Abstract
Aims
Effective diabetes self‐management increasingly depends on the interplay between health literacy, numeracy and digital health literacy, given the growing integration of advanced digital tools into diabetes care routines. Little is known about the current state of these skills among people with insulin‐treated diabetes. Therefore, this study aims to assess (digital) health literacy and numeracy in people with diabetes on intensive insulin therapy and to explore their associations with glycaemic control, health behaviours, clinical outcomes and patient‐reported outcomes.
Methods
The Exploring Diabetes Health Literacy and Numeracy across Europe (EDUCATE) study is a multicentre cross‐sectional study aiming to recruit 209 adults with type 1, type 2, or pancreatogenic diabetes on intensive insulin therapy in four European outpatient clinics. Participants will be asked to complete questionnaires, record their dietary patterns, and wear both a physical activity tracker and a blinded continuous glucose monitor for two weeks. The primary outcome is health literacy, assessed using the validated Health Literacy questionnaire. Secondary outcomes include numeracy, digital health literacy, glycaemic outcomes, health behaviour (e.g., diet and physical activity), and patient‐reported‐outcomes (e.g., quality‐of‐life and diabetes distress).
Results
Findings will be diseminated through peer‐reviewed scientific journals, and academic conferences or media outlets to inform the wider public.
Conclusion
EDUCATE will assess digital health literacy and numeracy in people with diabetes on intensive insulin regimens across four European countries. A deeper understanding of the current landscape of health literacy and its association with glycaemic outcomes may support the development of targeted interventions. These interventions are aimed at empowering people with diabetes and reducing socio‐economic and cultural health disparities.
Keywords: diabetes mellitus, (digital) health literacy, glycaemic control, lifestyle, numeracy, patient‐reported outcomes, self‐management
1. INTRODUCTION
Achieving optimal glycaemic control remains a challenge for the majority of people with diabetes, particularly those on insulin therapy. 1 Effective adherence to treatment regimens depends heavily on an individual's ability to read, understand and apply self‐management instructions. Over the past decade, advanced digital tools have been increasingly integrated into diabetes care to further support people with diabetes in achieving the recommended glycaemic targets. 2 However, not all individuals benefit equally from these innovations. Hypoglycaemia remains a common and feared acute complication of insulin therapy, even among users of automated insulin delivery systems. Health literacy, digital health literacy and numeracy each play a distinct but interconnected role in the successful use of digital diabetes tools. 3 Health literacy can be defined as ‘the knowledge and competence to access, understand, appraise, and apply health information in order to make judgements and take decisions in everyday life concerning healthcare, disease prevention, and health promotion to maintain or improve quality of life’. 4 Similarly, numeracy—a key component of health literacy—can be defined as ‘the ability to understand and use numbers in daily life’, 5 whereas digital health literacy refers to ‘someone's ability to use digital devices, software and applications effectively’. 2
Together, these three domains encompass a range of complex skills that are essential for effective diabetes self‐management. 3 These include the ability to comprehend physiological concepts, such as insulin sensitivity and time‐in‐range metrics, to master the use of digital tools (e.g., mounting and pairing a sensor with a smartphone) and to calculate insulin doses based on carbohydrate intake. These domains can overlap and reinforce each other. For instance, a person with sufficient digital skills may operate a medical device correctly, yet lack the ability to accurately interpret the glycaemic metrics. Conversely, someone with advanced carbohydrate‐counting skills may struggle to access or navigate diabetes applications.
The ADA/EASD consensus statements recommend tailoring self‐management education toward an individual's needs, considering factors such as cognitive function, literacy, and socio‐cultural, financial, geographical and lifestyle factors. 6 It further emphasises that individual needs and barriers should remain a central component of ongoing diabetes care. The majority of studies on the prevalence of health literacy and numeracy in the diabetes population were conducted in the United States, and only a few of these studies included continuous glucose monitoring (CGM) data. 3 , 5 Sparse information exists regarding (digital) health literacy and numeracy in the current (high‐tech) era in the European diabetes population, its associations with health behaviour (i.e., food intake and physical activity), and its impact on glycaemic parameters and patient‐reported outcomes (PROs). This study will therefore assess (digital) health literacy and numeracy and their association with health behaviour, glycaemic status and patient‐reported outcomes in a contemporary cohort of people with diabetes from four European countries representing various cultural backgrounds. The primary objective is to assess health literacy in people with diabetes requiring intensive insulin therapy.
2. METHODOLOGY
2.1. Study design
A multinational, multicentre, cross‐sectional study will be conducted in four European outpatient clinics from Denmark, Germany, Greece and the Netherlands. The study will last 2 weeks per participant after inclusion and run for approximately 1 year. This study has been reviewed and approved by the Maastricht University/Maastricht University Medical Centre+ (MUMC+) Medical Ethics Research Committee: NL8402.068.24.
2.2. Study population
Adults (18–80 years) with type 1, type 2 or pancreatogenic diabetes on intensive insulin therapy, defined as using either multiple daily injections with basal and bolus insulin or continuous subcutaneous insulin infusion or hybrid‐closed loop systems, are eligible for participation in the study. Enrolment will be distributed across the four outpatient clinics in Denmark (Copenhagen University Hospital—North Zealand [NOH]), Germany (Otto‐von‐Guericke Magdeburg University [OVGU]), Greece (National and Kapodistrian University of Athens—Laiko General Hospital [NKUA]) and the Netherlands (MUMC+) to reflect diversity across Europe. Participants will be recruited via public advertising and targeted recruitment in the outpatient clinics.
2.3. Inclusion and exclusion criteria
Adults (18–80 years), diagnosed with type 1, type 2 or type 3c diabetes, who use intensive insulin treatment for at least 1 year and can provide informed consent, will be included. Primary exclusion criteria are other subtypes of diabetes and comorbidities (i.e., unstable cardiovascular disease, active malignancy and kidney failure). Participants with severe cognitive, hearing or visual impairment that would interfere with completing the questionnaires or receiving study instructions will also be excluded. Detailed inclusion and exclusion criteria are shown in Table S1.
2.4. Outcomes of interest
2.4.1. Primary outcomes
The primary outcome of this study is health literacy, measured with the validated Health Literacy Questionnaire (HLQ). 7
2.4.2. Secondary outcomes
Secondary outcomes are numeracy skills assessed with the Diabetes Numeracy Test‐15 (DNT‐15), 8 digital health literacy assessed by Readiness and Enablement Index for Health Technology (READHY), 2 health behaviour [i.e., dietary pattern recorded via the goFOOD™Lite application, 9 the Maastricht Food Frequency Questionnaire (FFQ) and the chrono‐nutrition questionnaire in the Netherlands, 10 physical activity recorded with activPal4™ (PAL Technologies Ltd., Glasgow, Scotland), smoking and alcohol status]. Other outcomes of interest include patient‐reported outcomes (i.e., quality of life, diabetes treatment satisfaction, diabetes distress, awareness of hypoglycaemia, and attitudes towards hypoglycaemia), standard glucose metrics derived from blinded CGM using the Dexcom G7 (Dexcom Inc., San Diego, USA), and haemoglobin A1c (HbA1c) values obtained from patient records.
2.5. Study procedures
2.5.1. Participant recruitment and screening
To ensure the inclusion of participants with a broad range of health literacy levels, all people attending one of the above‐mentioned outpatient clinics will be informed about the study via their healthcare team and recruitment flyers. Diverse advertisement channels (e.g., social media, diabetes newsletters, magazines) will be used. Persons indicating an interest will receive the participant information sheet and the informed consent form. To accommodate individuals with limited literacy skills, an audio version of the participant information will be prepared and disseminated (if needed). All participants will be given time to reflect on their potential participation and ask questions before deciding, after which they will be asked to sign the informed consent form.
2.5.2. Study visits
The study will consist of two visits over the course of 2 weeks (Figure 1).
FIGURE 1.
EDUCATE study timeline and overview. Study devices (i.e., Dexcom G7, ActivPAL4™, goFOOD™Lite); CGM, continuous glucose monitoring; DNT‐15, Diabetes Numeracy Test; DTSQs, Diabetes Treatment Satisfaction Questionnaire status version; HLQ, Health Literacy Questionnaire; PROs, patient‐reported outcome questionnaires; READHY, Readiness and Enablement Index for Health Technology (i.e., ADDQoL‐19, Audit of Diabetes Dependent Quality of Life‐19; PAID, Problem Areas in Diabetes; A2A, Attitudes to Awareness of Hypoglycaemia; HFS‐II, Hypoglycaemia Fear Survey‐II; Gold score; Clarke questionnaire; Pedersen‐Bjergaard method); FFQ, food frequency questionnaire.
During visit 1, clinical data and demographics will be recorded through interview and from the participant's electronic health record, and a routine physical examination (including anthropometric data and blood pressure) will be performed. Participants will complete the HLQ, DNT‐15, and Diabetes Treatment Satisfaction Questionnaire status‐version (DTSQs). 11 The study devices (i.e., activPAL4™ and the blinded Dexcom G7) will be mounted, and the goFOOD™Lite application will be installed on the participant's smartphone.
For the subsequent 2 weeks, participants will wear the activPAL4™ and the Dexcom G7 CGM. As the Dexcom G7 CGM only records for 10 days, participants will be instructed to switch to a new CGM on day 10. All participants receive extensive instructions, verbal and written, on how to replace and pair the CGM. If needed, a virtual or in‐person meeting will be arranged to assist with CGM replacement. Participants are asked to complete a set of questionnaires [i.e., READHY, 2 Audit of Diabetes Dependent Quality of Life‐19 (ADDQoL‐19), 12 Problem Areas in Diabetes (PAID), 13 Attitudes to Awareness of Hypoglycaemia (A2A), 14 Hypoglycaemia Fear Survey‐II (HFS‐II), 15 Clarke questionnaire, 16 Pedersen‐Bjergaard method 17 and Gold score 18 ] at home through the electronic case report form in REDCap. Dietary patterns will be recorded by participants via the goFOOD™Lite 9 smartphone application by capturing meal images.
Visit 2 marks the end of the study. Study devices will be collected, and outstanding questionnaires will be completed. Participants living in the Netherlands will additionally complete the Maastricht FFQ and the chrono‐nutrition questionnaire. 10
2.6. Data collection
Study data will be gathered from electronic health records, the study devices, self‐reported questionnaires and by conducting patient interviews. All study measurements and procedures will be performed by trained research staff using standardised protocols. All data will be stored in an electronic case report form, REDCap. 19
2.6.1. Demographics
Detailed data on demographics (e.g., age, gender, ethnicity, socio‐economic status using standardised and country‐specific categories, etc.) will be obtained.
2.6.2. Anthropometric data
Anthropometric data (i.e., height, weight and waist‐to‐hip circumference) and blood pressure measured using an automated oscillometric office blood pressure device (average of three readings) will be collected via physical examination.
2.6.3. Clinical/laboratory data
Data relating to the onset and progression of diabetes (i.e., glycaemic status, complications, duration, treatment and comorbidities) will be collected from the participant's electronic health record and/or by interview.
2.6.4. Questionnaires
Several questionnaires will be administered in this study; a brief description of each is provided below. All questionnaires are validated, widely used in clinical trials and available in multiple languages.
Health literacy (HLQ) 7
The HLQ is a translated and culturally adapted instrument to assess health literacy. The nine domains of the HLQ (with 44 items in total) are intended to assess individuals' experiences in comprehending, accessing, and using health information and services.
Numeracy skills (DNT‐15) 8
The DNT‐15 is designed to assess numeracy skills in people with type 1 and type 2 diabetes. This performance‐based questionnaire tests a wide range of applied quantitative skills essential to the daily care of people with diabetes.
Individual's health technology readiness (READHY) 2
Health technology skills will be assessed using the Readiness and Enablement Index for Health Technology questionnaire.
Quality of life (ADDQoL‐19) 12
The ADDQoL‐19 will be used to assess quality of life in individuals with type 1 and type 2 diabetes. This validated instrument captures both overall quality of life and the specific impact of diabetes on daily living.
Diabetes treatment satisfaction (DTSQs) 11
The DTSQs will be administered to assess diabetes treatment satisfaction.
Problem areas in diabetes (PAID) 13
Diabetes‐related emotional distress will be assessed using the PAID questionnaire. This instrument describes negative emotions related to diabetes (e.g., fear, anger, frustration).
Attitudes to awareness (A2A) 14
The A2A will be administered to assess the attitudes to awareness of hypoglycaemia.
Fear of hypoglycaemia (HFS‐II) 15
The short version of the HFS‐II will be administered to assess fear of hypoglycaemia in people with diabetes.
Awareness of hypoglycaemia
Hypoglycaemia awareness will be assessed using the Gold score, 18 the Clarke questionnaire, 16 and the updated Pedersen‐Bjergaard method. 17
2.6.5. Glycaemic parameters
Standard glucose metrics 20 will be collected via a blinded Dexcom G7 CGM. All glucose data will be transferred from the Dexcom G7 reader to REDCap during the second visit. The latest HbA1c will be retrieved from the electronic health records. If no value is available for the past 3 months, HbA1c will be measured in venous blood.
2.6.6. Health behaviour
Physical activity
Physical activity will be recorded and quantified by the activPAL4™ monitor. The activPAL™ is well validated for assessing step counts and classifying physical activity intensity in natural environments. 21
Dietary intake
goFOOD™Lite 9 is used as a smartphone‐based food diary, whereby users can capture meal images or barcodes of packaged products along with their timestamps and maintain a history of the entries.
Participants living in the Netherlands will complete the Maastricht FFQ 10 to obtain comprehensive data on habitual food, energy and nutrient intake. This validated instrument contains 253 food items, assessing the intake of energy, macronutrients, types of fatty acids, dietary fibre and several essential micronutrients. Based on the FFQ data, the Dutch healthy diet index 2015 will be calculated, which measures diet quality. 22 To evaluate habitual timing of dietary intake (i.e., frequency, regularity and time window), a chrono‐nutrition questionnaire will be administered. 23 This questionnaire requests participants to log the habitual clock times for waking up, eating and drinking.
Smoking and alcohol consumption
Smoking status and alcohol consumption will be collected directly from the participants.
2.7. Data management
Data obtained during the trial will be defined and stored in accordance with the FAIR principles (Findability, Accessibility, Interoperability and Reusability). The handling of personal data will comply with the EU General Data Protection Regulation (GDPR) and national laws. REDCap is a secure, GDPR and Health Insurance Portability and Accountability Act‐compliant electronic case report form (eCRF), which will be used for data storage. 19 Meal images captured via the goFOOD™Lite app are securely stored on the GDPR‐compliant goFOOD™ server located on the secured network of the University of Bern, with no confidential information transmitted. All data collected throughout the study will be pseudonymised and will be stored for the next 15 years.
2.8. Statistical considerations
2.8.1. Sample size calculation
To explore the health literacy skills among the participants, a sample size was calculated with a confidence level of 95%, a standard error of precision of 0.1 and a standard deviation (SD) of 0.62 based on a previous study addressing health literacy in people with type 1 diabetes. 24 Because of cultural, educational and healthcare differences in the four participating countries, we anticipated a greater variety, for which the SD was increased to 0.7, resulting in a sample size of 188 participants. To account for a 10% discontinuation rate, the final sample size was set at 209 participants.
2.8.2. Statistical analysis plan
Results will be presented as mean ± SD for normally distributed variables and as median with first and third quartiles for skewed data. The independent‐sample t‐test or the Wilcoxon rank‐sum test will be used to compare differences between continuous variables, depending on whether the data are normally or non‐normally distributed, respectively. The chi‐squared test will be used for categorical variables and Fisher's exact test (in case of expected low cell counts of 5 or less).
The association of (digital) health literacy and numeracy scores with participant characteristics (i.e., glycaemic control, health behaviour, and PROs) will be estimated using linear regression models. Demographic and clinical characteristics, including age, gender, HbA1c, treatment modality, diabetes duration and clinical site, will be included in the multivariable analysis as potential confounders. Educational status will not be included in the model because of the high collinearity between this variable and health literacy, which potentially induces overadjustment. The regression coefficient will be presented with its 95% confidence intervals and p‐values. Log‐transformations or non‐parametric tests will be performed if the non‐linearity assumptions are not met.
CGM data will be handled in accordance with the recommendations outlined in the CGM consensus statement for clinical trials. 20
Statistical significance will be inferred at a two‐tailed p‐value of <0.05, and missing data will be imputed using multiple imputations with fully conditional specifications. The number of imputations will be set to the percentage of incomplete records. We will follow the American Statistical Association Statement and primarily focus on clinical relevance, and only secondarily on the level of significance. 25 The latest version of R will be used for the data analysis (R Core Team [2024], R Foundation for Statistical Computing, Vienna, Austria).
2.9. Dissemination
Findings will be documented and disseminated in scientific manuscripts submitted to peer‐reviewed scientific journals for publication. To inform participants and the wider public, findings will be presented at national and international conferences, shared on social media and included in newsletters.
3. DISCUSSION
Although often overlooked, digital health literacy and numeracy are key elements in diabetes self‐management. This has become even more important due to the emergence of sophisticated and accessible technologies aimed at supporting people with diabetes, to optimize their self‐management and enhance autonomy. These technological advancements include CGM, continuous subcutaneous insulin infusion (CSII), bolus calculators, automated insulin delivery (AID) systems and mobile health applications. Over the years, and particularly following the COVID‐19 pandemic, the implementation of digital consultations and telehealth has expanded. This shift may exacerbate difficulties for individuals with low digital health literacy in understanding, accessing and applying health information in their daily self‐management. Understanding and adapting diabetes self‐management strategies to attain glycaemic targets is crucial to promote long‐term health benefits, such as preventing vascular complications, improving general well‐being and reducing diabetes‐related distress. In Western Europe, the life expectancy gap for people with type 1 diabetes is up to 17 years and for type 2 diabetes up to 16 years, depending on the age of onset. 26 , 27 Effective diabetes management is imperative to close this life expectancy gap.
Better understanding of (digital) health literacy levels among people with insulin‐treated diabetes and its associations with health behaviour, glycaemic status and PROs is critical, given ongoing technological advancements. Individuals with limited health literacy encounter multiple barriers to accessing and effectively using these technologies because they lack the skills necessary to navigate apps, interpret data, troubleshoot device issues, or understand user interfaces. The extensive data generated by CGM devices can lead to information overload, often leaving people overwhelmed. 28 A recent study in Canada reported that the use of CGM and insulin pumps was lowest among the most socio‐economically deprived, potentially contributing to disparities in glycaemic control and prevalence of severe hypoglycaemia. 29 Disparities in needed care may be further influenced by differences between diabetes types. Individuals with type 1 diabetes and individuals on MDI often require more intensive management, including frequent blood glucose monitoring and regular insulin administration. For these individuals, domains like numeracy and reading comprehension are particularly vital for effective self‐care. 5 The management of type 2 diabetes often involves oral pharmacotherapy and lifestyle changes, where competencies in communication and the ability to understand and implement lifestyle changes play a significant role. 3
Previous studies suggest that higher health literacy and diabetes education can improve glycaemic control measured by CGM metrics. A Dutch cross‐sectional study found that lower health literacy was associated with less diabetes knowledge, higher HbA1c levels, and reduced adherence to self‐care behaviours (e.g., physical activity and self‐monitoring). 30 (Digital) health literacy, lifestyle behaviours, psychological factors and technology in diabetes management interact in complex ways. Although this interaction is not yet fully elucidated, it is both measurable and suitable for systematic characterisation. Nevertheless, to our knowledge, no single European study has comprehensively examined the combined roles of (digital) health literacy, numeracy, CGM, lifestyle and PROs. By including CGM‐, health behaviour‐ and PRO data, the EDUCATE study may help to disclose whether and to what extent disparities in health literacy contribute to factors that limit the use of technology in people with diabetes. The EDUCATE study will provide insights tailored to diverse socio‐economic, cultural contexts and health literacy status, offering unique perspectives on factors relevant for individualising diabetes care. By addressing these challenges, the healthcare system can enhance support for individuals with diabetes, helping to achieve optimal management and improve quality of life.
AUTHOR CONTRIBUTORS
EJdB and BEdG designed the study. EJdB, SMJvK and BEdG designed the statistical analysis plan. EJdB wrote the first version of the manuscript with input from CFD and BEdG. CH‐S, NMD, SB‐M, MTO, LAR, IP, AW, JP, PRM, EZ, GK, SA, AM, KM, SMJvK, SM, MMJvG, UP‐B, BEdG and CFD critically reviewed and edited the manuscript. All authors gave final approval of the version submitted for publication.
FUNDING INFORMATION
This research has received funding from the European Union under the Horizon Europe Research and Innovation Actions (RIA) programme, and the Swiss Confederation—State Secretariat for Education, Research and Innovation (SERI) within project grant no. 101057730.
CONFLICT OF INTEREST STATEMENT
MTO received speaker fees from Novo Nordisk and serves on the scientific advisory board for decide Clinical Software GmbH (GlucoTab). UP‐B has received speakers' fees from Sanofi and Novo Nordisk and has served on advisory boards for Sanofi, Novo Nordisk and Vertex. BEdG has received research support from Novo Nordisk. All other authors have no competing interests to disclose. The authors declare that there are no relationships or activities that might bias, or be perceived as biased, in this work.
ETHICS STATEMENT
This study was approved by the Medical Research Ethics Committee in the Netherlands (trial registration number: NL‐OMON57080).
Supporting information
Table S1.
ACKNOWLEDGEMENTS
The authors would like to express their gratitude to the members of the patients advisory committee within the MELISSA project and to Dexcom International Limited for supporting this study. The authors acknowledge the ownership of all instruments and questionnaires by their respective rights holders and express their gratitude for the permission to use these instruments. The Diabetes Treatment Satisfaction Questionnaire (DTSQs) and the Audit of Diabetes‐Dependent Quality of Life (ADDQoL19) are copyrighted by Prof. Clare Bradley, Health Psychology Research, UK. These questionnaires were licensed for use in this trial by Health Psychology Research Ltd. (HPR Ltd).
den Brok EJ, Hornborg‐Svensson C, Dimitropoulou NM, et al. Study protocol for exploring diabetes numeracy and health literacy across Europe (EDUCATE): A multicentre cross‐sectional study. Diabet Med. 2025;42:e70140. doi: 10.1111/dme.70140
Contributor Information
Elisabeth J. den Brok, Email: lisa.denbrok@maastrichtuniversity.nl.
Cassy F. Dingena, Email: cassy.dingena@maastrichtuniversity.nl.
DATA AVAILABILITY STATEMENT
The data sets generated and/or analysed during the EDUCATE study will be available from the corresponding author upon reasonable request or published with future publications.
REFERENCES
- 1. Abel ED, Gloyn AL, Evans‐Molina C, et al. Diabetes mellitus—Progress and opportunities in the evolving epidemic. Cell. 2024;187(15):3789‐3820. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Kayser L, Rossen S, Karnoe A, et al. Development of the multidimensional readiness and enablement index for health technology (READHY) tool to measure individuals' health technology readiness: initial testing in a cancer rehabilitation setting. J Med Internet Res. 2019;21(2):e10377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Marciano L, Camerini A‐L, Schulz PJ. The role of health literacy in diabetes knowledge, self‐care, and glycemic control: a meta‐analysis. J Gen Intern Med. 2019;34:1007‐1017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Sørensen K, Pelikan JM, Röthlin F, et al. Health literacy in Europe: comparative results of the European health literacy survey (HLS‐EU). European J Public Health. 2015;25(6):1053‐1058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Cavanaugh K, Huizinga MM, Wallston KA, et al. Association of numeracy and diabetes control. Ann Intern Med. 2008;148(10):737‐746. [DOI] [PubMed] [Google Scholar]
- 6. Chung WK, Erion K, Florez JC, et al. Precision medicine in diabetes: a consensus report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2020;63(9):1671‐1693. doi: 10.1007/s00125-020-05181-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Osborne RH, Batterham RW, Elsworth GR, Hawkins M, Buchbinder R. The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ). BMC Public Health. 2013;13:1‐17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Huizinga MM, Elasy TA, Wallston KA, et al. Development and validation of the diabetes numeracy test (DNT). BMC Health Serv Res. 2008;8:1‐8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Papathanail I, Abdur Rahman L, Brigato L, et al. The nutritional content of meal images in free‐living conditions—automatic assessment with goFOODTM. Nutrients. 2023;15(17):3835. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. van Dongen MCJM, Wijckmans‐Duysens NEG, den Biggelaar LJCJ, et al. The Maastricht FFQ: development and validation of a comprehensive food frequency questionnaire for the Maastricht study. Nutrition. 2019;62:39‐46. [DOI] [PubMed] [Google Scholar]
- 11. Lewis KS, Bradley C, Knight G, Boulton AJ, Ward JD. A measure of treatment satisfaction designed specifically for people with insulin‐dependent diabetes. Diabet Med. 1988;5(3):235‐242. [DOI] [PubMed] [Google Scholar]
- 12. Bradley C, Todd C, Gorton T, Symonds E, Martin A, Plowright R. The development of an individualized questionnaire measure of perceived impact of diabetes on quality of life: the ADDQoL. Qual Life Res. 1999;8:79‐91. [DOI] [PubMed] [Google Scholar]
- 13. Welch GW, Jacobson AM, Polonsky WH. The problem areas in diabetes scale: an evaluation of its clinical utility. Diabetes Care. 1997;20(5):760‐766. [DOI] [PubMed] [Google Scholar]
- 14. Rogers HA, De Zoysa N, Amiel SA. Patient experience of hypoglycaemia unawareness in type 1 diabetes: are patients appropriately concerned? Diabet Med. 2012;29(3):321‐327. [DOI] [PubMed] [Google Scholar]
- 15. Grabman J, Vajda Bailey K, Schmidt K, et al. An empirically derived short form of the Hypoglycaemia fear survey II. Diabet Med. 2017;34(4):500‐504. [DOI] [PubMed] [Google Scholar]
- 16. Clarke WL, Cox DJ, Gonder‐Frederick LA, Julian D, Schlundt D, Polonsky W. Reduced awareness of hypoglycemia in adults with IDDM: a prospective study of hypoglycemic frequency and associated symptoms. Diabetes Care. 1995;18(4):517‐522. [DOI] [PubMed] [Google Scholar]
- 17. Pedersen‐Bjergaard U, Færch L, Thorsteinsson B. The updated Pedersen‐Bjergaard method for assessment of awareness of hypoglycaemia in type 1 diabetes. Dan Med J. 2022;69(9):A01220041. [PubMed] [Google Scholar]
- 18. Gold AE, Macleod KM, Frier BM. Frequency of severe hypoglycemia in patients with type I diabetes with impaired awareness of hypoglycemia. Diabetes Care. 1994;17(7):697‐703. [DOI] [PubMed] [Google Scholar]
- 19. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377‐381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Battelino T, Alexander CM, Amiel SA, et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 2023;11(1):42‐57. [DOI] [PubMed] [Google Scholar]
- 21. Edwardson CL, Winkler EAH, Bodicoat DH, et al. Considerations when using the activPAL monitor in field‐based research with adult populations. J Sport Health Sci. 2017;6(2):162‐178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Looman M, Feskens EJM, de Rijk M, et al. Development and evaluation of the Dutch healthy diet index 2015. Public Health Nutr. 2017;20(13):2289‐2299. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Świątkiewicz I, Mila‐Kierzenkowska C, Woźniak A, et al. Pilot clinical trial of time‐restricted eating in patients with metabolic syndrome. Nutrients. 2021;13(2):346. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Olesen K, Reynheim ALF, Joensen L, et al. Higher health literacy is associated with better glycemic control in adults with type 1 diabetes: a cohort study among 1399 Danes. BMJ Open Diabetes Res Care. 2017;5(1):e000437. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Wasserstein RL, Lazar NA. The ASA statement on p‐values: context, process, and purpose. Am Stat. 2016;70:129‐133. [Google Scholar]
- 26. Kaptoge S, Seshasai SRK, Sun L, et al. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high‐income countries: 23 million person‐years of observation. Lancet Diabetes Endocrinol. 2023;11(10):731‐742. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Rawshani A, Sattar N, Franzén S, et al. Excess mortality and cardiovascular disease in young adults with type 1 diabetes in relation to age at onset: a nationwide, register‐based cohort study. Lancet. 2018;392(10146):477‐486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Patton SR, Clements MA. Psychological reactions associated with continuous glucose monitoring in youth. J Diabetes Sci Technol. 2016;10(3):656‐661. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Simba S, Von Oettingen JE, Rahme E, Ladd JM, Nakhla M, Li P. Socioeconomic disparities in glycemic management in children and youth with type 1 diabetes: a retrospective cohort study. Can J Diabetes. 2023;47(8):658‐664. [DOI] [PubMed] [Google Scholar]
- 30. Van Der Heide I, Uiters E, Rademakers J, Struijs JN, Schuit AJ, Baan CA. Associations among health literacy, diabetes knowledge, and self‐management behavior in adults with diabetes: results of a dutch cross‐sectional study. J Health Commun. 2014;19(sup2):115‐131. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1.
Data Availability Statement
The data sets generated and/or analysed during the EDUCATE study will be available from the corresponding author upon reasonable request or published with future publications.