Developing and validating a social media and dietary habits questionnaire using the integrated behavioural model for young adults

Abstract

Aim

To develop a structured questionnaire based on the integrated behavioural model (IBM) framework to assess social media use and dietary habits and to evaluate its validity and reliability among university students.

Materials and methods

The study targeted undergraduate students, aged 18–25 years, from universities in Riyadh, Saudi Arabia, who actively used at least one major social media platform. The questionnaire was designed using IBM constructs, focusing on five dietary habit domains potentially influenced by social media. The questionnaire was validated through expert assessment, pilot testing, and iterative revisions to enhance clarity. The final version consists of 38 items. Internal consistency was assessed using Cronbach's alpha, and construct validity was evaluated via factor analysis, including principal component extraction and Varimax rotation. The Kaiser–Meyer–Olkin measure and Bartlett's test of sphericity were used to confirm data suitability for factor analysis.

Results

The study included 401 participants (mean age: 20.75 years). Social media usage was prevalent, with 51.9% of participants reporting more than 4 hours of daily use. The 38-item scale demonstrated excellent internal consistency, with Cronbach's α values between 0.953 and 0.956. Factor analysis confirmed construct validity, identifying eight factors related to dietary behaviours, including dietary habits, attitudes, and knowledge. Each domain exhibited strong factor loadings, supporting the integrity of the construct.

Conclusion

Our theory-based questionnaire may serve as a reliable and valid tool to examine the association between social media usage and dietary behaviours in young adults. Future research should validate the instrument in more diverse populations to enhance its applicability.

Keywords

Social media diet dietary habits behavioural integrated behavioural model university students

Introduction

Health behaviours significantly shape the overall well-being of an individual, and in today's digital age, social media has become a major influence on daily life.¹ Individuals can spend an average of 4 hours per day on social media, with women typically engaging more than men.² A study by Lim et al. involving 165 young adults aged between 18 and 24 years revealed that 89.1% used social media three or more times a day, underscoring its strong influence among this age group.³ Platforms such as Instagram, Snapchat, X, Facebook, and WhatsApp enable users to communicate, share information, and connect globally, playing a crucial role in shaping behaviours and interactions.^4,5 These platforms are particularly impactful for adolescents and young adults, who use them to form relationships and express personal ideas.⁶ With the rapid rise of technology, social media now plays a vital role in daily life, influencing not only interpersonal interactions but also lifestyle habits, including health-related behaviours.⁷

Nutrition is a popular topic on social media; however, misinformation is widespread due to a lack of professional oversight.² Social media platforms provide easy access to food-related information, including recipes and diet trends, which may influence individuals to adopt restrictive eating patterns, such as eliminating entire food groups without adequate understanding of their nutritional needs.^8–10 Prolonged use of these platforms (i.e. more than 2 h daily) has also been associated with unhealthy dietary patterns—such as an increased preference for calorie-dense, nutrient-deficient foods—and reduced physical activity.¹¹ Both behaviours elevate the risk of developing chronic conditions such as metabolic syndrome.^12,13 For instance, individuals exposed to food advertisements on social media one to three times a week were more likely to engage in unhealthy eating behaviours including frequent snacking and fast food consumption.¹¹ Furthermore, emerging evidence highlights both positive and negative effects of social media on nutritional choices, including the promotion of diet trends, shaping of body image perceptions, and contribution to disordered eating behaviours such as restrictive dieting and binge eating.^9,10,14

In addition to its role in nutrition, social media has been linked to various other health behaviours. Research shows that it can affect physical activity levels, with some studies suggesting reduced exercise owing to increased screen time, while others highlight the use of social media as a tool for promoting physical activity interventions and peer support.^15,16 Social media is also used to spread awareness about the risks of substance use and has potential in both prevention and cessation efforts.¹⁷ Furthermore, platforms may influence stress management and emotional well-being, with both supportive communities and negative comparisons contributing to mental health outcomes.¹⁸ Notably, emerging evidence also indicates a weak but statistically significant positive association between social media use and emotional eating among women, indicating that social media may subtly impact eating behaviours through emotional or psychological pathways.¹⁹ These findings collectively underscore the wide-reaching influence of social media on lifestyle behaviours beyond diet, emphasising its role in health promotion and risk across multiple domains.

Among the various types of content, cooking videos have become a popular, accessible way to learn about food, health, and nutrition, influencing viewers’ choices, and eating intentions.²⁰ Existing literature, including a scoping review by Chung et al., highlights the influence of social media on adolescents’ dietary behaviours, demonstrating both beneficial effects, such as increased consumption of fruit and vegetables, and detrimental impacts, such as unhealthy eating patterns associated with fast food advertising.²¹ However, most evidence is observational and does not establish causality. Nonetheless, these findings emphasise the significant role social media plays in shaping dietary patterns.

Waszak et al. analysed widely circulated health-related web links on Polish social media platforms over the period from 2012 to 2017, revealing that 40% contained misinformation, with vaccine-related content being particularly affected.²² Over 450,000 shares involved misleading information, and more than 20% of harmful links originated from a single source. Similar trends have been observed in the domain of nutrition. Fernández et al. (2025) reported the widespread dissemination of nutrition-related misinformation across social media platforms,²³ often driven by non-experts and influencers. Supporting this, Patwardhan et al. (2024) found that social media exposure shapes young adults’ perceptions of food healthiness and their dietary behaviours.²⁴ These findings highlight the urgent need for evidence-based, theory-driven strategies, such as a structured questionnaire, to understand and address the impact of online health misinformation on eating behaviours.

Several behavioural theories have been used to explain health and dietary behaviours, including the Health Belief Model, the Theory of Planned Behaviour, and the Social Cognitive Theory. While these models offer valuable insights, they often overlook factors such as environmental constraints, habit formation, and the gap between intention and behaviour.²⁵ To address these limitations, the integrated behavioural model (IBM) provides a more comprehensive framework for analysing motivations and predicting health-related behaviours, focusing on attitude, intention, subjective norms, and perceived control.²⁵ The IBM also highlights four additional factors that influence the translation of intention into behaviour: knowledge, environmental constraints, behavioural salience, and habit formation. Knowledge equips individuals to act while reducing environmental barriers, ensuring behaviours are feasible. Salience influences motivation, and once behaviours become habits, intention plays a smaller role.²⁵

As highlighted in previous studies,^21,26 the broad impacts of social media on college students’ health, academic performance, and social interactions necessitate a holistic examination of its influence on dietary behaviours. While prior research has investigated the general influence of social media on health perceptions and behaviour,^1,27 relatively few studies have examined its influence on dietary habits through a theory-based approach, especially among college students.²⁸ Moreover, there is a lack of validated instruments grounded in behavioural theory to assess this relationship.

To address this gap, the current study developed a structured questionnaire based on the IBM framework to assess the relationship between social media use and dietary behaviour. The instrument captures key dimensions, such as knowledge, motivation, and behavioural skills, which are central to the IBM framework. Its validity and reliability were rigorously evaluated to ensure its consistency and appropriateness in measuring the targeted behaviours.

Materials and methods

Participants

An online cross-sectional survey was conducted between January and March 2024 using a targeted sample of college students in Riyadh, Saudi Arabia. Participants were undergraduate students (boys and girls), aged 18 to 25 years, studying at one of the universities in Riyadh city, and engaging with one or more of the social media platforms. Students who had graduated or could not read Arabic were excluded. To validate the questionnaire and focus on the young adults residing in Riyadh, the sample size was estimated using data from the General Authority for Statistics of Saudi Arabia (2024)²⁹ using the following epidemiological equations³⁰:

\frac{N p (1 - p) z 2 1 - α / 2}{d 2 (N - 1) + p (1 - p) z 2 1 - α / 2},

A confidence level (z) of 1.96 for 95%, an assumed proportion (p) of 50%, a margin of error (d) of 0.05, and a total population size (N) of 600,580. This resulted in an estimated minimum sample size of 384 participants. Participants were recruited through convenience sampling using a survey link distributed via social media platforms (e.g. WhatsApp, Telegram, X). Subsequently, a snowball sampling method was employed to extend the survey distribution to additional potential participants.

Ethical considerations

Ethical approval for the study was granted by the Institutional Review Board of Princess Nourah bint Abdulrahman University in Riyadh (approval number: 23–1112; approval date: 3 January 2024). Informed consent was obtained electronically; participants indicated their consent by selecting the ‘Agree’ option prior to beginning the survey. Participants had the right to choose whether to complete the survey, and any incomplete responses were excluded from the analysis.

Demographic data

Demographic information, including age, sex, marital status, academic discipline (including type of university and college), residence during studies, health status, any specific diet plan followed, and self-reported anthropometric data such as weight (kg) and height (cm), was collected. Body mass index (BMI, kg/m²) was subsequently calculated and classified to determine weight status: underweight (BMI < 18.5 kg/m²), normal weight (BMI 18.5–24.9 kg/m²), overweight (BMI 25–29.9 kg/m²), and obese (BMI ≥ 30 kg/m²). Moreover, participants’ social media usage was evaluated by requesting them to report their average daily usage time, with response options including: < 1 h, 1–2 h, 3–4 h, and >4 h.

Questionnaire development

The questionnaire was developed using previous instruments designed for college students.^4,31 Additional questions were incorporated to explore the specific research aims of the study. Ideas were carefully extracted and adapted to align with the IBM model, ensuring the framework effectively served the study objectives. In the end, we had a customised questionnaire consisting of eight constructs, each comprehensively tailored to meet the research aims. The flow diagram presented in Figure 1 illustrates the questionnaire development process. The questionnaire was first created in English and later translated into Arabic by the research team (Supplementary material). To ensure the accuracy of Arabic translation, the questionnaire was translated back into English by an independent translator, and the two versions were compared for consistency.

Figure 1.

Flow diagram illustrating the questionnaire development process.

Integrated behavioural model constructs

First construct: dietary habits

This section aimed to assess five domains that may be affected by social media usage³²: meal regularity, healthy diet adherence, dining out habits, meeting daily dietary recommendations, and trying new recipes. Participants rated the frequency of these behaviours on a scale, ranging from ‘never’ to ‘always’. Sample items included: ‘I regularly consume my main meals (breakfast, lunch, dinner)’, ‘I follow healthy dietary plans found on social media’, and ‘I experiment with new healthy recipes from social media’.

Second construct: attitude

This section aimed to evaluate participants’ attitudes toward the influence of social media on their dietary habits. Responses to five statements were collected using a Likert scale ranging from 1 (‘strongly disagree’) to 5 (‘strongly agree’). Representative examples of the items are ‘I believe social media has emphasised the importance of regular meal consumption’ and ‘I feel social media can assist me in preparing healthy food recipes at home’.

Third construct: perceived norms

This section consisted of six questions evaluating the influence of social media on dietary habits. Responses to five statements were collected using a Likert scale ranging from 1 (‘strongly disagree’) to 5 (‘strongly agree’). Representative examples of the items are: ‘Do you believe that following individuals in your social circle on social media encourages regular meal consumption?’ and ‘Do you believe that influencers’ opinions on social media influence your choice of healthy dishes at restaurants?’

Fourth construct: personal agency

This section included five questions assessing respondents’ confidence in the role of social media in influencing their dietary habits. Responses to five statements were collected using a Likert scale ranging from 1 (‘not confident’) to 5 (‘strongly confident’). Representative examples of the items are: ‘How confident are you that social media will assist you in consuming an appropriate amount of nutrients?’ and ‘How confident are you that social media will support you in maintaining regular meal consumption?’

Fifth construct: intention

This section consisted of five questions assessing respondents’ intentions to engage in healthy behaviours through social media. Responses to five statements were collected using a Likert scale ranging from 1 (‘strongly disagree’) to 5 (‘strongly agree’). Representative examples of the items are ‘I want to use social media to search for healthy food recipes’ and ‘I want to use social media to improve my regular consumption of main meals’.

Sixth construct: salience

This section assessed the perceived importance of respondents’ dietary intentions influenced by social media. Responses to five statements were collected using a Likert scale ranging from 1 (‘strongly disagree’) to 5 (‘strongly agree’). Representative examples of the items are ‘I believe it is important to have breakfast every day because of what I see on social media’ and ‘Seeing ideas and recipes for healthy home-cooked meals on social media encourages me to prioritise cooking at home’.

Seventh construct: skills

This section assessed respondents’ self-perceived ability to engage in nutrition-related behaviours via social media. Using a binary scoring system (1 = Yes, 0 = No or I am not sure), participants answered four questions. Sample items included: ‘I have the necessary skills to prepare healthy meals at home based on what I have learned from social media’ and ‘I have the necessary skills to follow a healthy diet based on recommendations on social media’.

Eighth construct: knowledge

This section evaluated respondents’ perception of acquiring essential nutritional knowledge through social media. Participants answered four questions using a binary scoring system (1 = Yes, 0 = No or I am not sure). Sample items included: ‘Is it important to consume a variety of nutrient-rich foods to ensure proper nutrient intake?’ and ‘Do you consider grilled vegetables with olive oil a healthy recipe?’

Validation process of the developed questionnaire

Validity

The validation process followed the methodology outlined in previous studies.³³ To verify the accuracy and relevance of the questionnaire, it was assessed by four experts in nutrition and health education, each with over 10 years of professional experience. Each item was rated on a scale from 1 to 5. Items receiving a rating of 4 or 5 were deemed satisfactory and required no modifications. For items rated 3 or below, experts provided justifications and suggestions for improvement. All feedback was carefully evaluated and incorporated, with particular attention given to refining language and enhancing clarity. Following expert feedback, several linguistic modifications were made to enhance clarity and precision. In the dietary habits section, question 1 was revised to explicitly specify main meals (breakfast, lunch, and dinner), and definitions for key concepts, such as healthy diets, healthy recipes, and healthy restaurants, were added to improve comprehension. In the perceived norms domain, the term ‘social media contacts’ was reworded for greater clarity. Similarly, in the attitude section, question 3 was rephrased to enhance readability. Additionally, two questions (questions 2 and 4) in the knowledge section were revised, and definitions for key domains and relevant platforms were incorporated to improve understanding. Minor linguistic refinements were applied throughout the questionnaire to ensure overall coherence and readability.

Following expert validation, the questionnaire underwent a pilot test with a small sample of college students (n = 15) who satisfied the study's inclusion criteria. In-person interviews were carried out, during which participants were asked to assess the clarity of each item and provide comments or suggestions. Their feedback led to further refinement. It is important to note that no items were removed or merged based on the expert review or pilot testing; all modifications were limited to linguistic improvements and enhanced clarity. Upon finalisation, the questionnaire was digitised and formatted for electronic distribution.

The final questionnaire was assessed for construct validity and internal consistency, with a total of 401 participants enrolled in the study. Exploratory factor analysis with varimax rotation was used to assess construct validity. The Kaiser–Meyer–Olkin (KMO) measure and Bartlett's test of sphericity were applied to evaluate sampling adequacy and the strength of correlations among the variables. Construct validity refers to the extent to which the items of an instrument correspond with the theoretical construct they aim to measure. It evaluates the extent to which the intended independent variable (construct) is represented by the observed or proxy variable (indicator).³⁴ Factor analysis was employed to assess construct validity when an indicator variable, comprising multiple items under different domains, was used.³⁵ The number of factors selected was based on the inflection point observed in the scree plot and the clarity of factor interpretation.

Reliability

Reliability testing was conducted to ensure that the questionnaire consistently yielded expected results and exhibited internal homogeneity. Cronbach's α was used to assess the internal consistency of the scales and the degree of interrelation among the items within each scale. A Cronbach's α value between 0.5 and 0.7 indicated acceptable consistency, while values ≥ 0.8 indicated excellent consistency.³⁶

Statistical analysis

Data were analysed using IBM SPSS Statistics version 26.0 for Windows (IBM Corp., Chicago, IL, USA). Descriptive statistics, including mean, standard deviation, median, interquartile range, and frequency distributions, were calculated to summarise both categorical and continuous variables. The internal consistency of the dietary habits scale was evaluated using Cronbach's α. To examine the relationships between individual items and their corresponding subscale scores, Pearson's correlation coefficients were computed. Convergent validity was also assessed. To determine the construct validity of the scale, factor analysis was conducted. The suitability of the 38 items for factor analysis was first evaluated using the correlation matrix, the KMO measure of sampling adequacy, and Bartlett's test of sphericity. Factor extraction was performed using the principal component method, with the number of extracted factors limited to eight. Eigenvalues were calculated to assess the proportion of variance explained by each factor. Varimax rotation was applied to obtain a clearer factor structure with eight rotated components. Statistical significance was established at p-values ≤ 0.05, and 95% confidence intervals were reported to indicate the precision of the estimates.

Results

Sociodemographic characteristics

A total of 401 participants, with a mean age of 20.75 years, responded to this study. Of them, 74.6% were women, and 96.3% were unmarried. More than 50% of the participants had a normal BMI. The majority of participants were from the Government University, with 37.9% from the health department, 23.4% from the humanities department, and 32.9% from the sciences department. The remaining participants were from the College of Community Service and Applied Studies. More than 90% of the participants were residing off-campus during their studies, and only 10% had health problems. Only 11.7% of the participants were following a specific dietary plan. Approximately 51.9% of the participants were spending > 4 h on social media platforms (Table 1).

Table 1.

Descriptive characteristics of the study participants (N = 401).

Characteristics	Number/ Mean	(%)/(SD)
Age (Years)^a	20.75	(1.86)
Sex Men Women	102 299	(25.4) (74.6)
Marital status Married Unmarried	15 386	(3.7) (96.3)
BMI Underweight Normal Obese	63 236 102	(15.7) (58.9) (25.4)
Type of college Health sciences Humanities Scientific Community service and applied studies	152 94 132 23	(37.9) (23.4) (32.9) (5.7)
Type of University Government Private	391 10	(97.5) (2.5)
Current residence during studies On-campus Off-campus	23 378	(5.7) (94.3)
Any medical or health problems Yes No	40 361	(10.0) (90.0)
Follow a specific dietary plan Yes No	47 354	(11.7) (88.3)
Number of hours spent using social media
> 4 h	208	(51.9)
3–4 h	121	(30.2)
1–2 h	61	(15.2)
< 1 h	11	(2.7)

SD: standard deviation; BMI: body mass index (kg/m²).

Reliability

The 38-item scale with eight domains was used to assess the questionnaire's reliability among the 401 study participants. The mean (standard deviation) of the responses to these 38 items ranged between a minimum of 1.14 (0.88) for an item under the Salience domain, ‘The constant exposure to diet-related content on social media influences my overall dietary choices’, and a maximum of 3.69 (1.29) for an item under the Knowledge domain, ‘Is it important to know how many calories are in dishes before ordering from the menu in the restaurant?’ Reliability refers to an instrument's (scale's) capacity to consistently measure a specific attribute and the extent to which its items are conceptually aligned. The Cronbach's α values ranged from 0.953 to 0.956 (for all 38 items), reflecting the α value when an item was deleted, and these values were statistically significant (Table 2).

Table 2.

Item analysis of the eight constructs of the integrated behavioural model (IBM) internal consistency.

Domains and items	Mean (standard deviation)	Correlated item total correlation	Alpha if item deleted
Dietary habits: 1. Eat main meals regularly.2. Follow healthy diets you see on social media.3. Eat out at healthy restaurants you see on social media per week.4. Maintain your adequate daily nutritional requirement because of nutrition Apps.5. Prepare new healthy recipes you find on social media at home.	3.06 (1.25)2.46 (1.18)2.27 (1.23)2.28 (1.34)2.73 (1.33)	.402.498.373.384.524	.955.955.955.955.954
Attitude: 1. I think social media has made me feel that it is important to eat regularly.2. I feel that social media can help me prepare healthy food recipes at home.3. When I see healthy restaurants and foods on social media, I like to try them.4. I think that social media has made me realise how important it is to meet my daily nutritional requirements.5. Social media has positively influenced my dietary habits.	3.25 (1.27)3.41 (1.28)2.79 (1.26)3.55 (1.27)3.31 (1.31)	.657.729.602.731.748	.954.953.954.953.953
Perceived norms: 1. Your social media contacts encourage you to eat regularly through social media posts.2. Your social media contacts negatively affect how you feel about your weight.3. Following your contact through social media applications will enhance daily nutritional intake.4. Social media influencers have affected your choice to eat at healthy restaurants.5. Your social media contacts assist you in preparing healthy food recipes at home.6. Your social media contacts assist you in adopting a healthy diet.	2.57 (1.37)2.67 (1.35)2.51 (1.24)2.90 (1.30)2.83 (1.34)2.83 (1.32)	.557.424.569.607.637.629	.954.955.954.954.954.954
Personal agency: 1. Social media will help you consume an adequate intake of nutrients.2. Social media will help you prepare healthy meals at home.3. Social media will influence your regular consumption of meals.4. Your decision to dine at a healthy restaurant is influenced by social media posts.5. Your decision to eat healthy is influenced by information and recommendations from social media.	2.88 (1.32)3.43 (1.26)3.09 (1.28)2.91 (1.31)3.16 (1.29)	.682.750.742.649.693	.953.953.953.954.953
Intention: 1. I intend to use social media to look for healthier food recipes to prepare.2. I intend to use social media to adopt a healthy diet.3. I intend to use social media to improve eating main meals regularly.4. I plan to try new healthy restaurants and foods I see on social media.5. I intend to use social media apps to remind myself to meet my adequate nutrition requirements.	3.48 (1.37)3.38 (1.32)3.47 (1.28)3.36 (1.31)3.30 (1.29)	.742.767.772.702.715	.953.953.953.953.953
Salience: 1. I think it's important to eat breakfast every day because of what I see and read on social media2. The constant exposure to diet-related content on social media influences my overall dietary choices3. Seeing healthy homemade meal ideas and recipes on social media platforms encourages me to place greater importance on cooking at home.4. When I use social media apps, I become more aware of how important it is to meet my daily food needs.	1.24 (0.85)1.14 (0.88)1.17 (0.87)1.33 (0.84)	.295.315.368.419	.955.955.955.955
Skills: I have the necessary skills to1. Prepare healthy meals at home inspired by what I have learned from social media.2. Follow a healthy diet based on recommendations on social media3. Find and try new healthy restaurants based on social media posts4. Find and try nutritional apps for proper nutritional intake	1.63 (0.69)1.30 (0.89)1.37 (0.81)1.69 (0.65)	.378.270.389.450	.955.956.955.955
Knowledge: 1. Is it important to consume a variety of nutrient-rich foods to ensure proper nutrient intake for the day?2. Are oven-roasted vegetables with a small amount of olive oil (5 ml) considered a healthy recipe?3. Is it important to know how many calories are in dishes before ordering from the menu in the restaurant?4. Eating my main meals regularly is beneficial to my health	3.51 (1.41)3.49 (1.29)3.61 (1.29)3.58 (1.28)	.570.720.758.745	.954.953.953.953

Table 3 shows the descriptive statistics (means, standard deviations, medians, and interquartile ranges) of the eight domains and their internal consistency reliability values. The Cronbach's α for these domains ranged from 0.701 for Knowledge to 0.935 for Intention. The overall internal consistency for all domains was 0.949, with a 95% confidence interval of 0.940 to 0.960.

Table 3.

Descriptive statistics, reliability, and internal consistency (Cronbach’s α) of the eight constructs and the overall dietary habits scale.

Domain	Mean (SD)	Median (IQR)	Cronbach's α (95% CI)
Dietary habits	12.8 (4.8)	12.0 (7.0)	0.813 (0.78 to 0.84)
Attitudes	16.3 (5.3)	17.0 (6.0)	0.892 (0.87 to 0.91)
Perceived norms	17.0 (4.8)	17.0 (8.0)	0.889 (0.87 to 0.91)
Personal agency	15.5 (5.4)	16.0 (7.0)	0.895 (0.88 to 0.91)
Intention	17.0 (5.9)	19.0 (7.0)	0.935 (0.92 to 0.94)
Salience	14.2 (4.6)	15.0 (5.0)	0.899 (0.88 to 0.91)
Skills	4.9 (2.6)	4.0 (5.0)	0.749 (0.71 to 0.79)
Knowledge	6.0 (2.2)	6.0 (4.0)	0.701 (0.65 to 0.75)
All domains	–	–	0.949 (0.94 to 0.96)

SD: standard deviation; IQR: interquartile range; 95% CI: 95% confidence interval.

Validity

The correlation analysis of the 38 items in the instrument was statistically significant (Table 4). The factor loadings for each domain were as follows: dietary habits (0.384–0.796), attitude (0.388–0.725), perceived norms (0.599–0.796), personal agency (0.279–0.645), intention (0.752–0.841), salience (0.371–0.652), skills (0.661–0.762), and knowledge (0.659–0.702). The analysis shows no multicollinearity as the determinant of the correlation matrix was 0.0001, exceeding the required threshold of 0.00001. In other words, all 38 items of the scale showed moderate correlations, with none being excessively high, indicating that no items needed to be removed. Each domain exhibited strong factor loadings for its respective items, supporting the integrity of the construct.

Table 4.

Factor loadings of the eight constructs of the IBM.

Domains and their items	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5	Factor 6	Factor 7	Factor 8
Dietary habits:Item1Item2Item3Item4Item5				0.3840.7550.7640.7960.741
Attitude:Item1Item2Item3Item4Item5			0.7250.6810.3880.6990.702
Perceived norms:Item1Item2Item3Item4Item5Item6		0.7540.7060.7760.5990.7860.796
Personal agency:Item1Item2Item3Item4Item5							0.4250.2790.4500.6450.572
Intention:Item1Item2Item3Item4Item5	0.8260.8270.8410.7520.754
Salience:Item1Item2Item3Item4								0.6520.4140.3710.409
Skills:Item1Item2Item3Item4					0.6610.7620.6960.739
Knowledge:Item1Item2Item3Item4						0.6910.6590.6830.702

IBM: integrated behavioural model.

The analysis showed a KMO measure of 0.937 and demonstrated a statistically significant Bartlett's test of sphericity (p < 0.0001), suggesting that the correlation matrix differs significantly from an identity matrix. In the process of extracting factors, Eigenvalues and the percentage of variance explained by each factor were examined. The eight extracted factors together accounted for 64.53% of the total variance, with the first factor alone contributing 38.99%. Factor loadings for the 38 items across the eight extracted factors were obtained; higher absolute loading values indicated a stronger contribution of the factor to the corresponding variable. The loading indicated that the eight factors each contributed to their respective items. The scree plot, which graphs the Eigenvalues against the factors, is useful for identifying the appropriate number of factors. We observed that the curve began to flatten approximately between factors 8 and 9. The eight factors were dietary habits, attitudes, perceived norms, personal agency, intention, salience, skills, and knowledge (Figure 2).

Figure 2.

Scree plot showing the relation between the Eigenvalues and factor extraction.

Discussion

This study focused on the development of a theory-based questionnaire, grounded in the IBM, designed to assess the potential influence of social media use on dietary behaviours. IBM was selected because it integrates key constructs from established theories, such as the Theory of Planned Behaviour and Social Cognitive Theory, and is recognised as one of the most comprehensive frameworks for predicting health-related behaviours. Unlike the previous models, IBM provides a more holistic approach by considering not only behavioural intention but also environmental constraints, habit strength, and personal agency,²⁵ making it particularly relevant for evaluating the impact of social media usage on dietary habits.

The questionnaire was developed to explore the impact of social media use on dietary habits across five key domains: meal regularity, healthy dietary adherence, dining out habits, meeting daily dietary recommendations, and trying new recipes.³² These domains were selected based on their susceptibility to social media influence. To comprehensively evaluate the correlation between social media usage and eating behaviours, the questionnaire incorporated key constructs from the IBM model, including attitude, perceived norms, personal agency, intention, salience, skills, and knowledge.²⁵

The study employed a rigorous process for questionnaire development and validation, ensuring the final version was well-established. To enhance its accuracy and relevance, the questionnaire was reviewed by four experts in nutrition and health education. Its reliability was then assessed among young adults, a population known for high exposure to screen time.^4,32 Various validity assessment methods were applied, including content validity, pre-testing, internal consistency reliability, construct validity, and exploratory factor analysis.^29,34 All validation steps followed established methodologies from previous studies,^33,37 confirming the questionnaire as a valid and reliable tool.

The questionnaire was administered to a homogeneous population in terms of education level and marital status, consisting of university students with a mean age of 20.75 years. More than 90% of the participants resided off-campus, which may have influenced their food choices and lifestyle behaviours as on-campus students typically have more structured meal options. This population exhibited a low level of adherence to structured eating habits, a concern given that young adults in this age group spend a significant amount of time in environments where such influences shape their health awareness and dietary behaviours.³⁸ However, sample homogeneity limits the generalisability of these findings. Students from similar backgrounds may exhibit specific behavioural patterns shaped by their academic and social environment, which may not reflect those of the broader population. Therefore, caution is warranted when applying these findings to other demographic groups, such as non-students, older adults, or individuals with different socioeconomic or cultural backgrounds. Additionally, a high level of social media usage was observed, with 51.9% of students spending over 4 hours daily on these platforms, suggesting substantial exposure to food-related content that may influence dietary behaviours. This finding is consistent with prior studies reporting widespread social media use among Arab and international university students,^39,40 as well as evidence linking such exposure to food choice behaviours.^24,41

The reliability of the 38-item scale, as measured by Cronbach's α, was high (0.953–0.956), indicating strong internal consistency. Domain-specific reliability values ranged from 0.701 (knowledge) to 0.935 (intention), demonstrating acceptable internal consistency across all domains.³⁶ The knowledge domain exhibited the lowest reliability, which aligns with previous studies.^42–44 This may be attributed to the fact that constructs such as attitudes, intentions, or perceived norms tend to measure subjective perceptions and are often more homogeneous in content. In contrast, knowledge is more fact-based, with right-or-wrong answers, making it difficult to represent with consistent internal patterns.^45,46 Furthermore, Cronbach's α values ranging from 0.5 to 0.8 (mostly between 0.6 and 0.7) have been reported for studies that measure nutrition knowledge.³⁹ It is worth noting that a Cronbach's α for scales with fewer than 10 items may be influenced by some degree of variation in responses to the subscale items.⁴⁴ In general, high reliability scores indicate that the instrument can consistently measure social media behaviour and dietary habits, supporting its applicability in similar populations.³⁶

Furthermore, the factor analysis provides strong support for the structural validity of the developed questionnaire, with each of the eight domains demonstrating appropriate factor loadings. Dietary habits, attitude, perceived norms, and intention revealed strong factor loadings (>0.7 for most items), indicating that these constructs are well-defined and contribute to our understanding of social media usage and eating behaviour. Relatively lower loadings were found in the personal agency domain, especially for certain items (e.g. 0.279), which may indicate that participants’ perceptions of their control over their eating habits vary. Similarly, salience had moderate loadings (0.371 and 0.652), suggesting that although this construct is important, its influence may be less pronounced. These findings align with previous research on health behaviour models, which emphasise the role of attitude, intention, and perceived norms in shaping dietary choices.^47–49 The high loadings for skills and knowledge further support the idea that nutrition-related competencies are crucial for behaviour change. The data structure was appropriate for identifying underlying constructs, as demonstrated by the KMO value (0.937) and the significant Bartlett's test (p < 0.0001), which validated the sufficiency of the factor analysis. Overall, our results confirmed that the questionnaire is well structured and captured the main aspects of social media influences on dietary behaviour among the target population.

Strengths and limitations

To the best of our knowledge, this is the first study to apply a well-recognised behavioural model to explore and predict the relationship between social media use and dietary habits among young adults from Saudi Arabia. A key strength of the current study is the development of a validated, reliable, theory-based survey instrument that aligns with an applied health behaviour model. This tool can be adopted by other researchers to assess the effect of social media use on dietary habits across various populations and provide practical insights for designing interventions. Policymakers and health educators can use IBM constructs, such as attitudes, perceived norms, and behavioural control, to develop targeted campaigns, while practitioners can tailor counselling and digital health messages to address specific beliefs and influences.

However, a notable limitation of the current study is the homogeneity of the sample, which consisted solely of university students from a specific background. This may limit the external validity and generalisability of our findings to other age groups or populations with different social media usage patterns and dietary behaviours. Additionally, the use of convenience sampling further restricts the representativeness of the sample. The instrument demonstrated good internal consistency, as indicated by a satisfactory Cronbach's α obtained with input from four health experts. Future studies should consider involving a larger and more diverse panel of experts to strengthen content validity. Additionally, test–retest reliability in this study was not assessed; internal consistency was measured as a widely accepted alternative.³⁰ Another limitation is the absence of open-ended questions, which could have provided richer qualitative insights to support subjective analysis and cross-validation of the instrument. Future research should validate this instrument in more diverse populations and examine its performance in longitudinal and intervention-based studies to confirm its broader applicability. Such efforts could inform the development of national-level policy and health programs aimed at improving dietary habits across diverse demographic groups.

Conclusion

In conclusion, the structured 38-item questionnaire we developed based on IBM constructs may serve as a reliable and valid instrument to examine the correlation between social media use and dietary behaviours among young adults. The questionnaire exhibited high internal consistency (Cronbach's α: 0.701–0.935) and solid construct validity, supporting its reliability in capturing key behavioural constructs. Our findings indicate that the IBM model is useful for instrument development in this context. It can facilitate future research, track behaviour, and promote healthy eating habits in today's digital world.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076251374136 - Supplemental material for Developing and validating a social media and dietary habits questionnaire using the integrated behavioural model for young adults

Supplemental material, sj-docx-1-dhj-10.1177_20552076251374136 for Developing and validating a social media and dietary habits questionnaire using the integrated behavioural model for young adults by Nahla Mohammed Bawazeer in DIGITAL HEALTH

Footnotes

Acknowledgements

The author would like to thank all the experts who participated in the validation process and all participants for their time and contributions to the reliability assessment of the study. Special thanks go to Sara Almalki, Ruba Alanazi, Rimaz Alamri, Rana Alanzi, and Raghad Alhanaya for their valuable assistance with data collection.

ORCID iD

Nahla Mohammed Bawazeer

Ethical considerations

The Institutional Review Board of Princess Nourah bint Abdulrahman University in Riyadh reviewed the protocol and approved the study (approval number: 23–1112; approval date: January 3, 2024).

Consent to participate

All participants gave informed consent by pressing the ‘Agree’ button on the survey. Participants had the option of not completing the survey, and their incomplete data were not used in the analysis.

Author contributions

NB contributed to conceptualisation, supervision, data analysis and interpretation, and manuscript writing.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Deanship of Scientific Research, Princess Nourah Bint Abdulrahman University, (grant number PNURSP2025R369).

Declaration of conflicting interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

Data can be made available upon request.

Supplemental material

Supplemental material for this article is available online.

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