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Factors that influence the adoption of a school-based eHealth alcohol prevention program among Spanish personnel: a mixed methods study
樱花视频 volume听25, Article听number:听436 (2025)
Abstract
Background
Evidence-based research has shown that using eHealth interventions effectively reduces risk behaviors such as alcohol consumption, a public health problem worldwide. However, despite its benefits, there has been a poor intention to adopt such innovations, and limited resources exist to understand factors influencing the uptake decision to use school-based eHealth alcohol prevention programs. This study aims to identify the factors that influence the adoption of a computer-tailored eHealth alcohol prevention program among school personnel in Spain.
Methods
A cross-sectional study employing an exploratory sequential mixed methods research design was carried out. First, interviews were conducted with ten Spanish school counselors to assess factors influencing the adoption of the school-based eHealth program by exploring participants' awareness and salient beliefs concerning attitudes, social influences, and self-efficacy. Second, an online quantitative questionnaire was developed based on the qualitative research findings. Third, the new questionnaire was administered to Spanish school personnel (N鈥=鈥100), including the school management team, school counselors, and teachers. Rogers' Diffusion of Innovations theory and the Integrated Change Model frameworks were used as theoretical bases for understanding the adoption process.
Results
School personnel with a strong intention to adopt the program (intenders) perceived significantly more advantages and positive innovation attributes, than those participants with a weaker intention to adopt (non-intenders). Intenders perceived a higher personal relevance and responsibility towards using the program, more self-efficacy (e.g., ability to understand, manage time and incorporate the program) and positive social influences from their colleagues, as well as greater willingness in preparing action plans, such as monitor students鈥 alcohol consumption and discussing the program with coworkers, than the non-intenders group. Advantages and social support were found to explain a significant portion of the variance in the adoption intention.
Conclusions
This study suggests that health intervention researchers should develop strategies to enhance educators' pro-innovation attitudes, personal relevance and responsibility, and perceived ease of use towards adopting an eHealth program. Furthermore, our results highlight that fostering school personnel's acceptance of the intervention and planning goal-oriented actions are crucial elements in optimizing adoption promotion of eHealth programs in school settings.
Introduction
Drinking alcohol among adolescents is a public health problem in many countries. Alcohol consumption is associated with several causes of death in this age group, such as unintentional injuries in road traffic accidents, suicide and interpersonal violence [1]. Among European adolescents aged 15-to 16-years-old, 47% consumed alcohol during the past 30听days, and an average of 13% reported intoxication during the same period. Hungary, Greece, Czechia, Austria, Germany and Denmark were the countries with the highest consumption of alcohol in adolescents, with more than three-fifths (61鈥74%) in the last 30听days [2]. In Spain, according to the National Drug Use Survey, alcohol remains the most widely consumed psychoactive substance among the young population; they found that 73.6% of adolescents between 14鈥18听years of age have consumed alcohol in the last 12听months, and 56.6% in the last 30听days. Moreover, 28.2% have experimented binge drinking or high-intensity drinking, which is described as the consumption of five or more standard glasses of alcohol by men and four or more by women in about two hours [3, 4].
School-based programs are a popular delivery setting for promoting youths鈥 health and preventing behaviors that increase the risk of illness, injury, and premature death [5]. School settings are considered suitable places for learning interventions since children and adolescents stay for significant amounts of time [6, 7]. Alerta Alcohol (A-A), a cultural adaptation of a Dutch intervention [8], is an example of an effective school-based alcohol prevention eHealth program for Spanish adolescents [9,10,11,12]. Despite its effectiveness, suggestions for improvement were considered and changes have been introduced, resulting in a 9-session program Alerta Alcohol 2.0 [11, 12], where students access a website via computer, mobile phone, or tablet. During the lessons, adolescents receive animated video-delivered tailored messages to increase alcohol drinking awareness, change their risk perception, foster self-efficacy, and reduce positive attitudes toward alcohol consumption and binge drinking. The A-A original design was adopted in 15 schools [11], and the A-A 2.0 in 24 schools, both in the Andalusia region. There is no record of other existing eHealth programs aiming to prevent adolescent alcohol consumption in the region either country; nevertheless, more than three-fourths of schools (86%) rejected the invitation to adopt the A-A 2.0 program [12], indicating an urgent need to understand which factors are influencing this decision. The content and development of the Alerta Alcohol original design and Alerta Alcohol 2.0 programs are described in detail elsewhere [11, 12].
Advancements in implementation science have increased the recognition among researchers that efforts to adapt and disseminate evidence-based programs should be approached systematically and rigorously [13]. Understanding these influencing factors will improve the development of appropriate strategies for promoting school adoption and, therefore, the diffusion of existing and subsequent health programs. This paper utilizes the current approach to develop a dissemination strategy for Alerta Alcohol 2.0.
Theoretical frameworks
Rogers' Diffusion of Innovations theory (DIT) is a valuable framework for understanding the adoption phase of an intervention. Rogers' Diffusion of Innovations Theory (DIT) is an important framework for understanding the adoption phase of new ideas or practices. Diffusion refers to how members of a social system learn about, evaluate, and respond to innovations they perceive as new. The adoption process occurs within this broader context. This phase听is听defined as the initial decision or action to try or employ an innovation or evidence-based practice (e.g., the encouragement of school administrators and teachers to commit to initiating a health program) [14]. Specific attributes of innovations have been shown to influence the speed of adopting an innovation (i.e., rate of adoption) [15, 16]. The innovation attributes perceived by potential adopters are: 1) Perceived cost of adopting and implementing innovation, 2) the relative advantage of the innovation (e.g., health program) compared with what is being used, 3) compatibility of the innovation with the existing values, past experiences, and needs of intenders or potential adopters, 4) low complexity in understanding and using the new practice, 5) trialability, which is how easily potential users can explore the innovation, and 6) observability of the results. Each of these attributes of innovation must be considered as predictors for a successful diffusion and likelihood of adoption of health interventions in school settings [17, 18].
The I-Change Model (ICM), developed by de Vries [19], is another theoretical model that provides explanations concerning the potential adopters' psychological characteristics. The ICM states that behavioral change is a process that occurs in three stages: awareness (pre-motivational), motivation (motivational), and actions (post-motivational), which are influenced by information factors (i.e., the quality of messages, channels, and sources used) and preceding factors (e.g., environmental/organizational and psychological factors) (see Fig.听1). The model explains that the awareness phase (e.g., cognizance, knowledge, risk perceptions, and cues to action) plays a fundamental role in creating individuals' motivation; therefore, sufficient awareness influences the motivational component [12, 19]. The motivation phase is determined by an individual's attitudes (advantages and disadvantages) towards behavior, social influences (social modeling, norms, and support), and self-efficacy (participant's belief of self-competence and the perceived school organizational capability). Altogether, the latter factors determine the intention of a person to modify a specific behavior. For instance, when there is a high intention to change, having preparatory and coping plans towards anticipated barriers, as well as self-efficacy and skills, increases the likelihood that intentions will be transferred into actions and, therefore, accomplish the goal behavior [19].
The Integrated Change Model (The I-Change Model) [20]
In addition to the previously mentioned factors influencing the adoption process, research has identified 麓perceived personal relevance麓 as an important characteristic affecting the initial decision to utilize a health program in school settings [21,22,23]. Therefore, we integrated this factor into the motivational component of the ICM.
In line with understanding the intention to adopt school-based health programs some studies have demonstrated the influence of factors such as organizational context, innovative characteristics, and individual motivation. Concerning the organizational aspects, schools are more likely to adopt an innovation in the presence of suitable integration of intervention material into the existing curriculum and perceived involvement in decision-making [11, 18, 21, 22, 24], clear communication to raise awareness regarding the health program within schools, and active support of school administrators [22,23,24]. Regarding the characteristics of the innovation, it has been shown that compatibility with school values, priorities, and student needs [18, 21, 22, 25]; a relative advantage in terms of cost-effectiveness compared with other preventive health programs [18, 23]; and simplicity in comprehending the program [17] are positively associated with the adoption phase. Positive attitudes toward the program, perception of benefits and need for innovation [18, 22, 24], and high self-efficacy [17, 18, 25] are individuals鈥 motivational factors that facilitate the adoption. Moreover, perceived personal relevance and role responsibility [21, 22, 25] influence individuals鈥 intention to uptake the program.
Although the studies above identified factors associated with the dissemination process of school-based health prevention programs, studies addressing factors influencing the adoption of school-based eHealth alcohol prevention programs were not found. Furthermore, most studies, including the adoption phase as part of their research, assessed school interventions that were, targeting overweight-obesity prevention and already implemented [18, 22, 24, 26]; therefore, findings from these studies concerning reach, cost-effectiveness, and fidelity barriers may not generalize to digital interventions [27, 28]. There is a shortage of studies that employ a theory-based framework to examine this issue [7, 11, 12, 21, 24, 29, 30], and research on technology-based interventions aimed at preventing alcohol use among adolescents is still in its early stages [27, 28]. In addition, despite the evidence about phase-specific factors impacting the dissemination continuum [24], there is a gap regarding the adoption stage since studies on technology and non-technology adolescents' alcohol interventions mainly focused on evaluating the methodology and effectiveness of the implementation phase [7, 27, 28]. A study exploring adolescent substance use program adoption [23] found that compatibility with students' needs and relative advantage against other interventions were the most valued factors influencing program use among stakeholders; however, the research was based on the adoption of a person-delivered screening brief intervention provided by school health centers 鈥 a characteristic which might be difficult to extrapolate to our research context.
The current study aims to identify factors influencing the adoption of a CT eHealth alcohol prevention program听(Alerta Alcohol) in Spanish schools. The first objective was to assess whether sociodemographic factors and decision-making styles differ among participants with higher intention to adopt (intenders) and lower intention to adopt (non-intenders). The second objective involved analyzing differences in awareness (knowledge, risk perception, and perceived severity), motivation (attitudes, personal relevance, self-efficacy), intention, and preparatory actions between intenders and non-intenders. The third objective was to identify those factors explaining the variance of intent to adopt the eHealth program. Overall, the study results seek to inform intervention planners and researchers about the organizational and individuals鈥 motivational determinants in the uptake of e-health alcohol prevention programs to increase voluntary adoption and implementation in school settings.
Methods
Study design
The study used a mixed-methods research (MMR) design defined as 鈥渞esearch in which the investigator collects and analyzes data, integrates the findings, and draws inferences using both qualitative and quantitative approaches鈥︹ [31]. It employed an exploratory sequential mixed methods research design [32, 33]. In the first phase, a qualitative approach was undertaken to assess factors influencing the adoption of the school-based eHealth program by exploring participants' awareness and salient beliefs concerning attitudes, social influences, and self-efficacy. Based on the I-Change Model, a deductive approach was used to develop the semi-structured interview questions [34]. In the second phase, a quantitative questionnaire was developed based on the qualitative findings. This step was followed by a third phase where the new instrument was applied (see the diagram in Fig.听2). As the study followed a sequential mixed methods design, the results of the qualitative method were essential for integrating and planning the questionnaire development in the quantitative method (i.e., the quotes became items; the codes, variables; and the themes, scales) [35]. This study followed the MMR guideline [36] for scientific rigor, transparency, and high quality in reporting research (see the Checklist in Additional file 1).
Diagram of exploratory sequential mixed methods research design; adapted from Creswell [32]
Study 1: Qualitative method
Participant selection and sampling
Interviews were conducted with ten school counselors working in public and subsidized private secondary and upper secondary schools from Andalusia, Spain, in March 2022 [37]. Interviewing continued until data saturation was reached. One of the school counselors鈥 key roles is the adoption of innovations to promote prevention and human development; hence, this population is relevant in the decision-making process in Spanish schools [38]. Purposive sampling was employed to recruit counselors involved in the adoption of health programs in their schools [39]. A researcher from the University of Seville contacted the participants by email or phone. All the interviewed school counselors agreed to adopt A-A 2.0 and implemented the program in the past; however, 4 out of 10 stopped implementing it before they were interviewed. The latter enriched the understanding of factors influencing intervention adoption and maintenance.
Data collection
The interview protocol (see Additional file 2) was designed based on previous qualitative studies using the I-Change model [40, 41]. Due to COVID-19, context interviews with the school personnel were conducted online using Microsoft teams. Personal data was not shared with the other researchers. Interviewers obtained verbal consent from the school counselors. The interview development was participatory and interactive; one female and one male-trained interviewer facilitated it, and its average time was 40听min. Participants had the opportunity to raise additional topics other than those included in the protocol. Confidentiality and safeguarding of data were informed to the participants verbally. Each participant provided informed consent prior to each interview.
Finally, we attempt to follow the rigor and transparency of the qualitative guidelines and approach reliability within the coding process by using 1) multiple coders to analyze data (two trained health researchers), 2) researchers鈥 expertise with the conceptual framework (ICM), which leads to a consensual interpretation of the data, and 3) member checked by returning transcripts to participants for comments, which is a relevant tool for qualitative reliability.
Data analysis
The audio recordings were transcribed verbatim. Data transcripts were analyzed by using Atlas.ti (version 22 Mac). Data analysis was performed following a theoretical thematic coding approach [42]. Descriptive data were identified and coded based on the predefined research questions and theoretical model. Coding was done by a health psychologist researcher and a second experienced researcher checked the coding results. Both researchers discussed and agreed upon the final selected themes.
Study 2: Quantitative method
Sample size
The G* Power program version 3.1.9.3. was used to calculate the required sample size of 102 participants based on the desired statistics analysis accepting a significant p-value鈥<鈥0.05, the statistical power of 0.80 and a medium effect size of 0.50 to detect meaningful differences and avoid the risk of Type 2 errors.
Although the program is currently only implemented in the Andalusia region, online questionnaires were sent to participants in all the Spanish territory since one of the future goals is national-wise dissemination. Since shared decision-making positively influences the adoption of health innovations in school settings [22, 24], this quantitative study targeted school management staff, counselors, and teachers from secondary education (students aged 12鈥16), upper secondary education (ages 16鈥18), and non-university technical-vocational education (ages 16 and older, or exceptionally 15) in public and private school systems across Spain. In Spain, the Ministry of Education, Vocational Training, and Sports (MEFPD) [43] governs schools, working in close collaboration with autonomous communities, which are responsible for distributing funding, providing curricular guidance, and monitoring education standards. A random sample of 5,927 schools was selected from a total of 8,853 eligible institutions listed in the General Data of the MEFPD. Additionally, Facebook groups for school personnel from various Spanish regions (e.g., Interim Teachers of Madrid) were utilized for participant recruitment.
Recruitment and procedures
Invitations to participate, including anonymous hyperlinks of the questionnaire, were sent through Qualtrics XM Platform to the official school email provided by MECD and shared via Facebook groups. Data were collected during May and June 2022.
A brief description of the program Alerta Alcohol including its objectives, intensity, and duration, was shown on the first page of the questionnaire. An information letter, consent form, and main researchers鈥 names were also displayed. Individuals were informed about their voluntary participation and the possibility of withdrawing at any moment during the study. The respondents had the possibility of winning one of three 鈧10 vouchers if they filled out the online questionnaire completely. Three email reminders were sent to encourage participation among non-responders.
Data collection
Questionnaire development was based on previous studies designed using the I-Change Model. The obtained results of Study 1 were used to develop the online questionnaire with a total of 5 domains and 11 subdimensions. The questionnaire was pilot tested in terms of understanding and content by people from the target group and adapted according to their feedback. It was estimated that completing the questionnaire would take 15听min. These participants were not eligible to fill in the final questionnaire.
Questionnaire
Sociodemographic factors
Participants were asked to indicate their age, gender (1鈥=鈥塮emale, 2鈥=鈥塵ale, 3鈥=鈥塷ther, 4鈥=鈥塸refer not to say), school position title (1鈥=鈥塵anagement team, 2鈥=鈥塻chool counselor, 3鈥=鈥塼eacher), working educational level (1鈥=鈥塻econdary, 2鈥=鈥塽pper secondary education, 3鈥=鈥塶on-university technical-vocational education), region where they are working, (1鈥=鈥堿ndalusia, 2鈥=鈥塷ther), years of working in current profession (mean, SD), and hours working per week (mean, SD).
Decision-making styles
Decision-making styles were measured by assessing centralization and formalization. These measures were adapted from two dimensions of the听Organizational Structure scale [44]. Centralization (伪鈥=鈥0.396) and formalization (伪鈥=鈥0.347) were measured with three items each on a seven-point Likert scale (i.e., 1.鈥淚 frequently participate in the decision-making carried out in my center鈥, 2.鈥淎ny decision I make must be approved by my boss鈥, and 3. 鈥Even small matters should be referred to someone higher up for a fine response鈥; 1.鈥淢ost of the school's teaching staff follow their own rules鈥, 2.鈥淢y work is strictly regulated by rules and procedures鈥, and 3.鈥淭he school personnel's adherence to rules is constantly monitored鈥, respectively). Due to the low 伪鈥檚 in centralization and formalization, constructs were calculated and interpreted as indexes instead of as scales.
Awareness
Knowledge concerning alcohol consumption in Spanish adolescents was measured using six items (e.g., 鈥淐urrent data shows that more than a third of Andalusian adolescents drank excessively/binge in the last 30听days鈥, 鈥淚t has been shown that high alcohol consumption in adolescents favors school performance鈥) rated on seven-point Likert scale (0鈥=鈥塱ncorrect, 1鈥=鈥塩orrect; 伪鈥=鈥0.610). Participants were asked to rate the perceived risk (0鈥=鈥塿ery unlikely to 7鈥=鈥塿ery likely; 伪鈥=鈥0.868) of their student鈥檚 alcohol consumption by eight questions (e.g., 鈥淗ow likely is it that your students consume alcohol?鈥, 鈥淗ow is alcohol consumption likely to impair the socio-emotional stability of your students?鈥). Perceived severity (0鈥=鈥塶ot very serious to 7鈥=鈥塿ery serious; 伪鈥=鈥0.954) of those risks was assessed by eight items (e.g., 鈥淗ow serious do you think alcohol consumption is in your students?鈥, 鈥淗ow serious do you think it is that alcohol consumption harms the socio-emotional stability of your students?鈥). To see the scale of the complete items, see Table听3.
Motivational factors
Attitudes were rated on a seven-point Likert scale (0鈥=鈥塻trongly disagree to 7鈥=鈥塻trongly agree). The advantages (伪鈥=鈥0.913) of adopting the eHealth program were measured by nine questions; example items include: 鈥淯se the Alerta Alcohol program will contribute to reduce alcohol consumption in adolescents in my school鈥 and 鈥淯se the Alerta Alcohol program generates beneficial changes in the behavior of students鈥. Seven items measured the intervention adoption's perceived disadvantages (伪鈥=鈥0.755). For example: 鈥淯se the Alerta Alcohol program requires too much effort from the teachers鈥 and 鈥淯se the Alerta Alcohol program is difficult to incorporate into the existing school curriculum鈥. Personal relevance concerning the importance of adopting the program was assessed by six items (e.g., 鈥淚 am concerned about the consumption of alcohol in the students from my school鈥, I consider that promoting the health of adolescents is part of the school鈥檚 role鈥; 0鈥=鈥塻trongly disagree to 7鈥=鈥塻trongly agree; 伪鈥=鈥0.866). Rogers's [15] innovation attributes were also measured for the latter subdimensions'. Perceived social support to adopt the program consisted of six items (e.g., 鈥淢y co-workers would support the use of a web intervention to prevent and reduce alcohol consumption in adolescents鈥, 鈥淭he students would support the use of Alerta Alcohol in my school鈥; 0鈥=鈥塻trongly disagree to 7鈥=鈥塻trongly agree; 伪鈥=鈥0.906). Social norms to adopt the program consisted of five items (e.g., 鈥淢y co-workers think that a web intervention should be used to prevent and reduce the alcohol consumption of my students鈥, 鈥淭he students' family thinks that a web intervention should be used to prevent and reduce my students' alcohol consumption鈥;0鈥=鈥塻trongly disagree to 7鈥=鈥塻trongly agree; 伪鈥=鈥0.939). Finally, self-efficacy to use the program was assessed by 15 items (7鈥=鈥塿ery easy; 0鈥=鈥塿ery difficult; 伪鈥=鈥0.886) The subdimension included items such as: 鈥淯sing Alerta Alcohol is鈥 when the teachers understand it鈥 and 鈥淯sing Alerta Alcohol is鈥 when there are not enough computers鈥. To see the scale of the complete items, see Tables 4 and 5.
Outcome variable: intention
The intention to adopt the health program was measured by three items (0鈥=鈥塻trongly disagree to 7鈥=鈥塻trongly agree; 伪鈥=鈥0.799). Participants were asked to indicate their intention to use the program 1) in the next three, 2) in twelve months, 3) or someday in the future.
Action
Preparatory actions, defined as the person鈥檚 ability to plan specific actions toward a goal behavior (using the health program) was measured by five items (e.g., 鈥淚 have the intention of informing myself about the students' alcohol consumption鈥, 鈥淚 have the intention of searching about the content and effectiveness of the program鈥; 0鈥=鈥塻trongly disagree to 7鈥=鈥塻trongly agree; 伪鈥=鈥0.918).
Data analysis
Data were analyzed using SPSS version 27 (SPSS Inc., Chicago, IL, USA). The single imputation method -implying the replacement of missing values with a plausible guess, for instance, the mean of the distribution- was used for two secondary variables that were missing a small number of values (<鈥2%). Questionnaires presenting missing values within the outcome variable (i.e., intention scale) were excluded from further analysis [45]. Descriptive analyses such as frequency, mean, and proportion were calculated. Chi-square tests for discrete variables were used to compare respondents with a high intention to adopt versus a low intention to adopt the program. Differences between intenders and non-intenders for continuous variables were analyzed using independent sample t-tests. Cut-off points to create intenders and non-intenders鈥 groups were based on the median of the adoption intention summatory variable (x蛡鈥=鈥12). The G* Power program version 3.1.9.3. was used to calculate the effect sizes of the differences between means. Correlations were used to test associations between the evaluated dimensions and the intention to adopt the health program. Finally, a multiple linear regression analysis using the forward method was run to assess how well the model explained the variance of intention to adopt. Standardized regression coefficients (尾) were interpreted to understand the independent effects of the selected predictor variables (i.e., sociodemographic characteristics, awareness and motivational factors, preparatory actions) on the outcome of interest (i.e., intention). P-values less than 5% were considered for significant association.
Results
Qualitative study
Five general themes were explored from school counselors鈥 perceptions towards the adoption of the eHealth intervention: perceived risk and severity of their students鈥 alcohol consumption, perceived personal relevance and role responsibility towards the use of an eHealth program, advantages and disadvantages of adopting the health program, social influences in favor and against the use of the program, factors hindering and facilitating the adoption of the intervention in their schools and the intention to adopt the intervention.
Table 1 provides an overview of the resulting themes, codes, and quotations from the qualitative study and its corresponding scales, variables, and items in the quantitative study showing the connection and integration between both phases.
Quantitative study
Sociodemographic characteristics
Only one-third of the recipients opened the email invitations (1,955 out of 5,927). In all, 56 people participated, representing 1% of the recipients. Additionally, 46 participants completed the questionnaire via Facebook distribution. A total of 102 school personnel participated in the study. Two questionnaires were excluded from further analysis since data related to the intention variable were missing, resulting in a final sample of 100 participants. The sample consisted of 59 women, 40 men, and one non-binary participant with an average age of 45 (SD鈥=鈥10.54). Most of the participants were teachers (63%) working at a secondary level (46%) of a public school (75%) based in Andalusia (46%). The mean working experience of the participants was 15听years (SD鈥=鈥10.91) with an average of 30听h per week (SD鈥=鈥9.27). Twenty-one participants (21%) responded that they had heard of the Alerta Alcohol intervention program before receiving the questionnaire. The characteristics of the final sample (N鈥=鈥100), also divided into intenders to adopt (47%) and non-intenders to adopt (53%), are presented in Table听2.
Differences between intenders and non-intenders
Sociodemographic characteristics and decision-making styles.
Table 2 shows that significantly more intenders had heard of the Alerta Alcohol program before they received the questionnaire than non-intenders.
Concerning decision-making styles, centralization (t鈥=鈥1.151, P鈥=鈥0.252) and formalization (t鈥=鈥夆垝0.216, P鈥=鈥0.830) constructs showed no significant differences between groups.
Differences in awareness
Regarding the awareness factor (see Table听3), results showed no significant differences in knowledge, risk perception, or perceived severity between intenders and non-intenders. However, marginally significant p-values in knowledge and perceived severity suggested a difference between groups believing that alcohol consumption favors students' school performance (P鈥=鈥0.065) and how serious students' alcohol consumption is during weekends (P鈥=鈥0.093), respectively.
Differences in motivational factors
As presented in Table听4, intenders perceived significantly more advantages and positive innovation attributes (i.e., relative advantage, compatibility with students鈥 and school needs, simplicity) toward the health program than those non-intenders. The former group was more convinced about the positive contribution of the intervention to their school curriculum, its effectiveness, and its attractiveness in reducing alcohol consumption in their students compared with other health programs. Intenders believed the adoption of the program would generate beneficial changes in adolescents' behavior, physical health, and social context. They perceived a greater contribution of the program in improving the student's academic achievement. Positive attitudes factor and items presented medium to large effect sizes (d鈥=鈥0.5 鈥 0.90). Moreover, intenders presented a higher perceived personal relevance to using the program and a greater need to prioritize the reduction of alcohol consumption over other addictive substances in their students. Concerning social influences (see Table听5), intenders perceived significantly more support and stronger positive norms from their co-workers and students鈥 family when adopting the health program than non-intenders. Intenders also expressed significantly higher levels of perceived self-efficacy when teachers are well-informed, understand the content, and feel capable of implementing the program. They also reported that using an eHealth program is more accessible if the intervention is easily incorporated into the school's curriculum and when there is support and communication from coworkers, students' families, and the A-A research team. The latter items showed medium effect sizes (d鈥=鈥0.5 鈥 0.70).
Differences in preparatory actions
Intenders showed a significantly higher intention to plan specific actions towards the use of the program comparing with non-intenders. For instance, in informing themselves about the students' alcohol consumption (t鈥=鈥4.018, P鈥<鈥0.001), searching about the content and effectiveness of the program (t鈥=鈥6.135, P鈥<鈥0.001), listening (t鈥=鈥5.210, P鈥<鈥0.001), discussing (t鈥=鈥5.962, P鈥<鈥0.001), and motivating (t鈥=鈥6.203, P鈥<鈥0.001) their co-workers about the possibility of using the intervention. All items included in this factor presented large effect sizes (d鈥夆墹鈥0.81).
Correlations between ICM constructs and intention to adopt
Supplementary Table听1 (see the table in Additional file 3) shows that the intention to adopt the health program revealed significant Spearman correlation coefficients with ICM constructs. Intention to adopt showed positive moderate correlations with advantages (rs鈥=鈥0.496, P鈥<鈥0.01), social support (rs鈥=鈥0.465, P鈥<鈥0.01), social norms (rs鈥=鈥0.386, P鈥<鈥0.01), self-efficacy (rs鈥=鈥0.342, P鈥<鈥0.01) and personal relevance (rs鈥=鈥0.320, P鈥&濒迟;鈥0.01). The variable preparatory actions showed a positive strong correlation with intention (rs鈥=鈥0.627, P鈥&濒迟;鈥0.01).
Factors that explain the intention to adopt the eHealth program
A multiple linear regression analysis was conducted using the forward method to identify factors that explained the variance of intention to adopt the eHealth program. Linear regression assumptions were tested and met: 1) no multicollinearity (tolerance: values between 0.66 and 0.73, VIF: values between 1.36鈥1.51, correlation coefficients less than 0.7), 2) linearity (observable linear relationships in scatterplots), 3) homoscedasticity (no observable pattern in the scatterplot), 4) normality (observable linear pattern in the P-P plot), and 5) no residuals autocorrelations (D-W鈥=鈥2.085). Multiple linear regression analysis showed that sociodemographic and awareness factors were nonsignificant towards the intention to adopt the program. When adding motivational factors, the results showed that advantages (尾鈥=鈥0.319, \(P\) = 0.002) and social support (尾鈥=鈥0.290, \(P\) <0.005) had unique and significant contributions explaining the intention to adopt. These factors explained 28% of the intention to adopt the eHealth program. Furthermore, by adding preparatory actions, advantages (尾鈥=鈥0.178, \(P\) = 0.066) and social support (尾鈥=鈥0.150, \(P\) = 0.120) were no longer significant whereas making preparatory actions (尾鈥=鈥0.440, \(P\) <0.001) showed a positive association with the adoption intention. This final model accounted for 41% of the variance in the adoption intention.
Discussion
Principal findings
Findings of this study demonstrates that intenders recognized significantly more intervention advantages and positive attributes, such as effectiveness in reducing student alcohol consumption and contributions to their social, physical, emotional and academic needs. Moreover, they showed a greater responsibility to adopt the program, stronger support from colleagues and families, higher perceived ease, and greater agreement to engage in specific preparatory actions related to program implementation.
In line with previous studies, most of the assessed sociodemographic factors (i.e., age, gender, school position, school type, working experience, weekly working hours) presented nonsignificant differences in adoption intentions [18, 46, 47]. It is conceivable that these results were due to the small sample size and insufficient power. Nonetheless, many reviewed studies [21, 23, 26, 30] do not report these variables or were only analyzed descriptively. This may be because the theoretical frameworks used do not consider these variables influential in adopting an innovation [15, 29, 48]. However, it was found that participants who knew about the existence of the eHealth program were more likely to intend to adopt it. Intense exposure to a message targeting at least three audiences 鈥 the general public, public health workforce, and decision-makers [49] 鈥 is critical to increasing the likelihood of audience reception [50]. Thus, efforts to disseminate and communicate the existence of the health program are required.
Study results revealed that awareness factors (i.e., knowledge, perceived risk, and severity) did not differ significantly between groups. Although both groups showed high means scores in almost all risk and severity items (<鈥4.5 and鈥<鈥5.5, respectively), findings showed no correlation with the intention to adopt the program. The latter aligns with diverse behavioral model statements, which mention that a greater perceived risk alone may not be enough to induce intention and increase the likelihood of action [15, 51, 52]. Moreover, according to the items results, intervention developers may inform decision-makers about adolescents鈥 higher rates of alcohol consumption over other addictive substances and increase their awareness of alcohol consumption severity and its related health risks.
Concerning motivational factors, intenders were found to be more convinced of the advantages of using the eHealth program in their schools than non-intenders. Results are consistent with previous studies [22, 24, 26, 30, 48], supporting the fact that perceived beliefs and affects toward innovation influence individual acceptance and usage intentions [53]. Despite these significant differences, both groups perceived that the eHealth alcohol intervention's best advantage was its positive contribution to their school curriculum (mean 5.82, SD 0.93); nevertheless, both groups considered that A-A's effectiveness (i.e., relative advantage) over other interventions was its minor advantage (mean 4.69, SD 1.06). Perceiving a preventive service as more beneficial and cost-effective than another has shown to be the strongest motivator to adopt innovations [15, 23]. Therefore, showing eHealth programs鈥 benefits in terms of cost-effectiveness, as well as its attractiveness compared with traditional interventions is crucial for better intervention marketing and dissemination. Moreover, both groups perceived the program as beneficial in improving their students' academic achievement. The same results were found in another substance use study [25], suggesting a need to promote an association between alcohol consumption and poor school outcomes in this population.
Furthermore, personal relevance regarding using A-A obtained the highest items scores within all motivational factors for both groups. However, non-intenders perceived that adopting a program to reduce their students' alcohol consumption was less relevant and not a priority compared with intenders. It has been reported that school personnel may struggle with their perceived role responsibility in the usage of health interventions as they believe it is outside of their traditional and professional duty [7, 25, 26]. Behavioral models [54] state that even if people are aware of the health risks, maximizing personal relevance increases engagement and prompt decision to action. For this reason, reconciling the importance of delivering health education as part of the school's academic mission is a challenge that must be addressed by health intervention developers [17].
In line with prior findings, perceiving social support and stronger social norms (mainly from the counseling team, teachers, and students鈥 families) among significant others shows to be a facilitator in the adoption intention [46, 48, 53, 55]. For instance, an individual may be inspired to adopt the intervention if their peers recognize this innovation as important and advantageous [53]. These results are supported by Weinstein et al. [51], who states that people are more likely to act when relevant others view a problem as serious and suggest that action is desirable. Results revealed that non-intenders had a lower perception of self-efficacy than intenders. However, both groups reported that the easiness perception to use the program increases when the program is flexible to incorporate into the existing curriculum, not time demanding, attractive for students, and offers constant feedback and advice from the creator team. These findings are consistent with a systematic review which founds that school personnel reporting heavy workloads and time pressures may feel unable to incorporate the program into the regular curriculum and, in turn, express rejection in adopting a new practice [7, 22, 24]. Moreover, support and involvement from health promoters and, particularly, from the program team, explaining that web-based interventions are easy to administer, accessible at all times, not requiring widespread training or supervision, and are inexpensive compared to traditional person-delivered programs may foster their capability perception to use the program [27]. Therefore, being more explicit in the recognition of these advantages while diffusing the innovation is crucial for increasing the A-A program rate of adoption.
As expected from the literature [56], school personnel with a higher intention to adopt the program presented a greater willingness to plan specific actions. Therefore, motivating decision-makers to plan preparatory actions is a step further to increase the likelihood of translating intentions into actions and achieving the desired goal of adopting the intervention.
Results were consistent with Rogers' theory [15]. Intenders to adopt 1) perceived a relative advantage of the eHealth program over other traditional programs (e.g., higher positive attitudes), 2) found higher compatibility between the innovation and their role, school values, and student's needs (e.g., higher positive attitudes and personal relevance) and 3) believed that the program is easy to understand and use (e.g., higher self-efficacy) in comparison with non-intenders. Similar results have also been found in other studies about school-based health adoption programs [17, 18, 23].
Although this study found significant differences between intenders and non-intenders in most ICM constructs, the linear regression analysis showed that positive attitudes and social support were uniquely related to the intention to adopt the program. The influence of the other constructs was mediated through these two factors. This attitude-mediating nature is consistent with innovation adoption frameworks based on organizational settings, proposing this variable as a crucial mediator between external variables and individuals' acceptance of an innovation [53]. Other studies in hospital settings also found attitudes and social norms as unique facilitators explaining the variance in the adoption process [46, 55].
Strengths and limitations
A major strength of this study is the use of a mixed methods design and social-behavioral change theory-based framework, which guided the development of a questionnaire enriching the level of comprehension and contributing to theory and knowledge development through a broader context [32]. Secondly, this study leads specific adoption strategies for e-alcohol programs; to our knowledge, it is the first study identifying factors influencing the adoption of a school-based eHealth alcohol prevention program. Most diffusion literature has focused on the implementation stage, leaving aside pre- and post-implementation phases [30, 57]. Additionally, contrary to previous studies, school personnel who did not use A-A also responded to questionnaires, which gives a better comprehension and scope of the program's perceived need. Finally, this study is a valuable literature contribution to research in Hispanic cultural contexts since previous studies are not based on these countries. Accessibility to digital resources in schools remains a significant obstacle in these contexts [58]. This study better explains the needs and perceptions of stakeholders looking to promote adequate context-based eHealth implementation policies.This study presented certain limitations. First, school counselors unfamiliar with the program were not interviewed, unlike in the quantitative study. Moreover, it would be more convenient to ask participants whether their schools are already using the program rather than if they have ever heard about it. Second, although some school factors were considered (e.g., school position, type of school, availability of digital resources), future studies could benefit from a more in-depth organizational facilitators and barriers assessment. For instance, the decision-making style scales used in the current study showed an unacceptable internal consistency, which may be attributable to the few numbers of items per scale [59]. An appropriate evaluation of decision-making style (i.e., formalization and centralization constructs) would be relevant since this organizational factor is associated with the likelihood of adoption [21, 30, 47]. Additionally, although the study was representative in terms of age, gender, school type and working educational level [60, 61], the sample cannot be considered nationally representative (Andalusian school personnel national representation: 18% vs Andalusian study representation: 46%). Furthermore, we missed exploring whether participants were already using or would intend to use other traditional programs to prevent alcohol abuse in their schools. Future research should measure whether the existence of other traditional programs influences the adoption of digital innovation at schools.听Furthermore, according to the ICM, having preparatory actions strengthens the likelihood of translating intention into action, meaning these action plans come after the intention is established. Nevertheless, the structure of item statements in the questionnaire (e.g., 鈥淚 intend to inform myself about the content and effectiveness of the Alerta Alcohol program鈥) may be confusing to elucidate whether those actions are carried out pre- or post-intentional processes. In this sense, an alternative interpretation may be possible, implying that preparatory actions may also strengthen and increase a person's likelihood of intention. It may be suggested that specific action plans and intentions have a bidirectional relationship and, thus, interactive effects [20]. Further research concerning this reciprocal relationship is needed. Moreover, data was collected by a cross-sectional design; longitudinal research is needed to observe changes across the variables over time. Finally, fostering effective educational dissemination, accurate delivery of emails, and outreach systems in the study context is needed. Due to the small number of participants, the generalization of results must be interpreted with caution.
Implications of the study
Based on the study results, publicizing the health program鈥檚 existence among the public and relevant stakeholders is suggested. Encourage prioritization of alcohol prevention interventions by increasing awareness about the severity of the problem and the high rates of adolescent consumption must be addressed. Furthermore, health intervention researchers should design strategies tailored to enhance pro-innovation attitudes 鈥 for instance, fostering a relative advantage perception of using an eHealth intervention over other traditional programs and emphasizing its positive impact on student鈥檚 academic outcomes. Efforts towards recognizing the importance of delivering health education as part of the school鈥檚 mission and responsibility are relevant to facilitate adoption. Program developers must create clear guidelines explaining the content and the educator鈥檚 expected role during the sessions, as constant communication with all the school personnel is crucial to increase the perceived ease of use. Finally, ensuring a supportive environment towards the acceptance of the intervention among decision-makers and planning specific actions with the intention to use the program are also strategies for adoption promotion.
Conclusions
This study highlights the relevance of motivational factors influencing the intention of adopting an innovation. Perceiving positive attitudes, innovation attributes, social support, personal responsibility, and easiness towards the program use were critical facilitators of adoption intention. The present findings would help intervention planners and researchers develop effective adoption strategies to optimize the dissemination of computer-tailored eHealth programs in school settings by increasing the likelihood of adoption intention. Further research is needed to identify whether similar factors influence adoption, particularly technology-based health interventions.
Data availability
No datasets were generated or analysed during the current study.
Abbreviations
- A-A:
-
Alerta Alcohol
- CT:
-
Computer-tailored
- DIT:
-
Diffusion of Innovations theory
- ICM:
-
Integrated Change Model
- MMR:
-
Mixed-methods research
- MEFPD:
-
Ministry of Education, Vocational Training and Sports
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Acknowledgements
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Funding
This study is part of 鈥淎LERTA ALCOHOL An Animation-Versus Text-Based Computer-Tailored Game Intervention to Prevent Alcohol Consumption and Binge Drinking in Adolescents鈥 project. The project is financed by a grant from the Spanish Ministry of Health, Consumption and Social Welfare through the "National Plan on Drugs" program; contract 2018I016.
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EARP and HdV conceptualized and design the study. EARP and PFL collect data for the qualitative analysis. EARP developed the data analysis; HdV, PFL, MLS acted as consensus reviewers. All listed authors participated in the interpretation of the results. EARP wrote the first and subsequent drafts of the manuscript. MLS and HdV contributed to the critical revision on drafts of the manuscript before approving the final version. All authors read and approved the final manuscript.
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The study was approved by the Faculty of Health, Medicine, and Life Sciences Ethics Review Committee (FHML-REC/2022/057) of Maastricht University. Each participant provided informed consent prior to each interview. To protect privacy, personal data for contacting the participant (i.e., email and phone number) and audio recordings were deleted once the research analysis was finished.
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The authors declare no competing interests.
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Supplementary Information
Additional file 2:听Interview protocol. The file contains the interview guide which includes the used themes, questions, and prompts.
Additional file 3:听Table听6. The file shows the obtained correlations between ICM constructs and intention to adopt variable.
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Rosado-Pulido, E.A., Fern谩ndez-Le贸n, P., Lima-Serrano, M. et al. Factors that influence the adoption of a school-based eHealth alcohol prevention program among Spanish personnel: a mixed methods study. 樱花视频 25, 436 (2025). https://doi.org/10.1186/s12889-025-21574-1
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DOI: https://doi.org/10.1186/s12889-025-21574-1