Nutritional myths in adolescents and young adults with type 1 diabetes: a pilot study

Abstract

Background

The global prevalence of type 1 diabetes mellitus (T1DM) is steadily increasing, particularly among children and young adults. Health-related myths can significantly influence patients’ dietary behaviors and treatment adherence, thereby compromising disease management and metabolic outcomes.

Methods

This cross-sectional study included 190 adolescents and young adults with T1DM attending a pediatric endocrinology outpatient clinic. Data on demographics, BMI, HbA1c values, and nutrition-related myths were collected through face-to-face interviews. Statistical analyses, including chi-square tests and Spearman correlation coefficients, were performed using SPSS 22.0 software. Logistic regression analysis was conducted to identify independent predictors of metabolic control status (p < 0.05).

Results

Participants had a mean diabetes duration of 7.5 ± 4.63 years, with a mean HbA1c of 7.9 ± 1.44%. Approximately 27.0% of adolescents and 20.0% of young adults were overweight or obese. Only 29.0% of individuals had good metabolic control (HbA1c <%7), while 71.0% had HbA1c ≥ 7. An increase in diabetes duration was found to elevate the risk of poor metabolic control by 1.107 times, whereas a higher total number of answers was associated with a 0.696-fold decrease in this risk (p < 0.05).

Conclusion

Improved knowledge about nutrition myths is associated with better metabolic control among adolescents and young adults with T1DM. Structured education programs tailored to this population may contribute to improved glycemic outcomes. A multidisciplinary team approach is essential to effectively deliver educational content and reinforce evidence-based dietary practices.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40795-025-01115-0.

Introduction

Type 1 diabetes mellitus (T1DM) is an autoimmune condition characterized by the destruction of pancreatic β-cells, resulting in partial or complete insulin deficiency [1]. Although T1DM can occur at any age, it is most prevalent in children and young adults, and its incidence is increasing globally, including in Turkey [2, 3]. According to the International Diabetes Federation, in 2022, approximately 8.75 million people had T1DM worldwide, of whom 1.52 million were under the age of 20. National data from Turkey reported 29,000 individuals under 20 years of age diagnosed with T1DM in the same year [4].

Effective diabetes management requires daily insulin therapy, regular blood glucose monitoring, and structured education. These practices are particularly challenging during childhood and adolescence but are essential for preventing complications and maintaining health [5, 6]. However, access to comprehensive diabetes education—particularly in areas such as insulin use, nutrition, and physical activity—remains limited in many countries, especially among socioeconomically disadvantaged populations [3]. Evidence indicates that strict glycemic control reduces the risk of both microvascular and macrovascular complications [7–9]. The American Diabetes Association (ADA) and the International Society for Pediatric and Adolescent Diabetes (ISPAD) define good metabolic control as an HbA1c value below 7% [9, 10].

Myths are defined as stories shared by a group of people that form a part of cultural identity. Health-related myths have a strong influence on individuals’ lives, including the way they live and the way they seek treatment for diseases. Therefore, understanding false beliefs and misconceptions about diseases such as diabetes and changing these perceptions considering scientific data is essential to providing optimal care and health education to patients and healthy individuals [11]. Additionally, the increasing prevalence of diabetes causes diabetes and nutrition therapy to become more visible and popular. The recent surge in social media usage and related posts may lead to the proliferation of myths alongside positive and negative information about nutrition [12, 13].

Health-related myths, which often originate from cultural traditions, can influence treatment-seeking behaviors and health-related decisions. Misconceptions regarding diabetes and its nutritional management are widespread and may hinder optimal care [11]. The growing prevalence of diabetes and the increased visibility of nutrition-related content on social media platforms have contributed to the spread of both accurate and inaccurate information [12, 13]. Although rapid access to nutrition information is beneficial, it also facilitates the spread of misinformation [14–16]. Misconceptions may shape dietary behaviors and negatively affect metabolic outcomes in individuals with diabetes [17–19]. Therefore, assessing the nutritional beliefs of patients with T1DM is critical for identifying and correcting misinformation. This study aims to determine the frequency of nutritional myths in adolescents and young adults with T1DM and to investigate the relationship between these myths and metabolic control.

Methods

Study plan and participants

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Clinical Research Ethics Committee of Cerrahpaşa Medical Faculty (Approval No: E-83045809-604.01.02-28243). Written informed consent was obtained from all participants and from parents or guardians of individuals under 18 years of age. The study enrolled 190 adolescents and young adults with a confirmed diagnosis of T1DM who attended the Pediatric Endocrinology Outpatient Clinic at Cerrahpaşa Medical Faculty. The G*Power software was used to determine the required sample size. According to the results of analysis confidence level of 80%, response distribution of 50% and accepted margin of error of 5%, we calculated a sample size of 156.

A structured face-to-face questionnaire was administered to collect demographic data (age, weight, height), recent HbA1c levels, and beliefs related to nutrition myths in diabetes. Only patients diagnosed with T1DM for at least one year and presenting for routine follow-up were included. Individuals with other diabetes types (e.g., type 2 diabetes or MODY) were excluded.

Anthropometric measurements and biochemical parameters

Body Mass Index (BMI) values were calculated using the individuals’ body weight and height values. BMI for age Z-scores were evaluated using the World Health Organization (AnthroPlus) references in 10-18-year-old adolescents. While making this classification, for age, those with a BMI Z score <-1 SD were classified as underweight, those with a BMI Z score between − 1 and 1 SD were classified as normal, those with a BMI Z score between > 1 and 2 SD were classified as overweight, and those with a BMI Z score of > 2 SD were classified as obese [20]. At the same time, WHO BMI cut-off values were used when making BMI classification for individuals aged 19–22 years. Accordingly, those with a BMI (kg/m²) value of < 18.5 kg/m² were classified as underweight, those with 18.5–24.9 kg/m² were classified as normal, those with 25.0–29.9 kg/m² were classified as overweight, and those with ≥ 30 kg/m² were classified as obese [21].

HbA1c values were classified based on ADA and ISPAD guidelines: values < 7% were considered indicative of good metabolic control, whereas values ≥ 7% indicated poor metabolic control [9, 10].

Questions about nutritional myths in diabetes

Nutrition myth items were adapted from evidence-based resources on the official websites of the American, British, and Australian Diabetes Associations [22–24]. Additionally, frequently asked questions encountered by clinical diabetes care teams in Turkey were included to capture context-specific beliefs. After piloting the initial 30-item survey with 20 participants, five items were excluded due to ambiguity. The final instrument contained 25 statements (Supplementary File 1), and each correct answer was scored.

Statistical analysis

The statistical analyses were conducted using the SPSS 22.0 program. Descriptive variables of the individuals were presented as mean and standard deviation (SD), while BMI classifications were expressed as numbers and percentages. According to the metabolic control of the individuals evaluated by HbA1c, the rates of correct answers to myths about nutrition in T1DM were expressed as number and %, and the chi-square tests were employed for the group comparisons. The mean correct numbers of individuals were reported according to their metabolic control levels, with differences between groups determined through independent t-tests. The relationship between the number of correct answers given by adolescents and young adults with T1DM regarding myths about nutrition in diabetes and age, diabetes age, HbA1c levels, and BMI was evaluated with the Spearmen correlation coefficient. Multivariate analysis involved logistic regression analysis to identify independent predictors of metabolic status based on HbA1c, utilizing potential risk factors identified in previous analyses. The statistical significance level was set at 0.05.

Results

The general characteristics of participants were shown in Table 1. A total of 190 adolescent and young adult individuals with T1DM, including 83 males (44%) and 107 females (56%) between the ages of 10–22 years, participated in the study. 75.8% (n:144) of the individuals were between the ages of 10–18, and 24.2% (n: 46) were between the ages of 19–22. The mean age of the individuals was 15.5 ± 3.41 years, with an average diabetes age of 7.5 ± 4.63 years. The mean of the last measured HbA1c value was 7.9 ± 1.44%. According to the last HbA1c measurements, 29.0% (n = 55) of the participants exhibited good metabolic control (HbA1c < 7%), while 71.0% (n = 135) had poor control (HbA1c ≥ 7%). There was no statistically significant difference in the parameters shown in Table 1 according to gender (p > 0.05).

Table 1.

General characteristics of type 1 diabetics

	Males (n: 83)		Females (n: 107)		Total (n: 190)
	n	%	n	%	n	%	p
Age groups
10–18 years	69	83.1	75	70.1	144	75.8	0.052
19–22 years	14	16.9	32	29.9	46	24.2
Age (years) (mean ± SD)	15.5 ± 3.23		15.6 ± 3.55		15.5 ± 3.41		0.053
Diabetes age (years) (mean ± SD)	7.1 ± 4.64		7.8 ± 4.61		7.5 ± 4.63		0.382
Last HbA1c (%) (mean ± SD)	7.9 ± 1.44		7.9 ± 1.44		7.9 ± 1.44		0.103
Metabolic control according to HbA1c
Good (< 7%)	27	32.5	28	26.2	55	29.0	0.420
Poor (≥ 7%)	56	67.5	79	73.8	135	71.0

Chi-square and T test. SD: standard deviation

As illustrated in Figs. 1 and 69% of adolescents had a normal body weight based on BMI-for-age Z-scores, while 27% were classified as overweight or obese. Among young adults, 78% were of normal weight, and 20% were overweight or obese.

Fig. 1.

Distribution of adolescents (a) and young adults (b) with T1DM according to BMI classification

Participants’ answers to various nutrition myths were analyzed according to their HbA1c status, as presented in Table 2. The frequency of correct answers to several myths—such as those about sour fruits, sugar-free labels, herbal supplements, bedtime snacking, and the roles of macronutrients—was significantly higher among participants with good metabolic control (p < 0.05). An analysis of correct response rates revealed that certain nutritional myths are still prevalent among individuals with type 1 diabetes. Only 35.3% correctly answered the statement “As long as I count carbohydrates, I can drink sugary beverages,” indicating a lack of awareness regarding glycemic impact. The statement “I can estimate the carbohydrate amount without using measuring tools” was correctly answered by 67.4%, suggesting limited understanding of portion control and accurate carbohydrate counting. Low correct response rates to statements such as “Sour fruits contain fewer carbohydrates” and “It is appropriate to treat hypoglycemia with chocolate or biscuits” point to persistent misconceptions related to food choices and rapid-acting glucose sources.

Table 2.

Evaluation of correct answers to Myths according to HbA1c levels of type 1 diabetics

	HbA1c				Total (n:190)		p
	<%7 (n:55)		≥%7 (n:135)
	n	%	n	%	n	%
1. Eating too much sugar may have caused my diabetes.	55	100.0	116	85.9	171	90.0	0.003*
2. Drinking plain soda can lower my blood sugar.	26	47.3	66	48.9	92	48.4	0.083
3. Sour fruits contain less carbohydrates.	41	74.5	73	54.1	114	60.0	0.009*
4. Individuals with diabetes should not eat sugary fruits such as grapes, figs, and melons because they contain too much sugar.	50	90.9	86	63.7	136	71.6	0.000*
5. I should eat sweets or ice cream when my blood sugar is low.	30	54.5	55	40.7	85	44.7	0.083
6. If I eat only fish and salad in a meal, I don’t need to take insulin because there are no carbohydrates, and my blood sugar won’t increase.	37	67.3	74	54.8	111	58.4	0.114
7. All products labeled sugar-free are carbohydrate-free.	54	98.2	115	85.2	169	88.9	0.010*
8. Cinnamon and various herbal supplements can balance my blood sugar.	30	54.5	49	36.3	79	41.6	0.021*
9. When my blood sugar drops, I can eat chocolate or biscuits instead of fruit juice or sugar. They both raise my blood sugar because they are sugary.	43	78.2	88	65.2	131	68.9	0.079
10. I cannot eat fruit alone because it will raise my blood sugar quickly; I must always have a protein source like milk with it.	24	43.6	55	40.7	79	41.6	0.713
11. If my blood sugar is below 200 before going to bed, I should have an additional snack.	48	87.3	77	57.0	125	65.8	0.000*
12. If my blood sugar is 300, I should walk and drink water	49	89.1	81	60.0	130	68.4	0.000*
13. Instead of diet or sugar-free drinks, I can drink sugary ones as long as I calculate carbohydrates; it won’t be a problem.	20	36.4	47	34.8	67	35.3	0.839
14. I can tell if my blood sugar is dropping or rising without checking my blood sugar.	39	70.9	77	57.0	116	61.1	0.075
15. Only carbohydrates affect my blood sugar, whereas protein and fats do not	53	96.4	103	76.3	156	82.1	0.001*
16. Because I have diabetes, I should not eat sweet food like sugar and ice cream.	51	92.7	91	67.4	142	74.7	0.298
17. Because I have diabetes, my immunity is lower, and I can get sick more easily.	33	60.0	46	34.1	79	41.6	0.001*
18. Two apples of the same weight; the green one has fewer carbohydrates than the red one.	38	69.1	77	57.0	115	60.5	0.123
19. If I eat yoghurt and lemon before going to bed in the evening, it can help balance my blood sugar.	41	74.5	64	47.4	105	55.3	0.001*
20. I don’t need to use measures like spoon or scale when eating because I believe I can estimate the carbohydrates just by looking.	34	61.8	94	69.6	128	67.4	0.298
21. Diet and diabetic products are sugar-free, so I can eat as much as I want.	54	98.2	111	82.2	165	86.8	0.003*
22. It is necessary to have 3 snacks.	32	58.2	51	37.8	83	43.7	0.010*
23. If my blood sugar drops below 150 at 03:00, I should drink milk.	44	80.0	51	37.8	95	50.0	0.000*
24. If my fasting blood sugar is high, I should increase my long-acting insulin.	50	90.9	102	75.6	152	80.0	0.016*
25. If I measure my blood sugar at 80 before exercising, I start exercising right away.	-	-	1	0.7	1	0.5	0.522

Chi-square test. *p < 0.05

As shown as Fig. 2, the total number of correct answers to 25 queried myths assed based on the level of metabolic control as indicated by HbA1c. The average number of correct answers (17.8 ± 2.61) in individuals with type 1 diabetes who had good metabolic control significantly higher than those with poor metabolic control (13.7 ± 3.92) (p < 0.05).

Fig. 2.

Evaluation of total correct answers according to metabolic control level (Independent t-test)

A Spearman correlation analysis revealed a significant negative relationship between the total number of correct answers and HbA1c values (p < 0.001), indicating that better nutritional knowledge was associated with improved metabolic control. No significant associations were observed with age, BMI, or diabetes duration (Table 3).

Table 3.

Relationship between total correct answers and some characteristics of type 1 diabetics

	Total Correct Answers
	r*	p
Age (year)	0.063	0.386
Duration of diabetes (year)	-0.001	0.990
Last HbA1c (%)	-0.600	< 0.001
BMI (kg/m2)	-0.045	0.537

*Spearman correlation coefficient

The regression analysis presented in Table 4 examines the predictors affecting poor metabolic control, as indicated by HbA1c levels. An increase in diabetes duration was found to elevate the risk of poor metabolic control by 1.107 times, whereas a higher total number of answers was associated with a 0.696-fold decrease in this risk (p < 0.05).

Table 4.

Regression analysis of predictors affecting the level of poor metabolic control (HbA1c ≥%7)

Risk factor	OR (%95 CI)	p
Gender	1.369 (0.632–2.964)	0.426
Duration of diabetes	1.107(1.016–1.207)	0.020
Total Correct Answers	0.696 (0.615–0.789)	< 0.001

CI: Confidence interval; OR: Odds Ratio

Discussion

Adequate and balanced nutrition is essential for effective diabetes management, ensuring metabolic control, and reducing the risk of complications [25]. Accurate dissemination of nutritional information is crucial for diabetes education [26]. However, misinformation spread through social media and personal perceptions may lead to information pollution, reinforcing myths that negatively impact metabolic control and body weight ( [12, 27]– [28]). These myths, shaped by cultural beliefs, education levels, and social misconceptions, can hinder diabetes care. Therefore, identifying and correcting these myths and false beliefs is crucial for effective education and care provision [11]. When considering the most common myths that were incorrectly answered in our study, these findings emphasize the need for enhanced education on carbohydrate counting, appropriate food selection, and insulin management in individuals with T1DM.

The global prevalence of obesity has tripled worldwide since 1975 [29], which significantly affects individuals with T1DM, with obesity rates ranging from 2.8 to 37.1% [30]. In this study, 27% of adolescents and 20% of young adults with T1DM were overweight or obese. Obesity complicates diabetes control by increasing insulin requirements [31]. While insulin therapy and emotional eating contribute to weight gain, obesity may also play a causal role in T1DM development [32]. Obesity exacerbates insulin resistance, high insulin requirements, cardiometabolic risk, and the risk of developing chronic complications in patients with T1DM ( [30, 33]– [34]). In this regard, breaking the cycle of weight gain and increasing insulin requirements in individuals with T1DM is a critical goal [31]. Despite the absence of specific guidelines, multidisciplinary management involving hypocaloric diets, physical activity, and behavioral therapy remains best practice ( [30]– [31]).

Good metabolic control reduces diabetes compllications [8, 35]. In this study, only 29.0% of the individuals had HbA1c levels indicating good metabolic control. Similarly, a study in Saudi Arabia reported elevated HbA1c (≥ 7.5%) in 94.7% of children with T1DM [36]. Maintaining normoglycemia and nutritional adequacy is essential for growth and development in pediatric diabetes [10]. Personalized meal planning, cultural adaptations, and psychosocial considerations enhance glycemic control [25].

Dietary recommendations in diabetes emphasize healthy eating for all children, young people, families, aiming to improve diabetes outcomes and reduce cardiovascular risk [25]. A study in Nigeria identified myths and limited understanding as barriers to diabetes self-management, highlighting the need for individualized and culturally sensitive strategies [37]. In this context, nutrition education in diabetes plays a pivotal role. This study found significantly higher correct answer rates to nutrition-related myths among individuals with good metabolic control, suggesting a negative association between nutritional misconceptions and HbA1c levels. Structured education can therefore support both knowledge and disease management.

In a cross-sectional study in Delhi, the most common misconception (22%) was that “consuming more sugar causes diabetes,” especially among women and less-educated individuals [11]. In our study, 14.1% of poorly controlled individuals believed excessive sugar intake caused their diabetes, while none with good control agreed. Misconceptions about herbal remedies and cinnamon were prevalent among both groups, with higher rates among those with poor control. Similar misconceptions were reported in other studies, including beliefs about sensing high blood sugar or the harms of insulin ( [17, 38]– [39]). These highlight the importance of education in dietary strategies and insulin use.

Studies in developing countries show disparities in knowledge, attitudes, and practices (KAP) scores based on age, education, and socioeconomic status [40, 41]. Parental knowledge correlates with glycemic outcomes, as shown by associations between NKS scores and HbA1c [41]. Social support, professional interventions, and community-based programs further enhance self-management ( [42]– [43]).

Dietary habits also affect glycemic outcomes. Individuals with T1DM consume fewer carbohydrates but more fats and proteins. Higher fiber intake and nutrition knowledge are linked to better HbA1c outcomes [44]. The misconceptions that only carbohydrates affect blood sugar remains common, particularly among those with poor metabolic control. Therefore, nutrition therapy in diabetes should focus on glycemic control, carbohydrate counting, balanced eating patterns, and complication prevention.

This study has limitations. Demographic variables such as education and income were not analyzed in detail, limiting the exploration of associations between myths and sociodemographic factors. Additionally, metabolic control was assessed only through HbA1c; including cardiovascular risk markers may provide a more comprehensive evaluation.

Conclusion

This study demonstrates a clear association between nutritional misconceptions and poorer metabolic control in individuals with diabetes. From a public health perspective, addressing these misconceptions through targeted educational interventions holds the potential to improve glycemic outcomes and reduce diabetes-related complications. Emphasizing evidence-based nutrition education as a core component of diabetes management may enhance patients’ quality of life and facilitate long-term disease control. Moreover, public awareness campaigns that incorporate common societal beliefs, food-related attitudes, and prevailing nutrition trends can support early diagnosis and more effective self-management of the disease. For optimal impact, nutrition education should be delivered by a multidisciplinary team and tailored to the cultural and psychosocial context of individuals. Future studies involving larger and more diverse populations are needed to develop and evaluate structured education programs that effectively address misconceptions and promote sustainable behavior change.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

Conceptualization: DGK, FA, EB, HT, OE. Study and analysis: DGK, SA. Data Analysis: SA, EY, FA. Data interpretation: DGK, SA, EY, FA. Writing–review and editing: FA, SA, EY, DGK. Validation and finalization: FA, DGK, EB, HT, OE.

Funding

The authors did not receive any financial support or funding.

Data availability

The data will be provided from the corresponding author upon reasonable request.

Declarations

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.

Human ethics and consent to participate

This study followed the Helsinki Declaration. All participants signed an informed consent form, and this study was approved by the Cerrahpaşa Medical Faculty Clinical Research Ethics Committee (Registration number: E-83045809-604.01.02-28243). Informed consent from parents was obtained for minor participants.

Clinical trial number

Not applicable.