58:06","publishedOnDateReadable":"February 21st, 2026"},"versionCreatedAt":"2025-10-03 11:45:17","video":"","vorDoi":"10.1186/s12889-026-26412-6","vorDoiUrl":"https://doi.org/10.1186/s12889-026-26412-6","workflowStages":[]},"version":"v1","identity":"rs-7599976","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7599976","identity":"rs-7599976","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}