does carbohydrate counting from diabetes onset improve...

Vt 2016

Master thesis, 30 hp

Department of food and nutrition

Does carbohydrate counting from diabetes

onset improve glycemic control in children

and adolescents with type 1 diabetes?

A clinical prospective study with a cross sectional

questionnaire.

Förbättras glykemisk kontroll av kolhydraträkning från debut hos barn och ungdomar med diabetes typ 1? En klinisk prospektiv studie med en tvärsnittsenkät.

Elisabeth Jelleryd

ABSTRACT

Background Carbohydrate counting is a method used to calculate insulin doses to meals, in

the treatment of diabetes type 1. Few studies are available with a clear consensus on its

efficacy and effect on anthropometrics in children and adolescents.

Aim To evaluate if carbohydrate counting as treatment method in diabetes type 1 improved

glycemic control and anthropometrics compared to conventional treatment, one and two years

after onset in children and adolescents at Astrid Lindgren children’s hospital. A secondary

aim was to explore patients and caregivers perception of insulin dosage to meals with focus

on efficacy, time consumption and adherence.

Method A clinical prospective study was performed on data collected from the Swedish

pediatric quality registry (Swediabkids). Children with diabetes onset between 2010 and 2014

registered at Astrid Lindgren Children’s hospital (n=371) were included and divided into two

groups, carbohydrate counters and non-carbohydrate counters. Normal distribution was

assumed and parametric tests were performed. The registry data was complemented with a

web-based questionnaire providing information on perception of carbohydrate counting,

answered by 78 subjects.

Results Carbohydrate counting reduced insulin requirements (p<0.001) and eliminated

differences between pump- and pen users (p<0.001) as well as differences between boys and

girls. Glycemic control was not improved by carbohydrate counting one and two years after

diabetes onset (p=0.233, p=0.295). An adverse effect was increased body mass index standard

deviation score (BMI-sds) (p=0.044), especially amongst girls (p=0.038).

Conclusion Carbohydrate counting lowers insulin requirements with maintained glycemic

control. Contradictory, greater weight gain was found in the carbohydrate counting group,

especially among girls. A plausible explanation is that carbohydrates have taken focus off

protein- and fat intake in combination with a more liberal approach to energy dense foods,

causing excess energy intake. The strength of carbohydrate counting does not lie in its ability

to lower HbA1c-values but as a helpful tool, which patients are happy to use.

SAMMANFATTNING

Bakgrund Kolhydraträkning är en metod som används för att beräkna insulindoser till

måltider, i behandlingen av diabetes typ 1. Få studier finns för att ge en samlad konsensus

gällande dess effekt på glykemisk kontroll och tillväxt hos barn och ungdomar.

Syfte Att utvärdera om införandet av kolhydraträkning som behandlingsmetod vid diabetes

typ 1 påverkat metabol kontroll och tillväxt i jämförelse med konventionell metod, ett och två

år efter diabetesdebut. Ett andra syfte var att utforska patienters och vårdnadshavares

uppfattning om insulindosering till måltider med fokus på effektivitet, tidskonsumtion och

följsamhet.

Metod En klinisk prospektiv studie utfördes med data inhämtad från Nationellt

kvalitetsregister för barn och ungdomar med diabetes (Swediabkids). Barn och ungdomar som

debuterade med diabetes typ 1 på Astrid Lindgrens barnsjukhus mellan 2010 och 2014

(n=371) inkluderades i studien och delades in i två grupper baserat på debutdatum;

kolhydraträknare och icke-kolhydraträknare. Materialet bedömdes som normalfördelat och

parametriska test utfördes. En tvärsnittsenkät administrerades till studiedeltagarna för att

införskaffa fördjupad information om patienters och vårdnadshavares uppfattning om

insulindosering till måltider. Den webbaserade enkäten besvarades av 78 deltagare.

Resultat Kolhydraträkning reducerade insulinbehovet (p<0.001) och jämställde

insulinbehovet mellan pump- och pennanvändare (p<0.001) liksom skillnader mellan pojkar

och flickor inom gruppen. Glykemisk kontroll förändrades inte av kolhydraträkning ett och

två år efter debut (p=0.233, p=0.295). En oönskad effekt av kolhydraträkningen var en ökning

i BMI-sds (p=0.044), speciellt hos flickor (p=0.038).

Slutsats Kolhydraträkning från diabetesdebut sänker insulinbehov med bibehållen

glykemisk kontroll. Motsägelsefullt, så fanns en viktökning i gruppen som använde

kolhydraträkning, speciellt hos flickor. En möjlig förklaring är att kolhydrater har tagit fokus

från protein- och fettintag tillsammans med en mer frikostig syn på energität mat, vilket har

orsakat ökat energiintag. Styrkan i kolhydraträkning ligger inte i dess förmåga att förbättra

glykemisk kontroll men som ett användarvänligt verktyg som patienterna är nöjda med.

TABLE OF CONTENT

1 BACKGROUND ............................................................................................................................. 6 1.1 DIABETES AND NUTRITIONAL MANAGEMENT ............................................................................ 6 1.2 DIABETES IN CHILDREN AND ADOLESCENTS ............................................................................. 6 1.3 DIABETES AND CARBOHYDRATE COUNTING.............................................................................. 6 1.4 DIABETES COMPLICATIONS ........................................................................................................ 7 1.4 CARBOHYDRATE COUNTING AT ASTRID LINDGREN CHILDREN’S HOSPITAL ............................. 7

2 AIM .................................................................................................................................................. 8

3 METHOD ........................................................................................................................................ 8 3.1 STUDY DESIGN ........................................................................................................................... 8 3.2 DATA COLLECTION .................................................................................................................... 8

3.2.1 Registry data ...................................................................................................................... 9 3.2.2 Questionnaire ................................................................................................................... 10

3.3 STATISTICAL ANALYSIS ........................................................................................................... 10 3.3.1 Registry data .................................................................................................................... 11 3.3.2 Questionnaire ................................................................................................................... 11

3.4 ETHICAL CONSIDERATIONS ...................................................................................................... 11

4 RESULTS ...................................................................................................................................... 11 4.1 REGISTRY DATA RESULTS ........................................................................................................ 11

4.1.1 Glycemic control, HbA1c ................................................................................................. 12 4.1.2 BMI-sds ............................................................................................................................ 13 4.1.3 Insulin requirements ......................................................................................................... 14 4.1.4 Insulin administration, pen and pumps. ........................................................................... 15 4.1.5 Gender differences ........................................................................................................... 15 4.1.6 Hypoglycemia ................................................................................................................... 17

4.2 RESULTS FROM THE QUESTIONNAIRE ...................................................................................... 17

5 DISCUSSION ................................................................................................................................ 19 5.1 METHOD DISCUSSION............................................................................................................... 19 5.2 RESULT DISCUSSION ................................................................................................................ 20

6 CONCLUSION ............................................................................................................................. 22

7 ACKNOWLEDGEMENTS.......................................................................................................... 22

8 REFERENCES .............................................................................................................................. 23 Appendix 1. Survey on insulin dosing to meals and diabetes type 1. Appendix 2. Invitation to participate in survey.

6

1 BACKGROUND

1.1 Diabetes and nutritional management

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease characterized by

hyperglycaemia due to destruction of the insulin secreting beta cells (1, 2). The treatment is

exogenous insulin therapy as well as nutritional management. Recommendations and

guidelines for glycaemic control include a variety of insulin regimens as well as a number of

diet approaches. Among those approaches are fixed meal plans, exchange or portion systems,

glycemic index, and carbohydrate counting (1).

The carbohydrate content of a meal is the main factor affecting the postprandial glycaemic

response (1-3). Different approaches to regulate carbohydrate intake, as well as fat and protein

intake, are available to modify insulin dosage to meals in order to achieve acceptable

postprandial blood glucose values. Historically, diet approaches often meant that the

carbohydrate content was precisely controlled or restricted (3). In the 1990s, carbohydrate

counting became a meal planning approach with less rules and structure regarding food intake

after it was one of four meal planning approaches in the Diabetes Control and Complications

Trial (DCCT)(1, 4). The DCCT concluded that it was an effective method in helping people

achieve glycemic control while allowing flexibility in their food choice (4).

1.2 Diabetes in children and adolescents

The International Society for Paediatric and Adolescent diabetes (ISPAD) guidelines, as well as

the American Diabetes Association (ADA), emphasize that children and adolescents should not

restrict their carbohydrate intake (1, 2). Optimal growth and development must be achieved in

children and adolescents, why restriction of carbohydrates as well as controlled and rigid meal

plans could interfere with the energy intake and affect a child’s growth. Flexible eating and

simple meal planning approaches is often desirable when working with children since it can be

difficult to predict a child’s intake and changes in food preferences (2). Dietary regulation and

lack of freedom are often reported by adolescents to be among the worst aspects of living with

diabetes (3).

1.3 Diabetes and carbohydrate counting

Carbohydrate counting is included in official guidelines for the treatment of T1DM but the use

of the method varies greatly between countries as well as nationally (1, 2, 5). Three levels of

carbohydrate counting have been identified; basic, intermediate and advanced (1). Calculating a

bolus dose of insulin using advanced carbohydrate counting consists of three parts; correction

insulin, meal insulin and an adjustment fraction for increasing or decreasing the bolus size if

there are changes in activity level or health status. Knowledge of your insulin sensitivity,

insulin to carbohydrate (I:C) ratios as well as carbohydrate contents in food is required when

using advanced carbohydrate counting (5).

Though there are some studies such as the DCCT, which included an adolescent population,

there is a lack of studies on young people with T1DM and carbohydrate counting (1, 3, 4). A

few interventions of education programs have been piloted with contradicting results but all

reported improved quality of life outcomes (6, 7).

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1.4 Diabetes complications

Complications from T1DM include both acute and long-term complications as well as

increased body weight (1, 8, 9). The risk of hypoglycemic events is a limiting factor in

achieving optimal glycemic control, especially due to the emotional distress for both children

and caregivers. Young children and adolescents as well as a high mean HbA1c are associated

with increased risk of hypoglycemia (9). In the DCCT, an adolescent subgroup had higher rates

of severe hypoglycemic events as well as higher HbA1c compared to the adult cohort (10).

New evidence indicate that incidence of severe hypoglycemia are declining (9). Studies where

carbohydrate counting together with intensive insulin therapy has been used describe

contradicting results on the effect of severe hypoglycemic events (10-12).

Long-term complications from T1DM include microvascular and macro vascular effects such

as retinopathy, nephropathy and neuropathy (2, 13). Intensive treatment at T1DM onset has

been shown to be a predictor for improved glycemic control as well as the avoidance of

complications later in life (14-16). The DCCT concluded that intensive insulin therapy, such as

multiple daily injections and continuous subcutaneous insulin infusion could reduce diabetic

complications (4).

Children with T1DM, at all ages and both sexes, are heavier than their non-diabetic peers (1).

Preventing overweight has become an important aspect in the treatment of T1DM and there are

guidelines for weight management (1). Because carbohydrate counting allows flexible eating it

has raised the question of overeating with weight gain as a consequence. The limited number of

studies on children and carbohydrate counting offer little guidance as results on weight changes

are diverging (5, 11, 17).

In weight as well as metabolic control, gender differences has been found (18). Åkesson and

collaborators explored HbA1c as well as other clinical variables at diabetes onset and at follow

up, and found clear gender differences in where girls had higher HbA1c at onset and

persistently over the years as well as an association between girls and higher body mass index

(BMI).

1.4 Carbohydrate counting at Astrid Lindgren children’s hospital

Astrid Lindgren Children’s Hospital in Stockholm has two pediatric diabetes clinics that reach

approximately 1000 children and adolescents, making it the largest clinic in Sweden.

According to the Swedish Pediatric Quality Registry (Swediabkids), Astrid Lindgren

Children’s Hospital rank average when comparing HbA1c results to other pediatric clinics in

Sweden (19). With a desire to improve the treatment for children and adolescents at Astrid

Lindgren Children’s Hospital, carbohydrate counting was introduced at January 1, 2012. All

newly diagnosed T1DM patients and their families were taught advanced level of carbohydrate

counting for insulin dosage to meals.

If the modification of standard treatment, to include advanced level of carbohydrate counting,

has had an effect on patients’ glycemic control has not yet been evaluated. Therefore, it is

interesting and important to evaluate clinical variables as well as how the patients and their

caregivers perceive carbohydrate counting. T1DM is a complex disease and can put great strain

on both patient and their caregivers. Adding extra workload in form of carbohydrate counting,

to families that are already strained from the disease itself could be questionable, therefore it

was important to understand how patients and caregivers perceived the method.

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2 AIM

The aim of this study was to evaluate if carbohydrate counting as a treatment method in T1DM

has affected glycemic control and anthropometrics compared to conventional treatment, one

and two years after onset in children and adolescents at Astrid Lindgren Children’s Hospital. A

secondary aim was to explore patients and caregiver’s perception of insulin dosage to meals

with regard to sense of efficacy, time consumption and adherence.

3 METHOD

3.1 Study design

This was a clinical prospective registry study combined with a cross sectional survey. Choice

of study method was prompted by the availability of registry data and the clear shift from one

treatment method to another (20). A quantitative study was the best way to assess metabolic

and anthropometric markers and to fulfill the aim. The cross sectional survey was a study-

specific, web-based questionnaire with questions regarding patients and caregiver’s perception

of methods to determine insulin dosage to meals. The survey enabled a better understanding of

the registry data and offered a viewpoint from the patient and their families.

3.2 Data collection

Data was collected in two ways; by extracting data from the Swediabkids registry, a sub-

registry for children and adolescent diabetes, which is a part of the national diabetes registry,

and by a web-based questionnaire (Figure 1). Data was obtained from the registry in October

2015. Invitation to fill out the questionnaire was sent out in the first week of November 2015.

9

488 patients diagnosed with T1DM

115 patients excluded for not having three visits with

registered HbA1c, TDI or height and weight.

373 patients met inclusion criteria

Two patients excluded. One for initially being diagnosed with

diabetes type 2. One had duplicate registrations in the

registry.

371 subjects included

3 subjects were not invited due to

protected identities

368 subjects were invited to answer a web-based

questionnaire

Non-carbohydrate counters (non-CHC)

n = 181

Carbohydrate counters (CHC)

n = 190

78 answers were collected during a 2-week response

time

Figure 1. Flow chart of data collection and subject selection. Data was collected from Swediabkids registry of

patients at Astrid Lindgren Children’s hospital, Solna- and Huddinge clinics, in October 2015.

3.2.1 Registry data

In Sweden, physicians register all children and adolescents with T1DM, in Swediabkids (21).

Every clinical visit to the endocrinologist or diabetes nurse is registered with one or all of the

following measures; HbA1c, weight and height, insulin regimens and requirements and

occurrence of severe hypoglycemia.

Data was obtained from the Swediabkids registry for 488 children and adolescents, aged 0 to 17

years, who were diagnosed with T1DM at Astrid Lindgren Children’s Hospital during the

period January 1, 2010, to December 31, 2013. Time range was chosen to include patients with

diabetes onset two years prior and post introduction of carbohydrate counting. All available

data on the following markers were extracted; date of diabetes onset, gender, age, weight,

height, HbA1c, total daily insulin (TDI) Units/kg, insulin regimen (insulin pump or pen

injections), basal insulin, rapid-acting insulin and registered episodes of severe hypoglycemia.

Inclusion criteria were as following: having registry data with minimum one metabolic or

anthropometric marker, at three months (30 – 120 days), one year (240 – 485 days) and two

years (610 – 850 days) after diabetes onset. Patients who did not meet those criteria were

excluded (n = 115). One subject was excluded for being diagnosed with diabetes type 2 prior to

the final diagnosis of T1DM and one person had been registered twice. This rendered a total of

371 subjects included in the study. Occurrence of additional visits within the assigned time

period rendered a structural assortment of data where information was ranked. Visits with

registered HbA1c, Units/kg, weight and height, in that order, were chosen over visits where

information was lacking. Occurrence of >1 registered visits with all data available, within a

10

time period the closest to the time period (90, 365 and 730 days, respectively) was chosen.

Subjects were divided into two groups based on date of diabetes onset, prior to 2012 were non-

carbohydrate counters (non-CHC) and after 2012 were carbohydrate counters (CHC). Internal

omissions were registered visits with one or more metabolic or anthropometric measures

missing. Due to missing values all analyses were performed on varying number of subjects.

3.2.2 Questionnaire

A study-specific questionnaire was designed using the online tool Google Forms

(www.google.com/forms) to gather information regarding perception of insulin dosage to meals

using carbohydrate counting or another method (Appendix 1). The questionnaire was sub-

divided into four parts. The first part included nine general background questions regarding the

responders’ diabetes answered by all with a final question that separated them into non-CHC or

CHC. The non-CHC group then answered a separate part with nine questions regarding their

approach to insulin dosage to meals as well as their opinions on their method and questions

regarding the flexibility in food choices. The CHC group answered a separate part with 23

questions regarding carbohydrate counting. Specifically, they were asked about their I:C-ratios,

insulin sensitivity factors, time consumption, how they estimate carbohydrate amounts and

contents of food and how they calculate their insulin dose. This group was also asked about

their opinion of the method and flexibility in food choices. Both groups had an opportunity to

answer the final part of the questionnaire, which was an open-ended question about insulin

dosage to meals. Responders aged less than 13 years of age were encouraged to receive

assistance from an adult.

Invitations by letter were sent to 368 subjects included in the study to answer a web-based

questionnaire (Appendix 2). Invitations were not sent to three subjects due to having protected

personal data. Subjects had a two-week response time and after one week all subjects received

a reminder by mail. In total, 78 answers were collected. External omission was n = 290 (79%).

Based on diabetes onset, 33 responders were assigned to the non-CHC group and 45 to the

CHC group. However, 69 of the responders, stated that they used carbohydrate counting

rendering them to answer questions about carbohydrate counting. Nine subjects answered

questions based on alternative methods to assess insulin doses to meals.

3.3 Statistical analysis

The data was analyzed using IBM SPSS Statistics version 23.0. The significance level was set

at p <0.05. Variables were tested for normality through visual inspection of histograms and

QQ-plots as well as assessing the means and standard deviation. Normality was assumed and

parametric tests were performed. Standard scores (Z-scores) of variables were calculated to

detect outliers; values falling outside the normal distribution. Independent t-test were used to

analyze differences between groups and paired t-test and multivariate ANOVA were used for

differences within groups, regarding HbA1c, TDI Units/kg and BMI standard deviation score

(sds). Categorical data were analyzed using Pearson Chi-square test and Chi-Square goodness

of fit.

To analyze occurrence and eventual differences in severe hypoglycemic events in the two

groups, all registered visits within 28 months were used to perform analyzes (n=371). Chi-

square test was used to analyze differences between the groups.

11

3.3.1 Registry data

Data is presented as mean and standard deviation. To examine sample distribution and

normality of data, subjects were stratified into groups based on HbA1c, BMI-sds and age.

HbA1c values were categorized as “low HbA1c” (<45 mmol/mol), “HbA1c within the clinics’

target range” (45-55 mmol/mol), “moderately high HbA1c” (55-70 mmol/mol) and “high

HbA1c” (>70 mmol/mol). BMI-sds were categorized as “underweight” (<-1, 0 SD), “normal

weight” (-1,0 – 1,3 SD), “overweight” (1,3 – 2,3 SD) and “obesity” (>2,3 SD) (22). Age was

categorized based on autonomy and puberty; “toddlers and pre-school children” (< 6 years),

school age (6 to 10 years), older school age (10 to 14 years) and adolescents (>14 years).

The change over time in BMI-sds, HbA1c and TDI Unit/kg, was calculated as the difference

between the three time periods, respectively, for each individual.

3.3.2 Questionnaire

Differences in answers between the non-CHC group and the CHC group were assessed by

using Pearson Chi-square test. Further statistical analyses were not performed due to the small

group of responders and the distribution of the responders with a large majority of carbohydrate

counters.

3.4 Ethical considerations

This study was based on data from the Swediabkids registry as a part of a quality control,

performed recurrent at the diabetes clinics at the hospital. Using data from the Swediabkids

registry solely on patients listed at Astrid Lindgren children’s hospital does not require

approval from ethical committees. Administrators of the registry at Astrid Lindgren children’s

hospital have approved the quality control and the extraction of data. The collected data were

treated confidentially, personal numbers were eliminated and replaced with study ID-number.

There was an ethical consideration to link questionnaire answers with registry data. To respect

ethical principles of the Declaration of Helsinki the following precautions to protect the

subject’, anonymity was taken. The questionnaire was administered to patients as an invitation

to participate and the information stated that participation was voluntary (Appendix 2). In order

to connect questionnaire answers with registry data responders were asked to voluntarily leave

personal identification numbers. All subjects, filling out the questionnaire, was informed that

the data was handled according to the personal data act. A member of the Swediabkids board

and the administrator of the registry at Astrid Lindgren children’s hospital were responsible for

a code key, whom decoded the questionnaire answers and thereafter returned them to me, the

researcher, so that the identities of the patients remain concealed.

4 RESULTS

4.1 Registry data results

Characteristics of the subjects are presented in Table 1. The mean age was significantly higher

in the CHC group at diabetes onset. Figure 2 show the subjects divided by age groups, which

reveal that there are significantly more 14-18-year olds in the CHC group (p=0.015).

12

Table 1. Characteristics of participants, at three months, one year and two years after diabetes onset, which

occurred 2010-2013. Astrid Lindgren Children’s hospital, Solna and Huddinge Clinics, 2015. Data presented as

means ± standard deviation where applicable.

Non-Carbohydrate

counters Carbohydrate counters p-value

Number 181 190

Age at onset 8.4 ± 4.34 9.3 ± 4.41 0.0401

Boys / Girls (n) 91 / 90 97 / 93 0.804

1

HbA1c (mmol/mol)

At 3 mo

At 1 yr

At 2 yr

52.5 ± 10.0 (n=174)

53.8 ± 11.2 (n=179)

56.2 ± 11.7 (n=175)

51.3 ± 9.82 (n=180)

52.5 ± 10.7 (n=187)

55.0 ± 10.6 (n=189)

0.2512

0.2332

0.2952

BMI-sds

At 3 mo

At 1 yr

At 2 yr

0.23 ± 1.00 (n=169)

0.31 ± 1.03 (n=178)

0.35 ± 1.01 (n=168)

0.28 ± 1.13 (n=169)

0.38 ± 1.05 (n=180)

053 ± 1.02 (n=177)

0.7032

0.4842

0.0962

TDI (Unit/kg)

At 3 mo

At 1 yr

At 2 yr

0.54 ± 0.24 (n=176)

0.66 ± 0.27 (n=179)

0.72 ± 0.33 (n=169)

0.49 ± 0.21 (n=174)

0.58 ± 0.28 (n=184)

0.65 ± 0.26 (n=179)

0.0312

0.0062

0.0462

Pen / Pump (n)

At 3 mo

At 1 yr

At 2 yr

115/19 (n=134)

125/43 (n=168)

96/81 (n=177)

135/15 (n=150)

124/60 (n=184)

90/97 (n=187)

0.2791

0.1491

0.2441

BMI-sds = Body Mass Index standard deviation score

TDI = Total daily insulin 1 Chi-Square test performed.

2 Independent T-test performed.

Figure 2. Age distribution between groups at 3 months (total n=371). P-values show tested differences between

each age group as well as for differences between all groups. Statistical analyses used were Chi-square and Chi-

square goodness of fit. Astrid Lindgren Children’s hospital, Solna and Huddinge Clinics, 2015.

4.1.1 Glycemic control, HbA1c

There was no significant difference in HbA1c between the groups, though the mean values

were all lower in the CHC group, Table 1. There was an increase in HbA1c over time for all

subjects (p<0.001), Figure 3. There were no significant differences between the groups when

subjects were stratified into defined ranges of HbA1c at any time point (p=0.697, p=0.617,

p=0.688, respectively).

13

Figure 3. a) Differences in mean HbA1c between time points (n=169-186). Significant increase in mean over time in

HbA1c analyzed using multivariate ANOVA. b) Changes in HbA1c from 3 mo-1 yr, 1-2 yrs and 0-2 yrs (n=169-186).

No Significant differences in change between time points between the groups, analyzed by using Independent T-test. The

value at 3 months is set to 0 to visualize the change. P-values are read left to right in the figure, 3 mo-1 yr, 1-2 yrs and 0-

2 yrs. Astrid Lindgren Children’s hospital, Solna and Huddinge Clinics, 2015.

4.1.2 BMI-sds

There were no mean differences in BMI-sds between the non-CHC group and the CHC group,

Table 1. BMI-sds increased over time in both groups but the change was significantly different

between the groups, Figure 4. The change in BMI-sds between year one and year two increased

significantly in the CHC group compared to the non-CHC group, Figure 4. Categorizing

subjects into weight categories showed no significant difference in distribution between the

non-CHC group and the CHC group at any time point (p=0.138, p=0.450, p=0.332,

respectively).

14

Figure 4. a) Differences in mean BMI-sds between time points (n=156-169). Significant increase over time between the

groups and a significant difference in change between the groups, analyzed using multivariate ANOVA. b) Changes in

BMI-sds from 3 mo-1 yr, 1-2 yrs and 0-2 yrs (n=156-169). Significant differences in change between time points

between the groups analyzed by using Independent T-test. The value at 3 months is set to 0 to visualize the change. P-

values are read left to right in the figure, 3 mo-1 yr, 1-2 yrs and 0-2 yrs. Astrid Lindgren Children’s hospital, Solna and

Huddinge Clinics, 2015.

4.1.3 Insulin requirements

Mean total daily insulin Units/kg increased over time in both groups, Table 1. Mean values

were significantly lower in the CHC group at all time points (p=0.031, p=0.06, p=0.046,

respectively). There was a difference in TDI over time between the groups, with lower insulin

requirement in the CHC group (p<0.001). Changes in TDI between three months and one year

and between year one and two show no significant differences, Figure 5.

Figure 5. a) Differences in mean TDI between time points (n=163-174). Significant difference over time between time

points and a significant difference in change between the groups, analyzed using multivariate ANOVA. b) Changes in

TDI from 3 mo-1 yr, 1-2 yrs and 0-2 yrs (n=163-174). No significant differences in change between time points between

the groups analyzed by using Independent T-test. The value at 3 months is set to 0 to visualize the change. P-values are

read left to right in the figure, 3 mo-1 yr, 1-2 yrs and 0-2 yrs. Astrid Lindgren Children’s hospital, Solna and Huddinge

Clinics, 2015.

15

4.1.4 Insulin administration, pen and pumps.

The use of insulin pumps increased proportionally in the two groups at all time points, Table 1.

At year two 46 % of the subjects used insulin pumps in the non-CHC group versus 52 % in the

CHC group. In the non-CHC group the pen users had a higher increase in TDI Units/kg

between year one and two than the pump users (p<0.001). This difference in insulin

requirements was not seen in the CHC group.

4.1.5 Gender differences

Table 2 shows characteristics of the non-CHC group and the CHC group divided by gender.

There were no significant differences in glycemic control between girls and boys in either

group, though a tendency towards higher HbA1c is found in girls in the non-CHC group.

Differences in insulin requirements were seen between gender in the non-CHC group, where

girls had a significantly higher TDI at year 2 (p=0.034). Boys in the non-CHC group had a

higher TDI at year 1 than boys in the CHC group (p=0.037). The same tendency can be found

for girls in the CHC-group at year one and two.

Figure 6 visualize that the change in BMI-sds were different between the groups. Girls in the

CHC-group had a significantly greater increase in BMI-sds between year one and two and

between three months and two years, respectively (p=0.044, p=0.038). No significant

differences were found in changes over time in HbA1c, TDI or BMI-sds between girls and boys

within the two groups.

16

Table 2. Characteristics of participants from a gender perspective, presented as means at three months, one year and two years after diabetes onset, which occurred 2010-2013.

Astrid Lindgren Children’s hospital, Solna and Huddinge Clinics, 2015. Differences between groups tested for significance with independent t-test. Data presented as means

±standard deviation (±SD) where applicable.

Non-carbohydrate counters Carbohydrate counters

P for differences between non-

CHC and CHC group

Boys Girls p-value Boys Girls p-value Boys Girls

HbA1c (mmol/mol)

At 3 mo

At 1 yr

At 2 yr

52.0 ± 9.63 (n=87)

53.2 ± 10.11 (n=90)

54.5 ± 10.93 (n=88)

52.9 ± 10.50 (n=87)

54.4 ± 12.10 (n=89)

57.9 ± 12.19 (n=87)

0.553

0.462

0.061

50.8 ± 10.08 (n=94)

51.9 ± 11.10 (n=95)

54.4 ± 9.29 (n=97)

51.8 ± 9.57 (n=86)

53.0 ± 10.22 (n=92)

55.5 ± 11.77 (n=92)

0.510

0.497

0.480

0.405

0.416

0.939

0.448

0.358

0.197

BMI-sds

At 3 mo

At 1 yr

At 2 yr

0.18 ± 0.99 (n=87)

0.25 ± 1.04 (n=90)

0.31 ± 1.02 (n=83)

0.30 ± 1.02 (n=82)

0.36 ± 1.01 (n=88)

0.38 ± 1.00 (n=85)

0.438

0.451

0.670

0.23 ± 1.16 (n=84)

0.37 ± 1.08 (n=90)

0.45 ± 1.06 (n=88)

0.33 ± 1.10 (n=85)

0.40 ±1.02 (n=90)

0.61 ± 0.97 (n=89)

0.563

0.882

0.288

0.752

0.440

0.402

0.844

0.845

0.124

TDI (Units/kg)

At 3 mo

At 1 yr

At 2 yr

0.52 ± 0.23 (n=90)

0.64 ± 0.24 (n=90)

0.66 ± 0.27 (n=85)

0.56 ± 0.25 (n=86)

0.68 ± 0.30 (n=89)

0.77 ± 0.37 (n=84)

0.208

0.309

0.034

0.46 ± 0.20 (n=88)

0.56 ± 0.27 (n=92)

0.63 ± 0.26 (n=92)

0.52 ± 0.21 (n=86)

0.60 ± 0.29 (n=92)

0.68 ± 0.27 (n=87)

0.060

0.311

0.190

0.071

0.037

0.365

0.198

0.071

0.066

Pen / Pump1

At 3 mo

At 1 yr

At 2 yr

62/10 (n=72)

64/23 (n=87)

46/45 (n=91)

53/9 (n=62)

61/20 (n=81)

50/36 (n=86)

1.000

0.860

0.366

67/10 (n=77)

61/32 (n=93)

49/46 (n=95)

68/5 (n=74)

63/28 (n=92)

41/51 (n=92)

0.279

0.639

0.381

1.000

0.261

1.000

0.166

0.399

0.074 TDI = Total daily insulin

BMI-sds = Body Mass Index standard deviation score 1 Number of pen- and pump users. Significance test using Chi-square tests

17

Figure 6. Change in BMI sds between 3 mo-1 yr, 1-2 yr and 3 mo-2 yr girls (n=77-86) and boys (n=76-86) separately

between the non-CHC group and the CHC group. The value at three months is set to 0 to visualize the difference. Groups

compared with independent t-test. P-values are read left to right in the figure 3 mo-1 yr, 1-2 yrs and 3 mo-2 yrs. Astrid

Lindgren Children’s hospital, Solna and Huddinge Clinics, 2015.

4.1.6 Hypoglycemia

There was no significant difference in frequency of hypoglycemia between the two groups, 37

in the non-CHC group vs. 31 in the CHC group (p=0.354).

4.2 Results from the questionnaire

Of the 78 responders 69 used carbohydrate counting (88.5 %). Of the 33 subjects who were in

the non-CHC group, 31 had learned carbohydrate counting over time and four had then

stopped. Two stated that they were not using, or had never used, carbohydrate counting. Three

subjects had been taught carbohydrate counting at diabetes onset but stopped.

A review of the responders show that mean T1DM duration was approximately the same as

the non-responders, 3,75 years vs. 4 years (p=0,169). The responders had a lower mean age

than the non-responders, 11,3 years vs. 12,5 years (p=0,035). The responders had a lower

HbA1c than the non-responders (p=0.003). When tested within each group, the same was

found for the CHC-group (p=0.014) but not for the non-CHC (p=0.099).

Figure 7 show that responders from the CHC group continued to use the method and used the

method to a greater extent compared to the non-CHC. This is also shown when the questions

were turned around; CHC are more likely to use the method to all their meals and non-CHC

were more likely to skip counting to meals.

18

Figure 7. Distribution of answers to the question if responders use carbohydrate counting and to what extent

(n=69) and if they skip counting carbohydrates to meals (n=69). Pearson Chi square performed to measure

statistical significance. Astrid Lindgren Children’s Hospital, Solna and Huddinge Clinics, 2015.

When asking if carbohydrate counting was a good method for insulin dosing to meals 96 % of

the responders marked “Yes, always” or “Yes, mostly” and 4 % marked “Yes, sometimes”.

When asked about time consumption, 96 % marked “quick” or “fairly quick” to count

carbohydrates to meals, 4 % stated “not so quick”.

Voluntary comments were left by 27 (34,5 %) responders at the end of the questionnaire,

which gave a good and varied depiction of the subjects’ perception of carbohydrate counting.

Of those comments, 37 % were assessed to be positive, stating that they were pleased with the

method. A quote from one person: “We think it is a very good method that we are happy to

have been taught”. Even though many comments were positive, 26 % indicated that the

method did not always work and that there were more confounders than carbohydrates that

needed to be taken into account when dosing insulin to meals. “Sometimes carbohydrate

counting works really well, sometimes it doesn’t work at all even though the same quotas are

19

used to the same foods”. Another quote: “To assess insulin doses you need to, besides

carbohydrate content, take into consideration the food consistency, fat- and fibre content as

well as activity and its intensity”. One perceptive comment was “We started with

carbohydrate counting so we don’t know anything else” but other subjects who were taught

the method after diabetes onset commented; “carbohydrate counting gives better doses than

before, but it’s still tricky with blood sugar and diabetes”. Some stated that carbohydrate

counting gets easier with time; “Very complicated at first, very flexible now”.

Conclusively a comment from a subject that perceived carbohydrate counting as positive but

would rather not do it at all, “Carbohydrate counting is better than guessing but carbohydrate

counting is also bad, because carbohydrate counting means you have diabetes and diabetes

sucks”.

5 DISCUSSION

5.1 Method discussion

Results presented in this study must be interpreted with caution. T1DM is a complex disease

with many confounding factors making it hard to single out one specific cause for change.

Annual reports from the Swediabkids registry show that mean HbA1c-levels have been

reduced the last five years, implying that time is a confounding factor (19). Other

confounding factors in this observational study are advancement in technical aids over time.

With this in mind, the time period in this study was limited and carbohydrate counting was

the predominant change made during that period.

Previous studies on carbohydrate counting and children and adolescents are few and have

small sample sizes (3, 17, 23). In a review from 2014, on the effect of carbohydrate counting

in T1DM patients, only four studies out of 27 were on children and adolescents and the

largest sample size of those studies were 44 (5). Therefore, the large sample size in this study

makes it of particular interest and makes any findings in this study important. The sample size

also gives power to statistical analyses and a good generalizability. Furthermore, all subjects

in the CHC group were taught carbohydrate counting from diabetes onset, which, to my

knowledge is the only study where no intervention occurred.

A limitation was that the sample selection was based on patients who were diagnosed with

T1DM prior to or after a change in treatment method at a cut-off time point. This does not

ensure that all subjects use the new method post cut-off, nor does it ensure that subjects

continue using the method within the two-year period studied or to what level the method is

used. The same goes for the subjects included as non-CHC, which is a finding in the

questionnaire where almost all responders used carbohydrate counting four to six years after

diabetes onset. Though some subjects might not have used carbohydrate counting it should

not influence the results due to the large sample size.

Using data from a registry offers good reliability, as the same data is available to extract at

any time. A limitation in the data used is the reporting to the registry, where human error is

possible when physicians and nurses add values into a database. To account for this, z-scores

were calculated to find outliers making it possible to detect values that divert remarkably from

the mean. The reporting of TDI to the registry is an approximated number and may or may

not be close to the actual insulin requirement. Furthermore, the use of carbohydrate counting

20

involve a change from patients using standard doses to meals, where calculating a mean TDI

was easy, to the use of I:C ratios where the insulin doses vary, making it more difficult to

accurately assess a mean TDI. Though the entry of registry data could entail human error,

which is a limitation, it is likely to be relatively constant over time. It is possible that

differences in basal insulin regimen could affect TDI but analyzes were not made due to

restriction in the scope of this thesis paper.

The response rate in the questionnaire was low and almost all responders were using

carbohydrate counting making it statistically difficult to compare answers between the groups.

The questionnaire was not validated and therefore answers need to be interpreted with this in

mind. A potential limit was the short response time as well as the need for internet access.

The responders had a significantly lower HbA1c than the non-responders as well as lower

mean age. A limit in the study is therefore lack of perspective from older patients who are not

using carbohydrate counting and with poor metabolic control. It is plausible that a great deal

of parents and caregivers have answered the questionnaire, and therefore the perspective from

the child itself is lost. The view on carbohydrate counting could be different from the

caregiver’s perspective and the child’s perspective and those potential differences are not

distinguished by the questionnaire. However, comparisons could be made between non-CHC

group and the CHC-group regarding their perceptions of the method carbohydrate counting.

In retrospect an advantage would have been to analyze registry data prior to administering the

questionnaire, offering an opportunity to tailor questions and better complement the analyzed

data. Except specific limitations mentioned the questionnaire did fulfill its aim to get a better

understanding of carbohydrate counting in aspects of time consumption, efficacy and

adherence.

5.2 Result discussion

The main outcomes from this study were that glycemic control was maintained after

introduction of carbohydrate counting but insulin requirements were reduced. Though insulin

requirements are uncertain values, these should remain constant over time and thus not

explaining the differences seen between the groups. A weight gain was found amongst the

carbohydrate counters, particularly in girls. No differences in hypoglycemic events were

found.

HbA1c did not differ between the CHC group and the non-CHC group. These findings are not

unique, instead it reflects the contradicting results reported by others (6, 7). One randomized

controlled trial showed a significant reduction in HbA1c two years after intervention (17).

Schmidt concludes his review from 2014 that the effect of carbohydrate counting on HbA1c

cannot be determined (5). What differentiates this present study from previous made is the

short diabetes duration and that no intervention occurred, both groups were taught different

insulin dosing methods from diabetes onset. The absence of an intervention is a strength since

the intervention itself can affect outcome in a study. On the other hand, confounders can

affect results in observational studies. In this study pubertal development and diabetes

duration could have had an effect on the result. At diabetes onset all children go in to

remission, which varies in duration from months to over a year and may mask poor metabolic

control. To further evaluate if carbohydrate counting affects glycemic control more long-term

studies examining between-group differences in remission duration are needed. Furthermore,

the absence of results on HbA1c does not speak against carbohydrate counting, instead it tells

us that conventional methods also work.

21

Carbohydrate counting was found to reduce insulin requirements in this study. It also

eliminated differences between insulin requirements in pen- and pump users. Subjects in the

non-CHC group with pen treatment increased their insulin requirements over time and insulin

requirement was lowered in pump users. These results resemble the findings of Brorsson’s

comparison between pen - and pump users, where patients treated with pumps had a lower

insulin requirement over time compared to pen users (24). Interestingly, the difference found

between pen- and pump users in the non-CHC group and in Brorsson’s study was not found

in the CHC group where no difference was found. This suggests that the key difference in

lower insulin requirements is the use of carbohydrate counting. To further stress the insulin

lowering qualities that carbohydrate counting tend to have, the larger portion of adolescents in

the CHC group should imply higher insulin requirements due to their increased insulin

resistance (25), but instead the opposite was found.

Carbohydrate counting had an adverse effect on weight. A greater rise in BMI-sds was seen in

the CHC group compared to the non-CHC group. Other studies present contradicting results

on weight changes following carbohydrate counting but few are on growing populations (5).

Studies on adolescents and carbohydrate counting have found weight increases (10, 23) thus a

possible explanation for the weight gain is the significantly larger portion of adolescents in

the CHC group. On the other hand a greater weight gain was surprising as insulin

requirements were lower in the CHC group. Insulin is an anabolic hormone considered to

promote weight gain, why expected results would be that decreased insulin requirements

should be followed by reduced weight (25). Instead the opposite was found. A plausible

explanation is that protein- and fat intake have been disregarded when focus have been on

carbohydrates, causing excess intake of calories but no need for extra insulin (3). An

increased flexibility in food choices could also promote an increased intake of sweets and

other high-energy dense foods causing an excess energy intake (3, 4). The findings in this

study suggest that carbohydrate counting from onset cause excess weight gain and that

adolescents might be at higher risk. Principles of good nutrition must not be forgotten when

teaching carbohydrate counting and focus on all nutrients is of importance to prevent weight

gain.

The weight increase seen in the CHC group was more prominent in girls than in boys,

revealing a gender difference. Gender differences in diabetic populations are not uncommon,

girls tend to have poorer glycemic control and weigh more (1, 18). Possible explanations for

this can only be hypothesized but societal pressure, not wanting to stand out and girls being

more declined to demand their space might be bad combinations with insulin injections and

blood sugar monitoring. However, this does not explain differences between the CHC group

and the non-CHC group. There were larger mean differences between girls and boys in the

non-CHC group, which were reduced in the CHC group, implying that carbohydrate counting

could offer more gender-neutral care. Pubertal development is not evaluated in this study but

could affect outcomes, especially BMI, since there are more subjects in the ages 14-18 in the

CHC group than the non-CHC group. More quantitative research is needed to fully

understand these findings.

Carbohydrate counting comes to its full use when learning it from diabetes onset. The CHC

group used the method more frequently and they more often considered the method to work,

assessed by acceptable postprandial blood glucose values. Time consumption was considered

to be quick, or fairly quick by almost all responders. Based on questionnaire answers there is

a frustration over the many confounding factors affecting blood glucose levels, but it was

thought that carbohydrate counting offered more stability to unstable blood glucose values

22

without increased work, which is ultimately positive. Diabetes is a difficult disease to fully

control due to many confounding factors but carbohydrate counting seems to offer increased

stability, which is appreciated by patients and caregivers. The majority of comments were

positive towards counting carbohydrates, which resemble previous studies that have found

improved quality of life outcomes when this method has been introduced (1, 3). Also, the fact

that almost all patients who were not taught carbohydrate counting from onset had started

using the methods speaks on its behalf. A treatment method that patients have faith in and

want to use will maintain glycemic control as well as improve their quality of life. Taking into

consideration that the questionnaire provided limited data and therefore inadequate statistical

power, comments are valuable for clinical practice. The insight into the patient’s and

caregivers perception of carbohydrate counting offers valuable information that will improve

education to new patients, as well as a increased understanding of insulin dosage to meals

which ultimately offers better care.

Carbohydrate counting stresses the importance of a dietician in the diabetes team. Not only is

the dietician important to adequately teach patients and caregivers carbohydrate counting but

as findings in this study suggest, also prevent, detect and treat weight gain. This was an

important finding already in 1993 in the DCCT study where the importance of adherence to

diet and the occurrence of undesirable weight gain demonstrates the need for the dieticians’

expertise in the diabetes team (26). Improving treatment methods, such as carbohydrate

counting, that patients appreciate is of great value to patients’ mental and physical health.

Lack of dietary freedom is considered as one of the worst aspects of living with diabetes (3)

and to continue to develop carbohydrate counting and dietary advice and combine it with

technological advancements will increase patients’ freedom and achieve increased health.

6 CONCLUSION

Carbohydrate counting from diabetes onset lowers insulin requirements with maintained

glycemic control. Contradictive to the lowered insulin requirement, greater weight gain was

found in the group who used carbohydrate counting, especially among girls. A plausible

explanation is that carbohydrates have taken focus off protein- and fat intake and allowed a

more liberal approach to energy dense foods causing excess energy intake. Adherence to diet

in general, as well as carbohydrate counting, to prevent weight gain should be a focus in

clinical practice. The strength of carbohydrate counting does not lie in its ability to lower

HbA1c-values but as a user-friendly tool. Patients and their caregivers perceive the method as

a good tool that allows better insulin doses to meals, which gets easier by time and that they

are happy to use.

7 ACKNOWLEDGEMENTS

I would like to thank my colleagues at the diabetes clinics at Astrid Lindgren Children’s

hospital, especially Eva Örtkvist, Annika Janson, Torun Torbjörnsdotter, Anna-Lena Brorsson

and Lena Gummeson Nilsson, for their help, support and sharing of knowledge. Thank you

also to Anna Pettersson for invaluable help with statistics and coaching. Thanks to Jimmy

Jelleryd for creating invitations and a website for the questionnaire. A special thank you to

my husband, Andreas, for giving me the time to write this, for listening even though most of

the words are greek to him and for being understanding and never complaining over all the

late nights and weekends that I’ve spent away from him and our children.

23

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Pediatric and Adolescent Diabetes. ISPAD Clinical Practice Consensus Guidelines 2014.

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2014;15 Suppl 20:135-53.

2. Silverstein J, Klingensmith G, Copeland K, Plotnick L, Kaufman F, Laffel L, et al. Care

of children and adolescents with type 1 diabetes: a statement of the American Diabetes

Association. Diabetes Care. 2005;28(1):186-212.

3. Kawamura T. The importance of carbohydrate counting in the treatment of children with

diabetes. Pediatr Diabetes. 2007;8 Suppl 6:57-62.

4. Anderson EJ, Richardson M, Castle G, Cercone S, Delahanty L, Lyon R, et al.

Nutrition interventions for intensive therapy in the Diabetes Control and Complications

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5. Schmidt S, Schelde B, Nørgaard K. Effects of advanced carbohydrate counting in

patients with type 1 diabetes: a systematic review. Diabet Med. 2014;31(8):886-96.

6. Waller H, Eiser C, Knowles J, Rogers N, Wharmby S, Heller S, et al. Pilot study of a

novel educational programme for 11-16 year olds with type 1 diabetes mellitus: the

KICk-OFF course. Arch Dis Child. 2008;93(11):927-31.

7. von Sengbusch S, Müller-Godeffroy E, Häger S, Reintjes R, Hiort O, Wagner V. Mobile

diabetes education and care: intervention for children and young people with Type 1

diabetes in rural areas of northern Germany. Diabet Med. 2006;23(2):122-7.

8. The Diabetes Control and Complications Trial Research Group. Effect of Intensive

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Diabetes: 18 Years of Follow-up in the DCCT/EDIC. Diabetes. 2015 Feb;64(2):631-42.

9. Ly TT, Maahs DM, Rewers A, Dunger D, Oduwole A, Jones TW, et al. ISPAD Clinical

Practice Consensus Guidelines 2014. Assessment and management of hypoglycemia in

children and adolescents with diabetes. Pediatr Diabetes. 2014;15 Suppl 20:180-92.

10. The Diabetes Control and Complications Trial Research Group. Effect of intensive

diabetes treatment on the development and progression of long-term complications in

adolescents with insulin-dependent diabetes mellitus: Diabetes Control and

Complications Trial. J Pediatr. 1994;125(2):177-88.

11. DAFNE Study Group. Training in flexible, intensive insulin management to enable

dietary freedom in people with type 1 diabetes: dose adjustment for normal eating

(DAFNE) randomised controlled trial. BMJ. 2002;325(7367):746.

12. Alemzadeh R, Berhe T, Wyatt DT. Flexible insulin therapy with glargine insulin

improved glycemic control and reduced severe hypoglycemia among preschool-aged

children with type 1 diabetes mellitus. Pediatrics. 2005;115(5):1320-4.

13. Donaghue KC, Wadwa RP, Dimeglio LA, Wong TY, Chiarelli F, Marcovecchio ML, et

al. ISPAD Clinical Practice Consensus Guidelines 2014. Microvascular and

macrovascular complications in children and adolescents. Pediatr Diabetes. 2014;15

Suppl 20:257-69.

14. Nordwall M, Arnqvist HJ, Bojestig M, Ludvigsson J. Good glycemic control remains

crucial in prevention of late diabetic complications--the Linköping Diabetes

Complications Study. Pediatr Diabetes. 2009;10(3):168-76.

15. Shalitin S, Phillip M. Which factors predict glycemic control in children diagnosed with

type 1 diabetes before 6.5 years of age? Acta Diabetol. 2012;49(5):355-62.

16. Viswanathan V, Sneeringer MR, Miller A, Eugster EA, DiMeglio LA. The utility of

hemoglobin A1c at diagnosis for prediction of future glycemic control in children with

type 1 diabetes. Diabetes Res Clin Pract. 2011;92(1):65-8.

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17. Gökşen D, Atik Altınok Y, Ozen S, Demir G, Darcan S. Effects of carbohydrate counting

method on metabolic control in children with type 1 diabetes mellitus. J Clin Res Pediatr

Endocrinol. 2014;6(2):74-8.

18. Åkesson K, Hanberger L, Samuelsson U. The influence of age, gender, insulin dose,

BMI, and blood pressure on metabolic control in young patients with type 1 diabetes.

Pediatr Diabetes. 2015;16(8):581-6.

19. Samuelsson U. Årsrapport 2014. Nationellt register för barn- och ungdomsdiabetes.

Swediabkids. https://swediabkids.ndr.nu/Documents/NDR-Child/AnnualReport-

2014.pdf2014 [cited 2015 21/12].

20. Ejlertsson G. Statistik för hälsovetenskaperna. 2:4 ed. Lund: Studentlitteratur AB; 2012.

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22. Cole TJ. The development of growth references and growth charts. Ann Hum Biol.

2012;39(5):382-94.

23. Alemzadeh R, Palma-Sisto P, Parton E, Totka J, Kirby M. Beneficial effects of flexible

insulin therapy in children and adolescents with type 1 diabetes mellitus. Acta Diabetol.

2003;40(3):137-42.

24. Brorsson AL, Viklund G, Örtqvist E, Lindholm Olinder A. Does treatment with an

insulin pump improve glycaemic control in children and adolescents with type 1

diabetes? A retrospective case-control study. Pediatr Diabetes. 2015;16(7):546-53.

25. Hamilton J, Daneman D. Deteriorating diabetes control during adolescence:

physiological or psychosocial? J Pediatr Endocrinol Metab. 2002;15(2):115-26.

26. Delahanty L, Simkins SW, Camelon K. Expanded role of the dietitian in the Diabetes

Control and Complications Trial: implications for clinical practice. The DCCT Research

Group. J Am Diet Assoc. 1993;93(7):758-64, 67.

https://swediabkids.ndr.nu/Documents/NDR-Child/AnnualReport-2014.pdf2014

https://swediabkids.ndr.nu/Documents/NDR-Child/AnnualReport-2014.pdf2014

https://swediabkids.ndr.nu2016/

Appendix 1. Web based questionnaire

“Survey on insulin doses to meals and diabetes type 1”

“Undersökning om insulindosering till måltider vid diabetes typ 1”

Appendix 1. (1/9)

Undersökning om insulindosering till måltider vid

diabetes typ 1

Genom att svara på frågorna hjälper du oss att förstå hur det är att räkna ut en insulindos vid varje måltid.

Barn/Ungdomar som är yngre än 13 år behöver ta hjälp av en förälder för att besvara frågorna. Är du äldre

än 13 år får du svara själv men gärna med hjälp av en förälder.

Deltagande i enkätundersökningen är frivilligt. Deltagandet är anonymt och man kommer inte att kunna

koppla ihop identitet med resultat i studien. Vi kommer be er att fylla i ert personnummer. Detta är frivilligt

men genom att ni fyller i personnumret kan vi få bättre förståelse för hur det är att dosera insulin till

måltider och blodsockerkontroll. Alla persondata hanteras enligt lagen om PUL. Alla persondata hanteras

av en person som sitter i styrgruppen för Swediabkids, det nationella diabetesregistret där alla era data

från era besök hos läkare eller sköterska registreras.

*Obligatorisk

BAKGRUNDSFRÅGOR

1. Vänligen ange personnummer. 10 siffror,

XXXXXXXXXX

Personnummer behövs på barnet/ungdomen med

diabetes för att kunna koppla ihop svaren från den

här enkäten med data från Swediabkids. Alla

persondata hanteras enligt personuppgiftslagen

(PUL). Ingen koppling kommer att kunna göras till

personen med diabetes i studien.

2. Vilket kön har du/barnet? *

Markera endast en oval.

Pojke

Flicka

Annat

3. Hur gammal är du/barnet som har diabetes? *


0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

4. När fick du/barnet diabetes? *

Ange år och månad. Ex. 201101

Appendix 1. (9/9)

5. Hur injicerar du/barnet insulin? *


Med insulinpenna (spruta)

Med insulinpump

6. Var var ditt/barnets HbA1c senast du/ni besökte

diabetesmottagningen?

Om du inte minns eller vill svara, så hoppar du över

frågan och går vidare till nästa.

7. Behöver du/barnet hjälp för att räkna ut eller injicera insulindos? *

Hjälp avser all hjälp där en vuxen behöver närvara eller konsulteras.


Ja

Nej

Ibland

8. Tar du/barnet insulinet efter måltiden? *

Efter måltiden avser då du/barnet ätit färdigt.


Ja, alltid

Ja, ofta

Ja, ibland

Sällan

Nej, aldrig

9. Använder du/ni kolhydraträkning för att beräkna insulindos till måltid? *

Med kolhydraträkning menas att du/barnet utgår ifrån mängden kolhydrater som ska ätas för att räkna ut hur

mycket insulin som kommer att krävas till måltiden.


Ja, alltid Fortsätt till frågan 19.

Ja, oftast Fortsätt till frågan 19.

Ja, ibland Fortsätt till frågan 19.

Nej, jag har gjort det förut men inte längre

Nej, jag har aldrig använt kolhydraträkning

FRÅGOR TILL DIG/ER SOM INTE ANVÄNDER

KOLHYDRATRÄKNING

10. Hur gör du/ni huvudsakligen för att bestämma insulindos till en måltid? *

Frågan avser den metod, eller på vilket sätt du/ni gör för att komma fram till hur mycket insulin som

behövs till en måltid.


Använder standarddoser som läkare/sköterska ordinerat

Doserar utifrån känsla/erfarenhet utifrån hur mycket jag/barnet ska äta

Doserar utifrån hur mycket insulin jag/barnet brukar ta

Övrigt:

Appendix 1. (9/9)

11. Tycker du/ni att sättet (metoden) du använder för att dosera insulin fungerar bra? *

En välfungerande metod bör karakteriseras av att blodsockret blir som önskat


Ja, alltid

Ja, oftast

Ja, ibland

Nej, sällan

Nej, aldrig

12. Om du/ni svarade sällan eller aldrig på förra frågan. Varför tycker du inte metoden är bra?

Det skulle vara hjälpsamt om du ville förklara varför du inte tycker metoden är bra.

13. Rättar du/ni till (korrigerar) blodsockret genom att ta en extra insulindos om det blir högt EFTER

måltiden? *


Ja, alltid

Ja, oftast

Ja, ibland

Sällan eller aldrig

14. Om du svarade Ja på föregående fråga: Hur bestämmer du/ni huvudsakligen insulindosen för

att rätta till (sänka) blodsockret?


Använder standarddoser som läkare/sköterska ordinerat

Jag går på känsla utifrån vad blodsockret är

Jag tar samma dos insulin varje gång

Övrigt:

15. Hur stor skillnad är det i storlek på dina/barnets insulindoser till olika måltider? *

Här efterfrågas hur mycket insulindoserna varieras, alltså skillnaden mellan den största och minsta

insulindosen som du/barnet kan ta till en måltid. T.ex. om dosen till måltiden som minst är 1 E, och till

en annan måltid kan var som störst 7 E så är skillnaden 6 E. Variationen kan bero på måltidens innehåll

och storlek eller om man behöver extra insulin till ett högt blodsocker, alltså efterfrågas den totala

insulindosen som tas i samband med en måltid.


1 E

2 E

3 E

4 E

5 E

6 E

7 E

8 E

9 E

10 E

>10 E

Appendix 1. (9/9)

16. Anpassar eller ändrar du/barnet sitt matintag för att det ska passa med din/barnets diabetes? *

T.ex. hur mycket som äts eller vilka maträtter/livsmedel som ska ätas.


Ja, alltid

Ja, ofta

Ja, ibland

Nej, sällan

Nej, aldrig

17. Finns det livsmedel/maträtter som du/barnet undviker att äta helt eller delvis pga din/barnets

diabetes? *


Nej

Ja

18. Om du har svarat Ja på föregående fråga, vänligen kryssa för de livsmedel och/eller maträtter

som du/ni undviker att äta eller väljer att äta mindre av eller äter mer sällan än du skulle önska

pga din/barnets diabetes.

Flera val möjliga

Markera alla som gäller.

Pizza

Hamburgare

Kebab

Godis

Läsk, saft

Chips

Glass

Bröd

Pasta

Ris

Potatis

Pannkakor

Fruktyoghurt

Charkuterivaror, ex. korv, skinka

Övrigt:

Fortsätt till frågan 42.

FRÅGOR TILL DIG/ER SOM KOLHYDRATRÄKNAR

19. Hur lärde du/ni er kolhydraträkning? *


Av personalen på sjukhuset när jag/barnet fick diabetes

Av personalen på sjukhuset i samband med att jag/barnet fick pump

Av personalen på sjukhuset för att jag/barnet bad om det eller fick rekommenderat att lära mig

det

Jag/familjen har lärt sig själv eller fått hjälp av någon som använder kolhydraträkning

Övrigt:

20. Hur länge har du/barnet kolhydraträknat? *

Avrunda till år och halvår.


1/2 år

1 år

1 1/2 år

Appendix 1. (9/9)

2 år

2 1/2 år

3 år

Mer än 3 år

21. Hur många kolhydratkvoter har du/barnet? *

Kolhydratkvoten är det som hjälper dig att beräkna hur mycket insulin du behöver till en viss mängd

kolhydrater och skrivs, t.ex. 10 g/E, vilket betyder att 1 E insulin tar hand om 10 gram kolhydrater. Man

kan använda en eller flera kvoter på ett dygn.


1 kolhydratkvot

2 kolhydratkvoter

3 kolhydratkvoter

4 kolhydratkvoter

5 kolhydratkvoter

6 kolhydratkvoter

7 kolhydratkvoter

Jag har inte någon kolhydratkvot

22. Brukar du/ni ändra dina/barnets kolhydratkvoter? *

Flera alternativ möjliga.


Nej, jag har aldrig ändrat dem

Ja, jag ändrar dem själv

Ja, jag ändrar dem med hjälp av mina föräldrar

Ja, läkare/sköterska ordinerar nya kvoter när jag besöker diabetesmottagningen

23. Händer det att du/ni väljer att INTE kolhydraträkna till en måltid? *


Ja, varje dag

Ja, någon gång i veckan

Ja, någon gång i månaden

Nej, sällan

Nej, aldrig

24. Om det händer att du/ni inte kolhydraträknar en måltid, vad är orsaken/orsakerna?

Flera alternativ möjliga. (Om du har svarat Nej på föregående fråga så kan du hoppa över den här

frågan och fortsätta med nästa)


Det tar för lång tid att kolhydraträkna vissa måltider

Jag vet inte hur mycket kolhydrater det är i ett/flera av livsmedlen

Jag vill inte att de jag umgås med ska veta att jag har diabetes

Jag vet hur mycket insulin jag behöver till den måltiden så jag tar en standarddos

Situationen tillåter det inte, t.ex. på restaurang

Jag glömmer att ta insulindos till måltiden

Övrigt:

25. Tänk tillbaka på de senaste 2 veckorna. Hur har du/ni gjort för att ta reda på hur mycket

kolhydratrika livsmedel som ligger på tallriken?

Här efterfrågas MÄNGDEN potatis, ris, pasta, bröd m.m. som portionen innehåller. Flera alternativ

möjliga, kryssa för de som stämmer och rangordna dem utifrån det som är vanligast med 1, näst

vanligast 2 osv. Om du inte använder något av alternativet så kryssar du inte.

Markera endast en oval per rad.

Väger/mäter maten med hjälp av våg och mått

Appendix 1. (9/9)

Jag uppskattar med hjälp av bilder

Jag tittar på tallriken och vet hur mycket det är

Jag lägger upp lika mycket varje gång (standardportioner)

Annat.

1 (Vanligast)

2

3

4

5

26. Om du/ni har kryssat för Annat på föregående fråga, vänligen förklara hur du/ni gör för att ta

reda på hur mycket potatis, ris, pasta m.m. det är på tallriken.

27. Tänk tillbaka på de senaste 2 veckorna. Hur har du/ni gjort för att räkna ut insulindosen till

måltiderna?

Frågan avser beräkningen där du dividerar kolhydratinnehållet i måltiden med din kolhydratkvot för att

få fram insulindosen du ska ta. Om det är ett barn som inte själv beräknar insulindoser avser frågan hur

den vuxne gör. Flera alternativ är möjliga, kryssa för de som stämmer och rangordna dem utifrån hur

du oftast utför beräkningen de senaste 2 veckorna, det sätt som du använt mest (vanligast) med 1, näst

vanligast 2 osv. Du behöver endast kryssa/rangordna de beräkningssätt som du använder.


Använder miniräknare

Använder en app i telefon/surfplatta

Använder insulinpumpen

Använder huvudräkning

Annat

1 (Vanligast)

2

3

4

5

28. Om du/ni har kryssat för Annat på föregående fråga, vänligen förklara hur du/ni gör för att

beräkna din/barnets insulindos.

29. Tänk tillbaka på de senaste 2 veckorna. Hur har du/ni gjort för att ta reda på hur många gram

kolhydrater det är i ett livsmedel eller en maträtt? Rangordna svaren.

Frågan avser hur ni bestämmer kolhydratinnehållet i t.ex. bulgur, pytt i panna, pizza etc. Flera alternativ

är möjliga. Rangordna efter det sätt du/ni vanligast använder, där 1 motsvarar det sätt ni vanligast

använder och 2 det näst vanligaste osv.


Jag/vårdgivare använder en kolhydratlistasom jag/barnet har fått från sjukhuset

Jag/vårdgivare söker på internet, t.ex. livsmedelsverket.

Jag/vårdgivare använder en app i telefonen/surfplatta

Jag/vårdgivare tittar på näringsdeklarationen på livsmedlets förpackning.

Jag/vårdgivare har lärt mig kolhydratinnehållet i de flesta livsmedel jag äter och vet därför kolhydratinnehållet i

olika livsmedel

Annat

1 (vanligast)

2

3

4

5

6

30. Om du har kryssat för Annat på föregående fråga, vänligen förklara hur du/ni gör för att ta reda

Appendix 1. (9/9)

på kolhydratinnehållet i ett livsmedel eller maträtt.

31. Hur lång tid upplever du att det tar för dig/er att räkna ut och ta insulindosen till en måltid? *

Kryssa för alternativet som du tycker stämmer in oftast om tidsåtgången varierar


Snabbt

Ganska snabbt

Inte så snabbt

Långsamt

Väldigt långsamt

32. Hur många minuter uppskattar du att det i genomsnitt tar att räkna ut och ta insulindos till

måltiderna? *


1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

>30

33. Är det någon skillnad hur snabbt det går att beräkna insulindos nu mot när du först började med

kolhydraträkning? *


Snabbare

Ingen skillnad

Långsammare

34. Rättar du/ni till (korrigerar) blodsockret genom att ta en extra insulindos om det blir högt EFTER

måltiden? *


Appendix 1. (9/9)

Ja, alltid

Ja, ofta

Ja, ibland

Sällan eller aldrig

35. Hur bestämmer du/ni insulindos för att korrigera ett högt blodsocker? *


Använder korrigeringskvot och beräknar hur mycket insulin jag/barnet behöver

Använder standarddoser som läkaren ordinerat

Går på känsla utifrån hur högt blodsockret är

Övrigt:

36. Tycker du/ni att kolhydraträkning är en bra metod för att bestämma hur mycket insulin du ska ta

till måltiderna? *

En välfungerande metod bör karakteriseras av att blodsockret blir som önskat


Ja, alltid

Ja, oftast

Ja, ibland

Nej, sällan (vänligen motivera nedan)

Nej, aldrig (vänligen motivera nedan)

37. Om du svarade sällan eller aldrig på förra frågan. Varför tycker du inte metoden är bra?

Det skulle vara hjälpsamt om du ville förklara varför du inte tycker att metoden är bra.

38. Hur stor skillnad är det i storlek på dina/barnets insulindoser till olika måltider? *

Här efterfrågas hur mycket insulindoserna varieras, alltså skillnaden mellan den största och minsta

insulindosen som du/barnet kan ta till en måltid. T.ex. om dosen till måltiden som minst är 1 E, och till

en annan måltid kan var som störst 7 E så är skillnaden 6 E. Variationen kan bero på måltidens innehåll

och storlek eller om man behöver extra insulin till ett högt blodsocker, alltså efterfrågas den totala

insulindosen som tas i samband med en måltid.


1 E

2 E

3 E

4 E

5 E

6 E

7 E

8 E

9 E

10 E

>10 E

39. Anpassar eller ändrar du/barnet sitt matintag för att det ska passa med din/barnets diabetes? *

T.ex. hur mycket som äts eller vilka maträtter/livsmedel som ska ätas.


Ja, alltid

Ja, ofta

Appendix 1. (9/9)

Ja, ibland

Nej, sällan

Nej, aldrig

40. Finns det livsmedel/maträtter som du/ni undviker att äta helt eller delvis pga din/barnets

diabetes? *


Ja

Nej

41. Om du har svarat Ja på föregående fråga, vänligen kryssa för de livsmedel och/eller maträtter

som du/ni helt undviker att äta eller väljer att äta mindre av eller äter mer sällan än du skulle

önska pga din/barnets diabetes.

Flera val möjliga


Pizza

Hamburgare

Kebab

Godis

Läsk, saft

Chips

Glass

Bröd

Pasta

Ris

Potatis

Pannkakor

Fruktyoghurt

Charkuterivaror, ex. korv, skinka

Övrigt:

Nästan klar! En sista valfri fråga där du/ni har möjlighet att fritt berätta om hur det är att dosera insulin till måltider.

42. Är det något du skulle vilja lägga till eller berätta om din upplevelse av insulindosering till

måltider som inte har kommit fram i enkäten?

Appendix 2. (1/1)