# No association between carbohydrate-counting knowledge and disordered eating behaviors in adults with type 1 diabetes

Laura Albaladejo, Melissa Ferguene, Béatrice Genoux, Lucien Marchand, Hélène Du Boullay, Sandrine Lablanche, Céline Vermorel, Aurélie Gauchet, Jean-Luc Bosson, Cécile Bétry

► To cite this version:

Laura Albaladejo, Melissa Ferguene, Béatrice Genoux, Lucien Marchand, Hélène Du Boullay, et al. No association between carbohydrate-counting knowledge and disordered eating behaviors in adults with type 1 diabetes. Diabetes & Metabolism, 2026, 52 (2), pp.101735. 10.1016/j.diabet.2026.101735. hal-05497253

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Original article

# No association between carbohydrate-counting knowledge and disordered eating behaviors in adults with type 1 diabetes

<figure><img src="image_2.png" /></figure>

Laura Albaladejo<sup>a, *, e</sup>, Melissa Ferguene<sup>b</sup>, Béatrice Genoux<sup>c</sup>, Lucien Marchand<sup>d</sup>, Hélène du Boullay<sup>e</sup>, Sandrine Lablanche<sup>f</sup>, Céline Vermorel<sup>a</sup>, Aurélie Gauchet<sup>g</sup>, Jean-Luc Bosson<sup>h</sup>, Cécile Bétry<sup>i</sup>

## ARTICLE INFO

**Keywords:**

Carbohydrate counting

Disordered eating behaviors

Eating behavior

Intensive insulin therapy

Type 1 diabetes

## ABSTRACT

**Aims:** Carbohydrate counting enables flexible prandial insulin dosing in type 1 diabetes but remains cognitively demanding. Concerns persist that such sustained attention to food may contribute to disordered eating behaviors. The primary aim of this study was to examine whether carbohydrate-counting knowledge is associated with disordered eating behaviors.

**Methods:** This cross-sectional study (NCT07021456) was conducted online. Participants completed questionnaires assessing carbohydrate-counting knowledge (Gluciquizz), disordered eating behaviors (DEPS-R), and likely eating disorders (SCOFF-F). Additional questionnaires evaluated quality of life (ADDQoL), diabetes-related distress (PAID-5), and fear of hypoglycemia (HFS-II short form). Elevated DEPS-R was defined as a score ≥ 20, and likely eating disorders as SCOFF-F ≥ 2.

**Results:** A total of 100 adults with type 1 diabetes were included. No correlation was observed between Gluciquizz and DEPS-R (ρ = -0.03, 95% CI (-0.23 to 0.17), P = 0.73). Similarly, Gluciquizz scores did not differ between participants with SCOFF-F < 2 and ≥ 2 (P = 0.745). Diabetes-related distress was significantly higher among participants with elevated DEPS-R scores (PAID-5 median 15 vs 8; P = 0.006), whereas ADDQoL and HFS-II did not differ significantly.

**Conclusion:** In this selected adult population with type 1 diabetes, carbohydrate-counting knowledge was not associated with disordered eating behaviors. However, positive DEB screening was linked with higher diabetes-related distress, supporting the importance of psychosocial assessment.

## Introduction

In type 1 diabetes, lifelong exogenous insulin is essential for survival, while optimizing insulin therapy is key to maintain glycemic control and prevent microvascular and macrovascular complications. To achieve these goals, insulin must be administered multiple times per day before

each carbohydrate-containing meal. Over time, dietary management has evolved from fixed-carbohydrate regimens to individualized adjustment of prandial insulin based on meal composition. Carbohydrate counting has therefore become the reference method to promote flexibility and patient autonomy [1-4].

However, the sustained attention required for carbohydrate

<footnote><sup>a</sup> Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC 38000 Grenoble, France</footnote>

<footnote><sup>b</sup> Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC 38000 Grenoble, France</footnote>

<footnote><sup>c</sup> Department of Endocrinology, Diabetology and Nutrition, Centre hospitalier Grenoble Alpes 38000 Grenoble, France</footnote>

<footnote><sup>d</sup> Department of Endocrinology, Diabetology and Nutrition, Hôpital Saint Joseph Saint Luc 69007 Lyon, France</footnote>

<footnote><sup>e</sup> Department of Endocrinology, Diabetology and Nutrition, Centre hospitalier metropole Savoie, Chambéry, France</footnote>

<footnote><sup>f</sup> Univ. Grenoble Alpes, Department of Endocrinology, Diabetology and Nutrition, Centre hospitalier Grenoble Alpes, INSERM U1055, Grenoble, France</footnote>

<footnote><sup>g</sup> Université Savoie Mont Blanc, Université Grenoble Alpes, LIP/PC2S, Grenoble, France</footnote>

<footnote><sup>h</sup> Univ. Grenoble Alpes, Clinical Investigation Center-Technological Innovation, INSERM CIC1406, CHU Grenoble Alpes, Public Health department 38000 Grenoble, France</footnote>

<footnote><sup>i</sup> Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, Department of Endocrinology, Diabetology and Nutrition 38000 Grenoble, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: laura.albaladejo@univ-grenoble-alpes.fr (L. Albaladejo).</footnote>
estimation may, in some individuals, reinforce rigid eating attitudes and heighten anxiety related to food and meals [5]. This concern is partic- ularly relevant in a population already at increased risk for disordered eating behaviors and eating disorders [6–8]. Beyond eating behaviors, adults with type 1 diabetes face a substantial psychosocial burden, encompassing diabetes-related distress, reduced quality of life, and fear of hypoglycemia, which are closely interrelated [9,10]. Despite these interconnections, few studies have examined carbohydrate counting, psychosocial burden, and eating disorder dimensions simultaneously. Moreover, existing research has often relied on simplified or dichoto- mous assessments of carbohydrate counting (e.g., self-reported use vs non-use), making it difficult to distinguish carbohydrate-counting knowledge, real-life practice, and perceived cognitive or psychological burden [11]. This methodological heterogeneity limits the interpreta- tion of reported associations between carbohydrate counting and disordered eating behaviors.

In the present study, we specifically focused on carbohydrate-
counting knowledge, rather than on real-life counting practice or
perceived burden. In this context, we designed the DIABEA (DIAbetes
BEhavior and eAting) study, a national mixed-methods project aimed at
exploring the relationship between carbohydrate counting knowledge
and disordered eating behaviors in adults with type 1 diabetes. The
present article reports the quantitative component of this study, whose
primary objective was to examine the association between
carbohydrate-counting knowledge, assessed using the validated Gluci-
quizz questionnaire, and the risk of disordered eating behaviors, eval-
uated with the Diabetes Eating Problem Survey Revised (DEPS-R) [12,
13]. We hypothesized that higher carbohydrate-counting knowledge
may be associated with greater self-reported disordered eating behav-
iors, while acknowledging that knowledge may only partially reflect the
complexity of everyday carbohydrate-counting experiences. A second-
ary objective was to explore psychosocial functioning, including
diabetes-related distress, quality of life, and fear of hypoglycemia.

Methods

Ethical considerations

The DIABEA study was conducted in accordance with the ethical
principles of the 1964 Declaration of Helsinki and its later amendments,
as well as Good Clinical Practice guidelines (ICH E6). The study protocol
was approved by a French ethics committee for the protection of in-
dividuals (IDRCB: 2023-A02313-44). This study was registered on
ClinicalTrials.gov (NCT07021456).

Study design

The DIABEA study is a French, prospective, mixed-methods obser-
vational study coordinated by the tertiary Grenoble Alpes University
Hospital (Grenoble, France). Recruitment was conducted nationwide
through diabetologists across various healthcare settings and com-
plemented by announcements disseminated via patient associations and
social media. Interested individuals were able to access the online
eligibility questionnaire by scanning a QR code included in the study
information materials.

Eligible participants were adults (≥ 18 years) with a confirmed
diagnosis of type 1 diabetes for at least three months, fluent in French,
and able to provide informed consent. Exclusion criteria are refusal to
allow use of study data or inability to complete questionnaires online.

After enrolment, participants completed a videoconference assess-
ment during which the Gluciquizz, DEPS-R, and SCOFF-F questionnaires
were administered. This was followed by self-administered online
questionnaires assessing quality of life, fear of hypoglycemia, and dia-
betes distress (ADDQoL, HFS-II, and PAID-5). A subsample of partici-
pants subsequently took part in an optional qualitative interview.

Data collection

Study data were collected and managed using REDCap (Research Electronic Data Capture version 14.4.1) hosted at Grenoble Alpes University Hospital [14,15]. All data were pseudonymized and stored on secure hospital servers. The following variables were collected: clinical and sociodemographic variables (age, gender, body mass index, BMI, socioeconomic status, diabetes duration, treatment modality), together with a set of standardized questionnaires. Information on prior formal training in carbohydrate counting and HbA1c levels was not collected. Validated French versions were used for all questionnaires except for the HFS-II, for which no validated translation exists; the French version of the ADDQoL was used under license from the rights holder [16–18]. The questionnaires used in the study were as follows:

Gluciquizz. A 36-item questionnaire designed to assess theoretical knowledge of carbohydrate counting. Scores range from 0 to 36, with higher scores indicating better knowledge [12].

*Diabetes Eating Problem Survey-Revised (DEPS-R)*. A 16-item tool validated in type 1 diabetes to evaluate disordered eating behaviors. Each item is rated on a 6-point Likert scale (0–5), yielding a total score from 0 to 80; higher scores indicate greater risk. A threshold ≥ 20 is commonly used to identify patients at risk [19].

SCOFF-F. The Sick, Control, One stone, Fat, Food (SCOFF) ques-
tionnaire is a 5-item screening tool used to identify eating disorders,
with scores ranging from 0 to 5 and ≥ 2 positive responses indicating a
likely eating disorder [20]. The SCOFF-F refers to its validated French
version.

*Audit of Diabetes-Dependent Quality of Life (ADDQoL)*. The ADDQoL includes 19 items assessing the impact of diabetes on various life domains. Each item combines an importance rating (0–3) and an impact rating (−3 to +1); the overall score is calculated by weighting impact by importance and averaging across items, yielding a total score ranging from −9 to +3 and providing an individualized measure of diabetes-related quality of life [21].

*Problem Areas in Diabetes – 5 item version (PAID-5)*. PAID-5 is a validated short-form measure of diabetes-related emotional distress derived from the original 20-item PAID scale. It includes five items rated on a five-point Likert scale, yielding a total score ranging from 0 to 20, with higher scores indicating greater diabetes-related distress [22].

*Hypoglycemia Fear Survey–II short form (HFS-II)* [23]. An eight-item questionnaire assessing fear of hypoglycemia across two subscales (worry and behavior). Items are rated on a five-point Likert scale, yielding a total score ranging from 0 to 32, with higher scores reflecting greater fear [24].

Statistical analysis

The sample size was based on detecting a correlation coefficient:
with n = 100, α = 0.05, and 80% power, the minimum detectable cor-
relation was 0.28. Continuous variables are described as median [IQR],
and categorical variables as counts (percentages). The association be-
tween Gluciquizz score and DEPS-R was assessed using Spearman's rank
correlation, with the effect size reported along with its 95% confidence
interval. Stratified analyses were additionally performed according to
treatment modality (hybrid closed-loop system vs. multiple daily in-
jections and pump therapy), assessing the correlation between Gluci-
quizz score and DEPS-R within each group. Group comparisons of
median scores were performed using the Mann–Whitney U test. Analyses
were conducted in Jamovi (v2.3). Statistical significance was set at two-
sided P < 0.05.

Results

Study population

Of the 125 participants assessed for eligibility, 25 were excluded (7
did not meet the inclusion criteria and 18 were non-respondents or were assessed after the inclusion period had closed), resulting in 100 participants included in the quantitative study, of whom 22 also participated in the qualitative study (Fig. 1). Baseline sociodemographic and clinical characteristics of the study population are presented in Table 1. The participants comprised 43 males (43%) and 57 females (57%).

### Relationship between Gluciquizz, DEPS-R and SCOFF-F

Among the 100 participants, 89 (89%) had a DEPS-R score < 20 and 11 (11%) had a DEPS-R score ≥ 20; overall, 21 participants (21%) screened positive on the SCOFF-F (score ≥ 2), including 17 with DEPS-R < 20 and 4 with DEPS-R ≥ 20. No association was detected between Gluciquizz and DEPS-R scores ($p = -0.035$, 95% CI ($-0.23$ to $0.17$), $P = 0.73$) (Fig. 2). Gluciquizz scores did not differ significantly between participants with DEPS-R < 20 and ≥ 20 (Fig. 2 and Fig. 3), nor between those with SCOFF-F < 2 and ≥ 2 (26.0 [23.0; 27.0] vs 25.5 [23.0; 27.3], $P = 0.75$). In participants using hybrid closed-loop, the Spearman correlation between carbohydrate-counting knowledge and DEPS-R score was $-0.014$ (95% CI $-0.30$ à $0.27$, $P = 0.93$, n=47). In participants using multiple daily injections or pump therapy, the Spearman correlation was $-0.003$ (95% CI $-0.27$ à $0.27$, $P = 0.98$, n=53).

### Comparison of psychosocial factors by DEPS-R group

Participants with DEPS-R ≥ 20 (n = 11) had significantly higher PAID-5 compared with those with DEPS-R < 20 (n = 87) ($P < 0.01$) (Fig. 4A). No significant differences between DEPS-R groups were observed for HFS-II or ADDQoL (Fig. 4B & 4C).

## Discussion

The main objective of this study was to assess whether carbohydrate-counting knowledge was associated with disordered eating behaviors and secondary with likely eating disorders in adults with type 1 diabetes. Further objectives explored the relationships between disordered eating behaviors and psychosocial factors, including diabetes-related distress, quality of life, and fear of hypoglycemia.

The most striking result of this study is that no association was detected between carbohydrate-counting knowledge and disordered eating behaviors in this selected population. Our results contrast with previous reports, although only a few studies have examined the relationship between disordered eating and carbohydrate counting, and no study has specifically addressed the question of carbohydrate-counting knowledge [11]. Two studies concluded that carbohydrate counting was associated with a lower risk of disordered eating behaviors or eating disorders [25,26].

<table><caption>**Table 1**
Sociodemographic and clinical characteristics of the study population (n=100)
BMI: body mass index.</caption><thead><tr><th></th><th>Median (IQR) or n (%)</th></tr></thead><tbody><tr><td>Age, years</td><td>37.5 [29.0; 54.5]</td></tr><tr><td>BMI, kg/m<sup>2</sup></td><td>24.8 [22.3; 27.5]</td></tr><tr><td>Socioeconomic status</td><td></td></tr><tr><td>Craftsmen, tradesmen, and business owners:</td><td>4 (4)</td></tr><tr><td>Managers and higher intellectual professions:</td><td>38 (38)</td></tr><tr><td>Intermediate professions (e.g., technicians, mid-level professionals):</td><td>15 (15)</td></tr><tr><td>Clerical workers and employees:</td><td>2 (2)</td></tr><tr><td>Manual workers:</td><td>9 (9)</td></tr><tr><td>Unemployed:</td><td>12 (12)</td></tr><tr><td>Retired:</td><td></td></tr><tr><td>Diabetes duration, years</td><td>≤ 1: 6 (6)<br/>1- ≤ 5: 10 (10)<br/>5- < 10: 12 (12)<br/>≥ 10: 72 (72)</td></tr><tr><td>Treatment</td><td></td></tr><tr><td>Multiple daily injections</td><td>15 (15)</td></tr><tr><td>Pump therapy</td><td>38 (38)</td></tr><tr><td>Hybrid closed-loop</td><td>47 (47)</td></tr><tr><td>Gluciquizz [0-36]</td><td>26 [23,27]</td></tr><tr><td>DEPS-R [0-80]</td><td>9 [6,14]</td></tr><tr><td>SCOFF-F [0-5]</td><td>< 2: 79 (79)<br/>≥ 2: 21 (21)</td></tr><tr><td>ADDQoL * [-9-3]</td><td>-2 [-3; -1]</td></tr><tr><td>PAID-5 * [-0-20]</td><td>8 [5,14]</td></tr><tr><td>HFS-II* [0-32]</td><td>12 [8,15]</td></tr></tbody></table>

*n=98**

At least two explanations may account for this discrepancy. The first relates to differences in study populations—children in one and a mixed sample of adults and children in the other in whom carbohydrate counting may be more structured and partly supervised by caregivers [27]. The second concerns the assessment method: our study used the Gluciquizz, a validated tool measuring the level of carbohydrate-counting knowledge, with a median score within the same range as previously reported, 24, 25, and 26 out of 36, for multiple daily injections, insulin pumps and automated insulin delivery systems respectively [28,29]. In contrast, Sanlier et al [25] and Philippi et al [26] relied on a dichotomous definition of practice, assessing carbohydrate counting through a single yes/no question asking participants whether they practiced it. Such a binary approach overlooks key dimensions, including the type of counting (basic vs. advanced), the depth of knowledge, and the frequency of use—all factors that may shape real-world strategies and depend on prior education in carbohydrate counting.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Flowchart of study DIABEA.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Boxplots of Gluciquizz in adult participants with type 1 diabetes, stratified by DEPS-R category (< 20, n=89 et ≥ 20, n=11). DEPS-R: Diabetes eating problem survey-revised.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Boxplots of PAID-5, HFS-II and ADDQoL in adult participants with type 1 diabetes, stratified by DEPS-R category (< 20, n=87 et ≥ 20, n=11). ADDQOL: Audit of diabetes-dependent quality of life; PAID-5: Problem areas in diabetes-5 items version; HFSII: Hypoglycemia Fear Survey-II short form.

**P** < 0.01.</figcaption></figure>

Beyond the primary analysis, several secondary findings emerged that further illuminate the psychosocial context of disordered eating in type 1 diabetes. In our cohort, participants with higher DEPS-R scores also reported significantly greater diabetes-related distress, under-scoring the psychological burden associated with disturbed eating in this population. This association is consistent with prior evidence showing that elevated DEPS-R scores often coincide with increased emotional distress and impaired psychosocial functioning, confirming the overlap between markers of disturbed eating and diabetes-related distress [11, 24,30]. In youth, a study combining the PAID-5 and the DEPS-R identified a strong positive correlation between diabetes distress and disturbed eating, mediated by self-regulatory fatigue, suggesting that emotional strain may amplify maladaptive eating patterns [31]. Collectively, these findings support the view that disturbed eating behaviors in type 1 diabetes are part of a broader psychosocial burden. In our study, no significant differences were observed between DEPS-R groups in fear of hypoglycemia or diabetes-specific quality of life (ADDQoL). In our study, no significant differences were observed

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Boxplots with overlaid individual jittered data points of PAID-5, HFS-II and ADDQoL in adult participants with type 1 diabetes, stratified by DEPS-R category (< 20, n=87 et ≥ 20, n=11).</figcaption></figure>

between DEPS-R groups in fear of hypoglycemia or diabetes-specific quality of life (ADDQoL). This absence of difference in fear of hypoglycemia may be explained by several contextual factors. Indeed, the widespread use of advanced diabetes technologies, such as continuous glucose monitoring and closed-loop insulin delivery systems, has been shown to reduce fear of severe hypoglycemia, potentially homogenizing this psychosocial dimension within our cohort [32]. In addition, individual variability in hypoglycemia experiences may further attenuate differences in fear of hypoglycemia between groups [33]. In addition, for the quality of life, several pediatric studies have reported significant negative correlations between DEPS-R scores and general or diabetes-specific quality of life among adolescents with type 1 diabetes [34]. In contrast, a previous study assessing DEPS-R, PAID, WHO-5 (a well-being index), and HFS-II in a larger cohort of 250 participants
reported significant correlations between DEPS-R scores and both WHO-5 and HFS-II. Differences in sample size and population characteristics may partly explain this discrepancy, as their monocentric design, involving patients managed by a single physician, could have led to a more homogeneous sample than ours [30].

Some limitations should be acknowledged when interpreting the results. First, the carbohydrate-counting score Gluciquizz assesses declarative knowledge rather than practical skills, which may not fully reflect real-life carbohydrate-counting behavior. First, our findings should be interpreted as indicating that carbohydrate-counting knowledge alone is not a marker of disordered eating risk in this sample. However, this does not exclude the possibility that real-life carbohydrate-counting practices or the perceived cognitive burden associated with them may be linked to disordered eating in other populations or contexts. We nevertheless selected Gluciquizz because of its validation, objectivity, and reproducibility, which makes it more relevant than the binary classifications used in previous studies that simply distinguished between individuals who reported counting or not counting carbohydrates [12,29,35]. Second, responses to the DEPS-R and SCOFF-F questionnaires may have been influenced by social desirability bias, which represents a major threat to validity when assessing disordered eating behaviors in adults with type 1 diabetes. The stigmatizing nature of insulin omission and compensatory behaviors may have led some participants to underreport eating-related symptoms or attitudes [36]. This risk may have been amplified by the fact that questionnaires were completed during a videoconference rather than through fully anonymous self-administration, potentially contributing to an underestimation of disordered eating behaviors and attenuation of observed associations. Although the DEPS-R and SCOFF-F are usually self-administered, we opted for third-party administration to minimize missing data, which may have inadvertently increased social desirability bias. To limit this risk, we recruited participants across multiple centers, ensured they were not personally known to investigators [37]. Third, the external validity of our findings is limited by the characteristics of the study population. Recruitment relied partly on online channels (patient associations and social media), and participants were predominantly long-standing, technologically equipped, and likely highly engaged in diabetes self-management, with a high proportion using insulin pumps and automated insulin delivery systems. Such a profile likely underrepresents individuals with lower access to education or technology, those more recently diagnosed, or those with more severe disordered eating behaviors who may be less inclined to volunteer or less comfortable with videoconference-based assessments. As a result, our findings should not be generalized to the broader population of adults with type 1 diabetes [38,39]. Nevertheless, the prevalence of positive SCOFF-F scores in our sample was consistent with previously reported rates in adult populations [8]. In contrast, the prevalence of positive DEPS-R scores was lower than the rates generally observed in the literature [34,40]. Furthermore, recruitment was diversified through multiple channels—including hospitals, patient associations, and social media—to ensure broad representation and include individuals beyond those routinely followed in tertiary diabetes care centers. Third, regarding the assessment of eating-disorder risk, the SCOFF-F is a generic screening tool and is not specific to diabetes, as it does not capture diabetes-related behaviors. Although a diabetes-adapted version (mSCOFF) has been proposed, it has not been validated in French and was developed in a pediatric population. Previous studies have shown no meaningful differences between the mSCOFF and the SCOFF-F in identifying eating-disorder risk in individuals with type 1 diabetes, supporting the use of the SCOFF-F in the present study [36]. Finally, we did not collect information on prior formal training in carbohydrate counting or HbA1c levels. These factors may influence both carbohydrate-counting knowledge and the risk of disordered eating behaviors and therefore represent potential unmeasured confounders. Their absence limits our ability to adjust for metabolic control or educational exposure and may have contributed to residual

confounding. We chose not to include these parameters because, in this context, self-reported information was considered insufficiently reliable for robust analysis.

Looking ahead, several perspectives emerge regarding the assessment and understanding of eating behaviors and carbohydrate counting in type 1 diabetes. On one hand, several studies in type 1 diabetes have reported a general trend toward carbohydrate restriction, factor that may not be adequately captured by existing tools such as the DEPS-R or SCOFF [41–43]. The Gluciquizz provides a standardized measure of carbohydrate-counting knowledge but does not assess daily implementation or behavioral patterns. Further research is therefore needed to capture both dimensions—the diversity of disordered eating behaviors, including carbohydrate restriction, and the variability of carbohydrate-counting practices. It would be valuable to systematically profile strategies along three axes: (i) knowledge level (e.g., Gluciquizz score), (ii) adherence or frequency of counting, and (iii) the type of insulin–food adjustment strategy (e.g., visual estimation, fixed ratios, context-based adjustments, digital tools). Standardizing how carbohydrate counting is assessed would also facilitate cross-study comparisons and strengthen the evidence base on its clinical and behavioral implications. Historically, structured education programs on flexible insulin dosing in relation to carbohydrate intake—such as the DAFNE program—have demonstrated improvements in both glycemic control and quality of life, highlighting the clinical importance of these skills at a population level [44]. However, DAFNE did not include systematic evaluation of eating behaviors, and its one-week format may not reflect current educational modalities. Future longitudinal studies should therefore assess how eating behaviors evolve after diabetes diagnosis in relation to both carbohydrate-counting knowledge and practice, while accounting for confounding factors such as treatment modality and the growing use of advanced insulin delivery technologies, which may profoundly reshape patients’ relationship with food [45].

## Conclusion

In this selected adult population with type 1 diabetes, carbohydrate-counting knowledge alone was not associated with an increased risk of disordered eating behaviors. This finding indicates that knowledge, as assessed in this study, is not in itself a marker of disordered-eating risk. However, individuals screening positive for disordered eating behaviors reported higher levels of diabetes-related distress, underscoring the clinical importance of systematic psychosocial assessment in this population. Altogether, these results support integrating psychosocial screening alongside nutritional support and education, while recognizing that the real-life practice and perceived burden of carbohydrate counting may warrant further investigation.

## AI assistance acknowledgment

During the preparation of this work, the author used ChatGPT to assist with language and readability improvements. After using this tool, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.

## Funding sources

This work received funding from the Roche Fellowship awarded through the paramedical call for projects of the "Société Francophone du Diabète", and from the association of patients in France (AFD 42).

## CRediT authorship contribution statement

**Laura Albaladejo:** Writing – review & editing, Writing – original draft, Validation, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Melissa Fer-guene:** Data curation. **Béatrice Genoux:** Writing – review & editing,
Project administration. Lucien Marchand: Writing – review & editing, Investigation. Hélène du Boullay: Writing – review & editing, Investigation. Sandrine Labanche: Writing – review & editing, Investigation. Céline Vermorel: Writing – review & editing, Formal analysis. Aurélie Gauchet: Writing – review & editing, Supervision, Methodology. Jean-Luc Bosson: Writing – review & editing, Supervision, Methodology. Cécile Bétry: Writing – review & editing, Writing – original draft, Supervision, Methodology, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests
Laura Albaladejo reports administrative support was provided by Bourse Roche. Laura Albaladejo reports financial support was provided by association of patients in France (AFD 42). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors would like to thank all the healthcare professionals and collaborators who contributed to the inclusion of patients in this study.

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